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Voor het bijwonen van de openbare verdediging

van mijn proefschrift

What goes up must come down

Glucose variability and glucose control in diabetes

and critical illness

Op vrijdag 17 juni 2011om 10:00 uur

in de AgnietenkapelOudezijds Voorburgwal 231

Amsterdam

U bent van harte uitgenodigd voor de receptie ter plaatse

na afloop van de verdediging

Sarah Siegelaar

Jan Olphert Vaillantlaan 157

1086 XZ Amsterdam

06-41430800

[email protected]

Paranimfen

Olivier Siegelaar

[email protected]

Margo Klomp

[email protected]

Wh

at goes u

p m

ust co

me d

own

Sarah E

. Siegelaar

What goes up must come downGlucose variability and glucose control in diabetes and critical illness

Sarah E. Siegelaar

What goes up must come down

Glucose variability and glucose control

in diabetes and critical illness

What goes up must come down: glucose variability and glucose control in diabetes and critical illnessAcademic thesis, University of Amsterdam, Amsterdam, the Netherlands

ISBN: 978-90-9026133-1

Author: Sarah E. SiegelaarLay-out: Barbara ten BrinkCover: Design by Barbara ten BrinkPrint: Gildeprint Drukkerijen, Enschede, The Netherlands

© S.E. Siegelaar, Amsterdam 2011All rights reserved. No part of this publication may be reproduced, stored, or transmitted in any form or by any means, without written permission of the author.

Printing of this thesis was financially supported by: Stichting Asklepios, Universiteit van Amsterdam, AstraZeneca BV, sanofi-aventis Netherlands BV, Boehringer Ingelheim BV.

What goes up must come downGlucose variability and glucose control

in diabetes and critical illness

ACADEMISCH PROEFSCHRIFT

ter verkrijging van de graad van doctor aan de Universiteit van Amsterdam op gezag van de Rector Magnificus

prof.dr. D.C. van den Boomten overstaan van een door het college voor

promoties ingestelde commissie, in het openbaar te verdedigen in de Agnietenkapel

op vrijdag 17 juni 2011, te 10:00 uur

door

Sarah Elaine Siegelaar geboren te Heemstede

PROMOTIECOMMISSIE

Promotores: Prof. dr. J.B.L. Hoekstra

Co-promotor: Dr. J.H. de Vries

Overige leden: Prof. dr. E. Fliers

Prof. dr. E.S. Kilpatrick

Prof. dr. J.A. Romijn

Prof. dr. Y.M. Smulders

Prof. dr. D.F. Zandstra

Faculteit der Geneeskunde

Financial support by the Netherlands Heart Foundation and the Dutch Diabetes Research

Foundation for the publication of this thesis is gratefully acknowledged

CONTENTS

Chapter 1 Introduction

PART I Glucose variability

Chapter 2 Glucose variability; does it matter?

Chapter 3 Mild hyperglycaemia disturbs vascular homeostasis in

humans

Chapter 4 No relevant relationship between glucose variability and

oxidative stress in well regulated type 2 diabetes patients

Chapter 5 A randomised clinical trial comparing the effect of basal

insulin and inhaled mealtime insulin on glucose variability

and oxidative stress

Chapter 6 Glucose variability does not contribute to the development

of peripheral and autonomic neuropathy in type 1 diabetes:

data from the DCCT

Chapter 7 A decrease in glucose variability does not reduce

cardiovascular event rates in type 2 diabetes patients after

acute myocardial infarction: a reanalysis of the HEART2D

study

9

17

41

59

73

85

97

PART II Glucose control in critical illness

Chapter 8 Mean glucose during intensive care unit admission is related

to mortality by a U-shaped curve in surgical and medical

patients: a retrospective cohort study

Chapter 9 Accuracy and reliability of continuous glucose monitoring

in the intensive care unit: a head-to-head comparison of two

subcutaneous glucose sensors in cardiac surgery patients

Chapter 10 Microcirculation and its relation with continuous

subcutaneous glucose sensor accuracy in cardiac surgery

patients in the intensive care unit

Chapter 11 Special considerations for the diabetic patient in the intensive

care unit; targets for treatment and risks of hypoglycaemia

Chapter 12 The effect of diabetes on mortality in critically ill patients:

a systematic review and meta-analysis

Chapter 13 - Summary and future considerations

- Samenvatting en toekomstperspectief

- Authors’ affiliations

- List of publications

- Dankwoord

- Curriculum Vitae

107

123

137

153

171

197

Chapter 1

Introduction

Sarah E. Siegelaar

10

Glucose: essential for life but harmful in excess. Because of this paradox, the glycaemic

balance is a tightly regulated feed-back system in the healthy human body; unrestrained

increases in plasma glucose are prohibited by the action of insulin, and counterregulatory

hormones, such as glucagon, prevent plasma glucose to decrease to dangerously low

levels. As a result, plasma glucose levels in healthy humans almost never exceed 7.8

mmol/l1; the upper limit of what is considered normal. There are, however, conditions

where this equilibrium is being disturbed, resulting in chronic as well as acute

hyperglycaemia. Hyperglycaemia is known to induce endothelial damage, probably

because a mitochondrial glucose overload leads to formation of free oxygen radicals,

so-called oxidative stress 2, but the exact pathophysiology is not fully elucidated yet. This

thesis pictures different types of hyperglycaemia and examines whether hyperglycaemia

is always harmful and, as a result, should by all means be avoided.

Chronic hyperglycaemia is the defining feature of diabetes mellitus, as a result of an

absolute (type 1 diabetes mellitus) or relative (type 2 diabetes mellitus) shortage of

insulin. It affects the micro- and macrovasculature, causing damage to multiple organs 3;4. We know it is beneficial for patients with diabetes to decrease the high glucose

values and aim at HbA1c values below 7% for newly diagnosed patients and perhaps

somewhat less strict for patients with established disease and complications 5. This seems

straightforward, but unfortunately, it is not that easy. Patients with similar mean glucose

or HbA1c levels can have markedly different daily glucose profiles, with differences both

in number and duration of glucose excursions; so called glucose variability, the topic

of Part I of this thesis.

It has been suggested that high glucose variability induces vascular damage independent

from average glycaemia, which would have consequences for diabetes treatment 6. In

Chapter 2 an overview of different glucose variability measures is given and the current

literature regarding its effects in various populations is reviewed. To better understand

the effect of glucose variability, in Chapter 3 we studied whether the effects of mild

hyperglycaemia on vascular homeostasis are glucose-dependent or have a threshold above

which damage starts. The independent effect of glucose variability on oxidative stress

is investigated in Chapter 4 and Chapter 5, including type 2 diabetes patients treated

with oral glucose lowering drugs or insulin, respectively. A more clinical question is

addressed in Chapter 6 where datasets of the large Diabetes Control and Complications

Trial (DCCT) were reanalysed to assess the effect of glucose variability on the development

of neuropathy in type 1 diabetes. In Chapter 7 we describe the first trial that specifically

lowered glucose variability in type 2 diabetes patients assessing the effect on future

cardiovascular event rates.

Introduction

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That glucose homeostasis can be disturbed during critical illness is discussed in

Part II of this thesis. This so called “stress-hyperglycaemia” due to inflammatory and

neuro-endocrine derangements 7, is common in critically ill patients and associated

with mortality 8. In 2001, van den Berghe et al.9 startled the intensive care community

by publishing the results of a randomised controlled trial investigating the effect of

intensive insulin therapy on outcome, which showed that lowering plasma glucose to

normoglycaemic levels dramatically decreased mortality. But again, practice seemed not

to be as easy as proposed: the results from van den Berghe et al. could not be reproduced

and accumulating evidence suggests that the use of intensive insulin therapy is perhaps

even harmful in some patients 10. In Chapter 8 the optimal target range for mean glucose

during intensive care unit admission is explored further.

As heavy the disagreement is on whether strict or less-strict glycaemic control should

be applied in the intensive care unit, all are united that marked hyperglycaemia and

severe hypoglycaemia should be avoided. At present, time-consuming intermittent

blood sampling has to be performed to achieve glycaemic control, and moreover, no

information is available about glucose values in-between measurements. Subcutaneous

continuous glucose monitoring (CGM) could therefore be of value in achieving glycaemic

control. However, accuracy and reliability of the available systems seems uncertain in

critically ill patients 11;12. Therefore, we performed a head-to-head comparison of two

subcutaneous CGM systems in patients admitted to the intensive care unit after cardiac

surgery, presented in Chapter 9. The sometimes decreased accuracy of subcutaneous

CGM systems in the critically ill might result from alterations in the microcirculation

because needle-type glucose sensors measure glucose in the interstitial fluid and not

directly in blood. This has been investigated in Chapter 10, where the microcirculation

and its relation with continuous glucose sensor accuracy were studied in cardiac surgery

patients.

Not all patients with critical illness-induced hyperglycaemia are similar. More and more

the idea evolves that chronic hyperglycaemia in critically ill patients with diabetes is

pathophysiologically different from acute hyperglycaemia in those without previously

diagnosed diabetes. This could mean that treatment targets and strategies should differ

between these populations. The relation between hyperglycaemia and mortality as well as

the effect of intensive insulin therapy in critically ill patients with diabetes is discussed

in Chapter 11. Finally, in Chapter 12 the results of a meta-analysis looking at differences

in mortality between patients with and without diabetes when admitted to the intensive

care unit are shown, which allows us to put acquired knowledge into perspective.

In the end, this thesis is about glucose peaks and their consequences: must all what

goes up come down?

12

References1. Polonsky KS, Given BD, Hirsch LJ, et al (1988) Abnormal patterns of insulin secretion in non-insulin-dependent

diabetes mellitus. N Engl J Med 318: 1231-12392. Brownlee M (2001) Biochemistry and molecular cell biology of diabetic complications. Nature 414: 813-8203. UK Prospective Diabetes Study (UKPDS) Group (1998) Intensive blood-glucose control with sulphonylureas or

insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). UK Prospective Diabetes Study (UKPDS) Group. Lancet 352: 837-853

4. Nathan DM, Cleary PA, Backlund JY, et al (2005) Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes. N Engl J Med 353: 2643-2653

5. Patel A, MacMahon S, Chalmers J, et al (2008) Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes. N Engl J Med 358: 2560-2572

6. Monnier L, Mas E, Ginet C, et al (2006) Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes. JAMA 295: 1681-1687

7. Dungan KM, Braithwaite SS, Preiser JC (2009) Stress hyperglycaemia. Lancet 373: 1798-18078. Krinsley JS (2003) Association between hyperglycemia and increased hospital mortality in a heterogeneous

population of critically ill patients. Mayo Clin Proc 78: 1471-14789. Van den Berghe G, Wouters P, Weekers F, et al (2001) Intensive insulin therapy in the critically ill patients.

N Engl J Med 345: 1359-136710. Finfer S, Chittock DR, Su SY, et al (2009) Intensive versus conventional glucose control in critically ill patients.

N Engl J Med 360: 1283-129711. Logtenberg SJ, Kleefstra N, Snellen FT, et al (2009) Pre- and postoperative accuracy and safety of a real-time

continuous glucose monitoring system in cardiac surgical patients: a randomized pilot study. Diabetes Technol Ther 11: 31-37

12. Price GC, Stevenson K, Walsh TS (2008) Evaluation of a continuous glucose monitor in an unselected general intensive care population. Crit Care Resusc 10: 209-216

Introduction

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Part I

Glucose variability

Chapter 2

Glucose variability; does it matter?

Sarah E. Siegelaar, Frits Holleman, Joost B.L. Hoekstra and

J. Hans DeVries

Endocrine Reviews 2010; 31(2):171-182

18

Abstract

Overall lowering of glucose is of pivotal importance in the treatment of diabetes,

with proven beneficial effects on microvascular and macrovascular outcomes.

Still, patients with similar glycosylated haemoglobin levels and mean glucose

values can have markedly different daily glucose excursions. The role of this

glucose variability in pathophysiological pathways is the subject of debate. It is

strongly related to oxidative stress in in vitro, animal, and human studies in an

experimental setting. However, in real-life human studies including type 1 and

type 2 diabetes patients, there is neither a reproducible relation with oxidative

stress nor a correlation between short-term glucose variability and retinopathy,

nephropathy, or neuropathy. On the other hand, there is some evidence that long-

term glycaemic variability might be related to microvascular complications in

type 1 and 2 diabetes. Regarding mortality, a convincing relationship with short-

term glucose variability has only been demonstrated in nondiabetic, critically

ill patients. Also, glucose variability may have a role in the prediction of severe

hypoglycaemia. In this review, we first provide an overview of the various

methods to measure glucose variability. Second we review current literature

regarding glucose variability and its relation to oxidative stress, long-term

diabetic complications, and hypoglycaemia. Finally, we make recommendations

on whether and how to target glucose variability, concluding that at present we

lack both the compelling evidence and the means to target glucose variability

separately from all efforts to lower mean glucose while avoiding hypoglycaemia.

OutlineI. Introduction

II. Different methods for glucose variability measurement

III. Contribution of glucose variability to oxidative stress

IV. Contribution of glucose variability to diabetic complications and poor outcomes

in critically ill patients

V. Glucose variability as a predictor of severe hypoglycaemia

VI. Clinical recommendations

A. Should glucose variability be a target for intervention?

B. Available options to target glucose variability

VII. Conclusions and future perspectives


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I. Introduction

Patients with similar mean glucose or glycosylated haemoglobin (HbA1c) values can have

markedly different daily glucose profiles, with differences both in number and duration

of glucose excursions. Hyperglycaemia is thought to induce oxidative stress and interfere

with normal endothelial function by overproduction of reactive oxygen species, which

results in diabetic complications through several molecular mechanisms 1,2 (Figure 1).

In addition, glucose variability might contribute to these processes as well. Since the

publication of the results of the Diabetes Control and Complications Trial (DCCT) in the

early 1990s 3,4, the topic of glucose variability as a contributor to diabetic complications

has been debated. It was suggested that glucose variability might explain the difference

in microvascular outcome between the intensively and conventionally treated type 1

diabetes patients with the same mean HbA1c throughout the trial 5. Although this

hypothesis was refuted recently by the statisticians of the DCCT/Epidemiology of Diabetes

Interventions and Complications (EDIC) themselves 6, subsequent hypotheses on the

relation of glucose variability to oxidative stress in type 2 diabetes patients and to

mortality in patients with stress hyperglycaemia have been postulated.

Figure 1 Potential mechanism by which hyperglycaemia-induced mitochondrial superoxide overproduction activates four pathways of hyperglycaemic damage (Reproduced with permission from M. Brownlee: Nature 414:813-820, 2001 1, © Macmillan Publishers, Ltd).

20

Glucose variability and lack of predictability are issues that diabetes patients and doctors

encounter in daily practice. In this review article, we will first provide an overview of the

various methods to measure glucose variability. Second, we review the current evidence

for the relation between glucose variability and oxidative stress, long-term diabetic

complications, and severe hypoglycaemia. Lastly, we will make recommendations for

treatment with regard to targeting glucose variability. We performed a structured literature

search using PubMed and EMBASE according to the PICO (patient, intervention, comparison

and outcome) method 7, including relevant literature published online up to March 2009.

Especially in type 2 diabetes, postprandial hyperglycaemia contributes to individual

glucose variability. However, because postprandial hyperglycaemia is different from glucose

variability as defined above, we will not discuss this further, other than to say that the

positive relationship between postprandial hyperglycaemia and cardiovascular risk supports

the possibility that glucose variability may be related to cardiovascular risk as well 8.

II. Measures of glucose variability

There are several methods to quantify glucose variability, but there is no universally accepted

“gold standard”. Table 1 describes the formulas underlying the different measures and

their characteristics. Most authors consider glycaemic variability as a standard of intraday

variation, reflecting the swings of blood glucose in a diabetic patient as a consequence of

diminished or absent autoregulation and the shortcomings of insulin therapy.

The easiest way to get an impression of the glucose variability in an individual patient is

to calculate the SD of glucose measurements and/or the coefficient of variation (CV), if

one wishes to correct for the mean. It is possible to calculate SD and CV from seven-point

glucose curves, facilitating their use in daily practice. On the other hand, when obtaining

seven-point glucose curves, certain peaks or nadirs will always be missed simply because

they occur between two measurements, making this method less accurate. Calculating

SD and CV from continuous glucose measurement (CGM) data seems preferable, but in

daily practice it is impossible to obtain CGM data from each individual patient. Also, the

extent to which CGM assessed SD differs from that calculated from seven-point profiles

has not, to our knowledge, been formally investigated.

In 1964, Schlichtkrull et al. 9 defined a new measure, the M-value, trying to quantify

glycaemic control of diabetes patients. It is a measure of the stability of the glucose

excursions in comparison with an “ideal” glucose value of 6.6 mmol/l (120 mg/dl),

developed using six self-monitored blood glucose (SMBG) values per 24 h in 20 patients with

type 1 diabetes. Later, other “ideal” glucose levels from 4.4 to 5.6 mmol/l (80 to 100 mg/dl)


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Table 1 Formulas used in describing glucose variability

Variability measure

Formula Explanation of symbolsDiscriminating feature

SDxi = individual observationx– = mean of observationsk = number of observations

easy to determine, extensively used

CVs = standard deviationx– = mean of observations

easy to determine, SD corrected for mean

adjusted M-value

and

MGR = M-value for glucose readingsMw = correction factor for n <24GRt = glucose reading at time tIGV = ideal glucose valueti = time in minutes after start of observations of the ith observationGmax = maximum glucose readingGmin = minimum glucose reading

not a pure variability measure

MAGE if

λ = each blood glucose increase or decrease (nadir-peak or peak-nadir)n = number of observationsv = 1 SD of mean glucose for 24-hr period

used most extensively

CONGA

and

k* = number of observations where there is an observation n x 60 minutes agom = n x 60Dt = difference between glucose reading at time t and t minus n hours ago

specifically developed for CGM

MODD inter-day variation

SD, standard deviation; CV, coefficient of variation; MAGE, mean amplitude of glycaemic excursions; CONGA, continuous overall net glycaemic action; MODD, mean of daily differences; SMBG, self monitored blood glucose; CGM, continuous glucose monitoring. Units are in mmol/l or mg/dl depending on the unity of the glucose values measured. To convert glucose values from mg/dl to mmol/l multiply by 0.0555.

were proposed to obtain the best formula 10. In the final formula, choice of the ideal

glucose value is left up to the investigator, making it difficult to compare different

studies that use different ideal glucose values. The M-value is zero in healthy controls,

rising with increasing glycaemic variability or poorer glycaemic control, making it

difficult to distinguish between patients with either high mean glucose or high glucose

variability. Moreover, because hypoglycaemia has a greater impact on the M-value than

∑(xi – x–)2

k – 1

sx–

MGR + MW

whereMGR =

∑ 10log GRt

IGV

tk

t=t1

3

n

20MW = Gmax – Gmin

∑ λ–nλ v

∑tk*

t=t1

(Dt – D–)2

k* – 1

whereDt = GRt – GRt–m

∑tk*

t=t1

Dt

k*D– =

k*

GR1 – GRt–1440∑tk*

t=t1

22

hyperglycaemia, it is more a clinical than a mathematical indicator of glycaemic control.

In 1970, Service et al. 11 described a method that is is widely used nowadays: the mean

amplitude of glycaemic excursions (MAGE). Developed using hourly blood glucose

sampling for 48 hrs, this method generates a value for the variation around a mean

glucose value by summating the absolute rises or falls encountered in a day. The reference

point here is the mean glucose value rather than an arbitrarily chosen ideal value.

Arbitrarily, it ignores excursions of less than 1 SD. This may incorrectly disregard possibly

important smaller excursions. MAGE was originally defined from hourly glucose sampling

for 48 hrs in 14 patients. Thus, it has never been formally validated for calculation from

seven-point glucose profiles; neither do we know how the MAGE calculated from CGM

data corresponds to the originally developed value.

An intraday measurement of glycaemic variability specifically developed for use on CGM

data was proposed in 1999 by McDonnell et al. 12, i.e., continuous overlapping net glycaemic

action (CONGA-n). It is calculated as the SD of the summated differences between a current

observation and an observation n hours previously. Because CONGA does not require

arbitrary glucose cutoffs or arbitrarily defined rises and falls, it seems to be a more

objective manner to define glucose variability than M-value or MAGE. It is proposed for

CONGA-1, CONGA-2, and CONGA-4, but it is unknown which, if any, of these is preferable.

When examining glucose variability, the interday variation in blood glucose is also of

interest. In 1972, Molnar et al. 13 observed different day to day glucose patterns in patients

with a similar MAGE. They proposed the absolute mean of daily differences (MODD) as

a supplement to the MAGE and mean blood glucose. The MODD is the mean absolute

value of the differences between glucose values on 2 consecutive days at the same time.

In daily practice, eating habits play an interfering role because different mealtimes will

influence MODD. Developed using hourly blood sampling during 48 hrs, the validity of

its use on seven-point glucose curve data or CGM is unknown.

The most straightforward and easy way to measure interday variability is calculating the

SD of fasting blood glucose concentrations 14. However, it is more a measure of long-term

glucose variability because it takes values of at least 2 consecutive days to calculate. Above

all, fasting glucose variability neglects the variability in al other intraday glucose values.

Besides the commonly used measurements described above, several other methods have

been proposed that have not gained widespread use: the blood glucose rate of change,

computed for CGM, describing the magnitude of temporal fluctuations of blood glucose

levels using logarithmically transformed glucose data 15-17; the mean absolute difference

of consecutive glucose values, validated for SMBG curves 18; the “J”-index, defined as the


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square of the mean plus SD of glucose measurements, excluding severe and persistent

hypoglycaemia, which is validated for SMBG curves 19; and the lability index, based on the

change in glucose levels over time 20. The complexity of the calculations 17 or substantial

similarity with other measures 18,19 probably underlie their limited use.

The MAGE is most commonly used for CGM data and SD/CV for SMBG curves. It has to be

mentioned that blood glucose values are seldom normally distributed, a mathematical

condition for use of the SD 16. In literature, this limitation is mostly ignored. However,

for SMBG strong correlations between variability measures, expressed as SD and mean

absolute difference, have been described 18. Using data from a previous study 21, we also

identified strong and significant correlations between cited variability measures (r =

0.63-0.93; P = 0.01; our unpublished data), suggesting a high degree of overlap between

the different measures when using CGM data. Because the SD correlates highly with

all other variability measures, it seems of little concern that the SD does not take the

number of glycaemic swings into account (Figure 2), whereas the calculation of MAGE,

MODD and CONGA is based on this. Whether calculating MAGE, MODD, CONGA or other

measures simultaneously helps to get additional insight in pathophysiological processes

needs further investigation. A further complication is that the time needed to reliably

assess a given standard of variability is not known. Preliminary results suggest this may

take several days of CGM measurements 22.

In addition to methods to quantify glucose variability derived from direct glucose

measurements, serum determination of 1,5-anhydroglucitol (1,5-AG) has been suggested

as a measure of glycaemic excursions 23. 1,5-AG is a polyol kept within stable limits

in subjects with glucose values in the normal range. Its reabsorption in the kidney

is inhibited by excessive excretion of urinary glucose; the higher the plasma glucose

concentration, the lower the plasma 1,5-AG concentration 24. Urinary glucose appears

at a plasma glucose concentration of approximately 8.8-10.5 mmol/l (160-190 mg/dl),

so despite a very quick response of this marker to changes in plasma glucose levels, it

seems of little use detecting glucose fluctuations below this range. Also, the correlation

between glucose variability and 1,5-AG is weak when HbA1c values are above 8% 25,26.

Measurement of 1,5-AG concentrations seems therefore only of use when looking at

hyperglycaemic excursions, i.e., postprandial hyperglycaemia in patients with an HbA1c

below 8%.

In summary, we suggest SD as the preferable method when quantifying variability from

CGM data since this is the easiest and best validated measure. Also, as further explained

below, SD was the measure used in the only field so far where a relation between glucose

variability and hard outcomes could be demonstrated, i.e., mortality in intensive care

unit (ICU) patients.

24

Figure 2 Two fictitious patients with identical mean glucose and SD, but different patterns of variability A and B are two different patients with different patterns of variability but the same mean glucose (6.0 mmol/l)

and SD (2.1 mmol/l). SD is calculated as the square root of the variance: , where xi is the sample

of the ith observation, x̄ the mean of all observations, and k the number of observations.

III. Contribution of glucose variability to oxidative stress

The current hypothesis about the link between hyperglycaemia and diabetic

complications suggests that the hyperglycaemia-driven formation of reactive oxygen

species enhances four mechanisms of tissue damage: the polyol pathway, the hexosamine

pathway, protein kinase C (PKC) activation, and formation of advanced glycation end-

products 1 (Figure 1). It should, however, be noted that at this time no human intervention

studies have been published that establish a causal relation between oxidative stress and

micro- or macrovascular complications 27. Moreover, daily antioxidant supplementation

does not reduce the risk of cardiovascular events and microvascular complications 28.

However strong the evidence supporting the concept of hyperglycaemia-induced oxidative

stress may be, the role of glycaemic variability in the formation of oxidative stress is

much more controversial. In vitro, animal and human studies in experimental settings

consistently report a deleterious effect of intermittent high glucose, either larger than or

as large as constant high glucose, despite less total glucose exposure, but these findings

cannot be reproduced in real-life human studies.

Quagliaro et al. 29 and Piconi et al. 30 demonstrated that intermittent high glucose

levels stimulate reactive oxygen species overproduction leading to increased cellular

apoptosis in human umbilical vein endothelial cells compared to a stable high glucose

environment. In these studies, three groups of cells were compared, each group receiving

a different fresh medium every 24 hrs for 14 days: a continuously normal glucose medium

∑(xi – x–)2

k – 1


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(5 mmol/l), a continuously high glucose medium (20 mmol/l) and normal and high

glucose media alternating every 24 hrs (5 mmol/l and 20 mmol/l, respectively).

The effect of glycaemic variation vs. constant high glucose was also studied in cells of

the kidney. Takeuchi et al. 31 examined the effects of periodic changes in extracellular

glucose concentration on matrix production and proliferation of cultured rat mesangial

cells. Mesangial cell matrix production, measured as collagen III and IV protein

production and DNA level, was examined as a marker of cell proliferation and

nephropathy development 32,33. Three groups of cells were used, receiving a different

glucose medium every 24 hrs (5 mmol/l, alternating 5 and 25 mmol/l, and 25 mmol/l,

respectively) for 10 days. They reported a significantly larger collagen III and IV protein

and DNA production in the alternating glucose group compared with the continuous

high glucose group. No mechanism for these effects was demonstrated.

Jones et al. 34 investigated the effects of constant and intermittently increased glucose

on human kidney proximal tubule cells (PTC) and cortical fibroblasts (CF). In this study,

cell growth was assessed by thymidine uptake as an index of DNA synthesis, collagen

synthesis as a marker of extracellular matrix production, and protein content. They

exposed three groups of cells for 4 days to 6.1 mmol/l, 25 mmol/l or alternating 6.1 and

25 mmol/l glucose with daily medium change. Overall, the alternating glucose cells

showed larger thymidine uptake (PTC and CF) and more collagen synthesis (CF) than the

cells exposed to a stable high glucose medium. Nevertheless, no differences between

the high and intermittent glucose groups were found in cell protein content in both

PTC and CF. On the cytokine level, alternating high glucose activated more TGF-β1 and

IGF binding protein-3 than stable high glucose, suggesting more collagen synthesis,

potential apoptosis, and biological activity of IGF-1, which has been implicated in the

development of diabetic nephropathy 35,36.

Horváth et al. 37 built on these findings and compared the effect of nontreated diabetes

(continuous high glucose) with intermittently insulin-treated diabetes (oscillating

glucose) on the development of endothelial dysfunction in 19 male Wistar rats. After

10 days of insulin treatment, they monitored blood glucose levels every 6 hrs for 48

hrs in total. After these 48 hrs the rats were killed, and organs were harvested. Their

main finding was that the intermittently treated rats showed a significantly larger

impairment in endothelial function compared with the nontreated animals despite

lower total glucose exposure, with indications for an effect of the poly (ADP-ribose)

polymerase pathway.

The human studies performed are less consistent in their findings. Ceriello et al. 38

performed a normoinsulinemic hyperglycaemic glucose clamp study investigating the

relation between glucose variability, oxidative stress (assessed as plasma 3-nitrotyrosine

26

and 24-hr excretion rates of free 8-iso-prostaglandin F2α [8-iso-PGF2α]), and endothelial

function, measured by flow-mediated dilatation. Type 2 diabetic patients as well as

healthy controls were studied. They suggested that an oscillating glucose level has more

deleterious effects on endothelial function and enhances oxidative stress more than a

constant high glucose level. To mimic glucose variability, glycaemia was increased from

5 to 15 mmol/l and back every 6 hrs for 24 hrs. Stable hyperglycaemia conditions at 10

and 15 mmol/l for 24 hrs were the comparators.

It can be debated how many consecutive periods with alternating degrees of glycaemia

are necessary to reliably assess glycaemic variability rather than the effect of repeated

stimuli. From the field of pituitary function assessment, it is known that repeated

stimuli can result either in extinction of the response or exaggerated response 39. Also,

in everyday life, glucose swings of a patient with diabetes have a duration of less than 6

hrs and occur more frequently than the two 6-hr cycles used in the study performed by

Ceriello et al. 38. As already acknowledged in one of these manuscripts 31, the duration

of alternating glycaemia is also an important comment on the in vitro studies described

earlier since they alternate their glucose media every 24 hrs.

Three studies investigated the correlation between glucose variability assessed using

CGM and oxidative stress in a nonintervention design (Figure 3). These studies

calculated the MAGE to assess glucose variability and 24-hr urinary excretion rates of

8-iso-PGF2α to assess oxidative stress. The first study was performed by Monnier et al. 40

in 21 type 2 diabetes patients. They found a strong correlation between glucose

variability and oxidative stress (r = 0.86; P < 0.001). The second study was performed

by Wentholt et al. 21 in 25 type 1 diabetes patients. They expected to find an even stronger

correlation because of the greater glucose variability in type 1 diabetes patients, but

they could not confirm the findings of Monnier (r = 0.28; no P-value reported). A possible

explanation for this discrepancy is that the studies used a different method to quantify

8-iso-PGF2α excretion rates. Tandem mass spectrometry, used by Wentholt, is not

hampered by cross-reactivity of structurally (un)related components of 8-iso-PGF2α,

whereas the immunoassay used by Monnier is more susceptible to interference 40.

To solve this contradiction, our group reexamined this relationship in 24 type 2

diabetes patients quantifying urinary 8-iso-PGF2α excretion rates with tandem mass

spectrometry 41. We could not reproduce a relationship between glucose variability

and oxidative stress (r = 0.12; P = 0.53).

One intervention trial has been performed to assess the effect of lowering glucose

variability on oxidative stress 42. This crossover trial compared the effect of a basal insulin

regimen and a mealtime insulin regimen on glucose variability and oxidative stress in

type 2 diabetes using CGMS data (n = 40). Although glucose variability tended to be lower


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Figure 3 Different relations between glucose variability and oxidative stress in type 2 and type 1 diabetesCorrelation between glucose variability, expressed as MAGE, and oxidative stress, expressed as urinary excretion rate of 8-iso-PGF2α, in type 2 (A) and type 1 (B) diabetes patients. A, r = 0.86; B, r = 0.26. (Panel A is reproduced with permission from L. Monnier et al.: JAMA 295:1681-1687, 2006 40, © American Medical Association. Panel B is reproduced from Figure 3 with kind permission from I.M. Wentholt et al.: Diabetologia 51:183-190, 2008 21, © Springer Science + Business media.)

(9%; P = nonsignificant) in the mealtime insulin group, no difference in oxidative stress

was found. If anything, there was more oxidative stress in the mealtime insulin group.

Again, no correlation between glucose variability and oxidative stress determined by 24-hr

urinary excretion rates of 8-iso-PGF2α was seen in these insulin-treated type 2 patients.

In this study, 8-iso-PGF2α was quantified by tandem mass spectrometry.

Summarizing, in vitro studies do show a relationship between glycaemic variability and

oxidative stress-induced apoptosis and renal cell proliferation in cultured human or rat

cells. These findings are confirmed in an animal study, but this relation could not be

consistently reproduced in human studies. Differences in duration and frequency of the

periods with alternating glycaemia as well as differences in methods used for oxidative

stress quantification are possible explanations for these discrepant findings.

IV. Contribution of glucose variability to diabetic complications and poor outcomes in critically ill patients

The most important issue for clinical practice is whether glucose variability contributes

to morbidity and mortality irrespective of the pathophysiological mechanism. This issue

was studied retrospectively in type 1 diabetes patients 6,43-47 and in critically ill patients

at the adult 48-50 and pediatric 51,52 ICU.

The DCCT, a randomised controlled trial which included 1,441 patients with type

1 diabetes, presented statistical models in 1995 suggesting a connection between

28

variability in blood glucose and the occurrence of microvascular complications 4. At

similar HbA1c levels throughout the study, patients from the conventionally treated

group were thought to be at higher risk for microvascular complications, particularly

progression of retinopathy, than those in the intensively treated group. Kilpatrick et al. 43,44

independently performed analyses of the data of the DCCT showing that the variability

in blood glucose around a patient’s mean value (SD) was not related to the development

or progression of either retinopathy or nephropathy in type 1 diabetes patients. More

than 10 years later, the DCCT statisticians themselves corrected their previous findings

and refuted the relation suggested earlier 6. As opposed to short-term glucose variability,

long-term fluctuations in glycaemia, expressed as HbA1c variability, may contribute to

the development of retinopathy and nephropathy in the DCCT group 45.

Bragd et al. 46 performed a prospective observational study in 100 type 1 diabetes patients,

collecting five-point self monitoring glucose data for 4 weeks. Data on the incidence and

prevalence of micro- and macrovascular complications as well as peripheral neuropathy

were obtained during an 11-year follow-up. This study confirmed the findings of the

studies mentioned previously in this section, finding no relationship between short-term

glucose variability measured as SD and microvascular complications. However, they

found that glucose variability was significantly related to the presence of peripheral

neuropathy and was a borderline predictor of its incidence (hazard ratio, 1.73; P = 0.07),

suggesting that the nervous system may be vulnerable to glycaemic variability. On the

other hand, recent analysis of the more extensive DCCT datasets did not show any

relation between glucose variability and the prevalence of diabetic peripheral as well

as autonomic neuropathy 47.

A single study in type 2 diabetes patients examined the effect of glucose variability on

retinopathy 53. The coefficient of variation of fasting plasma glucose was retrospectively

calculated in 130 patients without retinopathy at baseline with an average follow-up

of 5.2 years. The frequency of glucose measurements ranged from quarterly to yearly,

so long-term variability of fasting plasma glucose was assessed. The highest quartile of

variation in fasting plasma glucose contributed to diabetic retinopathy independently

from and in addition to HbA1c (odds ratio, 3.68; P = 0.049). This finding is in line with

the above-noted relation of long-term fluctuations in glycaemia to the development of

retinopathy in type 1 diabetes 45.

Recently, a randomised controlled trial was published comparing the effects of a prandial

and a basal insulin regimen with respect to cardiovascular outcomes in type 2 diabetes

patients after acute myocardial infarction (HEART2D Trial 54). The authors concluded

that a significant difference in postprandial glucose values, while achieving comparable

HbA1c values, was not associated with a difference in cardiovascular outcome. Glucose


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variability was not separately assessed, but visual evaluation of the mean glucose profiles

collected during the study seems to show a difference in glucose variability in favour

of the prandial insulin group that did not translate into improved outcome (Figure 4).

Glucose variability has also been studied in critically ill patients. Three different groups

performed retrospective analyses of glucose variability as a predictor of mortality at the

adult ICU 48-50. All three groups concluded that glucose variability measured as SD was

a significant predictor of mortality in critically ill patients independently from severity

of illness. The finding that mortality significantly increased with variability in different

strata of mean glucose level 50, contributes to the suggestion that variability is a predictor

of mortality independent from mean glucose level (Figure 5). Egi et al. 49 performed a

subgroup analysis of patients with diabetes. Interestingly, in this group glucose control,

as assessed by the SD and mean glucose, displayed no relation with survival in contrast

to patients without diabetes. These results may suggest that patients with diabetes “get

accustomed” to fluctuating glucose levels, making them less devastating.

Figure 4 Glycaemic measures in a randomised controlled trial comparing a prandial with a basal insulin regimen (A) Mean (SD) HbA1c by treatment strategy. (B) Seven-point mean SMBG profiles at baseline (dotted line) and throughout the study (solid line) by treatment strategy. (Reproduced from Figure 2 with permission from I. Raz et al.: Diabetes Care 32:381-386, 2009 54 © American Diabetes Association in the format Journal via Copyright Clearance Center.

30

Figure 5 Hospital mortality related to mean glucose and glycaemic variability Q1, lowest quartile of glycaemic variability; Q4, highest quartile of glycaemic variability. To convert mean glucose from mg/dl to mmol/l, multiply by 0.0555. (Reproduced from Figure 1 with permission from J.S. Krinsley: Crit Care Med 36:3008-3013, 2008 50, © Wolters Kluwer Health.)

Not only in the adult ICU, but also in two different pediatric ICUs (PICUs), the influence

of glycaemic variability was studied. Wintergerst et al. 52 retrospectively reviewed all PICU

admissions of 1 year, excluding patients with a known diagnosis of diabetes mellitus

(n = 1094). Glucose variability was assessed as the mean of the absolute differences

between sequential glucose values divided by the differences in collection time. The

second retrospective cohort analysis was performed by Hirshberg et al. 51. They included

all PICU admissions with a length of stay of more than 24 hrs in 1 year, excluding patients

above 18 years of age, patients with known diabetes mellitus or when insulin therapy

was administered during PICU stay (n = 863). Glucose variability was described as a

patient who suffered from both hyperglycaemia (≥8.3 mmol/l) and hypoglycaemia (≤3.3

mmol/l) during PICU stay, which occurred in 6.8% of all patients. Both of these studies

confirmed the earlier described adult data showing that glucose variability is associated

with mortality and increased length of stay in this population, and they even show a

stronger association than hyperglycaemia, although only the latter study adjusted for

severity of illness in multivariate analysis.

Van den Berghe et al. 55 published a landmark trial in 2001 showing a dramatic 42%

relative reduction in mortality in the surgical ICU when blood glucose was normalised

to 4.4-6.1 mmol/l compared to 9.9-11.0 mmol/l. Recently, the purported benefits of tight

glycaemic control in the ICU have been challenged. The NICE-SUGAR study 56 showed that

intensive glucose control (4.5-6.0 mmol/l compared with <10 mmol/l) increased mortality

among adults in the ICU (odds ratio, 1.14; confidence interval, 1.02-1.28). One possible


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explanation for these conflicting results is a differential effect on glucose variability

in these studies because this is strongly associated with mortality in this population 48-50. The results of the van den Berghe study showed a substantially lower SD in the

intensively treated group (SD of morning blood glucose, 19 vs. 33 mg/dl in the intensively

vs. conventionally treated groups, respectively) as opposed to the NICE-SUGAR study

where SD of morning blood glucose was equal in both groups, 25 and 26 mg/dl in the

intensively and conventionally treated groups, respectively.

We can draw a few conclusions from these studies. First, a relation between short-term

glucose variability and microvascular or neurological complications has not been proven

in type 1 diabetes patients and has not been investigated in type 2 diabetes. Second,

no studies have been performed investigating the influence of glucose variability on

macrovascular complications and death in either type 1 or type 2 diabetes patients,

but the HEART2D trial suggests that lowering glucose variability does not improve

cardiovascular outcome in type 2 diabetes patients after acute myocardial infarction.

In contrast, glucose variability is clearly related to mortality in critically ill patients

without diabetes, but intervention trials are still lacking.

V. Glucose variability as a predictor of severe hypoglycaemia

Hypoglycaemia is a complication of diabetes treatment with sometimes severe

consequences, such as seizures, accidents, coma, and death. The frequency of severe

hypoglycaemia increases exponentially when lowering blood glucose 3. Because lowering

blood glucose is the main goal of the treatment of diabetes, occurrence of hypoglycaemia

is a frequent problem. Much harm could be avoided if it were possible to predict severe

hypoglycaemia. Unfortunately, only a modest percentage of future severe hypoglycaemic

episodes can be predicted from known variables such as history of severe hypoglycaemia

and hypoglycaemia awareness 57,58.

In the search for possible predictors, glucose variability is a plausible candidate because

severe hypoglycaemia is preceded by blood glucose disturbances 59, and several studies

reported a decline in the occurrence of hypoglycaemia coinciding with lower glucose

variability 60-62. In 1994, Cox et al. 63 described glucose variability as a more powerful

predictor of future severe hypoglycaemia than HbA1c. In this study, 87 type 1 diabetes

patients prone to severe hypoglycaemia were included. Fifty SMBG readings were

collected during 2 to 3 weeks, and severe hypoglycaemia occurrence was recorded for

the subsequent 6 months.

32

The Diabetes Outcomes in Veterans Study (DOVES) 64 developed and subsequently validated

a model for predicting hypoglycaemia based on the idea that hypoglycaemia is more

likely if the mean blood glucose is low or if negative deviations from the mean are large.

The 195 insulin-treated type 2 diabetes patients included collected SMBG glucose values

four times daily for 8 weeks and had three follow-up visits in 1 year. In this model, the

risk of hypoglycaemia of any severity (blood glucose ≤3.33 mmol/l) appeared to be unique

to each subject and was as much related to glucose variability as to the mean glucose

value. The authors suggested that minimizing glucose variability is a plausible method

for offsetting the increased risk of hypoglycaemia associated with tight glycaemic control.

Unfortunately, how glycaemic variability could be targeted separately remains unclear.

Kilpatrick et al. 65 used the datasets of the DCCT to establish whether mean blood glucose

and/or glucose variability add to the predictive value of HbA1c for hypoglycaemia risk

in type 1 diabetes. This is the only study aiming to predict hypoglycaemia within 24 hrs

after SMBG collection. In this model, glucose variability, calculated as the SD of daily

blood glucose and MAGE, was independently predictive of hypoglycaemia just like mean

blood glucose. Concerning night-time hypoglycaemic events, variability at the end of

the day seemed predictive, suggesting that patients who suffer from this complication

could aim at reducing glycaemic fluctuations rather than let their blood glucose run

higher at bedtime.

From the above, it can be concluded that glucose variability is larger in patients with

diabetes who suffer from hypoglycaemia, in particular severe hypoglycaemia. Also,

glucose variability seems a predictor of severe hypoglycaemia, but it is more difficult to

answer the question whether it is an independent predictor of future hypoglycaemia.

None of the studies reviewed here performed an analysis to examine whether glucose

variability remains a predictor of hypoglycaemia when correcting for known predictors

such as history of severe hypoglycaemia and hypoglycaemia unawareness. It may be

useful to aim at lower glucose variability in those who suffer from severe hypoglycaemia

while at the same time trying to prevent a rise in mean blood glucose and HbA1c, but

a specific intervention trial is lacking.

VI. Clinical recommendations

A. Should glucose variability be a target for intervention?According to the reviewed literature, glucose variability could be investigated as a

separate treatment target in nondiabetic, critically ill patients, but with the introduction

of strict glucose regulation at the ICU, diminishing hyperglycaemic glucose excursions is

already a goal of therapy 55,66. Also, prevention and treatment of hypoglycaemia will be a


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target anyway, although data on whether hypoglycaemia in the ICU is related to increased

mortality are conflicting 55,56,66-70. Glucose regulation with alertness for hypoglycaemia

should remain the intervention of choice until interventions specifically targeting

variability become available and are shown to result in improved outcome.

In insulin-treated diabetes patients with severe hypoglycaemia, it is often unavoidable

to reduce insulin doses to avoid subsequent episodes. However, a reduction in insulin

potentially leads to a deterioration of glucose control 71. Theoretically, therapies specifically

aiming to lower glucose variability might prevent severe hypoglycaemia while leaving

general glucose regulation unaffected. Again, trials supporting this notion are lacking.

As described above, there is little evidence to target glucose variability in general for

its limited effects on outcome. But one could think of other reasons to treat glucose

variability on an individual basis. It has been shown that within-day variability is an

independent predictor of the HbA1c achieved in type 1 diabetes patients receiving

multiple daily insulin therapy, with the largest variability correlating with the highest

HbA1c levels 72. One of the possible explanations for this is that glucose variability

reflects unexpected hypoglycaemic episodes due to a variable response to insulin

injections. This might lead to patient fear of hypoglycaemia and a possible deterioration

of glycaemic control when avoiding hypoglycaemia by resisting raising insulin dosage

or physical activity and a subsequent reduction in the patients’ quality of life 73. Clinical

investigations correlating glycaemic variability and quality of life are lacking, however.

Another important consequence of large intraindividual glucose variability is that the

patient has to perform SMBG more frequently, which is a burden for most diabetes

patients both from a psychological and a financial point of view.

B. Available options to target glucose variabilityAs for outpatients with type 1 or type 2 diabetes, long-acting insulin analogues seem

to improve glucose stability; treatment with long-acting analogues has been shown to

diminish hypoglycaemia and glucose variability 74-76. Prandial insulins, and even more

short-acting analogues, diminish postprandial hyperglycaemia and consequently glucose

variability specifically in type 2 diabetes patients 77,78. In comparison to the long-acting

analogue insulin glargine, the glucagon-like peptide-1 receptor agonist exenatide reduced

glucose variability with a similar reduction in HbA1c 79. Furthermore, compared to

multiple daily insulin injections, the use of continuous subcutaneous insulin infusion

is in type 1 diabetes associated with a decrease in glucose variability 60,80,81. Whether

diminishing glycaemic variability in these patient groups translates into improved

outcome is unknown, although it has been shown that patients with the largest glucose

variability benefit the most from switching from multiple daily insulin to continuous

subcutaneous insulin infusion, achieving significant lower HbA1c values 72.

34

VII. Conclusions and future perspectives

According to the literature we may conclude that glucose variability seems related to oxidative

stress in in vitro and animal studies and, although not consistently, in an experimental setting

in type 2 diabetes patients. In a clinical setting, glucose variability is related to mortality in

non-diabetic, critically ill subjects and is associated with (severe) hypoglycaemia in insulin-

treated diabetes patients. However, at this time there is no supportive evidence for targeting

glucose variability separately from mean glucose and/or HbA1c values.

There is no “gold” standard for determining glucose variability. Until added value for

other measures is shown, a simple SD seems the best way to quantify glucose variability.

CGM readings seem preferable to SMBG measurements to capture all variability, but no

data are available comparing these two methods in assessing glucose variability.

The only way to establish the utility of targeting glycaemic variability would be further

studies specifically aimed at lowering glucose variability, to investigate its influence on

hard outcomes.


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58. Gold AE, Frier BM, MacLeod KM, Deary IJ (1997) A structural equation model for predictors of severe hypoglycaemia in patients with insulin-dependent diabetes mellitus. Diabetic Medicine 14:309-315

59. Kovatchev BP, Cox DJ, Farhy LS, Straume M, Gonder-Frederick L, Clarke WL (2000) Episodes of severe hypoglycemia in type 1 diabetes are preceded and followed within 48 hours by measurable disturbances in blood glucose. J Clin Endocrinol Metab 85:4287-4292

60. Jeha G, Karaviti L, Anderson B, Smith E, Donaldson S, McGirk T, Haymond M (2005) Insulin pump therapy in preschool children with type 1 diabetes mellitus improves glycemic control and decreases glucose excursions and the risk of hypoglycemia. Diabetes Technology & Therapeutics 7:876-884

61. Kudva Y, Basu A, Jenkins G et al. (2007) Glycemic variation and hypoglycemia in patients with well-controlled type 1 diabetes on a multiple daily insulin injection program with use of glargine and ultralente as basal insulin. Endocr Pract 13:244-250

62. Saudek C, Duckworth WC, Giobbie-Hurder A et al. (2006) Implantable insulin pump vs multiple-dose insulin for non-insulin-dependent diabetes mellitus: a randomized clinical trial. Department of Veterans Affairs Implantable Insulin Pump Study Group. JAMA 276:1322-1327

63. Cox DJ, Kovatchev BP, Julian DM, Gonder-Frederick LA, Polonsky WH, Schlundt DG, Clarke WL (1994) Frequency of severe hypoglycemia in insulin-dependent diabetes mellitus can be predicted from self-monitoring blood glucose data. J Clin Endocrinol Metab 79:1659-1662

64. Murata GH, Hoffman RM, Shah JH, Wendel CS, Duckworth WC (2004) A probabilistic model for predicting hypoglycemia in type 2 diabetes mellitus: the diabetes outcomes in veterans study (DOVES). Arch Intern Med 164:1445-1450

65. Kilpatrick, Rigby, Goode, Atkin (2007) Relating mean blood glucose and glucose variability to the risk of multiple episodes of hypoglycaemia in type 1 diabetes. Diabetologia 50:2553-2561

66. Van den Berghe G, Wilmer A, Hermans G et al. (2006) Intensive insulin therapy in the medical ICU. N Engl J Med 354:449-461

67. Krinsley JS (2004) Effect of an intensive glucose management protocol on the mortality of critically ill adult patients. Mayo Clin Proc 79:992-1000

68. Vriesendorp TM, DeVries JH, van Santen S et al. (2006) Evaluation of short-term consequences of hypoglycemia in the intensive care unit. Crit Care Med 34:2714-2718

69. Brunkhorst FM, Engel C, Bloos F et al. The German Competence Network Sepsis (SepNet) (2008) Intensive insulin therapy and pentastarch resuscitation in severe sepsis. N Engl J Med 358:125-139

70. Devos P, Preiser JC, Melot C (2007) Impact of tight glucose control by intensive insulin therapy on ICU mortality and the rate of hypoglycemia: final results of the Glucontrol study. Intensive Care Medicine 33: Suppl 2:S189

71. The Diabetes Control and Complications Trial Research Group (1996) The absence of a glycemic threshold for the development of long-term complications: the perspective of the Diabetes Control and Complications Trial. Diabetes 45:1289-1298

72. Pickup J, Kidd J, Burmiston S, Yemane N (2006) Determinants of glycaemic control in type 1 diabetes during intensified therapy with multiple daily insulin injections or continuous subcutaneous insulin infusion: importance of blood glucose variability. Diabetes/Metabolism Research and Reviews 22:232-237

73. Hartman I (2008) Insulin analogs: impact on treatment success, satisfaction, quality of life, and adherence. Clinical Medicine & Research 6:54-67

74. Riddle MC, Rosenstock J, Gerich J (2003) The treat-to-target trial: randomized addition of glargine or human

38

NPH insulin to oral therapy of type 2 diabetic patients. Diabetes Care 26:3080-308675. Hermansen K, Davies M, Derezinski T, Martinez Ravn G, Clauson P, Home P, on behalf of the Levemir Treat-to-

Target Study Group (2006) A 26-Week, randomized, parallel, treat-to-target trial comparing insulin detemir with NPH insulin as add-on therapy to oral glucose-lowering drugs in insulin-naive people with type 2 diabetes. Diabetes Care 29:1269-1274

76. White NH, Chase HP, Arslanian S, Tamborlane WV (2009) Comparison of glycemic variability associated with insulin glargine and intermediate-acting insulin when used as the basal component of multiple daily injections for adolescents with type 1 diabetes. Diabetes Care 32:387-93

77. Anderson JJ, Brunelle R, Keohane P, Koivisto V, Trautmann M, Vignati L, DiMarchi R (1997) Mealtime treatment with insulin analog improves postprandial hyperglycemia and hypoglycemia in patients with non-insulin-dependent diabetes mellitus. Multicenter Insulin Lispro Study Group. Arch Intern Med 157:1249-1255

78. Kang S, Creagh FM, Peters JR, Brange J, Volund A, Owens DR (1991) Comparison of subcutaneous soluble human insulin and insulin analogues (AspB9, GluB27; AspB10; AspB28) on meal-related plasma glucose excursions in type I diabetic subjects. Diabetes Care 14:571-577

79. McCall AL, Cox DJ, Brodows R, Crean J, Johns D, Kovatchev B (2009) Reduced daily risk of glycemic variability: comparison of exenatide with insulin glargine. Diabetes Technology & Therapeutics 11:339-344

80. Bruttomesso D, Crazzolara D, Maran A et al. (2008) In type 1 diabetic patients with good glycaemic control, blood glucose variability is lower during continuous subcutaneous insulin infusion than during multiple daily injections with insulin glargine. Diabet Med 25:326-332

81. Alemzadeh R, Palma-Sisto P, Holzum M, Parton E, Kicher J (2007) Continuous subcutaneous insulin infusion attenuated glycemic instability in preschool children with type 1 diabetes mellitus. Diabetes Technol Ther 9:339-347


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Chapter 3

Mild hyperglycaemia disturbs vascular homeostasis in humans

Sarah E. Siegelaar, Bregtje A. Lemkes, Max Nieuwdorp, Wim Kulik,

Joost C. Meijers, Joost B.L. Hoekstra and Frits Holleman

Submitted for publication

42

Abstract

Hyperglycaemia induces oxidative stress, disturbs endothelial function, damages

the endothelial glycocalyx and causes a prothrombotic shift in coagulation and

fibrinolysis. Little is known about the exact blood glucose level necessary to

start these processes. The aim of this study was to determine at which level of

glycaemia these changes occur. A stepwise hyperglycaemic clamp was performed

in eleven healthy human males at blood glucose (BG) levels of 6, 8 and 10 mmol/l

for two hours each while suppressing endogenous insulin release. Oxidative

stress, assessed by malondialdehyde, showed a gradual increase highly correlating

with BG. Coagulation, assessed by prothrombin fragments F1+2, significantly

increased at 6 mmol/l and was followed by an increase in both plasmin-

antiplasmin complexes and d-dimer levels at 8 mmol/l, indicating fibrinolysis

activation. The endothelial glycocalyx, measured by hyaluronic acid levels,

showed no relevant change during the clamp. Hyaluronidase showed a gradual

decrease indicating increased hyaluronidase substrate binding by shedding of

glycocalyx constituents, significant at 10 mmol/l. There was no indication of a

cumulative effect of glycaemia over time on any of the parameters. These data

indicate that oxidative stress as well as coagulation activation already starts at

near normal BG levels, while endothelial glycocalyx changes occur at 10 mmol/l.


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Introduction

Patients with diabetes mellitus are at high risk of developing cardiovascular disease. A

combination of accelerated atherosclerosis and a shift towards a pro-coagulant state leads to

atherothrombotic events in nearly two thirds of all patients with diabetes 1. Hyperglycaemia,

its defining feature, has been shown to cause both endothelial dysfunction, a precursor

of atherosclerosis, and activation of the coagulation system 2;3. Endothelial dysfunction is

paired with damage to the endothelial glycocalyx, the protective layer of proteoglycans and

glycosaminoglycans lining the luminal side of all blood vessels. Hyperglycaemia induced

disruption of the endothelial glycocalyx results in a pro-atherogenic state, characterised

by increased vascular permeability, coagulation activation and increased cellular adhesion

and migration 4. The formation of reactive oxygen species (ROS) is an important mechanism

by which hyperglycaemia leads to endothelial (glycocalyx) damage, since high doses of

anti-oxidants are able to attenuate this damage 5. Furthermore, hyperglycaemia induced

ROS formation may also affect the coagulation system, by influencing gene transcription

of coagulation and fibrinolytic factors 6;7.

It is unclear, however, at which glucose level these vascular changes first occur and

whether this is an on-off phenomenon with a threshold or a continuous relationship. This

distinction is also of importance given the recent debate about the impact of glycaemic

variability on the development of complications in patients with diabetes 8;9. Some have

argued that a high variation in blood glucose levels throughout the day has a greater

impact on pro-atherogenic processes than a stable high glucose 10-12. If so, a threshold

phenomenon should exist for these processes, since a dose-dependent effect would lead

to comparable outcome when the mean blood glucose levels are equal.

Most studies investigating the effects of hyperglycaemia on vascular homeostasis have

described effects of a glucose level of 10 mmol/l or higher 2;5, but epidemiological studies

suggest that vascular damage actually starts at near normal glucose levels. Even in the

lower glucose ranges a linear relationship between HbA1c, fasting plasma glucose and

vascular complications of diabetes was demonstrated in patients with both type 1 and

type 2 diabetes 13;14. Moreover, impaired glucose tolerance and impaired fasting glucose,

both representing only mildly elevated glucose levels, already carry an increased risk

for macrovascular disease 15.

In the present study we describe the effects of only mildly elevated glucose levels on

oxidative stress, the endothelial glycocalyx and the thrombotic system in healthy males,

studied by performing a stepwise glucose clamp while suppressing endogenous insulin

levels.

44

Results

ProtocolWe investigated whether near-normal glucose levels were associated with endothelial

dysfunction by means of a stepwise hyperglycaemic clamp while suppressing endogenous

insulin production by octreotide infusion. Blood glucose (BG) levels were maintained at

6, 8 and 10 mmol/l successively for 2 hrs per level (Figure 1). We obtained blood samples

every 30 minutes during the clamp. Also, the day after the clamp a fasting blood sample

was obtained to assess the recovery after the glucose load.

PatientsIn total, 14 healthy non-smoking Caucasian males with a fasting plasma glucose level

≤5 mmol/l without risk factors for macrovascular disease as measured by BMI, blood

pressure, cholesterol and triglyceride levels were included in the study. Of those, one

dropped out due to febrile illness before the study day and two subjects were excluded

before analysis of the blood samples due to poor performance of the hyperglycaemic

clamp, resulting in 11 subjects who were included in the final analyses. Baseline

characteristics of the included subjects are listed in Table 1.

Table 1 Baseline characteristics

n = 11

Age, years 24.3 (3.6)

BMI, kg/m2 21.6 (1.9)

Systolic blood pressure, mmHg 115.1 (12.3)

Diastolic blood pressure, mmHg 68.9 (8.5)

Fasting plasma glucose, mmol/l 4.7 (0.2)

HbA1c, % 5.3 (0.2)

Total cholesterol, mmol/l 4.0 (0.9)

LDL cholesterol, mmol/l 2.2 (0.9)

HDL cholesterol, mmol/l 1.5 (0.3)

Triglycerides, mmol/l 0.7 (0.4)

Values are expressed as means (SD). BMI, body mass index; HDL, high density lipoprotein; LDL, low density lipoprotein

Hyperglycaemic clamp During the clamp endogenous insulin levels were adequately suppressed. Plasma insulin

levels after 1 hr of octreotide infusion and at the end of the clamp were comparable with


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fasting levels in all patients (median <15 pmol/l, maximum 43 pmol/l). Mean glucose

levels of all included time points are depicted in Figure 1.

Figure 1 Glucose clamp Glucose values obtained during the clamp. Data are expressed as mean (SD) of the previous 30 minutes. Dotted lines represent the time points where glucose infusion was increased to go to the next glucose level.

Oxidative stressOxidative stress was assessed by quantitative determination of malondialdehyde (MDA)

in plasma using high performance liquid chromatography tandem mass spectrometry

(HPLC-MS/MS). MDA is a reactive and potentially mutagenic aldehyde which is formed

as a result of lipid peroxidation caused by hyperglycaemia induced formation of

ROS. Lipid peroxidation is thought to be an important part of the pathogenesis of

atherosclerosis 16 and the metabolites are frequently used as biomarkers for oxidative

stress 17.

Plasma MDA levels during the glucose clamp are depicted in Figure 2 and Table 2.

Plasma MDA levels did not increase after 1 hr octreotide infusion (median [IQR]

6.6 [6.2-7.7] μmol/l to 6.8 [6.0-8.0] μmol/l, P = 0.89). After the start of the glucose

infusion, plasma MDA increased gradually accompanying the increase in blood

glucose with a strong correlation between MDA and blood glucose levels (ρ = 0.82, P

<0.001, Spearman correlation). Median MDA levels at the 8 mmol/l glucose plateau

were significantly higher than after 1 hr octreotide infusion (9.9 [9.3-10.6] μmol/l,

P = 0.02) and were further increased substantially at the 10 mmol/l plateau (11.8

[10.8-12.5] μmol/l, P = 0.01). No cumulative effect of glucose over time during each

plateau was observed (Friedman test for repeated measures). The day after the clamp

median plasma MDA levels had returned to baseline (6.4 [5.6-6.9] μmol/l, P = 0.24).

46

Tab

le 2

Pla

sma

leve

ls o

f th

e p

aram

eter

s o

f in

tere

st d

uri

ng

the

glu

cose

cla

mp

Mar

ker

Un

itB

asel

ine

t =

1 h

r6

mm

ol/

l8

mm

ol/

l10

mm

ol/

lt

= 24

hrs

MD

Aμm

ol/l

6.6

(6.2

-7.7

)6.

8 (6

.0-8

.0)

8.0

(7.6

-9.6

)9.

9 (9

.3-1

0.6)

*11

.8 (1

0.8-

12.5

)*6.

4 (5

.6-6

.9)

F1+2

pm

ol/l

140

(118

-151

)17

0 (1

24-4

74)

508

(275

-171

4)*

731

(446

-109

5)*

608

(445

-817

)*13

6 (1

23-1

69)

vWF

%66

(50-

127)

63 (5

3-11

3)59

(39-

86)*

58 (3

9-10

7)*

55 (4

2-78

)*76

(58-

130)

ETP

nM

.min

1271

(116

7-14

15)

1237

(107

4-14

29)

1297

(108

2-14

25)

1263

(113

7-14

40)

1296

(114

3-14

86)*

1328

(117

0-14

81)^

Peak

th

rom

bin

nM

222

(200

-264

)20

9 (1

86-2

44)

208

(180

-245

)18

7 (1

74-2

45)

209

(189

-243

)22

6 (2

11-2

65)

PAP

µg/l

366

(268

-460

)33

5 (2

44-9

34)

540

(271

-854

)60

3 (3

98-1

060)

*61

5 (5

08-7

11)

426

(277

-650

)

d-d

imer

mg/

l0.

00 (0

-0.1

8)0.

00 (0

-0.0

5)0.

08 (0

-0.2

3)0.

28 (0

-0.5

1)*

0.36

(0.0

4-0.

51)*

0.19

(0-0

.39)

^

HA

ng/

ml

49.6

(48.

1-50

.2)

49.5

(47.

9-50

.2)

49.9

(48.

8-50

.9)

49.9

(47.

0-51

.5)*

50.3

(47.

2-51

.2)*

51.6

(50.

4-54

.5)^

Hya

luro

nid

ase

U/m

l51

.6 (4

3.6-

55.3

)49

.4 (4

4.0-

58.3

)45

.4 (3

6.3-

48.6

)48

.3 (3

5.4-

55.2

)36

.0 (3

3.4-

41.4

)*49

.5 (4

4.1-

53.0

)

At

each

glu

cose

pla

teau

th

e m

edia

n (

IQR

) va

lues

of

the

mea

n v

alu

e p

er p

atie

nt

are

dep

icte

d. M

DA

, m

alon

dia

ldeh

yde;

F1+

2, p

roth

rom

bin

fra

gmen

t 1+

2; v

WF,

von

W

ille

bran

d f

acto

r; P

AP,

pla

smin

-an

tip

lasm

in c

omp

lex;

ETP

, en

dog

enou

s th

rom

bin

pot

enti

al; H

A, h

yalu

ron

ic a

cid

. ^

P <0

.05

com

par

ed t

o ba

seli

ne;

* P

<0.

05 c

omp

ared

to

t =

1


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Figure 2 Oxidative stress Oxidative stress was assessed by plasma malondialdehyde (MDA) levels during the glucose clamp. Data are depicted as medians with interquartile ranges. Dotted lines represent the timepoints where glucose infusion was increased to go to the next glucose level. *P <0.05 compared to t = 1, after 1 hr of octreotide infusion.

Coagulation We determined the effects of increasing glucose levels on coagulation by measuring

prothrombin fragment 1+2 (F1+2) and von Willebrand factor (vWF). F1+2 are released

when thrombin is formed from prothrombin and therefore provide an in vivo measure

of thrombin formation. VWF plays a major role in haemostasis by ensuring the arrest

of blood platelets at sites of injury, and by binding of coagulation factor VIII, but it is

also an established marker of endothelial dysfunction.

The effects of the stepwise increase in blood glucose on these markers of coagulation

are depicted in Figure 4 and Table 2. No significant effects on F1+2 levels or vWF were

detected after octreotide infusion. Median F1+2 levels showed a significant increase

from 170 (124-474) pmol/l to 508 (275-1714) pmol/l when the glucose level was raised to 6

mmol/l, further increased to 731 (446-1095) pmol/l at 8 mmol/l and remained at a stable

high level at 10 mmol/l with no significant differences between the glucose plateaus.

The following day F1+2 levels had returned to baseline. After raising the glucose level to

6 mmol/l vWF levels dropped to 59% (39-86, P = 0.02). An increase of glucose to 8 mmol/l

led to a further decrease of vWF levels to 58% (39-107, P = 0.05), but raising the glucose

level to 10 mmol/l did not cause further significant changes. After 24 hrs, vWF levels

had returned to baseline values.

Finally, we determined the endogenous thrombin potential (ETP), which represents the

48

balance between pro- and anti-coagulant processes in plasma and provides an ex vivo

measure for overall coagulability. No significant effects of the octreotide run-in period

or any of the blood glucose levels on peak thrombin values could be detected (Table

2). ETP showed a significant decrease from 1271 (1167-1415) nM.min to 1237 (1074-1429)

nM.min after the 1 hr octreotide period (P = 0.01) and a subsequent small increase to

1296 (1143-1486) nM.min at the 10 mmol/l glucose plateau (P = 0.01). The following day,

ETP remained higher than baseline at 1328 (1170-1481) nM.min (P = 0.05).

Figure 3 Endothelial glycocalyx Shedding of endothelial glycocalyx components was assessed by plasma hyaluronan levels (left panel) and activity of the regulatory enzyme hyaluronidase (right panel). Data are depicted as medians with interquartile ranges. Dotted lines represent the time points where glucose infusion was increased to go to the next glucose level. *P <0.05 compared to t = 1, after 1 hr of octreotide infusion.

Figure 4 Coagulation Coagulation was assessed by prothrombin fragment 1+2 (F1+2; left panel) and von Willebrand factor (vWF; right panel) plasma levels. Data are depicted as medians with interquartile ranges. Dotted lines represent the time points where glucose infusion was increased to go to the next glucose level. *P <0.05 compared to t = 1, after 1 hr of octreotide infusion.


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Figure 5 Fibrinolysis Fibrinolysis was assessed by plasmin-alpha-antiplasmin (PAP) complexes (left panel) and d-dimer (right panel) plasma levels. Data are depicted as medians with interquartile ranges. Dotted lines represent the time points where glucose infusion was increased to go to the next glucose level. *P <0.05 compared to t = 1, after 1 hr of octreotide infusion.

Fibrinolysis Fibrinolysis was assessed by measuring plasmin-alpha2-antiplasmin (PAP) complexes

and d-dimer (Figure 5 and Table 2). PAP complexes serve as an indicator of recent in

vivo fibrinolytic activity, since alpha2 antiplasmin is the most important circulating

inhibitor of plasmin, the main enzyme in the fibrinolytic system. D-dimer is a fibrin

degradation product, which is dependent on the amount of fibrin that is generated

(coagulation) as well as the ability of the fibrinolytic system to degrade the generated

fibrin (fibrinolysis).

Neither PAP complexes nor d-dimer had changed significantly after the 1-hr octreotide

infusion period (t = 1; Figure 5). Median PAP levels were not significantly different at

a blood glucose level of 6 mmol/l when compared to t = 1, but did show a significant

increase at a blood glucose of 8 mmol/l (335 [244-934] μg/l to 603 [398-1060] μg/l, P = 0.01).

PAP levels remained at a stable high level when blood glucose was further increased to

10 mmol/l and returned to baseline the following day. Median d-dimer levels showed an

increasing trend from 0.00 (0.00-0.05) mg/l to 0.08 (0.00-0.23) mg/l when the glucose level

was raised to 6 mmol/l (P = 0.07). At 8 mmol/l d-dimer levels had risen to 0.28 (0.00-0.51)

mg/l (P = 0.04, compared to t = 1) and increased further to 0.36 (0.004-0.51) mg/l when

blood glucose was raised to 10 mmol/l (P = 0.02, compared to t = 1). After 24 hrs, d-dimer

levels remained higher than at t = 0, at 0.19 (0.00-0.39) mg/l (P = 0.04).

No cumulative effect of any of the coagulation and fibrinolysis parameters was detected

at the three examined blood glucose levels, except for F1+2 at a blood glucose level of 6

mmol/l (p=0.02, Friedman test for repeated measures).

50

Endothelial glycocalyxThe effect of elevation of blood glucose levels on the endothelial glycocalyx was

assessed by plasma measurement of its main component hyaluronic acid (HA) and its

regulatory enzyme hyaluronidase to detect shedding from the glycocalyx. Plasma HA

and hyaluronidase levels were unaffected by 1 hr octreotide infusion (t = 1). When raising

the blood glucose level to 6 mmol/l, median HA levels remained unaffected but a raise

to 8 and 10 mmol/l showed a significant, but small increase compared to t = 1 (from

49.5 [47.9-50.2] ng/ml to 49.9 [47.0-51.5] ng/ml, P = 0.038, and to 50.3 [47.2-51.2] ng/ml, P

= 0.008). This increase persisted after 24 hrs. Plasma hyaluronidase activity showed a

gradual decrease during the clamp, with significantly lower activity at a blood glucose

level of 10 mmol/l (36.0 [33.4-41.4] U/ml compared to 51.6 [43.6-55.3] U/ml at t = 1, P =

0.005). After 24 hrs, these levels had returned to baseline values (Figure 3 and Table 2).

Discussion

In this study we show that oxidative stress, represented by MDA levels, showed a stepwise

increase during the clamp, mimicking the glucose curve. The coagulation system was

activated even at near normal glucose levels of 6 mmol/l, resulting in a significant

increase in prothrombin fragments 1+2 (F1+2) indicating thrombin formation. This was

followed by activation of the fibrinolytic system, as measured by PAP complexes and

d-dimer, at a glucose level of 8 mmol/l. Relevant endothelial glycocalyx changes were not

detected using biochemistry techniques, except for a decrease in hyaluronidase activity

when the glucose concentration was raised to 10 mmol/l.

To our knowledge this is the first study examining the effects of isolated mild

hyperglycaemia, with a maximum of 10 mmol/l, on vascular homeostasis. Previous studies

on oxidative stress show glucose dependent formation of reactive oxygen species (ROS)

with blood glucose levels above 10 mmol/l 2;10, which is comparable with our findings and

in vitro studies 18. Our data suggest that hyperglycaemia dependent ROS formation is dose-

dependent rather than an on-off phenomenon. This is depicted in Figure 2, showing no

cumulative effect within the different glucose plateaus but only an increase in oxidative

stress when blood glucose is increased to the next level. Data reported by Ceriello et al. 10

support this finding showing higher plasma nitrotyrosine levels at a plasma glucose of

15 mmol/l compared to 11 mmol/l as well as no further increase in plasma nitrotyrosine

levels when stabilizing plasma glucose. Also, in type 2 diabetes patients an impressive

correlation between MDA and blood glucose (ranging from 6 to 14 mmol/l) was found

after a mixed-meal test suggesting an insulin-independent effect 19.

Our results are in line with previous observations, which have shown that hyperglycaemia


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activates the coagulation 5;20 as well as the fibrinolytic system 5. Unlike oxidative stress,

our results indicate that the glucose induced-activation of the coagulation system is an

on-off phenomenon showing a more than threefold increase in thrombin generation,

measured by F1+2 levels, triggered by a blood glucose level of only 6 mmol/l. This

hypothesis is supported by the observation that maximum levels are reached quickly

and show no increase, perhaps even a decrease, at the highest glucose level. Moreover,

the maximum levels of F1+2 and d-dimer are comparable with the levels reached during

a hyperglycaemic clamp at a blood glucose of 15 mmol/l previously performed by our

group 5. The timing of the increase in fibrinolytic activity, closely following the coagulant

activity, suggests that the increased fibrinolytic activity is secondary to the coagulation

activation. Conversely, diabetes mellitus is associated with impairment in fibrinolysis 21,

which we did not detect in our study. However, Stegenga et al. 20 showed that fibrinolysis

was mainly affected by hyperinsulinemia as opposed to hyperglycaemia, and insulin was

suppressed throughout our experiments.

ETP changed only minimally during and after the clamp. This indicates no relevant

change in the thrombin generating capacity of the coagulation system itself, but

rather suggests that glucose is a trigger for the in vivo activation of coagulation. VWF

levels showed a maximal decrease of 5%. This modest change could be due to increased

binding to blood platelets, known to be activated by hyperglycaemia, or caused by

physical inactivity of the participants. VWF levels have been shown to increase after

physical exercise 22 and previous control experiments by our group have shown a similar

decreasing effect of a 6-hr saline infusion in healthy males (M. Nieuwdorp, unpublished

data).

We did not detect relevant changes in plasma HA levels and a decrease in hyaluronidase

activity was found only at a glucose level of 10 mmol/l. Previous investigations show

marked increase in HA shedding from 70 ng/ml at baseline to 112 ng/ml with blood

glucose levels of 15 mmol/l 5, suggesting that the trigger for direct endothelial damage as

reflected by loss of glycocalyx lies above a blood glucose level of 10 mmol/l. Statistically,

there was a change in plasma HA levels at 8 and 10 mmol/l. However, the maximum

increase was only 1.6% indicating no significant biological effect. This is supported by

the limited effects on vWF levels, which are also considered a marker for endothelial

damage. The decrease in hyaluronidase activity at 10 mmol/l does indicate substrate

binding to this enzyme. This substrate may consist of other glycosaminoglycans than

HA shed from the glycocalyx, such as heparan sulphate or chondroitin sulphate, since

these are also bound by hyaluronidase.

The results of our study are in line with epidemiological data, which show that the

increase in cardiovascular risk already starts at mildly elevated glucose levels 13-15.

52

Nonetheless, our results indicate that glucose-induced activation of the coagulation

system and ROS formation are completely reversible after 24 hrs. Therefore, these changes

may not be considered to be pathological in healthy subjects who spent the greater part

of the day with glucose levels below 6.1 mmol/l 23. Conversely, patients with diabetes or

pre-diabetes by definition have a fasting glucose level of >5.6 mmol/l if untreated 24, and

are exposed to glucose levels above 6 mmol/l throughout the day. This may interfere

with the reversibility of the changes in coagulation and oxidative stress, and translate

to pathological effects. Moreover, in diabetes inappropriate activation of the coagulation

system may not be counterbalanced because of the fibrinolytic impairment associated

with this disease 21. Our results do not support a role for glucose variability in coagulation

activation and ROS formation, since coagulation activation occurred even at a blood

glucose of 6 mmol/l and the relationship between blood glucose and oxidative stress

was continuous.

Several aspects of our study need comment. First, this study was specifically designed to

assess the effects of mild hyperglycaemia on several components of vascular homeostasis

and was therefore performed under full suppression of insulin levels. In disease states,

such as type 2 diabetes or stress-hyperglycaemia during severe illness, high glucose

levels are accompanied by high insulin levels and therefore our results cannot be

extrapolated directly to these settings. However, glucose levels are highly predictive of

vascular complications 14;25 although insulin levels are highly variable in these patients.

Second, given the design of our experiment, it can be argued that the effects we detected

may not be glucose specific, but rather result from the osmotic effect of raising blood

glucose or from prolonged administration of octreotide. However, previous work from

our group has shown no effect on coagulation or fibrinolysis in a control experiment

during which octreotide was administered in combination with mannitol infusion for six

hours, serving as a time and osmolality control 5. Moreover, in our study no significant

effect on any of the parameters after one hour of octreotide infusion was detected. This

is supported by literature, showing no significant vaso-active or haemostatic effects of

this dose of octreotide 26;27.

In conclusion, our results show that glucose-induced changes to vascular homeostasis

already start at near normal glucose levels. Furthermore our study reveals a dose-

dependent effect of glucose on MDA formation and an on-off phenomenon for glucose

induced coagulation activation, while changes to the endothelial glycocalyx occur at

glucose levels of 10 mmol/l or higher. These results give us more insight in the glucose

driven mechanisms of vascular complications in humans. To elucidate the difference

between acute and chronic mild hyperglycaemia on vascular homeostasis, further studies

are needed.


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Methods

PatientsThe study was approved by the institutional medical ethical committee and conducted

according to Declaration of Helsinki principles. Participants signed informed consent

prior to inclusion after oral and written explanation of the study.

Stepwise hyperglycaemic clamp protocol (Figure 6) After an overnight fast, two catheters for venous access were placed, one in every arm.

First, basal measurements of haemostasis and ROS formation were performed. Octreotide

was dissolved in saline 0.9% and albumin 20% (proportion 59:1 in a 60 ml syringe)

and administered at a final concentration of 30 ng/kg/min octreotide, to suppress

endogenous insulin production 5. To confirm that this infusion did not influence the

parameters of interest, the basal measurements were repeated after 1 hr of octreotide

infusion. Hereafter, glucose infusion with 20% glucose solution was started to reach the

desired glucose concentration based on a steady state principle 28. The plasma glucose

concentration was held constant at the desired plateau for 2 hrs by determination of

the plasma glucose concentration every 5 minutes and adjusting the glucose solution

accordingly. When a stable glucose concentration was reached, glycocalyx dimension,

ROS formation, and haemostasis parameters were measured every 30 minutes; 4 times

per plateau, the last measurement being the baseline value of the next step. Glucose

infusion was then increased to reach the next level of glycaemia and measurements were

repeated. In total, the actual clamp took 7 hrs. Blood samples were centrifuged within

1 hr after collection and stored at -80°C.

Figure 6 Glucose clamp protocol Depicted in the lower boxes are the times from baseline where the several actions were performed. An arrow indicates an assessment point for oxidative stress, glycocalyx and coagulation/fibrinolysis parameters.

54

Oxidative stressPlasma MDA concentration was determined using high HLPC-MS/MS as described by Pilz 29,

with minor modifications. Total (free and bound) malondialdehyde (MDA) in human

plasma samples was determined as the 2,4-dinitrophenylhydrazine (DNPH) derivative.

After addition of the stable isotopically labeled analogue (2H2-MDA) as internal standard

(IS), alkaline hydrolyzation, deproteinization and derivatization with DNPH, MDA-

hydrazone was analyzed by HPLC-MS/MS and positive electrospray ionization. Using

an Acquity UPLC system (Waters Corporation, Milford, MA), samples were injected on

a LC-18-DB analytical column (250 · 4.6 mm, 5 μm particles, Supelco) hyphenated to a

Quattro Premier XE mass spectrometer (Waters Corporation, Milford, MA). Analytes and

IS were eluted with acetonitrile/water/acetic acid (50/50/0.2) and detected in multiple

reaction monitoring (MRM) mode for the transitions m/z 235 m/z 159; m/z 237 m/z

161. Samples were quantified against calibration standards.

Endothelial glycocalyxHyaluronic acid was measured by a commercially available ELISA kit (Corgenix, Inc.,

Broomfield, Colorado, USA). In short, HA reacted with hyaluronic acid binding protein.

Thereafter horseradish peroxidase was added to form complexes with bound HA.

After addition of a chromogenic substrate the intensity of the colour was measured in

optical density units with a spectrophotometer at 450 nm. Hyaluronidase activity was

determined by a previously described assay 30;31.

Coagulation and fibrinolysisD-dimer was measured on an automated coagulation analyzer (Behring Coagulation

System, BSC) using protocols and reagents from the manufacturer (Siemens Healthcare

Diagnostics, Marburg, Germany). Antigen levels of vWF were assayed by ELISA using

antibodies from Dako (Glostrup, Denmark). F1+2 and PAP were determined by ELISA

from Siemens Healthcare Diagnostics and DRG (Marburg, Germany), respectively. The ETP

was determined using the Calibrated Automated Thrombogram as described by Hemker

et al. 32 and the Thrombinoscope manual (Maastricht, the Netherlands). Coagulation

was triggered by recalcification in the presence of 5 pM recombinant human tissue

factor (Innovin, Siemens Healthcare Diagnostics), 4 μM phospholipids, and 417 μM

fluorogenic substrate Z-Gly-Gly-Arg-AMC (Bachem, Bubendorf, Switzerland). Fluorescence

was monitored using the Fluoroskan Ascent fluorometer (ThermoLabsystems, Helsinki,

Finland), and the ETP and peak thrombin were calculated using the Thrombinoscope

software.

Data interpretationThe study was conducted to assess the influence of a certain level of plasma glucose on

the parameters described above. We excluded samples taken at a certain glucose plateau


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when the desired glucose level was exceeded by more than 1 mmol/l since crossing the

desired glucose level could interfere with the study results. For example, when a plasma

glucose level of 7.1 mmol/l occurred at any point during the 6 mmol/l plateau phase,

all subsequent samples taken at the 6 mmol/l plateau were excluded from analysis.

Moreover, samples were only included in the analysis when the desired glucose level was

truly reached. This was determined by calculation of the mean glucose level of the 30

minutes before sampling which had to be within 0.5 mmol/l of the desired glucose level.

Statistical analysisBaseline characteristics are expressed as mean (SD) and outcome parameters as median

(IQR). Differences between plateaus were assessed by a Wilcoxon signed ranks test for

paired data. The influence of time on the measurements at each glucose level was

assessed using the Friedman test. All analyses were performed using Predictive Analytics

Software (PASW) statistics version 18.0 (SPSS Inc., Chicago, IL, USA). A P-value <0.05 was

considered statistically significant.

AcknowledgementsWe would like to thank Jeroen Sierts from the Laboratory of Experimental Vascular

Medicine and Arno van Cruchten from the Laboratory Genetic Metabolic Diseases,

both at the Academic Medical Centre, Amsterdam, the Netherlands, for their excellent

laboratory support.

56

References1. Pignone M, Alberts MJ, Colwell JA, et al (2010) Aspirin for primary prevention of cardiovascular events in

people with diabetes: a position statement of the American Diabetes Association, a scientific statement of the American Heart Association, and an expert consensus document of the American College of Cardiology Foundation. Circulation 121: 2694-2701

2. Ceriello A, Esposito K, Piconi L, et al (2008) Glucose “peak” and glucose “spike”: Impact on endothelial function and oxidative stress. Diabetes Res Clin Pract 82: 262-267

3. Lemkes BA, Hermanides J, DeVries JH, Holleman F, Meijers JC, Hoekstra JB (2010) Hyperglycemia: a prothrombotic factor? J Thromb Haemost 8: 1663-1669

4. Nieuwdorp M, Meuwese MC, Vink H, Hoekstra JB, Kastelein JJ, Stroes ES (2005) The endothelial glycocalyx: a potential barrier between health and vascular disease. Curr Opin Lipidol 16: 507-511

5. Nieuwdorp M, van Haeften TW, Gouverneur MC, et al (2006) Loss of endothelial glycocalyx during acute hyperglycemia coincides with endothelial dysfunction and coagulation activation in vivo. Diabetes 55: 480-486

6. Iwasaki Y, Kambayashi M, Asai M, Yoshida M, Nigawara T, Hashimoto K (2007) High glucose alone, as well as in combination with proinflammatory cytokines, stimulates nuclear factor kappa-B-mediated transcription in hepatocytes in vitro. J Diabetes Complications 21: 56-62

7. Min C, Kang E, Yu SH, Shinn SH, Kim YS (1999) Advanced glycation end products induce apoptosis and procoagulant activity in cultured human umbilical vein endothelial cells. Diabetes Res Clin Pract 46: 197-202

8. Siegelaar SE, Holleman F, Hoekstra JB, DeVries JH (2010) Glucose variability; does it matter? Endocr Rev 31: 171-182

9. Kilpatrick ES (2009) Arguments for and against the role of glucose variability in the development of diabetes complications. J Diabetes Sci Technol 3: 649-655

10. Ceriello A, Esposito K, Piconi L, et al (2008) Oscillating Glucose Is More Deleterious to Endothelial Function and Oxidative Stress Than Mean Glucose in Normal and Type 2 Diabetic Patients. Diabetes 57: 1349-1354

11. Monnier L, Colette C, Mas E, et al (2010) Regulation of oxidative stress by glycaemic control: evidence for an independent inhibitory effect of insulin therapy. Diabetologia 53: 562-571

12. Siegelaar SE, Barwari T, Kulik W, Hoekstra JB, DeVries JH (2011) No relevant relationship between glucose variability and oxidative stress in well-regulated type 2 diabetes patients. J Diabetes Sci Technol 5: 86-92

13. Cheng YJ, Gregg EW, Geiss LS, et al (2009) Association of A1C and fasting plasma glucose levels with diabetic retinopathy prevalence in the U.S. population: Implications for diabetes diagnostic thresholds. Diabetes Care 32: 2027-2032

14. The Diabetes Control and Complications Trial Research Group (1996) The absence of a glycemic threshold for the development of long-term complications: the perspective of the Diabetes Control and Complications Trial. Diabetes 45: 1289-1298

15. Haffner SM, Stern MP, Hazuda HP, Mitchell BD, Patterson JK (1990) Cardiovascular risk factors in confirmed prediabetic individuals. Does the clock for coronary heart disease start ticking before the onset of clinical diabetes? JAMA 263: 2893-2898

16. Uchida K (2000) Role of reactive aldehyde in cardiovascular diseases. Free Radical Biology and Medicine 28: 1685-1696

17. Nielsen F, Mikkelsen BB, Nielsen JB, Andersen HR, Grandjean P (1997) Plasma malondialdehyde as biomarker for oxidative stress: reference interval and effects of life-style factors. Clin Chem 43: 1209-1214

18. Brownlee M (2001) Biochemistry and molecular cell biology of diabetic complications. Nature 414: 813-82019. Bunck MC, Cornér A, Eliasson B, et al. (2010) One year treatment with exenatide vs. Insulin Glargine: effects

on postprandial glycemia, lipid profiles, and oxidative stress. Atherosclerosis 212:223-22920. Stegenga ME, van der Crabben SN, Levi M, et al (2006) Hyperglycemia stimulates coagulation, whereas

hyperinsulinemia impairs fibrinolysis in healthy humans. Diabetes 55: 1807-181221. Grant PJ (2007) Diabetes mellitus as a prothrombotic condition. J Intern Med 262: 157-17222. Lippi G, Maffulli N (2009) Biological influence of physical exercise on hemostasis. Semin Thromb Hemost

35: 269-27623. Borg R, Kuenen JC, Carstensen B, et al (2010) Real-life glycaemic profiles in non-diabetic individuals with

low fasting glucose and normal HbA1c: the A1C-Derived Average Glucose (ADAG) study. Diabetologia 53: 1608-1611

24. American Diabetes Association (2011) Diagnosis and Classification of Diabetes Mellitus. Diabetes Care 34: S62-S69


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25. UK Prospective Diabetes Study (UKPDS) Group (1998) Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet 352: 837-853

26. Williams SB, Goldfine AB, Timimi FK, et al (1998) Acute hyperglycemia attenuates endothelium-dependent vasodilation in humans in vivo. Circulation 97: 1695-1701

27. Witzig TE, Kvols LK, Moertel CG, Bowie EJ (1991) Effect of the somatostatin analogue octreotide acetate on hemostasis in humans. Mayo Clin Proc 66: 283-286

28. Gottesman I, Mandarino L, Gerich J (1983) Estimation and kinetic analysis of insulin-independent glucose uptake in human subjects. Am J Physiol 244: E632-E635

29. Pilz J, Meineke I, Gleiter CH (2000) Measurement of free and bound malondialdehyde in plasma by high-performance liquid chromatography as the 2,4-dinitrophenylhydrazine derivative. J Chromatogr B Biomed Sci Appl 742: 315-325

30. Frost GI, Stern R (1997) A microtiter-based assay for hyaluronidase activity not requiring specialized reagents. Anal Biochem 251: 263-269

31. Nieuwdorp M, Mooij HL, Kroon J, et al (2006) Endothelial glycocalyx damage coincides with microalbuminuria in type 1 diabetes. Diabetes 55: 1127-1132

32. Hemker HC, Giesen P, al DR, et al (2003) Calibrated automated thrombin generation measurement in clotting plasma. Pathophysiol Haemost Thromb 33: 4-15

Chapter 4

No relevant relationship between glucose variability and oxidative stress in well-regulated type 2 diabetes patients

Sarah E. Siegelaar, Temo Barwari, Wim Kulik, Joost B.L. Hoekstra

and J. Hans DeVries

Journal of Diabetes Science and Technology 2011; 5(1):86-92

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Abstract

Background: A strong relationship between glycaemic variability and oxidative

stress in poorly regulated type 2 diabetes on oral medication has been reported.

However, this relationship was not seen in type 1 diabetes. The purpose of this

study is to re-examine the relation between glycaemic variability and oxidative

stress in a cohort of type 2 diabetes patients on oral medication.

Methods: Twenty-four patients with type 2 diabetes on oral glucose lowering

treatment underwent 48 hrs of continuous glucose monitoring (CGMS® System

GoldTM, Medtronic MiniMed) and simultaneous collection of two consecutive

24-hr urine samples for determination of 15(S)-8-iso-prostaglandin F2α (PGF2α)

using high-performance liquid chromatography tandem mass spectrometry.

Standard deviation (SD) and mean amplitude of glycaemic excursions (MAGE)

were calculated as markers of glycaemic variability.

Results: Included in the study were 66.7% males with a mean age (range) of 59

(36-76) years and a mean (SD) HbA1c of 6.9% (0.7). Median (interquartile range

[IQR]) urinary 15(S)-8-iso-PGF2α excretion was 176.1 (113.6-235.8) pg/mg creatinine.

Median (IQR) SD was 1.7 (1.3-2.2) mmol/l and MAGE 4.7 (3.1-5.9) mmol/l. Spearman

correlation did not show a significant relation for SD (ρ = 0.15, P = 0.49) or MAGE

(ρ = 0.23, P = 0.29) with 15(S)-8-iso-PGF2α excretion. Multivariate regression analysis

adjusted for age, sex, HbA1c, and exercise did not alter this observation.

Conclusions: We did not find a relevant relationship between glucose variability

and 15(S)-8-iso-PGF2α excretions in type 2 diabetes patients well-regulated with

oral medication that would support an interaction between hyperglycaemia and

glucose variability with respect to the formation of reactive oxygen species.

No effect of glucose variability on oxidative stress in well regulated T2DM

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Introduction

The main cause of morbidity and mortality in both type 1 and type 2 diabetes is the

development of micro- and macrovascular complications. Several studies have shown a

direct relation between glucose exposure, as measured by haemoglobin A1c (HbA1c) level,

and complications of diabetes 1;2. The molecular mechanisms that explain this relation

between hyperglycaemia and diabetic complications have not been fully elucidated. A

suggested common pathway by which hyperglycaemia leads to these complications is

the formation of reactive oxygen species (ROS) by glucose overload in the mitochondria.

This could lead to vascular damage through several molecular mechanisms 3. However,

HbA1c only explains around 25% of the variation in risk of developing complications 4,

suggesting the contribution of other factors. It has been suggested that glycaemic

variability contributes to diabetes complications via formation of ROS 5.

Monnier et al. 6 were the first to study the role of glucose variability in oxidative stress

formation in type 2 diabetes. They measured 24-hr urinary excretion rates of free 8-iso-

prostaglandin F2α (PGF2α), which is considered a good marker of oxidative stress 7;8.

Continuous glucose monitoring (CGM) was performed for determination of glycaemic

variability. They found a strong correlation between 24-hr PGF2α excretion rates and

glucose variability in type 2 diabetes patients on diet and/or oral antihyperglycaemic

drugs. Subsequently, Wentholt et al. 9 investigated the role of glucose variability in

activation of oxidative stress in type 1 diabetes patients using the same oxidative stress

marker and the same method to determine glucose variability. Despite more pronounced

glycaemic variability, no relationship was found between oxidative stress and glucose

variability. They found oxidative stress to be increased in type 1 diabetes.

In type 1 diabetes patients, other pathways than glucose variability might be involved in

activation of oxidative stress, perhaps explaining these conflicting findings. Moreover,

different techniques were used to assess the PGF2α excretion rate. The immunoassay is less

specific than the tandem mass spectrometry technique used by Wentholt et al., as already

acknowledged by Monnier et al. 10;11. Interestingly, a study in 2010 by Monnier confirmed

the findings of Wentholt regarding the absence of a relation between glucose variability

and oxidative stress in type 1 diabetes patients. Even more, type 1 diabetes patients and

healthy controls did not differ in 8-iso-PGF2α excretion 12. Also their results suggest that

the relation between glucose variability and ROS formation in type 2 diabetes patients

depends on the HbA1c level, a relation only seen at higher HbA1c levels.

The aim of this study is to evaluate the role of acute glucose fluctuations in the activation

of oxidative stress in type 2 diabetes patients using oral glucose-lowering agents, using

tandem mass spectrometry for the assessment of PGF2α excretion rates.

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Methods

PatientsTwenty-eight patients with type 2 diabetes were randomly recruited from the outpatient

clinic of the Academic Medical Centre in Amsterdam, The Netherlands, between April 2008

and February 2009. Inclusion criteria were a diagnosis of type 2 diabetes for more than 6

months and treatment with oral glucose-lowering agents or diet. Patients were excluded in

case of use of steroid or nonsteroidal anti-inflammatory drugs, insulin treatment, an acute

illness during the 3-month period prior to the investigation, or an estimated glomerular

filtration rate (GFR) of less than 60 ml/min/1.73m2 according to the Cockcroft-Gault formula 13 because of a potential influence on oxidative stress production. Also the use of heparin

or oral anticoagulants (except for aspirin) was not allowed as this could cause bleeding

during sensor insertion. The study was approved by the local ethics committee and patients

gave written informed consent after written and oral explanation of the study.

Study designOn day 1, after giving written informed consent, the continuous glucose monitor (CGMS®

System GoldTM, Medtronic MiniMed, Northridge, CA, USA) was inserted subcutaneously in

the abdominal wall. Patients were provided with home blood glucose meters (OneTouch®

Ultra®, LifeScan, Inc., Milpitas, CA, USA) and were instructed to perform the required

sensor calibration procedure four times daily according to manufacturers instructions.

Urine was collected for two consecutive 24-hr periods. Patients were asked to store

the urine jars in the refrigerator, to avoid ex vivo formation of isoprostanes. Systolic

and diastolic blood pressure was measured and venous blood samples were drawn for

the following laboratory measurements: fasting plasma glucose (FPG), HbA1c, total

cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL)

cholesterol, triglycerides, and creatinine. Relevant patient characteristics were recorded

(e.g., medical history, current medication use, body mass index [BMI], smoking habits).

The reported amount of physical exercise was graded as sedentary, moderately active,

active, and fit. On day 4, patients returned to the clinical trial room with the two urine

collection jars. From each 24-hr period (day 2 and day 3 of the study) a 7-ml urine sample

was stored in a freezer at -80 °C until analysis of all urine samples in one run. For the

analyses, the 15(S)-8-iso-PGF2α excretion rates over 48 hrs were used calculated by averaging

the first- and second-day samples. Lastly, the continuous glucose monitor was removed

and data was downloaded and stored electronically. Only glucose data obtained during

the 48 hrs of urine sampling was used for further analysis.

Laboratory MeasurementsHaemoglobin A1c was measured using an high-performance liquid chromatography

(HPLC) assay (Variant II, Bio-Rad Laboratories, Montreal, Quebec, Canada). Activation of


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oxidative stress was estimated by determining the urinary isoprostane excretion (15(S)-8-iso-

PGF2α), using HPLC tandem mass spectrometry (HPLC-MS/MS). Urine samples were collected

and stored without additives as 7-ml aliquots at -80 °C. Initial creatinine concentrations

were established by colourimetric Jaffé assay. Two millilitres of sample were mixed with 2H4-

labelled 15(S)-8-iso-PGF2α as internal standard and applied to an 8-iso-PGF2α immunoaffinity

column (Cayman Chemical Company, Ann Arbor, MI, USA). After washing, the extract was

eluted, evaporated to dryness (60 °C, N2) and reconstituted in 200-μl 0.05 mol/l formic

acid-ethanol (75:25, v/v); 50 μl was injected on the HPLC-MS/MS system. Chromatographic

separation was achieved on a modular HPLC system (Surveyor®, Thermo Finnigan, San Jose,

CA, USA) consisting of a cooled autosampler (T = 12 °C), a low-flow quaternary MS pump

and analytical HPLC column: Alltima C8, 2.1 x 150 mm, 5 μm (Alltech, Lexinton, KY, USA).

Samples were eluted with a flow rate of 200 μl/min and a programmed linear gradient

between A (0.01% HCOOH in H2O; v/v) and B (CH3CN): from t = 0 min 45% A, 55% B toward t = 3

min 30% A, 70% B toward t = 3.1 min 100% A until t = 6 min. MS/MS analyses were performed

on a TSQ Quantum AM (Thermo Finnigan, San Jose, CA, USA) operated in the negative ion

electrospray ionisation mode. The surface-induced dissociation was set at 2 V; spray voltage

was 3500 V, and the capillary temperature was 400°C. In the MS/MS experiments argon was

used as collision gas at a pressure of 0.2 Pa; collision energy was 26 eV for the optimised

transitions: m/z 353.24 m/z 193.10 and m/z 357.24 m/z 197.10. The interassay (n = 5) and

intraassay (average of 5 days, n = 3) variability allowed for determination at physiological

concentrations with a coefficient of variation of <7%.

Assessment of glycaemic variabilityIn literature, there is no universally accepted “gold” standard to measure variability

in glucose values 14. We calculated the SD of the glucose measurements and the mean

amplitude of glycaemic excursions (MAGE) as described by Service et al. 15 because SD is

the best mathematically validated measure and both are commonly used in literature.

The MAGE over 48 hrs is the mean of the absolute differences between peak and nadir

values over 48 hrs. The peaks and nadirs are defined as glucose values preceded and

followed by an increase and decrease or a decrease and increase, respectively. Only

increases or decreases larger than 1 SD of the mean glucose are taken into account. If

a decrease of more than 1 SD was the first excursion, only peak-to-nadir excursions (>1

SD) were included in the calculation of the MAGE and vice versa.

Assessment of postprandial glucose excursionsWe determined the incremental areas above preprandial glucose values (breakfast, lunch,

dinner) over a 4-hr period following the beginning of each meal using the trapezoidal

rule 16. The six incremental areas of each patient during the 48 hrs of continuous glucose

monitoring were summed and averaged to calculate the mean postprandial incremental

area under the curve (AUCpp) 6.

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Statistical analysisMeans and standard deviations of patient characteristics, urinary excretion of 15(S)-8-iso-

PGF2α, and glucose variability measures were assessed using standard statistics. Levels of

urinary 15(S)-8-iso-PGF2α in the samples from both days were compared using a Wilcoxon

signed-rank test. Spearman correlation was calculated in order to evaluate the relation

between glycaemic variability measures, HbA1c, FPG, postprandial glucose excursions,

and the excretion of urinary 15(S)-8-iso-PGF2α. The effect of glucose variability on 15(S)-

8-iso-PGF2α excretion was also assessed in a multivariate regression model adjusting for

variables that have been reported to be involved in oxidative stress activation, i.e., sex,

age, smoking, and HbA1c 17, and for variables that showed a relation with urinary 15(S)-

8-iso-PGF2α excretion in Spearman correlation analysis. Analyses were performed using

SPSS version 16.0.2.

Results

From the 28 recruited patients, 4 patients were excluded from data analysis; 2 patients

showed an estimated GFR of less than 60 ml/min/1.73m2, in 1 patient the glucose sensor

failed to record any data and 1 patient did not perform the urine collection adequately.

From the remaining 24 patients, 16 were male, mean (range) age was 58.9 years (36-76)

and mean (SD) HbA1c was 6.9% (0.7). Median (interquartile range [IQR]) urinary 15(S)-

8-iso-PGF2α excretion was 176.1 (113.6-235.8) pg/mg creatinine. There was no significant

difference between the first- and second-day urine samples (Wilcoxon signed-rank test,

P = 0.95). Median (IQR) SD and MAGE were 1.7 (1.3-2.2) and 4.7 (3.1-5.9) mmol/l, respectively.

Patient characteristics are listed in Table 1.

Spearman correlation did not reveal a significant relation for any glucose variability

parameter with 15(S)-8-iso-PGF2α excretion (SD, ρ = 0.15, P = 0.49; MAGE, ρ = 0.23, P = 0.29,

Figure 1]. Multivariate regression analysis was performed to adjust for sex, age, smoking,

and HbA1c 17, and for the variable that showed a significant correlation with 15(S)-8-iso-

PGF2α, i.e., the amount of physical exercise. This did not alter the results from Spearman

correlation described above (SD, r2 = 0.003, P = 0.77; MAGE, r2 = <0.001, P = 0.98).

No significant correlation was found between 15(S)-8-iso-PGF2α excretion and HbA1c (ρ =

0.27, P = 0.20) or AUCpp (ρ = 0.14, P = 0.51), FPG (ρ = 0.27, P = 0.20) and the mean of sensor

glucose measurements (ρ = 0.23, P = 0.29), Table 2. The plots do not reveal a pattern that


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Characteristics Patients (n = 24)

Age, years 58.9 (36-76)

Men/women, n 16/8

Diabetes duration, years 7.2 (4.2)

Diabetes treatment, n (%)MetforminSulfonylureaRosiglitazone

23 (96)15 (63)2 (8)

Other treatments, n (%)ACE inhibitorStatinAspirin

9 (38)19 (79)7 (29)

Cigarette smoking, n (%) 2 (8)

BMI, kg/m2 30.5 (5.5)

Systolic blood pressure, mmHg 135 (17)

Diastolic blood pressure, mmHg 82 (10)

Plasma creatinine, μmol/l 76.3 (13.1)

Total cholesterol, mmol/l 4.18 (0.80)

HDL cholesterol, mmol/l 1.10 (0.20)

LDL cholesterol, mmol/l 2.31 (0.71)

Triglycerides, mmol/l 1.71 (0.72)

HbA1c, % 6.9 (0.7)

FPG, mmol/l 8.0 (1.8)

Mean sensor glucose, mmol/l 8.1 (1.5)

AUCpp, mmol/l/hr 7.2 (3.0)

Markers of glucose variability (median [IQR])SD, mmol/lMAGE, mmol/l

1.7 (1.3-2.2)4.7 (3.1-5.9)

Urinary 15(S)-8-iso-PGF2α, pg/mg creatinine (median [IQR]) 176.1 (113.6-235.8)

Data are means (SD) or means (range), unless stated otherwise in parentheses. BMI, body mass index; FPG, fasting plasma glucose; AUCpp, 4-hour postprandial incremental area under the curve; SD, standard deviation; MAGE, mean amplitude of glycaemic excursions; IQR, Interquartile Range. To convert mean glucose, AUCpp, MAGE and SD from mmol/l to mg/dl divide by 0.0555.

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would suggest a threshold phenomenon. Also, no significant correlations were found

for sex, age, BMI, smoking, systolic or diastolic blood pressure, or lipid concentrations

(Table 2). A multivariate regression model with age, sex, smoking, exercise, and HbA1c

as covariates did show significant inverse relations between 15(S)-8-iso-PGF2α excretion

rates and age (r = -0.49, P = 0.01) and the amount of physical exercise (r = -0.61, P = 0.004).

Figure 1 Correlations between glycaemic markers and oxidative stress X-axis: glycaemic markers over 48 h of glucose measurements expressed as SD, MAGE, AUCpp and mean blood glucose. Y-axis: oxidative stress, expressed as 15(S)-8-iso-PGF2α in pg/mg creatinine, over 48 hrs of urine collection.


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Table 2 Spearman correlation coefficients (ρ) between urinary excretion rates of

15(S)-8-iso-PGF2α, clinical characteristics and glycaemic markers

Sex Age BMI Exercise HbA1c FPG MBG SD MAGE AUCpp

FPG 0.09 0.33 -0.12 -0.15 0.48 a

MBG -0.08 0.36 -0.22 -0.20 0.63b 0.85 b

SD -0.29 -0.04 -0.45 a -0.04 0.43 a 0.34 0.45 a

MAGE -0.27 -0.13 -0.41 a 0.01 0.44a 0.38 0.44 a 0.95 b

AUCpp -0.41 a -0.18 -0.28 0.03 0.39 0.29 0.35 0.85 b 0.86 b

8-isoPGF -0.09 -0.27 0.23 -0.50a 0.27 0.27 0.23 0.15 0.23 0.14

a P <0.05; b P <0.01

Discussion

We did not find a relevant association between markers of glycaemic variability and

oxidative stress, estimated by 15(S)-8-iso-PGF2α excretion rates, in patients with type 2

diabetes who were well-regulated with oral glucose lowering agents only. At first, these

results seem in contrast with earlier data, showing a firm correlation between glycaemic

variability and oxidative stress in type 2 diabetes patients using oral hypoglycaemic

agents 6, despite use of the same continuous glucose monitor and mathematical methods

to assess glucose variability.

A possible explanation for the seemingly opposing findings in type 2 diabetes patients

might be the difference in glucose regulation between the study populations. The mean

glucose and HbA1c of the original Monnier population was markedly higher than in

our population (mean glucose 10.5 and 8.1 mmol/l, HbA1c 9.6 and 6.9%, respectively)

though the mean MAGE was roughly comparable (4.2 and 4.7 mmol/l respectively). In a

paper published by the same group 12 there seemed to be a modifying effect of HbA1c

on the relation between glucose variability and ROS formation in patients with type 2

diabetes on oral medication. No effect of MAGE on the formation of 15(S)-8-iso-PGF2α was

seen in the group with an HbA1c below the median of 8.2%, while people with higher

MAGE values had more oxidative stress in the subgroup with an HbA1c above the median.

Thus, the absence of a relation between glucose variability and oxidative stress in our

population with a mean HbA1c of 6.9% is in agreement with their findings. It might be

possible that the fluctuations observed in patients with a high HbA1c level reach a higher

maximum glucose value and in that way cross a threshold for oxidative stress. In line

68

with this is the observation that the AUCpp in the Monnier population is much larger

than in our well regulated population (22.6 and 7.2 mmol/l/hr respectively) suggesting

that the variability in the Monnier population depended more on large postprandial

excursions which are known to enhance oxidative stress 18.

Another difference between our results and those reported by Monnier et al. is a

difference in technique for quantification of oxidative stress. Monnier et al. used an

enzyme immunoassay (EIA) to quantify urinary excretion of 15(S)-8-iso-PGF2α, whereas

we used HPLC-MS/MS, the same method used by Wentholt et al. Measurement of F2-

isoprostanes by way of mass spectrometry has been reported to be more specific and

sensitive than measurement by immunoassay 10;11. Studies have shown that MS is not

hampered by cross reactivity of structurally (un)related components of 8-iso-PGF2α in that

way selectively determining 15(S)-8-iso-PGF2α, whereas several substances in biological

fluids that are not products of lipid peroxidation interfere with the immunoassay that

includes its enantiomer ent-15(S)-8-iso-PGF2α in the quantification of oxidative stress 8;11.

This is also why in literature 8-iso-PGF2α levels are often reported to be higher when

assessed by EIA compared to MS.

The excretion rates of 15(S)-8-iso-PGF2α in our type 2 population (median [IQR] 176.1 [113.6-

235.8] pg/mg creatinine) are of the same magnitude as the excretion rates in the type 1

diabetes patient group reported by Wentholt and colleagues (median [IQR] 161 [140–217]

pg/mg creatinine) that were significantly higher than in their healthy control group

(median [IQR] 118 [101–146] pg/mg creatinine). Also, when we match 9 of our patients with

9 of the controls used in the Wentholt study according to age and sex, the PGF excretion

rate in our well-regulated type 2 diabetes patients is significantly larger (median [IQR]

controls 108.2 [90.3-144.0] and patients 243.3 [147.8-287.5], P = 0.006, Mann-Whitney U Test).

The excretion rates of the patient group reported by Monnier et al. are nearly threefold

higher (mean [SD] 482 [206] pg/mg creatinine) than our patient group. This disparity is

likely to be caused by the different methods used to assess 8-iso-PGF2α excretion rates as

explained earlier, together with the higher mean glucose and HbA1c of the Monnier

population, as a higher mean glucose is likely to cause more oxidative stress 3;19.

Our findings are supported by the only intervention trial assessing the effect of lowering

glucose variability on oxidative stress in type 2 diabetes patients 20. This crossover trial

comparing prandial vs. basal insulin did not find any influence of lowering glucose

variability on oxidative stress, assessed by the 24-hr excretion rates of 8-iso-PGF2α,

measured by HPLC-MS/MS. Additionally, no correlation between glucose variability (MAGE)

and oxidative stress was found in this insulin-treated population. Again, this is in line

with the recent findings of Monnier, who reported no relation between glucose variability

and urinary excretion of 15(S)-8-iso-PGF2α in insulin-treated type 2 diabetes patients.


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Despite the vast amount of studies using F2-isoprostanes as a biomarker of oxidative stress,

not all (patho) physiological factors contributing to the formation of F2-isoprostanes are

known 17. Multivariate regression analysis showed the amount of physical exercise and

age to be inversely related to oxidative stress activation in our study population. This has

been reported in another study, although for both parameters, also positive relations

and no relations have been reported (for review, see Basu and Helmersson 17).

A limitation of this study is that from the broad spectrum of markers of oxidative stress

we only measured the urinary excretion of F2-isoprostanes. Measurement of another

marker(s) could have complemented the present data. On the other hand, measurement

of F2-isoprostanes is regarded the best option for addressing lipid peroxidation 7;8. A

further limitation is the absence of people with higher HbA1c values on oral medication

in our cohort. We simply could not find such patients, who apparently had already

received treatment intensification according to current treatment guidelines.

Conclusions

We found no relevant relationship between glycaemic variability, assessed by continuous

glucose monitoring, and 15(S)-8-iso-PGF2α excretion, assessed by HPLC-MS/MS in a

population of well regulated type 2 diabetes patients. These results argue against a role

of glycaemic variability in the activation of oxidative stress in this group of patients,

and together with findings from literature suggest that the relation between glucose

variability and oxidative stress is only seen at higher HbA1c levels.

AcknowledgementsWe thank H. van Lenthe, Laboratory Genetic Metabolic Diseases, Academic Medical

Centre, Amsterdam, the Netherlands, who performed all the laboratory analyses of the

urine samples for determination of 15(S)-8-iso-PGF2α.

70

References1. The Diabetes Control and Complications Trial Research Group (1993) The Effect of Intensive Treatment of

Diabetes on the Development and Progression of Long-Term Complications in Insulin-Dependent Diabetes Mellitus. N Engl J Med 329: 977-986

2. Stratton IM, Adler AI, Neil HA, et al (2000) Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ 321: 405-412

3. Brownlee M (2001) Biochemistry and molecular cell biology of diabetic complications. Nature 414: 813-8204. The Diabetes Control and Complications Trial Research Group (1995) The relationship of glycemic exposure

(HbA1c) to the risk of development and progression of retinopathy in the diabetes control and complications trial. Diabetes 44: 968-983

5. Hirsch IB, Brownlee M (2005) Should minimal blood glucose variability become the gold standard of glycemic control? J Diabetes Complications 19: 178-181

6. Monnier L, Mas E, Ginet C, et al (2006) Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes. JAMA 295: 1681-1687

7. Morrow JD, Hill KE, Burk RF, Nammour TM, Badr KF, Roberts LJ II (1990) A Series of Prostaglandin F2-Like Compounds are Produced in vivo in Humans by a Non-Cyclooxygenase, Free Radical-Catalyzed Mechanism. Proc Natl Acad Sci 87: 9383-9387

8. Roberts LJ, Morrow JD (2000) Measurement of F2-isoprostanes as an index of oxidative stress in vivo. Free Radic Biol Med 28: 505-513

9. Wentholt IME, Kulik W, Hoekstra JBL, de Vries JH (2008) Glucose fluctuations and activation of oxidative stress in type 1 diabetes patients. Diabetologia 51: 183-190

10. Proudfoot J, Barden A, Mori TA, et al (1999) Measurement of urinary F(2)-isoprostanes as markers of in vivo lipid peroxidation-A comparison of enzyme immunoassay with gas chromatography/mass spectrometry. Anal Biochem 272: 209-215

11. Saenger AK, Laha TJ, Edenfield MJ, Sadrzadeh SM (2007) Quantification of urinary 8-iso-PGF2alpha using liquid chromatography-tandem mass spectrometry and association with elevated troponin levels. Clin Biochem 40: 1297-1304


13. Cockcroft DW, Gault MH (1976) Prediction of creatinine clearance from serum creatinine. Nephron 16: 31-4114. Siegelaar SE, Holleman F, Hoekstra JB, DeVries JH (2010) Glucose Variability; Does It Matter? Endocr.Rev. 31:

171-18215. Service FJ, Molnar GD, Rosevear JW, Ackerman E, Gatewood LC, Taylor WF (1970) mean amplitude of glycemic

excursions, a measure of diabetic instability. Diabetes 19: 644-65516. Rohlfing CL, Wiedmeyer HM, Little RR, England JD, Tennill A, Goldstein DE (2002) Defining the Relationship

Between Plasma Glucose and HbA1c: Analysis of glucose profiles and HbA1c in the Diabetes Control and Complications Trial. Diabetes Care 25: 275-278

17. Basu S, Helmersson J (2005) Factors regulating isoprostane formation in vivo. Antioxid Redox Signal 7: 221-235

18. Ceriello A, Bortolotti N, Motz E, et al (1998) Meal-generated oxidative stress in type 2 diabetic patients. Diabetes Care 21: 1529-1533

19. Davi G, Ciabattoni G, Consoli A, et al (1999) In Vivo Formation of 8-Iso-Prostaglandin F2α and Platelet Activation in Diabetes Mellitus : Effects of Improved Metabolic Control and Vitamin E Supplementation. Circulation 99: 224-229

20. Siegelaar SE, Kulik W, van Lenthe H, Mukherjee R, Hoekstra JB, DeVries JH (2009) A randomized controlled trial comparing the effect of basal insulin and inhaled mealtime insulin on glucose variability and oxidative stress. Diabetes, Obesity and Metabolism 11: 709-714


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Chapter 5

A randomised clinical trial comparing the effect of basal insulin and inhaled mealtime insulin on glucose variability and oxidative stress

Sarah E. Siegelaar, Wim Kulik, Henk van Lenthe, Robin Mukherjee,

Joost B.L. Hoekstra and J. Hans DeVries

Diabetes, Obesity and Metabolism 2009; 11(7):709-714

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Abstract

Aim: To assess the effect of three times daily mealtime inhaled insulin therapy

compared with once daily basal insulin glargine therapy on 72-hr glucose

profiles, glucose variability and oxidative stress in type 2 diabetes patients.

Methods: In an inpatient crossover study, 40 subjects with type 2 diabetes

were randomised to receive 9 days of inhaled insulin three times daily before

meals or 9 days of glargine administered in the morning before breakfast in a

randomised order. During the last 72 hrs in each phase, glucose was measured

with continuous glucose monitoring. Activation of oxidative stress was measured

by determining the 15(S)-8-iso-PGF2α secretion in 24-hr urine samples.

Results: Inhaled insulin improved overall and postprandial glucose control

significantly better than insulin glargine (P <0.0001). There was a trend towards a

greater reduction in glucose variability (8-9%) in the inhaled group (P = 0.1430 and

P = 0.3298 for mean amplitude of glycaemic excursions (MAGE) and mean of daily

differences, respectively). Oxidative stress, estimated by determining the urinary

isoprostane excretion (15(S)-8-iso-PGF2α), was equally reduced from baseline by

both treatments. No correlation was found between glucose variability and

oxidative stress in both groups.

Conclusions: This study showed a mealtime insulin approach to improve

glycaemic control more than a basal insulin approach. These findings indicate

also that lowering glucose using insulin treatment lowers oxidative stress over

time, at least for the study period of 9 days, in type 2 diabetes patients. Contrary

to earlier data, we found no correlation between glucose variability (MAGE) and

oxidative stress (15(S)-8-iso-PGF2α) in this study.

Effect of basal and inhaled mealtime insulin on glucose variability and oxidative stress

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Introduction

It has been suggested that glucose variability, considered in combination with

haemoglobin A1c (HbA1c), is a more reliable indicator of blood glucose control and

the risk for long-term complications than HbA1c alone 1-3, and if so, this has important

bearings for the people with diabetes. Still the impact of glucose variability on

macrovascular complications and its effect in type 2 diabetic patients has not been

firmly established.

In an attempt to provide evidence for this hypothesis, Monnier et al. 4 showed a strong

correlation between glucose variability (expressed as mean amplitude of glycaemic

excursions [MAGE]) and oxidative stress (measured as 8-isoprostane excretion) in type

2 diabetes patients, suggesting a relationship between glucose variability and diabetic

complications. However, in type 1 diabetes patients, a disease with more pronounced

glucose variability, we could not find a correlation between MAGE and urinary

8-isoprostane excretion 5. To further test this hypothesis, an intervention study aiming

at reducing variability specifically in the intervention group without affecting mean

glycaemia more than in the control group is rational. Hirsch and Brownlee 1 suggested

to perform a randomised controlled trial comparing a regimen of mealtime and basal

insulin with a regimen of basal insulin alone in newly diagnosed type 2 diabetes patients.

The present study compared a mealtime insulin regimen (inhaled insulin) with a basal

insulin regimen (insulin glargine) in a crossover design in type 2 diabetes patients failing

on oral medication. In a study by Bretzel et al. 6 using a similar design with a rapid-acting

analogue rather than inhaled insulin, indeed overall glycaemia was reduced by both

treatments to a similar extent, while postprandial values were lower in the mealtime

insulin group and fasting glucose was lower in the basal insulin group. Thus, glucose

variability was by necessity more reduced in the mealtime insulin group, making it

possible to study glucose variability in both groups independent from the influence of

overall glycaemia. We report the first intervention study specifically targeting glucose

variability in insulin-treated type 2 diabetes patients.

Methods

Subjects The study population consisted of patients with type 2 diabetes mellitus poorly controlled

on a combination of two oral agents. Patients had to be at least 18 years old with type 2

diabetes diagnosed ³6 months prior to study entry and currently be treated on a stable

dose of at least 2 oral hypoglycaemic agents for at least 1 month prior study entry,

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with or without adjunct subcutaneous insulin. Patients had to have a screening HbA1c

³6.5% and ≤10.5% and a BMI ≤44 kg/m2. A complete list of in- and exclusion criteria can

be found in the paper describing the glycaemic outcome of this study. (Hompesch et

al.7). The study was carried out in accordance with the principles of the Declaration of

Helsinki and of Good Clinical Practice. All local regulatory requirements were followed.

Before entering the study, patients gave written informed consent after a detailed oral

and written explanation of the study procedures.

Study design and procedureThis was a prospective, open-label, randomised, two-period crossover trial. The study

consisted of a screening visit and two 9-day inpatient periods separated by a 7- to 10-day

washout period. Subjects agreed to participate in this study by signing an informed

consent. Subjects were randomised 1:1 to inhaled insulin (Exubera®, Pfizer, New York,

NY, USA) three times daily before meals or glargine (Lantus®, sanofi-aventis, Paris,

France) administered in the morning of the second day before breakfast, then switched

treatments for the second phase (Figure 1). Patients who had existing subcutaneous

insulin regimens discontinued those therapies prior to each inpatient stay but resumed

them during washout. All patients did continue their pre-study oral diabetic treatments

throughout the study.

Figure 1 Study design

On the fifth day of each inpatient stay, vascular access for frequent venous blood

sampling was secured and subjects were subsequently connected to an automated

glucose monitoring system (CGMS® system gold, Medtronic MiniMed, Northridge, CA, USA)


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inserted in the abdominal wall for 72 hrs. Average interstitial glucose (IG) levels calibrated

to blood glucose were stored at 5-min intervals. The CGMS device was calibrated according

to the manufacturer’s instructions. For analysis, only data generated in the 72 hrs between

06:00 hrs on day 6 and 06:00 hrs on day 9 for each phase were used. During both inpatient

CGMS glucose profile periods, urine and blood samples were collected. Urinary 8-iso-PGF2α

was sampled from 24-hr urine collection on days 1 and 8 of each inpatient period.

Laboratory measurements Activation of oxidative stress was estimated by determining the urinary isoprostane

excretion (15(S)-8-iso-PGF2α), using high performance liquid chromatography tandem

mass spectrometry (HPLC-MS/MS). Urine samples were collected and stored without

additives as 7 ml aliquots at -80 °C. Initial creatinine concentrations were established

by colourimetric Jaffé assay. Two millilitres of sample were mixed with 2H4-labelled 15(S)-

8-iso-PGF2α as internal standard and applied to an 8-iso-PGF2α immunoaffinity column

(Cayman Chemical Company, Ann Arbor, MI, USA). After washing, the extract was eluted,

evaporated to dryness (60 °C, N2) and reconstituted in 200 μl 0.05 mol/l formic acid-

ethanol (75:25, v/v); 50 μl was injected on the HPLC-MS/MS system. Chromatographic

separation was achieved on a modular HPLC system (Surveyor, Thermo Finnigan, San

Jose, CA, USA) consisting of a cooled autosampler (T = 12°C), a low-flow quaternary MS

pump and analytical HPLC column: Alltima C8, 2.1 x 150 mm, 5 μm (Alltech, Lexinton,

KY, USA). Samples were eluted with a flow rate of 200 μl/min and a programmed linear

gradient between A (0.01% HCOOH in H2O; v/v) and B (CH3CN): from t = 0 min 45% A,

55% B towards t = 3 min 30% A, 70% B towards t = 3.1 min 100% A until t = 6 min. MS/

MS analyses were performed on a TSQ Quantum AM (Thermo Finnigan) operated in the

negative ion electrospray ionisation mode. The surface-induced dissociation was set at

2 V; spray voltage was 3500 V and the capillary temperature was 400°C. In the MS/MS

experiments argon was used as collision gas at a pressure of 0.2 Pa; collision energy was

26 eV for the optimised transitions: m/z 353.24 m/z 193.10. The interassay (n = 5) and

intraassay (average of 5 days, n = 3) variability allowed for determination at physiological

concentrations with a coefficient of variation of <7%.

Assessment of glycaemic variabilityInterday glycaemic variability

The day-to-day variation of the glucose pattern was calculated with the mean of the daily

differences (MODD), which is defined as the mean of the absolute differences between

glucose values on day 2 and the corresponding values on day 1, at the same time 8;9.

Intraday glycaemic variability

This was calculated by the MAGE 10. The MAGE over 24 hrs is the mean of the absolute

differences between peak and nadir values over 24 hrs, with peaks (nadirs) defined as

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glucose values preceded and followed by an increase (decrease) and decrease (increase),

respectively, in excess of at least 1 SD of the mean glucose. If a decrease of more than 1

SD was the first excursion, only peak-to-nadir excursions (>1 SD) were included in the

calculation of the MAGE and vice versa.

Statistical analysisNo sample size calculations were performed since this was an exploratory study.

Pharmacodynamic endpoints, including the mean 72-hr glucose profiles; the area

under the IG concentration-time curve over 72 hrs (IG-AUC0-72h); the 3-day mean IG-AUC0-

24h; postmeal IG-AUC0-4h for breakfast, lunch, and dinner; the 3-day mean IG-AUC0-6h at

night time; the 3-day mean maximum glucose concentration after breakfast IG-Cmax;

and the time to IG-Cmax (IG-tmax) were derived from the two glucose exposure profiles

measured with the CGMS. Glycaemic exposure measures (AUCs) were calculated using the

trapezoidal rule. To evaluate the variability of glucose exposure, the following parameters

were calculated: the SD of the mean 72-hr glucose concentration, the MAGE over 72 hrs 10, and the MODD of paired glucose levels over 72 hrs 8;9 using standard statistics. The

differences in 15(S)-8-iso-PGF2α levels are analyzed using a prespecified mixed effects model

procedure in SAS (version 8.02, SAS Institute, Cary, NC, USA) with treatment, sequence

and period as fixed effects and patient within sequence considered a random effect.

Normality assumptions were checked as necessary, with log transformation to improve

this. Correlation was calculated and univariate regression analysis was performed to

evaluate the relation between the excretion rate of 15(S)-8-iso-PGF2α and the markers

of glycaemic variability. Locally weighted polynomial regression (LOESS) curves were

added in Figure 2. Statistical significance for all results is expressed through 95% CIs,

whereby a finding is deemed significant when neither side of the confidence interval

crosses 1.0 (or 100%).

Results

A total of 40 patients with type 2 diabetes (male, 29; age, 57 ± 10 y; BMI, 31.9 ± 4.6 kg/m²;

HbA1C, 7.9 ± 1.0%; mean prebreakfast capillary glucose at randomization 8.1 ± 2.2 mmol/l;

type 2 diabetes duration 10.2 years [range 0.8 – 27.0]) were enrolled. Diabetes was treated

with oral antidiabetic medication (39 patients received metformin, 28 sulfonylurea, 11

rosiglitazone, and 5 pioglitazone); five patients were also treated with glargine. During

the study, two patients did not complete the two treatment sequences. Two patients

discontinued during glargine treatment, one during the first period (and consequently

did not receive inhaled insulin in the second period), and one during the second period

(having completed treatment on inhaled insulin in the first period).


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Figure 2 Correlation between oxidative stress and glucose variability in both groups Scatter plot showing correlation between 24-hr urinary excretion rates of 8-iso-PGF2α and mean amplitude of glycaemic excursions (MAGE) in both groups. This figure shows no correlation between 8-iso-PGF2α and MAGE: r = 0.18 (inhaled) and r = 0.28 (glargine). The lines are LOESS curves; 8-iso-PGF2α is expressed in nmol/mmol creatinine.

The mealtime targeted approach with inhaled insulin improved overall and postprandial

glucose control (expressed as total glycaemic exposure over 72 hrs; mean glucose

concentration over the final 72-hr period; the 3-day mean glycaemic exposure; 4-hr

postmeal glycaemic exposure after breakfast, lunch, dinner; and the 3-day mean

maximum glucose levels after breakfast) significantly better than the basal insulin

approach using glargine (P <0.0001; Table 1).

As hypothesised, glucose variability was more reduced in the inhaled group compared

to the glargine group, however not significantly (8-9% reduction; P = 0.1430, P = 0.3298

and P = 0.1613 for MAGE, MODD and SD respectively) (Table 1).

Oxidative stress, estimated by determining the urinary isoprostane excretion (15(S)-8-

iso-PGF2α), was reduced by both treatments (Table 2). There was no evidence of a period

effect, looking at the baseline 8-isoprostane values after the washout periods that were

higher than after finishing treatment. There was a trend towards a somewhat greater

reduction in oxidative stress in the glargine group compared to the inhaled group.

We found no correlation between oxidative stress (urinary isoprostane excretion) and

glucose variability (expressed as MAGE and MODD) (Figure 2). The correlation coefficients

(r) for the inhaled and glargine group were 0.18 and 0.28 for MAGE and 0.17 and 0.20

for MODD respectively.

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Table 1 Glycaemic exposure and variability of glycaemic exposure

Inhaled (n = 38)

Glargine (n = 38)

Inh/Glap

(95% CI) P-valueq

Glycaemic exposure (mean ± SD)

Mean 72 hrs (mmol/l) 5.3 ± 0.6 6.0 ± 1.1 88 (84-93) <0.0001

AUC72h (mmol/l·hr) 380.3 ± 45.3 426.3 ± 89.2 89 (84-93) <0.0001

AUC0-24h 3-day mean (mmol/l·hr) 126.5 ± 15.1 142.5 ± 27.9 89 (84-93) <0.0001

AUC0-4h post breakfast (mmol/·l·hr) 25.9 ± 4.8 27.7 ± 52 93 (87-99) 0.0318

AUC0-4h post lunch (mmol/·l·hr) 19.0 ± 4.0 24.6 ± 5.7 78 (72-84) <0.0001

AUC0-4h post dinner (mmol/l·hr) 20.5 ± 4.0 26.3 ± 5.7 78 (72-84) <0.0001

AUC0-6h night time (mmol/l·hr) 29.6 ± 4.6 31.0 ± 6.4 96 (91-103) 0.2345

Cmax post breakfast (mmol/l·hr) 8.3 ± 1.8 8.8 ± 1.7 94 (87-100) 0.0572

tmax post breakfast (hrs) 1.5 ± 0.6 1.8 ± 0.7 83 (71-97) 0.0180

Variability of glycaemic exposure (mean ± SD)

SD 72 hrs (mmol/l) 1.5 ± 0.6 1.6 ± 0.6 92 (81-104) 0.1613

MAGE 72 hrs (mmol/l) 3.5 ± 1.4 3.7 ± 1.3 91 (79-104) 0.1430

MODD 72 hrs (mmol/l) 1.4 ± 0.5 1.5 ± 0.6 93 (81-107) 0.3289

p Treatment/reference ratio (%) of the estimates of the geometric means from the mixed model fitted to the natural-log transformed endpoint data. q P-value is for the estimated treatment effect between inhaled and glargine from the mixed model fitted to the natural-log transformed endpoint data. AUC, area under concentration-time curve; Cmax, maximum concentration; tmax, time to maximum concentration; SD, standard deviation; MAGE, mean amplitude of glycaemic excursions; MODD, mean of daily differences.

Table 2 15(S)-8-iso-PGF2α of creatinine

BaselineMean (SD)

End of treatmentMean (SD)

∆r

Mean (SD)99% CIs

Inhaled 0.06 (0.03) 0.05 (0.03) 0.01 (0.02) -0.020, 0.000

Glargine 0.06 (0.03) 0.05 (0.02) 0.02 (0.02) -0.030, -0.010

15(S)-8-iso-PGF2α of creatinine is expressed in nmol/mmol creatinine. r ∆ represents the difference in 15(S)-8-iso-PGF2α concentrations from baseline to end of treatment. s 99% confidence interval is chosen because of the small design of the study.


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Discussion

The present study assessed the effect of three times daily mealtime insulin (inhaled)

therapy compared to once daily basal insulin (glargine) therapy on 72-hr glucose profiles,

glucose variability and oxidative stress in type 2 diabetes patients.

At least two important conclusions can be drawn. First, this study reveals that glucose

lowering using insulin treatment lowers oxidative stress (Table 2). In both groups,

there was a significant decline in 8-isoprostane production during treatment. As there

was no indication of a period effect, a time effect can be excluded. Lowering oxidative

stress as a result of lowering glycaemia was so far only reported as a momentary effect.

Ceriello et al. 11 described earlier a significant reduction of postprandial oxidative

stress in type 1 diabetes patients when reducing postprandial glucose excursions with

pramlintide, an amylin analogue. They examined nitrotyrosine, oxidised LDL and total

radical-trapping antioxidant parameter during a 4-hr postprandial period. They found a

correlation between the extent of postprandial glycaemia and the oxidative stress. Our

data strengthen their findings and extend the hypothesis of an oxidative stress lowering

effect by lowering glucose to a period of 8 days.

Second, we found no correlation between oxidative stress, measured as 8-iso-PGF2α in

urine, and glucose variability, defined as MAGE (Table 1, Figure 2). This is in accordance

with an earlier study in type 1 diabetes patients 5. On the other hand, Monnier et al. 4

reported a strong relationship between glucose variability and oxidative stress in type 2

diabetes patients. An explanation for the lack of correlation between glucose variability

and oxidative stress can be a methodological issue. We used next to immunoaffinity

isolation highly selective HPLC tandem MS for detection instead of the less specific

enzyme immunoassay to quantify 8-isoprostanes: HPLC-MS/MS is not hampered by cross-

reactivity of structurally (un)related components of 8-iso-PGF2α, whereas the immunoassay

is more susceptible to interference, as acknowledged in the earlier Monnier report 4. An

alternative possibility is that a relationship between glucose variability and oxidative

stress only exists in non-insulin treated type 2 diabetes patients. Recently, Ceriello

reported a clamp study 3 suggesting that oscillating glucose can have more deleterious

effects than constant high glucose on endothelial function and oxidative stress. However,

in this study only two glucose excursions were elicited, what in our opinion shows that

a second repetitive episode of acute hyperglycaemia elicits more oxidative stress than

the first 12, which is somewhat different than glucose variability over the whole day.

Looking at the consequence of oxidative stress, that is vascular complications, Gordin

et al. 13 detected no correlation between glucose variability (expressed as MAGE) and

arterial stiffness, considered an early sign of macrovascular complications, in type 1

diabetes patients.

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Overall glycaemic control was significantly better with a mealtime insulin approach

using inhalation insulin compared to a once daily approach using glargine. Literature

is not in agreement on this subject, varying from no difference between both regimens 6 to significantly better glycaemic control (expressed as decrease in HbA1c) when using

a mealtime approach in patients with type 2 diabetes starting with insulin therapy 14.

The overall better glycaemic control in the mealtime insulin group confounded the

comparison in oxidative stress between both groups, as lowering glycaemia lowers

oxidative stress.

In our study, glucose variability in the mealtime insulin group was somewhat lower

than in the once daily insulin group (Table 1) albeit not significantly. We think that the

non-significance is likely mainly explained by the small and therefore underpowered

study group. As postprandial hyperglycaemia accounts for the major part of glucose

variability 15, certainly in type 2 diabetes patients who experience few hypoglycaemic

episodes, one would expect glucose variability to be smaller with a mealtime insulin

approach. Glucose variability in the inhaled group was enlarged by the twofold higher

incidence of mild and moderate hypoglycaemia in the inhaled group 7. It is therefore

likely that the variability in the mealtime insulin group would have been lower if overall

glycaemic control would have been the same. It is also possible that glucose variability in

the basal insulin group is lower than expected. This could be explained by the residual

beta-cell function of the patients treated until recently with oral medication, mitigating

the postprandial glucose to rise even without mealtime insulin administration.

We have no clear explanation why oxidative stress seemed somewhat more lowered in

the glargine group. From the literature one would have expected lower oxidative stress in

the mealtime insulin group, resulting either from better overall glycaemic control in the

inhaled group or from reduced variability 16;17 . Possibly, the lower oxidative stress level

in the basal insulin group is a spurious finding. Again, a larger trial would be needed

to answer this question more definitively.

In conclusion, this study shows that lowering glucose lowers oxidative stress in type 2

diabetes patients not only as a momentary effect, extending the existing data of Ceriello

to a longer period 11. Second, we found no correlation between glucose variability and

oxidative stress in insulin-treated type 2 diabetes patients. Finally, a non-significant

almost 10% decline in glucose variability did not result in lower oxidative stress in

insulin-treated type 2 diabetes patients.

AcknowledgementsThis study was supported financially by Pfizer Inc.


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References1. Hirsch IB, Brownlee M (2005) Should minimal blood glucose variability become the gold standard of glycemic

control? J Diabetes Complications 19: 178-1812. Brownlee M, Hirsch IB (2006) Glycemic variability: a hemoglobin A1c-independent risk factor for diabetic

complications. JAMA 295: 1707-17083. Ceriello A, Esposito K, Piconi L, et al (2008) Oscillating glucose is more deleterious to endothelial function

and oxidative stress than mean glucose in normal and type 2 diabetic patients. Diabetes 57: 1349-13544. Monnier L, Mas E, Ginet C, et al (2006) Activation of oxidative stress by acute glucose fluctuations compared

with sustained chronic hyperglycemia in patients with type 2 diabetes. JAMA 295: 1681-16875. Wentholt IME, Kulik W, Hoekstra JBL, DeVries JH (2008) Glucose fluctuations and activation of oxidative

stress in type 1 diabetes patients. Diabetologia 51: 183-1906. Bretzel RG, Nuber U, Landgraf W, Owens DR, Bradley C, Linn T (2008) Once-daily basal insulin glargine versus

thrice-daily prandial insulin lispro in people with type 2 diabetes on oral hypoglycaemic agents (APOLLO): an open randomised controlled trial. Lancet 371: 1073-1084

7. Hompesch M, Kollmeier A, Rave K, et al (2009) Glycemic exposure is affected favorably by inhaled human insulin (Exubera) as compared with subcutaneous insulin glargine (Lantus) in patients with type 2 diabetes. Diabetes Technol Ther 11: 307-313

8. McDonnell CM, Donath SM, Vidmar SI, Werther GA, Cameron FJ (2005) A novel approach to continuous glucose analysis utilizing glycemic variation. Diabetes Technol Ther 7: 253-263

9. Molnar GD, Taylor WF, Ho MM (1972) Day-to-day variation of continuously monitored glycaemia. Diabetologia 8: 342-348

10. Service FJ, Molnar GD, Rosevear JW, Ackerman E, Gatewood LC, Taylor WF (1970) Mean amplitude of glycemic excursions, a measure of diabetic instability. Diabetes 19: 644-655

11. Ceriello A, Piconi L, Quagliaro L, et al (2005) Effects of pramlintide on postprandial glucose excursions and measures of oxidative stress in patients with type 1 diabetes. Diabetes Care 28: 632-637

12. Brownlee M (2005) The pathobiology of diabetic complications: a unifying mechanism. Diabetes 54: 1615-1625

13. Gordin D, Rönnback M, Forsblom C, Mäkinen V, Saraheimo M, Groop P-H (2008) Glucose variability, blood pressure and arterial stiffness in type 1 diabetes. Diabetes Res Clin Pract 80: e4-e7

14. Kazda C, Hulstrunk H, Helsberg K, Langer F, Forst T, Hanefeld M (2006) Prandial insulin substitution with insulin lispro or insulin lispro mid mixture vs. basal therapy with insulin glargine: A randomized controlled trial in patients with type 2 diabetes beginning insulin therapy. J Diabetes Complications 20: 145-152

15. McCall AL, Cox DJ, Crean J, Gloster M, Kovatchev BP (2006) A novel analytical method for assessing glucose variability: using CGMS in type 1 diabetes mellitus. Diabetes Technol Ther 8: 644-653

16. Flores L, Rodela S, Abian J, Claria J, Esmatjes E (2004) F2 isoprostane is already increased at the onset of type 1 diabetes mellitus: effect of glycemic control. Metabolism 53: 1118-1120

17. Shamir R, Kassis H, Kaplan M, Naveh T, Shehadeh N (2008) Glycemic control in adolescents with type 1 diabetes mellitus improves lipid serum levels and oxidative stress. Pedriatics Diabetes 9: 104-109

Chapter 6

Glucose variability does not contribute to the development of peripheral and autonomic neuropathy in type 1 diabetes: data from the DCCT

Sarah E. Siegelaar, Eric S. Kilpatrick, Alan S. Rigby, Steven L. Atkin,

Joost B.L. Hoekstra and J. Hans DeVries

Published in abbreviated form, Diabetologia 2009; 52(10):2229-2232

86

Abstract

Aims/hypothesis: While the presence of an effect of glycaemic variability

on retinopathy and nephropathy has been negated, it is unknown whether

glycaemic variability may influence neuropathy. We analysed data from the

Diabetes Control and Complications Trial (DCCT) dataset to assess whether

glycaemic variability is a risk factor for the development of diabetic neuropathy.

Methods: Seven-point glucose profiles were collected quarterly during the DCCT

in 1,441 type 1 diabetes patients. Peripheral and autonomic neuropathies were

assessed at baseline and at 5 and 4 years follow-up, respectively. The effect of

glycaemic variability, expressed as standard deviation (SD) and mean amplitude

of glycaemic excursions (MAGE), on the development of neuropathy in addition to

HbA1c and mean glucose was assessed using a logistic regression model, adjusted

for age, sex, disease duration, treatment group and prevention cohort.

Results: Glucose variability had no significant effect on the incidence of clinical

neuropathy confirmed by autonomic or electromyography abnormalities (SD,

odds ratio [OR] 1.07, 95% confidence interval [CI] 0.83-1.35; MAGE, 1.06 [0.96-

1.20]) or clinical neuropathy alone (SD, 0.95 [0.77-1.18]; MAGE, 1.01 [0.91-1.11]). It

appeared to have a significant effect on overall autonomic dysfunction but not

when adjusting for HbA1c or mean glucose (SD, OR 1.08 and 1.09, CI 0.82-1.44

and 0.80-1.48, respectively; MAGE, OR 1.09 and 1.10, CI 0.96-1.23 and 0.97-1.25

respectively).

Conclusions: Glucose variability was not an additional risk factor in the

development of diabetic peripheral or autonomic neuropathy over and above

HbA1c or mean glucose in the DCCT.

No contribution of glucose variability to development of neuropathy in T1DM

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Introduction

Diabetes is one of the most common causes of small fibre neuropathy causing sensory

symptoms as pain and numbness as well as autonomic dysfunction. Large fibre

involvement is frequent, which makes neuropathy an important complication of

diabetes, potentially leading to disability and premature deaths 1;2. It is thought that

hyperglycaemia causes direct damage to the nerve parenchyma as well as indirect

hyperglycaemia-induced neuronal ischemia by decreases in neuronal flow 3. Good

glycaemic control is a proven robust measure to delay or prevent the development of

diabetic polyneuropathy 4;5. Other cardiovascular risk-factors such as body mass index and

hypertension are associated with this complication in type 1 diabetes and are therefore

assessed in prevention programmes 4.

It is suggested that, in addition to hyperglycaemia, glucose variability can contribute to

the severity and development of diabetic neuropathy because the nervous system may be

particularly susceptible to glycaemic fluctuations 6. On the other hand, glucose variability

is not related to the development of retinopathy and nephropathy in type 1 diabetes 6;7.

To determine any additional effect of glucose variability, above that assessed by HbA1c

and mean glucose, on peripheral and autonomic diabetic neuropathy, we analyzed the

data from the Diabetes Control and Complications Trial (DCCT).

Methods

The datasetsWe used for this study the datasets collected during the DCCT (publicly accessible at,

www.gcrc.med.umn.edu/gcrc/downloads/dcct.html, accessed 23-27 January 2009). The

DCCT was a 9-year follow-up study of 1,441 patients with type 1 diabetes comparing the

effects of intensive versus conventional treatment on the development of microvascular

complications and neuropathy 8. A standardised neurologic history, physical examination

and nerve conduction studies were done by DCCT neurologists at baseline, 5 years, and

study end. Autonomic nervous system tests were performed at baseline and biennially

thereafter 9. We included only data from baseline to 4 years (autonomic function data)

or 5 years of follow-up in the analyses as more than 50% of the patients did not have

records of glucose data after 5 years of follow-up.

Definition of eventsClinical neuropathy was defined as abnormal findings in two or more of the following

categories in the absence of other known causes of neuropathy: neuropathic symptoms

(dysesthesias, paresthesias, hypersensitivity to touch and burning pain), sensory deficits

88

(light touch, position, temperature and pin-prick) or deep tendon reflexes 9. The nerve

conduction studies consisted of median motor and sensory, peroneal motor and sural

sensory nerve conduction velocities; distal latencies and amplitudes; and median and

peroneal motor F-wave latencies using a standard protocol 9. Abnormal nerve conduction

was considered present when at least one measured attribute was abnormal in at least

two anatomically distinct nerves. Autonomic nervous system function was assessed using

three tests: beat-to-beat heart rate variation (R-R variation) during deep breathing and

during a standardised Valsalva maneuver, and postural blood pressure testing 9. Abnormal

autonomic function was determined as at least one abnormal autonomic function

test. The main neurological endpoint of the DCCT was the development of confirmed

clinical neuropathy, defined as clinical neuropathy confirmed by either abnormal nerve

conduction or autonomic nervous system testing.

We studied the effect of glucose variability on the main neurological endpoint, i.e.

confirmed clinical neuropathy, and on the DCCT-defined secondary endpoints separately:

clinical neuropathy, abnormal nerve conduction studies, and abnormal autonomic

function. In addition, we determined its effect on the subvariables median motor F-wave

latency, sural amplitude, sensory signs, and beat-to-beat heart-rate variation (with Valsalva

ratio <1.5), as these variables tend to be the first affected by diabetes.

Glycaemic variablesDuring the DCCT a seven-point blood glucose profile was collected every 3 months (pre

breakfast, post breakfast, pre lunch, post lunch, pre supper, post supper and bedtime). An

additional data point was collected during the night, but since this was only measured

in <1% of the subjects, it is left out of further analysis. We included all profiles with five

observations or more during the 24-hr period, extrapolating missing values from the

surrounding points 10. Mean blood glucose was calculated by the area under the curve

(AUC) using the trapezoidal rule 11. Variability of blood glucose was calculated as the SD of

daily blood glucose around the mean from each quarterly visit (within-day SD) 12 and the

mean amplitude of glycaemic excursions (MAGE) 13. Last, we calculated the mean SD from

individual glucose data transformed to a symmetric distribution according to Kovatchev 14. Glucose variability from baseline to 4 or 5 years was assessed as the mean SD and

mean MAGE from the first quarter till the 16th or 20th quarter of follow-up, respectively.

Statistical analysisThe relationship between glucose variability and the development of each diabetic

neuropathy variable was assessed by a logistic regression model from which odds ratios

(OR) and 95% confidence intervals (CI) were calculated. Patients with a positive baseline

score on the neuropathy parameter studied were excluded from analysis. All regression

models were adjusted for the following baseline covariates: age (years), sex, disease


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duration (years), randomization treatment (conventional vs. intensive) and prevention

cohort (primary vs. secondary). Finally, the additional effect of glucose variability on

neuropathy separate from the effect of HbA1c and mean glucose (AUC) was computed

using the same technique. Statistical analysis was performed using SPSS version 16.0.2.

A P-value <0.05 was considered significant.

Results

The main characteristics of the patients in the group analysed for confirmed clinical

neuropathy are listed in Table 1. Of the 1,441 patients in total, 1,160 were included in

this specific analysis. Ninety-two patients were excluded from the analysis because they

had a positive score at baseline, and 189 patients had missing data on confirmed clinical

neuropathy at baseline (n = 3) or at 5 years (n = 186). The numbers of participants with

data analysed in the other specific neuropathy groups are listed in Table 2.

Table 1 Patient characteristics in the group analysed for confirmed clinical

neuropathy

Confirmed clinical neuropathy

Yes (n = 108) No (n = 1052) P - value

Age at baseline, years 28.28 (6.77) 26.40 (7.10) 0.008

Male sex, n (%) 52 (48) 533 (53) 0.38

Diabetes duration at baseline, months 79.59 (45.62) 68.60 (49.79) 0.02

Conventional treatment, n (%) 80 (74) 517 (49) <0.001

Primary prevention cohort, n (%) 35 (32) 503 (48) 0.002

HbA1c (%) 9.10 (1.58) 8.08 (1.43) <0.001

Mean glucose, mmol/l 13.51 (3.33) 11.52 (3.78) <0.001

MAGE, mmol/l 8.00 (1.96) 7.55 (1.90) 0.02

SD, mmol/l 4.24 (0.89) 4.05 (0.93) 0.04

SD TF, mmol/l 0.75 (0.17) 0.81 (0.16) <0.001

Data are means (SD), unless stated otherwise in parentheses. For this analysis patients with a positive or missing score for confirmed clinical neuropathy at baseline were excluded (neuropathy, n = 92; missing, n = 3). Patients with a missing score at 5 years are also excluded from the analysis (n = 186). P-values are comparisons between groups (independent samples t-test). MAGE, mean amplitude of glycaemic excursions; SD, standard deviation; SD TF, standard deviation obtained from glucose data transformed according to Kovatchev et al.: transformed blood glucose = 1.794*([log{BG}]1.026 - 1.861) 14.

90

Tab

le 2

Bin

ary

log

isti

c re

gre

ssio

n a

nal

ysis

rel

atin

g th

e ef

fect

of

dif

fere

nt

gly

caem

ic v

aria

ble

s to

neu

rolo

gic

al

com

pli

cati

on

s, a

s d

efin

ed b

y th

e D

CC

T

Co

nfi

rmed

cli

nic

al n

euro

pat

hya

Cli

nic

al n

euro

pat

hy

aA

uto

no

mic

neu

rop

ath

y b

Ab

no

rmal

ner

ve c

on

du

ctio

n a

n =

108

/116

0 c

n =

148

/111

3 c

n =

79/

1258

cn

= 2

07/8

13 c

mod

elO

R (

95 %

CI)

PO

R (

95%

CI)

PO

R (

95%

CI)

PO

R (

95%

CI)

P

Hb

A1c

1.64

(1.3

7-1.

95)

<0.0

011.

40 (1

.20-

1.64

)<0

.001

1.51

(1.2

3-1.

85)

<0.0

011.

62 (1

.39-

1.90

)<0

.001

AU

C1.

17 (1

.09-

1.26

)<0

.001

1.13

(1.0

5-1.

21)

0.00

11.

15 (1

.05-

1.26

)0.

003

1.15

(1.0

7-1.

23)

<0.0

01

SD1.

07 (0

.83-

1.35

)0.

670.

95 (0

.77-

1.18

)0.

661.

30 (0

.99-

1.70

)0.

061.

17 (0

.96-

1.42

)0.

12

SD (H

bA

1c)

0.86

(0.6

7-1.

10)

0.24

0.82

(0.6

6-1.

03)

0.08

1.08

(0.8

2-1.

44)

0.58

0.94

(0.7

5-1.

14)

0.46

SD (A

UC

)0.

85 (0

.65-

1.10

)0.

210.

78 (0

.61-

0.99

)0.

041.

09 (0

.80-

1.48

)0.

590.

95 (0

.76-

1.19

)0.

68

MA

GE

1.06

(0.9

6-1.

20)

0.23

1.01

(0.9

1-1.

11)

0.90

1.16

(1.0

3-1.

30)

0.01

1.07

(0.9

8-1.

17)

0.14

MA

GE

(Hb

A1c

)1.

00 (0

.89-

1.12

)0.

950.

96 (0

.86-

1.06

)0.

371.

09 (0

.96-

1.23

)0.

180.

98 (0

.90-

10.8

)0.

74

MA

GE

(AU

C)

1.00

(0.8

8-1.

12)

0.93

0.94

(0.8

5-1.

05)

0.29

1.10

(0.9

7-1.

25)

0.13

1.00

(0.9

1-1.

10)

0.93

SD T

F0.

14 (0

.04-

0.52

)0.

003

0.15

(0.0

5-0.

48)

0.00

10.

63 (0

.13-

3.07

)0.

570.

16 (0

.06-

0.47

)0.

001

SD T

F (H

bA

1c)

0.36

(0.0

9-1.

37)

0.13

0.25

(0.0

7-0.

86)

0.03

1.42

(0.2

6-7.

71)

0.68

0.62

(0.2

0-1.

87)

0.39

SD T

F (A

UC

)0.

33 (0

.08-

1.39

)0.

130.

26 (0

.08-

0.87

)0.

031.

27 (0

.25-

6.29

)0.

770.

61 (0

.19-

1.94

)0.

40

HbA

1c, A

UC

, SD

, MA

GE

and

SD

TF

rep

rese

nt

mea

ns

from

qu

arte

rly

visi

t 1-

16b

or 1

-20a . c

Pat

ien

ts w

ith

a p

osit

ive

neu

rop

ath

y sc

ore

at fi

ve y

ears

/com

ple

te a

nal

ysis

gro

up

p

er p

aram

eter

. All

mod

els

are

adju

sted

for

base

lin

e co

vari

ates

(sex

, age

, dis

ease

du

rati

on, p

reve

nti

on c

ohor

t, r

and

omiz

atio

n t

reat

men

t). S

D (H

bA1c

), M

AG

E (H

bA1c

), SD

TF

(HbA

1c),

SD (A

UC

), M

AG

E (A

UC

) an

d S

D T

F (A

UC

) are

six

dis

tin

ct m

odel

s ad

dit

ion

ally

ad

just

ed f

or H

bA1c

or

AU

C a

par

t fr

om t

he

base

lin

e co

vari

ates

. OR

, od

ds

rati

o;

CI,

con

fid

ence

in

terv

al; A

UC

, are

a u

nd

er t

he

curv

e; S

D, s

tan

dar

d d

evia

tion

; MA

GE,

mea

n a

mp

litu

de

of g

lyca

emic

exc

urs

ion

s; S

D T

F, s

tan

dar

d d

evia

tion

obt

ain

ed f

rom

gl

uco

se d

ata

tran

sfor

med

acc

ord

ing

to K

ovat

chev

: tra

nsf

orm

ed b

lood

glu

cose

= 1

.794

*([l

og{B

G}]

1.02

6 -1.8

61) 14

.


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91

Logistic regression analysis showed no effect of glucose variability, computed as the mean

SD and mean MAGE from the seven-point glucose profiles from quarterly visit 1-20 (first

5 years), on confirmed clinical neuropathy, the main neuropathy endpoint of the DCCT

(Table 2). Dividing the variability parameters in quartiles and performing the analysis

per randomization group did not change the outcome (data not shown).

No effect of glycaemic variability on clinical neuropathy was seen, with exception of

a small protective effect of the SD adjusted for AUC (Table 2). In addition, no effect of

glycaemic variability was seen on the incidence of sensory signs (SD, 1.00 [0.82-1.22], P =

0.99; MAGE, 1.02 [0.93-1.12], P = 0.69) as well as in separate analysis of the F-wave latency

of the median nerve (SD, 1.12 [0.84-1.49], P = 0.44; MAGE, 1.03 [0.90-1.17], P = 0.67) and the

amplitude of the sural nerve (SD, 1.27 [1.00-1.60], P = 0.05; MAGE, 1.05 [0.95-1.17], P = 0.34).

Glycaemic variability seemed to have an effect on autonomic neuropathy, but this effect

disappeared when adjusting the model for HbA1c or AUC (Table 2). Analysing both

randomization groups separately also did not reveal a relation over HbA1c (data not

shown). Separate examination of the three autonomic function parameters showed that

only for beat-to-beat heart rate variation during a Valsalva manoeuvre did the effect

remain significant when adjusting for mean glucose (SD, 2.64 [1.17-5.94], P = 0.02; MAGE,

1.42 [1.07-1.90], P = 0.02), but not when adjusting for HbA1c (SD, 1.84 [0.90-3.76], P = 0.09;

MAGE 1.30 [0.98-1.72], P = 0.07). There was no effect of glycaemic variability on beat-to-

beat heart-rate variation during deep breathing and postural blood pressure testing

(data not shown).

HbA1c and AUC itself were strong predictors of any form of neuropathy as described

above and transformation of the individual glucose data according to Kovatchev 14 did

not alter the results (Table 2).

Discussion

In this study, glycaemic variability did not influence the development of neuropathy

over HbA1c or mean glucose. HbA1c and mean glucose itself were strong predictors for

the development of diabetic neuropathy. These results are in line with earlier analysis

of DCCT data describing no influence of glycaemic variability on the development or

progression of retinopathy and nephropathy 7.

Bragd et al. 6 found that glucose variability (SD) was a borderline predictor of the

incidence of peripheral neuropathy in 100 type 1 diabetes and with a follow-up period

of 11 years (P = 0.07; HR 1.73, range 0.94-3.19). Peripheral neuropathy in their study was

92

defined as sensory neuropathy, as indicated by monofilament testing, and an abnormal

EMG and/or vibration test. This same study showed a significant relationship between

SD and the prevalence of peripheral neuropathy (P = 0.03; OR 2.34, range 1.06-5.20),

perhaps suggesting that the nervous system may be particularly susceptible to glycaemic

fluctuations 6. Another cross-sectional study investigated the relation between glucose

variability and the presence of pain in 20 type 1 diabetes patients with established

peripheral neuropathy. Compared to the group without symptoms (n = 10), the group with

painful symptoms had more glycaemic excursions, although there was no difference in

MAGE 15. Since the groups were neither matched nor the effect adjusted for mean glucose,

the significantly larger mean glucose in the painful group is more likely to explain the

difference between the groups. In the DCCT no separate distinction was made for pain

as a symptom so the outcome measure is not exactly comparable.

We did find a relation between glucose variability and two neuropathy parameters: the

autonomous parameter beat-to-beat heart rate variation <20 combined with a Valsalva

ratio >1.5 as well as clinical neuropathy, both independent from AUC. These results are

likely the consequence of multiple testing. When adjusting for multiple testing using the

Holm method 16 a P-value of 0.0125 would be needed to reject the H0 hypothesis, which

is smaller than the P-value of 0.02 and 0.04 we found for the autonomic neuropathy

parameter and clinical neuropathy respectively. Multiple testing is also the most likely

explanation for the odds ratio’s smaller than 1 found for some of the clinical neuropathy

parameters (Table 1).

What strengthens our results is that we did not find a relationship between glucose

variability and sensory signs or median motor F-wave latency and sural amplitude, the

earliest indicators of diabetic neuropathy. As diabetic neuropathy is mostly a small-fibre

disease, sensory signs are usually the presenting sign of the disease and they are a stable

and reliable measure of disease status or progression 17. Although EMG studies measure

large-fibre function, median motor F-wave latency and sural amplitude are the most

sensitive of all EMG measures to detect diabetic neuropathy 18;19.

A limitation of this study is that the variability parameters are calculated from seven-

point glucose curves by self-monitoring. Continuous glucose monitoring (CGM) might

detect fluctuations occurring between two measurements that would be missed by self-

monitoring of blood glucose. Also the DCCT participants did not collect all profiles as

required, resulting in missing values. However, they were highly motivated, thus limiting

missing data to a minimum. Another difficulty is that the neuropathy variables were

infrequently scored. We decided to focus on events up to 4 (autonomic neuropathy) and

5 (clinical neuropathy) years because after 5 years of follow-up in more than 50% of the

patients the glucose data has not been recorded. It might be possible that the analysis


Ch

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93

at this point has been hampered by a power problem due to too few events. Possibly the

DCCT/Epidemiology of Diabetes Interventions and Complications (EDIC) follow-up will

provide more endpoints as the same neuropathy parameters assessed in the DCCT are

measured in years 13 or 14 of its follow-up (2007-8; www.niddkrepository.org). These

data have not yet been released.

In conclusion, glucose variability was not a risk factor separate from HbA1c or mean

glucose in the development of diabetic peripheral neuropathy in the DCCT.

AcknowledgementsThe Diabetes Control and Complications Trial (DCCT) and its follow-up the Epidemiology

of Diabetes Interventions and Complications (EDIC) study were conducted by the DCCT/

EDIC Research Group and supported by National Institute of Health (NIH) grants and

contracts and by the General Clinical Research Center Program, NCRR. This manuscript

was not prepared under the auspices of the DCCT/EDIC study and does not represent

analyses nor conclusions of the DCCT/EDIC study group nor the NIH.

94

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Rev 16: 408-4334. Tesfaye S, Chaturvedi N, Eaton SEM, et al (2005) Vascular Risk Factors and Diabetic Neuropathy.

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therapy on the development and progression of neuropathy. Ann Intern Med 122: 561-5686. Bragd J, Adamson U, Backlund LB, Lins PE, Moberg E, Oskarsson P (2008) Can glycaemic variability, as

calculated from blood glucose self-monitoring, predict the development of complications in type1 diabetes over a decade? Diabetes & Metabolism 34: 612-616

7. Kilpatrick ES, Rigby AS, Atkin SL (2006) The effect of glucose variability on the risk of microvascular complications in type 1 diabetes. Diabetes Care 29: 1486-1490

8. The DCCT Research Group (1993) The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus.N Engl J Med 329:977-986

9. The Diabetes Control and Complications Trial Research Group (1988) Factors in development of diabetic neuropathy. Baseline analysis of neuropathy in feasibility phase of the Diabetes Control and Complications Trial (DCCT). Diabetes 37: 476-481

10. Kilpatrick ES, Rigby AS, Atkin SL (2008) Mean blood glucose compared with HbA1c in the prediction of cardiovascular disease in patients with type 1 diabetes. Diabetologia 51: 365-371

11. Rohlfing CL, Wiedmeyer HM, Little RR, England JD, Tennill A, Goldstein DE (2002) Defining the relationship between plasma glucose and HbA1c: analysis of glucose profiles and HbA1c in the DCCT. Diabetes Care 25: 275-278

12. Moberg E, Kollind M, Lins P, Adamson U (1993) Estimation of blood-glucose variability in patients with insulin-dependent diabetes mellitus. Scand J Clin Lab Invest 53: 507-514

13. Service FJ, Molnar GD, Rosevear JW, Ackerman E, Gatewood LC, Taylor WF (1970) Mean amplitude of glycemic excursions, a measure of diabetic instability. Diabetes 19: 644-655

14. Kovatchev BP, Cox DJ, Gonder-Frederick LA, Clarke W (1997) Symmetrization of the blood glucose measurement scale and its applications. Diabetes Care 20: 1655-1658

15. Oyibo SO, Prasad YDM, Jackson NJ, Jude EB, Boulton AJM (2002) The relationship between blood glucose excursions and painful diabetic peripheral neuropathy:a pilot study.Diabetic Medicine 19:870-873

16. Aickin M, Gensler H (1996) Adjusting for multiple testing when reporting research results: the Bonferroni vs Holm methods. Am J Public Health 86: 726-728

17. Bril V (1999) NIS-LL: The primary measurement scale for clinical trial endpoints in diabetic peripheral neuropathy. European Neurology 41: 8-13

18. Sima AAF (1992) Structure-function interactions in the therapeutic response of diabetic neuropathy. Journal of Diabetes and its Complications 6: 64-68

19. Perkins BA, Bril V (2003) Diabetic neuropathy: a review emphasizing diagnostic methods. Clinical Neurophysiology 114: 1167-1175


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Chapter 7

A decrease in glucose variability does not reduce cardiovascular event rates in type 2 diabetes patients after acute myocardial infarction: a reanalysis of the HEART2D study

Sarah E. Siegelaar, Lisa Kerr, Scott J. Jacober and J. Hans DeVries

Diabetes Care 2011; 34(4): 855-857

98

Abstract

Objective: To assess the effect of intraday glucose variability (GV) on

cardiovascular outcomes in a reanalysis of Hyperglycaemia and Its Effect After

Acute Myocardial Infarction on Cardiovascular Outcomes in Patients With Type

2 Diabetes Mellitus (HEART2D) study data.

Research Design and Methods: Type 2 diabetes patients after acute myocardial

infarction were randomised to an insulin treatment strategy targeting

postprandial (PRANDIAL; n = 557) or fasting/interprandial (BASAL; n = 558)

hyperglycaemia. GV was calculated as mean amplitude of glycaemic excursions

(MAGE), mean absolute glucose (MAG) change, and standard deviation (SD).

Results: The PRANDIAL strategy resulted in an 18% lower MAG than BASAL (mean

[SEM] difference 0.09 [0.04] mmol/l/hr, P = 0.02). Also, MAGE and SD were lower in

the PRANDIAL group, however not significantly. HbA1c levels and cardiovascular

event rates were comparable between groups.

Conclusions: A PRANDIAL strategy demonstrated lower intraday GV vs. a BASAL

strategy with similar overall glycaemic control but did not result in a reduction

in cardiovascular outcomes. This does not support the hypothesis that targeting

GV would be beneficial in reducing subsequent secondary cardiovascular events.

Decreasing glucose variability does not reduce cardiovascular event rates in T2DM after MI

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Introduction

Short-term variation in blood glucose (BG) levels is a daily challenge to patients with diabetes.

It confers a possible increased risk for hypoglycaemia, and it has been suggested that glucose

variability (GV) is related to cardiovascular risk 1-3 However, reanalysis of the Diabetes Control

and Complications Trial (DCCT) and DCCT/Epidemiology of Diabetes Interventions and

Complications (EDIC) dataset examining the predictive value of GV on microvascular and

neurological complications did not show an effect of GV independent from mean glucose

and HbA1c 4-6, and randomised controlled trials (RCTs) specifically targeting GV are lacking 7;8.

To assess the effect of intraday GV on cardiovascular outcomes we re-examined data

from Hyperglycaemia and Its Effect After Acute Myocardial Infarction on Cardiovascular

Outcomes in Patients With Type 2 Diabetes Mellitus study (HEART2D; clinical trial

registry number NCT00191282, clinicaltrials.gov) 9.

Research Design and Methods

The HEART2D study included 1,115 type 2 diabetic patients who had had a recent

myocardial infarction; patients well-controlled with diet or treated with intensive

insulin therapy were excluded. It was designed to investigate possible differences

between two insulin treatment strategies on time until first combined cardiovascular

event (a composite of cardiovascular death, nonfatal myocardial infarction, nonfatal

stroke, coronary revascularization, or hospitalization for acute coronary syndrome) 9.

Within 21 days after admission for acute myocardial infarction, patients were randomised

to one of two insulin treatment strategies: one targeted postprandial hyperglycaemia

with thrice-daily premeal insulin lispro (PRANDIAL; n = 557), and the other targeted

fasting/interprandial hyperglycaemia with once-daily insulin glargine or twice-daily

NPH (BASAL; n = 558). The study succeeded in achieving similar HbA1c levels in both

strategies, which allowed the authors to look at effects of targeting postprandial glucose

values independent from glycaemic control. There was no significant difference between

groups in time to first combined cardiovascular event (hazard ratio 0.98 [95% CI 0.8-

1.21]). Though the PRANDIAL group achieved significantly lower mean postprandial

blood glucose values, the between-group difference was less than expected. The trial was

halted early due to futility with a lower than expected overall number of cardiovascular

events. We evaluated the effect of glycaemic variability to help further the interpretation

of HEART2D results.

In the present analysis we calculated mean GV in both strategies from seven-point self-

measured BG profiles collected prior to study visits during the study period, obtained

100

over 24 hrs from breakfast to breakfast the next morning. This adds to the original

analysis since postprandial excursions contribute to GV, but GV encompasses more than

postprandial excursions alone. Since no gold standard for quantifying GV exists 7 we

calculated mean absolute glucose (MAG) change, mean amplitude of glycaemic excursions

(MAGE) and standard deviation (SD). MAG is the summated change in glucose per unit

of time (MAG = |ΔGlucose|/ΔTime) (Figure 1), which showed in the intensive care unit

a stronger association with mortality than SD 10. This is the first time MAG is used in a

diabetic population. MAGE is the average of all BG increases or decreases that are >1 SD

of all BG measures 11. Differences between regimens and those experiencing vs. those

not experiencing a cardiovascular event were assessed using a pattern mixed-model

repeated-measurement analysis. The model included strategy, baseline GV, randomisation

factors, and an additional factor for study duration (defined as ≤30, >30 and ≤42, and

>42 months) 9.

Results

The original HEART2D study analysis showed that HbA1c did not differ between groups

during the study (mean [SEM] PRANDIAL 7.7% [0.1], BASAL 7.8% [0.1], P = 0.4). We found

that the PRANDIAL strategy resulted in a significantly lower MAG compared with the

BASAL strategy (mean [SEM] PRANDIAL 0.40 [0.03] mmol/l/hr, BASAL 0.49 [0.02] mmol/

l/h, difference 0.09 [0.04] mmol/l/hr, P = 0.02). Also MAGE and SD were lower in the

PRANDIAL group, however not significantly (MAGE, PRANDIAL 3.14 [0.22] mmol/l, BASAL

3.32 [0.17] mmol/l, difference 0.18 [0.27] mmol/l, P = 0.50; SD, PRANDIAL 1.42 [0.09] mmol/l,

BASAL 1.58 [0.07] mmol/l, difference 0.16 [0.11] mmol/l, P = 0.15). Additionally, taking

the two randomization groups together, there was no difference in GV between those

experiencing vs. those not experiencing a cardiovascular event (mean [SEM] MAG 0.47

[0.03] mmol/l/hr vs. 0.44 [0.02] mmol/l/hr, P = 0.57; MAGE 3.35 [0.23] mmol/l vs. 3.16 [0.16]

mmol/l, P = 0.49; SD 1.56 [0.10] mmol/l vs. 1.48 [0.07] mmol/l, P = 0.52).

Conclusions

We found that in type 2 diabetes patients after acute myocardial infarction an insulin

regimen targeting postprandial hyperglycaemia lowered intraday GV compared with

a basal insulin strategy, with similar overall glycaemic control. However, GV decreases

shown in the PRANDIAL strategy did not translate into a reduction in cardiovascular

outcomes compared with treatment with BASAL strategy.


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It might be possible that these negative results are explained by the patient group

studied, i.e., type 2 diabetic patients with advanced atherosclerosis. Another possibility

is that patients with diabetes, in contrast with critically ill patients without previously

diagnosed diabetes 10, are not affected by GV because of the ability of cells to adapt to

the harmful effects of changing ambient glucose.

A strong point of the present study is the assessment of GV by MAG. MAG takes GV

into account to its fullest extent as opposed to MAGE, which neglects glycaemic swings

smaller than 1 SD and assesses only the increases or decreases of the glucose profile 11.

Furthermore, MAG calculates glucose change over time, whereas SD does not take time

into account (Figure 1).

In conclusion, the results of the present analysis showed that targeting postprandial

glucose decreased intraday GV by 18% without a corresponding reduction in subsequent

secondary cardiovascular events at least in this population. Further studies looking at

different groups of patients are needed to investigate whether reducing GV will reduce

cardiovascular risk independently from HbA1c.

Figure 1 Two fictitious patients with identical mean glucose, SD, and mean amplitude of glycaemic excursions (MAGE), but different patterns of variability expressed by mean absolute glucose change (MAG).

102

References1. Borg R, Kuenen JC, Carstensen B, et al (2011) HbA(1c) and mean blood glucose show stronger associations

with cardiovascular disease risk factors than do postprandial glycaemia or glucose variability in persons with diabetes: the A1C-Derived Average Glucose (ADAG) study. Diabetologia 54: 69-72

2. Nalysnyk L, Hernandez-Medina M, Krishnarajah G (2010) Glycaemic variability and complications in patients with diabetes mellitus: evidence from a systematic review of the literature. Diabetes Obes Metab 12: 288-298


4. Kilpatrick ES, Rigby AS, Atkin SL (2006) The effect of glucose variability on the risk of microvascular complications in type 1 diabetes. Diabetes Care 29: 1486-1490

5. Siegelaar SE, Kilpatrick ES, Rigby AS, Atkin SL, Hoekstra JB, DeVries JH (2009) Glucose variability does not contribute to the development of peripheral and autonomic neuropathy in type 1 diabetes: data from the DCCT. Diabetologia 52: 2229-2232

6. Kilpatrick ES, Rigby AS, Atkin SL (2009) Effect of glucose variability on the long-term risk of microvascular complications in type 1 diabetes. Diabetes Care 32: 1901-1903


8. Siegelaar SE, Kulik W, van Lenthe H, Mukherjee R, Hoekstra JB, DeVries JH (2009) A randomized clinical trial comparing the effect of basal insulin and inhaled mealtime insulin on glucose variability and oxidative stress. Diabetes Obes Metab 11: 709-714

9. Raz I, Wilson PW, Strojek K, et al (2009) Effects of prandial versus fasting glycemia on cardiovascular outcomes in type 2 diabetes: the HEART2D trial. Diabetes Care 32: 381-386

10. Hermanides J, Vriesendorp TM, Bosman RJ, Zandstra DF, Hoekstra JB, DeVries JH (2010) Glucose variability is associated with intensive care unit mortality. Crit Care Med 38: 838-842

11. Service FJ, Molnar GD, Rosevear JW, Ackerman E, Gatewood LC, Taylor WF (1970) mean amplitude of glycemic excursions, a measure of diabetic instability. Diabetes 19: 644-655


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Part II

Glucose control in critical illness

Chapter 8

Mean glucose during intensive care unit admission is related to mortality by a U-shaped curve in surgical and medical patients: a retrospective cohort study

Sarah E. Siegelaar, Jeroen Hermanides, Heleen M. Oudemans- van Straaten,

Peter H.J. van der Voort, Robert J. Bosman, Durk F. Zandstra and

J. Hans DeVries

Critical Care 2010; 14(6):R224

108

Abstract

Introduction: Lowering of hyperglycaemia in the intensive care unit (ICU) is

widely practised. We investigated in which way glucose regulation, defined as

mean glucose concentration during admission, is associated with ICU mortality

in a medical and a surgical cohort.

Methods: Retrospective database cohort study including patients admitted

between January 2004 and December 2007 in a 20-bed medical/surgical ICU in

a teaching hospital. Hyperglycaemia was treated using a computerised algorithm

targeting for glucose levels of 4.0-7.0 mmol/l. Five thousand eight hundred

twenty-eight patients were eligible for analyses, of whom 1,339 patients had a

medical and 4,489 had a surgical admission diagnosis.

Results: The cohorts were subdivided in quintiles of increasing mean glucose.

We examined the relation between these mean glucose strata and mortality. In

both cohorts we observed the highest mortality in the lowest and highest strata.

Logistic regression analysis adjusted for age, sex, Acute Physiology and Chronic

Health Evaluation II (APACHE II) score, admission duration and occurrence of

severe hypoglycaemia showed that in the medical cohort mean glucose levels

<6.7 mmol/l and >8.4 mmol/l and in the surgical cohort mean glucose levels

<7.0 mmol/l and >9.4 mmol/l were associated with significantly increased ICU

mortality (OR 2.4-3.0 and 4.9-6.2 respectively). Limitations of the study were its

retrospective design and possible incomplete correction for severity of disease.

Conclusions: Mean overall glucose during ICU admission is related to mortality

by a U-shaped curve in medical and surgical patients. In this cohort of patients a

“safe range” of mean glucose regulation might be defined approximately between

7.0 and 9.0 mmol/l.

Mean glucose during ICU admission is related to mortality by a U-shaped curve

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Introduction

Owing to inflammatory and neuro-endocrine derangements in critically ill patients,

stress hyperglycaemia associated with high hepatic glucose output and insulin resistance

is common in the intensive care unit (ICU) 1. This stress hyperglycaemia is associated

with poor outcome 2. Moreover, several studies report a deleterious effect of glycaemic

variability over and above mean glucose after correction for severity of disease 3-6.

In 2001, van den Berghe et al. 7 published the first randomised controlled trial (RCT)

comparing normalization of glycaemia by intensive insulin treatment (IIT) with

conventional glycaemic control in a surgical ICU (glucose target: 4.4 to 6.1 mmol/l vs.

10.0 to 11.1 mmol/l). The authors reported an impressive reduction in mortality with IIT.

The same group failed to reproduce these findings in the entire population of patients

in their medical ICU 8; however, mortality was lower in the predefined subgroup of

patients receiving IIT for more than 3 days. After the data were pooled from both RCT’s,

IIT seemed to be associated with a reduction in mortality 9. On the basis of these “Leuven

trials”, many hospitals decided to implement protocols and target normalization of

glucose levels to improve patient care.

Recently, after the publication of two inconclusive multicentre studies (the Volume

Substitution and Insulin Therapy in Severe Sepsis [VISEP] 10 and the GluControl 11;12 studies)

followed by the NICE-SUGAR (Normoglycaemia in Intensive Care Evaluation- Survival

Using Glucose Algorithm Regulation) trial 13, doubt was cast upon the benefits of tight

glycaemic control; the NICE-SUGAR trial investigators reported an absolute increase in

deaths at 90 days with IIT (glucose target: 4.5 to 6.0 mmol/l versus 8.0 to10.0 mmol/l). A

recently published meta-analysis including this latter trial showed that intensive insulin

therapy significantly increased the risk of hypoglycaemia and conferred no overall

mortality benefit among critically ill patients 14. The goal of this study is to report glucose

and mortality data from cohorts of patients with a medical and a surgical admission

diagnosis from a general ICU of a teaching hospital in The Netherlands.

Materials and methods

Cohorts, setting, and data collectionWe collected information about patients admitted between January 2004 and December

2007 in a 20-bed medical/surgical ICU in a teaching hospital (Onze Lieve Vrouwe Gasthuis

[OLVG], Amsterdam, the Netherlands) (the OLVG cohort). All data was anonymous and

collected retrospectively, so no ethical approval was necessary. On average, one nurse

took care of two patients, depending on the severity of disease. All beds were equipped

110

with a clinical information system (MetaVision; iMDsoft, Tel Aviv, Israel) from which

all clinical and laboratory data were extracted. The glucose regulation algorithm was

implemented successfully in 2001 15, targeting for glucose values of between 4.0 and 7.0

mmol/l. The glucose protocol was started for every patient at the time of arrival at the

ICU. Insulin infusion was started when admission blood glucose exceeded 7.0 mmol/l.

When admission glucose was lower than 7.0 mmol/l, blood glucose was further measured

every 2 hrs and insulin was started when necessary (that is, when blood glucose exceeded

7.0 mmol/l). The nursing staff was instructed to use a dynamic computerised algorithm

to adjust the insulin infusion rate, depending on the current glucose value and the rate

of glucose change (based on the previous five measurements). The software also provided

the time the next glucose measurement was due, which could vary from 15 min up to 4

hrs. Routinely, enteral feeding was started within 24 hrs after admission, aiming at 1,500

kcal per 24 hrs, and subsequently adjusted to the patient’s requirements, except for the

uncomplicated cardiac surgery patients who do not receive enteral feeding if extubated

within 24 hrs. A duodenal feeding tube was inserted in case of persistent gastric retention.

The tight glucose algorithm was deactivated when patients resumed normal eating.

We excluded readmissions, patients with a withholding care policy, and patients with

only one glucose value measured during admission. From the clinical information system,

we collected demographic variables, mortality rates in the ICU, and glucose values. As

severity of disease measure, we used the Acute Physiology and Chronic Health Evaluation II

(APACHE II) score 16. Informed consent was not required according to Dutch Ethical Review

Board regulations, because a retrospective analysis of anonymous data was performed.

Glucose measuresFor each patient, we calculated the mean overall glucose during admission from all

glucose values measured during admission and the mean morning glucose from the first

value available between 5:00 and 7:00 hrs per patient per day. Glucose values mentioned

in this paper stand for mean overall glucose unless stated otherwise. We calculated the

standard deviation (SD) and the mean absolute glucose (MAG) change 6 per patient as

markers of glycaemic variability. Glucose was obtained from arterial blood samples by

means of a handheld glucose measurement device (AccuChek; Roche/Hitachi, Basel,

Switzerland). Results were automatically stored in the clinical information system.

Data interpretationThe cohort characteristics are presented as mean (SD) or as median and interquartile range

(IQR), depending on the distribution of the data. The mean glucose values and SD’s were

divided into five strata with equal numbers of patients per group. For each stratum, the

ICU mortality was calculated. Subsequently, we performed a logistic regression analysis

to calculate the odds ratio (OR) with 95% confidence intervals (CI) for ICU mortality per


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glucose stratum. The stratum with the lowest mortality incidence was used as a reference.

In this model we adjusted for age, sex, severity of disease (APACHE II score), occurrence of

severe hypoglycaemia (≤2.2 mmol/l), and admission duration (that is, ≤ or > 24 hrs). The last

adjustment was done because glucose values are higher and have a wider range in the first 24

hrs of admission, biasing the patients with longer admission times and corresponding lower

mean glucose values. In a second model, adjustment for occurrence of mild hypoglycaemia

(≤4.7 mmol/l), which is also independently associated with mortality 17, was made.

Results

In total, 5,828 patients were eligible for analyses of the mean glucose for the OLVG

population after excluding 656 readmissions, 86 patients with a withholding care policy,

and 160 patients with only one glucose value measured. This cohort consisted of 1,339

patients with a medical admission diagnosis (the “medical” population) and 4,489 patients

with a surgical admission diagnosis (the “surgical” population). In the medical cohort, a

median (IQR) of 34 (15-65) glucose values per patient were collected and in the surgical

cohort a median (IQR) of 10 (5-14) values. The median (IQR) admission duration was 64 (30-

129) hrs in the medical and 22 (18-28) hrs in the surgical cohort.

Mean glucoseThe overall mean (SD) glucose values of the medical and surgical populations were

7.9 (2.7) and 8.1 (1.6) mmol/l (Table 1). The mean glucose values of the first 24 hrs of

admission were higher and had a wider range than did the mean glucose values after

24 hrs (medical: mean [SD] 8.4 [3.3] mmol/l, range 3.7-40.2 mmol/l and 7.0 [1.4] mmol/l,

range 3.2-31.1 mmol/l; surgical: mean [SD] 8.3 [1.9] mmol/l, range 0.6-27.5 mmol/l and 7.6

[1.7] mmol/l, range 3.2-15.7 mmol/l). The mean morning glucose was 7.4 [2.6] mmol/l in

the medical population and 7.7 [2.3] mmol/l in the surgical population. After dividing

the mean glucose of both populations into five equally sized strata, the lowest mean

glucose stratum ranged from 6.7 mmol/l and lower in the medical cohort and from 7.0

mmol/l and lower in the surgical cohort. The highest stratum ranged 8.5 mmol/l and

higher in the medical cohort and 9.5 mmol/l and higher in the surgical cohort. Mean

glucose ranges per stratum and corresponding mortality rates per cohort are displayed

in Figure 1. This results in a U-shaped curve relationship between mean glucose and

mortality in both cohorts, with high ICU mortality in the lowest and highest glucose

strata (medical: 26.9% and 35.6%; surgical: 3.6% and 1.4%). Logistic regression analysis

showed that in both populations mean glucose values in the lowest and highest strata

were associated with a significant higher OR for ICU mortality compared to the stratum

with the lowest mortality (Figure 2). This results in “safe ranges” of 6.7 to 8.5 mmol/l

in the medical, and 7.0 to 9.5 mmol/l in the surgical cohort. The non-linear U-shaped

112

Tab

le 1

Ch

arac

teri

stic

s o

f th

e st

ud

ied

co

ho

rts,

div

ided

by

mea

n g

luco

se r

ange

s

Med

ical

pop

ula

tion

Su

rgic

al p

opu

lati

on

Tota

ln

= 1

,339

≤ 6.

6 m

mol

/ln

= 2

68“s

afe

ran

ge”

n =

804

≥ 8.

5 m

mol

/ln

= 2

67To

tal

n =

4,4

89≤

6.9

mm

ol/l

n =

898

“saf

e ra

nge

”n

= 2

,694

≥ 9.

5 m

mol

/ln

= 8

97

Age

, yea

rs (m

ean

± S

D)

61.8

± 1

6.9

59.0

± 1

8.4

62.5

± 1

6.2

62.4

± 1

7.0

66.0

± 1

2.0

66.8

± 1

2.5

65.4

± 1

2.1

67.2

± 1

1.3

Gen

der,

fem

ale

(%)

38.2

37.3

37.7

40.4

33.2

36.6

32.0

33.4

APA

CH

E II

sco

re (m

ean

± S

D)

24.6

± 8

.824

.8 ±

9.1

24.1

± 8

.125

.8 ±

10.

215

.1 ±

4.6

16.3

± 5

.214

.8 ±

4.5

14.7

± 4

.2

Dia

bete

s M

elli

tus

(%)

0.6

0.4

0.5

1.1

15.4

23.7

16.4

4.1

Die

d IC

U (%

)20

.926

.914

.135

.61.

63.

61.

01.

4

Die

d h

ospi

tal (

%)

31.3

35.4

26.6

41.2

4.3

7.5

3.9

2.7

Mor

nin

g gl

uco

se, m

mol

/l (m

ean

± S

D)

7.4

± 2.

65.

9 ±

1.0

7.1

± 1.

210

.3 ±

4.5

7.7

± 2.

35.

8 ±

1.2

7.3

± 1.

710

.6 ±

1.9

Ove

rall

glu

cose

, mm

ol/l

(mea

n ±

SD

)7.

9 ±

2.7

6.0

± 0.

67.

3 ±

0.5

11.6

± 4

.18.

1 ±

1.6

6.4

± 0.

57.

9 ±

0.7

10.7

± 1

.1

Hyp

ogly

caem

ia in

cide

nce

(%)

9.9

18.7

8.8

4.5

1.8

4.8

1.3

0.1

SD, m

mol

/l (m

edia

n [I

QR

])2.

0 [1

.5-2

.9]

1.6

[1.2

-1.9

]2.

0 [1

.6-2

.6]

3.8

[2.7

-5.4

]1.

8 [1

.3-2

.3]

1.6

[1.3

-2.0

]1.

8 [1

.4-2

.4]

1.9

[1.4

-2.6

]

MA

G, m

mol

/l/h

r (m

edia

n [I

QR

])0.

8 [0

.5-1

.1]

0.5

[0.3

-0.8

]0.

8 [0

.6-1

.0]

1.4

[0.9

-2.0

]0.

6 [0

.4-0

.8]

0.5

[0.4

-0.7

]0.

6 [0

.4-0

.9]

0.5

[0.3

-0.7

]

Cal

oric

inta

ke p

er 2

4 h

rs (m

ean

± S

D)

1103

.0 ±

758

.411

59.3

± 1

108.

611

07.1

± 5

07.2

1033

.6 ±

944

.531

5.0

± 39

2.3

427.

7 ±

466.

632

2.8

± 38

7.5

181.

5 ±

268.

9

Use

of i

nsu

lin

(%)

88.5

79.5

93.3

82.8

64.0

93.1

71.8

11.6

Insu

lin

dos

e, IU

/hou

r (m

edia

n [I

QR

])1.

4 [0

.8-2

.4]

0.6

[0.4

-1.0

]1.

4 [0

.9-2

.1]

3.4

[2.0

-6.2

]1.

2 [0

.7-1

.9]

1.0

[0.7

-1.5

]1.

3 [0

.8-2

.0]

1.5

[0.7

-3.2

]

Use

of v

asop

ress

or d

rugs

(%)

86.0

19.4

11.8

15.4

94.8

94.1

94.2

97.0

Use

of c

orti

coid

s (%

)92

.591

.094

.886

.999

.199

.099

.199

.1

Mec

han

ical

ven

tila

tion

(%)

81.6

81.7

85.0

71.2

97.9

97.3

97.9

98.6

CV

VH

(%)

16.7

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Ch

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relationship between mean glucose and ICU mortality was supported by significance

of the quadratic transformation of the mean glucose levels in this logistic regression

model (P<0.001). The characteristics of our populations, also subdivided in groups with

low, “safe range” and high glucose values, are displayed in Tables 1 and 2.

Figure 1 ICU mortality (y-axis) per mean glucose stratum (x-axis) (A) Medical population. (B) Surgical population.

Other glycaemic measuresOverall, 9.9% and 1.8% of the medical and surgical patients, respectively, sustained at

least one hypoglycaemic episode, defined as a glucose value of not more than 2.2 mmol/l,

during ICU admission. Seventeen point five percent of all deaths during ICU admission

concerned patients who had experienced severe hypoglycaemia (both groups). Twenty-

eight percent of the patients who were in the lowest mean glucose strata and who died in

the ICU experienced hypoglycaemia, and 72% did not. The incidence of severe and mild

(≤4.7 mmol/l) hypoglycaemia in the different mean glucose strata is reported in Figure 3.

When we adjusted the logistic regression model for occurrence of mild hypoglycaemia

with a cutoff value of 4.7 mmol/l, which is also independently associated with mortality 17,

the OR (CI) for ICU mortality in the lowest glucose stratum remained significant (medical:

2.6 [1.6-4.4], P <0.001; surgical: 4.9 [1.1-22.1], P = 0.04).

In the medical cohort, glucose variability, both when expressed as the median of

individual SD’s and MAG changes 6, linearly increased with increasing glucose strata

(SD median [IQR] 1.6 [1.2-1.9] to 3.8 [2.7-5.4] mmol/l, P for trend <0.001; MAG 0.5 [0.3-0.8]

to 1.4 [0.9-2.0] mmol/l/h, P for trend 0.007). However, in the surgical cohort, no consistent

trend in glucose variability across the glucose strata was seen (SD median [IQR] 1.8 [1.3-

2.3] mmol/l; MAG 0.6 [0.4-0.8] mmol/l/hr). Adjusting the logistic regression model for

variability did not change the above-described relationship between mean glucose and

mortality (data not shown).

114

Figure 2 Odds ratio (OR) for mortality (y-axis) per glucose stratum (x-axis) with the highest OR in the lowest and highest strata (A) Medical population. (B) Surgical population. Logistic regression model was adjusted for age, sex, APACHE II (Acute Physiology and Chronic Health Evaluation II) score, admission duration (≤ and > 24 hrs), and occurrence of severe hypoglycaemia. *P <0.05, **P <0.001. CI, confidence interval

Figure 3 Hypoglycaemia incidence (y-axis) per mean glucose stratum (x-axis) (A) Medical population. (B) Surgical population. The y-axis represents the percentage of patients experiencing at least one severe (≤2.2 mmol/l, left bars) and mild (≤4.7 mmol/l, right bars) hypoglycaemic event.

Discussion

The salient finding of this investigation is that in this mixed medical and surgical cohort

of critically ill patients, mean glucose values of between approximately 7.0 and 9.0 mmol/l

during ICU stay were associated with the lowest OR for ICU mortality, while mean values

of below 7.0 and greater than 9.0 mmol/l confer significantly higher OR’s. These results

were attained while using a dynamic glucose algorithm that targeted for glucose values of

between 4.0 and 7.0 mmol/l. The finding that hyperglycaemia is associated with increased

mortality is in accordance with published literature 2;18;19. Also, the U-shaped curve we

found, with increased mortality in the lower and upper parts, is described earlier in


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patients with myocardial infarction during admission 20-22, more generally in patients

with type 2 diabetes mellitus 23, and in the ICU setting 24-26, corroborating this finding.

The optimum glucose levels in the ICU setting reported previously are somewhat lower

than we found. This is possibly due to differences in inclusion criteria or uncertainty

about the practice of tight glycaemic control 26, lack of regression analysis between the

strata 25, or a different method to assess mean glucose 24. Another difference between our

and other ICU cohorts is the high percentage of patients admitted after cardiac arrest

(Table 2), a population with a high mortality rate. Also, the percentage of patients with

diabetes in our cohort might be underestimated since we scored diabetes only when the

patient used anti-hyperglycaemic drugs. However, how these factors might influence the

position of the U-curve in relation to the x-axis is not known.

Hypoglycaemia is associated with increased risk of ICU and hospital mortality 17;27-29.

In our population, the incidence of hypoglycaemia was highest in the lowest mean

glucose cohorts in which mortality was higher as well. In addition, a significant

percentage of the patients who died had experienced a hypoglycaemic episode. However,

hypoglycaemia can account only partially for the high mortality rate in the lowest mean

overall glucose stratum since 72.0% of the non-survivors did not experience severe

hypoglycaemia. Also, when the logistic regression model was adjusted for occurrence

of severe or mild hypoglycaemia, the OR for mortality remained significantly higher for

those patients with a mean glucose in the lowest quintile. However, it might be possible

that some hypoglycaemic episodes were not recorded due to intermittent sampling,

or were underestimated because of the AccuChek point-of-care meter used for glucose

measurements, the results of which tend to be higher than those obtained from the

laboratory 30;31. Therefore, the contribution of hypoglycaemia to ICU death could be

underestimated and needs further research using continuous glucose measurement. An

alternative explanation for increased mortality at lower glucose values might be that

tissues with insulin-independent glucose uptake may suffer from insufficient glucose

availability at lower concentrations. In our cohort, glucose variability increased with

increasing glucose strata in the medical cohort. In the surgical cohort, no consistent

relationship was found. Since glucose variability is associated with mortality 6, it is

unlikely that this contributes to the higher mortality in the lower glucose strata.

In the NICE-SUGAR study, the mean glucose of the IIT group (6.4 mmol/l) falls into the

stratum with increased mortality compared to the conventional group (8.0 mmol/),

which lies in the safe range of both OLVG populations (Figure 1) 13. Thus, the findings

of the NICE-SUGAR trial are in accordance with the mortality data from our cohort.

This is in contrast with the data of both Leuven studies. The means of the IIT groups of

both the Leuven studies (6.1 mmol/l in the medical population 8 and 5.7 mmol/l in the

surgical population 7) fall into the lowest mean glucose stratum in the corresponding

116

Tab

le 2

Per

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98“s

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Ch

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117

OLVG cohorts, in which mortality is highest. The means of the conventional groups in

the Leuven studies (8.5 mmol/l in the medical as well as in the surgical population 7;8)

lie in the safe ranges of both OLVG populations (Figure 1).

A possible explanation for the low mortality of the Leuven IIT group might be the way

of feeding. In a recent paper, Marik and Preiser 32 suggested that the use of intravenous

calories could explain differences between populations treated with IIT, with a

positive effect of IIT in patients who receive most of their calories intravenously. In our

population, as opposed to the Leuven studies, only 0.7% of carbohydrates were given

parenterally. In populations predominantly fed parenterally, the relationship between

mean overall glucose and mortality might be different. Also, glycaemic swings are a

known risk factor of ICU death and might contribute to differences in mortality rate 4;5.

However, it is unlikely that differences in glucose variability explain the higher mortality

in our cohort compared with the Leuven IIT group as the medians [IQR] of the individual

median SD’s are roughly comparable (Leuven medical 1.99 [1.57-2.66] mmol/l 33 and OLVG

medical 2.03 [1.54-2.86] mmol/l). In addition, other explanations have been proposed to

explain the diverging outcomes of Leuven and NICE-SUGAR 34.

The mean glucose of the OLVG population (medical: 7.9 mmol/l; surgical: 8.1 mmol/l)

was higher than the target range, which was between 4.0 and 7.0 mmol/l. Other studies

of IIT also did not reach their target range, illustrating the difficult implementation of

this therapy 10;12;13. The high percentage of corticosteroid treatment in our population

might have contributed (Table 1). Also, the relatively short ICU duration of stay in the

predominantly surgical population of the OLVG explains that mean glucose is slightly

higher than the target (median ICU stay was 22 hrs in our cohort compared to 3 days in

the Leuven cohort and 4.2 days “on algorithm” in the NICE SUGAR study) because of the

time needed to reach target. Glucose values were indeed higher and had a wider range

in the first 24 hrs of admission. Furthermore, our patients were treated in a normal-care

setting without the extra stimuli of a trial setting to achieve the target. It should be noted

that mean glucose does not equal time in target range, since the protocol requires more

frequent sampling when not in target, thus falsely inflating the mean.

In our logistic regression model, we adjusted for severity of disease and admission

duration less or more than 24 hrs, since both high and low glucose levels could be a

manifestation, rather than a cause, of severe disease. Glucose values are higher and have

a wider range in the first 24 hrs of admission, biasing the patients with longer admission

times and corresponding lower mean glucose values. A limitation of our correction for

severity of disease is the use of the APACHE II score, because the use of APACHE II to

predict mortality is not validated for cardiac surgery patients. However, this adjustment

is the best available method 35.

118

Conclusions

In our mixed cohort of surgical and medical patients, the mean glucose during ICU

stay was related to mortality by a U-shaped curve; a “safe range” for mean glucose

can be defined as between approximately 7.0 and 9.0 mmol/l, while both higher and

lower mean values are associated with higher mortality. This finding applied to the

surgical as well as the medical patients. Hypoglycaemia seems to only partially explain

the high mortality rate in the lowest mean glucose quintile, and glucose variability

does not. Second, comparison of the combined Leuven, NICE-SUGAR, and our cohorts

demonstrates that the increased mortality in the IIT group of NICE-SUGAR is in line with

our U-shaped curve but that the low mortality in the intensively treated Leuven group

is not. The percentage of calories given parenterally may influence the relationship

between mean glucose and mortality. We await further studies, but according to these

findings, we recommend treating hyperglycaemia at the ICU in a moderately intensive

way in both medical and surgical patients, targeting for mean glucose values of between

approximately 7.0 and 9.0 mmol/l and avoiding hypoglycaemia. This “safe range” should

be studied prospectively in randomised clinical trials.

Key Messages

- During ICU admission, mean glucose relates to mortality by a U-shaped curve.

- A mean glucose range of 7.0 to 9.0 mmol/l is associated with the lowest mortality in

our cohort.

- Occurrence of hypoglycaemia does not fully explain the high mortality in the lower

glucose strata.


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References1. Dungan KM, Braithwaite SS, Preiser JC (2009) Stress hyperglycaemia. Lancet 373: 1798-18072. Krinsley JS (2003) Association between hyperglycemia and increased hospital mortality in a heterogeneous

population of critically ill patients. Mayo Clin Proc 78: 1471-14783. Dossett LA, Cao H, Mowery NT, Dortch MJ, Morris J, May AK (2008) Blood Glucose Variability Is Associated

with Mortality in the Surgical Intensive Care Unit. American Surgeon 74: 679-6854. Egi M, Bellomo R, Stachowski E, French CJ, Hart G (2006) Variability of blood glucose concentration and

short-term mortality in critically ill patients. Anesthesiology 105: 244-2525. Krinsley JS (2008) Glycemic variability: A strong independent predictor of mortality in critical ill patients.

Crit Care Med 36: 3008-30136. Hermanides J, Vriesendorp TM, Bosman RJ, Zandstra DF, Hoekstra JB, DeVries JH (2010) Glucose variability

is associated with intensive care unit mortality. Crit Care Med 38: 838-8427. Van den Berghe G, Wouters P, Weekers F, et al (2001) Intensive Insulin Therapy in Critically Ill Patients. N

Engl J Med 345: 1359-13678. Van den Berghe G, Wilmer A, Hermans G, et al (2006) Intensive Insulin Therapy in the Medical ICU. N Engl

J Med 354: 449-4619. Van den Berghe G, Wilmer A, Milants I, et al (2006) Intensive Insulin Therapy in Mixed Medical/Surgical

Intensive Care Units. Diabetes 55: 3151-315910. Brunkhorst FM, Engel C, Bloos F, et al (2008) Intensive Insulin Therapy and Pentastarch Resuscitation in

Severe Sepsis. N Engl J Med 358: 125-13911. Devos P, Preiser JC, Melot C (2007) Impact of tight glucose control by intensive insulin therapy on ICU

mortality and the rate of hypoglycemia: final results of the Glucontrol study. Intensive Care Medicine 33: Suppl 2: S189

12. Preiser JC, Devos P, Ruiz-Santana S, et al (2009) A prospective randomised multi-centre controlled trial on tight glucose control by intensive insulin therapy in adult intensive care units: the Glucontrol study. Intensive Care Med 35: 1738-1748

13. The NICE-SUGAR Study Investigators (2009) Intensive versus Conventional Glucose Control in Critically Ill Patients. N Engl J Med 360: 1283-1297

14. Griesdale DEG, de Souza RJ, van Dam RM, et al (2009) Intensive insulin therapy and mortality among critically ill patients: a meta-analysis including NICE-SUGAR study data. CMAJ 180: 821-827

15. Rood E, Bosman RJ, van der Spoel JI, Taylor P, Zandstra DF (2005) Use of a computerized guideline for glucose regulation in the Intensive Care Unit improved both guideline adherence and glucose regulation. J Am Med Inform Assoc 12: 172-180

16. Knaus WA, Draper EA, Wagner DP, Zimmerman JE (1985) APACHE II: A severity of disease classification system. Crit Care Med 13: 818-829

17. Hermanides J, Bosman RJ, Vriesendorp TM, et al (2010) Hypoglycaemia is related with intensive care unit mortality. Crit Care Med 38: 1430-1434

18. Umpierrez GE, Isaacs SD, Bazargan N, You X, Thaler LM, Kitabchi AE (2002) Hyperglycemia: An Independent Marker of In-Hospital Mortality in Patients with Undiagnosed Diabetes. J Clin Endocrinol Metab 87: 978-982

19. Krinsley JS (2006) Glycemic control, diabetic status, and mortality in a heterogeneous population of critically ill patients before and during the era of intensive glycemic management: six and one-half years experience at a university-affiliated community hospital. Semin Thorac Cardiovasc Surg 18: 317-325

20. Kosiborod M, Inzucchi SE, Krumholz HM, et al (2008) Glucometrics in Patients Hospitalized With Acute Myocardial Infarction: Defining the Optimal Outcomes-Based Measure of Risk. Circulation 117: 1018-1027

21. Pinto DS, Skolnick AH, Kirtane AJ, et al (2005) U-Shaped Relationship of Blood Glucose With Adverse Outcomes Among Patients With ST-Segment Elevation Myocardial Infarction. J Am Coll Cardiol 46: 178-180

22. Pinto DS, Kirtane AJ, Pride YB, et al (2008) Association of Blood Glucose With Angiographic and Clinical Outcomes Among Patients With ST-Segment Elevation Myocardial Infarction (from the CLARITY-TIMI-28 Study). Am J Cardiol 101: 303-307

23. Currie CJ, Peters JR, Tynan A, et al (2010) Survival as a function of HbA1c in people with type 2 diabetes: a retrospective cohort study. Lancet 375: 481-489

24. Bagshaw SM, Egi M, George C, Bellomo R (2009) Early blood glucose control and mortality in critically ill patients in Australia. Crit Care Med 37: 463-470

25. Egi M, Bellomo R, Stachowski E, et al (2008) Blood glucose concentration and outcome of critical illness: the impact of diabetes. Crit Care Med 36: 2249-2255

26. Falciglia M, Freyberg RW, Almenoff PL, D’Alessio DA, Render ML (2009) Hyperglycemia-related mortality in

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critically ill patients varies with admission diagnosis. Crit Care Med 37: 3001-300927. Bagshaw SM, Bellomo R, Jacka M, et al (2009) The impact of early hypoglycemia and blood glucose variability

on outcome in critical illness. Critical Care 13: R9128. Krinsley JS, Grover A (2007) Severe hypoglycemia in critically ill patients: Risk factors and outcomes. Crit

Care Med 35: 2262-226729. Vriesendorp TM, DeVries JH, van Santen S, et al (2006) Evaluation of short-term consequences of hypoglycemia

in an intensive care unit. Crit Care Med 34: 2714-271830. Hoedemaekers CWE, Klein Gunnewiek JMT, Prinsen MA, Willems JL, Van der Hoeven JG (2008) Accuracy of

bedside glucose measurement from three glucometers in critically ill patients *. Crit Care Med 36:3062-3066 31. Karon BS, Gandhi GY, Nuttall GA, et al (2007) Accuracy of Roche Accu-Chek Inform Whole Blood Capillary,

Arterial, and Venous Glucose Values in Patients Receiving Intensive Intravenous Insulin Therapy After Cardiac Surgery. Am J Clin Pathol 127: 919-926

32. Marik PE, Preiser JC (2010) Toward understanding tight glycemic control in the ICU: a systematic review and metaanalysis. Chest 137: 544-551

33. Meyfroidt G, Keenan DM, Wang X, Wouters PJ, Veldhuis JD, Van den Berghe G (2010) Dynamic characteristics of blood glucose time series during the course of critical illness: effects of intensive insulin therapy and relative association with mortality. Crit Care Med 38: 1021-1029

34. Van den Berghe G, Schetz M, Vlasselaers D, et al (2009) Intensive insulin therapy in critically ill patients: NICE-SUGAR or Leuven blood glucose target? J Clin Endocrinol Metab 94: 3163-3170

35. Kramer AA, Zimmerman JE (2008) Predicting Outcomes for Cardiac Surgery Patients After Intensive Care Unit Admission. Semin Cardiothorac Vasc Anesth 12: 175-183


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Chapter 9

Accuracy and reliability of continuous glucose monitoring in the intensive care unit: a head-to-head comparison of two subcutaneous glucose sensors in cardiac surgery patients

Sarah E. Siegelaar, Temo Barwari, Jeroen Hermanides,

Peter H.J. van der Voort and J. Hans DeVries

Published in abbreviated form, Diabetes Care 2011; 34(3): e31

124

Abstract

Objective: To investigate accuracy and reliability of two different continuous

glucose monitoring (CGM) devices in patients who underwent cardiac surgery.

Methods: We performed a prospective, observational, investigator-initiated

study in a 20-bed intensive care unit (ICU) of a teaching hospital. We studied 60

consecutive patients who underwent cardiac surgery. Two CGM devices (Guardian

Real-Time, Medtronic Minimed; FreeStyle Navigator, Abbott Diabetes Care) were

placed subcutaneously in the abdominal wall before surgery. Both devices

were calibrated simultaneously upon arrival at the ICU and further according

to manufacturers’ instructions. An arterial reference blood glucose value was

measured every two hours. Relative absolute deviation (RAD) between reference

and sensor glucose was calculated in six 5-minute intervals from the reference

glucose, to assess a possible delay.

Results: Of the 1,017 reference glucose values measured 77.7% could be paired

with a Guardian and 91.8% with a Navigator glucose value, missing values

indicating technical problems with the devices: unintentional signal loss (both

systems) or an interruption of real-time representation of glucose values after

delayed recalibration (Guardian). Median [IQR] RAD was significantly smaller for

Navigator than for Guardian measurements at the first and second interval (11%

[8-16] and 10% [8-16] compared to 14% [11-18] and 14% [11-17], P = 0.05 and 0.001).

The delay was estimated to be 5-9 minutes for the Navigator and 15-19 minutes

for the Guardian.

Conclusions: FreeStyle Navigator performed better regarding accuracy and

reliability than the Guardian Real-Time in cardiac surgery patients at the ICU.

Use of this device seems feasible in these patients.

Accuracy and reliability of CGM in the ICU

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Introduction

Occurrence of hyperglycaemia in the intensive care unit (ICU) is common, also in patients

without a known history of diabetes. Severe illness causes hormonal changes resulting

in hyperglycaemia through increased gluconeogenesis in the liver and increased insulin

resistance. This transient so called stress-hyperglycaemia is associated with increased

mortality 1. Several trials assessed the effect of intensive insulin therapy on outcome in

this patient group with conflicting outcomes 2;3. However, glycaemic control remains a

widespread practice, although the target range is unclear. Besides hyperglycaemia also

hypoglycaemia, as a consequence of intensive insulin therapy or due to severe illness,

and glucose variability are independently associated with mortality 4;5.

At present, intermittent manual blood sampling has to be performed to achieve glycaemic

control. This method is time consuming, certainly when the patients’ glucose levels

fluctuate. Moreover, no information is available for the period in-between measurements

with perhaps unnoticed hypoglycaemic episodes. Continuous glucose monitoring (CGM)

could therefore be of value in achieving glycaemic control, providing real-time glucose

values as well as an alarm function to alert for glucose values outside a predefined range,

and information on rapid increases or decreases in glucose levels 6.

Promising as CGM in the ICU may be, the accuracy and reliability of these devices is

uncertain in critically ill patients 7-9. Different studies report “acceptable” differences

between sensor and reference glucose values, but it can be debated how large an

acceptable deviation in the ICU may be, also because it is known from outpatient

data that accuracy is worse in the hypoglycaemic range 10. CGM measurements reflect

interstitial rather than plasma glucose levels, so microcirculatory changes seen in the

critically ill might influence CGM function. However, in patients who underwent cardiac

surgery a good correlation between arterial and interstitial glucose was found using an

experimental micro dialysis system 11, suggesting that this patient group lends itself for CGM.

In this study we investigated the accuracy and reliability of two different CGM devices,

the Guardian® Real-Time (Medtronic Minimed, Northridge, CA) and FreeStyle Navigator®

(Abbott Diabetes Care, Alameda, CA), postoperatively in cardiac surgery patients in an

investigator-initiated trial.

Methods

PatientsWe performed a prospective observational study in a 20-bed medical/surgical ICU in

126

the Onze Lieve Vrouwe Gasthuis (OLVG; Amsterdam, the Netherlands) to obtain glucose

monitoring data from the two devices. The study was approved by the Institutional Ethical

Review Board according to the declarations of Helsinki. We included subsequent patients

above the age of 18 who were planned to undergo elective cardiac surgery; coronary artery

bypass grafting (CABG) and/or valve surgery. We excluded patients with an abdominal

condition which would prohibit sensor insertion. Eligible patients received an information

letter at least one week before hospital admission. Before the planned surgery patients

were asked to give written informed consent after oral explanation of the study. During

ICU admittance Acute Physiology and Chronic Health Evaluation (APACHE) IV predicted

mortality score was calculated for the first 24 hrs of admission and Sequential Organ

Failure (SOFA) score was obtained daily. Also the European System for Cardiac Operative

Risk Evaluation (euroSCORE) score, a method of calculating predicted operative mortality

risk for patients undergoing cardiac surgery, was recorded for every patient.

Glucose monitoringTwo needle-type sensors, Guardian® Real-Time (Guardian; Medtronic Minimed,

Northridge, CA) and FreeStyle Navigator® (Navigator; Abbott Diabetes Care, Alameda,

CA), were inserted in the abdominal wall on either side of the umbilicus and calibrated

before surgery to allow stabilization of the signal. Upon arrival at the ICU after surgery,

the device’s internal clock was matched with the bedside computer and both devices

were calibrated simultaneously. Further calibrations were performed according to

manufacturers’ instructions. Except from the calibrations, all sensor dealings were

performed solely by the investigators. The devices were removed after 48 hrs of ICU

admission or earlier when the patient was discharged.

During the study an arterial blood glucose value was measured with the AccuChek

handheld glucose measurement device (Performa II, lot 320098, Roche/Hitachi®, Basel,

Switzerland) as a reference value every two hours. These samples were used as calibration

when needed and as reference otherwise. An in-house quality assurance study showed

that the slope of the regression between this point-of-care measurement method and

arterial glucose measurement by blood gas analysis was 1.0 (95% CI 1.00-1.02, n = 1393,

sample range 2.9-30.0 mmol/l; Passing-Bablok regression) and 95% of the absolute

differences between reference and point-of-care measurement were lower than 15%,

thereby meeting the ISO 15197 guideline. All results were stored in the ICU’s clinical

information system (iMD-Soft; MetaVision, Tel Aviv, Israel). Using a dynamic computerised

algorithm implemented in 2001 12, glucose values between 5.0 and 8.0 mmol/l were

targeted. The glucose protocol was started for every patient at time of arrival at the ICU.

Insulin infusion was started when admission blood glucose exceeded 8.0 mmol/l. When

admission glucose was lower than 8.0 mmol/l, blood glucose was measured every 2 hrs

and insulin was started when blood glucose exceeded 8.0 mmol/l. The nursing staff was


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instructed to adjust the insulin infusion rate depending on the current glucose value and

the rate of glucose change based on the previous five measurements. The 2-hr reference

glucose values were used as input in this algorithm. The nursing staff did not act upon

the sensor glucose values.

Data interpretation and statisticsThe Guardian device displays the average sensor glucose every five minutes and stores

all these values. The Navigator device refreshes the displayed glucose every minute and

stores the value of every tenth minute. The relative absolute deviation (RAD) [|sensor

value-reference glucose|/reference glucose] between reference and sensor glucose values

was calculated to assess the accuracy of the devices. For this purpose we linked the

reference value to the first available sensor value after the reference value using the

exact sampling times of both devices obtained after downloading the individual data. To

assess a possible delay of the CGM devices the reference value was linked to subsequent

sensor values up to 30 minutes after the reference value. We calculated the interval

between each reference-sensor pair in minutes and created six five-minute intervals (0-4,

5-9, 10-14, 15-19, 20-24, 25-29 minutes) in which we could match reference with sensor

glucose values. These five-minute intervals permit a fair comparison between both sensors

independent from the sampling frequency, since the Navigator stores data only every

tenth minute and the Guardian every five minutes. The median RAD per patient per

interval was calculated for each sensor and subsequently both sensors were compared

using a Wilcoxon signed ranks test for not normally distributed paired data. Assessment

of the lag time on the RAD per sensor was calculated using repeated measures ANOVA.

All analyses were performed using SPSS version 16.0.

For each sensor, all paired samples of reference glucose values and matching next sensor

values were plotted in a Clarke error grid 13. Also, the absolute differences between sensor

readings and reference glucose measurements were plotted against the average of the

two in a Bland-Altman plot 14.

Results

We included 61 patients in the study, of whom 1 patient dropped out due to cancellation

of surgery because of intercurrent febrile illness. In total we included 60 patients in

the final analysis of whom 48 were males. The median (range) age was 65 (25-85) years

and 26.7% of the patients were previously diagnosed with diabetes. The majority of the

patients underwent only a CABG procedure (53.3%). Median (IQR) APACHE IV PM and

maximum SOFA scores were 0.01 (0.003-0.02) and 6.0 (5.3-7.0). Patient characteristics are

reported in Table 1.

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Patients, n=60

Male sex, n (%) 48 (80.0)

Age, years 65.0 (59.0-73.8)

Diabetes, n (%) 16 (26.7)

Procedure, n (%)

CABGValve surgeryCABG + valve surgery

32 (53.3)16 (26.7)12 (20.0)

APACHE IV PM 0.01 (0.003-0.02)

SOFA max 6.0 (5.3-7.0)

euroSCORE 4.0 (2.0-5.0)

ICU stay, hours 23.0 (19.0-45.8)

ICU readmission, n (%) 6 (10.0)

Death in ICU/hospital, n 0

Glucose ICU, mean (SD) 8.2 (2.1)

Data are given in median (IQR) unless stated otherwise. CABG, coronary artery bypass grafting; ICU, intensive care unit; APACHE, Acute Physiology and Chronic Health Evaluation score; SOFA, sequential organ failure assessment score; euroSCORE, European System for Cardiac Operative Risk Evaluation. Valve surgery includes mitral valve plasty, tricuspid valve plasty, aortic valve replacement or a combination of these.

ReliabilityDuring the study 1,017 reference glucose values were collected. Of these 91.8% could be

paired with a Navigator and 77.7% with a Guardian glucose value in the first data storage

interval. Missing values indicated technical problems with the device: unintentional

signal loss (Guardian: 19 patients; Navigator: 1 patient), interruption of real-time

representation of glucose values after delayed recalibration (Guardian) or temporary

failure of data-recording (Navigator: 4 patients). In 7 patients a new Guardian sensor

had to be placed due to sensor failure. In 2 patients a new Navigator sensor was placed

due to a disconnection between the actual sensor and fixation plate.

AccuracyMedian (IQR) RAD was significantly smaller for Navigator compared with Guardian

glucose measurements at intervals 0-4 and 5-9 minutes after the reference glucose (11%

[8-16] versus 14% [11-18], P = 0.05 and 10% [8-14] versus 14% [11-17], P = 0.001; Figure 1). The

lowest RAD of the Navigator was observed 5-9 minutes after the reference glucose, but

no significant effect of time was seen (P = 0.74, repeated measures ANOVA). The accuracy

of the Guardian did show a delay with the lowest RAD after 15-19 minutes (11% [8-13], P =

0.01; Figure 1). The results did not differ among subgroups of patients with or without


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diabetes mellitus (Mann-Whitney U Test; data not shown).

Clarke error grids of reference glucoses with corresponding next sensor values are shown

in Figure 2 for each CGM device separately. For the comparisons with arterial reference

glucose values 81.8% of the Navigator and 73.2% of the available Guardian glucose values

fell in zone A. 17.7% of the Navigator and 25.2% of the Guardian glucose values fell in

zone B. Five of the 934 Navigator values fell in zone C or D (0.5%) and none in zone E

compared to 13 of the 790 Guardian values in zone C, D or E (1.3%).

To evaluate variations in accuracy over the range of measured glucose concentrations,

absolute differences between sensor readings and reference glucose values were plotted

(Figure 3). The variation in accuracy was larger with the Guardian looking at the range

between the 5th and 95th percentile (Guardian -3.03 to 2.27 mmol/l, range 5.30 mmol/l;

Navigator -1.83 to 2.53 mmol/l, range 4.36 mmol/l). There was no consistency in direction

of the error. Both positive and negative differences were seen, resulting in median (IQR)

differences coming close to zero (Navigator 0.10 [-0.60-0.90] mmol/l, Guardian 0.24 [-0.75-

1.07] mmol/l). No trend was observed visually for more inaccuracy in the hypo- and

hyperglycaemic ranges (Figure 3). We did not perform separate analyses to assess accuracy

during hypoglycaemia due to too few hypoglycaemic events: no severe hypoglycaemic

events (≤2.2 mmol/l) were measured and only 34 of 1,017 reference glucose values were

mildly hypoglycaemic (≤4.7 mmol/l) 5.

Figure 1 Head-to-head comparison of the accuracy of both sensors Relative Absolute Deviation (RAD) between reference and sensor glucose values at different intervals after the reference glucose measurement. RAD’s are displayed in medians. *P = 0.05, **P = 0.001

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Figure 2 Clarke error grids of glucose measurements (A) Navigator. (B) Guardian. Each grid shows data pairs of reference glucose values at the x-axis with proximate sensor values (within 10 minutes for the Navigator and within 5 minutes for the Guardian) at the y-axis.

Figure 3 Bland-Altman plots of glucose measurements (A) Navigator. (B) Guardian. The x-axis represents the average of sensor and reference glucose values in mmol/l. The y-axis represents the absolute difference between sensor and reference glucose values in mmol/l. The dashed line represents the median difference (Navigator 0.10 and Guardian 0.24 mmol/l). The dotted lines represent the 5th and 95th percentile (Navigator -1.83-2.53 and Guardian -3.03-2.27 mmol/l).

Discussion

We report that the FreeStyle Navigator CGM system performed better than the Guardian

Real-Time in accuracy, defined by MAD in comparison to AccuChek arterial glucose

measurements, as well as reliability, determined by the technical error rates, in

postoperative cardiac surgery patients during ICU stay.

To our knowledge, the only study comparing these two devices is a clamp study by

Kovatchev and colleagues 15 in type 1 diabetes patients, concluding that the numerical


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accuracy was comparable during normoglycaemia. However, the Navigator performed

significantly better during hypoglycaemia. It has to be noted that in our study we used

a new version of the Navigator with 1-hr initiation duration instead of the older version

with 10-hr initiation duration.

Regarding accuracy, our Guardian results are comparable with those from Logtenberg

et al. 7 who also performed a study in cardiac patients at the ICU, and reported a median

RAD of 12.3% during the ICU period. No studies using the Navigator at the ICU have

been published so far. Looking at the accuracy of both devices in our ICU population

compared to data in type 1 diabetes patients, this is comparable for the Guardian 16 and

even somewhat better for the Navigator 17. This suggests that at least in the population

of cardiac surgery patients CGM use seems feasible.

It is subject of debate whether sensor accuracy in the range that is acceptable for patients

with diabetes is also accurate enough for the critically ill patient. Hypoglycaemia is to be

avoided since already a single episode of low plasma glucose is independently associated

with mortality 5 and sedation makes it difficult to rely on hypoglycaemic symptoms.

Potentially dangerous sensor readings are those in the higher range while the reference

glucose is in the (near) hypoglycaemic range (Clark error grid zones D and E; Figure 2).

For the Navigator this occurred 3 times (0.3% of all sensor readings) and for the Guardian

5 times (0.6% of all sensors readings). We think that this low percentage of potentially

dangerous sensor readings is likely to be outweighed by hypoglycaemic episodes that

are likely to be prevented by continuous glucose monitoring 18. Of note, in the present

study the number of low glucose measurements is too small to draw conclusions on the

accuracy of the devices during hypoglycaemia.

We found a significant time-lag of the Guardian with optimal accuracy 15-19 minutes

following reference glucose. This is in accordance with Wei et al. 19 who found a median

delay of 16 minutes in their population of type 1 diabetes patients. The optimal

accuracy of the Navigator was reached 5-10 minutes after the reference glucose, however

no significant effect of time was found. All subcutaneous sensor measurements are

accompanied by a physiological delay of 0-10 minutes required for glucose to equilibrate

across the capillary endothelial barrier 20 suggesting the Navigator has a minimal

technological delay due to data processing and filtering. Our findings are different from

Garg et al. 21 who found a system time-lag of 15 minutes for the Navigator in adults

with type 1 diabetes. However, they used an earlier version of the Navigator with 10-hr

initiation time in their study which might explain these different findings and suggest

improvement of the new system with 1-hr initiation time. The differences between the

Guardian and Navigator and between different versions of the same device are intriguing;

however information on data processing is proprietary. The time-lag found favours the

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Navigator for clinical use, as decision making will depend on real-time sensor values.

Also regarding reliability, the Navigator performed significantly better. Moreover, the

technical failure rates might be reduced when these systems attended to on a 24/24 hour

basis; most of the connection problems occurred at night when no member of the study

team was available. The nurses were blinded to the sensor glucose values and therefore

the alarms were set off, so a connection problem at night was only noticed when there

was a need for recalibration. However, the high frequency of connection problems of

the Guardian system suggests some kind of interference with other ICU equipment not

occurring with the Navigator system. The technical problems we experienced with the

Guardian device were described earlier, although not quantified 22.

As a reference method we used an arterial glucose measurement performed by the

AccuChek handheld device, since clinical decision rules in our and many other ICUs are

based on this measurement. Mortality is decreased by targeting hyperglycaemia relying on

point-of-care measurements 3;23, so we compared a new method with one proven effective.

Recently it is debated however how accurate this point-of-care meter is in comparison

with laboratory glucose measurements. Accuracy seems unacceptably decreased in older

patients with high disease severity scores and high ICU mortality 24. On the other hand

Meynaar et al. 25 concluded that AccuChek measurement has acceptable accuracy for use

in the ICU. As our patient group is characterised by relatively low mortality rates and

severity scores, AccuChek measurements should have been sufficiently accurate. This

is further substantiated by our in-house study comparing the AccuChek with a robust

laboratory reference method, which showed agreement as required by the ISO guideline

between the two methods. But even with the availability of a possible superior reference

method, the inaccuracy of the AccuChek seems random, so that the outcome of our

comparison between the two sensors would be unaffected.

In conclusion, we report that the FreeStyle Navigator CGM system performed better in

accuracy as well as reliability compared to the Guardian Real-Time in cardiac surgery

patients at the ICU. Remarkably, the RAD of both sensors was quite good as compared

to known data for outpatients. We think that this device can be used in this group

of ICU patients characterised by low disease severity scores and low mortality rates.

Further studies will concentrate on patient factors influencing sensor performance and

different populations of critically ill patients to allow an even better definition of the

ICU population who might benefit from the now available systems. Also, whether or not

the use of CGM truly improves glycaemic control and mortality has to be the subject of

further research.


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AcknowledgementsThis study was supported by a European Foundation for the Study of Diabetes (EFSD)/

LifeScan research grant. The sensors used were provided free of charge by Medtronic

Minimed and at a discounted rate by Abbott Diabetes Care.

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References1. Krinsley JS (2003) Association between hyperglycemia and increased hospital mortality in a heterogeneous

population of critically ill patients. Mayo Clin Proc 78: 1471-14782. Finfer S, Chittock DR, Su SY, et al (2009) Intensive versus conventional glucose control in critically ill patients.

N Engl J Med 360: 1283-12973. Van den Berghe G, Wilmer A, Milants I, et al (2006) Intensive insulin therapy in mixed medical/surgical

intensive care units: benefit versus harm. Diabetes 55: 3151-31594. Hermanides J, Vriesendorp TM, Bosman RJ, Zandstra DF, Hoekstra JB, Devries JH (2010) Glucose variability

is associated with intensive care unit mortality. Crit Care Med 38: 838-8425. Hermanides J, Bosman RJ, Vriesendorp TM, et al (2010) Hypoglycaemia is related with ICU mortality. Crit

Care Med 38: 1430-14346. De Block C, Manuel-Y-Keenoy B, Van Gaal L, Rogiers P (2006) Intensive Insulin Therapy in the Intensive Care

Unit. Diabetes Care 29: 1750-17567. Logtenberg SJ, Kleefstra N, Snellen FT, et al (2009) Pre- and postoperative accuracy and safety of a real-time

continuous glucose monitoring system in cardiac surgical patients: a randomized pilot study. Diabetes Technol Ther 11: 31-37

8. Price GC, Stevenson K, Walsh TS (2008) Evaluation of a continuous glucose monitor in an unselected general intensive care population. Crit Care Resusc 10: 209-216

9. Rabiee A, Andreasik RN, Abu-Hamdah R, et al (2009) Numerical and clinical accuracy of a continuous glucose monitoring system during intravenous insulin therapy in the surgical and burn intensive care units. J Diabetes Sci Technol 3: 951-959

10. Wentholt IM, Hoekstra JB, Devries JH (2007) Continuous glucose monitors: the long-awaited watch dogs? Diabetes Technol Ther 9: 399-409

11. Ellmerer M, Haluzik M, Blaha J, et al (2006) Clinical evaluation of alternative-site glucose measurements in patients after major cardiac surgery. Diabetes Care 29: 1275-1281


13. Clarke WL, Cox D, Gonder-Frederick LA, Carter W, Pohl SL (1987) Evaluating clinical accuracy of systems for self-monitoring of blood glucose. Diabetes Care 10: 622-628

14. Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1: 307-310

15. Kovatchev B, Anderson S, Heinemann L, Clarke W (2008) Comparison of the numerical and clinical accuracy of four continuous glucose monitors. Diabetes Care 31: 1160-1164

16. Mazze RS, Strock E, Borgman S, Wesley D, Stout P, Racchini J (2009) Evaluating the accuracy, reliability, and clinical applicability of continuous glucose monitoring (CGM): Is CGM ready for real time? Diabetes Technol Ther. 11: 11-18

17. Weinstein RL, Schwartz SL, Brazg RL, Bugler JR, Peyser TA, McGarraugh GV (2007) Accuracy of the 5-day FreeStyle Navigator Continuous Glucose Monitoring System: comparison with frequent laboratory reference measurements. Diabetes Care 30: 1125-1130

18. Holzinger U, Warszawska J, Kitzberger R, et al (2010) Real time continuous glucose monitoring in critically ill patients - a prospective, randomized trial. Diabetes Care 33: 467-472

19. Wei C, Lunn DJ, Acerini CL, et al (2010) Measurement delay associated with the Guardian(R) RT continuous glucose monitoring system. Diabet Med 27: 117-122

20. Wentholt IM, Vollebregt MA, Hart AA, Hoekstra JB, Devries JH (2005) Comparison of a needle-type and a microdialysis continuous glucose monitor in type 1 diabetic patients. Diabetes Care 28: 2871-2876

21. Garg SK, Voelmle M, Gottlieb PA (2010) Time lag characterization of two continuous glucose monitoring systems. Diabetes Res Clin Pract 87: 348-353

22. Jacobs B, Phan K, Bertheau L, Dogbey G, Schwartz F, Shubrook J (2010) Continuous glucose monitoring system in a rural intensive care unit: a pilot study evaluating accuracy and acceptance. J.Diabetes Sci Technol 4: 636-644

23. Umpierrez GE, Isaacs SD, Bazargan N, You X, Thaler LM, Kitabchi AE (2002) Hyperglycemia: an independent marker of in-hospital mortality in patients with undiagnosed diabetes. J Clin Endocrinol Metab 87: 978-982


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24. Hoedemaekers CW, Klein Gunnewiek JM, Prinsen MA, Willems JL, Van der Hoeven JG (2008) Accuracy of bedside glucose measurement from three glucometers in critically ill patients. Crit Care Med 36: 3062-3066

25. Meynaar IA, van SM, Tangkau PL, et al (2009) Accuracy of AccuChek glucose measurement in intensive care patients. Crit Care Med 37: 2691-2696

Chapter 10

Microcirculation and its relation with continuous subcutaneous glucose sensor accuracy in cardiac surgery patients in the intensive care unit

Sarah E. Siegelaar, Temo Barwari, Jeroen Hermanides,

Peter H.J. van der Voort, Joost B.L. Hoekstra and J. Hans DeVries


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Abstract

Objective: Continuous glucose monitoring (CGM) could be helpful in glucose

regulation in critically ill patients but accuracy of the systems is uncertain.

The accuracy might be influenced by impaired microcirculation. Therefore, we

investigated the microcirculation and its relation with accuracy of two CGM

devices in patients after cardiac surgery.

Methods: In a prospective, observational study in a 20-bed intensive care unit

(ICU) we included 60 patients who were about to undergo cardiac surgery. Two

CGM devices (Guardian Real-Time, Medtronic Minimed; FreeStyle Navigator,

Abbott Diabetes Care) were placed before surgery. Relative absolute deviation

(RAD) between CGM and arterial reference glucose was calculated to assess

accuracy. Microcirculation was measured by microvascular flow index (MFI),

perfused vessel density (PVD) and proportion of perfused vessels (PPV) using

sublingual sidestream dark-field imaging, and tissue oxygenation (StO2) obtained

with near-infrared spectroscopy.

Results: Thirty-two patients underwent only a CABG procedure. Median (IQR)

APACHE IV PM was 0.01 (0.003-0.02). StO2 significantly increased during ICU

admission (max 91.2% [3.9] after 6 hrs) and decreased thereafter, stabilizing after

20 hrs. The increase in StO2 was accompanied by a decrease in PVD. MFI and PPV

did not show a time effect. Microcirculatory variables were not associated with

sensor accuracy. For the Navigator lower peripheral temperature (b = -0.008, P=

0.003), higher APACHE IV PM (b = 0.017, P <0.001) and age (b = 0.002, P = 0.037)

and for the Guardian lower peripheral temperature (b = -0.006, P = 0.048) were

significantly associated with decreased sensor accuracy.

Conclusions: This study showed that microcirculation was impaired in patients

after cardiac surgery but to a limited extent only compared with septic patients

and healthy controls. The impairment in microcirculatory variables was not

related to sensor accuracy but peripheral temperature (both sensors), patient

age and APACHE IV PM (Navigator) were.

Microcirculation and its relation with subcutaneous CGM accuracy

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Introduction

Intensive glucose control is widely practised in the ICU 1. However, the associated

frequent glucose measurements are time consuming for the nursing staff and there

is no information available about glucose values in between those measurements.

Occurrence of hypoglycaemia is independently associated with mortality in the ICU 2.

Continuous glucose monitoring could therefore be a step forward by decreasing severe

hypoglycaemia frequency 3 and possibly by increasing time within blood glucose target

range. Although we recently reported promising results of continuous glucose sensor

accuracy in cardiac surgery patients 4, a patient population that seems to benefit most

from intensive insulin treatment 5;6, other studies reported suboptimal accuracy of the

commercially available systems 7;8.

A part of the accuracy problem in critically ill patients could result from the fact

that current commercially available needle-type continuous glucose sensors measure

glucose concentrations in the interstitial fluid and not directly in blood. The transport

of molecules such as glucose to the interstitial fluid is dependent on glucose supply

to the tissue and therefore on microcirculatory function. In critically ill patients the

microcirculatory function is altered 9;10 which might negatively affect sensor performance.

In this investigator-initiated study we investigated the microcirculation in patients

after cardiac surgery in the ICU and assessed whether microcirculatory variables and

other patient-related factors were associated with accuracy of the Guardian® Real-Time

(Medtronic Minimed, Northridge, CA) and FreeStyle Navigator® (Abbott Diabetes Care,

Alameda, CA) continuous glucose monitoring systems.

Materials and Methods

PatientsWe performed a prospective observational study in a 20-bed mixed ICU in the Onze

Lieve Vrouwe Gasthuis (OLVG; Amsterdam, the Netherlands). The study was approved

by the Institutional Review Board. We included patients above the age of 18 who were

to undergo elective cardiac surgery; coronary artery bypass grafting (CABG) and/or

valve surgery. We excluded patients with an abdominal condition which would impair

sensor insertion. Eligible patients received an information letter at least one week before

hospital admission and were asked to give written informed consent after additional

explanation of the study the day before surgery.

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Glucose monitoringAfter we obtained informed consent the two needle-type sensors (the Guardian® Real-

Time, Medtronic Minimed, Northridge, CA and the FreeStyle Navigator®, Abbott Diabetes

Care, Alameda, CA) were inserted in the abdominal wall on either side of the umbilicus

the day before surgery and calibrated to allow stabilization of the signal. Upon arrival

at the ICU after surgery, the device’s internal clock was synchronised with the bedside

computer and both devices were calibrated simultaneously. Further calibrations were

performed according to manufacturers’ instructions. The devices were removed after

48 h of ICU admission or earlier when the patient was discharged.

During the study a reference arterial blood glucose value was measured with the

AccuChek handheld glucose measurement device (Performa II, lot 320098, Roche/Hitachi®,

Basel, Switzerland) every two hours. These samples were used as calibration when needed

and as reference otherwise. An in-house quality assurance study showed that the slope

of the regression between this point-of-care measurement method and arterial glucose

measurement by blood gas analysis was 1.0 (95% CI 1.00-1.02, n = 1,393, sample range

2.9-30.0 mmol/l; Passing-Bablok regression) and 95% of the absolute differences between

reference and point-of-care measurement were lower than 15%, thereby meeting the ISO

15197 guideline 11. All results were stored in the ICU’s clinical information system (iMDsoft;

MetaVision®, Tel Aviv, Israel). Using a dynamic computerised algorithm implemented in

2001 12, glucose values between 5.0 and 8.0 mmol/l were targeted. The nursing staff was

instructed to adjust the insulin infusion rate depending on the current glucose value

and the rate of glucose change based on the previous five measurements. The nurses

did not act upon the sensor glucose values.

Measurement of the microcirculationWe assessed the microcirculation by recording the sublingual microcirculation, after

gentle removal of excess saliva, using a handheld sidestream dark-field (SDF) camera.

This method has been described in detail previously 13. Imaging was performed as soon

as possible after the patient arrived at the ICU and 2 and 4 hours thereafter, as well as

at 8:00, 12:00 and 16:00 hrs the next days until the patientwas discharged from the ICU

with a maximum of 48 hrs after ICU admission. At each timepoint SDF recordings of at

least three different sublingual sites were recorded, stored and later scored in random

order to prevent bias. During the scoring procedure recordings were excluded when there

was no flow visible in the large vessels indicating a pressure artefact, presence of excess

saliva making it impossible to reliably visualise all vessels, and/or excess movement of

the recording. We analyzed small vessels with a diameter <20 μm in line with previous

literature 14 since these capillaries and small venules are considered to be most important

in nutrient, i.e. glucose, transport. Together with every SDF recording a reference plasma

glucose measurement was obtained to calculate sensor accuracy.


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We scored the microvascular flow index (MFI), the proportion of perfused vessels (PPV)

and the perfused vessel density (PVD) per sublingual site per patient. The three outcomes

per timepoint per patient were then averaged according to the consensus statement

on how to evaluate the microcirculation 15 using the automated vascular analysis (AVA)

programme version 3.0 (Department of Medical Technological Development, Academic

Medical Centre, and MicroVision Medical, Amsterdam, the Netherlands). In brief, for PVD

and PPV the vessel density was calculated as the number of vessels crossing 3 horizontal

and 3 vertical equidistant lines divided by the total length of the lines. Perfusion of the

crossing vessels was scored as follows: 0 = no flow, 1 = intermittent flow (flow present

<50% of the recording), 2 = sluggish flow (flow present >50% but <100% of the recording

or continuous very slow flow), 3 = continuous flow. PVD was then calculated as the

number of crossing vessels with flow present (scores 2 or 3) reported in n/mm. PPV is

the proportion of vessels with flow present (scores 2 or 3) 9. For the MFI the predominant

type of flow in four quadrants was determined according to the same scoring system.

The MFI is the sum of these flow scores divided by the number of quadrants where the

vessel type is visible 16;17. The heterogeneity index (HI) per timepoint for the PVD was

calculated by dividing the difference between the lowest and the highest value by the

mean to objectify intra-site differences per timepoint 18.

The MFI was scored by two different investigators (SES and TB), compared and revised

by consensus when the separate scores differed more than one point. The final score

was obtained by averaging the separate scores. The PPV and PVD were scored by one

investigator (SES) and intra-observer variability was determined by reanalyzing 17

randomly chosen movie sequences again after 4 weeks. The intraclass correlation

coefficient (ICC) was calculated using a two-way random model to determine absolute

agreement. The ICC (95% CI) for PVD was 0.94 (0.84-0.98) and for PPV 0.94 (0.84-0.98),

indicating good intra-observer agreement.

Additionally, tissue oxygenation (StO2) was measured with near-infrared spectroscopy

to assess microcirculatory function (InSpectratm StO2 Tissue Oxygenation Monitor,

Hutchinson Technology, Hutchinson, MN, USA). The StO2 is a measure of oxygen

consumption of the tissue and a predictor of organ dysfunction 19. It reflects the ratio of

oxygenated haemoglobin to total haemoglobin in the microcirculation measured by the

absorption of near infrared light (wavelength 700-1000 nm). The probe was placed on the

thenar muscle at ICU arrival and removed after 48 hrs or upon discharge from the ICU.

Other variablesBesides microcirculatory variables, other factors possibly influencing CGM accuracy

were obtained at the time of every reference glucose measurement: mean arterial

pressure (MAP), peripheral and rectal temperature, pulse oximeter oxygen saturation

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and dosing of vasoactive medication. The Acute Physiology and Chronic Health Evaluation

IV predicted mortality (APACHE IV PM) was calculated for the first 24 hrs of admission

and the Sequential Organ Failure (SOFA) score was obtained daily. Also the European

System for Cardiac Operative Risk Evaluation (euroSCORE) score, a method of calculating

predicted operative mortality risk for patients undergoing cardiac surgery, was recorded

for every patient.

Data interpretation and statisticsWe calculated the relative absolute deviation (RAD) [|sensor value-reference glucose|/

reference glucose] between reference glucose and the next available sensor glucose value

within 5 minutes to assess the real-time accuracy of the devices. To describe patient

characteristics and overall circulatory function the median (IQR) or mean (SD) of the

mean values per patient were calculated (Table 1 and 2). First, associations between sensor

accuracy and individual circulatory variables were assessed using a linear mixed-effects

model for repeated measures. Second, we built a multivariable model for each sensor

with those variables having a significant association with the accuracy of that specific

sensor. Last, the microcirculatory variables (MFI, PVD, PPV and StO2) were forced in turn

into the definite model to assess a possible independent effect on sensor accuracy. All

analyses were performed using Predictive Analytics Software (PASW) statistics version

18.0 (SPSS Inc., Chicago, IL, USA). A P-value <0.05 was considered statistically significant.

Results

We included 61 patients in the study, of whom 1 patient dropped out due to cancellation

of surgery because of intercurrent febrile illness. In total 60 patients were available for

the final analysis of whom 48 were males. The median (range) age was 65 (25-85) years

and 16 of the patients were previously diagnosed with type 2 diabetes. The majority of

the patients underwent only a CABG procedure (53.3%). The median (IQR) euroSCORE

score was 4.0 (2.0-5.0). Median (IQR) APACHE IV PM and maximum SOFA scores were 0.01

(0.003-0.02) and 6.0 (5.3-7.0). Patient characteristics are reported in Table 1.

MicrocirculationIn total, SDF recordings at 246 time points met our quality criteria for MFI assessment,

with a median (IQR) of 4 (3-5) time points per patient. For PVD and PPV scoring only 178

time points were suitable due to excessive movement of one of the three recordings of

that timepoint (median [IQR] 3 [2-4] per patient). The median (IQR) MFI was 2.8 (2.7-2.9),

PVD 9.3 (8.4-10.0) vessels/mm, PPV 0.97 (0.96-0.99), HIpvd 0.17 (0.13-0.21) and StO2 90.3%

(87.1-92.0) (Table 2). Microcirculation variables were not different between patients with

or without diabetes. The mean (SD) StO2 significantly increased during the first hours of


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Table 1 Patient characteristics

Patients, n = 60

Male sex, n (%) 48 (80.0)

Age, years 65.0 (59.0-73.8)

Diabetes, n (%) 16 (26.7)

Procedure, n (%)

CABGValve surgeryCABG + valve surgery

32 (53.3)16 (26.7)12 (20.0)

APACHE IV PM 0.01 (0.003-0.02)

SOFA max 6.0 (5.3-7.0)

euroSCORE 4.0 (2.0-5.0)

ICU stay, hours 23.0 (19.0-45.8)

ICU readmission, n (%) 6 (10.0)

Death in ICU/hospital, n 0

Glucose ICU, mmol/l (mean [SD]) 8.2 (2.1)

RAD Navigator, % 11 (8-15)

RAD Guardian, % 14 (11-18)

Dopamine, μg/kg/min (n=60) 1.62 (1.03-2.34)

Nitroglycerine, μg/kg/min (n=57) 0.22 (0.16-0.34)

Enoximon, μg/kg/min (n=40) 1.19 (0.80-1.71)

Ketanserine, μg/kg/min (n=4) 0.26 (0.08-0.39)

Noradrenaline, μg/kg/min (n=1) 0.09

Values are depicted as median (IQR) unless stated otherwise, calculated from the mean per patient during ICU admission. APACHE IV PM, Acute Physiology and Chronic Health Evaluation IV predicted mortality; CABG, coronary artery bypass grafting; euroSCORE, European System for Cardiac Operative Risk Evaluation; RAD, relative absolute deviation; SOFA max, maximum sequential organ failure assessment score per patient during the study period; Valve surgery includes mitral valve plasty, tricuspid valve plasty, aortic valve replacement or a combination of these.

ICU admission, from 88.6% (5.6) to a maximum of 91.2% (3.9) after 6 hrs, and decreased

thereafter, stabilizing after 20 hrs (Figure 1). This increase in StO2 was accompanied with

a decrease in PVD (b= -0.697, SE=0.267, p=0.01, linear mixed-effects model for repeated

measures). In line with this observation we found that the PVD was lowest in the first

eight hours after ICU admission compared with the next day (median [IQR] 9.2 [8.1-9.9]

and 9.4 [8.6-10.5], P = 0.049 Wilcoxon signed ranks test). The MFI, PPV and HIpvd did not

change over time. The arterial oxygen content (CaO2; haemoglobin concentration*pulse

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oximeter oxygen saturation*1.34) was stable during ICU admission (mean [SD] 12.6 [1.3]

ml O2/100 ml).

Table 2 Haemodynamic and microvascular variables

Patients, n = 60

MFI small vessels 2.8 (2.7-2.9)

PPV small vessels 0.97 (0.96-0.99)

PVD small vessels, n/mm 9.3 (8.4-10.0)

Heterogeneity index PVD 0.17 (0.13-0.21)

StO2, % 90.3 (87.1-92.0)

SpO2, % 97.2 (95.7-98.6)

Temperature rectal, ◦C 36.8 (36.4-37.1)

Temperature peripheral, ◦C 32.8 (31.6-33.6)

HF, beats/min 84.2 (74.4-90.0)

MAP, mmHg 73.6 (69.3-81.0)

Values are depicted as median (IQR), calculated from the mean per patient during ICU admission. HF, heart frequency; MAP, mean arterial pressure; MFI, microvascular flow index; PPV, proportion of perfused vessels; PVD, perfused vessel density; SpO2, peripheral oxygenation; StO2, tissue oxygenation.

CGM accuracyOverall, the Navigator sensor performed significantly better than the Guardian

sensor (median [IQR] RAD 11% [8-16] and 14% [11-18], p=0.05 4). Results of this head-to-

head comparison are described in Chapter 9 of this thesis. In a linear mixed-effects

model for repeated measures, variables that were individually associated with worse

Navigator sensor accuracy were higher age, a diagnosis of diabetes, decreased peripheral

temperature, increase in ketanserine, dopamine or enoximon dose and higher APACHE IV

PM. Only decreased peripheral temperature and increasing dopamine use were associated

with decreased accuracy of the Guardian sensor (Table 3A). None of the microcirculation

variables nor sex, MAP, norepinephrine dose and nitroglycerine dose showed significant

associations with accuracy of one of the sensors.

Subsequently, we built a multivariable model per sensor with the significantly associated

factors (Table 3A). Due to co-linearity between APACHE IV PM and ketanserine dose we

only included APACHE IV PM in the final model. This model showed that for the Navigator

only higher APACHE IV PM (b=0.017, SE= 0.004, p<0.001), lower peripheral temperature

(b= -0.008, SE= 0.003, p=0.003) and higher age (b=0.002, SE=0.001, p=0.037) remained

significantly associated with an increase in sensor RAD, i.e. worse sensor accuracy.


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Figure 1 Tissue oxygenation in relation with arterial oxygen content during ICU admission Development of mean (SE) tissue oxygenation (StO2, left y-axis) and arterial oxygen content (CaO2, right y-axis) during intensive care unit admission (x-axis).

Figure 2 Sensor accuracy in relation with peripheral temperature Relative absolute deviation (RAD) of both sensors stratified by peripheral temperature quartiles, depicted as median (IQR). The highest RAD is seen below 31.00 degrees Celsius and the lowest RAD above 34.11 degrees Celsius. *P <0.05

For the Guardian a decrease in peripheral temperature remained associated with

an increase in RAD (b= -0.006, SE= 0.003, p=0.048). Figure 2 shows crude accuracy of

both sensors stratified by peripheral temperature. Lastly, we successively forced the

microcirculatory variables in these final models to assess a possible independent effect on

sensor accuracy. Forcing these variables in turns did not reveal a significant association

with sensor accuracy (Table 3B).

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Table 3 Covariates associated with sensor accuracy

A

Univariate Multivariate

Navigator b SE p b SE p

APACHE IV PM 0.017 0.010 <0.001 0.017 0.004 <0.001

Age 0.002 0.001 <0.001 0.002 0.001 0.037

Peripheral temperature -0.009 0.003 0.001 -0.008 0.003 0.003

Dopamine dose 0.008 0.003 0.006 -0.001 0.004 0.785

Enoximon dose 0.016 0.006 0.008 0.009 0.007 0.207

DM no vs. yes -0.038 0.016 0.019 -0.024 0.018 0.177

Ketanserine dose 0.353 0.058 <0.001

Guardian

Peripheral temperature -0.006 0.003 0.036 -0.006 0.003 0.048

Dopamine dose 0.007 0.003 0.046 0.004 0.004 0.336

B

Navigator b SE p

+ MFIs -0.021 0.041 0.61

+ PVD -0.003 0.007 0.63

+ PPV 0.147 0.307 0.63

+ StO2 0.002 0.001 0.12

Guardian

+ MFIs -0.101 0.063 0.11

+ PVD 0.009 0.009 0.33

+ PPV -0.256 0.448 0.57

+ StO2 -0.001 0.001 0.61

Linear mixed-effects model for repeated measures, with the different patients as subjects and the two hour reference glucose measurements as repeated measures, using the AR(1) repeated covariance structure. Sensor accuracy, i.e. relative absolute deviation was the dependent variable. Covariates were put into the model as fixed effects. A: individual associations of variables with sensor accuracy (univariate) and basic model of both sensors including the variables with individual significant association with sensor accuracy (multivariate). B: forced introduction of the microcirculatory variables in turns does not show a significant association with sensor accuracy. APACHE IV PM, acute physiology and chronic health evaluation IV predicted mortality; DM, diabetes mellitus; MFIs, microvascular flow index of small vessels; PVD, perfused vascular density; PPV, proportion of perfused vessels; SE, standard error; StO2, tissue oxygen saturation.


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Discussion

In the current study we found microcirculatory impairment to a limited extent in cardiac

surgery patients in the first hours of ICU admission after surgery during a median

follow-up of 23 hrs. Microcirculatory impairment was reflected by a decrease in PVD

and a transient increase in StO2 but no differences in PPV and MFI. The microcirculatory

variables showed no relationship with continuous glucose sensor accuracy. Variables

associated with worse sensor accuracy were higher age, lower peripheral temperature

and higher APACHE IV PM for the Navigator sensor and lower peripheral temperature

only for the Guardian sensor.

Previous studies of microcirculatory function in postoperative cardiac surgery patients

show various results. Some found normal MFI values at ICU arrival 20 and PVD one

hour after cardiopulmonary bypass 21, whereas De Backer et al. 22 found a decrease in

PPV at the end of on-pump surgery persisting until 24 hrs after surgery. Of note, the

latter is the only study besides the present study with more than 1 hr follow-up during

postoperative ICU admission of cardiac surgery patients. Looking at the absolute values

of the measured microcirculation variables, the MFI, PPV and HI found in our population

are roughly comparable with healthy controls measured by Trzeciak 18. PVD in our group

was somewhat smaller than in this control group and not as low as in sepsis patients 9.

Unfortunately, we did not have the opportunity to measure microcirculatory function

pre-operatively and therefore we are unable to assess pre- to postoperative changes.

We found an increase in StO2 after ICU admission with a peak after 6-12 hrs (Figure 1) and

a gradual decrease thereafter stabilizing to normal levels under 90% after 20 hrs of ICU

stay. No studies measuring StO2 in patients after cardiac surgery are known to us. Together

with the increase in StO2, the lowest PVD values were found in the first 8 hrs of ICU

admission and the increase in StO2 was significantly associated with the decrease in PVD

in mixed model analysis. Endotoxin release and subsequent triggering of the inflammatory

response observed in on-pump surgery could explain these findings. Endotoxin levels

peak shortly after surgery 23;24 and the inflammatory response, as represented by plasma

IL-6 concentration, is found to be highest after 6 hrs 23 which is comparable with the

observed peak in StO2. A much larger inflammatory response is seen in sepsis and this is

accompanied by large microcirculatory changes 9 and also a possible increase in StO2 due

to impaired oxygen offloading in a hyperdynamic flow state 25. The changes seen in our

population could thus be due to similar but much smaller changes as those seen in sepsis

caused by distributive shock, due to arterial-venous shunting of the microcirculation

reducing the number of perfused vessels thereby disabling oxygen offloading. This

hypothesis is supported by the stable CaO2 during ICU admission (Figure 1) which makes

it unlikely that the increased StO2 could be explained by an increase in oxygen supply.

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Microcirculatory variables did not influence accuracy of both sensors. As far as we

know no previous studies assessing this relation have been performed. This finding

suggests that microcirculatory status does not impair glucose transport in cardiac

surgery patients; at least not to the extent that it has impact on subcutaneous sensor

performance. The patients included in this study had a relatively good microcirculation

as measured by MFI, PPV and PVD; although differences between patients were quite

large (PVD ranged 5.9-13.8 vessels/mm). It remains to be established whether in patients

with worse overall microcirculatory variables, i.e. patients with the sepsis syndrome,

sensor performance is affected.

A decrease in peripheral temperature did influence the accuracy of both sensors

negatively. This finding is in contrast with one other study performed in pediatric cardiac

surgery patients using the older Guardian RT CGM system not showing a relationship 26.

It is however plausible that the temperature of the skin influences sensor accuracy since

the optimal reaction temperature for the glucose oxidase enzyme incorporated in the

sensors is 30-40 degrees Celsius 27 and skin temperature during and shortly after cardiac

surgery is often below that lower limit. Nevertheless, the decreased sensor accuracy

needs to be put into clinical perspective as the median (IQR) RAD of both sensors during

peripheral temperatures under 31 degrees Celsius is still relatively low (Navigator: 10.5%

[4.8-20.0], Guardian: 14.7% [6.6-27.7]; Figure 2).

The performance of the Navigator was also negatively influenced by higher age and higher

APACHE IV PM. As we hypothesised, the highest RAD was found in the more severely ill

patients, but apparently the used microcirculatory variables did not mediate this effect

in those with the highest APACHE IV PM. Also APACHE IV PM in our patient group was

relatively low; therefore these results cannot be extrapolated to patients with higher

severity of disease scores. Notably, the accuracy of the Navigator was influenced by more

variables than the accuracy of the Guardian. Possibly the inaccuracy of the Guardian is

largely dependent on sensor-related factors overruling the influence of patient-related

variables.

Conclusions

This study showed that in cardiac surgery patients microcirculation was impaired after

surgery, reflected by a transient increase in StO2 and decrease in PVD, but was overall

quite good compared with septic patients and healthy controls. The impairment in

microcirculatory variables was not related to sensor accuracy but peripheral temperature

(both sensors) and age as well as APACHE IV PM (Navigator) was. These results support

CGM use in cardiac surgery patients characterised by low severity of illness. Further


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studies need to assess the influence of microcirculatory changes on sensor accuracy in

more severely ill patients.

AcknowledgementsThis study was supported by a European Foundation for the Study of Diabetes (EFSD)/

LifeScan research grant. The sensors used were provided free of charge by Medtronic

Minimed and at a discounted rate by Abbott Diabetes Care.

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References1. Inzucchi SE, Siegel MD (2009) Glucose control in the ICU--how tight is too tight? N Engl J Med 360: 1346-13492. Hermanides J, Bosman RJ, Vriesendorp TM, et al (2010) Hypoglycaemia is related with ICU mortality. Crit

Care Med 38: 1430-14343. Holzinger U, Warszawska J, Kitzberger R, et al (2010) Real time continuous glucose monitoring in critically

ill patients - a prospective, randomized trial. Diabetes Care 33: 467-4724. Siegelaar SE, Barwari T, Hermanides J, Stooker W, van der Voort PHJ, Devries JH (2011) Accuracy and reliability

of continuous glucose monitoring at the ICU; a head to head comparison of two subcutaneous glucose sensors in cardiac surgery patients. Diabetes Care in press

5. Van den Berghe G, Wouters P, Weekers F, et al (2001) Intensive Insulin Therapy in Critically Ill Patients. N Engl J Med 345: 1359-1367

6. Furnary AP, Gao G, Grunkemeier GL, et al (2003) Continuous insulin infusion reduces mortality in patients with diabetes undergoing coronary artery bypass grafting. J Thorac Cardiovasc Surg 125: 1007-1021

7. Logtenberg SJ, Kleefstra N, Snellen FT, et al (2009) Pre- and postoperative accuracy and safety of a real-time continuous glucose monitoring system in cardiac surgical patients: a randomized pilot study. Diabetes Technol Ther 11: 31-37


9. De Backer D, Creteur J, Preiser JC, Dubois MJ, Vincent JL (2002) Microvascular blood flow is altered in patients with sepsis. Am.J.Respir.Crit Care Med 166: 98-104

10. De Backer D, Creteur J, Dubois MJ, Sakr Y, Vincent JL (2004) Microvascular alterations in patients with acute severe heart failure and cardiogenic shock. Am Heart J 147: 91-99

11. International Organization for Standardization (2003) In vitro diagnostic test systems - Requirements for blood-glucose monitoring systems for self-testing in managing diabetes mellitus. ISO 15197


13. Goedhart PT, Khalilzada M, Bezemer R, Merza J, Ince C (2007) Sidestream Dark Field (SDF) imaging: a novel stroboscopic LED ring-based imaging modality for clinical assessment of the microcirculation. Opt Express 15: 15101-15114

14. Elbers PW, Ozdemir A, Heijmen RH, et al (2010) Microvascular hemodynamics in human hypothermic circulatory arrest and selective antegrade cerebral perfusion. Crit Care Med 38: 1548-1553

15. De Backer D, Hollenberg S, Boerma C, et al (2007) How to evaluate the microcirculation: report of a round table conference. Crit Care 11: R101

16. Boerma EC, Mathura KR, van der Voort PH, Spronk PE, Ince C (2005) Quantifying bedside-derived imaging of microcirculatory abnormalities in septic patients: a prospective validation study. Crit Care 9: R601-R606

17. Spronk PE, Ince C, Gardien MJ, Mathura KR, Oudemans-van Straaten HM, Zandstra DF (2002) Nitroglycerin in septic shock after intravascular volume resuscitation. Lancet 360: 1395-1396

18. Trzeciak S, Dellinger RP, Parrillo JE, et al (2007) Early microcirculatory perfusion derangements in patients with severe sepsis and septic shock: relationship to hemodynamics, oxygen transport, and survival. Ann Emerg Med 49: 88-98

19. Cohn SM, Nathens AB, Moore FA, et al (2007) Tissue oxygen saturation predicts the development of organ dysfunction during traumatic shock resuscitation. J Trauma 62: 44-54

20. den Uil CA, Lagrand WK, Spronk PE, et al (2008) Impaired sublingual microvascular perfusion during surgery with cardiopulmonary bypass: a pilot study. J Thorac Cardiovasc Surg 136: 129-134

21. Bauer A, Kofler S, Thiel M, Eifert S, Christ F (2007) Monitoring of the sublingual microcirculation in cardiac surgery using orthogonal polarization spectral imaging: preliminary results. Anesthesiology 107: 939-945

22. De Backer D, Dubois MJ, Schmartz D, et al (2009) Microcirculatory alterations in cardiac surgery: effects of cardiopulmonary bypass and anesthesia. Ann Thorac Surg 88: 1396-1403

23. Boelke E, Storck M, Buttenschoen K, Berger D, Hannekum A (2000) Endotoxemia and mediator release during cardiac surgery. Angiology 51: 743-749

24. Oudemans-van Straaten HM, Jansen PG, Hoek FJ, et al (1996) Intestinal permeability, circulating endotoxin, and postoperative systemic responses in cardiac surgery patients. J Cardiothorac Vasc Anesth 10: 187-194

25. Elbers PW, Ince C (2006) Mechanisms of critical illness--classifying microcirculatory flow abnormalities in distributive shock. Crit Care 10: 221


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26. Piper HG, Alexander JL, Shukla A, et al (2006) Real-time continuous glucose monitoring in pediatric patients during and after cardiac surgery. Pediatrics 118: 1176-1184

27. Takamatsu S, Takano H, Binh-Khiem N, et al (2010) Liquid-phase packaging of a glucose oxidase solution with parylene direct encapsulation and an ultraviolet curing adhesive cover for glucose sensors. Sensors 10: 5888-5898

Chapter 11

Special considerations for the diabetic patient in the intensive care unit: targets for treatment and risks of hypoglycaemia

Sarah E. Siegelaar, Joost B.L. Hoekstra and J. Hans DeVries

Best Practice & Research: Clinical Endocrinology and Metabolism 2011; in press

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Abstract

Due to the diabetes pandemic the number of diabetic patients admitted to the

ICU increases. Diabetic patients admitted to the ICU are more vulnerable for

developing complications as compared to non-diabetic patients, but this does

not directly translate into higher mortality rates. However, mortality might

differ per admission diagnosis. Hyperglycaemia is common in diabetic as well

as non-diabetic critically ill patients, but probably chronic hyperglycaemia

is pathophysiologically different from acute hyperglycaemia. As opposed

to non-diabetic patients, there is discussion about the association between

hyperglycaemia and mortality in diabetic patients. They do not seem to benefit

from strict glycaemic control and also glucose variability appears less harmful,

although clinical trials in diabetic populations have not been performed yet.

Diabetes is a risk factor for hypoglycaemia and evidence suggests that even near-

normal glucose levels are associated with worse outcome. Taking this together,

it is suggested to strive for moderate targets when treating hyperglycaemia in

critically ill diabetic patients.

Special considerations for the diabetic patient in the ICU

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I. Introduction

Hyperglycaemia is common in all critically ill patients, not only in those with a prior

diagnosis of diabetes mellitus. Hyperglycaemia in the critically ill is associated with

increased mortality 1 but large intervention studies evaluating the treatment of intensive

care unit (ICU) hyperglycaemia by intensive insulin therapy, show conflicting results 2-4.

There is no definite answer to the question whether and how tight hyperglycaemia of

critically ill patients has to be treated. Also, there may be a difference in outcome between

subgroups of patients. More and more the idea evolves that chronic hyperglycaemia

in critically ill patients with diabetes is pathophysiologically different from acute

hyperglycaemia in those without previously diagnosed diabetes, with consequences in the

hyperglycaemic as well as in the hypoglycaemic range. This could mean that treatment

targets and strategies in patients with diabetes should differ from those without diabetes.

If this hypothesis is true, the next question is whether undiagnosed diabetes should

be treated as patients with known diabetes or as patients without diabetes and a big

challenge for ICU physicians would be to unmask all patients with diabetes admitted

at the ICU. A large proportion of the patients admitted are unconscious hampering

adequate history taking and the medical history may be incomplete. Plasma glucose

values will not distinguish between those with and without diabetes due to the fact

that hyperglycaemia is also very common in non-diabetic critically ill patients. Therefore

some plead to measure HbA1c in all admitted patients to diagnose pre-existing diabetes 5, but this discussion lies outside the scope of this review.

In this review we will give an overview of the current literature on morbidity and

mortality in diabetic ICU patients with special consideration to glucose regulation,

insulin treatment and hypoglycaemia. For this purpose we searched MEDLINE for

studies conducted at any ICU concerning patients with diabetes, solely or in comparison

with patients without diabetes, without any restriction with respect to publication

date. Randomised controlled trials as well as observational studies were included.

We distinguished between studies regarding glucose regulation and studies looking

at morbidity and mortality of patients with diabetes at the ICU independent from

glucose regulation. Where possible the results are presented separately for different

subpopulations of ICU patients, for example cardiac surgery patients.

II. The diabetic patient in the ICU

A. Are diabetic patients at higher risk for morbidity?As a result of the diabetes pandemic in the western world, the number of diabetic patients

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admitted at the ICU also increases. Are there any consequences of this increase? It is

known that patients with diabetes are vulnerable for the development of complications

during ICU admission: immune cell functions are hampered 6;7 which theoretically

promotes the incidence of various kinds of infections and there is a prothrombotic shift

in coagulation and fibrinolysis 8. Also the presence of diabetic complications as micro-

and macrovascular damage might have an effect on morbidity and perhaps mortality

as compared to non-diabetic individuals.

Nearly all studies looking at the incidence of complications in diabetic patients admitted

at the ICU support the hypothesis that they have increased morbidity. In cardiac surgery

patients all studies confirm that diabetes is a risk factor for postoperative complications

such as infections and, perhaps as a consequence, longer ICU and hospital stay 9-11. The

same goes for diabetic trauma patients 12;13. Also in mixed ICU populations diabetes seems

to be a risk factor for the development of (severe) infections 14-16 and acute organ failure 17. Interestingly, diabetic patients seem to have a decreased risk of developing acute

respiratory distress syndrome (ARDS) 18;19. This could be explained by the abovementioned

impairment in immune function promoting infections, as the overzealous activation

and recruitment of circulating neutrophils into the lung is involved in the pathogenesis

of ARDS 19. However, although the evidence points to an increased complication risk in

diabetic patients admitted at the ICU, there are also studies which could not confirm

this for development of nocosomial pneumonia 20 and bacteriuria 21 and as far as the

authors are aware no meta-analysis has been performed so far.

B. Are diabetic patients at higher risk for mortality? With a probably higher complication risk and increased length of stay, one would also

expect an increased mortality risk for diabetic ICU patients. Studies in cardiac surgery

patients indeed show increased mortality rates for all diabetic patients 9;22 or in those

with long-term complications 23. On the other hand, medical diabetic patients or patients

admitted in mixed ICU’s having an infection do not seem to have increased mortality 24-27. Data from mixed ICU populations without further specification are less conclusive.

Egi et al. 28showed even a lower adjusted mortality risk for diabetic patients which was

confirmed by the publication of the largest cohorts reported so far, 1,509,890 and 36,414

diabetic patients 29. But also increased 17;30;31 and similar mortality rates 1;18;32 have been

described.

It is intriguing to conclude that some diabetic ICU patients are perhaps protected and

others have a higher mortality risk. It might be that for example diabetic patients

undergoing cardiac surgery have a larger number of affected coronary vessels contributing

to the higher mortality in that group. The relative tolerance for hyperglycaemia, which

will be discussed in the next section, might contribute to decreased mortality rates,


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but all proposed explanations are still only speculative. To further clarify the relation

between diabetic status and mortality, a meta-analysis making distinction between

subgroups of patients is needed.

III. Glucose regulation

A. Is hyperglycaemia deleterious to diabetic ICU patients? In patients without diabetes marked critical illness associated hyperglycaemia

is undisputedly related to morbidity and mortality 1;33. Critical illness-induced

hyperglycaemia is caused by inflammatory and neuro-endocrine derangements in these

patients leading to high hepatic glucose output and insulin resistance 34. In patients with

diabetes hyperglycaemia is already a pre-existing situation, although critical illness may

of course further derange blood glucose values by the above mentioned mechanisms.

Several studies examined the relation between hyperglycaemia and outcome in critically

ill patients with diabetes. In diabetic cardiac surgery patients hyperglycaemia above 11.1

mmol/l seems independently associated with increased wound infection rates 22;35 as

well as mortality and length of hospital stay 35 (to convert from mmol/l to mg/dl divide

by 0.0555). Contrary, Reyes et al. 36 did not find an association between hyperglycaemia

and complication rates in this group of patients, but in their study glucose levels were

very well regulated (mean [SD] postoperative BG 7.6 [2.5] mmol/l).

In mixed ICU populations the relationship between hyperglycaemia and mortality in

diabetic patients is less clear. Three studies containing large cohorts of diabetic patients

do report a relation between hyperglycaemia and mortality. Rady et al. 37 retrospectively

analyzed 1,083 ICU patients with diabetes and found that patients with median glucose

levels above 11.1 mmol/l showed increased mortality compared with patients with median

glucose levels between 4.4 and 11.1 mmol/l. In the latter group median glucose was

not related to mortality rates. Graham et al. 29 performed a retrospective analysis of

36,414 diabetic patients from the Mayo Clinic (Rochester, MN, USA). They found increased

hospital mortality rates in patients with peak glucose levels above 9.1 mmol/l compared

with patients with peak glucose levels between 7.2 and 9.1 mmol/l. Interestingly, patients

with peak glucose levels lower than 7.2 mmol/l were also found to have higher mortality

rates. However, no analyses were performed adjusting for possible confounders. Falciglia

et al. 38 did adjust for an important confounder: severity of disease. In a retrospective

cohort of 78,142 diabetes patients subdivided into groups with increasing mean glucose

levels, increasing hyperglycaemia was significantly associated with higher hospital

mortality compared to normoglycaemia (3.9-6.1 mmol/l).

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In contrast with these findings, Egi et al. 28 could not confirm the deleterious effect

of hyperglycaemia in diabetic patients. In a cohort of 728 patients with diabetes, no

significant difference in ICU- and hospital mortality was found when analyzing four

equally sized groups of patients with increasing mean glucose levels, the glucose of

the lowest group ranging from 8.1 mmol/l to below. Also mean glucose values were

comparable between diabetic survivors and non-survivors (mean [SD] glucose 9.5 [2.9]

and 9.6 [2.8] mmol/l, respectively) 39. Two other studies including 574 (21.2%) 40 and 188

(22.7%) 26 diabetic patients investigated the effect of admission glucose on mortality but

no effect of hyperglycaemia higher than 11.1 mmol/l was seen. It might be possible that

the degree of pre-existing hyperglycaemia, expressed as HbA1c, alters the association

between acute glycaemia and mortality. A recent study including 415 patients with

diabetes shows that in patients with preadmission HbA1c levels above 7.0%, the higher

the glucose levels during admission, the lower the hospital mortality, in contrast to

patients with HbA1c levels under 7.0%, where higher glucose levels during admission

translate into higher mortality rates 41.

Without exception it has been found that at any given mean glucose level in the

hyperglycaemic range the mortality of patients with diabetes is lower than the mortality

of non-diabetic patients. The cut-off values where this effect occurs vary however. Rady

showed already an increased mortality rate and Falciglia an increased adjusted odds-ratio

for mortality in non-diabetic patients compared to diabetic patients in the subgroup

with a median 37 or mean 38 glucose between 6.2 and 8.0 mmol/l. This was not confirmed

in other studies which report a significant difference in mortality in favour of patients

with diabetes only for a mean glucose level of 8.0 mmol/l and above 39 or a peak glucose

level of 9.1 mmol/l and above 29. Another study investigating only admission glucose

values above 11.1 mmol/l shows also lower mortality rates for patients with diabetes 26.

The different effects of hyperglycaemia in ICU patients with and without diabetes suggest

that acute hyperglycaemia in critical illness and chronic hyperglycaemia in diabetes are

two distinct pathophysiological entities. Adaptation to hyperglycaemia might be a key

mechanism. Acute hyperglycaemia and inflammation induce e.g. oxidative stress which

causes endothelial damage 42. It is possible that patients with diabetes are already adapted

to these insults and therefore better tolerate episodes of hyperglycaemia compared with

non-diabetic patients, whose cellular adaptation mechanisms are not yet activated.

Attractive as it may be, this hypothesis is not substantiated any further in the literature.

B. Do diabetic patients benefit from intensive insulin therapy?Apart from whether there is any association between hyperglycaemia and mortality in

diabetic ICU patients, the relevant clinical question is whether they would benefit from

glucose lowering therapy. Several large clinical studies addressed this question but none


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of them was specifically designed to look at the effect of intensive insulin therapy (IIT)

in diabetes patients. The results presented here are therefore derived from sub-analyses

except for three trials investigating intensive insulin therapy in diabetes patients during

cardiac surgery. Characteristics of the trials are presented in Table 1.

In 2001 van den Berghe et al. awoke the intensive care community publishing the results

of the so-called first Leuven study 3. They reported that IIT, with an achieved mean [SD]

morning blood glucose of 5.7 [1.1] mmol/l, in the surgical ICU significantly reduced

ICU and hospital mortality compared with conventional treatment (mean [SD] 8.5

[1.8] mmol/l), with an absolute mortality reduction from 8.0 to 4.6%. This was mainly

attributed to patients with an ICU stay of more than 5 days (mortality reduction from

20.2 to 10.6%). 204 of the 1548 patients included in this study had a history of diabetes.

Subanalysis of this group showed somewhat less survival benefit of IIT in diabetic patients

with an ICU stay longer than 5 days (16.0 to 9.5%), although it remained a significant

effect. In the second Leuven study 4, performed in a medical ICU, the reduction in

mortality with IIT was restricted to patients with an ICU stay of more than 3 days (52.5

to 43.0%, P = 0.009). For the subgroup of patients with diabetes (n = 203) IIT showed no

survival benefit overall and also not in those admitted to the ICU for more than 3 days.

Other randomised controlled trials (RCT’s) investigating the effect of intensive insulin

therapy on mortality with sub-analyses for patients with diabetes were performed

at mixed surgical and medical ICU’s, without making distinction between surgical

and medical patients 2;43-46. In all these studies IIT did not show survival benefit over

conventional glucose control in patients with diabetes. Also when analyzing pooled data

from both Leuven trials no benefit of IIT was seen in the diabetic patients 46. The achieved

glucose levels in the IIT group of the pooled analysis ranged from 5.8 to 6.5 mmol/l and in

the conventional group from 8.0 to 9.5 mmol/l. The NICE-SUGAR trial 2 published in 2009

included the largest population of diabetes patients (n = 1211) and achieved mean (SD)

glucose values of 6.4 (1.0) and 8.0 (1.3) mmol/l in the total population. In this subgroup

the benefit tended towards the conventional treatment, just like the overall outcome.

Pooling these data for the purpose of this manuscript, using Review manager version 5

(The Cochrane Collaboration, Oxford, UK), shows no benefit of intensive or conventional

treatment regarding mortality with little heterogeneity (I2 = 17%; Figure 1). The negative

results in the mixed populations are supported by analyses comparing mortality rates

before and after the implementation of an IIT protocol showing no significant decrease

in mortality with IIT in patients with diabetes 47;48.

The use of IIT in cardiac surgery patients however shows more positive results. Lazar et

al. 49 showed in 141 diabetic patients who underwent cardiac surgery that those receiving

IIT during surgery had a survival advantage over the initial two years after surgery and

160

Tab

le 1

Mai

n c

har

acte

rist

ics

of

inte

rven

tio

n s

tud

ies

on

in

ten

sive

in

suli

n t

her

apy

in d

iab

etic

IC

U p

atie

nts

Inte

nsi

veC

on

ven

tio

nal

Mo

rtal

ity

(%)

Stu

dy

Typ

ePo

pu

lati

onn

DM

ach

ieve

d B

G95

% C

I/SD

ach

ieve

d B

G95

% C

I/SD

IIT

Cty

pe

van

den

Ber

ghe

2001

3R

CT

Surg

ical

204

5.7

1.1

8.5

1.8

4.0

5.8

ICU

Surg

ical

>5d

ICU

46n

an

an

an

a9.

5*16

.0IC

U

van

den

Ber

ghe

2006

4R

CT

Med

ical

203

5.7

na

8.5

na

39.6

35.0

Hos

pit

al

Med

ical

≥3d

ICU

117

na

na

na

na

47.4

49.2

Hos

pit

al

van

den

Ber

ghe

2006

46R

CT

Mix

ed40

75.

81.

38.

41.

813

.013

.5IC

U

Mix

ed40

75.

81.

38.

41.

823

.222

.0H

osp

ital

Bru

nkh

orst

200

8 44

RC

TM

ixed

163

6.2

6.1-

6.3

8.4

8.2-

8.6

25.0

31.9

28-d

ay

Ara

bi 2

008

43

RC

TM

ixed

208

6.4

1.0

9.5

1.9

12.9

20.3

ICU

De

La R

osa

2008

45R

CT

Mix

ed61

6.5

5.6-

7.8

8.2

6.8-

10.0

31.0

37.5

28-d

ay

Fin

fer

2009

2R

CT

Mix

ed1,

211

6.4

1.0

8.0

1.3

31.7

27.7

90-d

ay

Kri

nsl

ey 2

006

47Pr

e-p

ost

Mix

ed53

27.

7n

a10

.4n

a19

.222

.6H

osp

ital

Kri

nsl

ey 2

009

48Pr

e-p

ost

Mix

ed94

27.

16.

2-8.

310

.28.

1-12

.626

.529

.4H

osp

ital

Laza

r 20

04 49

R

CT

Car

dia

c su

rger

y14

17.

50.

214

.80.

31.

4*10

.02-

year

Furn

ary

2003

50Pr

e-p

ost

Car

dia

c su

rger

y3,

554

9.8

1.7

11.9

2.3

2.5*

5.3

Hos

pit

al

Sum

mar

y of

th

e m

ain

ch

arac

teri

stic

s of

th

e in

terv

enti

on s

tud

ies

inve

stig

atin

g th

e ef

fect

of

inte

nsi

ve in

suli

n t

her

apy

(IIT

) in

cri

tica

lly

ill p

atie

nts

wit

h d

iabe

tes.

Glu

cose

va

lues

ach

ieve

d a

re o

f th

e to

tal

pop

ula

tion

an

d d

epic

ted

in

mm

ol/l

wit

h 9

5% c

onfi

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ce i

nte

rval

or

stan

dar

d d

evia

tion

. Ou

tcom

e is

giv

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per

cen

tage

s.

*P =

<0.

05 f

avou

rin

g II

T. S

ee f

or m

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anal

ysis

Fig

ure

1. B

G, b

lood

glu

cose

; C

, con

ven

tion

al t

reat

men

t; D

M, d

iabe

tes

mel

litu

s; I

CU

, in

ten

sive

car

e u

nit

; p

re-p

ost,

era

of

con

ven

tion

al t

reat

men

t (p

re) c

omp

ared

wit

h a

n e

ra o

f in

ten

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tre

atm

ent

(pos

t); R

CT,

ran

dom

ised

con

trol

led

tri

al.


Ch

apte

r 11

161

Figure 1; Meta-analysis of RCT’s on intensive insulin therapy in mixed medical/surgical diabetic patients in the ICU Figure 1 legend: Meta-analysis of randomised controlled trials comparing intensive insulin therapy (IIT) with conventional treatment in mixed medical/surgical patients with diabetes admitted at the intensive care unit.

also shorter postoperative length of stay, decreased episodes of recurrent ischemia and

less wound infections. This RCT was supported by a pre-post analysis showing that IIT

was protective for hospital mortality 50 and decreased length of stay but interestingly

not post-operative infection rates 51 after cardiac surgery. It has to be noted however

that the achieved blood glucose levels in the studies including cardiac surgery patients

lie above those conducted in the ICU. The achieved mean glucose in the intervention

group ranged between 7.5 and 9.8 mmol/l, which is roughly comparable with the mean

glucose of the conventionally treated ICU groups, and in the conventionally treated

group between 11.9 and 14.8 mmol/l, which are glucose levels associated with increased

mortality and morbidity in the observational studies 22;35. Therefore it is unknown

whether more intensive glycaemic control aiming at glucose levels below 7.0 mmol/l

is beneficial compared to moderate glycaemic control in cardiac surgery patients. A

study performed by Gandhi et al. in 2006 52 was designed to assess this question and

they achieved glucose levels after surgery of 6.3 (SD 1.6) mmol/l in the IIT group and 8.7

(2.3) mmol/l in the conventionally treated group. Unfortunately, the number of diabetic

patients was too small (n = 37) and the overall mortality rate too low (1%) to perform

subgroup analyses, though all 4 deaths occurred in the IIT group.

In summary, these data show that survival in mixed surgical/medical diabetic ICU

patients treated with strict glycaemic control (mean glucose 5.8-6.5 mmol/l) is not

different from patients treated with moderate glycaemic control (mean glucose 8.0-9.5

mmol/l). In diabetic cardiac surgery patients, moderate glycaemic control (mean glucose

7.5-9.8 mmol/l) has shown better results than loose glycaemic control (mean glucose

11.5-14.8 mmol/l). This implies that moderate glycaemic control aiming at glucose levels

between 7.5 and 10.0 mmol/l is perhaps the best treatment for all critically ill diabetic

patients, also because the lower the target, the more hypoglycaemic events occur.

162

IV. Hypoglycaemia

An important side-effect of insulin treatment is the occurrence of hypoglycaemia.

All intervention studies of intensive insulin therapy report a substantial increase in

hypoglycaemia incidence with intensive insulin therapy compared to less intensive

therapy. Besides the use of insulin, also the presence of diabetes is a risk factor for the

occurrence of severe hypoglycaemia, defined by cut-off levels of 3.3 mmol/l 53, 2.5 mmol/l 54 as well as 2.2 mmol/l 55, independent of insulin dose at the time of the event 54. Patients

with a prior diagnosis of diabetes may have an impaired counterregulatory response

hampering adequate reaction to overdosed exogenous insulin, likely explaining these

findings.

In mixed populations, without distinction between patients with or without diabetes,

the occurrence of severe hypoglycaemia seems to be associated with mortality

independently from severity of disease 55;56. Moreover, there is evidence that not only

severe hypoglycaemia using the diabetes outpatient definition of 2.2 mmol/l but also

glucose levels already lower than 4.7 mmol/l are harmful in critically ill patients 56.

Unfortunately, such association studies have not been performed separately in diabetic

patients. One study comparing nadir glucose values between diabetic survivors and

non-survivors demonstrated significantly lower values in non-survivors (4.9 [2.6] and 5.7

[2.7] mmol/l, P = 0.02) 39.

When comparing mortality rates of patients with and without diabetes in the

lower glycaemic range a notable phenomenon is seen. Mortality rates at any given

hyperglycaemic glucose level are higher in non-diabetic patients compared with diabetic

patients as shown previously, but in the lower glucose range the opposite seems to

occur. Graham showed hospital mortality rates to be significantly higher in diabetic ICU

patients with peak glucose values below 7.2 mmol/l compared with non-diabetic patients

in the same glucose range (P = 0.004) 29. Krinsley reported similar findings for diabetic

versus non-diabetic patients with mean glucose values under 6.6 mmol/l 47. Both studies

show unadjusted results only. The finding that diabetic patients show higher absolute

mortality rates when having a lower mean glucose during admission was confirmed by

Egi et al. 39 for mean glucose values between 4.4 and 6.1 mmol/l. But when adjusting also

for severity of disease, the significant effect of diabetes on ICU and hospital mortality

in this glucose range disappeared, although a trend remained visible; OR (95% CI) 0.33

(0.10-1.16, P = 0.08) and 0.45 (0.18-1.14, P = 0.09) for non-diabetic patients versus diabetic

patients regarding ICU and hospital mortality, respectively. In the latter study no absolute

mortality difference between the two groups was seen looking at mean glucose values

lower than 4.4 mmol/l.


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These findings show that diabetic patients are not only at risk for severe hypoglycaemia

but also suggest a relative intolerance for normal and hypoglycaemic glucose values

compared with patients without diabetes, although firm evidence is lacking.

V. Glucose variability

Glycaemic variability is a consequence of severe illness and associated with intensive

insulin therapy. Also in diabetic outpatients, shortage of endogenous insulin production,

presence of insulin resistance and/or diminished counter-regulatory responses cause

instability of plasma glucose levels. Whether there is a negative effect of glucose

variability over and above pure hyperglycaemia seems dependent on the patient

population. In various adult and pediatric critically ill populations glucose variability

is strongly associated with mortality independent of the overall glycaemic status 48;57-60,

but the effect of short-term glucose fluctuations in patients with diabetes outside the

hospital remains subject of debate 61. However, no intervention study specifically aiming

at lowering glucose variability in diabetic patients at the ICU has been performed yet,

although emerging data in diabetes outpatients suggest that lowering glucose variability

does not result in improved outcome 62. It is therefore interesting to investigate whether

glucose variability is deleterious in diabetic critically ill patients.

Diabetic postoperative cardiac surgery patients 63 as well as diabetic patients in mixed ICU

populations 48;64 show larger glycaemic variability than non-diabetic patients. Only two

studies looked at the effect of glucose variability in critically ill diabetic patients. Egi et al.

did not find increasing mortality in quartiles of increasing glucose variability (assessed as

standard deviation) and ICU or hospital survival in 728 diabetic patients in a mixed ICU 28, except for an univariate comparison of mortality rates between the lowest and highest

glucose variability quartile, that is a standard deviation lower than 1.7 mmol/l versus 2.5-3.5

mmol/l, respectively (P = 0.002). In the non-diabetic population in this study the standard

deviation was an independent and strong predictor for mortality. Krinsley showed no

association between glucose variability (assessed as coefficient of variation) and mortality

in multivariate analysis of 942 diabetic predominantly medical ICU patients 48, which is in

contrast with their earlier findings in a population with only 23.8% patients with diabetes 58. There was however a marked increase in mortality across increasing coefficient of

variation strata in the subgroup of patients with the lowest mean glucose values (3.9-5.5

mmol/l). This is possibly due to the occurrence of hypoglycaemia, increasing both glucose

variability and mortality, but no analysis was presented adjusting for hypoglycaemia.

In conclusion, it is evident that patients with diabetes have higher glucose variability

during ICU stay but high glucose variability seems to be less harmful than in non-diabetic

164

patients. These results are in line with the observation that hyperglycaemia is more

detrimental in non-diabetic patients. It has to be noted though that no intervention

studies looking at the effect of specifically lowering glucose variability have been

performed in critically ill diabetic as well as in non-diabetic patients.

VI. The role of continuous glucose monitoring in the ICU

At this time glucose control is practiced by means of frequent point-of-care measurements.

Given the critical role of hypoglycaemia and glucose variability, the lack of information

in between those measurements may be of importance. Continuous glucose monitoring

(CGM) could be a useful tool in ICU glucose regulation by decreasing severe hypoglycaemia

frequency 65 and possibly increasing time in target range. However, accuracy results of

subcutaneous CGM systems are inconsistent and seem dependent on the population and

type of sensor used 66-69, but recent results in cardiac surgery and medical ICU patients

are promising 68;69. In addition to subcutaneous glucose monitoring, also intra-vascular

glucose monitoring devices are being developed with good results regarding accuracy 70. Future studies should investigate the benefit of these systems.

VII. Summary

As a consequence of the increasing incidence of diabetes throughout the world, the

number of diabetic patients admitted at the ICU is growing. This needs attention since

their treatment is in some aspects different from patients without diabetes. Nearly all

studies show that diabetic patients suffer from more complications and have longer

ICU and hospital length of stay. Mortality rates however are not simply higher. Diabetic

cardiac surgery patients do have a decreased survival as compared to non-diabetic

patients but data from medical ICU populations do not show a difference in mortality

between diabetic and non-diabetic patients. In mixed populations there is even evidence

that diabetic patients are relatively protected, however the data is not conclusive as there

are also studies showing equal or increased mortality rates. A meta-analysis on this topic

is needed. Hyperglycaemia is common in diabetic critically ill patients. There is discussion

about the association between hyperglycaemia and mortality in this patient group but

severe hyperglycaemia, above 11.1 mmol/l, is considered harmful and it seems useful

to lower these high values with insulin therapy. Considering the currently available

data on implementing insulin therapy, moderate glycaemic control is equally effective

in reducing mortality compared with strict glycaemic control, the latter increasing

hypoglycaemia substantially. This is of concern since diabetic patients are more prone

to develop severe hypoglycaemia which is associated with mortality and it is suggested


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that they are already intolerant for glucose values considered normoglycaemic, although

conclusive evidence is lacking. Therefore we recommend to treat critically ill diabetic

patients with moderately intensive insulin therapy aiming at blood glucose levels

between 7.5 and 10.0 mmol/l, in that way avoiding extreme hyperglycaemia as well as

normo- and hypoglycaemia. Currently there are insufficient arguments to specifically

lower glucose variability, but intervention trials on this topic are awaited.

Our conclusions regarding the glucose target for ICU admitted diabetic patients support

the recommendations of the American Association of Clinical Endocrinologists and

the American Diabetes Association 71, that for critically ill patients in general insulin

treatment should be initiated at a threshold of 10.0 mmol/l and a glucose range of 7.8

to 10.0 mmol/l should be maintained. This consensus statement does not distinguish

between patients with or without previously diagnosed diabetes. We think that it is

important to add the presence of diabetes to the clinical situations that increase the

risk for hypo- and hyperglycaemia.

Practice Points

- Diabetic patients are at high risk for developing complications in the ICU

- Glucose control needs attention also in diabetic patients

- We recommend to maintain glucose levels between 7.5 and 10.0 mmol/l in diabetic

patients

- Hypoglycaemia and extreme hyperglycaemia should be vigorously avoided since these

are associated with mortality

- Currently there are insufficient arguments to specifically lower glucose variability

Research Agenda

- A meta-analysis has to be performed to objectify the influence of diabetes on mortality

risk in (subgroups of) critically ill patients

- Larger trials are needed to assess the effect of intensive insulin therapy in different

critically ill diabetic populations

- The threshold level below which glucose values are harmful in critically ill diabetic

patients needs to be determined

- Trials are needed to assess the effect of specifically lowering glucose variability

166

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68. Siegelaar SE, Barwari T, Hermanides J, Stooker W, van der Voort PHJ, Devries JH (2011) Accuracy and reliability of continuous glucose monitoring at the ICU; a head to head comparison of two subcutaneous glucose sensors in cardiac surgery patients. Diabetes Care 34: e31

69. Brunner R, Kitzberger R, Miehsler W, Herkner H, Madl C, Holzinger U (2011) Accuracy and reliability of a subcutaneous continuous glucose-monitoring system in critically ill patients*. Crit Care Med

70. Skjaervold NK, Solligard E, Hjelme DR, Aadahl P (2011) Continuous measurement of blood glucose: validation of a new intravascular sensor. Anesthesiology 114: 120-125

71. Moghissi ES, Korytkowski MT, DiNardo M, et al (2009) American Association of Clinical Endocrinologists and American Diabetes Association consensus statement on inpatient glycemic control. Diabetes Care 32: 1119-1131


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Chapter 12

The effect of diabetes on mortality in critically ill patients; a systematic review and meta-analysis

Sarah E. Siegelaar, Maartje Hickmann, Joost B.L. Hoekstra,

J. Hans DeVries and Frits Holleman


172

Abstract

Objective: Critically ill patients with diabetes are at increased risk for development

of complications but the impact of diabetes on mortality is unclear. We conducted a

systematic review and meta-analysis to determine the effect of diabetes on short-term

mortality in critically ill patients, making a distinction between different ICU types.

Data Sources: We performed an electronic search of MEDLINE and EMBASE for

observational as well as intervention studies that reported on mortality of adult ICU

patients from May 2005 to May 2010.

Study Selection: Two reviewers independently screened the 3,220 publications

obtained for information regarding ICU, hospital or 30-day mortality of patients

with and without diabetes. We included 141 studies containing 12,489,574 patients,

including 2,705,624 deaths (21.7%). Of these patients at least 2,327,178 (18.6%) had

diabetes.

Data Extraction: The number of deaths among patients with and without diabetes

and/or mortality risk associated with diabetes was extracted. When only crude

survival data were provided, odds ratios (OR) and 95% confidence intervals (CI) were

calculated.

Data Synthesis: We used inverse variance with OR’s as the effect measure. A random

effects model was used because of anticipated heterogeneity. Overall no association

between diabetes and mortality risk was found. Analysis for ICU type showed a

disadvantage for patients with diabetes for all mortality definitions when admitted

at the surgical ICU (ICU mortality: OR [CI] 1.48 [1.04-2.11]; hospital mortality: 1.59

[1.28-1.97]; 30-day mortality: 1.62 [1.13-2.34]). In medical and mixed ICU’s no effect of

diabetes was seen. Sensitivity analysis showed that the disadvantage in the diabetic

surgical population was attributable to cardiac surgery (1.77 [1.45-2.16], P <0.00001)

and not to general surgery patients (1.21 [0.96-1.53], P = 0.11).

Conclusions: This meta-analysis showed that diabetes was not associated with

increased mortality risk in any ICU population except for those who underwent

cardiac surgery.

The effect of diabetes on mortality in critically ill patients

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Introduction

The proportion of patients with diabetes admitted to the ICU is growing as a result of

the worldwide increase in type 2 diabetes. In cardiac surgery patients the growth is even

more pronounced since patients with diabetes-associated, often complex multivessel

macrovascular complications are preferably treated with surgery rather than by

percutaneous intervention 1;2. It is known that diabetic patients admitted to the ICU are

more prone to develop complications 3-5, at least in part due to hampered immune cell

function associated with the disease 6;7. One would expect an increased mortality rate for

diabetic ICU patients but the literature is at this point conflicting, reporting increased,

equal or even decreased mortality rates compared to patients without diabetes. Also

there might be a difference in outcome between various ICU populations, for example

cardiac surgery and medical patients.

To better understand the role of diabetes in critical illness, we conducted a systematic

review and meta-analysis regarding short-term mortality, including observational as

well as intervention studies that reported ICU, hospital or 30-day mortality rates of ICU

admitted patients with diabetes.

Materials and methods

This study was conducted according to the recommendations of the Meta-analysis of

Observational Studies in Epidemiology (MOOSE) group 8.

Data sources and search strategyTogether with the clinical librarian at our institution, an electronic search of MEDLINE

and EMBASE from May 1st 2005 to May 1st 2010 was performed for observational as well as

intervention studies that reported on mortality of adult ICU patients. The five-year limit

was chosen because we expected that insulin treatment regimens and other therapies

would be comparable among studies in this rapidly evolving field. Text terms and medical

subheading (MeSH) terms for ‘intensive care unit’, ‘critical care’ and ‘mortality’ were

combined. Since a preliminary search showed that diabetes was not always the primary

interest of included studies and to prevent publication bias, ‘diabetes mellitus’ as a

search term was not used to narrow the search. To avoid possible treatment induced

bias, randomised controlled trials (RCT) comparing intensive insulin therapy regimes

were excluded 9. We limited our search to research performed in humans, use of English

language and adult populations. Unpublished studies were not identified.

174

Study selection Two reviewers (SES and MH) independently screened the records. Agreement on final

inclusion was reached by consensus. Inclusion and exclusion criteria were defined a priori.

A study was included when it reported crude ICU, hospital and/or 30-day mortality of ICU

admitted patients with and without diabetes and/or univariate or multivariate analysis

of mortality risk of diabetic patients represented as odds ratio (OR), hazard ratio (HR) or

relative risk (RR). Studies reporting 28-day or 30-day mortality rates were combined. We

excluded studies where diabetes was the reason for ICU admittance, i.e. ketoacidosis,

as well as studies concerning patients with gestational diabetes and ICU readmissions.

When it was not possible to obtain the full manuscript from our institutional library

or the internet, the corresponding author was contacted if the population included in

the study was larger than 500 patients.

Data extractionFrom the included publications the following data were extracted: first author, year

of publication, country where the work was performed, study design, type of ICU,

population specification, reported mortality type, definition of diabetes and the

number of patients with and without diabetes. Subsequently the number of deaths

among the patients with and without diabetes and/or the mortality risk associated

with the presence of diabetes was collected. We contacted the corresponding author

for additional information when the publication reported only a P-value for mortality

risk or when the exact number or proportion of patients with diabetes was not

reported.

Study qualityNo individual assessment of study quality was performed. We did not expect bias in

outcome reporting since death is a robust endpoint and it is unlikely that patients were

lost to follow-up as the primary outcome was counted in the hospital.

Data synthesis and statistical methodsThe meta-analysis was performed using Review manager version 5 (The Cochrane

Collaboration, Oxford, UK). Analyses were performed separately for ICU, hospital and

30-day mortality as outcome variables. Data were synthesised using inverse variance

with odds ratios (OR) as the effect measure. In the primary analysis unadjusted results

were used where possible. When only crude mortality data were provided, the OR, 95%

confidence interval (CI) and standard error (SE) were calculated. An OR >1 suggested that

diabetes was associated with an increased risk of death. We stratified the analyses by

ICU type: trauma, surgical, medical and mixed ICU’s. Data were pooled using a random

effects model because heterogeneity between studies was anticipated. Heterogeneity

was assessed using the I2 statistic.


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We performed sensitivity analyses in order to explore the robustness of the data and the

influence of the following factors on effect size by repeating the analysis 1) using a fixed

instead of a random model, 2) making distinction between cardiac and other types of

surgery and 3) for studies reporting unadjusted as well as adjusted outcomes to assess

the possible influence of confounders.

Results

Figure 1 summarises the study identification and selection process. After removal of

duplicates in the EMBASE and MEDLINE search, 3,220 potentially interesting records were

identified. Of these, we excluded 556 publications by review of the abstract. After review

of 2,664 full articles, 2,520 were excluded: 1,814 because no data regarding diabetes was

available at all, 579 because mortality data was not available, 35 publications reported

only on long-term mortality and 2 duplicate publications were identified. We were not

able to obtain the full texts of 90 potentially relevant records, of which 14 reported a

sample size of more than 500 participants. Three records were excluded by data extraction

as only a P-value for mortality was reported and after contacting the authors the raw

data could not be retrieved 10-12. Finally, 141 studies could be included in the analysis of

ICU mortality (n = 50 13-62), hospital mortality (n = 74 26;63-135) and 30-day mortality (n = 20 65;128;136-153). Three studies showed results of two outcome types; one reporting both ICU

and hospital mortality 26 and two reporting both hospital and 30-day mortality 65;128. Only

a few studies specified the type of diabetes of the patients included (n = 26). In most of

these studies diabetes was defined by use of glucose lowering drugs. Table 1 contains

the characteristics of the 141 included studies.

ICU mortality The analysis of ICU mortality contained 52,908 patients, including 7,576 deaths (Figure

2). Of those patients, at least 8,852 were diagnosed with diabetes (16.7%). Of two studies

the number of patients with diabetes could not be retrieved 59;62. Overall, pooling of the

data showed no survival advantage for either group (OR 1.03, CI 0.87-1.22, P = 0.74, I2 =

64%). Analysis per ICU type showed that in the surgical ICU patients without diabetes

did have a survival benefit over the patients with diabetes (OR 1.48, CI 1.04-2.11, P = 0.03,

I2 = 0%). No differences were observed in the trauma, medical and mixed ICU’s.

Hospital mortality The largest cohort could be analysed for hospital mortality: 12,403,355 patients (mortality

rate 21.7%) of whom 2,313,466 (18.7%) with diabetes (Figure 3). Pooling of all data did show

a small mortality increase for the patients with diabetes (OR 1.08, CI 1.00-1.15, p=0.04,

I2=70%). The disadvantage for patients with diabetes was attributable to the effect in

176

the surgical (OR 1.59, CI 1.28-1.97, P <0.0001, I2 = 56%) and trauma (OR 1.23, CI 1.12-1.36,

P <0.0001, I2 = 0%) population after stratifying to ICU type. For the medical and mixed

ICU’s no difference in outcome between the groups was seen.

30-day mortalityA total of 4,860 (25.5%) patients with diabetes and 14,180 patients without diabetes

were included in this analysis (Figure 4). For one study, including 14,271 patients, the

proportion of patients with and without diabetes could not be retrieved 152. Overall there

was no mortality difference between the patients with and without diabetes (OR 1.19,

CI 0.96-1.47, P = 0.10, I2 = 65%). Stratifying the data for ICU type showed a difference in

mortality favouring the patients without diabetes in the surgical ICU (OR 1.62, CI 1.13-

2.34, P = 0.009, I2 = 54%). Outcome did not differ between groups in medical and mixed

ICU’s. No separate analysis of trauma patients could be performed since none of the

studies reported 30-day mortality outcome in a trauma ICU.

Sensitivity analysesFor ICU and 30-day mortality the effect pointed to the same direction when using a

fixed instead of a random model; no benefit for patients with or without diabetes in

the trauma, medical and mixed population and a significant disadvantage for surgical

patients with diabetes. Regarding hospital mortality, the effect in the mixed and medical

ICU shifted towards an advantage for diabetes patients (medical: 0.89 [0.85-0.92], P

<0.00001; mixed: 0.90 [0.88-0.92], P <0.00001). This effect was attributable to the weight

of two very large studies. In the medical population the study of Martin et al. 101 had an

overall weight of 17.5% and in the mixed population the study of Graham et al. 26 weighed

69.9%. Performing the fixed analysis without these two large studies resulted in the same

conclusions as the random analysis; that is no effect.

For all outcomes we observed a mortality benefit for non-diabetic subjects in the

surgical ICU. This population consisted of cardiac as well as general surgery patients,

quite distinct regarding etiology of the underlying disease as compared to other ICU

populations. We performed a sensitivity analysis investigating whether the effect of

diabetes would be different for cardiac surgery or general surgery patients. Since the

effect was the same for ICU- hospital as well as 30-day mortality outcome and to increase

power, we performed the sensitivity analysis combining all surgical studies regardless

of mortality definition. This analysis showed that the increased mortality in patients

with diabetes in the surgical ICU was mainly attributable to the studies including

cardiac surgery patients (OR 1.77, CI 1.45-2.16, P <0.00001, I2 = 49%). Pooling of the general

surgery study data showed a trend towards higher mortality in diabetic patients (OR

1.21, CI 0.96-1.53, P = 0.11, I2 = 30%).


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Most studies reported only crude mortality data or unadjusted regression analyses. To

assess the possible influence of confounders we first looked at the data of five studies

reporting unadjusted as well as adjusted results for hospital mortality 26;65;75;95;121. Among

other parameters, these studies adjusted for severity of disease. Overall, no difference

in effect size was observed when pooling the unadjusted results (OR 1.06 [0.83-1.35]) vs.

the adjusted results (OR 1.02 [0.79-1.34]). Introduction of adjusted instead of unadjusted

data in the analysis of hospital mortality did not change the outcome except for a shift

towards non-significance in the analysis of the trauma patients (1.02 [0.93-1.13], P = 0.45,

I2 = 0%).

Figure 1 Study selection

178

Figure 2 ICU mortality Forest plot showing odds ratio (OR) and 95% confidence intervals (CI) of ICU mortality risk comparing patients with and without diabetes. When ‘0’ is displayed as number of diabetic or non-diabetic patients, the information was not available.


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180

Figure 3 Hospital mortality Forest plot showing odds ratio (OR) and 95% confidence intervals (CI) of hospital mortality risk comparing patients with and without diabetes.

Figure 4 30-day mortality Forest plot showing odds ratio (OR) and 95% confidence intervals (CI) of 30-day mortality risk comparing patients with and without diabetes. When ‘0’ is displayed as number of diabetic or non-diabetic patients, the information was not available.


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Discussion

This large meta-analysis shows no significant difference in mortality between critically

ill patients with and without diabetes, except for a survival advantage for patients

without diabetes admitted to the ICU after cardiac surgery. Sensitivity analyses show

good robustness of the data. If anything, using a fixed model shifts the outcome from

equal survival towards a small benefit for diabetic patients in the medical and mixed

cohorts for hospital mortality which can be attributed to the inclusion of two very large

cohorts 26;101.

It did not come as a surprise to find a negative impact of diabetes on survival in cardiac

surgery patients. It is known that the diabetic cardiac surgery population is different

from the non-diabetic population. As a result of diabetes they are more likely to have

3-vessel coronary artery disease, left main coronary artery stenosis and left ventricular

systolic dysfunction 154;155, all associated with worse outcome.

On the other hand, it is remarkable to find no increased mortality for patients with

diabetes in the medical and mixed populations. Patients with diabetes are known

to have a higher chance of developing complications such as sepsis and acute organ

failure when admitted to the ICU 3-5. These are associated with mortality, at least in

the non-diabetic population. The increased infection risk is the result of immune cell

dysfunction associated with the disease 6;7. Apparently diabetes is only a risk factor for

the development of complications but once acquired, mortality risk is equal or perhaps

even lower.

A possible explanation for the relative protection of critically ill patients with diabetes

could lie in the different effects of stress-induced hyperglycaemia in the two groups.

Hyperglycaemia is common in critically ill patients and not only in those with diabetes.

It is associated with mortality 156 but it has been shown that patients with diabetes are

less affected by high glucose levels compared to their non-diabetic companions 26;77;157;158.

It might be that the relative protection from stress-induced hyperglycaemia counteracts

the increased mortality risk due to an increased amount of complications. Further studies

are needed to unravel the exact effects of hyperglycaemia in patients with and without

diabetes.

Due to the nature of the included studies we mainly show unadjusted results. It might

be possible that the baseline characteristics between patients with and without diabetes

were different, influencing the outcome. If there were a difference, it would seem likely

that patients with diabetes were more severely ill at admission. Adjusting for severity

of illness would have decreased the OR’s, as was shown in the sensitivity analysis,

182

thus indicating only greater advantage for patients with diabetes. This effect is seen

in the hospital mortality outcome of the trauma population. It might be possible that

adjustment for severity of disease decreases the negative effect of diabetes now seen

in cardiac surgery patients. However, our results represent the mortality risk of the

average patient with and without diabetes, irrespective of the differences between the

populations associated with diabetes.

There are a few limitations associated with this analysis. First, the published data do

not allow differentiating between type 1 and type 2 diabetes or between insulin treated

and non-insulin treated diabetes, and it may be possible that there are differences in

outcomes between these groups. Second, we could not retrieve mortality data of three

studies. It is unlikely that including these studies could have shifted the results to a

disadvantage for patients with diabetes, since the P-values for mortality related to the

presence of diabetes in these studies were not significant. Third, of 90 potentially relevant

studies the full manuscript could not be retrieved. These studies have included together

48,263 patients, which would have contributed only 0.39% to the total population, so

little influence on the outcome is expected.

Conclusions

We show in this meta-analysis that patients with diabetes who are admitted at the

medical, mixed and trauma ICU have similar chances of survival compared to patients

without diabetes. Diabetes only significantly increases mortality risk in patients

admitted after surgery, more specifically after cardiac surgery, a population with distinct

characteristics of the underlying disease. Further studies are needed to unravel the

pathophysiological mechanisms by which patients with diabetes seem to be protected

in non-surgical settings, despite encountering higher complication rates.

AcknowledgementsWe acknowledge the contributions of Heleen C. Dyserinck, Clinical Librarian, Academic

Medical Centre, Amsterdam, for her help performing the search and Rob J. Scholten,

department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical

Centre, Amsterdam, for his excellent support in the data extraction and synthesis. None

of the authors report a conflict of interest relevant to the content of the manuscript. No

funding was involved in preparation of the manuscript.


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Tab

le 1

Ch

arac

teri

stic

s o

f th

e 14

1 st

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incl

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l va

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surg

ery

+ >7

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spec

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30d

315

21.9

n.s

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Alm

og20

06Is

rael

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ical

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CH

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12Pr

osp

ecti

ve30

d78

42.3

n.s

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2007

Ch

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dia

c su

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spec

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Hos

pit

al24

20.8

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dri

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2009

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pec

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ICU

170

31.2

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2005

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151

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106

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200

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2007

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nM

ixed

VAP

Ret

rosp

ecti

veH

osp

ital

183

25.1

Req

uir

ing

trea

tmen

t

Gam

ach

o-M

2008

Spai

nM

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Cat

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er r

elat

ed B

SIPr

osp

ecti

veH

osp

ital

6619

.7n

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Geo

rges

2009

Fran

ceM

edic

alA

cute

bac

teri

al m

enin

giti

sR

etro

spec

tive

ICU

8222

.0n

.s.

Gon

g20

06U

SAM

edic

alA

RD

SPr

osp

ecti

veIC

U21

115

.6n

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Gra

ham

2010

USA

Mix

edn

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Ret

rosp

ecti

veH

osp

ital

1,50

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NC

Pros

pec

tive

ICU

, hos

pit

al36

,414

16.3

n.s

.; n

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KA

, HO

NC


Ch

apte

r 12

185

Gri

nia

stos

2008

Gre

ece

Surg

ical

Ch

olec

yste

ctom

yPr

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ecti

veH

osp

ital

2420

.8n

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Hay

s20

06U

SAM

edic

alIs

chem

ic s

trok

eR

etro

spec

tive

Hos

pit

al23

522

.6n

.s.

Hen

ckae

rts

2009

Bel

giu

mM

edic

aln

.s.

Ret

rosp

ecti

veH

osp

ital

774

17.1

Insu

lin

, OA

D o

r d

iet

Hol

ley

2009

Wor

ldw

ide

Mix

edC

and

idem

iaR

etro

spec

tive

Hos

pit

al18

923

.3n

.s.

Hsu

2009

Taiw

anM

edic

alSe

pti

c m

enin

giti

sR

etro

spec

tive

Hos

pit

al40

57.5

n.s

.

Irib

arre

n-D

2009

Spai

nM

ixed

Mix

edPr

osp

ecti

veH

osp

ital

377

11.9

n.s

.

Jam

al20

09K

uw

ait

Mix

edA

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man

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ctio

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veH

osp

ital

2429

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Jen

q20

07Ta

iwan

Med

ical

Cir

rhos

isPr

osp

ecti

veH

osp

ital

134

25.4

n.s

.

Kao

2006

USA

Trau

ma

n.s

.R

etro

spec

tive

Hos

pit

al34

3,25

02.

7ID

DM

+ N

IDD

M

Kar

imi

2008

Iran

Surg

ical

CA

BG

Cro

ss-

sect

ion

al30

d8,

890

33.6

n.s

.

Kem

arre

c20

08Fr

ance

Surg

ical

Abd

omin

al s

epsi

sPr

osp

ecti

veIC

U27

33.3

n.s

.

Ken

nea

lly

2007

USA

Mix

edC

. dif

fici

le i

nfe

ctio

nR

etro

spec

tive

30d

278

28.1

n.s

.

Kes

2007

Cro

atia

Med

ical

Isch

emic

str

oke

Ret

rosp

ecti

ve30

d63

034

.9n

.s.

Kri

nsl

ey20

07U

SAM

ixed

n.s

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etro

spec

tive

Hos

pit

al5,

365

20.7

n.s

.

Labe

lle

2008

USA

Med

ical

Can

did

a B

SIR

etro

spec

tive

ICU

111

34.2

n.s

.

Lan

2006

Taiw

anM

edic

alIs

chem

ic s

trok

eR

etro

spec

tive

ICU

233

32.2

n.s

.

Lee

2007

Kor

eaM

edic

alC

AP

Ret

rosp

ecti

veH

osp

ital

8527

.1n

.s.

Lem

ant

2008

Fran

ceM

edic

alC

hik

un

guya

in

fect

ion

Pros

pec

tive

ICU

3333

.3n

.s.

Lero

y20

09Fr

ance

Mix

edIn

vasi

ve C

and

ida

infe

ctio

nPr

osp

ecti

veIC

U26

810

.8T

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1

Liao

2009

Taiw

anM

ixed

AR

DS

Pros

pec

tive

Hos

pit

al17

225

.0n

.s.

Lin

(a)

2009

Taiw

anM

edic

aln

.s.

Pros

pec

tive

Hos

pit

al20

138

.3n

.s.

Lin

(b)

2009

Taiw

anM

edic

alPu

lmon

ary

TBC

+ R

FR

etro

spec

tive

Hos

pit

al59

22.0

n.s

.

Lore

nte

2009

Spai

nM

edic

alSe

psi

sPr

osp

ecti

veIC

U19

225

.5n

.s.

Lun

del

in20

10Sp

ain

Mix

edn

.s.

Pros

pec

tive

ICU

3828

.9In

suli

n, O

AD

or

die

t

Mar

kogi

ann

akis

2009

Gre

ece

Surg

ical

Infe

ctio

nPr

osp

ecti

veIC

U12

315

.4n

.s.

Mar

tin

2006

USA

Med

ical

Sep

sis

Ret

rosp

ecti

veH

osp

ital

10,4

22,3

0118

.6n

.s.

Mar

tin

-L20

10Eu

rop

eM

edic

alC

AP

+ in

tuba

tion

Pros

pec

tive

ICU

137

24.1

n.s

.

186

Au

tho

rYe

arC

ou

ntr

yIC

U t

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Pop

ula

tio

nD

esig

nM

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alit

yN

pat

ien

ts%

DM

Dia

bet

es t

ype

Mas

cia

2008

Euro

pe

Mix

edN

euro

logi

cal

Ret

rosp

ecti

veIC

U27

36.

2n

.s.

Mit

chel

l20

06A

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rali

a/N

ZL

Mix

edn

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Pros

pec

tive

Hos

pit

al93

317

.5In

cl. i

nsu

lin

tr

eatm

ent

Mow

ery

(a)

2009

USA

Surg

ical

Gen

eral

su

rger

yPr

osp

ecti

veH

osp

ital

925

10.7

Typ

e 1

+ 2

Mow

ery

(b)

2009

USA

Trau

ma

Bra

in i

nju

ryPr

osp

ecti

veH

osp

ital

106

16.3

IDD

M +

NID

DM

Mu

rph

y20

09U

SAM

edic

alA

LI +

sep

tic

shoc

kR

etro

spec

tive

Hos

pit

al21

232

.1n

.s.

Mu

rth

y20

07U

SASu

rgic

alC

ard

iac

surg

ery

Ret

rosp

ecti

veIC

U68

614

.6In

suli

n, O

AD

or

die

t

Mu

sci

2009

Ger

man

ySu

rgic

alE

nd

ocar

dit

ic s

urg

ery

Ret

rosp

ecti

ve30

d22

122

.2n

.s.

Nai

r20

09U

SASu

rgic

alLi

ver

tran

spla

nta

tion

Pros

pec

tive

Hos

pit

al19

328

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isto

ry, i

nsu

lin

/O

AD

, HbA

1c >

7%

Nas

raw

ay20

06U

SASu

rgic

al>4

day

s IC

UR

etro

spec

tive

ICU

393

20.1

n.s

.

Nse

ir20

06Fr

ance

Med

ical

CO

PD +

>48

h M

VPr

osp

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veIC

U65

917

.8n

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On

g20

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aum

a>3

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s IC

UR

etro

spec

tive

ICU

205

20.0

n.s

.

Pate

l (a

)20

09U

SAM

edic

alC

and

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sep

tic

shoc

kR

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spec

tive

ICU

3135

.5n

.s.

Pate

l (b

)20

09U

SASu

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alC

ard

iac

surg

ery

+ w

oun

d i

nf.

Ret

rosp

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ve30

d12

456

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suli

n o

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AD

Pau

l 20

10Is

rael

Mix

edIn

fect

ion

Pros

pec

tive

30d

495

30.1

n.s

.

Pier

acci

2008

USA

Surg

ical

Surg

ery

Pros

pec

tive

Hos

pit

al94

64.

3n

.s.

Prat

ikak

i20

09G

reec

eM

ixed

Blo

odst

ream

in

fect

ion

Pros

pec

tive

ICU

148

18.9

n.s

.

Qu

ach

2009

Can

ada

Mix

edn

.s.

Ret

rosp

ecti

veH

osp

ital

3,77

812

.4IC

D-1

0 co

de

Rad

y20

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SAM

ixed

n.s

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spec

tive

Hos

pit

al52

219

.7n

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Ram

mae

rt20

09Fr

ance

Med

ical

CO

PD +

MV

Pros

pec

tive

ICU

116

19.8

n.s

.

Ran

ucc

i20

08It

aly

Surg

ical

Car

dia

c su

rger

yR

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spec

tive

Hos

pit

al4,

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17.8

Insu

lin

or

OA

D

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nta

m20

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iaM

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n.s

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osp

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veIC

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611

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Rel

los

2006

Gre

ece

Mix

ed>9

0yPr

osp

ecti

veH

osp

ital

6025

.0n

.s.

Rh

odes

2006

UK

Mix

edn

.s.

Pros

pec

tive

ICU

5213

.5n

.s.

Ria

chy

2008

Leba

non

Med

ical

Seve

re s

trok

ePr

o-/

retr

osp

ecti

veIC

U62

27.4

n.s

.


Ch

apte

r 12

187

Riv

ero-

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Hos

pit

al10

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26.9

n.s

.

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teri

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Ret

rosp

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veIC

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423

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2

Ros

amel

2005

Fran

ceSu

rgic

alEn

doc

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itic

su

rger

yR

etro

spec

tive

Hos

pit

al98

9.2

n.s

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Ros

e20

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alM

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ve t

ran

sfu

sion

Ret

rosp

ecti

veH

osp

ital

204

8.8

n.s

.

Ru

iz-B

2005

Spai

nM

edic

alA

MI

pos

t th

rom

boly

sis

Ret

rosp

ecti

veIC

U15

123

.8n

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Sail

ham

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SAM

edic

alC

. dif

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olit

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etro

spec

tive

Hos

pit

al19

929

.6n

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Sakr

2006

Euro

pe

Mix

edn

.s.

Pros

pec

tive

ICU

1,05

87.

7n

.s.

Sakr

2008

Euro

pe

Mix

edn

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Pros

pec

tive

Hos

pit

al1,

729

7.0

n.s

.

San

tin

i20

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Sep

sis

Ret

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veIC

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surg

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osp

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120

47.5

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mol

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eds

Sen

thu

ran

2008

Au

stra

lia

Med

ical

ESR

FR

etro

spec

tive

ICU

7044

.3n

.s.

Shah

2007

USA

Med

ical

n.s

.Pr

osp

ecti

veH

osp

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179

27.4

n.s

.

Silv

a20

06B

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lM

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AR

FR

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spec

tive

Hos

pit

al12

89.

4n

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Slei

man

2008

Ital

yM

edic

aln

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Ret

rosp

ecti

veH

osp

ital

1,15

528

.8H

isto

ry o

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edic

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n

Smit

h20

07U

SASu

rgic

alTr

aum

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yPr

osp

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veH

osp

ital

807

16.5

IDD

M +

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DM

Steg

enga

2010

Wor

ldw

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Med

ical

Sep

sis

Ret

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ecti

ve30

-day

830

22.7

n.s

.

Svir

cevi

c20

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nd

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rgic

alC

ard

iac

surg

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Ret

rosp

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veH

osp

ital

7,98

919

.8n

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Swob

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2008

Ger

man

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rgic

alSe

psi

s/se

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Ret

rosp

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veIC

U70

25.7

n.s

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Thie

lman

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Surg

ical

Car

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c su

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yPr

osp

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veH

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ital

254

31.1

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Tsai

2008

Taiw

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alE

CM

O +

acu

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sis

Pros

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tive

Hos

pit

al10

417

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Tsen

g (a

)20

09Ta

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Med

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Pan

crea

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s +

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Ret

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.

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)20

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SAM

edic

alPn

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Ret

rosp

ecti

veH

osp

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406

26.6

n.s

.

Vale

nte

2008

Ital

yM

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alST

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U31

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n.s

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Van

dij

ck20

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Hos

pit

al15

217

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2005

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U24

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n.s

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le20

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edic

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Ret

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2005

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21.2

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.

Wil

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2005

Au

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CA

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tive

ICU

9614

.6n

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Wu

2008

Ch

ina

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Ret

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Hos

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543

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2006

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335

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Yu20

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Pros

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ICU

4032

.5n

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Zan

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247

un

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2009

USA

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112. Rivero-Ayerza M, Scholte Op RW, Lenzen M, et al (2008) New-onset atrial fibrillation is an independent predictor of in-hospital mortality in hospitalized heart failure patients: results of the EuroHeart Failure Survey. Eur Heart J 29: 1618-1624

113. Rosamel P, Cervantes M, Tristan A, et al (2005) Active infectious endocarditis: postoperative outcome. J Cardiothorac Vasc Anesth 19: 435-439

114. Rose AH, Kotze A, Doolan D, Norfolk DR, Bellamy MC (2009) Massive transfusion--evaluation of current clinical practice and outcome in two large teaching hospital trusts in Northern England. Vox Sang 97: 247-253

115. Sailhamer EA, Carson K, Chang Y, et al (2009) Fulminant Clostridium difficile colitis: patterns of care and predictors of mortality. Arch Surg 144: 433-439

116. Sakr Y, Vincent JL, Ruokonen E, et al (2008) Sepsis and organ system failure are major determinants of post-intensive care unit mortality. J Crit Care 23: 475-483

117. Sayed S, Fischer S, Karck M, Hassouna A, Haverich A (2009) Effect of different preoperative patient characteristics on coronary surgery outcome: a comparative study between a developing and a developed country. J Card Surg 24: 275-280

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119. Silva Junior GB, Daher EF, Mota RM, Menezes FA (2006) Risk factors for death among critically ill patients with acute renal failure. Sao Paulo Med J 124: 257-263

120. Sleiman I, Morandi A, Sabatini T, et al (2008) Hyperglycemia as a predictor of in-hospital mortality in elderly patients without diabetes mellitus admitted to a sub-intensive care unit. J Am Geriatr Soc 56: 1106-1110

121. Smith RL, Chong TW, Hedrick TL, et al (2007) Does body mass index affect infection-related outcomes in the intensive care unit? Surg Infect 8: 581-588

122. Svircevic V, Nierich AP, Moons KG, Brandon Bravo Bruinsma GJ, Kalkman CJ, van DD (2009) Fast-track anesthesia and cardiac surgery: a retrospective cohort study of 7989 patients. Anesth Analg 108: 727-733

123. Thielmann M, Massoudy P, Neuhauser M, et al (2006) Prognostic value of preoperative cardiac troponin I in patients undergoing emergency coronary artery bypass surgery with non-ST-elevation or ST-elevation acute coronary syndromes. Circulation 114: I448-I453

124. Tsai CW, Lin YF, Wu VC, et al (2008) SAPS 3 at dialysis commencement is predictive of hospital mortality in patients supported by extracorporeal membrane oxygenation and acute dialysis. Eur J Cardiothorac Surg 34: 1158-1164

125. Tseng CC, Fang WF, Huang KT, et al (2009) Risk factors for mortality in patients with nosocomial Stenotrophomonas maltophilia pneumonia. Infect Control Hosp Epidemiol 30: 1193-1202

126. Vandijck DM, Oeyen SG, Buyle EM, Claus BO, Blot SI, Decruyenaere JM (2008) Hyperglycaemia upon onset of ICU-acquired bloodstream infection is associated with adverse outcome in a mixed ICU population. Anaesth Intensive Care 36: 25-29

127. Vardakas KZ, Michalopoulos A, Kiriakidou KG, Siampli EP, Samonis G, Falagas ME (2009) Candidaemia: incidence, risk factors, characteristics and outcomes in immunocompetent critically ill patients. Clin Microbiol Infect 15: 289-292

128. Vasile VC, Babuin L, Rio Perez JA, et al (2009) Long-term prognostic significance of elevated cardiac troponin levels in critically ill patients with acute gastrointestinal bleeding. Crit Care Med 37: 140-147

129. Vincent JL, Rello J, Marshall J, et al (2009) International study of the prevalence and outcomes of infection in intensive care units. JAMA 302: 2323-2329

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130. Wasir H, Mehta Y, Pawar M, et al (2006) Predictors of operative mortality following primary coronary artery bypass surgery. Indian Heart J 58: 144-148

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132. Ylipalosaari P, Ala-Kokko TI, Laurila J, Ohtonen P, Syrjala H (2006) Intensive care acquired infection is an independent risk factor for hospital mortality: a prospective cohort study. Crit Care 10: R66

133. Yoshimoto A, Nakamura H, Fujimura M, Nakao S (2005) Severe community-acquired pneumonia in an intensive care unit: risk factors for mortality. Intern Med 44: 710-716

134. Zangrillo A, Landoni G, Fumagalli L, et al (2006) Methicillin-resistant Staphylococcus species in a cardiac surgical intensive care unit: a 5-year experience. J Cardiothorac Vasc Anesth 20: 31-37

135. Zarrilli R, Casillo R, Di PA, et al (2007) Molecular epidemiology of a clonal outbreak of multidrug-resistant Acinetobacter baumannii in a university hospital in Italy. Clin Microbiol Infect 13: 481-489

136. Ailawadi G, Swenson BR, Girotti ME, et al (2008) Is mitral valve repair superior to replacement in elderly patients? Ann Thorac Surg 86: 77-85

137. Almog Y, Novack V, Megralishvili R, et al (2006) Plasma level of N terminal pro-brain natriuretic peptide as a prognostic marker in critically ill patients. Anesth Analg 102: 1809-1815

138. Chien JY, Jerng JS, Yu CJ, Yang PC (2005) Low serum level of high-density lipoprotein cholesterol is a poor prognostic factor for severe sepsis. Crit Care Med 33: 1688-1693

139. Connelly KA, Creati L, Lyon W, et al (2007) Early and late results of combined mitral-aortic valve surgery. Heart Lung Circ 16: 410-415

140. Cuthbertson BH, Croal BL, Rae D, et al (2009) N-terminal pro-B-type natriuretic peptide levels and early outcome after cardiac surgery: a prospective cohort study. Br J Anaesth 103: 647-653

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146. Kes VB, Solter VV, Supanc V, Demarin V (2007) Impact of hyperglycemia on ischemic stroke mortality in diabetic and non-diabetic patients. Ann Saudi Med 27: 352-355

147. Musci M, Weng Y, Hubler M, et al (2009) Predictors of early mortality in patients with active infective native or prosthetic aortic root endocarditis undergoing homograft aortic root replacement. Clin Res Cardiol 98: 443-450

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154. Waller BF, Palumbo PJ, Lie JT, Roberts WC (1980) Status of the coronary arteries at necropsy in diabetes mellitus with onset after age 30 years. Analysis of 229 diabetic patients with and without clinical evidence of coronary heart disease and comparison to 183 control subjects. Am J Med 69: 498-506

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in critically ill patients varies with admission diagnosis. Crit Care Med 37: 3001-3009158. Rady MY, Johnson DJ, Patel BM, Larson JS, Helmers RA (2005) Influence of individual characteristics on

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Chapter 13

Summary and future considerations

Samenvatting en toekomstperspectief

Authors’ affiliations

List of publications

Dankwoord

Curriculum Vitae

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This thesis is about the effects and treatment of glucose peaks in chronic and acute

hyperglycaemia. It addresses the question whether it is beneficial to curb these glucose

peaks: must all what goes up come down? In Part I, the consequences of glucose

variability in diabetes were studied with respect to oxidative stress, which is associated

with endothelial damage, and two chronic complications of diabetes, neuropathy and

cardiovascular events. Part II of this thesis discusses hyperglycaemia in critical illness.

First, an optimal glucose target range for critically ill patients was proposed and a

new method to reach this target range, subcutaneous continuous glucose monitoring

(CGM), was tested for accuracy and reliability. Second, the implications of the presence

of diabetes in a critical care setting were investigated with respect to glucose regulation

and mortality.

Part IIn Chapter 2 we give an overview of the available methods to measure glucose variability

and review the evidence for its importance in addition to mean glucose. A large variation

in the number and duration of glucose peaks exists between patients with similar

haemoglobin A1c levels, but to date there is no “gold standard” for quantifying glucose

variability. The standard deviation from the mean seems the most extensively used and

mathematically best validated measure. In in vitro, animal and experimental human

studies, glucose peaks increase oxidative stress. However, the evidence for an independent

effect of glucose variability on oxidative stress and long-term diabetic complications in

type 1 and type 2 diabetes patients is marginal, and possibly limited to poorly regulated

type 2 diabetes patients on oral glucose lowering drugs. Contrary, in the critically ill

glucose variability is indisputably associated with mortality.

To understand the different findings of the effect of glucose variability in different

diabetic populations, we investigated in Chapter 3 at which glucose level the glucose-

dependent effects on vascular homeostasis first occur, and whether this is an on-off

phenomenon with a threshold or a continuous relationship. A stepwise glucose clamp

was performed in healthy humans, stabilizing plasma glucose levels for two hours at

6.0, 8.0, and 10.0 mmol/l successively. The effect of increasing glucose on oxidative

stress, coagulation and fibrinolysis, and the endothelial glycocalyx were investigated.

The results of this study reveal that changes to vascular homeostasis start already at

near normal glucose levels. The increase in oxidative stress was dose-dependent and

coagulation activation showed a threshold already at 6.0 mmol/l. Absence of a threshold

or a threshold at such a low level do not support a role for glucose variability in oxidative

stress and coagulation activation.


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This conclusion was supported by the results described in Chapters 4 and 5. In Chapter

4, the relation between glucose variability, measured by continuous glucose monitoring,

and oxidative stress, measured by 24-hr excretion of 8-iso prostaglandin F2α, was assessed

in 24 well-regulated type 2 diabetes patients on oral glucose lowering drugs. No relevant

relationship was found between glucose variability and oxidative stress. In Chapter 5

a mealtime insulin approach was compared with a basal insulin regimen in a cross-

over study including 40 type 2 diabetes patients regarding glucose regulation and the

effects on oxidative stress. Addition of insulin to the patients’ medication significantly

lowered mean glucose as well as oxidative stress. However, again no relationship was

found between glucose variability and oxidative stress.

Oxidative stress is thought to induce vascular complications, but eventually it is an

indirect marker of disease, not a hard outcome. In Chapter 6 we investigated the effect of

glucose variability on the development of peripheral and autonomic neuropathy. For this

purpose data from the Diabetes Control and Complications Trial (DCCT) were reanalysed.

The DCCT was originally designed to assess the effect of intensive vs. conventional glucose

lowering treatment on the development of microvascular complications and included

1,441 type 1 diabetes patients. The development of neuropathy was strongly associated

with mean glucose but no additional effect of glucose variability was found.

Chapter 7 shows a reanalysis of The Hyperglycaemia and Its Effect After Acute Myocardial

Infarction on Cardiovascular Outcomes in Patients With Type 2 Diabetes Mellitus study

(HEART2D). This randomised controlled trial assessed the effect of a prandial insulin

regimen compared with a basal insulin regimen on future cardiovascular event rates

in type 2 diabetes patients after myocardial infarction, thereby specifically lowering

glucose variability. Overall glycaemic control was found to be similar in the two groups

but no differences in cardiovascular outcomes were seen despite eighteen percent lower

glucose variability in the prandial insulin group.

In conclusion, Part I of this thesis does not support a relationship between glucose

variability and oxidative stress or diabetic complications. Moreover, specifically lowering

glucose variability in type 2 diabetes patients did not result in a reduction in future

cardiovascular event rates. Therefore, there are currently insufficient arguments to

specifically lower glucose variability in patients with diabetes regarding complication risk,

and treatment should continue targeting mean glucose while avoiding hypoglycaemia

as much as possible.

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Part IIMarked hyperglycaemia has to be avoided in critically ill patients but there is debate

on the optimal glucose target. In Chapter 8 we investigated the relationship between

mean glucose during intensive care unit (ICU) admission and mortality in surgical as

well as medical patients who were treated according to the most recent guidelines. In

both cohorts, mean glucose appeared to be related to ICU mortality by a U-shaped curve,

and a “safe-range” for mean glucose could be defined between approximately 7.0 and 9.0

mmol/l. These results are in line with the data from the NICE-SUGAR (Normoglycaemia

in Intensive Care Evaluation- Survival Using Glucose Algorithm Regulation) trial and

suggest that lowering glucose to normoglycaemia is perhaps doing more harm than good.

Continuous glucose monitoring (CGM) could be a useful tool to achieve more time in the

glucose target range. We report in Chapter 9 a head-to-head comparison investigating the

accuracy and reliability of the Guardian Real-Time (Medtronic Minimed) and the FreeStyle

Navigator (Abbott Diabetes Care) CGM system in 60 cardiac surgery patients admitted to

the ICU after surgery. The FreeStyle Navigator performed significantly better in accuracy

as well as reliability compared to the Guardian Real-Time. Remarkably, accuracy of both

systems was quite good compared to known data for outpatients. These data support the

use of the FreeStyle Navigator in cardiac surgery patients. Whether the use of CGM truly

increases the time spent in the target range and lowers the incidence of hypoglycaemia

should be subject of further study.

We hypothesised that the accuracy of the CGM systems could be influenced by the

microcirculation. In Chapter 10 the microcirculation and its effect on CGM accuracy was

investigated in the same 60 patients after cardiac surgery. Impairment in microcirculatory

parameters was found during the first hours of ICU admission during a median follow-

up of 23 hours, but this impairment was not related to CGM accuracy. A decrease in

peripheral temperature did decrease the accuracy of the two systems, and an increase in

age of the patient as well as an increase in severity of disease influenced the accuracy of

the FreeStyle Navigator in a negative way. Further studies need to assess the influence of

more profound microcirculatory changes on sensor accuracy in more severely ill patients.

Challenging it is when acute meets chronic hyperglycaemia: the critically ill patient

with diabetes. Chapter 11 gives an overview of the current literature on morbidity and

mortality in ICU patients with diabetes with special consideration to glucose regulation,

insulin treatment and hypoglycaemia. Diabetes is considered to be a risk factor for the

development of complications while admitted in the ICU but this does not seem to

translate directly into higher mortality. The relation between diabetes and mortality is

further investigated in Chapter 12. Hyperglycaemia is common in critically ill patients

with diabetes and associated with mortality when above 11.1 mmol/l, but there is


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discussion about the detrimental effect of hyperglycaemia lower than 11.1 mmol/l. Also,

intensive insulin therapy seems not beneficial as compared with moderate glycaemic

control. Interestingly, at any given level of hyperglycaemia mortality rates are higher in

non-diabetic patients compared with diabetic patients, but in the lower glucose range

it is the other way round. Patients with diabetes are also vulnerable for developing

hypoglycaemic events which are strongly associated with mortality. We recommend to

maintain glucose levels between 7.5 and 10.0 mmol/l in critically ill patients with diabetes

while avoiding hypoglycaemia as well as extreme hyperglycaemia.

Finally, in Chapter 12 the results of a systematic review and meta-analysis are shown

assessing the effect of diabetes on mortality in different ICU types. In total, 141 studies

were included containing over 12.4 million patients, including 2.7 million (21.7%)

deaths and 2.3 million (18.6%) patients with diabetes. The meta-analysis showed that

patients with diabetes who are admitted at the medical, mixed and trauma ICU have

similar chances of survival compared to patients without diabetes. Diabetes significantly

increased mortality risk only in patients admitted after cardiac surgery, where it

distinctly influences the underlying coronary disease. Further studies are needed to

unravel the pathophysiological mechanisms by which patients with diabetes seem to

be protected in non-surgical settings, despite encountering higher complication rates.

From a clinical perspective, we may conclude from the findings described in Part II of this

thesis that the optimal glucose target range in critically ill patients lies above the range

considered normoglycaemic, irrespective of the diabetic status prior to admission. Marked

hyperglycaemia and hypoglycaemia should be avoided. Continuous glucose monitoring

shows good accuracy in cardiac surgery patients, and its accuracy seem independent

from microcirculatory parameters.

Future considerationsAs always, also this research raises new questions. A decrease in glucose variability seems

not to decrease cardiovascular event rates in type 2 diabetes patients after myocardial

infarction, but a randomised controlled trial specifically lowering glucose variability in

other patient groups has not been performed yet. The most promising results are to be

expected in the critically ill because epidemiological studies consistently show that in this

population glucose variability is associated with mortality. It will be a major challenge

to come up with an intervention in this population that lowers glucose variability while

leaving mean glucose unaffected, but it is the only way to investigate whether glucose

variability is causally related to mortality or only a manifestation of severe disease.

Continuous glucose monitoring might be a useful tool to increase time in target range

and decrease hypoglycaemia and perhaps also glucose variability in the critically ill. A

randomised controlled trial comparing the FreeStyle Navigator CGM system with point-

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of-care glucose measurements assessing these questions is currently being performed in a

mixed population of critically ill patients and the results of this trial are avidly waited for.

In addition to subcutaneous glucose monitoring, also intra-vascular glucose monitoring

devices are being developed with promising results regarding accuracy. However, clinical

trails will have to demonstrate that the possible beneficial effects will counterbalance

the costs and possible complications of its invasiveness. Finally, an intriguing question is

why critically ill patients with diabetes are less affected by hyperglycaemia than patients

without previously diagnosed diabetes, while hypoglycaemia seems more harmful.

ConclusionThe central question of this thesis is whether it is necessary to curb all glucose peaks.

From the studies presented in this thesis we conclude that this is not always the case.

In diabetes it is important to lower mean glucose while avoiding hypoglycaemia, but we

found that lowering of glucose to normoglycaemia in critically ill patients seems actually

harmful, in patients with and without diabetes. In addition, our studies show that

glucose variability does not need separate treatment in diabetes, while it is associated

with mortality in critically ill patients without diabetes. Thus, not all what goes up

must come down.


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204

Samenvatting

In een gezond lichaam wordt het glucose (suiker) gehalte in het bloed zeer strak

gereguleerd omdat zowel te hoge als te lage waarden schadelijk kunnen zijn. Als gevolg

hiervan overstijgt de glucoseconcentratie in het bloed bij gezonde mensen bijna nooit 7.8

mmol/l. Soms is dit evenwicht echter verstoord. Een chronisch verhoogd glucosegehalte

in het bloed (hyperglycemie) is karakteristiek voor de ziekte diabetes mellitus, maar

ook ernstig zieke patiënten zonder diabetes mellitus kunnen acute hyperglycemie

ontwikkelen. In beide gevallen is ernstige hyperglycemie schadelijk en geassocieerd

met het ontstaan van complicaties en zelfs overlijden.

Dit proefschrift gaat over de effecten en de behandeling van pieken in glucose die

optreden tijdens chronische en acute hyperglycemie. De centrale vraag is of het zinvol

is om deze glucosepieken altijd in te perken. In Deel I van dit proefschrift worden de

consequenties van schommelingen in glucose bij patiënten met diabetes besproken

in relatie tot oxidatieve stress, een proces welke geassocieerd is met schade aan de

binnenbekleding van de vaatwand, en twee chronische complicaties van diabetes,

schade aan de zenuwen en hart- en vaatziekten. In Deel II van dit proefschrift wordt

hyperglycemie besproken bij ernstig zieke patiënten die opgenomen zijn op de intensive

care. Allereerst stellen wij een veilig gebied voor glucoseregulatie voor. Ook testten wij

de nauwkeurigheid en betrouwbaarheid van het continu monitoren van glucose (CGM)

in het vetweefsel, een nieuwe methode die indirect het glucosegehalte in het bloed kan

monitoren. Tot slot hebben wij onderzocht wat de implicatie is van het hebben van de

ziekte diabetes tijdens een opname op de intensive care in relatie tot glucoseregulatie

en de kans op overlijden.

Deel IIn Hoofdstuk 2 wordt een overzicht gegeven van verschillende methoden om

schommelingen in glucose, ofwel glucose variabiliteit, te meten en bespreken we het

belang van glucose variabiliteit. Er bestaat namelijk een grote variatie in zowel het

aantal als de duur van glucosepieken bij patiënten met diabetes. Op dit moment is er

geen “gouden standaard” om glucose variabiliteit te kwantificeren. De standaarddeviatie

van het gemiddelde wordt het meest gebruikt in de literatuur en lijkt mathematisch

de best gevalideerde methode. In in vitro, dierexperimenteel en humane experimentele

studies, verhogen pieken in glucose oxidatieve stress. Bij patiënten met type 1 of type

2 diabetes mellitus is het bewijs voor een onafhankelijk effect van glucose variabiliteit

op het ontstaan van oxidatieve stress en complicaties echter marginaal, en mogelijk

beperkt tot een specifieke groep patiënten met type 2 diabetes die slecht ingesteld zijn

en behandeld worden met alleen orale glucoseverlagende middelen. In tegenstelling tot

patiënten met diabetes is glucose variabiliteit bij ernstig zieke patiënten opgenomen op

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Samenvatting

de intensive care (IC) onomstotelijk geassocieerd met overlijden.

In Hoofdstuk 3 onderzochten we bij welke glucoseconcentratie de effecten op

de vasculaire homeostase beginnen en of dit een aan/uit fenomeen is met een

drempelwaarde of een continue relatie. Hiervoor hebben we bij gezonde vrijwilligers de

glucoseconcentratie in het bloed stapsgewijs omhoog gebracht naar achtereenvolgens 6.0,

8.0 en 10.0 mmol/l. De effecten van glucose op oxidatieve stress, stolling en fibrinolyse,

en de endotheliale glycocalyx werden bestudeerd. De stijging in oxidatieve stress

bleek dosisafhankelijk en het activeren van het stollingssysteem gebeurde al bij een

drempelwaarde van 6.0 mmol/l glucose. De afwezigheid van een drempelwaarde of een

zeer lage drempelwaarde pleit tegen een rol voor glucose variabiliteit in het activeren

van oxidatieve stress of het stollingssysteem.

Deze conclusie wordt gesteund door de resultaten beschreven in Hoofdstuk 4 en 5. In

Hoofdstuk 4 onderzochten wij de relatie tussen glucose variabiliteit, gemeten met behulp

van CGM, en oxidatieve stress, bepaald door het meten van de 24-uurs uitscheiding van

de marker 8-iso prostaglandine F2α in de urine, bij 24 patiënten met type 2 diabetes

die goed gereguleerd waren en behandeld werden met alleen orale glucose verlagende

therapie. We vonden geen relevante relatie tussen glucose variabiliteit en oxidatieve

stress. In Hoofdstuk 5 werd het gebruik van drie keer daags een kortwerkend insuline

bij de maaltijd vergeleken met het gebruik van één keer daags een langwerkend insuline

wat betreft het effect op glucose regulatie en oxidatieve stress. In een cross-over design

werden 40 patiënten met type 2 diabetes geïncludeerd. Het toevoegen van insuline aan

de medicatie van de patiënten verlaagde de gemiddelde glucosewaarden en oxidatieve

stress significant. Echter, opnieuw werd geen relatie gevonden tussen glucose variabiliteit

en oxidatieve stress.

Het wordt aangenomen dat oxidatieve stress vooraf gaat aan vasculaire complicaties,

maar uiteindelijk is het een indirecte marker voor ziekte en geen harde uitkomstmaat.

In Hoofdstuk 6 onderzochten we het effect van glucose variabiliteit op het ontstaan

van perifere en autonome zenuwschade, neuropathie. Hiervoor hebben we data van de

Diabetes Control and Complications Trial (DCCT) opnieuw geanalyseerd. De DCCT was

oorspronkelijk opgezet om het effect van intensieve vs. conventionele glucoseverlagende

behandeling op het ontstaan van microvasculaire complicaties bij patiënten met type

1 diabetes te bekijken. Uit onze analyse bleek dat het ontstaan van neurologische

complicaties sterk geassocieerd was met de hoogte van de gemiddelde glucose, zoals

verwacht, maar we vonden geen effect van glucose variabiliteit.

206

In Hoofdstuk 7 wordt een secundaire analyse van The Hyperglycaemia and Its Effect

After Acute Myocardial Infarction on Cardiovascular Outcomes in Patients With Type 2

Diabetes Mellitus study (HEART2D) beschreven. Deze gerandomiseerde studie vergeleek

het effect van een kortwerkend insuline bij de maaltijden met een langwerkend insuline

op het ontstaan van toekomstige hart- en vaatziekten in patiënten met type 2 diabetes

die geïncludeerd werden na een acuut hartinfarct. De gemiddelde glucoseregulatie was

gelijk tussen de groepen, maar ondanks achttien procent minder glucose variabiliteit in

de maaltijdinsuline groep werden er geen verschillen in hart- en vaatziekten gevonden.

Concluderend ondersteunt Deel I van dit proefschrift een relatie tussen glucose

variabiliteit en oxidatieve stress of diabetische complicaties niet. Bovendien bleek

dat het specifiek verlagen van glucose variabiliteit bij patiënten met type 2 diabetes

niet resulteerde in een afname van het aantal hart- en vaatziekten in deze groep. Om

die redenen zijn er op dit moment onvoldoende argumenten om specifiek glucose

variabiliteit te verlagen bij patiënten met diabetes. De behandeling zal blijven bestaan

uit het verlagen van de gemiddelde glucose en het vermijden van te lage bloedglucose

concentraties, hypoglycemieën.

Deel IIHet is nodig om ernstige hyperglycemie te vermijden bij ernstig zieke patiënten. Er

is echter discussie over wat de streefwaarden voor glucose zouden moeten zijn. In

Hoofdstuk 8 onderzochten we de relatie tussen de gemiddelde bloedglucose concentratie

tijdens opname op de IC en de kans op overlijden in twee cohorten: patiënten opgenomen

met een internistische of een chirurgische reden. In beide cohorten bleek de gemiddelde

bloedglucose gerelateerd aan overlijden op basis van een U-vormige curve, met de

laagste kans op overlijden bij een gemiddelde glucose tijdens opname tussen 7.0 en

9.0 mmol/l. Deze resultaten zijn in overeenstemming met de resultaten van de NICE-

SUGAR (Normoglycaemia in Intensive Care Evaluation- Survival Using Glucose Algorithm

Regulation) studie, en suggereren dat het verlagen van de bloedglucose tot lagere

(normale) waarden wellicht meer kwaad dan goed doet in deze patiëntengroep.

Het continue monitoren van glucose (CGM) in het vetweefsel zou een nuttige methode

kunnen zijn om de bloedglucose beter te reguleren. In Hoofdstuk 9 vergelijken we de

nauwkeurigheid en de betrouwbaarheid van twee van deze apparaten: de Guardian Real-

Time (geproduceerd door Medtronic Minimed) en de FreeStyle Navigator (geproduceerd

door Abbott Diabetes Care). Voor deze studie zijn 60 patiënten geïncludeerd, opgenomen

op de IC na open hartchirurgie. De FreeStyle Navigator bleek nauwkeuriger en meer

betrouwbaar dan de Guardian Real-Time. Opmerkelijk was wel dat de nauwkeurigheid

van beide systemen behoorlijk goed was in vergelijking met resultaten die we kennen

van patiënten met diabetes buiten het ziekenhuis. Onze data ondersteunen het gebruik

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van de FreeStyle Navigator voor patiënten die open hartchirurgie ondergaan. Echter,

toekomstig onderzoek moet uitwijzen of het gebruik van deze apparaten daadwerkelijk

zorgt voor een betere glucoseregulatie en het voorkomen van hypoglycemieën.

Onze hypothese was dat de nauwkeurigheid van de CGM systemen zou kunnen worden

beïnvloed door de microcirculatie, het stelsel van de allerkleinste vaten in het lichaam. In

Hoofdstuk 10 wordt de microcirculatie van dezelfde 60 patiënten na open hartchirurgie

beschreven en het effect op de nauwkeurigheid van de CGM systemen geanalyseerd.

Het bleek dat de microcirculatie verslechterd was gedurende de eerste uren na de

operatie, maar deze verslechtering had geen invloed op de nauwkeurigheid van de CGM

systemen. Een verlaging van de perifere temperatuur verslechterde de nauwkeurigheid

van beide systemen wel. Een hogere leeftijd en ernstiger ziekte beïnvloedden alleen

de nauwkeurigheid van de FreeStyle Navigator negatief. Vervolgstudies zullen moeten

uitwijzen wat de invloed is van grotere veranderingen in de microcirculatie op de

nauwkeurigheid van CGM systemen bij ernstiger zieke patiënten.

Het wordt een uitdaging wanneer acute en chronische hyperglycemie samenkomen:

de ernstig zieke patiënt met diabetes mellitus. Hoofdstuk 11 geeft een overzicht van

de huidige literatuur over de morbiditeit en mortaliteit van patiënten met diabetes die

opgenomen zijn op de IC. Er wordt specifiek aandacht besteed aan glucoseregulatie,

behandeling met insuline en hypoglycemie. Diabetes is een risicofactor voor het ontstaan

van complicaties tijdens opname op de IC, maar dit betekent niet meteen dat patiënten

met diabetes ook eerder overlijden (de relatie tussen diabetes en overlijden wordt verder

besproken in Hoofdstuk 12). Hyperglycemie komt vaak voor bij ernstig zieke patiënten

met diabetes. Dit leidt tot een verhoogde kans op overlijden als de glucoseconcentratie

boven de 11.1 mmol/l uitkomt, maar er is discussie over het schadelijke effect van

hyperglycemie lager dan 11.1 mmol/l. Zeer intensieve insulinetherapie lijkt niet beter

te zijn voor patiënten met diabetes dan minder intensieve insulinetherapie. Opvallend is

dat bij elke mate van hyperglycemie de mortaliteit van patiënten zonder diabetes hoger is

dan die van patiënten met diabetes, maar dat voor lagere glucosewaarden het omgekeerde

geldt. Patiënten met diabetes zijn kwetsbaar voor het ontwikkelen van hypoglycemie,

wat sterk geassocieerd is met overlijden. Wij raden aan om de glucoseconcentratie bij

ernstig zieke patiënten met diabetes tussen de 7.5 en 10.0 mmol/l te houden en het

ontstaan van hypoglycemieën en ernstige hyperglycemie te vermijden.

Tot slot wordt in Hoofdstuk 12 het resultaat van een meta-analyse beschreven waarin het

effect van het hebben van diabetes op overlijden op verschillende types IC onderzocht

is. In totaal werden 141 studies in deze analyse geïncludeerd, die meer dan 12.4

miljoen patiënten bevatte inclusief 2.7 miljoen (21.7%) doden en 2.3 miljoen (18.6%)

patiënten met diabetes. De analyse liet zien dat patiënten met diabetes dezelfde kans

208

op overlijden hebben als patiënten zonder diabetes wanneer zij opgenomen zijn in een

internistische, trauma of gemengde IC. De aanwezigheid van diabetes verhoogde alleen

de kans op overlijden bij patiënten die opgenomen waren na hartchirurgie, maar bij

deze patiëntengroep worden de onderliggende afwijkingen van de kransslagaderen

in grote mate negatief beïnvloed door diabetes. Meer onderzoek is nodig om de

pathofysiologische mechanismen te ontrafelen die een rol spelen bij de relatieve

bescherming van patiënten met diabetes in een niet-chirurgische setting, ondanks

een hoger aantal complicaties.

Vanuit een klinisch perspectief kunnen we concluderen uit Deel II van dit proefschrift

dat het optimale doel voor glucoseregulatie bij ernstig zieke patiënten met en zonder

diabetes boven het “normale” niveau ligt. Extreme hyperglycemie en hypoglycemie

moeten worden vermeden. Continue glucose monitoring bij patiënten die hartchirurgie

hebben ondergaan is behoorlijk nauwkeurig en deze nauwkeurigheid lijkt onafhankelijk

te zijn van de microcirculatie. De ziekte diabetes draagt niet bij aan een verhoogde kans

op overlijden, tenzij de patiënt is opgenomen voor hartchirurgie.

ToekomstperspectiefZoals altijd, roept ook dit onderzoek nieuwe vragen op. Het lijkt erop dat het verlagen

van glucose variabiliteit er niet voor zorgt dat het aantal nieuwe hart- en vaatziekten

vermindert bij patiënten met type 2 diabetes opgenomen na een hartinfarct, maar

een gerandomiseerd onderzoek met deze vraagstelling is nog niet verricht in andere

patiëntgroepen. Het meeste resultaat kan verwacht worden bij ernstig zieke patiënten

opgenomen op de IC, aangezien epidemiologische studies in deze patiëntengroep

consequent laten zien dat glucose variabiliteit is geassocieerd met overlijden. Het zal

wel een grote uitdaging worden om een interventie te bedenken die alleen glucose

variabiliteit verlaagt en het gemiddelde glucose ongemoeid laat, maar het is de enige

manier om uit te zoeken of hoge glucose variabiliteit een onafhankelijke oorzaak

is voor overlijden of alleen een manifestatie van ernstige ziekte. CGM is mogelijk

bruikbaar om de glucoseregulatie te verbeteren en het aantal hypoglycemieën en

glucose variabiliteit te verminderen bij ernstig zieke patiënten. Op dit moment wordt

een gerandomiseerd onderzoek uitgevoerd op een gemengde IC die deze vragen

probeert te beantwoorden; het gebruik van de FreeStyle Navigator wordt vergeleken met

minder frequente standaard glucosemetingen. We zijn dan ook zeer benieuwd naar de

resultaten van dit onderzoek. Naast CGM in het vetweefsel wordt er ook meetapparatuur

ontwikkeld die de glucoseconcentratie continu en direct in het bloedvat meet. Tot

nu toe zijn deze resultaten veelbelovend. Maar ook hier geldt dat klinische studies

zullen moeten uitwijzen of de potentieel gunstige effecten opwegen tegen de kosten

en mogelijke complicaties. Tot slot is een intrigerende vraag die beantwoord moet gaan

worden waarom ernstig zieke patiënten met diabetes minder schade ondervinden van

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hyperglycemie dan patiënten zonder diabetes, terwijl hypoglycemie voor hun juist

schadelijker lijkt te zijn.

ConclusieDe centrale vraag in dit proefschrift is of het zinvol is om pieken in glucose altijd in te

perken. Op basis van de studies die gepresenteerd worden in dit proefschrift concluderen

we dat dit niet altijd het geval is. Voor patiënten met diabetes is het belangrijk om de

gemiddelde glucose te verlagen en het aantal hypoglycemieën te verminderen, maar we

hebben gezien dat bij ernstig zieke patiënten opgenomen op de IC het verlagen van de

glucoseconcentratie naar normale waarden juist schadelijk is, zowel bij patiënten met

als patiënten zonder diabetes. Daarnaast laten onze studies zien dat glucose variabiliteit

bij patiënten met diabetes niet apart behandeld hoeft te worden, maar bij ernstig zieke

patiënten zonder diabetes geassocieerd is met overlijden. Kortom, glucose pieken hoeven

niet altijd verlaagd te worden.

210

Authors’ affiliations

Alan S. Rigby

Academic Department of Cardiology, University of Hull and Hull-York Medical School,

Hull, United-Kingdom

Bregtje A. Lemkes

Department of Internal Medicine, Academic Medical Centre, Amsterdam

Durk F. Zandstra

Department of Intensive Care, Onze Lieve Vrouwe Gasthuis, Amsterdam

Eric S. Kilpatrick

Department of Clinical Biochemistry, Hull Royal Infirmary, Hull, United Kingdom

Frits Holleman


Heleen M. Oudemans- van Straaten


Henk van Lenthe

Laboratory Genetic Metabolic Diseases, Academic Medical Centre, Amsterdam

Jeroen Hermanides

Department of Internal Medicine and Department of Anaesthesiology, Academic Medical

Centre, Amsterdam

J. Hans DeVries


Joost B.L. Hoekstra


Joost C. Meijers

Department of Experimental Vascular Medicine, Academic Medical Centre, Amsterdam

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Authors’ affilliations

Lisa Kerr

Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana, USA

Maartje Hickmann


Max Nieuwdorp


Peter H.J. van der Voort


Robert J. Bosman


Robin Mukherjee

Statistical Department, Pfizer Inc., New York, New York, USA

Scott J. Jacober

Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana, USA

Steven L. Atkin

Department of Diabetes, Hull-York Medical School, Hull, United Kingdom

Temo Barwari


Wim Kulik

Laboratory Genetic Metabolic Diseases, Academic Medical Centre, Amsterdam

212


Siegelaar SE, Hoekstra JB, DeVries JH (2011) Special considerations for the diabetic patient

in the intensive care unit: targets for treatment and risks of hypoglycaemia. Best Pract

Res Clin Endocrinol Metab: in press

Siegelaar SE, Kerr L, Jacober SJ, DeVries JH (2011) A decrease in glucose variability does

not reduce cardiovascular event rates in type 2 diabetes patients after acute myocardial

infarction: a reanalysis of the HEART2D study. Diabetes Care 34(4):855-857

Siegelaar SE, Barwari T, Hermanides J, van der Voort PHJ, DeVries JH (2011) Accuracy and

reliability of continuous glucose monitoring in the intensive care unit; a head-to-head

comparison of two subcutaneous glucose sensors in cardiac surgery patients. Diabetes

Care 34(3): e31

Siegelaar SE, Barwari T, Kulik W, Hoekstra JB, DeVries JH (2011) No relevant relationship

between glucose variability and oxidative stress in well-regulated type 2 diabetes patients.

J Diabetes Sci Technol 5(1): 86-92

Siegelaar SE, Hermanides J, Oudemans- van Straaten HM, van der Voort PH, Bosman RJ,

Zandstra DF, DeVries JH (2010) Mean glucose during ICU admission is related to mortality

by a U-shaped curve in surgical and medical patients: a retrospective cohort study. Crit

Care 14(6): R224

Siegelaar SE, DeVries JH (2010) Strakke glucoseregualtie en de mogelijke rol voor continue

glucose monitoring op de intensive care. Intensive Care Capita Selecta: 15-22

Siegelaar SE, DeVries JH, Hoekstra JB (2010) Patients with diabetes in the intensive care

unit; not served by treatment, yet protected? Crit Care 14(2): 126 (commentary)

Siegelaar SE, Holleman F, Hoekstra JB, DeVries JH (2010) Glucose variability; does it

matter? Endocr Rev 31(2): 171-182

Siegelaar SE, Kilpatrick ES, Rigby AS, Atkin SL, Hoekstra JB, DeVries JH (2009) Glucose

variability does not contribute to the development of peripheral and autonomic

neuropathy in type 1 diabetes: data from the DCCT. Diabetologia 52(10): 2229-2232

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Siegelaar SE, Kulik W, van Lenthe H, Mukherjee R, Hoekstra JB, DeVries JH (2009) A

randomized clinical trial comparing the effect of basal insulin and inhaled mealtime

insulin on glucose variability and oxidative stress. Diabetes Obes Metab 11(7): 709-714

DeVries JH, Siegelaar SE, Holleman F, Hoekstra JB (2008) Intensive insulin therapy in

patients with type 2 diabetes. Lancet 372(9640): 717

Siegelaar SE, Olff M, Bour LJ, Veelo D, Zwinderman AH, van Bruggen G, de Vries GJ,

Raabe S, Cupido C, Koelman JH, Tijssen MA (2006) The auditory startle response in post-

traumatic stress disorder. Exp Brain Res 174(1): 1-6

214

Dankwoord

Ha, het is af! Ik wil graag iedereen danken die bijgedragen heeft aan de totstandkoming

van dit proefschrift en een aantal in het bijzonder:

Allereerst alle deelnemers aan de verschillende studies. Zonder jullie bijdrage is klinisch

wetenschappelijk onderzoek niet mogelijk.

Mijn promotor, prof. dr. J.B.L. Hoekstra. Beste Joost, wat ben ik blij dat je precies op

het moment belde dat ik twijfelde om er überhaupt aan te beginnen. Je zag het zitten

dat ik het eerste jaar promotieonderzoek en topsport combineerde. Je vond het een

uitdaging. Je positivisme en enthousiasme zijn zeer bijzonder: toen de studie waar ik

op aangenomen was na twee weken toch niet doorging, bij alle manuscripten die ik

aan je voorlegde, of als er weer eens een hardloopwedstrijdje werd voorgesteld. Je vond

het allemaal supermooi. Heel veel dank voor alles, en het is zeker waar: kies eerst je

promotor, daarna je onderwerp.

Mijn co-promotor, dr. J.H. de Vries (alias J. Hans DeVries). Beste Hans, ik weet niet of ik

had verwacht dat je op een druilerige dag in een regenpak langs de Amstel zou komen

fietsen om naar roeien te kijken. Het typeert je betrokkenheid. Enorm veel dank voor je

snelheid, scherpte en (licht?) cynische humor. Stukken kwamen met razende snelheid

terug, en passages waar ik zelf niet helemaal zeker over was werden er feilloos uitgepikt.

Dit zorgde voor een mooie flow, fijn!

De overige leden van de promotiecommissie, prof. dr. Fliers, prof. dr. Romijn, prof. dr.

Smulders en prof. dr. Zandstra. Hartelijk dank voor het kritisch beoordelen van dit

proefschrift en de bereidheid om plaats te nemen in mijn promotiecommissie. Dear

prof. dr. Kilpatrick, it was a pleasure working with you. I am honoured you are willing

to serve on my doctorate committee.

Frits, met de ondergang van de SMILING studie verdween helaas ook onze directe

samenwerking een beetje naar een zijspoor. Toch heb ik veel geleerd van je inventiviteit

en duidelijke mening.

Gabor, zonder jou was ik hier niet terechtgekomen. Ik hoop dat de wijn lekker was!

Mijn collegae. Bregtje, roomie en partner in crime, onze (maandag) chitchat en

wetenschappelijke discussies ga ik missen daar in Noord-Holland! Sanne, Jeroen, Anne,

Els, Yoeri, Arianne, Airin, Wanda en Josefine, dank voor de gezellige tijd en altijd

scherpe (politiek geëngageerde) lunchdiscussies. Studenten, dank voor jullie hulp. In het

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Dankwoord

bijzonder: Maartje, het werd een megaproject. Maar je liet je niet afschrikken, waarvoor

dank. En natuurlijk Temo, altijd vrolijke ‘superstudent’, zonder jou waren het nog veel

langere dagen geworden in het OLVG. Dank voor je initiatief en humor!

Gootje, bij de Appie to Go is veel besproken. We gaan zeker tijd maken voor fietsen,

schaatsen, roeien en koffietjes in dit drukke bestaan. Superleuk dat je mijn paranimf wilt

zijn! Andere vriendjes, vriendinnetjes en negds. Het waren, zijn en blijven mooie tijden!

Lieve schoonfamilie, wat een warm bad. Fijn dat ik jullie er zomaar bijkreeg!

Anne en Henk, lievelings tante en oom, ik werd groot bij de Wijnvriend. Heel stoer vond

ik dat. Dank voor jullie betrokkenheid!

Olie, broet, ik weet zeker dat ze me met jou als paranimf naast me niet fysiek zullen

durven aan te vallen. Heel bijzonder was het om samen in Beijing te zijn. Ik kom zeker

naar Cal als je met je hoed gaat zwaaien! En Tiets, van klein sussie naar grote sus,

superleuk dat je samen met As lekker dichtbij bent komen wonen. Ik ben trots op jullie!

Lieve mam en pap, in één adem, jullie hebben er voor gezorgd dat ik ben wie ik ben.

Schouders eronder en doorgaan, van jullie geleerd. Ik ben trots op zulke lieve ouders!

Lieve Cockie, en Joop hierboven, jullie waren je tijd ver vooruit en hebben me enorm

geïnspireerd. Dankjewel.

Tot slot, lieve Jel, gelukkig vond ik het langlaufen na de eerste keer nog steeds leuk, anders

had je me misschien wel ingeruild. Je onvoorwaardelijke steun was en is fantastisch,

ook al was ik moe, op trainingskamp of op onchristelijke tijden in het ziekenhuis. Het

leven is een mooi ommetje, maar dan wel samen met jou. Luv joe!

216

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Curriculum Vitae

Curriculum Vitae

Sarah Elaine Siegelaar werd op 4 oktober 1981 als oudste van drie kinderen

geboren te Heemstede. Ze leerde de beginselen van het zelfdoen op de Haarlemse

Montessorischool. Met veel plezier doorliep ze daarna het Stedelijk Gymnasium te

Haarlem waar ze in 1999 eindexamen deed. Ze werd direct ingeloot voor de studie

geneeskunde aan de Universiteit van Amsterdam en tijdens het begin van haar

studie begon ze met roeien bij de ASR Nereus. Studentenroeien werd topsport en

na successen op de wereldkampioenschappen in 2003 en Olympische spelen in

2004, rondde ze haar co-schappen af en behaalde haar artsdiploma in 2007. Ze

beëindigde ze haar actieve roeicarrière in 2008 na het behalen van een zilveren

medaille in de vrouwenacht bij de Olympische spelen in Beijing. In oktober 2007

startte ze met haar promotieonderzoek op de afdeling Klinische Diabetologie

onder leiding van prof. dr. Joost Hoekstra. Tijdens haar promotieonderzoek gaf ze

klinisch onderwijs op de Hogeschool van Amsterdam en coachte ze verschillende

roeiploegen op Nereus. Per 1 april 2011 is ze begonnen met de opleiding tot

internist in het Westfriesgasthuis te Hoorn. Sarah is in oktober 2009 getrouwd

met Jelle Luijnenburg.

Documents

UvA-DARE (Digital Academic Repository) What goes up must … · What goes up must come down: glucose variability and glucose control in diabetes and critical illness Academic thesis,