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.

Lipid metabolism has long been implicated in diabetes, but there has been a paucity of population-based studies of the plasma lipidome and incident diabetes in cohorts of early middle age. We used data from the US-based Coronary Artery Risk Development in Young Adults (CARDIA) Study to identify lipidomics associated with 15-year incident diabetes (n = 1,094; n = 162 incident diabetes; [mean (SD) age: 45 (3.6); 58% women; and 59% White race]). Plasma lipidomics was conducted using liquid-chromatography and infusion-mass spectrometry. Diabetes was defined at 5-, 10- and 15-year follow-ups as fasting glucose ≥ 126 mg/dl, 2-h glucose tolerance test ≥ 200 mg/dl, HbA1c ≥ 6.5%, or reported diabetic medication use. We tested associations between individual lipids and incident diabetes with interval-censored, multivariable-adjusted Cox proportional hazards regression, accounting for multiple comparisons. We used differential expression analysis to identify pathways upregulated and downregulated in participants who developed diabetes over the 15-year period. Finally, we used penalized regression (LASSO) to generate a lipid risk score for incident diabetes (0.7 training, 0.3 testing). In hazards regression, 156 lipids including glycerolipids, glycerophospholipids, and sphingolipids, were associated with incident diabetes. Of these, 56 lipids were also selected by LASSO regression as distinguishing participants who developed diabetes from those who did not. The lipid risk score’s ability to improve prediction of 15-year incident diabetes past sociodemographic, behavioral, and clinical covariates was limited to the training set. Pathways leading to diacylglycerols and ceramides were upregulated, while pathways leading to hexosylceramides, lysophosphatidylethanolamines, triacylglycerols, and lysophosphatidylcholines were downregulated in incident diabetes cases. Our results in this cohort of early middle-aged adults, supports further investigation into the roles of glycerophospholipid and sphingolipid metabolism in diabetes development, particularly for ceramides and hexosylceramides.

Metabolic-associated steatotic liver disease (MASLD) is marked by accumulation of hepatic triacylglycerols (TAG), but many other lipids have been implicated. Choline metabolism has been shown to be related to MASLD, specifically through phosphatidylcholines (PC) role in hepatic TAG removal through very low density lipoproteins (VLDL). There are a lack of population-based studies with integrated data on lipidomics, choline metabolites, and MASLD. We tested associations between the plasma lipidome, choline metabolites, and MASLD using data from the Coronary Artery Risk Development in Young Adults (CARDIA) Study. The analytic sample included 1,039 participants with data on choline metabolite, lipidomic, and liver attenuation data [mean (SD) age: 45 (4); 57% female; 57% White race]. MASLD (n = 234) was defined as mean CT-derived liver attenuation < 51HU. Plasma lipidomics and choline metabolites were quantified from stored fasting plasma using liquid-chromatography and infusion-mass spectrometry. In logistic regression adjusted for sociodemographics, lifestyle, and clinical variables, total TAGs, diacylglycerols (DAG), and dihydroceramides (DCER) were positively, and lactosylceramides (LCER) were inversely, associated with MASLD. Species-level results revealed diverging MASLD associations for PCs, based on FA composition. In choline metabolite models, betaine was inversely associated with MASLD. A lipidomic risk score (LRS) derived from penalized regression of MASLD on lipid species was associated positively with choline, and inversely with betaine. We contribute population-based results to a growing literature relating lipidomics and MASLD. In our data, FA composition is biologically relevant to MASLD, particularly for PCs and TAGs. Our results link choline metabolites to both the plasma lipidome and to incident MASLD, furthering efforts in biomarker development and supporting mechanistic evidence using population-level data.

Background: A healthy diet is crucial for preventing cardiovascular disease (CVD) by modulating gene expression, but the gene pathways remain poorly studied. Hypothesis: Healthy dietary intake is associated with a favorable blood transcriptomic profile, which in turn is associated with a lower CVD risk. Methods: Among HCHS/SOL participants with dietary and transcriptome data ( n = 6796), we performed cross-sectional analyses to identify whole-blood RNA sequencing (RNA-seq) signatures of a healthy dietary pattern assessed by Alternative Healthy Eating Index (AHEI), where higher scores indicate a healthier diet. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to characterize diet-related gene modules and examined their associations with incident CVD. Results: Out of 18989 RNA-seq features, we identified 375 RNA-seq signatures associated with AHEI (FDR- q <0.10; Fig. A ). These identified RNA-seq signatures were clustered into four co-expressed gene modules ( Fig. B ), including an adaptive immunity module and an innate immunity&oxidative stress module involving IL-17 and NF-κB signaling ( Fig. C ). The adaptive immunity module was positively associated with AHEI and with blood immune traits reflecting adaptive immune activation (e.g., higher proportions of activated CD4+ and CD8+ T lymphocytes). By contrast, the innate immunity&oxidative stress module was negatively associated with AHEI and positively with traits indicative of innate immune activation (e.g., higher neutrophil proportions; Fig. D ). Furthermore, higher levels of adaptive immunity module were associated with a lower CVD risk (HR [95% CI]: 0.84 [0.72–0.98]), whereas higher levels of innate immunity&oxidative stress module were associated with a higher CVD risk (1.19 [1.02–1.39]; Fig. E ). Conclusions: Adherence to a healthy dietary pattern is associated with a favorable blood transcriptomic profile related to adaptive and innate immune pathways, which may help explain the protective association between healthy diet and CVD.

Both short and long sleep duration have been associated with poor glycemic control and an increased risk of developing type 2 diabetes mellitus. Although sleep duration may differentially modify the effects of genetic risk factors for type 2 diabetes, this has not been systematically investigated. In the present study, we conducted genome-wide gene by sleep duration meta-analyses, separately assessing interactions of short and long sleep, for fasting glucose, fasting insulin, and hemoglobin A1c in up to 489,309 individuals without diabetes from seven different population groups. In total, 16 loci were identified to interact with sleep duration - six with short sleep and ten with long sleep. Of these, four loci were identified through cross-population meta-analysis. Mapped genes exhibit pathway connections to pericyte apoptosis, NMDA receptor activity, the GLUT1 receptor, neurological health, and sleep architecture. Eleven loci (VRK2, PCDH7, TFAP2A, CAP2, PAPPA, ZCCHC2, MYH9, SGIP1, JAKMIP3, RRAS2, MAPT) have not been reported in previous glycemic trait genome-wide association studies. Interaction loci identify divergent biological mechanisms for short and long sleep duration influencing glycemic control, suggesting specific pathways of intervention for precision medicine approaches to diabetes prevention and management.

BACKGROUND:Hypertension is a leading contributor to cardiovascular disease in people with HIV (PWH), yet its underlying molecular mechanisms remain poorly understood. METHODS:We conducted a cross-sectional methylome-wide association study (MWAS) of blood pressure (BP) and arterial hypertension in 1131 PWH (739 women, 392 men; mean age 43 years) in the MACS/WIHS Combined Study (MWCCS), to identify differentially methylated positions (DMPs) and regions (DMRs) associated with SBP, DBP, and hypertension. RESULTS:We identified 59 DMPs associated with SBP and 60 associated with DBP [false discovery rate (FDR) P  < 0.05], enriched in vascular, metabolic, and immune pathways. Top DMPs near ATP8B2 gene [3.8% DNA methylation (DNAm) per SD change in SBP], MIR378E gene (- 9.5% DNAm per SD change in SBP), SNORA114 gene (5.2% DNAm per SD change in DBP), and ANXA11 gene (- 7.9% DNAm per SD change in DBP). A total of 204 DMPs were associated with hypertension, including a top hypermethylated DMP near GSE1 gene [odds ratio = 0.014, 95% confidence interval (CI) 0.013-0.015, P  = 1.32e -16 ] per 1% increase in DNAm. Gene enrichment analysis showed associations with BP-relevant tissues including kidney, brain, vasculature, and adipose tissue. We found 23 DMRs associated with DBP, including a 19-DMP region in CTBP1 gene, and 136 DMRs associated with hypertension, the largest spanning BAZ2A and LMNTD1 genes. CONCLUSION:We identified HIV/antiretroviral therapy (ART)-specific and shared epigenetic loci associated with BP traits, implicating neurovascular, renometabolic, and immune-regulatory pathways in HIV/ART-related hypertension.

Genetic predisposition and alcohol consumption are risk factors for increased blood pressure (BP), but their interactions influencing BP remain understudied. We conducted population-specific and cross-population meta-analyses of genome-wide gene-alcohol (GxAlc) interactions affecting BP in >1.1M individuals from multiple populations. We identified 46 GxAlc interaction loci for BP, including 21 from one-degree-of-freedom interaction tests (PGxAlc<5×10-8; or <0.05/Meff, Meff independent BP associations at P<10-5), and 25 from two-degree-of-freedom tests of main and interaction effects (PGxAlc<0.05/M2df, M2df independent 2df-associations at P2df<5×10-8), including 7 novel and 39 known BP loci. The 12q24 locus highlights the genetic effect of BRAP-rs11066001 on BP, being ~6 times larger in current drinkers than in non-drinkers. Gene prioritization with 46 GxAlc loci identified 15 genes with ≥3 lines of evidence (location, literature, druggability, functional/regulatory annotation, or pathway analyses). Several loci showed sex- and population-specific effects and revealed biological pathways of alcohol's influence on BP, suggesting mechanisms underlying alcohol-induced hypertension.

Background: Metformin may offer anti-aging and cardiometabolic benefits beyond its typical role in glycemic control through regulating gene expression, but its relationship with whole-blood gene expressions remains unclear in human studies. Hypothesis: Metformin use is associated with gene expressions related to anti-aging pathways, and these signatures are associated with favorable cardiometabolic traits. Methods: In 794 participants with diabetes and free of cardiovascular disease and cancer from the HCHS/SOL, differential gene expression analysis was conducted between those with metformin use (n=338) and those without any anti-diabetic medication use (n=456) using whole-blood RNA-sequencing data. Pathway enrichment analysis based on the MSigDB database was conducted for biological interpretation. Spearman correlation analysis was applied to examine correlations between differential expression genes (DEGs) and cardiometabolic traits. Results: Among 14791 genes, we identified 36 upregulated and 43 downregulated DEGs associated with metformin use ( P FDR <0.05; Fig. a ). The gene-set enrichment analysis indicated that metformin use was associated with upregulating heme metabolism and angiogenesis pathways and downregulating apoptosis, coagulation, KRAS signaling, and complement system pathways ( P FDR <0.25; Fig. b ). These alterations indicate enhanced adaptive and repair accompanied by suppressed apoptosis and inflammatory responses, which are important pathways in anti-aging. Some upregulated DEGs, such as YPEL4 (cell cycle progression) and IGHG1 (encodes constant region of IgG1 antibodies) , showed negative correlations with unfavorable traits (e.g., HOMA-IR and CRP), while some downregulated DEGs, such as GNAL (regulating energy metabolism) and C1QB (complement system), showed positive correlations with unfavorable blood lipids ( Fig. d ). However, some DEGs showed unexpected results with cardiometabolic health, with metformin upregulated DEGs (e.g., ARHGEF37 , a gene involved in structure and signal transduction) positively associated with unfavorable cardiometabolic traits. Conclusions: Among Hispanics/Latinos with diabetes, metformin use is associated with alterations in whole-blood gene expressions related to several anti-aging pathways, some of which might be involved in the beneficial effects of metformin use on cardiometabolic health.

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.

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

While lipids have been extensively investigated, genetic regulation of the circulating lipidome in diverse populations remains poorly understood. We conducted a lipidome-wide GWAS of 830 lipid species in 2,287 Hispanic/Latino participants and performed predictive modeling across omics layers. We identified 7,593 genome-wide significant SNPs mapping to 208 genes. Conditional analysis disentangled the long-range linkage disequilibrium artifacts from the pleiotropic FADS1/2/3 cluster. Separately, we discovered an association at the GPLD1 locus for a circulating ceramide. Colocalization revealed shared genetic architecture with conventional lipids alongside distinct, species-specific pathways. Incorporating Native/Indigenous American eQTLs within a multi-omic framework uncovered 62 likely regulatory genes missed by European-centric gene expression models. Finally, genetically regulated predictive models demonstrated performance declining from transcriptomics to proteomics to lipidomics, reflecting increased distance from gene action along the molecular cascade. Our study provides a genetic landscape of lipid metabolism in a highly burdened population and highlights the challenges in predicting lipid abundance.

BACKGROUND:Pulmonary function is linked to cardiovascular disease risk; however, the underlying mechanisms remain unclear. We aimed to identify protein biomarkers associated with pulmonary function and examine their impact on incident chronic obstructive pulmonary disease, coronary heart disease, heart failure, and all-cause mortality. METHODS:Data from White and Black Americans in the Atherosclerosis Risk in Communities study (visit 2: N=11 354, mean age=57 years; visit 5: N=3517, mean age=75 years), a prospective cohort, were analyzed. Linear regression assessed associations between protein levels and pulmonary function measures, including forced expiratory volume in 1 second and forced vital capacity. The impact of the identified proteins on incident chronic obstructive pulmonary disease, coronary heart disease, heart failure, and mortality was estimated using logistic regression and Cox proportional hazards models. Pathway enrichment and Mendelian randomization explored underlying biological functions and causal effects. RESULTS:Of 4766 proteins analyzed, 364 were cross-sectionally associated with forced expiratory volume in 1 second (and forced vital capacity (false discovery rate<0.05). Ninety-four and 270 proteins had concordant positive and negative effects, respectively. Five pathways related to pulmonary and cardiac function were enriched. Of the 364 proteins, 112 were linked to all 4 outcomes, where 86 were associated with increased risk (odds ratio/hazard ratio [OR/HR], 1.05-1.42) and 26 with reduced risk (OR/HR, 0.69-0.96). Six proteins (STAT3 [signal transducer and activator of transcription 3], MIC-1 [growth differentiation factor 15], apoA-II [apolipoprotein A-II], TPST1 [protein-tyrosine sulfotransferase 1], integrin a1b1 [integrin alpha-I: beta-1 complex], and BLC [C-X-C motif chemokine 13]) showed potential inverse causal effects on with forced expiratory volume in 1 second and forced vital capacity, and integrin a1b1 demonstrated consistent inverse associations with chronic obstructive pulmonary disease, coronary heart disease, and heart failure risks. CONCLUSIONS:Proteins associated with pulmonary function may influence CVD risk. Six proteins, including integrin a1b1, represent promising targets for future interventions.

RATIONALE:As the global population ages, identifying risk factors for age-related diseases, such as COPD, is crucial for public health. Mosaic loss of Y chromosome (mLOY) in blood cells is an age-related somatic mosaicism event, but its relationship with pulmonary health remains undercharacterized. OBJECTIVES:To examine the association between mLOY and pulmonary outcomes in men. METHODS:Leveraging mLOY assessment (cell fraction ≥ 5%) in over 12 000 men, including 5097 from the COPDGene Study and 7235 from six additional cohorts in the Trans-Omics for Precision Medicine program, we investigated mLOY associations with respiratory outcomes and epigenetic aging using multivariable cross-sectional, longitudinal, and prospective models. Primary outcomes included spirometry, CT-based emphysema, and epigenetic pace of aging. RESULTS:The prevalence of mLOY increased with age. Cross-sectionally, mLOY was associated with airflow obstruction, with reduced FEV1/FVC of 0.018 [95% CI, -0.030 to -0.006] in COPDGene and 0.020 [95% CI, -0.027 to -0.013] in TOPMed. mLOY was also associated with greater CT-quantified lung emphysema and faster pace epigenetic aging. Longitudinally, mLOY was associated with faster FEV1 decline (∼55mL/year vs ∼38mL/year). Prospectively, mLOY was associated with higher odds of developing COPD [OR = 1.84, 95% CI, 1.10-3.07] and preserved ratio impaired spirometry (PRISm) [OR = 2.87, 95% CI, 1.09-7.56] among participants with normal lung function at baseline. Associations remained robust after adjusting for clonal hematopoiesis and telomere length. CONCLUSIONS:mLOY is associated with lower lung function, accelerated lung function decline, higher emphysema, and faster pace of aging, positioning mLOY as a potential biomarker of respiratory aging in men.

Background Lipoprotein (a) [Lp(a)] is an independent atherosclerotic cardiovascular disease risk factor, with levels largely determined by variation in the LPA gene. Despite interests in therapeutic Lp(a) lowering, the phenotypic effects of very low Lp(a) are incompletely characterized, with some studies suggesting increased type 2 diabetes risk. Objectives Estimate associations between very low Lp(a), inferred by LPA loss of function variant rs41272114, with type 2 diabetes and glycemic biomarkers. Methods A multi-population study of European, Hispanic/Latino, and South Asian populations from five cohort studies was assembled. Effects of the null allele rs41272114 (CC, wild-type; T, null allele) on type 2 diabetes and glycemic biomarkers (fasting glucose, fasting insulin, glycosylated hemoglobin (HbA1c), triglyceride-to-high-density lipoprotein ratio, and HOMA-IR) were estimated using multivariable-adjusted and cohort-specific linear or logistic regression models that were combined using inverse variance meta-analysis. Results Of n=519,892 participants, the T null allele was common (n=49,841 CT and n=2,039 TT) and n=38,320 participants had type 2 diabetes (prevalence range: 4.7, 29.5%). Participants with the TT genotype had a median Lp(a) ranging 1.5-1.9 nmol/L compared to median Lp(a) ranging 15.8-30.0 nmol/L in participants with the CC genotype. We did not observe increased odds of type 2 diabetes by rs412722114 genotype (ORCTvs.CC = 0.97, 95% CI: 0.94, 1.00; ORTTvs.CC = 1.01, 95% CI: 0.89, 1.15). Mean levels of glycemic biomarkers also did not differ by rs41272114 genotype. Conclusions In a large multi-population study with high burdens of type 2 diabetes, life-long very low Lp(a) did not increase type 2 diabetes risk.

BACKGROUND AND AIMS:Addiction-related behaviors, such as loss of control eating (LOC), cigarette smoking and alcohol consumption, have been associated with high body mass index (BMI). This study aimed to assess genetic and environmental contributions to these associations over time. DESIGN:A longitudinal twin study using data from waves 2 and 3 of the Center on Antisocial Drug Dependence study, employing additive genetic (A), shared environmental (C), nonshared environmental (E) influences and cross-lagged models. SETTING:Colorado, USA. PARTICIPANTS:The sample included 764 male and 997 female same-sex twins. MEASUREMENTS:BMI was calculated using self-reported height and weight. LOC was self-reported. Cigarettes smoked per day (CPD) and drinks per week (DPW) were assessed during interviews. FINDINGS:We conducted three cross-lagged models: LOC and BMI in males, LOC and BMI in females and CPD and BMI in females, after excluding small phenotypic correlations (|r| < 0.10). Trait stability over time was largely attributable to genetic factors, accounting for 62% of the variance in BMI (both sexes), 11% in LOC (males), 18% in LOC (females) and 56% in CPD (females) at wave 3. Residual effects were mostly from nonshared environmental factors, accounting for 38% of the variance in BMI (both sexes), 76% of LOC (females), 71% of LOC (males) and 44% of CPD (females) at wave 3. A small but statistically significant cross-lagged effect occurred from wave 2 BMI to wave 3 LOC, explaining 12% (males) and 3% (females) of the variance in wave 3 LOC, with genetic factors accounting for most of this effect. No cross-lagged effects emerged from LOC or CPD to BMI. CONCLUSIONS:Genetic factors contributing to higher body mass index at an earlier age may also increase the risk of developing loss of control eating later in life, highlighting the importance of early weight-related interventions to prevent the onset of disordered eating behaviors.

Large-scale multiancestry genome-wide association studies have identified hundreds of loci associated with type 2 diabetes (T2D) and glycemic traits, yet imputed genotyping arrays limit the detection of low-frequency and rare variants. Whole-genome sequencing (WGS) offers a more complete view of genetic variation, especially across diverse populations. We analyzed high-coverage (38×) WGS data from 21,913 T2D case subjects, 61,036 control subjects, and up to 50,011 individuals with no diabetes with fasting glucose, fasting insulin, and HbA1c from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine Program. We performed single-variant association testing, conditional analysis, fine-mapping, and Bayesian colocalization to identify genetic signals and assess regulatory relevance in diabetes-related tissues. We identified 76 distinct association signals across 34 loci, including novel variants at DUSP9 for T2D, and ROBO1, NDN, and MYT1 for HbA1c. Fine-mapping narrowed credible sets and improved causal variant resolution. Colocalization highlighted 80 expression signals in diabetes-related tissues, linking genetic associations to functional regulatory mechanisms. Our findings demonstrate the utility of WGS to uncover novel variants in diverse populations, enhance locus resolution, and link regulatory variation to disease-relevant tissues. This work refines the genetic architecture of T2D and glycemic traits and supports precision medicine efforts targeting diverse populations. ARTICLE HIGHLIGHTS:We aimed to improve understanding of the genetic architecture of type 2 diabetes and glycemic traits by leveraging whole-genome sequencing in diverse populations. Our goal was to identify novel variants, refine known loci, and link genetic signals to regulatory mechanisms through colocalization with expression quantitative trait loci. We discovered novel variants, significantly improved fine-mapping resolution, and identified 80 regulatory colocalization signals in diabetes-relevant tissues. These findings support precision medicine approaches by connecting genetic variation to functional biology in type 2 diabetes.

BACKGROUND:Joint use of multiple molecular layers can be useful to prioritize targets for mechanistic studies. Application of this approach to coronary disease in large populations is an emerging field. METHODS:We used reported circulating proteomic data (Somascan aptamer-based) from ≈3000 individuals in the CARDIA study (Coronary Artery Risk Development in Young Adults), measuring association with prevalent and 10-year incident coronary artery calcium (CAC) score. We used a multiparametric approach to prioritize circulating protein-CAC associations via genomics of circulating protein levels and coronary artery transcription. RESULTS:Proteins linked to prevalent/incident CAC in CARDIA implicated pathogenic mechanisms of vascular disease, including fibrosis and inflammation (GDF-15 [growth/differentiation factor 15], CDCP1 [CUB domain-containing protein 1], GSN [gelsolin], THBS2 [thrombospondin-2], chemokines, RNAS6 [ribonuclease K6]), oxidative lipid metabolism (CILP2; cartilage intermediate layer protein 2), extracellular matrix remodeling and signaling (MMPs [matrix metalloproteinases], TIMP-1 [tissue inhibitor of metalloproteinases 1], integrins), calcification (Notch 1, ARHGAP36 [Rho GTPase-activating protein 36]), and metabolism (GIP [gastric inhibitory polypeptide]), as well as new proteins not previously reported. Using proteome-wide association study genetic approaches, several targets with nominal evidence in CAC proteomics were associated with atherosclerosis or myocardial infarction in over 300K individuals, including PCSK9 (proprotein convertase subtilisin/kexin type 9) and APOC1. Finally, the coronary artery-specific transcriptome-wide association study of CAC yielded genes with previously implicated mechanistic roles in vascular homeostasis, inflammation, and metabolism, as well as genes without previously described function in CAC. Overlap across CAC proteomics and transcriptome-wide association study highlighted genes involved in vascular inflammation (S100A9), cardiac development (HES1 [transcription factor HES-1]), vessel wall structure (SPARCL1 [SPARC-like protein 1]), and vascular dysfunction or plaque (NOTCH3 [neurogenic locus notch homolog protein 3], TNFSF12 [tumor necrosis factor ligand superfamily member 12], S100A12 [protein S100-A12]). CONCLUSIONS:These results report population-level multiomics in human coronary calcification, presenting a method to identify disease-relevant targets through integration of human genetic approaches with multiomics.