Measurement of total body water (TBW) and total energy ...eprints.qut.edu.au/44135/1/Cornelia_Wishart_Thesis.pdf · Measurement of total body water (TBW) and total energy expenditure

Measurement of total body water (TBW) and total energy expenditure (TEE) using

stable isotopes

A thesis submitted in fulfillment of the requirements for the award of:

Master of Applied Science (Research)

By

Cornelia Wishart

Institute of Health and Biomedical Innovation

School of Human Movement Studies

Faculty of Health

Queensland University of Technology

ii

KEYWORDS

Total energy expenditure,

Total body water,

Isotope ratio mass spectrometry,

Stable isotopes, doubly labeled water,

Body composition,

Deuterium,

Oxygen‐18

iii

STATEMENT OF ORIGINAL AUTHORSHIP

“The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the

best of my knowledge and belief, the thesis contains no material previously

published or written by another person except where due reference is made.”

Signature

Date

iv

ABSTRACT

Understanding the relationship between diet, physical activity and health in humans

requires accurate measurement of body composition and daily energy expenditure.

Stable isotopes provide a means of measuring total body water and daily energy

expenditure under free‐living conditions. While the use of isotope ratio mass

spectrometry (IRMS) for the analysis of 2H (Deuterium) and 18O (Oxygen‐18) is well

established in the field of human energy metabolism research, numerous questions

remain regarding the factors which influence analytical and measurement error

using this methodology. This thesis was comprised of four studies with the following

emphases. The aim of Study 1 was to determine the analytical and measurement

error of the IRMS with regard to sample handling under certain conditions. Study 2

involved the comparison of TEE (Total daily energy expenditure) using two

commonly employed equations. Further, saliva and urine samples, collected at

different times, were used to determine if clinically significant differences would

occur. Study 3 was undertaken to determine the appropriate collection times for

TBW estimates and derived body composition values. Finally, Study 4, a single case

study to investigate if TEE measures are affected when the human condition changes

due to altered exercise and water intake.

The aim of Study 1 was to validate laboratory approaches to measure isotopic

enrichment to ensure accurate (to international standards), precise (reproducibility

of three replicate samples) and linear (isotope ratio was constant over the expected

concentration range) results. This established the machine variability for the IRMS

equipment in use at Queensland University for both TBW and TEE.

Using either 0.4mL or 0.5mL sample volumes for both oxygen‐18 and deuterium

were statistically acceptable (p>0.05) and showed a within analytical variance of 5.8

Delta VSOW units for deuterium, 0.41 Delta VSOW units for oxygen‐18. This variance

was used as “within analytical noise” to determine sample deviations. It was also

found that there was no influence of equilibration time on oxygen‐18 or deuterium

values when comparing the minimum (oxygen‐18: 24hr; deuterium: 3 days) and

v

maximum (oxygen‐18: and deuterium: 14 days) equilibration times. With regard to

preparation using the vacuum line, any order of preparation is suitable as the TEE

values fall within 8% of each other regardless of preparation order. An 8% variation

is acceptable for the TEE values due to biological and technical errors (Schoeller,

1988). However, for the automated line, deuterium must be assessed first followed

by oxygen‐18 as the automated machine line does not evacuate tubes but merely

refills them with an injection of gas for a predetermined time. Any fractionation

(which may occur for both isotopes), would cause a slight elevation in the values and

hence a lower TEE.

The purpose of the second and third study was to investigate the use of IRMS to

measure the TEE and TBW of and to validate the current IRMS practices in use with

regard to sample collection times of urine and saliva, the use of two TEE equations

from different research centers and the body composition values derived from these

TEE and TBW values.

Following the collection of a fasting baseline urine and saliva sample, 10 people (8

women, 2 men) were dosed with a doubly labeled water does comprised of 1.25g

10% oxygen‐18 and 0.1 g 100% deuterium/kg body weight. The samples were

collected hourly for 12 hrs on the first day and then morning, midday, and evening

samples were collected for the next 14 days. The samples were analyzed using an

isotope ratio mass spectrometer. For the TBW, time to equilibration was determined

using three commonly employed data analysis approaches. Isotopic equilibration

was reached in 90% of the sample by hour 6, and in 100% of the sample by hour 7.

With regard to the TBW estimations, the optimal time for urine collection was found

to be between hours 4 and 10 as to where there was no significant difference

between values. In contrast, statistically significant differences in TBW estimations

were found between hours 1‐3 and from 11‐12 when compared with hours 4‐10.

Most of the individuals in this study were in equilibrium after 7 hours.

The TEE equations of Prof Dale Scholler (Chicago, USA, IAEA) and Prof K.Westerterp

were compared with that of Prof. Andrew Coward (Dunn Nutrition Centre). When

vi

comparing values derived from samples collected in the morning and evening there

was no effect of time or equation on resulting TEE values.

The fourth study was a pilot study (n=1) to test the variability in TEE as a result of

manipulations in fluid consumption and level of physical activity; the magnitude of

change which may be expected in a sedentary adult. Physical activity levels were

manipulated by increasing the number of steps per day to mimic the increases that

may result when a sedentary individual commences an activity program. The study

was comprised of three sub‐studies completed on the same individual over a period

of 8 months.

There were no significant changes in TBW across all studies, even though the

elimination rates changed with the supplemented water intake and additional

physical activity. The extra activity may not have sufficiently strenuous enough and

the water intake high enough to cause a significant change in the TBW and hence the

CO2 production and TEE values. The TEE values measured show good agreement

based on the estimated values calculated on an RMR of 1455 kcal/day, a DIT of 10%

of TEE and activity based on measured steps.

The covariance values tracked when plotting the residuals were found to be

representative of “well‐behaved” data and are indicative of the analytical accuracy.

The ratio and product plots were found to reflect the water turnover and CO2

production and thus could, with further investigation, be employed to identify the

changes in physical activity.

vii

Table of contents

KEYWORDS ......................................................................................... 2

ABSTRACT ........................................................................................... 4

Table of contents ................................................................................ 7

LIST OF ABBREVIATIONS .................................................................... 15

List of Tables ..................................................................................... 18

List of Figures .................................................................................... 20

Chapter 1 ............................................................................................ 1

Background ......................................................................................... 1

Components of energy expenditure and its assessment .................................................. 3

Basal metabolic rate .......................................................................................................... 6

Thermic effect of food (TEF) .............................................................................................. 7

Total Energy Expenditure .................................................................................................. 7

Total body water ............................................................................................................... 9

Thesis overview ............................................................................................................... 11

Chapter 2 .......................................................................................... 12

2.1 Literature review for TEE ............................................................. 12

DLW theory and deviations ............................................................................................. 15

Isotopic fractionation and other corrections .................................................................. 15

DLW assumptions ............................................................................................................ 16

DLW reliability ................................................................................................................. 18

Translation into TEE ......................................................................................................... 19

Estimation of constant turnover rates (Ko‐1 Kd‐1) ............................................................ 20

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Estimation of isotope dilution space ............................................................................... 20

Conversion of CO2 into energy ........................................................................................ 21

Different equation constants used by Schoeller / Westerterp and Coward / Klein. ....... 21

Coward’s equation .......................................................................................................... 22

Respiratory quotient ....................................................................................................... 22

Schoeller’s equation ........................................................................................................ 23

Calculation of elimination rate ........................................................................................ 26

Curve fitting to multi‐point.............................................................................................. 26

Quality control using residuals to determine data integrity ........................................... 27

Residual plots for oxygen‐18 and deuterium .................................................................. 27

Ratio and product plots ................................................................................................... 27

2.2 TBW literature review ................................................................. 29

Definition of a plateau ..................................................................................................... 31

Definition of the protocols in use .................................................................................... 32

Calculations for TBW ....................................................................................................... 32

TBW plateau calculation .................................................................................................. 32

TBW intercept method calculation ................................................................................. 33

Hydration coefficient ....................................................................................................... 34

Derivation of FM and FFM from TBW ............................................................................. 35

Body composition methods ............................................................... 35

DXA .................................................................................................................................. 35

Multi‐frequency bioelectrical impedance analysis .......................................................... 35

Total body volume ........................................................................................................... 36

Rationale for thesis ........................................................................... 36

Research questions addressed in this thesis ...................................... 37

Chapter 3 .......................................................................................... 39

ix

IRMS background ............................................................................................................ 39

IRMS functionality ........................................................................................................... 40

IRMS at Queensland University of Technology (QUT) ..................................................... 42

Laboratory practices at QUT to control analytical and measurement error ................... 45

Standard preparation ...................................................................................................... 45

Quality control within a run ............................................................................................ 45

Correction for memory and drift ..................................................................................... 46

Temperature controlled environment ............................................................................ 46

Enriched and natural abundance standards ................................................................... 47

Research questions to be considered in regard to the IRMS ............... 47

What are the methodological and analytical variances in the IRMS? ............................. 48

Measurement error definition and description .............................................................. 48

Question 1: Equipment variation and drift (precision and reliability

data) measuring 2H and 18O (known standards) ................................ 50

Introduction and Background .......................................................................................... 50

Definitions of Measurement error and drift ................................................................... 50

Sample analysis ............................................................................................................... 51

Results for IRMS variability beam size and machine drift ............................................... 52

Key findings and statistical analysis for question 1 ......................................................... 52

Question 2: Do different sample volumes affect the results? ............. 53

Introduction and background .......................................................................................... 53

Study Design .................................................................................................................... 54

Key findings and statistical analysis to Question 2 –oxygen‐18 ...................................... 54

Key findings and statistical analysis to Question 2‐deuterium ....................................... 56

Question 3: Does equilibration time affect the results? ..................... 57


x

Sample equilibration time ............................................................................................... 57


Oxygen ‐18 results ‐time variability ................................................................................. 59

Deuterium results ‐time variability ................................................................................. 59

Question 4: Number of samples required for analysis ........................ 60


Number of samples required for TEE or TBW estimates. ............................................... 60

Sample numbers for batch analysis (Duplicate or triplicate) .......................................... 61

Study Design for sample numbers required .................................................................... 61

Key findings for question 4 .............................................................................................. 62

Question 5: Variation in order of sample preparation ....................... 63


Study Design .................................................................................................................... 63

Statistical analysis of different methods of preparation ................................................. 65


Discussion ......................................................................................... 66

Question 1: Machine variability ...................................................................................... 67

Question 2: Sample analysis with regard to volume ....................................................... 67

Question 3: Equilibration time ........................................................................................ 68

Question 4: Number of samples to be analyzed ............................................................. 68

Question 5: Variation in order of sample preparation (2H or 18O first) ........................... 69

Conclusion ....................................................................................................................... 70

Chapter 4 .......................................................................................... 70

Participants ...................................................................................................................... 71

Study design .................................................................................................................... 72

Dose alterations .............................................................................................................. 74

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Measurements ................................................................................................................ 74

Sample analysis ............................................................................................................... 75

Body size and Body Composition .................................................................................... 76

Body composition measurements ................................................................................... 76

Data analysis .................................................................................................................... 76

Research questions for TEE ............................................................................................. 77

TEE calculations used ...................................................................................................... 77

Part 1 ‐ IRMS data, TEE analysis and estimation ................................ 78

IRMS data for the 10 participants ................................................................................... 78

Key findings and statistical analysis – Question 1: Do different time points affect the

results .............................................................................................................................. 81

Key findings and statistical analysis – Question 2: Do different mediums affect the

results .............................................................................................................................. 81

Key findings and statistical analysis – Question 3: Do different regression equations

affect the TEE results?. .................................................................................................... 82

Discussion ........................................................................................................................ 82

To check the analytical variance the following measures were implemented ............... 84

Ko/kd ratio ....................................................................................................................... 84

Covariance graphs and residual plots .............................................................................. 85

Dose ................................................................................................................................. 85

Food Quotient (FQ).......................................................................................................... 85

Jack Knife system ............................................................................................................. 86

Conclusion ....................................................................................................................... 86

Part 2‐TBW analysis and estimation .................................................. 87

TBW research question: .................................................................................................. 87

Calculations for TBW ....................................................................................................... 87

TBW plateau calculation .................................................................................................. 87

xii

Key findings and statistical analysis –Question 1: Variability in time to

equilibration ..................................................................................... 89

TBW Data analysis for plateau estimation ...................................................................... 90

Key findings to optimal collection time ............................................. 92

Key findings and statistical analysis – Question 2: What is the

difference in TBW (kg) using different equations? ............................. 93

Results ............................................................................................................................. 94

Key findings and statistical analysis: ‐ Question 3: What is the impact of

time variability on body composition (i.e. %body fat) estimates derived

from TBW and do these differ from values obtained by other modalities

– DXA, BODPOD and BIA? ................................................................. 99

Results ............................................................................................................................. 99

Percent Body fat derived from TBW all collection times .............................................. 100

Discussion ....................................................................................... 106

Question 1: What is the variability in time to isotopic equilibration and

optimal enrichment plateau time point? ......................................... 106

Question 2: Differences in TBW estimations using different equations

....................................................................................................... 109

Question 3: What is the impact of time variability on body composition

i.e. percent body fat estimates derived from TBW and do these differ

from values obtained by other modalities – DXA, BODPOD, BIA? .... 109

Conclusion ..................................................................................................................... 110

Chapter 5 ........................................................................................ 111

xiii

The hypothesis addressed within in this Chapter is: Can the TEE data

and covariance graphs track the changes brought about by altered

fluid intake and activity? ................................................................. 111

Introduction and literature review .................................................. 111

Subject and method ........................................................................ 112

Design ............................................................................................. 112

Study 1: No interference with regard to water intake or activity, TEE baseline to be

established to represent normal daily living. ................................................................ 112

Study 2: The activity level was altered by increasing the number of daily steps taken.112

Study3: The fluid intake was changed from Week 1 to 2 while activity levels were kept

constant. ........................................................................................................................ 113

Protocol .......................................................................................... 113

Dosing, sample collection and analysis ......................................................................... 113

Measurements .............................................................................................................. 113

Measurement of the thermic effect of a meal .............................................................. 114

Statistical analysis/Results .............................................................. 114

Statistical analysis .......................................................................................................... 114

Results ........................................................................................................................... 114

Results ........................................................................................................................... 120

Covariance residuals ....................................................................... 120

Ratio plots and product plots .......................................................... 122

Discussion ....................................................................................... 125

Conclusion ....................................................................................... 125

Chapter 6 ........................................................................................ 128

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Research questions for analytical variance on the IRMS: ................. 128

Bibliography ................................................................................... 134

xv

LIST OF ABBREVIATIONS

CV Coefficient of variation

RMR Resting metabolic rate

REE Resting energy expenditure

BMR Basal metabolic rate

BMI Body Mass Index

NCD Non‐Communicable Diseases

FAO/WHO/UNU Food and Agricultural Organization /World Health

Organization /United Nations University

DIT Diet induced thermogenisis

NEAT Non exercise activity thermogenisis

TEF Thermic effect of food

AEE Activity energy expenditure

ExEE Exercise energy expenditure

PAL Physical activity level

QUT Queensland University of Technology

BURP Brisbane Urbane Regional Precipitation (tap water)

REF Reference

TBW Total body water

DLW Doubly labeled water

TEE Total energy expenditure

IRMS Isotope ratio mass spectrometry

xvi

CO2 Carbon dioxide

DXA Dual energy X‐ray absorptiometry

1H Hydrogen

2H Deuterium

18O Oxygen‐18

A Dose administered to subject

a Dose diluted for analysis

h Hours

SD Standard deviation

Pool sizes and rate constants

N (Nd or No) Pool size

K (kd or ko) Rate constant

Fractionation factors

f1 2H2O vapor/liquid

f2 H218O vapor/liquid

f3 C18O2/H218O

Mass spectrometric variables

δ Isotopic enrichment relative to a standard

V‐SMOW Vienna Standard Mean Ocean Water

SLAP Standard Light Antarctic Precipitation

FFM Fat‐Free Mass

FM Fat Mass

xvii

IAEA International Atomic Energy Agency

xviii

List of Tables

Table 1: Total Energy Expenditure measurements by various methods ....................... 5

Table 2.1: A comparison of equation constants used by Schoeller and Coward to .... 25

Table 3.0: Study design for question 1. ....................................................................... 43

Table 3.1: Mass spectrometer settings for H and CO2 ................................................. 50

Table 3.10: Triplicate or duplicate analysis (routine batch of deuterium) .................. 60

Table 3.11: IRMS Results for deuterium and oxygen‐18 preparations in delta V‐

SMOW units (‰) ...................................................................................................... 62

Table 3.12: Statistical comparison of the different methods of preparation ............. 63

Table 3.2: Isotope assay results to establish machine drift; deuterium and oxygen‐18

(n=20) Mean and SD of 20 samples shown ............................................................. 52

Table 3.3: Results for different sample volume sizes for Oxygen‐18 (N=20 for each

point). ....................................................................................................................... 53

Table 3.4: Statistical analysis for the different Oxygen‐18 volumes. .......................... 54

Table 3.5: Different Deuterium sample volume (N=20 for each point) ...................... 55

Table 3.6: Statistical analysis for Reference deuterium volume changes ................... 55

Table 3.7: Study design for question 3 ........................................................................ 57

Table 3.8: Results for Oxygen‐18 variability in 2 different equilibration batches ....... 58

Table 3.9: Results for Deuterium variability in 2 different equilibration batches ....... 58

Table 4.1. cont.: Urine enrichments in delta V SMOW units (‰). .............................. 76

Table 4.1: Urine enrichments in delta V‐SMOW units (‰) ......................................... 77

Table 4.10: Statistically non significant collection times shaded in blue .................... 91

Table 4.11: Results of TBW (kg) using different equations ......................................... 92

Table 4.12: Descriptive statistics for different equations of TBW estimation ............ 93

Table 4.13: Results of TBW (kg) using different equations ......................................... 95

Table 4.14: Statistical comparison of TBW (kg) using different equations ................. 96

Table 4.15: TBW (kg) and percent body fat data ......................................................... 98

Table 4.16: % Body fat values derived from different equipment and equations urine

h 6, saliva h 4 ............................................................................................................ 99

Table 4.17: Statistical comparison of percent body fat derivatives from TBW and

equipment measurements. ................................................................................... 100

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Table 4.18: Statistical comparison of % body fat derivatives from TBW and TEE

measurements ....................................................................................................... 101

Table 4.2: TEE (kcal/day) values for urine and saliva samples using Schoeller and

Coward equations at different time points across the day; .................................... 78

Table 4.3: Comparison of times for urine (Coward) n=10 ........................................... 79

Table 4.4: Comparison of Schoeller urine and saliva ................................................... 80

Table 4.5: Different equations comparison n=10 ........................................................ 80

Table 4.6: Jack Knife technique ................................................................................... 84

Table 4.7: Urine IRMS deuterium data ........................................................................ 87

Table 4.8: TBW (kg) for urine and saliva ...................................................................... 88

Table 4.9: Comparison of TBW (kg), (n=7) urine = U, saliva = S .................................. 90

Table 5.1: Dilution spaces and elimination rates per day ......................................... 114

Table 5.2: TEE (kcal/day), daily water intake and step count per week .................... 115

Table 5.3 Comparison of values using Jack knife method ......................................... 116

Table 5.4 Comparison of TBW (kg) values derived from TEE intercept during the 5

studies .................................................................................................................... 117

xx

List of Figures

Figure 1: Schematic diagram of Total Energy Expenditure components ...................... 6

Figure 2.1: Diagram to explain the principles of the DLW technique ......................... 15

Figure 2.2: Diagram to show elimination rates of Deuterium and Oxygen‐18 ........... 16

Figure 2.3: Covariance residual plots ........................................................................... 27

Figure 2.4: Figure of ratio plot indicating CO2 production .......................................... 27

Figure 2.5: Figure of product plot indicating water turnover ...................................... 28

Figure 3.1: Hydra 20/20 IRMS ABCA‐G unit, sampling carousel and Hydra 20/20 ...... 40

Figure 3.2: Diagram of Sercon Hydra 20/20 CF ‐IRMS ................................................. 42

Figure 4.1: Collection schedule for 14 days of sampling ............................................. 71

Figure 4.2: TBW (kg) urine (uncorrected) values over time in hours .......................... 89

Figure 4.3: Time to isotopic equilibration for all 3 methods for urine ........................ 89

Figure 4.4: Time to isotopic equilibration for all 3 methods ‐ saliva ........................... 90

Figure 4.5 Graphical representation of % Body Fat derived from uncorrected urine

plateau values and Schoeller TEE urine and saliva values ..................................... 102

Figure 4.6: Graphical representation of %body fat derived from uncorrected urine

plateau values and Coward TEE urine and saliva values ....................................... 103

Figure 5.1: Comparison of covariance residuals based on samples pre, 6 h, d 1, 2, 8,

10, 12, 13 ................................................................................................................ 119

Figure 5.2: Residual product plots (water turnover) based on samples pre, 6 h, d 1, 2,

8, 10, 12, 13 ............................................................................................................ 121

Figure 5.3: Ratio residual plots (CO2 production) based on samples pre, 6 h, d 1, 2, 8,

10, 12, 13 ................................................................................................................ 122

1

Chapter 1

Background

Changing global economic markets and industrialization have resulted in enormous

changes in lifestyle conditions for urban and rural populations (World Health

Organisation, 2003). Globally, the diets of many populations now consist of a larger

proportion of refined and processed foods and similarly, a more sedentary lifestyle

both at work and in the home has resulted in an imbalance between energy intake

and total daily energy expenditure. As a result, obesity and related chronic diseases

are widespread and constitute a significant proportion of the world’s public health

problems. It is not uncommon to find the coexistence of under nutrition and obesity

in the same community (Shetty, 2005; World Health Organisation, 2003).

Overweight affects 30‐80% of adults in the World Health Organization (WHO)

European region of which 20% are children and adolescents. One third of this

number is obese. Results from the Australian Diabetes, Obesity and Lifestyle Study

suggest that during the period 1999‐2000, over 7 million Australians aged 25+ were

classified as being overweight as defined by a BMI >25 kg.m‐2. Over 2 million of

these were defined as obese with a BMI > 30 kg.m‐2 (Australia's Health., 2000). The

prevalence of obesity continues to rise with the annual rate of increase growing

steadily such that the current rate is 10 times higher than in the 1970s (WHO,

2002). By 2010, WHO estimates that in Europe, 150 million adults and 15 million

children will be obese. The growing problem of obesity and associated non‐

communicable diseases (NCDs) such as diabetes, heart disease, hypertension,

cardiovascular diseases and cancers is now so common in developing countries that

it is dominating more traditional public health concerns such as malnutrition and

infectious disease (Hossain et al., 2007; Shetty, 2005; World Health Organisation,

2003).

With the increasing prevalence of obesity there is an urgent need for standardized

measurement techniques with the ability to provide accurate estimations of the

main determinants of obesity. In 1985 the FAO/WHO/UNU Expert Committee on

Energy Requirements recommended that energy requirements be based on energy

2

expenditure rather than the traditional approach of energy intake (Schoeller et al.,

1996). Traditionally, energy intake has been monitored using food diaries, repeated

24 h recall, diet history and food questionnaires. Each of these methods has inherent

strengths and weaknesses with the most problematic element being the

requirement that individuals be scrupulously honest and accurate with regard to

reporting their food intake.

In contrast, total energy expenditure (TEE) can be assessed using stable isotopes to

provide an accurate and objective measurement of a TEE in a free‐living individual.

The criterion measure for TEE is the doubly labeled water (DLW) technique; double

in that two stable isotopes are used, oxygen‐18 and deuterium. The technique was

developed in the early 1950s by Nathan Lifson. After dosing the individual with both

stable isotopes, the oxygen‐18 washes out of the body as water and carbon dioxide

and the deuterium only as water. The difference in the elimination rates of the two

isotopes, after adjusting for isotopic fractionation, is a measure of CO2 production

rate. The advantage of this technique is that sample collection only involves urine

collection at timed intervals following initial dosing. These samples are then analyzed

using Isotope Ratio Mass Spectrometry (IRMS) to determine the stable isotope

remaining in the body. Average daily energy expenditure in kcal/day can then be

calculated using standard stoichiometric equations.

From the perspective of the participant, the technique is simple and straightforward.

In brief, a pre‐dose sample is collected, the DLW dose is administered orally and then

timed urine samples are collected for the ensuing 14 days. The technique is robust

with regard to accuracy, but is subject to error if some of the dose water is lost, if the

participant switches to a new water supply that has a different isotopic background

value, or if the urine specimens are contaminated or mislabeled with regard to date

and time. The DLW technique also requires the calculation of energy expenditure

from the CO2 production so an error in the macronutrient composition of the diet

can lead to bias (Black et al., 1986). During the 10 years after the initial use of the

approach in humans, the technique was extensively validated against indirect

calorimetric methods. Many discussions ensued regarding aspects of the procedure

including the optimal number of samples that should be collected and which

3

correction factors should be used in the calorimetric equations. Many of the issues

regarding the specifics of the DLW technique have been resolved and it is now

widely considered the gold standard for the measurement of free‐living TEE. The

DLW technique has been extensively validated against near continuous indirect

calorimetry and has been reported to be accurate to 1‐2% with a coefficient of

variation of 2‐12% (Schoeller, 1988). In recent years the DLW technique and its

application in research has grown dramatically along with technical improvements in

IRMS. The wider availability and the lower cost of oxygen‐18 has also made it

possible to apply the DLW technique to studies involving larger numbers of

participants rather than the smaller numbers that characterized earlier studies.

The DLW technique has contributed greatly to the expansion of our knowledge of

human nutrition. A more meaningful insight has been provided into the energy

requirements of individuals in health and disease and provided a validation tool for

the prediction of dietary intake. Several carefully controlled studies using DLW have

shown that energy expenditure levels actually exceed the values obtained using self‐

reported energy intake (Black et al., 1986; Hill et al., 2004; Ritz et al., 1994). These

studies have demonstrated that the degree of underreporting in self‐reported

intakes ranges from 10 to 45%. Interestingly, the underestimate is proportional to

the BMI of the participant. In other words, overweight individuals report how much

food they eat with less accuracy than individuals of average weight or individuals

who are underweight (Black et al., 1986; Goran et al., 1998; Schoeller et al., 1995).

Therefore, results from food diaries should be treated with considerable caution

when calculating TEE from dietary intakes.

Components of energy expenditure and its assessment

Several chronic diseases, such as type 2 diabetes, cardiovascular disease, and certain

cancers are associated with physical inactivity and excessive energy consumption

(Mahabir et al., 2006). Energy intake and physical activity energy expenditure, the

two major modifiable components of energy balance, have become major public

health foci in the development of chronic diseases. Energy expenditure has been of

interest in the investigation of human nutrition for over 100 years (Speakman, 1998)

4

with much of the early work undertaken on basal metabolic rate (BMR) conducted at

the beginning of the 20th century and earlier (Delaney et al., 1996).

TEE is comprised of resting metabolic rate (RMR), thermic effect of food (TEF) (also

known as diet‐induced thermogenesis (DIT) and activity energy expenditure (AEE)

which is made up of physical activity level (PAL) and non‐exercise activity

thermogenesis (NEAT); TEE = TEF +AEE +NEAT.

In energy balance, energy intake equals energy output and any imbalance between

the two components leads to either a weight gain or loss consistent with the First

law of Thermodynamics which states that energy is neither created nor destroyed;

rather it is converted from one form to another. Numerous techniques are available

to assess each of the components of TEE with each differing in analytical and

technical requirements, precision and accuracy.

The DLW technique is considered the “gold standard” for measuring total daily

energy expenditure under free‐living conditions because of its accuracy (Schoeller,

1988) but it is relatively expensive and impractical for large scale epidemiological

studies (de Jonge et al., 2007; Tooze et al., 2007). Various measurement approaches

can be used to predict activity energy expenditure, for example using heart rate

monitors, accelerometers, and physical activity questionnaires; however it is

important that they are validated against the criterion method, DLW.

5

Table 1: Total Energy Expenditure measurements by various methods

(Delaney et al., 1996).

Measurement Equipment Advantages/Disadvantages

Total Energy Expenditure

(TEE)

Metabolic chamber Restrictive, expensive but

does provide a true

estimate of 24 h TEE.


by Doubly labeled water

Doubly Labeled Water

dose, Isotope Ratio Mass

Spectrometer

Accurate measurement, low

subject requirement,

expensive equipment and

dose. Does provide an

average TEE 24 h TEE value.

Resting Metabolic Rate Metabolic cart Predictive equations used

to estimate TEE.

Physical Activity Level

(PAL)

Heart rate monitors,

accelerometers, activity

diaries

TEE is predicted from

estimates made using the

equipment.

Energy expenditure for physical activity can be calculated as 0.9 × TEE‐Resting

Metabolic Rate. Assuming the thermal effect food (TEF) to be 10%.

6

Figure 1: Schematic diagram of Total Energy Expenditure components

As outlined in Figure 1, TEE is comprised of three main components: resting

metabolic rate or resting energy expenditure (RMR, REE), diet‐induced

thermogenesis or thermic effect of food (DIT, TEF) and activity energy expenditure

(AEE). AEE is comprised of exercise energy expenditure (ExEE) and non‐exercise

activity thermogenesis (NEAT).

Basal metabolic rate

Basal metabolic rate (BMR) can be basically defined as the minimal rate of energy

expenditure compatible with life (Shetty, 2005). BMR is measured under standard

conditions of immobility in the fasted state (12–14 hours after a meal) in an ambient

environmental temperature of between 26 and 30 degrees centigrade, which

ensures no activation of heat‐generating processes such as shivering. Commonly,

BMR is measured upon waking with the participant having slept in a metabolic ward

of a hospital. However, given the limitations of this requirement, RMR is more

commonly used as a substitute but is slightly (5%) greater than BMR. RMR

measurements use the same protocol as for the BMR but do not require participants

Total Energy Expenditure value

comprised of: of

1 = Resting Metabolic

Rate under near basal

conditions (60‐75%

TEE)

3 = Activity

Energy

Expenditure

(PAL + NEAT)

Prediction

equation

Measurement

e.g. indirect

calorimetry

Physical activity

questionnaires or

predictive values

assigned by intensity

Doubly

Labeled Water

2 = Thermic Effect of

Food (often assumed

to be 10% TEE)

7

to sleep in a metabolic ward which enables participants to sleep at home and arrive

at the laboratory by car.

Thermic effect of food (TEF)

The thermic effect of food is the increase in resting energy expenditure after the

ingestion of food and is comprised of two components. The obligatory component,

which includes the processes of digestion, absorption, and storage of nutrients, and

a facultative component, which is linked with oropharyngeal stimulation (Diamond

et al., 1985). Of the three main components of TEE, RMR accounts for approximately

60‐75%, TEF accounts for 7‐13%, and the thermic effect of physical activity accounts

for the remaining 15‐30%. When summing these mean values approximately 2‐7%

remains unaccounted for but may include the influences of drug‐induced

thermogenesis (e.g., smoking) and a thermoregulatory component (e.g., energy

produced in response to cold) (Elia, 1992).


An objective and reliable method is required for measuring the interaction between

physical activity and health in free‐living individuals. The basic requirements of such

a method are that it be sustainable over long periods of time without causing

discomfort to the participant or interfere with their lifestyle. Various techniques exist

which can be grouped into the following categories: behavioral observation,

questionnaires, physiological markers (heart rate monitors and calorimetry) and

motion sensors. Lack of adequate criterion measures when comparing field methods

for the measurement of physical activity has always been a major obstacle. Each of

the methods has inherent strengths and weaknesses, making it difficult to determine

the true validity of any one particular method (Montoye et al., 1996; Westerterp,

1999). Indirect calorimetry, specifically the DLW technique, has become the gold

standard for the validation of field methods to assess physical activity (Melanson et

al., 1996; Westerterp et al., 1995) where physical activity is defined as body

movement produced by skeletal muscles and resulting in energy expenditure.

An excellent physiological measure of physical activity is to combine the use of stable

isotopes in the DLW technique to determine total daily energy expenditure with

8

indirect calorimetry to measure RMR (Byrne et al., 2005). Indirect calorimetry

involves the measurement of respiratory gas exchange, oxygen uptake and carbon

dioxide output while a participant is resting comfortably in a temperature‐controlled

room whereas DLW, using two stable isotopes (18O and 2H) (Schoeller et al., 1980) is

a form of indirect calorimetry used while individuals go about their daily lives.

Typically, the DLW technique is used to measure TEE over a period of approximately

two weeks with the collection of an individual’s baseline urine or saliva sample; the

administration of a dose of 18O and 2H and subsequent collection of urine samples

over the next 14 days with samples analyzed using IRMS.

Deuterium, a stable isotope of hydrogen, and the stable isotope oxygen‐18, are

biological tracers that quickly equilibrate in body water. The deuterium

concentration, nearly all of which remains associated with water molecules,

decreases as a result of dilution of body water by new, unlabelled water and

evaporative loss. The loss may be in the form of consumed food, drink and water

produced during oxidation from foodstuffs, coupled with the simultaneous loss of

labelled water via evaporation from skin and lungs and via excretions and secretions.

The rate constant for deuterium is derived as the slope of deuterium enrichment

against time and is a measure of the rate of water movement through the body

(International Atomic Energy Agency, 2009).

Most of the labelled oxygen is lost as water but some is lost as carbon dioxide

because CO2 in body fluids is in isotopic equilibrium with body water due to the

action of carbonic anhydrase present in red blood cells. Thus the slope of oxygen

elimination is steeper than the line for deuterium with the difference between these

two elimination rates being proportional to carbon dioxide production. TEE can be

calculated from carbon dioxide production using common indirect calorimetric

equations. It should be noted that there are differences in calculating the isotope

elimination rates depending on the method used, the two‐point (Schoeller equation)

and the multi‐point (Coward equation). Using the two‐point method the fractional

turnover is calculated from fitting a straight line between two log transformed data

points, one at the beginning the other at the end of the study (Schoeller, 1988). The

9

multi‐point method uses the gradient of a straight line fitted to a log transformed

multi‐point isotope enrichment points. This regression equation is used to estimate

initial concentration, fractional turnover rates of the isotopes, TBW, carbon dioxide

flux and TEE (Haggarty et al., 1988).

One of the limitations of the DLW technique is that the final energy value is based on

carbon dioxide production which necessitates knowledge of the macronutrient

composition of the diet. The food quotient is derived either from a record of dietary

intake or using a standardized value (Black et al., 1986). The use of a standardized

value may introduce error into the estimation of CO2, however the literature

suggests that the error is “negligible and should not exceed 2%” (Haggarty et al.,

1988).

Total body water

Measuring total body water in humans using the isotope dilution technique is the

most accurate method of measuring the bodies water pool. Uncertainty over losses

and gains from the dynamic water pool can be overcome by using the intercept

method or plateau approach. Non‐aqueous exchange is possibly the only remaining

variable in estimating TBW from the deuterium dilution technique. However,

assessment of total body water provides a means for evaluating body composition

according to the 2‐C model (FM and FFM) or as part of a multi compartment model.

At birth the body is comprised of 70‐75% water but this proportion decreases as the

body matures. In lean adults, the TBW approximates 50% however it may be less

than 40% in obese adults. Measurement of TBW enables FFM to be estimated and

fat mass (FM) to be calculated as the difference between body mass and FFM. TBW

measurement and the derived body composition values are being increasingly used

in health‐related research with correction values applied to TBW estimates to

correct for age and gender (Cole et al., 1992). The gold standard approach for the

assessment of TBW is the deuterium dilution technique using IRMS and Von Hevesy

(1934) is credited as the first to use isotope tracers, however many others have

followed (Halliday et al., 1977; Pace et al., 1945; Schoeller et al., 1980). After

10

collection of a baseline sample the dose is administered and samples are then

collected at timed intervals.

Two approaches can be used for TBW estimation, the intercept or plateau method.

The intercept method involves a longer collection period (7‐14 days) of urine

samples and is usually part of the TEE protocol. The plateau method is shorter and

can be completed within a timeframe ranging from 3‐12 hours depending on the

population being studied. Time to isotopic equilibration varies between individuals

depending on age, activity and different protocols used in various laboratories

(Colley et al., 2007; Schoeller, 1988; Westerterp 1998). Variability also depends on

the sampling medium as saliva, blood and urine have different equilibration times.

Early researchers (Mendez et al., 1970; Schloerb et al., 1950) suggested an

equilibration time of 1.5 to 3 hours for urine samples. However more recently,

collection times have ranged from 6 hour post‐dose (Bauer et al., 2005; Rush et al.,

2003; Schoeller et al., 1980) to 10 hour after dosing in the evening (van Marken

Lichtenbelt et al., 1994). The equilibration time in elderly groups, with residual urine

post voiding will also be delayed which resulted in the development of an overnight

TBW protocol (Westerterp, 1999).

TBW analysis has overcome past difficulties as stable isotopes can be applied in

practice with greater precision than radioisotopes such as 3H. Automation in the

IRMS field has also improved sample throughput and reduced sample analysis costs

(Prosser et al., 1991) which has also resulted in the increased use of stable isotopes

in body composition studies. Gas‐isotope ratio mass spectrometry (G_IRMS) is

considered to be the best analytical tool for the accurate and precise determination

of 2H and 18O content in physiological samples. The first G‐IRMS was described by

Neir in 1940. The basic design of the Neir‐McKinney type of mass spectrometer

persists today though it is far superior to its 1940 counterpart in that it is fully

automated and computer driven. The result is that TBW measurement, using stable

isotope tracers, is clearly the most appropriate primary method of body composition

analysis suitable for field use. Depending on the size of the study, less exacting

techniques such as anthropometry may be chosen to assess the whole population,

with validation of such approaches performed using TBW analysis in a representative

11

sample. Alternatively, TBW analysis may be applied for nutritional assessment of the

whole population under study (Chamney et al., 2007; Slater et al., 2005).

Thesis overview

The thesis is organized by chapters to detail the findings from 3 main methodological

studies and a case study. The primary focus of the thesis is the measurement and

validation of TBW and TEE by IRMS.

Chapter 1 provides the rationale for the study and background information on TEE

and TBW.

Chapter 2 details a literature review for TEE and TBW and the equipment used in

their assessment. The research questions raised based on the literature review are

also posed.

Chapter 3 details the assessment of the IRMS functionality and analytical variance. A

major focus of this work (study 1) was the examination of variability of sample

volumes, order of preparation, and within‐ and between‐day repeatability and

variability using the IRMS.

Chapter 4 details the results for the TEE and TBW studies. The impact of different

sample collection times and hence isotopic equilibration was investigated with

regard to TBW and the impact of this variability on body composition estimates.

Further, two different TEE regression equations were applied to the data generated

to ascertain the energy expenditure (kcal) differences in resultant TEE. The two

equations are used by two leading research groups namely Prof. Dale Schoeller

(Chicago, USA; IAEA) and Prof. Andrew Coward (Dunn Nutrition Centre, UK). The

Schoeller approach uses a two‐point collection of samples and Coward approach

uses a multi‐point sample approach.

12

Chapter 5 details Study 4, a pilot study (n=1) to test the variability in TEE as a result

of manipulations in fluid consumption and levels of physical activity. Changes which

may be normal, in a sedentary person, undertaking a new exercise regime.

Chapter 6 is a general discussion of all studies with conclusions and recommended

applications and practices.

Chapter 2

2.1 Literature review for TEE

DLW is considered the gold standard or the criterion measure of TEE. Unlike

traditional indirect calorimetric methods where the participant is confined to a

whole body calorimeter or attached to a gas analyzer, the DLW technique allows for

the non‐invasive measurement of CO2 production and hence energy expenditure

while the person leads their normal life. The technique was first reported by

Schoeller and van Santen (1982) and the technique has subsequently been

extensively evaluated (Prentice, 1990; Schoeller et al., 2002; Speakman, 1998;

Westerterp, 1999).

The knowledge that respiration and ventilation are essential functions of life dates

back to at least Biblical times. For example, in the Old Testament Book of Psalms, it is

stated with respect to animals “When thou takest away their breath, they die”

(Psalm 104) (Speakman, 1998). Scientific study of animal respiration has a history

which dates back to the 1600s where experimental work was done on mice by

Mayrow who determined that the air consists of different parts, only some of which

are usable in respiration (Speakman, 1998). In the late 1700s, after the presence of

carbon dioxide and oxygen were discovered, French chemists Lavoisier and Sequin

ran more sophisticated experiments. The animals or humans were confined to

chambers to quantify their oxygen consumption and CO2 production; these were the

beginnings of the indirect calorimetry methods still used today to quantify energy

expenditure (Speakman, 1998). Lavoisier is credited with the reporting of the

13

principles of thermodynamics and formulated the concept of energy balance where

a steady state of body weight is achieved when energy intake equals expenditure

(Ritz et al., 1994).

The discovery of stable isotopes of oxygen and carbon dioxide and the fact that their

behavior and chemical reactions were similar to the lighter naturally occurring

isotopes, enabled researchers to use them as tracers for the behavior of oxygen and

carbon dioxide. The concept of stable isotopes was proposed in 1912 by Soddy but

the investigations increased more substantially after the World War I when Aston

constructed a mass spectrometer to investigate neon isotopes. By 1925, the isotopic

composition of 50 elements had been discovered. This increased to 66 by 1933, and

83 by 1948 (Budzikiewicz et al., 2005). A characteristic of stable isotopes is that they

occur naturally in varying concentrations, with 0.015% of all hydrogen being 2H, the

balance (99.985%) being 1H, while the percentage of oxygen which is 18O being

0.20%, the balance (99.8%) is 16O and 17O.

By 1949, Lifson had performed several experiments on mice using water enriched

with 18O. He demonstrated that the oxygen in body water was in complete isotopic

equilibration with the oxygen in respiratory carbon dioxide. The significance of this

discovery was the knowledge that labeled oxygen was eliminated from the body as

CO2 and H2O, and labeled hydrogen was eliminated as H2O. The difference between

the two elimination rates would be the CO2 production and would indirectly allow

for the calculation of energy production. It took a further 6 years to develop this

simple theory into a working protocol, and in 1955 Lifson et al. published their first

study using the protocol. This publication informed readers that “only 2 blood

samples were required to reconstruct the elimination curves, while the animal (12

mice) could perform a whole variety of natural behaviours.” However it took another

9 years before the method was used to study the metabolism of a wild animal

(Speakman, 1998; Westerterp et al., 2004). Due to the relatively high cost of 18O,

work in the 1970s was undertaken mainly on small free‐living animals such as a

mouse and a lizard each weighing less than 1 kg. The method was not applied to

human research due to prohibitive isotope costs, limited technical expertise and less

interest in applicable clinical conditions such as obesity. However, as the cost of 18O

14

declined through the 1980s and the technological developments in mass

spectrometry became more advanced, it became feasible to test and dose people,

while still being able to maintain precision and accuracy of results. The first DLW

measurements were made by Schoeller in 1980 and following that, validation studies

proliferated in the following years on infants and adults. Since the mid‐1990s, the

technique has been used in excess of 100 published studies and has remained at this

level (Speakman, 1998; Westerterp et al., 2004).

There are currently several DLW protocols utilized by different laboratories for TEE

sample collection and analysis. The two most commonly cited are Schoeller,

Westerterp: two‐point TEE being a baseline and 14‐day sample, and Coward, Klein:

the multi‐point TEE collecting a baseline and then several samples for the next 14

days. There are also different methods for calculating TEE after the samples are

analyzed, each based on different fractionation factors. The different approaches

result in differences in the calculated values for CO2 production from which a

calculation of daily TEE for a 14‐day period is generated (Goran et al., 1994). The

DLW technique has a precision of 4‐7% and a limited time window of 0.5‐3 biological

half‐lives of the tracer (Dolnikowski et al., 2005; Westerterp et al., 2004). A half‐life

in an infant with high water turnover would be 6‐9 days, 3 days in extreme

endurance exercise and 40 days in extremely sedentary elderly subjects (Westerterp,

1999).

Advances in IRMS instrumentation have enabled researchers to apply the DLW

technique in large studies involving hundreds of participants. However, it must be

understood that the technique is very sensitive to the precision of isotopic analysis

(Speakman, 2005) and large variations between estimates still exist between

laboratories (Roberts et al., 1995; Schoeller et al., 1995). Despite these drawbacks,

the DLW technique is considered the gold standard for TEE assessment and has

contributed significantly to human nutrition knowledge. The technique has been

applied in the following categories: the assessment of energy requirements in heath

and disease, the study of the etiology of obesity and the validation of tools for the

assessment of dietary intake (recommended by the FAO/WHO/UNU) (Schoeller,

1999).

15

DLW theory and deviations

Isotopic fractionation and other corrections

In the DLW technique, it is the rate of carbon dioxide production that is measured.

The measurement is not based on gaseous production but rather on the established

physiological relationship of carbon dioxide production and kinetics of water

turnover (Lifson, 1966; Speakman, 1998). The underlying theory of the technique is

that O atoms in expired CO2 have isotopically equilibrated with O atoms in the body

water. Therefore, when loading the body with a dose of 18O and 2H water, the 2H is

eliminated from the body as water predominantly in the form of urine and to a

lesser degree sweat and breath vapor. Most of the 18O is eliminated as water but

some is lost as carbon dioxide, as CO2 in body fluids is in isotopic equilibrium with

body water due to the action of carbonic anhydrase within the red blood cells

(IDECG, 1990). The difference between the elimination rates from the body water

pool, after adjusting for isotopic fractionation, is therefore proportional to CO2

production and hence energy expenditure can be calculated.

Figure 2.1: Diagram to explain the principles of the DLW technique

Where the Lifson equation states

rCO2=N (k18‐k2)/2

16

Within the equation above N = Body water pool, k2 and k18 are the elimination rates

of deuterium and oxygen‐18.

In summary, the concentrations of deuterium and oxygen‐18 administered in the

DLW dose decrease in concentration within the body water over time, but the total

body water pool remains constant.

Figure 2.2: Diagram to show elimination rates of Deuterium and Oxygen‐18

DLW assumptions

Despite the DLW technique being more accurate than other measures of TEE, there

are numerous assumptions and therefore potential sources of error (Lifson, 1966;

Schoeller, 1988). Specifically

The volume of the body water pool remains constant.

Body water is a dynamic system with a variety of inputs (drink, food, and

metabolic water) and outputs (urine, feces, sweat, breath). Enrichment of total

body water by labeled isotopes is not instantaneous, but occurs over a period of

a few hours and then starts to decrease over time as labeled hydrogen and

oxygen molecules leave the system (Schoeller, 1999; Westerterp, 1999).

The rates of water influx and water and CO2 efflux are constant.

The isotopes label only the H2O and CO2 in the body.

17

An error in dilution space can result because the isotope exchange is not limited to

body water alone. The consequence is that body water pools estimated by DLW can

be between 2‐6% greater than when determined by desiccation (Schoeller, 1988).

The hydrogen dilution space tends to be larger than the oxygen dilution space

suggesting that more exchange occurs with hydrogen. The postulated source of

exchange for hydrogen is with the pool of acidic hydrogen in protein (Haggarty et al.,

1988; Schoeller et al., 1980) and to a lesser degree in the synthesis of fat and

protein. Oxygen undergoes exchange with organic matter to a lesser degree and is

involved in bone mineral exchange. To correct for this source of error, correction

values are used such that the hydrogen dilution space is divided by 1.041 and the

oxygen by 1.007 (Racette et al., 1994).

The isotopes leave the body only in the form of CO2 and H2O

It is generally theorized that the sum of water and carbon dioxide flux through the

body is equal to the elimination of stable isotope from the body. However the day‐

to‐day variations are quite small and the general consensus is that alternative routes

of water loss in humans are insignificant as they do not exceed 1% (Coward, 1988;

Ritz et al., 1994; Schoeller et al., 1995).

The isotopic abundance of H2O and CO2 leaving the body are the same as

those in body water (no isotopic fractionation).

H2O or CO2 that has left the body does not re enter it again.

The natural abundance or “background” levels of the isotopes remain

constant during the measurement interval.

Individuals who have travelled within the 2 weeks before or after dose

administration should be excluded from a study because this can cause error due to

changes in deuterium and oxygen‐18 background abundance (Horvitz et al., 2001;

Schoeller et al., 2002). Not all the tap water supplies have water with the same

natural abundance, due to isotopic fractionation. This is determined by the storage

systems in use i.e. water in Brisbane (QLD, Australia) as a result of being stored in

18

open dams and exposed to excessive sunlight, will have a different abundance to

water stored underground or in places where there is excessive rainfall.

DLW reliability

The DLW technique can be performed with a coefficient of variation of 3‐5%

however certain physiological and analytical requirements are essential to maintain

accuracy and precision (Schoeller et al., 1995). Physiological variations that will result

in differences between repeat TEE measurements include changes in physical activity

levels, changes in body composition, variation in dilution space of the isotopes,

background variations in the water supply and variations in respiratory quotient (a

reflection of the substrate utilization mix) (Schoeller et al., 2002; Schoeller et al.,

1995). The respiratory quotient here is the largest variable contributing up to 3% of

the CV. Analytical errors which result in differences between repeat TEE

measurements are those associated with isotopic measurement and thereby affect

the isotopic elimination rates, dilution spaces. The degree of precision depends on

the isotope dose, the isotope elimination rate, the metabolic period (collection time)

and the number of points used to calculate the elimination rate and dilution space

(Prentice, 1990; Schoeller et al., 1996; Wong, 2003).

For the 2‐point method, it is recommended that the precision of the isotopic analysis

be better than 1/600 of the initial isotopic enrichment = 0.1 delta units for 18O and

1.1 delta units for 2H. In contrast, the multi‐point method can tolerate a reduction in

precision in proportion to the square root of the number of points analyzed as more

points make it easier to fit a regression line with a higher R2 value. Practically,

multiple analyses of a single set of samples from a subject should be analyzed and

the CO2 production rate should have a CV of 4% for a 2‐point protocol and 2‐3.5% for

the multi‐point protocol. This requirement should easily be met by individuals whose

2H elimination rate is <75% of 18O rate, but becomes more difficult in situations

where the 2H elimination rate is >85%, due to excessive fluid intake and elimination

(Prentice, 1990; Schoeller et al., 1996).

19

Increasing the dose given can diminish the analytical error, however oxygen‐18 is

expensive and deuterium, while cheaper, needs to be kept within an optimum dose

range to prevent isotopic effects.

Following are recommended conditions to maintain the 3‐5% CV for analysis

1. Recommended average doses given to adults based on body weight: DLW

dose = 1.25 g/kg 10% 18O and 0.1 g/kg 100% 2H to achieve recommended

post‐dose enrichment values: 110 ‰ and 700 ‰ for 18O and 2H,

respectively.

2. Final collection day enrichment values: 8‰ and 120‰ for 18O and 2H,

respectively to ensure that values obtained are within the sensitivity

range of the IRMS (International Atomic Energy Agency, 2009)

3. Typical elimination rates in temperate climates: 0.095 d‐1 and 0.07 d‐1 for

18O and 2H.

4. The optimal observation interval following the first sample is one to three

biological half‐lives of the isotopes (Lifson, 1966) i.e. from 2.5 days in

extremely active subjects to 30 days in sedentary elderly (Westerterp,

1999).

DLW has a relative accuracy of 1%, a laboratory‐dependent analytical precision of 3%

or greater, and a within‐subject repeatability of 5 to 8% (Schoeller et al., 2002),

thereby enabling researchers to confidently estimate TEE based on direct

calorimetric research data.

Translation into TEE

DLW dose

C18O2

Tracers eliminated over time

2H218O

20

Deuterium elimination (water output) rH2O = TBW kh

Oxygen elimination (water and carbon dioxide) rH2O +2rCO2 = TBW k0

Arithmetic difference rCO2 = TBW x (ko‐kh)/2

CO2 converted to energy equivalent by using the ratio of carbon dioxide production

to oxygen consumption, specifically RQ = CO2/O2. Weir’s equation is then applied to

estimate energy expenditure.

Estimation of constant turnover rates (Ko‐1 Kd‐1)

The measured enrichments of the prepared samples are used to calculate the

dilution spaces of each isotope. Collection times are converted to decimal times. The

abundance of the baseline 18O and 2H samples are subtracted from all subsequent

samples to determine final enrichment. The data is then log transformed and plotted

against decimal time to provide regression lines which represent the elimination

rates for both 18O (ko) and 2H (kd).

Estimation of isotope dilution space

TBW, that is, dilution space for (ND and NO) are calculated using the equation

NO and ND (mol) = (A x T/18.02a) x (Ea‐Et) / (Es‐Ep)(Schoeller et al., 2002).

Where

NO = isotope dilution space of oxygen‐18 (mol)

ND = isotope dilution space of deuterium (mol)

A = amount of dose given in subject in g

T = tap water (mL) used to dilute the dose prior to IRMS analysis

a = amount of dose in grams weighed out into the tap water

Ea = dilute dose value in Delta units

Et = Delta units value of tap water used to dilute the dose

21

Es = Intercept point calculated by extrapolating the log natural graph of enrichment

vs. time back to time zero

Ep = Pre‐dose sample value in Delta units

18.02 =convert to moles H2O

Conversion of CO2 into energy

The initial equation proposed by Lifson and McClintock (1966) was based on volume

of TBW and deuterium elimination rates of the isotopes as follows:

rH20 =NDkD (Lifson, 1966)

Where rH20 is the total body water elimination rate, kD is the elimination rate of

deuterium, ND is the deuterium dilution space (kg).

Lifson observed that oxygen in water is in rapid and complete isotopic equilibration

with the oxygen in CO2. Accordingly, a water molecule labeled with oxygen‐18 will

not only mix and exit with water but also with carbon dioxide. Therefore, oxygen,

water and elimination rate will not equal water elimination rate.

rH20 +2rCO2 = Noko (Lifson, 1966)

Where NO is the oxygen dilution space, kO is the oxygen elimination rate. The factor

of 2 adjusts for the fact that there are twice as many atoms of oxygen in each CO2

molecule as there are in each water molecule.

By substituting the rH2O values in the above equation, CO2 production rates can be

solved

rCO2 = ½ (NOkO‐NDkD)

Different equation constants used by Schoeller / Westerterp and Coward / Klein.

Coward and Schoeller have slightly different approaches from this point due to the

use of different fractionation constants and collection protocols. Coward uses a

multi‐point sample collection protocol which includes sampling prior to dosing and

then after dosing, at 5 h and thereafter day 1 through to 14.

22

In contrast, Schoeller uses a two‐point protocol which includes a pre‐sample and

then three samples within 6 hours of dosing (at 2, 3 and 4 hrs) and then two samples

within 1 h of each other on day 14.

Coward’s equation

To account for fractionation (differences in bond energy); corrections have to be

applied to rectify the relative abundances of the isotopes.

rCO2 = ½ (NOkO‐NDkD) Lifson’s equation

CO2 production rate = ½ [(No x ko) ÷ f3‐(ND x kD) (xf2+1‐x) ÷ f3(xf1+1‐x)]

(Coward, 1988)

Where

NO is the 18O dilution space,

kO is the 18O elimination rate,

ND is the 2H dilution space,

kD is the 2H elimination rate,

f1 is the 2H fractionation factor between water and water vapour,

f2 is the 18O fractionation factor between water and water vapor,

f3 is the 18O fraction factor between water and CO2,

x is the proportion of water loss that is fractionated.

Where f1 = 0.941, f2 = 0.992, f3 = 1.04, x = 0.25

Respiratory quotient

Respiratory quotient (RQ) can be calculated from indirect calorimetry and is the ratio

of carbon dioxide production to oxygen consumption, specifically RQ = CO2/O2.

An RQ = 0.85 is used in the Coward equation to determine the O2 mol/day (Black et

al., 1986).

23

O2 in moles /day = CO2/0.85

This figure is then multiplied by 22.414 (conversion factor from Charles’s Law to

convert moles of oxygen to O2 L/day).

O2 in L/day = O2 in moles/day x 22.414

By substituting the CO2 and O2 values obtained from the above equations the TEE

can be calculated using Weir’s equation (Weir, 1949).

Weir’s equation TEE kcal/day = (3.941 x O2) + (1.106 x CO2 x 22.4)

Where units are ‐ TEE in kcal/day, O2 in l/day, CO2 in l/day

Schoeller’s equation

rCO2 = ½ (Noko‐NDkD)

Due to the fact that the isotopic tracers deuterium and oxygen‐18 do not mimic

exactly the physiological behavior of hydrogen (1H) and oxygen‐16 which make up

99% of water, corrections must be made for isotopic fractionation. Non‐aqueous

isotope tracers, in the case of deuterium with acidic hydrogen in protein

overestimates the deuterium dilution space by 4.1% compared to TBW (Racette et

al., 1994) alone. In case of oxygen‐18 the major exchange is with inorganic

compounds such as oxygen in phosphate and carbon in bone mineral. This exchange

increases the oxygen‐18 dilution space by 0.7% compared to TBW (Schoeller, 1988).

Incorporating these factors makes the equation:

TBWo=No/1.007

TBWd=Nd/1.041

TBWavg=(TBWo+TBWd)/2

The calculation of water turnover rH2O is based on the relationship:

rH2O = (kh x Dh)/f

Where kh is the elimination rate

24

Dh is the dilution space

f is the fractionation correction factor for 2H leaving the body via breath water and

insensible cutaneous water.

rCO2 = ko x Do –kh x Dh/(2‐f3)‐(f2‐f1)/(2 x f3) ‐0.0246

ko, kh, Do, Dh are elimination rates and dilution spaces respectively for oxygen and

deuterium.

f1= 0.941 fractionation of 2H in water vapour

f2 = 0.992 fractionation of 180 in water vapour

f3 = 1.039 fractionation of CO2

0.0246 is the constant rate of fractionated water loss

By assuming that breath is 96% saturated with water vapor and that the small

amount of non‐sweat skin vapor loss is proportional to the exposed surface area, the

equation can be simplified to

rCO2= 0.0455 x TBW (1.007k0‐1.041kd)

Finally, it is necessary to calculate energy expenditure from the rate of CO2

production (Black et al., 1986). This requires an estimate of the respiratory exchange

ratio and then energy production can be calculated using Weir equation (Weir,

1949). TEE (kcal/d) = 22.4 x rCO2 * (1.10 + 3.90/RQ).

Where the units used are TEE in kcal/day, O2 in L/day, CO2 in L/day.

RQ is the assumed respiratory exchange ratio of 0.85 (Black et al., 1986).

25

Table 2.1: A comparison of equation constants used by Schoeller and Coward to

derive TEE.

Schoeller No and Nd in kg, Coward in g.

Schoeller Coward

No or Nd= (T x A/a) x (Ea‐Et) / (Es‐

Ep)/1000

No or Nd= (A x T/a) x (Ea‐Et) / (Es‐Ep)

TBWo=No/1.007

TBWd=Nd/1.041

TBWavg=(TBWo+TBWd)/2

TBWavg(kg)x 1000/18.0153

rCO2 mol/day = 0.455 x (TBWavg) x (1.007

Ko)‐(1.041Kd)

rCO2 production rate mol day =

[(No x Ko) ÷2f3‐(Nd x Kd)*(xf2+1‐x)÷2f3(xf1+1‐x)]

f1 = 0.941, f2 = 0.992

f3 = 1.04

O2 in moles /day = rCO2/0.85

RQ = 0.85

rCO2 L/day = rCO2 mol/day* 22.414 O2 in L/day = 02 in moles/day x 22.414

TEE (kcal/d) = rCO2 L/day * (1.10 +

3.90/0.85)

RQ = 0.85

TEE kcal/day = (3.941 x O2) + (1.106 x rCO2)

26

Calculation of elimination rate

In the calculation of pool sizes, 2 different techniques are in use.

Multi‐point slope intercept – same data used to measure slope and time zero using

linear regression of natural log transformed isotopic enrichments. This will be correct

if mixing is instantaneous and rate constants stay the same over time. If variations

are random, the error is usually reduced to 1%.

2‐point plateau used to calculate volume, using the start and two end points of the

metabolic period, using two of the three values measured. This will be correct

irrespective of isotope mixing if all isotopes lost during the equilibration period can

be accounted for.

It is suggested that Nd/No values are used as an initial screen in detecting analytical

problems (values1.0‐1.07) (Schoeller, 1999).

Curve fitting to multi‐point

To obtain values for rate constants using multi‐point data, an appropriate curve must

be fitted to determine elimination rates:

Log transformation is the simplest and it assumes that the errors are

proportional; that is the residuals (differences between actual and predicted

values from linear regression equation derived from the log transformed

data) are constant in size relative to enrichment.

Exponential fit where rate constants are estimated by used untransformed

data after inspecting residual plots to determine errors.

If the data is “well‐behaved” either fit will be suitable, however it is recommended

that users should calculate all data sets using as many different ways. If the results

do not agree within a predetermined level say 3%, the results need to be scrutinized.

27

Quality control using residuals to determine data integrity

Residual plots for oxygen‐18 and deuterium

Covariance residuals are the difference obtained between the calculated values,

based on the isotope (deuterium or oxygen‐18) elimination rates (kd and ko) and the

measured values derived from the IRMS. These differences are indicative of the

precision of analytical measurement, and also indicate changes in water intake and

activity (Prentice, 1990). They indicate whether assumption of constant proportional

error is valid, as week‐to‐week variations under normal living conditions are not

random but are covariant.

Figure 2.3: Covariance residual plots

Ratio and product plots

The residual ratio plots (division of log natural values for oxygen‐18 and deuterium)

are indicators of CO2 turnover. Where activity is not excessive, in that the person is

not breathing heavily, the plots follow the zero line.

Figure 2.4: Figure of ratio plot indicating CO2 production

28

The residual product plots (multiplication of log natural values for oxygen‐18 and

deuterium) are indicative of water turnover (Prentice, 1990). Deviations away from

the zero line are indicators where water turnover has fluctuated.

Figure 2.5: Figure of product plot indicating water turnover

As demonstrated in the equation comparison, the estimation of TEE (kcal/day) is

based on several equations which each provide the answer for the next equation in

the cascade.

29

2.2 TBW literature review

Total body water (TBW) measurements using deuterium were first proposed by von

Hevesy and Hofer in 1934 and are commonly used as a criterion for comparison with

alternate measurement techniques of TBW (Ellis, 2000). The basic principle

underlying the technique is that the volume of a body composition compartment can

be defined by the dose of the tracer relative to its concentration in that

compartment within a specific time period following dose administration (Ellis,

2000). TBW measurement forms an integral part of body composition analysis

relevant to health‐based research; and is suitable for large studies in the field. This is

also aided by the fact that as mass spectrometer instrumentation (IRMS) has

improved, and become more sensitive, deuterium costs have dropped enabling large

numbers of TBW samples to be analyzed using a minimal amount of the tracer

(deuterium) isotope. The field test and sampling protocol can be developed using

minimal equipment and expertise, involving only the administration of a precise

amount of isotope dose, collection of fluid samples, their storage and shipment to an

IRMS laboratory. Here, under more rigorous and controlled laboratory conditions,

the IRMS analysis of TBW samples collected, can be undertaken by experienced

technical staff.

Body composition measurements with isotope dilution are usually performed in the

post‐absorptive state, i.e. in the early morning after an overnight fast and before any

food or drink is consumed (Lukaski et al., 1985) or after a small breakfast (Schoeller

et al., 1980; Wong et al., 1988). In other studies, subjects consume the dose at night

before bedtime and the equilibration takes place overnight (Westerterp et al., 1995).

Baseline samples are collected prior to dosing the subjects with a known dose of

isotope 2H or 2H and 18O if DLW is used, then post‐dose samples can be collected in

the field, in the form of urine, saliva, or blood. It has been reported that deuterium

in body water rapidly equilibrates with these media (Colley et al., 2007; Janowski et

al., 2004; Mendez et al., 1970; Schloerb et al., 1950; Smith et al., 2002). After

administration, the time taken for the deuterium dose to reach a stable value i.e. a

30

plateau, in the body water pool is highly variable. To counter the variability, the IAEA

has recommended the collection of 2 post dose samples at hourly intervals after the

third hour. If equilibrium has been reached the post dose samples are within 95% of

each other. In younger subjects water turnover is quicker which is reflected in the

water turnover values. If collected too early, analyses of urine samples can be

misleading if equilibration has not been achieved (Janowski et al., 2004). Therefore,

to ensure accuracy in this dilution method, samples must be collected when true

isotopic equilibration values have been attained. Due to the continual isotope decay,

a true plateau never occurs and therefore a theoretical plateau is the aim within this

technique. Importantly, the timing of the post‐dose sample is also dependant on the

sampling medium (urine, saliva, blood) as there is a difference in the isotopic

equilibration time of each (Colley et al., 2007; Halliday et al., 1977; Janowski et al.,

2004; Schoeller et al., 1982; Schoeller et al., 1980; van Marken Lichtenbelt et al.,

1994; Wong et al., 1988). The chronological age of the participant group is also a

determining factor in sample collection time as older individuals typically take longer

to reach isotopic equilibration due to slower renal output (Janowski et al., 2004).

The primary challenge hampering the deuterium dilution technique is the inability to

validate the measurement with an established technique (Speakman, 1998). Earlier

research in this field conducted between 1950 and 1970 was of the opinion that the

desired equilibrium time for urine samples was between 1.5‐3 h (Ellis, 2000; Mendez

et al., 1970; Schloerb et al., 1950). More recent research however (Schoeller et al.,

1982; Schoeller et al., 1980; Wong, 2003; Wong et al., 1988) has suggested that an

equilibration time of 6 hours or more may be required as 3 h is insufficient to reach

an equilibrated state. Butte et al. (1989) reported that equilibrium was not reached

in 100% of the subjects within 6 h while Van Marken and Westerterp (1994)

suggested that it may take up to 10 h for equilibration in some individuals. Similarly,

Blanc et al. (2002) found that 3 h was insufficient time to reach isotopic plateau in

21% of the participants tested, again providing evidence for a longer equilibration

time. Jankowski et al. (2004) suggested that steady state enrichments were achieved

within a minimum of 2.5 h in young healthy adults, however recommended

collection of samples at 3 × 30 min intervals to verify the attainment of steady state

31

(2.5 + 1.5 = 4 h). More recent studies have reported post‐dose urine sampling at 5 h

(Bauer et al., 2005; Colley et al., 2007; Rush et al., 2003). In contrast to times for

urine equilibration, Jankowski (2004) found that saliva samples achieved a plateau

within 2 h of ingestion and remained stable for several hours. Further, they also

found that there was not a significant difference between urine and saliva samples,

2‐6 h after dosing.

After dose administration, deuterium enrichment in the total body water pools is

highly variable until it reaches a relatively stable value for a few hours, which is

defined as plateau. As equilibration is highly variable between body fluids,

population groups and individuals, different timing protocols exist for the deuterium

dilution technique (Colley et al., 2007; Janowski et al., 2004; Schoeller et al., 1980).

These include the plateau, intercept and overnight equilibration protocols. The

specific features of these protocols are often driven by the particular preferences of

researchers or research institutes. This lack of consistency in the use of the

technique has made comparisons between studies challenging, particularly the

interpretation of results. Irrespective of the chosen protocol, it is critical that full

isotopic equilibration is attained to ensure valid TBW values.

For all protocols at least two to three samples are collected: a pre‐dose sample to

account for background isotope levels; and one or more post‐dose samples collected

following equilibration of the deuterium with body water (Ellis, 2000). The main

differences between the protocols are in the collection times, dosing approaches

and the interpretation of the equilibration plateau.

Definition of a plateau

A plateau or equilibrium has been defined several ways by the following researchers:

1. A difference of < 3% between 2 consecutive time points (Fjeld, 1988).

However, a more conservative value of < 2% has more frequently been

suggested and is considered an acceptable limit for analytic considerations

(Blanc et al., 2002; Salazar et al., 1994; Schoeller et al., 2000).

2. Janowski et al. (2004) defined a reference value as the average of 3 samples

taken during the fourth hour post‐dosing and suggested that equilibrium

32

occurred when the enrichment became ≤ 2% of the reference value at hour

4.

3. In contrast, other researchers have used the 6 h enrichment value as the

reference point (Schoeller et al., 1980; Wong et al., 1989).

Definition of the protocols in use

Plateau protocol – pre‐dose urine sample followed by a dose of 0.5 g/10% D2O/kg

body weight, urine sample collected 4‐6 h later.

Intercept protocol – pre‐dose urine sample, then a dose of 1.25 g/10%18O and 0.1 g/

100% D2O per kg body weight and then urine samples collected at 6 h on dosing day

and the second void each morning for the next 14 days. Calculation is derived as part

of the TEE technique by elimination rates of the oxygen‐18 and deuterium.

Overnight protocol – a pre‐dose urine sample followed by a dose of 0.1 g/kg TBW of

5‐10% D2O and a 10‐h overnight equilibration and then a urine sample collection

(Westertep, van Marken Lichtenbelt, 1995),used mainly in elderly subjects with

slower water turnover.

Calculations for TBW

Two approaches to TBW estimations were used utilizing the deuterium dilution

technique; namely the plateau or the intercept method. Both approaches have been

extensively examined and found to provide similar results (Schoeller et al., 1980).

The plateau TBW method is determined within 1 day, while the intercept TBW

method is part of a total energy expenditure calculation and the collection time

frame is 14 days. The isotopic dilution spaces were calculated according to Cole and

Coward (1992) where:

TBW plateau calculation

Equation 1 N = TA/ a * [(Ea ‐ Et) (Es ‐ Ep)]

N is the dilution space in grams, A is the isotope given in grams, a is a portion of the

dose (in grams) retained for mass spectrometer analysis, T is the amount of tap

water (mL) in which a is diluted before analysis. Ea, Et, Ep and Es are the isotopic

33

enrichments (in units) of the portion of dose, the tap water used, the pre‐dose

sample of physiological fluid (i.e. urine or saliva) and the post‐dose sample of

physiological fluid, respectively. Isotopic abundance is measured in Delta SMOW

units.

TBW (L) was subsequently calculated using Equation 2:

Equation 2 TBW = (N/ 1.04) /1000

The division of N by 1.04 corrects for the exchange of deuterium with non‐aqueous

hydrogen. 1000 = transforms TBW in g (mL) to kg (L)

Based on the assumption that the hydration of FFM is 0.73 for adults (Wong, 2003),

FFM (kg) was calculated using Equation 3:

Equation 3 FFM = TBW /0.73

Percent body fat (%) was subsequently derived from FFM and body weight (W) using

Equation 4:

Equation 4 % Body Fat = [(W‐ FFM) W]*100

The optimal approach is to correct for all intake and loss of fluid (Schoeller et al.,

1995) however this is often not practical.

TBW intercept method calculation

The back extrapolation or intercept method involves collection of a baseline sample,

administration of the dose followed by the collection of a sample at a fixed time

point (6 hours for urine, 4 hours for saliva) and then each morning for the next 14

days. All collection times were recorded. Briefly, the natural logarithm of the

elimination of the tracer from body water is plotted against time and the intercept

gives the tracer dilution at the time of dosing. This method is usually incorporated

into the doubly labeled water (DLW) technique which uses 18O and 2H to calculate

carbon dioxide production (rCO2). TBW calculation is an intermediary step in the final

calculation according to Schoeller et al. (1986) listed below.

34

No or Nd (kg) = ((W × A/a) × (ΔDD/ΔBW)/ (1000)

TBWo= No/1.007

TBWd = Nd/1.041

TBW avg = (TBWd + TBWo)/2

TBWav(kg)=TBWav×1000/18.0153

W = amount of water (g) to dilute the dose for IRMS analysis

A = amount of dose (g) given to the subject

a = amount of dose weighed out (g) for the dose dilution

ΔDD = enrichment of diluted dose minus tap water enrichment

ΔBW = enrichment of post‐dose sample minus baseline sample enrichment

1000 = transforms TBW in g (mL) to kg (L)

1.007 = correction factor for non‐aqueous oxygen exchange

1.041 = correction factor for non‐aqueous hydrogen exchange

Hydration coefficient

Cellular hydration in all animals is strictly controlled as is that of extracellular fluid.

However the ratio of extracellular to intra cellular fluid space may change. As ECF

and ICF hydration differs (0.98 vs 0.70), their altered ratio is largely the basis of

hydration changes in early life and disease. The work of Pace and Rathburn (1945) is

the source of the commonly used hydration coefficient of 0.732 and more recently

this has been confirmed by Wang et al. (1999). During infancy, childhood and

pregnancy, this factor will vary due to changing TBW; therefore the equation used

below is based on Lohman’s tabulated data (1992):

Hydration factor <21 y = 0.792 ‐ 0.0028× Age (y)

However for individuals >21 y of age a hydration factor of 0.732 is applied.

35

Derivation of FM and FFM from TBW

References (Pace et al., 1945; Wong, 2003).

Weight (kg) = FM kg + FFM kg

FFM = TBW/ Hydration coefficient

FM (kg) = Weight (kg) – FFM (kg)

% Body fat = 100 × FM/Body weight

Body composition methods

The following body composition methods were used for body composition

assessment.

DXA

Values for bone mineral content (BMC), FFM, and body fat (BF) were obtained using

a Lunar Prodigy Advance DXA machine (GE Medical Systems Lunar), pencil beam

mode, software version Encore 2005 version 9.30.044. The instrument automatically

alters scan depth depending on the thickness of the subject, as estimated from age,

height, and weight. All scans were performed while the subjects were wearing light

indoor clothing and no metal objects. The typical scan time was 5‐min, depending on

height and weight. The radiation exposure per whole‐body scan is estimated to be

2µSv, which is lower than the daily background level. The same laboratory technician

positioned the participants, performed the scans, and executed the analyses using

the established standard protocol.

Multi‐frequency bioelectrical impedance analysis

TBW was assessed with a BIA analyzer (ImpediMed Imp SFB7), and whole‐body

resistance and reactance were assessed. Participants were in a supine position with

their arms and legs by their sides. After the skin was cleaned with alcohol, 4

electrodes were placed on the dorsal surfaces of the right hand and right foot. The

measurements were carried out 10 minutes after the participants assumed the lying

posture. Data were sampled in triplicate

36

Total body volume

Air displacement plethysmography was performed to assess body volume (BV) using

the BOD POD (Life Measurement, Inc., Concord, CA, software version 1.68). To

eliminate or account for the effects of clothing, skin surface area, and hair, each

participant wore a tight fitting swimsuit and hair was compressed with a tight fitting

swim cap. Body mass was measured to the nearest 100 g using an electronic scale

connected to the BOD POD computer.

The use of the above equations and equipment were used to estimate the TBW and

body composition values for Studies 2 and 3

Rationale for thesis

The use of stable isotopes in body composition and energy expenditure research is

based on the assumption that the methods being used quantify the isotopes

accurately and precisely, and that the equations subsequently applied are suitable

for the work being undertaken.

The DLW technique was not validated in humans until 1982 due to the prohibitive

cost of the isotopes. Progressive development in mass spectrometry technology

improved the precision of the instrumentation; and along with a reduction in the

cost of isotopes after 1990 the number of published studies using the technique

steadily increased to about 110 per year by 1995 (Speakman, 1998). This level of

publication has been maintained since. Despite the importance of the method to the

study of human energy expenditure in free‐living conditions, there remain some

inconsistencies in how the technique is employed in different laboratories (Roberts

et al., 1995). Further, there are possible variations in regression equations used and

equation constants employed in the post‐measurement data analysis process.

Therefore a series of methodological studies needs to be conducted to better

understand the impact of each of these protocol and data analysis options on the

resultant total energy expenditure values.The DLW technique for estimating TEE has

been utilised at QUT for approximately 14 years. Over time, and with increasing

demands on the laboratory equipment, a need was identified to determine if the

37

procedures in use were in agreement with the current literature and published

procedures, particularly with respect to the DLW technique and related equations.

The TBW technique, using the either deuterium dilution or as part of the TEE

estimation, was identified as a protocol to be validated with regard to collection

time, as it has an impact on the derived body composition values, and sampling

medium.

Research questions addressed in this thesis

Within the field of human energy research and body composition, the use of the

IRMS for isotope analysis is well established. Based on the literature reviews for both

TEE and TBW, numerous questions remain regarding factors which would influence

and impact analytical and measurement error within our laboratory. This has

resulted in the following questions being raised.

The methodological and analytical variances in the IRMS in TEE and TBW

measurements to establish baseline “noise”.

With regard to the TEE and TBW measurements in use in our laboratory, the

following methodological studies were undertaken.

Does the use of different regression equations and equation constants result

in different TEE values?

Does the use of saliva or urine affect the final TEE result?

What is the variability in time to isotopic equilibrium using plasma, saliva and

urine samples and the impact of this variability on estimates of TBW and

body composition (derived and measured)?

What is the difference in TBW using different equations (Intercept vs.

Plateau).

38

“If the human condition changes as a result of exercise or excessive water

intake does it affect the TEE result?”

39

Chapter 3

The Isotope Ratio Mass Spectrometer (IRMS)

IRMS background

Recognition of the existence of stable, non‐radioactive isotopes of the elements and

their measurement by mass spectrometry dates back to the work of Aston in the

early to mid‐1920s when he established the whole number rule of atomic weights.

The first use of stable isotopes for nutrition‐related research was in 1934 when 2H

enriched water became available (Wong et al., 1987).

Gas‐isotope ratio mass (G_IRMS)spectrometry had its beginning in 1940 when Alfred

Neir’s laboratory (Wood et al., 1940) designed the first “Neir‐type “ mass

spectrometer for the measurement of 2H content in hydrogen gas. It consisted of an

ion source, a permanent magnet and a single collector. The instrument also utilized a

high vacuum which ensured that the ions travelled from source to collector without

collision. The gas molecules were ionized and with electrons from the ion source and

the positively charged molecules were propelled by an accelerating potential into a

magnetic field where they were resolved into separate ion beams according to their

masses. As a single collector was used, adjustments had to be made to focus on each

ion beam. As this generated unreliable data, a dual collector system was developed

to undertake simultaneous measurements of two isotopic masses which are in use

today. With advances in electronic, vacuum, and computer technology, the modern

IRMS is fully automated and costs in the region of AUD$250‐300,000 (Wong et al.,

1987). After the gaseous samples have been introduced into the equipment, the

entire process of valve sequencing, ion source tuning, and focusing, vacuum

monitoring and data collection is fully automated and computerized.

As a result, stable isotope analysis has gained popularity as a research tool in many

different areas including ecology, nutrition and exercise physiology with dietary and

energy utilisation data collected from plants, animals and humans in the field

(Jardine et al., 2005). However, consistent with the more widespread application of

stable isotope techniques has been a tendency to continue to utilise established

approaches to analysis without question and on‐going critical appraisal. Some might

40

also suggest that there is also a knowledge gap between some IRMS operators and

the research community. It is critical that standard operating procedures in the field,

the interpretation of results and an appreciation of potential analytical errors be

clearly understood. There is a current need for researchers and technical staff alike,

to have a better understanding of the possible sources of analytical error in dosing

protocols, sample collection, collection times, sample analysis, equation utilisation

and finally, result interpretation with respect to assessment of TBW and TEE.

High accuracy in isotope measurements is required because energy expenditure is

calculated based on a small difference between the two isotope elimination rates.

The primary difficulties arise from fractionation of the isotopes during sample

preparation and measurement due to the different physical characteristics of the

heavy isotopes compared with the major isotope. Furthermore, sample matrix can

interfere with isotope equilibration (Delaney et al., 1996).

IRMS functionality

The isotope ratio mass spectrometer allows the precise measurement of a mixture

of stable isotopes. The analysis of stable isotopes is normally concerned with

measuring isotopic variations in mass dependent isotopic fractionation. Instruments

have been developed based on several techniques for mass separation and tuned to

specific needs. It is critical that the samples are processed before entering the mass

spectrometer so that only a single chemical species is analyzed at a given time.

Generally, the samples are either pyrolysed, combusted or equilibrated with a

specific gas to ensure that the desired species of hydrogen gas for 2H, carbon dioxide

for 18O measurements, nitrogen gas or sulphur are produced (Wong et al., 1987).

IRMS systems in use today consist of three major parts. The first is an electron

ionization source for producing ions from CO2, H2, N2 or SO2. The second is a single

sector magnet for separating the isotopomers of each of these ions. The third is an

array of Faraday cups for detecting simultaneously all of the isotopomers (for

example 1H, 2H or 16O, 18O) of a given molecular ion (Scrimgeour et al., 1993). The

two most common IRMS in use today are either dual‐inlet or continuous‐flow. In

dual‐inlet IRMS, purified gas obtained from a prepared sample is alternated rapidly

41

with a standardized gas of known isotopic concentration through a system of valves

so that a number of comparison measurements are made of both gases. In

continuous‐flow IRMS, sample preparation occurs before introduction into the IRMS,

and the purified gas from the sample is only measured once. Highest precision is

achieved using a dual‐inlet ion source. With slightly less precision but greater

throughput, samples can be analyzed with the continuous‐flow IRMS (Dolnikowski et

al., 2005).Sample preparation in IRMS studies is critically important as all compounds

of interest have to be converted to 2H or CO2.The ratio of D (O) atoms in liquid form

:atoms D(O) in gas should be kept high i.e. >100:1. Modern IRMS are very sensitive

and can produce excellent results if equilibration gas blends are used, allowing the

use of smaller(liquid) samples without causing the ratio of fluid: gas to reduce to a

point where the isotopic abundance of the added gas may alter the enrichment of

the equilibrated gas volumes. Many energy metabolism and obesity‐related studies

that use 18O and 2H as tracer isotopes use the equilibration technique to transfer the

label from the water in the biological fluid to the gas phase. In the case of

deuterium, the sample is equilibrated in the hydrogen gas in the presence of a

catalyst, 5% platinum on alumina (Prosser et al., 1994).

Figure 3.1: Hydra 20/20 IRMS ABCA‐G unit, sampling carousel and Hydra 20/20

42

IRMS at Queensland University of Technology (QUT)

The Hydra 20/20 in use at the Queensland University of Technology is a continuous‐

flow isotope ratio mass spectrometer system for measuring deuterium and oxygen‐

18 in aqueous samples following equilibration with a gas phase. It consists of a long

deuterium spur 20‐20 stable isotope analyzer with electromagnet (Sercon Cat.

No.9014), gas auto sampler (Sercon Cat. No.17008) and an ABCA–G module (Sercon

Cat. No.17010). Presently, gas samples for the isotope‐ratio mass spectrometer are

prepared by equilibration of the liquid sample with a gas. The usual gas for analysis

of 18O is 99.99% CO2 and that for 2H is 99.99% H2. CO2 is added to the liquid sample

and exchanges unlabelled O for 18O from the water (Wong et al., 1987). H2 is added

to the liquid sample and exchanges unlabelled H for 2H from the water, with

platinum on alumina as a catalyst (Scrimgeour et al., 1993). The gas sample is

introduced into the system where it is purified on‐line. A water trap of magnesium

per chlorate, in the ABCA‐G, removes moisture and the pure sample is carried in a

continuous stream of helium gas to the ionizer (in the Hydra 20/20). Helium gas is

the carrier of choice as a consequence of its inert nature, high ionization energy, and

low density. Within the Hydra 20/20, the sample gas in the helium flow is ionized by

electron impact from electrons emitted from a hot filament within a high vacuum.

The ions are separated in a magnetic field according to their mass to charge ratio

(m/z). The current for each isotope mass is measured as the charge generated by the

ions impact a detector, called a Faraday cup. A schematic diagram of the Ion Source

and magnetized vacuum fields, along with the Faraday cups is illustrated below.

Figure 3.2: Diagram of Sercon Hydra 20/20 CF ‐IRMS

43

Hydrogen has a mass to charge ratio 2(H2) and 3(DH) and has 2 collector cups

designated to collect mass 2 and mass 3. Oxygen‐18 measurements are undertaken

on CO2 molecular ions therefore there are 3 collector cups to collect mass 44, 45, and

45. The most abundant and naturally occurring CO2 has a charge of 44 (12C16O16O),

CO2 with 13C having a mass of 45 (13C16O16O), CO2 with oxygen‐18 has a mass of 46

(12C16O18O). The charge generated is standardized against an internationally

calibrated reference sample. The raw data is reprocessed using Hydra reprocessing

software with the resulting data being reported in Delta V‐SMOW units or ppm.

Correction is made for the H3+ by the Marquardt correction within the software

supplied by Sercon (Cheshire, UK) suppliers of the Hydra 20/20. Data from the IRMS

is expressed as unit less delta value per mil (‰) with respect to a standard. Units to

express isotopic abundance can be Delta V‐SMOW (Vienna Standard Mean Ocean

Water) or ppm, where the Delta V‐SMOW units can be written as δ or ‰. The

conversion between the units can be defined as 0 δ or 155.76 ppm for hydrogen and

0 δ or 2005 ppm for oxygen.

Where ((R sample‐R standard)/R standard) 1000 = deltas

The international standard for 1H/2H and 16O /18O is Vienna Standard Mean Ocean

Water (V‐SMOW)(Dolnikowski et al., 2005). These references are available from the

International Atomic Energy Agency (IAEA). V‐SMOW is an isotopic water standard

defined in 1968 by the IAEA. V‐SMOW is a recalibration of the original SMOW

definition and was created in 1967 by Harmon Craig and other researchers from

Scripps Institute of Oceanography who mixed distilled ocean waters collected from

different spots around the globe. V‐SMOW remains one of the major isotopic water

benchmarks in use today. However as these standards are expensive, laboratories

manufacture their own working standards which are then calibrated relative to the

international standard. The working standards are derived by various dilutions of the

100% deuterium oxide with tap water of known isotopic value.

Refer to Table 3.1 Mass Spectrometer Settings for more information with regard to actual

settings used when running the IRMS

44

Table 3.0: Mass spectrometer settings for H and CO2

Hydra‐IRMS source and detector settings for CO2 and H analysis

CO2, 13CO2 and C

16O18O H2 [2H1H]

HT (V) 2300 3654

Trap Current (μA) 98 500

Electron Energy (eV) ‐75 ‐75

Ion Repeller (V) ‐4.25 30

Beam Focus (%) 83 85

Head Amplifier Resistance

Beam 1 (MΩ) 100 100

Beam 2 (MΩ) 5000 100,000

Beam 3 (MΩ) 5000 Not applicable

HT‐High Tension settings

45

Laboratory practices at QUT to control analytical and measurement error

Standard preparation

Good laboratory practice requires that instruments used are precise and accurate. As

it is expensive and impractical to use a primary standard on a regular basis,

laboratory working standards are made up that will cover a range either at natural

abundance (tap water) or highly enriched (reference samples). These standards are

made from 99.95% D2O (Sigma Aldrich) and 10% H218O (Cambridge Isotope

Laboratories Inc.) to be within a Delta V‐SMOW range that will cover all the expected

biological values obtained from the urine and saliva samples assayed on the IRMS.

Large batches of each standard and tap water samples are bottled in sufficient

quantities to cover an annual use. The reference and tap water samples are analyzed

externally for QUT by Iso‐Analytical (Cheshire, UK) using a primary reference

standard obtained from the IAEA to ascertain the values of the samples. These

reference samples and tap water are then used in every batch for deuterium and

oxygen‐18 assays to determine the unknown sample values.

It is essential that all standards are calibrated against an international value so that

work undertaken in different laboratories can be compared on an equal basis. To

ensure linearity within the IRMS analysis a series of standards are made with

predetermined assigned values, ranging from 21 to 1380 delta V‐SMOW units. These

standards, with the same sample volume, are analyzed on a 12 monthly basis.

Quality control within a run

The following processes should be considered to ensure quality control within an

IRMS run.

1. Repeating samples from previous batches to check for repeatability.

2. Assaying reference samples i.e.QC samples that have been weighed out

to give values within an intermediate range of the tap water and

reference samples and have been repeatedly assayed to determine their

values.

3. Comparing the TBW with a predicted value and flagging samples that do

not fall within the range (Slater et al., 2005).

46

4. If no other prediction is available, the relationship with height3 can be

used: TBW = 7.4 / height3 (m3). If the measurement falls outside the 95%

confidence limits of 5.7‐9.6, the data and calculation should be checked

and reanalyzed if possible (Slater et al., 2005).

Correction for memory and drift

A small amount of specimen can adhere to the analytical system and exchange with

the next or next few samples. This is referred to as specimen to specimen ‘memory’.

To overcome this problem the samples are analyzed on the IRMS in triplicate and the

“best of three” approach is taken with regard to raw data conversion to Delta units.

Within a batch of samples being analyzed for TEE, low enriched samples (tap waters

and pre‐dose samples) are analyzed first followed by samples with gradual

enrichment increments. If a very high sample is analyzed prior to a low one, tap

water samples are inserted between the high and low samples as a ‘wash out’ to

prevent carry‐over.

Drifts can occur in the IRMS due to room temperature fluctuations, power supply

variances and equipment temperature. If the drift is small it does not need to be

corrected. Within the Hydra IRMS software is a drift correction facility to overcome

this problem. Another preventative measure is to space reference samples after

every 15 samples so that when the software calculates the delta values there is very

little drift.

Temperature controlled environment

It is essential to maintain a constant temperature during the equilibration to

minimize the isotopic effect. To prevent measurement errors, the whole batch

should be kept together in the same spot, preferably near the mass spectrometer, to

ensure constant temperature for equilibration and analysis. If the samples are

moved from one area to another, at least 24 h should be provided for the samples to

become temperature stable (correspondence Dr.C.Slater). On repeated occasions we

have shown the temperature within the mass spectrometer room to be 21°C as

recorded on a laboratory data logger; this is also the controlled temperature of

central air conditioning utilized at IHBI.

47

Enriched and natural abundance standards

The high water reference and tap water that were used as the standards for oxygen‐

18 and deuterium were analyzed externally at the ISO‐Analytical Laboratory in the

United Kingdom against an IAEA standard (IA R011, IA R019, and IA R016).

Research questions to be considered in regard to the IRMS

Within the research framework, a large portion of the studies undertaken in the use

of stable isotopes to measure TEE and TBW is based on the assumption that the

methods in use are actually quantifying the substance correctly, and within that

framework that the equations applied are suitable for the work being determined.

It is recognized that the precision of the DLW technique is 8‐9% and reliability can

vary considerably between laboratories (Roberts et al., 1995). Laboratory

measurements to measure isotopic enrichment will be validated to ensure accurate

(to international standards), precise (reproducibility of three replicate samples) and

linear (isotope ratio was constant over the expected concentration range) values.

This process will establish the machine variability for the IRMS equipment in use at

Queensland University of Technology, for both TBW and TEE measurement. Further,

research will be conducted to determine the correct sequence for sample analysis

with regard to deuterium and oxygen‐18 analysis, and also the maximum and

minimum sample volume that can be used so that the results would remain within

the analytical precision of the equipment. Additionally, sample equilibration time will

be evaluated to determine if the current protocols are statistically sound.

So to answer the research question raised at the end of Chapter 2. With regard to

the IRMS


measurements to establish baseline “noise”.

48

The following research questions were posed to determine the technical and

measurement error in the equipment and methodology currently in use at QUT.

1. What is the equipment variation (precision and reliability data) measuring 2H

and 18O (known standards)?

2. Do different sample volumes affect the results?

3. Does equilibration time affect the results?

4. Number of samples required for analysis?

5. What is the variation in order of sample preparation?

What are the methodological and analytical variances in the IRMS?

It is important to establish the inherent machine noise and to determine analytical

variations that would impact on the final estimations. Variations then become the

baseline and results only become statistically significant when raised above this

baseline “noise”. So in identifying this noise in the equipment is the bottom line

above which measurable true change can be established.

Measurement error definition and description

When undertaking measurements it is understood that the observed score is a

composite of the true value plus the random error (Xr) plus possible systematic error

(Xs). Random and systematic errors (Semyon et al., 2000).

X0 = Xt + Xr +Xs

X0 = observed score

Xt = true score

Xr = random error

Xs = systematic error

Random error is caused by any factors that randomly affect measurement of the

variable across the sample. Importantly, random error does not have any consistent

effects across the entire sample but rather, pushes observed scores up or down

49

randomly. Theoretically, this means that if we could see all the random errors in a

distribution they would have to sum to 0; that is, there would be as many negative

errors as positive errors. The important property of random error is that it adds

variability to the data but does not affect average value for a data set. Because of

this feature, random error is sometimes referred to as noise (Semyon et al., 2000).

In contrast, systematic error is caused by any factor(s) that systematically affect

measurement of the variable across the sample. For instance, if a weighing device is

set with the zero value registered at 1 gram, all samples measured with the scale will

be affected in the same way, in this example, measured systematically higher. Unlike

random error, systematic errors tend to be either positive or negative; because of

this, systematic error is sometimes considered to be bias in measurement.

Accurate measurement is central to scientific endeavor and minimizing

measurement error and obtaining consistency is a key goal in research. Reliability

and validity are considered the foundations of measurement as they represent

attempts to reduce measurement error. Reliability in measurement may actually

have nothing to do with accuracy or validity in measurement, as reliability only refers

to consistency. Measures that are relatively free of random error are referred to as

reliable; and measures that are relatively free of random and systematic error are

reliable and valid (Semyon et al., 2000).

The measurement process is usually defined by a number of steps to develop

reliable and valid measures. Measurement is the process of determining the value of

a substance which can be defined qualitatively and expressed quantitatively. The

process of experimentally determining the value is enabled by instrumentation such

as IRMS to measure the deuterium enrichments of urine samples, and scales to

measure changes in body weight. Numbers or units of measurement are central to

the definition of measurement for several reasons. For example, they can be

standardized and expressed in sanctioned units of measurement such as body

weight (kg), temperature (0C) or mass spectrometer measurements as delta V‐

SMOW units. They allow communication and uniformity in science as they can be

subjected to statistical analysis and are precise.

50

However, underneath the facade of precise, measureable and analyzable numbers is

the issue of accuracy and inaccuracy. Accuracy is defined as the closeness of a

measurement to the true value of the substance being measured. Inaccuracy is the

degree of deviation of a measurement result from the true value of the substance

and can be characterized as either measurement error or measurement uncertainty.

Question 1: Equipment variation and drift (precision and reliability data)

measuring 2H and 18O (known standards)

The objective of this study was to determine during an assay run of deuterium and

oxygen‐18 what technical error existed within repeated measurements of the same

samples due to machine variability.

Introduction and Background

Definitions of Measurement error and drift

When performing IRMS analyses, it is essential to maximize the precision of the TEE

or TBW measurement. In determining the machine error of the IRMS, the inherent

“background noise” range will be identified. Any sample measurement changes

which fall within this range will be deemed clinically not significant as they were a

product of inherent noise and not due to physiological changes. So samples may

show statistical significance but are clinically not significant. Other measurement

errors that may occur due to poor analytical techniques or inherent weakness in the

method need to be identified, quantified and controlled.

Drift (machine error)

Drift can be defined as a slow variation with time of the output of a measuring

system that is independent of a stimulus. The drift that occurs in the Hydra IRMS is

corrected in the Hydra Reprocessor software. This software is provided by Sercon™

as a tool that allows the operator to change the integration parameters and fix

errors made in a batch run. Using the Reprocessor will not alter the raw data

collected by the Hydra during the original analysis. Rather, the software will

“reprocess” the raw data using the designated reference water samples allocated

their predetermined analyzed value. This is then used to calculate the sample values

51

with results being determined between water references that are placed at

approximately 20 unknown sample intervals within the batch.

The study design tabulated below covers the volumes, preparation and conditions of

testing.

Table 3.1: Study design for question 1

Temperature 21°C

Equilibration time 18O = 24 h 2H = 72 h

Sample volume 0.5 mL

Sample 20 samples of tap water

known as BURP – Brisbane

Urban Regional Precipitation

18O value =

3.39 delta V‐

SMOW units

2H value =

16.54 delta

V‐SMOW

units

20 samples of enriched

Reference water (REF)

18O reference

value = 95.53

delta V‐

SMOW units

2H reference

value = 999.3

delta V‐

SMOW

Method of

preparation

Refer to sample and isotopic

analysis below

Equipment Europa Hydra 20‐20 IRMS

Sample analysis

The samples are prepared for analysis by platinum reduction (2H) and gas

equilibration, for 180 and 2H. These are then analyzed on an isotope ratio mass

spectrometer (HYDRA 20/20, Sercon, Cheshire CW5, UK) by a continuous flow

technique. A timed analysis protocol for each isotope is computer controlled, the

total run time for 2H is 200 seconds and for oxygen ‐18 it is 360 seconds. The

abundance of the stable isotopes of C, H, and O are measured in the gas (CO2, H2,)

present in the headspace of the Exetainer tube. The raw data generated by the IRMS

is then reprocessed by Hydra software and the Marquardt correction is made for H3+

ion production (coulombs) which would alter the results.

52

In the current study, analyses were completed in triplicate so that reliability of

measurement could be assessed for each sample. References and tap waters were

interspersed throughout the runs to allow for continual calibration. The raw data

was ‘stretched’ to allow for any drifting which occurred during the run. The delta 2H

and 18O values were normalized against two water standards, which had been

quantified against IAEA standards. This procedure calibrates the results to a natural

abundance value (tap water) and an enriched value (reference of known

concentration). Results were expressed as delta (δ) units per mil (‰) relative to

standard mean ocean water.

Results for IRMS variability beam size and machine drift

As beam size is an integral part of the analysis it is essential that during a run of

either deuterium or oxygen‐18 that there is no gas pressure drift and that the beam

sizes remain within a constant coefficient of variation (CV). To ensure the constancy

of the beam size, the samples are prepared by an automated system which injects

the controlled gas flow into each sample tube for a predetermined number of

seconds. On the IRMS currently in use at QUT, the CV is 3% for the hydrogen and CO2

gas with the beam sizes in the range of 2.72e‐7 for deuterium and 4.5e‐7 for oxygen‐

18 (routine analysis observation and findings)

Key findings and statistical analysis for question 1

In (Table 3.2) are the results for the 20 Burp and References samples analyzed to

determine machine drift. Based on results generated, a 1 SD of 5.84‰ is applied for

2H and 0.40‰ for 18O in the within‐run quality control assessment of the triplicate

sample results, as the inherent run “noise” when using the IRMS.

To establish the ranges for the references used within the runs, 2 SD has been

applied to the actual value, i.e. ±1.96 × the actual value. Triplicate samples with a SD

that does not fall within this 5.84‰ range should then be recalculated on 2 samples

and if the SD is still out of range the sample needs to be reanalyzed.

53

Table 3.2: Isotope assay results to establish machine drift; deuterium and oxygen‐

18 (n=20) Mean and SD of 20 samples shown

Sample Actual

deuterium ‰

Actual

oxygen‐18 ‰

Mean

Measured

deuterium

‰

SD of Measured Delta

SMOW units ( ‰)

BURP 16.54 16.49 2.13

REF 999.30 1006.75 5.84

BURP 3.39 3.39 0.40

REF 95.95 95.39 0.16

REF = enriched Reference water; BURP = Brisbane Urban Regional Precipitation (tap

water)

Question 2: Do different sample volumes affect the results?

The objective of this study was to determine if varying the sample volume would

affect the oxygen‐18 and deuterium results generated on the IRMS.

Introduction and background

In a typical mass spectrometer, 1 mL of H2 gas at Standard Temperature and

Pressure is sufficient for precise measurements of 2H/1H ratios (Wong et al., 1988).

This amount of H2 is equivalent to 0.8 mg of water or 9 mg of organic matter

containing 1% hydrogen. 1 mL of CO2 is equivalent to 2 mg water or 150 mg of

organic matter containing 1% oxygen. With correct inlet systems and valves this

volume can be dramatically reduced from 1 mL to 3 µL, significantly reducing the

volume of sample required (Wong et al., 1988).Sample volumes required for sample

preparation are generally small because the matrix effects for the isotopic

abundance is small. Roberts (1995) showed that the matrix effects can increase

measurement errors in urine specimens more so than for water because the

presence of residual solids, chemicals or proteins may lower the urine enrichment

values. A volume of 0.5 mL or less is recommended in the Sercon (Hydra 20/20

supplier) User’s Manual. For a 0.5mL aqueous sample and 11mL H2 there is a molar

ratio (liquid H2;gas H2) of 56, should samples of 250uL be used with 100% H2 this

54

ratio is only 28, but with 20% H2 it is 141. Minimising this variable will insure greater

precision. Other research facilities commonly use this volume (Scrimgeour et al.,

1993; Slater et al., 2005). To minimize any effects, all the samples and references

within a batch, must be the same volume.

Study Design

As per Question 1

Key findings and statistical analysis to Question 2 –oxygen‐18

Oxygen‐18: There was a statistical difference (p<0.05) between measured oxygen ‐

18 volumes 1 and 2 but not to volume 3 (Table 3.4). However volume 3 had a large

SD (Table3.3) which would have affected the statistical values. Volumes 1(0.5ml) and

2(0.4ml) had SDs within the established machine variation of 0.4 delta V‐SMOW

units (noise) previously determined (Research Question 1) and therefore would not

clinically or statistically impact the TEE calculations. However, analyses (Table 3.3)

using a volume of 0.3mL had a SD of 4.24 (reference) and 0.7 (Burp), and this is

beyond the acceptable limit of 0.4 delta V‐SMOW units for within‐run

reproducibility. Using a volume of 0.3ml may result in baseline “noise” elevating the

measured values above true value.

55

Table 3.3: Results for different sample volume sizes for Oxygen‐18 (N=20 for each

point

Sample Actual oxygen‐18

value ‰

Measured oxygen‐18

value ‰

SD samples

analyzed ‰

Volume 1 = 0.5mL

REF

95.52

96.33

0.38

BURP 3.39 3.39 0.37

Volume 2 = 0.4 mL

REF

95.52

95.55

0.12

BURP 3.39 3.38 0.25

Volume 3 =0.3 mL

REF

95.52

96.73

4.24

BURP 3.39 3.21 0.77

REF = enriched Reference water; BURP = Brisbane Urban Regional Precipitation

All values in Delta SMOW units

Table 3.4: Statistical analysis for the different Oxygen‐18 volumes

Volume1=0.5ml, volume2=0.4ml, volume 3=0.3ml

Volume Mean difference

Sig a 95% Confidence Limits

Lower Bound

Upper Bound

1 2* 0.76 0.00 0.56 0.95 3 2.08 0.11 ‐0.32 4.49

2 1* ‐0.76 0.00 ‐0.95 0.56 3 1.31 0.56 ‐1.20 3.84

3 1 ‐2.08 0.11 ‐4.4 0.32 2 ‐1.31 0.56 ‐3.84 1.20

*Significance p<0.05

56

Key findings and statistical analysis to Question 2‐deuterium

Deuterium: Despite statistical differences shown for all the volumes to each

other(Table 3.6), the differences are not clinically significant, as the SD for the

samples analyzed (Table 3.5) falls within the previously determined 1 SD error values

of 5.8 delta V‐SMOW units for deuterium. The difference seen between actual and

measured values is a product of inherent noise and not reflective of a physiological

change. The inherent noise variable being influenced by the molar ratio (liquid H2:

gas H2) in the samples. 500uL, 400uL and 300uL aqueous samples in a volume of

11mL H2 give ratios of 282, 226, 169 respectively. A ratio value >100 being the ideal

ratio and below this the variation will impact on the precision.

Table 3.5: Different Deuterium sample volume (N=20 for each point

Sample Actual deuterium value ‰

Measured deuterium run value ‰

SD of samples analyzed ‰

Volume 1 = 0.5 mL

REF 999.30 990.20

3.51

BURP 16.54 16.53 2.32

Volume 2 = 0.4 mL

REF 999.30 1005.17

4.40

BURP 16.54 16.54 2.22

Volume 3 = 0.3 mL

REF 999.30 998.55

3.94

BURP 16.54 16.53 1.89

All values in Delta SMOW units

57

Table 3.6: Statistical analysis for Reference deuterium volume changes

Volume1=0.5ml, volume2=0.4ml, volume 3=0.3ml

Measure Mean difference

Sig a 95% Confidence Limits

Lower Bound

Upper Bound

1 2* ‐13.57 0.00 ‐17.83 ‐9.31 3* ‐8.48 0.00 ‐11.23 ‐5.7

2 1* 13.57 0.00 9.31 17.83 3* 5.08 0.00 1.55 8.61

3 1* 8.48 0.00 5.73 11.23 2* ‐5.08 0.00 ‐8.61 ‐1.55

*Significance p<0.05

Volumes 1, 2, 3 showed significant differences p<0.05 to each other

Question 3: Does equilibration time affect the results?

The objective of this study was to determine if varying the equilibration time for

oxygen‐18 and deuterium, affects the values for assays run on the IRMS. The

Oxygen‐18 samples were left to equilibrate for a minimum of 24h a maximum of

14days. Deuterium samples equilibrated for a minimum of 3 days a maximum of 14

days.


Sample equilibration time

The samples must be converted to the gaseous state before being introduced to the

ion source of the mass spectrometer for gas‐isotope ratio measurements. H2 gas is

the preferred final gas product for 2H/1H IRMS measurements, and CO2 gas is the

preferred final product for 18O/16O isotope ratio measurements (Schoeller et al.,

1986; Wong et al., 1986). Isotopic equilibration may be reduced due to some aspect

of the analysis. For example when a urine sample is equilibrated with hydrogen gas,

it is subject to an isotope effect that reduces the deuterium abundance relative to

the starting value of the sample by about 28% (Wong et al., 1987). Thus a sample

with an enrichment of 200 ppm 2H will yield equilibrated hydrogen with 56 ppm 2H

(Prentice, 1990). Therefore, assuming that the same isotopic effects are uniform

58

across a prepared batch, it is essential that unknown samples and known reference

materials are prepared together in the same batch and allowed to equilibrate for the

same length of time (Scrimgeour et al., 1993) and personal communication with Dr.

C Slater and Iso Analytical,UK).

In this study, samples were arranged into a shorter (18O = 24h and 2H = 72h) and a

longer (18O = 14 days and 2H = 14 days) equilibration batches.

Table 3.7: Study design for question 3

Temperature 21°C

Equilibration time 18O = 24 h for batch 1,

14 days for batch 2,

2H = 72 h for batch 1,

14 days for batch 2

Sample volume 0.5 mL

Sample 20 x 2 samples of BURP tap

water

18O value =

3.39 delta V‐

SMOW units

(‰)

2H value =

16.54

delta V‐

SMOW

units (‰)

20 x 2 samples of REF 18O reference

value = 95.53

delta V‐

SMOW units

(‰)

2H

reference

value =

999.3

deltaV‐

SMOW

(‰)

Equipment Europa Hydra 20‐20 IRMS

Method of

preparation

As per question 1

BURP = Brisbane Urban Regional Precipitation; REF = enriched Reference water; V‐

SMOW = Vienna Standard Mean Ocean Water

59


Oxygen ‐18 results ‐time variability

Oxygen‐18: There was a significant difference (p<0.05) between batch 1 and 2 (Table

3.8). The standard deviation of 0.14 and 0.29 V‐SMOW delta units for Batch 1 and

2(respectively) is within the machine variation (noise) previously determined as 0.4

delta V‐SMOW units and therefore not clinically or statistically significant in the TEE

calculations.

Table 3.8: Results for Oxygen‐18 variability in 2 different equilibration batches

Sample Actual value Delta

SMOW ‰

Mean run value

Delta SMOW ‰

SD of samples

analyzed (‰)

Batch 1

REF*

BURP

95.52

3.39

95.26

3.38

0.14

0.35

Batch 2

REF*

BURP

95.52

3.39

95.50

3.39

0.28

0.27

*Significant difference (P <0.05) between batch 1 and 2 for the reference samples.

Deuterium results ‐time variability

Despite Batch1 and 2 showing statistically significant differences (Table 3.9), they are

not clinically significant as they are within the predetermined machine variation of

5.8 delta V‐SMOW unit levels.

60

Table 3.9: Results for Deuterium variability in 2 different equilibration batches

Sample Actual value Delta SMOW ‰

Mean run value Delta SMOW ‰

SD (‰)

Batch 1

REF*

BURP #

999.3

16.54

1008.73

16.31

5.14

4.04

Batch 2

REF*

BURP #

999.3

16.54

1002.07

16.54

4.16

3.88

*Significant difference (P <0.05) between batch 1 and 2 for reference samples

# Significant difference (P <0.05) between batch 1 and 2 BURP samples

Question 4: Number of samples required for analysis

The objective of this study was to determine the following

Number of samples to be analyzed

Are samples to be analyzed in duplicate or triplicate?


Number of samples required for TEE or TBW estimates.

Different protocol approaches

The number of samples collected and analyzed by different groups internationally

varies according to the specific protocols employed at each facility, for example:

D.A .Schoeller – pre‐dose, 3 and 5 h post‐dose and 2 samples on Day 14 within 1 h of

each other; (5 samples collected and analyzed).

A. Coward – pre‐dose, 5 h post–dose, then Days 1 to 14 using the second void of the

day collection; (16 samples collected but only 9 analyzed).

K. Westerterp – pre‐dose, overnight equilibration then second void collection,

followed by Days 2, 7 and 10 (5 samples collected and analyzed).

61

With regard to TEE analysis, one pre‐dose sample is always collected in sufficient

volume (5 mL) to ensure an adequate size of sample for a repeat assay if required. It

is always preferable that the sample is the second void of the day, as an earlier

sample would be a concentrated overnight sample and not representative of that

collection time point. The post‐dose samples are always collected at 4 and 6 hrs after

dosing to allow for equilibration of the isotopes within the body water pool, and

then on a daily basis (second void) for 14 days thereafter. However at QUT, only the

pre‐dose and Days 1, 2, 3, 7, 10, 12, 13, 14 have routinely been analyzed for TEE. The

other samples are kept in the freezer and analyzed if the results or elimination rates

are questionable.

Sample numbers for batch analysis (Duplicate or triplicate)

If there is adequate sample collected, all samples are prepared in triplicate within a

sample run to ensure that the results are statistically more robust. Importantly, if

there are any problems with one of the samples, there are always 2 others as a

backup.

Study Design for sample numbers required

Refer to the study design template (Table 3.0) in Question 1.

Samples were assayed in triplicate for 18O and 2H using 0.4 mL volumes as

previously determined as being suitable in Question2.

As shown in (Table 3.10),duplicate samples can be used to determine the results and

the IRMS is sensitive enough to ensure the sample SD falls within the baseline 5.8

Delta SMOW units previously determined in Question 1.

62

Table 3.10: Triplicate or duplicate analysis (routine batch of deuterium)

Beam size

% Element

Delta V‐SMOW units (‰)

Average Triplicate(‰)

Average duplicate Italics not used (‰)

SD triplicate (‰)

SD duplicate (‰)

Dummy 2.39E‐07

Reference 2.38E‐07 100



DD12 2.48E‐07 103.7 928.338 927.420 930.855 929.710 2.14 1.14

2.46E‐07 102.9 932.526 931.663

2.44E‐07 102.1 930.930 930.047

1164 2.24E‐07 93.84 17.637 4.950 6.8515 6.218 1.27 0.89

2.25E‐07 94.23 18.892 6.221

2.37E‐07 99.31 20.137 7.482

1165 2.41E‐07 100.6 677.440 673.280 672.776 674.340 2.76 0.71

2.34E‐07 98.04 681.576 677.469

2.32E‐07 97.08 676.445 672.272

1166 2.38E‐07 99.69 14.559 1.832 0.938 1.236 0.52 0.09

2.36E‐07 98.5 13.736 0.999

2.42E‐07 101 13.616 0.877

The SD of triplicate and duplicate values fall within the established 5.8 delta units

(‰)

Key findings for question 4

In a large batch of samples, each Exetainer tube (used for sample analysis), will

increase the test cost by approximately AU. $1 and will also decrease the number of

samples for analysis within a run (predetermined by the sample carousel size of 190).

Small sample sizes and the high costs of technical labor, mass spectrometer analysis

and interpretation time may often prohibit triplicate analysis.

However, triplicate samples allow a more accurate estimation, if one sample is

affected by an error e.g. tube contamination when pipetting or low beam size due to

faulty filling. The IRMS analyses using the automated filling system usually have a

very low between 2‐5% “failure” rate where the duplicate and triplicate values are

not within the predetermined “noise” range.

63

Question 5: Variation in order of sample preparation

The objective of this study was to determine if variation in sample preparation i.e., if

preparing and analyzing the oxygen‐18 then the deuterium or vice versa would have

an effect on the results and therefore the TEE value.


To prevent excessive tube and sample usage, when doing a TEE analysis, the same

sample tubes are used for both deuterium and oxygen‐18 analysis. Prior to the

automated tube filling system (currently in use on the Hydra 20/20), a manually

operated, sample preparation vacuum manifold was used. Both of these systems are

still available for use, but the automated system has superseded the manual system

due to its ease of use and time saved in manual labor.

Study Design

Urine samples from a subject (PRA 18) who was previously dosed 1.35 g/kg body

weight of a prepared DLW dose (1.25 g/kg of a 10% 18O solution and 0.1 g /kg 100%

D2O) and analyzed for TEE were reanalyzed. A sample volume of 0.4 mL was used.

One set of samples was prepared on the sample preparation vacuum line for

either:

18O prepared first then converting to 2H

or2H prepared first then converting to 18O.

The second set of samples was prepared automatically on the Hydra 20/20

(machine prep) for either:

18O prepared first then converting to 2H

or 2H prepared first then converting to 18O.

Tabled below are the results for the different preparation methods for deuterium

and oxygen‐18.

64

Table 3.11: IRMS Results for deuterium and oxygen‐18 preparations in delta V‐

SMOW units

Machine prep

2H first ,18O 2nd

Machine prep

18O first, 2H 2nd

Vacuum line prep

2H first ,18O 2nd

Vacuum line prep

18O first ,2H 2nd

Sample 2H 18O 2H 18O 2H 18O 2H 18O

Dilute dose

395.97 83.85 406.69 78.71 382.93 82.92 406.15 79.49

Tap 32.89 4.541 35.80 4.598 28.082 4.56 35.17 5.23

Pre 31.87 2.441 35.18 2.797 35.358 2.64 38.14 3.23

6 hr 640.7 139.01 645.31 131.5 641.77 138.52 651.53 131.79

Day 1 628.15 134.63 636.65 127.7 624.71 134.39 652.56 127.66

Day 2 576.94 119.22 585.71 112.7 579.15 118.73 577.50 112.93

Day 3 524.35 104.68 534.99 99.42 515.89 104.28 574.76 99.19

Day 12 281.21 42.58 285.69 40.81 276.51 42.40 288.87 406.67

Day 13 263.81 37.55 260.48 36.19 253.87 37.48 258.44 35.75

Day 14 228.66 33.32 241.08 32.14 241.78 33.41 275.23 31.75

TEE kcal/day

1920 1722 1835 1811

TEE = 1933 from previous analysis done 6 months earlier on the same samples, using the machine automated preparation system.

65

Statistical analysis of different methods of preparation

Table 3.12: Statistical comparison of the different methods of preparation

Results used from Table 3.11 to generate SE and p values

Measure1 = vacuum line preparation of deuterium first, 18O second

Measure 2 = vacuum line preparation of 18O first, deuterium second

Measure 3 = automated machine preparation of deuterium first, 180 second

Measure 4 = automated machine preparation of 18O first, deuterium second

Measure SE of measures P value 95% Confidence Interval

Lower

Bound

Upper Bound

1 2vac 5.43 0.06 ‐34.91 0.71

3 auto 1.67 0.49 ‐8.71 2.26

4auto 2.04 0.01* ‐16.94 ‐3.54

2 1vac 5.43 0.06 ‐0.71 34.91

3auto 4.63 0.08 ‐1.32 29.06

4auto 4.47 0.93 ‐7.81 21.51

3 1vac 1.67 0.49 ‐2.25 8.71

2vac 4.63 0.08 ‐29.06 1.31

4auto 1.55 0.01* ‐12.09 ‐1.94

4 1vac 2.04 0.01* 3.54 16.94

2vac 4.48 0.93 ‐21.51 7.81

3auto 1.54 0.01* 1.94 12.09

*Significant difference p<0.05

66


Samples analyzed using the vacuum line preparation, were not found to be

significantly different when order was compared (Table 3.12 ).However the

significance bordered on significant (P = 0.06) indicating that it may be advisable to

keep the preparation order the same. There being no difference would be attributed

to the nature of sample preparation, in that the sample Exetainer tubes are

completely evacuated prior to being filled with the required gas (Hydrogen for

deuterium; CO2 for Oxygen ‐18 analysis) and thereby preventing fractionation of the

isotopes in the samples.

Automated preparation show significance to each other (Table 3.12, measure 3 vs.

4) and to the vacuum line preparation of deuterium first (Table 3.12 measure 1).

When samples are prepared on the automated line system, the sample Exetainer

tubes are not evacuated, but merely filled for 30 sec by an automated injection

system, with the required gas, as explained above. The automated preparation of

oxygen‐18 first (variable 4) and then re‐preparation of deuterium on the same

sample, significantly (p<0.05) affects the samples due to isotopic fractionation of

deuterium within the sample. Oxygen‐18 is not affected by fractionation to the same

degree as deuterium as it is a much larger molecule.

When preparing the samples using the automated system, deuterium first followed

by oxygen‐18 on the same sample tubes. For the manual vacuum line either order of

sample preparation is suitable. The overriding principle is to treat the samples in

exactly the same way as the reference waters to limit the effects of fractionation and

septum leakage due to the same tube being used for both oxygen‐18 and deuterium

analysis.

Discussion

Before being confident in the use of any measurement protocol it is paramount that

the data collection process be shown to be both reliable and accurate. Therefore,

the purpose of the methodological investigations outlined in this chapter was to

67

systematically examine all the potentially confounding factors that would contribute

to experimental error within the TBW and TEE analysis.

Most of the evidence, with regard to experimental reliability and precision, in the

literature is equipment specific and pertinent to equipment which although it

performs the same analysis, is not the brand (Sercon ™Hydra 20/20) as is currently in

use at QUT. The experimental reliability of TBW and TEE have been examined in a

number of cross validation studies (Goran et al., 1994; Roberts et al., 1995) in several

laboratories and these show that energy expenditure by DLW compares well with

that obtained from indirect calorimetric measures in free living subjects. The

experimental reliability of TEE estimations is ±8% under tightly controlled conditions

and this is composed of analytical error of ±6% or and the balance biological error

(Coward et al., 1991; Goran et al., 1994). While we cannot completely control all

these errors, being informed of their existence we can minimize or avoid incorrect

laboratory practices or assumptions.

The use of DLW isotopes in studies has expanded to examine small differences in TEE

between groups e.g. lean vs. obese or in response to interventions. It is therefore

important to determine the precision and accuracy of the technique due to

experimental error prior to making any statistical interpretation and possibly making

a type 2 error.

Question 1: Machine variability

Based on results generated (Table 3.2), a 1 SD of 5.84 delta V‐SMOW units is applied

for 2H and 0.40 delta V‐SMOW units for 18O in the within‐run quality control

assessment of the triplicate sample results, as the inherent run “noise” when using

the IRMS.

Question 2: Sample analysis with regard to volume

The purpose of this study was to examine the appropriateness of the current

standard practice within the QUT laboratory of using a sample volume of 0.5ml.

Often samples have volumes which are inadequate for the current laboratory

protocols of triplicate assays; hence volume changes were investigated to determine

their impact on TEE results.

68

Volume changes for oxygen‐18 analyzed on the IHBI IRMS equipment have a SD

“within” the analytical variance levels of 0.4 delta V‐SMOW units for volumes of 0.5

and 0.4 mL but not for 0.3 mL (Table 3.3). Volume changes for deuterium analyzed

on the IHBI IRMS equipment fall within the variance levels of 5.8 delta V‐SMOW

units for all volumes (Table 3.5). Therefore, it is recommended that volumes of

either 0.5 or 0.4 mL be used for both deuterium and oxygen‐18 analysis.

Question 3: Equilibration time

The range of equilibration times recommended for oxygen‐18 is a minimum of 24 h

to a maximum of 14 d, whereas for deuterium the range is 72 h to 14 d.

Oxygen‐18: There was a statistically significant difference over time for the 18O

reference values but not for the burp values (Table 3.8.). However, the standard

deviation was within the 0.4 delta V‐SMOW units of the predetermined “within

machine” variability. Therefore, while being statistically different, this difference was

not of clinical importance as this degree of variability will normally occur within a run

due to machine variance.

Deuterium: Statistically different values for the deuterium reference indicate that

the equilibration time (3days versus 14days) does affect the values (Table 3.9).

However, as the SD is within the acceptable range of 1.96 5.8 delta V‐SMOW units

the results are not clinically significant. Although not clinically significant, the

prudent recommendation would be that the references and samples are prepared

simultaneously and analyzed together. In doing so, any upward or downward shift

that may occur over time will affect the reference and burp values equally.

Question 4: Number of samples to be analyzed

Protocol numbers

Analysis of as many samples (collected in the 14 day collection period) as possible

will help to ensure a stable elimination rate line if water turnover and/or activity is

excessive.

69

Sample triplicate or duplicate

In this study, the impact on experimental reliability of duplicate or triplicate sample

analysis was considered. As shown in Table 3.10 the IRMS is sensitive enough to

provide SD values that falls within the “machine noise” range for duplicate samples.

Analyses in triplicate would also provide duplicate values in case one sample is

invalid.

Question 5: Variation in order of sample preparation (2H or 18O first)

In this study .we examined the affect on final TEE values when experimental

procedures were changed i.e. different preparation protocols employed before

allowing the samples to equilibrate for the required assay time i.e. vacuum line

preparation or the automated online preparation system.

When the automated preparation of oxygen‐18 was undertakenin the first instance

(Table 3.12 measure 4) followed by re‐preparation of deuterium on the same

sample, there was a significant effect on the samples due to isotopic fractionation of

deuterium. This concurs with the literature (Scrimgeour et al., 1993) that when using

the equilibration method, deuterium must be prepared first.

However, the order of preparation of samples for TEE using the vacuum preparation

line does not affect the TEE values as they fall within the 8% deviation allowed,

regardless of order of preparation (Table 3.12). This conclusion is based on the

notion that 8% variation is deemed acceptable for the TEE values due to this value

reflecting biological and technical errors (Goran et al., 1994; Schoeller et al., 1996).

Within this 8% , the technical part of the error could be compounded and increased

due to the fact that by the nature of the vacuum line, any gas that is residual in the

tube would be evacuated to create a vacuum prior to being refilled and so there is

no fractionation. In the automated system, no vacuum is created and the samples

tubes are flushed with the required gas prior to standing for the set time, and then

being analyzed.

70

Conclusion

With regards to the vacuum line, as the TEE values fell within 8% of each other

regardless of preparation order, we can conclude that either order of preparation is

suitable. However, for the automated line, deuterium must be prepared first

followed by oxygen‐18 as the automated machine line does not evacuate tubes, but

merely refills them with an injection of gas for a predetermined time. Any

fractionation, (which may occur for both isotopes), would cause a slight elevation in

the values and hence a lower TEE. In Table 3.11 the lower TEE of 1722 kcal however,

does not fall within the 8% variable range of 1817‐2048 delta units (Roberts et al.,

1995; Schoeller et al., 2002) and would therefore be rejected.

The following is a proposed “checklist” to ensure smooth and accurate functioning of

the IRMS in our laboratory

The beam size variation due to gas flow must be kept to a CV of less than 2%.

References must be placed within the batch at intervals of no greater than 20

tubes to ensure there is no machine drift and to enable drift correction when

the software is applied to the raw data.

Standards used to calibrate the data (IAEA standards) should bracket the

range of expected isotopic values expected from the samples.

Run all samples in duplicate, or budgetary constraints allowing running with

triplicates is preferable to “break the tie” (Jardine et al., 2005).

Sample preparation – deuterium first then oxygen‐18.

Sample volume 0.5 or 0.4 mL.

Sample equilibration for deuterium: 3‐14 days and for oxygen ‐18: 1‐14 days.

All samples and references in a batch must be prepared at the same time.

Pre dose samples and natural abundance waters are analyzed before the

enriched samples in the run to ensure no memory effect.

Magnesium perchlorate water trap changed every 6 months.

71

Chapter 4

The purpose of this study was twofold in that it was used to answer TEE and TBW

questions raised in Chapter 2, where the methodological issues were questioned

after doing a TEE and TBW literature review. The questions were as follows:

Does the use of different regression equations and equation constants result



What is the variability in time to isotopic equilibrium using saliva and urine

samples and the impact of this variability on estimates of TBW and body

composition (derived and measured)?

What is the difference in TBW using different equations (Intercept vs.

Plateau)

Listed below is the information with regard to participant, protocol and

equipment utilized.

Participants

Ten healthy adults (2 males, 8 females) participants were recruited from the

Queensland University of Technology and completed all aspects of the study.

Participants were a convenient sample, not selected based on any age, activity level,

or body composition criteria. No renal or voiding dysfunctions were indicated. The

study protocol was approved by the Queensland University of Technology Human

Research Ethics Committee 0800000223.

The sample was a “convenience“sample of participants currently employed at IHBI.

All were healthy, non‐smoking, weight stable, and 4 were engaged in formal exercise

training. Of the 8 females in the cohort; 6 were pre‐menopausal and 2 were post‐

menopausal. The age of the participants ranged from 22‐56 yrs (mean 35 ± 11), and

BMI ranged from 19‐29 kg/m2 (mean 23 ± 3).

Study design

Urine samples were collected hourly for the first 12 hours after DLW dosing. From

this point onwards, samples were collected three times a day for 14 days, the second

morning void, midday and evening just prior to going to bed. Saliva samples were

72

collected every 15 minutes for the first 2 hours and then hourly for the next 10 hours

on day 1. The samples were then collected to coincide with the urine sample times.

During the first two hours, the participants fasted as drinking would interfere with

the saliva collection protocol, by diluting the saliva. A collection log was provided for

participants to note the actual time of sample collection each day. Participants were

instructed to collect the 2nd void of each day at roughly the same time each day. The

first void of the day is not collected as it includes an enrichment level representative

of the sleeping period. All collection times were recorded, as was all the fluid

ingested and voided.

Study timelines and design:

Day 1 Fasting urine and saliva sample.

DLW dose administered.

Urine samples collected hourly for the next 12 hrs, fluid intake and urine

output measured.

Saliva samples collected at 15 min intervals for the next 2 hrs and hourly for

next 10 hrs.

Days 2‐14 Urine and saliva collected early morning, midday and evening.

73

Figure 4.1: Collection schedule for 14 days of sampling

Collection schedule Day 1

Hours 1 3 4 5 6 7 8 9 10 11 12

Urine

Pre‐sample then

DLW dose * * * * * * * * * * * *

Saliva

Pre‐sample then

DLW dose **** **** * * * * * * * * * *

* = sample collection

**** = 15‐minute interval sample collection

Collection schedule Days 2‐14

Day 2 3 4 5 6 7 8 9 10 11 12 13 14

Urine

morning, midday ,

evening

Saliva

morning, midday ,

evening

74

Dose alterations

The first 4 participants were dosed using a protocol of 1.30 g/kg of DLW, comprised

of 1.25 g/kg 10% 18O and 0.05 g/kg 100% 2H.

This dose had previously been considered adequate, however in light of comparing

the equations and residuals in the pilot study, it was considered optimal to increase

the dose to 1.35 g/kg DLW (1.25g/kg 10% 18O and 0.1 g/kg 100% 2H) for the

remaining participants to ensure adequate post dose enrichment measurements of

18O to be 98‰ ± 10% (Horvitz et al., 2001; Schoeller et al., 1995) and deuterium

600‰ were achieved. The new dosing protocol was used for participants 1, 2, 5, 6, 9,

and 10.

In participants where endpoint (day 14) enrichment values of <8‰ for oxygen‐18

and <170‰ for deuterium, the previous day’s (day 13) results were considered the

endpoint. Any data that appeared aberrant on the regression equation for

elimination rates was also flagged to be removed from the TEE equation.

Measurements

Resting metabolic rate (RMR)

RMR was measured using an indirect calorimetry system (ParvoMedics True

One2400,Sandy, Utah, USA) which provides high resolution oxygen and carbon

dioxide output analysis and a pneumotach volume measurement system with pump

and flow controller. The

analyser was calibrated before each test with room air and standardized gases (O2 =

15.99%, CO2 = 1.0%, balance + Nitrogen). The pneumotach was calibrated using a 3 L

syringe. Participants were required to be fasted overnight prior to the test and have

refrained from physical activity in the preceding 24 hours. Participants were fitted

with a Polar Coded Transmitter™ (Polar Electro, Kempele, Finland) for the recording

of resting HR. Participants were positioned in a supine position, beneath a

ventilated hood and canopy system. They were asked to breathe normally during the

30 minute testing period. Generally, after discarding the first 10 minutes of the test,

the 10 minutes where the energy expenditure (or VO2) was lowest was used for the

analysis of RMR as long as the CV was for this period was <5%. The output from this

75

test included several variables, the most important of which included: ventilation,

oxygen consumption, carbon dioxide production and respiratory exchange ratio. The

Weir (1949) equation was used to convert the respiratory exchange ratio and oxygen

consumption into energy expenditure (kcal∙min‐1). The rate of energy expenditure

was then converted to daily energy expenditure (kcal∙d‐1).

Sample analysis

The deuterium and oxygen‐18 enrichment of the local tap water, the dose given, the

pre‐dose and post‐dose samples were measured using isotope ratio mass

spectrometry (Hydra, PDZ Europa, Crewe, UK) using the equilibration method of

Scrimgeour et al. (1993). Briefly, a 0.4 mL sample, along with a vial of 5% platinum on

alumina powder (Sigma‐Aldrich, Poole,United Kingdom), was placed in a septum

sealed container(Labco, High Wycombe, United Kingdom) and flushed for 2 min with

99% hydrogen gas. Samples were equilibrated at room temperature for a minimum

of 3 d before analysis. The head spaces in the containers were then analyzed for

deuterium enrichment with a continuous‐flow isotope ratio mass spectrometer

(Hydra20‐20; Europa Scientific, Crew, United Kingdom). The accuracy of the analyses

was checked by measuring an intermediate water standard within each batch of

samples. All samples were prepared and analyzed in triplicate. For the intercept

method, after completion of the deuterium analysis, the same Vacutainer tubes

were filled with 100% CO2 using the automated Hydra system. The tubes were left to

equilibrate for a minimum of 1‐14 days and then analyzed for 18O. All enriched

reference waters were prepared at the same time and in the same manner. Delta

units express isotopic enrichment relative to a standard, in this case, Vienna‐

Standard Mean Ocean Water. All assays were performed in triplicate with the CV in

the laboratory being < 2% and 1 SD being 5.8 delta V‐SMOW units for deuterium and

0.4 for oxygen‐18. Enriched water references, and natural abundance tap waters,

previously calibrated to an International Atomic Energy Agency Standard, were

interspersed throughout the runs to allow for continual calibration. These were

interspersed at 20 unknown sample intervals throughout each batch being analyzed.

The isotopic enrichment of the prepared samples and references were measured by

IRMS (20:20 Hydra Model, PDZ Europa, Crewe, UK). All analyses were completed in

76

triplicate so that reliability of measurement could be assessed for each sample. A

timed analysis protocol for each isotope is computer controlled, the total run time

for 2H is 200 seconds and for oxygen ‐18 it is 360 seconds.

Using the Sercon Reprocessor software, the raw data was analyzed and transferred

to an Excel spreadsheet. With the deuterium runs, corrections were made for

H3+interference, within the Reprocessor software. Calculations were done on this

data, using the known values of the enriched water standards (Ref) and the natural

abundance samples (Burp). The final data is expressed as delta units (‰), relative to

Vienna Standard Mean Ocean Water (V‐SMOW).

Body size and Body Composition

Body Mass Index (BMI) was calculated by dividing weight(kg) by height(m) squared.

BMI = body weight (kg)/height (m)2

Body composition measurements

Body composition measurements were performed on the same day during a visit to

the study laboratory, over a 1‐hour period. All measurements were performed by

the same technician under standardized conditions. Participants were required to

fast from the previous evening and also to avoid moderate to vigorous exercise

training for the previous 24 hours. Body composition estimates, using DXA, BODPOD,

and BIA, were completed on all participants in the post‐absorptive state prior to the

dose being administered. Refer to Chapter 2 – Body composition methods for more

detailed description of the methodology.

Data analysis

Statistical analyses were carried out using SPSS Version 16, 2007 (SPSS Inc., Chicago,

IL, USA). Since all TEE outcome variables were normally distributed, means and

standard deviations were used as summary statistics. Analysis of variance test with

repeated measures (RMANOVA) were conducted to test the significance of the

effects of different equation on the analysis of the samples, as well as the effect of

time of collection and correcting for covariance due to the different dose protocols.

77

Where significant differences were identified (p < 0.05), post‐hoc analyses using a

Bonferroni adjustment for multiple comparisons were employed. Regression

equations (Schoeller and Coward) were applied to both urine and saliva samples to

estimate the TEE values, elimination rates and dilution spaces.

Research questions for TEE

1. Do different time points affect the results?

2. Does the use of saliva or urine affect the final TEE result?

3. Does the use of different regression equations and equation constants result


TEE calculations used

(Explained in greater detail in Chapter 2)

The dilution space of each isotope (No, Nd)

N = [(WA)/a]*[(Ea‐Et)/Es

For the Schoeller equation rCO2 = 0.455 TBW (1.007ko‐1.041kd).

For the Coward equation CO2 production rate = ½ [(No x ko) ÷ f3‐(ND x kD) (xf2+1‐x) ÷

f3(xf1+1‐x)] (Coward, 1988).

Where TBW is the total body water, ko and kd are the oxygen and deuterium

elimination rates, respectively. From the rCO2, TEE was calculated using the modified

Weir equation (Weir, 1949) using a respiratory quotient of 0.85.

For both the Schoeller and Coward equations, the same samples and same number

of samples were analyzed, so both equations were treated as multi‐point analyses.

78

Part 1 ‐ IRMS data, TEE analysis and estimation

IRMS data for the 10 participants

Table 4.1: Urine enrichments in delta V‐SMOW units

Results are expressed as mean values of triplicate analyses

Participant 1 2 3 4 5

Sample

time

2H

Abundance

18O

Abundance

2H

Abundance

18O

Abundance

2H

Abundance

18O

Abundance

2H

Abundance

18O

Abundance

2H

Abundance

18O

Abundance

Pre 16.769 1.750 7.870 0.280 19.447 2.070 14.390 2.850 ‐3.434 0.548

5hr 1121.873 127.340 1120.862 120.940 993.706 116.070 559.950 118.550 1343.619 149.480

DAY1 1013.217 112.060 959.600 100.030 914.008 104.660 506.410 108.630 1280.061 137.198

DAY2 889.164 95.790 830.192 80.390 822.903 91.840 445.380 93.050 1127.866 120.463

DAY3 764.110 79.990 709.403 67.870 713.150 78.170 387.910 78.100 1031.355 102.299

DAY7 512.345 48.410 337.377 31.090 474.971 46.200 224.780 41.800 648.487 60.319

DAY8 464.480 42.330 288.340 26.210 413.640 39.420 200.700 34.970 591.793 52.483

DAY12 301.973 24.740 178.697 12.290 281.259 24.080 123.920 20.380 386.052 32.026

DAY13 283.456 22.280 135.227 9.780 254.459 21.420 101.790 17.880 350.881 27.423

DAY14 255.664 20.210 114.544 7.990 230.219 18.140 95.110 14.450 310.000 24.221

79

Table 4.1 cont.: Urine enrichments in delta V SMOW units

Results are expressed as mean values of triplicate analyses

Participant 6 7 8 9 10

Sample

time

2H

Abundance

18O 2H

Abundance

18O 2H

Abundance

18O 2H

Abundance

18O 2H

Abundance

18O

Abundance Abundance Abundance Abundance Abundance

Pre 10.111 0.983 6.754 ‐0.119 21.114 1.460 10.082 0.887 42.976 5.415

5hr 1044.552 108.011 552.981 115.464 570.197 118.824 1000.242 102.686 1165.088 132.746

DAY 1 1026.697 105.070 517.208 108.747 538.359 109.188 893.599 91.968 1070.475 117.985

DAY2 956.654 95.125 475.727 96.423 495.535 99.506 765.766 75.899 999.674 107.628

DAY3 894.976 87.247 448.144 87.362 469.483 91.410 655.087 63.460 935.573 97.929

DAY7 669.428 59.999 388.212 33.473 661.162 63.350

DAY8 618.789 53.839 329.457 28.384 606.489 57.133

DAY12 470.060 35.783 199.833 31.002 258.014 41.256 198.368 15.365 439.264 38.162

DAY13 437.693 32.291 179.856 27.192 245.451 38.118 176.785 12.657 395.552 32.464

DAY14 374.562 26.585 165.705 24.417 219.566 34.432 155.597 10.570 359.215 29.776

80

Table 4.2: TEE (kcal/day) values for urine and saliva samples using Schoeller and Coward equations at different time points across the day;

time 1 = morning, time 2 = midday, time 3 = evening.;* indicates different dose protocol used 1.30 or 1.35g DLW/kg

Gaps in the table due to equipment/laboratory error and insufficient saliva to repeat the analysis

SALIVA TEE URINE TEE

Schoeller Coward Schoeller Coward Dilution

space

Elimination

rate

BMIc

ID am mid pm Time1 Time2 Time3 Time1 Time2 Time3 Time1 Time2 Time3

1 2163 2052 1870 2034 1967 1890 2460 2460 2319 3003 3146 3217 1.003 0.19/0.16 24.7

2 2475 2775 2705 2753 2515 2482 2909 2897 3007 2922 2900 3027 1.033 0.14/0.11 20.8

*3 2668 2706 2854 2603 2992 2982 1.019 0.16/0.14 23.7

*4 3022 2849 2811 2589 2511 2578 2914 2736 3160 2866 2702 3225 1.037 0.13/0.11 29.0

5 2764 2904 3172 2510 2565 2889 2392 2442 2157 1801 1926 2270 1.068 0.12/0.09 24.6

6 2726 2733 2767 2694 2676 2712 2903 3305 3165 2801 3002 2939 1.040 0.09/0.07 27.8

*7 3467 3205 2525 2652 2701 2504 2680 2688 1.041 0.11/0.09 23.2

*8 2129 2265 2127 1959 2036 2006 2000 1973 2175 1872 1894 2128 1.048 0.09/0.07 22.9

9 2601 2572 2666 2414 2467 2557 2508 2267 2226 2269 2095 2268 1.04 0.17/0.14 17.7

10 1942 2088 1886 1792 1886 1826 2467 2271 2455 2322 2242 2408 1.046 0.14/0.11 20.2

81

Key findings and statistical analysis – Question 1: Do different time points affect

the results

When using the data from all 10 participants and irrespective of the dosing protocol

followed, there was a significant main effect of time on the Coward TEE values.

(Table 4.3) The equations differed between the morning and evening time points (p

<0.05). However, if the different dose protocols were used as a covariant, there was

no effect of time or equation.

When analyzing only data from the 6 participants (1, 2, 5, 6, 9, 10) where the new

dose protocol was implemented, no effect of equation (p = 0.98) or time (p = 0.57)

were shown.

Table 4.3: Comparison of times for urine (Coward) n=10

Time Mean Std. Error 95% Confidence Interval

Lower Bound Upper

Bound

1* 2496 134.74 2191.48 2801.41

2 2557 150.49 2217.71 2898.08

3* 2715 132.00 2416.16 3013.93

*p=0.04 for time points 1 and 3

Key findings and statistical analysis – Question 2: Do different mediums affect the

results

There was no statistical difference (Table 4.4, p=0.995) in the total energy

expenditure values determined from saliva and urine samples. So either medium

could be used.

82

Table 4.4: Comparison of Schoeller urine and saliva

Time points 1,2,3 Mean Std. Deviation N

for the different mediums

urine_Schoeller_1 2569 479.34 8



saliva_Schoeller_1 2477 322.42 8



.No significant differences between mediums

Key findings and statistical analysis – Question 3: Do different regression equations

affect the TEE results?.

There was no significant difference between the total energy expenditure equations

of Schoeller and Coward using using pair wise comparisons (Table 4.5, p=0.99).

Table 4.5: Different equations comparison n=10

Schoeller=1, Coward =2

Equations Mean difference

Significance 95% confidence level for difference Lower Bound Upper Bound

1 2 ‐.66 .99 ‐199.90 198.57

2 1 .66 .99 ‐198.57 199.90

Discussion

When making observations about a complex physiological system, only those parts

that are available for scrutiny are able to be analyzed, and deductions and

assumptions have to be employed to calculate TEE estimates. In a human or animal

this will entail dosing the participant with DLW and relying on the assumptions that

the body fluids collected (blood, urine or saliva) will have adhered to the

assumptions of:

1. Body water is a single compartment which the isotopes label.

83

2. 2H is lost only as water.

3. 18O is lost both as water and CO2.

4. Fractional output rates of water and CO2 are constant.

5. Background isotope rates remain constant.

6. Water and CO2 loss occurs at the same enrichment as coexist in the body

water.

Another influence on the derived TEE is analytical and biological error, which can be

controlled in the research protocol. Although the accuracy and precision of the DLW

technique has been established (Schoeller, 1988), an inter‐laboratory investigation

.Roberts et al (1995) demonstrated that the values are not universally comparable

amongst laboratories.

Data from the current study showed no significant difference between the

equations using pair ‐wise comparisons. However, the TEE values subsequently

derived showed a significant main effect of time. Samples collected in the morning

differed from those collected in the evening when analyzed with the Coward

equation. When data was corrected for the different dose protocols (1.30 g DLW/kg

body weight as opposed to 1.35 g DLW/kg body weight) there was no effect of time

or equation. Therefore these findings indicate that when collecting samples for TEE

analysis, it is preferable to collect the 14 day samples in the morning (second void)

or midday. We can further recommend that if a dosing protocol other than that

outlined in this (QUT) laboratory, evening samples should be treated with caution,

as there may be an error greater than the 8% allowed in the analytical control

measures. When comparing TEE values from other research data, consideration

should be given to TEE data collection time points, dosing protocols and equations

used. Finally, this study suggests that either urine or saliva samples can be used for

the DLW analysis with no statistical differences between sample mediums.

A number of factors may affect the accuracy and precision of the TEE estimations

and these can be considered as both biological and analytical error.

Biological error may include changes in physical activity, health status, and changes

in isotopic background i.e. water consumed. To control for biological error and to

84

counter changes in the isotopic background, all study participants remained in the

Brisbane area for the study duration. In contrast analytical error is determined by

factors the investigator may manipulate such as the dose of DLW provided, the

duration of the metabolic period, and sample processing and measurement error

during mass spectrometry.

A combination of biological variation and analytical error will account for an

experimental error of ± 8.5% (Goran et al., 1994; International Atomic Energy

Agency, 2009). Under tightly controlled diet and living conditions where RMR, diet,

and activity are constantly monitored the experimental reliability of the TEE result

reported by these studies was ± 8.5%. However, in more free‐living individuals; it is

suggested that a value of 12% may be more realistic to account for inherent intra‐

individual variation (Goran et al., 1994).

To ensure that the values obtained in the current study were a true representation

of physiological values the following protocols were adhered to:

To check the analytical variance the following measures were implemented

Analytical error was controlled by keeping the within‐run triplicate standard

deviation values for deuterium at ≤6‰ and oxygen‐18 at ≤0.4‰ (machine error

determined in Chapter 2). Levels above this would result in spurious results which

could affect the TEE results. Also by ensuring that the CV% of the beam size for the

analyzed samples to be less than 2%, as fluctuations in the beam size would affect

the final isotope value, and not be reflective of the true sample enrichment.

Ko/kd ratio

The Ko/kd ratio has a major influence on potential for error in the TEE estimation.

Any error in the pool size estimation or changes in the pool size there are large

changes in water intake or CO2 production resulting from excess activity, this can be

compensated for by increasing the number of samples to analyze or increasing the

dose of the DLW isotopes given, which was the protocol adhered to in this study. The

quality control checks used had a range of 1.2‐1.4 for the ko:kd ratio.

85

Covariance graphs and residual plots

The graphs for the 10 participants were covariant and the regression lines for the log

natural elimination rates of 18O and 2H were within an R2 value of 0.99.

Dose

The amount of dose water administered to each participant can alter the precision of

the TEE estimation. Too little isotope may result in low enrichment of the body fluid

at the conclusion of the study which can lead to increased measurement error

(Schoeller, 1983). The dosing protocol in use prior to this study was (1.30 g/kg TBW)

of DLW consisting of 0.05 g/kg body weight of 99% 2H2O, 1.25 g/kg body weight of

10% 18O. After observing the low day 14 values in participants who ingested large

fluid volumes, the protocol was adjusted to (1.35 g/kg TBW) of DLW consisting of 0.1

g/kg body weight of 99% 2H2O, 1.25 g/kg body weight of 10% 18O.

The new dosing methodology ensured that most of the individuals had endpoints

that were within the required values of 8‰ and 128‰ for 18O and 2H, respectively

for day 14(IAEA handbook). However in participants where the daily fluid intake was

in excess of 2 L, increased elimination rates often meant that the endpoint of day 14

was unsuitable.In these cases , Day 12 or 13 was used instead.

Food Quotient (FQ)

Another factor and possible limitation of the DLW technique is that the final energy

expenditure (kcal/day) value obtained is based on converting the measure of carbon

dioxide production, which necessitates knowledge of the macronutrient composition

of the diet. The food quotient(FQ), the ratio of food energy provided by the various

macronutrients in the diet, is determined from either a record of dietary intake or

using a standardized value for the common “mixed diet” (Black 1986).

The value used for the RQ in the equations was 0.85. The use of a standardized value

may introduce error into the estimation of CO2, though the literature suggests that

the error is “negligible and should not exceed 2‐3%”(Schoeller, 1983).

86

Jack Knife system

The “Jack Knife” system (Broemeling et al., 1993; Prentice, 1990; Schoeller, 1983) of

eliminating days to obtain a steady TEE value of ± 8% was used. This system allows

quantification of each individual’s results, enabling an objective decision to be made

with regard to the inclusion of a dataset in the final estimation. Refer to Table 4.6

where Schoeller 1,2 and 3 are morning, midday and evening results respectively

Table 4.6: Jack Knife technique

JACK KNIFE SCHOELLER 1

JACK KNIFE

SCHOELLER2

JACK KNIFE SCHOELLER 3

ID AV SD CV% AV SD CV% AV SD CV%

1 2458 194.3 7.9 2458 194.3 7.9 2308 110.8 4.8

2 2907 66.4 2.3 2896 90.9 3.1 3007 88.2 2.9

3 2668 132.4 5.0 2654 132.4 5.0 2877 105.8 3.7

4 2913 57.9 2.0 2737 56.2 2.1 3161 69.0 2.2

5 2390 66.2 2.8 2455 43.0 1.8 2173 160.1 7.4

6 2890 48.8 1.7 3301 74.7 2.3 3164 65.8 2.1

7 2535 21.9 0.9 2676 137.4 5.1 2698 33.8 1.3

8 2002 65.9 3.3 1965 83.5 4.3 2174 34.0 1.6

9 2400 99.0 4.1 2263 85.5 3.8 2216 31.3 1.4

10 2467 46.4 1.9 2269 27.8 1.2 2456 45.3 1.8

This table shows the values obtained across the 3 time points of the day (morning,

midday and evening) generated results that were within the CV of 8% expected for

TEE values generated by the Schoeller equation, indicating valid stable results.

Conclusion

Other measures should also be undertaken, for example resting metabolic rate, food

intake and body composition during the study period. The aim of these

measurements would be to provide equivalence between energy inputs and outputs

during the study period. Each measurement would provide, in part, validation of the

results using the DLW technique. However it is critical that the initial isotopic

87

enrichments are carefully examined with quality controls and covariance checks to

ensure the integrity of the data, regardless of which equation is used.

Part 2‐TBW analysis and estimation

TBW research question:

1. What is the variability in time to isotopic equilibrium using plasma,

saliva, and urine samples and the impact of this variability on

estimates of TBW and body composition?

2. What is the difference in TBW using different equations, specifically

the plateau methods (TBW corrected, uncorrected) versus the

intercept method.

Calculations for TBW

This is discussed in detail in Chapter 2 under TBW review. In brief two approaches to

TBW estimations were used utilizing the deuterium dilution technique; namely the

plateau or the intercept method. Both approaches have been extensively examined

and found to provide similar results (Schoeller et al., 1980). The plateau TBW

method is determined within 1 day, while the intercept TBW method is part of a

total energy expenditure calculation and the collection time frame is 14 days. The

isotopic dilution spaces were calculated according to Cole and Coward (1992) where:

TBW plateau calculation

Equation 1 N = WA/ a * [(Ea ‐ Et) (Es ‐ Ep)]

TBW (L) was subsequently calculated using Equation 2:

Equation 2 TBW = (N/ 1.04) /1000

Equation 3 FFM = TBW /0.73

Percent body fat (%) was subsequently derived from FFM and body weight (W) using

Equation 4:

Equation 4 % Body Fat = [(W‐ FFM) W]*100

88

The optimal approach is to correct for all intake and loss of fluid (Schoeller et al.,

1995) however this is often not practical. Where correction for fluid loss is listed the

following formula and protocols were applied; all fluids ingested and passed during

the 12 hour collection period were measured. The apparent TBW (kg) was calculated

using the TBW calculation (pg 78); corrections were then made for cumulative fluid

intake and loss by subtraction of these volumes from the apparent TBW values.

89

Key findings and statistical analysis –Question 1: Variability in time to equilibration

The raw data for urine deuterium in Delta V‐SMOW units is presented in Table 4.7.

Table 4.7: Urine IRMS deuterium data

Delta V‐SMOW deuterium values

ID 1 2 3 4 5 6 7 8 9 10

Hr 1 635.93 992.74 425.44 359.59 1127.59 949.11 474.39 411.06 545.97

2 1172.64 1108.49 599.27 1266.44 1195.02 1066.91 582.78 617.86 1038.44 1111.73

3 1032.47 582.57 1374.90 1163.98 1057.82 561.15 602.69 1030.57 1130.09

4 1138.59 1005.51 560.72 1378.27 1165.08 1040.77 546.39 591.64 1021.35 1128.82

5 1120.86 993.71 559.95 1343.62 1165.00 1029.85 552.98 581.78 1000.24 1105.13

6 1083.74 990.74 549.31 1151.54 1044.55 538.67 575.83 968.04 1076.76

7 1085.98 967.15 549.12 1340.60 1138.83 1031.37 534.38 571.13 955.61 1062.75

8 1077.04 947.23 556.53 1321.24 1125.27 1009.63 547.69 566.21 945.68 1049.09

9 1047.37 941.41 547.40 1005.97 547.15 569.74 941.06 1049.31

10 1046.28 932.00 550.44 1336.39 1021.00 1026.69 540.52 558.89 946.27 1033.75

11 926.66 530.33 1339.20 1116.90 1016.09 534.19 912.22 1016.86

12 1015.64 918.92 525.83 1320.20 1113.87 987.67 522.26 551.65 919.47 1012.82

90

Not all participants were able to provide a urine sample at each of the 12 post‐dose

time points, indicated by the gaps in the table. The IRMS deuterium data for the

saliva samples from hours 2‐12 were within the expected range of 96‐101% of the

urine deuterium values,( Janowski 2004) and are not tabulated.

Data of the 10 participants is presented in (Table 4.7) in TBW kg for 12 hour, data is

presented uncorrected for fluid intake or loss. As can be seen the initial values are

elevated for the first hour prior to equilibration taking place. Table 4.8 data was then

utilized for the plateau estimation.

Table 4.8: TBW (kg) for urine and saliva

Hour Urine TBW(Kg) Uncorr*.

Saliva TBW(Kg) uncorr.

1 56.5 ± 36.7 31.4 ± 3.9

2 32.9 ± 4.6 33.5 ± 3.9

3 33.5 ± 4.0 34.2 ± 4.6

4 33.7 ± 3.9 35.3 ± 3.7

5 34.1 ± 3.9 35.9 ± 3.8

6 33.9 ± 3.1 34.7 ± 3.8

7 34.9 ± 3.7 35.7 ± 4.5

8 35.2 ± 3.7 36.3 ± 4.6

9 34.8 ± 3.2 36.8 ± 4.1

10 35.5 ± 3.5 36.5 ± 4.2

11 36.0 ± 3.6 36.9 ± 4.7

12 36.3 ± 3.5 37.0 ± 4.2 Uncorr=uncorrected for fluid intake or loss

TBW Data analysis for plateau estimation

Based on the methods most widely discussed in the literature, the following three

methods of assessing TBW plateau equilibration were evaluated in the present study

(Colley, 2007; Salazar et al., 1994; Wong et al., 1988).

Method 1: Plateau defined as the earliest time‐point when consecutive enrichment

values become ≤ 2% different from the previous hour.

Method 2: Plateau defined as the earliest time‐point when consecutive enrichment

values become ≤ 3% different from the previous hour.

91

Method 3: Plateau defined as the earliest time‐point which is ≤ 2% different from

the enrichment level at hour 6.

In the urine analysis (Figure 4.3), using the most commonly cited method (1) of ≤ 2%

of previous value; the proportion of participants equilibrated at hour 4 was 60%. This

proportion increased to 90% by hour 6 and 100% by hour 7. The proportions for

saliva (Figure 4.4) were 60% at hour 4, 100% by hour 6 and 7.

Figure 4.2: TBW (kg) urine (uncorrected) values over time in hours

Figure 4.3: Time to isotopic equilibration for all 3 methods for urine

92

Figure 4.4: Time to isotopic equilibration for all 3 methods ‐ saliva

Key findings to optimal collection time

Depicted below in Table 4.9 are the points at which urine and saliva TBW (kg) values

show statistical differences, the shaded areas are statistically non significant

collection times. With 4 to 10 hour after dosing showing no statistical difference for

urine and 2 to 6 hours for saliva.

Table 4.9: Comparison of TBW (kg), (n=7) urine = U, saliva = S

Hour 3 4 5 6 7 8 9 10 12

2 U*

3 U* U* U* U* U* S* U*

4 S* S* U*

5 S* U*

6 U*

7 U*

8 U*

9 U*

10

12

U S,* Significance denoted p<0.05

93

Table 4.10: Statistically non significant collection times shaded in blue

Hour Urine TBW (Kg) Saliva TBW (Kg)

uncorr. n=10 uncorr. n=10

1 56.5 ± 36.7 31.4 ± 3.9

2 32.9 ± 4.6 33.5 ± 3.9

3 33.5± 4.0 34.2 ±4.6

4 33.7 ± 3.9 35.3 ±3.7

5 34.1 ± 3.9 35.9 ± 3.8

6 33.9 ± 3.1 34.7 ± 3.8

7 34.9 ± 3.7 35.7 ± 4.5

8 35.2 ± 3.7 36.3 ±4.6

9 34.8 ± 3.2 36.8 ± 4.1

10 35.5 ± 3.5 36.5 ± 4.2

11 36.0 ± 3.6 36.9 ± 4.7

12 36.3 ± 3.5 37.0 ± 4.2

Key findings and statistical analysis – Question 2: What is the difference in TBW

(kg) using different equations?

Table 4.11 displays the TBW values resulting from the use of different equations

when using the average of samples over 4‐10 hours for urine, 2‐6 hours for saliva. In

the table, “uncorrected” indicates that no correction was made for fluid intake or

loss. Both plateau and intercept methods were used to derive the TBW values. When

applying the intercept method, both the equations of Schoeller and Coward were

used.

Using this data for statistical analysis Table 4.12 shows that * Significant difference

(p<0.05) between measure 1 (urine) and measure 3 (urine corrected for fluid loss).

Also a # Significant difference (p<0.05) between measure 6 (Schoeller saliva) to

measure 8 (Coward saliva).

94

Results

Table 4.11: Results of TBW (kg) using different equations

Urine average value 4‐10 hours, saliva average value 2‐6 hours.

Plateau (uncorrected)

Urine Saliva

Urine D

plateau

(corrected for

urine loss)

Urine D

plateau

(corrected for

fluid intake)

TBW –TEE calc. Schoeller

Urine Saliva

TBW –TEE calc. Coward

Urine Saliva

Participant1 33.0 33.8 31.4 32.6 32.1 33.4 36.9 33.4

2 36.3 35.1 34.6 35.3 35.2 35.9 36.9 36.4

3 31.9 32.0 29.8 31.7 30.8 30.7 31.5 30.7

4 41.0 40.0 39.6 40.6 38.9 39.8 39.8 40.3

5 33.2 34.1 33.0 32.7 30.7 30.1 30.9 30.3

6 37.2 36.8 36.4 36.8 36.8 35.2 35.7 36

7 39.3 42.0 38.6 39.3 37.5 39.5 38.4 40.5

8 35.1 34.1 33.6 34.7 36.6 35.3 36.9 35.8

9 30.4 29.2 28.5 30.8 29.5 30.1 30.2 30.7

10 30.3 28.2 27.7 27.8 29.6 30.0 30.3 30.7

Average 34.8 34.5 33.3 34.2 33.8 34.0 35.4 34.5

SD 3.7 4.3 4.1 3.9 3.6 3.8 3.4 3.9

95

Table 4.12: Descriptive statistics for different equations of TBW estimation

(Data from table 4.11) n=10 demonstrates statistical changes over collection period urine 4‐10h, saliva 2‐6 h.

Methods Mean SD 95% Confidence Interval

Lower Bound Upper Bound

Urine uncorrected* 34.8 1.15 32.15 37.38

Saliva uncorrected 34.5 1.36 31.44 37.61

Urine corrected loss* 33.3 1.28 30.40 36.23

Urine corrected intake 34.2 1.24 31.42 37.03

Schoeller urine 33.8 1.13 31.20 36.34

Schoeller saliva# 34.0 1.19 31.29 36.70

Coward urine 35.4 1.08 32.89 37.80

Coward saliva# 34.5 1.24 31.66 37.29

* Significant difference (p<0.05) between measure 1 (urine) and measure 3 (urine corrected for fluid loss).

96

Most protocols are usually defined by a fixed collection time which does not cover this wide range for urine and saliva. At QUT, the preferred

collection times for urine and saliva are 6 h and 4 h, respectively. Table 4.13 displays these single TBW (kg) time points.

Statistical analysis of this data is shown in Table 4.14 where urine corrected for fluid loss (measure 3) showed statistical difference (p<0.05) to

measures 1, 2, 4, 7(respectively urine and saliva uncorrected, urine corrected for fluid intake and TBW derived from the Coward TEE equation).

All the other methods showed no significance to measure 1(urine uncorrected), the method currently in use.

97

Table 4.13: Results of TBW (kg) using different equations

urine 6 hour, saliva 4 hour

Participant Urine plateau

(uncorrected)

Urine Saliva

Urine plateau

(corrected for

urine loss)*

Urine plateau

(corrected for fluid

intake)

TBW ‐ TEE calc.

(urine)

Schoeller

TBW ‐ TEE

calc.

(saliva)

TBW ‐ TEE

calc. (urine)

Coward

TBW ‐ TEE

calc.

(saliva)

Average SD

1 33.0 33.4 31.6 32.7 32.1 33.4 36.9 33.4 33.2 1.8

2 35.4 35.8 34.1 34.9 35.2 35.9 36.9 36.4 35.3 1.2

3 32.1 32.2 29.8 32.1 30.8 30.7 31.5 30.7 31.1 0.8

4 41.1 40.6 39.8 40.6 38.9 39.8 39.8 40.3 40.1 0.7

5 32.9 34.2 32.4 34.4 30.7 30.1 30.9 30.3 32.2 2.4

6 36.6 36.5 35.7 36.1 36.8 35.2 35.7 36.5 36.1 0.5

7 39.7 41.4 39.1 39.7 37.5 39.5 38.4 40.5 39.4 1.1

8 35.0 34.2 33.6 34.4 36.6 35.3 36.9 35.8 35.2 1.2

9 30.4 29.8 29.3 29.3 29.5 30.1 30.2 30.7 29.7 0.8

10 30.2 28.4 28.2 29.6 30.5 30.3 30.7 29.3 1.2

Average 34.6 35.3 33.3 34.2 33.8 34.6 35.4 34.5

SD 3.7 3.8 4.0 4.2 3.6 3.8 3.4 3.9

98

Table 4.14: Statistical comparison of TBW (kg) using different equations

Urine 6 hour, saliva 4 hour

Measure Mean Std. Error 95% Confidence Interval Sig. to measure


Urine uncorrected 6h 1 35.1 1.22 32.42 37.90 1*3

Saliva uncorrected 4h 2 35.3 1.34 32.53 38.23 2*3

Urine corrected loss 6h 3 33.8 1.35 30.95 36.82 3*1,2,4,7

Urine corrected intake 6h 4 34.9 1.26 32.22 37.72

Schoeller urine 5 34.2 1.27 31.63 36.91

Schoeller saliva 6 34.4 1.29 31.67 37.34 6*8

Coward urine 7 35.9 1.00 33.50 38.33 7*3

Coward saliva 8 34.9 1.32 31.91 37.90 8*6

99

Key findings and statistical analysis: ‐ Question 3: What is the impact of time

variability on body composition (i.e. %body fat) estimates derived from TBW and

do these differ from values obtained by other modalities – DXA, BODPOD and BIA?

Results

The following tables listed below show the derived % Body fat data and the relative

statistical comparison of each section;

1. % Body fat derived from urine TBW data collected over all the collection time

points (hours 1‐12) (Table 4.15.)

2. A comparison of % Body fat values derived from BODPOD, BIA, DXA and TBW

(urine and saliva) over time points that are routinely in use i.e. Urine 6h.

Saliva 4h (Table 4.16)

3. A comparison of % Body fat values derived from TBW and TEE data (Table

4.17, Table 4.18)

100

Percent Body fat derived from TBW all collection times

Data of the 10 participants is presented in TBW kg for 12 hour. Not all the

participants could provide a sample every hour. Shaded area indicates sampling

times which were not statistically significantly different shown between hours 4‐10.

Table 4.15: TBW (kg) and percent body fat data

Hour Uncorr TBW(Kg) n=10 %Body Fat (n=10) derived from uncorr TBW

1 56.5 ± 36.7 ‐10.7 ± 51.0

2 32.9 ± 4.6 32.3 ± 4.5

3 33.5± 4.0 31.1 ± 6.1

4 33.7 ± 3.9 30.3 ± 6.3

5 34.1 ± 3.9 29.6 ± 6.3

6 33.9 ± 3.1 27.3 ± 6.3

7 34.9 ± 3.7 27.8 ± 7.3

8 35.2 ± 3.7 27.2 ±7.3

9 34.8 ± 3.2 25.5 ± 6.6

10 35.5 ± 3.5 25.9 ± 7.6

11 36.0 ± 3.6 25.4 ± 9.9

12 36.3 ± 3.5 24.8 ± 8.0

In the laboratory at IHBI, the % body fat values are derived from either TBW or DLW

estimations, but other measurements (DXA, BODPOD, and BIA) are also used to

verify the results. Raw data from the IRMS is presented in Table 4.16. The statistical

comparison of % body fat derived from TBW and equipment (Table 4.17), showed

significant statistical difference p<0.05between urine (hour 6) and saliva (hour 4).

The comparison of % body fat derived from TBW and TEE (Table 4.18), showed

significant differences between Schoeller and Coward saliva, when using all the data

for all 10 participants. When correcting for dose protocols, no statistical differences

were shown.

101

Table 4.16: % Body fat values derived from different equipment and equations urine h 6, saliva h 4

ID BODPOD BIA DEXA Urine Plat

uncorrected

6h

Saliva plat

uncorrected

4h

Urine

plateau

corrected

urine loss

6h

Urine

plateau

corrected

fluid

intake 6h

TBW ‐

TEE calc

(urine)

Schoeller

TBW ‐

TEE calc

(saliva)

TBW ‐

TEE calc

(urine)

Coward

TBW ‐

TEE calc

(saliva)

1 28.6 27.6 35.1 33.0 33.4 31.6 32.7 31.1 30.3 33.1 28.5

2 20.1 21.8 19.0 35.4 35.8 34.1 34.9 20.0 19.1 18.1 17.3

3 27.3 24.2 29.9 32.1 32.2 29.8 32.1 29.8 30.1 28.2 26.9

4 42.8 35.3 45.1 41.1 40.6 39.8 40.6 41.7 39.2 40.3 39.0

5 28.7 31.0 34.4 32.9 34.2 32.0 34.4 35.3 37.6 33.8 35.7

6 25.0 28.8 27.0 36.6 36.5 35.7 36.1 36.6 39.4 38.5 37.6

7 25.2 28.2 39.7 41.4 39.1 39.7 28.7 25.7 27.0 23.9

8 29.0 28.8 33.0 35.0 34.2 33.6 34.4 25.9 29.0 24.1 26.7

9 16.4 24.4 16.4 30.4 29.8 29.0 29.3 19.2 17.9 17.3 15.6

10 30.0 24.9 30.0 30.2 28.4 28.0 30.2 29.2 28.5 28.3

Av 27.3 27.7 29.8 34.6 35.3 33.3 34.2 29.9 29.8 28.9 28

SD 6.9 4.1 8.2 3.7 3.8 4 4 7 7.6 7.8 7.9

102

Table 4.17: Statistical comparison of percent body fat derivatives from TBW and equipment measurements

urine h 6 and saliva h 4.( data from table 4.2.10 )

Method Mean Std. Error 95% Confidence Interval


BODPOD 1 28.45 2.4 22.54 34.32

BIA 2*# 28.16 1.6 24.22 31.91

DXA 3 31.23 3.1 23.71 38.70

Urine uncorrected 6 4* 34.90 1.2 31.93 37.84

Saliva uncorrected 4 5# 34.81 1.2 31.80 37.83

* Significant difference (p<0.05) between method 2 and 4,

# Significant difference (p<0.05) between method 2 and 5.

103

Table 4.18: Statistical comparison of % body fat derivatives from TBW and TEE measurements

Method Measure Mean SD 95% Confidence

Interval


Urine uncorr 6h 1 35.0 1.1 32.5 37.4

Schoeller urine 2 30.1 2.4 24.6 35.5

Schoeller saliva 3* 29.9 2.6 24.0 35.8

Coward urine 4 29.0 2.6 22.9 35.1

Coward saliva 5* 28.1 2.6 21.9 34.3

* Significant difference (p<0.05) between method 3 and 5 when uncorrected for dose protocols

No statistical differences were shown when correcting for covariance due to the different dose protocols.

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When comparing body fat values, clear indication must be made of the calculations used to derive the TBW and subsequent % body fat. As

indicated in the above graphs, there appears that one method is systematically higher than the other, when comparing uncorrected urine TBW

with TEE and corrected values

Figure 4.5 Graphical representation of % Body Fat derived from uncorrected urine plateau values and Schoeller TEE urine and saliva values

105

Figure 4.6: Graphical representation of %body fat derived from uncorrected urine plateau values and Coward TEE urine and saliva values

.

106

Discussion

Body composition studies undertaken in the field or laboratory require an accurate

value for isotopic equilibration, often for diverse cohorts of participants living in

varying situations. A few important points must be mentioned with regard to the

current study and with potential implications for the interpretation and wider

application of findings. In most body composition studies, larger cohorts are usually

assessed, so n=10 would be a limiting factor. The findings may not be able to be

generalized to the wider population which may vary from the physiological status

and age of the small number of participants. The cohort studied here were fit,

healthy and well‐hydrated adults, but increased or decreased levels of activity will

likely have an influence on isotopic turnover rates (van Marken Lichtenbelt et al.,

1994). The participants were fasting in this study and not allowed to exercise or

consume excessive amounts of water in the first 4 hours, as small differences may

occur between the fed and fasted state. This may impact on the equilibration time

due to increased intestinal transport (Westerterp et al., 1995).

Question 1: What is the variability in time to isotopic equilibration and optimal

enrichment plateau time point?

This study provided an important extension of previous work undertaken at QUT

where variability in time to isotopic equilibration was investigated (Colley et al.,

2007). Variability in time to isotopic equilibration has important implications on the

administration of the deuterium dilution technique. A significantly longer time frame

of 12 hours was investigated in this study whereas previous work at QUT was limited

to 8 h. Due to the wide variability in the collection times used in other studies, for

example 3‐4 or 6 h (Janowski et al., 2004; Schoeller et al., 1980; van Marken

Lichtenbelt et al., 1994) we were interested in further exploration of this issue. Due

to the widespread use of saliva or urine as the sample medium for body composition

analyses, we investigated the variability in time to isotopic equilibration as per the 3

commonly defined methods, and across each of these two media. The 3 different

methods currently employed to define the optimal value for a plateau are as follows:

less than a 2% difference from the previous sample; less than 3%, or the 6 h sample.

Optimal post‐dose sample collection time is when the equilibration point or plateau

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has been reached and maintained. It is unrealistic to have different collection

protocols for each person; therefore it is critical to determine a time point for urine

collection when data is collected in field studies as variations in individuals will lead

to erroneous TBW and body composition values. Many individuals will reach isotopic

equilibration as early as 3‐4 h and most will do so within 6 h.

In the urine analysis in this study, using the most commonly cited method (1) of ≤ 2%

of the previous value; the proportion of participants equilibrated at hour 4 was 60%.

This proportion increased to 90% by hour 6 and to 100% by hour 7. The proportions

for saliva were 60 % at hour 4, 100% by hour 6 and 7. Using all three cited plateau

methods, all samples were equilibrated by 7 h.

Optimal collection time

Confusion may arise if the assumption is made that the same sampling time protocol

can be applied to various sampling media, i.e. urine, saliva and serum. The evidence

in support of a 3 h equilibration time in serum and breath is well established

(Schoeller et al., 1984). However, equilibration in urine is longer due to a longer time

for isotopic equilibration of the bladder contents relative to saliva, serum or breath.

As outlined in Table 4.14, urine samples (hours 2, 3, 12) showed a significant effect

of time, indicating a collection period between 4 and 10 hours and for the saliva

samples the optimum collection times would be between 2‐6 hours post dose. The

findings of this study are consistent with previous research (Colley et al., 2007;

Schoeller et al., 1980; Wong et al., 1989), which suggested that a minimum of 6 h is

required for isotopic equilibration. The finding that 100% of the participants were

equilibrated at hour 7 is not novel, as some researchers (Westerterp 1994) have

previously found that a 3‐4 h equilibration time is inadequate and a 10 h overnight

equilibration time yielded results that were more in line with TBW derived from the

underwater weighing technique. However, there are many study designs where an

overnight protocol may not be possible, for example when dosing in the morning is

undertaken along with other resting measures. The IAEA recommends a second and

third hourly post dose sample collection after the first 3 hr sample collection. If these

are within 95% of each other optimal collection time was achieved.

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As shown in this study (Fig.4.3), using methods 1 and 2 for urine, 90% of participants

had reached equilibration by hour 5, while only for method 2, 100% were

equilibrated by hour 6. The failure to reach equilibration may be caused by various

factors including edema, shock, or renal problems however none of the participants

in the study showed any of these symptoms. In this cohort of participants studied,

excessive water intake may be a cause of the lower % of equilibration. This may be

due to higher water turnover in some of the cohort, after hr 4, when restricted water

consumption was returned to normal personal intake. This intake varied in

individuals from between 50 mL to 500 mL per hr.

Collection of samples at a later, rather than earlier time point is preferable as most

participants in this study had reached an equilibrium point after 6 h. These results

concur with those of Schoeller et al. (1984) who recommend a 6 h post‐dose

collection time and van Marken Lichtenbelt and Westerterp (1994) who prefer an

overnight (10 h) equilibration protocol.

Fluid intake

Several studies using the deuterium dilution technique have strictly controlled diet

and fluid intake both before and after dosing (Blanc et al., 2002; Racette et al., 1994;

Schoeller et al., 1980) while others have acknowledged the practical limitations this

imposes in clinical or field settings (Isenring et al., 2004; Salazar et al., 1994).

In the present study, water intake was restricted as saliva samples were collected

every 15 min for the first 2 h and then hourly for the next 10 h. Water intake was

restricted to 20 min prior to saliva sampling as it would affect the saliva enrichment

as a result of dilution (Drews et al., 1992). All fluid intake and urine output was

recorded and corrected in the TBW equations (fluid intake and output). In field work

this can be very time consuming and inaccurate, so it is seldom reported. However,

equilibrium is generally reached earlier in the fed state, primarily because of

increased intestinal transport and absorption through the intestine wall (Hill et al.,

2004; van Marken Lichtenbelt et al., 1994). All the participants in this study were

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fasted, so equilibration may have been achieved earlier if they had not been in a

fasted state. However, despite this possibility, it is suggested that samples are

collected at a later time rather than earlier to cover any isotopic equilibration

inconsistencies caused by delayed bladder emptying in an older population and fluid

shifts in the body due to the fasting state and slower intestinal transport.

Question 2: Differences in TBW estimations using different equations

Using the equations shown in Table 4.12, there was a significant difference between

the equations used in the plateau method, urine TBW corrected for fluid loss

compared to the current method in use TBW (uncorrected). The other equations

showed no statistical differences. The plateau method currently in use at QUT,

where no correction is made for fluid intake or output of urine over the 6h

equilibration, showed no statistical difference to the TBW values generated by either

the Schoeller or Coward equations. This concurs with research completed by

Schoeller (1984) and Westerterp (1994) where no statistical differences were noted

for the different approaches in the intercept method using urine as the medium.

Question 3: What is the impact of time variability on body composition i.e. percent

body fat estimates derived from TBW and do these differ from values obtained by

other modalities – DXA, BODPOD, BIA?

To standardize the collection protocol, providing there is no negative cost factors

involved, the argument would be to collect the post‐dose sample at 6 h for urine.

Reference time‐points have been established by a number of researchers to

minimize collection error in sampling and hence standardize TBW results and derived

body composition measurements. Larger numbers than studied within this cohort

would have to be done to determine the clinical differences that may be observed,

by altering the collection times and hence derived % body fat values.

The values obtained following the QUT protocol of a 6 h urine and 4 h saliva

collection are shown in Table 4.16 and Table 4.17. BIA (measure 2) was statistically

different to uncorrected urine and saliva, (measures 4 and 5 respectively). The small

cohort number and diverse BMI ranges (19‐29) may have had an impact on the

statistical data. In Table 4.18, Coward saliva( measure 5) was statistically different to

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Schoeller saliva(measure 3), but not to any other measure. However, after

accounting for the different dosing protocols used, no statistical differences were

found.

Conclusion

The results of this study confirm the importance of consistent application of a well

structured protocol. There should not be changes made to the collection times

within the study, and a minimum equilibration time of 4 and not more than 10 hours

should be employed for urine samples. Further, given the differences between

sample mediums, using the same medium within each study is important. Finally

consistent use of equations to convert sample data to TBW values is important as

the various equations will influence the results.

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Chapter5

Chapter 5

The hypothesis addressed within in this Chapter is: Can the TEE data and

covariance graphs track the changes brought about by altered fluid intake and

activity?

Introduction and literature review

Determination of energy expenditure is fundamental to investigations of human

energy metabolism, particularly when examining the adaptation of energy

metabolism to the following conditions: growth, aging, over and underfeeding and

physical activity. The generally accepted technique for measuring energy

expenditure in free‐living human subjects is the doubly labeled water technique. It

has been extensively used in a variety of circumstances over the last twenty years

and its popularity has rapidly grown, as a result of its ease of use, non‐invasive

nature and the price reduction in oxygen‐18 due to increased production caused by

global demand.

The underlying principle of the doubly labeled water technique is based on the

kinetics of two stable isotopes of water, namely deuterium (2H2O) and oxygen‐

18(H218O). It is assumed that water labeled in the oxygen position will be eliminated

from the body both as CO2 and H2O, whereas the hydrogen labeled water will be

eliminated only as a function of water turnover. The difference between the two

elimination rates is a measure of CO2 production. Oxygen consumption can then be

calculated from the rates of CO2 production together with the food quotient data.

Subsequently energy expenditure can then be derived using Weir’s equation (Weir,

1949).

The DLW technique appears deceptively simple however there are many overlapping

complicating factors in play. These include changes in body composition during the

study period, changes in water intake, changing levels of physical activity, and

variations in baseline water intake (Goran et al., 1994; Herd et al., 2000; Speakman,

1998). The main component of the daily energy turnover in the average individual is

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the energy expenditure for maintenance processes, as reflected by the resting

metabolic rate (RMR). The remaining components of TEE are the Thermic Effect of a

meal (TEF) and the activity‐induced energy expenditure (AEE). TEF is a fraction of

<10% of energy intake, depending on the macronutrient composition of the food

consumed. AEE is the most variable component of the daily energy turnover, on

average ranging between 25–35% of TEE up to 75% in extreme situations during

heavy, sustained exercise (Goran et al., 1994; Westerterp 1998).

The purpose of this study was to examine the effects of alterations in physical

activity and water intake on the estimation of TEE in a sedentary person. This

objective was achieved by manipulating energy expenditure by increasing the

number of daily steps taken and by changing the daily fluid intake.

Subject and method

A 56 year‐old female was the participant; and not selected based on any age, activity

level, or body composition criteria. No renal or voiding dysfunctions were indicated.

Blood pressure was normal (120/80 mmHg), she was a non‐smoker and had been

weight stable for the last 6 months. The study was approved by the University

Human Research Ethics Committee 0800000223.

Design

The study was designed to measure TEE firstly at a baseline, and then repeat the

measurement of TEE when changes were made to either the number of daily steps

or fluid consumed (based on the baseline levels) over three studies across an 8‐

month period.

Study 1: No interference with regard to water intake or activity, TEE baseline to be

established to represent normal daily living.

Study 2: The activity level was altered by increasing the number of daily steps

taken.

The second study was comprised of altering the activity level over a 2 day block in

Week 1 and 2 of the 14‐day collection period. The daily steps were increased by

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3000 per day over the 2 day block from a starting point of approximately 40,000

steps in week 1( approximately 6‐7000 steps per day).

Study3: The fluid intake was changed from Week 1 to 2 while activity levels were

kept constant.

Within the third study, fluid intake was kept constant at a daily intake of 2 L and

activity levels were recorded for Week 1. In Week 2, the fluid intake was increased to

a daily intake of 4 L for the duration of the week and the activity mimicked that of

Week 1.

Protocol

Dosing, sample collection and analysis

After collecting an early morning fasting urine sample, a dose of DLW (1.30 g/kg

TBW) was orally ingested. This consisted of 0.05 g/kg body weight of 99% 2H2O

(Sigma Aldrich) and 1.25 g/kg body weight of 10% 18O. Due to low deuterium

enrichment values, the dose was increased for parts 2‐5 to 1.35 g DLW/kg body

weight consisting of 0.1 g 99% 2H2O and 1.25 g kg 10%18O. Subsequent to dosing a 6

h and thereafter daily second voided urines were collected for 14 days. All collection

times were noted and converted to decimal time to facilitate the calculations of TEE.

The samples were labeled and frozen at ‐20°C until the completion of the study,

where they were analyzed at the Queensland University of Technology (Brisbane,

Australia). All activity was measured with a pedometer and a Triaxial Actigraph GT3X

accelerometer. This was worn during all waking hours on the waistband directly

above the left knee, and programmed to collect data every 60 sec.

Measurements


Total EE was measured using the DLW technique. Two stable, non‐radioactive, non‐

toxic isotopes of hydrogen (deuterium, 2H) and oxygen (18O) in the form of water, i.e.

2H2O and H218O, were administered to the subject. The subject consumed 0.1 g/kg of

body weight of deuterium (100%) and 1.25 g/kg of body weight 18O (10%),

administered via a plastic cup and drinking straw. The dose consumed was recorded

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to two decimal places of a gram. A single urine sample was obtained before the

dose, and subsequently urine samples were collected 6‐h post‐dose and thence

every 24‐h for the following 14‐d. All samples were collected in 20 mL Universal

tubes and subsequently frozen. The subject was instructed to record the time each

urine sample was collected. The enrichment of both the deuterium and 18O samples

were measured in triplicate via isotope ratio mass spectrometry (Hydra, PDZ Europa,

UK), with the results being expressed in delta units as ‰ (per mil) relative to

standard mean ocean water (SMOW).TEE in kcal/day was calculated using the

Schoeller equation. (Refer Chapter 2).

An RMR was measured during the baseline study and this value was applied to all

the subsequent studies.

Measurement of the thermic effect of a meal

TEF was estimated as being 10% of TEE value estimated for each study.

Statistical analysis/Results

Statistical analysis

All values presented are averages of triplicate raw IRMS data readings for each

analysis. This data was used in the Schoeller et al. equation (Schoeller et al., 1987) to

generate TEE values and corresponding elimination rates and dilution spaces. The

“Jack knife” method (Broemeling et al., 1993) was employed to detect any

questionable results. Body composition values were derived from the TBW (kg)

values estimated by the intercept method (Refer chapter 2 for more detail). Steps

counts were calculated for each day and average weekly values are presented in the

tables below.

Results

Tabulated below are the results for each study. Where No and Nd are the dilution

spaces for deuterium and oxygen‐18 respectively. The Nd/No is the dilution space

ratio which should be in the range previously determined to be 1.015‐1.065 (IAEA,

Schoeller).This range is also an indicator if the data used is sound as variances from

these range are not physiologically possible or normal. The elimination rates are the

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average rate of elimination over the 14 day time period for both deuterium (kd)

which is lost as water and the oxygen‐18(ko) which is lost as both water and breath

vapor. Excessive fluid intakes and activity changes may alter these variables but it

will depend on the intensity of the exercise relative to the person’s fitness and size,

and the quantity of fluid ingested relative to their size. The Ko/Kd ratio is an indicator

of the elimination rate of both isotopes is (1.2‐1.4 ;IDECG report) for ambient

temperatures and low water turnover, which is not adhered to in table below due to

water and exercise variances.

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Table 5.1: Dilution spaces and elimination rates per day

Study No(kg) Nd(kg) Nd/No Elimination rates Elimination

rates

ko/kd

ko kd

Baseline 1 37.90 40.00 1.06 ‐0.13 ‐0.11 1.20

Increased steps days 4,8,9,14 2 37.52 39.36 1.05 ‐0.14 ‐0.12 1.22

Increased water, week 1=2L, week

2=4L 3 36.93 38.81 1.05 ‐0.15 ‐0.13 1.16

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Table 5.2: TEE (kcal/day), daily water intake and step count per week

Study Baseline

fluid

Increased

fluid

Steps

week1

Steps

week2

Total

steps

Change in

steps from

baseline (%)

RMR

(baseline

value

used)

wt kg Measured TEE

kcal/day

Change in

TEE from

baseline (%)

8%

variance

on baseline

TEE

Baseline 1 2L 2L 45899 42345 88244 1455 92.0 2281

Increased steps days

4,8,9,14 2 2L 2L 47227 53981 101208 15 1455 92.2 2657 16.5 >8%

Increased water,

week 1=2L, week

2=4L

3 2L 4L 44799 41939 86738 ‐2 1455 91.8 1995 ‐14.3 >8%

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Table 5.3 Comparison of values using Jack knife method

TEE (kcal day) average SD CV% SEM Study

minus 6 h d1 d2 d8 d10 d12 d13

2228 2243 2287 2300 2273 2287 2340 2280 37.04 1.62 13.12 1

2561 2634 2623 2638 2631 2562 2674 2617 41.65 1.52 15.73 2

2021 1949 1985 2001 1967 1952 2100 1996 52.62 2.61 19.90 3

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Table 5.4 Comparison of TBW (kg) values derived from TEE intercept during the 5 studies

Study TBW (kg) TBW (kg) TBW av.

(kg)

18O derived 2H derived

1 37.6 40.0 38.0

2 37.2 37.8 37.5

3 36.6 37.2 36.9

Average 37.1 38.3 37.5

SD 0.50 1.47 0.55

CV% 1.36 3.85 1.47

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Results

The age, weight, and height of the subject was 56 y, 92 kg, and 178 cm, respectively. The isotopic data is shown in Table 5.1. The dilution space

for 18O was approximately 37 450 mL and for 2H, 39 390 mL. The respective elimination rates of 18O and 2H are detailed in Table 5.2 and the

average rates were ko 0.1400 and kd 0.1200. Daily TEE was in kcal as per Table 5.2 and the average daily water turnover was 2103 L.

Covariance residuals

Covariance residuals are the difference obtained between the calculated values, based on the isotope (deuterium or oxygen‐18) elimination

rates (kd and ko) and the measured values derived from the IRMS. These differences are indicative of the precision of analytical measurement,

and also indicate changes in water intake and activity (Prentice, 1990).

The figures depicted below in the comparison of covariance residuals show good agreement between the deuterium and oxygen18 results,

indicating that the analytical estimation of both isotopes was precise across the 3 studies. Imprecision would be indicated by a deviation of the

points away from each other.

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Figure 5.1: Comparison of covariance residuals based on samples pre, 6 h, d 1, 2, 8, 10, 12, 13

Both oxygen‐18 and deuterium graphs track each other indicating analytical precision.

Study 1 baseline Study2 increased steps day 4, 8,9,14

Study 3 water wk 1 = 2 L, week 2= 4 L

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Ratio plots and product plots

The residual product plots depicted below are indicative of water turnover (Prentice, 1990).Deviations away from the zero line are indicators

where water turnover has fluctuated. In studies 2 and 3 there are variances around the zero line when compared to study 1. The residuals do

appear to be of a greater magnitude in the second part of study 3, where the water intake was increased. The product plots are indicators of

CO2 turnover and where activity is not excessive in that the person is not breathing heavily the plots follow the zero line.In studies 1, 2 and 3,

the activities may not have been of an excessive enough nature to increase the CO2 turnover as is indicated by the plots remaining around the

zero line..

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Figure 5.2: Residual product plots (water turnover) based on samples pre, 6 h, d 1, 2, 8, 10, 12, 13

Study 1 baseline Study 2 increased steps day 4, 8,9,14

Study 3 water wk 1=2 L, week 2 =4 L

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Figure 5.3: Ratio residual plots (CO2 production) based on samples pre, 6 h, d 1, 2, 8, 10, 12, 13

Study1 baseline Study 2 steps day 4, 8, 9, 14

Study 3 water wk 1=2 L, week 2 =4 L

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Discussion

A combination of biological variation and analytical error will account for an experimental

error of ± 8.5% (Goran et al., 1994; Prentice, 1990; Schoeller et al., 1995). Under tightly

controlled diet and living conditions where RMR, diet and activity are constantly monitored

the experimental reliability of the TEE result was ± 8.5%. However, in more free‐living

individuals it is suggested that a value of 12% may be more realistic to account for inherent

intra individual variation (Goran et al., 1994). In the laboratory an 8% variation may be

expected to account for technique, seasonal variation and machine error. In the current

study, based on the baseline measure it would be expected that, the average daily EE of the

participant could range between 2099 kcal and 2463 kcal applying the 8% rule.

The covariance values (Figure 5.1) track each other in the plots and are representative of

“well behaved” data and are indicative of the analytical accuracy. The ratio and product

plots (Figures 5.2, 5.3) indicate the water turnover and CO2 production and do reflect the

changes in activity. This is illustrated in Study 2 where extra steps were undertaken on days

4, 8, 9, 14(approx = 12,000 steps) and normal activity step values for the other days,

resulting in an increased CO2 production. The elimination rates show variability but fit with

the water intake and activity. Nd/No results were within the acceptable limits of 1.034 ±

0.03. The IDECG Workshop recommended that 1.015‐1.060 should be adopted as an

acceptable range. Where water intake was increased the Ko/Kd ratio falls slightly below 1.2

due to the K and Ko rate being increased. The recommended range in the literature for

ambient temperature and low water turnover is 1.2‐1.4 (Prentice, 1990).

Conclusion

Because of the importance of routine physical activity and leisure‐time exercise in the

prevention of disease and the maintenance of health, methods have been developed to

quantify physical activity at population levels. Amongst these methods are TEE by DLW,

accelerometers, heart rate monitors and physical activity diaries.

Within this study, accelerometer and the DLW technique were used to quantify the energy

cost of increasing daily activity as would be prescribed to a sedentary person undertaking a

fitness program. There were no significant changes in total body water across all the studies,

126

even though the elimination rates changed with extra water intake and activities. These may

not have been sufficiently strenuous or the water intake high enough to cause a significant

change in the TBW and hence the CO2 production and TEE values. RMR was only measured

at the start of study 1 and the value then used for all the other studies, as the participant

was weight stable and the activities were not physically strenuous.

As diet records were not kept for all 3 studies it was necessary to assume a respiratory

quotient of 0.85 rather than determine one based on diet composition. However, in effect,

changing the respiratory quotient to, for example, 0.90, changes the subject’s calculated EE

in study 1 from 2278 kcal to 2178 kcal, a difference of only 1%. Thus, an alteration of this

magnitude still falls close to the range of the subject’s EE as determined by the 8% variation

predetermined in literature (Goran et al., 1994; Schoeller et al., 1995).

An inevitable loss of precision will occur when DLW technique is used under high ambient

temperatures, causing high rates of water turnover with a consequent reduction in Ko/Kd

ratio (Prentice, 1990) . For this reason, the dose was increased to ensure analytical

precision. A potential error in DLW estimations is the measurements of the two

disappearances rates of 2H and 18O. This error can be magnified when the water turnover

rates are high, giving a lower ko/kd ratio. To counter this problem, more data points were

analyzed to measure the outflow rates of the isotopes.

Systematic changes in water flux and CO2 production may cause errors in the dilution space

calculations. When the variable pool model is used and both dilution spaces are calculated

by back extrapolation to time zero, the systematic errors are increased (Prentice, 1990;

Schoeller et al., 1986).To counter this pitfall in the multi‐point methodology, close

observation of the residual plots would identify outlier points which could be rejected end

the data recalculated or the Jack knife system used to identify questionable points.

The rationale of this case‐study was to test 1 person over 3 situations to determine if the

method was sensitive enough to detect the fluctuations in activity and water intake. The

findings of this series of studies demonstrate that changes in water turnover and activity can

be tracked. In study 2 the TEE values reflected the increase in added physical activity.

However the findings of study 3, where water intake was increased separate to increasing

physical activity levels, suggests that variability in fluid intake of this magnitude may cause

127

spurious TEE results. The TEE value obtained in study 3 was lower than expected due to an

isotopic washout effect which was not related to energy turnover. This can be seen from the

resulting TBW values for this period being lower than the TBW values calculated in studies 1

and 2. However to place meaningful estimations on these graphs, more data across a wider

spectrum of the population needs to be collected to determine what changes are inherent

noise and what reflect significant changes in energy expenditure.

128

Chapter 6

In conclusion, the data generated within this thesis has highlighted the following points for

the questions.


measurements to establish baseline “noise”

Research questions for analytical variance on the IRMS:

The following research questions were posed to determine the technical and measurement

error in the equipment and methodology in use within the laboratories at QUT and IHBI.

The following points have been confirmed with regard to sample preparation and analysis

on the IRMS at IHBI.

Volume changes for oxygen‐18 analyzed on the IHBI IRMS equipment have a SD within

the “within” analytical variance levels of 0.4 delta SMOW units for volumes of 0.5 and

0.4 mL but not for 0.3 mL. Therefore, it is recommended that volumes of either 0.3 or

0.4 mL be used for both deuterium and oxygen‐18 analysis. Volume changes for

deuterium analyzed on the IHBI IRMS equipment fall within the variance levels of 6 delta

SMOW units for all volumes. Despite being statistically significant it is not of clinical

significance in the TEE or TBW calculations as the standard error is within the “noise

range” of the equipment and would always need to be considered before any result is

significant.

The equilibration time for oxygen‐18 is a minimum of 24 h and a maximum of 2 weeks;

deuterium a minimum of 3 days and a maximum of 2 weeks does not affect the values

and gives results that are greater than the machine variability. The main proviso is that

the references and samples are prepared simultaneously and analyzed together. In

doing so, any upward or downward shift that may occur over time will affect the

reference and burp values equally.

Analyses in triplicate provide better statistical output. It also provides duplicate values in

case one sample is invalid. It is suggested that as many samples (collected in the 14 day

collection period) as possible be analyzed to ensure a stable elimination rate line if water

129

turnover and/or activity is excessive. The preferred samples to be analyzed are pre, 6 h,

d1‐3, d7, 8, d12‐14, cost and time permitting.

For the vacuum line, any order of preparation is suitable as the TEE values fall within 8%

of each other regardless of preparation order. An 8% variation is acceptable for the TEE

values due to biological and technical errors (Schoeller, 1988). However, for the

automated line deuterium must be assessed first followed by oxygen‐18 as the

automated machine line does not evacuate tubes but merely refills them with an

injection of gas for a predetermined time. Any fractionation (which may occur for both

isotopes), would cause a slight elevation in the values and hence a lower TEE.

The position of the sample in the carousel will not affect the TEE as the results have a CV

of 1.5%, which is within the laboratory dependant analytical precision in the literature

of 3% or greater(de Jonge et al., 2007; Schoeller et al., 1995).

To ensure that position is not a determining factor in TEE calculations the following

conditions should be adhered to:


References must be placed within the batch at intervals of no greater than 20 tubes to

ensure there is no machine drift and to enable drift correction when the software is

applied to the raw data.

Although temperature in the laboratory was not systematically altered, it was found that

within the laboratories at IHBI, as long as all the samples and standards to be analyzed

within a batch are prepared together and left together in a batch, prior to analysis,

temperature will not be a consideration.

With regard to the TEE and TBW measurements in use in our laboratory, the following

methodological studies were undertaken.

Does the use of different regression equations and equation constants result in

different TEE values?


130

In this study, the different equations of Schoeller and Coward were applied to the raw

data.

Given the differences in the equations, the TBW values for both were identical and there

was no significant effect of equation or time, but the TEE values derived showed a

significant main effect of time and equation (p<0.05) for samples collected in the

morning and evening. When correlations were made to correct for the different dose

protocols (1.30 g DLW/kg body weight as opposed to 1.35 g DLW/kg body weight) there

was no effect of time or equation.

To provide clarity for other researchers comparing TEE values, the equation used and an

indication of values obtained would provide more information for comparison. Other

measures should also be carried out such as resting metabolic rate, food intake and

body composition assessment during the study period. The aim of these measurements

would be to provide equivalence between energy inputs and outputs during the study

period. Each measurement would provide, in part, validation of the DLW technique

results. However, it is critical that the initial isotopic enrichments are carefully examined

with quality controls and covariance checks to ensure the integrity of the data,

regardless of which equation is used.

Within the estimation of TBW using stable isotopes

What is the variability in time to isotopic equilibrium using plasma, saliva and urine

samples and the impact of this variability on estimates of TBW and body composition

(derived and measured)?

What is the difference in TBW using different equations (Intercept vs Plateau)

Variability in time to equilibration ‐ the optimal time for urine collection is between

hours 4 and 10 where there was no significant difference between values. In contrast,

between hours 1 to 3 and from 11 h onwards, there was significant statistical difference

(p<0.05). Across the collection time frame subjects reached equilibration at varying time

points. However, for consistency and applicability across a range of people, 6 h should

be recommended as the time required reaching isotopic equilibration.

131

Optimal post‐dose sample collection time is when the equilibration point or plateau has

been reached and maintained. As it is unrealistic to have different collection protocols

for each person, it is critical to determine an optimal time point for urine collection in

field studies as individual variations will lead to erroneous TBW and body composition

values. Collection of samples at a later, rather than earlier time‐point, is preferable as

most of the individuals in this study were compliant after 7 h. Schoeller et al, (Schoeller,

1988) recommend a 6 h post‐dose collection time and Westerterp and van Marken

Lichtenbelt (Westerterp et al., 1995) prefer an overnight (10 h) equilibration protocol. To

cover any isotopic equilibration inconsistencies caused by delayed bladder emptying in

older populations, fluid shifts in the body due to fasting state and slower intestinal

transport, it is suggested that samples are collected at a later rather than earlier time.

The effect of fluid intake during the equilibration period was monitored. Several studies

using the deuterium dilution technique have strictly controlled diet and fluid intake both

before and after dosing (Blanc et al., 2002; Racette et al., 1994; Schoeller, 1983), while

others have acknowledged the practical limitations this imposes in clinical or field

settings (Isenring et al., 2004; Salazar et al., 1994). Water intake was restricted to 20 min

prior to saliva sampling as it would affect the saliva enrichment as a result of dilution

(Drews et al., 1992). All fluid intake and urine output was recorded and corrected in the

TBW equations (fluid intake and output). In field work this can be very time consuming

and inaccurate so it is seldom reported. However, equilibrium is generally reached

earlier in the fed state, primarily because of increased intestinal transport and

absorption through the intestine wall (van Marken Lichtenbelt, Westerterp et al. 1994).

All the participants in this study were fasted so perhaps equilibration may have been

achieved earlier if they had not been in a fasted state.

Differences in TBW estimations using different equations. Compared to the current

method in use (TBW derived from urine, uncorrected), only TBW derived from urine

samples corrected for fluid intake over the collection period was different (TBW = 34.82

± 3.75 L for uncorrected urine versus 33.10 ± 4.34 L for urine corrected for fluid loss, p =

0.008). The other equations showed no statistical differences.

132

Within Study 4

“If the human condition changes as a result of exercise or excessive water intake

does it affect the TEE result?”

There were no significant changes in total body water across all studies, even though the

elimination rates changed with extra water intake and activities. The extra activity may not

have been sufficiently strenuous or the water intake high enough to cause a significant

change in the TBW and hence the CO2 production and TEE values.

An inevitable loss of precision will occur when the DLW technique is used under high

ambient temperatures, causing high rates of water turnover with a consequent reduction in

Ko/Kd ratio (Prentice, 1990). For this reason, the dose was increased to ensure analytical

precision. A potential error in DLW estimations is the measurement of the two

disappearance rates of 2H and 18O. This error can be magnified when the water turnover

rates are high, giving a lower ko/kd ratio. To counter this problem, more data points were

analyzed to measure the outflow rates of the isotopes.

The covariance values (Figure 5.1) track in the plots and are representative of “well‐

behaved” data and are indicative of the analytical accuracy. The ratio and product plots

(Figures 5.2, 5.3) indicate the water turnover and CO2 production and do identify the

changes in activity.

133

The following is a proposed “checklist” to ensure consistent and accurate functioning of the

IRMS and results in the laboratory;


References must be placed within the batch at intervals of no greater than 20 tubes

to ensure there is no machine drift and to enable drift correction when the software

is applied to the raw data.

Standards used to calibrate the data (IAEA standards) should bracket the range of

expected isotopic values expected from the samples.

Run all samples in duplicate, or budgetary constraints allowing running with

triplicates is preferable to “break the tie” (Jardine et al., 2005).

Sample preparation – deuterium first then oxygen‐18.

Sample volume 0.5 or 0.4 mL.

Sample equilibration for deuterium: 3‐14 days and for oxygen ‐18: 1‐14 days.

All samples in a batch must be prepared at the same time.

Equation‐ Schoeller or Coward

Use either both as neither is more correct or accurate than the other.

Report both results as they may not report the same values, which would then give

other researchers a chance to evaluate your results against theirs in a true fashion.

Check the endpoint values for deuterium >128‰ and oxygen‐18>8‰, if not use the

previous day’s results.

Nd:No dilution space between 1.0‐1.07.

Calculate TEE by dropping one data point at a time ‐ an agreement within 8% for

each result generated.

Check TBW (kg) value by TBW (kg)/ Ht. ^3(m.). Flag results if they fall out of the 5.7‐

9.6 range.

R2 of the regression line for elimination rates should be >0.99.

Residual spaces co varies for deuterium and oxygen‐18.

134

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