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Diet plays a substantial role in the etiology, progression, and treatment of chronic disease and is best considered as a multifaceted set of modifiable input variables with pleiotropic effects on a variety of biological pathways spanning multiple organ systems. This brief review discusses key issues related to the design and conduct of diet interventions in rodent models of metabolic disease and their implications for interpreting experiments. We also make specific recommendations to improve rodent diet studies to help better understand the role of diet on metabolic physiology and thereby improve our understanding of metabolic disease.

Ultra-processed food consumption has increased worldwide, yet little is known about the potential links with taste preference and sensitivity. This exploratory study aimed to (i) compare sweet and salty taste detection thresholds and preferences following consumption of ultra-processed and unprocessed diets, (ii) investigate whether sweet and salty taste sensitivity and preference were associated with taste substrates (i.e. sodium and sugar) and ad libitum nutrient intake, and (iii) examine associations of taste detection thresholds and preferences with blood pressure (BP) and anthropometric measures following consumption of ultra-processed and unprocessed diets. In a randomized crossover study, participants (N = 20) received ultra-processed or unprocessed foods for 2 weeks, followed by the alternate diet. Baseline food intake data were collected prior to admission. Taste detection thresholds and preferences were measured at the end of each diet arm. Taste-substrate/nutrient intake, body mass index (BMI), and body weight (BW) were measured daily. No significant differences were observed in participant salt and sweet detection thresholds or preferences after 2 weeks on ultra-processed or unprocessed diets. There was no significant association between salt and sweet taste detection thresholds, preferences, and nutrient intakes on either diet arm. A positive correlation was observed between salt taste preference and systolic BP (r = 0.59; P = 0.01), BW (r = 0.47, P = 0.04), and BMI (r = 0.50; P = 0.03) following consumption of the ultra-processed diet. Thus, a 2-week consumption of an ultra-processed diet does not appear to acutely impact sweet or salty taste sensitivity or preference. Trial Registration: ClinicalTrials.gov Identifier NCT03407053.

Mice are widely used for diabetes and obesity research, but the quantitative contribution of physical activity to mouse energy metabolism is poorly understood. The energy cost of activity (PAEE) is typically determined by regression of total energy expenditure (TEE) versus physical activity. Confounding factors are 1) that muscle-produced heat reduces cold induced thermogenesis by brown fat and 2) that core body temperature (Tb) increases during physical activity. Minimizing cold induced thermogenesis addresses the first problem. Prior studies used housing at 30°C, but 30°C is now known to be below thermoneutrality in the dark phase. Furthermore, the Tb increase accompanying physical activity increases energy expenditure by non-activity processes; this was not considered previously. To account for these factors, we studied mice by indirect calorimetry at an ambient temperature of 35 °C while measuring Tb by telemetry and physical activity by beam break. We developed a model, partitioning TEE into PAEE and resting energy expenditure (REE) . Significant features of the model are i) physical activity measurements are aligned to the measured TEE using the time constant of the indirect calorimetry system, ii) the REE term is adjusted for the measured Tb using a Q10 (van’t Hoff coefficient) of 3.0, and iii) the REE term is scaled for body mass. In this model, the within mouse PAEE was robustly fit by linear regression, yielding PAEE of 22.5 ± 0.8 (light) and 21.7 ± 1.0 (dark) cal/1000 beam breaks (n=17) . The correlations between measured and model-calculated TEE were r = 0.888 ± 0.013 (light) and 0.888 ± 0.0 (dark) . Under usual housing conditions (ambient temperature of 23 °C) , the average physical activity was 1390 ± 130 (light) and 4940 ± 390 (dark) beam breaks/h. Based on these data, PAEE accounts for 8.6 ± 0.9 % (light) and 21.7 ± 1.8 % (dark) of TEE. This rigorous determination of PAEE advances understanding the role of physical activity in mouse energy metabolism, improving the predictive value of the mouse as a model for human disorders. Disclosure V.Skop: None. J.Guo: n/a. N.Liu: None. K.D.Hall: None. O.Gavrilova: None. M.L.Reitman: None. Funding Intramural NIDDK

OBJECTIVE: Physical activity is a major component of total energy expenditure (TEE) that exhibits extreme variability in mice. Our objective was to construct a general, physiology-based model of TEE to accurately quantify the energy cost of physical activity. METHODS: Spontaneous home cage physical activity, body temperature, TEE, and energy intake were measured with frequent sampling. The energy cost of activity was modeled considering six contributors to TEE (basal metabolic rate, thermic effect of food, body temperature, cold induced thermogenesis, physical activity, and body weight). An ambient temperature of 35 °C was required to remove the contribution from cold induced thermogenesis. Basal metabolic rate was adjusted for body temperature using a Q(10) temperature coefficient. RESULTS: We developed a TEE model that robustly explains 70–80% of the variance in TEE at 35 °C while fitting only two parameters, the basal metabolic rate and the mass-specific energy cost per unit of physical activity, which averaged 60 cal/km/g body weight. In Ucp1(−/−) mice the activity cost was elevated by 60%, indicating inefficiency and increased muscle thermogenesis. The diurnal rhythm in TEE was quantitatively explained by the combined diurnal differences in physical activity, body temperature, and energy intake. Incorporating body temperature into human basal metabolic rate measurements significantly reduced the inter-individual variation. CONCLUSIONS: The physiology-based model of TEE allows quantifying the energy cost of physical activity. While applied here to mice, the model should be generally valid across species. Due to the effect of body temperature, we suggest that basal metabolic rate measurements be corrected to a reference body temperature, including in humans. Having an accurate cost of physical activity allows mechanistic dissection of disorders of energy homeostasis, including obesity.

The carbohydrate–insulin model of obesity posits that high-carbohydrate diets lead to excess insulin secretion, thereby promoting fat accumulation and increasing energy intake. Thus, low-carbohydrate diets are predicted to reduce ad libitum energy intake as compared to low-fat, high-carbohydrate diets. To test this hypothesis, 20 adults aged 29.9 ± 1.4 (mean ± s.e.m.) years with body mass index of 27.8 ± 1.3 kg m−2 were admitted as inpatients to the National Institutes of Health Clinical Center and randomized to consume ad libitum either a minimally processed, plant-based, low-fat diet (10.3% fat, 75.2% carbohydrate) with high glycemic load (85 g 1,000 kcal−1) or a minimally processed, animal-based, ketogenic, low-carbohydrate diet (75.8% fat, 10.0% carbohydrate) with low glycemic load (6 g 1,000 kcal−1) for 2 weeks followed immediately by the alternate diet for 2 weeks. One participant withdrew due to hypoglycemia during the low-carbohydrate diet. The primary outcomes compared mean daily ad libitum energy intake between each 2-week diet period as well as between the final week of each diet. We found that the low-fat diet led to 689 ± 73 kcal d−1 less energy intake than the low-carbohydrate diet over 2 weeks (P

BACKGROUND: A possible driver of obesity is insensitivity (passive overconsumption) to food energy density (ED, kcal/g); however, it is unclear whether this insensitivity applies to all meals.OBJECTIVES: We assessed the influence of ED on energy intake (kcal) across a broad and continuous range of energy densities comprised of non-covertly manipulated, real-world meals. We also allowed for the possibility that the association between energy intake and ED is non-linear.METHODS: We completed a secondary analysis of 1,519 meals which occurred in a controlled environment as part of a study conducted by Hall and colleagues to assess the effects of food ultra-processing on energy intake. To establish the generalizability of the findings, the analyses were repeated in 32,162 meals collected from free-living humans using data from the UK National Diet and Nutrition Survey (NDNS). Segmented regressions were performed to establish ED 'breakpoints' at which the association between consumed meal ED and mean centered meal caloric intake (kcal) changed.RESULTS: Significant breakpoints were found in both the Hall dataset (1.41 kcal/g), and the NDNS dataset (1.75 and 2.94 kcal/g). Centered meal caloric intake did not increase linearly with consumed meal ED, and this pattern was captured by a two-component ('volume' and 'calorie content' (biologically derived from the sensing of fat, carbohydrate, and protein)) model of physical meal size (g), in which volume is the dominant signal with lower energy-dense foods and calorie content is the dominant signal with higher energy-dense foods.CONCLUSIONS: These analyses reveal that, on some level, humans are sensitive to the energy content of meals and adjust meal size to minimize the acute aversive effects of overconsumption. Future research should consider the relative importance of volume and calorie content signals, and how individual differences impact everyday dietary behavior and energy balance.

A recent Perspective article described the “carbohydrate-insulin model (CIM)” of obesity, asserting that it “better reflects knowledge on the biology of weight control” as compared with what was described as the “dominant energy balance model (EBM),” which fails to consider “biological mechanisms that promote weight gain.” Unfortunately, the Perspective conflated and confused the principle of energy balance, a law of physics that is agnostic as to obesity mechanisms, with the EBM as a theoretical model of obesity that is firmly based on biology. In doing so, the authors presented a false choice between the CIM and a caricature of the EBM that does not reflect modern obesity science. Here, we present a more accurate description of the EBM where the brain is the primary organ responsible for body weight regulation operating mainly below our conscious awareness via complex endocrine, metabolic, and nervous system signals to control food intake in response to the body's dynamic energy needs as well as environmental influences. We also describe the recent history of the CIM and show how the latest “most comprehensive formulation” abandons a formerly central feature that required fat accumulation in adipose tissue to be the primary driver of positive energy balance. As such, the new CIM can be considered a special case of the more comprehensive EBM but with a narrower focus on diets high in glycemic load as the primary factor responsible for common obesity. We review data from a wide variety of studies that address the validity of each model and demonstrate that the EBM is a more robust theory of obesity than the CIM.

Increased ultra-processed foods (UPFs) in the food supply have plausibly caused the rise in obesity prevalence and related chronic diseases. To address this public health concern, policies targeting reformulation or elimination of UPF categories will require improved understanding of the biological mechanisms whereby UPFs lead to overconsumption and poor health.

Objective The objective of this study was to measure changes in glucose, lipid, and inflammation parameters after transitioning from a baseline diet (BD) to an isocaloric ketogenic diet (KD). Methods Glucose homeostasis, lipid homeostasis, and inflammation were studied in 17 men (BMI: 25-35 kg/m2 ) during 4 weeks of a BD (15% protein, 50% carbohydrate, 35% fat) followed by 4 weeks of an isocaloric KD (15% protein, 5% carbohydrate, 80% fat). Postprandial responses were assessed following mixed-meal tests matched to compositions of the BD (control meal [CM]) and KD (ketogenic meal). Results Fasting ketones, glycerol, free fatty acids, glucagon, adiponectin, gastric inhibitory peptide, total and low-density lipoprotein cholesterol, and C-reactive protein were significantly increased on the KD. Fasting insulin, C-peptides, triglycerides, and fibroblast growth factor 21 were significantly decreased. During the KD, the glucose area under the curve was significantly higher with both test meals, and the insulin area under the curve was significantly higher only for the CM. Analyses of glucose homeostasis suggested that the KD insulin sensitivity decreased during the CM but increased during the ketogenic meal. Insulin-mediated antilipolysis was decreased on the KD regardless of meal type. Conclusions Switching to the KD was associated with increased cholesterol and inflammatory markers, decreased triglycerides, and decreased insulin-mediated antilipolysis. Glucose homeostasis parameters were diet dependent and test meal dependent.

BACKGROUND: Low-carbohydrate diets have been reported to significantly increase human energy expenditure when measured using doubly labeled water (DLW) but not by respiratory chambers. Although DLW may reveal true physiological differences undetected by respiratory chambers, an alternative possibility is that the expenditure differences resulted from failure to correctly estimate the respiratory quotient (RQ) used in the DLW calculations. OBJECTIVE: To examine energy expenditure differences between isocaloric diets varying widely in carbohydrate and to quantitatively compare DLW data with respiratory chamber and body composition measurements within an energy balance framework. DESIGN: DLW measurements were obtained during the final 2 wk of month-long baseline (BD; 50% carbohydrate, 35% fat, 15% protein) and isocaloric ketogenic diets (KD; 5% carbohydrate, 80% fat, 15% protein) in 17 men with a BMI of 25–35 kg/m(2). Subjects resided 2 d/wk in respiratory chambers to measure energy expenditure (EE(chamber)). DLW expenditure was calculated using chamber-determined RQ either unadjusted (EE(DLW)) or adjusted (EE(DLWΔRQ)) for net energy imbalance using diet-specific coefficients. Accelerometers measured physical activity. Body composition changes were measured by dual-energy X-ray absorptiometry (DXA) which were combined with energy intake measurements to calculate energy expenditure by balance (EE(bal)). RESULTS: After transitioning from BD to KD, neither EE(chamber) nor EE(bal) were significantly changed (∆EE(chamber )= 24 ± 30 kcal/d; P = 0.43 and ∆EE(bal )= −141 ± 118 kcal/d; P = 0.25). Similarly, physical activity (−5.1 ± 4.8%; P = 0.3) and exercise efficiency (−1.6 ± 2.4%; P = 0.52) were not significantly changed. However, EE(DLW) was 209 ± 83 kcal/d higher during the KD (P = 0.023) but was not significantly increased when adjusted for energy balance (EE(DLWΔRQ) = 139 ± 89 kcal/d; P = 0.14). After removing 2 outliers whose EE(DLW) were incompatible with other data, EE(DLW) was marginally increased during the KD by 126 ± 62 kcal/d (P = 0.063) and EE(DLW∆RQ) was only 46 ± 65 kcal/d higher (P = 0.49). CONCLUSIONS: DLW calculations failing to account for diet-specific energy imbalance effects on RQ erroneously suggest that low-carbohydrate diets substantially increase energy expenditure. This trial was registered at clinicaltrials.gov as NCT01967563.

Objective This study aimed to compare self-reported with objective measurements of energy intake changes (∆EI) during a 1-year weight-loss intervention with subjects randomized to low-carbohydrate versus low-fat diets. Methods Repeated body weight measurements were used as inputs to an objective mathematical model to calculate ∆EIModel and to compare with self-reported energy intake changes assessed by repeated 24-hour recalls (∆EIRecall ). Results ∆EIRecall indicated a relatively persistent state of calorie restriction of ~500 to 600 kcal/d at 3, 6, and 12 months with no significant differences between the diets. ∆EIModel demonstrated large early decreases in calorie intake > 800 kcal/d followed by an exponential return to ~100 kcal/d below baseline at the end of the year. Accounting for self-reported physical activities did not materially affect the results. Discrepancies between ∆EIModel and ∆EIRecall became progressively greater over time. The low-carbohydrate diet resulted in ∆EIModel that was 162 ± 53 kcal/d lower than the low-fat diet over the first 3 months (P = 0.002), but no significant diet differences were found thereafter. Conclusions Self-reported ∆EI measurements were inaccurate. Model-based calculations of ∆EI found that instructions to follow the low-carbohydrate diet resulted in greater calorie restriction than the low-fat diet in the early phases of the intervention, but these diet differences were not sustained.

Although many persons with obesity can lose weight by lifestyle (diet and physical activity) therapy, successful long-term weight loss is difficult to achieve, and most people who lose weight regain their lost weight over time. The neurohormonal, physiological, and behavioral factors that promote weight recidivism are unclear and complex. The National Institute of Diabetes and Digestive and Kidney Diseases convened a workshop in June 2019, titled "The Physiology of the Weight-Reduced State," to explore the mechanisms and integrative physiology of adaptations in appetite, energy expenditure, and thermogenesis that occur in the weight-reduced state and that may oppose weight-loss maintenance. The proceedings from the first session of this workshop are presented here. Drs. Michael Rosenbaum, Kevin Hall, and Rudolph Leibel discussed the physiological factors that contribute to weight regain; Dr. Michael Lowe discussed the biobehavioral issues involved in weight-loss maintenance; Dr. John Jakicic discussed the influence of physical activity on long-term weight-loss maintenance; and Dr. Louis Aronne discussed the ability of drug therapy to maintain weight loss.

Obesity and its consequences are among the greatest challenges in healthcare. The gut microbiome is recognized as a key factor in the pathogenesis of obesity. Using a mouse model, we show here that a wild-derived microbiome protects against excessive weight gain, severe fatty liver disease and metabolic syndrome during a 10-week course of high-fat diet. This phenotype is transferable only during the first weeks of life. In adult mice, neither transfer nor severe disturbance of the wild-type microbiome modifies the metabolic response to a high-fat diet. The protective phenotype is associated with increased secretion of metabolic hormones and increased energy expenditure through activation of brown adipose tissue. Thus, we identify a microbiome that protects against weight gain and its negative consequences through metabolic programming in early life. Translation of these results to humans may identify early-life therapeutics that protect against obesity.

OBJECTIVES: To investigate differences in ad libitum energy intake when inpatient adults were exposed to diets with equal calories and protein but varied widely in the ratio of carbohydrate to fat. METHODS: 16 adults without diabetes were admitted to the Metabolic Clinical Research Unit in the NIH Clinical Center for four continuous weeks and were randomized to receive either a plant-based, low-fat (LF) diet or an animal-based, low-carbohydrate (LC) diet for two weeks, followed by the alternate diet for two weeks. The LF diet was ∼75% carbohydrate and ∼10% fat, whereas the LC was ∼10% carbohydrate and ∼75% fat. The LF diet had ∼4-fold more fiber and was ∼60% of the energy density of the LC diet. Both diets were matched for protein and the presented calories were double each subject's maintenance energy requirements, as calculated from their measured resting energy expenditure multiplied by 1.6. Participants received three daily meals, had continuous access to snacks, and were instructed to eat as much or as little as they wanted. Leftovers were weighed to determine food intake. ProNutra software was used to calculate energy and nutrient intake. RESULTS: The study enrolled 9 men and 7 women with an age (mean ± SE) of 29 ± 1.7 years and body mass index (BMI) of 27.5 ± 1.5. During exposure to the LF diet, participants consumed 726 ± 84 kcal/d less than during the LC diet (P

OBJECTIVES: To explore postprandial responses to isocaloric meals after ∼2 weeks on an ad libitum Low Carbohydrate (LC) diet vs. a Low Fat (LF) diet. METHODS: 16 healthy volunteers without diabetes were admitted to the NIH Clinical Center and randomized to consume a LC or LF diet for 2 weeks immediately followed by 2 weeks of the alternate diet. The LC diet was composed of ∼75% fat, ∼10% carbohydrate, and ∼15% protein; the LF diet was ∼75% carbohydrate, ∼10% fat, and ∼15% protein. Daily meals and snacks were matched for presented calories and participants were instructed to consume as much or as little as desired. On day 13 of each diet after an 8 hour fast, participants consumed a liquid meal containing 30% of energy requirements with a macronutrient composition corresponding to the prevailing diet. Blood was drawn at 0, 10, 20, 30, 60, 90, 120, 180, 240, 300, 360 minutes post meal consumption. Plasma concentrations of glucose, lactate, insulin, c-peptide, free fatty acids, and triglycerides were measured. RESULTS: 7 females and 9 males with an age of (mean ± SE) 28.7 ±1.7 y and BMI of 27.5 ± 1.5 kg/m(2) completed the study. During the LC diet, baseline levels of triglycerides and lactate were significantly lower (−33.5 ± 9.1 mg/dl; P = 0.003, −0.18 ± 0.05 mM; P = 0.002, respectively) and glucose, insulin, and c-peptide also tended to be lower (−3.7 ± 2.0 mg/dl; P = 0.09, −2.2 ± 1.2 µU/ml; P = 0.08, −0.35 ± 0.17 ng/ml; P = 0.06, respectively) whereas free fatty acids were significantly higher (0.28 ± 0.06 mM; P = 0.0005) compared to the LF diet. Average postprandial levels of glucose, lactate, insulin, and c-peptide were significantly lower following the LC meal (−11 ± 3 mg/dl; P = 0.003, −0.88 ± 0.06 mM; P

Competing models of obesity and its treatment often contrast the relative roles of dietary fat versus carbohydrate. Advocates of low-carbohydrate diets posit that intake of high glycemic carbohydrates leads to elevated postprandial insulin thereby promoting body fat accumulation while increasing hunger and energy intake according to the carbohydrate-insulin model of obesity. Alternatively, proponents of low-fat diets argue that high fat intake promotes body fat storage due to passive overconsumption of energy resulting from the high energy density of dietary fat. To test these competing models, 20 adults without diabetes aged (mean±SE) 29.9±1.4 y with BMI=27.8±1.3 kg/m2 were admitted as inpatients to the NIH Clinical Center and randomized to consume ad libitum either a plant-based, low-fat (PBLF) diet (75.2% carbohydrate, 10.3% fat, non-beverage energy density = 1.1 kcal/g) or an animal-based, ketogenic, low-carbohydrate (ABLC) diet (75.8% fat,10.0% carbohydrate, non-beverage energy density = 2.2 kcal/g) for two weeks followed immediately by the alternate diet for two weeks. Three daily meals plus snacks amounting to twice each subject’s estimated energy requirements were provided and subjects were instructed to eat as much or as little as desired. The PBLF diet resulted in substantially greater glucose and insulin levels whereas the ABLC diet led to increased blood ketones of ~3 mM which is thought to suppress appetite. However, ad libitum energy intake was 689±73 kcal/d lower during the PBLF diet as compared to the ABLC diet (p

We read the recent article in Psychology of Sport and Exercise by Liu et al. ("A randomized controlled trial of coordination exercise on cognitive function in obese adolescents") with great interest. Our interest in the article stemmed from the extraordinary differences in obesity-related outcomes reported in response to a rope-jumping intervention. We requested the raw data from the authors to confirm the results and, after the journal editors reinforced our request, the authors graciously provided us with their data. We share our evaluation of the original data herein, which includes concerns that weight and BMI loss by the intervention appears extraordinary in both magnitude and aspects of the distributions. We request that the authors address our findings by providing explanations of the extraordinary data or correcting any errors that may have occurred in the original report, as appropriate.

BACKGROUND: High postprandial glucose excursions may increase risk for disease. Individuals have widely varying glucose responses to different meals, and precision nutrition approaches often seek to personalize diets to minimize postprandial glycemic responses as measured by continuous glucose monitors (CGMs). However, it is unknown whether different CGM devices result in concordant meal rankings according to postprandial glycemic excursions. OBJECTIVE: We explored whether meal rankings according to postprandial glycemic excursions differ between 2 simultaneously worn CGMs. METHODS: We collected 27,489 simultaneous measurements from Dexcom G4 Platinum and Abbott Freestyle Libre Pro CGMs during 28 inpatient days in 16 adults without diabetes. Simultaneous glucose measurements obtained for 2 h following 760 ad libitum meals were used to compare within-subject meal rankings between the CGM devices according to their incremental glucose response. RESULTS: Postprandial responses to ad libitum meals were highly variable, with the Abbott and Dexcom systems resulting in within-subject incremental mean ± SD glucose CVs of 91.7 ± 1.9% and 94.2 ± 2.7%, respectively. Within-subject meal rankings for incremental glycemic responses were relatively discordant between CGMs, with a mean Kendall rank correlation coefficient of 0.43 ± 0.05. Meals in the bottom compared with those in the top half of incremental glycemic responses ranked by Abbott resulted in 50 ± 10% (P = 0.0002) less glycemic reduction as measured by Dexcom, and vice versa. The missing glycemic reduction by eating meals ranked according to the discordant CGM was inversely correlated with each subject's Kendall rank correlation coefficient (r = −0.95; P

Background Mathematical models have been developed to predict body weight (BW) and composition changes in response to lifestyle interventions, but these models have not been adequately validated over the long term. Objective We compared mathematical models of human BW dynamics underlying 2 popular web-based weight-loss prediction tools, the National Institutes of Health Body Weight Planner (NIH BWP) and the Pennington Biomedical Research Center Weight Loss Predictor (PBRC WLP), with data from the 2-year Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE) study. Design Mathematical models were initialized using baseline CALERIE data, and changes in body weight (ΔBW), fat mass (ΔFM), and energy expenditure (ΔEE) were simulated in response to time-varying changes in energy intake (ΔEI) objectively measured using the intake-balance method. No model parameters were adjusted from their previously published values. Results The PBRC WLP model simulated an exaggerated early decrease in EE in response to calorie restriction, resulting in substantial underestimation of the observed mean (95% CI) BW losses by 3.8 (3.5, 4.2) kg. The NIH WLP simulations were much closer to the data, with an overall mean ΔBW bias of -0.47 (-0.92, -0.015) kg. Linearized model analysis revealed that the main reason for the PBRC WLP model bias was a parameter value defining how spontaneous physical activity expenditure decreased with caloric restriction. Both models exhibited substantial variability in their ability to simulate individual results in response to calorie restriction. Monte Carlo simulations demonstrated that ΔEI measurement uncertainties were a major contributor to the individual variability in NIH BWP model simulations. Conclusions The NIH BWP outperformed the PBRC WLP and accurately simulated average weight-loss and energy balance dynamics in response to long-term calorie restriction. However, the substantial variability in the NIH BWP model predictions at the individual level suggests cautious interpretation of individual-level simulations. This trial was registered at clinicaltrials.gov as NCT00427193.

Background: The responses to behavioral, pharmacological, or surgical obesity treatments are highly individualized. The Accumulating Data to Optimally Predict obesity Treatment (ADOPT) project provides a framework for how obesity researchers, working collectively, can generate the evidence base needed to guide the development of tailored, and potentially more effective, strategies for obesity treatment. Objectives: The objective of the ADOPT biological domain subgroup is to create a list of high‐priority biological measures for weight‐loss studies that will advance the understanding of individual variability in response to adult obesity treatments. This list includes measures of body composition, energy homeostasis (energy intake and output), brain structure and function, and biomarkers, as well as biobanking procedures, which could feasibly be included in most, if not all, studies of obesity treatment. The recommended high‐priority measures are selected to balance needs for sensitivity, specificity, and/or comprehensiveness with feasibility to achieve a commonality of usage and increase the breadth and impact of obesity research. Significance: The accumulation of data on key biological factors, along with behavioral, psychosocial, and environmental factors, can generate a more precise description of the interplay and synergy among them and their impact on treatment responses, which can ultimately inform the design and delivery of effective, tailored obesity treatments.

Objective The aim of this study was to explore how physical activity (PA) and energy intake (EI) changes were related to weight loss and regain following “The Biggest Loser” competition. Methods At baseline, week 6 and week 30 of the competition, and 6 years after the competition, body composition was measured via dual-energy x-ray absorptiometry, resting energy expenditure was measured by using indirect calorimetry, and EI and PA were measured by using doubly labeled water. Results Six years after the competition, median weight loss in 14 of “The Biggest Loser” participants was 13%, with those maintaining a greater weight loss (mean ± SE) of 24.9% ± 3.8% having increased PA by 160% ± 23%, compared with a PA increase of 34% ± 25% (P = 0.0033) in the weight regainers who were 1.1% ± 4.0% heavier than the precompetition baseline. EI changes were similar between weight loss maintainers and regainers (−8.7% ± 5.6% vs. −7.4% ± 2.7%, respectively; P = 0.83). Weight regain was inversely associated with absolute changes in PA (r = −0.82; P = 0.0003) but not with changes in EI (r = −0.15; P = 0.61). EI and PA changes explained 93% of the individual weight loss variability at 6 years. Conclusions Consistent with previous reports, large and persistent increases in PA may be required for long-term maintenance of lost weight.

Objective Obesity pharmacotherapies result in an exponential time course for energy intake whereby large early decreases dissipate over time. This pattern of declining drug efficacy to decrease energy intake results in a weight loss plateau within approximately 1 year. This study aimed to elucidate the physiology underlying the exponential decay of drug effects on energy intake. Methods Placebo-subtracted energy intake time courses were examined during long-term obesity pharmacotherapy trials for 14 different drugs or drug combinations within the theoretical framework of a proportional feedback control system regulating human body weight. Results Assuming each obesity drug had a relatively constant effect on average energy intake and did not affect other model parameters, our model correctly predicted that long-term placebo-subtracted energy intake was linearly related to early reductions in energy intake according to a prespecified equation with no free parameters. The simple model explained about 70% of the variance between drug studies with respect to the long-term effects on energy intake, although a significant proportional bias was evident. Conclusions The exponential decay over time of obesity pharmacotherapies to suppress energy intake can be interpreted as a relatively constant effect of each drug superimposed on a physiological feedback control system regulating body weight.

The design of well-powered in vivo preclinical studies is a key element in building the knowledge of disease physiology for the purpose of identifying and effectively testing potential antiobesity drug targets. However, as a result of the complexity of the obese phenotype, there is limited understanding of the variability within and between study animals of macroscopic end points such as food intake and body composition. This, combined with limitations inherent in the measurement of certain end points, presents challenges to study design that can have significant consequences for an antiobesity program. Here, we analyze a large, longitudinal study of mouse food intake and body composition during diet perturbation to quantify the variability and interaction of the key metabolic end points. To demonstrate how conclusions can change as a function of study size, we show that a simulated preclinical study properly powered for one end point may lead to false conclusions based on secondary end points. We then propose the guidelines for end point selection and study size estimation under different conditions to facilitate proper power calculation for a more successful in vivo study design.

Summary Exercise is a common component of weight loss strategies, yet exercise programs are associated with surprisingly small changes in body weight [1–4]. This may be due in part to compensatory adaptations, in which calories expended during exercise are counteracted by decreases in other aspects of energy expenditure [1, 5–10]. Here we examined the relationship between a rodent model of voluntary exercise— wheel running— and total daily energy expenditure. Use of a running wheel for 3 to 7 days increased daily energy expenditure, resulting in a caloric deficit of ∼1 kcal/day; however, total daily energy expenditure remained stable after the first week of wheel access, despite further increases in wheel use. We hypothesized that compensatory mechanisms accounted for the lack of increase in daily energy expenditure after the first week. Supporting this idea, we observed a decrease in off-wheel ambulation when mice were using the wheels, indicating behavioral compensation. Finally, we asked whether individual variation in wheel use within a group of mice would be associated with different levels of daily energy expenditure. Despite a large variation in wheel running, we did not observe a significant relationship between the amount of daily wheel running and total daily energy expenditure or energy intake across mice. Together, our experiments support a model in which the transition from sedentary to light activity is associated with an increase in daily energy expenditure, but further increases in physical activity produce diminishingly small increments in daily energy expenditure.

Obesity is associated with physical inactivity, which exacerbates the health consequences of weight gain. However, the mechanisms that mediate this association are unknown. We hypothesized that deficits in dopamine signaling contribute to physical inactivity in obesity. To investigate this, we quantified multiple aspects of dopamine signaling in lean and obese mice. We found that D2-type receptor (D2R) binding in the striatum, but not D1-type receptor binding or dopamine levels, was reduced in obese mice. Genetically removing D2Rs from striatal medium spiny neurons was sufficient to reduce motor activity in lean mice, whereas restoring Gi signaling in these neurons increased activity in obese mice. Surprisingly, although mice with low D2Rs were less active, they were not more vulnerable to diet-induced weight gain than control mice. We conclude that deficits in striatal D2R signaling contribute to physical inactivity in obesity, but inactivity is more a consequence than a cause of obesity. Fil: Friend, Danielle M.. National Institutes of Health; Estados Unidos Fil: Devarakonda, Kavya. National Institutes of Health; Estados Unidos Fil: O'Neal, Timothy J.. National Institutes of Health; Estados Unidos Fil: Skirzewski, Miguel. National Institutes of Health; Estados Unidos Fil: Papageorgiou, Ioannis. National Institutes of Health; Estados Unidos Fil: Kaplan, Alanna R.. National Institutes of Health; Estados Unidos Fil: Liow, Jeih San. National Institutes of Health; Estados Unidos Fil: Guo, Juen. National Institutes of Health; Estados Unidos Fil: Rane, Sushil G.. National Institutes of Health; Estados Unidos Fil: Rubinstein, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Fisiología, Biología Molecular y Celular; Argentina. University of Michigan; Estados Unidos Fil: Alvarez, Verónica A.. National Institutes of Health; Estados Unidos Fil: Hall, Kevin D.. National Institutes of Health; Estados Unidos Fil: Kravitz, Alexxai V.. National Institutes of Health; Estados Unidos

We investigated whether ultra-processed foods affect energy intake in 20 weight-stable adults, aged (mean±SE) 31.2±1.6 y and BMI=27±1.5 kg/m(2). Subjects were admitted to the NIH Clinical Center and randomized to receive either ultra-processed or unprocessed diets for 2 weeks immediately followed by the alternate diet for 2 weeks. Meals were designed to be matched for presented calories, energy density, macronutrients, sugar, sodium, and fiber. Subjects were instructed to consume as much or as little as desired. Energy intake was greater during the ultra-processed diet (508±106 kcal/d; p=0.0001), with increased consumption of carbohydrate (280±54 kcal/d; p

The mouse is widely used to study diabetes and obesity. Understanding thermal biology is essential for translating observations from mice to humans. For example, mice expend large amounts of energy to maintain body temperature, while humans do not. The thermoneutral zone (TNZ) for humans is defined as the ambient temperature (Ta) range where metabolic rate is at a minimum. Here we explore the mouse TNZ, focusing on its neglected upper boundary. Total light-phase energy expenditure (TEE) was quantitated by indirect calorimetry at various Ta, while also continuously measuring core body temperature (Tb). Broken line regression with nonlinear mixed model was used for data analysis. In wild type C57BL/6J mice, TEE decreased linearly with increasing Ta until a plateau was reached. The Ta at the break point (TNZ lower bound, called the Tlc) was 28.91 ±0.15 °C. Importantly, no increase in TEE was observed at Ta up to ∼4 °C above the Tlc. Tb was constant at cool Ta and began to rise at Ta = 28.95 ±0.12 °C. Thus, the Ta at which Tb starts to increase coincides with the Tlc and was not accompanied by an increased TEE. The generality of this unexpected observation was explored in multiple mouse models, including germline deletion of three temperature-sensing channels (TrpV1, TrpM8, TrpA1), neonatal ablation of Trpv1+ neurons, diet-induced obesity, and leptin deficiency. Over 12 cohorts (in total 66 mice), there was no difference between the Tlc and start of Tb increase (0.06 ±0.22 °C). Our novel results suggest a general property of mouse thermal physiology. Unlike humans, mouse Tb starts to increase right at the Tlc. There is no range of Tas over which both TEE and Tb are constant. The results suggest that the mouse TNZ is not the “zone” of textbooks, but rather is a fraction of a degree, a point. Disclosure V. Skop: None. J. Guo: None. N. Liu: None. C. Xiao: None. K.D. Hall: None. O. Gavrilova: None. M. Reitman: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (DK075063)

The protein leverage model of obesity posits that decreasing the protein fraction of the diet leads to compensatory increases in total energy intake in an attempt to maintain a target amount of absolute protein consumed. The resulting increased energy intake thereby causes weight gain. According to food balance sheets published by the Food and Agriculture Organization of the United Nations, while the absolute protein content of the US food supply has increased since the early 1970s, the fraction of available calories from protein has decreased by ~1% because of greater increases in available carbohydrate and fat. Counterintuitively, even such a small decrease in the protein fraction of the food supply has the potential to result in relatively large increases in energy intake according to the protein leverage model. Therefore, while the protein leverage effect is unlikely to fully explain the obesity epidemic, its potential contribution should not be ignored.

Very low-carbohydrate, high-fat ketogenic diets (KDs) induce a pronounced shift in metabolic fuel utilization that elevates circulating ketone bodies; however, the consequences of these compounds for host-microbiome interactions remain unknown. Here, we show that KDs alter the human and mouse gut microbiota in a manner distinct from high-fat diets (HFDs). Metagenomic and metabolomic analyses of stool samples from an 8-week inpatient study revealed marked shifts in gut microbial community structure and function during the KD. Gradient diet experiments in mice confirmed the unique impact of KDs relative to HFDs with a reproducible depletion of bifidobacteria. Invitro and invivo experiments showed that ketone bodies selectively inhibited bifidobacterial growth. Finally, mono-colonizations and human microbiome transplantations into germ-free mice revealed that the KD-associated gut microbiota reduces the levels of intestinal pro-inflammatory Th17 cells. Together, these results highlight the importance of trans-kingdom chemical dialogs for mediating the host response to dietary interventions.

Several putative explanations of the obesity epidemic relate to the changing food environment. Individual dietary macronutrients have each been theorized to be the prime culprit for population obesity, but these explanations are unlikely. Rather, obesity probably resulted from changes in the caloric quantity and quality of the food supply in concert with an industrialized food system that produced and marketed convenient, highly processed foods from cheap agricultural inputs. Such foods often contain high amounts of salt, sugar, fat, and flavor additives and are engineered to have supernormal appetitive properties driving increased consumption. Ubiquitous access to convenient and inexpensive food also changed normative eating behavior, with more people snacking, eating in restaurants, and spending less time preparing meals at home. While such changes in the food environment provide a likely explanation of the obesity epidemic, definitive scientific demonstration is hindered by the difficulty in experimentally isolating and manipulating important variables at the population level.

Objective To quantify the feedback control of energy intake in response to long-term covert manipulation of energy balance in free-living humans. Methods A validated mathematical method was used to calculate energy intake changes during a 52-week placebo-controlled trial in 153 patients treated with canagliflozin, a sodium glucose co-transporter inhibitor that increases urinary glucose excretion, thereby resulting in weight loss without patients being directly aware of the energy deficit. The relationship between the body weight time course and the calculated energy intake changes was analyzed using principles from engineering control theory. Results It was discovered that weight loss leads to a proportional increase in appetite resulting in eating above baseline by ∼100 kcal/day per kilogram of lost weight—an amount more than threefold larger than the corresponding energy expenditure adaptations. Conclusions While energy expenditure adaptations have often been considered the main reason for slowing of weight loss and subsequent regain, feedback control of energy intake plays an even larger role and helps explain why long-term maintenance of a reduced body weight is so difficult.

Background: The carbohydrate–insulin model of obesity posits that habitual consumption of a high-carbohydrate diet sequesters fat within adipose tissue because of hyperinsulinemia and results in adaptive suppression of energy expenditure (EE). Therefore, isocaloric exchange of dietary carbohydrate for fat is predicted to result in increased EE, increased fat oxidation, and loss of body fat. In contrast, a more conventional view that “a calorie is a calorie” predicts that isocaloric variations in dietary carbohydrate and fat will have no physiologically important effects on EE or body fat. Objective: We investigated whether an isocaloric low-carbohydrate ketogenic diet (KD) is associated with changes in EE, respiratory quotient (RQ), and body composition. Design: Seventeen overweight or obese men were admitted to metabolic wards, where they consumed a high-carbohydrate baseline diet (BD) for 4 wk followed by 4 wk of an isocaloric KD with clamped protein. Subjects spent 2 consecutive days each week residing in metabolic chambers to measure changes in EE (EEchamber), sleeping EE (SEE), and RQ. Body composition changes were measured by dual-energy X-ray absorptiometry. Average EE during the final 2 wk of the BD and KD periods was measured by doubly labeled water (EEDLW). Results: Subjects lost weight and body fat throughout the study corresponding to an overall negative energy balance of ∼300 kcal/d. Compared with BD, the KD coincided with increased EEchamber (57 ± 13 kcal/d, P = 0.0004) and SEE (89 ± 14 kcal/d, P < 0.0001) and decreased RQ (−0.111 ± 0.003, P < 0.0001). EEDLW increased by 151 ± 63 kcal/d (P = 0.03). Body fat loss slowed during the KD and coincided with increased protein utilization and loss of fat-free mass. Conclusion: The isocaloric KD was not accompanied by increased body fat loss but was associated with relatively small increases in EE that were near the limits of detection with the use of state-of-the-art technology. This trial was registered at clinicaltrials.gov as {"type":"clinical-trial","attrs":{"text":"NCT01967563","term_id":"NCT01967563"}}NCT01967563.

Objective To measure long-term changes in resting metabolic rate (RMR) and body composition in participants of “The Biggest Loser” competition. Methods Body composition was measured by dual energy X-ray absorptiometry, and RMR was determined by indirect calorimetry at baseline, at the end of the 30-week competition and 6 years later. Metabolic adaptation was defined as the residual RMR after adjusting for changes in body composition and age. Results Of the 16 “Biggest Loser” competitors originally investigated, 14 participated in this follow-up study. Weight loss at the end of the competition was (mean ± SD) 58.3 ± 24.9 kg (P < 0.0001), and RMR decreased by 610 ± 483 kcal/day (P = 0.0004). After 6 years, 41.0 ± 31.3 kg of the lost weight was regained (P = 0.0002), while RMR was 704 ± 427 kcal/day below baseline (P < 0.0001) and metabolic adaptation was −499 ± 207 kcal/day (P < 0.0001). Weight regain was not significantly correlated with metabolic adaptation at the competition's end (r = −0.1, P = 0.75), but those subjects maintaining greater weight loss at 6 years also experienced greater concurrent metabolic slowing (r = 0.59, P = 0.025). Conclusions Metabolic adaptation persists over time and is likely a proportional, but incomplete, response to contemporaneous efforts to reduce body weight.