Song CM, Lin TH, Huang HT, Yao JY. Illuminating diabetes via multi-omics: Unraveling disease mechanisms and advancing personalized therapy. World J Diabetes 2025; 16(7): 106218 [DOI: 10.4239/wjd.v16.i7.106218]
Corresponding Author of This Article
Jeng-Yuan Yao, PhD, Associate Professor, Key Laboratory of Functional and Clinical Translational Medicine, Fujian Province University, Xiamen Medical College, No. 1999 Guankou Middle Road, Jimei District, Xiamen 361023, Fujian Province, China. 201500080004@xmmc.edu.cn
Research Domain of This Article
Endocrinology & Metabolism
Article-Type of This Article
Review
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Diabetes. Jul 15, 2025; 16(7): 106218 Published online Jul 15, 2025. doi: 10.4239/wjd.v16.i7.106218
Illuminating diabetes via multi-omics: Unraveling disease mechanisms and advancing personalized therapy
Chen-Meng Song, Ta-Hui Lin, Hou-Tan Huang, Jeng-Yuan Yao
Chen-Meng Song, Ta-Hui Lin, Jeng-Yuan Yao, School of Public Health, Fujian Medical University, Fuzhou 350122, Fujian Province, China
Ta-Hui Lin, Hou-Tan Huang, Jeng-Yuan Yao, Key Laboratory of Functional and Clinical Translational Medicine, Fujian Province University, Xiamen Medical College, Xiamen 361023, Fujian Province, China
Co-first authors: Chen-Meng Song and Ta-Hui Lin.
Author contributions: Song CM and Lin TH contributed equally; Yao JY supervised all aspects, made critical revisions, and is responsible for correspondence; Song CM, Lin TH, and Huang HT carried out the literature review, interpreted the data, and drafted the original manuscript; All authors prepared the draft and approved the submitted version.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Jeng-Yuan Yao, PhD, Associate Professor, Key Laboratory of Functional and Clinical Translational Medicine, Fujian Province University, Xiamen Medical College, No. 1999 Guankou Middle Road, Jimei District, Xiamen 361023, Fujian Province, China. 201500080004@xmmc.edu.cn
Received: February 20, 2025 Revised: April 8, 2025 Accepted: May 27, 2025 Published online: July 15, 2025 Processing time: 146 Days and 7.2 Hours
Abstract
Diabetes mellitus (DM) comprises distinct subtypes-including type 1 DM, type 2 DM, and gestational DM - all characterized by chronic hyperglycemia and substantial morbidity. Conventional diagnostic and therapeutic strategies often fall short in addressing the complex, multifactorial nature of DM. This review explores how multi-omics integration enhances our mechanistic understanding of DM and informs emerging personalized therapeutic approaches. We consolidated genomic, transcriptomic, proteomic, metabolomic, and microbiomic data from major databases and peer-reviewed publications (2015-2025), with an emphasis on clinical relevance. Multi-omics investigations have identified convergent molecular networks underlying β-cell dysfunction, insulin resistance, and diabetic complications. The combination of metabolomics and microbiomics highlights critical interactions between metabolic intermediates and gut dysbiosis. Novel biomarkers facilitate early detection of DM and its complications, while single-cell multi-omics and machine learning further refine risk stratification. By dissecting DM heterogeneity more precisely, multi-omics integration enables targeted interventions and preventive strategies. Future efforts should focus on data harmonization, ethical considerations, and real-world validation to fully leverage multi-omics in addressing the global DM burden.
Core Tip: Metabolomics offers key insights into the distinct metabolic pathways of each diabetes mellitus subtype. By integrating multiple omics layers-genomic, transcriptomic, proteomic, and microbiomic - researchers can refine disease classification, identify novel biomarkers, and develop personalized interventions, substantially enhancing the efficacy of diabetes management.
