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OBJECTIVE Mathematical equations that predict resting energy expenditure (REE) are widely used to derive calorie prescriptions during weight-loss interventions. Although such equations are known to introduce group- and individual-level error into REE prediction, their validity has largely been assessed in weight-stable populations. Therefore, this study sought to characterize how weight change affects the validity of commonly used REE predictive models throughout a 12-month weight-loss intervention. METHODS Changes in predictive error of four models (Mifflin-St-Jeor, Harris-Benedict, Owen, and World Health Organization/Food and Agriculture) were assessed at 1-, 6-, and 12-month time points in adults (n = 66, 76% female, aged 18-55 years, BMI = 27-45 kg/m2 ) enrolled in a randomized clinical weight-loss trial. RESULTS All equations experienced significant negative shifts in bias (measured - predicted REE) toward overprediction from baseline to 1 month (p < 0.05). Three equations showed reversal of bias in the positive direction (toward underprediction) from baseline to 12 months (p < 0.05). Early changes in bias were correlated with decreased fat-free mass (p ≤ 0.01). CONCLUSIONS Changes in body composition and mass during a 12-month weight-loss intervention significantly affected REE predictive error in adults with overweight and obesity. Weight history should be considered when using mathematical models to predict REE during periods of weight fluctuation.

Measuring individual circadian phase is important to diagnose and treat circadian rhythm sleep-wake disorders and circadian misalignment, inform chronotherapy, and advance circadian science. Initial findings using blood transcriptomics to predict the circadian phase marker dim-light melatonin onset (DLMO) show promise. Alternatively, there are limited attempts using metabolomics to predict DLMO and no known omics-based biomarkers predict dim-light melatonin offset (DLMOff). We analyzed the human plasma metabolome during adequate and insufficient sleep to predict DLMO and DLMOff using one blood sample. Sixteen (8 male/8 female) healthy participants aged 22.4 ± 4.8 years (mean ± SD) completed an in-laboratory study with 3 baseline days (9 h sleep opportunity/night), followed by a randomized cross-over protocol with 9-h adequate sleep and 5-h insufficient sleep conditions, each lasting 5 days. Blood was collected hourly during the final 24 h of each condition to independently determine DLMO and DLMOff. Blood samples collected every 4 h were analyzed by untargeted metabolomics and were randomly split into training (68%) and test (32%) sets for biomarker analyses. DLMO and DLMOff biomarker models were developed using partial least squares regression in the training set followed by performance assessments using the test set. At baseline, the DLMOff model showed the highest performance (0.91 R2 and 1.1 ± 1.1 h median absolute error ± interquartile range [MdAE ± IQR]), with significantly ( p < 0.01) lower prediction error versus the DLMO model. When all conditions (baseline, 9 h, and 5 h) were included in performance analyses, the DLMO (0.60 R2; 2.2 ± 2.8 h MdAE; 44% of the samples with an error under 2 h) and DLMOff (0.62 R2; 1.8 ± 2.6 h MdAE; 51% of the samples with an error under 2 h) models were not statistically different. These findings show promise for metabolomics-based biomarkers of circadian phase and highlight the need to test biomarkers that predict multiple circadian phase markers under different physiological conditions.

PURPOSE This study aimed to determine if energy intake and appetite regulation differ in response to an acute bout of resistance exercise (REx) versus aerobic exercise (AEx). METHODS Physically inactive adults (n = 24, 35% ± 2% body fat, 50% female) completed three conditions: AEx (walking at 65%-70% heart rate max for 45 min), REx (1 set to failure of 12 exercises), and sedentary control (SED). Each condition was initiated in the postprandial state (35 min after breakfast). Appetite (visual analog scale for hunger, satiety, and prospective food consumption) and hormones (ghrelin, peptide YY (PYY), and glucagon-like peptide-1 (GLP-1)) were measured before and 30, 90, 120, 150, and 180 min after a standardized breakfast. Area under the curve was calculated using the trapezoid method. Ad libitum energy intake was evaluated at a lunch meal after the 180-min measurements. RESULTS No differences in ad libitum energy intake (REx, 991 ± 68; AEx, 937 ± 65; SED, 944 ± 76 kcal; P = 0.50) or appetite ratings (all, P > 0.05) were detected. The area under the curve for ghrelin, PYY, and GLP-1 were all lower after REx versus AEx (ghrelin: 130,737 ± 4928 for REx; 143,708 ± 7500 for AEx (P = 0.006); PYY: 20,540 ± 1177 for REx, 23,812 ± 1592 for AEx (P = 0.001); and GLP-1: 1314 ± 93 for REx, 1615 ± 110 for AEx (P = 0.013)). Neither exercise condition significantly differed from SED. CONCLUSIONS Acute REx lowers both orexigenic (ghrelin) and anorectic (PYY and GLP-1) gut peptides compared with acute AEx. Ad libitum energy intake did not increase compared with SED in either exercise condition, indicating both exercise modalities have appetite and energy intake suppressing effects. Future work is needed to determine if exercise of differing modalities influences chronic appetite regulation.

OBJECTIVE Identifying predictors of weight loss and clinical outcomes may increase understanding of individual variability in weight loss response. We hypothesized that baseline multiomic features, including DNA methylation (DNAme), metabolomics, and gut microbiome, would be predictive of short-term changes in body weight and other clinical outcomes within a comprehensive weight loss intervention. METHODS Healthy adults with overweight or obesity (n = 62, age 18-55 years, BMI 27-45 kg/m2 , 75.8% female) participated in a 1-year behavioral weight loss intervention. To identify baseline omic predictors of changes in clinical outcomes at 3 and 6 months, whole-blood DNAme, plasma metabolites, and gut microbial genera were analyzed. RESULTS A network of multiomic relationships informed predictive models for 10 clinical outcomes (body weight, waist circumference, fat mass, hemoglobin A1c , homeostatic model assessment of insulin resistance, total cholesterol, triglycerides, C-reactive protein, leptin, and ghrelin) that changed significantly (P

Objective The purpose of this study was to determine whether suppression of ovarian function (gonadotropin-releasing hormone agonist [GnRHAG ]) for 24 weeks in premenopausal women approaching menopause causes changes in body composition and a decline in free-living physical activity energy expenditure (PAEE) and whether endurance exercise training attenuates the changes. Methods Premenopausal women who were approaching menopause (mean [SD]: age 46 [3] years, BMI 26.3 [4.8] kg/m2 ) were randomized to 24 weeks of GnRHAG (n = 14), GnRHAG + Exercise (n = 11), or placebo (n = 9). Endurance exercise was performed 4 days per week with the goal of expending 200 to 300 kcal per session. Primary outcome measurements included body composition by dual-energy x-ray absorptiometry, total daily energy expenditure (TDEE), and PAEE by doubly labeled water, and resting energy expenditure (REE) by indirect calorimetry. Results Changes in TDEE, PAEE, REE, or body composition were not different between groups. However, within the GnRHAG group, fat mass increased (mean [SE]: total 1.7 [0.4] kg, trunk 0.9 [0.2] kg, leg 0.6 [0.2] kg) and fat-free leg mass decreased (mean [SE]: -0.4 [0.2] kg) significantly. Conclusions In premenopausal women approaching menopause, ovarian hormone suppression resulted in increased adiposity without alterations in TDEE, PAEE, or REE.

Objective This study aimed to perform a preliminary investigation of the impact of combined hormonal contraceptive (CHC) use on weight loss during an 18-month behavioral weight-loss trial. Methods Adults (n = 170; 18-55 years; BMI 27-42 kg/m2 ) received a weight-loss intervention that included a reduced-calorie diet, a progressive exercise prescription, and group-based behavioral support. Premenopausal women (n = 110) were classified as CHC users (CHC, n = 17) or non-CHC users (non-CHC, n = 93). Changes in weight were examined within groups using a linear mixed model, adjusted for age and randomized group assignment. Results At 6 M, weight was reduced from baseline in both CHC (mean, -6.7 kg; 95% CI: -9.8 to -3.7 kg) and non-CHC (-9.1 kg; -9.1 to -6.4 kg). Between 6 and 18 M, CHC regained weight (4.9 kg; 0.9 to 8.9 kg), while weight remained relatively unchanged in non-CHC (-0.1 kg; -1.8 to 1.6 kg). At 18 M, weight was relatively unchanged from baseline in CHC (-1.8 kg; -7.3 to 3.6 kg) and was reduced from baseline in non-CHC (-7.9 kg; -10.2 to -5.5 kg). Conclusions In this secondary data analysis, CHC use was associated with weight regain after initial weight loss. Prospective studies are needed to further understand the extent to which CHC use influences weight loss and maintenance.

Introduction Exercise can cause a decrease in serum ionized calcium (iCa) concentration, which stimulates parathyroid hormone (PTH) secretion and activates bone resorption. We postulated that dermal Ca loss during cycling exercise is the major determinant of the serum iCa, PTH, and bone resorption (C-terminal telopeptide of type 1 collagen [CTX]) responses. Methods To investigate this, women (n = 13) and men (n = 12) age 18 to 45 yr performed the same exercise bout under cool (18°C) and warm (26°C) conditions. Exercise was 60 min of cycling at ~75% of peak aerobic power. Sweat samples were obtained during exercise using a skin patch method, and blood samples were obtained before and during exercise and during 60 min of recovery. Results Sweat volume and estimated sweat Ca loss were 50% higher for the warm condition than the cool condition. Despite this, there were no differences between thermal conditions in the changes (mean, 95% confidence interval [95% CI]) in iCa (cool, -0.07 mg·dL; 95% CI, -0.16 to 0.03); warm, -0.07 mg·dL; 95% CI, -0.20 to 0.05), PTH (cool, 34.4 pg·mL; 95% CI, 23.6-45.2; warm: 35.8 pg·mL; 95% CI, 22.4-49.1), or CTX (cool, 0.11 ng·mL; 95% CI, 0.08-0.13; warm, 0.15 ng·mL; 95% CI, 0.11-0.18). Adjusting for exercise-related shifts in plasma volume revealed a marked decline in vascular iCa content in the first 15 min of exercise (cool, -0.85 mg·dL; 95% CI, -1.01 to -0.68; warm, -0.85 mg·dL; 95% CI, -1.05 to -0.66), before substantial sweat Ca loss had occurred. Conclusions This indicates that dermal Ca loss was not the primary trigger for the increases in PTH and CTX during exercise. Further research is necessary to understand the causes and consequences of the disruption in Ca homeostasis during exercise and specifically the extravascular shift in iCa.

Many adults cite exercise as a primary strategy for losing weight, yet exercise alone is modestly effective for weight loss and results in variable weight loss responses. It is possible that some of the variability in weight loss may be explained by the time of day that exercise is performed. Few studies have directly compared the effects of exercise performed at different times of the day (i. e., morning versus evening exercise). Results from these existing studies are mixed with some studies demonstrating superior weight and fat mass loss from morning exercise, while other studies have found that evening exercise may be better for weight management. Exercise timing may alter modifiable lifestyle behaviors involved in weight management, such as non-exercise physical activity, energy intake, and sleep. The purpose of this review is to summarize evidence for and against time-of-day dependent effects of exercise on weight management. Although limited, we also review studies that have examined the effect of exercise timing on other lifestyle behaviors linked to body weight regulation. While exercise at any time of day is beneficial for health, understanding whether there is an optimal time of day to exercise may advance personalized treatment paradigms for weight management.

BACKGROUND: Individuals with overweight or obesity commonly underreport energy intake (EI), but it is unknown if the tendency to underreport persists in formerly obese individuals who lose significant weight and maintain their weight loss over long periods of time. OBJECTIVE: Assess the accuracy of self-reported EI in successful weight loss maintainers (WLM) compared with controls of normal body weight (NC) and controls with overweight/obesity (OC). METHODS: Participants for this case-controlled study were recruited in 3 groups: WLM [n = 26, BMI (in kg/m(2)) 24.1 ± 2.3; maintaining ≥13.6 kg weight loss for ≥1 y], NC (n = 33, BMI 22.7 ± 1.9; similar to current BMI of WLM), and OC (n = 32, BMI 34.0 ± 4.6; similar to pre–weight loss BMI of WLM). Total daily energy expenditure (TDEE) was measured over 7 d using the doubly labeled water (DLW) method, and self-reported EI was concurrently measured from 3-d diet diaries. DLW TDEE and self-reported EI were compared to determine accuracy of self-reported EI. RESULTS: WLM underreported EI (median, interquartile range) (–605, –915 to –314 kcal/d) to a greater degree than NC (–308, –471 to –68 kcal/d; P < 0.01) but not more than OC (–310, –970 to 18 kcal/d; P = 0.21). WLM also showed a greater degree of relative underreporting (–25.3%, –32.9% to –12.5%) compared with NC (–14.3%, –19.6% to –3.1%; P = 0.02) but not OC (–11.2%, –34.1% to –0.7%; P = 0.16). A greater proportion of WLM was classified as underreporters (30.8%) than NC (9.1%; P = 0.05) but not OC (28.1%; P = 1.00). CONCLUSIONS: WLM underreported EI in both absolute and relative terms to a greater extent than NC but not OC. These findings call into question the accuracy of self-reported EI in WLM published in previous studies and align with recent data suggesting that WLM rely less on chronic EI restriction and more on high levels of physical activity to maintain weight loss. This trial was registered at clinicaltrials.gov as NCT03422380.

Background Substantial interindividual variability in response to behavioral weight loss interventions remains a critical challenge in obesity treatment. An improved understanding of the complex factors that contribute to this variability may improve obesity treatment outcomes. Objective To identify weight change trajectories during a behavioral weight loss intervention and to explore differences between trajectory groups in sociodemographic, biologic, behavioral, and psychosocial factors. Methods Adults (n = 170, 40 ± 9 years, BMI 34 ± 4 kg/m2, 84% female) participated in an 18‐month behavioral weight loss intervention. Weight was measured at 0, 3, 6, 9, 12, 15, 18, and 24 months. Among participants with at least two weights after baseline (n = 140), clusters of longitudinal trajectories of changes in weight were identified using a latent class growth mixture model. The association between baseline factors or changes in factors over time and trajectory group was examined. Results Two weight change trajectories were identified: “weight regainers” (n = 91) and “weight loss maintainers” (n = 49). Black participants (90%, 19/21) were more likely than non‐Black participants to be regainers versus maintainers (p

Purpose of Review This paper presents a review of the current literature in support of a model explaining the relationships between sleep health and risk for type 2 diabetes in adolescents. Recent Findings Short sleep duration is associated with risk of developing obesity in youth. Sleep restriction increases energy expenditure, but also increases hunger, appetite, and food intake, causing positive energy balance, impacting appetite-regulating hormones, and leading to increased eating late at night. Insufficient sleep may lead to reduced physical activity and greater sedentary behaviors. In addition, short sleep duration is associated with reduced insulin sensitivity. The cumulative negative consequences of insufficient sleep increase risk for type 2 diabetes. Applications to clinical care, public policy, and future research are discussed. Summary Insufficient sleep in adolescence increases risk for type 2 diabetes directly through impact on insulin sensitivity and indirectly through increased dietary intake, sedentary activity, and weight gain.

Objective The objectives of this study were to describe sitting, standing, and stepping patterns for people with lower limb amputation (LLA) and to compare sitting, standing, and stepping between people with dysvascular LLA and people with traumatic LLA. Methods Participants with dysvascular or traumatic LLA were included if their most recent LLA was at least 1 year earlier, they were ambulating independently with a prosthesis, and they were between 45 and 88 years old. Sitting, standing, and stepping were measured using accelerometry. Daily sitting, standing, and stepping times were expressed as percentages of waking time. Time spent in bouts of specified durations of sitting (90 minutes), standing (0–1, 1–5, and >5 minutes), and stepping (0–1, 1–5, and >5 minutes) was also calculated. Results Participants (N = 32; mean age = 62.6 [SD = 7.8] years; 84% men; 53% with dysvascular LLA) spent most of the day sitting (median = 77% [quartile 1 {Q1}–quartile 3 {Q3} = 67%–84%]), followed by standing (median = 16% [Q1–Q3 = 12%–27%]) and stepping (median = 6% [Q1–Q3 = 4%–9%]). One-quarter (median = 25% [Q1–Q3 = 16%–38%]) of sitting was accumulated in bouts of >90 minutes, and most standing and stepping was accrued in bouts of Conclusion Participants had high daily volumes of long durations of sitting. Further, these individuals accumulated most physical activity in bouts of Impact High levels of sedentary behavior and physical inactivity patterns may place people with LLA at greater mortality risk relative to the general population. Interventions to minimize sedentary behaviors and increase physical activity are potential strategies for improving poor outcomes of physical therapy after LLA.

Study Objectives Insufficient sleep is believed to promote positive energy balance (EB) and weight gain. Increasing weekend sleep duration to “recover” from weekday sleep loss is common, yet little is known regarding how weekend recovery sleep influences EB. We conducted a randomized controlled trial to assess how: (1) 2 days and 8 days of insufficient sleep and (2) ad libitum weekend recovery sleep impact EB (energy intake [EI] – energy expenditure [EE]). Methods Following ten baseline days with 9 h per night sleep opportunities, participants completed one of three 10-day experimental protocols with ad libitum EI: control (9 h sleep opportunities; n = 8; 23 ± 5 years [mean ± SD]); sleep restriction (SR; 5 h sleep opportunities; n = 14; 25 ± 5 years); sleep restriction with weekend recovery sleep (SR + WR; 5 days insufficient sleep, 2 days ad libitum weekend recovery sleep, 3 days recurrent insufficient sleep; n = 14; 27 ± 4 years). Results Twenty-four hour EB increased (p < 0.001; main effect) by an average of 797.7 ± 96.7 (±SEM) kcal during the 10-day experimental protocol versus baseline with no significant differences between groups. Percent change from baseline in 24 h-EE was higher (p < 0.05) on day 2 of insufficient sleep (SR and SR + WR groups; 10 ± 1%) versus adequate sleep (control group; 4 ± 3%). Conclusions In this between-group study, the effects of adequate sleep and insufficient sleep, with or without or weekend recovery sleep, on 24 h-EB were similar. Examining EB and body weight changes using within-subject cross-over designs and “free-living” conditions outside the laboratory (e.g. sleep extension) are needed to advance our understanding of the links between insufficient sleep, weekend recovery sleep and weight-gain.

BACKGROUND/OBJECTIVES: Individuals successful at weight loss maintenance engage in high amounts of physical activity (PA). Understanding how and when weight loss maintainers accumulate PA within a day and across the week may inform PA promotion strategies and recommendations for weight management. METHODS: We compared patterns of PA in a cohort of weight loss maintainers (WLM, n=28, maintaining ≥13.6 kg weight loss for ≥1 year, BMI 23.6 ± 2.3 kg/m(2)), controls without obesity (NC, n=30, BMI similar to current BMI of WLM, BMI 22.8 ± 1.9 kg/m(2)), and controls with overweight/obesity (OC, n=26, BMI similar to pre-weight loss BMI of WLM, 33.6 ± 5.1 kg/m(2)). PA was assessed during 7 consecutive days using the activPAL(™) activity monitor. The following variables were quantified; sleep duration, sedentary time (SED), light-intensity PA (LPA), moderate-to-vigorous intensity PA (MVPA), and steps. Data were examined to determine differences in patterns of PA across the week and across the day using mixed effect models. RESULTS: Across the week, WLM engaged in ≥60 minutes of MVPA on 73% of days, significantly more than OC (36%, p

BACKGROUND: Baseline cardiovascular fitness may be a significant predictor of future success in a comprehensive behavioral weight loss program (BWLP). Yet, few studies have examined the association between baseline fitness and future weight loss. PURPOSE: To determine the association between baseline fitness and changes in body weight and device-measured levels of moderate-to-vigorous physical activity (MVPA) during a BWLP. METHODS: Adults (n=85) were enrolled in an 18-month BWLP combining a calorie-restricted diet, group-based behavioral support, and 6 months of supervised exercise (progressing to 300 min/wk of moderate-intensity) followed by 12 months of unsupervised exercise. Data from 60 completers (age 41.0±9.5 years, BMI 34.6±4.2 kg/m2, 80% female) were used in this analysis. MVPA was measured over 1 week with the Sensewear Armband at months 0, 6, 12, and 18. Fitness (VO2max) was measured on a treadmill using indirect calorimetry and categorized based on published age and sex norms (Physical Fitness Specialist Certification Manual, 1997). A linear mixed effects model with unstructured covariance was used to examine the association between baseline fitness category and changes in body weight, total MVPA, and MVPA in bouts ≥10 min at the four time points. RESULTS: Of the 60 completers, 33% (n=20) were classified as having very poor fitness, 45% (n=27) poor, 18% (n=11) fair, 3% (n=2) good, and 0% (n=0) excellent or superior. Due to the low proportion of participants categorized as having fair or better fitness, we created a binary fitness variable (very poor vs. poor or better). Baseline BMI was higher in those in the very poor category compared to those in the poor or better category (36.2±4.2 vs 33.7±4.0, p=0.03). There were no significant differences between the two fitness categories in weight change at 6 or 12 months. However, at 18 months, mean weight loss was 4.3±1.7 kg in those in the very poor category and 8.2±1.2 kg in those in the poor or better category, with a marginally significant between-group difference (p=0.07). There were no differences in changes in total or bout MVPA. However, those with very poor fitness had lower bout MVPA at baseline vs. those with poor or better fitness (16±20 vs 33±31 min/d, p=0.03). At 18 months, both groups increased bout MVPA, however bout MVPA remained lower in the very poor vs. poor or better group (24±29 vs 42±29 min/d, p=0.03). Total MVPA showed a similar pattern. CONCLUSION: Baseline fitness may moderate 18-month weight loss, as those with very poor fitness lost less weight compared to those with poor or better fitness levels. Those with poor or better fitness at baseline achieved significantly higher mean levels of MVPA at 18 months compared to those with very poor fitness. Participants with very poor fitness at baseline may require additional exercise support during a BWLP to achieve the high levels of MVPA recommended for weight loss maintenance.

Increased risk of obesity and diabetes in shift workers may be related to food intake at adverse circadian times. Early morning shiftwork represents the largest proportion of shift workers in the United States, yet little is known about the impact of food intake in the early morning on metabolism. Eighteen participants (9 female) completed a counterbalanced 16 day design with two conditions separated by ~1 week: 8 h sleep opportunity at habitual time and simulated early morning shiftwork with 6.5 h sleep opportunity starting ~1 h earlier than habitual time. After wake time, resting energy expenditure (REE) was measured and blood was sampled for melatonin and fasting glucose and insulin. Following breakfast, post-prandial blood samples were collected every 40 min for 2 h and the thermic effect of food (TEF) was assessed for 3.25 h. Total sleep time was decreased by ~85 min (p &lt, 0.0001), melatonin levels were higher (p &lt, 0.0001) and post-prandial glucose levels were higher (p &lt, 0.05) after one day of simulated early morning shiftwork compared with habitual wake time. REE was lower after simulated early morning shiftwork, however, TEF after breakfast was similar to habitual wake time. Insufficient sleep and caloric intake during a circadian phase of high melatonin levels may contribute to metabolic dysregulation in early morning shift workers.

Study ObjectiveIdentify small molecule biomarkers of insufficient sleep using untargeted plasma metabolomics in humans undergoing experimental insufficient sleep.MethodsWe conducted a crossover laboratory study where 16 normal-weight participants (eight men; age 22 ± 5 years; body mass index < 25 kg/m2) completed three baseline days (9 hours sleep opportunity per night) followed by 5-day insufficient (5 hours sleep opportunity per night) and adequate (9 hours sleep opportunity per night) sleep conditions. Energy balanced diets were provided during baseline, with ad libitum energy intake provided during the insufficient and adequate sleep conditions. Untargeted plasma metabolomics analyses were performed using blood samples collected every 4 hours across the final 24 hours of each condition. Biomarker models were developed using logistic regression and linear support vector machine (SVM) algorithms.ResultsThe top-performing biomarker model was developed by linear SVM modeling, consisted of 65 compounds, and discriminated insufficient versus adequate sleep with 74% overall accuracy and a Matthew’s Correlation Coefficient of 0.39. The compounds in the top-performing biomarker model were associated with ATP Binding Cassette Transporters in Lipid Homeostasis, Phospholipid Metabolic Process, Plasma Lipoprotein Remodeling, and sphingolipid metabolism.ConclusionWe identified potential metabolomics-based biomarkers of insufficient sleep in humans. Although our current biomarkers require further development and validation using independent cohorts, they have potential to advance our understanding of the negative consequences of insufficient sleep, improve diagnosis of poor sleep health, and could eventually help identify targets for countermeasures designed to mitigate the negative health consequences of insufficient sleep.

Exercise is accepted as a method to improve weight loss maintenance; however, the mechanisms by which this occurs have yet to be elucidated. In this pilot study, 13 women with obesity underwent a structured weight loss program (goal 8%–10% weight loss) and were then randomized to either a 12-wk diet (n = 7) or an aerobic exercise training (n = 6) intervention aimed at maintaining weight loss. At baseline, post–weight loss, and following the weight loss maintenance interventions, measurements of appetite (hunger and satiety) and appetite-regulating hormones (leptin, ghrelin, peptide tyrosine tyrosine, and glucagon-like peptide 1) were obtained after an overnight fast and for 3 h after a standardized test meal. Ad libitum energy intake was measured at a lunch meal. During the weight loss phase, participants lost 9.1% ± 1.1% of baseline body weight. Participants in both groups maintained weight loss during the 12-wk weight loss maintenance intervention. No differences in fasting leptin (P = 0.68) or in ghrelin (P = 0.30), peptide tyrosine tyrosine (P = 0.93), and glucagon-like peptide 1 (P = 0.98) area under the curve were detected between groups. Similarly, ratings of hunger (P = 0.99) and satiety (P = 0.65) area under the curve after the standardized test meal also did not differ between the groups nor did ad libitum energy intake at lunch. In summary, the 12-wk diet and exercise interventions were equally effective at maintaining weight loss in women, and no differences in measures of appetite regulation and food intake were found.

Background Physical activity remains low and nearly unchanged from preoperative levels following total knee arthroplasty (TKA), and this is thought to underlie long-term functional limitations, secondary health problems, and higher health care costs after TKA. Objective Our objective is to determine whether a telehealth-based intervention could improve physical activity and functional outcomes after TKA. Design The design is a 2-arm, parallel, assessor-blinded, randomized controlled trial with baseline, midintervention, postintervention, and 6-month follow-up assessments. Setting The setting is one academic medical center and one Veterans Affairs health care system. Participants One hundred US military veterans (aged 50–85 years) scheduled for unilateral TKA will participate in this study. Intervention The telehealth-based intervention to change physical activity behavior will be delivered through 10 sessions each of 30 minutes over a 12-week period. Participants will be provided with a wearable physical activity monitor to receive feedback on step count and guide goal-setting. Control participants will receive telehealth-based education on nonbehavioral aspects of health for the same frequency and duration as the intervention group. For both groups, telehealth sessions will occur concurrently with standardized outpatient rehabilitation. Measurements The primary outcome will be change in physical activity, assessed as daily step counts measured using an accelerometer-based sensor. Secondary outcomes will be measured using the Life-Space Assessment questionnaire and change in physical function (30-Second Chair-Stand Test, Timed “Up & Go” Test, Six-Minute Walk Test, Western Ontario and McMaster Universities Osteoarthritis Index, and Veterans RAND 12-Item Health Survey). Limitations Participant and interventionist blinding is not possible. Conclusions This trial will assess the efficacy of a novel behavior-change intervention to improve physical activity and physical function in patients after TKA. Effective physical activity behavior change could provide clinicians with a technique to augment current practice and resolve poor physical activity outcomes, long-term health problems, and high costs following TKA.

BACKGROUND: Evidence in humans is equivocal in regards to whether resting energy expenditure (REE) decreases to a greater extent than predicted for the loss of body mass with weight loss, and whether this disproportionate decrease in REE persists with weight-loss maintenance. OBJECTIVES: We aimed to1) determine if a lower-than-predicted REE is present in a sample of successful weight-loss maintainers (WLMs) and 2) determine if amount of weight loss or duration of weight-loss maintenance are correlated with a lower-than-predicted REE in WLMs. DESIGN: Participants (18–65 y old) were recruited in 3 groups: WLMs (maintaining ≥13.6 kg weight loss for ≥1 y, n = 34), normal-weight controls [NCs, body mass index (BMI; in kg/m(2)) similar to current BMI of WLMs, n = 35], and controls with overweight/obesity (OCs, BMI similar to pre–weight-loss maximum BMI of WLMs, n = 33). REE was measured (REE(m)) with indirect calorimetry. Predicted REE (REE(p)) was determined via 1) a best-fit linear regression developed with the use of REE(m), age, sex, fat-free mass, and fat mass from our control groups and 2) three standard predictive equations. RESULTS: REE(m) in WLMs was accurately predicted by equations developed from NCs and OCs (±1%) and by 3 standard predictive equations (±3%). In WLMs, individual differences between REE(m) and REE(p) ranged from −257 to +163 kcal/d. A lower REE(m) compared with REE(p) was correlated with amount of weight lost (r = 0.36, P

Reducing estrogen in women results in decreases in energy expenditure, but the mechanism(s) remain largely unknown. We postulate that the loss of estrogens in women is associated with increased accumulation of bone marrow (BM)-derived adipocytes in white adipose tissue, decreased activity of brown adipose tissue, and reduced levels of physical activity. Regular exercise may counteract the effects of estrogen deficiency.

PURPOSE: To determine the effects of 5 months of ovarian hormone suppression in pre-menopausal women on objectively measured physical activity (PA). METHODS: Participants (age = 35±8 yr; body mass index = 27±6 kg·m(−2)) received monthly intramuscular injections of gonadotropin releasing hormone agonist therapy (GnRH(AG)) which suppresses pituitary gonadotropins and results in suppression of ovarian sex hormones. Women were randomized to receive concurrent transdermal E(2) (GnRH(AG)+E(2); n=30) or placebo (GnRH(AG)+PL, n=31). PA was assessed for 1 week before and during each month of the 5-month intervention using a hip-worn accelerometer (Actical, Mini Mitter Co., Inc., Bend, OR). Estimates of time spent in sedentary, light, and moderate-to-vigorous physical activity (MVPA) were derived using a previously published equation. Subsets of participants in each group were also randomized to a supervised progressive resistance exercise training program. RESULTS: Total MVPA tended towards being higher (p=0.08) in the GnRH(AG)+E(2) group at month 4. There were no significant effects of intervention or time in sedentary or light PA. In the subset of women who did not participate in structured exercise training for which Actical data were obtained (N=16 in each group), total MVPA was higher at month 4 (p=0.01). CONCLUSION: Physical activity levels appear to be maintained at a higher level in women undergoing pharmacological suppression of ovarian function with E(2) add back when compared with women treated with placebo. These data provide proof of concept data that E(2) contributes to the regulation of PA in humans. However, given the exploratory nature of this study, future confirmatory investigations will be necessary.

BackgroundSeveral indirect calorimetry (IC) instruments are commercially available, but comparative validity and reliability data are lacking. Existing data are limited by inconsistencies in protocols, subject characteristics, or single-instrument validation comparisons. The aim of this study was to compare accuracy and reliability of metabolic carts using methanol combustion as the cross-laboratory criterion.MethodsEight 20-minute methanol burn trials were completed on 12 metabolic carts. Respiratory exchange ratio (RER) and percent O-2 and CO2 recovery were calculated.ResultsFor accuracy, 1 Omnical, Cosmed Quark CPET (Cosmed), and both Parvos (Parvo Medics trueOne 2400) measured all 3 variables within 2% of the true value; both DeltaTracs and the Vmax Encore System (Vmax) showed similar accuracy in measuring 1 or 2, but not all, variables. For reliability, 8 instruments were shown to be reliable, with the 2 Omnicals ranking best (coefficient of variation [CV] ConclusionsOmnical, Parvo, Cosmed, and DeltaTrac had greater accuracy and reliability. The small number of instruments tested and expected differences in gas calibration variability limits the generalizability of conclusions. Finally, humidity and temperature could be modified in the laboratory to optimize IC conditions.

Introduction U.S. adolescents have high rates of insufficient sleep. School closures and stay-at-home orders were implemented to mitigate disease spread during the Coronavirus 2019 (COVID-19) pandemic. Without the restriction of imposed early school start times, we hypothesized that adolescents would have longer, later, and less variable sleep compared to pre-COVID-19. We further hypothesized these changes would be associated with increased and later light exposure. Methods High school students age 14–19 years with Results Participants (N=16) were 16.5 ±1.2-years-old at Pre-COVID-19, 70.6% female, 68.8% White, and 25.1% Hispanic. Youth were participating in online learning due to in-person school closures and only 2 participants (14.3%) had a set start time, while the remainder reported learning per their own schedule. Youth obtained approximately one hour more weekday sleep per night during the COVID-19 week compared to Pre-COVID-19 (p Conclusion An unintended effect of the switch to online learning may have been affording adolescents the opportunity to obtain longer and more regular sleep. Understanding the impact of these changed sleep behaviors on daytime functioning, academic performance, and health outcomes is particularly urgent as schools plan for the remainder of the academic year and eventual return to in-person learning. Support (if any) K23DK117021 to SLS

BACKGROUND: Prior data demonstrated three weeks of sleep restriction and concurrent circadian disruption uncoupled bone turnover markers (BTMs), indicating decreased bone formation and no change or increased bone resorption. The effect of insufficient sleep with or without ad libitum weekend recovery sleep on BTMs is unknown. METHODS: BTMs were measured in stored serum from 20 healthy adults randomized to one of three study groups consisting of a control group (N = 3 men; 9 h/night) or one of two nocturnal sleep restriction groups in an inpatient laboratory environment. One Sleep Restriction group (“SR”; N = 9; 4 women) had 5h sleep opportunity per night for nine nights. The other sleep restriction group had an opportunity for ad libitum Weekend Recovery sleep (“WR”; N = 8; 4 women) after four nights of 5h sleep opportunity per night. Food intake was energy balanced at baseline and ad libitum thereafter. Fasted morning BTM levels and hourly 24h melatonin levels were obtained on study days 3 (baseline), 5 (after 1 night of sleep restriction for WR and SR), and 11 (after a sleep restricted workweek with weekend recovery sleep in WR or 7 nights of sleep restriction in SR). Linear mixed-effects modeling was used to examine the effect of study duration (e.g., change over time), study condition, age, and sex on BTMs. Pearson correlations were used to determine associations between changes in BTMs and changes in weight and morning circadian misalignment (i.e., duration of high melatonin levels after wake time). RESULTS: There was no significant difference between the three study groups in change over time (p ≥ 0.4 for interaction between assigned group and time for all BTMs), adjusted for age and sex. There was no significant change in N-terminal propeptide of procollagen type I (P1NP), osteocalcin, or C-telopeptide of type I collagen (CTX) from baseline to day 11 (all p ≥ 0.3). In women

Purpose To compare energy intake (EI) and appetite regulation responses between men and women following acute bouts of aerobic (AEx), resistance exercise (REx), and a sedentary control (CON). Methods Men and women (n = 24; 50% male) with overweight/obesity, matched on age (32.3 ± 2 vs. 36.8 ± 2 yrs, p = 0.14) and BMI (28.1 ± 1.2 vs 29.0 ± 1.5 kg/m2, p = 0.64) completed 3 conditions: 1) AEx (65–70% of age-predicted maximum heart rate for 45 min); 2) REx (1-set to failure on 12 exercises); and 3) CON. Each condition was initiated in the post-prandial state (35 min following consumption of a standardized breakfast). Appetite (visual analog scale for hunger, satiety, and prospective food consumption [PFC]) and hormones (ghrelin, PYY, and GLP-1) were measured in the fasted state and every 30 min post-prandially for 3 h. Post-exercise ad libitum EI at the lunch meal was also measured. Results Men reported higher levels of hunger compared to women across all study conditions (AEx: Men: 7815.00 ± 368.3; Women: 5428.50 ± 440.0 mm x 180 min; p = 0.025; REx: Men: 7110.00 ± 548.4; Women: 6086.25 ± 482.9 mm x 180 min; p = 0.427; CON: Men: 8315.00 ± 429.8; Women: 5311.25 ± 543.1 mm x 180 min; p = 0.021) and consumed a greater absolute caloric load than women at the ad libitum lunch meal (AEx: Men: 1021.6 ± 105.4; Women: 851.7 ± 70.5 kcals; p = 0.20; REx: Men: 1114.7 ± 104.0; Women: 867.7 ± 76.4 kcals; p = 0.07; CON: Men: 1087.0 ± 98.8; Women: 800.5 ± 102.3 kcals; p = 0.06). However, when adjusted for relative energy needs, there was no difference in relative ad libitum EI observed between men and women. No differences in Area Under the Curve for Satiety, PFC, ghrelin, PYY, and GLP-1 were noted between men and women following acute exercise (all p > 0.05). Conclusions These data suggest that women report lower ratings of appetite following an acute bout of exercise or sedentary time when compared to men, yet have similar relative EI. Future work is needed to examine whether sex-based differences in appetite regulation and EI are present with chronic exercise of differing modalities.

Many breast cancer survivors (BCS) gain fat mass and lose fat-free mass during treatment (chemotherapy, radiation, surgery) and estrogen suppression therapy, which increases the risk of developing comorbidities. Whether these body composition alterations are a result of changes in dietary intake, energy expenditure, or both is unclear. Thus, we reviewed studies that have measured components of energy balance in BCS who have completed treatment. Longitudinal studies suggest that BCS reduce self-reported energy intake and increase fruit and vegetable consumption. Although some evidence suggests that resting metabolic rate is higher in BCS than in age-matched controls, no study has measured total daily energy expenditure (TDEE) in this population. Whether physical activity levels are altered in BCS is unclear, but evidence suggests that light-intensity physical activity is lower in BCS compared to age-matched controls. We also discuss the mechanisms through which estrogen suppression may impact energy balance and develop a theoretical framework of dietary intake and TDEE interactions in BCS. Preclinical and human experimental studies indicate that estrogen suppression likely elicits increased energy intake and decreased TDEE, although this has not been systematically investigated in BCS specifically. Estrogen suppression may modulate energy balance via alterations in appetite, fat-free mass, resting metabolic rate, and physical activity. There are several potential areas for future mechanistic energetic research in BCS (e.g., characterizing predictors of intervention response, appetite, dynamic changes in energy balance, and differences in cancer sub-types) that would ultimately support the development of more targeted and personalized behavioral interventions.

Altered gut microbiota has been linked to obesity and may influence weight loss. We are conducting an ongoing weight loss trial, comparing daily caloric restriction (DCR) to intermittent fasting (IMF) in adults who are overweight or obese. We report here an ancillary study of the gut microbiota and selected obesity-related parameters at the baseline and after the first three months of interventions. During this time, participants experienced significant improvements in clinical health measures, along with altered composition and diversity of fecal microbiota. We observed significant associations between the gut microbiota features and clinical measures, including weight and waist circumference, as well as changes in these clinical measures over time. Analysis by intervention group found between-group differences in the relative abundance of Akkermansia in response to the interventions. Our results provide insight into the impact of baseline gut microbiota on weight loss responsiveness as well as the early effects of DCR and IMF on gut microbiota.

Objective The objective of this study was to compare patterns of objectively measured moderate-to-vigorous physical activity (MVPA, ≥ 3.00 metabolic equivalents [METs]), light-intensity physical activity (LPA, 1.50-2.99 METs), and sedentary behavior (SB

It is widely assumed that structured exercise causes an additive increase in physical activity energy expenditure (PAEE) and total daily energy expenditure (TDEE). However, the common observation that exercise often leads to a less than expected decrease in body weight, without changes in energy intake, suggests that some compensatory behavioral adaptations occur. A small number of human studies have shown that adoption of structured exercise can lead to decreases in PAEE, which is often interpreted as a decrease in physical activity (PA) behavior. An even smaller number of studies have objectively measured PA, and with inconsistent results. In animals, high levels of imposed PA induce compensatory changes in some components of TDEE. Recent human cohort studies also provide evidence that in those at the highest levels of PA, TDEE is similar when compared to less physically active groups. The objective of this review is to summarize the effects of structured exercise training on PA, sedentary behavior, PAEE and TDEE. Using models from ecological studies in animals and observational data in humans, an alternative model of TDEE in humans is proposed. This model may serve as a framework to investigate the complex and dynamic regulation of human energy budgets.

Introduction Easily measuring individual circadian timing is increasingly important to inform personalized chronotherapy, screen for circadian disorders and circadian misalignment, and advance circadian research. Findings from multiple studies show that transcriptomics is a viable method to estimate dim-light melatonin onset (DLMO), but no published omics-based findings have predicted dim-light melatonin offset (DLMOff), and only one known study has used metabolomics to predict DLMO. Here, we developed and tested a plasma metabolomics-based biomarker of circadian phase using DLMO and DLMOff as phase markers. Methods Sixteen (8M/8F) healthy participants aged 22.4 ± 4.8y (mean ± SD) completed an in-laboratory study with 3 baseline days (9h sleep opportunity/night), followed by a randomized cross-over protocol with 9h sleep and 5h sleep conditions, each lasting 5 days. Blood was collected every 4h on the final 24h of each condition for untargeted metabolomics analyses. DLMO and DLMOff were determined during the final 24h of each condition. Samples from all conditions were randomly split into training (68%) and test (32%) datasets. DLMO and DLMOff models were developed using partial least squares regression in the training dataset and validated in the test dataset. Results When validating with the test dataset, R2 for the DLMO model was 0.60, median absolute error (MdAE) was 2.2 ± 2.8h (± interquartile range), and 44% of samples had MdAE under 2h. R2 for the DLMOff model was 0.62, MdAE was 1.8 ± 2.6, and 51% of samples had MdAE under 2h. The DLMOff model predicted baseline samples, under conditions of 9h sleep and controlled food intake, with an R2 of 0.91 and MdAE 1.1 ± 1.1h. Conclusion These findings show promise for metabolomics-based biomarkers of circadian phase and highlight the need for biomarker efforts to predict multiple circadian phase markers. Additional analyses with an independent validation dataset will help advance these initial findings. Support NIH-R01HL085705, NIH-R01HL109706, NIH-R01HL132150, NIH-K01HL145099, NIH-F32DK111161, and NIH-UL1TR000154; and Sleep Research Society Foundation 011-JP-16

The current obesity epidemic is staggering in terms of its magnitude and public health impact. Current guidelines recommend continuous energy restriction (CER) along with a comprehensive lifestyle intervention as the cornerstone of obesity treatment, yet this approach produces modest weight loss on average. Recently, there has been increased interest in identifying alternative dietary weight loss strategies that involve restricting energy intake to certain periods of the day or prolonging the fasting interval between meals (i.e., intermittent energy restriction, IER). These strategies include intermittent fasting (IMF; >60% energy restriction on 2−3 days per week, or on alternate days) and time-restricted feeding (TRF; limiting the daily period of food intake to 8−10 h or less on most days of the week). Here, we summarize the current evidence for IER regimens as treatments for overweight and obesity. Specifically, we review randomized trials of ≥8 weeks in duration performed in adults with overweight or obesity (BMI ≥ 25 kg/m2) in which an IER paradigm (IMF or TRF) was compared to CER, with the primary outcome being weight loss. Overall, the available evidence suggests that IER paradigms produce equivalent weight loss when compared to CER, with 9 out of 11 studies reviewed showing no differences between groups in weight or body fat loss.

Objective This study aimed to evaluate the impact of timing of exercise initiation on weight loss within a behavioral weight loss program. Methods Adults with overweight or obesity (N = 170; age 18-55 years; BMI 25-42 kg/m2 ; 83.5% women) were enrolled in an 18-month behavioral weight loss program consisting of a reduced-calorie diet, exercise, and group-based support. The standard group (STD) received a supervised exercise program (progressing to 300 min/wk of moderate-intensity aerobic exercise) during months 0 to 6. The sequential group (SEQ) was asked to refrain from changing exercise during months 0 to 6 and received the supervised exercise program during months 7 to 12. On completion of supervised exercise, both groups were instructed to continue 300 min/wk of moderate-intensity exercise for the study duration. Results At 6 months, the STD group exhibited greater reductions in body weight (-8.7 ± 0.7 kg) compared with the SEQ group (-6.9 ± 0.6 kg; P = 0.047). Between 6 and 18 months, the STD group regained more weight (2.5 ± 0.8 kg vs. 0.0 ± 0.8 kg; P = 0.02). At 18 months, there were no between-group differences in changes in weight (STD: -6.9 ± 1.2 kg; SEQ: -7.9 ± 1.2 kg), fat mass, lean mass, physical activity, or attrition. Conclusions Both immediate and delayed exercise initiation within a behavioral weight loss program resulted in clinically meaningful weight loss at 18 months. Thus, timing of exercise initiation can be personalized based on patient preference.

Insufficient sleep induces insulin resistance and is associated with an increased risk for developing obesity and cardiometabolic diseases, implicating sleep loss as a metabolic stressor. Fibroblas...

This study compared 24-h nutrient oxidation responses between a sedentary condition (SED) and a condition in which short 5-min bouts of moderate-intensity physical activity were performed hourly for nine consecutive hours over 4 days (MICRO). To determine whether any shifts in fuel use were due solely to increases in energy expenditure, we also studied a condition consisting of a single isoenergetic 45-min bout of moderate-intensity exercise (ONE). Twenty sedentary overweight or obese adults (10 men/10 women; 32.4 ± 6.3 yr; BMI, 30.6 ± 2.9 kg/m2) completed all three conditions (MICRO, SED, and ONE) in a randomized order. Each condition consisted of a 3-day free-living run-in followed by a 24-h stay in a whole-room calorimeter to measure total energy expenditure (TEE) and substrate utilization. Dietary fat oxidation was also assessed during the chamber stay by administering a [1-13C] oleic acid tracer at breakfast. Energy intake was matched across conditions. Both MICRO and ONE increased TEE relative to SED, resulting in a negative energy balance. HOMA-IR improved in both activity conditions. MICRO increased 24-h carbohydrate oxidation compared with both ONE and SED ( P < 0.01 for both). ONE was associated with higher 24-h total fat oxidation compared with SED, and higher 24-h dietary fat oxidation compared with both SED and MICRO. Differences in substrate oxidation remained significant after adjusting for energy balance. In overweight and obese men and women, breaking up sitting time increased reliance upon carbohydrate as fuel over 24 h, while a single energy-matched continuous bout of exercise preferentially relies upon fat over 24 h. NEW & NOTEWORTHY Insulin sensitivity, as assessed by HOMA-IR, was improved after 4 days of physical activity, independent of frequency and duration of activity bouts. Temporal patterns of activity across the day differentially affect substrate oxidation. Frequent interruptions of sedentary time with short bouts of walking primarily increase 24-h carbohydrate oxidation, whereas an energy-matched single continuous bout of moderate intensity walking primarily increased 24-h fat oxidation.

AB We have previously shown that motor-driven (passive) stationary cycling elevates energy expenditure (EE). Purpose: This study aimed to quantify how acute passive cycling affects glucose and insulin responses to an oral glucose tolerance test (OGTT) and basic cognition compared with sitting and moderate-intensity active cycling. Methods: Twenty-four physically inactive healthy males completed three trials in randomized order involving 30-min conditions of sitting, passive cycling, and moderate-intensity cycling. During each condition, EE was measured, and participants performed cognitive tests. After each condition, a 2-h OGTT was performed. Results: EE was significantly higher during the cycling conditions compared with sitting (1.36 +/- 0.58 and 6.50 +/- 1.73 kcal[middle dot]min-1 greater than sitting for passive and moderate-intensity, respectively). A significant correlation was found between body fat percentage and postsitting OGTT 2-h postplasma glucose (r2 = 0.30, P < 0.05); thus, participants were divided into lean (n = 11) and nonlean (n = 13) groups. In the nonlean group, compared with sitting, passive cycling lowered 2-h postplasma glucose (7.7 +/- 1.3 vs 6.9 +/- 1.6 mmol[middle dot]L-1, respectively, P < 0.05), and the Matsuda whole-body insulin sensitivity index (WBISI) was higher (2.74 +/- 0.86 vs 3.36 +/- 1.08, P < 0.05). In addition, passive and moderate-intensity cycling had similar beneficial effects on 2-h postplasma glucose and WBISI. Cognitive performance did not significantly differ between the sitting and passive cycling conditions. Conclusions: Two-hour postplasma glucose was lower and WBISI after acute passive cycling was higher in nonlean participants. Given that and the increase in EE without changes in cognitive performance, we propose passive cycling as a promising intervention to counteract some of the deleterious effects of prolonged sitting in the workplace. (C) 2016 American College of Sports Medicine

Objective To evaluate the safety and tolerability of alternate-day fasting (ADF) and to compare changes in weight, body composition, lipids, and insulin sensitivity index (Si) with those produced by a standard weight loss diet, moderate daily caloric restriction (CR). Methods Adults with obesity (BMI ≥30 kg/m2, age 18-55) were randomized to either zero-calorie ADF (n = 14) or CR (−400 kcal/day, n = 12) for 8 weeks. Outcomes were measured at the end of the 8-week intervention and after 24 weeks of unsupervised follow-up. Results No adverse effects were attributed to ADF, and 93% completed the 8-week ADF protocol. At 8 weeks, ADF achieved a 376 kcal/day greater energy deficit; however, there were no significant between-group differences in change in weight (mean ± SE; ADF −8.2 ± 0.9 kg, CR −7.1 ± 1.0 kg), body composition, lipids, or Si. After 24 weeks of unsupervised follow-up, there were no significant differences in weight regain; however, changes from baseline in % fat mass and lean mass were more favorable in ADF. Conclusions ADF is a safe and tolerable approach to weight loss. ADF produced similar changes in weight, body composition, lipids, and Si at 8 weeks and did not appear to increase risk for weight regain 24 weeks after completing the intervention.

OBJECTIVES: Intermittent fasting (IMF) is an alternative to the standard weight loss approach of daily caloric restriction (DCR). Although altered gut microbiota has been linked to obesity and may influence weight loss, it is unknown how the gut microbiota is impacted by these weight loss strategies or its association with responsiveness. In this study, we examine the gut microbial diversity and composition during an intervention of DCR versus IMF. METHODS: Fecal microbiota communities were profiled by 16S rRNA gene sequencing in 59 individuals with overweight and obesity (mean BMI: 33.1 (SD: 4.4) kg/m(2), age: 40.7 (SD: 9.8) years; 76.3% female) undergoing a comprehensive, group-based behavioral weight loss intervention of DCR (n = 25) versus IMF (n = 34) at baseline and 3-months into the intervention. Mixed effects linear regressions, permutational ANOVA, and ANCOM were used to examine differences in gut microbiota over time and by intervention group, and regression-based methods were used to examine the association between baseline gut microbiota and percentage change in weight and waist circumference. RESULTS: Overall, participants lost 5.9 (SD: 3.7) kilograms at 3 months. Weight loss within groups is not reported, as the trial is ongoing. Alpha diversity increased in both intervention groups (P 0.847). Microbiota composition (beta diversity) changed significantly (R2 = 7.1%; P = 0.001) over the course of the intervention, with no significant differences between groups (P > 0.325). Numerous taxa showed changes over time with differences between groups (FDR

Introduction Timing of food intake has emerged as a novel risk factor for weight gain and obesity. Higher evening food intake, especially during insufficient sleep, is associated with weight gain. We aimed to explore initial changes in evening food intake and the respiratory quotient (RQ) during insufficient sleep and subsequent weight gain. We also explored sex differences. Methods 28 healthy adults (14F) aged 26.3±4.5y completed a 14–16 daylong laboratory protocol. In their home environment participants maintained one week of ~9h/night sleep schedules and consumed energy balanced diets for 3 days prior to completing the laboratory protocol. The laboratory protocol consisted of 3 baseline days of 9h/night scheduled sleep with energy balanced diets followed by 10 days of 5h/night scheduled sleep with ad-libitum food intake, with (n=14) and without (n=14) weekend recovery sleep. RQ was assessed on days 3 and 5 in a whole room calorimeter. Evening (dinner and after-dinner snacks) energy intake and body weight were assessed daily. Results A significant sex by condition effect was observed for evening food intake such that men and women were similar at baseline, but men ate more than women during insufficient sleep, when controlling for body mass (p Conclusion Findings suggest that rapid changes in evening food intake during insufficient sleep contributes to subsequent weight gain during sustained insufficient sleep, especially in men. Support NIH HL109706, DK111161, TR001082, DK048520, Sleep Research Society Foundation grant 011-JP-16 and Office of Naval Research MURI (N00014-15-1-2809).

Background/objectives Circadian physiology has been linked to body weight regulation and obesity. To date, few studies have assessed the association between exercise timing and weight related outcomes. The aim of this secondary analysis was to explore the impact of exercise timing (i.e., 24 h clock time of exercise session) on weight loss and components of energy balance. Subjects/methods Overweight/obese (BMI 25.0–39.9 kg/m2), physically inactive, young adults (~51% female) completed a 10-month supervised exercise program (400 or 600 kcal/session for 5 days/week) or served as non-exercise controls (CON). Participants were categorized based on the time of day in which they completed exercise sessions (Early-Ex: >50% of sessions completed between 7:00 and 11:59 am; (n = 21), Late-Ex: >50% of sessions completed between 3:00 and 7:00 pm; (n = 25), Sporadic-Ex: n = 24), and CON; (n = 18)). Body weight, energy intake (EI; digital photography), and non-exercise physical activity (NEPA; accelerometer) were assessed at baseline, 3.5, 7, and 10 months. Total daily energy expenditure (TDEE; doubly labeled water), was assessed at baseline and 10 months. Results At month 10, weight loss was significantly greater in both Early-EX (−7.2 ± 1.2%; p p = 0.01) vs CON (+0.5 ± 1.0%), and Early-EX vs Late-EX (−2.1 ± 1.0%; p P > 0.05). A significant group × time interaction (p = 0.02) was observed for NEPA (counts/min), however, after adjusting for multiple comparisons, group effects were no longer significant. Conclusions Despite minimal differences in components of energy balance, Early-EX lost significantly more weight compared with Late-Ex. Although the mechanisms are unclear, the timing of exercise may be important for body weight regulation.

Study Objectives To evaluate the associations between sedentary time, total (total-PA), light (light-PA), moderate (MOD-PA), and vigorous (VIG-PA) physical activity with indices of sleep in postmenopausal women. Methods Baseline self-reported data from the Women’s Health Initiative Observational Study (n = 75 074) were used in this cross-sectional analysis. Total-PA, light-PA, MOD-PA, and VIG-PA were categorized by metabolic equivalents of the activity (MET-hour [hr]/week [wk]) and were estimated using validated questionnaires. Sedentary time was categorized by hr/day and was estimated via questionnaire. Logistic regression was used to examine the associations between these variables and short sleep (≤6 hr/night), long sleep (≥10 hr/night), poor sleep quality, and insomnia symptoms after adjustment for age, race, socioeconomic status, body mass index, health status, depressive symptoms, smoking status, alcohol use, hormone therapy, and comorbidities. Results Higher sedentary time (>11 hr/day) was associated with higher odds of short sleep (odds ratio [OR] = 1.80, 95% confidence interval [CI]: 1.72–1.88), poor sleep quality (OR = 1.85, 95% CI: 1.74–1.97), and insomnia symptoms (OR = 1.56, 95% CI: 1.49–1.64). Light-PA (>0 MET-hr/wk) was associated with lower odds of short sleep (OR = 0.96, 95% CI: 0.92–1.00), and higher amounts of total-PA (OR = 0.90, 95% CI: 0.84–0.97), light-PA (OR = 0.94, 95% CI: 0.89–1.00), and MOD-PA (OR = 0.91, 95% CI: 0.86–0.97) were associated with lower odds of poor sleep quality. Conclusions Our findings suggest that higher levels of light and moderate intensity physical activity are associated with better sleep quality, whereas higher amounts of sedentary time are associated with short sleep and lower quality sleep. Future studies should investigate the directionality of these associations and potential causal pathways.

Free fatty acids (FFA) are a primary fuel source for heart and skeletal muscle, particularly during fasting. In addition, both high fat feeding and insufficient sleep have been shown to increase nocturnal FFA in animal and human models, respectively. However, findings from studies of insufficient sleep have been limited by a focus on male subjects. Furthermore, the impact of ad libitum food intake during insufficient sleep on FFA has not been examined. We therefore examined potential sex differences in circulating 24-hour FFA profiles during habitual sleep and insufficient sleep under ad libitum feeding conditions. Healthy adults (7 females: 23.6±3.2 y, BMI 22.1±1.1 kg/m2; 7 males: 25.7±4.6 y, BMI 22.5±1.2 kg/m2; mean±SD) were studied after five inpatient days consisting of 9h scheduled sleep per night at habitual bedtime and five inpatient days consisting of 5h scheduled sleep beginning 2 hours later than habitual bedtime in a crossover design. On the 5th day in each condition, blood was sampled hourly beginning one hour after scheduled wake-time for 24 hours. In both conditions, participants were provided 3 scheduled meals and 2 snacks designed to provide approximately 130-150% of energy requirements, and had free access to snacks during scheduled waketime. Females showed a more robust FFA response to food intake during both habitual and insufficient sleep vs. men, likely due to positive energy balance for men in both conditions. In addition, both men and women displayed altered FFA patterns during insufficient sleep such that the nocturnal peak was later in the 24hour profile. We provide evidence of sex differences in 24-hour FFA profiles in response to overeating with and without insufficient sleep. The significance of these differences and how they may affect responses to sleep and/or metabolic perturbations is unknown. Future studies are necessary to determine whether differences in FFA may lead to sex differences in metabolic impairments in response to insufficient sleep. Disclosure J.L. Broussard: None. C.M. Depner: None. R. Markwald: None. R.H. Eckel: Advisory Panel; Self; Novo Nordisk Inc., Merck & Co., Inc. L. Perreault: Advisory Panel; Self; Novo Nordisk A/S. Speaker's Bureau; Self; Novo Nordisk A/S. Advisory Panel; Self; Merck & Co., Inc.. Speaker's Bureau; Self; Merck & Co., Inc., AstraZeneca, Janssen Pharmaceuticals, Inc., Boehringer Ingelheim Pharmaceuticals, Inc.. Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc.. Advisory Panel; Self; Sanofi. Speaker's Bureau; Self; Sanofi. J. Higgins: None. E. Melanson: None. K. Wright: Research Support; Self; Philips Respironics, CurAegis Technologies, Inc., Somalogics, National Institutes of Health, Pac-12, Office of Naval Research. Consultant; Self; National Heart, Lung, and Blood Institute, CurAegis Technologies, Inc., Circadian Therapeutics, LTD. Board Member; Self; Sleep Research Society. Consultant; Self; American Academy of Sleep Medicine, American Diabetes Association, American College of Chest Physicians.