Plasma acylcarnitines and amino acids in dyslipidemia: an integrated metabolomics and machine learning approach

Abstract

Abstract Background:‌ The Discovery of underlying intermediates associated with the development of dyslipidemia results in a better understanding of pathophysiology of dyslipidemia and their modification will be a promising preventive and therapeutic strategy for the management of dyslipidemia. Methods: The entire dataset in this study was a large cross-sectional study that included 1200 subjects and was stratified into four binary classes with normal and abnormal cases based on their levels of triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and non-HDL-C. The current study sought to first evaluate ​​plasma concentrations of 20 amino acids and 30 acylcarnitines in each class of dyslipidemia. Then, these attributes, along with baseline characteristics data, were used to check whether machine learning (ML) algorithms could classify cases and controls. Results: Taking this into account, the levels of dyslipidemia classes fluctuate during the day, which produces data fluctuation, our ML framework accurately predicts TG binary classes. Moreover, the findings showed that alanine, phenylalanine, methionine, C3, C14:2, and C16 had great power in differentiating patients with high TG from normal TG controls. Conclusions: The comprehensive output of this work, along with sex-specific attributes, will improve our understanding of the underlying intermediates involved in dyslipidemia.