Exome wide association study for blood lipids in 1,158,017 individuals from diverse populations.

Rare coding alleles play crucial roles in the molecular diagnosis of genetic diseases. However, the systemic identification of these alleles has been challenging due to their scarcity in the general population. Here, we discovered and characterized rare coding alleles contributing to genetic dyslipidemia, a principal risk for coronary artery disease, among over a million individuals combining three large contemporary genetic datasets (the Million Veteran Program, n = 634,535, UK Biobank, n = 431,178, and the All of Us Research Program, n = 92,304) totaling 1,158,017 multi-ancestral individuals. Unlike previous rare variant studies in lipids, this study included 238,243 individuals (20.6%) from non-European-like populations. Testing 2,997,401 rare coding variants from diverse backgrounds, we identified 800 exome-wide significant associations across 209 genes including 176 predicted loss of function and 624 missense variants. Among these exome-wide associations, 130 associations were driven by non-European-like populations. Associated alleles are highly enriched in functional variant classes, showed significant additive and recessive associations, exhibited similar effects across populations, and resolved pathogenicity for variants enriched in African or South-Asian populations. Furthermore, we identified 5 lipid-related genes associated with coronary artery disease (RORC, CFAP65, GTF2E2, PLCB3, and ZNF117). Among them, RORC is a potentially novel therapeutic target through the down regulation of LDLC by its silencing. This study provides resources and insights for understanding causal mechanisms, quantifying the expressivity of rare coding alleles, and identifying novel drug targets across diverse populations.

Improving Cardiovascular Disease Primary Prevention Treatment Thresholds in a New England Health Care System.

Background: Atherosclerotic cardiovascular disease (ASCVD) risk estimation based on the pooled cohort equation (PCE) overestimates in population-based cohorts. Whether it performs equally across disaggregated demographics in health care populations is less known. Objectives: The purpose of the study was to recalibrate PCE and rederive prevention thresholds in a contemporary health care system and evaluate its performance across sociodemographics. Methods: We retrospectively inspected electronic health records between 2010 to 2012 and 2020 to 2022 within Mass General Brigham health care in New England region. We compared performance of the original vs recalibrated PCE measured by calibration, discrimination, reclassification rate, and net benefit among 160,926 patients aged 40 to 79 years and without prior ASCVD or lipid-lowering medication. Results: Of the 160,926 patients (mean age: 54.6 ± 8.6 years; 61.4% female), 20,373 (12.7%) developed ASCVD over 10 years. The original PCE globally underestimated ASCVD risk (observed vs predicted incidence rate: 0.13 vs 0.05). Recalibration upclassified risk primarily among individuals with low-to-borderline risk by the original PCE and additionally identified 40% of patients who had undergone ASCVD events yet deemed statin-ineligible based on the original PCE. Treatment thresholds yielding the greatest net benefit were ≥24.0% for women (+23.3%) vs ≥26.0% for men (+18.7%), whereas ≥26.0% for White or other race (+24.7%) vs ≥14.0% Black or African American (+12.5%), respectively. Specifically, Hispanic or Latino and non-Hispanic Black patients conferred the greatest sensitivity improvement at ≥12.3% threshold compared to higher ≥23.6% among non-Hispanic Asian or Pacific Islanders. Generally, lower thresholds earlier in life were optimal. Conclusions: Recalibration and personalized treatment thresholds derived within a health system may improve prevention treatment allocation efficiency.

Long-term longitudinal analysis of 4,187 participants reveals insights into determinants of clonal hematopoiesis.

Clonal hematopoiesis of indeterminate potential (CHIP) is linked to diverse aging-related diseases, including hematologic malignancy and atherosclerotic cardiovascular disease (ASCVD). While CHIP is common among older adults, the underlying factors driving its development are largely unknown. To address this, we performed whole-exome sequencing on 8,374 blood DNA samples collected from 4,187 Atherosclerosis Risk in Communities Study (ARIC) participants over a median follow-up of 21 years. During this period, 735 participants developed incident CHIP. Splicing factor genes (SF3B1, SRSF2, U2AF1, and ZRSR2) and TET2 CHIP grow significantly faster than DNMT3A non-R882 clones. We find that age at baseline and sex significantly influence the incidence of CHIP, while ASCVD and other traditional ASCVD risk factors do not exhibit such associations. Additionally, baseline synonymous passenger mutations are strongly associated with CHIP status and are predictive of new CHIP clone acquisition and clonal growth over extended follow-up, providing valuable insights into clonal dynamics of aging hematopoietic stem and progenitor cells. This study also reveals associations between germline genetic variants and incident CHIP. Our comprehensive longitudinal assessment yields insights into cell-intrinsic and -extrinsic factors contributing to the development and progression of CHIP clones in older adults.

Polygenic Score for the Prediction of Postoperative Nausea and Vomiting: A Retrospective Derivation and Validation Cohort Study.

BACKGROUND: Postoperative nausea and vomiting (PONV) is a key driver of unplanned admission and patient satisfaction following surgery. Because traditional risk factors do not completely explain variability in risk, we hypothesize that genetics may contribute to the overall risk for this complication. The objective of this research is to perform a genome-wide association study of PONV, derive a polygenic risk score for PONV, assess associations between the risk score and PONV in a validation cohort, and compare any genetic contributions to known clinical risks for PONV. METHODS: Surgeries with integrated genetic and perioperative data performed under general anesthesia at Michigan Medicine and Vanderbilt University Medical Center were studied. PONV was defined as nausea or emesis occurring and documented in the PACU. In the Discovery Phase, genome-wide association studies were performed on each genetic cohort and the results were meta-analyzed. Next, in the Polygenic Phase, we assessed whether a polygenic score, derived from genome-wide association study in a derivation cohort from Vanderbilt University Medical Center, improved prediction within a validation cohort from Michigan Medicine, as quantified by discrimination (C-statistic) and net reclassification index. RESULTS: Of 64,523 total patients, 5,703 developed PONV (8.8%). We identified 46 genetic variants exceeding P<1x10-5 threshold, occurring with minor allele frequency > 1%, and demonstrating concordant effects in both cohorts. Standardized polygenic score was associated with PONV in a basic model, controlling for age and sex, (aOR 1.027 per standard deviation increase in overall genetic risk, 95% CI 1.001-1.053, P=0.044), a model based on known clinical risks (aOR 1.029, 95% CI 1.003-1.055, P=0.030), and a full clinical regression, controlling for 21 demographic, surgical, and anesthetic factors, (aOR 1.029, 95% CI 1.002-1.056, P=0.033). The addition of polygenic score improved overall discrimination in models based on known clinical risk factors (c-statistic: 0.616 compared to 0.613, P=0.028) and improved net reclassification of 4.6% of cases. CONCLUSION: Standardized polygenic risk was associated with PONV in all three of our models, but the genetic influence was smaller than exerted by clinical risk factors. Specifically, a patient with a polygenic risk score > 1 standard deviation above the mean, has 2-3% greater odds of developing PONV when compared to the baseline population, which is at least an order of magnitude smaller than the increase associated with having prior PONV/motion sickness (55%), having a history of migraines (17%), or being female (83%), and is not clinically significant. Furthermore, the use of a polygenic risk score does not meaningfully improve discrimination compared to clinical risk factors and is not clinically useful.

Deep learning-derived splenic radiomics, genomics, and coronary artery disease.

Background: Despite advances in managing traditional risk factors, coronary artery disease (CAD) remains the leading cause of mortality. Circulating hematopoietic cells influence risk for CAD, but the role of a key regulating organ, spleen, is unknown. The understudied spleen is a 3-dimensional structure of the hematopoietic system optimally suited for unbiased radiologic investigations toward novel mechanistic insights. Methods: Deep learning-based image segmentation and radiomics techniques were utilized to extract splenic radiomic features from abdominal MRIs of 42,059 UK Biobank participants. Regression analysis was used to identify splenic radiomics features associated with CAD. Genome-wide association analyses were applied to identify loci associated with these radiomics features. Overlap between loci associated with CAD and the splenic radiomics features was explored to understand the underlying genetic mechanisms of the role of the spleen in CAD. Results: We extracted 107 splenic radiomics features from abdominal MRIs, and of these, 10 features were associated with CAD. Genome-wide association analysis of CAD-associated features identified 219 loci, including 35 previously reported CAD loci, 7 of which were not associated with conventional CAD risk factors. Notably, variants at 9p21 were associated with splenic features such as run length non-uniformity. Conclusions: Our study, combining deep learning with genomics, presents a new framework to uncover the splenic axis of CAD. Notably, our study provides evidence for the underlying genetic connection between the spleen as a candidate causal tissue-type and CAD with insight into the mechanisms of 9p21, whose mechanism is still elusive despite its initial discovery in 2007. More broadly, our study provides a unique application of deep learning radiomics to non-invasively find associations between imaging, genetics, and clinical outcomes.

Integrative Metabolomics Differentiate Coronary Artery Disease, Peripheral Artery Disease, and Venous Thromboembolism Risks.

BACKGROUND: Arterial and venous cardiovascular conditions, such as coronary artery disease (CAD), peripheral artery disease (PAD), and venous thromboembolism (VTE), are genetically correlated. Interrogating underlying mechanisms may shed light on disease mechanisms. In this study, we aimed to identify (1) epidemiological and (2) causal, genetic relationships between metabolites and CAD, PAD, and VTE. METHODS: We used metabolomic data from 95 402 individuals in the UK Biobank, excluding individuals with prevalent cardiovascular disease. Cox proportional-hazards models estimated the associations of 249 metabolites with incident disease. Bidirectional 2-sample Mendelian randomization (MR) estimated the causal effects between metabolites and outcomes using genome-wide association summary statistics for metabolites (n=118 466 from the UK Biobank), CAD (n=184 305 from CARDIoGRAMplusC4D 2015), PAD (n=243 060 from the Million Veterans Project), and VTE (n=650 119 from the Million Veterans Project). Multivariable MR was performed in subsequent analyses. RESULTS: We found that 196, 115, and 74 metabolites were associated (P<0.001) with CAD, PAD, and VTE, respectively. Further interrogation of these metabolites with MR revealed 94, 34, and 9 metabolites with potentially causal effects on CAD, PAD, and VTE, respectively. There were 21 metabolites common to CAD and PAD and 4 common to PAD and VTE. Many putatively causal metabolites included lipoprotein traits with heterogeneity across different sizes and lipid subfractions. Small VLDL (very-low-density lipoprotein) particles increased the risk for CAD while large VLDL particles decreased the risk for VTE. We identified opposing directions of CAD and PAD effects for cholesterol and triglyceride concentrations within HDLs (high-density lipoproteins). Subsequent sensitivity analyses including multivariable MR revealed several metabolites with robust, potentially causal effects of VLDL particles on CAD. CONCLUSIONS: While common vascular conditions are associated with overlapping metabolomic profiles, MR prioritized the role of specific lipoprotein species for potential pharmacological targets to maximize benefits in both arterial and venous beds.

Modification of coronary artery disease clinical risk factors by coronary artery disease polygenic risk score.

BACKGROUND: The extent to which the relationships between clinical risk factors and coronary artery disease (CAD) are altered by CAD polygenic risk score (PRS) is not well understood. Here, we determine whether the interactions between clinical risk factors and CAD PRS further explain risk for incident CAD. METHODS: Participants were of European ancestry from the UK Biobank without prevalent CAD. An externally trained genome-wide CAD PRS was generated and then applied. Clinical risk factors were ascertained at baseline. Cox proportional hazards models were fitted to examine the incident CAD effects of CAD PRS, risk factors, and their interactions. Next, the PRS and risk factors were stratified to investigate the attributable risk of clinical risk factors. FINDINGS: A total of 357,144 individuals of European ancestry without prevalent CAD were included. During a median of 11.1 years of follow-up (interquartile range 10.4-14.1 years), CAD PRS was associated with 1.35-fold (95% confidence interval [CI] 1.332-1.368) risk per SD for incident CAD. The prognostic relevance of the following risk factors was relatively diminished for those with high CAD PRS on a continuous scale: type 2 diabetes (hazard ratio [HR]interaction 0.91, 95% CIinteraction 0.88-0.94), increased body mass index (HRinteraction 0.97, 95% CIinteraction 0.96-0.98), and increased C-reactive protein (HRinteraction 0.98, 95% CIinteraction 0.96-0.99). However, a high CAD PRS yielded joint risk increases with low-density lipoprotein cholesterol (HRinteraction 1.05, 95% CIinteraction 1.04-1.06) and total cholesterol (HRinteraction 1.05, 95% CIinteraction 1.03-1.06). CONCLUSION: The CAD PRS is associated with incident CAD, and its application improves the prognostic relevance of several clinical risk factors. FUNDING: P.N. (R01HL127564, R01HL151152, and U01HG011719) is supported by the National Institutes of Health.

Polygenic Scores and Preclinical Cardiovascular Disease in Individuals With HIV: Insights From the REPRIEVE Trial.

BACKGROUND: Coronary artery disease (CAD) is a leading cause of death among the 38.4 million people with HIV globally. The extent to which cardiovascular polygenic risk scores (PRSs) derived in non-HIV populations generalize to people with HIV is not well understood. METHODS AND RESULTS: PRSs for CAD (GPSMult) and lipid traits were calculated in a global cohort of people with HIV treated with antiretroviral therapy with low-to-moderate atherosclerotic cardiovascular disease risk enrolled in REPRIEVE (Randomized Trial to Prevent Vascular Events in HIV). The PRSs were associated with baseline lipid traits in 4495 genotyped participants, and with subclinical CAD in a subset of 662 who underwent coronary computed tomography angiography. Among participants who underwent coronary computed tomography angiography (mean age, 50.9 [SD, 5.8] years; 16.1% women; 41.8% African, 57.3% European, 1.1% Asian), GPSMult was associated with plaque presence with odds ratio (OR) per SD in GPSMult of 1.42 (95% CI, 1.20-1.68; P=3.8×10-5), stenosis >50% (OR, 2.39 [95% CI, 1.48-3.85]; P=3.4×10-4), and noncalcified/vulnerable plaque (OR, 1.45 [95% CI, 1.23-1.72]; P=9.6×10-6). Effects were consistent in subgroups of age, sex, 10-year atherosclerotic cardiovascular disease risk, ancestry, and CD4 count. Adding GPSMult to established risk factors increased the C-statistic for predicting plaque presence from 0.718 to 0.734 (P=0.02). Furthermore, a PRS for low-density lipoprotein cholesterol was associated with plaque presence with OR of 1.21 (95% CI, 1.01-1.44; P=0.04), and partially calcified plaque with OR of 1.21 (95% CI, 1.01-1.45; P=0.04) per SD. CONCLUSIONS: Among people with HIV treated with antiretroviral therapy without documented atherosclerotic cardiovascular disease and at low-to-moderate calculated risk in REPRIEVE, an externally developed CAD PRS was predictive of subclinical atherosclerosis. PRS for low-density lipoprotein cholesterol was also associated with subclinical atherosclerosis, supporting a role for low-density lipoprotein cholesterol in HIV-associated CAD. REGISTRATION: URL: https://www.reprievetrial.org; Unique identifier: NCT02344290.

Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases.

Polygenic risk scores (PRSs) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. We propose PRSmix, a framework that leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture for 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.20-fold (95% confidence interval [CI], [1.10; 1.3]; p = 9.17 × 10-5) and 1.19-fold (95% CI, [1.11; 1.27]; p = 1.92 × 10-6), and PRSmix+ improved the prediction accuracy by 1.72-fold (95% CI, [1.40; 2.04]; p = 7.58 × 10-6) and 1.42-fold (95% CI, [1.25; 1.59]; p = 8.01 × 10-7) in European and South Asian ancestries, respectively. Compared to the previously cross-trait-combination methods with scores from pre-defined correlated traits, we demonstrated that our method improved prediction accuracy for coronary artery disease up to 3.27-fold (95% CI, [2.1; 4.44]; p value after false discovery rate (FDR) correction = 2.6 × 10-4). Our method provides a comprehensive framework to benchmark and leverage the combined power of PRS for maximal performance in a desired target population.

Risk factors for clonal hematopoiesis of indeterminate potential in people with HIV: a report from the REPRIEVE trial.

ABSTRACT: Clonal hematopoiesis of indeterminate potential (CHIP), the clonal expansion of myeloid cells with leukemogenic mutations, results in increased coronary artery disease (CAD) risk. CHIP is more prevalent among people with HIV (PWH), but the risk factors are unknown. CHIP was identified among PWH in REPRIEVE (Randomized Trial to Prevent Vascular Events in HIV) using whole-exome sequencing. Logistic regression was used to associate sociodemographic factors and HIV-specific factors with CHIP adjusting for age, sex, and smoking status. In the studied global cohort of 4486 PWH, mean age was 49.9 (standard deviation [SD], 6.4) years; 1650 (36.8%) were female; and 3418 (76.2%) were non-White. CHIP was identified in 223 of 4486 (4.97%) and in 38 of 373 (10.2%) among those aged ≥60 years. Age (odds ratio [OR], 1.07; 95% confidence interval [CI], 1.05-1.09; P < .0001) and smoking (OR, 1.37; 95% CI, 1.14-1.66; P < .001) associated with increased odds of CHIP. Globally, participants outside of North America had lower odds of CHIP including sub-Saharan Africa (OR, 0.57; 95% CI, 0.4-0.81; P = .0019), South Asia (OR, 0.45; 95% CI, 0.23-0.80; P = .01), and Latin America/Caribbean (OR, 0.56; 95% CI, 0.34-0.87; P = .014). Hispanic/Latino ethnicity (OR, 0.38; 95% CI, 0.23-0.54; P = .002) associated with significantly lower odds of CHIP. Among HIV-specific factors, CD4 nadir <50 cells/mm3 associated with a 1.9-fold (95%CI, 1.21-3.05; P = .006) increased odds of CHIP, with the effect being significantly stronger among individuals with short duration of antiretroviral therapy (ART; OR, 4.15; 95% CI, 1.51-11.1; P = .005) (Pinteraction= .0492). Among PWH at low-to-moderate CAD risk on stable ART, smoking, CD4 nadir, North American origin, and non-Hispanic ethnicity associated with increased odds of CHIP. This trial was registered at www.ClinicalTrials.gov as NCT02344290.

Genetic Associations of Circulating Cardiovascular Proteins With Gestational Hypertension and Preeclampsia.

Importance: Hypertensive disorders of pregnancy (HDPs), including gestational hypertension and preeclampsia, are important contributors to maternal morbidity and mortality worldwide. In addition, women with HDPs face an elevated long-term risk of cardiovascular disease. Objective: To identify proteins in the circulation associated with HDPs. Design, Setting, and Participants: Two-sample mendelian randomization (MR) tested the associations of genetic instruments for cardiovascular disease-related proteins with gestational hypertension and preeclampsia. In downstream analyses, a systematic review of observational data was conducted to evaluate the identified proteins' dynamics across gestation in hypertensive vs normotensive pregnancies, and phenome-wide MR analyses were performed to identify potential non-HDP-related effects associated with the prioritized proteins. Genetic association data for cardiovascular disease-related proteins were obtained from the Systematic and Combined Analysis of Olink Proteins (SCALLOP) consortium. Genetic association data for the HDPs were obtained from recent European-ancestry genome-wide association study meta-analyses for gestational hypertension and preeclampsia. Study data were analyzed October 2022 to October 2023. Exposures: Genetic instruments for 90 candidate proteins implicated in cardiovascular diseases, constructed using cis-protein quantitative trait loci (cis-pQTLs). Main Outcomes and Measures: Gestational hypertension and preeclampsia. Results: Genetic association data for cardiovascular disease-related proteins were obtained from 21 758 participants from the SCALLOP consortium. Genetic association data for the HDPs were obtained from 393 238 female individuals (8636 cases and 384 602 controls) for gestational hypertension and 606 903 female individuals (16 032 cases and 590 871 controls) for preeclampsia. Seventy-five of 90 proteins (83.3%) had at least 1 valid cis-pQTL. Of those, 10 proteins (13.3%) were significantly associated with HDPs. Four were robust to sensitivity analyses for gestational hypertension (cluster of differentiation 40, eosinophil cationic protein [ECP], galectin 3, N-terminal pro-brain natriuretic peptide [NT-proBNP]), and 2 were robust for preeclampsia (cystatin B, heat shock protein 27 [HSP27]). Consistent with the MR findings, observational data revealed that lower NT-proBNP (0.76- to 0.88-fold difference vs no HDPs) and higher HSP27 (2.40-fold difference vs no HDPs) levels during the first trimester of pregnancy were associated with increased risk of HDPs, as were higher levels of ECP (1.60-fold difference vs no HDPs). Phenome-wide MR analyses identified 37 unique non-HDP-related protein-disease associations, suggesting potential on-target effects associated with interventions lowering HDP risk through the identified proteins. Conclusions and Relevance: Study findings suggest genetic associations of 4 cardiovascular disease-related proteins with gestational hypertension and 2 associated with preeclampsia. Future studies are required to test the efficacy of targeting the corresponding pathways to reduce HDP risk.

Clonal haematopoiesis of indeterminate potential predicts incident cardiac arrhythmias.

BACKGROUND AND AIMS: Clonal haematopoiesis of indeterminate potential (CHIP), the age-related expansion of blood cells with preleukemic mutations, is associated with atherosclerotic cardiovascular disease and heart failure. This study aimed to test the association of CHIP with new-onset arrhythmias. METHODS: UK Biobank participants without prevalent arrhythmias were included. Co-primary study outcomes were supraventricular arrhythmias, bradyarrhythmias, and ventricular arrhythmias. Secondary outcomes were cardiac arrest, atrial fibrillation, and any arrhythmia. Associations of any CHIP [variant allele fraction (VAF) ≥ 2%], large CHIP (VAF ≥10%), and gene-specific CHIP subtypes with incident arrhythmias were evaluated using multivariable-adjusted Cox regression. Associations of CHIP with myocardial interstitial fibrosis [T1 measured using cardiac magnetic resonance (CMR)] were also tested. RESULTS: This study included 410 702 participants [CHIP: n = 13 892 (3.4%); large CHIP: n = 9191 (2.2%)]. Any and large CHIP were associated with multi-variable-adjusted hazard ratios of 1.11 [95% confidence interval (CI) 1.04-1.18; P = .001] and 1.13 (95% CI 1.05-1.22; P = .001) for supraventricular arrhythmias, 1.09 (95% CI 1.01-1.19; P = .031) and 1.13 (95% CI 1.03-1.25; P = .011) for bradyarrhythmias, and 1.16 (95% CI, 1.00-1.34; P = .049) and 1.22 (95% CI 1.03-1.45; P = .021) for ventricular arrhythmias, respectively. Associations were independent of coronary artery disease and heart failure. Associations were also heterogeneous across arrhythmia subtypes and strongest for cardiac arrest. Gene-specific analyses revealed an increased risk of arrhythmias across driver genes other than DNMT3A. Large CHIP was associated with 1.31-fold odds (95% CI 1.07-1.59; P = .009) of being in the top quintile of myocardial fibrosis by CMR. CONCLUSIONS: CHIP may represent a novel risk factor for incident arrhythmias, indicating a potential target for modulation towards arrhythmia prevention and treatment.

Cumulative Diastolic Blood Pressure Burden in Normal Systolic Blood Pressure and Cardiovascular Disease.

BACKGROUND: The clinical significance of isolated diastolic hypertension defined by the 2017 American College of Cardiology/American Heart Association blood pressure (BP) guidelines remains inconsistent. We examined whether long-term diastolic burden predicts the first major adverse cardiovascular event in participants with sustained and untreated normal systolic BP. METHODS: The Mass General Brigham Biobank is a New England health care-based cohort recruited between 2010 and 2021. A total of 15 979 participants aged 18 to 64 years and without prior cardiovascular disease, antihypertensives, or high systolic BP were studied. The cumulative diastolic burden was determined as the area under the curve for diastolic BP (DBP) ≥80 mm Hg over 5 years before enrollment. Major adverse cardiovascular event was defined as a composite of first incident ischemic heart disease, stroke, heart failure, or all-cause death. RESULTS: Of the 15 979 participants, mean (SD) age at enrollment was 47.6 (14.3) years, 11 950 (74.8%) were women, and the mean (SD) systolic BP and DBP were 118.0 (12.9) and 72.2 (9.3) mm Hg, respectively. Over a median (interquartile range) follow-up of 3.5 (1.8-5.4) years, 2467 (15.4%) major adverse cardiovascular events occurred. Using Cox proportional hazards regression, each SD increase in cumulative DBP was independently associated with a hazard ratio (95% CI) of 1.06 (1.02-1.10) without effect modification by sex (P=0.65), age (P=0.46), or race/ethnicity (P=0.24). In addition to traditional risk factors, cumulative DBP modestly improved the discrimination C index (95% CI) from 0.74 (0.72-0.75) to 0.75 (0.74-0.76; likelihood ratio test, P=0.037). CONCLUSIONS: Among individuals with normal systolic BP, cumulative DBP may augment cardiovascular disease risk stratification beyond a single DBP measure and traditional risk factors.

Decoding Genetics, Ancestry, and Geospatial Context for Precision Health.

Mass General Brigham, an integrated healthcare system based in the Greater Boston area of Massachusetts, annually serves 1.5 million patients. We established the Mass General Brigham Biobank (MGBB), encompassing 142,238 participants, to unravel the intricate relationships among genomic profiles, environmental context, and disease manifestations within clinical practice. In this study, we highlight the impact of ancestral diversity in the MGBB by employing population genetics, geospatial assessment, and association analyses of rare and common genetic variants. The population structures captured by the genetics mirror the sequential immigration to the Greater Boston area throughout American history, highlighting communities tied to shared genetic and environmental factors. Our investigation underscores the potency of unbiased, large-scale analyses in a healthcare-affiliated biobank, elucidating the dynamic interplay across genetics, immigration, structural geospatial factors, and health outcomes in one of the earliest American sites of European colonization.

Dynamic Importance of Genomic and Clinical Risk for Coronary Artery Disease Over the Life Course.

Importance: Earlier identification of high coronary artery disease (CAD) risk individuals may enable more effective prevention strategies. However, existing 10-year risk frameworks are ineffective at earlier identification. Understanding the variable importance of genomic and clinical factors across life stages may significantly improve lifelong CAD event prediction. Objective: To assess the time-varying significance of genomic and clinical risk factors in CAD risk estimation across various age groups. Design Setting and Participants: A longitudinal study was performed using data from two cohort studies: the Framingham Offspring Study (FOS) with 3,588 participants aged 19-57 years and the UK Biobank (UKB) with 327,837 participants aged 40-70 years. A total of 134,765 and 3,831,734 person-time years were observed in FOS and UKB, respectively. Main Outcomes and Measures: Hazard ratios (HR) for CAD were calculated for polygenic risk scores (PRS) and clinical risk factors at each age of enrollment. The relative importance of PRS and Pooled Cohort Equations (PCE) in predicting CAD events was also evaluated by age groups. Results: The importance of CAD PRS diminished over the life course, with an HR of 3.58 (95% CI 1.39-9.19) at age 19 in FOS and an HR of 1.51 (95% CI 1.48-1.54) by age 70 in UKB. Clinical risk factors exhibited similar age-dependent trends. PRS significantly outperformed PCE in identifying subsequent CAD events in the 40-45-year age group, with 3.2-fold more appropriately identified events. The mean age of CAD events occurred 1.8 years earlier for those at high genomic risk but 9.6 years later for those at high clinical risk (p<0.001). Overall, adding PRS improved the area under the receiving operating curve of the PCE by an average of +5.1% (95% CI 4.9-5.2%) across all age groups; among individuals <55 years, PRS augmented the AUC-ROC of the PCE by 6.5% (95% CI 5.5-7.5%, p<0.001). Conclusions and Relevance: Genomic and clinical risk factors for CAD display time-varying importance across the lifespan. The study underscores the added value of CAD PRS, particularly among individuals younger than 55 years, for enhancing early risk prediction and prevention strategies.

Genome-wide association meta-analysis identifies risk loci for abdominal aortic aneurysm and highlights PCSK9 as a therapeutic target.

Abdominal aortic aneurysm (AAA) is a common disease with substantial heritability. In this study, we performed a genome-wide association meta-analysis from 14 discovery cohorts and uncovered 141 independent associations, including 97 previously unreported loci. A polygenic risk score derived from meta-analysis explained AAA risk beyond clinical risk factors. Genes at AAA risk loci indicate involvement of lipid metabolism, vascular development and remodeling, extracellular matrix dysregulation and inflammation as key mechanisms in AAA pathogenesis. These genes also indicate overlap between the development of AAA and other monogenic aortopathies, particularly via transforming growth factor ß signaling. Motivated by the strong evidence for the role of lipid metabolism in AAA, we used Mendelian randomization to establish the central role of nonhigh-density lipoprotein cholesterol in AAA and identified the opportunity for repurposing of proprotein convertase, subtilisin/kexin-type 9 (PCSK9) inhibitors. This was supported by a study demonstrating that PCSK9 loss of function prevented the development of AAA in a preclinical mouse model.

Bayesian multivariate genetic analysis improves translational insights.

While lipid traits are known essential mediators of cardiovascular disease, few approaches have taken advantage of their shared genetic effects. We apply a Bayesian multivariate size estimator, mash, to GWAS of four lipid traits in the Million Veterans Program (MVP) and provide posterior mean and local false sign rates for all effects. These estimates borrow information across traits to improve effect size accuracy. We show that controlling local false sign rates accurately and powerfully identifies replicable genetic associations and that multivariate control furthers the ability to explain complex diseases. Our application yields high concordance between independent datasets, more accurately prioritizes causal genes, and significantly improves polygenic prediction beyond state-of-the-art methods by up to 59% for lipid traits. The use of Bayesian multivariate genetic shrinkage has yet to be applied to human quantitative trait GWAS results, and we present a staged approach to prediction on a polygenic scale.

Long-term longitudinal analysis of 4,187 participants reveals new insights into determinants of incident clonal hematopoiesis.

Clonal hematopoiesis (CH), characterized by blood cells predominantly originating from a single mutated hematopoietic stem cell, is linked to diverse aging-related diseases, including hematologic malignancy and atherosclerotic cardiovascular disease (ASCVD). While CH is common among older adults, the underlying factors driving its development are largely unknown. To address this, we performed whole-exome sequencing on 8,374 blood DNA samples collected from 4,187 Atherosclerosis Risk in Communities Study (ARIC) participants over a median follow-up of 21 years. During this period, 735 participants developed incident CH. We found that age at baseline, sex, and dyslipidemia significantly influence the incidence of CH, while ASCVD and other traditional risk factors for ASCVD did not exhibit such associations. Our study also revealed associations between germline genetic variants and incident CH, prioritizing genes in CH development. Our comprehensive longitudinal assessment yields novel insights into the factors contributing to incident CH in older adults.

Representation of Race and Ethnicity in the Contemporary US Health Cohort All of Us Research Program.

Importance: To address systemic disparities in biomedical research, the All of Us (AoU) Research Program was created to identify the root causes and consequences of health outcomes in the US. However, the extent of AoU's racial and ethnic diversity is unknown. Objective: To quantify representation of key racial and ethnic groups in the accruing AoU nationwide health cohort and compare with their actual representation in the US. Design, Setting, and Participants: This cohort study compared the AoU program from May 2017 to June 2022 for individuals 18 years and older with the Decennial Survey 2020 (DEC) collected by the US Census Bureau. Exposures: Representation of non-Hispanic Asian, non-Hispanic Black or African American, Hispanic or Latino, non-Hispanic White, and uncategorized or multiple races in AoU. Main Outcomes and Measures: The extent of underrepresentation or overrepresentation of each racial group in the AoU program at both nationwide and state-level relative to DEC. Results: Of the 358 705 US adults in the AoU to date, individuals identified with the following race and ethnicity categories: 12 710 non-Hispanic Asian (3.5%), 73 348 non-Hispanic Black or African American (20.5%), 58 488 Hispanic or Latino (16.3%), 205 457 non-Hispanic White (57.3%), and 8702 uncategorized or reporting multiple categories (2.4%). Of 355 413 participants with available sex at birth and age data, 218 981 (61.6%) were female and had a mean (SD) age of 53.1 (17.0) years, 136 037 (38.28%) were male and had a mean (SD) age of 56.7 (17.0) years, and 395 reported nonbinary sex (0.1%), with a mean (SD) age of 55.4 (15.8) years. Compared with the referent US, non-Hispanic Black or African American individuals were overrepresented in the AoU by 8.73% (AoU, 20.5% [73 348 of 358 705] vs DEC, 11.7% [30 266 080 of 258 343 281]) and by relative scale, 1.94-fold. Non-Hispanic White individuals accounted for the greatest participation in the AoU with generally consistent dominance across all regions yet numerically underrepresented by absolute difference of -3.54% (95% CI, -3.70 to -3.38). Uncategorized or multiracial group in the AoU (2.4% [8702 of 358 705]) was 0.43-fold likely to be represented relative to the DEC (4.6% [11 922 096 of 258 343 281]) with an absolute difference of -2.19% (95% CI, -2.24 to -2.14). Moreover, non-Hispanic Asian individuals were underrepresented by -2.54% (95% CI, -2.60 to -2.48) prominently in most states. Individuals identifying as Hispanic or Latino were nominally underrepresented by -0.46% (95% CI, -0.58 to -0.34) (AoU, 16.3% [58 488 of 358 705] vs DEC, 16.8% [43 322 792 of 258 343 281]). Conclusions and Relevance: Recruitment trends for the ongoing AoU show relatively improved representation of some major race groups with geographic trends. These findings underscore the need to further tailor and augment recruitment and participation initiatives for diverse populations.

Multi-ancestry meta-analysis identifies 5 novel loci for ischemic stroke and reveals heterogeneity of effects between sexes and ancestries.

Stroke is the second leading cause of death and disability worldwide. Stroke prevalence varies by sex and ancestry, possibly due to genetic heterogeneity between subgroups. We performed a genome-wide meta-analysis of 16 biobanks across multiple ancestries to study the genetics of ischemic stroke (60,176 cases, 1,310,725 controls) as part of the Global Biobank Meta-analysis Initiative (GBMI) and further combined the results with previously published MegaStroke. Five novel loci for ischemic stroke (LAMC1, CALCRL, PLSCR1, CDKN1A, and SWAP70) were identified after replication in four additional datasets. One previously reported locus showed significant ancestry heterogeneity (ABO), and one showed significant sex heterogeneity (ALDH2). The ALDH2 association was male specific (males p = 1.67e-24, females p = 0.126) and was additionally observed only in the East Asian ancestry (male) samples. These findings emphasize the need for more diverse datasets with large sample sizes to further understand the genetic predisposition of stroke in different ancestry and sex groups.