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Heart Failure Polygenic Risk Predictions

Heart Failure Polygenic Risk Predictions


This study explores the genetic associations and risk prediction capabilities of polygenic risk scores (PRSs) derived from risk factors shared with heart failure (HF) in bi-racial populations. The risk factors considered include atrial fibrillation (AF), body mass index (BMI), coronary heart disease (CHD), systolic blood pressure (SBP), and type 2 diabetes (T2D). Using data from the Atherosclerosis Risk in Communities (ARIC) study, PRSs were constructed for these factors in White participants, and their associations with incident HF and its subtypes were assessed.


The study, conducted over 30 years with 8624 White ARIC participants, identified 1922 incident HF cases. PRSs for AF, BMI, and CHD showed significant associations with incident HF, with PRS for AF exhibiting the strongest association. Importantly, incorporating PRS for AF into the established HF risk equation improved risk prediction performance over a 10-year period.

Furthermore, the study found that PRS for AF was associated with both HF with reduced ejection fraction and HF with preserved ejection fraction. These associations and the enhanced risk prediction were replicated in ARIC study Blacks and Cardiovascular Health Study (CHS) Whites. Additionally, protein analyses revealed associations between PRS for AF and N-terminal pro-brain natriuretic peptide along with 98 other proteins.


In conclusion, PRS for AF demonstrated associations with incident HF and its subtypes, providing incremental value in risk prediction beyond established risk equations. This study highlights the potential utility of genetic risk scores in improving HF risk assessment and underscores the importance of considering genetic factors in understanding HF pathogenesis and prognosis.


Heart failure (HF) is a multifaceted condition affecting millions of American adults, with its prevalence steadily increasing. Studies have indicated a substantial genetic component to HF, with heritability estimated at 26%, implying a genetic predisposition to the disorder. Genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs) associated with HF and its risk factors like atrial fibrillation, coronary heart disease, and diabetes.


Risk prediction models incorporating clinical factors have been developed to identify individuals at heightened risk of HF onset for early intervention. Polygenic risk scores (PRSs), aggregating genetic effects from GWAS-derived SNPs, offer promise in refining disease risk prediction beyond clinical factors. While PRSs have shown efficacy in predicting outcomes like coronary heart disease and stroke, their application in HF risk prediction remains limited.


This study aimed to evaluate the added value of PRSs, derived from clinical risk factors, in predicting HF risk compared to established prediction models. Utilizing data from the Atherosclerosis Risk in Communities (ARIC) study, a longitudinal cohort with three decades of follow-up, and validating findings in the Cardiovascular Health Study (CHS), the research sought to elucidate underlying pathways associated with improved HF risk prediction using proteome data from ARIC.


By investigating the potential of PRSs to enhance HF risk prediction, this study aims to contribute valuable insights into refining disease surveillance and informing treatment strategies.


The ARIC study, consisting of 15,792 individuals primarily from Black and White ethnic backgrounds, and the CHS, focusing on men and women aged 65 years and older, are longitudinal studies aimed at investigating the origins of atherosclerosis, cardiovascular disease (CVD), and related risk factors. Enrolment for these studies occurred in various US communities between 1987 and 1990, with subsequent clinical visits over several years.


In the current analysis, participants with prevalent heart failure (HF) or lacking genetic information, relevant clinical risk factors, or HF follow-up were excluded. Incident HF was defined using hospitalization or death records, with further categorization into HF with preserved ejection fraction (HFpEF) and HF with reduced ejection fraction (HFrEF). Various clinical risk factors were assessed, including atrial fibrillation (AF), body mass index (BMI), coronary heart disease (CHD), systolic blood pressure (SBP), and type 2 diabetes (T2D).


The primary outcomes included incident HF, HFpEF, and HFrEF occurring during specific time frames, with meticulous attention to endpoint definitions and data collection methods. Statistical analyses involved constructing polygenic risk scores (PRSs) for each risk factor and assessing their associations with incident HF using Cox proportional hazards models. Additional analyses explored the predictive performance of PRSs and their associations with HF subtypes.


Furthermore, protein pathway analysis was conducted to elucidate the biological mechanisms underlying HF-associated PRSs. Linear regressions were performed to identify significant associations between PRSs and proteomic measures, followed by pathway enrichment analysis and protein-protein interaction network assessment.


These analyses were meticulously conducted using robust statistical methods and stringent criteria for statistical significance. The findings contribute valuable insights into the genetic and clinical determinants of HF risk, paving the way for improved risk prediction and personalized interventions in cardiovascular health.


The study delved into the intricacies of heart failure (HF) by examining the baseline characteristics and genetic factors among participants from two renowned cohort studies: the Atherosclerosis Risk in Communities (ARIC) study and the Cardiovascular Health Study (CHS). This comprehensive analysis provided valuable insights into the onset and progression of HF in diverse populations over extended periods.


Among ARIC study Whites, approximately one-third developed HF during the median follow-up of 27.2 years, highlighting the significant burden of this condition. Notably, individuals who developed HF exhibited a higher prevalence of comorbidities and unfavorable clinical factors, underscoring the complex interplay between cardiovascular health and other health parameters.


Similar trends were observed among ARIC study Blacks and CHS Whites, emphasizing the consistency of findings across different demographic groups. These findings contribute to a deeper understanding of HF epidemiology and underscore the need for tailored interventions based on individual risk profiles.


Polygenic risk scores (PRS) related to atrial fibrillation (AF), body mass index (BMI), and coronary heart disease (CHD) emerged as significant predictors of incident HF. The association between PRS and HF risk remained robust even after accounting for other established risk factors, highlighting the potential utility of genetic markers in risk stratification and preventive care.


Of particular interest was the PRS related to AF, which demonstrated a substantial improvement in the prediction of HF lifetime risk. Individuals in the highest quintile of AF PRS exhibited a three-fold higher risk of HF compared to those in the lowest quintile, underscoring the profound impact of genetic predisposition on cardiovascular health outcomes.


Furthermore, the study explored the association between AF PRS and HF subtypes, including HF with reduced ejection fraction (HFrEF) and preserved ejection fraction (HFpEF). The findings revealed consistent associations across different populations, shedding light on the genetic underpinnings of HF heterogeneity.


In addition to elucidating genetic risk factors, the study identified potential biological pathways underlying AF PRS, offering valuable insights into the molecular mechanisms driving HF pathogenesis. Proteins involved in cell adhesion, calcium ion binding, and molecular signaling pathways emerged as key players, providing a foundation for future research endeavors aimed at uncovering novel therapeutic targets for HF.


Overall, this study represents a significant advancement in our understanding of HF etiology and prognosis. By integrating genetic data with clinical parameters, it lays the groundwork for precision medicine approaches to HF management, with the potential to revolutionize patient care and improve outcomes in this high-risk population.


This longitudinal cohort study delved into the intricate relationship between Polygenic Risk Scores for Atrial Fibrillation (PRSAF) and the incidence of heart failure (HF), encompassing its subtypes, HF with preserved ejection fraction (HFpEF) and HF with reduced ejection fraction (HFrEF), specifically among individuals of White ethnicity. Notably, the robustness of the findings was reaffirmed through replication in independent cohorts representing both White and Black populations. 


The study’s key revelation centered on the substantial elevation in HF risk associated with PRSAF, irrespective of sex or age group. Particularly striking was the threefold increase in HF risk observed among individuals harboring the highest genetic predisposition, even after adjusting for conventional clinical risk factors. This underscores the pivotal role of genetic susceptibility in shaping HF outcomes, thus highlighting the potential utility of PRSs in refining HF risk stratification beyond conventional risk assessment tools.


Moreover, the study delved deeper into the underlying biological mechanisms linking PRSAF to HF pathogenesis through proteomic analyses. Here, the identification of specific proteins, such as NT-proBNP, ERBB1, and angiopoietin-2, provided invaluable insights into the molecular pathways implicated in HF development. Notably, NT-proBNP emerged as a key biomarker closely associated with PRSAF, underscoring its significance in HF diagnosis and prognostication. The association of ERBB1 and angiopoietin-2 with PRSAF opens up avenues for further research to elucidate their roles in HF etiology and progression.


Despite its strengths, such as its multi-cohort design and replication across diverse racial groups, the study was not without limitations. Reliance on data predominantly derived from cohorts of European ancestry may have skewed the findings, potentially overestimating the predictive performance of PRSAF. Additionally, the study’s focus on specific clinical risk factors and cohorts may limit the generalizability of its findings to broader populations.


In conclusion, this study represents a significant advancement in our understanding of the genetic underpinnings of HF and underscores the potential of PRSs in enhancing HF risk prediction. Moving forward, further research endeavors should aim to validate these findings in more diverse populations and explore the integration of PRSAF into comprehensive HF risk assessment models to inform personalized prevention and management strategies.

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