Background Coronary artery disease (CAD) is increasing among young adults. We aimed to describe the cardiovascular risk factors and long‐term prognosis of premature CAD. Methods and Results Using the Duke Databank for Cardiovascular Disease, we evaluated 3655 patients admitted between 1995 and 2013 with a first diagnosis of obstructive CAD before the age of 50 years. Major adverse cardiovascular events (MACEs), defined as the composite of death, myocardial infarction, stroke, or revascularization, were ascertained for up to 10 years. Cox proportional hazard regression models were used to assess associations with the rate of first recurrent event, and negative binomial log‐linear regression was used for rate of multiple event recurrences. Past or current smoking was the most frequent cardiovascular factor (60.8%), followed by hypertension (52.8%) and family history of CAD (39.8%). Within a 10‐year follow‐up, 52.9% of patients had at least 1 MACE, 18.6% had at least 2 recurrent MACEs, and 7.9% had at least 3 recurrent MACEs, with death occurring in 20.9% of patients. Across follow‐up, 31.7% to 37.2% of patients continued smoking, 81.7% to 89.3% had low‐density lipoprotein cholesterol levels beyond the goal of 70 mg/dL, and 16% had new‐onset diabetes mellitus. Female sex, diabetes mellitus, chronic kidney disease, multivessel disease, and chronic inflammatory disease were factors associated with recurrent MACEs. Conclusions Premature CAD is an aggressive disease with frequent ischemic recurrences and premature death. Individuals with premature CAD have a high proportion of modifiable cardiovascular risk factors, but failure to control them is frequently observed.

Background DNA methylation is implicated in many chronic diseases and may contribute to mortality. Therefore, we conducted an epigenome‐wide association study (EWAS) for all‐cause mortality with whole‐transcriptome data in a cardiovascular cohort (CATHGEN [Catheterization Genetics]).Methods and ResultsCases were participants with mortality≥7 days postcatheterization whereas controls were alive with≥2 years of follow‐up. The Illumina Human Methylation 450K and EPIC arrays (Illumina, San Diego, CA) were used for the discovery and validation sets, respectively. A linear model approach with empirical Bayes estimators adjusted for confounders was used to assess difference in methylation (Δβ). In the discovery set (55 cases, 49 controls), 25 629 (6.5%) probes were differently methylated (P<0.05). In the validation set (108 cases, 108 controls), 3 probes were differentially methylated with a false discovery rate–adjusted P<0.10: cg08215811 (SLC4A9; log2 fold change=−0.14); cg17845532 (MATK; fold change=−0.26); and cg17944110 (castor zinc finger 1 [CASZ1]; FC=0.26; P<0.0001; false discovery rate–adjusted P=0.046–0.080). Meta‐analysis identified 6 probes (false discovery rate–adjusted P<0.05): the 3 above, cg20428720 (intergenic), cg17647904 (NCOR2), and cg23198793 (CAPN3). Messenger RNA expression of 2 MATK isoforms was lower in cases (fold change=−0.24 [P=0.007] and fold change=−0.61 [P=0.009]). The CASZ1,NCOR2, and CAPN3 transcripts did not show differential expression (P>0.05); the SLC4A9 transcript did not pass quality control. The cg17944110 probe is located within a potential regulatory element; expression of predicted targets (using GeneHancer) of the regulatory element, UBIAD1 (P=0.01) and CLSTN1 (P=0.03), were lower in cases.ConclusionsWe identified 6 novel methylation sites associated with all‐cause mortality. Methylation in CASZ1 may serve as a regulatory element associated with mortality in cardiovascular patients. Larger studies are necessary to confirm these observations.

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We sought to determine if novel plasma biomarkers improve traditionally defined metabolic health (MH) in predicting risk of cardiovascular disease (CVD) events irrespective of weight. Poor MH was defined in CATHGEN biorepository participants (n > 9300), a follow-up cohort (> 5600 days) comprising participants undergoing evaluation for possible ischemic heart disease. Lipoprotein subparticles, lipoprotein-insulin resistance (LP-IR), and GlycA were measured using NMR spectroscopy (n = 8385), while acylcarnitines and amino acids were measured using flow-injection, tandem mass spectrometry (n = 3592). Multivariable Cox proportional hazards models determined association of poor MH and plasma biomarkers with time-to-all-cause mortality or incident myocardial infarction. Low-density lipoprotein particle size and high-density lipoprotein, small and medium particle size (HMSP), GlycA, LP-IR, short-chain dicarboxylacylcarnitines (SCDA), and branched-chain amino acid plasma biomarkers were independently associated with CVD events after adjustment for traditionally defined MH in the overall cohort (p = 3.3 × 10−4–3.6 × 10−123), as well as within most of the individual BMI categories (p = 8.1 × 10−3–1.4 × 10−49). LP-IR, GlycA, HMSP, and SCDA improved metrics of model fit analyses beyond that of traditionally defined MH. We found that LP-IR, GlycA, HMSP, and SCDA improve traditionally defined MH models in prediction of adverse CVD events irrespective of BMI.

BACKGROUND: Inherited primary arrhythmia syndromes and arrhythmogenic cardiomyopathies can lead to sudden cardiac arrest in otherwise healthy individuals. The burden and expression of these diseases in a real-world, well-phenotyped cardiovascular population is not well understood. METHODS: Whole exome sequencing was performed on 8574 individuals from the CATHGEN cohort. Variants in 55 arrhythmia-related genes (associated with 8 disorders) were identified and assessed for pathogenicity based on American College of Genetics and Genomics/Association for Molecular Pathology criteria. Individuals carrying pathogenic/likely pathogenic (P/LP) variants were grouped by arrhythmogenic disorder and matched 1:5 to noncarrier controls based on age, sex, and genetic ancestry. Long-term phenotypic data were annotated through deep electronic health record review. RESULTS: Fifty-eight P/LP variants were found in 79 individuals in 12 genes associated with 5 arrhythmogenic disorders (arrhythmogenic right ventricular cardiomyopathy, Brugada syndrome, hypertrophic cardiomyopathy, LMNA -related cardiomyopathy, and long QT syndrome). The penetrance of these P/LP variants in this cardiovascular cohort was 33%, 0%, 28%, 83%, and 4%, respectively. Carriers of P/LP variants associated with arrhythmogenic disorders showed significant differences in ECG, imaging, and clinical phenotypes compared with noncarriers, but displayed no difference in survival. Carriers of novel truncating variants in FLNC, MYBPC3, and MYH7 also developed relevant arrhythmogenic cardiomyopathy phenotypes. CONCLUSIONS: In a real-world cardiovascular cohort, P/LP variants in arrhythmia-related genes were relatively common (1:108 prevalence) and most penetrant in LMNA . While hypertrophic cardiomyopathy P/LP variant carriers showed significant differences in clinical outcomes compared with noncarriers, carriers of P/LP variants associated with other arrhythmogenic disorders displayed only ECG differences.

Introduction. The interpretation of variants of uncertain significance (VUS) remains a challenge in the care of patients with established or familial cardiovascular diseases. 56% of potential variants within known cardiovascular risk genes are characterized as VUS and unbiased machine learning algorithms trained upon large data resources can stratify VUS into higher vs. lower probability of contributing to a cardiovascular disease phenotype. Methods. ClinVar pathogenic or likely pathogenic (P/LP) and benign or likely benign (B/LB) from 47 genes previously associated with monogenic cardiovascular diseases (MCVDs) were identified. A random forest model was trained using six-fold cross validation on these variants to build a predictive model of variant pathogenicity using measures of evolutionary constraint, deleteriousness, splicogenicity, local pathogenic variation, cardiac-specific exon expression, and population allele frequency. Predicted pathogenicity was computed as a linear outcome coupled with a naïve Bayes classifier to determine an optimal cut-off to distinguish VUS of pathogenic interest versus VUS with low likelihood of pathogenicity. Performance of our model was validated using variants for which ClinVar pathogenicity assignment changed between 2014 to 2022. As a proof-of-concept we demonstrated the utility of our model in the (CATHeterization GENetics [CATHGEN]) cohort. Results. Random forest identified a top-ranked model using ClinVar known P/LP and B/LB variants that weighted evolutionary constraint (CADD score) most heavily. The model accurately prioritized variants for which ClinVar clinical significance had changed from 2014 to 2022 (precision recall AUC = 0.97) and had equal or greater performance when compared to conventional in-silico methods for predicting variant pathogenicity. In the CATHGEN cohort, there was a higher burden of VUS of pathogenic interest in individuals with DCM as compared to controls without DCM (p = 8.2x10− 15). Individuals in CATHGEN who harbored model predicted and ACMG/AMP reviewed pathogenic VUSs demonstrated that 27.6% had clinical evidence of the relevant disease. Lastly, variant prioritization using this model provided genetic diagnosis in 11.9% of CATHGEN patients diagnosed with HCM clinically who did not harbor a HCM genetic P/LP variant by initial ACMG/AMP review. Conclusion. We have developed a cardiac-specific model for prioritizing variants underlying familial cardiovascular disease syndromes. CVD-PP proves to have high performance in discriminating pathogenicity of VUS in MCVD genes. ACMG/AMP review and phenotyping of individuals carrying VUS of pathogenic interest in a large cardiovascular cohort support the clinical utility of this model.

BackgroundGenetic loci associated with risk of severe COVID-19 infection have been identified and individuals with complicated COVID-19 infections often have multiple comorbidities.ObjectiveIdentify known and unidentified comorbidities associated with genetic loci linked to risk of severe COVID-19 infection.MethodsA Phenome Wide Association Study (PheWAS) was conducted in 247,448 unrelated, white individuals from the UK Biobank to test the association of 1,402 unique phenotypes with ten genome-wide significant severe-COVID risk single nucleotide polymorphisms (SNP) identified from prior studies. A validation PheWAS was conducted in 2,247 white individuals from the CATHGEN.ResultsFour of the ten tested genetic loci showed significant phenotypic associations in UK Biobank after FDR adjustment. Vascular dementia significantly associated with rs7271165 near TMEM65 on 8q24.13 in individuals with the C risk allele (OR 5.66 [95% CI 2.21-11.85], q=0.049). We identified 40 novel phenotype associations with rs657152 on 9q34.2 coinciding with the ABO gene with individuals with the A COVID risk allele having higher odds of heart failure (OR 1.09 [95% CI 1.03-1.14], q=0.004), diabetes mellitus (OR 1.05 [95% CI 1.02-1.07], q=0.004) and hypercholesterolemia (OR 1.04 [95% CI 1.02-1.06], q=6.3×10−5). Eight phenotypes associated with rs1819040 near KANSL1 on 17q21.31 in individuals with the A risk allele including atrial fibrillation and flutter (OR 1.07 [95% CI 1.04-1.10], q=0.0084) and pulmonary fibrosis (OR 0.80 [95% CI 0.71-0.89], q=0.035). Ten novel phenotypic associations were identified in association with rs74956615 on 19p13.2 near the TYK2 gene including individuals with the A COVID risk allele having lower odds of psoriatic arthropathy (OR 0.31 [95% CI 0.20-0.47], q=4.5×10−5), rheumatoid arthritis (OR 0.83 [95% CI 0.64-0.83], p=1.4×10−6) and thyrotoxicosis with or without goiter (OR 0.77 [95% CI 0.68-0.87], p-6.9×10−5). Two associations for rs1819040 (KANSL1) and seven associations for rs74956615 (TYK2) validated in CATHGEN.ConclusionsUsing a broad PheWAS approach in a large discovery and validation cohort, we have identified novel phenotypic associations with risk alleles for severe COVID-19 infection. Interestingly, the ABO locus was associated with comorbidities that are also risk factors for severe COVID-19 infection, suggesting that this locus has pleiotropic effects and provides a potential mechanism for this association. The 19p13 locus was associated with lower risk of autoimmune disease, these findings may have broad implications for the importance of multiple comorbidities across both infectious and non-infectious diseases and may provide insight in the molecular function of the genes near these genetic risk loci.