Author:
Meng Jinli,Huang Fangfang,Shi Jing,Zhang Chenghui,Feng Li,Wang Suyuan,Li Hengyan,Guo Yongyue,Hu Xin,Li Xiaomei,He Wanlin,Cheng Jian,Wu Yunhong
Abstract
Abstract
Introduction
Metabolomic signatures of type 2 diabetes mellitus (T2DM) in Tibetan Chinese population, a group with high diabetes burden, remain largely unclear. Identifying the serum metabolite profile of Tibetan T2DM (T-T2DM) individuals may provide novel insights into early T2DM diagnosis and intervention.
Methods
Hence, we conducted untargeted metabolomics analysis of plasma samples from a retrospective cohort study with 100 healthy controls and 100 T-T2DM patients by using liquid chromatography–mass spectrometry.
Results
The T-T2DM group had significant metabolic alterations that are distinct from known diabetes risk indicators, such as body mass index, fasting plasma glucose, and glycosylated hemoglobin levels. The optimal metabolite panels for predicting T-T2DM were selected using a tenfold cross-validation random forest classification model. Compared with the clinical features, the metabolite prediction model provided a better predictive value. We also analyzed the correlation of metabolites with clinical indices and found 10 metabolites that were independently predictive of T-T2DM.
Conclusion
By using the metabolites identified in this study, we may provide stable and accurate biomarkers for early T-T2DM warning and diagnosis. Our study also provides a rich and open-access data resource for optimizing T-T2DM management.
Funder
Science and Technology Plan Projects of Tibet Autonomous Region
Sichuan Province Science and Technology Support Program
Medical Research project of Sichuan Province
the central government guides local projects
Hospital level key project of Hospital of Chengdu office of People’s Government of Tibetan Autonomous Region
Publisher
Springer Science and Business Media LLC
Subject
Endocrinology, Diabetes and Metabolism,Internal Medicine
Cited by
1 articles.
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