Exploring metabolic syndrome serum free fatty acid profiles based on GC–SIM–MS combined with random forests and canonical correlation analysis

摘要: Metabolic syndrome (MetS) is a cluster of metabolic abnormalities associated with an increased risk developing cardiovascular diseases or type II diabetes. Till now, the etiology MetS complex and still unknown. profiling powerful tool for exploring perturbations potential biomarkers, thus may shed light on pathophysiological mechanism diseases. In this study, fatty acid was employed to exploit disturbances discover biomarkers MetS. Fatty profiles serum samples from patients healthy controls were first analyzed by gas chromatography–selected ion monitoring–mass spectrometry (GC–SIM–MS), robust method quantitation acids. Then, supervised multivariate statistical random forests (RF) used establish classification prediction model MetS, which could assist diagnosis Furthermore, canonical correlation analysis (CCA) investigate relationships between free acids (FFAs) clinical parameters. As result, several FFAs, including C16:1n-9c, C20:1n-9c C22:4n-6c, identified as The results also indicated that high density lipoprotein-cholesterol (HDL-C), triglycerides (TG) fasting blood glucose (FBG) most important parameters closely correlated FFAs they should be paid more attention in practice monitoring than waist circumference (WC) systolic pressure/diastolic pressure (SBP/DBP). have demonstrated GC–SIM–MS combined RF CCA useful discovering possible