99EMF Pilot of Integrated Metabolomic and MicroRNA Analysis to Identify Myocardial Ischemia on Emergency Department Cardiac Stress Tests

Abstract

Acute Coronary Syndrome (ACS) is a growing global health problem, and precision medicine techniques integrating multi-omics data sets for biomarker discovery hold promise for the development of diagnostic indicators of ACS. In this pilot, we sought to assess the utility of an integrated analysis of metabolomic and microRNA data in peripheral blood to distinguish patients with abnormal cardiac stress testing from matched controls. We used biobank samples prospectively collected from ED patients placed in an ED-based observation unit who underwent stress testing for ACS. We isolated microRNA and quantified metabolites from plasma collected before and after stress testing in patients with myocardial ischemia on stress testing versus those with normal stress tests. We used tandem mass spectrometry (MS/MS) on a Quattro Micro instrument (Waters Corporation, Milford, MA) to analyze 64 different acylcarnitines and amino acids and extracted RNA with the Qiagen miRNeasy Serum/Plasma Kit (Qiagen, Frederick, MD) from plasma collected in EDTA tubes. We used a standard QIASeq miRNA Library Kit protocol (Qiagen, Frederick, MD) for library preparation. Library QC was done on the Agilent Bioanalyzer with the DNA High Sensitivity Assay to quantify all microRNA sequences. The combined metabolomic and microRNA data were analyzed jointly for case (ischemia) and 1:1 age and sex-matched control patients in a supervised, dimension-reducing discriminant analysis using the DIABLO (Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies) method in the mixOmics package in R. We recruited 7 patients with myocardial ischemia on ED cardiac stress testing (6 females, 85% Caucasian, Mean TIMI Score=3, 4 patients ultimately receiving percutaneous coronary intervention) with 1:1 age and sex-matched controls. Though underpowered for the number of analytes included, integrative analysis of the change in metabolite levels and microRNA expression in subjects pre- versus post-stress test showed modest performance for distinguishing cases from controls, with an overall error rate of 0.36 when both metabolite and microRNAs are considered (individual AUC = 0.94 and 0.96 for metabolite and microRNA data, respectively) (Figure 1). This pilot works shows a potential method for using a precision medicine approach to cardiac stress testing in ED patients undergoing workup for ACS. We are seeking analyses in larger sample sizes to test the validity of our findings.