Metabolic risk factors associated with sudden cardiac death (SCD) during acute myocardial ischemia

Forensic Sci Res. 2017 Jul 6;2(3):126-131. doi: 10.1080/20961790.2017.1343269. eCollection 2017.

Abstract

Sudden cardiac death (SCD) is the leading cause of death worldwide. Myocardial ischemia (MI) is the most common underlying causal disorder for SCD. Metabolic risks leading to SCD during acute MI are still not fully understood. Here, using tissue metabolomics, we aimed to investigate myocardial metabolic alterations relevant to SCD events in an acute MI rat model induced by coronary artery ligation (CAL). Thirty-four rats were successfully performed CAL, of which 13 developed lethal ventricular tachyarrhythmia (LVTA)-SCD and 7 developed severe atrioventricular block (AB)-SCD. Fourteen rats that survived within 70 min after the ligation were served as peer controls. The partial least squares-discriminant analysis plots demonstrated clear separations between the SCD rats and controls, indicating obvious differences in myocardial metabolome between these rats. The levels of isoleucine, lactate, glutamate choline, phosphorylcholine, taurine and asparagine in ischemic myocardia were positively associated with LVTA-SCD events; in contrast, the levels of alanine, urea, phenylalanine, linoleic acid, elaidic acid and stearic acid were inversely correlated with LVTA-SCD events. The levels of glutamate and urea were positively and negatively relevant to AB-SCD events, respectively. The dangerous metabolites indicated that lower levels of energy substrates, severe hypoxia, the inhibition of transamination and hyper sympathetic excitement and reactive oxygen species in myocardia were vulnerable to SCD during acute MI. The results suggest fatal metabolic alterations correlated with SCD events during acute MI, which could offer novel clues for the prevention or treatment of acute MI-related SCD.

Keywords: Forensic science; acute myocardial ischemia; forensic pathology; metabolic risk; sudden cardiac death; tissue metabolomics.

Grants and funding

This work was supported by the Natural Science Foundation [grant number 2015A408119346049] and Science and Technology Innovation project of Guangdong Province [grant number 2013KJCX0076].