40 A protein biomarker model for detection of cardiac arrhythmia and prediction of associated heart failure. (30th September 2020)

• Record Type: Journal Article
• Authors: Tonry, C; McDonald, K; Ledwidge, M; Herandez, B; Glezeva, N; Rooney, C; Morrissey, B; Pennington, S; Baugh, J; Watson, C
• Is Part Of: Heart. Volume 106(2020)Supplement 4
• Page Start: A27
• Page End: A27
• Publication Date: 2020-09-30
• Subjects: Heart -- Diseases -- Treatment -- Periodicals; Cardiology -- Periodicals; 616.12
• DOI: 10.1136/heartjnl-2020-ICS.40
• Languages: English
• ISSNs: 1355-6037

Abstract

Abstract : Introduction: Cardiac arrhythmia is strongly linked with heart failure (HF) and a primary cause of stroke. The condition affects around 37, 000 people in Northern Ireland although it is estimated that many thousands more remain undiagnosed. It is important to be able to diagnose cardiac arrhythmia early, so that appropriate interventions can be made to reduce risk of subsequent stroke or HF. Currently, diagnosis and management of cardiac arrhythmia is reliant on assessment of clinical risk factors, however, routine monitoring of circulating biomarkers would significantly improve accuracy for prediction of arrhythmia and associated adverse events. The aim of this study was to (i) identify protein biomarkers, which can predict cardiac arrhythmia and (ii) identify protein biomarkers that are predictive of HF in patients with arrhythmia. Methods: Multiple Reaction Monitoring mass spectrometry-based assays were developed for measurement of a selection of candidate protein biomarkers of cardiovascular injury. Assays were developed using nanoflow reverse phase C18 chromatographic ChipCube based separation, coupled to an Agilent 6460 triple quadrupole mass spectrometer. Optimised MRM assays were applied, in a sample blinded manner, for analysis of a cohort of 410 serum samples. This included 112 patients with cardiac arrhythmia as well as matched controls without cardiac arrhythmia. Results: MRM assays were established for measurement of 25 proteins. Individually, a number of the biomarker proteins show significant differential expression between patients with and without cardiac arrhythmia. An 11-protein biomarker model was identified, which was comparable to BNP in prediction of HF within the cardiac arrhythmia subset of patients (Protein panel AUC = 0.856 vs BNP AUC = 0.838). Combination of the 11 proteins with BNP notably enhanced the predictive capacity of BNP (AUC = 0.898). Conclusions/Implications: Through this study, assays have been developed for robust, multiplexed measurement of 25 cardiovascular disease-associated proteins in patient serum samples. A number of proteins were identified, which show significant expression changes in association with cardiac arrhythmia and will be further explored. Importantly, a statistical model revealed a panel of 11 proteins, which can predict HF in patients with cardiac arrhythmia, with comparable accuracy to BNP. This panel will need to be further validated in independent patient cohorts.