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Peer-reviewedCC BYInternational journal of medical sciences

Metabolomics in the Prediction and Early Diagnosis of Type 2 Diabetes Mellitus: Advances and Applications.

  • Xiaokai Wei1,2
  • Min He2,3
  • Haili Wang4
  • Xin Xiang1
  • Mengyuan Li2,3
  • Mengmeng Li1,2
  • Thanh Tuan Vu Le1,2
  • Zhongxian Wang1,2
  • Dingqi Zhang1
  • Lei Cheng1
  • Mengmeng Sun2,3
  • Hongfeng Wang2,3
4 affiliations
  1. 1College of Acupuncture and Tuina, Changchun University of Chinese Medicine, Changchun, Jilin, China.
  2. 2Jilin Provincial International Cooperation Key Laboratory of Traditional Medicine for Prevention and Treatment of Metabolic Diseases, Changchun University of Chinese Medicine, Jingyue Economic Development District, Changchun, Jilin, China.
  3. 3Northeast Asia Institute of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, Jilin, China.
  4. 4College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, Jilin, China.
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Abstract

Early detection and intervention can prevent the onset and progression of type 2 diabetes mellitus (T2DM); however, achieving early diagnosis and accurate prediction remains challenging. Metabolomics, a potent diagnostic tool in systems biology, makes it possible to systematically identify important biomarkers linked to type 2 diabetes, which provides vital proof for risk assessment and early diagnosis. This review summarizes research advances in metabolomics for T2DM prediction and early detection since 2010 by incorporating both the reported prospective and cross-sectional metabolic evidence, with a focus on putative biomarkers and their metabolic characteristics identified across diverse biological samples. Furthermore, it highlights the unique advantages of metabolomics in T2DM early warning, aiming to offer novel insights for future research on personalized prevention and early diagnostic strategies.

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Cite

BibTeXRIS
BibTeX
@article{wei2026metabolomics,
  title = {{Metabolomics in the Prediction and Early Diagnosis of Type 2 Diabetes Mellitus: Advances and Applications.}},
  author = {Xiaokai Wei and Min He and Haili Wang and Xin Xiang and Mengyuan Li and Mengmeng Li and Thanh Tuan Vu Le and Zhongxian Wang and Dingqi Zhang and Lei Cheng and Mengmeng Sun and Hongfeng Wang},
  journal = {International journal of medical sciences},
  year = {2026},
  month = sep,
  doi = {10.7150/ijms.126351},
  url = {https://europepmc.org/article/MED/42909386},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Metabolomics in the Prediction and Early Diagnosis of Type 2 Diabetes Mellitus: Advances and Applications.
AU  - Xiaokai Wei
AU  - Min He
AU  - Haili Wang
AU  - Xin Xiang
AU  - Mengyuan Li
AU  - Mengmeng Li
AU  - Thanh Tuan Vu Le
AU  - Zhongxian Wang
AU  - Dingqi Zhang
AU  - Lei Cheng
AU  - Mengmeng Sun
AU  - Hongfeng Wang
PY  - 2026
DA  - 2026/09/11
JO  - International journal of medical sciences
DO  - 10.7150/ijms.126351
UR  - https://europepmc.org/article/MED/42909386
AB  - Early detection and intervention can prevent the onset and progression of type 2 diabetes mellitus (T2DM); however, achieving early diagnosis and accurate prediction remains challenging. Metabolomics, a potent diagnostic tool in systems biology, makes it possible to systematically identify important biomarkers linked to type 2 diabetes, which provides vital proof for risk assessment and early diagnosis. This review summarizes research advances in metabolomics for T2DM prediction and early detection since 2010 by incorporating both the reported prospective and cross-sectional metabolic evidence, with a focus on putative biomarkers and their metabolic characteristics identified across diverse biological samples. Furthermore, it highlights the unique advantages of metabolomics in T2DM early warning, aiming to offer novel insights for future research on personalized prevention and early diagnostic strategies.
SN  - 1449-1907
ER  -