Multiple Plasma Biomarkers for Risk Stratification in Patients With Heart Failure and Preserved Ejection Fraction

Abstract

Background: Better risk stratification strategies are needed to enhance clinical care and trial design in heart failure with preserved ejection fraction (HFpEF).

Objectives: The purpose of this study was to assess the value of a targeted plasma multi-marker approach to enhance our phenotypic characterization and risk prediction in HFpEF.

Methods: In this study, the authors measured 49 plasma biomarkers from TOPCAT (Treatment of Preserved Cardiac Function Heart Failure With an Aldosterone Antagonist) trial participants (n = 379) using a Multiplex assay. The relationship between biomarkers and the risk of all-cause death or heart failure-related hospital admission (DHFA) was assessed. A tree-based pipeline optimizer platform was used to generate a multimarker predictive model for DHFA. We validated the model in an independent cohort of HFpEF patients enrolled in the PHFS (Penn Heart Failure Study) (n = 156).

Results: Two large, tightly related dominant biomarker clusters were found, which included biomarkers of fibrosis/tissue remodeling, inflammation, renal injury/dysfunction, and liver fibrosis. Other clusters were composed of neurohormonal regulators of mineral metabolism, intermediary metabolism, and biomarkers of myocardial injury. Multiple biomarkers predicted incident DHFA, including 2 biomarkers related to mineral metabolism/calcification (fibroblast growth factor-23 and OPG [osteoprotegerin]), 3 inflammatory biomarkers (tumor necrosis factor-alpha, sTNFRI [soluble tumor necrosis factor-receptor I], and interleukin-6), YKL-40 (related to liver injury and inflammation), 2 biomarkers related to intermediary metabolism and adipocyte biology (fatty acid binding protein-4 and growth differentiation factor-15), angiopoietin-2 (related to angiogenesis), matrix metalloproteinase-7 (related to extracellular matrix turnover), ST-2, and N-terminal pro-B-type natriuretic peptide. A machine-learning-derived model using a combination of biomarkers was strongly predictive of the risk of DHFA (standardized hazard ratio: 2.85; 95% confidence interval: 2.03 to 4.02; p < 0.0001) and markedly improved the risk prediction when added to the MAGGIC (Meta-Analysis Global Group in Chronic Heart Failure Risk Score) risk score. In an independent cohort (PHFS), the model strongly predicted the risk of DHFA (standardized hazard ratio: 2.74; 95% confidence interval: 1.93 to 3.90; p < 0.0001), which was also independent of the MAGGIC risk score.

Conclusions: Various novel circulating biomarkers in key pathophysiological domains are predictive of outcomes in HFpEF, and a multimarker approach coupled with machine-learning represents a promising strategy for enhancing risk stratification in HFpEF.

Keywords: HFpEF; Penn Heart Failure Study; TOPCAT trial; biomarkers; fibrosis; inflammation; kidney; liver.

Figure 1.. Correlations between biomarkers.

The heatmap represents the correlation between the biomarkers. The most important biomarker clusters, derived from cluster analyses are shown.

Figure 2.. Network connectivity backbone of all measured biomarkers.

The nodes representing individual biomarkers and the edges (connections) between nodes representing the correlation coefficient between a given biomarker (node) pair.

Figure 3.

Standardized hazard ratios for examined biomarkers. The volcano plots show the standardized HRs for DHFA (one model per biomarker) in unadjusted analyses (left) and adjusted for the MAGGIC risk score (right), plotted against the Log-10 P-value. The dashed lines indicate the non-corrected (lower line) and Bonferroni-corrected (upper line) level of significance.

Figure 4.. Permutation feature importance coefficients for biomarkers in the machine-learning model.

Biomarkers are ranked according to importance.

Figure 5.. Standardized hazard ratios and 95%CIs for the risk of DHFA.

HRs for the machine learning score vs. the MAGGIC risk score are presented for non-adjusted analyses and analyses adjusted for each other, in the derivation (TOPCAT) and validation (PHFS) samples.

Figure 6.. Harrel’s concordance statistic (c index) and 95%CIs for the prediction of DHFA.

Values are shown for the derivation (TOPCAT) and validation (PHFS) samples.

Central Illustration.. Multimarker-based Machine Learning Approach For Risk Prediction in Heart Failure with Preserved Ejection Fraction.

We performed multiplex-based measurements of 49 proteins related to key biologic pathways in the TOPCAT trial. We then derived a predictive model for outcomes using machine learning. We then validated the prognostic score in a separate cohort (Penn Heart Failure Study).