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.

The Global Leadership Initiative on Malnutrition (GLIM) consensus standardized malnutrition diagnosis, but incorporating low Body Mass Index (BMI) as a phenotypic criterion introduces a circularity bias in weight-stratified analyses and conceals "hidden hunger" in high obesity populations. Using nationally representative data from the U.S. NHANES 1999 2020 (N = 23,363 adults), we propose a "GLIM-adapted" framework that excludes BMI relying solely on weight loss and DXA-derived appendicular lean mass index (ALMI). Standard GLIM identified an 18.9% malnutrition prevalence, but low BMI was the sole indispensable phenotypic driver for only 233 individuals (5.2%). The GLIM-adapted framework maintained a highly comparable prevalence of 17.9% (n = 4,038). Thus, low BMI exhibits low diagnostic sensitivity in modern U.S. cohorts. Excluding BMI successfully bypasses circularity, unmasks qualitative muscle wasting under excess body weight, and establishes a robust, weight-independent standard for clinical and epidemiological research.

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 The consequences of prolonged exposure to obesity on biomarkers of myocardial stress and injury remain unclear. We examined associations of duration and cumulative severity of obesity from childhood to later adulthood with concentrations of N-terminal pro-B-type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin I (hs-cTnI), and growth differentiation factor-15 (GDF-15) at age 62 years. Methods The sample comprised 4,901 participants in the 1958 National Child Development Study who had measurements of NT-proBNP, hs-cTnI, or GDF-15 at age 62 years and at least four BMI assessments between 7 and 62 years of age. Individual BMI trajectories were modelled, from which total duration of obesity and mean excess BMI (area between the trajectory and the age-specific obesity threshold divided by duration) were derived. Associations of ever versus never having been obese, obesity duration, and mean excess BMI with biomarker concentrations were examined using general linear regression models with progressive adjustment for early-life characteristics; contemporaneous cardiometabolic factors; and health behaviours, socioeconomic position, and psychosocial factors. Nonlinear associations of obesity duration were estimated using restricted cubic splines and subsequently approximated using linear splines. Results Overall, 26.3% of participants had ever experienced obesity. There was no evidence of an association between ever versus never having been obese and NT-proBNP. Among men, ever having been obese was associated with 11.00% (95% CI, 4.62-17.37) higher hs-cTnI in the fully adjusted model, whereas there was no evidence of an association among women. Ever having been obese was associated with higher GDF-15, although the association attenuated substantially after adjustment for contemporaneous cardiometabolic factors. Longer obesity duration was associated with higher concentrations of all three biomarkers in fully adjusted models, but the pattern differed by biomarker: hs-cTnI increased approximately linearly with duration, whereas NT-proBNP and GDF-15 remained relatively stable up to approximately 25 years of obesity before increasing with longer duration. In fully adjusted models, each additional year of obesity beyond 25 years was associated with 1.80% (95% CI, 0.54-3.06) higher NT-proBNP and 0.81% (95% CI, 0.25-1.36) higher GDF-15, while each additional year across the full range of obesity duration was associated with 0.41% (95% CI, 0.07-0.76) higher hs-cTnI. Among participants who had ever experienced obesity, each 1 kg/m2 higher mean excess BMI was associated with 4.02% (95% CI, 1.79-6.24) higher NT-proBNP and 2.00% (95% CI, 0.49-3.51) higher hs-cTnI in fully adjusted models; there was no evidence of an association with GDF-15. Conclusions Longer duration of obesity was associated with higher concentrations of biomarkers reflecting myocardial stress, injury, and cellular stress at age 62 years, with associations particularly evident after approximately 25 years of obesity for NT-proBNP and GDF-15. These findings suggest that duration of obesity may provide clinically relevant information beyond current obesity status, supporting its consideration as an additional dimension of cumulative obesity burden in cardiovascular risk assessment.

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.

Background: Early postoperative weight loss may help predict later outcomes, but its reliability for longer-term weight loss and regain remains unclear. We assessed whether the percentage total weight loss (%TWL) 3 months after metabolic/bariatric surgery predicted later %TWL weight change/regain. Methods: This retrospective observational cohort study included 216 patients from a prospectively pre-recruited multicentre bariatric surgery cohort who underwent sleeve gastrectomy or Roux-en-Y gastric bypass and had 3-month %TWL and at least one later outcome. Linear regression assessed associations between 3-month %TWL and later %TWL plus weight change/regain outcomes. Adjusted incremental R2 quantified the additional variance explained by 3-month %TWL. Secondary procedure-stratified, diabetes-stratified, and baseline BMI comparator analyses are reported in the Supplementary Material. Results: Greater 3-month %TWL was associated with higher 1-year and 2-year %TWL in adjusted models (both p < 0.001) but not longer-term %TWL (p = 0.078). Incremental R2 was 38.23% for 1-year, 11.75% for 2-year %TWL and 2.25% for long-term %TWL. Greater 3-month %TWL was also associated with greater recurrent weight gain from 1 to 2 years and from 1 year to long-term follow-up. In secondary analyses, findings were broadly consistent across procedure and diabetes subgroups, while 3-month %TWL explained more additional variance in 1-year %TWL than baseline BMI Conclusion: 3-month %TWL predicted 1-year and 2-year outcomes but not longer-term %TWL, supporting its value as an early marker of postoperative weight trajectory. Heterogeneity in recurrent weight stability may contribute to weaker long-term prediction and warrants further study to determine whether early weight-loss patterns can identify patients requiring closer long-term weight management support.

Purpose: In high-income countries, lower socioeconomic position (SEP) is linked to higher childhood obesity prevalence but its association with cardiometabolic risk in those presenting in pediatric weight management services remain underexplored. Methods: In the Bern Obesity in Childhood and Adolescence Biorepository (BOCAB), we used the Swiss neighborhood index of SEP (0-100, 100=highest SEP) to assess cross-sectional associations with obesity severity and cardiometabolic risk factors, mediation by behavioral lifestyle factors and longitudinal associations between SEP and percentage of the 95th BMI percentile (%BMIp95) and waist circumference-to-height ratio (WHtR), adjusted for age and sex. Results: Among 886 participants (52% male, mean age 11.2 years [SD 3.1]), mean %BMIp95 was 124% (SD 17%). A 10-unit higher SEP was associated with lower %BMIp95 ({beta} -2.57; 95% CI -3.84 to -1.31) and lower WHtR ({beta} -0.006; -0.010 to -0.001), both partially mediated by weekly physical activity and screen time. Higher SEP was also associated with lower odds of insulin resistance (OR 0.80; 0.65 to 0.98), low HDL-cholesterol (OR 0.81; 0.67 to 0.98), elevated non-HDL-cholesterol (OR 0.74; 0.59 to 0.92) and elevated triglycerides (OR 0.83; 0.70 to 0.98). Among 419 participants with longitudinal data, annual reduction in %BMIp95 and WHtR did not differ by SEP. Conclusion: Lower SEP is associated with greater obesity severity and cardiometabolic risk among children and adolescents with obesity presenting at specialized tertiary care. Screening and early referral of lower-SEP children and adolescents with obesity should be encouraged, before cardiometabolic risk accumulates, particularly since treatment response did not differ once enrolled.

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.

The prevalence of obesity is increasing worldwide, especially amongst younger populations. As both brain and body are still under development, numerous studies have shown that adolescence is an especially sensitive period for the impact of external insults including unbalanced diets. To better understand and prevent the vulnerability to obesity and maladaptive food choice, it is crucial to explore the long-term impact of adolescent exposure to obesogenic diets on different feeding behaviours. To address this, we gave mice access to an obesogenic diet with a very high-fat content (vHFD) specifically during adolescence and investigated the long-lasting impact on feeding at adulthood months after a switch to a healthy diet. Using specific feeding tests, we found that both males and females vHFD-exposed mice exhibited an increased intake of both standard food and palatable high-fat foods, without impacting preference for non-caloric food rewards. Importantly, these sex-dependent alterations are not fully correlated with diet-induced weight gain but may support long-term vulnerability to overeating and the development or maintenance of obesity.

Background: Both fetal and maternal genomes contribute to birthweight, but the timing and relative magnitude of their effects during gestation remain unclear. Polygenic risk scores (PRSs), constructed from genome-wide association studies (GWAS), capture the cumulative influence of common birthweight-associated variants to quantify genetic predisposition to fetal growth. We constructed fetal and maternal PRSs for birthweight in two UK birth cohorts, the Pregnancy Outcome Prediction Study (POPS) and Born in Bradford (BiB), to evaluate their associations with estimated fetal weight (EFW) throughout pregnancy and with birthweight at delivery. We also investigated whether PRSs could improve risk stratification of infants born below the 10th centile by distinguishing those with pathological intrauterine growth restriction (IUGR) from those who are constitutionally small-for-gestational-age (SGA). Methods: In POPS and BiB, we used LDpred2-auto to estimate PRSs based on the largest available maternal and fetal GWAS for birthweight. We longitudinally assessed EFW associations with PRSs using linear mixed effects models in POPS and BiB. We evaluated birthweight associations with PRSs using linear models, where birthweight was adjusted for gestational age and fetal sex. We estimated R2 and conducted variance decomposition analyses for all models. Genetically-predicted birthweights were estimated from joint models of fetal and maternal PRSs plus ten genetic principal components (PCs). BiB analyses were conducted separately for genetically-inferred European (EUR) and South Asian (SAS) ancestry. Results: While PRSfetus was significantly associated with EFW at baseline ([~]22 weeks gestation) across cohorts, PRSmother was not. Both the interaction of PRSfetus and PRSmother with gestational age (GA) were significantly associated with EFW. The effect of PRSmother on EFW increased more quickly across gestation than the effect of PRSfetus in POPS, but this finding was not replicated in BiB. Jointly, fetal and maternal polygenic risk explained 4% of variance in EFW in POPS and 1% in BiB-EUR. In POPS, fetal and maternal PRSs showed independent contributions to EFW variance of 3% and 0.5%, respectively. Joint and univariable linear models showed significant positive associations of PRSfetus and PRSmother with observed birthweight. In POPS, PRSfetus and PRSmother accounted for [~]10% and [~]4% of variance in birthweight over covariates alone, respectively, which was replicated in BiB-EUR. Maternal and fetal genetic effects jointly explained 11% of birthweight variance in POPS, similar to BiB-EUR, at 12%; however, the variance explained was reduced by half in BiB-SAS, at 6%. Among POPS babies born <10th birthweight centile, the magnitude of the difference between observed and genetically-predicted birthweight was greater in the group judged clinically to be IUGR vs. SGA (p=0.008; point estimate=0.29, 95%CI: 0.12, 0.47). Conclusions: Maternal and fetal PRSs jointly account for [~]11-12% of birthweight variance in European ancestry groups, but much less in a South Asian cohort. In POPS, the effect of birthweight-associated SNPs on estimated fetal weight increases more rapidly across pregnancy for maternal than for fetal genetic effects. Deviation from genetically-predicted birthweight may be a useful metric to help distinguish growth-restricted from constitutionally small babies.

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.

Background: Incretin-based obesity medications (IOMs) produce substantial weight loss, but the incremental benefit of structured nutritional therapy when integrated with pharmacotherapy remains unclear. Objectives: To compare 12-month weight-loss outcomes between individualized carbohydrate-reduced nutritional therapy combined with IOMs (VINT+IOM) and IOM-based care without structured nutritional therapy (IOM only). Methods: This retrospective cohort study evaluated 4,269 adults enrolled in a comprehensive telemedicine obesity care program (VINT+IOM, n=1,658; IOM only, n=2,611). Both pathways included clinician-guided medication management and behavioral coaching. The primary analysis focused on GLP-1-naive participants receiving either semaglutide or tirzepatide without switching medications during follow-up. Adjusted weight changes were estimated using longitudinal linear mixed-effects models accounting for baseline demographic and clinical characteristics. Additional analyses evaluated weight-loss responders, prior GLP-1 exposure, and medication utilization. Results: Among GLP-1-naive semaglutide users, adjusted 12-month weight loss was greater with VINT+IOM than IOM only (30.7 vs. 24.1 lb; P=0.01), with corresponding observed weight loss of 13.3% versus 11.1%. Among tirzepatide users, adjusted weight loss was 45.9 versus 34.4 lb (P=0.003), with observed reductions of 19.7% versus 15.6%, respectively. Weight-loss responder rates consistently favored VINT+IOM. In the overall cohort, including GLP-1-naive participants, those with prior GLP-1 exposure, and participants with varying medication trajectories, adjusted weight loss was 29.4 versus 21.5 lb (P less than 0.001), with observed reductions of 12.7% versus 9.6%. Greater weight loss with VINT+IOM was also observed among participants with prior GLP-1 exposure. Weight loss continued between 6 and 12 months in both pathways. Medication coverage was high, with approximately 80% of participants achieving a proportion of days covered of at least 80% in both pathways. Conclusions: Integrating individualized carbohydrate-reduced nutritional therapy with IOM-based care was associated with greater 12-month weight loss than IOM-based care without structured nutritional therapy. These real-world findings support further evaluation of individualized nutritional approaches as an adjunct to incretin-based obesity pharmacotherapy.

Background & Aims: Childhood obesity is a complex, heterogeneous disease. Children with obesity are often grouped into metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) phenotypes, although this classification may oversimplify the underlying metabolic diversity. We aimed to characterise the metabolic profiles associated with the conventional MHO/MUO classification and to identify data-driven metabolic profiles in 267 children and adolescents with obesity. Methods: In this cross-sectional study, we used serum 1H-NMR spectroscopy to quantify lipoproteins, glycoproteins, low-molecular-weight metabolites, and lipid fractions. Univariate comparisons and partial least squares discriminant analysis (PLS-DA) were used to characterise NMR-derived variables associated with MHO and MUO groups. We applied unsupervised k-means clustering based on principal component scores to identify data-driven metabolic profiles independent of the predefined clinical phenotypes. Results: Forty-one NMR-derived variables differed between MUO and MHO (FDR q-value < 0.05), predominantly in the triglyceride-rich lipoprotein and glycoprotein profiles, of which 31 were elevated in MUO. PLS-DA discriminated between MHO and MUO (AUC = 0.84, Q2 = 0.33; permutation p < 0.001). Unsupervised clustering identified two data-driven metabotypes that only partially overlapped with the clinical phenotypes: 27% of children with MHO clustered within the adverse metabolic profile (Cluster 2), characterised by a triglyceride-rich and inflammation-associated profile, whereas 41% of children with MUO were assigned to the comparatively favourable profile (Cluster 1). Clustering further revealed additional differences in lipoprotein subclasses and lipid composition that the MHO/MUO classification did not capture. Conclusions: The conventional MHO/MUO classification captures an important component of metabolic variation in childhood obesity but does not fully capture cardiometabolic heterogeneity. Quantitative 1H-NMR metabolomics identified putative metabotypes characterised by differences in lipoprotein composition and cholesterol distribution beyond clinical phenotypes. These findings support the potential value of metabolomic profiling to refine cardiometabolic risk stratification in children with obesity; however, the relevance of these data-driven metabotypes requires confirmation in independent cohorts and longitudinal follow-up studies.

Background/Objectives: Childhood appetitive traits are heritable behavioural phenotypes hypothesized to link genetic susceptibility to obesity risk. Yet their genetic architecture and role in mediating polygenic adiposity risk remain poorly understood. Methods: We conducted the largest survey of childhood eating behaviour to date, allowing us to perform genome-wide association studies of six appetitive domains derived from 18 items of the parent-reported Children's Eating Behaviour Questionnaire in up to 31,018 eight-year-old children from the Norwegian Mother, Father and Child Cohort Study (MoBa). A trio-based design enabled decomposition of direct and indirect genetic effects on appetite and BMI. Results: We identified ten independent genome-wide significant loci for childhood eating behaviour, primarily across Food Responsiveness, Satiety Responsiveness, and Food Fussiness, eight of which lie at established childhood or adult BMI loci. Food Responsiveness and Satiety Responsiveness showed both phenotypic and genetic correlations with BMI trajectories from early childhood through adolescence. Statistical mediation analyses indicated that 22.1% and 10.4% of the aggregated genetic association with BMI at age 8 could be decomposed through these traits, respectively. Locus-specific patterns further suggested mechanistic pathways, with the FTO locus acting predominantly via Food Responsiveness, and the ADCY3 locus via Satiety Responsiveness. Trio analyses demonstrated that both BMI and eating behaviour associations were predominantly explained by children's inherited alleles, with minimal contribution from indirect effect from parental adiposity, although parental genetic liability influenced reporting of Satiety Responsiveness. Conclusions: Childhood appetitive traits capture a substantial proportion of genetic susceptibility to adiposity through distinct eating behaviour pathways (under standard mediation assumptions). These effects are primarily driven by the child's own genotype rather than indirect parental influences, positioning appetite as a plausible, biologically grounded target for early obesity prevention.

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.

Aims To characterize adults with obesity in a large integrated health system. Methods We created a cross-sectional cohort of adults with at least one body mass index (BMI) value >=30 kg/m2 in the prior 18 months, a recent in-person outpatient visit, and an assigned primary care provider. Electronic health record data were transformed into a patient-level analytic dataset and linked to neighborhood-level social determinants of health. Diagnoses, laboratory values, vital signs, and prescriptions were curated using reproducible phenotype definitions and internally reviewed vocabularies. Results The registry included 284,143 adults, of whom 48% had class I obesity. The cohort was 63% female, 27% black, and 48% lived in areas with high or very high social vulnerability. Common comorbidities included hypertension, type 2 diabetes, depression, and anxiety. HbA1c and cholesterol measurements were absent in more than 40% of the cohort during the prior two years. Overall, 22% of patients received a prescription for at least one weight loss drug in the past 2 years, including 18% who received a GLP-1 agonist approved for weight management. Conclusions This care-engaged obesity cohort was characterized by high representation of women and socially vulnerable populations, substantial cardiometabolic screening gaps, and broad adoption of GLP-1 agonists.

Background: Handgrip strength is associated with cardiometabolic abnormalities, but whether it adds information once detailed adiposity and muscle mass are known is unclear. Methods: We studied adults aged 40-59 years from NHANES 2011-2012 (development, n=508) and 2013-2014 (temporal validation, n=616). The outcome was at least two of four non-adiposity abnormalities (elevated blood pressure, fasting glucose or triglycerides, or low HDL cholesterol); waist circumference was not part of the outcome. A prespecified feature ladder added DXA body fat percentage and visceral adipose tissue to age, sex, BMI and waist circumference (M1), then appendicular skeletal muscle index (M2), then absolute (M3A, primary) or weight-normalised (M3B) grip strength. Logistic regression was primary; elastic-net and XGBoost were supportive. Development performance used nested cross-validation; frozen models were scored once in the validation cohort. Paired increments used 2000 stratified bootstrap resamples. Results: The outcome occurred in 249 (49.0%) and 277 (45.0%) participants. Adding adiposity increased the logistic regression AUC by 0.0209 (95% CI 0.0024-0.0402) in development and 0.0209 (0.0032-0.0375) in temporal validation. Adding absolute grip strength to M2 changed the AUC by -0.0046 (-0.0107 to 0.0013) and 0.0045 (-0.0009 to 0.0098), respectively. All 12 grip-strength AUC increments across algorithms, definitions and cohorts had intervals that included zero. Calibration was unchanged and the incremental net benefit was not consistently positive. Model attributions to grip strength varied by algorithm. Conclusions: In middle-aged adults, detailed adiposity, but not grip strength, added reproducible information for concurrent cardiometabolic risk stratification beyond conventional anthropometry and muscle mass.