AUTHOR=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 , Kakaei Ardeshir , Bandarian Fatemeh , Aghaei Meybodi Hamid , Larijani Bagher , Razi Farideh 

TITLE=Metabolomics profile and 10-year atherosclerotic cardiovascular disease (ASCVD) risk score

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 10 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2023.1161761

DOI=10.3389/fcvm.2023.1161761

ISSN=2297-055X

ABSTRACT=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.