# Spironolactone and Fibrosis in Heart Failure Risk: Machine Learning Analysis of HOMAGE Trial Plasma Proteomics

## TL;DR

This study uses machine learning to find proteins linked to spironolactone's effect on reducing heart fibrosis in patients at risk of heart failure.

## Contribution

Novel use of machine learning to identify proteins mediating spironolactone's anti-fibrotic effects in heart failure risk patients.

## Key findings

- Spironolactone reduces PICP via changes in collagen, metabolism, and immune biomarkers.
- Galectin-9 and thrombospondin-2 mediate spironolactone's effect on PICP reduction.
- Findings confirmed in patients with heart failure and preserved ejection fraction.

## Abstract

In the HOMAGE (Heart Omics in AGEing) trial, spironolactone reduced serum concentrations of procollagen Type I C‐terminal propeptide (PICP), a fibrosis biomarker, in patients at risk of heart failure. To elucidate the underlying mechanisms, multidimensional analyses including proteomics were conducted. Olink cardiovascular and inflammation panels (n = 276 proteins) were measured in plasma from 488 HOMAGE participants at baseline, 1 month, and 9 months after randomization. Proteins associated with PICP changes were identified using machine learning algorithms (MLAs). Selected candidates were further analyzed in patients with heart failure and preserved ejection fraction (Aldo‐DHF trial). Linear regression and mediation analyses assessed which MLA‐selected proteins mediated spironolactone's effects on PICP. MLAs consistently linked PICP reduction to changes in biomarkers of collagen (e.g., decreased COL1A1), fatty acid metabolism (e.g., increased FABP4), immune function (e.g., increased CCL24 and IL6RA, and decreased FLT3L), neurological function (e.g., increased DNER), cell–matrix interactions (e.g., increased galectin‐9 [GAL9] and decreased thrombospondin‐2 [THBS2]), and reduced NT‐proBNP. Mediation analysis suggested that changes in GAL9 and THBS2 were associated with spironolactone‐induced PICP reduction, which was confirmed in Aldo‐DHF patients. This study raises the hypothesis that spironolactone inhibits collagen synthesis via inflammatory, metabolic, and extracellular matrix pathways, and particularly through modulation of GAL9 and THBS2.

In HOMAGE, 276 plasma proteins were profiled using Olink panels. Machine learning algorithms identified galectin‐9 (GAL9), thrombospondin‐2 (THBS2), and NT‐proBNP as predictors of changes in procollagen type I C‐terminal propeptide (PICP) after spironolactone treatment. Decreasing THBS2 and increasing GAL9 were associated with PICP reduction in HOMAGE and Aldo‐DHF patients, suggesting a potential mediating role of these proteins in fibrosis modulation
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