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Two clusters of biomarkers were found to predict cardiovascular mortality, hospitalization related to heart failure (HF), or aborted cardiac arrest in patients with HF with preserved ejection fraction (HFpEF), according to a study published in the Journal of the American College of Cardiology.
The data and biosamples from 379 patients with HFpEF who participated in the multicenter, randomized TOPCAT trial (Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist; ClinicalTrials.gov identifier: NCT00094302) were analyzed. A total of 49 plasma biomarkers were measured to assess their value in predicting outcomes of HFpEF (ie, a composite of cardiovascular death, HF-related hospitalization, or aborted cardiac arrest).
Chosen protein analytes represented physiological processes associated with cardiovascular disease, as well as downstream effects (eg, atherothrombosis, angiogenesis, extracellular matrix turnover, cardiomyocyte injury, inflammation, adipocyte signaling and calcification/mineral metabolism).
A total of 6 dominant biomarker clusters were observed in the TOPCAT cohort, among which 2 large biomarker clusters were identified. Biomarkers of inflammation, fibrosis/tissue remodeling, liver fibrosis, and renal injury/dysfunction were included in these clusters.
Biomarkers that were predictive of the composite outcome included fibroblast growth factor-23, osteoprotegerin, tumor necrosis factor-alpha, soluble tumor necrosis factor-receptor, interleukin-6, YKL-40, fatty acid binding protein-4, growth differentiation factor-15, angiopoietin-2, matrix metalloproteinase-7, ST-2, and N-terminal pro–B-type natriuretic peptide.
A multimarker predictive model for the composite outcome was created using a tree-based pipeline optimizer platform. This model was then validated in an independent cohort of 156 patients with HFpEF who were enrolled in the Penn Heart Failure Study (PHFS). Over a median 2.83-year follow-up period, 69 patients in the PHFS cohort experienced the composite outcome.
In the machine learning model, a combination of biomarkers was predictive of the composite outcome in the independent PHFS group (standardized hazard ratio, 2.74; 95% CI, 1.93-3.90; P <.0001). The machine learning model, when added to the Meta-Analysis Global Group in Chronic Heart Failure Risk Score, was found to improve risk prediction of the composite outcome in the TOPCAT cohort.
Study limitations include the lack of information on tissue origins for the majority of circulating biomarkers, which raises uncertainty regarding whether these markers reflect systemic vs regional pathological responses.
“Our findings advance our understanding of circulating biomarker profiles in HFpEF and suggest that multimarker approaches can be implemented for enhancing risk stratification in this condition,” concluded the investigators.
Disclosure: This clinical trial was supported by Bristol-Myers Squibb. Several study authors declared affiliations with the pharmaceutical industry. Please see the original reference for a full list of authors’ disclosures.
Reference
Chirinos JA, Orlenko A, Zhao L, et al. Multiple plasma biomarkers for risk stratification in patients with heart failure and preserved ejection fraction. J Am Coll Cardiol. 2020;75(11):1281-1295.
