Abstract
AimsThere is a critical need for better biomarkers so that heart failure can be diagnosed at an earlier stage and with greater accuracy. The purpose of this study was to design a robust mass spectrometry (MS)‐based assay for the simultaneous measurement of a panel of 35 candidate protein biomarkers of heart failure, in blood. The overall aim was to evaluate the potential clinical utility of this biomarker panel for prediction of heart failure in a cohort of 500 patients.Methods and resultsMultiple reaction monitoring (MRM) MS assays were designed with Skyline and Spectrum Mill PeptideSelector software and developed using nanoflow reverse phase C18 chromatographic Chip Cube‐based separation, coupled to a 6460 triple quadrupole mass spectrometer. Optimized MRM assays were applied, in a sample‐blinded manner, to serum samples from a cohort of 500 patients with heart failure and non‐heart failure (non‐HF) controls who had cardiovascular risk factors. Both heart failure with reduced ejection fraction (HFrEF) patients and heart failure with preserved ejection fraction (HFpEF) patients were included in the study. Peptides for the Apolipoprotein AI (APOA1) protein were the most significantly differentially expressed between non‐HF and heart failure patients (P = 0.013 and P = 0.046). Four proteins were significantly differentially expressed between non‐HF and the specific subtypes of HF (HFrEF and HFpEF); Leucine‐rich‐alpha‐2‐glycoprotein (LRG1, P < 0.001), zinc‐alpha‐2‐glycoprotein (P = 0.005), serum paraoxanse/arylesterase (P = 0.013), and APOA1 (P = 0.038). A statistical model found that combined measurements of the candidate biomarkers in addition to BNP were capable of correctly predicting heart failure with 83.17% accuracy and an area under the curve (AUC) of 0.90. This was a notable improvement on predictive capacity of BNP measurements alone, which achieved 77.1% accuracy and an AUC of 0.86 (P = 0.005). The protein peptides for LRG1, which contributed most significantly to model performance, were significantly associated with future new onset HF in the non‐HF cohort [Peptide 1: odds ratio (OR) 2.345 95% confidence interval (CI) (1.456–3.775) P = 0.000; peptide 2: OR 2.264 95% CI (1.422–3.605), P = 0.001].ConclusionsThis study has highlighted a number of promising candidate biomarkers for (i) diagnosis of heart failure and subtypes of heart failure and (ii) prediction of future new onset heart failure in patients with cardiovascular risk factors. Furthermore, this study demonstrates that multiplexed measurement of a combined biomarker signature that includes BNP is a more accurate predictor of heart failure than BNP alone.
Highlights
Heart failure (HF) is a major global health issue, with recent estimates suggesting that there are more than 26 million HF patients worldwide
A statistical model found that combined measurements of the candidate biomarkers in addition to BNP were capable of correctly predicting heart failure with 83.17% accuracy and an area under the curve (AUC) of 0.90
An Multiple reaction monitoring (MRM)-mass spectrometry approach was employed for multiplexed measurement of 25 candidate protein biomarkers for HF
Summary
Heart failure (HF) is a major global health issue, with recent estimates suggesting that there are more than 26 million HF patients worldwide. The burden of this disease on the healthcare system is significant. There are a number of limitations with these biomarkers in the context of HF management; the biological variation of BNP or N-terminal proBNP is ~30% in chronic HF, and levels are further influenced by patient weight, comorbidities, and medications.[2] The natriuretic peptides are not useful in classifying types of HF, which is key to managing the disease effectively—especially in terms of risk stratification. The underlying mechanisms that contribute to HF remain poorly understood, and it is thought that biomarkers that reflect important pathophysiologic pathways involved in cardiovascular dysfunction would likely be of greater clinical value for diagnosis and prognosis of HF
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