Abstract 4123872: Unsupervised clustering analysis of large-scale proteomics data identifies molecular phenogroups of heart failure in a population-based cohort

Background: Heart failure (HF) is a heterogeneous syndrome with high mortality. Previous work characterizing HF subgroups primarily relied on clinical data to cluster patients, an approach constrained by the boundaries of documented clinical features. Clustering based on biomarker data, such as proteomics, may provide insights into underlying molecular processes. However, few studies have used this approach, and all used targeted cardiovascular proteomics panels and a restricted HF ejection fraction group. Hypothesis: We used unsupervised clustering analysis to characterize molecular phenogroups in a population-based HF cohort using a large-scale array of circulating plasma proteins. We hypothesized that distinct proteomic-defined phenogroups would exhibit differences in clinical characteristics, mortality, and protein expression. Methods: We measured 7151 SOMAmers (Slow off-rate modified aptamers) targeting human proteins via the SomaLogic 7K SomaScan assay in 1351 HF patients. After feature selection, 831 SOMAmers were used in k-means clustering of patients via R package “ConsensusClusterPlus.” Phenogroups were compared for clinical characteristics, all-cause mortality, and protein expression. Results: Three phenogroups were identified, exhibiting different clinical characteristics ( Table 1 ) and survival probabilities (5-year survival probability in phenogroup 1: 65% [61%, 68%], phenogroup 2: 45% [40%, 51%], and phenogroup 3: 26% [22%, 30%]). In covariate-adjusted Cox models, membership in phenogroups 2 and 3 was significantly associated with increased rate of mortality compared to phenogroup 1. Phenogroups did not differ by ejection fraction or New York Heart Association class. Pathway overrepresentation analysis of differentially expressed SOMAmers identified pathways associated with inflammatory response and immune cell trafficking. Conclusions: Unsupervised clustering analysis using large-scale proteomics data identified three HF phenogroups with different proteomic signatures, clinical characteristics, and outcomes. Future work is necessary to elucidate how molecular differences contribute to heterogeneity in HF to improve risk stratification beyond current recommended clinical guidelines.