MSACL 2019 US Abstract

Development of a Proteomic Plasma Biomarker Panel for Hypertrophic Cardiomyopathy

Wendy E Heywood (1), Gabriela Captur (2), Caroline Coats (3), Stefania Rosmini (2), Vimal Patel (3) Richard Collis (3) Nina Patel (1) Petros Syrris (3) Paul Bassett (3), Ben O’Brien (2), James C Moon (2,3), Perry M Elliott (2,3) Kevin Mills (1)
(1) Genetics & Genomic Medicine Unit, UCL Great Ormond St Instiute of Child Health, London, UK (2) Barts Heart Center, St Bartholomew’s Hospital, West Smithfield, London, EC1A 7BE, UK (3) Institute of Cardiovascular Science, University College London, Gower Street, London, WC1E 6BT, UK

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Wendy Heywood (Presenter) University College London

Presenter Bio: Wendy Heywood did her undergraduate degree in Molecular Genetics at Kings College London. She went on to attain her PhD on nutritional programming of pancreatic Glucokinase at University College London (UCL). She is a Senior Research Associate in Translational Mass spectrometry at UCL Great Ormond Street Institute of Child Health in London, UK. She also co-manages the UCL Biological Mass Spectrometry Centre and is an honorary clinical biochemist at Great Ormond St Hospital. Interests include biomarker discovery and development for rare Inborn Errors of Metabolism.

Relevant Financial Disclosures (within past 24 months) Honorarium/Expenses Freeline therapeutics Grant/Research Support Shire pharmaceuticals Discussing Products? NO, presenter will NOT present educational content related to products/services of the ineligible company(ies) listed above. Conflict mitigation NOT REQUIRED.

There is a need for new biomarkers for hypertrophic cardiomyopathy (HCM). Candidate biomarkers were identified using label free proteomics and developed into a multiplexed targeted proteomic assay. We assayed 110 patients with HCM and 97 controls. Six markers were significantly increased (P<0.006) in HCM. Markers correlated with left ventricular (LV) wall thickness, LV mass and % myocardial scar on cardiovascular magnetic resonance imaging. Supervised machine learning (ML) differentiated HCM from controls (area under the curve: 0.89). Four biomarkers as well as the ML score correlated with non-sustained ventricular tachycardia and estimated 5-year risk of sudden cardiac death.