Therapeutic Targets for Heart Failure Identified Using Proteomics and Mendelian Randomization

Abstract

Background: Heart failure (HF) is a highly prevalent disorder for which disease mechanisms are incompletely understood. The discovery of disease-associated proteins with causal genetic evidence provides an opportunity to identify new therapeutic targets.

Methods: We investigated the observational and causal associations of 90 cardiovascular proteins, which were measured using affinity-based proteomic assays. First, we estimated the associations of 90 cardiovascular proteins with incident heart failure by means of a fixed-effect meta-analysis of 4 population-based studies, composed of a total of 3019 participants with 732 HF events. The causal effects of HF-associated proteins were then investigated by Mendelian randomization, using cis-protein quantitative loci genetic instruments identified from genomewide association studies in more than 30 000 individuals. To improve the precision of causal estimates, we implemented an Mendelian randomization model that accounted for linkage disequilibrium between instruments and tested the robustness of causal estimates through a multiverse sensitivity analysis that included up to 120 combinations of instrument selection parameters and Mendelian randomization models per protein. The druggability of candidate proteins was surveyed, and mechanism of action and potential on-target side effects were explored with cross-trait Mendelian randomization analysis.

Results: Forty-four of ninety proteins were positively associated with risk of incident HF (P<6.0×10^-4). Among these, 8 proteins had evidence of a causal association with HF that was robust to multiverse sensitivity analysis: higher CSF-1 (macrophage colony-stimulating factor 1), Gal-3 (galectin-3) and KIM-1 (kidney injury molecule 1) were positively associated with risk of HF, whereas higher ADM (adrenomedullin), CHI3L1 (chitinase-3-like protein 1), CTSL1 (cathepsin L1), FGF-23 (fibroblast growth factor 23), and MMP-12 (matrix metalloproteinase-12) were protective. Therapeutics targeting ADM and Gal-3 are currently under evaluation in clinical trials, and all the remaining proteins were considered druggable, except KIM-1.

Conclusions: We identified 44 circulating proteins that were associated with incident HF, of which 8 showed evidence of a causal relationship and 7 were druggable, including adrenomedullin, which represents a particularly promising drug target. Our approach demonstrates a tractable roadmap for the triangulation of population genomic and proteomic data for the prioritization of therapeutic targets for complex human diseases.

Keywords: Mendelian randomization analysis; drug target prediction; heart failure; proteomics.

Figure 1.

A flow chart of the study design and a schematic illustration of cis-MR. AF indicates atrial fibrillation; BMI, body mass index; CAD, coronary artery disease; cis-MR, Mendelian randomization using cis-acting protein quantitative trait loci instruments; DBP, diastolic blood pressure; eGFR, glomerular filtration rate; GWAS, genome-wide association study; HF, heart failure; LD, linkage disequilibrium; MR, Mendelian randomization; pQTL, protein quantitative trait loci; SBP, systolic blood pressure; and T2DM, type 2 diabetes mellitus.

Figure 2.

Observational and MR estimates of protein–heart failure association. A, Circular heatmap of association from 40 proteins associated with incident heart failure in observational studies (P<0.05/83=0.0006). The 2 circular lanes refer to results from 2 analyses: (1) observational analysis and (2) cis-MRwith partially correlated instruments. Color represents direction of effect and strength of association with heart failure measured by P value. B, Forest plot of risk ratio (hazard ratio from observational analysis and odds ratio from MR analysis) from 17 proteins associated with heart failure in MR analysis (P<0.05/40=0.001). Colored dots and error bars indicate the point estimate and 95% CIs. The gray violin plots around the MR estimates illustrate the distributions of odds ratio point estimates estimated from combinations of up to 30 instrument selection parameters and 4 MR models in multiverse sensitivity analysis, with medians of distribution shown as vertical lines within the violin plot. Proteins with consistent direction of effect as indicated by multiverse sensitivity analysis are highlighted in bold and italic font. Full protein names are provided in Table S2. cis-MR indicates Mendelian randomization using cis-acting protein quantitative trait loci instruments; and MR, Mendelian randomization.

Figure 3.

Estimated effect of prioritized circulating protein levels with HF and related traits. Left, Approximate relative risks of HF per doubling circulating protein levels as estimated with meta-analysis of observational data and cis-MR. Right, A matrix of estimated causal effect size of prioritized circulating protein levels (rows) on HF and related traits (columns) from cis-MR analysis as represented by bullet points. The size of the bullet represents the magnitude of estimated causal effect measured in absolute Z score. Bullet points with a darker shade indicate associations that survived multiple testing at P value <0.0009 (α=0.05/[8 proteins*7 traits, excluding HF]). Red indicates a risk/trait-increasing effect, and blue indicates a risk/trait-decreasing effect. ADM indicates adrenomedullin; AF‚ atrial fibrillation; BMI, body mass index; CAD, coronary artery disease; CHI3L1‚ chitinase 3-like 1; CSF-1‚ colony stimulating factor 1; CTSL1‚ cathepsin L; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; FGF-23‚ fibroblast growth factor 23; Gal-3‚ galectin-3; HF, heart failure; KIM-1‚ kidney injury molecule 1; MMP-12‚ matrix metallopeptidase 12; MR, Mendelian randomization; SBP, systolic blood pressure; and T2D, type 2 diabetes.