Wenwen Qi,1 Chunjing Yang,2,3 Jingfeng Li,2,3 Li Bao2,3 

1Department of Geriatrics, Beijing Shijitan Hospital, Capital Medical University, Beijing, People’s Republic of China; 2Department of Pharmacy, Beijing Shijitan Hospital, Capital Medical University, Beijing, People’s Republic of China; 3Beijing Key Laboratory of Bio-Characteristic Profiling for Evaluation of Rational Drug Use, Beijing, People’s Republic of China

Correspondence: Li Bao, Department of Pharmacy, Beijing Shijitan Hospital, Capital Medical University, No. 10 Tieyi-Road, Haidian District, Beijing, 10038, People’s Republic of China, Tel +86-10-63926405, Email baoli3712@bjsjth.cn

Purpose: Comprehensive analysis of serum lipidomics is important for the treatment and prevention of type 2 diabetes (T2DM). The purpose of this study was to provide a profile of lipid changes in the serum of T2DM patients and identify potential lipid biomarkers.
Patients and Methods: In this study, we collected clinical physiological parameters and determined the serum lipid profiles of 30 T2DM patients and 30 matched healthy volunteers using the UPLC-MS method. T test and multivariate statistical analyses were used to identify candidate lipid predictors using the GraphPad Prism 9.5 software and MetaboAnalyst 5.0 online platform.
Results: Based on the above test, 1162 lipid metabolites were detected, of which 267 were significantly altered in the T2DM group (FDR < 0.05), which belonged to the five main lipid classes. Eleven lipids were identified as potential lipid biomarkers with the specific screening criteria (variable importance in the projection (VIP) > 1.0; P < 0.05; log2(Fold Change) > 1) in the MetaboAnalyst 5.0 online platform. The Pearson rank correlation test showed that ten differential lipids were significantly correlated with T2DM-related physiological parameters (2h-loaded blood glucose and HbAc1 (glycated haemoglobin)). ROC curve analyses revealed that the combined 11 lipids or LPI classes can be as candidate features for the development of an integrated diagnostic biosignature for T2DM.
Conclusion: The results of this study revealed important changes in lipids in T2DM patients, expanded the knowledge of lipid levels and T2DM progression, and provided important metabolic information for the therapy and diagnosis of T2DM.

Keywords: diabetes mellitus type 2 (T2DM), lipidomic, UPLC-MS