This study explored the effects of three diets differing in the amount and type of carbohydrates on peripheral immune markers. After a 15% weight loss with a very-low-carbohydrate diet (VLCD), participants were randomized to isocaloric diets: VLCD, high-carbohydrate diet with high starch content (HC-Starch), or high-carbohydrate diet with high sugar content (HC-Sugar). Changes in peripheral immune markers were assessed in 44 participants using multimodal analyses before randomization and after 10 weeks on the diet. In HC-Starch, cytometry by time of flight (CyTOF) revealed significant shifts in proportions of circulating natural killer (NK) cells and increased activation markers in NK cells, monocytes, T cells, dendritic cells, and neutrophils. In agreement, transcriptomics showed upregulation of immune and metabolic pathways with HC-Starch but reductions with HC-Sugar. Cytokine analysis and mouse experiments suggested that increased adipsin and complement activation could underlie these changes. Following weight loss with VLCD, starch-rich diets serve as an unrecognized driver of inflammation. This study was registered at ClinicalTrials.gov: NCT03394664.

Diet plays a crucial role in health, with low-carbohydrate diets often proposed to exert metabolic benefits. We aim to investigate metabolomic adaptations in 164 adults with overweight or obesity who were randomly assigned to high- (n = 54), moderate- (n = 53), or low-carbohydrate (n = 57) diets during a 20-week weight-loss maintenance phase of the Framingham State Food Study [(FS)2], a controlled, parallel feeding trial (ClinicalTrials.gov: NCT02068885). We measure fasting plasma metabolites by liquid chromatography-tandem mass spectrometry using samples from 147 participants who completed the study (n = 45, 48, and 54 in the high-, moderate-, and low-carbohydrate diet groups, respectively). Significant associations (False Discovery Rate<0.05) are identified between carbohydrate-to-fat ratio (CFR) and diet-induced changes in 148 of 479 metabolites at 20 weeks, with nearly all showing consistent trends at 10 and 20 weeks. Phosphatidylcholines plasmanyls/plasmalogens, phosphatidylethanolamines plasmanyls/plasmalogens, and sphingomyelins generally decrease with higher CFR, whereas lysophosphatidylcholines, lysophosphatidylethanolamines, and triglycerides generally increase. Our findings are largely reproducible in an independent feeding trial involving diets with similar CFR (Popular Diets Study, ClinicalTrials.gov: NCT00315354). Eleven triglyceride species (≤3 double bonds), linked to type 2 diabetes risk, increase with higher CFR. Our findings demonstrate metabolomic changes caused by varying CFR dietary patterns, offering potential insights into mechanisms that could guide targeted dietary intervention strategies. Increasing dietary carbohydrate-to-fat ratio in a randomized controlled feeding study altered circulating small molecules (metabolites), including ones associated with diabetes risk, underscoring important metabolic effects of dietary composition.

OBJECTIVE:When treated with a macronutrient-balanced hypocaloric diet, do male individuals who have overweight and obesity lose relatively more dual-energy x-ray absorptiometry (DXA)-measured lean soft tissue (LST) mass than female individuals? Are there changes in bone mineral content (BMC), and if so, how do they impact relative reductions in LST compared to fat-free mass (FFM; LST plus BMC)? Are decrements in fat, LST, and FFM predictable from the magnitude of weight loss or baseline body composition? METHODS:To answer these questions, DXA studies were conducted before and after a 9- to 12-week calorie-restriction period in 43 male and 97 female individuals who lost a mean (SD) of 10.8% (2.2%) and 10.7% (1.6%) of their baseline weight, respectively. RESULTS:The proportion of weight loss as LST was significantly (p < 0.001) larger in male (mean [SD], 0.33 [0.11] kg) than female individuals (0.25 [0.11] kg); BMC paradoxically increased, thereby leading to a significantly smaller reduction in FFM than LST in the male (-3.87 [1.73] kg vs. -3.92 [1.74] kg; p < 0.001) and female individuals (-2.22 [1.18] kg vs. -2.24 [1.18] kg; p < 0.001), and three different analyses showed that the composition of weight loss tracked as predicted a priori from weight change and baseline body composition. CONCLUSIONS:These observations provide insights into and future guidance for analyzing the DXA-measured body composition changes associated with newer pharmacotherapies for weight loss.

Childhood obesity is a complex chronic condition, such that effective management requires intensive programming and sustained access to treatment. Integrated care models are useful for designing and delivering services to treat children with overweight or obesity. For this narrative mini-review, we searched PubMed (January 1, 2010, to December 31, 2024) using broad terms in 3 categories-care models, condition of interest (obesity), and population of interest (children/youth). This resulted in identification of 2 foundational models, the Chronic Care Model (CCM) and the Patient/Family-Centered Medical Home (PFCMH), which distinguish key elements of integrated care for childhood obesity (treatment with self-management support, team-based care, child/family activation and engagement, collaborative community linkages, and care coordination) and considerations for implementing such models (accessibility to care, virtual care, interprofessional education, and information systems and clinical decision support). Drawing upon the CCM and PFCMH, we designed an integrated care model with the child/family at the center and coordinated wraparound services pertaining to sectors influencing child health (health care, community, and family home). We concluded by noting the need to further study, adapt, scale, and fund strategies for implementing integrated care models and underscoring the importance of relevant outcome measures to drive ongoing quality improvement and sustainability.

A positive association between sugar-sweetened beverages (SSBs) and diabetes risk has been shown, with inconsistent evidence between artificially sweetened beverages (ASBs) and diabetes. Moreover, it is uncertain if physical activity can mitigate the negative effects of these beverages on diabetes development. Therefore, we aimed to evaluate the independent and joint associations between SSB or ASB consumption and physical activity on the risk of type 2 diabetes. We followed 64,029 women in the Nurses’ Health Study (1980–2016), 88,340 women in the Nurses’ Health Study II (1991–2017) and 39,436 men in the Health Professionals Follow-up Study (1986–2016). SSB and ASB consumption was calculated from food-frequency questionnaires administered every 4 years, while physical activity data were collected biennially. A validated supplementary questionnaire on diabetes symptoms, diagnostic tests and treatment confirmed type 2 diabetes cases. Multivariable Cox proportional hazards regression models were used to calculate HRs and 95

An in fl uential 2-wk cross-over feeding trial without a washout period purported to show advantages of a low-fat diet (LFD) compared with a low-carbohydrate diet (LCD) for weight control. In contrast to several other macronutrient trials, the diet order effect was originally reported as not signi fi cant. In light of a new analysis by the original investigative group identifying an order effect, we aimed to examine, in a reanalysis of publicly available data (16 of 20 original participants; 7 female; mean BMI, 27.8 kg/m 2 ), the validity of the original results and the claims that trial data oppose the carbohydrate - insulin model of obesity (CIM). We found that energy intake on the LCD was much lower when this diet was consumed fi rst compared with second (a difference of - 1164 kcal/d, P = 3.6 x 10 -13 ); the opposite pattern was observed for the LFD (924 kcal/d, P = 2.0 x 10 -16 ). This carry-over effect was signi fi cant ( P interaction = 0.0004) whereas the net dietary effect was not ( P = 0.4). Likewise, the between-arm difference (LCD - LFD) was - 320 kcal/d in the fi rst period and + 1771 kcal/d in the second. Body fat decreased with consumption of the LCD fi rst and increased with consumption of this diet second ( - 0.69 + 0.33 compared with 0.57 + 0.32 kg, P = 0.007). LCD- fi rst participants had higher beta -hydroxybutyrate levels while consuming the LCD and lower respiratory quotients while consuming LFD when compared with LFD- fi rst participants on their respective diets. Change in insulin secretion as assessed by Cpeptide in the fi rst diet period predicted higher energy intake and less fat loss in the second period. These fi ndings, which tend to support rather than oppose the CIM, suggest that differential (unequal) carry-over effects and short duration, with no washout period, preclude causal inferences regarding chronic macronutrient effects from this trial.

Background: Evaluating effects of different macronutrient diets in randomized trials requires well defined infrastructure and rigorous methods to ensure intervention fidelity and adherence. Methods: This controlled feeding study comprised two phases. During a Run-in phase (14 -15 weeks), study participants (18 -50 years, BMI, >= 27 kg/m 2 ) consumed a very -low -carbohydrate (VLC) diet, with home delivery of prepared meals, at an energy level to promote 15 +/- 3% weight loss. During a Residential phase (13 weeks), participants resided at a conference center. They received a eucaloric VLC diet for three weeks and then were randomized to isocaloric test diets for 10 weeks: VLC (5% energy from carbohydrate, 77% from fat), highcarbohydrate (HC) -Starch (57%, 25%; including 20% energy from refined grains), or HC -Sugar (57%, 25%; including 20% sugar). Outcomes included measures of body composition and energy expenditure, chronic disease risk factors, and variables pertaining to physiological mechanisms. Six cores provided infrastructure for implementing standardized protocols: Recruitment, Diet and Meal Production, Participant Support, Assessments, Regulatory Affairs and Data Management, and Statistics. The first participants were enrolled in May 2018. Participants residing at the conference center at the start of the COVID-19 pandemic completed the study, with each core implementing mitigation plans. Results: Before early shutdown, 77 participants were randomized, and 70 completed the trial (65% of planned completion). Process measures indicated integrity to protocols for weighing menu items, within narrow tolerance limits, and participant adherence, assessed by direct observation and continuous glucose monitoring. Conclusion: Available data will inform future research, albeit with less statistical power than originally planned.

IntroductionTo achieve and maintain adequate weight, people with cystic fibrosis (CF) May often consume energy-dense, nutrient-poor foods high in added sugars and refined carbohydrates; however, little is known about the glycemic and metabolic effects of dietary composition in this patient population. The objective of this pilot study was to investigate the safety and tolerability of a low glycemic load (LGL) diet in adults with CF and abnormal glucose tolerance (AGT).MethodsTen adults with CF and AGT completed this prospective, open-label pilot study. Mean age was 27.0 ± 2.1 years, 64% were female, and all had pancreatic insufficiency. Each participant followed his/her typical diet for 2 weeks, then transitioned to a LGL diet via meal delivery service for 8 weeks. The primary outcome was change in weight from baseline to study completion, with safety established if no significant decline was noted. Other key safety outcomes included change in hypoglycemia measured by patient report and continuous glucose monitoring (CGM). Exploratory outcomes included changes in other CGM measures, body composition by dual energy X-ray absorptiometry (DXA), and patient reported outcomes.ResultsThere were no significant changes in weight or in subjectively-reported or objectively-measured hypoglycemia. Favorable non-significant changes were noted in CGM measures of hyperglycemia and glycemic variability, DXA measures of fat mass, and gastrointestinal symptom surveys.DiscussionA LGL dietary intervention was safe and well tolerated in adults with CF and AGT. These results lay the groundwork for future trials investigating the impact of low-glycemic dietary interventions on metabolic outcomes in the CF population.

Interventions in community settings, where children spend substantial out of school time, may enhance access to evidence-based lifestyle interventions. The Boys and Girls Club of Lawrence (BGCL) and New Balance Foundation Obesity Prevention Center at Boston Children's Hospital partnered to revise, enact, and evaluate BGCL's existing Healthy Living Club and then flexibly expand the program to increase access. The BGCL is within walking distance of three public housing communities and easily accessible to members, of whom 90% identify as Hispanic. The interventions comprised nutrition sessions and either fitness activity sessions (N+FA Cycle 1, n = 63, 26 hours; N+FA Cycle 2, n = 94, 27 hours) or academic basketball practices (N+AB Cycle 2, n = 99, 72-80 hours), leveraging time already in the schedule where fitness could be intentionally promoted by coaches. Among children aged 8-15 years, mean [95% confidence interval (CI)] changes (beginning to end) in percentage above the BMI median were significant [N+FA Cycle 1: -2.4 (-4.1, -0.8); N+FA Cycle 2: -4.3 (-5.4, -3.1); N+AB Cycle 2: -5.5 (-6.9, -4.1)]. Lifestyle interventions, implemented with flexibility in existing programs, had beneficial impact, indicating potential of community-academic partnerships.

Background: Whether physical activity could mitigate the adverse impacts of sugar -sweetened beverages (SSBs) or artificially sweetened beverages (ASBs) on incident cardiovascular disease (CVD) remains uncertain. Objectives: This study aimed to examine the independent and joint associations between SSB or ASB consumption and physical activity and risk of CVD, defined as fatal and nonfatal coronary artery disease and stroke, in adults from 2 United States -based prospective cohort studies. Methods: Cox proportional hazards models were used to calculate hazard ratios (HRs) and 95% CIs between SSB or ASB intake and physical activity with incident CVD among 65,730 females in the Nurses' Health Study (1980-2016) and 39,418 males in the Health Professional's Follow-up Study (1986-2016), who were free from chronic diseases at baseline. SSBs and ASBs were assessed every 4-y and physical activity biannually. Results: A total of 13,269 CVD events were ascertained during 3,001,213 person -years of follow-up. Compared with those who never/rarely consumed SSBs or ASBs, the HR for CVD for participants consuming >= 2 servings/d was 1.21 (95% CI: 1.12, 1.32; P -trend < 0.001) for SSBs and 1.03 (95% CI: 0.97, 1.09; P -trend = 0.06) for those consuming >= 2 servings/d of ASBs. The HR for CVD per 1 serving increment of SSB per day was 1.18 (95% CI: 1.10, 1.26) and 1.12 (95% CI: 1.04, 1.20) for participants meeting and not meeting physical activity guidelines (>= 7.5 compared with <7.5 MET h/wk), respectively. Compared with participants who met physical activity guidelines and never/rarely consumed SSBs, the HR for CVD was 1.47 (95% CI: 1.37, 1.57) for participants not meeting physical activity guidelines and consuming >= 2 servings/wk of SSBs. No significant associations were observed for ASB when stratified by physical activity. Conclusions: Higher SSB intake was associated with CVD risk regardless of physical activity levels. These results support current recommendations to limit the intake of SSBs even for physically active individuals.

ObesityEarly View LETTER TO THE EDITOR Caution needed on causal inferences in obesity David S. Ludwig, Corresponding Author David S. Ludwig [email protected] orcid.org/0000-0003-3307-8544 New Balance Foundation Obesity Prevention Center, Boston Children's Hospital, Boston, Massachusetts, USA Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA Correspondence David S. Ludwig, Boston Children's Hospital, 300 Longwood Ave., Boston, MA 02115, USA. Email: [email protected]Search for more papers by this authorCara B. Ebbeling, Cara B. Ebbeling New Balance Foundation Obesity Prevention Center, Boston Children's Hospital, Boston, Massachusetts, USA Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USASearch for more papers by this authorMark I. Friedman, Mark I. Friedman Monell Chemical Senses Center, Philadelphia, Pennsylvania, USASearch for more papers by this author David S. Ludwig, Corresponding Author David S. Ludwig [email protected] orcid.org/0000-0003-3307-8544 New Balance Foundation Obesity Prevention Center, Boston Children's Hospital, Boston, Massachusetts, USA Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA Correspondence David S. Ludwig, Boston Children's Hospital, 300 Longwood Ave., Boston, MA 02115, USA. Email: [email protected]Search for more papers by this authorCara B. Ebbeling, Cara B. Ebbeling New Balance Foundation Obesity Prevention Center, Boston Children's Hospital, Boston, Massachusetts, USA Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USASearch for more papers by this authorMark I. Friedman, Mark I. Friedman Monell Chemical Senses Center, Philadelphia, Pennsylvania, USASearch for more papers by this author First published: 06 May 2024 https://doi.org/10.1002/oby.24050Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. REFERENCES 1Gao L, Hu S, Yang D, et al. The hedonic overdrive model best explains high-fat diet-induced obesity in C57BL/6 mice. Obesity (Silver Spring). 2024; 32: 733-742. 10.1002/oby.23991 CASPubMedWeb of Science®Google Scholar 2Ludwig DS, Apovian CM, Aronne LJ, et al. Competing paradigms of obesity pathogenesis: energy balance versus carbohydrate-insulin models. Eur J Clin Nutr. 2022; 76(9): 1209-1221. 10.1038/s41430-022-01179-2 PubMedWeb of Science®Google Scholar 3Ludwig DS, Aronne LJ, Astrup A, et al. The carbohydrate-insulin model: a physiological perspective on the obesity pandemic. Am J Clin Nutr. 2021; 114(6): 1873-1885. 10.1093/ajcn/nqab270 CASPubMedWeb of Science®Google Scholar 4Ludwig DS, Ebbeling CB. The carbohydrate-insulin model of obesity: beyond "calories in, calories out". JAMA Intern Med. 2018; 178(8): 1098-1103. 10.1001/jamainternmed.2018.2933 PubMedWeb of Science®Google Scholar 5Friedman MI, Ramirez I, Wade GN, Siegel LI, Granneman J. Metabolic and physiologic effects of a hunger-inducing injection of insulin. Physiol Behav. 1982; 29(3): 515-518. 10.1016/0031-9384(82)90275-X CASPubMedWeb of Science®Google Scholar 6Fonseca V, McDuffie R, Calles J, et al. Determinants of weight gain in the action to control cardiovascular risk in diabetes trial. Diabetes Care. 2013; 36(8): 2162-2168. 10.2337/dc12-1391 PubMedWeb of Science®Google Scholar 7Torbay N, Bracco EF, Geliebter A, Stewart IM, Hashim SA. Insulin increases body fat despite control of food intake and physical activity. Am J Physiol. 1985; 248(1 Pt 2): R120-R124. CASPubMedGoogle Scholar 8Yang CH, Fagnocchi L, Apostle S, et al. Independent phenotypic plasticity axes define distinct obesity sub-types. Nat Metab. 2022; 4(9): 1150-1165. 10.1038/s42255-022-00629-2 CASPubMedGoogle Scholar 9Ludwig DS, Ebbeling CB, Bikman BT, Johnson JD. Testing the carbohydrate-insulin model in mice: the importance of distinguishing primary hyperinsulinemia from insulin resistance and metabolic dysfunction. Mol Metab. 2020; 35:100960. 10.1016/j.molmet.2020.02.003 CASPubMedWeb of Science®Google Scholar 10Tordoff MG, Pearson JA, Ellis HT, Poole RL. Does eating good-tasting food influence body weight? Physiol Behav. 2017; 170: 27-31. 10.1016/j.physbeh.2016.12.013 CASPubMedWeb of Science®Google Scholar Early ViewOnline Version of Record before inclusion in an issue ReferencesRelatedInformation

Background: Consumption of sugar-sweetened beverages (SSBs) or artificially sweetened beverages (ASBs) and physical activity are independently associated with type 2 diabetes (T2D) risk; however, it is unknown whether there is an interaction of SSB/ASB intake and physical activity on risk of T2D. Methods: We examined the independent and joint associations between habitual SSB/ASB intake and physical activity with incident T2D risk among 196,101 women and men from the Nurses’ Health Study (NHS, 1980-2016), NHSII (1991-2017), and Health Professional’s Follow-up Study (HPFS, 1986-2016), who were free from chronic diseases at baseline. Cox proportional hazards regressions were used to estimate hazard ratios and 95% confidence intervals (HR; CI), adjusting for demographic and lifestyle T2D risk factors. Results: There were 20,430 incident T2D cases over follow-up of 36, 26, and 30 years in NHS, NHSII, and HPFS, respectively. In multivariable-adjusted models, we confirmed that participants with higher SSBs, ASBs and lower physical activity were independently at higher T2D risk, compared to lower intakes and higher activity levels. In joint analyses for these exposures, participants who did not meet physical activity guidelines and consumed gt 2 servings/day of SSBs had a significantly higher risk of T2D than those who met physical activity guidelines and never/rarely consumed SSBs (1.51; 1.43, 1.60); we observed similar findings for ASBs: 1.29; 1.23, 1.36). Among participants who met physical activity guidelines, those who consumed gt 2 servings/day of SSBs had a HR of 1.23 (1.16, 1.30); the HR for ASBs was 1.07 (1.02, 1.13). Consistent results were observed for women and men. Conclusions: Long-term habitual intake of SSBs or ASBs combined with lower physical activity was associated with higher risk of T2D in three large prospective cohort studies. These findings suggest that even when individuals are physically active, higher consumption of SSBs is associated with a higher risk of T2D. Our results support recommendations and policies to limit the intake of SSB and increase physical activity levels.

Background: The Diet Intervention Examining The Factors Interacting with Treatment Success (DIETFITS) trial demonstrated that meaningful weight loss can be achieved with either a "healthy low-carbohydrate diet" (LCD) or "healthy low-fat diet" (LFD). However, because both diets substantially decreased glycemic load (GL), the dietary factors mediating weight loss remain unclear.Objectives: We aimed to explore the contribution of macronutrients and GL to weight loss in DIETFITS and examine a hypothesized relationship between GL and insulin secretion.Design: This study is a secondary data analysis of the DIETFITS trial, in which participants with overweight or obesity (aged 18-50 y) were randomized to a 12-mo LCD (N = 304) or LFD (N = 305).Results: Measures related to carbohydrate intake (total amount, glycemic index, added sugar, and fiber) showed strong associations with weight loss at 3-, 6-, and 12-mo time points in the full cohort, whereas those related to total fat intake showed weak to no associations. A biomarker of carbohydrate (triglyceride/HDL cholesterol ratio) predicted weight loss at all time points (3-mo: 13 [kg/biomarker z-score change] = 1.1, P = 3.5 x 10-9; 6-mo: 13 = 1.7, P = 1.1 x 10-9; and 12-mo: 13 = 2.6, P = 1.5 x 10-15), whereas that of fat (low-density lipoprotein cholesterol + HDL cholesterol) did not (all time points: P = NS). In a mediation model, GL explained most of the observed effect of total calorie intake on weight change. Dividing the cohort into quintiles of baseline insulin secretion and GL reduction revealed evidence of effect modification for weight loss, with P = 0.0009 at 3 mo, P = 0.01 at 6 mo, and P = 0.07 at 12 mo.Conclusions: As predicted by the carbohydrate-insulin model of obesity, weight loss in both diet groups of DIETFITS seems to have been driven by the reduction of GL more so than dietary fat or calories, an effect that may be most pronounced among those with high insulin secretion. These findings should be interpreted cautiously in view of the exploratory nature of this study.Trial Registration: ClinicalTrials.gov (NCT01826591).

Background: Recent 3-dimensional optical (3DO) imaging advancements have provided more accessible, affordable, and self-operating opportunities for assessing body composition. 3DO is accurate and precise in clinical measures made by DXA. However, the sensitivity for monitoring body composition change over time with 3DO body shape imaging is unknown. Objectives: This study aimed to evaluate the ability of 3DO in monitoring body composition changes across multiple intervention studies. Methods: A retrospective analysis was performed using intervention studies on healthy adults that were complimentary to the cross-sectional study, Shape Up! Adults. Each participant received a DXA (Hologic Discovery/A system) and 3DO (Fit3D ProScanner) scan at the baseline and follow-up. 3DO meshes were digitally registered and reposed using Meshcapade to standardize the vertices and pose. Using an established statistical shape model, each 3DO mesh was transformed into principal components, which were used to predict whole-body and regional body composition values using published equations. Body composition changes (follow-up minus the baseline) were compared with those of DXA using a linear regression analysis. Results: The analysis included 133 participants (45 females) in 6 studies. The mean (SD) length of follow-up was 13 (5) wk (range: 3-23 wk). Agreement between 3DO and DXA (R2) for changes in total FM, total FFM, and appendicular lean mass were 0.86, 0.73, and 0.70, with root mean squared errors (RMSEs) of 1.98 kg, 1.58 kg, and 0.37 kg, in females and 0.75, 0.75, and 0.52 with RMSEs of 2.31 kg, 1.77 kg, and 0.52 kg, in males, respectively. Further adjustment with demographic descriptors improved the 3DO change agreement to changes observed with DXA. Conclusions: Compared with DXA, 3DO was highly sensitive in detecting body shape changes over time. The 3DO method was sensitive enough to detect even small changes in body composition during intervention studies. The safety and accessibility of 3DO allows users to self-monitor on a frequent basis throughout interventions. This trial was registered at clinicaltrials.gov as NCT03637855 (Shape Up! Adults; https://clinicaltrials.gov/ct2/show/NCT03637855); NCT03394664 (Macronutrients and Body Fat Accumulation: A Mechanistic Feeding Study; https://clinicaltrials.gov/ct2/show/NCT03394664); NCT03771417 (Resis-tance Exercise and Low-Intensity Physical Activity Breaks in Sedentary Time to Improve Muscle and Cardiometabolic Health; https://clinicaltrials.gov/c t2/show/NCT03771417); NCT03393195 (Time Restricted Eating on Weight Loss; https://clinicaltrials.gov/ct2/show/NCT03393195), and NCT04120363 (Trial of Testosterone Undecanoate for Optimizing Performance During Military Operations; https://clinicaltrials.gov/ct2/show/NCT04120363).

BACKGROUND The aim of obesity treatment is to promote loss of fat relative to lean mass. However, body composition changes with calorie restriction differ among individuals. OBJECTIVES The goal of this study was to test the hypothesis that insulin secretion predicts body composition changes among young and middle-age adults with high BMI (in kg/m2) following major weight loss. METHODS Exploratory analyses were conducted with pre-randomization data from 2 large feeding trials: the Framingham, Boston, Bloomington, Birmingham, and Baylor study (FB4; n = 82, 43.9% women, BMI ≥27) and the Framingham State Food Study [(FS)2; n = 161, 69.6% women, BMI ≥25]. Participants in the 2 trials consumed calorie-restricted moderate-carbohydrate or very-low-carbohydrate diets to produce 12-18% weight loss in ∼14 wk or 10-14% in ∼10 wk, respectively. We determined insulin concentration 30 min after a 75-g oral glucose load (insulin-30) as a measure of insulin secretion and HOMA-IR as a measure of insulin resistance at baseline. Body composition was determined by DXA at baseline and post-weight loss. Associations were analyzed using general linear models with adjustment for covariates. RESULTS In FB4, higher insulin-30 was associated with a smaller decrease in fat mass (0.441 kg per 100 μIU/mL increment in baseline insulin-30; P = 0.005; -1.20-kg mean difference between the first compared with the fifth group of insulin-30) and a larger decrease in lean mass (-0.465 kg per 100 μIU/mL; P = 0.004; 1.27-kg difference). Participants with higher insulin-30 lost a smaller proportion of weight loss as fat (-3.37% per 100 μIU/mL; P = 0.003; 9.20% difference). Greater HOMA-IR was also significantly associated with adverse body composition changes. Results from (FS)2 were qualitatively similar but of a smaller magnitude. CONCLUSIONS Baseline insulin dynamics predict substantial individual differences in body composition following weight loss. These findings may inform understanding of the pathophysiological basis for weight regain and the design of more effective obesity treatment. Registered at clinicaltrials.gov as NCT03394664 and NCT02068885.