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World J Diabetes. Aug 15, 2026; 17(8): 121422
Published online Aug 15, 2026. doi: 10.4239/wjd.121422
Artificial intelligence-based chest computed tomography assessment of bone, vascular, and epicardial adipose changes in type 2 diabetes
Yuan-Kang Liu, Fan-Yu Wu, Cui-Ping Jiang, Yu-Xuan Xia, Ying Li, Wen-Wen Zeng, Shu-Ya Zhang, Dan-Ni Fan, Meng-Xin Zhang, Tong-Chi Liu, Xiao-Ping Wu, Yue Dong, Wen Chen, Ying-Hua Zhao, Tao Li, Zhen Li, Xiang-Yang Xu
Yuan-Kang Liu, Fan-Yu Wu, Cui-Ping Jiang, Yu-Xuan Xia, Ying Li, Wen-Wen Zeng, Shu-Ya Zhang, Dan-Ni Fan, Meng-Xin Zhang, Xiang-Yang Xu, Department of Radiology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430077, Hubei Province, China
Yuan-Kang Liu, Cui-Ping Jiang, Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
Tong-Chi Liu, Xiao-Ping Wu, Department of Radiology, Xi’an Central Hospital, Xi’an 710003, Shaanxi Province, China
Yue Dong, Wen Chen, Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan 442000, Hubei Province, China
Ying-Hua Zhao, Tao Li, Department of Radiology, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, Guangdong Province, China
Zhen Li, Department of Radiology, Tongji Hospital, Wuhan 430030, Hubei Province, China
Co-first authors: Yuan-Kang Liu and Fan-Yu Wu.
Co-corresponding authors: Zhen Li and Xiang-Yang Xu.
Author contributions: Liu YK and Wu FY jointly undertook study design, data collection, model development and validation, manuscript revision, statistical analysis, and drafting of the manuscript (including preparation of figures and tables) as co-first authors; Jiang CP and Xia YX assisted with data sorting and image analysis and participated in polishing the manuscript; Li Y, Zeng WW, and Zhang SY participated in the drafting of the manuscript; Fan DN and Zhang MX were involved in data acquisition; Liu TC, Wu XP, Dong Y, Chen W, Zhao YH, and Li T provided materials, clinical, and technical support; Li Z and Xu XY critically revised the manuscript as co-corresponding authors; all authors reviewed and approved the final manuscript for submission.
AI contribution statement: AI tools (specifically ChatGPT) were used solely for linguistic refinement and formatting assistance. No AI tool was involved in the generation of research data, interpretation of results, or formulation of conclusions. All AI-generated outputs were critically reviewed and revised by the authors.
Supported by National Key Research and Development Program of China, No. 2024YFC2419300; and National Natural Science Foundation of China, No. 62131009.
Institutional review board statement: The study protocol was approved by the Ethics Review Committee of all participating centers (Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology; Xi’an Central Hospital; Taihe Hospital, Hubei University of Medicine; the Third Affiliated Hospital of Southern Medical University; Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology) (approval No. TJ-IRB202412092). The study was conducted in accordance with the Declaration of Helsinki.
Informed consent statement: Informed consent was waived due to the study’s retrospective design. All patient data were de-identified prior to analysis.
Conflict-of-interest statement: All authors declare no conflict of interest in publishing the manuscript.
Data sharing statement: Technical appendix, statistical code, and dataset available from the corresponding author upon reasonable request.
Corresponding author: Xiang-Yang Xu, PhD, Department of Radiology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 39 Yanhu Avenue, Wuchang District, Wuhan 430077, Hubei Province, China.
1993ly0538@hust.edu.cn
Received: March 27, 2026
Revised: May 8, 2026
Accepted: June 24, 2026
Published online: August 15, 2026
Processing time: 131 Days and 21.5 Hours
BACKGROUND
Type 2 diabetes mellitus (T2DM) is a complex multisystem disorder in which chronic inflammation often drives both bone loss and accelerated atherosclerosis. Although coronary artery calcification (CAC) and epicardial adipose tissue (EAT) are established cardiovascular risk markers, their integrated relationship with bone mineral density (BMD) in T2DM remains underexplored.
AIM
To develop an artificial intelligence (AI)-based framework to simultaneously quantify BMD, CAC, and EAT from routine chest computed tomography and investigate their associations in T2DM.
METHODS
This retrospective study included 4589 participants, including 1369 patients with T2DM and 3220 controls, from five tertiary hospitals. An AI framework was used to quantify thoracic vertebral BMD, the Agatston coronary artery calcium score, EAT volume, and EAT density. Propensity score matching was performed to control for confounding bias, and multivariable regression models were used to assess independent associations. Restricted cubic splines were used to evaluate nonlinear relationships between imaging biomarkers. Subgroup analyses were conducted to examine the consistency of associations across disease duration and glycemic control groups.
RESULTS
After adjustment for age, sex, and metabolic risk factors, T2DM remained independently associated with higher EAT density (β = 4.131, P < 0.001) and severe CAC (odds ratio = 3.656, P < 0.001). Restricted cubic splines analysis revealed a significant negative linear association between BMD and EAT volume (P < 0.001) and a positive linear association between EAT density and severe CAC (P = 0.007). Notably, a J-shaped nonlinear relationship was observed between BMD and EAT density (P for nonlinearity < 0.001). Subgroup analysis showed that the negative correlation between BMD and EAT volume was significantly stronger in patients with T2DM than in controls, particularly among those with moderate glycemic control and shorter disease duration.
CONCLUSION
T2DM was associated with adverse cardiovascular-bone profiles. Automated AI-based opportunistic screening using chest computed tomography provides a “one-stop” tool for early identification of high-risk multisystem phenotypes in T2DM, thereby facilitating more precise individualized interventions.
Core Tip: We developed an artificial intelligence-based opportunistic chest computed tomography framework that simultaneously quantifies bone mineral density, coronary artery calcification, and epicardial adipose tissue in type 2 diabetes mellitus. The study reveals a linear bone mineral density-epicardial adipose tissue (EAT) volume association, a linear EAT density-severe coronary artery calcification association, and a J-shaped bone mineral density-EAT density relationship, supporting routine chest computed tomography as a one-stop tool for multisystem risk stratification in diabetes.
