Abstract

Background: Peripheral blood somatic genetic variants such as those in clonal hematopoiesis of indeterminate potential (CHIP) increase the risk of both ischemic and non-ischemic cardiomyopathy but mechanisms beyond inflammation are unclear. Research question/Objectives: We sought to understand the prevalence and impact of CHIP and extended somatic variants (ExSV) using a new deep learning algorithm in heart failure patients on the outcome of all-cause mortality and leverage novel partition scores to identify pathways involved. Methods: CHIP and ExSV were evaluated using a novel proprietary deep learning algorithm in a heart failure cohort at Yale using whole exome sequencing and somatic variants identified were validated using UKB and TOPMED cohorts. Cox regression including somatic variants on the outcome of death was performed. Partition score analysis integrating patient demographics, co-morbidities, genomic and transcriptomic data was performed to identify traits and biological pathways implicated in heart failure Results: CHIP was highly prevalent in the 189 patient Yale heart failure cohort at 42% with mean age of 66. Fifty percent had diabetes and 46% had CAD. Using UKB and TOPMED cohorts, novel “CHIP-like” somatic variants were identified that conferred increased or decreased risk of heart failure. Some ExSV were associated with increased risk of death. Partition score assessment identified top traits associated with increased risk of heart failure, including LDL, triglycerides, diabetes, BMI and CAD and pathway analysis identified that metabolic pathways including cholesterol metabolism, glucose signaling and AMPK were implicated in the heart failure phenotype mediated by somatic variants (Table). Conclusions: CHIP and ExSV are prevalent in heart failure patients, impact the outcome of death and metabolic pathways are implicated in mediating the effects of somatic variants/CHIP in heart failure

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