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World J Diabetes. Nov 15, 2026; 17(11): 125698
Published online Nov 15, 2026. doi: 10.4239/wjd.125698
Published online Nov 15, 2026. doi: 10.4239/wjd.125698
Integrating traditional Chinese medicine syndromes with clinical variables to predict lower extremity arterial disease in type 2 diabetes
Ying-Wei Lv, Ai-Juan Jiang, Yu-Meng Geng, Min Hu, Yu-Qing Wang, Lin Ma, Nuo Shen, School of Integrated Chinese and Western Medicine, Anhui University of Chinese Medicine, Hefei 230012, Anhui Province, China
Jin-Dong Zhao, Department of Endocrinology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei 230031, Anhui Province, China
Author contributions: Lv YW, Jiang AJ, Geng YM, Hu M, Ma L, Shen N, Wang YQ and Zhao JD designed the research; Lv YW, Jiang AJ, Geng YM and Hu M performed the research; Ma L and Shen N contributed new analytic tools; Lv YW, Wang YQ, and Zhao JD analyzed the data; and Lv YW, Jiang AJ and Geng YM wrote the paper.
AI contribution statement: The authors take full responsibility and accountability for all content of this manuscript, including any portions for which AI tools were used as assistive technologies. All AI-assisted outputs were carefully reviewed, validated, and approved by the authors. AI tools were not used to generate original scientific data, perform independent scientific analyses, or draw scientific conclusions.
Supported by Anhui Provincial Natural Science Foundation Project, No. 2308085MH307; The "Basic-Clinical Integration" Special Project of Category A for 2025 of Anhui University of Chinese Medicine, No. JCLCA2025024; and National Natural Science Foundation of China, No. 82505372.
Institutional review board statement: This study was approved by Medical Ethics Committee of the First Affiliated Hospital of Anhui University of Chinese Medicine the (Approval No. 2021MCZQ11).
Informed consent statement: Patient consent was waived by the Medical Ethics Committee because this was a retrospective study using anonymized data.
Conflict-of-interest statement: The authors declare that they have no competing interests.
Data sharing statement: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Corresponding author: Ai-Juan Jiang, PhD, Professor, School of Integrated Chinese and Wes
Received: July 14, 2026
Revised: August 9, 2026
Accepted: October 9, 2026
Published online: November 15, 2026
Processing time: 114 Days and 19.6 Hours
Revised: August 9, 2026
Accepted: October 9, 2026
Published online: November 15, 2026
Processing time: 114 Days and 19.6 Hours
Core Tip
Core Tip: This study integrated Western medicine demographic and clinical characteristics, laboratory indicators, and traditional Chinese medicine syndrome features to construct prediction models for lower extremity arterial disease (LEAD) in type 2 diabetes mellitus using multiple machine learning algorithms. The XGBoost model based on integrated Western and Chinese medicine features exhibited the highest efficacy and has potential for the early identification and treatment of LEAD.