Obesity and Diabetes Clinical Research Section (P.P., J.K ... · 15/08/2013 · 1 Lower “Awake...

Lower “Awake and Fed Thermogenesis” Predicts Future Weight Gain in Subjects with 1

Abdominal Adiposity 2

3

Paolo Piaggi, Jonathan Krakoff, Clifton Bogardus, Marie S. Thearle 4

Obesity and Diabetes Clinical Research Section (P.P., J.K., C.B., M.S.T.), National Institute of 5

Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona, USA, 6

85016; and Obesity Research Center, Endocrinology Unit, (P.P.), University Hospital of Pisa, Pisa, 7

Italy, 56124 8

9

Last Names: Piaggi, Krakoff, Bogardus, Thearle. 10

Abbreviated title: AFT predicts weight change in obese subjects. 11

Keywords: Energy Expenditure, Respiratory Chamber, Thermic Effect of Food, Weight Change 12

Prediction, Visceral Obesity. 13

Word count: 3986 14

Number of figures and tables: 6 15

References: 42 16

17

Corresponding author: Paolo Piaggi, PhD, Phoenix Epidemiology and Clinical Research Branch, 18

National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 19

4212 North 16th

Street, Phoenix, AZ 85016 Phoenix, Arizona 85016. 20

E-mail: [email protected]; [email protected] 21

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Diabetes Publish Ahead of Print, published online August 23, 2013

Abstract 22

Awake and fed thermogenesis (AFT) is the energy expenditure (EE) of the non-active fed condition 23

above the minimum metabolic requirement during sleep, and is composed of the thermic effect of 24

food and the cost of being awake. AFT was estimated from whole room 24-h EE measures in 509 25

healthy subjects (368 Native Americans and 141 Whites) while subjects consumed a eucaloric diet. 26

Follow-up data was available for 290 Native Americans (median follow-up time: 6.6 years). 27

AFT accounted for approximately 10% of 24-h EE, and explained a significant portion of 28

deviations from expected energy requirements. Energy intake was the major determinant of AFT. 29

AFT, normalized as a percentage of intake, was inversely related to age, fasting glucose 30

concentration, and showed a nonlinear relationship with waist circumference and BMI. Spline 31

analysis demonstrated that AFT becomes inversely related to BMI at an inflection point of 29 32

kg/m2. The residual variance of AFT after accounting for covariates predicted future weight change 33

only in subjects with a BMI>29 kg/m2. 34

AFT may influence daily energy balance, is reduced in obese individuals, and predicts future weight 35

gain in these subjects. Once central adiposity develops, a blunting of AFT may occur that then 36

contributes to further weight gain. 37

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INTRODUCTION 38

Daily energy expenditure (24-h EE) can be considered to consist of the minimum energy required to 39

sustain life, usually represented by sleeping metabolic rate (SLEEP); the energy cost of being 40

awake; the thermic effect of food (TEF); and the contribution from physical activity (1). Several 41

studies have been conducted to assess the role of 24-h EE in the etiology of obesity. In a Native 42

American population with a high prevalence of obesity, a relatively low rate of EE is a risk factor 43

for future weight and fat mass gains (2). TEF (also known as diet-induced thermogenesis or specific 44

dynamic action) is defined as the increase in EE in response to food intake (3). The relationship of 45

TEF with weight change is not clear. In cross sectional analyses, authors report that TEF is lower in 46

obese subjects (4-7) while others report no difference (8-11). The directionality of the cause-and-47

effect relationship that might exist between TEF and obesity has not been fully established. Brudin 48

et al. (12) demonstrated that the postprandial rise in EE was diminished to the level of obese 49

subjects in lean subjects with artificial thermal insulation of the abdomen. These results indicate 50

that thermic insulation provided by abdominal adiposity may limit the body’s capacity to generate 51

EE in response to food intake. Differing relationships between TEF and body fat distribution in 52

obese individuals might explain the divergent results obtained in previous studies. Some studies 53

have found that TEF increases in obese subjects after weight loss, but results are not consistent (4; 54

13-15). In one study, TEF as calculated in a respiratory chamber over 24 hours, but which also 55

included the energy cost of being awake, was not a predictor of future weight gain (16). 56

In studies utilizing 24 hours of indirect calorimetry to estimate TEF, TEF is often calculated as the 57

increase in 24-h EE over SLEEP (16-18) rather than using the awake basal energy expenditure 58

during fasting. Therefore, this estimate of TEF also includes the energy cost of being awake. The 59

cost of being awake (CoA) is defined as the difference between basal and sleeping metabolic rate, 60

representing the energy cost of waking conscious and unconscious activities (19). Using a solitary 61

24-h EE measure, it is difficult to separate CoA from TEF, in part because they are overlapping 62

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biologic phenomena with both including such bodily functions as gut motility and increased 63

utilization of macronutrients (3). Accordingly, in this study we have defined awake fed 64

thermogenesis (AFT) as the difference in a subject’s EE during the fasting, sleeping state and the 65

fed, sedentary, awake state, i.e., the energy cost of being awake and fed exclusive of physical 66

activity. 67

The aims of this study were to assess the determinants of AFT; to test whether the unexplained 68

variability of AFT after accounting for its determinants is related to long-term weight change; and 69

to assess whether any such relationship is affected by adiposity measures. 70

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RESEARCH DESIGN AND METHODS 71

A total of 509 subjects (368 Native Americans and 141 Whites; 62% men) between the ages of 18 72

and 55 years were admitted to our clinical research unit in Phoenix, AZ, between 1985 and 2005 for 73

a longitudinal study of the pathogenesis of obesity. All subjects were determined to be healthy by 74

physical examination, medical history, and laboratory tests. Exclusion criteria included a diagnosis 75

of type 2 diabetes mellitus by a 75g OGTT (20), other medical conditions, or use of medications 76

known to affect energy metabolism. 77

Because all subjects had data available for body composition measures, obesity was defined both 78

according to the NIH guidelines (21) as well as according to the World Health Organization criteria 79

(22) . Only results using BMI are reported since the method of classifying adiposity did not alter the 80

results, and BMI is gender independent and used more frequently in clinical settings. 81

Before participation, volunteers were fully informed of the nature and purpose of the study, and 82

written informed consent was obtained. The experimental protocol was approved by the 83

Institutional Review Board of the National Institute of Diabetes and Digestive and Kidney Diseases 84

(NIDDK). 85

Of the 509 subjects, 290 Native Americans had a follow-up visit at least one year after the 24-h EE 86

measurement, with complete data for both body weight and glucose tolerance (23). In a subgroup of 87

193 subjects, body composition data was also available. Only subjects less than 55 years of age who 88

remained free from diabetes, and women who were not pregnant at follow-up were included in the 89

longitudinal analysis. 90

Study protocol and respiratory chamber 91

Upon admission, subjects were given a weight maintaining diet (50% carbohydrate, 30% fat and 92

20% protein) for three days before any tests were performed. The weight maintaining energy needs 93

were calculated as previously described based on gender, weight and BMI (24). 94

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Subjects’ EE was measured by indirect calorimetry while they resided for 24 hours within a 95

respiratory chamber and its components derived as previously reported (1). The total energy content 96

of the four meals given (intake) was calculated using previously described equations (25). The rate 97

of EE was measured continuously for 23¼ hours, averaged for each 15-minute interval, and 98

extrapolated to 24 hours. All unconsumed food was returned to the metabolic kitchen for weighing 99

for accurate calculation of intake. Energy balance (enbal) was calculated as the difference between 100

intake and 24-h EE. Only subjects with enbal within 20% of 24-h EE during the stay in the 101

respiratory chamber were included in the analysis. The EE in the inactive state (EE0 activity) was 102

calculated as the intercept of the regression line between EE and spontaneous physical activity 103

(SPA) between 11:00 and 01:00 (6). Subjects’ SPA was measured by radar sensors and expressed as 104

the percentage of time when activity was detected (26). Only chambers with an average radar 105

activity <15.0% were included in the analysis. SLEEP was defined as the average EE of all 15-min 106

nightly periods between 01:00 and 05:00 during which SPA was less than 1.5% (<0.9 sec/min). The 107

starting point to calculate SLEEP was chosen to minimize the influence of TEF on SLEEP. AFT 108

was defined as the increase in a subject’s EE from the sleeping condition to the awake, non-active, 109

and fed condition. AFT was calculated as the difference between EE0 activity and SLEEP (Figure 1). 110

The reproducibility of the AFT estimate was evaluated in 16 subjects who had two chamber 111

sessions within three months of one another. 112

Body composition, body fat distribution and analytic measurements 113

Body composition was estimated by underwater weighing until August 1993 and thereafter by total 114

body dual energy X-ray absorptiometry (DPX-1; Lunar Radiation Corp., Madison, WI, USA). 115

Percent body fat measurements from the DXA scan were made comparable to underwater values 116

using a conversion equation (27). 117

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Waist circumference (WC) and thigh circumference were measured at the umbilicus and the gluteal 118

fold in the supine and standing position, respectively. Plasma glucose concentrations were measured 119

with the glucose oxidase method (Beckman Instruments, Fullerton, CA). 120

Statistical analysis 121

Statistical tests used to compare groups of subjects included Student’s t test for difference in mean 122

values, Mann-Whitney U test for skewed variables, Chi-square test for difference in counts and 123

frequency. The Kolmogorov-Smirnov test was used to assess normality of data; the safe logarithmic 124

transformation, i.e., sign(Rate of weight change)·LOG10[1+abs(Rate of weight change)], was 125

applied to the rate of weight change due to its skewed distribution, and in order to handle negative 126

values. Pearson’s (R) and Spearman’s (ρ) correlation coefficients were employed for Gaussian and 127

skewed variables, respectively. 128

Locally weighted regression (28) (nonparametric approach) and nonlinear regression (parametric 129

approach) were employed to detect nonlinear relationships between AFT and the anthropometric 130

measures. The former method was used to visually detect potential nonlinear relationships on the 131

scatterplot with no underlying assumptions about the data distribution, and without specifying a 132

regression function. Subsequently, nonlinear regression was used to determine if a quadratic 133

function was a statistically better fit for the data compared to the linear equation. If a nonlinear 134

relationship was found, a spline regression analysis was then performed to objectively identify the 135

inflection point (knot) that achieved the best fit of the data using a piecewise linear model. The 136

equation for the one-knot spline model was: 137

AFT = intercept + A*X + B*(X−knot)*(X ≥ knot) + ε 138

where AFT and X are the dependent and independent variables, respectively; intercept, A, B, and 139

knot are the model parameters; (X ≥ knot) is a dummy variable with value 0 for X<knot and 1 for 140

X≥knot; ε is the error term. 141

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The reproducibility of the AFT measurement in duplicate chambers was quantified by the 142

coefficient of variation (CV) and the intraclass correlation coefficient (ICC) of the paired 143

measurements. Multivariate regression analysis was used to identify the independent predictors of 144

AFT among demographic, glycemic and body composition measures. The residuals were then 145

calculated and considered as the unexplained variability of AFT. This residual variance of AFT was 146

tested as a potential predictor of body weight change in longitudinal analyses. A P-value less than 147

0.05 was considered significant. No correction was made for multiple tests, as all analyses were pre-148

planned, exploratory, and of independent interest (29). Data are presented as mean±standard 149

deviation (SD) or median with interquartile range (IQR). Analyses were performed using SPSS 150

(version 21, IBM Corp, Armonk, NY, USA). 151

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RESULTS 152

The baseline characteristics of the study cohort are shown in Table 1. 153

Validity of AFT 154

AFT varied widely among subjects, ranging from 9 to 656 kcal/14·h and accounting, on average, for 155

10.0±3.7% of 24-h EE (range: 0.4 to 25.4%). In 16 subjects with repeated measures, the AFT 156

measure had a CV of 24% and an ICC of 0.69 despite differences in body weight (CV =2.1%) and 157

energy intake (CV=8.3%). 158

Determinants of AFT 159

AFT was positively related to intake (ρ=0.32), fat free mass (FFM, ρ=0.31), and inversely 160

associated with age (ρ=−0.15, P=0.001) and fasting plasma glucose concentration (FPG, ρ=−0.09, 161

P=0.04). In a multivariate model including age, gender, ethnicity, percent body fat (%BF), FFM 162

and FPG, a total of 15% of the variance in AFT was explained (Table 2). Similar results were 163

obtained if FM or WC was substituted for %BF in the multivariate model or by including energy 164

intake among the predictors, but the best-fit model was with %BF as assessed by R2 and 165

multicollinearity. 166

When normalized as a percentage of intake, AFT was on average 10.4±4.0% and inversely related 167

to age (ρ=−0.19, Figure 2A) and FPG (ρ=−0.17, P<0.001, Figure 2B). The relationship between 168

AFT and BMI was found to be nonlinear (Figure 2C). The quadratic nonlinear model showed a 169

better fit of data as compared to the linear model (mean±SEM, linear term: 0.171±0.122, P=0.16; 170

quadratic term: −0.003±0.001, P=0.04), and a knot value equal to 29 kg/m2 achieved the highest R

2 171

by spline regression analysis (Figure 3A). AFT was inversely related to BMI for values greater than 172

29 kg/m2 (ρ=−0.19, P<0.001, N=334), but not for BMI≤29 (ρ=0.10, P=0.20, N=175, interaction 173

term P=0.01).The relationship between AFT and BMI was similar if subjects were categorized as 174

obese (BMI≥30 kg/m2) and non-obese (interaction term P=0.007). Sensitivity analyses using the 175

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residuals of AFT after accounting for intake in a regression model or expressing AFT as percent of 176

24-h EE led to similar relationships. 177

There was also a nonlinear relationship between AFT and WC (Figure 2D) with a significant 178

quadratic term by nonlinear regression (linear term: 0.182±0.085, P=0.03; quadratic term: 179

−0.002±0.001, P=0.02). The inflection point found by spline analysis was equal to 103 cm in men 180

(Figure 3B) and 95 cm in women (Figure 3C). 181

AFT and 24-h energy balance 182

Energy balance ranged between −572 to 480 kcal/day and, on average, was lower than zero 183

(−96±186 kcal/day, P<0.001). AFT was inversely related to enbal both as an absolute value 184

(ρ=−0.29) and as a percent of intake (ρ=−0.28, P<0.001). After adjustment for age, gender, 185

ethnicity, FM and FFM in a multivariate model, 50% of the variance in enbal was explained by 186

daily mean radar activity (partial R2=18%, P<0.001), SLEEP (partial R

2=32%) and AFT (partial 187

R2=23%), such that a 50-kcal increase in AFT independently corresponded to an average 42-kcal 188

decrease (95% CI: 35 to 49 kcal) in enbal. There was no difference in the relationship between AFT 189

and enbal between subjects with a BMI less than or greater than 29 kg/m2 (P=0.49). 190

AFT and long-term weight change 191

In a longitudinal analysis of 290 Native Americans (median follow-up time: 6.6 years, IQR: 3.9-192

10.7 years), body weight change was on average 8.2±13.0 kg (range: −27.9 to 71.4 kg, P<0.001), or 193

9.4±14.5% (range: −33.7 to 68.9%) of initial body weight, with a mean rate of weight change of 194

1.3±2.3 kg per year (1.4±2.5% of initial weight). The rate of weight change was similar between 195

sexes (P=0.86). The baseline characteristics of this longitudinal cohort were not different from the 196

Native American subjects included in the cross-sectional analyses (Table 1). 197

Because we found that the BMI inflection point where the relationship between BMI and AFT 198

changed was 29 kg/m2, we assessed whether any association between the unexplained variance in 199

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AFT at baseline and rate of weight change differed for subjects with a baseline BMI above or below 200

29 kg/m2 (Figure 4A). AFT was inversely related to rate of percent weight change in subjects with a 201

BMI>29 kg/m2

(ρ=−0.20, P=0.005, N=204), but not in subjects with a baseline BMI≤29 kg/m2 202

(ρ=0.12, P=0.26, N=86) (interaction term P=0.02). For a subject with a BMI>29 kg/m2, a 100 kcal 203

decrease from the predicted AFT value corresponded to an average 0.3% increase in body weight 204

per year (0.4 kg/yr). Similar results were obtained in sensitivity analyses done to test the robustness 205

of the statistical model. These included: 1) using the clinical BMI cut-off for defining obesity 206

(BMI≥30 kg/m2); 2) replacing %BF with FM or WC in the baseline model for AFT residuals; 3) a 207

subset analysis utilizing only subjects with normal glucose regulation and, 4) using the WC 208

inflection point (instead of BMI) to categorize subjects (Figure 4B). AFT was inversely related to 209

rate of percent weight change in subjects with a WC above the gender-dependent threshold 210

(ρ=−0.22, P=0.003, N=189), but not in subjects below the threshold (ρ=0.10, P=0.32, N=101) 211

(interaction term P=0.02). 212

In the 193 Native Americans with follow-up data for body composition measures, the relationship 213

between AFT and the rate of FM change was again different between subjects with a baseline BMI 214

above or below 29 kg/m2 (interaction term P=0.04). AFT was not associated with the rate of FFM 215

change (ρ=0.07, P=0.30) in either group. 216

217

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DISCUSSION 218

We investigated the concept of “awake and fed thermogenesis” as a component of 24h EE and its 219

role in body weight regulation in a cohort of 509 healthy subjects. AFT represents approximately 220

10% of 24-h EE, and is inversely related to age and glucose tolerance. AFT explains as much 221

deviation from expected 24-h enbal as SLEEP. AFT was not clinically important in subjects with a 222

BMI≤29 kg/m2. However, in subjects with a BMI>29 kg/m

2, AFT was inversely related to body 223

adiposity, and further, lower-than-expected values of AFT were predictive of long-term weight gain 224

in these subjects in longitudinal analyses. 225

AFT represents the non-activity related increase in EE beyond the minimum requirements observed 226

during sleep. Theoretically, it comprises both the energy cost of being awake and the energy 227

expended for eating, digesting, and storing macronutrients, two obligatory conditions required for 228

survival. Both CoA and TEF contribute to human body thermogenesis, the former being the energy 229

necessary to perform normal awake functions while the latter is the energy expended in response to 230

food intake. On average, TEF is a function of consumed macronutrients while CoA is more likely to 231

be composed of internal, individual-specific factors such as genotype and efficiency of cellular 232

processes. These two components of energy expenditure are heavily intertwined as obtaining, 233

consuming and digesting of food compose a large portion of the waking hours. Feeding happens 234

uniquely during the awake state and involves not only a mechanical digestive phase but also the 235

sensory response to anticipation of food intake represented, in part, by the cephalic phase (30), 236

therefore, it is not only difficult but possibly overly simplistic to distinguish between these two 237

contributions during normal living. 238

The advantage of using a whole room indirect calorimeter to assess the components of EE is that 239

EE is measured continuously over 24 hours. This overcomes the problem with the shorter duration 240

of ventilated hood experiments in which the rise of EE after food intake may not be fully captured. 241

The TEF can last up to six hours and the response to more than one meal may overlap (8; 31; 32). 242

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Hence, to fully understand the impact of TEF in everyday life, it is appropriate to measure EE 243

during consumption of multiple meals until EE returns to baseline after a night of fasting. In 244

addition, because CoA and TEF are so intricately intertwined, a 24-h EE measure allows for 245

measurement of AFT, which may be of greater biological relevance. A previous study estimated an 246

EE variable using a similar calculation as the one we used, calling it TEF (16). However, CoA was 247

also included within this calculation such that the EE variable was actually AFT (16), but the CV 248

was higher (48%) compared with ours (24%), which may be partly explained by differences in food 249

intake and weight in duplicate measurements. In that study (16), AFT was not associated with 250

weight change in longitudinal analysis; however, it was not reported whether there were differences 251

between obese and non-obese subjects. The effect of AFT solely in subjects with higher BMIs 252

might explain the difference between our findings compared to other studies. We detected a 253

nonlinear relationship between AFT and BMI by spline analysis with an inflection point at 29 254

kg/m2, with similar results if the clinical threshold for obesity (BMI=30 kg/m

2) is used instead. 255

Energy intake was found to be the major determinant of AFT. This is likely due to the known linear 256

relationship between TEF and caloric intake (10; 32). When expressed as a percentage of intake, 257

AFT was also normalized to body size since the diet given in the chamber was calculated according 258

to body size (25). Several studies have demonstrated an inverse relationship between TEF and age 259

(33; 34). CoA has also previously been reported to be inversely related to age (19). Lower values of 260

AFT with increasing age may be related to a reduced sympathetic nervous system (SNS) response. 261

The mitochondrial dysfunction that occurs with aging may also explain some of the age associated 262

diminished thermogenic response (35; 36). The inverse relationship between AFT and FPG is likely 263

due to the decreased thermogenesis in response to food intake previously observed with insulin 264

resistance (9). However, the differences in the relationship between AFT and weight change 265

observed between subjects with a BMI above or below 29 kg/m2 in our larger group were verified 266

in the subset of NGR subjects, indicating that this finding is independent of the effects of insulin 267

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resistance. The finding that both age and FPG were independent determinants of AFT is consistent 268

with a previous study (33). 269

A thermogenic defect has been proposed as a possible cause of future weight gain (37). In our 270

study, AFT explained additional variation in the deviation from expected 24-h enbal even after 271

accounting for SLEEP and SPA, indicating a role of AFT in daily energy balance and, possibly, 272

weight regulation. Our longitudinal results show that a negative deviation from the predicted AFT 273

was associated with an increase in body weight per year, but only in subjects with a BMI>29 kg/m2. 274

The relationship between AFT and future body weight only in subjects with a greater amount of 275

adiposity may explain, in part, the reported relationship between relative deviations from expected 276

24-h EE and future weight change (2). In addition, ethnic differences in body habitus may account 277

for the conflicting results between studies evaluating the relationship between 24-h EE and risk of 278

weight gain. For example, a positive relationship between EE variance and future weight gain was 279

observed in lean Nigerian adults (38), whereas we recently confirmed a negative association in an 280

overweight Native American population (2). 281

The unexplained variance of AFT predicted weight change but only in individuals with WC greater 282

than the data-derived inflection point. Although the underlying mechanisms of AFT’s role in weight 283

regulation are not clearly established, these findings support the hypothesis that individuals with 284

central adiposity have a reduced ability to transfer the heat generated after food consumption across 285

the abdominal wall compared to lean individuals. In a study assessing heat exchange with a glucose 286

load, overall heat loss throughout the study was lower in obese females compared to lean controls 287

and the obese subjects (5) had a lower TEF after the glucose load. It has also been demonstrated 288

(12) that artificial body insulation can reduce heat transfer across the abdominal wall after food 289

consumption in lean subjects. These findings supports the idea that lean individuals generate a 290

higher TEF in order to maintain core body temperature in contrast to obese individuals who have 291

limited postprandial heat loss due to the insulating properties of central adiposity. Body heat loss in 292

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obese subjects may be reduced in part due to the insulating properties of adipose tissue such that 293

there is a attenuated capacity for heat loss into the environment as well as a decreased need to 294

generate heat with cooler temperatures (39). Support for this theory can be found as early as 1902 in 295

a study demonstrating that the minimum metabolism of a dog was reached at a lower temperature 296

when the dog was obese versus when it was emaciated (40). 297

Our study population has a high prevalence of Pima Indians, whose body distribution differs from 298

Caucasians (41). Although the prevalence of obesity and type 2 diabetes is higher in Native 299

American populations, in general, findings from this study population have been replicated in other 300

study populations (42). Although AFT is an estimate derived from 24-h EE measures, it 301

demonstrated reasonable reproducibility in replicate measures. In addition, the analysis of a large 302

cohort of subjects with long-term longitudinal data allowed us to identify its determinants and to 303

test whether AFT plays a role in future body weight regulation. Although the explained variance of 304

AFT was relatively low, all the associations are concordant with prior literature on TEF and CoA, 305

and results were consistent in both the cross-sectional and longitudinal analyses. Our longitudinal 306

results indicate that although AFT may not be an important contributor to weight maintenance in 307

lean individuals, once abdominal adiposity develops, a relative reduction in AFT increases the risk 308

of further weight gain and may be a potential impediment to weight loss attempts. While it is 309

unknown if the lower AFT caused obesity to develop or if the excess adiposity led to blunting of the 310

AFT in the cross-sectional analysis, in the longitudinal analysis we demonstrate that a lower 311

baseline AFT (regardless of whether it was due to excess adiposity or other factors) predicts future 312

weight gain. We, therefore, hypothesize that once AFT is blunted in subjects with a BMI>29 kg/m2, 313

there is an even greater predisposition to further weight gain and worsening of obesity. 314

In conclusion, this study describes the combined role of CoA and TEF, which we have termed 315

“awake fed thermogenesis”, in body weight regulation. AFT is related to glucose tolerance and age, 316

and it explains variance in deviations from daily energy balance. AFT is negatively correlated with 317

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body adiposity in individuals with a BMI greater than 29 kg/m2 and a WC of 103 cm or 95 cm in 318

men and women, respectively. A relative reduction in this thermogenic component of 24-h EE 319

favors further weight gain in individuals with abdominal adiposity indicating that, once obesity 320

develops, a decreased ability to transfer heat to the external environment may predispose to further 321

weight gain. Although weight gain does not occur without an excess of food intake, a decreased rate 322

of thermogenesis may play a significant role in energy requirements and thus the maintenance of, 323

and risk for, further adiposity. 324

325

ACKNOWLEDGMENTS 326

The authors would like to thank the nursing, clinical and dietary staffs and laboratory technicians of 327

the clinical research center for their valuable assistance and care of the volunteers. This work was 328

supported by the Intramural Research Program of the National Institutes of Health (NIH), National 329

Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). No potential conflicts of interest 330

relevant to this article were reported. 331

P.P. is the guarantor of this work and, as such, had full access to all the data in the study and takes 332

responsibility for the integrity of the data and the accuracy of the data analysis. P.P. wrote the 333

manuscript and analyzed data. J.K, C.B, M.S.T. contributed to discussion and reviewed/edited the 334

manuscript. M.S.T. and all other authors critically revised the draft and approved the final report. 335

336

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438

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TABLES 439

Table 1. Demographic, anthropometric and metabolic characteristics of the study population. 440

Entire population at baseline

(N = 509)#

Subjects with

follow-up data

for body weight

(N = 290)##

Subjects with

follow-up data for

body composition

(N = 193)###

All subjects

(N = 509)

Native

Americans

(N=368)

Whites

(N=141)

Non-obese

(N=207)

Obese

(N=302)

Baseline

measurements

Baseline

measurements

Age (years) 29.3 ± 8.0 28.0 ± 7.6* 32.6 ± 8.2 29.3 ± 8.6 29.2 ± 7.7 27.9 ± 7.5 27.0 ± 6.7

Gender

• Men

• Women

315 (61.9%)

194 (38.1%)

217 (59.0%)

151 (41.0%)

98 (69.5%)

43 (30.5%)

144 (69.6%)

63 (30.4%)

171 (56.6%)

131 (43.4%)

173 (59.7%)

117 (40.3%)

123 (63.7%)

70 (36.3%)

Ethnicity

• Native Americans

• Whites

368 (72.3%)

141 (27.7%)

368 (100%)

0 (0%)

0 (0%)

141 (100%)

126 (60.9%)

81 (39.1%)

242 (80.1%)

60 (19.9%)

290 (100%)

0 (0%)

193 (100%)

0 (0%)

Body weight (kg) 94.8 ± 24.8 96.2 ± 24.2 91.2 ± 25.9 73.8 ± 11.2† 109.2 ± 21.0 94.6 ± 23.7 93.9 ± 23.9

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BMI (kg/m2) 33.2 ± 8.5 34.3 ± 8.2* 30.6 ± 8.6 25.6 ± 3.1† 38.5 ± 6.9 33.7 ± 7.7 33.4 ± 7.7

Waist circumference (cm) 107.3 ± 19.1 109.2 ± 17.7* 101.7 ± 21.6 89.6 ± 10.2† 118.6 ± 14.2 108.1 ± 17.2 107.3 ± 17.5

Thigh circumference (cm) 66.0 ± 9.0 66.1 ± 8.4 65.5 ± 10.6 58.2 ± 5.3† 70.9 ± 7.2 65.8 ± 8.2 65.3 ± 8.3

Body fat (%) 31.5 ± 9.3 33.3 ± 7.9* 26.8 ± 11.0 24.1 ± 7.9† 36.4 ± 6.6 33.0 ± 7.9 32.1 ± 8.1

Fat mass (kg) 31.3 ± 15.0 32.9 ± 14.0* 26.8 ± 16.7 17.9 ± 6.5† 40.0 ± 12.2 32.1 ± 13.4 31.2 ± 13.7

Fat free mass (kg) 63.8 ± 13.4 63.1 ± 13.5* 65.8 ± 13.1 55.7 ± 9.6† 69.1 ± 12.9 62.5 ± 13.4 62.8 ± 13.3

Fasting plasma glucose

(mg/dL)

87.5 ± 9.7 87.8 ± 10.3 86.8 ± 8.0 84.6 ± 8.7† 89.5 ± 9.9 87.5 ± 10.3 86.7 ± 9.7

2-hour plasma glucose

(mg/dL)

117.3 ± 31.0 118.1 ± 30.4 115.3 ± 32.6 107.3 ± 29.4† 124.2 ± 30.3 117.4 ± 28.9 116.0 ± 28.8

Glucose tolerance status‡

NGR

IGR

365 (71.7%)

144 (28.3%)

264 (71.7%)

104 (28.3%)

101 (71.6%)

40 (28.4%)

172 (83.1%)

35 (16.9%)

193 (63.9%)

109 (36.1%)

209 (72.1%)

81 (27.9%)

143 (74.1%)

50 (25.9%)

IFG 42 (8.3%) 36 (9.8%) 6 (4.3%) 8 (3.9%) 34 (11.3%) 27 (9.3%) 16 (8.3%)

IGT 127 (25.0%) 90 (24.5%) 37 (26.2%) 32 (15.5%) 95 (31.5%) 70 (24.1%) 45 (23.3%)

Energy intake

(kcal/day)§

2271 ± 362 2270 ± 361 2274 ± 365 2039 ± 268 2430 ± 330 2246 ± 365 2253 ± 367

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24-h energy expenditure

(kcal/day)

2367 ± 420 2383 ± 418 2325 ± 424 2093 ± 315† 2555 ± 378 2354 ± 421 2364 ± 416

24-h energy balance

(kcal/day)

−96 ± 186 −113 ± 181* −50 ± 191 −53 ± 172† −125 ± 190 −108 ± 186 −111 ± 189

24-h sleeping metabolic rate

(kcal/day)

1667 ± 303 1675 ± 299 1644 ± 314 1473 ± 210† 1799 ± 285 1652 ± 298 1653 ± 306

EE0 activity

(kcal/14·hrs)

1208 ± 210 1211 ± 206 1202 ± 220 1078 ± 168† 1297 ± 189 1197 ± 211 1200 ± 215

Awake fed thermogenesis

(kcal/14·hrs)

236.1 ± 97.5 233.3 ± 98.2 243.4 ± 95.4 219.0 ± 97.9† 247.8 ± 95.6 233.5 ± 97.1 235.9 ± 99.9


(% of energy intake)

10.4 ± 4.0 10.3 ± 4.1 10.7 ± 3.8 10.7 ± 4.2 10.3 ± 3.9 10.4 ± 4.0 10.5 ± 4.2

Values in each cell are reported as mean ± SD. *: P<0.05 vs. Whites; †: P<0.05 vs. obese (BMI≥30 kg/m2) 441

#: 250 (68%) full heritage Pima Indians and 118 (32%) mixed heritage Native Americans. ##: 195 (67%) full heritage Pima Indians and 95 (33%) mixed heritage 442

Native Americans. ###: 133 (69%) full heritage Pima Indians and 60 (31%) mixed heritage Native Americans. 443

‡: Normal Glucose Regulation (FPG <100 mg/dL and 2hPG <140 mg/dL); Impaired Glucose Regulation (FPG: 100-125 mg/dL and/or 2hPG: 140-199 mg/dL); 444

Impaired Fasting Glucose (FPG: 100-125 mg/dL); Impaired Glucose Tolerance (2hPG: 140-199 mg/dL) according to American Diabetes Association diagnostic 445

criteria (20). 446

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§: Total energy intake of meals provided during the chamber session, based on a unit-specific equation including body size and gender. 447

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Table 2. Multivariate models for the determinants of AFT. 448

Explained

Variance

(global P-value)

Age

(years)

Gender

(Male=0

Female=1)

Ethnicity

(White=0

Native American=1)

Body fat

(%)

Fat Free Mass

(kg)

Fasting

Glucose

(mg/dL)

Intercept


(kcal/14·hrs)

R2 = 0.151*

(P<0.001)

−2.5*

(−3.6 to −1.4)

Partial R=−0.21

−44*

(−72 to −15)

Partial R=−0.14

−25.0*

(−46 to −4)

Partial R=−0.11

1.9*

(0.5 to 3.3)

Partial R=0.12

1.3*

(0.5 to 2.2)

Partial R=0.15

−1.1*

(−2.0 to −0.2)

Partial R=−0.11

363*

(262 to 463)


(% of energy intake)

R2 = 0.077*

(P<0.001)

−0.10*

(−0.15 to −0.06)

Partial R=−0.20

−0.9

(−2.1 to 0.3)

Partial R=−0.07

−0.9*

(−1.8 to −0.1)

Partial R=−0.10

0.03

(−0.03 to 0.09)

Partial R=0.05

−0.03

(−0.06 to 0.01)

Partial R=−0.08

−0.05*

(−0.09 to −0.01)

Partial R=−0.12

21.6*

(17.2 to 25.9)

Beta coefficients in each cell are reported as mean value with 95% CI, along with partial correlation. *: P<0.05 449

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FIGURES 450

Figure 1. Time course of energy expenditure (EE, thick black line, left y-axis) and 451

spontaneous physical activity (SPA, thin black line, right y-axis) during 24 hours in a 452

respiratory chamber. 453

454

Spontaneous physical activity (SPA) is measured by a radar system based on the Doppler Effect and 455

expressed as percent over the time interval. Energy expenditure in the inactive state (EE0 activity) is 456

derived as the intercept of the regression line between EE and SPA during the daily hours (11 AM – 457

11 PM). Sleeping metabolic rate (SLEEP) is calculated as the mean EE during the nightly hours (1 458

– 5 AM) when SPA is lower than 1.5%. Awake and fed thermogenesis (AFT) is calculated as the 459

difference between EE0 activity and SLEEP. 460

461

09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 00 01 02 03 04 05 060.5

0.7

0.9

1.1

1.3

1.5

1.7

EE [kcal/min]

0

10

20

30

SPA [%]

SMR

EE0activity

AFT

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Figure 2. Relationships between AFT (percentage of energy intake) and age, fasting plasma 462

glucose concentration, BMI and waist circumference. 463

464

Inverse associations between awake and fed thermogenesis (AFT, expressed as percentage of 465

energy intake) with age (Panel A) and fasting plasma glucose concentration (Panel B). The best-fit 466

line is displayed in both panels. 467

A

C

B

D

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Nonlinear relationships between AFT vs. BMI (Panel C) and AFT vs. waist circumference (Panel 468

D). Locally weighted regression curves are displayed (percentage of fitted points=50%, weight 469

function: tri-cube). 470

471

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Figure 3. Results of spline regression analysis on the relationships between AFT (percentage 472

of energy intake) and BMI and waist circumference. 473

474

The piece-wise linear curve is shown for BMI in the global cohort of 509 subjects (knot value=29 475

kg/m2, Panel A), and for waist circumference in 315 men (knot value=103 cm, Panel B) and 194 476

women (knot value=95 cm, Panel C). 477

0,5 cm

A B C

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Figure 4. Lower values of AFT predict the rate of weight change in obese subjects and in 478

individuals with a higher waist circumference. 479

480

Differing relationships between awake and fed thermogenesis (AFT, adjusted for age, gender, 481

ethnicity, %BF, FFM and FPG) and rate of percent body weight change in 204 obese subjects with 482

a BMI≥29 kg/m2 (

Panel A, right) vs. 86 non-obese subjects with a BMI<29 kg/m2 (Panel A, left), as 483

well as in 189 subjects with a WC above the gender-dependent threshold (≥103 cm for men and ≥95 484

cm for women, Panel B right) compared to 101 subjects below the threshold (Panel B left). The 485

BMI≤29 kg/m2

ρ=0.12

P=0.26

B

A

WomenMen

WomenMen

WomenMen

WomenMen

ρ=−0.20

P=0.005

ρ=0.10

P=0.32

ρ=−0.22

P=0.003

BMI>29 kg/m2

WC: <103 cm (men), <95 cm (women) WC: ≥103 cm (men), ≥95 cm (women)

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median follow-up time is 6.6 years. Men and women are shown as black and white circles, 486

respectively. 487

Rate of weight change on y-axis is calculated as the difference between follow-up and initial 488

weight, normalized to initial weight and to follow-up time (i.e., rate of percent weight change), and 489

is reported on a safe-logarithmic scale. A relatively linear rate of weight gain over time has been 490

previously described in longitudinal studies of Native Americans (23). 491

492

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