Key result
Combining novel blood-based protein biomarkers with BNP measurement improved the prediction of HF subtypes, achieving an AUC of 0.931 for HFpEF and 0.975 for HFrEF compared to BNP alone.
Why the study?
Limited echocardiography access delays heart failure diagnosis, and the utility of combining BNP with other blood-based biomarkers to predict new-onset HF subtypes remains underexplored.
Does combining novel blood-based protein biomarkers with BNP improve the diagnostic prediction of heart failure subtypes compared to BNP alone?
Observational (n=92)
No
Does combining novel blood-based protein biomarkers with BNP improve the diagnostic prediction of heart failure subtypes compared to BNP alone?
Absolute Event Rate: 0.975% vs 0.825%
Adding specific proteomic biomarkers to standard BNP testing significantly improves the diagnostic accuracy for differentiating HFpEF and HFrEF in patients with suspected heart failure.
May aid HF subtype differentiation in suspected cases; leaves open prospective validation before changing practice.
BACKGROUND: Limited access to echocardiography can delay the diagnosis of suspected heart failure (HF), which in turn postpones the initiation of optimal guideline-directed medical therapy. Although natriuretic peptides like B-type natriuretic peptide (BNP) are valuable biomarkers for diagnosing and managing HF, the utility of combining BNP with other blood-based biomarkers to predict subtypes of new-onset HF remains underexplored. OBJECTIVES: This study sought to investigate and evaluate the diagnostic significance of adding blood-based biomarkers to BNP for identifying heart failure with preserved ejection fraction (HFpEF) or reduced ejection fraction (HFrEF), with the goal of enhancing diagnostic assays beyond BNP measurements. METHODS: We identified candidate blood protein biomarkers using untargeted proteomics workflows from a cohort of individuals recruited to the STOP-HF trial who were at risk of HF and subsequently developed either HFpEF or HFrEF over time ("HF progressors"; n = 40). Candidate biomarkers were verified in an independent cohort (n = 52) from a community-based rapid access HF diagnostic clinic. The biological processes associated with these proteins were assessed, and the diagnostic values of biomarker panels were evaluated using a machine learning approach. RESULTS: Within HF progressors, we identified 3 proteins associated with HFpEF development: vascular cell adhesion protein 1 (VCAM1), insulin-like growth factor 2 (IGF2), and inter-alpha-trypsin inhibitor heavy chain 3 (ITIH3). Additionally, 4 proteins were linked to HFrEF development: C-reactive protein (CRP), interleukin-6 receptor subunit beta (IL6RB), phosphatidylinositol-glycan-specific phospholipase D (PHLD), and noelin (NOE1). These findings were verified in an independent cohort to distinguish HF subtypes from controls. Moreover, a random forest algorithm demonstrated that combining these candidate biomarkers with BNP measurement significantly improved the prediction of HF subtypes. CONCLUSIONS: We identified candidate proteins linked to HFpEF and HFrEF in a longitudinal HF progressor cohort and validated them in a community-based cohort. Adding these proteins to BNP led to a significant improvement in HF subtype prediction. Study results have clinical implications for blood-based screening of HF subtypes using panels of biomarkers, particularly in resource-limited settings.
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Karuna et al. (2025) conducted an observational in Heart failure subtypes (HFpEF and HFrEF) (n=92). Multiplexed blood-based biomarker panels (BNP plus novel proteins) vs. BNP measurement alone was evaluated on Prediction of HF subtypes (AUC). Combining novel blood-based protein biomarkers with BNP measurement improved the prediction of HF subtypes, achieving an AUC of 0.931 for HFpEF and 0.975 for HFrEF compared to BNP alone.
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