Proteomic analysis of cardiorespiratory fitness for prediction of mortality and multisystem disease risks.

Despite the wide effects of cardiorespiratory fitness (CRF) on metabolic, cardiovascular, pulmonary and neurological health, challenges in the feasibility and reproducibility of CRF measurements have impeded its use for clinical decision-making. Here we link proteomic profiles to CRF in 14,145 individuals across four international cohorts with diverse CRF ascertainment methods to establish, validate and characterize a proteomic CRF score. In a cohort of around 22,000 individuals in the UK Biobank, a proteomic CRF score was associated with a reduced risk of all-cause mortality (unadjusted hazard ratio 0.50 (95% confidence interval 0.48-0.52) per 1 s.d. increase). The proteomic CRF score was also associated with multisystem disease risk and provided risk reclassification and discrimination beyond clinical risk factors, as well as modulating high polygenic risk of certain diseases. Finally, we observed dynamicity of the proteomic CRF score in individuals who undertook a 20-week exercise training program and an association of the score with the degree of the effect of training on CRF, suggesting potential use of the score for personalization of exercise recommendations. These results indicate that population-based proteomics provides biologically relevant molecular readouts of CRF that are additive to genetic risk, potentially modifiable and clinically translatable.

Volumetric brain MRI signatures of heart failure with preserved ejection fraction in the setting of dementia.

Heart failure with preserved ejection fraction (HFpEF) is an important, emerging risk factor for dementia, but it is not clear whether HFpEF contributes to a specific pattern of neuroanatomical changes in dementia. A major challenge to studying this is the relative paucity of datasets of patients with dementia, with/without HFpEF, and relevant neuroimaging. We sought to demonstrate the feasibility of using modern data mining tools to create and analyze clinical imaging datasets and identify the neuroanatomical signature of HFpEF-associated dementia. We leveraged the bioinformatics tools at Vanderbilt University Medical Center to identify patients with a diagnosis of dementia with and without comorbid HFpEF using the electronic health record. We identified high resolution, clinically-acquired neuroimaging data on 30 dementia patients with HFpEF (age 76.9 ± 8.12 years, 61% female) as well as 301 age- and sex-matched patients with dementia but without HFpEF to serve as comparators (age 76.2 ± 8.52 years, 60% female). We used automated image processing pipelines to parcellate the brain into 132 structures and quantify their volume. We found six regions with significant atrophy associated with HFpEF: accumbens area, amygdala, posterior insula, anterior orbital gyrus, angular gyrus, and cerebellar white matter. There were no regions with atrophy inversely associated with HFpEF. Patients with dementia and HFpEF have a distinct neuroimaging signature compared to patients with dementia only. Five of the six regions identified in are in the temporo-parietal region of the brain. Future studies should investigate mechanisms of injury associated with cerebrovascular disease leading to subsequent brain atrophy.

LNK/SH2B3 loss of function increases susceptibility to murine and human atrial fibrillation.

AIMS: The lymphocyte adaptor protein (LNK) is a negative regulator of cytokine and growth factor signaling. The rs3184504 variant in SH2B3 reduces LNK function and is linked to cardiovascular, inflammatory, and hematologic disorders including stroke. In mice, deletion of Lnk causes inflammation and oxidative stress. We hypothesized that Lnk-/- mice are susceptible to atrial fibrillation (AF) and that rs3184504 is associated with AF and AF-related stroke in humans. During inflammation, reactive lipid dicarbonyls are a major component of oxidative injury, and we further hypothesized that these mediators are critical drivers of the AF substrate in Lnk-/- mice. METHODS AND RESULTS: Lnk-/- or wild-type (WT) mice were treated with vehicle or 2-hydroxybenzylamine (2-HOBA), a dicarbonyl scavenger, for 3 months. Compared to WT, Lnk-/- mice displayed increased AF duration that was prevented by 2-HOBA. In the Lnk-/- atria, action potentials were prolonged with reduced transient outward K+ current, increased late Na+ current, and reduced peak Na+ current, proarrhythmic effects that were inhibited by 2-HOBA. Mitochondrial dysfunction, especially for complex I, was evident in Lnk-/- atria, while scavenging lipid dicarbonyls prevented this abnormality. Tumor necrosis factor-α (TNF-α) and interleukin-1ß (IL-1ß) were elevated in Lnk-/- plasma and atrial tissue, respectively, both of which caused electrical and bioenergetic remodeling in vitro. Inhibition of soluble TNF-α prevented electrical remodeling and AF susceptibility, while IL-1ß inhibition improved mitochondrial respiration but had no effect on AF susceptibility. In a large database of genotyped patients, rs3184504 was associated with AF, as well as AF-related stroke. CONCLUSIONS: These findings identify a novel role for LNK in the pathophysiology of AF in both experimental mice and in humans. Moreover, reactive lipid dicarbonyls are critical to the inflammatory AF substrate in Lnk-/- mice and mediate the proarrhythmic effects of pro-inflammatory cytokines, primarily through electrical remodeling.

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.

A prognostic molecular signature of hepatic steatosis is spatially heterogeneous and dynamic in human liver.

Metabolic dysfunction-associated steatotic liver disease (MASLD) prevalence is increasing in parallel with an obesity pandemic, calling for novel strategies for prevention and treatment. We defined a circulating proteome of human MASLD across ≈7000 proteins in ≈5000 individuals from diverse, at-risk populations across the metabolic health spectrum, demonstrating reproducible diagnostic performance and specifying both known and novel metabolic pathways relevant to MASLD (central carbon and amino acid metabolism, hepatocyte regeneration, inflammation, fibrosis, insulin sensitivity). A parsimonious proteomic signature of MASLD was associated with a protection from MASLD and its related multi-system metabolic consequences in >26000 free-living individuals, with an additive effect to polygenic risk. The MASLD proteome was encoded by genes that demonstrated transcriptional enrichment in liver, with spatial transcriptional activity in areas of steatosis in human liver biopsy and dynamicity for select targets in human liver across stages of steatosis. We replicated several top relations from proteomics and spatial tissue transcriptomics in a humanized "liver-on-a-chip" model of MASLD, highlighting the power of a full translational approach to discovery in MASLD. Collectively, these results underscore utility of blood-based proteomics as a dynamic "liquid biopsy" of human liver relevant to clinical biomarker and mechanistic applications.

Outcomes of Heart Transplant Donation After Circulatory Death.

BACKGROUND: Heart transplantation using donation after circulatory death (DCD) allografts is increasingly common, expanding the donor pool and reducing transplant wait times. However, data remain limited on clinical outcomes. OBJECTIVES: We sought to compare 6-month and 1-year clinical outcomes between recipients of DCD hearts, most of them recovered with the use of normothermic regional perfusion (NRP), and recipients of donation after brain death (DBD) hearts. METHODS: We conducted a single-center retrospective observational study of all adult heart-only transplants from January 2020 to January 2023. Recipient and donor data were abstracted from medical records and the United Network for Organ Sharing registry, respectively. Survival analysis and Cox regression were used to compare the groups. RESULTS: During the study period, 385 adults (median age 57.4 years [IQR: 48.0-63.7 years]) underwent heart-only transplantation, including 122 (32%) from DCD donors, 83% of which were recovered with the use of NRP. DCD donors were younger and had fewer comorbidities than DBD donors. DCD recipients were less often hospitalized before transplantation and less likely to require pretransplantation temporary mechanical circulatory support compared with DBD recipients. There were no significant differences between groups in 1-year survival, incidence of severe primary graft dysfunction, treated rejection during the first year, or likelihood of cardiac allograft vasculopathy at 1 year after transplantation. CONCLUSIONS: In the largest single-center comparison of DCD and DBD heart transplantations to date, outcomes among DCD recipients are noninferior to those of DBD recipients. This study adds to the published data supporting DCD donors as a safe means to expand the heart donor pool.

Identifying Patients With Peripheral Artery Disease Using the Electronic Health Record: A Pragmatic Approach.

BACKGROUND: Peripheral artery disease (PAD) is underdiagnosed due to poor patient and clinician awareness. Despite this, no widely accepted PAD screening is recommended. OBJECTIVES: The authors used machine learning to develop an automated risk stratification tool for identifying patients with a high likelihood of PAD. METHODS: Using data from the electronic health record (EHR), ankle-brachial indices (ABIs) were extracted for 3,298 patients. In addition to ABI, we extracted 60 other patient characteristics and used a random forest model to rank the features by association with ABI. The model identified several features independently correlated with PAD. We then built a logistic regression model to predict PAD status on a validation set of patients (n = 1,089), an external cohort of patients (n = 2,922), and a national database (n = 2,488). The model was compared to an age-based and random forest model. RESULTS: The model had an area under the curve (AUC) of 0.68 in the validation set. When evaluated on an external population using EHR data, it performed similarly with an AUC of 0.68. When evaluated on a national database, it had an AUC of 0.72. The model outperformed an age-based model (AUC: 0.62; P < 0.001). A random forest model with inclusion of all 60 features did not perform significantly better (AUC: 0.71; P = 0.31). CONCLUSIONS: Statistical techniques can be used to build models which identify individuals at high risk for PAD using information accessible from the EHR. Models such as this may allow large health care systems to efficiently identify patients that would benefit from aggressive preventive strategies or targeted-ABI screening.

GWAS of random glucose in 476,326 individuals provide insights into diabetes pathophysiology, complications and treatment stratification.

Conventional measurements of fasting and postprandial blood glucose levels investigated in genome-wide association studies (GWAS) cannot capture the effects of DNA variability on 'around the clock' glucoregulatory processes. Here we show that GWAS meta-analysis of glucose measurements under nonstandardized conditions (random glucose (RG)) in 476,326 individuals of diverse ancestries and without diabetes enables locus discovery and innovative pathophysiological observations. We discovered 120 RG loci represented by 150 distinct signals, including 13 with sex-dimorphic effects, two cross-ancestry and seven rare frequency signals. Of these, 44 loci are new for glycemic traits. Regulatory, glycosylation and metagenomic annotations highlight ileum and colon tissues, indicating an underappreciated role of the gastrointestinal tract in controlling blood glucose. Functional follow-up and molecular dynamics simulations of lower frequency coding variants in glucagon-like peptide-1 receptor (GLP1R), a type 2 diabetes treatment target, reveal that optimal selection of GLP-1R agonist therapy will benefit from tailored genetic stratification. We also provide evidence from Mendelian randomization that lung function is modulated by blood glucose and that pulmonary dysfunction is a diabetes complication. Our investigation yields new insights into the biology of glucose regulation, diabetes complications and pathways for treatment stratification.

Genome-wide association analysis and Mendelian randomization proteomics identify drug targets for heart failure.

We conduct a large-scale meta-analysis of heart failure genome-wide association studies (GWAS) consisting of over 90,000 heart failure cases and more than 1 million control individuals of European ancestry to uncover novel genetic determinants for heart failure. Using the GWAS results and blood protein quantitative loci, we perform Mendelian randomization and colocalization analyses on human proteins to provide putative causal evidence for the role of druggable proteins in the genesis of heart failure. We identify 39 genome-wide significant heart failure risk variants, of which 18 are previously unreported. Using a combination of Mendelian randomization proteomics and genetic cis-only colocalization analyses, we identify 10 additional putatively causal genes for heart failure. Findings from GWAS and Mendelian randomization-proteomics identify seven (CAMK2D, PRKD1, PRKD3, MAPK3, TNFSF12, APOC3 and NAE1) proteins as potential targets for interventions to be used in primary prevention of heart failure.

Multiancestry Genome-Wide Association Study of Aortic Stenosis Identifies Multiple Novel Loci in the Million Veteran Program.

BACKGROUND: Calcific aortic stenosis (CAS) is the most common valvular heart disease in older adults and has no effective preventive therapies. Genome-wide association studies (GWAS) can identify genes influencing disease and may help prioritize therapeutic targets for CAS. METHODS: We performed a GWAS and gene association study of 14 451 patients with CAS and 398 544 controls in the Million Veteran Program. Replication was performed in the Million Veteran Program, Penn Medicine Biobank, Mass General Brigham Biobank, BioVU, and BioMe, totaling 12 889 cases and 348 094 controls. Causal genes were prioritized from genome-wide significant variants using polygenic priority score gene localization, expression quantitative trait locus colocalization, and nearest gene methods. CAS genetic architecture was compared with that of atherosclerotic cardiovascular disease. Causal inference for cardiometabolic biomarkers in CAS was performed using Mendelian randomization and genome-wide significant loci were characterized further through phenome-wide association study. RESULTS: We identified 23 genome-wide significant lead variants in our GWAS representing 17 unique genomic regions. Of the 23 lead variants, 14 were significant in replication, representing 11 unique genomic regions. Five replicated genomic regions were previously known risk loci for CAS (PALMD, TEX41, IL6, LPA, FADS) and 6 were novel (CEP85L, FTO, SLMAP, CELSR2, MECOM, CDAN1). Two novel lead variants were associated in non-White individuals (P<0.05): rs12740374 (CELSR2) in Black and Hispanic individuals and rs1522387 (SLMAP) in Black individuals. Of the 14 replicated lead variants, only 2 (rs10455872 [LPA], rs12740374 [CELSR2]) were also significant in atherosclerotic cardiovascular disease GWAS. In Mendelian randomization, lipoprotein(a) and low-density lipoprotein cholesterol were both associated with CAS, but the association between low-density lipoprotein cholesterol and CAS was attenuated when adjusting for lipoprotein(a). Phenome-wide association study highlighted varying degrees of pleiotropy, including between CAS and obesity at the FTO locus. However, the FTO locus remained associated with CAS after adjusting for body mass index and maintained a significant independent effect on CAS in mediation analysis. CONCLUSIONS: We performed a multiancestry GWAS in CAS and identified 6 novel genomic regions in the disease. Secondary analyses highlighted the roles of lipid metabolism, inflammation, cellular senescence, and adiposity in the pathobiology of CAS and clarified the shared and differential genetic architectures of CAS with atherosclerotic cardiovascular diseases.

Association of Genetically Predicted Fibroblast Growth Factor-23 with Heart Failure: A Mendelian Randomization Study.

BACKGROUND AND OBJECTIVES: Elevated fibroblast growth factor-23 (FGF23) has been consistently associated with heart failure, particularly heart failure with preserved ejection fraction, among patients with CKD and in the general population. FGF23 may directly induce cardiac remodeling and heart failure. However, biases affecting observational studies impede robust causal inferences. Mendelian randomization leverages genetic determinants of a risk factor to examine causality. We performed a two-sample Mendelian randomization to assess causal associations between FGF23 and heart failure. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Genetic instruments were genome-wide significant genetic variants associated with FGF23, including variants near PIP5K1B, RGS14, LINC01229, and CYP24A1. We analyzed data from the Heart Failure Molecular Epidemiology for Therapeutic Targets and BioVU biobanks to examine associations of the four variants with overall heart failure, heart failure with preserved ejection fraction, and heart failure with reduced and mid-range ejection fraction. We developed an eGFR polygenic risk score using summary statistics from the Chronic Kidney Disease Genetics Consortium (CKDGen) genome-wide association study of eGFR in >1 million individuals and performed stratified analyses across eGFR polygenic risk score strata. RESULTS: Genetically determined FGF23 was not associated with overall heart failure in the Heart Failure Molecular Epidemiology for Therapeutic Targets consortium (odds ratio, 1.13; 95% confidence interval, 0.89 to 1.42 per unit higher genetically predicted log FGF23) and the full BioVU sample (odds ratio, 1.32; 95% confidence interval, 0.95 to 1.84). In stratified analyses in BioVU, higher FGF23 was associated with overall heart failure (odds ratio, 3.09; 95% confidence interval, 1.38 to 6.91) among individuals with low eGFR-polygenic risk score (<1 SD below the mean), but not those with high eGFR-polygenic risk score (P interaction = 0.02). Higher FGF23 was also associated with heart failure with preserved ejection fraction among all BioVU participants (odds ratio, 1.47; 95% confidence interval, 1.01 to 2.14) and individuals with low eGFR-polygenic risk score (odds ratio, 7.20; 95% confidence interval, 2.80 to 18.49), but not those high eGFR-polygenic risk score (P interaction = 2.25 × 10-4). No significant associations were observed with heart failure with reduced and midrange ejection fraction. CONCLUSION: We found no association between genetically predicted FGF23 and heart failure in the Heart Failure Molecular Epidemiology for Therapeutic Targets consortium. In BioVU, genetically elevated FGF23 was associated with higher heart failure risk, specifically heart failure with preserved ejection fraction, particularly among individuals with low genetically predicted eGFR. PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2022_07_28_CJN00960122.mp3.

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.

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.

Genome-wide association study reveals novel genetic loci: a new polygenic risk score for mitral valve prolapse.

AIMS: Mitral valve prolapse (MVP) is a common valvular heart disease with a prevalence of >2% in the general adult population. Despite this high incidence, there is a limited understanding of the molecular mechanism of this disease, and no medical therapy is available for this disease. We aimed to elucidate the genetic basis of MVP in order to better understand this complex disorder. METHODS AND RESULTS: We performed a meta-analysis of six genome-wide association studies that included 4884 cases and 434 649 controls. We identified 14 loci associated with MVP in our primary analysis and 2 additional loci associated with a subset of the samples that additionally underwent mitral valve surgery. Integration of epigenetic, transcriptional, and proteomic data identified candidate MVP genes including LMCD1, SPTBN1, LTBP2, TGFB2, NMB, and ALPK3. We created a polygenic risk score (PRS) for MVP and showed an improved MVP risk prediction beyond age, sex, and clinical risk factors. CONCLUSION: We identified 14 genetic loci that are associated with MVP. Multiple analyses identified candidate genes including two transforming growth factor-ß signalling molecules and spectrin ß. We present the first PRS for MVP that could eventually aid risk stratification of patients for MVP screening in a clinical setting. These findings advance our understanding of this common valvular heart disease and may reveal novel therapeutic targets for intervention.

Exploration of an alternative to body mass index to characterize the relationship between height and weight for prediction of metabolic phenotypes and cardiovascular outcomes.

OBJECTIVE: Body mass index (BMI) is the most commonly used predictor of weight-related comorbidities and outcomes. However, the presumed relationship between height and weight intrinsic to BMI may introduce bias with respect to prediction of clinical outcomes. A series of analyses comparing the performance of models representing weight and height as separate interacting variables to models using BMI were performed using Vanderbilt University Medical Center's deidentified electronic health records and landmark methodology. METHODS: Use of BMI or height-weight interaction in prediction models for established weight-related cardiometabolic traits and metabolic syndrome was evaluated. Specifically, prediction models for hypertension, diabetes mellitus, low high-density lipoprotein, and elevated triglycerides, atrial fibrillation, coronary artery disease, heart failure, and peripheral artery disease were developed. Model performance was evaluated using likelihood ratio, R 2, and Somers' Dxy rank correlation. Differences in model predictions were visualized using heat maps. RESULTS: Compared to BMI, the maximally flexible height-weight interaction model demonstrated improved prediction, higher likelihood ratio, R 2, and Somers' Dxy rank correlation, for event-free probability for all outcomes. The degree of improvement to the prediction model differed based on the outcome and across the height and weight range. CONCLUSIONS: Because alternative measures of body composition such as waist-to-hip ratio are not routinely collected in the clinic clinical risk models quantifying risk based on height and weight measurements alone are essential to improve practice. Compared to BMI, modeling height and weight as independent, interacting variables results in less bias and improved predictive accuracy for all tested traits. Considering an individual's height and weight opposed to BMI is a better method for quantifying individual disease risk.

Characterizing a Clinical Trial - Representative, Real-World Population with Heart Failure with Reduced Ejection Fraction.

PURPOSE: Given known differences between real-world and clinical trial populations, we characterized demographics, clinical characteristics, and outcomes using real-world (RW) data for patients with heart failure with reduced ejection fraction (HFrEF), including those similar to subjects enrolled in an HFrEF clinical trial to better understand patient populations that could benefit from novel therapies. PATIENTS AND METHODS: Using Vanderbilt University Medical Center electronic health records (2006-2019), two RW cohorts of HFrEF patients were identified. The "Clinical Cohort" was based on a validated HFrEF algorithm and left ventricular ejection fraction (LVEF) ≤40%. The "GALACTIC-HF-like Cohort" mirrored enrollment requirements of the GALACTIC-HF clinical trial including hospitalizations, medications, laboratory values, and LVEF ≤35%. RESULTS: Median age at index for the Clinical Cohort (N = 3954) and GALACTIC-HF-like Cohort (N = 1541) were 65 and 61 years, respectively; both were 67% male and 80% white. Over half had coronary artery disease (55% Clinical vs 64% GALACTIC-HF-like); hypercholesterolemia was common (69% Clinical vs 74% GALACTIC-HF-like). Chronic kidney disease (31 vs 21%), atrial fibrillation (32 vs 29%), and cardiac resynchronization or implantable cardioverter defibrillator (26 vs 23%) were higher in the GALACTIC-HF-like Cohort. ACE inhibitor use was high in both groups but more common in the GALACTIC-HF-like Cohort (71% and 82%, respectively). Beta-blockers or loop diuretics were used by >90% of both cohorts. HF hospitalization rates were 261 (95% CI 224, 297) per 1000 person-years in Clinical versus 523 (484, 562) in GALACTIC-HF-like Cohort (median follow-up of 2.9 and 4.2 years, respectively). CONCLUSION: Approximately 40% of RW HFrEF patients met criteria for the GALACTIC-HF trial. While findings of ongoing clinical trials may be directly generalizable to this sizable proportion of patients, future trials should examine whether the majority of patients with lower prevalence of comorbidities and rate of HF hospitalization could benefit from emerging HF treatments.

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.

Arrhythmia Variant Associations and Reclassifications in the eMERGE-III Sequencing Study.

BACKGROUND: Sequencing Mendelian arrhythmia genes in individuals without an indication for arrhythmia genetic testing can identify carriers of pathogenic or likely pathogenic (P/LP) variants. However, the extent to which these variants are associated with clinically meaningful phenotypes before or after return of variant results is unclear. In addition, the majority of discovered variants are currently classified as variants of uncertain significance, limiting clinical actionability. METHODS: The eMERGE-III study (Electronic Medical Records and Genomics Phase III) is a multicenter prospective cohort that included 21 846 participants without previous indication for cardiac genetic testing. Participants were sequenced for 109 Mendelian disease genes, including 10 linked to arrhythmia syndromes. Variant carriers were assessed with electronic health record-derived phenotypes and follow-up clinical examination. Selected variants of uncertain significance (n=50) were characterized in vitro with automated electrophysiology experiments in HEK293 cells. RESULTS: As previously reported, 3.0% of participants had P/LP variants in the 109 genes. Herein, we report 120 participants (0.6%) with P/LP arrhythmia variants. Compared with noncarriers, arrhythmia P/LP carriers had a significantly higher burden of arrhythmia phenotypes in their electronic health records. Fifty-four participants had variant results returned. Nineteen of these 54 participants had inherited arrhythmia syndrome diagnoses (primarily long-QT syndrome), and 12 of these 19 diagnoses were made only after variant results were returned (0.05%). After in vitro functional evaluation of 50 variants of uncertain significance, we reclassified 11 variants: 3 to likely benign and 8 to P/LP. CONCLUSIONS: Genome sequencing in a large population without indication for arrhythmia genetic testing identified phenotype-positive carriers of variants in congenital arrhythmia syndrome disease genes. As the genomes of large numbers of people are sequenced, the disease risk from rare variants in arrhythmia genes can be assessed by integrating genomic screening, electronic health record phenotypes, and in vitro functional studies. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier; NCT03394859.

Heart failure clinical care analysis uncovers risk reduction opportunities for preserved ejection fraction subtype.

Heart failure (HF) has no cure and, for HF with preserved ejection fraction (HFpEF), no life-extending treatments. Defining the clinical epidemiology of HF could facilitate earlier identification of high-risk individuals. We define the clinical epidemiology of HF subtypes (HFpEF and HF with reduced ejection fraction [HFrEF]), identified among 2.7 million individuals receiving routine clinical care. Differences in patterns and rates of accumulation of comorbidities, frequency of hospitalization, use of specialty care, were defined for each HF subtype. Among 28,156 HF cases, 8322 (30%) were HFpEF and 11,677 (42%) were HFrEF. HFpEF was the more prevalent subtype among older women. 177 Phenotypes differentially associated with HFpEF versus HFrEF. HFrEF was more frequently associated with diagnoses related to ischemic cardiac injury while HFpEF was associated more with non-cardiac comorbidities and HF symptoms. These comorbidity patterns were frequently present 3 years prior to a HFpEF diagnosis. HF subtypes demonstrated distinct patterns of clinical co-morbidities and disease progression. For HFpEF, these comorbidities were often non-cardiac and manifested prior to the onset of a HF diagnosis. Recognizing these comorbidity patterns, along the care continuum, may present a window of opportunity to identify individuals at risk for developing incident HFpEF.