MSACL 2019 EU Abstract

Serum Metabolomics Using LC-MS Reveals Potential Biomarker of Myocardial Ischemia

Sunhee Jung (1,2), Jueun Lee (1), Do Hyun Ryu (2), Geum-Sook Hwang (1,3)
(1) Integrated Metabolomics Research Group, Western Seoul Center, Korea Basic Science Institute, Seoul 03759, Republic of Korea, (2) Department of Chemistry, Sungkyunkwan University, Suwon 16419, Republic of Korea, (3) Department of Chemistry and Nano Science, Ewha Womans University, Seoul 03759, Republic of Korea

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Sunhee Jung (Presenter) Korea Basic Science Institute

Relevant Financial Disclosures (within past 24 months) No relevant financial relationship(s) to disclose. Conflict mitigation NOT REQUIRED.

Introduction
Cardiovascular disease (CVD), one of the leading cause of death worldwide, are influenced by a wide range of genetic, dietary, and environmental factors. As CVD is a complex pathophysiological disease, more intensive research is needed to elucidate the diagnostic approach. Metabolomics, a field of omics science that comprehensively analyze low-molecular-weight compounds in biological systems, can be used in patient omics profiling, diagnosis, and monitoring. In the present study, we aimed to identify biomarker candidates for myocardial infarction (MI) using mass spectrometry-based metabolomics.
Methods and Results
We performed metabolomic profiling of serum samples from MI patients (70 MI patients and 70 controls) in a discovery phase using UPLC/Q-TOF MS. And then, we analyzed serum sample (274 MI patients and 273 controls) in independent validation phase using UPLC/TQ MS. In discovery set, serum 11 metabolites, including purines, carnitine, acyl-carnitines, amino acids, taurine, and organic acids, showed significantly dysregulated levels in MI using Mann-Whitney test and ranked ANCOVA adjusted age and sex. Further, inosine and hypoxanthine were discriminated between MI and normal sample (area under the curve (AUC) value > 0.8) in the discovery set. Therefore, we conducted a targeted analysis of metabolites related to purine metabolism in the validation set. Serum concentrations of hypoxanthine, inosine, xanthine, and xanthosine were significantly higher in MI patients compared to the normal sample adjusted age and sex (p<0.001). Also, the diagnostic model of hypoxanthine, which was constructed based on the ROC curve of the MI patients and normal sample, showed the AUCs ranged from 0.8 to 0.864.
Conclusion
The current study demonstrated that hypoxanthine could be used as a biomarker candidate to distinguish MI patients from normal and metabolomics technologies are well suited for identifying biomarkers for CVD and provide value for clinical diagnosis of CVD.