LC/MS-based polar metabolite profiling reveals gender differences in serum from patients with myocardial infarction

J Pharm Biomed Anal. 2015 Nov 10:115:475-86. doi: 10.1016/j.jpba.2015.08.009. Epub 2015 Aug 11.

Abstract

Myocardial infarction (MI), a leading cause of death worldwide, results from prolonged myocardial ischemia with necrosis of myocytes due to a blood supply obstruction to an area of the heart. Many studies have reported gender-related differences in the clinical features of MI, but the reasons for these differences remain unclear. In this study, we applied ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF MS) and various statistical methods-such as multivariate, pathway, and correlation analyses-to identify gender-specific metabolic patterns in polar metabolites in serum from healthy individuals and patients with MI. Patients with diagnosed MI (n=68), and age- and body mass index-matched healthy individuals (n=68), were included in this study. The partial least-squares discriminant analysis (PLS-DA) model was generated from metabolic profiling data, and the score plots showed a significant gender-related difference in patients with MI. Many pathways were associated with amino acids and purines; amino acids, acylcarnitines, and purines differed significantly between male and female patients with MI. This approach could be utilized to observe gender-specific metabolic pattern differences between healthy controls and patients with MI.

Keywords: Gender-related differences; Metabolic profiling; Myocardial infarction; UPLC/Q-TOF MS.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biomarkers / blood
  • Case-Control Studies
  • Chromatography, Liquid / methods*
  • Discriminant Analysis
  • Female
  • Humans
  • Least-Squares Analysis
  • Male
  • Mass Spectrometry / methods*
  • Metabolomics / methods*
  • Multivariate Analysis
  • Myocardial Infarction / blood*
  • Myocardial Infarction / diagnosis
  • Sex Factors

Substances

  • Biomarkers