The gut microbiome is emerging as a key modulator of human energy balance. Prior studies in humans lacked the environmental and dietary controls and precision required to quantitatively evaluate the contributions of the gut microbiome. Using a Microbiome Enhancer Diet (MBD) designed to deliver more dietary substrates to the colon and therefore modulate the gut microbiome, we quantified microbial and host contributions to human energy balance in a controlled feeding study with a randomized crossover design in young, healthy, weight stable males and females (NCT02939703). In a metabolic ward where the environment was strictly controlled, we measured energy intake, energy expenditure, and energy output (fecal and urinary). The primary endpoint was the within-participant difference in host metabolizable energy between experimental conditions [Control, Western Diet (WD) vs. MBD]. The secondary endpoints were enteroendocrine hormones, hunger/satiety, and food intake. Here we show that, compared to the WD, the MBD leads to an additional 116 ± 56 kcals (P < 0.0001) lost in feces daily and thus, lower metabolizable energy for the host (89.5 ± 0.73%; range 84.2-96.1% on the MBD vs. 95.4 ± 0.21%; range 94.1-97.0% on the WD; P < 0.0001) without changes in energy expenditure, hunger/satiety or food intake (P > 0.05). Microbial 16S rRNA gene copy number (a surrogate of biomass) increases (P < 0.0001), beta-diversity changes (whole genome shotgun sequencing; P = 0.02), and fermentation products increase (P < 0.01) on an MBD as compared to a WD along with significant changes in the host enteroendocrine system (P < 0.0001). The substantial interindividual variability in metabolizable energy on the MBD is explained in part by fecal SCFAs and biomass. Our results reveal the complex host-diet-microbiome interplay that modulates energy balance.

Objective: This study investigated the associations of both physical activity time (PA) and energy expenditure (EE) with weight and fat mass (FM) loss in patients following Roux‐en‐Y gastric bypass (RYGB) surgery.Methods: Ninety‐six nondiabetic patients were included in this analysis. Post‐RYGB patients were randomized in one of two treatments: A 6‐month exercise training program (RYBG+EX) or lifestyle educational classes (RYGB). Body composition was assessed by dual‐energy X‐ray absorptiometry and computed tomography. Components of PA and EE were quantified by a multisensory device. Dose‐response relationships of both PA and EE with weight loss and body composition were explored according to quartiles of change in steps per day.Results: Patients in the highest quartiles of steps per day change lost more FM (3rd = −19.5 kg and 4th = −22.7 kg, P < 0.05) and abdominal adipose tissue (4th = −313 cm², P < 0.05), maintained skeletal muscle mass (3rd = −3.1 cm² and 4th = −4.5 cm², P <  0.05), and had greater reductions in resting metabolic rate. Decreases in sedentary EE and increases in light EE and age were significant predictors of both Δweight and ΔFM (R² = 73.8% and R² = 70.6%, respectively).Conclusions: Nondiabetic patients who perform higher, yet still modest, amounts of PA following RYGB have greater energy deficits and lose more weight and FM, while maintaining higher skeletal muscle mass.

Physiological variables such as maximal oxygen uptake (VO₂max), velocity at maximal oxygen uptake (vVO₂max), running economy (RE) and changes in lactate levels are considered the main factors determining performance in long-distance races. The aim of this review was to present the mathematical models available in the literature to estimate performance in the 5000 m, 10,000 m, half-marathon and marathon events. Eighty-eight articles were identified, selections were made based on the inclusion criteria and the full text of the articles were obtained. The articles were reviewed and categorized according to demographic, anthropometric, exercise physiology and field test variables were also included by athletic specialty. A total of 58 studies were included, from 1983 to the present, distributed in the following categories: 12 in the 5000 m, 13 in the 10,000 m, 12 in the half-marathon and 21 in the marathon. A total of 136 independent variables associated with performance in long-distance races were considered, 43.4% of which pertained to variables derived from the evaluation of aerobic metabolism, 26.5% to variables associated with training load and 20.6% to anthropometric variables, body composition and somatotype components. The most closely associated variables in the prediction models for the half and full marathon specialties were the variables obtained from the laboratory tests (VO₂max, vVO₂max), training variables (training pace, training load) and anthropometric variables (fat mass, skinfolds). A large gap exists in predicting time in long-distance races, based on field tests. Physiological effort assessments are almost exclusive to shorter specialties (5000 m and 10,000 m). The predictor variables of the half-marathon are mainly anthropometric, but with moderate coefficients of determination. The variables of note in the marathon category are fundamentally those associated with training and those derived from physiological evaluation and anthropometric parameters.

The literature is replete with clinical studies that characterize the structure, diversity, and function of the gut microbiome and correlate the results to different disease states, including obesity. Whether the microbiome has a direct impact on obesity has not been established. To address this gap, we asked whether the gut microbiome and its bioenergetics quantitatively change host energy balance. This paper describes the design of a randomized crossover clinical trial that combines outpatient feeding with precisely controlled metabolic phenotyping in an inpatient metabolic ward. The target population was healthy, weight-stable individuals, age 18-45 and with a body mass index ≤30 kg/m2. Our primary objective was to determine within-participant differences in energy balance after consuming a control Western Diet versus a Microbiome Enhancer Diet intervention specifically designed to optimize the gut microbiome for positive impacts on host energy balance. We assessed the complete energy-balance equation via whole-room calorimetry, quantified energy intake, fecal energy losses, and methane production. We implemented conditions of tight weight stability and balance between metabolizable energy intake and predicted energy expenditure. We explored key factors that modulate the balance between host and microbial nutrient accessibility by measuring enteroendocrine hormone profiles, appetite/satiety, gut transit and gastric emptying. By integrating these clinical measurements with future bioreactor experiments, gut microbial ecology analysis, and mathematical modeling, our goal is to describe initial cause-and-effect mechanisms of gut microbiome metabolism on host energy balance. Our innovative methods will enable subsequent studies on the interacting roles of diet, the gut microbiome, and human physiology. NCT02939703. The present study reference can be found here: https://clinicaltrials.gov/ct2/show/NCT02939703.

This study aimed to analyze the modulating effect of competition seriousness and competition level in the testosterone and cortisol responses in professional soccer player. Ninety five (95) soccer players were included in this study (professional, n = 39; semiprofessional, n = 27; amateur, n = 29) before and after training, friendly game and official games. Repeated measures ANOVA showed higher testosterone levels (F_(1,89) = 134, p < 0.0001, η²_p = 0.75) in professional soccer players, when compared with semiprofessional (p < 0.0001) or amateur athletes (p < 0.0001). After winning a competition game an increase in testosterone levels was observed in professionals (t = −3.456, p < 0.001), semiprofessionals (t = −4.400, p < 0.0001), and amateurs (t = −2.835, p < 0.009). In contrast, this momentary hormonal fluctuation was not observed after winning a friendly game or during a regular training day. Additionally, statistical analysis indicated that cortisol levels were lower in professional (t = −3.456, p < 0.001) and semiprofessional athletes (t = −4.400, p < 0.0001) than in amateurs (t = −2.835, p < 0.009). In soccer players a rise in testosterone was only observable when the team was faced with an actual challenge but did not support a different response between categories. Thus, the desire to achieve a goal (and keep the social status) may be one of the key reasons why testosterone levels rise promptly. Conversely, testosterone did not change after friendly games, which suggests these situations are not real goals and the players do not perceive an actual threat (in terms of dominance) more than the preparation for their next competitive game.