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PubMed2026

Obesity Polygenic Risk and Healthy Lifestyle Interactions on Weight Trajectories in Women and Men.

BACKGROUND: Genetics and environmental factors contribute to obesity risk, but the extent to which healthy behaviors can offset genetic susceptibility remains unclear. We examined the interaction between obesity polygenic risk and a composite healthy lifestyle score on body mass index (BMI) trajectories in women and men. METHODS: We analyzed 13&#x2009;780 women from the Nurses' Health Study and 8242 men from the Health Professionals Follow-Up Study, all of European ancestry and free of major chronic disease at baseline. The lifestyle score comprised American Heart Association Essential 8 components (nonsmoking, physical activity, healthy eating, adequate sleep) plus moderate alcohol intake, modeled as a time-varying variable. A genome-wide polygenic score for BMI was derived from genome-wide association study. Adjusted linear mixed-effects models estimated associations and interactions on biennial BMI measures over up to 26&#x2009;years. RESULTS: Each SD increase in the polygenic score was associated with 1.80&#x2009;kg/m2 (95% CI, 1.72-1.87) and 1.12&#x2009;kg/m2 (95% CI, 1.06-1.19) higher BMI in women and men, respectively. Significant interactions between the polygenic score and healthy lifestyle score (both P<0.05) showed a dose-response attenuation of the genetic effects with healthier lifestyles. Comparing the healthiest with the least healthy lifestyle groups, genetic effects on BMI were 35% lower in women and 28% lower in men. In sensitivity analyses, higher diet quality and physical activity consistently attenuated genetic associations in both cohorts, whereas current smoking showed similar effects in women only. CONCLUSIONS: Adherence to a healthier lifestyle attenuated the association between obesity polygenic risk and BMI in a dose-response manner.

Humans↗
PubMed2024

Obesity: exploring its connection to brain function through genetic and genomic perspectives.

Obesity represents an escalating global health burden with profound medical and economic impacts. The conventional perspective on obesity revolves around its classification as a "pure" metabolic disorder, marked by an imbalance between calorie consumption and energy expenditure. Present knowledge, however, recognizes the intricate interaction of rare or frequent genetic factors that favor the development of obesity, together with the emergence of neurodevelopmental and mental abnormalities, phenotypes that are modulated by environmental factors such as lifestyle. Thirty years of human genetic research has unveiled >20 genes, causing severe early-onset monogenic obesity and ~1000 loci associated with common polygenic obesity, most of those expressed in the brain, depicting obesity as a neurological and mental condition. Therefore, obesity's association with brain function should be better recognized. In this context, this review seeks to broaden the current perspective by elucidating the genetic determinants that contribute to both obesity and neurodevelopmental and mental dysfunctions. We conduct a detailed examination of recent genetic findings, correlating them with clinical and behavioral phenotypes associated with obesity. This includes how polygenic obesity, influenced by a myriad of genetic variants, impacts brain regions associated with addiction and reward, differentiating it from monogenic forms. The continuum between non-syndromic and syndromic monogenic obesity, with evidence from neurodevelopmental and cognitive assessments, is also addressed. Current therapeutic approaches that target these genetic mechanisms, yielding improved clinical outcomes and cognitive advantages, are discussed. To sum up, this review corroborates the genetic underpinnings of obesity, affirming its classification as a neurological disorder that may have broader implications for neurodevelopmental and mental conditions. It highlights the promising intersection of genetics, genomics, and neurobiology as a foundation for developing tailored medical approaches to treat obesity and its related neurological aspects.

Humans↗
PubMed2026

Genetic determinants of obesity: mechanisms, clinical implications, and targeted therapies.

PURPOSE: Obesity is a major global health crisis with rising prevalence in both pediatric and adult populations, leading to an increased risk of cardiovascular, metabolic, and other chronic complications affecting all organ systems. A clear understanding of the genetic contributors to polygenic, syndromic, and monogenic obesity is essential for early diagnosis and targeted management. METHODS: Advances in genome-wide association studies (GWAS) and sequencing technologies have greatly expanded our understanding of the genetic alterations underlying this multifaceted disease and have helped in delivering personalized treatment. RESULTS: The pathogenesis of common, polygenic obesity is related to a complex interplay between genetic susceptibility and environmental factors. Syndromic obesity, a less common form, is characterized by early-onset accompanied by additional features such as developmental delay, dysmorphic traits, and various organ system involvement. The rarest form, monogenic obesity, is characterized by severe early-onset non-syndromic obesity caused by mutations in single genes regulating appetite within the hypothalamus. These monogenic obesity cases, though infrequent, have been instrumental in elucidating key pathways involved in hunger and satiety. CONCLUSION: This review provides a comprehensive summary of the most recent findings on the genetic basis of obesity across all age groups, highlighting clinical implications and emerging therapeutic opportunities.

Humans↗
PubMed2025

Polygenic prediction of body mass index and obesity through the life course and across ancestries.

Polygenic scores (PGSs) for body mass index (BMI) may guide early prevention and targeted treatment of obesity. Using genetic data from up to 5.1 million people (4.6% African ancestry, 14.4% American ancestry, 8.4% East Asian ancestry, 71.1% European ancestry and 1.5% South Asian ancestry) from the GIANT consortium and 23andMe, Inc., we developed ancestry-specific and multi-ancestry PGSs. The multi-ancestry score explained 17.6% of BMI variation among UK Biobank participants of European ancestry. For other populations, this ranged from 16% in East Asian-Americans to 2.2% in rural Ugandans. In the ALSPAC study, children with higher PGSs showed accelerated BMI gain from age 2.5&#x2009;years to adolescence, with earlier adiposity rebound. Adding the PGS to predictors available at birth nearly doubled explained variance for BMI from age 5 onward (for example, from 11% to 21% at age 8). Up to age 5, adding the PGS to early-life BMI improved prediction of BMI at age 18 (for example, from 22% to 35% at age 5). Higher PGSs were associated with greater adult weight gain. In intensive lifestyle intervention trials, individuals with higher PGSs lost modestly more weight in the first year (0.55&#x2009;kg per s.d.) but were more likely to regain it. Overall, these data show that PGSs have the potential to improve obesity prediction, particularly when implemented early in life.

Adolescent↗
PubMed2026

Joint Effects of Long-Term Obesity and Genetic Susceptibility on Sex-Specific Brain Aging.

OBJECTIVE: This study aimed to examine the associations of longitudinal obesity trajectories and polygenic risk with sex-specific brain aging. METHODS: We analyzed 35,092 UK Biobank participants (16,484 males and 18,608 females). Sex-specific XGBoost models estimated multimodal brain age. We derived 16-year longitudinal obesity trajectories from repeatedly collected anthropometric measurements. Polygenic risk scores were constructed based on 55 independent genetic loci. Multivariable logistic regression examined associations of obesity trajectories and genetic risk with brain age acceleration. RESULTS: A total of 8198 (49.73%) males and 9089 (48.84%) females had accelerated brain aging. High genetic risk significantly increased brain age acceleration odds (males: OR&#x2009;=&#x2009;1.39; females: OR&#x2009;=&#x2009;1.34). Crucially, the high-stable obesity trajectory exerted a stronger effect in males (OR&#x2009;=&#x2009;1.90, 95% CI: 1.64-2.21) than in females (OR&#x2009;=&#x2009;1.25, 95% CI: 1.12-1.40), with the joint presence of high genetic risk and high-stable obesity amplifying risk to an OR of 2.78 in males and 1.57 in females. Conversely, shifting from obesity to non-obesity reduced risk by 30% in males and 18% in females. CONCLUSIONS: These findings underscore long-term obesity as a critical, sex-dimorphic driver of accelerated brain aging, and midlife weight management offers robust neuroprotection even in genetically susceptible individuals.

brain aging↗
PubMed2026

The association between GLP-1R expression and cardiovascular-kidney-metabolic-related diseases in non-diabetic and non-obese population: evidence triangulation using Mendelian randomization, observational and polygenic score association analysis.

BACKGROUND: Glucagon-like peptide-1 receptor (GLP-1R) agonists are emerging as promising therapies for cardiovascular-kidney-metabolic (CKM) related diseases in individuals with type 2 diabetes mellitus (T2DM) or obesity. But their effects in non-obese and non-diabetic individuals are unclear. This study triangulates evidence using Mendelian randomization (MR), polygenic scores (PGS) and observational analyses to estimate the associations of GLP-1R expression with chronic kidney disease (CKD), heart failure (HF) and metabolic dysfunction-associated steatotic liver disease (MASLD). METHODS: For the MR analysis, instruments mimicking GLP-1R expression were identified using pancreas-specific cis-expression quantitative trait loci from GTEx (N&#x2009;&#x2264;&#x2009;305). MR-Robust method was used as the primary MR approach. PGS and observational analyses were performed both in non-diabetic and non-obese individuals separately. A genome-wide association study (GWAS) for MASLD (14,231 cases and 348,091 controls) was performed in the general population using data from UK Biobank. RESULTS: GLP-1R expression showed robust effects on CKD (odds ratio [OR] 0.96, 95%CI 0.95 to 0.97, q&#x2009;=&#x2009;1.7&#x2009;&#xd7;&#x2009;10-&#x2009;10 ), HF (OR&#x2009;=&#x2009;0.96, 95%CI 0.94 to 0.97, q&#x2009;=&#x2009;2.5&#x2009;&#xd7;&#x2009;10-&#x2009;8) and MASLD (OR&#x2009;=&#x2009;0.96, 95%CI 0.93 to 0.98, q&#x2009;=&#x2009;1.3&#x2009;&#xd7;&#x2009;10-&#x2009;3) in the general population. Consistent results were observed in validation analyses. Furthermore, PGS and observational analyses among non-T2DM and non-obese individuals found little evidence to support its association with CKD, HF or MASLD. GWAS analysis identified eight conditionally independent variants associated with MASLD, in which rs563199662 was a new signal located at TFPI region. CONCLUSIONS: This study provides multilayered evidence for GLP-1R expression in mitigating CKD, HF and MASLD risks in the general population, while de-prioritized its effect on CKM-related diseases in non-obese and non-diabetic individuals. Further clinical trials are needed to validate the effects of GLP-1R agonists in relative health population.

Humans↗
PubMed2025

Validation of a genome-wide polygenic score for body mass index in South Asians.

Obesity is a complex disorder, manifested by the interaction of inherited and environmental factors and modulated by a person's lifestyle habits. India has witnessed more than a two-fold increase in the number of overweight adults in the last 30 years. The polygenic risk score (PRS) quantitatively measures an individual's risk for common diseases. The PRS for obesity have been validated in the Caucasian population but not in the South Asian (SAS) population. In this study, we benchmarked and validated the existing genome-wide PRS model of obesity with 2.1 million variants in the SAS population. We analyzed a total of 14,263 individuals from three different South Asian cohorts. We compared the risk score with the body mass index (BMI) categories (underweight, normal weight, overweight, and obese) in all three cohorts. High PRS was associated with increased BMI in all the three cohorts. This study also compared validation results from another population-specific PRS model for the BMI. We conclude that high PRS is associated with high BMI in South Asians. Our study suggests that the PRS score can perhaps be an early predictor of overweight and obesity in the South Asian population.

South Asian↗
PubMed2025

Polygenic scores for obstructive sleep apnoea reveal pathways contributing to cardiovascular disease.

BACKGROUND: Obstructive sleep apnoea (OSA) is a common chronic condition, with obesity its strongest risk factor. Polygenic scores (PGSs) summarise the genetic liability to phenotype and can provide insights into relationships between phenotypes. Recently, large datasets that include genetic data and OSA status became available, providing an opportunity to utilise PGS approaches to study the genetic relationship between OSA and other phenotypes, while differentiating OSA-specific from obesity-specific genetic factors. METHODS: Using race/ethnic diverse samples from over 1.2 million individuals from the Million Veteran Program, FinnGen, TOPMed, All of Us (AoU), Geisinger's MyCode, MGB Biobank, and the Human Phenotype Project, we developed and assessed PGSs for OSA, both without (BMIunadjOSA-PGS) and with adjustment for the genetic contributions of BMI (BMIadjOSA-PGS). FINDINGS: Adjusted odds ratios (ORs) for OSA per 1 standard deviation of the PGSs ranged from 1.38 to 2.75. The associations of BMIadjOSA- and BMIunadjOSA-PGSs with CVD outcomes in AoU shared both common and distinct patterns. Only BMIunadjOSA-PGS was associated with type 2 diabetes, heart failure, and coronary artery disease, while both BMIadjOSA- and BMIunadjOSA-PGSs were associated with hypertension and stroke. Sex stratified analyses revealed that BMIadjOSA-PGS association with hypertension was driven by females (OR = 1.1, p-value = 0.002, OR = 1.01 p-value = 0.2 in males). OSA PGSs were also associated with body fat measures with some sex-specific associations. INTERPRETATION: Distinct components of OSA genetic risk are related and independent of obesity. Sex-specific associations with body fat distribution measures may explain differing OSA risks and associations with cardiometabolic morbidities between sexes. FUNDING: R01AG080598.

Humans↗
PubMed2024

Obesity-enriched gut microbe degrades myo-inositol and promotes lipid absorption.

Numerous studies have reported critical roles for the gut microbiota in obesity. However, the specific microbes that causally contribute to obesity and the underlying mechanisms remain undetermined. Here, we conducted shotgun metagenomic sequencing in a Chinese cohort of 631 obese subjects and 374 normal-weight controls and identified a Megamonas-dominated, enterotype-like cluster enriched in obese subjects. Among this cohort, the presence of Megamonas and polygenic risk exhibited an additive impact on obesity. Megamonas rupellensis possessed genes for myo-inositol degradation, as demonstrated in&#xa0;vitro and in&#xa0;vivo, and the addition of myo-inositol effectively inhibited fatty acid absorption in intestinal organoids. Furthermore, mice colonized with M.&#xa0;rupellensis or E.&#xa0;coli heterologously expressing the myo-inositol-degrading iolG gene exhibited enhanced intestinal lipid absorption, thereby leading to obesity. Altogether, our findings uncover roles for M.&#xa0;rupellensis as a myo-inositol degrader that enhances lipid absorption and obesity, suggesting potential strategies for future obesity management.

Inositol↗
PubMed2026

Multivariate, Multi-Omic Analysis in 799,429 Individuals Identifies 134 Loci Associated with Somatoform Traits.

INTRODUCTION: Somatoform traits (e.g., health anxiety, somatic preoccupation, and bodily distress symptoms) are prevalent and pose challenges to clinical practice. Understanding their genetic basis could improve diagnostic and therapeutic approaches. METHODS: Using available summary statistics, we conducted a multivariate genome-wide association study (GWAS) and multi-omic analysis of four somatoform traits - fatigue, irritable bowel syndrome, pain intensity, and health satisfaction - in 799,429 individuals genetically similar to European reference panels. RESULTS: The GWAS identified 134 loci associated with a somatoform common factor, including 44 loci not significant in the input GWAS and 8 novel loci for somatoform traits. Novel loci were mechanistically informative, mapping to the DNM1 gene and the protocadherin gene cluster (PCDHA1-4), which are involved in nociceptor sensitization and synaptogenesis, respectively. Gene-property analyses highlighted an enrichment of genes involved in synaptic transmission and enriched expression in 11 brain tissues and the pituitary. Across two brain transcriptomic datasets, we identified 16 high-confidence genes whose expression in enriched tissues was associated with somatoform traits. There was substantial polygenic overlap (76-83%) between the somatoform and externalizing, internalizing, and general psychopathology factors. Somatoform polygenic scores were associated with obesity, type 2 diabetes, and tobacco use disorder in independent biobanks. Drug repurposing analyses suggested potential therapeutic targets, including MEK inhibitors, while Mendelian randomization analyses indicated potentially protective effects of gut microbiota. DISCUSSION: Consistent with emerging medical and genetic knowledge, somatoform traits have a shared etiology and considerable polygenic overlap with psychopathology. The biological insights from drug repurposing and Mendelian randomization analyses could provide promising avenues for treatment development.

Genetics↗
PubMed2026

A multi-ancestry polygenic risk score for body mass index predicts longitudinal weight change.

BACKGROUND: Identifying individuals at risk for future weight gain is challenging, partly because associations with traditional clinical risk factors may be biased by confounding and reverse causation. Polygenic risk scores (PRS) provide a stable, lifelong measure of genetic predisposition to obesity. However, existing PRS have not been evaluated for their association with longitudinal weight change in adulthood and often lack generalizability across diverse genetic ancestry groups. METHODS: We conducted ancestry-specific genome-wide association study meta-analyses of body mass index (BMI) in populations of European, African or African American, Admixed American, East Asian, and South Asian ancestries and developed ancestry-specific PRS. A multi-ancestry polygenic risk score (MAPRS) was trained using ancestry-specific PRS in a model selection dataset (N&#x2009;=&#x2009;39,685) from the All of Us Research Program (AoU). We evaluated the MAPRS in an independent AoU model evaluation dataset (N&#x2009;=&#x2009;158,743) for BMI prediction and in a separate AoU test dataset (N&#x2009;=&#x2009;78,219) with repeated measurements over 1.5-2.5 years for weight change prediction. The outcomes included change in BMI and&#x2009;&#x2265;&#x2009;10% or&#x2009;&#x2265;&#x2009;5% total body weight (TBW) gain. We further examined the relationship between MAPRS and 12 clinical risk factors commonly comorbid with obesity in relation to weight change. RESULTS: The MAPRS captured 7.05% of the variance in measured BMI in the AoU model evaluation dataset and demonstrated improved generalizability across all non-European genetic ancestry groups. In the AoU test dataset, conditioned on baseline BMI at the second-to-last measurement, a one SD increase in MAPRS was associated with a 0.16 kg/m2 increase in future BMI (standard error&#x2009;=&#x2009;0.012 kg/m2; p-value&#x2009;=&#x2009;2.2&#x2009;&#xd7;&#x2009;10-39), 1.27-fold increased odds of experiencing&#x2009;&#x2265;&#x2009;10% TBW gain (95% CI: 1.24-1.31; p-value&#x2009;=&#x2009;1.4&#x2009;&#xd7;&#x2009;10-55), and 1.15-fold increased odds of experiencing&#x2009;&#x2265;&#x2009;5% TBW gain (95% CI: 1.13-1.18; p-value&#x2009;=&#x2009;2.8&#x2009;&#xd7;&#x2009;10-39). These associations were observed across all genetic ancestry groups and remained highly consistent after adjustment for any clinical risk factor. In contrast, most clinical risk factors demonstrated inconsistent or weaker associations with weight change outcomes. CONCLUSIONS: We developed an MAPRS for BMI that represents a robust and generalizable risk factor for longitudinal weight gain in adulthood, providing a foundation for genetically informed risk stratification and earlier, more targeted obesity prevention strategies.

Humans↗
PubMed1979

Determinants of plasma uric acid.

Obesity, alcohol consumption, and hematocrit provide an index of plasma uric acid, which in path analysis has a cultural heritability of 0.11 in children and 0.23 in parents, a small maternal effect, and a genetic heritability of 0.25 in both generations. Preliminary evidence for a major locus is destroyed by the omission of one exceptional child. There is no evidence against the polygenic hypothesis for hyperuricemia in the Japanese-American population studied.

Adult↗
PubMed2026

Genetic evidence for causality of late chronotype on metabolic syndrome in East Asians and Europeans.

CONTEXT: The impact of chronotype-defined as an individuals' inherent preference of sleep timing-and its genetic determinants on metabolic syndrome (MetS) has been less studied. OBJECTIVE: This study investigated the causal relationship between late chronotype and MetS using Mendelian randomization (MR) analysis, based on data from the Taiwan Biobank (TWB) and parallel analyses in the UK Biobank (UKB). METHODS: A total of 36,845 participants from TWB served as the discovery cohort, and 235,639 participants from UKB served as the replication cohort. Late chronotype was defined in TWB as a preference for bedtime after midnight, and in UKB as self-report as being an 'evening' person. The association between late chronotype and MetS, along with its components, was evaluated in TWB, and validated in UKB. Genome-wide association analyses for late chronotype were first conducted in TWB and then meta-analyzed with UKB. Polygenic risk scores (PRS) for late chronotype were constructed and tested for association with MetS. Causality between late chronotype and MetS was examined using one-sample MR analysis in TWB and validated in UKB. RESULTS: Late chronotype was significantly associated with MetS, as well as with central obesity, hyperglycemia, and hypertriglyceridemia, in both TWB and UKB (all P&#xa0;<&#xa0;0.0083, considering Bonferroni correction). The constructed PRS of late chronotype also showed significant associations with MetS and several of its components (several P&#xa0;<&#xa0;0.0083, considering Bonferroni correction). Findings from the one-sample MR analysis indicated a potential causal effect of late chronotype on MetS. CONCLUSIONS: This study provides evidence of a robust association between late chronotype and MetS across populations of diverse ancestry, including Taiwanese and European.

Humans↗
PubMed2026

Refining the Genetic Contribution to Type 2 Diabetes Subtypes.

BACKGROUND: Type 2 diabetes (T2D) is a complex and highly heterogeneous disease driven in part by genetic predisposition and can be stratified into clinical subgroups to aid disease management. We recently grouped T2D subjects in the Qatar Biobank (QBB) cohort into Severe Insulin-Deficient Diabetes (SIDD), Severe Insulin-Resistant Diabetes (SIRD), Mild Obesity-Related Diabetes (MOD) and Mild Age-Related Diabetes (MARD) subtypes. Herein, we focused on the genetic makeup of these subtypes. METHODS: We used the QBB cohort (n&#x2009;=&#x2009;13,808), of whom 2687 were with T2D, and comprehensively assessed polygenic risk scores (PGS) across T2D subtypes, investigated genetic loci associated with each subtype by leveraging the most recent and largest GWAS for T2D, evaluated SNP associations across T2D genetic clusters, and identified protein interaction pathways associated with these distinct T2D subtypes. RESULTS: MOD showed consistently lower PGS compared with other T2D subtypes across all tested scores. SIDD showed more associations with SNPs mapping to residual glycemic cluster compared with other T2D subtypes. The incremental analysis of PGS004838 demonstrated a high &#x394;AUC of 0.101 for SIDD and a moderate &#x394;AUC of 0.068 for SIRD, but not for MOD and MARD. Protein interaction analyses identified candidate subtype-associated gene networks linked to pathways related to glucose homeostasis in SIDD, insulin signalling and hepatic metabolism in SIRD, body fat distribution in MOD and vascular-related processes in MARD. CONCLUSION: We found heterogeneous genetic architectures across clinically defined T2D subtypes in a Middle Eastern population. Our findings provide evidence supporting differential polygenic burden, subtype genetic associations and subtype-associated biological pathways across T2D subtypes. These observations support the utility of subtype-based genetic analyses for improving biological understanding of T2D heterogeneity.

Humans↗
PubMed2024

Increased Genetic Risk for &#x3b2;-Cell Failure Is Associated With &#x3b2;-Cell Function Decline in People With Prediabetes.

Partitioned polygenic scores (pPS) have been developed to capture pathophysiologic processes underlying type 2 diabetes (T2D). We investigated the association of T2D pPS with diabetes-related traits and T2D incidence in the Diabetes Prevention Program. We generated five T2D pPS (&#x3b2;-cell, proinsulin, liver/lipid, obesity, lipodystrophy) in 2,647 participants randomized to intensive lifestyle, metformin, or placebo arms. Associations were tested with general linear models and Cox regression with adjustment for age, sex, and principal components. Sensitivity analyses included adjustment for BMI. Higher &#x3b2;-cell pPS was associated with lower insulinogenic index and corrected insulin response at 1-year follow-up with adjustment for baseline measures (effect per pPS SD -0.04, P = 9.6 &#xd7; 10-7, and -8.45 &#x3bc;U/mg, P = 5.6 &#xd7; 10-6, respectively) and with increased diabetes incidence with adjustment for BMI at nominal significance (hazard ratio 1.10 per SD, P = 0.035). The liver/lipid pPS was associated with reduced 1-year baseline-adjusted triglyceride levels (effect per SD -4.37, P = 0.001). There was no significant interaction between T2D pPS and randomized groups. The remaining pPS were associated with baseline measures only. We conclude that despite interventions for diabetes prevention, participants with a high genetic burden of the &#x3b2;-cell cluster pPS had worsening in measures of &#x3b2;-cell function.

Humans↗
PubMed2025

Predicting Weight Loss After Vertical Sleeve Gastrectomy Using a Whole-genome Sequencing-derived Polygenic Risk Score in the All of Us Cohort.

OBJECTIVE: To create a genome-wide polygenic risk score (PRS) to improve prediction of a 12-month percentage weight loss (WL) after vertical sleeve gastrectomy (VSG). BACKGROUND: Variability in post-VSG WL is not well explained by clinical factors. The All of Us program provides access to a 414,830 short-read whole-genome sequencing resource, enabling unbiased discovery of genetic predictors after VSG. METHODS: VSG counts, demographic, anthropomorphic and vital sign information were obtained from the linked electronic health record. The discovery cohort (DC) included participants from version 7 carried into version 8 while the validation cohort (VC) included those newly added to v8. We defined good responders and nonresponders as having WL&#xb1;1SD from the mean. Following quality filtering, we applied a 2-stage penalized-regression, followed by elastic-net logistic regression, to identify 1583 stable variants and derive &#x3b2;-weights. We then tested this PRS on the DC into a prediction model. RESULTS: We identified 395 participants in the DC and 336 participants in the VC, respectively. Of these, VSG, 44 were classified as good responders (&#x2265;37% WL) and 55 as nonresponders (&#x2264;19% WL). In the VC, 55 were classified as good responders and 48 as nonresponders. Adding the PRS to models to clinical predictors increased the area under the curve following logistic regression by 0.03; P <4.3 &#xd7; 10 -14 , random forest by 0.03; P <9.1 &#xd7; 10 -7 , decision tree by 0.05; P = 1.2 &#xd7; 10 -3 , and gradient boosting by 0.08; P <8.3 &#xd7; 10 -10 . CONCLUSIONS: Use of short-read whole-genome sequencing from All of Us (AoU) can be effectively used to generate PRS to enhance predictive WL accuracy. This work has implications for outcomes of both bariatric surgery and other surgical procedures.

Humans↗