Heart failure (HF) is a common condition for which the underlying processes are yet unknown. The identification of disease-associated proteins with causal genetic evidence opens the door to novel treatment targets. Researchers used affinity-based proteomic assays to evaluate the observational and causal correlations of 90 cardiovascular proteins. First, they used a fixed-effect meta-analysis of four population-based studies with a total of 3,019 people and 732 HF incidents to evaluate the correlations of 90 cardiovascular proteins with incident heart failure. Mendelian randomization was used to study the causative effects of HF-associated proteins, with cis-protein quantitative loci genetic instruments found from genomewide association studies in over 30,000 people. They used a Mendelian randomization model that accounted for linkage disequilibrium between instruments to improve the precision of causal estimates, and we tested the robustness of causal estimates using a multiverse sensitivity analysis that included up to 120 combinations of instrument selection parameters and Mendelian randomization models per protein. With a cross-trait Mendelian randomization study, the druggability of candidate proteins was assessed, and the mechanism of action and potential on-target side effects were investigated.
The incidence of incident HF was positively linked with 44 of the ninety proteins (P<6.0×10–4). Higher levels of CSF-1 (macrophage colony-stimulating factor 1), Gal-3 (galectin-3), and KIM-1 (kidney injury molecule 1) were positively associated with HF risk, while higher levels of ADM (adrenomedullin), CHI3L1 (chitinase-3-like protein 1), CTSL1 (cathepsin L1), FGF-23 (fibroblast growth factor 23), and MMP-12 (matr Therapeutics targeting ADM and Gal-3 are now being tested in clinical trials, and all of the remaining proteins, with the exception of KIM-1, were thought to be druggable.
We found 44 circulating proteins linked to incident HF, 8 of which had a causal association and seven of which were druggable, including adrenomedullin, which is a very attractive drug target. The method offered a logical path for triangulating population genomic and proteomic data to prioritize treatment targets for complicated human illnesses.
Reference:www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.121.056663
