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Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. Disclosure
  9. REFERENCES

Objective: Obesity is a prevalent condition in industrialized societies and is increasing around the world. We sought to assess the relative importance of resting energy expenditure (REE) and activity EE (AEE) in two populations with different rates of obesity.

Methods and Procedures: Women of African descent between 18 and 59 years of age were recruited from rural Nigeria and from metropolitan Chicago. Total EE (TEE) was measured using the doubly labeled water (DLW) technique and REE by indirect calorimetry; AEE was calculated as the difference between TEE and the sum of REE plus a factor for the thermic effect of food. In the analyses all EE parameters were adjusted for body size using a regression method. Comparisons were made between the groups and associations between EE and adiposity examined.

Results: A total of 149 Nigerian and 172 African-American women completed the protocol. All body size measurements were lower in the Nigerian women. Adjusted TEE and REE were higher in the Nigerian cohort but adjusted AEE did not differ significantly. Adjustment for parity, seasonality, and recent illness did not modify mean AEE or adiposity. In neither cohort was there a meaningful association between measures of AEE and adiposity.

Discussion: In these cohorts of women from very different environments, AEE did not differ significantly nor was it associated cross-sectionally with adiposity. If generalizable, these findings suggest that reduction in AEE may have less of a role in the development of obesity than anticipated. The possibility remains that variation in type and duration of activity plays a role not captured by total AEE.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. Disclosure
  9. REFERENCES

Over the past 20 years, societies as geographically and culturally distant as Barbados, Russia, Kuwait, and Japan have experienced rapid increases in relative weight affecting both children and adults (1). Despite substantial heterogeneity in current prevalences of obesity in these populations, the trends have been remarkably similar. In the United States the upturn in the slope of the trend for BMI occurred in the mid-1980's and a fivefold increase in the rate of change per year has been observed subsequently for every major sex/race/age/socioeconomic group (2,3). Clearly some “common source exposure” is shifting the population distribution of weight rightward and almost all segments of the societies that participate in the world economy are being affected. Although it is obvious that long-term declines in EE and increases in relative calorie intake must be the cause, very little is known in a direct, quantitative way about their impact. Direct measurement of EE to evaluate its potential contribution to population risk has only been undertaken among the Pima Indians living in the southwest United States (4).

In the United States, minority women have been at particular risk of obesity; the current prevalence among African-American women is 45% (5). In contrast, in contemporary populations in West Africa the prevalence remains low. Based on surveys in a rural Yoruba community in southwest Nigeria, the prevalence among women is 6–8% (6,7). These and similar observations have led to the inference that mean EE in developing economies is likely to be greater than in industrialized societies as a result of mechanization of work, transport, and domestic life (8,9,10). As noted, however, only the cross-cultural comparison of the Pima Indians in rural Mexico and on US reservations provides direct measurement of the various parameters of EE (4). Although this study supports the hypothesis of declining physical activity, additional studies among related populations with divergent rates of overweight and obesity will be required to confirm the generalizatiblity of this finding. Whether individual-level predictors of obesity vary across populations, and whether that variation is large enough to account for observed differences in obesity prevalence, are therefore important unresolved questions in the epidemiology of obesity.

Through an existing international collaborative study of chronic disease in populations of African descent, we undertook a comparison of components of the energy budget in women from a rural community in southwest Nigeria and metropolitan Chicago, IL. The levels and distribution of parameters of energy expenditure (EE), i.e., total EE (TEE), resting EE (REE) and activity EE (AEE) were measured directly and the cross-sectional relationships between EE and measures of adiposity were determined.

Methods and Procedures

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. Disclosure
  9. REFERENCES

Participants

The Department of Preventive Medicine and Epidemiology at Loyola University School of Medicine and the University of Ibadan, Nigeria, initiated a cross-cultural, population-based research program on hypertension in 1992 involving communities in West Africa, the Caribbean, and the United States (11,12). The participants enrolled in the present women's study of obesity and EE were recruited from the existing database of the prior participants who were sampled from households. The protocol was approved by the Institutional Review Board of Loyola University School of Medicine and the Ethics Committee of University College Hospital, University of Ibadan. Written informed consent was obtained from all participants and all relevant documents including the informed consent forms were translated into Yoruba for use at the Nigerian site.

In Nigeria, the study was conducted in two rural villages near the border with the Republic of Benin (Igbo-Ora and Idere). In the United States, participants were recruited from the predominantly African-American community of Maywood, a suburb adjacent to Chicago, and the Austin neighborhood, which is located just within the western boundary of the city. The primary occupation of the women in Nigeria was market trading (43%) with a substantial proportion also engaged in subsistence farming (21%). In contrast, the women in the US site were employed in a wide variety of occupations with the most common being certified nurse assistant (8%) followed by customer service representative (6%); ∼40% were unemployed at the time of the study.

Women were asked to participate if they were between the ages of 18 and 59 years and were in general good health and not pregnant or lactating at the time of the baseline examination. Other exclusion criteria included plans to travel outside the study area during the doubly labeled water (DLW) measurement period, active efforts to lose weight, a known thyroid condition, or orthopedic or other conditions which restricted movement. The same standardized examination protocol was used in both Nigeria and the United States. This included measurement of TEE using the DLW method, REE by indirect calorimetry, and body composition using isotope dilution. Additional anthropometric measurements and information on medical history were collected.

The research teams used clinic space convenient to the participant populations in both Nigeria (in Igbo-Ora) and the United States (in Maywood). Participants arrived at the respective clinics around 7 am following an 8–10-h overnight fast. The baseline examination required a minimum of 5 h to complete after which the participants were provided with a light meal and instructions for completion of the DLW protocol.

TEE

Free-living TEE (MJ/day) was measured over a period of 10–14 days using the DLW technique, as previously described in detail (13). A 10-day measurement period was used in Nigeria due to potentially more rapid water turnover in tropical regions, whereas a 14-day period was used in the United States. All EE data are expressed per day accounting for the differential length in measurement periods. Otherwise sampling procedures were similar for both the Nigerian and US sites. Briefly, deuterium and 18O elimination rates were calculated by the two-point method with the use of isotopic enrichment relative to baseline and the time difference between the third postdose and final urine samples (14). Participants provided a baseline urine sample and drank a premade dose of DLW prior to the measurement of REE. The dose included 0.14 g deuterium and 0.20 g 18O per kg fat-free mass (FFM) (estimated). Urine samples were then collected at 2, 4, and 5 h following the isotope administration. In Nigeria, a midpoint spot urine sample was collected on day 5 and an endpoint sample was collected on day 10 after the isotope administration. In the United States, two urine samples were collected on day 14, the first void of the morning and again ≥2 h later in the clinic. Regardless of site, participants returned to the respective clinics on day 10 or 14 where they provided the final urine sample and their weights were recorded. All urine samples were analyzed for isotope abundance at the Stable Isotope Core Laboratory of the University of Wisconsin, Madison.

REE

Following the collection of a baseline urine sample and the administration of the isotopes, REE and resting respiratory quotient were measured using the same indirect calorimeter (Delta Trac II, Viasys Medical Systems, Palm Springs, CA) in both sites. Fasting 30-min measurements were completed following a minimum of a 20–30-min period of supine lying to acclimate the participant to the instrument. The DeltaTrac II operates as an open-circuit canopy system with a paramagnetic oxygen sensor, infrared carbon dioxide analyzer, and onboard computer. Briefly, rates of oxygen consumption and carbon dioxide production are calculated and used to calculate REE utilizing the modified equation of Weir (15). Prior to each measurement, the indirect calorimeter was calibrated using a gas of known concentration and alcohol burn tests were performed monthly. These measures indicated that the unit operated within 2% at all times. The last 20–30 min of data was used to determine the average REE and was expressed as MJ/day.

AEE

AEE (MJ/day) was calculated as: AEE = (0.9 × TEE - REE) where the term “(0.9 × TEE)” represents the estimated 10% of TEE expended as the thermic effect of food (16). Expenditure in activity is also presented as the physical activity level (PAL), the ratio of TEE to REE, a metric used extensively in international studies (17).

Anthropometry and body composition

At each site, height was measured, without shoes, to the nearest 0.1 cm using wall-mounted stadiometer (Seca, San Jose, CA). Weight was measured without shoes and in light clothing to the nearest 0.1 kg using a calibrated electronic scale (Health-o-meter, Bridgeview, IL). Height and weight measurements were used to calculate BMI as weight (in kg)/height2 (in m). Waist circumference was measured to the nearest 0.1 cm at the narrowest part of the torso between the lowest rib and the iliac crest while hip circumference was measured to the nearest 0.1 cm at the point of maximum extension of the buttocks.

Body composition was measured using the isotope dilution method as previously described by Schoeller et al. (18). Total body water was calculated for both the deuterium and 18-oxygen dilution and the two values were averaged. FFM was calculated from total body water using a constant hydration factor (0.73) (ref. 19) and fat mass (FM) was calculated as the difference between body weight and FFM.

Medical history and other information

All participants were asked about their medical history including presence of known chronic diseases, smoking and drinking habits, usual occupation, time since last illness requiring time off from work, and number of pregnancies. Additionally, the season in which the measurements were obtained was recorded in order to determine impact of seasonality on body weight and AEE. In Nigeria, the wet season was determined to be from the beginning of April to the end of October and the dry season from the beginning of November to the end of March (20). A similar delineation of the seasons was used for the US cohort, with baseline examinations conducted between November to April designated as winter and those between May and October as summer.

Statistical analysis

All statistical analyses were completed using Stata, version 9.0 (Stata, College Station, TX). EE data are presented both in absolute measures and after adjustment for body size and composition. TEE, REE, and AEE were adjusted for FFM and FM using a regression method as described previously (21). Age was not a significant determinant of any EE parameter and thus was not used in the adjustment by the regression method. Means and s.d. were calculated for all variables. Student's t-test was used to assess differences between the two sites. Analysis of covariance was used to assess whether there was an association between AEE or percent body fat and number of pregnancies adjusting for age and whether any difference in association existed between sites.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. Disclosure
  9. REFERENCES

Two hundred six women were enrolled in Nigeria and 195 in the United States. In both sites, a small number of women younger than 18 years were inadvertently recruited (11 in Nigeria and 6 in the United States), in addition, one woman from each site died within 12 months of enrollment (the Nigerian woman from cancer and the US woman from cardiovascular disease). These 19 women were excluded from the analyses although the results did not differ with their inclusion. At the time this study was initiated, there was a significant shortage of 18O-labeled water available for research (22) and, consequently, it was possible to assess TEE by DLW only in a subset of participants: 149 in Nigeria and 172 in the United States. The TEE subset from Nigeria was younger (31.9 years vs. 33.7 years) and had a lower mean BMI (22.6 kg/m2 vs. 23.1 kg/m2) than the total Nigerian cohort; there were no differences between the US participants with or without a TEE measurement. All subsequent analyses are restricted to the TEE subset.

Characteristics of participants are presented by site in Table 1. The mean age of the Nigerian participants was slightly younger than the US participants when analyses were restricted to those in the TEE subset. In contrast, every measure of body size and composition was significantly greater among the US women whether comparing the total sample or those in the TEE subset as presented. The participants were classified according to the BMI categories for differing grades of under- and overweight (Table 2) (ref. 23). As can be observed, there was a marked rightward shift in the distribution by BMI categories in the United States relative to Nigerian women. Overall, 6.7% of the Nigerian cohort had a BMI >30 compared to 51.2% in the United States.

Table 1.  Participant characteristics by site
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Table 2.  Distribution of participants by BMI category and site
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EE parameters are presented in Table 3. The absolute values for TEE and REE were significantly lower among the Nigerian women, reflecting their smaller body size, whereas absolute AEE levels did not differ significantly. In contrast, TEE and REE were greater among the Nigerian women after adjustment for FFM and FM (P < 0.001), while neither adjusted AEE nor PAL differed significantly between the sites. The frequency distributions of unadjusted AEE and AEE adjusted for FFM and FM for both the Nigerian and US cohorts are presented in Figure 1. This figure illustrates the fact that not only do the two cohorts have the same mean adjusted AEE but they also have comparable distributions. The mean respiratory quotient was higher among the Nigerian women reflecting the higher carbohydrate intake (Luke, unpublished data). The difference in mean adjusted TEE between the sites (0.53 MJ/day or 125 kcal/day; P < 0.001) was due to both higher REE (mean difference of 0.27 MJ/day or 65 kcal/day; P < 0.001) and higher AEE (mean difference of 0.20 MJ/day or 50 kcal/day; P = 0.12) in the Nigerian women; the remaining 0.06 MJ/day is the difference in estimated thermic effect of food. In effect, therefore, although all three adjusted EE measures were 5–6% higher in Nigerians, the absolute differences in the means or variances by EE category determined the result of statistical tests. Preliminary analyses were performed both with and without participants with BMI <18.5 (suggestive of chronic energy deficiency) and, as there are no differences in any mean EE measure, the underweight women were kept in the analyses.

Table 3.  Energy expenditure (EE) by site
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Figure 1. (a) Frequency distributions of activity energy expenditure (MJ/day) for the Nigerian cohort (solid line) and the US cohort (dashed line). The y-axis values correspond to the relative frequency scaled so that the total area under the curve equals 1 as in theoretical probability density functions. (b) Frequency distributions of activity energy expenditure adjusted for fat-free mass and fat mass (MJ/day) for the Nigerian cohort (solid line) and the US cohort (dashed line). The y-axis values correspond to the relative frequency scaled so that the total area under the curve equals 1, as in theoretical probability density functions.

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Due to the increased risk of acute infectious disease in Nigeria and its potential impact on body weight and activity levels, participants were asked to estimate the date of the last bout of illness severe enough to keep them from their occupation. Fourteen percent of the Nigerian women reported such an illness within the previous month (n = 21); 57% of these individuals indicated they suffered from malaria and another 33% from “general body aches.” Mean BMI, percent body fat, and adjusted AEE did not differ between the participants reporting illness in the previous month and the remaining Nigerian cohort, therefore, they were kept in the analyses.

The season in which the TEE measurements were completed had no impact on either adjusted AEE levels or body weight in Nigeria or the United States. Adjusted AEE was 3.42 ± 1.34 MJ/day vs. 3.29 ± 1.17 MJ/day (P = 0.55) during the rainy and dry seasons, respectively, in Nigeria while mean weight was virtually identical. Likewise, adjusted AEE was 3.43 ± 1.12 MJ/day in the winter and 3.17 ± 1.13 MJ/day during the summer months, again with no differences in body weight among the US women.

Slightly over half of the Nigerian women reported having been pregnant (51.7%) compared with 75.6% of the US women. The number of pregnancies, however, was higher among the Nigerian women who had been pregnant (4.8 ± 2.3) than among the US women (2.3 ± 1.5) (P < 0.001). There was no observable association between number of pregnancies and BMI or percent body fat in either site. There was, however, a positive association between adjusted AEE and number of pregnancies among the US women (r = 0.20, P < 0.05) but not the Nigerian women. In a model including adjusted AEE as the outcome measure and number of pregnancies and age as the exposure measures, there was no difference in the association by site as assessed by analysis of covariance. Thus parity apparently had at most a minimal effect on adiposity or AEE in either Nigeria or in the United States.

There were no significant associations between adjusted TEE or REE and measures of adiposity in the Nigerian cohort; among the US women low-level positive associations between several variables were observed (TEE vs. BMI, r = 0.19; TEE vs. percent body fat, r = 0.15; REE vs. % fat, r = 0.18; all P < 0.05). In addition, there were only weak, nonsignificant associations between measures of adiposity and adjusted AEE, e.g., correlations with BMI r = 0.03 and 0.10, respectively, for Nigeria and with percent body fat r = −0.11 and −0.12, respectively, for the United States. These correlations were not modified by the addition of age or parity to the models. The lack of association between adiposity and adjusted AEE persisted after stratifying the samples by BMI status as suggested by Hemmingsson and Ekelund (24), i.e., participants with BMI ≥30 showed no stronger an association than those <30.

In Table 4, mean adjusted TEE, REE, and AEE and PAL values are presented by site for underweight, normal weight, overweight, and obese participants. Due to small numbers in several categories, chronic energy deficiency I and II data were combined as were data for all BMI categories ≥30. There were no significant differences in the measures of AEE between the Nigerian and US women for any category of body weight, whereas both TEE and REE differed between the cohorts at every BMI category except underweight.

Table 4.  Adjusted energy expenditure (EE) parameters by BMI category and site
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Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. Disclosure
  9. REFERENCES

In this comparative study of EE and obesity, we observed that although body composition differed dramatically between women living in rural Nigeria and Chicago, the levels of AEE were indistinguishable. As in previous reports (7,25), the Nigerian women were significantly shorter, weighed less, and had less body fat than their US counterparts. After adjustment for body size and composition, TEE was higher in the Nigerian than the US cohort by 0.53 MJ/day (125 kcal/day) more than half of which was due to a higher REE; neither AEE nor PAL differed significantly between the cohorts. Mean EE levels were not influenced by parity or season of measurement in either cohort. Unlike many previous studies of EE and adiposity, most of which were smaller in size or based on indirect measurement, there was no meaningful association between measures of EE and BMI or percent body fat in either the Nigerian or the US cohort. Since DLW is insensitive to variation in patterns of activity, it remains possible that intensity, duration, or other characteristics of EE may contribute to adiposity differences between these cohorts of women. However, neither PAL nor AEE, as currently conceptualized and measured, appear to be important.

The concept of the epidemiologic or nutrition transition has been widely used to provide a framework for the process of modernization of lifestyle. Although relatively little data exist on PALs using objective measurement, lower EE in more industrialized societies is generally accepted as one of the driving factors in the increase of overweight and obesity in those societies (8,9,10). It seems apparent that as societies become more mechanized, the EE associated with occupational activity and the chores of daily living would decrease markedly and indirect evidence exists that does support this notion (8). It is then reasonable to make the assumption that overall AEE, and by extension TEE, would also be decreased in more industrialized societies. Much of the research in developing countries, however, suggests that this scenario may not accurately reflect reality. Although most of the extant studies did not utilize the DLW technique, there is substantial experience measuring EE in developing countries in order to ascertain energy intake requirements (26). A comprehensive review of these data concluded that there was no evidence to suggest systematically higher EE in developing countries (27). The findings presented from this study of women in Nigeria and the United States are obviously consistent with this general conclusion. In contrast, Esparza et al. (28) reported dramatically lower TEE and AEE, i.e., on the order of 2.1 MJ/day (∼500 kcal/day) and much higher adiposity among adult Pima Indians living in southern Arizona compared with Pima Indians living in the rural mountainous region in northern Mexico. Pima Indians living a more traditional lifestyle in Mexico were clearly expending more energy than a related population in the United States, and this increased EE could have made an important contribution to the differences in obesity prevalence. Life in tropical regions may of course be different from that of northern Mexico. Ferro-Luzzi et al. have suggested that individuals in environments of marginal energy supplies may take advantage of compensatory inactivity in order to conserve energy (29).

Although the difference in mean BMI between the Nigerian and US women was comparable to that between the two Pima Indian populations, the difference in TEE was only ∼25% of that observed in the Pima Indian study. The major difference between the cohorts of women was in REE, with only a 0.20 MJ/day (48 kcal/day) difference in AEE. In contrast to the present findings on REE, we previously reported no significant difference in REE adjusted for body composition between adults of both sexes in Nigeria and the United States (30). A similar absolute difference in REE between the samples was present in the earlier data, however, the sample size of the earlier study was not large enough to render this difference of 0.27 MJ/day (65 kcal/day) significant. The explanation for lower REE among the US women is not obvious. These are two populations that are genetically related, broadly speaking; population-specific allelic markers suggest that on average they share roughly 80% of their genetic material (31). It is possible that isotope dilution provided a less than optimal estimate of total body water, from which FFM and FM were estimated, which would be more pronounced in the much more obese US cohort. More likely, however, is that there is a slight, but significant, difference in the composition of the FFM component of the two cohorts. Recently, Gallagher et al. reported that lower REE among African-American adults than white adults might in part be due to a lower total volume of high metabolically active organs (32). The reverse may be happening here: the US women in the present study are much taller and heavier, with higher FFM, than their Nigerian counterparts. The relative proportion of low metabolically active tissue, i.e., skeletal muscle and bone, may therefore be higher among the US women, contributing to a lower overall REE. Regardless of the etiology of the higher average REE of the Nigerian women, it is unlikely to play a significant role in the difference in obesity rates. In a large prospective cohort of Nigerian adults, we previously reported finding no association between REE and weight gain (33). With the exception of two influential studies among adult Pima Indians (4,34), there have been no reports supporting REE as a determinant of weight change (35).

Physical activity has been defined and measured differently in different studies, complicating generalizable statements about their potential relationship. The use of DLW for measuring TEE combined with measurement of REE lends itself to defining physical activity as either the PAL or AEE in MJ/d, neither of which were found to be related to adiposity in our study. The absence of a relationship between adiposity and PAL, however, is not entirely unexpected since it is now recognized that PAL is highly confounded by body weight and only reflects differences in physical activity when the groups or persons being compared are of similar weight (36,37).

Our finding of no relationship between adiposity and AEE in both the Nigerian and the US cohorts is inconsistent with findings from many other studies, including our own; in general, of course, the reported observation from cross-sectional data is an inverse association between BMI and AEE (38,39). On the other hand, our findings are not completely unprecedented. In a combined analysis of 22 studies, Westerterp and Goran reported no association between AEE and body fat in women, whereas in men the correlation was −0.35 (40). Our previous studies have also indicated a weaker relationship between AEE and adiposity in women than in men (25). Neither inclusion of parity nor season of measurement improved the correlations appreciably suggesting that factors other than EE are influencing adiposity in women living in rural Nigeria and in suburban United States.

By default, a difference in energy intake between the cohorts is likely to be the most important determinant of differences in obesity prevalence. We have been unable to accurately assess dietary intake in our Nigerian participants because of an absence of a comprehensive macronutrient database. Careful measurement of quantity and composition of food in high risk vs. low risk settings represents an important additional challenge for the epidemiology of obesity.

Analysis of the body size—AEE relationship depends critically on the method of adjustment of the EE variables. As can be observed in Table 4, there is a linear increase in mean expenditure by BMI classification for all EE variables except PAL; this increase is particularly striking for the Nigerian cohort. It may be that the accepted adjustments for body composition are simply not adequate and this becomes obvious when comparing two populations with such different body sizes and composition. Specifically, AEE is a product of the time spent in physical activity, the intensity of the physical activities, and the weight of the subject performing those physical activities (41,42). Thus a simple linear adjustment for weight may be insufficient. However, despite attempts to generate better models incorporating nonlinear terms, we could not improve on the basic adjustment procedures displayed above.

Weinsier and co-workers (43) compared different measures of physical activity including intensity and duration in addition to PAL, AEE, and concluded that these measures represent different domains and have different relationships with body weight and adiposity. It may therefore be inappropriate to equate any single measure with the term physical activity. For example, we found that the values for AEE were not significantly different despite a 44% greater weight in the US cohort as well as an 11% greater percent body fat. Because the energy costs of a given activity increase proportionally with weight (41), our findings would suggest that the time spent in physical activities in the US cohort was less. Unfortunately, we do not have any reliable measures of the time domains of physical activity. It is worth noting that Weinsier et al. (44) and Levine et al. (45) identified differences in some of these domains between lean and obese samples in which AEE was similar. Finally, it should also be noted that the 6% lower AEE in the United States would, if extended over a period of years, lead to substantial excess in calories stores. However, this interpretation is unwarranted given the lack of statistical significance in the two-group comparison and the absence of within-group associations.

While alternative explanations of our results may be plausible, the most parsimonious interpretation suggest that our expectation of high EE in developing countries may in fact be erroneous. While we did observe higher adjusted TEE among the Nigerian cohort of women, this was driven primarily by increased REE; AEE did not differ significantly between the cohorts. A better understanding of energy intake and time domains in physical activity in both populations will be necessary to form a complete picture of energy balance.

Acknowledgment

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. Disclosure
  9. REFERENCES

This research was supported in part by grants from the National Institutes of Health (DK56781, HL45508, and GK30031).

REFERENCES

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. Disclosure
  9. REFERENCES
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