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ABSTRACT Background Early catheter ablation for atrial fibrillation (AF) improves outcomes, but the optimal timing, especially in heart failure (HF) subtypes, remains unclear. Objective To evaluate the impact of ablation timing on outcomes in patients with HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF). Methods We used the TriNetX database (2010–2024) to identify HF patients with AF who underwent ablation. Patients were classified as early ablation (≤ 1 year of first AF diagnosis) or delayed ablation (1–3 years), with propensity score matching (PSM) applied to balance the baseline characteristics. The primary outcome was AF recurrence, defined by repeat ablation, direct current cardioversion (DCCV), or antiarrhythmic drug (AAD) use after a 3‐month blanking period. Secondary outcomes included stroke, hospitalization, and mortality over 5 years. Results After PSM, delayed ablation was associated with higher AF recurrence in both HFrEF (HR = 1.28, p < 0.001) and HFpEF (HR = 1.21, p = 0.02), as seen in multivariate analysis. In HFrEF, delayed ablation was associated with higher incidence of re‐do ablation, AAD use, and DCCV, while in HFpEF, higher recurrence with delayed ablation was associated with increased AAD use. Both cohorts had similar stroke and mortality rates between early and delayed ablation. Conclusions Delayed ablation in HFrEF and HFpEF was linked to higher AF recurrence, driven by increased redo ablations, DCCV, AAD use in HFrEF, and higher AAD use in HFpEF. Stroke, HF hospitalization and mortality rates were similar, suggesting earlier ablation may enhance rhythm control over other outcomes.

Background Admixture mapping can be leveraged to identify genomic loci associated with disease traits in admixed individuals such as Hispanic Americans. Methods and Results We characterized fine‐scale population structure of participants (n=105 108) in the multiethnic eMERGE‐III (electronic Medical Records and Genomics) cohort, using reference panels from the 1000 Genomes Project and a Native American cohort. We defined “genetically admixed Hispanic Americans” as those having ≥10% Native American genetic ancestry (n=5025), in addition to European (specifically, Iberian) and African genetic ancestry. This population descriptor was subsequently applied to 3 other cohorts: the MESA (Multi‐Ethnic Study of Atherosclerosis; n=1176), WHI (Women’s Health Initiative; n=2873), and the HCHS/SOL (Hispanic Community Health Study/Study of Latinos; n=7804). We inferred local ancestry along the genome in genetically admixed Hispanic Americans in each cohort using RFMix2 with phased haplotypes from SHAPEIT2. Local ancestry proportions were used for admixture mapping of lipid traits (low‐density lipoprotein cholesterol, high‐density lipoprotein cholesterol, total cholesterol, and triglycerides) using extended linear mixed models that incorporated multiple or single ancestries. After accounting for fixed‐effect covariates and random effects, ancestry proportions at the following loci were associated with lipid traits in the HCHS/SOL cohort: 11q12.2 ( P =6.8×10 −7 ) with low‐density lipoprotein cholesterol, 2p23.3 ( P =5.2×10 −7 ) with total cholesterol, and 11q23.3 ( P =7.3×10 −12 ) with triglycerides. The LDL cholesterol and triglyceride loci replicated in at least one independent cohort, whereas the total cholesterol locus showed suggestive evidence for replication in the eMERGE cohort ( P =0.073). Native American (NAMR) ancestry appeared to drive the LDL‐C association, whereas both NAMR and Iberian population in Spain (IBS) ancestries contributed to the triglyceride signal. Conclusions Admixture mapping in genetically admixed Hispanic Americans identified genomic loci influencing lipid traits and the different ancestral origins driving these associations.

Background: Sudden cardiac death (SCD) is a leading cause of mortality in the U.S., with emerging evidence pointing to racial disparities and the impact of social vulnerability. Yet, geographic patterns integrating race and county-level vulnerability remain underexplored. Objective: To identify U.S. counties where socially vulnerable communities, stratified by race/ethnicity, experience disproportionate burdens of SCD-related mortality. Methods: We analyzed county-level data (1999-2020), integrating age-adjusted mortality rates (AAMR) for SCD with the 2020 CDC Social Vulnerability Index (SVI). Counties were categorized into SVI quartiles (Q1: Low to Q4: High Social Vulnerability). Analyses were stratified by race/ethnicity: Black or African American, Hispanic/Latino, White, American Indian/Alaska Native (AI/AN), and Asian/Pacific Islander (API). We used RStudio (v2023.06.1) for data cleaning, ANOVA testing, and descriptive analysis. “Unreliable” mortality values were handled as missing or set to zero, with only valid AAMR > 0 included. Hotspot counties were identified by selecting the top decile of AAMR SCD within Q4 SVI counties. Results: Significant disparities emerged across SVI quartiles for Black (p = 0.0003), Hispanic (p < 0.0001), and White (p < 0.0001) groups. Mean AAMR more than doubled from low to high SVI counties in Black communities (17.0 to 39.4 per 100,000). White and Hispanic groups showed smaller but consistent increases. API (p = 0.60) or AI/AN (p = 0.63) groups showed no significant trends. Madison Parish, LA appeared in the top-10 highest-burden counties across all groups. Black populations faced extreme rates in some SVI counties (e.g., Madison Parish, LA: 351.4 per 100,000), while API groups had low AAMR even in vulnerable areas. Data misclassification limited interpretation in the Hispanic group. Conclusions: Black, Hispanic, and White populations showed significant differences in SCD mortality tied to social vulnerability. Black populations in the most vulnerable areas experienced more than twice the rate of mortality compared to those in less vulnerable areas, suggesting a strong correlation between social vulnerability and SCD mortality in Black communities. API and AI/AN groups showed no significant associations, likely due to small sample sizes. Madison Parish, LA reflects the compounded impact of structural disadvantage. Findings support targeted interventions in socially and racially vulnerable counties to reduce preventable cardiac deaths.

Background: Chronic kidney disease (CKD) has been linked to elevated inflammatory biomarkers, yet few studies have evaluated these associations using iothalamate-based measures of kidney function, highlighting the need for further research. Research question: Is CKD associated with elevated inflammatory markers when defined by measured glomerular filtration rate (mGFR) and urinary albumin-to-creatinine ratio (UACR)? Methods: Participants from the Genetic Epidemiology Network of Arteriopathy (GENOA) cohort were included if they had data available for at least one inflammatory marker at visit 2. 17 inflammatory markers were assessed, including C-reactive protein (CRP), interleukin-6 (IL-6), IL-18, tumor necrosis factor receptors 1 and 2 (TNFR1, TNFR2), serum amyloid A, intercellular and vascular adhesion molecules (ICAM, VCAM), E-selectin, P-selectin, monocyte chemoattractant protein (MCP), myeloperoxidase (MPO), receptor for advanced glycation end products (RAGE), matrix metalloproteinases 2 and 9 (MMP-2, MMP-9), and tissue inhibitors of metalloproteinases 1 and 2 (TIMP-1, TIMP-2). Kindey function was assessed using the urinary clearance of iothalamate. CKD was defined as mGFR < 60 mL/min/1.73 m 2 or UACR ≥ 30 mg/g. To further assess whether associations persisted without albuminuria, we also defined CKD solely by mGFR < 60 mL/min/1.73 m 2 . Generalized estimating equations (GEE) were used to assess associations between CKD status and inflammatory markers, accounting for familial clustering. Exponentiated beta coefficients from GEE models were used to present geometric mean (GM) ratios of each biomarker by CKD status, adjusted for demographics, lifestyle factors, cardiovascular risk factors, and medication use. Results: Among 1,010 participants (mean age 61 ± 10 years; 67% women; 48% Black adults), 207 were classified as having CKD. After adjusting for potential confounders, individuals with CKD had significantly higher levels of hsCRP, VCAM, TNFR1, and TNFR2 compared to those without CKD (GM ratios > 1; p < 0.05), while the remaining inflammatory markers showed no significant differences ( Figure 1 ). Importantly, these elevations remained significant when CKD was defined by mGFR alone ( Figure 2 ). Conclusion: Our findings suggest that CKD, defined by mGFR, is associated with specific inflammatory biomarkers, underscoring the need for mechanistic studies to investigate these associations.

Background: Susceptibility to cardiometabolic diseases (CDs) is shaped by genetic, socio-environmental, and lifestyle factors. We evaluated the associations of disease-specific polysocial risk scores (PSSs)— derived from social determinants of health (SDOH) and lifestyle factors—and genetic risk factors with six CDs, across self-identified race/ethnicity (SIRE) groups in the eMERGE IV cohort. Methods: The six CDs included: atrial fibrillation, coronary heart disease, chronic kidney disease, hyperlipidemia, obesity, and type 2 diabetes. Disease-specific PSSs were developed using machine-learning models based on 65 SDOH and lifestyle factors. Participants were grouped based on their SIRE as Asian, Black, Hispanic/Latino, and White. We evaluated PSS distributions across SIRE groups. Genetic risk was assessed through disease-specific polygenic risk scores (PRSs), monogenic etiologies, and family history (FamHx). High PSS and high PRS were defined as the top 5th percentile. Associations were tested using multivariable logistic regression; model performance was assessed using the C-statistic. Results: Among 20178 participants (50±15 years, 68% female, 40% non-White), the most frequently associated PSS components were physical activity, sleep, self-perceived health, and income. PSS distributions varied across SIRE groups, with Whites having the lowest and Black and Hispanic/Latinos the highest scores for most CDs (Figure 1). All PSSs were significantly associated with their respective CDs, independent of confounders (OR per 1 SD increase: 1.19–2.68; Figure 2-A). No significant interactions were observed between PSSs and PRSs or monogenic etiologies. However, significant interactions were noted between PSSs and FamHx for CKD and obesity. PSS and PRS effects on the odds of CDs were independent (Figure 2-B) and additive (Figure 2-C). Incorporating PSSs into models that included clinical and genetic risk factors improved predictive performance of risk prediction models (Figure 3). Conclusion: PSSs, reflecting socio-environmental and lifestyle disadvantage, varied significantly across SIRE groups and were associated with risk of six CDs, independent of and additive to PRS. Given the substantial variation in socio-environmental and lifestyle risks across SIRE groups, integrating these factors into risk prediction models for CDs may help address health disparities and enhance preventive efforts, especially in marginalized populations.

Background: We investigated the impact of genetic risk factors for coronary heart disease (CHD) -polygenic risk score (PRS), familial hypercholesterolemia (FH), and family history (FamHx)- on CHD risk estimates, across the age spectrum, in two diverse cohorts of US adults -eMERGE IV (eIV) and All of Us (AoU). Methods: CHD was defined as myocardial infarction, unstable angina, and coronary revascularization. Self-identified race/ethnicity (SIRE) was used as a population descriptor. We calculated a PRS for CHD (PRS CHD , PGS004698), ascertained FH as the presence of pathogenic/likely pathogenic variants in FH genes, and defined FamHx as early-onset CHD in first-degree relatives. We employed Pooled Cohort Equations (PCE) to estimate the 10y risk of CHD for adults ≥40y and modeled the association of conventional risk factors with CHD in adults <40y. AoU served as the training set, and eIV as the testing set. We analyzed the impact of PRS CHD and FamHx on CHD risk estimates by a) using multivariable logistic regression and Cox proportional hazard models, assessing discrimination and the extent of risk reclassification; and b) net benefit analysis and decision curves to assess the performance of prediction models across actionable thresholds. Results: We analyzed data for 19348 participants from eIV (age 50±15, 68% female, 41% non-White) and 239645 participants from AoU (age 55±17, 61% female, 48% non-White). Genetic risk factors were significantly associated with CHD. PRS CHD performance varied by SIRE groups, while FamHx was consistent. The effects of PRS CHD and FamHx on CHD were independent and additive (Figure 1). In adults ≥40y, incorporating PRS CHD and FamHx into PCE improved discrimination (C-statistic increased from 0.719 to 0.753; P -diff=9.1×10 -3 , Figure 2) and reclassified risk in 19% and 20% of participants at the 7.5% and 10% 10y CHD risk thresholds, respectively. Between the 7.5% and 10% 10y CHD risk thresholds, incorporating PRS CHD and FamHx into the PCE improved the net benefit of the risk prediction models across White, Black, and Hispanic/Latino groups (Figure 3). Conclusion: PRS CHD and FamHx were independently and additively associated with CHD in two large diverse cohorts in the US. Incorporating PRS CHD and FamHx into PCE improved risk discrimination, reclassified risk in a significant portion of participants, and improved net benefit of the PCE across all three major SIRE groups, motivating the addition of these factors to clinical risk calculators.

Introduction/Background: CHIP is a risk factor for cardiovascular (CV) disease, cancer, and all-cause mortality. Previous work has shown that CHIP, and particularly larger CHIP clones, are associated with adverse CV outcomes, yet the molecular pathways through which CHIP impacts CV risk are poorly defined. Hypothesis/Research Question: We hypothesize that the integration of whole blood transcriptomics and methylomics will provide novel insights into the pathophysiology of CHIP. Methods/Approach: Whole blood DNA methylation profiling, transcriptomics, and whole exome sequencing with CHIP calling for variant allele frequencies (VAF) of ≥2% (CHIP) and ≥10% (large CHIP) were performed for 507 ISCHEMIA and ISCHEMIA-CKD participants with moderate-severe ischemia. We identified transcriptomic and methylomic differences between participants with CHIP and large CHIP vs no CHIP using DESeq2 and limma, adjusted for age, sex, and race/ethnicity. Gene set enrichment analysis (GSEA) and probe set enrichment analysis (PSEA) were performed to identify pathway-level alterations in transcription and methylation, respectively. Results/Data: Clinical characteristics of study participants are described in Fig 1A . Compared to no CHIP (n=391), transcriptomics identified 6 differentially expressed genes (DEGs) in CHIP (n=116) and 27 DEGs in large CHIP (n=35) (p-adj<0.05; abs(logFC)>0.25) (Fig 1B) . Compared to no CHIP, methylation identified no differentially methylated probes in CHIP and 6 in large CHIP (padj<0.20, abs(logFC)>0.03). GSEA identified 137 pathways significantly different in both CHIP and large CHIP vs. no CHIP (padj<0.05), while PSEA identified 724 and 2356 pathways (padj<0.20), respectively. Given its stronger relationship with methylation and transcription, downstream analyses focused on large CHIP. Integrating these data, we found 58 pathways to be both hypomethylated and transcriptionally upregulated in large CHIP, including azurophil granule-related pathways implicated in neutrophil degranulation (Fig 1C) . Further investigation into the gene-probe pairs driving the azurophil granule pathway enrichment in large CHIP revealed hypomethylation and increased transcription of genes implicated in neutrophil extracellular trap formation, including ELANE, PTRN3, AZU1, and CTSG (Fig 1D) . Conclusions: Integration of the methylome and transcriptome suggests large CHIP is linked to neutrophil-mediated immune pathways in patients with stable coronary artery disease.

Background: Early catheter ablation for atrial fibrillation (AF) improves outcomes, but the optimal timing, especially in heart failure (HF) subtypes, remains unclear. Objective: To evaluate the impact of ablation timing on outcomes in patients with HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF). Methods: We used the TriNetX database (2010–2024) to identify HF patients with AF who underwent ablation. Patients were classified as early ablation (≤1 year of first AF diagnosis) or delayed ablation (1–3 years), with propensity score matching (PSM) applied to balance the baseline characteristics. The primary outcome was AF recurrence, defined by repeat ablation, direct current cardioversion (DCCV), or antiarrhythmic drug (AAD) use after a 3-month blanking period. Secondary outcomes included stroke, hospitalization, and mortality over 5 years. Results: After PSM, delayed ablation was associated with higher AF recurrence in both HFrEF (HR 1.25, p < 0.001) and HFpEF (HR 1.27, p = 0.01), as seen in multivariate analysis. In HFrEF, delayed ablation was associated with higher incidence of re-do ablation, AAD use, and DCCV, while in HFpEF, higher recurrence with delayed ablation was associated with increased AAD use. Both cohorts had similar stroke and mortality rates between early and delayed ablation. Conclusions: Delayed ablation in HFrEF and HFpEF was linked to higher AF recurrence, driven by increased redo ablations, DCCV, AAD use in HFrEF, and higher AAD use in HFpEF. Stroke and mortality rates were similar, suggesting earlier ablation may enhance rhythm control without affecting long-term survival.

Background: Hypertension and systemic inflammation are independently associated with adverse cardiovascular outcomes. While hypertension is a known risk factor for left ventricular (LV) hypertrophy, the role of inflammation in LV structural changes remains unclear. It is also uncertain whether concomitant inflammation and hypertension ("inflammatory hypertension") exacerbate LV hypertrophy. Research Question: Is inflammatory hypertension associated with increased LV mass index (LVMI) compared to hypertension without inflammation? Methods: We analyzed baseline data from Black adults enrolled in the Genetic Epidemiology Network of Arteriopathy (GENOA) cohort in Jackson, Mississippi. Inflammation was defined by high sensitivity C-reactive protein (hsCRP) >3 mg/L, and hypertension as systolic blood pressure (SBP) >130 mmHg. Participants were classified into four groups: noninflammatory-normotensive (reference group), noninflammatory-hypertensive, inflammatory-normotensive, and inflammatory-hypertensive. The primary outcome was LVMI, assessed by echocardiography. Associations between groups and LVMI were evaluated using generalized estimating equations to account for familial clustering and adjusted for covariates including age, sex, education, smoking status, body mass index, number of antihypertensive medications, statin use, cholesterol, and diabetes. Results: Among 1,027 participants (mean age 64 ± 8.5 years; 71% women), baseline LVMI differed by inflammation-hypertension status. Compared with the LVMI in noninflammatory-normotensive cohort (n=108; 74.5 g/m 2 [70.4–78.6]), significantly higher LVMI was observed in noninflammatory-hypertensive (n=343; 83.6 g/m 2 [81.3–85.9], p<0.001) and inflammatory-hypertensive groups (n=497; 84.7 g/m 2 [82.8–86.7], p<0.001). There was no statistically significant difference between inflammatory-hypertensive and noninflammatory-hypertensive groups (84.7 vs 83.6 g/m 2 )(p=0.460). The inflammatory-normotensive cohort also did not differ from the reference group (74.9 g/m 2 [70.2–79.5], p=0.901). Secondary analyses using elevated IL-6 to define inflammation yielded similar findings ( Figure 1 ). Conclusion: In individuals with inflammatory hypertension, hypertension appears to be the primary driver of LV hypertrophy. Further studies in larger, diverse populations are needed to definitively evaluate the potential additive effect of inflammatory hypertension on LV remodeling.

Introduction: Higher mtDNA-CN reflects better mitochondrial function, while lower levels indicate mtDNA release and increased inflammation. Although mtDNA-CN serves as a marker of cardiovascular health, the importance and mechanism of risk associated with mtDNA in high-risk individuals with chronic coronary artery disease (CAD) remains poorly understood. To address this gap, we leveraged the ISCHEMIA Biorepository to: (1) evaluate the association between mtDNA-CN and all-cause death (ACD); and (2) define a transcriptomic profile associated with high mtDNA-CN and ACD risk for high-risk patients with CAD. Hypothesis: Higher mtDNA-CN is associated with reduced ACD risk and inflammation in high-risk patients enrolled in the ISCHEMIA Biorepository. Methods: Low pass whole genome sequencing and whole blood RNA-Seq were performed on 594 ISCHEMIA and ISCHEMIA-CKD participants with moderate-severe ischemia. Data were processed using the NYU deciphEHR pipeline and mtDNA was quantified using mitoAnalyzer. mtDNA-CN was median split, with samples having values ≤ median classified as the 'low' group and those > median as the 'high' group. Unadjusted group differences were assessed using Chi-squared tests. Survival analysis was performed using Cox proportional hazard models, adjusted for age, sex, diabetes, eGFR, dialysis status, ischemia severity, and left ventricular ejection fraction. Adjusted differential gene expression (DGE) and gene set enrichment analysis (GSEA) were performed to explore molecular mechanisms underlying mtDNA-CN. Results: Median age was 67 (IQR: 57–78); 19% were female, 84% white, and 6% Hispanic. Hypertension (84%), diabetes (43%), and obesity (45%) were common; 26% had eGFR <60 mL/min/1.73 m 2 . Median mtDNA-CN was 153 counts per sample. After multivariate adjustment , mtDNA-CN > median was associated with a 48% reduction in ACD (aHR = 0.52, 95% CI: 0.30–0.90; P = 0.020) (Figure 1A) . DGE revealed downregulation of inflammatory genes in the mtDNA-CN group, including S100A9 (FC=-.41, p=3.32e-10) and S100A12 (FC=-.56, p=3.32e-10). Inflammatory pathways such as the inflammasome complex (NES=-2.17, p=9.32e-03) and interleukin-1 production (NES= -2.02, p=6.60e-03) were downregulated among participants in the high mtDNA-CN ( Figure 1B) . Conclusion: Higher mtDNA-CN is associated with lower risk of ACD and reduced inflammasome activity, suggesting that enhanced mitochondrial health may mitigate inflammasome-related pathology linked to ACD in patients with CAD.