Modeling the Epidemiologic Transition: Energy Expenditure, Obesity and Diabetes
Conditions: Obesity, Diabetes
Sponsor: Loyola University

 
Official Title: Modeling the Epidemiologic Transition: Energy Expenditure, Obesity and Diabetes 
 
NCT Number:  NCT02925156 
 
Date: 05/27/2016 
 

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PI: Amy Luke, PhD 
Co-PI: Ramon Durazo, PhD., Guichan Cao M.S., Bamidele Tayo, PhD., Richard Cooper, MD., Lara 
Dugas, PhD., (Daniel, Chiang, STAR Student), Rania Kaoukis, MD, Brian Layden, MD ( Northwestern 
Memorial) 
 
 
A. SPECIFIC AIMS      
 
 
 
Populations all over the world are experiencing rapid increases in the prevalence of obesity and 
diabetes [1-5].  To date, the public health response to the emerging obesity epidemic has been almost totally 
ineffective.  As a first line of response, professional bodies and government organizations have issued 
prevention guidelines, all of which include recommendations on levels of activity energy expenditure (AEE) [6-
10].  However, even if fully implemented, it is not clear that the current recommendations on AEE would impact 
the trend in age-related weight gain.  In fact, there is virtually no direct evidence that can be brought to bear on 
the question of whether the obesity epidemic has resulted primarily or even partially from society-wide declines 
in habitual physical activity (PA).   
 
 
To date, epidemiologic research on AEE has relied on very crude measurement tools—primarily 
questionnaires.  These self-reported data capture only a small fraction of the total variance in activity and are 
potentially confounded when they focus on leisure-time PA.  By contrast, direct measurement with doubly 
labeled water (DLW) provides a precise, unbiased estimate of all forms of non-resting energy expenditure.  
DLW has also been used to validate the new generation of accelerometers which are efficient measurement 
tools for larger studies.   
 
 
In this application we request support to apply these objective measurement tools to examine 
the “ecology of AEE”.  The study will take place in 5 countries, spanning the range of the activity-obesity 
spectrum, and combine both within-population person-level and between-population ecological analyses.  First, 
we will examine whether an individual’s amount or pattern of AEE is related to adiposity in a diverse sample of 
2,500 (i.e., 500 subjects/site x 5 sites).  Second, we will test the ecological hypothesis that a decline in levels of 
AEE is an important cause of the rapid increases in obesity that are currently taking place in many societies.  
As an exploratory aim we will examine the role of selected adipocytokines and the appetite hormone ghrelin.   
 
 
Accordingly, we propose to test the following Hypotheses:   
 
1)  AEE is negatively related to percent body fat in the 5 study populations at baseline, independent of     
dietary intake.  
 
 
2)  AEE is negatively related to change in body weight during follow-up, independent of dietary intake. 
 
 
3)  Population mean levels of AEE are negatively related to population mean levels of obesity/relative 
 
weight. 
 
4)  Adipocytokines (e.g., adiponectin and leptin) and hormones (e.g., ghrelin) influence weight regulation 
and insulin sensitivity and interact with AEE.   
5)  Heavy metals, such as arsenic, mercury, lead and cadmium, are positively associated with obesity and 
markers of diabetes and CVD risk. 
 
6) Human gut and oral microbiome and plasma metabolites are associated with obesity through short chain 
fatty acids, a byproduct of gut fermentation of complex carbohydrates and is mediated by lifestyle and 
geographic location.   
 
 
 
To test these hypotheses we will complete the following Specific Aims:   
 

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1)  Recruit 500 participants, ages 25-45, from each of 5 populations (total N = 2,500); measure AEE with 
accelerometry; collect 24-hour dietary recalls; assess fasting glucose/insulin, ghrelin, and adipocytokines 
(adiponectin, leptin). 
 
2)  Measure AEE using DLW in a random subset of 75 participants from each of the 5 populations at 
baseline (total n = 375).   
 
3)  Repeat AEE measurement with accelerometry on all participants at 24 months; repeat weight at 12 and 
24 months and body composition at 24 months. 
 
4)  Examine the relationship between AEE and body composition within and among populations at baseline 
and at follow-up; assess inter-relations of AEE and diet, glucose, insulin, ghrelin and adipocytokines. 
 
5) Measure serum 25(OH)D and intact parathyroid hormone (iPTH) levels in 500 adults from the 5 METS 
sites (total N=2,500) at baseline and determine associations with latitude, diet, physical activity, 
adiposity, blood pressure (BP), and biochemical risk factors for CVD (eg, insulin, glucose, adiponectin, 
leptin, cholesterol).  
6) Examine the association between baseline serum 25(OH)D and iPTH levels and changes in body 
composition and blood pressure over 6.5 years of follow-up. 
7) Repeat measurement of serum 25(OH)D and iPTH at third follow-up examination (approximately 3.5 
years after baseline) and measure BMD using DXA in the available cohort.    
8) Identify a metabolite profile that predicts progression to IGT/T2D in diverse black populations spanning 
the epidemiologic transition. 
9) We hypothesize that the plasma metabolite profile at baseline (2009) will be significantly associated 
with changes in diabetic status over the same 6-year period (2009 to 2015), i.e. normal glucose 
tolerance (NGT) to IGT and/or T2D. 
Examine whether this metabolite profile is common between sites, and characterize the lifestyle 
components, including diet, and physical activity, which may account for any country differences. 
10) We hypothesize that, in addition to a baseline metabolite profile, lifestyle factors (improved diet quality, 
and greater physical activity) that vary according to epidemiologic transition are also associated with 
development of IGT/T2D over a 6-year period. 
11) To evaluate the independent and combined effects of metals (As, Cd, Pb, Hg) on body composition and 
markers of CM risk at baseline in 2,500 individuals living in 5 diverse countries. Hypothesis 1: Baseline 
metals concentrations are positively associated with a) elevated adiposity / obesity and b) markers of 
CM risk (central obesity, impaired fasting glucose (IFG), insulin resistance (IR), hyperlipidemia, high 
blood pressure), adjusting for diet, physical activity, and other potentially important confounding 
variables. 
12) To evaluate the independent and combined effects of metals (As, Cd, Hg, Pb) on incident obesity, IGT, 
IR and hypertension after 8 years of follow-up in the full cohort. Hypothesis 2: Exposure to metals is 
associated with increased risk of new-onset a) obesity, b) impaired glucose tolerance, c) insulin 
resistance, and d) hypertension in individuals who were healthy at baseline, adjusting for diet, physical 
activity, and other potentially important confounding variables.  
13) To explore the independent and combined effects of metals (As, Cd, Hg, Pb) on adipose-derived 
bioactive molecules/hormones as intermediate markers of diabetes risk.  
14) Hypothesis 3: Exposure to metals is associated with lower adiponectin/leptin ratio, adjusting for diet and 
physical activity and other potentially important confounding variables, and modifies the effects of 
adipose-derived hormones on body weight regulation, fat composition, and insulin sensitivity. 
15) Examine the associations between obesity, plasma metabolites and gut/oral microbiome. 
16) As a proof of concept examine the contributions of the oral/gut microbiome to the development of 
obesity and type 2 diabetes in sterile mice, using samples identified from in specific aim 15. 
 
 
B. BACKGROUND AND RATIONALE 

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B.1 Physical Activity and Risk of Obesity 
 
 
 
The energy budget is defined by the direct relationship between energy intake and expenditure.  
Obesity can only result from an excess of calories consumed over calories expended. It is often assumed, 
therefore, that a similarly straightforward, simple relationship exists between variation in AEE within the range 
of normal for a population and the risk of weight gain.  The second conclusion which follows from this line of 
reasoning is that population-level weight gains taking place have resulted—at least in part—from declining 
AEE, and small increases in AEE for individuals will prevent weight gain. In fact, neither of these conclusions is 
justified by theory alone or supported by evidence.  Increases in expenditure would normally be accompanied 
by increases in intake, while excess intake can be stimulated independently by high caloric density of food, 
changes in food availability, and eating patterns.  As pointed out repeatedly by investigators in this field [17-
20], the regulation of energy stores and body composition must be seen as a complex, dynamic process 
influenced by the interplay of factors that modify both intake and expenditure.  An individual ultimately uses 
both biological stimuli, via the hormones that control satiety, and social cues to achieve energy balance [21-
24].   Whether variation in patterns of activity observed in modern, free-living populations plays a key role in 
this process is unknown.  Since recommendations to increase AEE to reduce weight gain are at the core of 
current public health policy this issue deserves careful study.   
 
 
Large shifts in chronic disease have been associated with recognizable shifts in lifestyle—for example 
cigarette smoking and lung cancer, and animal fat intake and heart disease [25, 26].  “Macro-level” or 
ecological relationships are, of course, subject to confounding.  However, these relationships must be present 
for an etiologic hypothesis to be correct, and their absence is a serious weakness for a hypothesis.  While 
lifestyle trends that increase risk of obesity are obviously taking place, it is difficult to identify clear temporal 
associations.  As clearly demonstrated in the trends from the CDC’s Behavioral Risk Factor Surveillance 
System (BRFSS), the US experienced 
an abrupt upward deviation of the trend 
for BMI in the mid-1980s, resulting in 
the current “obesity epidemic” [27] 
(Figure 1).  However, there was no 
temporally related break in activity 
patterns or eating habits [27].  In the 
absence of direct evidence most 
observers have defaulted to a “common 
sense” explanation that people are 
“eating more and exercising less”.  
Unfortunately, this formulation is both 
trivial conceptually and unsubstantiated 
empirically.  A more sober assessment 
was offered recently by a well-known 
obesity expert:  “Clearly some kind of change in energy balance has occurred, but we have surprisingly little 
ability to explain exactly why this has happened.  Why was BMI relatively stable and why did it begin to 
increase fairly suddenly?  Despite the intense attention paid to obesity and numerous hypotheses that have 
been put forward, we still lack explanations supported by data, and we lack understanding of the mechanisms 
driving these changes” [28].  The central purpose of our proposed project is to test whether change in 
AEE can be identified as a contributory mechanism to the population-wide weight gain, and, if so, to 
quantify its importance.   
 
B.2 Activity Energy Expenditure and Risk of Future Weight Gain  
 
 
Under experimental conditions, increases in activity with control of energy intake will obviously lead to 
weight loss.  However, more relevant to population prevention efforts is the question of whether variation in 
normal activity mitigates the tendency to gain weight.  Despite the view that this relationship is self-evident, the 
data are not convincing.  By far, the largest number of studies has employed questionnaires.  In most 
analyses, categories of sedentary activities (e.g., hours of TV watching) or leisure time sports are used as the 
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30
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
BMI
Black male
Hispanic male
White male
Black female
Hispanic female
White female
Total
Figure 1.  BMI Trend in US Population  

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exposure measures.  In two specialized cohorts, i.e., Nurses and Health Professionals, sub-sets of significant 
relationships were observed (e.g., TV watching and obesity, increase in vigorous PA and reduction in waist 
size) [29, 30].  In other prospective surveys, change in PA was an important predictor of weight change, 
explaining 4.4% of change in body weight in a UK cohort [31] and predicting weight change in both black and 
white adults over 10 years of follow-up in CARDIA [32].  Occupational activity was predictive of weight change 
in China [31].  An older review of 16 prospective, observational studies examining the role of PA in the 
prevention of weight gain among white males found inconsistent results: 3 studies reported an inverse 
association between baseline PA and weight change, 2 showed a positive association, 4 reported an inverse 
association between follow-up PA and weight change, and 7 had no association [33].  In addition, other recent 
large community studies have shown no association [34, 35].  Questionnaires are both insensitive and 
potentially biased instruments.  A RECENT COMPREHENSIVE REVIEW OF VALIDATION STUDIES FOUND 
THAT ON AVERAGE THE CORRELATION BETWEEN AEE BY QUESTIONNAIRE AND DLW WAS ~ 0.2 
(Neilson et al).  The challenge in this field is clearly to move toward objective measurement tools. 
 
 
DLW has been used in 4 observational and longitudinal studies with a minimum follow-up of 12 months 
in adults (cf. Appendix A, Table 3) [36-39].  Only 3 of these studies—including two of our own—examined 
weight gain in a population sample [36, 38, 39].  While all three studies found an association between AEE and 
body composition at baseline, variation in AEE did not predict future weight change.  These results are similar 
to our larger pilot data on women in Chicago and 
Nigeria (cf. Preliminary Studies).  Some 
interventional studies, focusing on the risk of 
regaining weight after dieting, have suggested 
that higher AEE is preventive [40, 41].  
Accelerometers, either alone or in combination 
with heart rate monitors, have so far received 
limited attention in population studies of this 
question.  While several studies have quantified 
activity, none have yet reported longitudinal 
results.   A varied literature also exists among 
children using these methods, with similarly 
equivocal results (cf. Appendix A, Table 4 for 
summary).  To our knowledge, at the present 
time there are no data using objective 
measurements that permit the “macro-level” or 
ecological hypothesis of AEE versus obesity to 
be tested.   
 
The question framed in this section is at the 
heart of our proposal, and therefore we want to 
be certain it is stated with complete clarity.  For sake of emphasis, we repeat the key point.  Whether or not 
variation in AEE by itself influences body fat stores is a fundamental biological and public health question.  
Under normal circumstances increased activity stimulates increased energy intake which allows the organism 
to remain in balance.  At the present time the “accepted wisdom” states that AEE added to a daily life routine of 
humans will reduce the risk of obesity.  In fact, the available data do not support that “optimistic” view.  For 
example, in a recent well controlled clinical trial involving 468 overweight or obese women, graded increases in 
exercise led to highly consistent increases in fitness, but did not result in participant weight loss [42].  This 
result is consistent with the general finding in the literature on this question which shows that increased AEE 
without intentional calorie restriction does not reduce weight [43-45]. 
 
 
Further, it is repeatedly stated that the environment of modern society is “obesogenic” in part because 
of the greatly reduced need for physical exertion [46-48].  Once again, while it seems “self evident” that 
industrialized societies require less AEE across a range of domains, the empirical data on this question lead to 
the opposite conclusion.  As summarized in a recent review of the world’s literature, “The seemingly obvious 
conclusion that energy expenditure is systematically higher in Third World countries is not supported by the 
evidence…  (Based on a review of the data) we conclude, therefore, that there are no systematic differences in 
the level of habitual activity between developed and developing countries” [49].   
Figure 2. Mean BMI in 5 Countries of African Origin, 
25-45 year olds 
15
20
25
30
35
40
Nigeria
South Africa
Jamaica
Seychelles
USA
Body Mass Index
0
10
20
30
40
Obesity Prevalence %
Men
Women
Prevalence

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While AEE has many indisputable health benefits, its role in the regulation of body weight requires 
careful additional study.    
 
 B.3 Population Trends in Weight 
 
 
 
Long-term trends in weight have been tracked in only a few countries.  Somewhat surprisingly, what 
has emerged, as noted above, is a long period of stability until the last two decades of the 20th century when 
an abrupt upturn took place.  In the US, this inflection point is sharply defined at 1985 (cf, Figure 1) [27].  In 
Norway, for example, the upturn occurred around 1990 [50].  Many developing countries are also experiencing 
even more rapid increases in relative weight, which in turn have led to an abrupt up-turn in the prevalence of 
CV risk factors and diabetes. Trend data analyses of longitudinal cohorts in Nigeria, Jamaica, and the US 
demonstrate that while they all gained weight during follow-up, the rate of change varied markedly with the 
steepest slope in Jamaica (0.4 kg/y in Nigeria vs. 1.4 kg/y in Jamaica) [51].  The 5 countries selected for our 
study span the full range of the obesity spectrum (Figure 2).  The rapidity of the obesity trends has been 
particularly striking in developing countries.  We would therefore expect that the risk relationships would be 
most clearly delineated in that setting.   
 
B.4 Trends in Physical Activity – The Critical Unmeasured Variable  
 
 
 
While in the US we have very good data on obesity prevalence beginning in the 1960’s, there are no 
comparable population-level trends in AEE or total EE [31].  Trends in occupation and transportation clearly 
suggest declining activity, however, this trend has been going on for over three-quarters of a century in 
industrialized countries while the upturn in obesity is much more recent [31].  Data from a recent WHO project 
on a subset of African countries demonstrate an emerging relationship between questionnaire-based levels of 
inactivity and obesity (Figure 3); while intriguing, these results are driven primarily by one country.   
Questionnaires, as the sole direct source of information, are clearly limited.  For example, data from the US 
suggest that leisure-time AEE has increased 
consistently since 1989 while the prevalence of obesity 
has almost doubled [52].  Belatedly, we have come to 
recognize that surveillance for AEE will be crucial to 
unravel the causes of this epidemic, and it must be 
conducted using objective measures.   
A recent comprehensive review by a panel of 
experts advising the National Heart, Lung, and Blood 
Institute (NHLBI) highlighted the importance of this missing 
piece of information needed to solve the puzzle of the rise 
in obesity.  First, they noted that while reduced activity and 
increased consumption have obviously fueled the current 
epidemic, “the decrease in energy expenditure over the last 
decade and its contribution to the obesity epidemic have 
not been quantified” [53].  The second missing data item, in 
many ways the complement of the first, will also be 
addressed by this study:  “Insufficient data are available to 
determine the amount of physical activity needed for the 
primary prevention of weight gain” [53].  An estimate of this 
“permissive minimum,” which we view as being analogous 
to the “recommended daily allowance” of nutrients, is 
needed to guide public health policy and recommendations.  
We highlight these issues because of our near-universal experience that many in the epidemiology community 
have come to view DLW as a novelty or specialty tool rather than a robust measurement technique that could 
be more widely used in population research.  We are proposing, to the contrary, that an important new set of 
questions can be addressed with systematic use of these tools.  Given the dramatic decrease in cost for DLW, 
this is a particularly propitious moment to pursue this neglected agenda.   
 
Figure 3. Physical Inactivity and Obesity in the 
AFRO Region 

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B.5 Assessment of Energy Balance 
 
 
 
The premise of this project rests on the assertion that epidemiologic research has not yet applied 
sufficiently rigorous methods in studies of PA.  Questionnaires are both limited in scope—because they focus 
on sub-domains, such as leisure activity—and biased, because a sedentary lifestyle has taken on increasingly 
negative social connotations and will influence responses [54, 55].  The only technique that measures total EE 
directly in free living people relies on stable isotopes, i.e., doubly labeled water (DLW).  Total EE is the sum of 
resting EE and voluntary PA.  The 3rd component, the thermic effect of food, is only about 10% of EE in adults 
and thus numerically minor.  Subtracting resting EE and a fixed estimate of the thermic effect of food yields EE 
in PA.  Resting EE can be measured using a metabolic cart or estimated from lean body mass (cf. Methods for 
details).  In contemporary populations, variation in time spent standing or “fidgeting” may therefore be a major 
contributor to non-resting EE, and it cannot be measured with questionnaires [56].  Recent data from Health 
ABC, a NIH-sponsored study of the elderly that was one of the first to use DLW in an epidemiologic setting, 
demonstrates the value added by this technique.  In that cohort, questionnaires captured much less than half 
of the variation in AEE [55].   
 
The Health ABC study also demonstrates the potential value of objective measurement of AEE as an 
exposure variable.  Among the 302 community-dwelling elderly individuals, the hazard ratio for all-cause 
survival was 0.3 in the top vs. bottom tertiles of EE [55]; this large survival advantage was not captured by the 
questionnaire, demonstrating the enormous gain in information observed when objective, precise measures 
are applied. 
 
Considerable effort has now been invested in making better accelerometers and validating them 
against DLW.  In preparation for this grant we conducted a comprehensive review of the published literature 
(see Appendix A, Table 1).  The current generation of triaxial accelerometers is now sensitive, robust and easy 
to use.  Based on 4 separate studies (total n = 91), the weighted correlation between kcal/day from the 
accelerometer and DLW was 0.62 (see Appendix A, Table 2) [57-60].  On-going methodological work (cf 
D.4.1.1) should further improve precision and make it feasible to move this research forward rapidly.  
 
 
Data on energy intake (EI), and as well as macronutrient composition (eg, % of calories from fat, 
carbohydrate and protein) are also important for assessing energy balance. Measurement of intake in free-
living people can be difficult, however [61-67].  Food frequency questionnaires (FFQ) are known to have 
important limitations, both in estimation of total calories and composition [68].  Furthermore, no single FFQ 
would be appropriate for our heterogeneous populations.  Multiple twenty-four hour recalls are therefore the 
best option for international comparisons, even though they are labor-intensive [69-72].  Energy adjustment will 
also improve estimation of composition [68, 73].  Likewise, intake of manufactured foods, soda, etc, which are 
measured with less error may be key aspects of an obesogenic environment, especially in the context of 
rapidly modernizing societies. 
 
B.6 Rationale for the Proposed Study Design 
 
 
 
In our experience, studying AEE to gain insight into the pathogenesis of obesity remains controversial.  
We therefore describe explicitly the logic of the hypothesis testing we propose.   As noted, energy balance can 
be described in a simple equation with two variables, viz, in a weight stable individual energy intake = energy 
expenditure (EI = EE).  How energy stores are actually regulated, and correspondingly dysregulated in obesity, 
is much more complicated, however.  For most individuals, obesity results from the gradual accumulation of 
excess energy stores over many years.  In the US, average weight gain is ~1.5–2.0 lbs/year, which is 
equivalent to an excess of ~30 kcal/day [74].  This can result from excess intake, reduced expenditure, or a 
combination of the two.  The rationale for this project is based on the assumption that persons with low levels 
of AEE are, on average, at increased risk of accumulating excess energy stored as fat.  About 70% of total EE 
is required for resting metabolism and there is relatively little variation in resting metabolic rate/kg among 
people; AEE accounts for most of the between-person variation.  Alternatively of course, since weight gain 
represents the final balance of EI and EE, consistent over-consumption could also be a co-factor or the 
determining factor.  Assuming both are operative, persons in the quadrant of “low AEE”, “high EI” would be at 
the highest risk.  EI is difficult to measure in free-living people.  The hypothesis which can be rigorously tested, 
because precise, objective measures are available, is whether baseline PA is related to future change in 

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weight.  Since EE and EI are not perfectly balanced (if they were there would be no change in weight over 
time), EE will have a separate, independent influence on change in energy stores.  Given that AEE is a highly 
stable trait (cf. Section C), persistence of low AEE will lead to future increase in weight, and this can occur in 
combination with, but independent of, excess EI.   
 
 
We acknowledge that the energy balance models presented here are simplified to allow valid 
hypothesis testing.  We submit, however, that the basic model is no different from established risk factor 
mechanisms.  Thus, a diet high in saturated fat and cholesterol leads to a gradual increase in serum LDL over 
a lifetime, independent of other modifying factors and without the assumption that diet composition continues 
to change over time.  The same logic applies to the impact of a high salt diet on blood pressure.  In our 
framework low AEE is therefore being characterized as a classic chronic disease lifestyle risk factor.  AEE 
should be detectable as a risk factor when measured with precision in an adequately powered sample.   
 
B.7 
Rationale for Cross-Population or “Ecological” Studies  
 
 
 
We have now missed the historical moment when obesity risk factors changed in the US.  By sampling 
from global populations across the range from low to high risk, we can compress the time dimension and 
model this transition with standard epidemiologic methods.  In the past, this “population as the unit of analysis” 
design has made crucial contributions to research in common disease.  The key role of nutrition as the 
underlying cause of the CHD epidemic was first recognized by ecological comparisons [75].  The “Seven 
Countries Study” subsequently demonstrated the relationship of individual nutrients to atherosclerosis [75-78].  
“Intersalt” employed a similar design to study mineral intake and blood pressure [79, 80], and our recently 
completed “International Collaborative Study on Hypertension in Blacks” (ICHSHIB) used population 
comparisons to examine a broad range of risk factors and physiologic processes in hypertension (cf C.2.3) 
[81].  Similar studies have suggested etiologic factors for breast, colon and stomach cancer. The study 
proposed here will extend this methodology into the area of obesity for the first time.  The advantages and 
disadvantages of this design are outlined in general terms in the section below and in more detail under 
Methods (Section D). 
 
 
Descriptive epidemiology is usually the only available tool that can be used to examine etiologic 
processes in chronic disease.  For many “common source” diseases, however, methodological problems 
undermine its effectiveness.  Within a homogeneous society some degree of exposure may be universal, and 
the relevant quantitative variation can be hard to characterize. This is often complicated by large within-person 
relative to between-person variation in the exposures which makes person-level analyses imprecise.  The large 
contrasts obtained by comparing social groups as the unit of analysis and the marked shifts that occur among 
migrants can help focus attention on the key exposures.  For example, in ICSHIB low correlations were noted 
between BMI and Na with BP at the individual level (.01-.15); however, these rose to 0.85 when populations 
were the unit of analysis.  As with all study designs, however, there are trade-offs in cross-cultural studies.  For 
example, a concern sometimes raised is the “ecologic fallacy,” whereby group-level relationships are falsely 
inferred to be causal at the individual level [82].  We are well aware of this potential threat and address it 
further in the analytic section of the Methods.  With consistency at both levels and control for confounding, 
however, these relationships can be inferred with confidence.  Likewise, once a strong hypothesis has been 
established, larger within-population studies can be justified. 
 
B.8 
Adipocytokines, Appetite Hormones and Regulation of Body Fat Stores.   
 
 
Once considered metabolically inert, adipose tissue has now been shown to secrete multiple cytokine 
hormones [83-85].   In addition, other components of the system that regulates body fat stores have been 
identified [21, 86].  Despite initial enthusiasm, from this long list—which includes, among others, leptin, 
adiponectin, ghrelin, resistin, visfatin, omentin, NPY3, serum amyloid A, TNF-α, RBP4, IL-6, IL-10—only for the 
first 3 is there reasonable evidence of a physiologic function in regulating fat mass or influencing EE [22, 87, 
88].  Leptin accurately reflects fat mass, and, while not an “adipostat”, appears to function at the lower range to 
prevent depletion of calorie reserves [89].  Adiponectin is a potent insulin sensitizing agent, and in prospective 
studies—including our own—it predicts onset of glucose intolerance [90, 91].  Surprisingly, adiponectin is not 
higher in women than men despite higher body fat, and it varies cross-culturally with BMI [92], suggesting a 
more complex role that has not yet been defined.  Ghrelin, which is secreted by the stomach, clearly 

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contributes to appetite control but is more complex to study in observational cohorts [93, 94].  Although data in 
the literature are inconsistent as to whether or not these hormones, in concert with insulin, play a role in the 
regulation of energy balance [95-103], four recent studies suggest associations with EE independent of body 
composition for all three, adiponectin, leptin and ghrelin [104-107], and we will measure them in all subjects.  
Additional inter-relations to EE from our pilot data are described in section C below.   
 
B.9 Summary 
 
Although the “obesity epidemic” has received enormous attention in recent years, the mechanisms 
through which the energy budget is being perturbed are still not well understood.  In particular, the role of 
declining AEE as a causal mechanism in population-wide increases in relative weight deserves much greater 
attention.  Despite widespread perception that variation in normal activity levels (as opposed to voluntary 
increases in high-intensity activity, as typical of trials) is correlated with change in weight, the data do not 
support this conclusion.  Research in this vital area has been handicapped by lack of access to precise 
objective methods of measurement.  A new generation of epidemiologic research will be required before we 
can base causal inferences or public health recommendations on something more than assumptions.  As with 
other chronic CVD, international comparisons could be particularly informative.   
The test of our study hypothesis will be “two-sided”.  That is to say, the presence or absence of an 
association can be given a clear interpretation.  Of course, given the body of evidence that increased AEE has 
health advantages, absence of an association would not be taken to suggest that AEE is of no value.  Rather, 
the correct interpretation would be that declining AEE is unlikely to be the driving force behind the rise in 
obesity, nor will modest increases in AEE be adequate to reverse the trends.   
 
C. PRELIMINARY STUDIES 
 
C.1 Experience in International Collaborative Research 
 
C.1.1 Experience of the PI 
 
 
 
The applicant is a nutrition epidemiologist with a primary interest in AEE and obesity.  Her doctoral 
thesis involved the development of improved laboratory methods for the analysis of stable isotopes to measure 
AEE [108-111].  At Loyola she has been involved in large-scale cross-cultural studies comparing CV risk 
status, nutrition and AEE in populations of African descent in Nigeria, Jamaica and Chicago [112-120].  She is 
currently PI on a grant to define AEE and obesity risk in women in rural Nigeria and Chicago; the preliminary 
data from Nigeria-Chicago comparisons provide part of the framework for this study.  This project represents 
the largest cross-cultural application of DLW to population samples.  As part of a related project, she has 
collected resting EE data on 2,500 individuals in Nigeria, Jamaica, and the US—the largest such sample ever 
studied [114, 121].   
In Chicago she has supervised studies enrolling more than 2,000 African-American and Mexican-
American participants over the last 6 years. She recruited and supervised bilingual staff and established 
effective working relationships with communities and local institutions.  The PI is also working as a consultant 
with the Tropical Medicine Research Institute of the University of the West Indies in Kingston, Jamaica, on an 
International Atomic Energy Agency-sponsored project [118].  The project is designed to assess EE and intake 
in both rural and urban older adults using the DLW method and their relationship to parameters of glucose 
tolerance in populations at elevated risk of diabetes.   
 
C.1.2 Experience within the Department  
 
 
 
The applicants have extensive experience in collaborative research.  In 1992, the Department initiated 
the largest international study of hypertension yet to be conducted, involving 12,000 participants in 8 countries 
of African origin [112, 122-124].  This research project has produced comparable population prevalence 
estimates, new insights into physiologic pathways, estimates of disease burden from prospective cohorts, and 
numerous genetic analyses [125-128].  The study has been continuously funded by the NIH for 15 years.  The 
PI has participated actively in all aspects of this collaborative program and has extensive field experience in 

Version Date: Version Date; 05/27/2016 
Africa and the Caribbean.  The Department of Preventive Medicine has additional collaborative research in 
Mexico, Cuba and Puerto Rico.   
 
 
C.2  Previous Research on Physical Activity, DLW, Body Composition and Weight Change 
 
C.2.1   Physical Activity by Accelerometer 
Accelerometers have been used by the applicants to 
measure AEE in all of the study sites; in addition, our 
consultants, Drs. Ekelund and Brage, have published 
extensively on the methodology and validation of 
accelerometry [129-134].  In preparation for this 
application, preliminary data have been collected in all 5 
populations using the proposed AEE protocol (Table 1).  
In each of the field sites, men and women were recruited 
and wore the accelerometer (Actical, Respironics/Mini-
Mitter, Bend, OR) for a minimum of 7 consecutive days.   
Our experience with the accelerometers was 
overwhelmingly positive at each of the sites.  Acceptance 
of the instruments by the participants was uniformly high, 
there were no instances of instrument failure with 
accompanying loss of data, no reports of excessive 
participant burden, and virtually no missing data due to 
non-compliance by participants (cf. D4.1.1 for further 
details).  Over the course of 3 months we enrolled 198 adults.  As can be observed in Table 1, there is a range 
of mean activity EE measured by the accelerometers across the sites with the highest levels recorded among 
Jamaican and South African males and the lowest among women from Nigeria and the Seychelles.   
 
Table 1.  Participant Characteristics by Site – mean (SD) 
  
Jamaica 
Maywood 
Nigeria 
Seychelles 
S. Africa 
n (M/F) 
41 (18/23) 
36 (16/20) 
38 (20/18) 
45 (22/23) 
38 (16/22) 
Age (yrs) 
37.9 (8.6) 
38.3 ± 10.1 
38.9 ± 13.2 
35.2 ± 5.7 
27.2 ± 5.0 
BMI 
 
 
 
 
 
     males 
23.6 (3.5) 
27.3 ± 6.5 
22.5 ± 2.9 
26.6 ± 3.3 
21.8 ± 2.0 
     females 
30.6 (5.3) 
33.7 ± 9.4 
26.2 ± 6.4 
26.9 ± 6.4 
29.8 ± 7.3 
Activity Counts 
 
 
 
 
 
     males (cts/d) 
364040 (158584) 
313988 ± 217868 
241191 ± 103401 
275078 ± 130813 
379480 ± 159859 
     females 
209277 (94637) 
280929 ± 175078 
181312 ± 105667 
170670 ± 76242 
293548 ± 182584 
AEE (kcal/d) 
 
 
 
 
 
     males 
1050 (306) 
1090 ± 387 
688 ± 253 
902 ± 254 
911 ± 329 
     females 
884 (306) 
1090 ± 566 
608 ± 230 
617 ± 292 
1000 ± 514 
AEE/kg (kcal/kg/d) 
 
 
 
 
 
     males 
15.06 (3.76) 
13.25 ± 5.71 
10.45 ± 3.83 
11.45 ± 3.08 
14.37 ± 5.35 
     females 
11.13 (3.75) 
12.42 ± 5.66 
9.14 ± 3.88 
8.73 ± 3.16 
13.29 ± 6.06 
AEE adj. for wt 
 
 
 
 
 
     males (kcal/d) 
1094 (271) 
985 ± 426 
761 ± 262 
866 ± 239 
1011 ± 319 
     females 
838 (287) 
970 ± 532 
660 ± 220 
667 ± 227 
999 ± 467 
Pop. Mean BMI* 
 
 
 
 
 
     males  
23.7 (4.5) 
26.7 (5.5) 
22.6 (4.5) 
25.1 (4.4) 
22.9 (6.0) 
     females 
28.0 (6.4) 
29.8 (8.1) 
22.9 (6.0) 
27.6 (6.3) 
27.1 (9.1) 
*  Population mean BMI for 25-45 year olds based on representative sampling 
 
BMI by Mean Daily Activity Counts
y = -9E-06x + 29.516
R2 = 0.0447
10
15
20
25
30
35
40
45
50
55
60
10
100010
200010
300010
400010
500010
600010
700010
800010
900010
Activity Counts
BMI
Figure 4 

Version Date: Version Date; 05/27/2016 
The absolute activity EE levels are supported by data on the % of time spent in sedentary vs. moderate 
activity, with Jamaican and South African males, who were primarily laborers, spending less time in sedentary 
activity than Nigerian or Seychelles women, as recorded by the accelerometer (Table 2).  There was an 
inverse association between activity counts and BMI (Figure 4) indicating a cross-sectional relationship 
between adiposity and PA.  We acknowledge that heterogeneity exists by gender, but caution against 
interpretation of sub-groups given the small sample size.  
Due to varying degrees of literacy amongst our populations, we did not require participants to record 
activity in a log while wearing the accelerometer.  We did, however, have participants in Nigeria and the 
Seychelles complete the Global Physical Activity Questionnaire (GPAQ) via interviewer.  The purpose of the 
GPAQ is to obtain internationally comparable data on self-reported, health-related AEE done in the past 7 
days.  The correlation for activity EE (kcal/d) between the GPAQ and the accelerometer, r = 0.51 (p<0.002), 
did not differ significantly between Nigeria and Seychelles.  In both sites the GPAQ overestimated AEE by as 
much as 145 kcal/d.  The GPAQ or other comparable questionnaire used in conjunction with accelerometry 
can provide useful information on types and patterns of PA. 
 
Table 2.  Average Percent of Awake Time Spent in Varying Levels of Physical Activity by Site 
  
Jamaica 
Maywood 
Nigeria 
Seychelles 
S. Africa 
  
(Mean ± SD) 
Sedentary Activity (%) 
43.3 ± 11.3 
46.6 ± 15.6 
57.7 ± 13.6 
53.0 ± 11.3 
40.9 ± 20.7 
     males 
39.7 ± 8.9 
46.3 ± 14.4 
56.8 ± 14.4 
49.6 ± 9.5 
41.5 ± 18.4 
     females 
46.0 ± 12.4 
46.8 ± 16.8 
58.7 ± 13.1 
56.3 ± 12.1 
40.4 ± 22.7 
Light Activity (%) 
31.9 ± 7.4 
27.9 ± 6.1 
22.3 ± 6.3 
28.8 ± 6.0 
33.6 ± 11.2 
     males 
30.3 ± 8.9 
27.7 ± 5.6 
21.7 ± 7.2 
29.9 ± 5.7 
32.2 ± 9.5 
     females 
33.1 ± 5.8 
28.0 ± 6.6 
23.0 ± 5.4 
27.7 ± 6.2 
34.5 ± 12.4 
Moderate Activity (%) 
24.7 ± 9.9 
25.2 ± 12.7 
19.9 ± 8.4 
17.9 ± 6.2 
25.1 ± 11.5 
     males 
29.6 ± 9.0 
25.5 ± 11.9 
21.4 ± 8.4 
20.0 ± 4.8 
25.4 ± 11.9 
     females 
20.9 ± 9.0 
24.9 ± 13.5 
18.3 ± 8.4 
15.9 ± 6.7 
24.9 ± 11.5 
Vigorous Activity (%) 
0.13 ± 0.42 
0.40 ± 0.78 
0.05 ± 0.13 
0.30 ± 0.65 
0.47 ± 0.82 
     males 
0.28 ± 0.60 
0.58 ± 1.09 
0.08 ± 0.18 
0.52 ± 0.85 
0.84 ± 0.90 
     females 
0.01 ± 0.04 
0.25 ± 0.37 
0.01 ± 0.02 
0.09 ± 0.23 
0.20 ± 0.65 
 
C.2.2 Doubly Labeled Water Studies 
 
C.2.2.1  DLW & Accelerometer in South Africa 
 
 
 
In South Africa, DLW was used to assess free-living total daily EE in a small sample of adults (n = 27).  
Of this total, 18 also wore the Actical accelerometer.  
The characteristics of the participants wearing the 
accelerometer and undergoing DLW did not differ 
from those listed in Table 1 for South Africa.  The 
total daily EE measured using DLW was 2600 ± 655 
kcal/d and using the accelerometer was 2775 ± 990 
kcal/d; the correlation was r=0.6.  This pilot study was 
conducted in Khayelitsha, the community in which we 
propose to carry out this larger study.  The correlation 
between total EE measured using DLW and the 
accelerometer was comparable to our weighted 
correlation calculated from published literature 
(Appendix A, Table 2), giving us confidence that the 
accelerometer is measuring the same domain in the 
field setting that it is in more tightly controlled 
laboratory settings. 
 
Table 3.  Participant Characteristics – Loyola-
Ibadan Women’s Study - mean (SD) 
 
Nigeria 
(n = 150) 
US 
(n = 180) 
Age (y) 
32.9 (11.4) 
33.9 (10.8) 
Weight (kg) 
58.1 (12.5) 
83.6 (21.6) 
BMI 
23.0 (5.1) 
30.9 (7.7) 
Fat-free mass (kg) 
40.4 (5.3) 
48.9 (8.6) 
Fat mass (kg) 
17.9 (8.8) 
34.8 (15.4) 
% Body Fat 
29.3 (8.6) 
39.8 (9.0) 
Total EE (kcal/d) 
2262 (398) 
2442 (462) 
Resting EE (kcal/d) 
1284 (157) 
1400 (212) 
PAL (TEE/REE) 
1.77 (0.27) 
1.75 (0.24) 
AEE/kg (kcal/kg/d) 
13.5 (6.0) * 
10.0 (3.8) 
Adj. AEE (kcal/d) 
810 (311) 
765 (299) 
* p<0.05 

Version Date: Version Date; 05/27/2016 
C.2.2.2  Physical Activity and Weight Change 
 
The Loyola-Ibadan Women’s Study was initiated in 2000 to study the relationship between resting EE, 
AEE, and weight change in women from two contrasting social settings.  In both metropolitan Chicago and 
rural Nigeria, women were enrolled in a longitudinal survey in which EE was measured at baseline using DLW, 
and weight and body composition were measured for 3 consecutive years (Table 3) [135].  Mean change in 
weight during the follow-up interval was 0.73 and 0.76 kg/y for the Nigerian and US women, respectively.  
Contrary to our hypothesis, no association was observed between activity EE at baseline and weight change in 
either site [136].  This result underscores our prior assertion that variation in AEE in free-living populations has 
not been convincingly shown to predict risk of obesity.  Unfortunately, the sample size in this cohort is 
somewhat small, and we were limited to two sites.   
 
C.2.3 Energy Expenditure, Body Composition and Markers of Chronic Disease  
 
Our international collaborative study on hypertension has led to new insights on the relationship 
between EE, body composition and markers of chronic disease.  In our initial survey, we noted a robust 
correlation between resting EE and BP.  This observation was subsequently confirmed in a total sample of 
1,567 individuals from independent samples in Nigeria, Jamaica and the US [121].  Resting EE is in turn 
strongly correlated with weight and BMI, since adiposity leads to an obligatory increase in muscle mass 
required for movement.  Multivariate analysis subsequently confirmed, however, that the impact of resting EE 
on BP is independent of BMI and weight.  In fact, no measures of adiposity remain significant after accounting 
for resting EE [121].  This observation suggests that at least one important mechanism by which obesity leads 
to increases in BP is through the correlated increase in muscle mass and resting EE.  To further test this 
hypothesis, we recently completed a person-level meta-analysis of body composition and BP in 18,072 
individuals from the populations we propose to include in this study [137].  Persons at the upper end of the BMI 
distribution have higher percent body fat, thus a unit increase in BMI leads to a smaller increase in lean mass 
than a comparable increase in BMI among the lean.  Under the assumption that it is the increase in lean mass 
that influences BP, the slope of the BMI-BP relationship should be steeper among the lean.  In meta-
regression analysis of 13 population samples, we, in fact, observed a strong negative relationship between 
mean BMI and slope of the BMI-BP correlation for both SBP and DBP (p < 0.001).  This relationship had never 
been detected in studies of individual populations and demonstrates the value of studies that sample from a 
broad range of social contexts.  Using this international comparative design, we have also detected novel 
relationships between BMI and physiologic variables 
(e.g., leptin and angiotensinogen) as well as impacts of 
environmental factors on the heritability of common 
traits (e.g., height, BP, and BMI) [137].  We have also 
reported on the relationship between PA, measured 
using DLW, and blood pressure; in a small sample of 
Nigerian, Jamaican, and African-American adults, 
activity EE, independent of body size or composition, 
was found to be inversely associated with both SBP 
and DBP [48]. 
The Loyola-Ibadan’s Women’s Study allowed us 
to examine the relationship between components of the 
energy budget and hormones and cytokines associated 
with obesity and glucose homeostasis.  Along with 
measurements of total, resting and activity EE, fasting 
blood samples were obtained from the women in both 
rural Nigeria and suburban Maywood.  Among the significant associations recorded was an inverse association 
between activity EE and fasting insulin after controlling for body size and composition among the Maywood 
women (r=-0.24, p<0.05), but none among the Nigerian women.  This lack of association among the Nigerian 
women is likely due to the relatively modest variability in insulin levels compared to the Maywood cohort (10±6 
vs 24±19 μU/mL).  As can be observed in Figure 5, the distribution of insulin concentrations is much narrower 
among the Nigerian than the US women.  These data illustrate the obvious importance of AEE in maintenance 
0
5
1 0
1 5
2 0
2 5
3 0
3 5
4 0
4 5
5 0
>5 0
Fa s ting Ins ulin (uU/m L)
0
1 0
2 0
3 0
4 0
5 0
6 0
Pe rc e nt of Popula tion
Distr ibution of Fasting Insulin
Nigeria
US

Version Date: Version Date; 05/27/2016 
of glucose homeostasis.  Interestingly, in regression models resting EE was a significant determinant of both 
leptin (p<0.005) and adiponectin (p<0.05) concentrations in both cohorts of women after controlling for body 
composition and age.  In addition, resting EE was inversely associated with ghrelin in the Nigerian cohort 
(p<0.01).  These observations require confirmation.  It has been hypothesized that high adiponectin may be 
protective of the increased risk of obesity that theoretically accompanies low resting EE [106], however, our 
group demonstrated recently that there was no association between low resting EE and subsequent weight 
gain[138].  This preliminary data does suggest a strong link between hormones and the energy budget and 
deserve further investigation in larger samples.    
 
C.2.4   Dietary Intake Across the 5 Sites 
 
 
Dietary intake data have been collected in each of our 5 study sites.  In the US and Jamaica our group has 
significant experience in the collection and analysis of 24-hour recall and FFQ data with analysis completed 
using the Nutrient Data System for Research (NDSR) [139-141].  In Nigeria, until very recently a nutrient 
database specific to Nigerian foods was not available and thus dietary analysis was limited to foods and food 
patterns; a site-specific database is now available and nutrient analyses will be conducted on previously 
collected 24-hour recalls [142].  In the Seychelles, our co-investigator has been working with dietitians at the 
Ministry of Health to create and validate a nutrient database specific to Seychelles using the UK system, WISP 
(personal communication, S. Romain & P. Bovet) [143, 144].  In South Africa, our new co-investigator Dr. 
Steyn, has extensive experience with dietary intake measurement at the national level [145] and developed a 
system with colleagues at the MRC to improve estimation of portion sizes, the Dietary Assessment and 
Education Kit (DAEK); there is a South Africa-specific nutrient database available.  
In Table 4, the most frequently consumed foods, assessed by repeated, multiple pass 24-hour dietary 
recalls, are presented for each of the 5 study sites.   
 
Table 4.  Most Frequently Reported Foods and Energy Intake by Site – 24-hr Dietary Recall Data   
 
Jamaica 
(n = 270) 
Maywood 
(n = 197) 
Nigeria  
(n = 125) 
Seychelles 
(n = 243) 
South Africa 
(n = 563) 
Instrument 
24-hr recall 
24-hr recall 
24-hr recall 
24-hr recall 
24-hr recall 
Most frequent 
Brown bread 
Soft drinks 
White bread 
Rice 
Sugar 
#2 
Cheese 
Chicken 
Pepper stew 
Sugar 
Maize porridge 
#3 
Rice 
Potatoes 
Cow peas 
Whole milk 
Bread 
#4 
Beans 
White bread 
Vegs-greens 
White bread  
Potatoes 
#5 
Chicken 
Beef 
Beef 
Tea 
Milk 
Energy intake (kcal/d) 
2130 
2520 
2475* 
2150 
1860 
Fat (% kcal) 
31% 
41 % 
26%* 
26% 
26% 
* estimated from sub-sample of recalls  
 
It is of interest to note – illustrating the rapid environmental change developing countries are undergoing - that 
in rural Nigeria 8 years ago white bread accounted for only 2.3% of the foods, compared to 24.9% in 2007; 
cassava, the most frequently reported food in 1999, was not in the top 5 in 2007 (unpublished observation, A. 
Luke).  In Seychelles, there was a 17% decrease in the consumption of fresh fish and concomitant increase in 
dairy and red meat intake between 1993 and 2003 (personal communication, P. Bovet).     
 
D. EXPERIMENTAL DESIGN AND METHODS 
 
D.1 Project Overview 
 
D.1.1 Participant Recruitment and Baseline Exam 
 
 
 
Population-based samples, ages 25-45, will be drawn from communities in 5 countries: Nigeria, 
Seychelles, South Africa, Jamaica and the US; 500 per site, 2500 total.  Based on our overall experience with 

Version Date: Version Date; 05/27/2016 
longitudinal studies, we experienced 2% annual attrition in Nigeria and 10% in the US site, we anticipate no 
more than 20% attrition by the 24-month examination, resulting in a final ‘N’ of at least 400 per site (Table 5).    
 
D.1.2 Hypotheses to be Tested 
 
1) PA is negatively related to percent body fat in the study populations at baseline, independent of dietary  
intake. 
2) PA is negatively related to change in body weight during follow-up, independent of dietary intake. 
3) Mean levels of AEE are negatively related to mean levels of obesity/relative weight among the 
populations. 
4)  Adipocytokines and hormones influence weight regulation and insulin sensitivity and interact with AEE.   
 
Table 5.  Proposed Study Measures 
 
 
Baseline 
(N) 
12-Mo 
Follow-Up 
(N) 
24-Mo 
Follow-Up 
(N) 
 
Use of Data 
Physical Activity 
 
 
 
 
       Accelerometer 
2500 
 
2500 
Exposure 
       DLW 
375 
 
 
Concordance, exposure  
       Questionnaire 
2500 
 
 
Exposures 
Body Composition 
 
 
 
 
       BIA 
2500 
 
2500 
Outcome: baseline & change 
       Isotope Dilution 
500 
 
500 
Calibrate BIA change data 
Dietary Intake 
2500 
 
 
Covariate 
Anthropometrics 
2500 
2500 
2500 
Outcome:  baseline & change 
Blood pressure 
2500 
 
 
Outcome 
Adipocytokines/Hormones 
2500 
 
 
Outcome 
 
D.2 Study Organization and Time Line 
 
The Department of Preventive Medicine in Chicago will serve as the coordinating center.  During the 
first 6 months, a detailed protocol will be finalized, and staff from all sites will come to Chicago for a group 
training session.  Staff at each site will be responsible for translating and back translating all patient materials, 
including consent forms and questionnaires, into the local language.  These materials will be piloted on 10 
people at each site in the first 6 months.  The DLW protocol and concurrent accelerometer baseline protocol 
for the 75 selected individuals/site will be conducted between month 6 and 18 in all sites.  The baseline 
accelerometer protocol for the additional 425 participants/site will be carried out over the course of a year 
beginning at 6 months.  The follow-up examinations will begin at 1.5 years, for a 2.0-year period.  As discussed 
previously, these will involve measuring weight for all 500 participants/site at 12 and 24 months of follow-up 
with accelerometer and body composition measurements at 24 months of follow-up.  Laboratory analyses of 
the DLW samples will commence at approximately year 1.  Deuterium analyses for body composition 
assessments will be conducted in years 1 and 4.  Fasting hormone and glucose samples will be collected at 
the baseline examination and stored at -80C; the samples will be analyzed in year 4.  Primary data analysis 
and report writing will be conducted in year 4.  Figure 6 shows a graphical representation of the previously 
discussed timeline.  A detailed timeline can be found in Appendix B. 

Version Date: Version Date; 05/27/2016 
Communication between investigators will be maintained by monthly conference calls coordinated by 
the Loyola group.  The project coordinator and/or PI will travel to each site annually for site visits with staff and 
co-investigators.   
 
 
 
D.3 Clinic Overview 
 
D.3.1 Clinic Examinations and Data Acquisition 
 
 
 
Each of the sites has been conducting community-based CV research and has community-centered 
clinics from which the present study will be based (see below).  For participants enrolled in the DLW/deuterium 
protocol, the initial clinic examination will last approximately 4 hours.  They will return to the clinic, or be visited 
by a staff member at their home, on day 5 (± 1 day) to provide a midpoint urine sample and to ensure the 
accelerometer is comfortable and being worn properly.  They will be asked to return to the clinic on day 9 (± 1 
day) to provide a final urine sample and have the accelerometer removed. For the participants enrolled in the 
accelerometer protocol, the clinic examination will last approximately 1 hour.  A staff member will check on 
each participant on day 2 to ensure that the accelerometer is comfortable and being worn properly, and the 
participant will return on day 9 to return the accelerometer and have a final body weight measurement taken.  
All anthropometric measurements, BP, and blood collection will be made at the initial clinic 
examination, with participants fasted from the evening prior.  Demographic data and medical history, including 
age, gender, season, birth place, occupation, education, tobacco use, alcohol use and pregnancy history, will 
also be collected at this time.  At 12 and 24 months after the baseline examination, all participants will be 
recontacted for follow-up clinic examinations. 
 
D.3.2 Sampling Procedures 
 
 
 
In each of the 5 sites, 75 individuals aged 25-45 years will be recruited for the DLW protocol and 425 
will be recruited for the accelerometer protocol, with equal numbers of men and women.  We will exclude 
individuals with obvious chronic or infectious diseases (including active malaria), pregnant or lactating women, 
and HIV positive individuals (see D.3.3 for HIV testing procedures).  Any individual that has a condition 
preventing normal AEE will also be excluded from our study.  Population-based surveys have been carried out 
in each of the sites; the sampling procedures used previously will be employed in the present study.  These 
study sites were chosen because mean BMIs and prevalence of obesity span the healthy physiological range. 
 
Jamaica – Spanish Town is an urban area 25 km from the center of Kingston.  Once the capital of colonial 
Jamaica, Spanish Town is an established community with a population of 92,000 in 1991; about 45,000 
individuals are between the ages of 25 and 74.  A community-based study of hypertension and diabetes 
(n=3500) has been completed [123].  Districts were randomly sampled; beginning from a fixed point in each 
district (e.g., the north-west corner), door-to-door screening took place.  Examinations will be carried out in the 
local health clinic and specimens taken immediately to the Univ. of the West Indies for processing.   
 
     Figure 6.  Time Line   
  
Year 1 
Year 2 
Year 3 
Year 4 
Cross-Site Training/Methods Development 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
  
Baseline Weight/Activity Measurement  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
Doubly Labeled Water Studies  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
Dietary Methods/Database Development (SA) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Follow-Up Weight/Activity Measurement 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
Laboratory/Dietary Analyses 
  
 
 
 
  
  
  
  
  
  
  
  
 
 
 
  
Data Analyses & Report Writing 
  
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  

Version Date: Version Date; 05/27/2016 
US – Maywood is an African-American working class community adjacent to the western border of Chicago, 
Illinois. Approximately 3,500 participants have been examined in our clinic since 1991 for both observational 
and interventional studies [123].  Participation rates for observational components of these studies have been 
60% or greater.  Project staff are residents of the community and conduct all recruitment and home visits.  We 
have now contacted individuals on approximately two thirds of the blocks in Maywood and are well known to 
the community.  The Department clinic is on campus, within walking distance of most neighborhoods and is 
well known to local residents.  We will also recruit from the adjoining west side of Chicago.   
 
Nigeria – Developed in 1965 as a teaching site for the University College Hospital (UCH), University of Ibadan, 
Igbo-Ora is a rural community with a population of 50,000 in the Yoruba-speaking region of southwest Nigeria.  
All permanent houses have been mapped and assigned numbers by public health workers.  Most residents are 
subsistence farmers or petty traders.  Loyola has been engaged in collaborative research with the UCH Dept of 
Social & preventive Medicine since 1992 and maintains a permanent facility in Igbo-Ora.  Ten-thousand adults 
are being followed in a prospective cohort study on hypertension, and 3,000 family members have been 
enrolled in genetic studies [123, 124, 126, 127].  Prior studies on EE and obesity have been carried out in this 
community.   
 
Seychelles – The Republic of Seychelles is an archipelago with 81,000 inhabitants located approximately 
1,500 km east of Kenya in the Indian Ocean, and approximately 2,000 km north of the island of Mauritius. 
Population-based surveys in 1989 and 2004 have shown increasing prevalence of several cardiovascular risk 
factors in the adult population [146, 147].  The sampling method will be a sex- and age-stratified random 
sample generated from the national census.   
 
South Africa – Khayelitsha, the 3rd largest township in South Africa, is adjacent to the city of Cape Town.  The 
population is about 500,000 people with 80 percent of the residents living in shacks and 40 percent 
unemployed.  There are no hospitals in Khayelitsha, but there are three community health clinics.  
Investigators in Cape Town have recently been funded to conduct a population-based 5-year prospective study 
on AEE and diabetes in 1000 members of this urban community.  A sex- and age-stratified random sample will 
be generated from the national census.   
 
D.3.3   HIV Testing in South Africa 
 
 
 
The prevalence of HIV in the rural regions of Nigeria in which this study will be conducted is less than 
2% [148], and less than 1% in the Seychelles [149]; however, the prevalence is as high as 12% in the Western 
Cape, South Africa [150].  The hyper-metabolic state produced by HIV infection can significantly impact body 
weight well before other signs of infection appear.  In order to eliminate the impact of HIV infection, we will 
work with the community health clinics in South Africa in which study enrollment will take place.  All participants 
will be tested for HIV and undergo pre- and post-test counseling both before study enrollment at baseline and 
again at follow-up (cf. E.2.2 Protection Against Risk).  The officials at LifeLine Clinic in Khayelitsha, CapeTown, 
have agreed to counsel, test, and treat all participants in the screening process of this study.  All the above 
options are free of charge in South Africa as a result of both national and international efforts.  HIV-positive 
individuals will be excluded from our study.   
 
 
D.4 Measurement Procedures 
 
D.4.1 Activity Energy Expenditure 
 
Total daily EE is comprised of three components: resting EE, AEE, and thermic effect of food.  In this 
study, we are most interested in AEE and its relationship to weight change and insulin sensitivity.  AEE will be 
measured by two methods: accelerometers and DLW.  All participants in each site will wear an activity monitor 
for 6-9 days, while a subset will undergo the DLW procedure as well.  Our previous experience with 
measurement of EE indicates that both resting EE and AEE are very stable traits, with within-person 
correlations of 0.9 and 0.8 at intervals of 1-2 years, respectively [135].  For those participants in the DLW 

Version Date: Version Date; 05/27/2016 
subset, resting EE will be measured by indirect calorimetry, and thermic effect of food will be estimated as 10% 
of total daily EE.  All methods are described below.   
 
D.4.1.1 Physical Activity by Accelerometer 
 
PA will be measured using the Actical activity monitor (Respironics/Mini-Mitter, Bend, OR) with all 
participants.  Previous studies have shown that accelerometer-based activity monitors can discriminate 
differing intensities of activity [151-156], making it possible to adequately characterize each of the study 
communities with regard to overall intensity of PA.  We will record the data as activity counts/min and kcal/d.  
The activity monitor records the intensity, duration and frequency of physical motion through the use of an 
accelerometer which produces a variable electrical current based on the combination of the amplitude and 
frequency of motion.  Accelerometers are omnidirectional motion sensors that count the vertical and horizontal 
acceleration of the user.  This information is stored within the instrument as activity counts per epoch, or 
specified subunit of time, e.g., per minute.  As the intensity of the activity increases, so does the number of 
activity counts per epoch.  The Actical monitor is lightweight, waterproof and made to be worn on the wrist, 
waist or ankle.  Consistent placement of the activity monitor ensures comparability of data.  For this study, the 
monitor will be worn at the waist.  There are concerns about the ability of the accelerometer to discriminate 
between some activities with differing energy expenditures, eg, stair climbing versus level walking.  As 
described below, our consultants are developing methods to improve accuracy of these instruments.   
There is controversy over the number of days of recording needed to adequately capture habitual 
activity levels; the suggested range is from 4 to 9 days [152].  We used the Spearman-Brown prophecy formula 
with calculated, site-specific intraclass correlation coefficients [157] to estimate the level of reliability achieved 
for a given number of days of monitoring in each site (Figure 7) [152].  Most studies use 0.8 as an acceptable 
level of reliability [152, 158-161].  For our data, all sites achieve this level with five days of monitoring (ie, 
excluding the first and last partial days of monitoring which are dropped from analysis), however the 
Seychelles are on the borderline.  Given the practicality of six days of monitoring (i.e., participants will receive 
the monitor and return the monitor on the same day of the week), we have chosen to monitor all participants 
for six days (i.e., a total of 8 after taking the two partial days into consideration).  With six days of monitoring, 
the expected level of reliability will be 0.83-0.92.   
The Actical activity monitor will be initialized 
for each participant on the morning of the initial 
clinic examination, using the on-site computer.  
The participant’s ID, epoch period (1 minute), start 
date and time, and mode will be entered into the 
main program.  Use of the activity monitor will be 
explained to the  participant: the participant will be 
asked to wear the activity monitor at all times over 
the next 8 days, including during sleep; the only 
time the monitor should be removed is while 
bathing, showering, or swimming.  The activity 
monitor will be placed on the participant on a belt 
at the waist, positioned just behind the left hip 
bone.  The participant will be trained in proper 
handling of the accelerometer, including removal 
and placement of the monitor on the belt as well as 
placement of the belt on the body, in order to ensure accurate measurements.  If the participant has problems 
sleeping with the monitor, they will be encouraged to remove it during sleep hours.  An additional belt will be 
provided in case it gets wet and needs to be swapped out.  Staff members will contact participants within 24 
hours after the initiation of the monitoring session to ensure that there is no discomfort with the monitors.  In 
addition, the monitor will be checked at the midpoint home or clinic visit to ensure proper placement and 
recording.  On day 8, each participant will return to the clinic, and the staff member will verify end date and 
time and download the data.  Data will be presented as activity counts per minute for the whole day and during 
hours the participant is awake, as well as kcal/day or METs/wk in AEE as calculated using the accompanying 
software [153, 155, 156, 162, 163].   
Figure 7. Levels of Reliability Achieved for a Given Number of 
Monitored Days 
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Number of Monitored Days
Level of Reliability
S. Africa n=37
Maywood n=30
Nigeria n=22
Jamaica n=13
Seychelles n=38

Version Date: Version Date; 05/27/2016 
Investigators at each site have significant experience with accelerometer-based activity monitors and 
with the validation of monitors using the DLW method.  Staff will be collectively trained on the use and handling 
of the Actical.  In addition, we will validate the Actical activity monitor for estimation of energy expenditure in 
AEE at each site through the comparison with DLW results.  Validation of this instrument will help in future 
studies for the quantification of AEE at significantly lower cost than available with DLW.   
 
 
Over the years, we have had extensive experience with several models of accelerometers, activity 
monitors, and heart rate monitoring devices.  Missing data is a well-documented problem with activity monitors 
in general and can result from instrument failure or non-compliance of the participant, i.e., the monitor is simply 
removed for part of the measurement period.  In our experience, all cases of truncated data collection (which 
occurred in less than 2% of participants) had at least 4 days of data collected over the 7-9 day measurement 
period, providing at least some data for analysis.  Methods of handling missing data are further discussed in 
the analytical section.   
 
 
The consultants on this project, Drs. Brage and Ekelund, are currently working on advanced methods of 
analysis for data generated from accelerometers.  They have demonstrated that converting the output from 
accelerometry to units of acceleration (in m/s2) from counts provides substantial additional information by 
capturing speed of motion [130].  After adjustment for built-in frequency filtering by the specific accelerometer 
model, acceleration is linearly associated with activity counts, as well as with EE.  Brage and Ekelund are also 
developing methods using Hidden Markov Models and other mathematical approaches to improve the 
accuracy of accelerometer data, particularly with regard to recognition of patterns of activity that allow further 
differentiation among activities.  The DLW data from this project will therefore help improve the accuracy of 
accelerometery as a tool in survey research, while improving the precision of the data collected in our project.      
 
D.4.1.2 Physical Activity by Questionnaire 
 
 
 
All participants will have AEE assessed by questionnaire, in their own language, using the Global 
Physical Activity Questionnaire version 2 (GPAQ) [164].  The GPAQ was developed by the World Health 
Organization (WHO) as part of the WHO STEPwise approach to chronic disease risk-factor surveillance 
(STEPS manual available online at http://www.who.int/chp/steps/manual/en/) [165] to produce reliable and 
valid estimates of physical activity for use in culturally diverse populations in developing countries.  
Approximately 50 developing countries are currently using the GPAQ for physical activity data collection.  The 
main outcome variables are: a categorical variable of total physical activity (high, moderate and low) and a 
continuous variable of total physical activity within each domain of work, transport and leisure.   
 
D.4.2 Body Composition 
 
D.4.2.2 Bioelectrical Impedance Analysis 
 
Body composition will also be assessed in all participants using BIA.  BIA measures the impedance to 
the flow of an applied mild alternating current by body tissues.  The measured impedance of body tissues can 
be used to calculate total body water, from which fat-free mass and fat mass can be calculated [170].  BIA is a 
relatively inexpensive method to estimate body composition in populations.   
The participants will be placed in the supine position with limbs abducted.  Current-supplying electrodes 
will be placed on the dorsal surfaces of the right hand and foot at the metacarpals and metatarsals, 
respectively.  Detection electrodes will be placed at the pisiform prominence of the right wrist and the anterior 
surface of the true ankle joint [170].  The single-frequency instrument (BIA Quantum, RJL Systems, Clinton 
Township, MI) will be attached to electrodes and generate an excitation current of 800 μA at 50kHz.  
 
 
The investigators on this application have extensive experience in the application of BIA in field 
situations and have developed equations for use in Nigerian, Jamaican and African-American adult and child 
populations [112, 119].  Unless evidence from the ID analyses, described above, indicates a need for the 
development of separate equations for the other African sites, we propose to use the pre-existing validated BIA 
equations.  The ID data will be used to calibrate BIA for change in body composition within individuals over the 
2-year follow-up.  
 
D.4.2.3 Dual X-ray Absorptiometry (DXA) 

Version Date: Version Date; 05/27/2016 
Participants will undergo a bone mineral density measurement using dual x-ray absorptiometry (DXA).  
The bone scan takes approximately 30 minutes and will require the participant to remove all metal 
jewelry and piercings. To ensure women are not unknowingly pregnant, they will be asked the date of 
their last period, given a pregnancy test and will be excluded if they are pregnant. Individuals with 
pacemakers will be excluded. 
 
D.4.3 Anthropometrics  
 
At the initial clinic visit, height, weight, waist and hip circumferences, and blood pressure will be 
measured.  Weight of the participant will be measured without shoes and dressed in light clothing to the 
nearest 0.1 kg using a standard balance (Health-o-meter, Bridgeview, IL).  Height will be measured using a 
stadiometer without shoes and head held in the Frankfort plane to the nearest 0.1 cm.  Waist circumference 
will be measured to the nearest 0.1 cm at the umbilicus.  Hip circumference will be measured to the nearest 
0.1 cm at the point of maximum extension of the buttocks.  For both waist and hip circumferences, repeat 
measures will be taken.  If two measurements differ by more than 0.5 cm, a third measurement will be taken. 
 
 
 
D.4.4 Dietary Intake 
 
 
Dietary intake is not the primary focus of this application, however, we recognize that estimates of 
EI and diet composition data may improve the analysis of energy balance.  We are cognizant of the challenges 
involved in comparing dietary intake across multiple populations [69-72, 171].  Using both INTER-MAP and 
EPIC as models, we will attempt to collect and measure dietary intake, with appropriate quality control 
measures, in these 5 populations at varying stages of the epidemiologic transition.  We regard this as an 
additional contribution to the long-term effort to collect international comparative diet data for epidemiologic 
studies, and one that will continue to be refined.    
The dietary component of this project will be directed by Dr. Steyn at the Medical Research Council 
(MRC) of South Africa.  Dr. Steyn has extensive experience in assessing the diets of multiple countries in sub-
Saharan Africa [145, 172-175], and has developed a system for portion size estimation in collaboration with a 
MRC colleague which has been utilized in South Africa and Kenya (DAEK, [176]).  Quality control measures 
will include centralized training for all interviewers led by Dr. Steyn (cf. section D.5); training will include 
discussion and documentation of site-specific prompts and portion sizes for the dietary interviews, decision on 
standardized coding of the recalls, and methods for the audiotape recording of the interviews.  We will use the 
five indicators suggested by INTERMAP to assess performance quality:  evaluation of audiotapes, number of 
foods coded, coding error rates, duration of the recall and total energy intake (kcal or kJ) per recall [171].  Dr. 
Steyn and/or a post-doctoral fellow will travel to each during pilot training to insure proper implementation of 
the protocol.  The Nutrient Data System for Research (NDSR; U. Minnesota, Minneapolis, MN) will be modified 
and utilized for nutrient analysis.  The first year will be spent setting up the NDSR to accommodate those 
foods/recipes local to each site using the currently available site-specific nutrient databases and the previously 
collected 24-hour recall data (cf. Table 4).  In the 3 African countries, this will not be an excessive number of 
foods – eg, in Nigeria only 75 different foods/food mixtures mentioned in over 200 collected recalls. 
In brief, each participant will complete at least two 24-hour recalls using the multiple pass method, one 
at the initial baseline examination and the second when the activity monitor is collected.  The DLW subset of 
participants will have 3 dietary recalls - the third recall conducted at the midpoint urine sample collection.  With 
these multiple recalls, usual intake distributions of nutrients can be estimated (cf. Section D.7.1.3; [177-179]. At 
each site, the 24-hour recalls will be collected by trained interviewers using pen and paper; an audiotape 
recording will also be made of the recall interviews and used to augment the written record where necessary, 
as well as for quality control.  The 24-hour recalls will be scanned and sent via e-mail to the MRC in South 
Africa where data entry and analysis will take place.  Endpoints of interest will be total EI and macronutrient 
composition (ie, % kcals from fat, carbohydrate and protein), as well as indicators of intake of processed foods 
(ie, # of sweetened beverages, pre-packaged foods and restaurant fast foods per day) and of intake of fruits, 
vegetables and site-specific staples (cf. Table 4).  While we recognize the importance of micronutrients for 

Version Date: Version Date; 05/27/2016 
overall health and well-being of individuals, many nutrient databases are lacking sufficient data on local food 
micronutrient content.    
 
D.4.4 Blood Pressure 
 
Blood pressure will be measured using the protocol and training procedures developed for our ongoing 
international hypertension studies.  Blood pressure will be measured using the Omron Automatic Digital Blood 
Pressure Monitor (model HEM-747Ic, Omron Healthcare, Bannockburn, IL, USA).  With the antecubital fossa 
at heart level, three pulse and blood pressure readings will be taken.   
 
D.4.5 Biochemistry assays 
 
Participants will be asked to fast from the evening prior to the baseline clinic examination.  One 10-mL 
lavender-top (adipocytokines, ghrelin and insulin) and one 5-mL grey-top vacutainer (glucose) will be drawn for 
fasting blood samples.  The blood samples will be processed and plasma separated within two hours of 
collection and stored at -20C (in the field) and -80C in the laboratory.  Plasma will be aliquotted into 6 cryovials.  
Fasting plasma glucose will be measured using the glucose oxidase method at each site at the time of 
collection using the instrument available in each collaborator’s laboratory.  Two vials will be shipped or carried 
with the PI, along with the DLW samples, to Loyola for the assays.  Insulin, total ghrelin, leptin and adiponectin 
will be measured using RIA kits (Linco Research, Inc., St. Charles, MO).  In addition, a small plasma aliquot 
will be shipped to the Swedish Metabolomics Unit for metabolite analysis from all 2,500 participants.  The 
analysis will focus on known, as well as novel metabolites linked to obesity and type two diabetes mellitus.  
Again, samples from all sites will be batched together, and blinded replicates will be included.  Our experience 
with the insulin and leptin RIA indicates an intra-assay CV of <8%, ghrelin is <6% and adiponectin is ~9%.  The 
3 remaining cryovials will remain in the laboratory of origin in case of shipping mishap or laboratory error. 
 
a. Participants will have a blood sample drawn to assess vitamin D status. 
 
b. Participants will provide a stool sample. 
 
c. Participants will provide a sputum sample. 
 
D.4.6 Mouse model 
Following the identification of obesity-related fecal/oral microbiome in the METS participant samples, and as a 
proof of research concept, fecal/oral microbiome samples, collected in D.4.5, will be transplanted in sterile 
mice, who will be evaluated for the development of adiposity and type 2 diabetes.  All the mice work will be 
preformed at Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University, under 
the supervision of Dr Brian Layden.  Specifically, male mice will have their gut microbiota ablated by oral 
gavage of ampicillin over a 2 week period.  After the final oral antibiotic dose, mice will have the fecal transfer, 
once a week for 4 weeks.  At the end of each week, mice will receive an intraperitoneal glucose tolerance test, 
in addition to adiposity measurements. Mice will be sacrificed after 4 weeks for tissue collection.  This proof of 
concept study has been approved by the Northwestern University Institutional Animal Care and Use 
Committee 
 
D.4.7 Skin Pigmentation 
 
 
Participants will have their skin pigmentation measured by a handheld DSM II ColorMeter at the inner 
upper right and left arms. 
 
   
D.5 Centralized Training of Staff and Certification 
 
In the first 6 months, after protocols and procedures have been approved by the investigators, a 
centralized training session for all staff will be held in Chicago.  Over the course of one week, all staff will be 

Version Date: Version Date; 05/27/2016 
trained by previously certified project coordinators on procedures to be used for the study.  All methods have 
been employed in previous studies at the coordinating institution, Loyola University.  The project coordinator 
from Loyola will be responsible for the training and certification on the DLW and accelerometer methods, and 
anthropometric, body composition and blood pressure measurements.  Dr. Steyn and her coordinator will be 
responsible for training and certification on the dietary intake methods.     
 
D.6 International Transport of Samples 
 
International shipment of biological samples has become complicated by security precautions.  Based 
on prior experience, it has been decided that samples to the US, i.e., DLW and plasma samples, will be carried 
by investigators as they travel to/from the annual investigator meeting if there is evidence of problems with 
shipping, e.g., from Jamaica.  After appropriate aliquotting, the samples will be packed on dry ice in insulated 
shipping containers and checked as baggage.  In the case of Nigeria and South Africa, no such problems have 
arisen to date; therefore, samples will be shipped directly on dry ice by an express shipping company.  For 
DLW samples transported to Loyola, they will subsequently be re-packed on dry ice and shipped overnight to 
the University of Wisconsin in Madison, Stable Isotope Core Laboratory.  Duplicates of all samples will be 
stored in the laboratories of origin. 
 
D.7  
Statistical Considerations 
 
 
 
Statistical analyses will be supervised by Dr. Durazo.  He will generate reports on accrual, missing data 
items, and alerts (if any).  With the assistance of the PI and the programmer, he will undertake the analyses 
outlined below.  A data coordinating center (DCC) will be established to manage and administer data transfers 
between sites.  Data quality control and assurance processes will be put in place by the DCC.  
 
D.7.1  Statistical Analysis 
 
In the initial stage of the analysis, a complete set of descriptive tables will be produced by gender within 
site.  The distributional properties of each variable will be examined and appropriate re-scaling undertaken, if 
necessary.  All outliers (i.e. > 3 SD’s) will be identified and either verified as correct or set to missing.  When 
possible, internal validity will be assessed by the use of bivariate scatter plots of variables known to be 
correlated (e.g., resting EE vs. BMI or weight vs. BP).  These plots provide a useful search procedure for 
unanticipated outliers and/or non-linear relationships. 
As in any other longitudinal study, some measurements may be unavailable in a subset of the study 
participants.  A brief description of methods used to handle non-ignorable missing data are presented at the 
end of this section (cf. D.7.4) 
 
We describe below the analytic procedures and the power calculation, in relation to the following 4 
primary hypotheses as stated in the Aims: 
 
1)  AEE is negatively related to % body fat at baseline, independent of dietary intake. 
2)  AEE is negatively related to change in body weight during follow-up, independent of dietary intake. 
3)  Mean levels of AEE are negatively related to mean levels of obesity/relative weight among the 
populations. 
 
 
4)  Adipocytokines and hormones influence weight regulation and insulin sensitivity and interact with  
 
 
AEE.   
 
D.7.1.1    Weight and Body Composition are Related to AEE at Baseline 
 
D.7.1.1.1  AEE, Weight and Body Composition   
 
 
 
Two measures of AEE will be available.  At baseline, we will have 500 participants/site with data from 
the accelerometer, inclusive of those who have both DLW and accelerometer (total N, all sites = 2500; 6 days 

Version Date: Version Date; 05/27/2016 
of monitoring).  We will have data generated by DLW for 75 participants/site (total N, all sites = 375).  The 
structure of the analysis will be similar with both exposure measures.   
 
  
We anticipate that data for all the primary outcomes will not conform entirely to a normal Gaussian 
distribution.  Using BMI, PAL and AEE data collected in previous studies in Maywood (n = 567) and Nigeria (n 
= 735), we examined the probability distributions.  A formal test statistic was performed that combines kurtosis 
and skewness [180] as implemented in STATA with p-values less than 0.001, suggesting deviations from 
normality.  Kernel density estimates of the BMI, AEE, and PAL distributions are depicted in Figure 7. After 
applying the ladder of power approach suggested by Tukey [181], we found that BMI, AEE and PAL for both 
sites appear to deviate from normality.  Although no transformation is preferred after application of the ladder 
of powers method, two potential transformations are suggested by this approach, namely log X and X-1.  
Tukey’s approach followed by formal testing will be carried out once data on all subjects in the study has been 
collected and a decision about data transformations has been made.  Despite the lack of normality exhibited 
and corroborated by the statistical tests performed, a visual exploration of Figure 7 suggests only slight 
deviations from normality, mostly due to well-known skewness of these distributions.  Thus, we anticipate the 
statistical methods that make use of normally distributed data will be appropriate to analyze these data.  Of 
course, the degree of appropriateness of these methods will depend on their robustness to deviations from 
normality.  If it is necessary, appropriate transformations will be carried out before statistical tests are 
performed.   
Maywood 
 
 
Nigeria 
 
 
 
Body Mass Index 
Activity Energy Expenditure 
Physical Activity Level 
Figure 7. Distribution estimates of main analysis variables BMI, AEE, and PAL in two sites: Maywood (US) and 
Nigeria.  
 
 
Following transformation of the appropriate exposure and outcome variables, Fisher’s Z-transformation 
test will be applied in order to test hypotheses involving correlations.  The resulting parametric test will be more 
powerful than other distribution-free approaches.  The results will be corroborated by applying Spearman rank 
correlations and another parametric test based on the concordance coefficient introduced by Lin [182] and later 
extended by Chinchilli and colleagues to include repeated measures [183].  Lin’s concordance correlation 
allows the comparison of the correlations accounting for possible differences in the mean values of the two 
variables under consideration.  Furthermore, approximate permutation tests [184, 185] will be implemented 
using each of the three statistics: Pearson, Spearman and Lin’s concordance, to compare agreement of the 
association between AEE and BMI across the three groups under investigation. 
  
Correlation coefficients will be estimated using the Pearson correlation and confidence intervals 
constructed after applying the Fisher’s Z-transformation.  Furthermore, computer intensive statistical methods, 
such as bootstrapping, will be used to construct confidence intervals for both parametric and nonparametric 
approaches.  Comparisons of these correlations will be carried out using this statistic for 1 and 2 samples 
[186], as well as approximate permutation tests [184, 185].  In addition, multivariate regression analysis will be 
fit to adjust for potential confounders.  Confounders will include age, gender, and any other significant 
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Version Date: Version Date; 05/27/2016 
predictors defined by the univariate analyses.  Potential non-linear functions will be explored to see if they fit 
the data better.  Sub-group analyses will be undertaken, with participants in each site divided by gender.   
 
 
We have no evidence to suggest that the relationship between AEE and BMI is non-linear in the 
parameters.  However, we will perform all our analyses using multivariate linear regression methods allowing 
for non-linearity of the covariates.  Standard statistical methods based on the F-statistic and R2 will be used to 
decide the best fitting model to our data.  It is anticipated that there will be more than one “best model” 
obtained.  We will choose one based on two approaches.  First, subject matter considerations will eliminate 
some models.  Second, a computer intensive approach based on bootstrap sampling will be implemented in a 
fashion similar to that introduced by Sauerbrai and Schumaker [187] to aid automated variable selection. 
 
 
Repeat accelerometer measures will be made in all individuals at the 2 year exam (with estimated 20% 
attrition, N=2000).   We will use these data to assess tracking of AEE levels and identify potential cross-site 
variation in seasonal effects, etc. A second measure will offer important information about potential between-
site heterogeneity that might help explain anomalous findings (i.e., weak or absent relationships at a particular 
site).  If appropriate, we could also use this data in a “regression-dilution” type of analysis that corrects for the 
underestimation of exposure-outcome effects from “noise” in individual exposure levels.)    
 
D.7.1.1.2  AEE, Adipocytokines and Diabetes 
 
 
 
We will have measures of fasting glucose and insulin, adiponectin, ghrelin and leptin on this unique 
sample of 5 populations of African origin.   The following additional variables will be collected: age, gender, 
place of birth, prior medical history, occupation, education, height, weight, activity by questionnaire, pregnancy 
history, and season of exam.   
 
 
In the analysis phase we will first compare the descriptive characteristics in each population (e.g., mean 
adipocytokine/hormone values and prevalence of hyperglycemia).  We will explore the univariate correlation 
structure for the continuous variables listed above.  We will then construct multivariable regression models to 
assess the potential independent role of AEE as a predictor of risk factor status independent of body 
composition.  Risk factor status will be examined as continuous and categorical outcomes.  For instance, how 
is insulin level affected by AEE after accounting for body composition?  
 
 
An important focus of these analyses will be looking for potential heterogeneity of risk relationships 
across sites.  Three main aspects are of interest, namely whether the AEE-risk factor (e.g. insulin level) 
relationship is present, or of the same magnitude, in each sample and whether there exists co-variation with 
the mean level of AEE; testing for interaction will be undertaken where appropriate.  Precisely,  
Relative effect of AEE on risk factor: We will examine the magnitude of the association using 1) regression 
coefficients; 2) standardized regression coefficients.  This approach allows within and between site 
comparisons. It is recognized that cross-site comparisons could be problematic when using standardized 
regression coefficients since the standard deviation of the variables involved may differ across sites. In this 
case, standardized regression coefficients will be obtained using pooled estimates of the mean and standard 
deviation. 
Co-variation of the association with mean level of AEE: Multilevel models will be applied, in which the site 
specific variable, mean AEE, will be used to estimate the effect of the average AEE on the regression 
coefficient. 
Interactions between AEE and site: Multiplicative interaction terms will be included in regression models to 
assess how much site modifies the association between AEE and the risk factor. We are fully aware of the lack 
of statistical power to test this type of hypotheses and thus results will be exploratory in nature.  Thus, one 
might anticipate that anthropometric and AEE measures are stronger in low risk population samples than in the 
US.  In addition, we will test whether the slope of the exposure-outcome relationships are influenced by the 
mean BMI of the population using multilevel regression models.  
 
 
Questionnaire data on AEE will be summarized using methods previously developed [188].  Mean 
activity levels and prevalence of inactivity will be assessed in relation to objective measures of EE and the 
relevant outcome variables.  As a sub-analysis we will describe types of activities reported to assess overall 
population patterns.   
 
D.7.1.2  AEE and Weight Change 
 

Version Date: Version Date; 05/27/2016 
 
 
The study design contemplates 3 weight determinations over time: one at baseline, a second after 12 
months, and a final assessment 2 years after entering the study.  To better understand the associations 
between AEE and body composition, the analysis plan will be carried out in incremental steps corresponding to 
the degree of sophistication of the statistical methods used.  
1) Descriptive: Weight change is computed for each subject as the subject’s last weight minus baseline 
weight.  The correlation between weight change and AEE will be computed. 
2) Regression of weight change (as computed above) on AEE will be performed, adjusting for potential 
confounders of the association. 
3) AEE will be dichotomized at the median value, resulting in high and low AEE groups.  Weight change 
will be estimated for each individual as the slope of the regression of weight on time (0, 12, and 24 
months).  Average weight change across AEE groups will be compared.  That is, individual regression 
lines of weight on time will be obtained for each individual, and the average regression among “Low 
PA” will be compared to the average regression among “High PA” subjects.  
4) Multilevel analysis (i.e., mixed effect models), which generalizes the previous methods, will be used in 
our final analysis.  This approach will allow for the use of all the available information (i.e., there will be 
no need to categorize AEE values), thus improving the statistical power [189].  Furthermore, this 
method allows one to account for the within-subject correlation of the weight determinations and to 
account (adjust for) subject-specific covariates that may confound the associations.  In addition, the 
dependence of weight change on AEE values can be specifically modeled by the use of random 
coefficients.  Specifically, 
 
 
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confounders.  Confounders will include demographic variables and EI variables (e.g. total intake, percent 
calories from fat, etc). This approach corresponds to a multivariate nutrient density method suggested in 
the literature [73]. The effect of type of food most frequently consumed (e.g. vegetables, industrially 
processed foods, etc) will be accounted for by including dummy variables in our multivariate regression 
models. Note that the parameter β1i, in the Level-1 equation, represents the weight change of the i-th 
subject.  Thus, the Level-2 equation for this parameter models the dependence of weight change on AEE 
after adjusting for other covariates.  A similar interpretation for the intercept parameter, β0i, holds. In both 
instances site-specific factors could be included. 
 
The multilevel approach can be extended to accommodate time-varying covariates, which we will apply 
to investigate the effect of changing AEE on weight change. In addition, the effect of AEE in the i-th subject 
(∆AEEi) on weight change will be assessed by replacing AEEi in the Level 2 step of the multi-level analysis 
above by (∆AEEi). 
 
Finally, one additional co-variate capturing exposure information will be created which will define the 
pattern of AEE.  After examining the within-person distribution over the monitored days (eg, within-person 
SD) we will use both continuous and categorical transformations to characterize “consistent vs variable” 
patterns.  Our pilot data suggest substantially more consistent patterns in Nigeria vs. the US and this may 
impact on the AEE-weight relationship.    
 
D.7.1.3  Comparison of Mean AEE Levels Among Sites 
 

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The 2 sample t-test will be used to compare means of the primary continuous variables, e.g., BMI, PA, 
weight, and height.  Relevant co-variates will be identified from the within-site analyses and added using 
multivariable models [186, 190].  We recognize that some modifying factors within sites may not have the 
same significance across sites, and we will be very careful how variables are chosen for these analyses.  
Finally, we will test whether the regression slopes of the PA-BMI relationship varies significantly among any 
pair of sites (i.e., test for interaction).   
The association between mean levels of AEE and mean levels of obesity/relative weight will be 
assessed using regression analysis, following a meta-regression type of approach.  That is, mean AEE values 
will be regressed on mean obesity/relative weight values, weighting by the inverse of the squared standard 
error of the mean AEE values [191].  This is a widely-used approach in meta-analysis [192]. It is recognized 
that EI is an important potential confounder of the associations at the individual level. We will estimate the 
median (and other quantiles of the distribution) usual intake of nutrients (e.g. fat, percent fat) using methods 
developed by the ISU research group [179, 193] and include them in the meta-regression models. 
 
D.7.2  Power Analysis 
 
 
 
A statistical justification for the selected sample size is provided for each aim of the study according to 
the suggested analytical plan.  Sample size estimation for each of these hypotheses will be obtained, and the 
maximum number required to test them with sufficient power (80% or higher) will be selected. 
 
1) PA is negatively related to percent body fat and CV risk factors in the study populations at baseline. 
 
It has been previously estimated (cf. Preliminary Studies) that the correlation between PA, 
measured with DLW, and body composition is between -0.6 and -0.5.  A correlation of -0.33 or smaller 
(larger in absolute value) will be detectable with 83% power and maximum probability of type I error alpha 
= 5% using a one-sided Fisher’s Z test and 75 subjects per site [194].  
 
2) PA is negatively related to change in body fat composition. 
 
 
a)  Physical Activity Measured by Accelerometer: 
 
 
PA, measured by the accelerometer, is associated with changes in body composition.  For 
design purposes we will use the approach described above in the analysis section (see D.7.1.2.  
method 3).  That is, two AEE groups will be formed by using the median AEE as the cut point. A 
method developed by Frison and Pocock [195] will be applied to compare mean changes between 
groups in a longitudinal analysis, adjusting for baseline values.  In this method the following 
assumptions are made: the correlation between any 2 weight determinations is ~ 0.60 and the 
minimum acceptable difference between the means is 0.25 standard deviations (i.e., effect size equals 
0.25) [186].  There will be one baseline measure of weight and two follow-up measurements. An 
analysis of covariance approach will be used to adjust for baseline values.  Thus, to detect the pre-
specified minimum difference with 90% statistical power we will need to accrue 460 subjects per AEE 
group (230 in the Low and 230 in the High group).  We will accrue a total of 500 individuals per site in 
anticipation of a conservative total attrition rate of ~ 20%.   
   
 
 
b)  Physical Activity Measured by Doubly Labeled Water: 
 
 
The correlation between AEE and change in body composition is anticipated to be weaker (i.e., 
smaller in absolute value) than that observed between AEE and body composition at baseline.  Data 
from previous studies (cf. Section C: Preliminary Studies) suggest that the association between AEE 
and body composition remains constant across studies.  
 
 
Although the study is not powered to test for differences in the association across sites (i.e., 
effect modification), we will undertake this task to ensure that large differences among the correlation 
coefficients do not exist.  In addition, sites in which the correlation coefficient is deemed to be similar 
will be combined, and then comparisons of the correlations will be performed.  For the sake of 
illustration, assume that 2 sites (n = 150 individuals) are compared to other 2 sites (n = 150 individuals).  
Thus, a two-sample, one-sided test of the correlations, assuming -0.4 versus -0.65, will yield over 91% 
power, whereas the power will be over 80% for comparison of -0.45 and -0.65, and between -0.35 and -
0.6 the power is greater than 85%. Combining the data samples from all 5 sites and assuming constant 

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association will demonstrate a correlation between AEE and change in body composition of -0.15 or 
smaller (larger in absolute value) with over 83% power. 
 
3) Mean levels of AEE are negatively related to mean levels of obesity/relative weight among the 
populations. 
 
As previously demonstrated, sample sizes within sites will allow us to rule out correlations larger 
than 0.33 (absolute value), even when AEE is determined by DLW.  Furthermore, we have estimated (cf. 
Preliminary Studies) that the within-site correlation between AEE and body composition of individual data 
could be as large as 0.6.  We anticipate that the correlation between mean AEE levels and mean body 
composition will exceed 0.85.  This estimate is justified in the following manner.  A Fisher’s Z-
transformation 5%-significance test will have over 55% power to detect this correlation with 5 values 
corresponding to the 5 sites of the study.  However, after dividing our samples by gender there will be 10 
observations (2 X 5 countries), resulting in over 95% statistical power. Assuming an effective sample size 
of 7, to account for within-country correlations, the statistical power is 80%. Note that additional power will 
be gained when weighted linear regression methods are applied to estimate the correlation (slope) 
between AEE and body composition using meta-regression.  Given the strong a prior hypothesis we can 
also compare mean AEE in the groups with the highest and lowest rates of obesity to test the ecological 
association.   
 
In this instance, we acknowledge that we are forced to make the assumption that important 
relationships will be inherently strong statistically.  This assumption is justified since the unit of the analysis 
will be the average for the entire group in each site, and therefore estimated with precision.  For example, 
in our study of 11,000 individuals in 8 populations we demonstrated a correlation of 0.85 between mean 
BMI and prevalence of hypertension [123].  To enhance our ability to find these relationships, we have 
selected populations from across the full range of BMI (e.g., from 21-30).  We are also fully aware of the 
possibility of “ecological confounding” in these analyses.  Thus, while a strong correlation could be 
observed, the causal process could be entirely different from, but simply co-linear with, the AEE 
relationship.  We will guard against this outcome by testing the individual-level relationships first, as 
discussed previously. 
 
D.7.3  Data Management 
 
 
 
The data coordinating center (DCC) will be established in Chicago, where the study statistician and 
statistical programmers are housed.  Dr. Durazo has significant experience supervising DCCs.  He has 
maintained large data repositories on international collaborative studies at Loyola for over 10 years.  He also 
was the director of the DCC for the Chicago Initiative to Raise Asthma Health Equity for which he was in 
charge of maintaining a database of asthma screening on over 50,000 children from the Chicago Public 
Schools.  Data entry will be the responsibility of each site coordinator.  Data will be sent to the DCC along with 
copies of the paper forms used to collect them for storage, editing, cleaning, and preparation for publication.  
Data quality and assurance processes will be implemented and kept in place for the duration of the study.  All 
data files will be stripped from personal identifiers to ensure participant confidentiality.  Data backups will be 
performed regularly following the guidelines of the Department of Preventive Medicine IT. 
 
D.7.4  Missing Data 
 
 
Briefly, missing data will be handled statistically by using mixed effects models, which will (1) allow use of 
all data even if some time points are missing, (2) accommodate subject measurements that were not obtained 
at the precise time, (3) use growth curve models, which is a special case of mixed effects models, which again 
permits use of the full data set despite missing values and (4) allow for correlations of variables within a 
participant.  The mixed models approach assumes that the data is missing at random (MAR) and, as such, will 
not bias the analysis.  However, if it is determined that the data may not be randomly missing and thus non-
ignorable, then two well accepted approaches to handle this situation will be implemented, namely selection 
models [196, 197] and pattern mixture models [198-201].  Selection models comprise to steps. In the first, a 
predictive model for drop-out (missingness) is developed as a function of variables measured prior to dropout 
(e.g. baseline information). Thus, the probability (propensity) of missingness is estimated for each participant. 

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Then, in the second step, the propensity score is entered into the regression model as a covariate to adjust for 
the effect of drop-outs.  We will also apply mixed-effect selection models to account for non-ignorable missing 
data [200]. In pattern-mixture models we first group the data according to a missing pattern. For instance, in a 
study with 3 time point determinations there will be total of 8 (23) missing data patterns (e.g. OOO, OOM, 
OMM, OMO, where O stands for observed and M for missing). Then, a mixed-effects model is fit to the data 
with the same covariates as the original mixed-effects model, in addition to a grouping variable representing 
the missing data patterns to account for drop-out. Formally, dummy variables are created to represent each of 
the data patterns. The final model includes all the covariates, the dummy variables and interactions of these 
dummy variables with other variables in the model. This approach allows determination of i) the degree of 
dependence of the outcome variable on the missing data patterns; ii) the extent of the influence of the missing 
data patterns on the association between other variables and outcome (effect modification). Overall estimates 
of the association between a given variable and outcome can be estimated by averaging over all missing data 
patterns.   
 
D.8 Limitations  
 
This study is based on the premise that objective measurement of EE will be required to fully 
understand the causes of the on-going secular trends in obesity.  Given prior research on the role of specific 
sedentary activities, it is reasonable to test the hypothesis that AEE will vary across the social settings we have 
chosen to study.  The value of our study will be in quantifying the overall magnitude of this difference and its 
within-site relationship.  We recognize that it is possible that increases in energy intake—rather than 
expenditure—could be the determining, or even sole factor, in weight gain and diabetes risk; demonstrating the 
absence of a PA-obesity relationship will directly support this alternative hypothesis.  We will capture data on 
EI from questionnaires as well, although we recognize its limitations. 
On the other hand, the net positive energy balance required to cause gradual weight gain can be quite 
subtle (in the range of 20-40 kcal/d for adults).  Given that this net excess is likely to be due to a combination of 
both decreased AEE and increased caloric intake, the contrast in population means for AEE could be hard to 
define.  As in all such etiologic studies, therefore, we run the risk of a false negative due to low power.  This 
result, however, would not be uninformative since it would be important to know that large mean shifts in AEE 
were not driving this epidemic.  At the same time, a wide distribution of AEE levels can be anticipated among 
participants within each site, and we anticipate finding important relationships in the individual-level analyses.  
The entire success of the project, therefore, does not rest on the group comparisons.    
 
 
The focus of this study is on the net outcome of a complex regulatory system in natural populations.  
The class of effects that include the interaction of socially-embedded behavior, food patterns, cultural norms, 
required and voluntary PA, etc., can only be examined in observational studies.  Controlled experiments are 
therefore not useful to address this question.  Long-term prospective studies have clear advantages over 
cross-sectional and short-term studies; however, they are also difficult to conduct and do not fit within the NIH 
funding framework.  In most instances where epidemiologic research has tried to define risk for chronic 
disease, cross-sectional studies have effectively summarized the cumulative effect of prior exposures and 
have consistently identified true relationships among CVD risk factors (e.g., BMI vs. BP, glucose, and 
cholesterol).  Clearly, studies of much longer duration are ultimately necessary to support causal inferences 
and will be needed in the future.   
 
 
This project involves recruitment in 5 countries, collection of a complex set of data, and follow-up for 
two years.  The methods must be rigorously standardized to ensure comparability.  We recognize the 
challenge posed by these aspects of the study.  We have many years of experience in cross-cultural research 
and have implemented standardized data collection protocols for similar complex surveys.  Our study of the 
African diaspora was both the largest international collaborative study ever conducted on hypertension and the 
only one that has yielded standardized cross-cultural prevalence estimates [81, 202].  We regard project 
described in this application as a major research opportunity which could have long-term significance.  It will be 
the sole research focus of the PI who has invested more than 6 years bringing this project to maturity.  We are 
therefore committed to a maximal effort at study management and are convinced that we can obtain complete 
high quality data from all the study sites.    
 
 

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 HUMAN SUBJECTS  
  
E.1  Risks to the Subjects  
  
  This study will involve the collection of medical information, activity data, plasma and urine samples from 
2,500 human participants, ages 25-45 years, from communities in 5 countries.  The participants will be 
recruited from residential neighborhoods in Igbo-Ora, Nigeria; Cape Town, South Africa; Seychelles; Spanish 
Town, Jamaica; and Maywood, IL, USA.  Participants will be recruited for this study as the primary aim is to 
determine whether or not differences in energy expenditure are responsible for the differences in relative 
weight, change in relative weight over time, and diabetes risk between these countries.  
  
E.1.1  Sources of Materials  
  
  Information, activity monitoring data, and plasma samples will be collected from all participants (n = 2,500); 
urine samples will be collected on a subset participating in the DLW component of the study (n = 375).  
Demographic, anthropometric, PA, environmental, and lifestyle data will be collected on all participants.  They 
will be assigned an identification number upon enrollment into the study.  Linkage of identification number with 
name, address, birth date or any other identifying information will be kept by the data manager at the 
respective institutions in a separate, password-protected data file.  Only the data managers and study 
investigators will have access to the linkage file.  AEE data, anthropometrics, and specimens will be collected 
for this study by specifically trained field staff at each of the institutions.  Demographic, health history, and 
questionnaire data will be collected by a one-on-one interview.  The participants will be provided with an 
extensive explanation of the intended study in his/her native language.  The data will be coded, cleaned and 
double entered by trained staff at Loyola.    
  
E.1.2  Potential Risks  
  
  The potential risk to the research participants includes hematoma at the site of venipuncture and possible 
disturbed sleep due to the presence of the activity monitor.  The second risk can be alleviated by removal of 
the monitor during the night.  There are no risks associated with the DLW or the questionnaires proposed for 
this study.  There are no alternate treatments or procedures offered.  Participation is strictly voluntary and 
participants may choose to leave the study at any time during the protocol.  
  
E.2   Adequacy of Protection Against Risks  
  
E.2.1   Recruitment and Informed Consent  
  
  Recruitment in all sites will be conducted through health professionals in the respective communities.  The 
procedure has been agreed upon by the respective institutions.  The institutional review procedures at Loyola 
will be initiated once this application has been submitted to NIH for review.  At that time, an informed consent 
document will be created and translated/back-translated into each local language.  Our investigators and staff 
are trained to explain the study in detail to all participants, review the informed consent documents, and 
answer any questions that may arise.  There is no aspect of the study that will be withheld from any participant.    
    
  
  

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E.2.2   Protection Against Risk  
  
  The study protocol itself has minimal risk to the participant or family.  All phlebotomy staff have been trained 
and certified, so risk of bleeding and hematoma trauma will be minimized.  
  Confidentiality will be protected in several ways.  The participant will be assigned a unique identification 
number upon enrollment into the study.  This ID will be used on all subsequent materials, including laboratory 
samples, activity monitoring data and questionnaires.  The electronic file linking the name and ID number will 
be accessed only by the data manager in a separate passworded file at the respective study sites and 
available only to study investigators.  
   Appropriate referral will be made for any detected conditions, including elevated fasting serum glucose or 
blood pressure.  The study, however, will not assume responsibility for treatment or provision of drugs.  
  Due to the relatively high prevalence of HIV in many regions of sub-Saharan Africa, we will undergo 
screening in Khayelitsha (South Africa).  All participants will be tested for HIV and undergo pre- and post-test 
counseling both before study enrollment at baseline and again at follow-up.  The LifeLine Khayelitsha Clinic, 
CapeTown, will counsel and test all individuals recruited for the study and treat all those found to be HIV-
positive.  This is policy in South Africa, and we have received confirmation from the officials at the LifeLine 
Khayelitsha Clinic that all those recruited for the study will be provided for.  All counseling, testing as well as 
treatment for HIV/AIDS is free of charge in South Africa as a result of both national and international efforts.  
HIV-positive individuals will be excluded from our study, as mentioned previously, because of its effects on 
metabolism and body weight.    
  
E.3  Potential Benefits of the Proposed Research to the Subjects and Others  
  
  Participants will be asked to join this study for its potential scientific and public health benefits.  All individuals 
in the study may benefit directly from the screening activities, e.g., blood pressure, BMI, and fasting glucose.  
The potential benefits to others include the knowledge of AEE requirements for prevention of excess weight 
gain.  The research risks are reasonable in relation to the anticipated benefits because the possibility of a 
hematoma, the only real risk to a participant in this study, is no greater than that from a trip to a doctor’s 
appointment.  Yet, we have the chance to increase the body of knowledge which will inform national and 
international recommendations on AEE guidelines.  
  
 E.4  Importance of the Knowledge to be Gained    
  
  Obesity is an increasing problem worldwide and carries a large risk for diabetes and CVD.  We lack an 
adequate understanding of the underlying determinants of this problem.  This study will provide an answer to 
one facet of the puzzle and will allow us to define a minimum threshold of AEE above which excess weight 
might be minimized.  In addition, there are few objectively obtained data on AEE expenditure.  National and 
international recommendations have been made based on relatively sparse information.  The data collected 
from this study will greatly enhance the extant literature on this topic.  Again, the risks of this study are very 
minor relative to these potential gains.  
  
E.5  Data and Safety Monitoring Plan – N/A  
   
  
 
 
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