Multiplexed measurement of candidate blood protein biomarkers of heart failure

Abstract

Aims: There 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 results: Multiple 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].

Conclusions: This 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.

Keywords: BNP; Biomarkers; Heart failure; MRM; Proteomics.

Figure 1

Workflow for MRM assay development and optimisation. The figure illustrates the process of MRM assay design and optimisation. Proteotypic peptides were carefully selected using Skyline and Peptide Selector software as well as data from previous in‐house MRM‐based studies and relevant publications. MRM assays were initially developed in depleted serum and further optimized in crude serum. Synthetic peptides were used to optimize parameters for low abundant peptide targets. Working assays were ultimately compiled into a single MRM method for measurement of 25 proteins. MRM, multiple reaction monitoring.

Figure 2

Performance of biomarkers and BNP in prediction of heart failure. (A) Receiver operating curve demonstrating predicate value of BNP alone (green), clinical information alone (red), BNP combined with peptides (amber), and BNP combined with peptides and clinical information (blue). ‘Clinical’ information refers risk factors: patient age, sex, and body mass index. AUC, area under the curve.

Figure 3

Survival analysis of patients with low and high LRG‐1 protein expression at baseline. CI, confidence interval.