REVIEW article
Front. Endocrinol.
Sec. Diabetes: Molecular Mechanisms
Volume 16 - 2025 | doi: 10.3389/fendo.2025.1624878
Metabolomics Uncovers the Diabetes Metabolic Network: From Pathophysiological Mechanisms to Clinical Applications
Provisionally accepted
- 1City University of Hong Kong, Kowloon, Hong Kong, SAR China
- 2Faculty of Medicine, Heidelberg University, Heidelberg, Germany
- 3Guangxi Hospital Division of The First Affiliated Hospital, Sun Yat-sen University, Nan Ning, China
- 4Sun Yat-sen University, Guangzhou, China
- 5Department of Hypertension and Vascular Disease, The First Affiliated Hospital of. Sun Yat-sen University, Guangzhou, China
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Diabetes mellitus (DM) represents a complex metabolic disorder posing urgent diagnostic and therapeutic challenges worldwide. Traditional biomarkers such as HbA1c and OGTT fail to capture the dynamic nature of metabolic remodeling underlying DM pathophysiology.Metabolomics, by offering real-time, systems-level insights into small-molecule dynamics, has emerged as a promising strategy for both early disease detection and therapeutic target discovery. Recent studies have highlighted the diagnostic and prognostic value of metabolites, including branched-chain amino acids, lipid derivatives, and bile acids. Despite its immense potential, the clinical application of metabolomics remains hindered by technical limitations, such as cross-cohort standardization and data interpretation complexity. Future advances integrating artificial intelligence and multi-omics strategies may transform metabolomics from an exploratory tool to a clinical mainstay in diabetes management. This review offers a comprehensive synthesis of recent advances in metabolomics-driven diabetes research, with a particular focus on elucidating key metabolic pathways, identifying emerging biomarkers, and exploring translational opportunities. To fully realize the clinical potential of metabolomics, further efforts toward analytical standardization, cross-cohort validation, and the integration of artificial intelligence-powered tools will be essential to bridge the gap from bench to bedside in diabetes care.
Keywords: Metabolomics, Diabetes Mellitus, metabolic reprogramming, biomarkers, clinical translation, precision medicine By doing so, we seek to
Received: 10 May 2025; Accepted: 07 Aug 2025.
Copyright: © 2025 Xu, Zhou, Xie and Ning. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Zhongxing Ning, Guangxi Hospital Division of The First Affiliated Hospital, Sun Yat-sen University, Nan Ning, China
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