Common Ancestry-Specific Ion Channel Variants Predispose to Drug-Induced Arrhythmias.

BACKGROUND: Multiple reports associate the cardiac sodium channel gene (SCN5A) variants S1103Y and R1193Q with type 3 congenital long QT syndrome and drug-induced long QT syndrome. These variants are too common in ancestral populations to be highly arrhythmogenic at baseline, however: S1103Y allele frequency is 8.1% in African Americans and R1193Q 6.1% in East Asians. R1193Q is known to increase late sodium current (INa-L) in cardiomyocytes derived from induced pluripotent stem cells but the role of these variants in modulating repolarization remains poorly understood. METHODS: We determined the effect of S1103Y on QT intervals among African-American participants in a large electronic health record. Using cardiomyocytes derived from induced pluripotent stem cells carrying naturally occurring or genome-edited variants, we studied action potential durations (APDs) at baseline and after challenge with the repolarizing potassium current (IKr) blocker dofetilide and INa-L and IKr at baseline. RESULTS: In 1479 African-American participants with no confounding medications or diagnoses of heart disease, QT intervals in S1103Y carriers was no different from that in noncarriers. Baseline APD was no different in cells expressing the Y allele (SY, YY cells) compared with isogenic cells with the reference allele (SS cells). However, INa-L was increased in SY and YY cells and the INa-L blocker GS967 shortened APD in SY/YY but not SS cells (P<0.001). IKr was increased almost 2-fold in SY/YY cells compared with SS cells (tail current: 0.66±0.1 versus 1.2±0.1 pA/pF; P<0.001). Dofetilide challenge prolonged APD at much lower concentrations in SY (4.1 nmol/L [interquartile range, 1.5-9.3]; n=11) and YY (4.2 nmol/L [1.7-5.0]; n=5) than in SS cells (249 nmol/L [22.3-2905]; n=14; P<0.001 and P<0.01, respectively) and elicited afterdepolarizations in 8/16 SY/YY cells but only in 1/14 SS cells. R1193Q cells similarly displayed no difference in baseline APD but increased IKr and increased dofetilide sensitivity. CONCLUSIONS: These common ancestry-specific variants do not affect baseline repolarization, despite generating increased INa-L. We propose that increased IKr serves to maintain normal repolarization but increases the risk of manifest QT prolongation with IKr block in variant carriers. Our findings emphasize the need for inclusion of diverse populations in the study of adverse drug reactions.

Clonal hematopoiesis of indeterminate potential and outcomes after heart transplantation: A multicenter study.

Cardiac allograft vasculopathy (CAV) is a leading cause of late graft failure and mortality after heart transplantation (HT). Sharing some features with atherosclerosis, CAV results in diffuse narrowing of the epicardial coronaries and microvasculature, with consequent graft ischemia. Recently, clonal hematopoiesis of indeterminate potential (CHIP) has emerged as a risk factor for cardiovascular disease and mortality. We aimed to investigate the relationship between CHIP and posttransplant outcomes, including CAV. We analyzed 479 HT recipients with stored DNA samples at 2 high-volume transplant centers, Vanderbilt University Medical Center and Columbia University Irving Medical Center. We explored the association between the presence of CHIP mutations with CAV and mortality after HT. In this case-control analysis, carriers of CHIP mutations were not at increased risk of CAV or mortality after HT. In a large multicenter genomics study of the heart transplant population, the presence of CHIP mutations was not associated with an increased risk of CAV or posttransplant mortality.

An algorithm to identify cases of pulmonary arterial hypertension from the electronic medical record.

BACKGROUND: Study of pulmonary arterial hypertension (PAH) in claims-based (CB) cohorts may facilitate understanding of disease epidemiology, however previous CB algorithms to identify PAH have had limited test characteristics. We hypothesized that machine learning algorithms (MLA) could accurately identify PAH in an CB cohort. METHODS: ICD-9/10 codes, CPT codes or PAH medications were used to screen an electronic medical record (EMR) for possible PAH. A subset (Development Cohort) was manually reviewed and adjudicated as PAH or "not PAH" and used to train and test MLAs. A second subset (Refinement Cohort) was manually reviewed and combined with the Development Cohort to make The Final Cohort, again divided into training and testing sets, with MLA characteristics defined on test set. The MLA was validated using an independent EMR cohort. RESULTS: 194 PAH and 786 "not PAH" in the Development Cohort trained and tested the initial MLA. In the Final Cohort test set, the final MLA sensitivity was 0.88, specificity was 0.93, positive predictive value was 0.89, and negative predictive value was 0.92. Persistence and strength of PAH medication use and CPT code for right heart catheterization were principal MLA features. Applying the MLA to the EMR cohort using a split cohort internal validation approach, we found 265 additional non-confirmed cases of suspected PAH that exhibited typical PAH demographics, comorbidities, hemodynamics. CONCLUSIONS: We developed and validated a MLA using only CB features that identified PAH in the EMR with strong test characteristics. When deployed across an entire EMR, the MLA identified cases with known features of PAH.

Unbiased characterization of atrial fibrillation phenotypic architecture provides insight to genetic liability and clinically relevant outcomes.

Background: Atrial Fibrillation (AF) is a common and clinically heterogeneous arrythmia. Machine learning (ML) algorithms can define data-driven disease subtypes in an unbiased fashion, but whether the AF subgroups defined in this way align with underlying mechanisms, such as high polygenic liability to AF or inflammation, and associate with clinical outcomes is unclear. Methods: We identified individuals with AF in a large biobank linked to electronic health records (EHR) and genome-wide genotyping. The phenotypic architecture in the AF cohort was defined using principal component analysis of 35 expertly curated and uncorrelated clinical features. We applied an unsupervised co-clustering machine learning algorithm to the 35 features to identify distinct phenotypic AF clusters. The clinical inflammatory status of the clusters was defined using measured biomarkers (CRP, ESR, WBC, Neutrophil %, Platelet count, RDW) within 6 months of first AF mention in the EHR. Polygenic risk scores (PRS) for AF and cytokine levels were used to assess genetic liability of clusters to AF and inflammation, respectively. Clinical outcomes were collected from EHR up to the last medical contact. Results: The analysis included 23,271 subjects with AF, of which 6,023 had available genome-wide genotyping. The machine learning algorithm identified 3 phenotypic clusters that were distinguished by increasing prevalence of comorbidities, particularly renal dysfunction, and coronary artery disease. Polygenic liability to AF across clusters was highest in the low comorbidity cluster. Clinically measured inflammatory biomarkers were highest in the high comorbid cluster, while there was no difference between groups in genetically predicted levels of inflammatory biomarkers. Subgroup assignment was associated with multiple clinical outcomes including mortality, stroke, bleeding, and use of cardiac implantable electronic devices after AF diagnosis. Conclusion: Patient subgroups identified by unsupervised clustering were distinguished by comorbidity burden and associated with risk of clinically important outcomes. Polygenic liability to AF across clusters was greatest in the low comorbidity subgroup. Clinical inflammation, as reflected by measured biomarkers, was lowest in the subgroup with lowest comorbidities. However, there were no differences in genetically predicted levels of inflammatory biomarkers, suggesting associations between AF and inflammation is driven by acquired comorbidities rather than genetic predisposition.

Genetic Determinants of Body Mass Index and Fasting Glucose Are Mediators of Grade 1 Diastolic Dysfunction.

Background Early (grade 1) cardiac left ventricular diastolic dysfunction (G1DD) increases the risk for heart failure with preserved ejection fraction and may improve with aggressive risk factor modification. Type 2 diabetes, obesity, hypertension, and coronary heart disease are associated with increased incidence of diastolic dysfunction. The genetic drivers of G1DD are not defined. Methods and Results We curated genotyped European ancestry G1DD cases (n=668) and controls with normal diastolic function (n=1772) from Vanderbilt's biobank. G1DD status was explored through (1) an additive model genome-wide association study, (2) shared polygenic risk through logistic regression, and (3) instrumental variable analysis using 2-sample Mendelian randomization (the inverse-variance weighted method, Mendelian randomization-Egger, and median) to determine potential modifiable risk factors. There were no common single nucleotide polymorphisms significantly associated with G1DD status. A polygenic risk score for BMI was significantly associated with increased G1DD risk (odds ratio [OR], 1.20 for 1-SD increase in BMI [95% CI, 1.08-1.32]; P=0.0003). The association was confirmed by the inverse-variance weighted method (OR, 1.89 [95% CI, 1.37-2.61]). Among the candidate mediators for BMI, only fasting glucose was significantly associated with G1DD status by the inverse-variance weighted method (OR, 4.14 for 1-SD increase in fasting glucose [95% CI, 1.55-11.02]; P=0.005). Multivariable Mendelian randomization showed a modest attenuation of the BMI association (OR, 1.84 [95% CI, 1.35-2.52]) when adjusting for fasting glucose. Conclusions These data suggest that a genetic predisposition to elevated BMI increases the risk for G1DD. Part of this effect may be mediated through altered glucose homeostasis.

Heart Failure Risk Associated With Rheumatoid Arthritis-Related Chronic Inflammation.

Background Inflammation may contribute to incident heart failure (HF). Rheumatoid arthritis (RA), a prototypic inflammatory condition, may serve as a model for understanding inflammation-related HF risk. Methods and Results Using the Vanderbilt University Medical Center electronic health record, we retrospectively identified 9889 patients with RA and 9889 control patients without autoimmune disease matched for age, sex, and race. Prevalent HF at entry into the electronic health record or preceding RA diagnosis was excluded. Incident HF was ascertained using International Classification of Diseases, Ninth Revision (ICD-9), codes and medications. Over 177 566 person-years of follow-up, patients with RA were at 21% greater risk of HF (95% CI, 3-42%) independent of traditional cardiovascular risk factors. Among patients with RA, higher CRP (C-reactive protein) was associated with greater HF risk (P<0.001), while the anti-inflammatory drug methotrexate was associated with ≈25% lower HF risk (P=0.021). In a second cohort (n=115) of prospectively enrolled patients with and without RA, we performed proteomics and cardiac magnetic resonance imaging to discover circulating markers of inflammation associated with cardiac structure and function. Artemin levels were higher in patients with RA compared with controls (P=0.009), and higher artemin levels were associated with worse ventricular end-systolic elastance and ventricular-vascular coupling ratio (P=0.044 and P=0.031, respectively). Conclusions RA, a prototypic chronic inflammatory condition, is associated with increased risk of HF. Among patients with RA, higher levels of CRP were associated with greater HF risk, while methotrexate was associated with lower risk.