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Positive association between resting energy expenditure and weight gain in a lean adult population^1,2,3

¹ From the Department of Preventive Medicine and Epidemiology, Loyola University Stritch School of Medicine, Maywood, IL (AL, RD-A, GC, and RC); the University College Hospital, University of Ibadan, Ibadan, Nigeria (AA and BT); the Human Genome Center, Howard University, Washington, DC (AA); and the Department of Social and Preventive Medicine, SUNY at Buffalo, Buffalo, NY (BT)

² Supported in part by grants from the National Institutes of Health (DK 56781, HL 45508, and HL 54001).

³ Address reprint requests to A Luke, Department of Preventive Medicine and Epidemiology, Loyola University Stritch School of Medicine, 2160 South First Avenue, Maywood, IL 60153. E-mail: aluke{at}lumc.edu.

	ABSTRACT

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Background:Weight gain in adulthood is common, from modest gains in developing countries to substantial increases in Western societies. Evidence of the importance of energy expenditure in adult weight change has been limited to studies conducted in Pima Indians, in whom resting energy expenditure (REE) was found to be inversely associated with weight gain.

Objective:The aim was to determine whether REE was predictive of weight change in lean Nigerian adults.

Design:Weight was measured in 744 adults on 2–4 occasions over 5.5 y. REE was measured in the second follow-up examination. Sex-specific, mixed-effects models with REE, fat-free mass, and age as fixed effects were used to test the association between REE and weight change.

Results:Adults aged >19 y (n = 352 men and 392 women) were included in these analyses. At baseline, the mean (±SD) age was 45.9 ± 16.1 y for the whole population; the mean weight was 61.4 ± 10.7 and 58.1 ± 12.1 kg and body mass index (in kg/m²) was 21.4 ± 3.2 and 23.1 ± 4.0 for men and women, respectively. Over a mean 5.5 y of follow-up, the age-adjusted weight gain was 0.42 kg/y for the men and 0.59 kg/y for the women. In mixed-effects models, REE was positively associated with weight gain in both men and women (P < 0.001). No significant association was observed in participants who lost weight.

Conclusions:In contrast with observations in overweight Pima Indians, REE adjusted for body size and composition was positively associated with weight gain in lean Nigerian adults. This suggests either that the potential for differential regulation of body weight in lean compared with overweight populations exists or that the increased REE in this population was the result, rather than cause, of weight gain.

Key Words: Resting metabolic rate • body composition • Nigerians • weight gain

	INTRODUCTION

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
A potential role for variation in metabolic rate in weight regulation has obvious theoretical appeal. In the first systematic observation of this question, low relative resting energy expenditure (REE) was reported to be a risk factor for subsequent weight gain over 4 y in adult Pima Indians (1). This inverse association between adjusted REE and body weight gain was subsequently confirmed in a separate sample of Pima Indians in 2003 (2). On the basis of this evidence, the potential role of low relative REE as a predictor of weight gain in adults has become an influential idea in obesity research.

Numerous subsequent studies have reported lower levels of REE, adjusted for body size and composition, in children and women of African descent than in age-matched persons of European descent (3–9). As a result of these findings, several investigators have suggested that a lower REE may be an important determinant of the higher prevalence of obesity observed in African-Americans than in European-Americans. However, in a comparative study of African-Americans and rural Nigerians, two genetically related populations with dramatically different prevalences of overweight and obesity, we documented similar REE levels, casting doubt on the independent causal role of this component of energy expenditure at the population level (10). The hypothesis that a relatively low REE would confer a selective advantage in persons of comparable size in an energy-restricted environment, whereas it leads obesity in an energy-replete environment, is consistent with an energy balance approach to weight maintenance. The observation that low relative REE has become a physiologic liability and risk factor for excess energy storage and weight gain in adults in modern environments has not, however, been replicated in any population other than the Pima Indians of Arizona. In fact, two other studies conducted in the United States, one in nonobese white men and the other in postobese women, found no association between REE and weight change (11, 12).

From our longitudinal studies of blood pressure and relative weight in rural Nigeria, we observed that, on average, adults continue to gain weight through their sixth decade of life. Preliminary data from a small sample of women (n = 30) in these communities with REE measurements supported the hypothesis that low relative REE predicted weight gain in adulthood, whereas no association was observed in African-American women of the same age (13). On the basis of this small pilot study, we hypothesized that REE could be a significant contributor to the regulation of energy balance in an environment with minimal exposure to Western diets and labor-saving devices. To test this hypothesis, we examined a larger cohort of rural Nigerians who have now been followed up to 7 y.

	SUBJECTS AND METHODS

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Subject recruitment
The present study was a component of the International Collaborative Study on Hypertension in Blacks (ICSHIB), which is described in detail elsewhere (14). Data were collected in Igbo-Ora and Idere, two rural villages in the Oyo State of southwestern Nigeria. The study protocol was reviewed and approved by the Institutional Review Boards of the University of Ibadan, Ibadan, Nigeria, and Loyola University, Chicago, IL. Written informed consent was obtained in Yoruba, the primary language in this region, from all study participants.

For the parent ICSHIB study, the communities were sampled based on the probability-proportional-to-size method (15, 16). In brief, after identification of appropriate geographic subunits (ie, family compounds in these villages), a random sample was generated that was proportional to the size of each subunit, and all eligible participants were invited to participate in the study. In the second phase of ICSHIB, nuclear families were identified through middle-aged probands from the first phase, and all available relatives from extended families who were aged >12 y were enrolled (17, 18). Over 7 y (mean: 5.5 y), the participants were asked to return for measurements of blood pressure, weight, and body composition, which resulted in 1–3 follow-up examinations. REE was measured during the second follow-up clinic examination.

Survey methods
A screening exam was completed by trained research staff with the use of a standardized protocol (19). Local interviewers obtained a medical history and a family pedigree in the Yoruba language. At each clinic examination, the participants’ height and weight were measured and body composition estimated by bioelectrical impedance analysis.

Height was measured to the nearest 0.1 cm by using a stadiometer consisting of a steel tape attached to a straight wall and a wooden headboard. The headboard was positioned while the participant wore no shoes, with their feet and back against the wall and head held in the Frankfort horizontal plane. Body weight was measured to the nearest 0.2 kg with the use of calibrated electronic scales. Body mass index (BMI) was calculated as weight (in kg)/height² (in m).

Body composition was estimated by bioelectrical impedance analysis with the use of a single-frequency (50 kHz) impedance analyzer (model BIA 101Q; RJL Systems, Clinton Township, MI). A tetrapolar placement of electrodes was used on the right hand and foot (20). Fat-free mass (FFM) and fat mass (FM) were estimated from measured resistance by using an equation previously validated in this population (21).

Measurement of resting energy expenditure
REE was measured on an outpatient basis by using respiratory gas exchange. Under supervision by one of the investigators (AL), certified staff made all measurements with the use of a single indirect calorimeter (DeltaTrac II Metabolic Monitor; Viasys Medical Systems, Palm Springs, CA) in a clinic setting in Nigeria over the course of 12 mo. The DeltaTracII is an open circuit canopy system with a paramagnetic oxygen sensor, infrared carbon dioxide analyzer, and onboard computer. Rates of oxygen consumption and carbon dioxide production were calculated as the difference between inspired and expired gas concentrations with the use of a known, fixed flow rate. By using the modified Weir equation (22), REE was calculated from oxygen consumption and carbon dioxide production values.

The examination room was thermoneutral relative to the environment; air circulation was maintained by overhead fans. All measurements were done in the morning, which is the coolest part of each day. The average ambient temperature was about 29°C, and no seasonal variation in mean REE was noted. The instrument was calibrated for ambient barometric pressure bimonthly.

To control for the thermic effect of food on the REE measurement, the participants were asked to fast from 2000 the previous evening. Compliance with the request for fasting was assessed by monitoring the respiratory quotient (RQ) during the REE measurement; any participant with a RQ > 1.00, which suggests recent food consumption, was asked to return for a second visit (n = 5). No restrictions were made on the participants’ activities on the day preceding the REE measurement; however, no participant engaged in strenuous activity, either work-related activities or voluntary exercise, before arriving at the clinic on the day of the examination. On arrival, the participant was allowed to sit quietly for 30 min before the measurement; they then rested in a supine position for 15 min to become acclimated to the examination room and bed. A clear Lucite hood was placed over the participant’s head, and respiratory gas rates, REE, and RQ were recorded each minute for 30–45 min. In all participants, REE was stable (<10% variability) for 20 min. The first 10–15 min of recorded data were not used in the calculation of average REE. The instrument was calibrated daily with the use of an external gas of known composition. Alcohol burn tests conducted biweekly indicated that the instrument was accurate to within 2% at all times; replicate measurements (up to 1.5 y apart; n = 45) indicated the intra-individual CV of REE was 3.3%.

Statistical methods
Participant characteristics are presented as means ± SDs. Three methods were used to assess the relation between REE and weight change. The first method, the change model, computed weight change as the difference between the last measured weight and the baseline weight divided by the time between weight determinations (expressed in kg/y). This variable was then regressed on the centered (value minus the average) adjusted REE (CREE; adjusted for FFM, FM, age, and sex) to estimate the mean weight change per year for a participant of average REE (intercept) and the effect of adjusted REE on weight change (slope). The change model was also used to assess the relation between REE and weight change with only the prospective data, ie, the difference in weight between the REE examination and the final examination. The primary drawback to this change model was the inability to use all measurements of weight recorded over the entire follow-up period and the inability to account for within-person variability in weight change.

The second method used to assess the REE-weight change relation was descriptive to a certain extent. Weight change per year was estimated by regressing weight on time (in y) for each participant. The resulting person-level slope was subsequently regressed against CREE and adjusted for FFM, FM, age, and sex. All available weight data could thus be used with this two-stage model; however, within-person variability was again not taken into account.

The third method, which built on the previous two methods, was a multilevel analysis (2 levels), ie, a mixed-effects, random coefficient model (23–25). With this mixed-effects model, the weight determinations over time (ie, the level 2 measures) were nested within participants (ie, the level 1 measures). Briefly, W_ij represented the weight of the ith participant at T_j, where T_j is time measured from baseline to the jth weight determination, and CREE_i denoted the centered adjusted REE (adjusted REE minus the mean adjusted REE) for the ith participant in the study.

(1)

(2)

(3)

where , the random error associated with the j-th weight determination of the i-th person, has a constant variance ² [ N(0, ²)]. The baseline mean weight (ß₀) and rate of weight change over time (ß₁) not only depend on REE, but also vary randomly across persons with variance ₀² [₀ N(0, ₀²)] and ₁² [₁ N(0, ₁²)], respectively. The correlation (corr) between the slope and intercepts random coefficients is represented by ₀₁ [corr (₀, ₁) = ₀₁]. All error terms (, ₀, and ₁) are assumed to have a normal distribution (N) with mean zero.

The models above can be rewritten as:

(4)

Thus, the effect of REE on weight change per year is measured by the interaction coefficient ₁, the mean weight at baseline for a participant of average REE is given by ₀, the average effect of REE on weight is estimated by ₁, ß_0i corresponds to mean weight at baseline, and ß_1i corresponds to weight change over time for participant i. The advantages of this multilevel approach are that all available information on all participants could be used for analysis and the within-person variability in weight change was taken into account. The mixed-effects model was also used to assess the relation between REE and weight change by weight change status, ie, in participants who experienced a mean weight gain compared with those who experienced a mean weight loss.

A total of 208 families, with 3.5 members per family, were studied. Our previous study of the heritability of REE in this population estimated the sibling-pair correlation of REE to be about 0.15 (26). Thus, the models previously described included a random-effect term to account for potential intrafamily correlations. In addition, all analyses were repeated with data only from unrelated persons, eg, spouses or a single person from each family. Results did not differ significantly from those presented below, thus data are not presented. All statistics were performed with SAS version 8.1 (SAS Institute, Cary, NC).

	RESULTS

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
The ICSHIB Family Study enrolled 3103 participants; REE was measured on a subset of 1342 persons from 219 families. For the present analyses, we used data from all participants who were aged 20 y at baseline, underwent REE and concurrent body composition measurements, and had 1 follow-up clinic examination (ie, 2 weight measurements). In addition, we omitted 12 participants with a baseline BMI < 16.4, which is indicative of grade III chronic energy malnutrition (27), from the analyses. These criteria resulted in an analytic data sample of 744 participants from 208 families. Two hundred twenty-two persons in this sample completed 4 examinations, 206 completed 3 examinations, and 316 completed only 2 clinic examinations over a mean 5.5 y of follow-up (maximum follow-up: 7.3 y).

Baseline participant characteristics are presented in Table 1. As expected, the men weighed significantly more than did the women, with larger FFM levels and, consequently, higher REE values. In contrast, the women had a significantly greater mean BMI, FM, and percentage body fat. At baseline, 13.3% of the participants had BMIs between 16.4 and 18.5, which suggested grade I or II chronically low energy intakes (27). Analyses conducted with and without these participants yielded parameter estimates that were indistinguishable and we thus chose to retain these participants in the final sample.

Table 2

Table 3

The mixed-effects model approach built on the previous 2 statistical techniques and allowed for the inclusion of all weight measurements while accounting for within-person variability in weight change and potential correlations between weight change and baseline weight. The estimated mean weight change per year and the effect of REE on weight change were very similar to those obtained from the 2 simpler models. SE estimates of these coefficients, however, were larger than those obtained from the simpler models. In all 3 models, there was a difference in weight change per year of 0.11–0.22 kg for persons whose adjusted REEs differed from the cohort average by 500 kJ/d. From the mixed-effects model, it was also possible to determine the effect of REE on average weight in the cohort; there was a predicted 7.5 kg difference in mean weight at baseline for persons whose adjusted REEs differed by 500 kJ/d.

After stratifying the cohort by weight change status, ie, the participants who experienced a mean weight gain and those who experienced a mean weight loss or no change, the observed positive association between REE and weight change was present only in those with weight gain (Table 4). An association between REE and weight loss was not observed.

Figure 1

View larger version (19K): [in this window] [in a new window]   FIGURE 1.. Estimates of weight change per year (ie, last measured weight minus the baseline weight divided by time between measurements), plotted against adjusted resting energy expenditure (REE) values, centered at their mean value (CREE). The estimated regression line obtained from the mixed-effects model was weight change per year = 0.532 + 0.00044 x CREE (P < 0.001).

	DISCUSSION

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

As noted, previous data on this question obtained in a study conducted on Pima Indians have shown a negative association between REE and weight change in an overweight population, a population with a much larger average weight change than that observed in our population (1.4 kg/y compared with 0.5 kg/y, respectively) (2). The differences observed between the Pima Indian sample and the Nigerian sample are consistent with various potential explanations. It is unlikely that the differences in the observations are simply the result of sampling variation or type 1 error. Both samples were reasonably large, and the negative association was observed in two independent cohorts of Pima Indians. In our sample, a significant result was observed in both men and women. Because REE is strongly correlated with FFM (r 0.80), it is possible that the method of assessing body composition could lead to biased results. The precision of our body composition measurements was previously verified with the use of deuterium dilution, however, and we used several modeling approaches, which led to identical results. Also note that there appears to be nothing extraordinary about REE among Nigerians. We previously reported that we observed no differences in REE adjusted for body composition between population-based samples of Nigerian and African-American adults, nor did the mean Nigerian REE differ from estimates derived from predictive equations that were based on body composition (10).

Assuming both observations are unbiased, these contradictory results raise the possibility that different physiologic mechanisms underlie the opposite ends of the distribution of body composition and obesity risk. The proposed explanation for the previous finding of an association between negative REE and weight change assumed that the reduced REE had been protective against periods of food shortage over much of the course of human evolution (1, 2, 31, 32). Our data, however, suggest that this version of the "thrifty gene" construct is not universally applicable, because the Nigerian population we studied has never experienced long periods of calorie excess and, in fact, continues to experience substantial mortality from undernutrition (33, 34). The hypothesis that a reduced REE, although protective in times of food shortage, is a determinant of obesity in times of energy surfeit is attractive, yet has not proven true in populations other than the Arizona Pima Indians (1, 2, 11, 12, 35).

It is possible, however, that the increased relative REE observed in our Nigerian sample was a result, rather than a determinant, of weight change. The energetic equivalent of 1 kg body mass in adults was estimated to be 30 MJ (36); therefore, the energy cost of tissue accretion and maintenance may explain at least a portion of the 500 kJ/d excess in REE that was associated with an increase of 0.22 kg/y over the mean values in the present cohort. Whether REE was functioning as a determinant or outcome of weight gain in these lean Nigerian adults, the effect was small, with only 1% of the variation in weight change over 6 y explained by relative REE. More important than the effect of REE on weight change from a public health standpoint, however, are the research findings that energy intake and physical activity are associated with weight gain in adults (2, 30).

In our cohort of Nigerian adults, mean weight change was 0.48 kg/y and REE adjusted for body composition and age was positively associated with weight change over 5.5 y of follow-up. This finding is in contrast with observations in Pima Indians, suggesting that too little is understood about metabolic rate and weight to make assumptions on obesity risk based on relative REE. The mechanisms regulating both factors are likely to be highly complex and may vary between populations.

	ACKNOWLEDGMENTS

 
AL was involved in the conception, design, data collection, and data interpretation for this project. RD-A and GC analyzed the data and were involved in its interpretation. AA and BT were involved in data collection and interpretation. RC assisted in the interpretation of the data. All authors report no conflict of interest.

	REFERENCES

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