In-depth profile of biosignatures for T2DM cohort utilizing an integrated targeted LC-MS platform

Abstract

Abstract The profiling of metabolites provides an immediate snapshot that depicts crucial physiological information, holding immense potential for the early diagnosis and prognosis of diseases, including diabetes. Herein, we proposed an optimized and in-depth target-based metabolome platform through an integration of six distinct conditions, including a normal phase, a pre-column chemical derivatization and four reversed phase separation methods for the quantification of a total of 1609 small molecules (32 sub-classes) in serum after normalization using isotope-labeled internal standards. After undergoing rigorous methodological validation and comprehensive comparison with untargeted strategies, we present a new dataset of metabolomic profile encompassing a cohort of 200 healthy individuals and 100 newly diagnosed Type 2 diabetes mellitus (T2DM) patients from the northern region of China. The overall differential analysis results indicated obvious metabolic disturbance of amino acid, fatty acids, lysophosphatidyl-choline and triacylglycerol in T2DM. We hereby make these technical validation results and the profiling dataset publicly available to the scientific community, showcasing its exceptional sensitivity and robustness as an invaluable tool for the comprehensive targeted metabolome analysis.