Hou BW, Ran Z, Li YT, Zhang J, Chu YQ, Gharaibeh NM, Li XM. Magnetic resonance imaging derived biomarkers for the diagnosis of type 2 diabetes with insulin resistance: A pilot study. World J Diabetes 2025; 16(9): 110183 [DOI: 10.4239/wjd.v16.i9.110183]
Corresponding Author of This Article
Xiao-Ming Li, PhD, Professor, Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095 Jiefang Road, Wuhan 430030, Hubei Province, China. lilyboston2002@tjh.tjmu.edu.cn
Research Domain of This Article
Radiology, Nuclear Medicine & Medical Imaging
Article-Type of This Article
Observational Study
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. Sep 15, 2025; 16(9): 110183 Published online Sep 15, 2025. doi: 10.4239/wjd.v16.i9.110183
Magnetic resonance imaging derived biomarkers for the diagnosis of type 2 diabetes with insulin resistance: A pilot study
Bo-Wen Hou, Zheng Ran, Yi-Tong Li, Jing Zhang, Yong-Qiang Chu, Nadeer M Gharaibeh, Xiao-Ming Li
Bo-Wen Hou, Zheng Ran, Yi-Tong Li, Jing Zhang, Yong-Qiang Chu, Nadeer M Gharaibeh, Xiao-Ming Li, Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
Co-first authors: Bo-Wen Hou and Zheng Ran.
Author contributions: Hou BW and Ran Z designed the research and wrote the original manuscript; Li YT and Zhang J performed the data analysis; Chu YQ and Gharaibeh NM performed the statistics analysis and language polishing; Li XM supervised this research. Hou BW and Ran Z contribute equally to this study as co-first authorship. All authors have read and approved the final manuscript.
Supported by National Natural Science Foundation of China, No. 81930045 and No. 31630025.
Institutional review board statement: This study is conducted under the approval from the Institutional Review Board of Tongji Hospital (Approval No. TJ-IRB20220633).
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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: Xiao-Ming Li, PhD, Professor, Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095 Jiefang Road, Wuhan 430030, Hubei Province, China. lilyboston2002@tjh.tjmu.edu.cn
Received: June 3, 2025 Revised: July 1, 2025 Accepted: August 15, 2025 Published online: September 15, 2025 Processing time: 104 Days and 1.2 Hours
Abstract
BACKGROUND
Insulin resistance (IR) plays a critical role in the musculoskeletal metabolic disorders associated with type 2 diabetes mellitus (T2DM).
AIM
To develop multiparametric magnetic resonance imaging (MRI)-derived biomarkers and diagnostic models for non-invasive identification and stratification of IR.
METHODS
Parameters of paravertebral muscles and vertebra were evaluated using quantitative chemical shift-encoded MRI and diffusion tensor imaging protocols. Tripartite cohort analyses were conducted through Kruskal-Wallis H tests with post hoc Dunn-Bonferroni correction for MRI-derived metrics. Diagnostic performance for T2DM-IR was assessed after selecting the most significant features through Z-score standardization and multinomial logistic regression models.
RESULTS
This study evaluated 97 subjects (control: 39 subjects, T2DM-IR: 18 subjects, T2DM patients without IR: 40 subjects) using multiparametric MRI protocols. Significant intergroup differences were observed in the cross-sectional area (P = 0.047) and apparent diffusion coefficient (P = 0.027) of the psoas, and the cross-sectional area (P = 0.042) of the erector. More intramyocellular lipid (IMCL) in the psoas (P = 0.001) and erector (P = 0.004) were found in the T2DM-IR group. Multinomial receiver operating characteristic curve analysis demonstrated that IMCL of the erector performed better (area under the curve = 0.838, sensitivity: 0.800, specificity: 0.938) in the diagnosis of T2DM-IR.
CONCLUSION
IMCL in erector emerges as a highly discriminative metric for T2DM-IR diagnosis. Multiparametric MRI enables non-invasive quantification of early musculoskeletal metabolic injury, providing reliable biomarkers for IR identification and stratification.
Core Tip: This study reveals that insulin resistance (IR) in type 2 diabetes mellitus exacerbates myosteatosis via intramyocellular lipid (IMCL) accumulation and fat infiltration in paravertebral muscles, alongside vertebral fat fraction increases. Multiparametric magnetic resonance imaging sequences (quantitative Dixon and diffusion tensor imaging) identified key biomarkers (e.g., IMCL, fat-muscle ratios), and erector IMCL specifically showed high diagnostic accuracy for type 2 diabetes mellitus-IR. Multiparametric magnetic resonance imaging evaluation enables non-invasive quantification of early musculoskeletal metabolic injury, providing reliable imaging biomarkers for IR identification and stratification.
