Body weight gain in free-living Pima Indians: effect of energy intake vs expenditure

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1 (2003) 27, & 2003 Nature Publishing Group All rights reserved /03 $25.00 PAPER Body weight gain in free-living Pima Indians: effect of energy intake vs expenditure PA Tataranni 1 *, IT Harper 1, S Snitker 2, A Del Parigi 1, B Vozarova 1, J Bunt 1, C Bogardus 1 and E Ravussin 3 1 Clinical Diabetes and Nutrition Section, NIDDK-NIH-DHHS, Phoenix, AZ, USA; 2 University of Maryland, Baltimore, MD, USA; and 3 Pennington Biomedical Research Center, Baton Rouge, LA, USA BACKGROUND: Obesity results from a chronic imbalance between energy intake and energy expenditure. However, experimental evidence of the relative contribution of interindividual differences in energy intake and expenditure (resting or due to physical activity) to weight gain is limited. OBJECTIVE: To assess prospectively the association between baseline measurements of daily energy metabolism and weight changes by studying free-living adult Pima Indians, one of the most obese populations in the world. DESIGN: A study of the pathogenesis of obesity in the Pima Indians living in Southwestern Arizona. The participants were 92 nondiabetic Pima Indians (64M/28F, y, 3579% body fat; mean7s.d.). At baseline, free-living daily energy metabolism was assessed by doubly labeled water and resting metabolic rate (RMR) by indirect calorimetry. Data on changes in body weight ( kg) over a follow-up period of 473 y were available in 74 (49M/25F) of the 92 subjects. RESULTS: The baseline calculated total energy intake (r ¼ 0.25, P ¼ 0.028) and RMR (r ¼ 0.28, P ¼ 0.016) were significantly associated with changes in body weight. The baseline energy expenditure due to physical activity was not associated with changes in body weight. CONCLUSION: Using state-of-the-art methods to assess energy intake and expenditure in free-living conditions, we show for the first time that the baseline calculated total energy intake is a determinant of changes in body weight in Pima Indians. These data also confirm that a low RMR is a risk factor for weight gain in this population. (2003) 27, doi: /sj.ijo Published online 16 September 2003 Keywords: risk factor; indirect calorimetry; doubly labelled water; energy expenditure; physical activity Introduction Obesity is a highly prevalent disease associated with increased morbidity and mortality, but its etiology remains largely undefined. 1 The Pima Indians living in Southwestern Arizona are one of the most obese populations in the world. 2 Despite the very high prevalence of obesity, further weight gain is readily observed among adult members of this Native American community when followed longitudinally. 3 We have previously shown that the risk of gaining weight is increased in adult Pimas with a relatively low resting metabolic rate (RMR). 4 However, because a low metabolic rate explained less than 15% of weight gain over a 2-y followup period, we believe that an innate tendency to overeat in the presence of unlimited and easily accessible food is likely *Correspondence: PA Tataranni, Clinical Diabetes and Nutrition Section, NIDDK-NIH-DHHS, 4212 N. 16th St., Rm. 541, Phoenix, AZ 85016, USA. antoniot@mail.nih.gov Received 22 January 2003; revised 29 May 2003; accepted 2 August 2003 to be a major cause of obesity in this and other populations. 5 Furthermore, studies of resting energy expenditure are conducted in experimental settings that artificially limit the diversity of human behaviors that influence energy expenditure, such as the variable amount of daily physical activities, another possible important etiologic risk factor of weight gain in the Pimas and other populations. The advent of doubly labeled water and mass spectrometry has made it possible to measure total energy expenditure (TEE), as well as energy expenditure due to physical activity in free-living individuals. 6,7 Short-term energy balance and TEE can be used to calculate the total energy intake. 8 Data from doubly labeled water studies show that on average, and after taking into account differences in body size and composition, obese individuals consume the same amount of calories and expend the same amount of energy when compared with lean individuals. 9,10 It has also been reported that obese individuals are less active than lean individuals. 11,12 However, cross-sectional relationships do not

2 establish causality. Prospective and longitudinal studies represent the only ways to identify risk factors that antecede the development of obesity. 13 The doubly labeled water technique offers the unique opportunity to measure both energy intake and expenditure in free-living individuals and prospectively test their role in the etiology of obesity. The aim of the present study was to extend our previous findings of metabolic risk factors of weight gain in resting conditions and assess the contribution of interindividual differences in daily energy intake and expenditure to body weight changes in free-living adult Pima Indians. We examined (1) the cross-sectional relationship between the physical characteristics of the subjects and daily energy metabolism and (2) the prospective relationship between baseline daily energy metabolism and body weight changes. Methods Experimental subjects A total of 92 subjects were recruited either from among the participants in ongoing studies on the metabolic risk factors of obesity at the Clinical Diabetes and Nutrition Section (CDNS) of the NIH in Phoenix, AZ (n ¼ 60) or from among the participants in an epidemiological study of the Gila River Indian Community at the NIH clinic of the Hu Hu Kam Hospital in Sacaton, AZ (n ¼ 32). All subjects were healthy, nondiabetic 14 and not taking any medications known to influence energy metabolism. A total of 60 volunteers were admitted to the CDNS in Phoenix, where they were fed a weight maintenance diet for 3 days and fasted overnight prior to measurements of energy metabolism. In all, 32 volunteers were admitted to the NIH outpatient clinic in Sacaton, where measurements of energy metabolism were performed on admission day after an overnight fast. In all subjects, body composition was assessed by underwater weighing, with simultaneous determination of residual lung volume 15 or total body dual energy X-ray absorptiometry. 16 Percent body fat, fat mass (FM) and fat-free mass (FFM) were calculated as previously described 17 and a conversion equation was used to make measurements comparable between the two methods. 18 Body weights and diabetes status at follow-up were available in 74 Pima Indians. The follow-up visit was selected as the visit where each individual had reached the maximum weight on record. Six of the 74 individuals were diagnosed with diabetes at this visit. The NIDDK IRB and the Gila River Indian Community Tribal Council approved these studies, and the subjects gave informed consent. Materials and methods Indirect calorimetry. The measurement of daily resting energy expenditure and substrate oxidation using a respiratory chamber has been described before. 19 In brief, volunteers admitted to the CDNS in Phoenix entered the chamber at 0745 and remained in it until 0700 the following morning. The rate of energy expenditure was measured continuously, calculated for every 15-min interval of the 23 h in the chamber, and then extrapolated to 24 h. The intercept (INT) of the linear regression between energy expenditure and activity measured by radar from 0800 to 2300 (15 h) was obtained. RMR was then calculated as (INT [0.1* 24-h EE]) to account for the thermic effect of food. 20 Calibration procedures, precision, accuracy, response time and variability of the measurements using a respiratory chamber have been previously published. 19 In 32 outpatients studied at the NIH Clinic in Sacaton, RMR was measured using a Deltatrac metabolic chart (SensorMedics Co., Yorba Linda, CA, USA). After an overnight fast, subjects rested comfortably on a bed for 10 min, after which RMR was measured over 30 min. Doubly labeled water ( 2 H 18 2 O). The measurement of daily energy expenditure and energy intake in free-living conditions using 2 H 18 2 O has been described before. 8,10,21,22 Briefly, after an overnight fast, subjects were given a water dose of 2.54 g/kg total body water. The dose consisted of a 1 : 20 (wt : wt) mixture of 99.9% deuterium oxide and 10% 18 O-water. Timed urine collections were obtained at approximately 2, 3 and 5 h after dosing. To allow for isotope equilibration, the first urine sample (0 2 h) was discarded. Subjects were then discharged and resumed their normal daily activities. On day 8 (7 days later), two more 4-h urine collections were completed. Deuterium ( 2 H) and 18 O enrichments were determined on D-S and MAT-251 isotope mass spectrometers (Finnigan Co., San Jose, CA, USA). As the composition of the diet was not known, TEE was calculated using CO 2 production derived from the isotopic decay rates and energy equivalent of CO 2 at a respiratory quotient of The energy expended for physical activity (EEACT) was calculated from the (TEE [RMR þ 0.1 * TEE]) to take into account the estimate of the thermic effect of food. The physical activity level (PAL) was calculated as the ratio of TEE and RMR. Calculated total energy intake (ctei). To calculate the total energy intake, body weight was measured on a mechanical beam and lever scale (Phoenix: 108-MAC, Acme Scale Co., San Leandro, CA, USA; Sacaton: Detecto, Cardinal Scale Co., Webb City, MO, USA) on the day of dosing and 7 days later. The caloric equivalent of changes in body energy stores (ENBAL) was calculated by assuming that two-thirds of this short-term body weight change (dwt) was metabolic and one-third was composed of water. It was further assumed that three-fourths of the metabolic weight change was FM and one-fourth was FFM. 23 For weight gain, the values of 13.2 kcal/g FM and 2.2 kcal/g FFM gained were used. For weight loss, 9 kcal/g FM and 1 kcal/g FFM lost were used Thus, the formulas used for calculation of the total energy intake were as follows: (1) ctei ¼ TEE þ EnBal (2) EnBal ¼ dwt (3) dwt ¼ 2/3metabolic þ 1/3water 1579

3 1580 (4) metabolic dwt ¼ 3/4FM þ 1/4FFM (5) if þ dwt-13.2 kcal/gfm, 2.2 kcal/gffm (6) if dwt-9 kcal/gfm, 1 kcal/gffm Statistical analysis. All analyses were performed using SAS software procedures (SAS Institute, Cary, NC, USA). Data are presented as mean7s.d., unless otherwise specified. Differences between males and females were tested using unpaired Student s t-tests. In 92 subjects with baseline data, age, sex and body composition (FFM and FM) were entered in stepwise regression models to assess their contribution to the interindividual variability of the metabolic variables (ie, RMR, TEE, ctei, EEACT and PAL). Only demographic/ anthropometrical variables entering the stepwise model with a Po0.05 were then used in multiple linear regression analyses to adjust the metabolic variables for their determinants. In 74 subjects with follow-up measurements, changes in body weight were calculated as follow-up minus baseline body weight and adjusted for baseline age and duration of follow-up (DWT). These were the only contributing variables in a multiple regression model also including sex and baseline weight and/or body composition. In the same individuals, short-term body weight changes were calculated as day 8 minus day 1 body weight (dwt) during the 7 days when TEE was measured. The baseline relationship between adjusted metabolic variables and body weight changes was examined by simple correlation analysis. Results Cross-sectional analysis The physical characteristics of the 92 subjects are summarized in Table 1. Table 2 shows the predictive equations for the Table 2 Predictive equations for the main metabolic variables derived from data in 92 nondiabetic Pima Indians Equation R 2 TEE (kcal/day) ¼ FFM 0.56 ctei (kcal/day) ¼ FFM 0.08 RMR (kcal/day) ¼ FFM+4FM 0.80 EEACT (kcal/day) ¼ male 5 age 0.08 PAL ¼ FM 0.08 TEE ¼ total energy expenditure; FFM ¼ fat-free mass; ctei ¼ calculated total energy intake; RMR ¼ resting metabolic rate; FM ¼ fat mass; EEACT ¼ activity energy expenditure; PAL ¼ physical activity level. All parameter estimates Po0.05. Table 3 Relationship between baseline metabolic variables and changes in body weight in 74 nondiabetic Pima Indians dwt (g/day) Body weight change (kg) dwt (g/day) F 0.31 (0.008) ENBAL (kcal/day) F 0.29 (0.011) TEE (kcal/day) 0.22 (0.056) 0.20 (0.089) ctei (kcal/day) F 0.25 (0.028) RMR (kcal/day) 0.17 (0.140) 0.28 (0.016) EEACT (kcal/day) 0.12 (0.292) 0.03 (0.772) PAL 0.10 (0.414) 0.03 (0.800) Pearson s correlation coefficients and level of significance (in parentheses). Body weight change was adjusted for baseline age and duration of follow-up. dwt ¼ short-term body weight change during the 7 days when doubly labeled water measurements were performed; ENBAL ¼ caloric equivalent of changes in body energy stores during the same period; TEE ¼ total energy expenditure; ctei ¼ calculated total energy intake; RMR ¼ resting metabolic rate; EEACT¼ activity energy expenditure; PAL¼ physical activity level. TEE, TEI, RMR, EEACT and PAL were adjusted for their anthropometric determinants prior to analysis. Table 1 Indians Physical and metabolic characteristics of 92 nondiabetic Pima All Males Females N Age (y) 35 [19 70] Height (cm) 167 [ ] ** Weight (kg) 90 [49 134] FFM (kg) 57 [43 83] ** FM (kg) 33 [5 60] ** Body fat (%) 35 [9 52] ** dwt (g/day) 99 [ ] ENBAL (kcal/day) 705[ ] TEE (kcal/day) 2894 [ ] ** ctei (kcal/day) 3608 [ ] RMR (kcal/day) 1845 [ ] * EEACT (kcal/day) 760 [ ] PAL 1.58 [ ] Mean7s.d. [range in parentheses]. Significantly different from males: *Po0.05, **Po0.01. dwt ¼ short-term body weight change during the 7 days when doubly labeled water measurements were performed; FFM ¼ fat-free mass; FM ¼ fat mass; ENBAL ¼ caloric equivalent of changes in body energy stores during the same period; TEE ¼ total energy expenditure; ctei ¼ calculated total energy intake; RMR ¼ resting metabolic rate; EEACT ¼ activity energy expenditure; PAL ¼ physical activity level. TEE and RMR were not different between men and women after adjustment for body composition. main metabolic variables. To exclude a possible effect of the methodology used for the assessment of RMR, a variable identifying measurement by Deltatrac vs respiratory chamber was added to the model that already included FFM and FM. The resulting parameter estimate was not significant ( þ 20 kcal/day for the respiratory chamber, P ¼ 0.49). Relationship between metabolic variables and changes in body weight Changes in body weight (Z4 months) were available in 74 individuals (49M/25F, y, 3679% body fat), whose general characteristics were not different from the group in the cross-sectional analysis. The average body weight change was kg (range 9 to 26 kg). The average follow-up time was y (range y). Table 3 shows the relationships between baseline metabolic variables and body weight changes, adjusted for baseline age and duration of follow-up. ctei was positively correlated (Figure 1), while RMR was negatively correlated with changes in body weight. Energy expenditure due to physical activity and PAL was not associated with changes in body weight.

4 Body weight change (kg) r = 0.25; p = Baseline Calculated Total Energy Intake (kcal/d) Figure 1 Relationship between baseline ctei (adjusted for FFM) and body weight change (adjusted for baseline age and duration of follow-up) in 74 adult nondiabetic Pima Indians. Discussion In this study, we measured daily energy metabolism in free-living adult Pima Indians to assess the contribution of energy intake and energy expenditure to the positive energy balance that leads to obesity. We found an association between baseline ctei, RMR, but not energy expenditure due to physical activity (EEACT) or PAL and changes in body weight. Using state-of-the-art methods, these data indicate for the first time that a high-energy intake is a risk factor for obesity in humans. They also confirm that a low RMR is a risk factor for weight gain in the Pima population. The primacy of energy intake over energy expenditure in the pathogenesis of weight gain has been debated since obesity research began. This issue, however, has been largely unsolvable because energy intake cannot be measured accurately in unconfined humans. 27 Our measure of ctei (ie, TEE þ ENBAL) in adult Pimas was several hundred calories higher than previously estimated in this population from self-administered questionnaires 28,29 and exceeded TEE by more than 600 kcal/day. More importantly, we show that a positive short-term energy balance and the resulting estimates of ctei were associated with weight gain in this population. These findings are generally consistent with the hypothesis 5 and some evidence 30 that excessive energy intake is a major cause of obesity in humans. Similar to previously described predictors of body weight gain, 13 however, ctei explained only 6% of the variability of changes in body weight. Thus it seems unlikely, given the current methodologies available for measuring energy metabolism in humans, that it will be possible to resolve experimentally the question of whether energy intake plays a greater role than energy expenditure in the pathogenesis of obesity. A relatively low RMR was shown to predict weight gain in Pimas, 4 but not in other populations. 31,32 Here, we confirm our previous finding that RMR and long-term body weight changes are negatively related. The role of physical activity as a risk factor for obesity remains controversial A high level of spontaneous physical activity seems to protect against weight gain, 38 but longitudinal studies have provided conflicting evidence on the relationship between EEACT, PAL and the development of obesity. 39,40 In this small group of adult Pima Indians, very few of whom met the definitions of active (ie, PALZ ) or very active (ie, PALZ ) people, we found little evidence that a low EEACT or PAL antecedes the development of obesity. Since PAL was lower in obese individuals, as previously reported by others 11 and us, 12 we conclude that this is a secondary event. Recognizing that the week-long measurement by doubly labeled water may be insufficient to capture the true level of habitual physical activity in people, we submit that longitudinal studies with repeated measurements of both metabolic variables and body weight will be a better test of these hypotheses. We would like to point out a number of limitations in our study, some of which are inherent to the methodologies currently available to study energy metabolism in humans. Caution should especially be exercised in the interpretation of our results on ctei. The relatively large short-term weight gain of approximately 100 g/day, which is several fold higher than expected based on the annual rate of weight gain in the same population, makes the kcal/day in excess of daily energy expenditure a likely overestimate of the true energy balance. The positive energy balance necessary to deposit 6 kg of body weight over 4 y can be calculated to be a sustained kcal/day excess Proviso these calculations are of limited value, since they are nondynamic and do not take into account the fact that the measured positive energy balance is likely to be attenuated over time by the over-compensatory increase in energy expenditure caused by the weight gain. 43,44 We have previously been unable to show a relationship between ctei and self-reported energy intake. 8 ctei, as estimated in this study, depended on accurate measurements of body weight and rested on many metabolic assumptions For example, the calculations of the caloric equivalent of changes in body energy stores during the week-long measurement must be interpreted with caution, since we do not have any experimental evidence that all subjects gained or lost the same amount of water and metabolically active tissue. We believe that rigorous validation studies are needed to establish how closely ctei approximates the true daily energy intake in humans. Furthermore, only replication of these findings in other populations can establish if the results can be generalized beyond the subjects included in the present study. In conclusion, this study provides the first evidence that baseline ctei is a determinant of long-term changes in body weight in free-living Pima Indians. Our data also confirm that a low RMR is a risk factor for weight gain in this population. 1581

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