Xianyong Yin

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 8–67 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 (β = 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 (β = 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.

Introduction: Acrylamide has been shown to induce oxidative stress and β-cell toxicity in animal studies; however, population-based evidence remains limited and inconsistent. Therefore, this study aimed to evaluate the associations between acrylamide (AA) hemoglobin biomarkers and the risk of prediabetes and diabetes in the US population. Methods: Data were obtained from the 2013-2016 National Health and Nutrition Examination Survey (NHANES). Participants were classified into diabetes, prediabetes, and healthy control groups by their glycometabolic status. The associations between prediabetes and diabetes with AA biomarkers, specifically hemoglobin adducts of acrylamide (HbAA), glycidamide (HbGA), the Hb- GA/HbAA ratio, and their combined levels (HbAA + HbGA), were investigated using multivariate logistic regression and restricted cubic spline models. Results: The fully adjusted logistic regression analyses indicated that participants in the highest HbGA quartile had a significantly elevated risk of prediabetes (odd ratio [OR] = 2.47, 95% confidence interval [CI]: 1.52-4.02, P = 0.007; Ptrend = 0.005) and diabetes (OR = 2.46, 95% CI: 1.27-4.73, P = 0.019; Ptrend = 0.043). The HbGA/HbAA ratio showed a positive correlation with prediabetes (OR = 2.10, 95% CI: 1.05-4.20, P = 0.042; Ptrend = 0.024) and diabetes (OR = 5.58, 95% CI: 2.10-14.79, P = 0.008; Ptrend < 0.001). In contrast, HbAA levels were not significantly associated with either prediabetes (OR = 1.49, 95% CI: 0.86-2.61, P = 0.116; Ptrend = 0.088) or diabetes (OR = 0.92, 95% CI: 0.47-1.81, P = 0.744; Ptrend = 0.351). Discussion: The divergent patterns observed for HbAA and the HbGA/HbAA ratio may reflect a shift from exogenous exposure to intrinsic metabolic capacity across different stages of disease progression. Conclusion: Higher HbGA levels and elevated HbGA/HbAA ratios were associated with an increased prevalence of prediabetes and diabetes. Further research is needed to clarify the causal role of acrylamide exposure in abnormal glucose metabolism.

Aims/hypothesis: A recent study has suggested eight clusters of genetic variants associated with type 2 diabetes. We aimed to characterise metabolite associations for these eight clusters. Methods: We constructed type 2 diabetes overall and cluster-partitioned polygenic risk scores (PRSs) in 10,015 Finnish men with 979 named plasma metabolites measured in Metabolon HD4 mass spectrometry platform. We evaluated metabolite–PRS associations using linear regression. We also performed a mediation analysis to examine whether metabolites statistically accounted for part of the association between genetic risk and incident type 2 diabetes that developed in a mean of 13.6 years’ follow-up. Results: We identified 337 metabolites significantly associated with type 2 diabetes genetic risk, including 242 exclusive to cluster-partitioned PRSs. Of the significant metabolites, 26 exhibited significantly heterogeneous associations across clusters. We identified significant enrichment for 33 metabolic pathways among the cluster-associated metabolites. Notably, metabolites for the two pancreatic beta cell-related clusters exhibited enrichment in distinct pathways: the beta cell + proinsulin (PI) cluster in fructose, mannose and galactose metabolism; and the beta cell − PI cluster in branched-chain amino acid metabolism. Mediation analysis suggested that >50% of the associated metabolites showed patterns statistically consistent with a mediating role in the associations between PRSs and incident type 2 diabetes. Conclusions/interpretation: This study underscores the value of type 2 diabetes clustering and highlights metabolic heterogeneity across clusters. The findings have the potential to guide personalised interventions. Data availability: The datasets generated during and/or analysed in the current study are available in dbGaP (accession ID: phs000743.v4.p1 and phs004033.v1.p1).

We conducted the largest-ever cross-ancestry GWAS meta-analysis for SLE with 25,109 cases and 271,084 controls comprising East Asian (EA), Southeast Asians (SEA) and Europeans (EUR). We identified 279 independent non-MHC associations, including 51 unreported SLE associations, among which EA-driven associations were identified at PIK3AP1, FOXK1, UBASH3A, and CTDSP1. We further identified a significant interactive association between FOXK1 and IRF5 variants. Statistical fine-mapping together with SNP-to-gene inference and functional prediction identified plausible causal non-coding variants in PRKD3 and TSPAN32, as well as variants in enhancers for CD83, IL12A, c-MYC, and l-MYC, along with an EA-specific missense variant rs55882956 in TYK2. Transcription factors (TFs) linked to fine-mapped enhancer variants contributed to trans-acting regulatory mechanisms in SLE, with strong heritability enrichment at their binding sites across various immune cell types. These include a disproportionate representation of B cell TFs that are themselves risk genes. MYC protein showed the largest enrichment at its binding sites in B cells. In addition, Prioritizing MYC binding sites of B cells in polygenic risk score improved SLE prediction. We also identified 67 candidate druggable genes, providing mechanistic insights and highlighting opportunities for therapeutic development in SLE.

Epidemiological studies have established that autoimmune diseases (AD) increase cardiovascular disease (CVD) risk. We aimed to elucidate their shared genet

OBJECTIVE: We aimed to identify plasma metabolites significantly associated with existing type 2 diabetes (T2D), further characterize their associations with T2D-related laboratory measures and lifestyle-related risk factors, and evaluate their potential mediating and predictive roles for incident T2D. RESEARCH DESIGN AND METHODS: We performed metabolomic profiling in 10,163 Finnish men in the Metabolic Syndrome in Men (METSIM) study at baseline. Of these participants, 1,412 had prevalent T2D and 1,234 developed incident T2D during an average follow-up period of 13.6 years. We tested for associations of 979 named metabolites with prevalent T2D. For significant metabolites, we further evaluated their associations with 24 T2D-related laboratory measures of five groups and five T2D lifestyle-related risk factors. We performed an exploratory mediation analysis to examine the mediation effects of metabolites on incident T2D for the five risk factors. We evaluated metabolite associations with incident T2D and built exploratory metabolite predictive models for incident T2D. RESULTS: We identified 193 plasma metabolites significantly associated with prevalent T2D at baseline, including 71 previously unreported. Of these 193 metabolites, 88 were associated with incident T2D. In participants with prevalent T2D, the 193 metabolites showed associations with zero to 24 (mean 10.2) T2D-related laboratory measures. Eighty-one metabolites partially mediated the associations of the five risk factors with T2D onset under standard mediation assumptions. The significant metabolites showed improved predictive ability for future T2D. CONCLUSIONS: This study identifies T2D plasma metabolic biomarkers for further investigation in women and other populations. The findings enhance our understanding of T2D biology.

Type 2 diabetes is associated with a range of non-cardiovascular non-oncologic comorbidities. To move beyond associations and evaluate causal effects between type 2 diabetes genetic predisposition and 21 comorbidities, we apply Mendelian randomization analysis using genome-wide association studies across multiple genetic ancestries. Additionally, leveraging eight mechanistic clusters of type 2 diabetes genetic profiles, each representing distinct biological pathways, we investigate causal links between cluster-stratified type 2 diabetes genetic predisposition and comorbidity risk. We identify causal effects of type 2 diabetes genetic predisposition driven by distinct genetic clusters. For example, the risk-increasing effects of type 2 diabetes genetic predisposition on cataracts and erectile dysfunction are primarily attributed to adiposity and glucose regulation mechanisms, respectively. We observe opposing effect directions across different genetic ancestries for depression, asthma and chronic obstructive pulmonary disease. Our findings leverage the heterogeneity underpinning type 2 diabetes genetic predisposition to prioritize biological mechanisms underlying causal relationships with comorbidities.

Polygenic risk scores hold prognostic value for identifying individuals at higher risk of type 2 diabetes. However, further characterization is needed to understand the generalizability of type 2 diabetes polygenic risk scores in diverse populations across various contexts. We systematically characterize a multi-ancestry type 2 diabetes polygenic risk score among 244,637 cases and 637,891 controls across diverse populations from the Population Architecture Genomics and Epidemiology Study and 13 additional biobanks and cohorts. Polygenic risk score performance is context dependent, with better performance in those who are younger, male, without hypertension, and not obese or overweight. Additionally, the polygenic risk score is associated with various diabetes-related cardiometabolic traits and type 2 diabetes complications, suggesting its utility for stratifying risk of complications and identifying shared genetic architecture between type 2 diabetes and other diseases. These findings highlight the need to account for context when evaluating polygenic risk score as a tool for type 2 diabetes risk prognostication and the potentially generalizable associations of type 2 diabetes polygenic risk score with diabetes-related traits, despite differential performance in type 2 diabetes prediction across diverse populations. Our study provides a comprehensive resource to characterize a type 2 diabetes polygenic risk score.

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 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 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.