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Proteomics-based clustering outperforms clinical clustering in identifying people with heart failure with distinct outcomes
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Proteomics-based clustering outperforms clinical clustering in identifying people with heart failure with distinct outcomes
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Proteomics-based clustering outperforms clinical clustering in identifying people with heart failure with distinct outcomes
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Journal Article
Proteomics-based clustering outperforms clinical clustering in identifying people with heart failure with distinct outcomes
2025
Overview
Background
Heart failure (HF) clustering typically relies on clinical characteristics which may not reflect underlying pathophysiology relevant for personalized medicine. We aimed to identify plasma protein profiles of HF patients with reduced ejection fraction (HFrEF).
Methods
Using latent class analysis, we derived clusters based on 1) clinical characteristics, and 2) proteomics (SomaScan) from 379 HFrEF patients (median age 64 years [Q1 56; Q3 72], 73% male). Survival analysis assessed associations with major cardiovascular (CV) events (HF hospitalization, CV death, or advanced therapy), HF hospitalization, CV death, and all-cause mortality. Associations were validated in 511 external patients (median age 72 years [Q1 63; Q3 79], 70% male). We identified differentially expressed proteins and explored whether proteins are targets of developmental or approved drugs.
Results
We show that clinical clustering identifies three patient clusters without distinct disease progression. Contrary to this, clustering based on plasma proteomics identifies three patient clusters with clear differences in disease, which are validated in the external cohort. The slowly progressing cluster 1 includes younger patients with fewer comorbidities, while the rapidly progressing cluster 3 consists of older patients with more atrial fibrillation and renal failure, and the hospitalization cluster 2 is intermediate in many characteristics. Medication use is similar across clusters. Relative to cluster 1, patients in cluster 2 have an increased risk of major CV events (HR 2.31, 95%CI 1.23; 4.36) and HF hospitalization (HR 2.30, 95%CI 1.10; 4.78). Patients in cluster 3 experienced increased event rates of major CV events (HR 5.84), HF hospitalization (6.50), CV death (8.58), and all-cause mortality (5.07). Twelve proteins are differentially expressed across the identified clusters, including druggable CD2, GDF-15, ABO, IGFBP-1, IGFBP-2, and RNase1.
Conclusions
Proteomics-based clustering identifies three HFrEF clusters associated with distinct outcomes that remain undetected using only clinical characteristics.
Plain language summary
Heart failure affects millions of people worldwide, but symptoms and disease course varies greatly. People are often grouped based on basic clinical characteristics, which may miss important biological differences. In this study, we analyse blood proteins from people with heart failure and compare grouping based on these to a grouping based on clinical characteristics. We identify three biological groups of people with heart failure, and each group has a different future risk of hospitalization and death. The results are confirmed in an independent patient group. Our findings suggest that protein profiling can reveal hidden disease subtypes, which could help tailor treatments and improve outcomes for heart failure patients. We also identify proteins that could provide promising drug targets for specific patient groups.
van Vugt, She, Kardys et al. derive and validate a publicly available clustering algorithm based on data to identify HFrEF patient clusters. Proteomics-based clustering reveals three distinct groups with markedly different risks of hospitalization, cardiovascular events, and mortality, undetected by clinical data alone.
