Metabolomics profile and 10-year atherosclerotic cardiovascular disease (ASCVD) risk score - Dehghanbanadaki, Hojat; Dodangeh, Salimeh; Parhizkar Roudsari, Peyvand; Hosseinkhani, Shaghayegh; Khashayar, Pouria; Noorchenarboo, Mohammad; Rezaei, Negar; Dilmaghani-Marand, Arezou; Yoosefi, Moein; Arjmand, Babak; Khalagi, Kazem; Najjar, Niloufar; et al. (Frontiers in Cardiovascular Medicine, 2023)
Background: The intermediate metabolites associated with the development of atherosclerotic cardiovascular disease (ASCVD) remain largely unknown. Thus, we conducted a large panel of metabolomics profiling to identify the new candidate metabolites that were associated with 10-year ASCVD risk. Methods: Thirty acylcarnitines and twenty amino acids were measured in the fasting plasma of 1102 randomly selected individuals using a targeted FIA-MS/MS approach. The 10-year ASCVD risk score was calculated based on 2013 ACC/AHA guidelines. Accordingly, the subjects were stratified into four groups: low-risk (n=620), borderline-risk (n=110), intermediate-risk (n=225), and high-risk (n=147). 10 factors comprising collinear metabolites were extracted from principal component analysis. Results: C4DC, C8:1, C16OH, citrulline, histidine, alanine, threonine, glycine, glutamine, tryptophan, phenylalanine, glutamic acid, arginine, and aspartic acid were significantly associated with the 10-year ASCVD risk score (p-values≤ 0.044). The high-risk group had higher odds of factor 1 (12 long-chain acylcarnitines, OR=1.103), factor 2 (5 medium-chain acylcarnitines, OR=1.063), factor 3 (methionine, leucine, valine, tryptophan, tyrosine, phenylalanine, OR=1.074), factor 5 (6 short-chain acylcarnitines, OR=1.205), factor 6 (5 short-chain acylcarnitines, OR=1.229), factor 7 (alanine, proline, OR=1.343), factor 8 (C18:2OH, glutamic acid, aspartic acid, OR=1.188), and factor 10 (ornithine, citrulline, OR=1.570) compared to the low-risk ones; the odds of factor 9 (glycine, serine, threonine, OR=0.741), however, were lower in the high-risk group. ‘D-glutamine and D-glutamate metabolism’, ‘phenylalanine, tyrosine, and tryptophan biosynthesis’, and ‘valine, leucine, and isoleucine biosynthesis’ were metabolic pathways having the highest association with borderline/intermediate/high ASCVD events, respectively. Conclusions: Abundant metabolites were found to be associated with ASCVD events in this study. Utilization of this metabolic panel could be a promising strategy for early detection and prevention of ASCVD events.