Explore the vast range of titles available.

Dilated cardiomyopathy (DCM) is an important cause of heart failure and the leading indication for heart transplantation. Many rare genetic variants have been associated with DCM, but common variant studies of the disease have yielded few associated loci. As structural changes in the heart are a defining feature of DCM, we report a genome-wide association study of cardiac magnetic resonance imaging (MRI)-derived left ventricular measurements in 36,041 UK Biobank participants, with replication in 2184 participants from the Multi-Ethnic Study of Atherosclerosis. We identify 45 previously unreported loci associated with cardiac structure and function, many near well-established genes for Mendelian cardiomyopathies. A polygenic score of MRI-derived left ventricular end systolic volume strongly associates with incident DCM in the general population. Even among carriers of TTN truncating mutations, this polygenic score influences the size and function of the human heart. These results further implicate common genetic polymorphisms in the pathogenesis of DCM. Structural changes to the left ventricle are characteristic of dilated cardiomyopathy (DCM), a disease for which many rare genetic variants are known. Here, Pirruccello et al. report GWAS of seven cardiac MRI measurements in the left ventricle and describe shared loci and polygenic association with DCM.

Identification of individuals at highest risk of coronary artery disease (CAD)—ideally before onset—remains an important public health need. Prior studies have developed genome-wide polygenic scores to enable risk stratification, reflecting the substantial inherited component to CAD risk. Here we develop a new and significantly improved polygenic score for CAD, termed GPS Mult , that incorporates genome-wide association data across five ancestries for CAD (>269,000 cases and >1,178,000 controls) and ten CAD risk factors. GPS Mult strongly associated with prevalent CAD (odds ratio per standard deviation 2.14, 95% confidence interval 2.10–2.19, P  < 0.001) in UK Biobank participants of European ancestry, identifying 20.0% of the population with 3-fold increased risk and conversely 13.9% with 3-fold decreased risk as compared with those in the middle quintile. GPS Mult was also associated with incident CAD events (hazard ratio per standard deviation 1.73, 95% confidence interval 1.70–1.76, P  < 0.001), identifying 3% of healthy individuals with risk of future CAD events equivalent to those with existing disease and significantly improving risk discrimination and reclassification. Across multiethnic, external validation datasets inclusive of 33,096, 124,467, 16,433 and 16,874 participants of African, European, Hispanic and South Asian ancestry, respectively, GPS Mult demonstrated increased strength of associations across all ancestries and outperformed all available previously published CAD polygenic scores. These data contribute a new GPS Mult for CAD to the field and provide a generalizable framework for how large-scale integration of genetic association data for CAD and related traits from diverse populations can meaningfully improve polygenic risk prediction. A polygenic risk score for coronary artery disease developed using data from individuals of five different ancestries has increased accuracy across diverse populations.

Left ventricular mass is a risk marker for cardiovascular events, and may indicate an underlying cardiomyopathy. Cardiac magnetic resonance is the gold-standard for left ventricular mass estimation, but is challenging to obtain at scale. Here, we use deep learning to enable genome-wide association study of cardiac magnetic resonance-derived left ventricular mass indexed to body surface area within 43,230 UK Biobank participants. We identify 12 genome-wide associations (1 known at TTN and 11 novel for left ventricular mass), implicating genes previously associated with cardiac contractility and cardiomyopathy. Cardiac magnetic resonance-derived indexed left ventricular mass is associated with incident dilated and hypertrophic cardiomyopathies, and implantable cardioverter-defibrillator implant. An indexed left ventricular mass polygenic risk score ≥90 th percentile is also associated with incident implantable cardioverter-defibrillator implant in separate UK Biobank (hazard ratio 1.22, 95% CI 1.05-1.44) and Mass General Brigham (hazard ratio 1.75, 95% CI 1.12-2.74) samples. Here, we perform a genome-wide association study of cardiac magnetic resonance-derived indexed left ventricular mass to identify 11 novel variants and demonstrate that cardiac magnetic resonance-derived and genetically predicted indexed left ventricular mass are associated with incident cardiomyopathy. A genome-wide association study of cardiac magnetic resonance-derived left ventricular mass index including 43,000 UK Biobank participants reveals 12 associations (11 novel), implicating genes involved in cardiac contractility and cardiomyopathy.

Large-scale sequencing has enabled unparalleled opportunities to investigate the role of rare coding variation in human phenotypic variability. Here, we present a pan-ancestry analysis of sequencing data from three large biobanks, including the All of Us research program. Using mixed-effects models, we performed gene-based rare variant testing for 601 diseases across 748,879 individuals, including 155,236 with ancestry dissimilar to European. We identified 363 significant associations, which highlighted core genes for the human disease phenome and identified potential novel associations, including UBR3 for cardiometabolic disease and YLPM1 for psychiatric disease. Pan-ancestry burden testing represented an inclusive and useful approach for discovery in diverse datasets, although we also highlight the importance of ancestry-specific sensitivity analyses in this setting. Finally, we found that effect sizes for rare protein-disrupting variants were concordant between samples similar to European ancestry and other genetic ancestries ( β Deming  = 0.7–1.0). Our results have implications for multi-ancestry and cross-biobank approaches in sequencing association studies for human disease. Gene-based rare variant analyses for 601 diseases across 748,879 individuals from three biobanks identify 363 significant associations and highlight important considerations for multi-ancestry and cross-biobank sequencing studies.

Routine endurance exercise confers numerous health benefits, and high intensity exercise may accelerate and magnify many of these benefits. To date, explanatory molecular mechanisms and the influence of exercise intensity remain poorly understood. Circulating factors are hypothesized to transduce some of the systemic effects of exercise. We sought to examine the role of exercise and exercise intensity on the human plasma proteome. We employed an aptamer-based method to examine 1,305 plasma proteins in 12 participants before and after exercise at two physiologically defined intensities (moderate and high) to determine the proteomic response. We demonstrate that the human plasma proteome is responsive to acute exercise in an intensity-dependent manner with enrichment analysis suggesting functional biological differences between the moderate and high intensity doses. Through integration of available genetic data, we estimate the effects of acute exercise on exercise-associated traits and find proteomic responses that may contribute to observed clinical effects on coronary artery disease and blood pressure regulation. In sum, we provide supportive evidence that moderate and high intensity exercise elicit different signaling responses, that exercise may act in part non-cell autonomously through circulating plasma proteins, and that plasma protein dynamics can simulate some the beneficial and adverse effects of acute exercise.

The Thrombolysis in Myocardial Infarction (TIMI) risk scores for Unstable Angina/Non-ST-elevation myocardial infarction (UA/NSTEMI) and ST-elevation myocardial infarction (STEMI) and the Global Registry of Acute Coronary Events (GRACE) risk scores for in-hospital and 6-month mortality are established tools for assessing risk in Acute Coronary Syndrome (ACS) patients. The objective of our study was to compare the discriminative abilities of the TIMI and GRACE risk scores in a broad-spectrum, unselected ACS population and to assess the relative contributions of model simplicity and model composition to any observed differences between the two scoring systems. ACS patients admitted to the University of Michigan between 1999 and 2005 were divided into UA/NSTEMI (n = 2753) and STEMI (n = 698) subpopulations. The predictive abilities of the TIMI and GRACE scores for in-hospital and 6-month mortality were assessed by calibration and discrimination. There were 137 in-hospital deaths (4%), and among the survivors, 234 (7.4%) died by 6 months post-discharge. In the UA/NSTEMI population, the GRACE risk scores demonstrated better discrimination than the TIMI UA/NSTEMI score for in-hospital (C = 0.85, 95% CI: 0.81-0.89, versus 0.54, 95% CI: 0.48-0.60; p<0.01) and 6-month (C = 0.79, 95% CI: 0.76-0.83, versus 0.56, 95% CI: 0.52-0.60; p<0.01) mortality. Among STEMI patients, the GRACE and TIMI STEMI scores demonstrated comparably excellent discrimination for in-hospital (C = 0.84, 95% CI: 0.78-0.90 versus 0.83, 95% CI: 0.78-0.89; p = 0.83) and 6-month (C = 0.72, 95% CI: 0.63-0.81, versus 0.71, 95% CI: 0.64-0.79; p = 0.79) mortality. An analysis of refitted multivariate models demonstrated a marked improvement in the discriminative power of the TIMI UA/NSTEMI model with the incorporation of heart failure and hemodynamic variables. Study limitations included unaccounted for confounders inherent to observational, single institution studies with moderate sample sizes. The GRACE scores provided superior discrimination as compared with the TIMI UA/NSTEMI score in predicting in-hospital and 6-month mortality in UA/NSTEMI patients, although the GRACE and TIMI STEMI scores performed equally well in STEMI patients. The observed discriminative deficit of the TIMI UA/NSTEMI score likely results from the omission of key risk factors rather than from the relative simplicity of the scoring system.

Purpose To evaluate the diagnostic yield and clinical relevance of clinical genome sequencing (cGS) as a first genetic test for patients with suspected monogenic disorders. Methods We conducted a prospective randomized study with pediatric and adult patients recruited from genetics clinics at Massachusetts General Hospital who were undergoing planned genetic testing. Participants were randomized into two groups: standard-of-care genetic testing (SOC) only or SOC and cGS. Results Two hundred four participants were enrolled, 202 were randomized to one of the intervention arms, and 99 received cGS. In total, cGS returned 16 molecular diagnoses that fully or partially explained the indication for testing in 16 individuals (16.2% of the cohort, 95% confidence interval [CI] 8.9–23.4%), which was not significantly different from SOC (18.2%, 95% CI 10.6–25.8%, P  = 0.71). An additional eight molecular diagnoses reported by cGS had uncertain relevance to the participant’s phenotype. Nevertheless, referring providers considered 20/24 total cGS molecular diagnoses (83%) to be explanatory for clinical features or worthy of additional workup. Conclusion cGS is technically suitable as a first genetic test. In our cohort, diagnostic yield was not significantly different from SOC. Further studies addressing other variant types and implementation challenges are needed to support feasibility and utility of broad-scale cGS adoption.

Genetic studies may help identify causal pathways; therefore, we sought to identify genetic determinants of ideal CVH and their association with CVD outcomes in the multi-population Veteran Administration Million Veteran Program. An ideal health score (IHS) was calculated from 3 clinical factors (blood pressure, total cholesterol, and blood glucose levels) and 3 behavioral factors (smoking status, physical activity, and BMI), ascertained at baseline. Multi-population genome-wide association study (GWAS) was performed on IHS and binary ideal health using linear and logistic regression, respectively. Using the genome-wide significant SNPs from the IHS GWAS, we created a weighted IHS polygenic risk score (PRSIHS) which was used (i) to conduct a phenome-wide association study (PheWAS) of associations between PRSIHS and ICD-9 phenotypes and (ii) to further test for associations with mortality and selected CVD outcomes using logistic and Cox regression and, as an instrumental variable, in Mendelian Randomization. The discovery and replication cohorts consisted of 142,404 (119,129 European American (EUR); 16,495 African American (AFR)), and 45,766 (37,646 EUR; 5,366 AFR) participants, respectively. The mean age was 65.8 years (SD = 11.2) and 92.7% were male. Overall, 4.2% exhibited ideal CVH based on the clinical and behavioral factors. In the multi-population meta-analysis, variants at 17 loci were associated with IHS and each had known GWAS associations with multiple components of the IHS. PheWAS analysis in 456,026 participants showed that increased PRSIHS was associated with a lower odds ratio for many CVD outcomes and risk factors. Both IHS and PRSIHS measures of ideal CVH were associated with significantly less CVD outcomes and CVD mortality. A set of high interest genetic variants contribute to the presence of ideal CVH in a multi-ethnic cohort of US Veterans. Genetically influenced ideal CVH is associated with lower odds of CVD outcomes and mortality.

A key public health need is to identify individuals at high risk for a given disease to enable enhanced screening or preventive therapies. Because most common diseases have a genetic component, one important approach is to stratify individuals based on inherited DNA variation 1 . Proposed clinical applications have largely focused on finding carriers of rare monogenic mutations at several-fold increased risk. Although most disease risk is polygenic in nature 2 – 5 , it has not yet been possible to use polygenic predictors to identify individuals at risk comparable to monogenic mutations. Here, we develop and validate genome-wide polygenic scores for five common diseases. The approach identifies 8.0, 6.1, 3.5, 3.2, and 1.5% of the population at greater than threefold increased risk for coronary artery disease, atrial fibrillation, type 2 diabetes, inflammatory bowel disease, and breast cancer, respectively. For coronary artery disease, this prevalence is 20-fold higher than the carrier frequency of rare monogenic mutations conferring comparable risk 6 . We propose that it is time to contemplate the inclusion of polygenic risk prediction in clinical care, and discuss relevant issues. Genome-wide polygenic risk scores derived from GWAS data for five common diseases can identify subgroups of the population with risk approaching or exceeding that of a monogenic mutation.

Despite the known benefits of cardiac rehabilitation in patients with coronary artery disease, referral rates to rehabilitation programs remain low. We determined the incidence and determinants of cardiac rehabilitation referral rates for patients undergoing percutaneous coronary intervention (PCI). The incidence and predictors of referral to cardiac rehabilitation were assessed among 145,661 consecutive patients undergoing PCI and surviving to hospital discharge across 31 hospitals in the Blue Cross Blue Shield of Michigan Cardiovascular Consortium between 2003 and 2008. The 6-year cardiac rehabilitation referral rate was 60.2%. Younger age, male gender, white race, and presentation with acute or severe disease (ie, acute myocardial infarction [AMI] in the previous 24 hours and ST-elevation myocardial infarction) were associated with increased referral to rehabilitation (all P < .0001). Most medical comorbidities were associated with decreased referral. Referral rates for cardiac rehabilitation were below the rates of other AMI quality-of-care indicators and more variable across hospital sites. Race-specific referral rates differed significantly in the lowest referring hospitals (P < .0001) but not in the highest referring hospitals (P = .16). Women had a 0.7% relative decrease in referral as compared to men (P = .0188) in the highest referring hospitals but a 26.7% relative decrease in referral in the lowest referring hospitals (P = .02). Over one third of patients undergoing PCI are not referred for cardiac rehabilitation. Referral rates are below the rates of other AMI quality-of-care performance measures and more variable across sites. Racial and gender disparities in referral to rehabilitation exist but are concentrated at the lowest referring hospitals.