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Contents lists available at SciVerse ScienceDirect

Primary Care Diabetes

j o u r n a l h o m e p a g e : h t t p : / / w w w . e l s e v i e r . c o m / l o c a t e / p c d

riginal research

primary care register for impaired glucose handling (IGH):mpact on cardiometabolic profile

drian Hugh Healda,d,∗,1, Humphrey Knapmanb, Sunil Naira, Tom Chambersc,aniela Radforda, Teresa Rushtonb, Simon George Andersond,1

Department of Medicine, Leighton Hospital, Crewe, United KingdomKiltearn Medical Centre, Nantwich, United KingdomMedical School, University of Manchester, Manchester, United KingdomCardiovascular Sciences Research Group, Core Technology Facility (3rd Floor), University of Manchester, 46 Grafton Street, Manchester,nited Kingdom

r t i c l e i n f o

rticle history:

eceived 28 September 2009

eceived in revised form

6 December 2011

ccepted 7 February 2012

vailable online 4 May 2012

eywords:

mpaired glucose handling

ntervention

ardiovascular risk

rimary care

a b s t r a c t

Objective: Diet and exercise reduce the incidence of diabetes in high-risk individuals as

does Metformin, although less dramatically. Here we evaluated if lifestyle and pharma-

cological intervention, for people at risk of diabetes, resulted in an improvement in their

cardiometabolic risk profile.

Research design/methods: In a primary care based study, 92 individuals screened opportunisti-

cally and identified to have impaired glucose handling were offered detailed lifestyle advice,

at 6 monthly intervals, with targeting of cardiovascular risk factors. Duration of follow-up

was 4 years. The relation between fasting and 2 h glucose with different cardio-metabolic

risk factors over time was assessed using multi-level modeling.

Results: There was no significant weight reduction. At 24 months, mean fasting glucose

level (6.4 mmol/L (95% CI 6.0–6.8)) was slightly lower than at baseline (6.6 mM (95% CI:

6.4–6.9), F = 3.67; p < 0.001). For men and women combined, systolic blood pressure (mean dif-

ference = −6 mmHg, p = 0.013), total cholesterol (−0.66 mmol/L, p < 0.0001) and triglycerides

(−0.13 mmol/L, p = 0.133) fell, whilst HDL-cholesterol (0.12 mmol/L, p = 0.047) rose. Diabetes

developed in 18/92 participants during follow-up (up to 4 years).

Five per cent of participants were started on Metformin, 88.5% on lipid lowering agents

and 85.4% on anti-hypertensive agents. After adjusting for age, sex and BMI, 2 h glucose

was independently and negatively associated with HDL-cholesterol ( ̌ = −2.17, p = 0.041), and

positively with systolic BP ( ̌ = 0.24, p = 0.004, per 5 mmHg).

Abbreviations: ANOVA, analysis of variance; BP, blood pressure; BMI, body mass index; CI, confidence interval; GLS, generalised leastquares; HbA1c, glycosylated haemoglobulin; HDL, high density lipoprotein; IQR, interquartile range; IFG, impaired fasting glycaemia;GH, impaired glucose handling; IGT, impaired glucose tolerance; LDL, low density lipoprotein; NSF, National Service Framework; OGTT,ral glucose tolerance test; PCTs, Primary Care Trusts; RCT, randomised controlled trial; Type 2 DM, Type 2 diabetes mellitus; WHO, Worldealth Organisation.∗ Corresponding author at: Leighton Hospital, Crewe CW1 4QJ, UK. Tel.: +44 4 1270612353; fax: +44 1270273353.

E-mail address: Adrian.Heald@manchester.ac.uk (A.H. Heald).1 These authors contributed equally to this work.751-9918/$ – see front matter © 2012 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.oi:10.1016/j.pcd.2012.02.002

214 p r i m a r y c a r e d i a b e t e s 6 ( 2 0 1 2 ) 213–219

Targeted intervention had an effective role in improving lipid and BP profile in individuals

with impaired glucose handling, with limited impact on glycaemia and no impact on weight.

More work needs be done to evaluate the potential benefit of insulin sensitizing agents in

this setting.

ry Ca

imum 30 min walk each day. A trained primary care nurse anda dietician led these individualised sessions. The actual num-ber of sessions (up to 48 months from the date first screened)

© 2012 Prima

1. Introduction

The number of people with diabetes continues to grow world-wide with an estimated 2.1 million people in the UK, a countrywith historically relatively well funded health care, diagnosedand a further 600,000 potentially undiagnosed [1,2]. Interna-tionally and across socio-economic groups there are widedisparities [3].

There is strong evidence that much of the cardiovascu-lar damage in type 2 diabetes (type 2 DM) occurs long beforethe formal diagnosis of diabetes is made [4,5]. The corol-lary of this is that focussed intervention may reduce incidentcardiovascular disease in individuals with impaired glucosetolerance/impaired fasting glucose (or impaired glucose han-dling (IGH)) [6]. In relation to glycaemia, behavioural changesto modify diet, reduce weight and increase exercise and phys-ical activity have been proven in clinical trials to be effectivein preventing or delaying the onset of diabetes in people withimpaired glucose tolerance (IGT) [7,8] as has the use of Met-formin [9,10].

There is a great deal of debate about what can be done inthe primary care setting to reduce cardiovascular risk in peo-ple with impaired fasting glycaemia (IFG) or IGT, more simplydescribed as impaired glucose handling (IGH) and to reducethe incidence of type 2 DM in this group [11,12]. One strategyis actively to target people identified to have IGH with lifestyleadvice and aggressive treatment of cardiovascular risk factors.This is in accordance with standard 1 of The UK National Ser-vice Framework for Diabetes (2001) [13]. Ample evidence frommeta-analyses of randomised controlled trials (RCTs) evaluat-ing interventions to delay or prevent type 2 DM indicates thatboth lifestyle and pharmacological intervention reduces theincidence [9]. The question is how far the RCT results can betranslated to real life clinical practice. The challenge to sustainthe benefits of these intervention strategies to reduce incidenttype 2 DM in a primary care setting is ongoing [14].

The aim of this study was to assess if provision of targetedlifestyle intervention through a primary care based register,for individuals with IGH modifies their cardiometabolic riskprofile.

2. Methods

2.1. Patient selection (Fig. 1)

This locally funded initiative was undertaken at a singleGP practice in Cheshire, UK. Screening was performed in

any individual presenting de novo with any of the followingrisk factors – body mass index of ≥27 kg/m2, symptomaticmacrovascular disease (angina, myocardial infarction, stroke,transient ischaemic attack, intermittent claudication), chronic

re Diabetes Europe. Published by Elsevier Ltd. All rights reserved.

obstructive pulmonary disease and asthma. The aim was totreat all individuals to the targets applicable to patients withtype 2 DM as recommended in the National clinical guidelinefor management in primary and secondary care [15]. Partici-pant current use of anti-hypertensive, lipid lowering or othermedication was obtained from the general practitioner (GP)records.

2.2. Anthropometric assessments

Trained staff used a standardised protocol for baseline andfollow-up measurements. Weight (using precision scales;SECA, Birmingham, UK) and height were measured in lightclothing and without shoes. BMI was calculated as weight(kg) divided by height (in m) squared (m2). Blood pressure (BP)was measured sitting, in rested participants, using a validatedsemi-automatic ‘Omron HEM-705CP’ monitor (Omron Health-care, Kyoto, Japan).

2.3. Interventions

All participants were offered lifestyle advice according to astructured proforma addressing active targeting of cardiovas-cular risk factors including macronutrient intake, total dailycalorie intake and exercise, with a recommendation of a min-

Fig. 1 – Flow chart of patient journey.

p r i m a r y c a r e d i a b e t e s 6 ( 2 0 1 2 ) 213–219 215

Table 1 – Baseline characteristics of men and women with impaired glucose handling (IGH).

Male (n = 50) Female (n = 42) p value

Age (years) 66.2 (63.0–69.4) 67.1 (63.6–70.5) F = 0.13; p = 0.717BMI (kg/m2) 29.1 (27.3–31.0) 30.2 (28.2–32.3) F = 0.66; p = 0.420Fasting glucose (mmol/L) 6.1 (6.4–6.8) 6.7 (6.5–6.9) F = 0.22; p = 0.6402 h P-P glucose (mmol/L) 7.6 (6.8–8.4) 8.4 (7.5–9.3) F = 2.01; p = 0.160HbA1c (%) 6.0 (5.8–6.3) 6.3 (5.9–6.6) F = 1.24; p = 0.272Systolic BP (mmHg) 138 (134–142) 140 (135–145) F = 0.43; p = 0.514Diastolic BP (mmHg) 81 (78–84) 78 (75–81) F = 1.87; p = 0.176Total cholesterol (mmol/L) 4.7 (4.5–5.0) 5.5 (5.2–5.8) F = 12.3; p < 0.001LDL-cholesterol (mmol/L) 3.0 (2.7–3.2) 3.2 (2.9–3.5) F = 1.85; p = 0.178HDL-cholesterol (mmol/L) 1.15 (1.06–1.24) 1.33 (1.22–1.44) F = 6.21; p = 0.015

= pos

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Triglycerides (mmol/L) 1.8 (1.5–2.1)

Data are means (95% CI). ANOVA for differences between means. P-P

as negotiated between the patient and health care profes-ional. Blood pressure (BP) lowering treatment was initiatedf BP was consistently greater than 140/80 mmHg (9) and lipidowering treatment if total cholesterol was ≥5 mmol/L and/orDL-cholesterol was ≥3 mmol/L [15].

.4. Ongoing biochemical assessment

ll participants were asked to fast from 10 pm on the eveningrior to assessments at baseline and before each subsequentxamination annually. The aim was to undertake a 75 g orallucose tolerance test (OGTT) at baseline and prospectively.owever not all respondents elected to undergo an OGTT. Of

he total respondents 83/92 consented to a (OGTT) at base-ine (the remaining had fasting bloods only). Fasting (and 2 host glucose load) bloods were taken for determination of glu-ose, HbA1c and lipid levels. All biochemical measurementsere performed at Leighton Hospital, Crewe, UK on a Vitros

.1 Autoanalyser (Ortho diagnostics, Rochester, NY, USA).

.5. Statistical analyses

he data were analysed using the statistical package Inter-ooled Stata version 10.1 (Stata Corp, Texas). Not all subjectsad data available for every time point during the 48-onth follow-up period. Some data were collected up to

0 months. Comparative analyses between variables of inter-st were therefore assessed using baseline and data collectedt 24 months as the numbers attending sessions after thiseriod decreased significantly. Anthropometric and metabolicata are expressed as arithmetic means with 95% confidence

ntervals (CI) or medians and interquartile range. Compari-on of means was by t-test or analysis of variance (ANOVA).omparison of proportions used Chi-squared tests. A non-arametric test (Stata module – nptrend, an extension of theilcoxon rank-sum test) was used to assess trend across

rdered groups [16]. A generalised least squares multilevelegression (GLS) model was used with cross-sectional time-eries models. Maximum-likelihood random-effects modelsay be used to describe relationships across time in a longitu-

inal dataset with multiple missing data points. Fasting or 2 hlucose as the dependent variable was fitted against explana-ory cardio-metabolic risk factors including blood pressurend lipids, adjusting for age, sex and BMI.

1.9 (1.5–2.2) F = 0.11; p = 0.738

t-prandial.

3. Results

3.1. Baseline characteristics

At baseline, 104 participants consented to an initial assess-ment. Twelve respondents were found to have type 2 DM at thepoint of screening (baseline) and were therefore excluded fromfurther follow-up in this study. The remaining 92 individuals(45% female) with either impaired glucose tolerance (IGT) orimpaired fasting glycaemia (IFG) were followed-up up prospec-tively. Opportunistic screening at six monthly intervals over aperiod of 48 months was undertaken in these participants.

As shown in Table 1, there were no differences in age, BMIor BP for men and women at baseline. Baseline BMI in menwas 29.1 (95% CI 27.3–31.0) and 30.3 (95% CI 28.2–32.3) kg/m2 inwomen. No significant differences were noted for fasting and2 h glucose, however mean HDL (p < 0.001) and total cholesterol(p = 0.015) levels were greater in women at baseline.

3.2. Follow-up and pharmacologic therapy

The mean number of lifestyle advice sessions attended was2.2. In the course of follow-up, 5.5% of participants werestarted on Metformin. 72% were started on lipid lowering and85.4% on BP lowering treatment. At the end of the study, 14.6%of patients were on no anti-hypertensive treatment, 30.3% ofpatients were on 1 antihypertensive agent, 30.3% of patientswere on 2 antihypertensive agents, 16.9% of patients wereon 3 antihypertensive agents and 7.9% of patients were on4 antihypertensive agents. Similarly 11.5% were on no phar-macological lipid lowering treatment, 81.6% of patients wereon statins alone, 3.5% on Ezetimibe, 2.3% on the combinationof statin and Ezetimibe, none on fibrates alone and 1.1% on astatin plus fibrate combination.

3.3. Cardiometabolic outcome data

Diabetes developed in 18/92 (19.5%) participants during thefollow-up period (up to 48-months). At the mid-point interval(24-month), mean fasting glucose for men and women com-

bined was lower than at baseline (6.4 (95% CI 6.0–6.8) vs 6.6(95% CI 6.4–6.9) mmol/L; F 3.67, p < 0.001, p < 0.001). Trends inmetabolic variables over time are shown in Fig. 2 (median andinterquartile range (IQR)) and Table 2 (mean, 95% CI). Analyses

216 p r i m a r y c a r e d i a b e t e s 6 ( 2 0 1 2 ) 213–219

Fig. 2 – Box plots of median (IQR and range) of fasting glucose, HbA1c, BMI, cholesterol, triglycerides and systolic bloodpressure (SBP) over study period. Thick horizontal lines are medians; bottom and top box borders are 25th and 75thpercentiles; and whiskers are data range. Follow-up numbers are as follows: baseline 92: 6 months 56: 12 months 64:

mo

18 months 56: 24 months 53: 30 months 33: 36 months 29: 42

at longer follow-up intervals gave no difference between firstand last fasting glucose measurements due to low numbers of

subjects returning for follow-up.

There was no significant change in weight over the follow-up period (at 24 or 48 months) for whole group, despite the

Table 2 – Mean (95% CI) of the cardiometabolic variables over ti

Baseline 6 months 12 m

BMI (kg/m2) 29.6 (28.3–30.9) 28.6 (26.7–30.6) 28.3 (2Fasting glucose (mmol/L) 6.6 (6.4–6.9) 6.4 (6.0–6.7) 6.3 (52 h P-P glucoseb (mmol/L) 8.0 (7.4–8.6) – –

Total cholesterol (mmol/L)a 5.1 (4.9–5.3) 4.6 (4.3–4.9) 4.5 (4LDL (mmol/L) 3.1 (2.9–3.2) 2.4 (2.1–2.7) 2.6 (2HDL (mmol/L)a 1.23 (1.16–1.30) 1.24 (1.13–1.35) 1.29 (1Triglycerides (mmol/L) 1.8 (1.5–2.1) 1.7 (1.3–2.2) 1.6 (1SBP (mmHg) 139 (136–142) 136 (133–140) 137 (1DBP (mmHg) 80 (78–82) 78 (75–80) 77 (7HbA1c (%) 6.1 (5.9–6.3) 6.3 (6.1–6.7) 6.2 (6

a Age and sex adjusted means.b Less than 10 respondents had a 2 h post-prandial glucose at each time p

nths 17; 48 months 16.

targeted lifestyle (diet and exercise) advice. A trend for anincrease in HbA1c over time from baseline was observed

(Table 2). A significant reduction in systolic BP (by 6.0 (95%CI 1–11) mmHg, p = 0.013), diastolic blood pressure (by 4(95% CI 1–7) mmHg, p = 0.015), total cholesterol (0.66 (95% CI

me.

onths 18 months 24 months 30 months

6.7–29.9) 29.0 (27.1–30.8) 29.0 (27.0–30.8) 31.6 (28.7–34.6).9–6.6) 6.4 (6.0–6.8) 6.4 (6.0–6.8) 6.6 (6.1–7.1)

– – –.3, 4.8) 4.6 (4.4–4.9) 4.2 (3.9–4.5) 3.9 (3.3–4.6).3–2.8) 2.5 (2.2–2.8) 2.3 (2.0–2.6) 2.2 (1.8–2.7).18–1.40) 1.34 (1.22–1.47) 1.32 (1.19–1.45) 1.14 (0.96–1.32).2–2.0) 2.0 (1.6–2.5) 1.4 (1.0–1.9) 1.6 (1.0–2.2)33–140) 134 (130–138) 133 (130–137) 136 (132–141)5–80) 75 (73–78) 76 (74–78) 77 (74–80).0–6.5) 6.4 (6.1–6.7) 6.3 (6.1–6.6) 6.5 (6.2–6.9)

oint after baseline.

p r i m a r y c a r e d i a b e t e s 6 ( 2 0 1 2 ) 213–219 217

Table 3 – Regression models of the determinants of fasting and 2 h over time.

Dependent variables

Fasting glucose (per mmol/L) ̌ coefficients (95% CI)

2 h P-P glucose (per mmol/L) ̌ coefficients (95% CI)

Model 1a Systolic BP (per 5 mmHg) 0.06 (0.01, 0.11); p = 0.019 0.24 (0.08, 0.40); p = 0.004Model 2a Diastolic BP (per 5 mmHg) 0.10 (0.01, 0.19); p = 0.030 0.30 (0.02, 0.57); p = 0.033Model 3a HDL (per 1 mmol/L) −0.20 (−0.72, 0.32); p = 0.448 −2.17 (−4.23, −0.09); p = 0.041Model 4a LDL (per 1 mmol/L) 0.23 (0.01, 0.45); p = 0.011 0.06 (−0.69, 0.71); p = 0.868Model 5a Triglycerides (per 1 mmol/L) 0.20 (−0.02, 0.42)†; p = 0.07 −0.02 (−0.61, 0.57); p = 0.952

a Age, sex and BMI adjusted.† Significant if BMI excluded from model ( ̌ = 0.25 (0.06, 0.44), p = 0.011). P-P = post-prandial.

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.4–1.0) mmol/L, p < 0.0001) and LDL-cholesterol (by 0.6 (95%I 0.1–0.2) mmol/L, p = 0.002) was also observed. Triglyceride

evels fell by 0.13 mmol/L ((95% CI: −0.07 to 0.48); p = 0.135)t the mid-point. However this reduction was not significant.here was an indication pointing to an increase in mean agend sex-adjusted HDL-cholesterol levels (0.12 mmol/L (95% CI:.22–0.02)) over time (p for trend = 0.047).

Finally, GLS models were used to determine the naturef the relation between fasting and post-prandial 2 h glucoseith different cardio-metabolic risk factors over time (Table 3).fter adjusting for age, sex and BMI, over the follow-up period

h glucose was independently and negatively associated withDL-cholesterol ( ̌ = −2.17, p = 0.041), and positively with sys-

olic BP ( ̌ = 0.24, p = 0.004 per 5 mmHg), also with diastolicP ( ̌ = 0.30, p = 0.033 per 5 mmHg). The relation with totalholesterol was not significant. Similar findings were notedsing fasting glucose as a dependent variable. Thus over time,elative to 2 h glucose, cholesterol and diastolic BP fell andDL-cholesterol rose.

. Discussion

.1. Summary of main findings

ur results demonstrate that the measures instituted to helpeople with impaired glucose handling to reduce weight wereot effective. However, in this study interventions for lower-

ng of blood pressure and serum lipids were effective. It is ofote that only a small proportion of patients were started onetformin.

.2. Comparison with other studies

n the event of more individuals being started on Metformin,here might have been a greater reduction in fasting bloodlucose [10] with the caveat that in the Diabetes Preventionrogramme [10], Metformin was less effective than lifestylentervention in preventing progression to type 2 DM.

The importance of timely targeting of people with IGH forntensive cardiovascular risk factor management resonates

ith the recently published findings of the ACCORD studyhere the use of intensive therapy in patients with established

ype 2 DM actually increased mortality [17,18]. In other wordsuch of the damage to the cardiovascular system may have

already occurred before people are diagnosed with type 2 DM[11,12]. Nevertheless, timely intervention in newly diagnosedDutch patients with Type 2 DM has been shown to improvecardiovascular risk factor levels without untoward impact onhealth related quality of life measures [19].

Convincing evidence for a relation between abnormal glu-cose tolerance and an increased coronary artery disease riskhas been provided by the DECODE study with data from morethan 10 prospective European cohort studies including morethan 22,000 subjects [19,20]. Significantly increased mortalitywas also observed in subjects with IGT.

More targeted lifestyle intervention addressing balance ofmacronutrients, total calorie intake and exercise, for weightand waist circumference reduction is manifestly necessary.There needs to be clarification internationally and amonghealth care providers of what pharmacological interven-tions are appropriate for impaired glucose handling, eitherMetformin (6) or a glitazone for those who are Metformin intol-erant [21,22]. Weight reducing drugs may also have a role here[23], although there is currently only one agent available theUK at present, with the current suspension of other agents.

A key finding of an earlier study [24] was the great dispar-ity between the aspirations of clinicians and researchers withregard to the development of effective measures to reduce therisk of developing type-2 DM at a primary care and popula-tion level, and the knowledge and expectations or willingnessto change of individuals who actually are at risk. Cliniciansshould not assume that patients share their model of risk [25].

4.3. Strengths and limitations

A strength of this study is its duration and the regularity offollow-up. This has resulted in a high level of detail about theparticipants in relation to their cardiometabolic outcomes. Aweakness is the relatively high attrition rate over time withincomplete data from all individuals. Another valid criticismis that insufficient data was collected in terms of family his-tory of cardiovascular disease [26] to build a cardiovascular10 year risk score in our study. Also the lifestyle interventionmight have been more intensively targeted. Finally, screeningwas not systematic. However the individuals included are rep-

resentative of the type of patients currently being included inUK GP registers for IFG and IGT.

Follow-up interval in our study was more variable thananticipated. As is the case for patients with proven diabetes

e t e s

r

218 p r i m a r y c a r e d i a b

mellitus, there is a need for a clear structure for follow-upinterval, preferably built within a template in the GP electronicrecord to avoid ‘opportunistic measurement’. Furthermore itis important to ensure that all health care professionals work-ing in the practice are signed up to the project to ensure joinedup thinking.

Finally we do not have any data in relation to adherence todiet and exercise advice given nor its specific impact.

We now have a modular intervention program targetingobese individuals with type 2 DM across may GP practices inCheshire. This would have been utilised if it had been availableat the time of this study. The low percentage of patients startedon Metformin probably also influenced our results.

4.4. Suggested further research

The next step would be to increase the sample size acrosshealth care communities (such as Primary Care Trust areas) todetermine the prevalence of IGH and the variability accordingto geography, socio-economic indices and ethnic variation. Wealso plan to determine the effectiveness of GP surgery-basedlifestyle intervention programs already in place, at reducingtype 2 DM incidence and the sustainability of weight reduc-tion achieved after termination of the formal period of anyintervention.

4.5. Implications for practice

More targeted lifestyle intervention for weight and waist cir-cumference reduction is manifestly necessary. There needsto be clarification internationally and among health careproviders of what pharmacological interventions are appro-priate for impaired glucose handling, either Metformin orother insulin sensitising agents. Lifestyle modulating pro-grams and weight reducing drugs also have a role here.

5. Conclusion

In conclusion, targeted intervention has an effective role inimproving lipid and BP profile in impaired glycaemia. Howeverthe intervention as reported here, was less successful for gly-caemia and weight. While we have effective pharmacologicalstrategies for dealing with dyslipidaemia and hypertension,the tools currently utilised for addressing dysglycaemia man-ifestly have much less impact.

More work needs be done across our heath economies, toevaluate the impact of impaired glycaemia registers on localdiabetes incidence and the role of specific pharmacotherapeu-tic strategies, such as Metformin in the setting of impaired

glucose handling, in reducing the numbers of people progress-ing to type 2 DM.

Conflict of interest statement

The authors state that they have no conflict of interest.

6 ( 2 0 1 2 ) 213–219

Acknowledgements

We acknowledge the help of Geoff Leigh of Central and East-ern Cheshire PCT as we do that of EMIS®, without whose helpthis project could not have come to fruition. S.G.A. is an NIHRAcademic Clinical Fellow in Cardiovascular Medicine.

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