1,721,203 research outputs found
Supplemental data "The Role of Gut Hormones on Diet-induced Metabolic Flexibility in Healthy Adults"
No full textSupplemental data, "The Role of Gut Hormones on Diet-induced Metabolic Flexibility in Healthy Adults
Supplemental Data, "Exome sequencing identifies a nonsense variant in DAO associated with reduced energy expenditure in American Indians"
No full textSupplemental Data, "Exome sequencing identifies a nonsense variant in DAO associated with reduced energy expenditure in American Indians
Metabolic Determinants of Weight Gain in Humans
Full text linkOne of the fundamental challenges in obesity research is to identify subjects prone to weight gain so that obesity and its comorbidities can be promptly prevented or treated. The principles of thermodynamics as applied to human body energetics demonstrate that susceptibility to weight gain varies among individuals as a result of interindividual differences in energy expenditure and energy intake, two factors that counterbalance one another and determine daily energy balance and, ultimately, body weight change. This review focuses on the variability among individuals in human metabolism that determines weight change. Conflicting results have been reported about the role of interindividual differences in energy metabolism during energy balance in relation to future weight change. However, recent studies have shown that metabolic responses to acute, short‐term dietary interventions that create energy imbalance, such as low‐protein overfeeding or fasting for 24 hours, may reveal the underlying metabolic phenotype that determines the degree of resistance to diet‐induced weight loss or the propensity to spontaneous weight gain over time. Metabolically “thrifty” individuals, characterized by a predilection for saving energy in settings of undernutrition and dietary protein restriction, display a minimal increase in plasma fibroblast growth factor 21 concentrations in response to a low‐protein overfeeding diet and tend to gain more weight over time compared with metabolically “spendthrift” individuals. Similarly, interindividual variability in the causal relationship between energy expenditure and energy intake (“energy sensing”) and in the metabolic response to cold exposure (e.g., brown adipose tissue activation) seems, to some extent, to be indicative of individual propensity to weight gain. Thus, an increased understanding and the clinical characterization of phenotypic differences in energy metabolism among individuals (metabolic profile) may lead to new strategies to prevent weight gain or improve weight‐loss interventions by targeted therapies on the basis of metabolic phenotype and susceptibility to obesity in individual persons
Metabolic Factors Determining the Susceptibility to Weight Gain: Current Evidence
Full text linkPurpose of Review
There is substantial inter-individual variability in body weight change, which is not fully accounted by differences in daily energy intake and physical activity levels. The metabolic responses to short-term perturbations in energy intake can explain part of this variability by quantifying the degree of metabolic “thriftiness” that confers more susceptibility to weight gain and more resistance to weight loss. It is unclear which metabolic factors and pathways determine this human “thrifty” phenotype. This review will investigate and summarize emerging research in the field of energy metabolism and highlight important metabolic mechanisms implicated in body weight regulation in humans.
Recent Findings
Dysfunctional adipose tissue lipolysis, reduced brown adipose tissue activity, blunted fibroblast growth factor 21 secretion in response to low-protein hypercaloric diets, and impaired sympathetic nervous system activity might constitute important metabolic factors characterizing “thriftiness” and favoring weight gain in humans.
Summary
The individual propensity to weight gain in the current obesogenic environment could be ascertained by measuring specific metabolic factors which might open up new pathways to prevent and treat human obesity
Big Data and Precision Medicine
No full textThe increasing availability of biological data collected at different levels (e.g., cellular, tissue and whole-body levels) in large cohorts of individuals can be exploited to identify disease-related data features that can be used for tailoring medical treatments to each individual with the goal of ultimately improving population health. To achieve this goal, the analysis of Big Data by traditional (e.g., classical data mining approaches) and more sophisticated techniques (e.g., artificial intelligence algorithms) is expected to lead to individualized diagnosis and improved treatment by identifying the pathophysiology mechanisms underlying multiple, chronic medical conditions and diseases. The increasing relevance of Big Data analysis in precision medicine will improve clinical care by the prevention and early detection of diseases, personalization of interventions, ultimately improving health in the years to come
Do Statins Blunt the Beneficial Effect of Aerobic Exercise on Metabolic Flexibility?
Full text linkCommentar
How can we assess “thrifty” and “spendthrift” phenotypes?
Full text linkPurpose of review: There is a large inter-individual variability in the magnitude of body weight change that cannot be fully explained by differences in daily energy intake and physical activity levels and that can be attributed to differences in energy metabolism. Measuring the short-term metabolic response to acute changes in energy intake can better uncover this inter-individual variability and quantify the degree of metabolic thriftiness that characterizes an individual's susceptibility to weight gain and resistance to weight loss. This review summarizes the methods used to identify the individual-specific metabolic phenotype (thrifty vs. spendthrift) in research and clinical settings. Recent findings: The metabolic responses to short-term fasting, protein-imbalanced overfeeding, and mild cold exposure constitute quantitative factors that characterize metabolic thriftiness. Summary: The energy expenditure response to prolonged fasting is considered the most accurate and reproducible measure of metabolic thriftiness, likely because the largest energy deficit best captures interindividual differences in the extent of metabolic slowing. However, all the other dietary/environmental challenges can be used to quantify the degree of thriftiness using whole-room indirect calorimetry. Efforts are underway to identify alternative methods to assess metabolic phenotypes in clinical and outpatient settings such as the hormonal response to low-protein meals
VO2max is associated with measures of energy expenditure in sedentary condition but does not predict weight change
No full textBackground/Objectives
Energy expenditure measured under sedentary conditions predicts weight change but evidence that directly measured VO2max is associated with weight change is lacking. The aim of this study was to determine the associations of VO2max with measures of predominantly sedentary 24-h thermogenesis, and subsequent weight change.
Subjects/Methods
Three hundred fifty-seven individuals (162 females; 27 Blacks, 72 Caucasians, and 258 American Indians) had measures of body composition, resting metabolic rate (RMR), and intermittent treadmill run test for assessment of VO2max. On a separate day, 24-h energy expenditure (EE), diet-induced thermogenesis (DIT) expressed as “awake and fed” thermogenesis (AFT), sleeping metabolic rate (SMR), and spontaneous physical activity (SPA) were measured in a whole-room indirect calorimeter. Follow-up weight for 217 individuals was available (median follow-up time, 9.5 y; mean weight change, 12.4 ± 14.9 kg).
Results
After adjustment for fat free mass, fat mass, age, sex, and race, a higher VO2max was associated with a higher RMR (β = 68.2 kcal/day per L/min, P < 0.01) and 24-h EE (β = 62.2 kcal/day per L/min, P < 0.05) and including additional adjustment for energy intake higher AFT (β = 66.1 kcal/day per L/min, P = 0.01). Neither SMR (P > 0.2) nor SPA (P > 0.8) were associated with VO2max. VO2max at baseline did not predict follow-up weight after adjustment for baseline weight, follow-up time, sex, and race (P > 0.4).
Conclusion
VO2max is associated with measures of EE including 24-h EE, RMR and DIT implying a common mechanism regulating the energetics of skeletal muscle during exercise and thermogenesis. However, this did not translate to VO2max as a predictor of weight change
SAT-106 Plasma Interleukine-6 (IL-6) Concentration Is a Determinant of Free-Living Weight Change in Healthy Humans
No full textBackground: IL-6 is a multifunctional cytokine secreted by leukocytes and endothelial cells in multiple organs. Mice with IL-6 deficiency show mature-onset obesity due to decreased energy expenditure (EE) but no change in food intake. In humans, IL-6 concentration increases with obesity; however, the causal relationship with future weight gain is unclear. We investigated if fasting plasma IL-6 concentration predicts weight change in lean and overweight individuals. Methods: While residing in our clinical research unit, forty-nine healthy, weight-stable volunteers (37.5±10.8 y, 26.7±4.0 kg/m2BMI, 29.7±9.1% body fat; mean±SD, 39 men) with normal glucose regulation had 24-h EE measurements in a whole-room indirect calorimeter during energy balance and consuming a standard diet (50% carbohydrate, 20% protein). After at least 3 days on weight-maintaining diet and an overnight fast, plasma was collected for measurement of IL-6 concentrations by ELISA (Enzo Life Sciences, Farmingdale, NY; intra-assay CV=7.9%, inter-assay CV=10.5%, range=1.52-50 pg/mL) on 6 different days with values averaged to increase precision. Dual-energy X-ray absorptiometry (DXA) was used to measure body composition. Volunteers returned for follow up assessment of body weight and composition by DXA after 6 months (n=38) and 1 year (n=32). Results: Fasting IL-6 concentrations (geometric mean, 95% CI: 11.5, 10.0-13.2 pg/mL) did not differ by gender or race (p>0.05) nor were associated with age, BMI, % body fat, fat mass (FM), or fat-free mass (FFM). After adjustment for body composition and other known EE determinants, fasting IL-6 concentration was not associated with 24-h EE (p=0.33), sleeping EE (p=0.85), or respiratory quotient (p=0.76). However, higher IL-6 concentration was associated with weight gain at 6 months (r=0.51, p=0.001) and at 1 year (r=0.45, p=0.009), reflecting increases in FM (r=0.42, p=0.01 and r=0.50, p=0.04, respectively), but not FFM (p=0.09 and p=0.40). In a linear model, fasting IL-6 concentration at baseline was an independent predictor of weight gain at 6 months and 1 year [β=13.2 (95% CI: 5.7-20.8) and 15.1 kg (1.6-28.7) per 10-fold increase, p=0.001 and 0.03; respectively] after accounting for baseline weight, age, and sex. Conclusion: Although not influencing EE, higher plasma concentration of IL-6 predicts future weight and FM gain in humans, suggesting a potentially novel role of IL-6 in food intake and overeating
Protein oxidation in non-exercising healthy adults under varying dietary conditions: Physiological determinants, effects on fuel partitioning, and implications for body weight regulation
No full textBackground: Protein oxidation (PROTOX) typically accounts for the smallest fraction of daily energy expenditure (24hEE) in humans compared to carbohydrate and lipid oxidation. However, inter-individual differences in PROTOX may explain differences in fuel partitioning and body weight change. We aimed to elucidate the physiological determinants of PROTOX under controlled 24-h dietary conditions, including eucaloric feeding, fasting, and overfeeding diets with variable protein content. Methods: Eighty-six weight-stable healthy volunteers with normal glucose regulation (67 M/19F; age: 37 ± 10 years; BMI: 26.7 ± 4.5 kg/m2, body fat by DXA: 29.0 ± 9.8 %) underwent 24hEE measurements by whole-room calorimetry during energy balance (20 % protein, 50 % carbohydrate), different overfeeding diets (200 % of the daily eucaloric requirement), including three normal-protein (20 %) diets (balanced: 50 % carbohydrate; high-carbohydrate: 75 % carbohydrate; high-fat: 60 % fat), low-protein (3 %) and high-protein (30 %), and 24-h fasting in a randomized crossover design. Urine samples were collected during each 24-h dietary intervention for quantification of PROTOX and catecholamine excretion rates by nitrogen excretion and high-performance liquid chromatography, respectively. Results: PROTOX during energy balance (mean ± SD: 372 ± 78 kcal/day) was positively associated with protein intake (r = 0.39, p < 0.001), fat free mass (r = 0.35, p < 0.001), but not with fat mass (p = 0.24). Higher PROTOX was associated with higher 24-h urinary norepinephrine (partial r = 0.27, p = 0.01), but not epinephrine (p = 0.48), excretion rates. During normal-protein diets, higher PROTOX was associated with lower lipid oxidation, but not carbohydrate oxidation. Inter-individual variability in PROTOX did not predict changes in weight or body composition over two years. Conclusion: Dietary protein content, lean body mass, and sympathetic nervous system activity are key determinants of PROTOX. Although PROTOX did not predict free-living weight gain, increased PROTOX is associated with decreased lipid oxidation, underscoring its role in fuel partitioning and whole-body energy and substrate balance
- …
