Changes in energy metabolism in response to 48 h of overfeeding and fasting in Caucasians and Pima Indians

Weyer, Christian; Vozarova, Barbora; Ravussin, Éric; Tataranni, P. Antonio

doi:10.1038/sj.ijo.0801610

Cited by 78 publications

(65 citation statements)

References 34 publications

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“…However, when compared with underfeeding, small or no mass-independent increases in REE were observed (Ravussin et al, 1985;Diaz et al, 1992;Lammert et al, 2000); for example, after 9 days of overfeeding, at 1.6-fold of maintenance requirements, body weight ( þ 3.2 kg), FFM ( þ 1.4 kg) and REE ( þ 7.7%) increased in normal weight men, but there were nearly no mass-independent changes in REE (Ravussin et al, 1985). By contrast, 48 h of overfeeding at 200% of energy requirements increased the sleeping metabolic rate by B18% (or 350 kcal per day) without measurable changes in body composition (Weyer et al, 2001). This is in line with data obtained from non-obese individuals after controlled overfeeding ( þ 1000 kcal above maintenance requirements until a 10% weight gain was attained) and a subsequent weight stabilization, resulting in massindependent increases in REE of 27 kcal per day (Leibel et al, 1995).…”

Section: Metabolic Adaptations During Weight Changesmentioning

confidence: 90%

“…The first evidence for this concept was obtained from intra-individual and interindividual comparisons of metabolic responses to short-term overfeeding and fasting (Weyer et al, 2001). From these studies, two different phenotypes became evident: first, the spendthrift phenotype showing large increases in REE to overfeeding, but small decreases in response to fasting; and second, the thrifty phenotype, characterized by large decreases in energy expenditure in response to fasting at small increases to overfeeding (Weyer et al, 2001). These data suggest intra-individual and interindividual differences in 'elasticity' in response to either fasting or overfeeding.…”

Section: Metabolic 'Elasticity'mentioning

confidence: 99%

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Functional body composition: insights into the regulation of energy metabolism and some clinical applications

et al. 2009

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The application of advanced methods and techniques and their continuous development enable detailed body composition analyses (BCAs) and modeling of body composition at different levels (e.g., at atomic, molecular, organ-tissue and whole body level). Functional body composition integrates body components into regulatory systems (e.g., on energy balance). Regulation of body weight is closely linked to the mass and function of individual body components. Fat mass is part of the energy intake regulatory feedback system. In addition, fat-free mass (FFM) and fat mass are both determinants of resting energy expenditure (REE). Up to 80% of the variance in energy intake and energy expenditure is explained by body composition. A deviation from normal associations between body components and function suggests a metabolic disequilibrium (e.g., in the REE-FFM relationship or in the plasma leptin-fat mass association) that may occur in response to weight changes and diseases. The concept of functional body composition adds to a more sophisticated view on nutritional status and diseases, as well as to a characterization of biomedical traits that will provide functional evidence relating genetic variants.

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Section: Metabolic Adaptations During Weight Changesmentioning

confidence: 90%

Section: Metabolic 'Elasticity'mentioning

confidence: 99%

Functional body composition: insights into the regulation of energy metabolism and some clinical applications

et al. 2009

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“…However, this theory does not accord with the general experience of individuals when dieting. Furthermore, under the settling point model, we would not expect to see any compensatory changes in energy expenditure or intake to resist changes in energy balance -yet such changes are routinely observed (Norgan and Durnin, 1980;Leibel et al, 1995;Horton et al, 1995;Dulloo et al, 1997;Dulloo and Jacquet, 1998;Goldberg et al, 1998;Weyer et al, 2001;Galgani and Santos, 2016;Hall et al, 2011Hall et al, , 2012Johannsen et al, 2012;Polidori et al, 2016). A review of 32 controlled feeding studies in humans concluded that the responses were most consistent with the set-point rather than the settling point model (Hall and Guo, 2017).…”

Section: Settling Point Theorymentioning

confidence: 99%

The evolution of body fatness: trading off disease and predation risk

Speakman

2018

Journal of Experimental Biology

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Human obesity has a large genetic component, yet has many serious negative consequences. How this state of affairs has evolved has generated wide debate. The thrifty gene hypothesis was the first attempt to explain obesity as a consequence of adaptive responses to an ancient environment that in modern society become disadvantageous. The idea is that genes (or more precisely, alleles) predisposing to obesity may have been selected for by repeated exposure to famines. However, this idea has many flaws: for instance, selection of the supposed magnitude over the duration of human evolution would fix any thrifty alleles (famines kill the old and young, not the obese) and there is no evidence that hunter-gatherer populations become obese between famines. An alternative idea (called thrifty late) is that selection in famines has only happened since the agricultural revolution. However, this is inconsistent with the absence of strong signatures of selection at single nucleotide polymorphisms linked to obesity. In parallel to discussions about the origin of obesity, there has been much debate regarding the regulation of body weight. There are three basic models: the setpoint, settling point and dual-intervention point models. Selection might act against low and high levels of adiposity because food unpredictability and the risk of starvation selects against low adiposity whereas the risk of predation selects against high adiposity. Although evidence for the latter is quite strong, evidence for the former is relatively weak. The release from predation ∼2-million years ago is suggested to have led to the upper intervention point drifting in evolutionary time, leading to the modern distribution of obesity: the drifty gene hypothesis. Recent critiques of the dual-intervention point/ drifty gene idea are flawed and inconsistent with known aspects of energy balance physiology. Here, I present a new formulation of the dual-intervention point model. This model includes the novel suggestion that food unpredictability and starvation are insignificant factors driving fat storage, and that the main force driving up fat storage is the risk of disease and the need to survive periods of pathogen-induced anorexia. This model shows why two independent intervention points are more likely to evolve than a single set point. The molecular basis of the lower intervention point is likely based around the leptin pathway signalling. Determining the molecular basis of the upper intervention point is a crucial key target for future obesity research. A potential definitive test to separate the different models is also described.

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“…We observed a larger deficit on 24-h energy balance in the diabetic group compared with the nondiabetic group, which may be due to using nondiabetes prediction equations to estimate energy requirements. Energy imbalance is known to influence substrate oxidation (32) and, over longer periods of under-or overfeeding, also EE rest (33). In addition, urinary glucose excretion has been reported to be 3.1% of 24-h energy expenditure (103 Ϯ 112 kcal/day) in obese type 2 diabetic individuals on treatment (34), which would contribute to an even higher energy deficit.…”

Section: Energy Expenditure In Type 2 Diabetesmentioning

confidence: 99%

Increased 24-h Energy Expenditure in Type 2 Diabetes

Bitz¹,

Toubro²,

Larsen³

et al. 2004

Diabetes Care

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OBJECTIVE -The aim of this study was to determine whether overweight and obese individuals with type 2 diabetes have higher basal and 24-h energy expenditure compared with healthy control subjects before and after adjustment for body composition, spontaneous physical activity (SPA), sex, and age.RESEARCH DESIGN AND METHODS -Data from 31 subjects with type 2 diabetes and 61 nondiabetic control subjects were analyzed. The 24-h energy expenditure, basal metabolic rate (BMR), and sleeping energy expenditure (EE sleep ) between 1:00 A.M. and 6:00 A.M. were measured in whole-body respiratory chambers. Body composition was assessed by dual-energy X-ray absorptiometry (DXA).RESULTS -No significant differences in unadjusted EE sleep , BMR, and 24-h energy expenditure were observed between the type 2 diabetic group and the control group. After adjustment for fat-free mass (FFM), fat mass, SPA, sex, and age, EE sleep and BMR were, respectively, 7.7 and 6.9% higher in the type 2 diabetic group compared with the control group. This was equivalent to 144 Ϯ 40 kcal/day (P ϭ 0.001) and 139 Ϯ 61 kcal/day (P ϭ 0.026), respectively. Adjusted 24-h energy expenditure was 6.5% higher in the type 2 diabetic group compared with the nondiabetic control subjects (2,679 Ϯ 37 vs. 2,515 Ϯ 23 kcal/day, P ϭ 0.002). In multiple regression analyses, FFM, fat mass, SPA, and diabetes status were all significant determinants of EE sleep and 24-h energy expenditure, explaining 83 and 81% of the variation, respectively.CONCLUSIONS -This study confirms reports in Pima Indians that basal and 24-h energy expenditure adjusted for body composition, SPA, sex, and age are higher in individuals with type 2 diabetes compared with nondiabetic control subjects and may be even more pronounced in Caucasians. Diabetes Care 27:2416 -2421, 2004T he development of type 2 diabetes depends on both genetic susceptibility and environmental factors (1). An inappropriately high-energy intake and a sedentary lifestyle are wellknown risk factors for obesity, and there is solid evidence that excessive fat mass is the major cause of type 2 diabetes (2,3). Furthermore, large intervention trials have demonstrated that even a moderate, sustained weight loss in high-risk individuals can reduce the incidence of type 2 diabetes (4,5). However, genetic and disease-related differences in energy expenditure may also be important etiological determinants.A low resting energy expenditure (EE rest ) is a risk factor for weight gain (6). Paradoxically, cross-sectional studies have indicated an ϳ5% increased EE rest in Pima Indians with type 2 diabetes compared with nondiabetic individuals after adjustment for age and body composition (7-9). Other studies found a decreased thermogenic response to meals (10) and insulin/glucose clamp infusions among type 2 diabetic subjects compared with nondiabetic individuals (11). Thus, the integrated effect on total 24-h energy expenditure has been reported to be similar or only slightly elevated in Pima Indians with type 2 diabetes compared with nondia...

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Changes in energy metabolism in response to 48 h of overfeeding and fasting in Caucasians and Pima Indians

Cited by 78 publications

References 34 publications

Functional body composition: insights into the regulation of energy metabolism and some clinical applications

Functional body composition: insights into the regulation of energy metabolism and some clinical applications

The evolution of body fatness: trading off disease and predation risk

Increased 24-h Energy Expenditure in Type 2 Diabetes

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