Background: A comprehensive, replicated atlas of circulating metabolites for incident coronary heart disease (CHD) across race diverse populations is lacking and metabolite signatures of early-onset CHD remain largely unidentified. Methods: We conducted a two-stage metabolome wide-association analysis using Cox regression model for incident CHD, with discovery analyses in 22,742 CHD-free individuals with 1245 blood metabolites profiled from 7 multi-ethnic cohorts (1,124 incident cases over 7.5~17.0 yrs of follow-up) in TOPMed, and replication analyses in 32,615 CHD-free individuals from 7 multi-ethnic cohorts (3,365 incident cases over 7.5~19.3 years) (Fig1.a). Random-effect meta-analysis was used to pool results from each cohort in these two stages. We further evaluated the associations of identified metabolites with incident CHD diagnosed at different ages. Results: We identified 189 metabolites (FDR<0.05) associated with incident CHD, with 127 metabolites (p<0.05) replicated (Fig1.b). Over 90% of these replicated metabolites showed positive associations, with the majority belonging to glycerolipids, phosphatidylethanolamine, fatty acids, lactoyl amino acid, histidine, aromatic amino acids, branched amino acids (Fig1.b). In the Study of Latinos (SOL, n=13,322), 14 out of these 127 metabolites were associated with incident CHD diagnosed before age 50 yrs (FDR<0.05; Fig1.c), including the known atherogenic metabolites (e.g., cholesterol, fibrinopeptide A), harmful microbial derived trimethylamine N−oxide, sugar sweeteners (e.g., mannitol/sorbitol, erythritol), markers of insulin resistance, inflammation and oxidative stress (e.g., mannose, erythronate, gluconate, suberoylcarnitine), and novel metabolites not previously linked to CHD (e.g., C−glycosyltryptophan, hydroxymalonate, and methyl glucopyranoside). Further, associations of these metabolites with CHD diagnosed at younger age tend to be stronger than those with late-onset cases (e.g., the hazard ratio per SD increase in mannose decreased from 4.4 for CHD diagnosed at age 45 to 1.5 for case diagnosed at age 65; Fig1.d). Adding metabolites to conventional risk factors improved AUC of CHD risk prediction from 0.78 to 0.84 (p<0.001) (Fig1.e). Conclusion: We provide the most comprehensive, replicated, multi-ethnic atlas of circulating metabolites for incident CHD, identify a set of early-onset CHD metabolite markers, and demonstrate significant gains in CHD risk prediction with identified metabolites.

The human metabolome reflects complex metabolic states affected by genetic and environmental factors. However, metabolites associated with type 2 diabetes (T2D) risk and their determinants remain insufficiently characterized. Here we integrated blood metabolomic, genomic and lifestyle data from up to 23,634 initially T2D-free participants from ten cohorts. Of 469 metabolites examined, 235 were associated with incident T2D during up to 26 years of follow-up, including 67 associations not previously reported across bile acid, lipid, carnitine, urea cycle and arginine/proline, glycine and histidine pathways. Further genetic analyses linked these metabolites to signaling pathways and clinical traits central to T2D pathophysiology, including insulin resistance, glucose/insulin response, ectopic fat deposition, energy/lipid regulation and liver function. Lifestyle factors-particularly physical activity, obesity and diet-explained greater variations in T2D-associated versus non-associated metabolites, with specific metabolites revealed as potential mediators. Finally, a 44-metabolite signature improved T2D risk prediction beyond conventional factors. These findings provide a foundation for understanding T2D mechanisms and may inform precision prevention targeting specific metabolic pathways.

Most genetic variants associated with complex traits are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterized 14,324 RNA-sequencing samples from the Trans-Omics for Precision Medicine program and performed expression and splicing quantitative trait locus (e/sQTL) analyses in six tissues and cell types, including whole blood (n = 6454) and lung (n = 1291). We detected tens of thousands of secondary cis-e/sQTLs, showing that secondary cis-e/sQTL discovery remains unsaturated. We fine-mapped UK Biobank-derived genome-wide association study (GWAS) signals from 164 traits and identified e/sQTL colocalizations for 10,611 GWAS signals, including 7096 that colocalize with secondary e/sQTLs. Our results suggest that even larger e/sQTL analyses will uncover additional secondary e/sQTLs, further benefiting GWAS interpretation.

Type 2 diabetes (T2D) is a heterogeneous disease shaped by genetic pathways related to insulin resistance and β-cell dysfunction, but how this heterogeneity is reflected molecularly remains unclear. We integrated partitioned polygenic scores (pPS) with proteomic and metabolomic profiling to define molecular signatures of T2D and their clinical relevance. We analyzed UK Biobank participants with genomic, proteomic, and metabolomic data. In a disease-free training subset, we used LASSO regression to identify multi-omic signatures associated with each pPS by jointly modeling proteins and metabolites. In an independent testing set, we constructed multi-omic scores and examined their associations with clinical traits and diabetes-related outcomes. Mediation analyses were used to investigate putative causal pathways. Key findings were evaluated in the Multi-Ethnic Study of Atherosclerosis (MESA). We identified distinct multi-omic signatures that capture the molecular architecture of T2D genetic risk across physiological subtypes. Compared with genetic scores alone, multi-omic pPS showed larger effect sizes and better disease discrimination. These scores recapitulated subtype-specific physiology and were associated with T2D risk. The Beta-Cell 2 multi-omic score showed marked stratification for insulin use, which was replicated in MESA, where it also predicted future insulin use. Mediation analyses implicated lipoprotein remodeling and fatty acid metabolism in the Lipodystrophy 1 cluster, accounting for 30-45% of the total effect of pPS on T2D risk. Integrating process-specific genetic risk with circulating multi-omic profiles reveals biologically distinct endotypes of T2D and supports a framework for improved patient stratification and risk assessment.

BACKGROUND:Knowledge of proteomic mechanisms explaining the link between psychosocial stress and cardiovascular disease is limited. This study aimed to (1) identify plasma proteins associated with psychosocial factors and (2) assess associational pathways between psychosocial factors, identified proteins, and incident cardiovascular disease events in a discovery cohort, JHS (Jackson Heart Study), and 2 replication cohorts, the CHS (Cardiovascular Health Study), and the MESA (Multi-Ethnic Study of Atherosclerosis). METHODS:JHS participants from exam 1 (2000-2004) with SomaScan 1.3k platform proteomics data were included (n=2143, mean age=55.3). Depressive symptoms and perceived stress scores were measured via the 20-item Center for Epidemiological Studies scale and an 8-item perceived stress scale adapted for the JHS, respectively. Multivariable linear regression models were used to test the association between psychosocial factors and plasma proteins, controlling for age, sex, proteomics batch, and estimated glomerular filtration rate. Bonferroni correction was used for multiple testing (P<3.782×10-5) and meta-analyses were performed across cohorts. Mediation analyses with Cox proportional hazards models were used to evaluate potential proteomic pathways in the association between psychosocial factors and coronary heart disease, heart failure, and stroke in JHS. RESULTS:Angiopoietin-2 (β=0.014, SE=0.003, P<0.001), contactin-5 (β=-0.017, SE=0.003, P<0.001), growth/differentiation factor 15 or macrophage inhibitory cytokine 1 (β=0.014, SE=0.002, P<0.001), neural cell adhesion molecule 120 (β=-0.016, SE=0.003, P<0.001), and KYNU (kynureninase; β=0.014, SE=0.003, P<0.001) were each significantly associated with depressive symptoms, with angiopoietin-2, contactin-5, macrophage inhibitory cytokine 1, and neural cell adhesion molecule 120 replicating in CHS and MESA. Leukotriene A-4 hydrolase was associated with perceived stress (β=0.0235, SE=0.005, P<0.001). Macrophage inhibitory cytokine 1 partially accounted for the association between depressive symptoms and incident coronary heart disease in JHS (23%; P<0.001). CONCLUSIONS:Novel associations between psychosocial factors, plasma proteins, and cardiovascular disease were identified in JHS. Circulating proteomic profiles across 3 cardiovascular disease cohorts showed differences in protein concentrations by psychosocial measures. Future investigations should identify additional potentially targetable proteomic mechanisms by which psychosocial factors contribute to disease.

Abstract INTRODUCTION Polygenic scores (PGSs) for sleep traits are potentially more stable, and less subject to confounding than measured sleep traits. Leveraging data from five observational cohorts, we aim to assess the associations between PGS for six common sleep traits, and global cognitive function (GCF) among middle-aged to older adults. METHODS In each cohort, GCF was defined as the first principal component (PC) of multiple cognitive measures and was projected from baseline (first selected visit) to measures from a subsequent follow up visit. Poor GCF was defined as having GCF < 1 standard deviation (SD) of the age-adjusted GCF distribution median. We estimated sleep PGS associations with baseline GCF, poor baseline GCF, GCF change between baseline and follow-up, and incident poor GCF at follow-up. Models adjusted for age, sex, study center, race/ethnicity, genetic PCs, and education. Results were meta-analyzed via fixed effects meta-analysis. RESULTS Estimates are reported per 1 SD increase in PGS. A higher PGS for long sleep was associated with lower GCF at baseline (estimate = -0.02 SD, 95% CI: -0.03 to 0.00, p = 0.01) and higher risk of poor GCF at baseline (odds ratio, OR = 1.04, 95% CI: 1.00 to 1.09, p = 0.06). In addition, a higher PGS for BMI-adjusted OSA was associated with higher risk of poor GCF at baseline (OR = 1.11, 95% CI: 1.00 to 1.22, p = 0.04). DISCUSSION Genetic predisposition to long sleep and OSA is associated with poorer cognitive function in a meta-analysis of more than 20,000 middle-aged and older adults.

BACKGROUND:NPs (natriuretic peptides) are bioactive hormones crucial for regulating blood pressure, glucose homeostasis, and lipid metabolism. Despite the high heritability of circulating NP levels, the genetic determinants of NP regulation, particularly across ancestries and sexes, remain poorly understood. The objective of the current study was to identify genetic variants associated with NT-proBNP (N-terminal pro-B-type NP) levels in a multiancestry study population. METHODS:Whole genome sequencing and array-based data from 81 213 individuals without heart failure were analyzed from the Trans-Omics for Precision Medicine cohorts, UK Biobank, All of Us Research Program, and REGARDS (Reasons for Geographic and Racial Differences in Stroke) study to identify common, rare, and structural variants associated with NT-proBNP levels. The main outcome of the study was rank-based inverse normal and standardized NT-proBNP levels. Genetic associations with NT-proBNP were examined, followed by gene prioritization, transcriptome-wide association studies, colocalization, and rare variant analyses. RESULTS:Nine novel loci and 3 previously reported loci were identified to be associated with NT-proBNP levels. Novel structural variants were detected across 12 loci. Similar effect sizes were observed for both common and rare variants. Key genes such as BAG3 (10q26.11) and SLC39A8 (4q24) were identified through gene prioritization, with prior animal models supporting their therapeutic relevance. Rare variant analysis identified 6 masks with significant associations, specifically non-coding masks, suggesting regulatory modulation of NT-proBNP. CONCLUSIONS:This study identifies novel common, rare, and structural variants associated with NT-proBNP levels, highlighting the contribution of both coding and regulatory non-coding variation. These findings advance our understanding of the genetic architecture of NT-proBNP and may inform future cardiometabolic therapeutic strategies.

Population stratification is one of the source of inflation in epigenome-wide association studies (EWAS) when not properly accounted for. To address this, we developed methylation population scores (MPSs) to predict genetic principal components (GPCs) using a feature selection approach. We used multi-ethnic DNA methylation data from Illumina EPIC arrays across five cohorts, including MESA (n = 929), CARDIA (n = 1123), JHS (n = 1365), ARIC (n = 2338), and HCHS/SOL (n = 1475), randomly splitting participants into training (85%) and test (15%) sets. Within each cohort, associations between GPCs and CpG sites were estimated using linear regression adjusting for age, sex, smoking and alcohol use, race/ethnicity, body mass index, and cell type proportions, followed by meta-analysis and selection of CpGs with FDR <0.05. We then applied a two-stage weighted least squares Lasso regression to construct MPSs, adjusting for the aforementioned covariates. In the test dataset, MPSs showed strong correlation with GPCs, with R² ranging from 0.27 (MPS7 vs. GPC7) to 0.98 (MPS1 vs. GPC1). Visualization demonstrated that MPSs recapitulated the pattern shown by GPCs in differentiating self-reported White, Black, and Hispanic/Latino groups and outperformed methylation-based principal components constructed using alternative published methods. Additionally, MPSs showed comparable performance to GPCs in reducing inflation in EWAS. Overall, MPSs uses supervised learning with covariate adjustment to capture genetic structure across diverse populations, and provide a reliable estimate of population structure in the data and can complement GPCs when genetic data are absent.

Mitochondrial heteroplasmic variant has been increasingly recognized as a potential contributor to common complex diseases, yet its relationship with cardiometabolic disorders (CMDs) remains poorly understood. Leveraging deep whole-genome sequencing data from 16,882 participants across six multi-ancestry TOPMed cohorts, we systematically evaluated the associations between rare heteroplasmic variants and eight CMD traits, including body mass index (BMI), obesity, blood pressure, hypertension, blood glucose, diabetes, low-density lipoprotein (LDL), and hyperlipidemia. Using a previously developed statistical framework, we identified heteroplasmic variants according to three coding definitions and performed gene-based burden, SKAT, SKAT-O and ACAT-O tests within sixteen mitochondrial DNA (mtDNA) genes. We identified twelve significant gene-trait associations after Bonferroni correction, with consistent effect directions across coding definitions. The strongest association was observed between hyperlipidemia and heteroplasmic variants in CO1 gene (OR=0.28, 95% CI=(0.17, 0.46), p=3.4E-7) among EA (European Americans). Additional associations were detected for BMI, adjusted SBP (systolic blood pressure), BG (blood glucose), diabetes, and adjusted LDL. These findings highlight the contribution of heteroplasmic variation within mtDNA to cardiometabolic phenotypes and provide new insight into mitochondrial involvement in CMD pathophysiology.

Background Chronic kidney disease (CKD) is a global health problem which is associated with poor outcomes, and its prevalence is expected to increase. Identifying novel risk factors for CKD may lead to improved outcomes. Circulating saturated fatty acids (SFAs) have been posited as contributors to CKD risk. Objectives We aimed to evaluate associations between circulating SFAs (measured in phospholipids in 7 cohorts, serum or plasma total in 5 cohorts, and cholesterol esters in 1 cohort) and incident CKD in 13 cohorts, and to pool results by meta-analysis across the studies. Methods SFAs were measured in 13 cohorts in the Fatty Acids Outcomes Research Consortium, including 18,193 participants with estimated glomerular filtration rate >60 mL/min/1.73 m2 across 9 countries. Associations between each SFA [palmitic acid (16:0), stearic acid (18:0), arachidic acid (20:0), behenic acid (22:0), and lignoceric acid (24:0)] and incident CKD (defined as an estimated glomerular filtration rate <60 mL/min/1.73 m2 and ≥25% decrease from baseline) were assessed by Cox or Poisson regressions. Results were pooled using inverse variance weighted meta-analysis. Results In total, 2554 participants developed CKD over a weighted median follow-up of 7.6 y. After adjustment, higher concentrations of 18:0 were associated with a lower risk of CKD with minimal heterogeneity (relative risk per interquintile range: 0.87; 95% confidence interval: 0.80, 0.95, P = 0.003, I2 = 14.7%). These associations remained consistent in secondary and sensitivity analyses. We did not observe significant associations of other SFAs with CKD. Conclusions In a meta-analysis of 18,193 participants across 9 countries, we observed no indication that SFA increased CKD risk, whereas higher 18:0 concentrations were associated with a lower risk of CKD. Future research is needed to assess mechanisms by which SFA 18:0 may exert kidney-protective effects, and how circulating SFA 18:0 concentrations may be altered.

Introduction: All-cause and cause-specific mortality remain major measures of public health burden, despite the rising number of individuals achieving longevity (≥85 years). Although many mortality-related metabolites have been identified, metabolite predictors of long-term mortality and longevity across diverse populations remain understudied. We aim to identify novel metabolites associated with mortality and longevity. Methods: Circulating metabolite profiling was performed across seven cohorts in the Trans-Omics for Precision Medicine project. Cox models were used to examine the associations of 1,121 metabolites with all-cause, cardiovascular (CV), cancer, and respiratory mortality. Logistic regression was used to assess longevity, defined as living past 85 years at the end of follow-up, adjusting for clinical risk factors (CRF). Random-effects meta-analysis was used to estimate joint effects, and subgroup analyses were conducted by sex and race. Replication was performed using independent samples. Results: During an average follow-up of ten years among 26,091 participants (57% women, 41% Whites), there were 6,315 deaths, including 1,649 (26%), 1,387 (22%), and 314 (5%) from CV, cancer, and respiratory diseases, and 4,216 participants achieved longevity. A total of 183, 101, 12, and 23 metabolites were discovered and replicated (FDR < 0.05) for all-cause, CV, cancer, and respiratory mortality, with a range of 20% to 98% risk difference per SD increase of the metabolite. Nearly half of the metabolites were novel, and carnitines, glycerophospholipids, ceramides, and sphingolipids were leading pathways. A metabolite risk score derived from all-cause mortality-related metabolites improved the prediction of all-cause mortality by an average of 3.3%, using Harrell’s C, beyond CRF across participating cohorts. In the longevity analyses, 38 metabolites were discovered and replicated, and 31 were shared with all-cause mortality (correlation r = -0.97). Among the seven metabolites uniquely linked to longevity, taurocholate, glycocholate, and glycoursodeoxycholate suggested distinct bile acid metabolism, possibly driven by enterohepatic or microbiome-related processes. Subgroup analyses of all-cause mortality and longevity by sex and race revealed no significant heterogeneity across strata. Conclusions: We identified circulating metabolites associated with mortality and longevity, providing insight into slowing aging and the identification of at-risk populations.

STUDY OBJECTIVES:Excessive daytime sleepiness (EDS), influenced by environmental and social-behavioral factors, is reported by a subset of patients with sleep apnea-a group that may be at elevated cardiovascular risk. However, it is unclear whether sleep apnea with and without EDS have distinct genetic underpinnings. In this study, we perform gene-by-EDS interaction analyses for apnea hypopnea index, a diagnostic marker of sleep apnea severity, to understand EDS's influence on its underlying genetic risk. METHODS:Discovery interaction analyses for common variants and gene-based rare variants were conducted respectively using multi-ethnic Trans-Omics for Precision Medicine (N = 11 619) data, followed by replication and subsequent meta-analysis in additional Trans-Omics for Precision Medicine-imputed data (N = 8904). The 1 degree-of-freedom (1df) G × E test and the 2df joint G,G × E tests were utilized. Sex-stratified analyses were additionally performed. RESULTS:Discovery analysis revealed two common intronic variants-rs13118183 (CCDC3) and rs281851 (MARCHF1)-and three rare variant gene sets mapped to SCUBE2, TMEM26, and CPS4FL-to exhibit interaction with EDS. Meta-analysis revealed EDS interaction with 11 rare variant gene sets mapped to UBLCP1, MED31, RAP1GAP, CPNE5, MYMX, YY1, ZNF773, YBEY, IQCB1, PI4K2B, and CORO1A. CONCLUSION:Genetic loci reveal connections to cardiovascular risk, insulin resistance, thiamine deficiency, and resveratrol mechanism. Discovered genetic signals may offer insight into pertinent biological pathways for sleep apnea patients with an excessively sleepy subtype. Statement of Significance Sleep apnea is a complex sleep disorder. Exemplifying this is the disparately varying estimates of presence of excessive daytime sleepiness (EDS) in patients, and persistent EDS that lingers despite treatment. Some data indicate that the excessively sleepy subtype of sleep apnea carries heightened cardiovascular risk. Whether EDS influences genetic risk factors underlying sleep apnea has not yet been investigated. This study addresses this gap, as the first genome-wide gene × EDS interaction study for apnea hypopnea index, the standard sleep apnea severity metric. Genetic loci that have been previously unconsidered for sleep apnea are revealed. Discovered interaction signals highlight pathways in metabolism, genes associated with cardiometabolic traits, and therapeutic agents influencing obesity, blood pressure, oxidative stress, and apnea hypopnea index.

Measures from affinity-proteomics platforms often correlate poorly, challenging interpretation of protein associations with genetic variants and phenotypes. Here, we examine 2157 proteins measured on both SomaScan 7k and Olink Explore 3072 across 1930 participants with genetic similarity to European, African, East Asian, and Admixed American ancestry references. Inter-platform correlation coefficients for these 2157 proteins follow a bimodal distribution (median r = 0.30). We evaluate protein measure associations with genetic variants, and find approximately 25-30

Abstract Plasma proteomics is increasingly used for biomarker discovery and predictive modeling, yet diurnal protein trajectories remain insufficiently characterized. In our review of recent proteomic biomarker studies, 43% of the identified biomarkers had previously been reported to display 24-h rhythmicity. We demonstrate that ignoring these short-term dynamic effects compromises the robustness of reported models predicting health outcomes. We integrated a population-scale multi-ethnic longitudinal cohort with repeated measures over 10 years, with two cohorts of healthy adults undergoing frequent plasma sampling across days under controlled circadian, sleep and food-intake conditions. This design enabled estimation of short-term intraindividual variability (ST), long-term intraindividual variability (LT), population-level variability (POP) and genetic effects (GEN) across 7,289 protein targets. ST, LT, POP, and GEN define diverse protein trajectories, including rapid dynamics, long-term change, and individual-specific signatures. Using and generalizing this framework will facilitate covariate selection, study design, biomarker prioritization, and variability-aware modeling by users of proteomic data. Graphical abstract

High-throughput affinity-based proteomics has advanced biomedical research, yet fundamental, persistent discordance between mainstream platforms (SomaScan and Olink) routinely undermines the replication of findings. This platform-driven non-replication complicates downstream biological validation and biomarker prioritization. Here, we develop a machine learning-based framework for cross-platform protein value imputation to resolve this translational bottleneck. Using paired proteomic data measured by both SomaScan and Olink from 5,325 participants of the Multi-Ethnic Study of Atherosclerosis, we developed models to impute cross-platform measurements and applied them to two independent and demographically distinct cohorts (Cardiovascular Health Study [N=3,171] and UK Biobank [UKB; N=41,405]) for external validation. Our bi-directional model 1) established an imputation performance-based protein fidelity index, validated against gold-standard measurements from Atherosclerosis Risk in Communities study (N=101) and Nurses' Health Study (N=54), 2) enabled imputation of platform-exclusive protein measurements, and 3) facilitated calibration of overlapping proteins. We demonstrate the utility of this framework through three applications: 1) fidelity-informed analyses enhanced the replication of biomarker discovery, 2) recovery of SomaScan signals that were previously inaccessible in UKB's original Olink measurements, and 3) improved replication performance for overlapping proteins. Our study offers a translational roadmap that allows researchers to achieve reliable epidemiological replication, target specific assays for future optimization, and prioritize biological signal over platform noise.

Despite evidence for a genetic component, few genetic associations with lung function decline have been identified. We aimed to evaluate genome-wide associations and putative downstream functionality of genetic variants for lung function decline. We conducted genome-wide association study (GWAS) analyses of decline in FEV1, FVC, and FEV1/FVC in 52,056 White (N = 44,988), Black (N = 5,788), Hispanic (N = 550), and Chinese American (N = 730) participants across seven general population cohorts. GWAS analyses were stratified by cohort, ancestry, and sex. Results were combined in cross-ancestry and ancestry-specific meta-analyses. Significant variants available in two independent COPD-enriched cohorts were tested for replication. We identified 361 distinct genome-wide significant (p < 5E-08) variants for one or more of the FEV1, FVC, and FEV1/FVC decline phenotypes, which overlapped with previously reported genetic signals for pulmonary traits. Four variants, or 10.3

Introduction:Sex hormones shape biological sex differences and alter the onset and severity of sleep and metabolic diseases in a sex-specific manner. To better understand relationships and underlying mechanisms, we develop summary proteomics and metabolomics scores for sex hormones and investigate their associations with sleep and metabolic disorders. Methods:We used proteome- (n= 3680) and metabolome- wide (n= 1649) data from the baseline exam of the Multi-Ethnic Study of Atherosclerosis (MESA) cohort to develop female- and male-specific omics scores for sex hormones including total (Total T), bioavailable (Bio T), and free (Free T) testosterone, estradiol (E2) and sex hormone binding protein (SHBG). Each omics dataset was randomly split assigning 80% of participants to a training dataset and the remaining 20% to a test dataset. We applied linear regression with bootstrap standard errors, adjusting for age, BMI, self-reported race/ ethnicity and study site, to identify sex hormone-associated proteins and metabolites (i.e FDR< .05). Lasso penalized regression was then used to select independent features, from which weighted protein (ProtS) and metabolite scores (MetS) were constructed as weighted sums, and examined in the validation dataset. Subsequently, we conducted sex-stratified association analysis of the validated omics scores using data from MESA baseline, exams 4 (proteomics) and 5 (proteomics, metabolomics) with sleep and metabolic phenotypes, timepoints where sex hormones were not measured. Results:All constructed omics scores were significantly associated with their corresponding hormones in the test dataset. Higher omics scores of SHBG and lower omics scores of Free T were associated with lower diabetes risk in both sexes; and higher E2 scores with higher incident hypertension risk only in men. In males, Total T had protective diabetes associations, whereas in females they were linked to greater risk. Similarly, higher ProtS-Free T and lower ProtS-SHBG were associated with increased risk for OSA in both sexes. Finally, higher E2 scores were associated with higher risk of insomnia only in males. Conclusions:Summary omics-based scores reveal sex-specific cross-sectional associations with sleep and incident metabolic disorders. These findings highlight the potential of these omics proxies to improve risk stratification and generate insights into mechanisms linking sex hormones with disease.

Multiple germline and somatic genomic factors are associated with risk of coronary artery disease, but there is no single measure of risk that integrates all information from a DNA sample. To address this gap, we develop an integrated genomic model that includes six germline and somatic genetic drivers for coronary artery disease, including polygenic risk score, genetically-proxied proteomic/metabolomic risk scores, and clonal hematopoiesis of indeterminate potential. We evaluated its predictive power in the UK Biobank (N = 391,536), and validate it using data from the TOPMed program (N = 34,177). The 10-year coronary artery disease risk based on the integrated genomic model profile ranges from 1.1% to 15.5% in the UK Biobank and from 3.8% to 33.0% in TOPMed, with a more pronounced gradient in males than females. The integrated genomic model captures the cumulative effect of multiple genetic drivers, identifying individuals at high risk for coronary artery disease despite lacking any single high-risk genetic factor, as well as individuals at low risk despite carrying known high-risk factors. In middle age, the integrated genomic model augments the performance of the Pooled Cohort Equations, a clinical risk calculator for coronary artery disease. While the integrated genomic model yields only modest incremental predictive value over polygenic risk score at the population level, it identifies approximately 13% of high-risk individuals not detected by polygenic risk score alone.

Objective The TrialNet Oral Insulin Prevention Trial (TN07) tested oral insulin to prevent Stage 3 type 1 diabetes in 560 Stage 1 relatives of individuals with type 1 diabetes (T1D). Of the three pre-defined risk strata, participants in Secondary Stratum 1 (SS1), characterized by low first-phase insulin release (n=55), responded significantly better to oral insulin. We aimed to identify genetic factors associated with treatment response. Research Design and Methods The TEDDY-T1DExomeChip was used to genotype 552 participants with available DNA. Cox models examined associations between response to oral insulin and HLA haplotypes, 33 pre-selected T1D-associated SNPs, the T1D genetic risk score-2 (T1D-GRS2), and type 2 diabetes (T2D)-associated polygenic scores. For primary analyses, p-values were Benjamini-Hochberg (BH)-corrected for multiple comparisons; results not passing correction were considered nominal. Results GLIS3 rs7020673 was significantly associated with response to oral insulin in SS1 (BH-corrected p-value=0.031 without and p=0.022 with covariate adjustment). Additional nominal associations included better response with HLA-DRB1*04:01-DQA1*03:01-DQB1*03:02 (HR=0.22 vs HR=1.09; unadjusted/adjusted p=0.031/0.045) in SS1, and worse responses with TNFAIP3 and CTLA4 in at least one stratum. In exploratory analyses, participants with T1D-GRS2 >12.5 responded better to oral insulin (HR=0.68) than those with T1D-GRS2 ≤12.5 (HR=2.10; unadjusted/adjusted p=0.003/0.006) in the overall cohort, and lower proinsulin- and obesity-partitioned T2D polygenic scores were associated with greater treatment benefit in SS1 and in another secondary stratum, respectively. Conclusions Genetic differences distinguish responders from non-responders to oral insulin for T1D prevention. Genetics may enable precision medicine by identifying individuals likely to benefit from T1D-modifying therapies.