International Journal of Obesity

Obesity is a growing public health challenge, with body mass index (BMI) influenced by both genetic and environmental factors. While the role of direct genetic transmission is well established, evidence for genetic nurture effects, in which parental genotypes impact offspring through the environment, has remained mixed. This study investigates direct genetic transmission and genetic nurture effects on BMI across ages, using parent-offspring trios and pairs from the Dutch Lifelines cohort study (N = 18,897 offspring, aged 8 to 67 years). We leveraged the latest multi-ancestry BMI polygenic score (PGS) to construct transmitted (PGS-T) and non-transmitted (PGS-NT) polygenic scores, where PGS-NT consists of parental alleles not passed on to offspring and serves as a proxy for genetic nurture. Linear mixed models showed a large effect of PGS-T on offspring BMI (Beta = 0.416, p < 0.001), corresponding to a 1.85 kg/m2 increase per SD increase in PGS-T. PGS-NT had a small but significant effect (Beta = 0.026, p = 0.013), consistent with a genetic nurture effect accounting for approximately 6.6% of the effect of direct transmission. Parent-of-origin analyses showed that maternal PGS-NT effects were larger than paternal effects. PGS-T interactions with age indicated that direct transmission effects increased in childhood and stabilized in adulthood, while PGS-NT effects remained stable across age. Our findings suggest that direct genetic transmission is the dominant influence on BMI, while results are consistent with small genetic nurture effects that are driven by the maternal side.

Background: As global obesity rates have increased, so too have efforts to manage obesity. This work estimates how many people maintain a lifelong healthy weight, have weight loss potential, and who successfully lose weight without medical support. Methods: Using the 1946 National Survey of Health and Development (1946NSHD; n=4,423), the 1958 National Child Development Study (1958NCDS, n=16,749) and the 1970 British Cohort Study (1970BCS; n=15,612), we quantify the prevalence of lifetime healthy weight, overweight and obesity by ages 51-55, and compare prevalence in early adulthood to the 2000-02 Millenium Cohort Study (2001MCS; n=9,675). We identified those who maintained, lost, gained, or cycled weight up to ages 50-55 (1958NCDS) and 46-54 (1970BCS), relative to their highest body mass index (BMI) before age 42, and examined predictors of group membership using multinomial regression models. Findings: In 1970BCS one-in-five people maintained a healthy BMI into their fifties, whilst 43% experienced obesity at least once, up from 25% in 1946NSHD. In 2001MCS 19% already had obesity by age 23, compared to 1-2% in the oldest cohorts. Across cohorts, those who maintained a healthy BMI were more socioeconomically advantaged, while those who experienced obesity were the most disadvantaged. Among those with obesity, a similar proportion lost weight in both cohorts (~13%), whilst 33-39% continued weight gain. Few potential drivers were associated with weight loss after adjusting for peak BMI, whilst socioeconomic disadvantage predicted further weight gain, as did the intention to lose weight. Interpretation: Weight loss from obesity is rare and the rate has remained consistent over time, whilst weight gain into obesity is common, and prevalence of lifetime obesity has increased.

Background: Vietnam and similar low-middle-income countries face a double/triple burden of overweight-obesity, thinness, and stunting. We examined how pre-pubertal BMI affects longitudinal height trajectories in a large Vietnamese cohort. Methods: We analyzed annual school health data from Hanoi, Ho Chi Minh City, and Haiphong (2018-2025). Children with greater than or equal to 3 visits (n = 40,887) were classified by WHO BMI category (thinness, normal, overweight, obesity) at sex-specific pre-pubertal index ages (males 11 years, females 9 years). Height trajectories were visualized via LOESS smoothing. Between-group differences were tested using Kruskal-Wallis with Dunn post-hoc tests and bootstrap median differences. BMI transitions assessed whether weight-status normalization recovered height potential. Results: At index ages, obese children were taller than normal-BMI peers (+5.1 cm at 11 years, peaking at +5.8 cm at 12 years for males and +4.0 cm at 9 years for females), while thin children were shorter (-3.0 to -4.2 cm). Differences narrowed progressively, becoming non-significant by 15 years in females (p = 0.885) and 17 years in males (p = 0.201). BMI normalization was associated with convergence toward normal-weight peers while persistent thinness showed no recovery. Results were consistent across ages, cities, and sensitivity analyses. Conclusions: Pre-pubertal BMI shapes the tempo, not outcome, of pubertal height gain, supporting early, sex-specific nutritional intervention without compromising adult height.

Objective: To examine changes in the neural valuation of high- versus low-calorie stimuli following bariatric surgery and determine whether these changes relate to weight loss at 24 months. Methods: Adults undergoing bariatric surgery completed fMRI scans before surgery and at 4, 12 and 24 months post-surgery while performing the Becker-DeGroot-Marschak auction task to assess willingness-to-pay (WTP) for food stimuli. Linear mixed-effect models tested longitudinal changes in WTP-related blood oxygen level-dependent (BOLD) associations and their interactions with total weight loss at 24 months. Results: WTP for high-calorie foods decreased significantly after surgery, whereas valuation of low-calorie foods remained stable. At 4 months, WTP-BOLD associations for high- versus low-calorie stimuli were enhanced within the frontoparietal control network and right lateral orbitofrontal cortex relative to pre-surgery. These early postoperative changes did not correlate with 24-month weight loss. Instead, greater 24-month weight loss correlated with both pre-surgical and long-term changes (24 months versus pre-surgery) in WTP-BOLD associations within the precuneus, inferior parietal cortex and visual cortex. Conclusion: Bariatric surgery induces early reductions in the valuation of high-calorie foods, potentially through enhanced cognitive control and aversive processing. However, long-term weight-loss success appears more strongly related to trait-like neural differences in self-referential and attentional processing.

Background Lifestyle interventions are central to obesity prevention and management, yet interindividual variability in response remains incompletely understood. Here, we leveraged genetically defined, distinct obesity endotypes to examine lifestyle-body mass index (BMI) associations across biological pathways. Methods In the UK Biobank, we analyzed 305,713 participants with partitioned polygenic scores (pPSs) representing 10 obesity endotypes. We evaluated interactions between endotype-specific genetic susceptibility and physical activity, diet, sedentary behavior, and sleep on BMI using multivariable linear regression. Primary findings were externally evaluated in the All of Us Research Program using Fitbit-derived lifestyle measures. Results Favorable lifestyle behaviors were associated with lower BMI for all obesity endotypes, but the magnitude of these associations varied significantly across endotypes. Higher endotype-specific pPSs strengthened the benefits of physical activity (7 endotypes), healthy diet (3 endotypes), nonsedentary behavior (5 endotypes), and adequate sleep (7 endotypes) on BMI. Distinct endotypes demonstrated the greatest responsiveness to different lifestyle domains, with the metabolically unhealthy endotype showing the strongest interaction with physical activity, metabolically healthy endotype with sedentary behavior, hypothalamic dysregulation endotype with diet, and hypoinsulin 2 endotype with sleep, corresponding to differences in BMI of 0.22-0.49 kg/m2 between the highest and lowest pPS deciles. These interaction patterns were consistent in the All of Us cohort. Conclusions Obesity endotypes modify the association between lifestyle behaviors and BMI, demonstrating that responsiveness to lifestyle behaviors is heterogeneous and pathway dependent. These findings provide a framework for precision obesity prevention by identifying individuals who may derive greater benefit from specific lifestyle interventions.

Background: Self reported height and weight are prone to misreporting, which can bias BMI estimates. This study identifies misreporting determinants, develops calibration equations and examines how measured, self-reported, and calibrated BMI affect estimates of obesity prevalence and socioeconomic inequalities. Methods: We analysed survey-weighted, sex stratified data from 3,404 adults (18-64 years) in the Portuguese National Food, Nutrition and Physical Activity Survey (IAN-AF 2015-2016), including self reported and measured anthropometry. Misreporting determinants were assessed using multinomial logistic regression. Calibration equations for height and weight were estimated using measured values, self-reports, age, region of residence and education level. Calibrated BMI was derived from predicted values. Obesity prevalence was estimated for each BMI assessment method (30 kg/m^2). Education, income and employment inequalities in obesity were compared across BMI methods using prevalence difference and ratio, slope index and relative indexes of inequality. Results: Height is systematically overreported and weight underreported, with misreporting increasing with age and BMI. Calibration eliminates underestimation of obesity prevalence from self-reported BMI, bringing calibrated estimates close to measured values. Regarding education-related inequalities in obesity, calibration widen disparities among women, whereas among men corrects the overestimation observed from self-reported BMI. Income and employment-inequality patterns are similar across BMI methods. Conclusions: Among Portuguese adults, the systematic and socially patterned misreport of self-reported anthropometry affects obesity prevalence and inequality estimates. Calibration based on simple sociodemographic models improves validity and equity of obesity surveillance and could be routinely integrated into national surveys to strengthen monitoring of obesity and its socioeconomic distribution.

Current recommendations suggest that women achieve a healthy weight before becoming pregnant, but evidence supporting the benefits of preconception weight loss are limited and inconsistent, with some evidence of risks. The Maternal Preconception Weight Trajectory (MatTrack) study will evaluate the impact of preconception weight change on maternal, pregnancy, and child outcomes. In this paper, we present methods used to construct the cohort and evaluate baseline characteristics. The MatTrack cohort was derived from electronic health record data from four Kaiser Permanente regions. Inclusion criteria addressed data quality, availability, and enrollment; maternal age ([&ge;]18 years); and date (pregnancy onset date in 2006-2020, delivery [&le;]12/31/2020). Starting body mass index (BMI) was calculated using weight closest to 24 months prior to pregnancy onset date and adult height. Preconception weight change rates from 24 months prior to and through pregnancy onset date were estimated using linear mixed effects models. Descriptive analyses characterized the baseline characteristics of the cohort. The cohort includes 297,592 pregnancies with the full spectrum of starting BMI: underweight (2.3%), normal weight (41.8%), overweight (28.4%); and obesity class I (15.3%), II (7.4%), and III (5.0%). The cohort is demographically diverse, with 8.3% covered by Medicaid; and 44.5%, 9.2%, and 10.9% Hispanic, non-Hispanic Black, or non-Hispanic Asian, respectively. Preconception weight change rates (kg/year) span weight loss to gain, with the greatest loss in those with obesity class III [median (10th, 90th percentile): -0.8 (-9.5, 4.9)] and the greatest gain in those with underweight [median (10th, 90th percentile): 1.1 (-0.7, 3.6)]. Longitudinal data from this cohort of nearly 300,000 linked maternal-child dyads will enable examination of associations between preconception weight loss and pregnancy, maternal, and child health outcomes. Findings will strengthen the evidence base for preconception weight management guidelines.

Background: Cardiometabolic health typically declines with age and is worse among individuals living with obesity. Weight loss medications have modified the potential for weight loss across the life course, but it remains unclear whether weight reduction in later midlife contributes to improved cardiometabolic health, or if continuing to gain weight may continue to worsen cardiometabolic health. Methods: Using the nationally representative 1958 National Child Development Study (NCDS), a British birth cohort, associations were examined using lagged linear regression between weight change between ages 50-55 and health outcomes at age 62 (n=6,309 high-density lipoprotein (HDLc) and low-density lipoprotein (LDLc) cholesterol, systolic and diastolic blood pressure (SBP and DBP), heart rate, triglycerides, C-reactive protein (CRP), and glycated haemoglobin (HbA1c). Models accounted for prior biomarker levels at age 44. We also explored impacts of weight change on subsequent body composition. Results: Those who gained weight into or within obesity had less favourable cardiometabolic profiles and experienced faster deterioration of cardiometabolic markers between the ages of 44 and 62 than those remaining in healthy weight (e.g. SBP: 5.726, 95% CI: 2.660 to 8.793, p < 0.001; CRP: 0.802, 95% CI: 0.409 to 1.196, p < 0.001). Those who lost weight from obesity had similar rates of cardiometabolic biomarker deterioration to the healthy weight group (SBP: 0.947, 95%CI: -6.605 to 8.499, p=0.806; CRP: 0.140, 95% CI: -0.774 to 1.055, p= 0.764). Conclusion: Weight change in midlife tends towards increasing obesity and associated adverse cardiometabolic risk. Those who lose weight experienced improved cardiometabolic profiles. By viewing midlife as a modifiable stage of the life course, this study highlights opportunities to promote cardiometabolic health, and limit the speed of health decline.

Adiposity in adolescence can have detrimental effects on neural maturation, and is associated with incompletely understood alterations in brain structures and circuits. To address this important knowledge gap, N=3,341 youth from the Adolescent Brain Cognitive Development (ABCD) two-year follow-up cohort (median(IQR) age=12.0(1.1) years; 51.0% females), with structural MRI and resting-state fMRI were studied. Topological properties reflecting network strength, resilience, efficiency and modularity, information transfer, regional feedback costs associated with controllability of network dynamics, and morphometric characteristics were examined as a function of body mass index (BMI), body roundness index (BRI), overweight, obesity, and persistent excess BMI (across assessments). Youth who slept less, spent more time on electronic devices, were Hispanic and/or from lower-income families had higher BMI/BRI ({beta}=0.07-4.95, 95%CI=[0.03,7.05]), and higher odds of overweight or obesity (aOR=1.01-1.72, 95%CI=[1.01,2.28]). Higher BMI/BRI, overweight, obesity and/or persistent excess BMI were associated with weaker and less resilient networks supporting decision-making, control, emotional regulation, reward processing and social function ({beta}=-0.11 to -0.01, CI=[-0.16, -0.01]), and more topologically fragile thalamus and basal ganglia ({beta}=0.05-0.10, CI=[0.01,0.14], p<0.03). They were also associated with lower control costs in cognitive and topological hubs (including the precuneus and prefrontal regions) that play central roles in regulating brain dynamics, aberrant information transfer in limbic, frontoparietal, salience, somatomotor and cerebellar regions, and lower thickness and/or volume of distributed (including prefrontal) regions, and functional hubs ({beta}=-0.14 to -0.02, CI=[-0.18,-0.01]). Thus, adiposity in adolescence is associated with widespread structural and alterations of brain networks supporting developing cognitive processes, and fundamental mechanisms that control these networks dynamics.

Background: Obesity is becoming a global health crisis, and it leads to various metabolic disorders. Body mass index fails to differentiate fat mass from lean mass and systematically misclassifies adiposity risk - a limitation particularly pronounced in South Asian adults, who exhibit characteristically elevated visceral adiposity and reduced appendicular lean mass at a normal BMI. The 2025 Lancet Commission explicitly recommends direct adiposity measurement beyond BMI for obesity diagnosis. Weight loss interventions - whether dietary, behavioural, or pharmacological - are consistently associated with concurrent reductions in both fat mass and lean mass, making body composition monitoring essential beyond scale weight alone. Although DEXA is globally accepted as a gold standard for body composition analysis, the accessibility of DEXA is limited, particularly in resource-constrained low and middle-income countries such as India. BIA devices are a convenient low-cost option to DEXA and can be used for body composition analysis more frequently than a DEXA scan to provide longitudinal data. The aim of this study is to validate 8 electrode BIA devices as a viable alternative to DEXA scan for the South Asian population. Methods: A prospective cross-sectional validation study was conducted following ethics committee approval, with a priori sample size estimation ( = 0.05, power = 80%). Fifty-eight healthy adults (n=58) underwent three BIA measurements and one DEXA scan each. To ensure statistical independence, the three BIA readings per participant were averaged, yielding 58 final measurements for validation. Body fat percentage, lean mass and fat mass were evaluated using Python with statistical analyses like Bland Altman analysis, Pearson correlation, ICC and regression analysis. Results: In this BIA vs DEXA study, the Pearson correlation was strong across all three outcomes (fat%: r = 0.97; fat mass: r = 0.98; lean mass: r = 0.96), with ICC (2,1) values of 0.94, 0.97, and 0.91 confirming excellent absolute agreement. Mean absolute error was 3.40% for fat percentage, 1.96 kg for fat mass, and 3.37 kg for lean mass. BIA systematically underestimated body fat percentage (bias -1.96%, 95% CI: -2.91% to -1.01%; LoA: -9.04% to +5.12%) and fat mass (bias -0.72 kg, 95% CI: -1.38 to -0.07 kg; LoA: -5.59 to +4.14 kg), while overestimating lean mass by +3.08 kg (95% CI: +2.34 to +3.82 kg; LoA: -2.46 to +8.62 kg). Conclusions: The 8-electrode BIA device shows clinically acceptable agreement with DEXA for body composition assessment in healthy Indian adults. It offers a radiation-free, cost-effective, accessible, and portable alternative to DEXA, making it suitable for longitudinal monitoring and trend detection. The device is particularly valuable for obesity screening and for tracking body composition changes during weight loss interventions at the population level, addressing the critical need for accessible body composition assessment in resource-limited settings.

Resolving the inner conflict between improving eating habits (change talk) and sticking to unhealthy ones (sustain talk) is a key target in communication-based behavioral change interventions such as motivational interviewing (MI). Recent work has shown that this inner conflict affects how tastiness and healthiness are traded off in dietary decision-making. The effect varied with body mass index (BMI). Here we aimed to identify why participants with higher BMI shifted toward healthier food choices after listening to change talk. An evidence accumulation model found that BMI affected health evidence sampling when listening to change talk, and taste evidence sampling when listening to sustain talk. A serial mediation analysis showed that the effect of BMI on change-talk-induced health evidence sampling was explained by stronger resting-state connectivity in the ventromedial prefrontal cortex within the default mode network (DMN), which in turn predicted greater motivation to change eating habits. These cross-sectional findings indicate that the intrinsic functional organization of valuation-related regions within the DMN is associated with motivation to change. They provide evidence that these neural and behavioral factors need to align with contextual cues, such as weight status (as reflected by BMI), and with reasons for behavioral change to promote healthier decision-making.

ABSTRACT IMPORTANCE Childhood obesity is a growing global health concern associated with adverse physical, psychiatric, and neurodevelopmental outcomes. Although previous neuroimaging studies have linked obesity to widespread alterations in brain structure and function, it remains unclear how well multimodal neuroimaging measures and genetic markers can predict future weight gain and inform early intervention strategies. OBJECTIVE To evaluate the predictive utility of multimodal MRI measures and polygenic risk scores for obesity in estimating proportional body weight at baseline and predicting weight gain over one year in preadolescent children. DESIGN, SETTING, AND PARTICIPANTS This study used data from the Adolescent Brain Cognitive Development (ABCD) Study, a large-scale, multisite longitudinal cohort of children aged 9 to 10 years (N = 11,880). Analyses included baseline data collected between 2016 and 2018, and one-year follow-up data collected between 2018 and 2020 across multiple imaging sites. MAIN OUTCOMES AND MEASURES Elastic net regression models were applied to structural MRI (including diffusion tensor imaging) and resting-state functional MRI data to predict baseline triponderal mass index (TMI), a weight-for-height measure that more accurately reflects adiposity in children than body-mass index (BMI). Longitudinal classification models were developed to predict excess weight gain relative to normative developmental trajectories at one-year follow-up. Models were evaluated with and without the inclusion of polygenic risk scores and other non-imaging covariates. Generalizability was assessed using leave-one-site-out cross-validation. RESULTS Structural MRI measures predicted baseline TMI with an R^2 of 0.21, whereas resting-state functional MRI measures predicted TMI with an R^2 of 0.08. Classification models predicted one-year weight gain with area under the receiver operating characteristic curve (AUC) values of 0.73 for structural MRI and 0.60 for resting-state functional MRI. Including polygenic risk scores and other covariates improved model performance (structural MRI: R^2 = 0.25, AUC = 0.75; resting-state functional MRI: R^2 = 0.15, AUC = 0.69). Leave-one-site-out cross-validation revealed reduced generalizability across imaging sites (structural MRI R^2 = 0.13-0.17; resting-state functional MRI R^2 = 0.02-0.09; structural MRI AUC = 0.73-0.74; resting-state functional MRI AUC = 0.60-0.67). CONCLUSIONS AND RELEVANCE Multimodal MRI measures were associated with proportional body weight and demonstrated modest predictive utility for future weight gain in preadolescent children, explaining up to one fifth of the variance in weight-related outcomes. The addition of genetic and non-imaging variables improved prediction accuracy, underscoring the multifactorial nature of childhood obesity. However, the observed decline in performance under site-wise cross-validation highlights the need to address site-related variability to enhance reproducibility and generalizability in neuroimaging-based predictive models of pediatric obesity.

Introduction Body composition, rather than body weight alone, is an increasingly important health metric, and preservation of lean mass has become a central concern in obesity treatment, aging, and chronic disease management. Dual-energy X-ray absorptiometry (DXA) provides accurate assessment of fat and lean tissue, but its cost and logistical requirements limit repeated measurement. Computer-vision approaches show promise for estimating adiposity from smartphone images, but lean-mass estimation remains less established. Methods We evaluated a computer-vision body composition model, applied to consumer-grade smartphone photographs, against DXA in a held-out validation sample of 195 adults from an ongoing cross-sectional study. Body fat percentage and total lean mass percentage were co-primary outcomes; for total lean mass percentage, an image-only configuration (no added covariates) was pre-specified as primary. Agreement was quantified using Lin's concordance correlation coefficient (CCC) as the lead statistic, with Pearson correlation, mean absolute error, root mean square error, mean bias, and Bland-Altman limits of agreement. In secondary analyses, appendicular lean mass and total lean mass percentage were each estimated with and without routine anthropometric and demographic inputs (body weight, height, age, and sex). Results Total lean mass percentage agreed with DXA from image features alone (CCC 0.916). Body fat percentage, estimated with routine inputs added, agreed at least as closely (CCC 0.930). Adding routine inputs barely changed agreement for total lean mass percentage but markedly improved it for appendicular lean mass, an absolute quantity that scales with body size. Conclusions A smartphone-image-based model estimated both body fat and lean mass with strong agreement to DXA, with lean mass percentage from image features alone. The approach needs no fixed equipment or ionizing radiation. Whether it can track change over time, including in incretin-based weight loss where lean mass preservation is a concern, was not assessed in this cross-sectional study.

Body mass index (BMI) alone may underestimate clinically relevant obesity because it does not capture central fat distribution. We compared obesity prevalence in Mexico using BMI-only criteria, adiposity-confirmed criteria, and the clinical obesity definition proposed by the Lancet Diabetes and Endocrinology Commission. We conducted a population-based, cross-sectional study of 13,160 adults aged 18 years or older who participated in the 2018-2019 Mexican National Health and Nutrition Survey (ENSANUT). Obesity prevalence was estimated through survey-weighted analyses that accounted for the complex sampling design. The weighted prevalence of obesity based on BMI was 34.5% (95% CI, 33.1-35.9), while 30.9% (95% CI, 29.6-32.2) met criteria for clinical obesity. One quarter of individuals with clinical obesity had a BMI under 30 kg/m2, a phenotype more common among older adults. Half of adults with a BMI under 30 kg/m2 showed elevated central adiposity. BMI alone underestimates clinically relevant obesity in Mexican adults. Adding waist-based measurements could improve the identification of individuals with excess fat and metabolic risk, both in clinical settings and population monitoring.

Abstract Introduction: Birthweight reflects fetal growth from conception. The complications arising from the extremes of fetal growth are well known, but addressing these effectively depends on understanding how and when fetal growth is affected by various maternal and fetal factors. We aimed to compare associations of fetal genetic and maternal environmental factors with fetal weight mid-pregnancy and birthweight. Material and methods: We studied 3110 mother-child pairs from three population-based birth cohorts (UK and China). Estimated fetal weight at ~20 weeks gestation (EFW20) and birthweight were the outcomes of interest. Exposures were fetal genetic factors (fetal sex and fetal birthweight genetic score [BW GS]) and maternal factors (maternal BW GS, BMI, fasting plasma glucose [FPG], smoking, age, and parity). Associations were studied using multivariable linear regressions within cohorts and meta-analysed. Results: All exposures were associated with both EFW20 and birthweight, apart from maternal FPG and smoking which were associated with birthweight only. Male fetal sex and a higher maternal BW GS were consistently associated with higher EFW20 and birthweight, whereas the fetal BW GS, maternal FPG, BMI and smoking showed more marked associations later in pregnancy. Maternal age and parity showed directionally opposite associations with EFW20 and birthweight. Conclusions: Fetal genetic and maternal environmental factors vary in their effect on fetal growth in mid-pregnancy compared with late pregnancy. These findings contribute to our understanding of growth across pregnancy and may inform the timing of clinical monitoring of fetal growth and interventions targeting modifiable maternal factors.

Childhood obesity is a major global health concern, yet the long-term effects of early-life adiposity on adult fat distribution and underlying biological pathways remain poorly understood. In particular, it is unclear whether childhood BMI differentially influences specific adipose tissue depots and whether circulating proteins mediate these relationships. We conducted a Mendelian randomisation study using genetic instruments for childhood BMI across 12 timepoints from ages 3 to 18 years. Two-sample MR was applied to five MRI-derived adult fat depots (abdominal subcutaneous (ASAT), gluteofemoral (GFAT), visceral (VAT), liver, and pancreatic fat) in UK Biobank. We further implemented a two-step MR framework to assess whether 2,940 circulating plasma proteins (Olink) mediate observed associations, integrating temporal, statistical, and directional evidence across childhood. Genetically predicted higher childhood BMI from approximately age 7 years onwards was associated with increased ASAT and GFAT in adulthood, but showed little evidence of association with visceral, liver, or pancreatic fat, with effects persisting into adolescence. Two-step MR identified 140 proteins influenced by childhood BMI during a developmentally sensitive window, of which seven showed directionally consistent evidence of mediation. These proteins included ACAN, CCL7, and CLIC5 for abdominal subcutaneous fat, and CLIC5, BMP10, CLEC10A, KIT, and IGSF3 for gluteofemoral fat, highlighting partially distinct biological pathways across depots. Childhood BMI exerts depot-specific effects on adult fat distribution, particularly influencing subcutaneous adipose tissue. Circulating proteins provide evidence of potential mediating pathways, supporting the existence of developmentally sensitive biological mechanisms linking early-life adiposity to adult body composition. These findings underscore childhood as a critical period for shaping long-term adipose tissue distribution and highlight potential molecular targets for future intervention strategies.

Consumer-grade bioelectrical impedance analyzers have become widely used for body composition assessment, yet their accuracy varies considerably across devices. The Hume Pod is a popular consumer-grade analyzer marketed as being highly accurate, but independent validation is lacking. The purpose of this study was to evaluate the reliability and validity of the Hume Pod relative to both a four-compartment (4C) model and dual-energy X-ray absorptiometry (DXA). Sixty-seven adults (42 females, 25 males; age 37.2 +/- 13.5 years, body mass index: 24.6 +/- 4.9 kg/m2, body fat percentage [BF%]: 26.4 +/- 10.2%) completed duplicate Hume Pod assessments alongside DXA and 4C evaluations. Reliability was evaluated using the technical error of measurement (TEM) and intraclass correlation coefficients (ICC). Validity was assessed using equivalence testing, Lin's concordance correlation coefficient (CCC), standard error of the estimate (SEE), Bland-Altman analysis, and additional tests. The Hume Pod demonstrated strong reliability, with ICCs >/= 0.993 and TEMs of 0.8% for BF% and 0.6 kg for fat mass (FM) and fat-free mass (FFM). Relative to the 4C model, BF%, FM, and FFM estimates were statistically equivalent (all p<0.05), with strong agreement (CCC=0.95-0.98), low SEE values (3.1%, 2.3 kg, and 2.2 kg, respectively), moderate limits of agreement (+/-6.1%, +/-4.5 kg, and +/-4.5 kg), and no proportional bias. Compared with DXA, generally strong agreement was also observed. These findings indicate that the Hume Pod demonstrates strong reliability and validity compared with laboratory reference methods for body composition estimation, supporting its potential use as a consumer body composition assessment.

Importance: In adolescence, attempts to lose weight are common, but their long-term impacts on mental and physical health are not known. Objective: To investigate the association between adolescent dieting and exercising to lose weight and adult trajectories of depressive symptoms and body mass index (BMI). Design: A longitudinal cohort study of children born between April 5 and 11, 1970, and followed up to age 51 years. Setting: Adolescents in the 1970 British Cohort Study in England, Wales and Scotland. Participants: A total of 4,650 adolescents with available exposure data. Exposures: Self-reported lifetime dieting or exercising for weight loss measured at age 16 years. Main Outcomes and Measures: Depressive symptoms measured with the nine-item Malaise Inventory, and BMI derived from self-reported height and weight, at ages 26, 30, 34, 42, 46, and 51 years. Results: Among 4,650 adolescents (56.7% girls, 97.7% White), 1,938 (41.7%) had dieted and 343 (7.4%) had exercised for weight loss by age 16 years. In fully adjusted analyses controlling for a wide range of child- and family-based confounders including prior BMI and emotional difficulties, there was evidence that adolescents who had dieted had higher adult depressive symptom trajectories (adjusted mean difference [aMD] 0.13, 95% CI 0.03-0.24, p=0.015) and higher and increasing adult BMI trajectories than those who had not dieted. There was also evidence that adolescents who exercised for weight loss had higher adult depressive symptom (aMD 0.18, 95% CI 0.02-0.34, p=0.031), and BMI trajectories (aMD 0.37, 95% CI -0.03, 0.78, p=0.071), though evidence of the latter was weak. Conclusions and Relevance: Behaviours aimed at weight loss occurring in adolescence might be a shared risk factor for depressive symptoms and high BMI in adulthood. If causal, these findings could suggest that reducing pressures to lose weight in adolescence may help prevent poor mental and physical health across the lifecourse.

Background Anthropometric measures do not adequately capture heterogeneity in body fat distribution and corresponding cardiometabolic risk, whereas magnetic resonance imaging (MRI) enables precise differentiation and quantification of adipose tissue compartments and ectopic fat. We aimed to validate previously derived MRI-based body composition subphenotypes and their cardiometabolic risk profiles in two independent European cohorts. Methods Using deep learning-based image analysis, we quantified bone marrow, visceral, subcutaneous, cardiac, renal sinus, hepatic, skeletal muscle, and pancreatic fat in the imaging substudies of two population-based cohorts: the German National Cohort (NAKO, N=29,314, age range 19-74 years) and the UK Biobank (N=36,109, age range 40-69 years). Body composition subphenotypes, previously identified by k-means clustering, were evaluated using a rigorous statistical cluster validation framework with method-based and results-based approaches. In NAKO, cross-sectional associations between subphenotypes and estimated cardiovascular disease risk scores were examined using linear regression. In UK Biobank, longitudinal associations between subphenotypes and incident cardiometabolic outcomes, ascertained through hospital record linkage, were analysed using Cox regression. Findings All five body composition subphenotypes were robustly validated across both cohorts, and showed distinct fat distribution patterns and cardiometabolic risk profiles: I "lean", II "average adiposity", III "bone and muscle adiposity", IV "hepato-abdominal adiposity", and V "general and pancreatic adiposity". Subphenotypes I-III showed progressive adipose tissue remodelling patterns likely reflecting ageing trajectories. The "hepato-abdominal adiposity" subphenotype showed highest risk of incident diabetes, whereas the "general and pancreatic adiposity" subphenotype showed highest overall cardiovascular disease burden and metabolic impairment. Interpretation MRI-derived body composition subphenotypes represent distinct fat distribution patterns that reflect ageing- and disease-related processes, which supports the potential of body composition phenotyping for improved cardiometabolic risk stratification and targeted prevention.

Pubertal growth spurt characteristics vary substantially across populations, but no study has characterized these parameters in Vietnamese children using the SITAR (SuperImposition by Translation And Rotation) framework, despite Vietnam rapid ongoing nutritional transition. We fitted sex specific SITAR models to longitudinal height data from 15,491 children (9,047 boys, 6,444 girls, 2018 to 2025) to estimate population and individual age at peak height velocity (APHV) and peak height velocity (PHV), and used multivariable linear mixed effects and logistic regression models to identify prepubertal predictors of pubertal timing and intensity. Population mean APHV was 12.2 years in boys and 9.2 years in girls, with PHV of 9.3 and 7.0 cm/year, respectively. This is earlier than previously reported Korean, Colombian, Ethiopian and US cohorts. Individual variability in pubertal timing was nearly twice as large in girls as in boys. After multivariable adjustment, higher baseline BMI-for-age z-score independently predicted earlier pubertal timing in both sexes, whereas height-for-age z-score and prepubertal height velocity showed opposite direction associations (earlier timing in boys but later timing in girls). A significant secular trend toward earlier puberty was observed in girls (~1 year per decade) but not in boys. Logistic models predicting early pubertal timing achieved acceptable discrimination in boys (AUC=0.76) and modest discrimination in girls (AUC=0.70). This first SITAR-based characterization study reveals earlier pubertal timing in these Vietnamese urban schoolchildren than comparator populations, pronounced sex-specific associations between prepubertal BMI status and height status, and a marked secular trend in earlier puberty in girls warranting further surveillance. Keywords: puberty, growth spurt, SITAR, peak height velocity, adolescent growth, Vietnam, BMI, secular trend