International Journal of Obesity

BackgroundMost research on obesity trends and projections has focused on changes across calendar years. However, as the risk of disease increases with cumulative exposure to obesity, it is crucial to characterize the obesity landscape through a life-course perspective and across birth cohorts. ObjectiveTo enhance the accuracy of obesity epidemic projections throughout life course and across birth cohorts in the US. DesignCross-sectional and cohort study. SettingUnited States ParticipantsIndividuals participated in three National Health Examination Surveys (NHES) from 1959 to 1970 and 18 National Health and Nutrition Examination Surveys (NHANES) from 1971 to 2020. MeasurementsBody mass index (BMI) distributions by sex, race, and birth cohort. ResultsBy leveraging over 40 years of cross-sectional and longitudinal data from nationally representative surveys, we developed models to estimate historical and future BMI distributions in the US for both children and adults throughout their life course. We also calculated life-years of exposure to overweight and obesity, according to sex, race, and birth cohort. Our findings reveal significant increases in these metrics among birth cohorts since 1965 and highlight differential trends by sex and race for the 1965, 1985, and 2005 cohorts. LimitationsAssumption that model parameters will hold in the future. ConclusionOur approach significantly expands upon previous models by projecting life course with continuous BMI distributions informed by longitudinal trajectories, explicitly accounting for variations in birth cohorts. Primary Funding SourceNational Institutes of Health.

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.

BackgroundThough viewed as problematic for measuring individual-level adiposity, Body Mass Index (BMI) is often considered "good enough" for population inference and epidemiological research. However, we demonstrate that BMI produces statistically invalid population-level estimates of associations between key demographic risk factors (e.g., self-reported sex, race, age) and obesity when compared to more direct adiposity measurements. Further, we demonstrate how novel statistical calibration techniques can enable more valid population inference using widely available BMI data alongside a limited subset of "gold standard" measurements. MethodsUsing National Health and Nutrition Examination Survey data (2011-2023), we compare associations, broken down by demographic groups, across three different purported adiposity measures: BMI, Waist Circumference (WC), and whole-body total fat percentage from Dual-energy X-ray absorptiometry (DXA) scans. We then apply a statistical procedure for conducting inference on predicted data to calibrate BMI-based prevalence estimates toward the "gold standard" DXA-based measurements, allowing for valid population inference even for time periods where only BMI data are available. FindingsBMI-measured adiposity yields substantially different - and often contradictory - conclusions about the association between obesity status and lifestyle factors compared to the more direct, DXA-based measurements. Most concerning, the directions and magnitudes of the associations between racial groups may differ depending on whether BMI or DXA-based measurements are used. Similarly, self-reported sex-based differences in obesity prevalence show opposite patterns across measurement types. Our validation results confirm that our calibration method overcomes this challenge and successfully approximates DXA-based associations using primarily BMI-based measurements. InterpretationOur study provides empirical evidence that uncorrected BMI-based inference leads to invalid population-level estimates about the associations between obesity status and key predictors. The statistical calibration approach we present offers a practical solution for obesity researchers who must rely on BMI or similar anthropometric measures due to cost or data availability constraints, enabling more valid population inference without requiring comprehensive "gold standard" adiposity measurements.

BackgroundPrevious studies have presented conflicting findings regarding the potential causal relationships between leisure-time physical activity (LTPA) and body mass index (BMI). Here, we use individual-level data and apply a triangulation framework that incorporates three complementary methods to investigate the bidirectional causal associations between LTPA and BMI. MethodsWe used data from a longitudinal Finnish twin cohort with four measurement points spanning 36 years. The data included 22,696 twin individuals aged 18-50 years at baseline (52.4% women); 8,527 had genetic data available. We applied three analytical approaches suggested to strengthen causal inference in observational studies: Random intercept cross-lagged path model (RI-CLPM) for longitudinal data, one-sample Mendelian Randomization (MR) and Direction of Causation (DoC and MR-DoC) twin models for cross-sectional data at each measurement point. ResultsAll three approaches provided evidence for a causal effect of higher BMI on lower LTPA, particularly at the later follow-up stages. Only twin models suggested a negative causal effect of LTPA on BMI. Men and women showed mainly similar effects. ConclusionsEvidence triangulation across the three methodologies provided support for a causal effect of higher BMI on lower LTPA, whereas the evidence for a reverse effect was less convincing. Our results indicate that the role of high BMI in limiting LTPA becomes more important with advancing age, while also highlighting the importance of accounting for timing when studying the causal effects of LTPA on BMI and vice versa.

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.

Overweight, defined by a body mass index (BMI) between 25 and 30, has been associated with enhanced survival among older adults in some studies. However, whether being overweight is causally linked to longevity remains unclear. To investigate this, we conducted a Mendelian randomization (MR) study of lifespan 85+ years, using overweight as an exposure variable and data from the Health and Retirement Study and the Long Life Family Study. An essential aspect of MR involves selecting appropriate single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs). This is challenging due to the limited number of SNP candidates within biologically relevant genes that can satisfy all necessary assumptions and criteria. To address this challenge, we employed a novel strategy of creating additional IVs by pairing SNPs between candidate genes. This strategy allowed us to expand the pool of IV candidates with new composite SNPs derived from eight candidate obesity genes. Our study found that being overweight between ages 75 and 85, compared to having a normal weight (BMI 18.5-24.9), significantly contributes to improved survival beyond age 85. Results of this MR study thus support a causal relationship between overweight and longevity in older adults.

Weight gain is often associated with the pleasure of eating foods rich in calories and lack of willpower to reduce such food cravings, but empirical evidence is sparse. Here we investigated the role that connectivity within the brains hedonic valuation system (BVS, the ventral striatum and the ventromedial prefrontal cortex) at rest plays (1) to predict weight gain or loss over time and (2) for homeostatic hormone regulation. We found that intrinsic connectivity within the BVS at rest (RSC) predicted out-of-sample weight changes over time in lean and obese participants. Counterintuitively, such BVS RSC was higher in lean versus obese participants before the obese participants underwent a drastic weight loss intervention (Roux-en-Y gastric bypass surgery, RYGB). The RYGB surgery increased BVS RSC in the obese after surgery. The obese participants increase in BVS RSC correlated with decreases in fasting state systemic leptin, a homeostatic hormone signalling satiety that has been previously linked to dopamine functioning. Taken together, our results indicate a first link between brain connectivity in reward circuits in a more tonic state at rest, homeostatic hormone regulation involved in dopamine functioning and ability to lose weight. Significance statementWith obesity rates on the rise, advancing our understanding of what factors drive peoples ability to lose and gain weight is crucial. This research is the first to link what we know about the brains hedonic valuation system (BVS) to weight loss and homeostatic hormone regulation. We found that connectivity at rest (RSC) within the BVS system predicted changes in weight, differentiated between lean and obese participants, and increased after a weight loss intervention (gastric bypass surgery). Interestingly, the extent to which BVS RSC improved after surgery correlated to decreases in circulating levels of the satiety hormone leptin. These findings are the first to reveal the neural and hormonal determinants of weight loss, combining hedonic and homeostatic drivers of (over-)eating.

IntroductionEducational attainment (EA) is negatively associated with body mass index (BMI) but less is known about the association between EA and adult BMI change. We analyzed the role of genetic and environmental factors in the association between EA and BMI trajectory components over adulthood. Data and methodsPooled data from 59,490 twins aged 31-99 years (49% women) across 11 cohorts with EA and repeated measures of BMI were used. BMI trajectory components (baseline BMI and BMI change per decade) were estimated using linear mixed-effects (LME) and delta slope methods. EA was derived by regressing years of education on birth year and cohort. Associations between EA and BMI trajectories were evaluated with LME models in both cohort-specific and pooled data. Genetic and environmental contributions were evaluated using structural equation modeling. ResultsEA was more strongly negatively associated with baseline BMI and BMI change (mean of 1.31 and 1.32 kg/m2 per decade in men and women, respectively) in women ({beta} = - 0.14 kg/m{superscript 2}, 95% CI: -0.15 to -0.12; {beta} = -0.02 kg/m{superscript 2}/decade, 95% CI: -0.03 to -0.01, respectively) than in men ({beta} = -0.07, 95% CI: -0.08 to -0.06; {beta} = -0.01, 95% CI: -0.02 to - 0.001, respectively). The associations between baseline BMI and EA were explained by genetic factors in men (rA = -0.10) and by both genetic (rA = -0.17) and unique environmental factors (rE = -0.07) in women. For BMI change, the associations with EA were explained solely by genetic factors (rA = -0.04 in men; -0.06 in women). ConclusionIndividuals with higher EA tend to have lower baseline BMI and slower BMI increases across adulthood. The majority of the associations are primarily genetically mediated.

BackgroundThere is a notion that tobacco smoking would have weight control effect based on the appetite suppressive effect of nicotine. However, a causal effect from being an ever smoker on obesity-related traits in the general population has yet been determined. MethodsThis Mendelian randomization (MR) analysis instrumented 378 genetic variants associated with being an ever smoker which mostly initiated in adolescents or young adulthood, identified from a genome-wide association study (GWAS) meta-analysis of 1.2 million individuals. The outcome data for body mass index, waist circumference, hip circumference, and waist-to-hip ratio was collected in 337,318 white British ancestry UK Biobank participants with 40-69 ages. Replication analysis was performed for GWAS meta-analysis for body mass index including the GERA/GIANT data including 364,487 mostly European samples. Summary-level MR by inverse variance weighted method and pleiotropy-robust MR methods, including median-based and MR-Egger regression, was performed. ResultsSummary-level MR analysis indicated that genetically predicted being an ever smoker is causally linked to higher body mass index [+0.28 (0.18, 0.38) kg/m2], waist circumference [+0.88 (0.66, 1.10) cm], hip circumference [+0.40 (0.23, 0.57) cm], and waist-to-hip ratio [+0.006 (0.005, 0.007)]. The results were consistently supported by pleiotropy-robust MR analysis. In the replication analysis, genetically predicted being an ever smoker was again significantly associated with higher body mass index [+0.03 (0.01, 0.05) kg/m2]. ConclusionInitiation of tobacco use may consequently lead to worse obesity-related traits of the general population in middle-to-old ages.

BackgroundMany loci associated with obesity have been reported in previous genome-wide association studies (GWASs). However, it remains unclear whether variants at all these loci contributed to onset of obesity or whether one or a few variants cause obesity when obesity is a genetically heterogeneous population. ObjectiveTo investigate the genetic architecture of obesity by clustering a population with obesity into clusters using obesity-related factors. MethodsThis study was based on the Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study and the Community-Based Cohort Study. As the Step-1, a GWAS with body mass index (BMI) as an outcome was performed for all 48,365 eligible participants. As the Step-2, we then assigned the 13,067/48,365 participants with obesity (BMI [&ge;] 25 kg/m2) using the k-prototype to 5 clusters. Obesity-related factors (such as age, nutrient intake, physical activity, sleep duration, difference between weight at age 20 and current weight, smoking, alcohol drinking, psychological distress, and birth weight) were used for clustering. Subsequently, participants in each cluster and those with a BMI < 25 kg/m2 were combined, and GWASs were performed according to the 5 clusters. Additionally, a sub-analysis using data from the UK Biobank was conducted to compare the results. ResultsThe Step-1 detected 18 genes, most of which were reportedly associated with obesity or obesity-related topics in previous studies. The result of Step-2, of the 18 genes detected in Step-1, LINC01741, CRYZL2P-SEC16B, and SEC16B were significantly related to Cluster 2, FTO, PMAIP1, and MC4R to Cluster 3, and BDNF, BDNF-AS, LINC00678, and KIF18A to Clusters 4 and 5. In the sub-analysis, a similar phenomenon was observed in which separate obesity-related genes were detected for each cluster. ConclusionsOur data support the notion that a decreased sample size with increased homogeneity may reveal insights into the genetic architecture of obesity.

Background and AimSocioeconomically disadvantaged people are more likely to have high body mass index (BMI). However whether socioeconomic position moderates genetic susceptibility to high BMI, and whether this effect differs by sex, remains unclear. We aimed to investigate whether educational attainment (EA) moderates the association between genetic liability for high BMI and BMI trajectories across adulthood in females and males. MethodsWe analyzed data from 69,314 participants in the Trondelag Health Study (HUNT), a population-based cohort with genotyping and repeated BMI measures. A polygenic index for BMI (BMI PGI) was calculated, and participants were categorized by EA level. Using linear mixed-effects models stratified by sex, we tested the interaction between BMI PGI, EA, and age on BMI trajectories. ResultsThe relationship between BMI PGI and BMI was non-linear, showing a steeper slope in the upper deciles, and was modified by sex (p<0.001). Sex-stratified analysis showed that EA moderated the effect of BMI PGI on BMI in females (p=0.003) but not in males (p=0.089). Among highly educated females, the BMI difference between the top and bottom BMI PGI deciles was-0.99 kg/m{superscript 2} [95%CI: -1.67 to -0.30] smaller than among those with low education. In males, the corresponding difference was -0.16 kg/m{superscript 2} [-0.71 to 0.39]. Genetic influences on BMI trajectories showed consistent age-dependent patterns across all educational groups, though trajectories differed by sex. Females experienced a steady increase in BMI until age 60, after which it declined. Males had an early rapid increase, then stabilization, followed by a slight late-life decline. ConclusionHigher EA consistently moderates the effect of genetic liability for high BMI in females throughout adulthood, but this protective effect is absent in males. This sex difference suggests that gender-related socioeconomic factors may modulate the expression of BMI-related genetic variants, warranting further investigation into the underlying mechanisms.

We highlight a robust correlation between COVID-19 mortality and obesity prevalence using available country level data on COVID-19 mortality as of August 10, 2020. Such association is robust to controlling for other potential comorbidity factors: diabetes, cardio-vascular, and respiratory diseases, further to a set of demographics, urban, and economic, and containment policies controls. We estimate that .6 log point increase in obesity prevalence, or 1 standard deviation, is associated with about an extra .9 log point per 100,000 deaths (or 50% of a standard deviation, .5{sigma}).

ObjectiveEvaluate the impact of bariatric surgery on monthly earnings and employee status among working-age adults, and examine variations across sociodemographic characteristics. DesignRetrospective longitudinal cohort study using national, linked administrative datasets. SettingHospital inpatient services in England between 1 April 2014 and 31 December 2022. Participants40,662 individuals who had a bariatric surgery procedure and obesity diagnosis during the study period, with no bariatric surgery history in the previous 5 years, and were 25 to 64 years old at the date of surgery. We also included 49,921 individuals sampled from the general population who had not had bariatric surgery matched by age and sex to those in the cohort who had bariatric surgery. Main outcome measuresMonthly employee pay - for all months and only months where the individual was in paid employment - expressed in 2023 prices; paid employee status. ResultsAmong people living with obesity who had bariatric surgery, there was a sustained increase in monthly employee pay from six months after surgery with a mean increase of {pound}84 per month 5 years after surgery compared with the six months before surgery. Among those in paid employment, there was a sustained increase in the probability of being a paid employee from 4 months after bariatric surgery, with a mean increase of 4.3 percentage points 5 years after surgery. The increases in pay and probability of employment were greater for males. The increase in employee pay was not sustained over the 5-year follow up time for the youngest age groups. ConclusionsBariatric surgery is associated with an increased probability of being employed, resulting in increased earnings. These findings suggest that living with obesity negatively impacts labour market outcomes and that obesity management interventions are likely to generate economic benefits both to individuals and on a macroeconomic level by increasing the likelihood of employment of people living with obesity.

BackgroundArtificial intelligence (AI) analytics have not been applied to global burden of disease (GBD) risk factor data to study population health. The comparative risk assessment (CRA) systematic literature review-based methodology for population attributable fractions (PAFs in percents) calculations has not been utilised for quantifying dietary and other risk factors for body mass index kg/M2 (BMI). MethodsInstitute of Health Metrics and Evaluation (IHME) staff and volunteer collaborators analysed over 12,000 GBD risk factor surveys of people from 195 countries and synthesized the data into representative mean cohort BMI and risk factor values. We formatted IHME GBD data relevant to BMI and associated risk factors. We empirically explored the univariate and multiple regression correlations of BMI risk factors with worldwide BMI to derive a BMI multiple regression formula (BMI formula). Main outcome measures included the performances of the BMI formula when tested with all nine Bradford Hill causality criteria each scored on a 0-5 scale: 0=negative to 5=very strong support. FindingsThe BMI formula derived, with all foods in kilocalories/day (kcal/day), BMI formula risk factor coefficients were adjusted to equate with their PAFs. BMI increasing foods had "+" signs and BMI decreasing foods "-" signs. Total BMI formula PAF=80.96%. BMI formula=(0.37%*processed meat + 4.23%*red meat + 0.02%*fish + 2.24%*milk + 5.67%*poultry + 1.77%*eggs + 0.34%*alcohol + 0.99%*sugary beverages + 0.04%*corn + 0.72%*potatoes + 8.48%*saturated fatty acids + 3.89%*polyunsaturated fatty acids + 0.27%*trans fatty acids - 2.99%*fruit - 4.07%*vegetables - 0.37%*nuts and seeds - 0.45%*whole grains - 1.49%*legumes - 8.62%*rice - 0.10%*sweet potatoes - 7.45% physical activity (METs/week) - 20.38%*child underweight + 6.02%*sex (male=1, female=2))*0.05012 + 21.77. BMI formula versus BMI: r=0.907, 95% CI: 0.903 to 0.911, p<0.0001. Bradford Hill causality criteria test scores (0-5): (1) strength=5, (2) experimentation=5, (3) consistency=5, (4) dose-response=5, (5) temporality=5, (6) analogy=4, (7), plausibility=5, (8) specificity=5, and (9) coherence=5. Total score=44/45. InterpretationNine Bradford Hill causality criteria strongly supported a causal relationship between the BMI formula derived and mean BMIs of worldwide cohorts. The artificial intelligence methodology introduced could inform individual, clinical, and public health strategies regarding overweight/obesity prevention/treatment and other health outcomes. FundingNone Research in contextO_ST_ABSEvidence before this studyC_ST_ABSComparative risk assessment (CRA) systematic literature review-based methodology has been used in worldwide global burden of disease (GBD) analysis to determine population attributable fraction(s) (PAF(s)) for one or more risk factors for various health outcomes. So far, CRA has not been applied to derive PAFs for dietary and other risk factors for worldwide BMI. Artificial intelligence (AI) analytics has not yet been applied to worldwide GBD data as an alternative to the CRA methodology for determining risk factor PAFs for health outcomes. Added value of this study{square}A multiple regression derived BMI formula (BMI formula) including PAFs of 20 dietary risk factors, physical activity, childhood severe underweight, and sex satisfied all nine Bradford Hill causality criteria. The BMI formula also plausibly predicted the long-term BMI outcomes related to various dietary and physical activity scenarios. All the BMI formulas 24 risk factor PAFs were consistent in sign (+ or -) with the preponderance of previously published studies on those risk factors related to BMI. Implications of all the available evidenceThe AI analytics methodology of GBD data modeling of BMI and associated risk factors infers causality of the BMI formula estimates with BMI worldwide and BMIs of subsets. This methodology may enable multiple regression formulas for risk factors of health outcomes for a range of non-communicable diseases--testable by Bradford Hill causality criteria.

Background: 39M children worldwide are overweight or have obesity, accelerating risk for adult non-communicable diseases. Presently, interventions to prevent obesity have had limited success due to poor timing and lack of personalisation. Objective: We aimed to identify early-life predictors of childhood obesity (ChOB) that could aid targeting specific population subsets for obesity prevention interventional studies. Methods: Data were from the Raine Study Gen2 participants (n=1494). Anthropometric and genetic predictors evaluated included birthweight (BW), early-life BMI (1-3 years), and three polygenic scores (PGS) [two BW-PGSs (BW-PGS2016 and BW-PGS2019) and a ChOB-PGS], developed from BW and ChOB genome-wide-association-studies, respectively. Multivariate analyses were performed to investigate associations between predictors and child-BMI (5-, 8-, 10-years). Results: BW-PGS2019 associate with child-BMI at 5-years. BW-PGS2016 was not associated with child-BMI. Remaining predictors positively associate with child-BMI at 5-, 8- and 10-years (p<0.001). Early-life BMI, ChOB-PGS and BW accounted for up to 38.7%, 5.8% and 3.4% of the variability in child-BMI, respectively. Conclusions: Our data suggest early-life BMI is a better predictor of child-BMI than ChOB-PGS, and BW, accounting for up to ten-fold more variance in child-BMI. Future interventional studies to mitigate obesity could target early-life BMI as a marker to identify children at the highest risk.

ObjectiveChildhood obesity is a serious health concern that is not yet fully understood. Previous research has linked obesity with neurobehavioral factors such as behavior, cognition, and brain morphology. The causal directions of these relationships remain mostly untested. MethodsWe filled this gap by using the Adolescent Brain Cognitive Development study cohort comprising 11,875 children aged 9-10. First, correlations between body mass percentile and neurobehavioral measures were cross-sectionally analyzed. Effects were then aggregated by neurobehavioral domain for causal analyses. Direction of Causation twin modeling was used to test the direction of each relationship. Findings were validated by longitudinal cross-lagged panel modeling. ResultsBody mass percentile correlated with measures of impulsivity, motivation, psychopathology, eating behavior, and cognitive tests (executive functioning, language, memory, perception, working memory). Higher obesity was also associated with reduced cortical thickness in areas of the frontal and temporal lobe but with increased thickness in parietal and occipital brain areas. Similar although weaker patterns emerged for cortical surface area and volume. Twin modeling suggested causal effects of childhood obesity on eating behavior ({beta}=.26), cognition ({beta}=.05), cortical thickness ({beta}=.15), and cortical surface area ({beta}=.07). Personality/psychopathology ({beta}=.09) and eating behavior ({beta}=.16) appeared to causally influence childhood obesity. Longitudinal evidence broadly supported these findings. Results regarding cortical volume were inconsistent. ConclusionsResults supported causal effects of obesity on brain functioning and morphology, consistent with effects of obesity-related brain inflammation on cognition. The present study highlights the importance of physical health for brain development during childhood and may inform interventions aimed at preventing or reducing pediatric obesity.

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.

BackgroundObesity is a risk factor for both cardiovascular disease (CVD) and cancer. The traditional body mass index (BMI)-based obesity definition (BMI [&ge;]30 kg/m{superscript 2}) has limitations across adulthood and ancestry. The Global Commission on Clinical Obesity in 2025 proposed a new definition incorporating central adiposity measures. The magnitude of increased disease risk among the newly classified obese persons remains undetermined. We compared the associations between obesity definitions and incident CVD and obesity-related cancer in a community-based cohort. MethodsWe analyzed 14,834 Atherosclerosis Risk in Communities (ARIC) study participants followed for a median of 25 years. Participants were classified as obese using the traditional definition (BMI [&ge;]30 kg/m{superscript 2}) and the new definition (BMI [&ge;]25 kg/m{superscript 2} plus [&ge;]1 central adiposity measure, or two central adiposity measures as follows: waist circumference [&ge;]102 cm Male/ [&ge;]88 cm Female; waist-to-hip ratio >0.90 Male/ >0.85 Female; waist-to-height ratio >0.5). We identified participants classified as obese only by the new definition. Cox models adjusted for demographics, socioeconomic, and clinical risk factors estimated the adjusted hazard ratios (aHR). A subgroup analyses by age, sex, and race, and time-dependent sensitivity analyses were conducted. Results54% and 27% were classified as obese, respectively, according to the new-only (median BMI 26 kg/m2) versus the traditional (median BMI 33 kg/m2) definition; 19% were non-obese by both. 7% of the "obese by new-only definition" group (i.e. BMI 25-<30 kg/m2 and central adiposity) reverted to non-obese status. Compared to non-obese, both new-only (aHR 1.25, 95% CI 1.15-1.35) and traditionally-defined (aHR 1.65, 95% CI 1.51-1.81) obesity were associated with higher CVD risk. The same pattern was noted in coronary heart disease and heart failure. For obesity-related cancers, the traditional definition (aHR 1.36, 95% CI 1.16-1.59), but not the new-only definition (aHR 1.09, 95% CI 0.95-1.26), conferred significantly higher risk. The traditional definition was also associated with higher risk of total and colorectal cancers. Time-dependent analysis and subgrouping by sex, race, and age at time of diagnosis yielded similar results. ConclusionBoth obesity definitions identified increased CVD risk, with significant trends across groups. Only traditionally-defined obesity consistently showed increased risk for total and obesity-related cancers. The newer, more sensitive, definition identified a large cohort with intermediate CVD risk, not previously captured by BMI alone, highlighting a distinct risk profile, suggesting potential for targeted interventions. These findings warrant careful consideration of potentially inflated risk in patients defined as obese by the new criteria. FundingNHLBI, NCI, NPCR, AHA

BackgroundSome nuclei of the hypothalamus are known for their important roles in maintaining energy homeostasis and regulating food intake. Moreover, obesity has been associated with hypothalamic inflammation and morphological alterations, as indicated by increased volume. However, the reversibility of these changes after bariatric surgery-induced weight loss remains underexplored. ObjectiveThe aim of this study was to characterize volume changes in hypothalamic subunits up to two years following bariatric surgery and to determine whether these differences were associated with changes in metabolic parameters and levels of gastrointestinal appetite-regulating hormone levels. MethodsParticipants with severe obesity undergoing bariatric surgery were recruited. They completed high-resolution T1-weighted brain magnetic resonance imaging (MRI) before bariatric surgery and at 4, 12 and 24 months post-surgery. Blood samples collected during the fasting and postprandial states were analyzed for glucagon-like peptide 1 (GLP-1), peptide YY (PYY), and ghrelin concentrations. The hypothalamus was segmented into 5 subunits per hemisphere using a publicly available automated tool. Linear mixed-effects models were employed to examine volume changes between visits and their associations with variables of interest. ResultsA total of 73 participants (mean age 44.5 {+/-} 9.1 years, mean BMI 43.5 {+/-} 4.1 kg/m2) were included at baseline. Significant volume reductions were observed in the whole left hypothalamus 24 months post-surgery. More specifically, decreases were noted in both the left anterior-superior and left posterior subunits at 12 and 24 months post-surgery (all p<0.05, after FDR correction). These reductions were significantly associated with the percentage of total weight loss (both subunits p<0.001), improvements in systolic blood pressure (both subunits p<0.05), and an increase in postprandial PYY (both subunits p<0.05). ConclusionThese results suggest that some hypothalamic morphological alterations observed in the context of obesity could potentially be reversed with bariatric surgery induced-weight loss.

Background/ObjectivesThis study aimed to predict body mass index (BMI) trajectories from childhood to early adulthood using explainable artificial intelligence, integrating polygenic scores (PGS), maternal, early-life, and familial factors to identify key predictors of obesity risk and inform prevention strategies. Subjects/MethodsWe analysed longitudinal data from the Raine Study Gen2 cohort, recruiting 2 868 participants. This observational study, without randomization or case-control design, collected BMI measurements at ages 8, 10, 14, 17, 20, 23, and 27 years. We applied Kolmogorov-Arnold Networks (KAN) alongside conventional machine learning models, integrating epidemiological variables (maternal and paternal anthropometrics, parental education, early-life skinfold measurements) with seven BMI-related PGS. The analysis spanned from childhood to early adulthood, with no intervention administered. ResultsThe KAN model, combining epidemiological and PGS data, achieved predictive performance with R{superscript 2} ranging from 0.81 at age 8 to 0.34 at age 27. BMI z-score at age 5 was the dominant predictor in early years, with PGS influence increasing post-adolescence. Maternal and paternal anthropometric measures, parental education, and early-life skinfold measurements were significant contributors. The interpretable KAN model revealed the dynamic interplay of genetic and environmental factors, with early-life BMI z-score and PGS emerging as key drivers of BMI trajectories across life stages. ConclusionsThese findings highlight the dynamic interplay of genetic and environmental factors across life stages, underscoring the potential of early-life BMI as a biomarker for obesity risk. Our interpretable model offers actionable insights for targeted obesity prevention strategies.