Measurement of total body water (TBW) and total energy ... · Measurement of total body water (TBW)...
Transcript of Measurement of total body water (TBW) and total energy ... · Measurement of total body water (TBW)...
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
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KEYWORDS
Total energy expenditure,
Total body water,
Isotope ratio mass spectrometry,
Stable isotopes, doubly labeled water,
Body composition,
Deuterium,
Oxygen‐18
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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
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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
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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
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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.
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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
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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
Introduction and background .......................................................................................... 57
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Sample equilibration time ............................................................................................... 57
Key findings and statistical analysis for question 3 ......................................................... 59
Oxygen ‐18 results ‐time variability ................................................................................. 59
Deuterium results ‐time variability ................................................................................. 59
Question 4: Number of samples required for analysis ........................ 60
Introduction and background .......................................................................................... 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
Introduction and background .......................................................................................... 63
Study Design .................................................................................................................... 63
Statistical analysis of different methods of preparation ................................................. 65
Key findings and statistical analysis for question 5 ......................................................... 66
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
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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
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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
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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
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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
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IAEA International Atomic Energy Agency
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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
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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
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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
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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
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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)
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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.
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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.
Total Energy Expenditure
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).
Total Energy Expenditure
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
The methodological and analytical variances in the IRMS in TEE and TBW
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.
Introduction and background
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
Key findings and statistical analysis for question 3
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?
Introduction and background
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
Reference 2.33E‐07 100
Reference 2.39E‐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.
Introduction and background
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
Key findings and statistical analysis for question 5
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
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 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
in different TEE values?
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
urine_Schoeller_2 2543 346.78 8
urine_Schoeller_3 2583 448.12 8
saliva_Schoeller_1 2477 322.42 8
saliva_Schoeller_2 2529 422.09 8
saliva_Schoeller_3 2500 478.03 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
Lower Bound Upper Bound
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
Lower Bound Upper Bound
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.
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Table 4.18: Statistical comparison of % body fat derivatives from TBW and TEE measurements
Method Measure Mean SD 95% Confidence
Interval
Lower Bound Upper Bound
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
109
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 Energy Expenditure
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,
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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
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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.
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Chapter 6
In conclusion, the data generated within this thesis has highlighted the following points for
the questions.
The methodological and analytical variances in the IRMS in TEE and TBW
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
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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:
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.
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?
Does the use of saliva or urine affect the final TEE result?
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;
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 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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