Prediction of Acute Coronary Syndromes by Urinary Proteome Analysis

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2017 Mar 9

PMID 28273075

Citations 11

Authors

Nay M Htun

Dianna J Magliano

Zhen-Yu Zhang

Jasmine Lyons

Thibault Petit

Esther Nkuipou-Kenfack

Adela Ramirez-Torres

Constantin von Zur Muhlen

David Maahs

Joost P Schanstra

Claudia Pontillo

Martin Pejchinovski

Janet K Snell-Bergeon

Christian Delles

Harald Mischak

Jan A Staessen

Jonathan E Shaw

Thomas Koeck

Karlheinz Peter

Affiliations

Soon will be listed here.

Abstract

Identification of individuals who are at risk of suffering from acute coronary syndromes (ACS) may allow to introduce preventative measures. We aimed to identify ACS-related urinary peptides, that combined as a pattern can be used as prognostic biomarker. Proteomic data of 252 individuals enrolled in four prospective studies from Australia, Europe and North America were analyzed. 126 of these had suffered from ACS within a period of up to 5 years post urine sampling (cases). Proteomic analysis of 84 cases and 84 matched controls resulted in the discovery of 75 ACS-related urinary peptides. Combining these to a peptide pattern, we established a prognostic biomarker named Acute Coronary Syndrome Predictor 75 (ACSP75). ACSP75 demonstrated reasonable prognostic discrimination (c-statistic = 0.664), which was similar to Framingham risk scoring (c-statistics = 0.644) in a validation cohort of 42 cases and 42 controls. However, generating by a composite algorithm named Acute Coronary Syndrome Composite Predictor (ACSCP), combining the biomarker pattern ACSP75 with the previously established urinary proteomic biomarker CAD238 characterizing coronary artery disease as the underlying aetiology, and age as a risk factor, further improved discrimination (c-statistic = 0.751) resulting in an added prognostic value over Framingham risk scoring expressed by an integrated discrimination improvement of 0.273 ± 0.048 (P < 0.0001) and net reclassification improvement of 0.405 ± 0.113 (P = 0.0007). In conclusion, we demonstrate that urinary peptide biomarkers have the potential to predict future ACS events in asymptomatic patients. Further large scale studies are warranted to determine the role of urinary biomarkers in clinical practice.

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