Zhao XF, Tang SS, Ji HY, An XD, Kang XM, Jiang LL, Lian FM. Potentially modifiable predictors in models for undiagnosed and incident type 2 diabetes: A systematic review. World J Diabetes 2026; 17(11): 123851 [DOI: 10.4239/wjd.123851]
Corresponding Author of This Article
Feng-Mei Lian, MD, Professor, Institute of Metabolic Diseases, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, No. 5 Beixiange Xicheng District, Beijing 100053, China. lfm565@sohu.com
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Zhao XF, Tang SS, Ji HY, An XD, Kang XM, Jiang LL, Lian FM. Potentially modifiable predictors in models for undiagnosed and incident type 2 diabetes: A systematic review. World J Diabetes 2026; 17(11): 123851 [DOI: 10.4239/wjd.123851]
World J Diabetes. Nov 15, 2026; 17(11): 123851 Published online Nov 15, 2026. doi: 10.4239/wjd.123851
Potentially modifiable predictors in models for undiagnosed and incident type 2 diabetes: A systematic review
Xue-Fei Zhao, Shan-Shan Tang, Hang-Yu Ji, Xue-Dong An, Xiao-Min Kang, Lin-Lin Jiang, Feng-Mei Lian
Xue-Fei Zhao, Shan-Shan Tang, Hang-Yu Ji, Xue-Dong An, Xiao-Min Kang, Lin-Lin Jiang, Department of Endocrinology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
Shan-Shan Tang, Department of Endocrinology, Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou 215009, Jiangsu Province, China
Feng-Mei Lian, Institute of Metabolic Diseases, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
Author contributions: Lian FM and Ji HY conceived and designed the study; Zhao XF and Tang SS developed the search strategy, performed the systematic literature search, and independently screened the titles, abstracts, and full texts according to the eligibility criteria, with Ji HY serving as the third reviewer to resolve any discrepancies; Zhao XF, Tang SS, and Kang XM independently extracted data from the included studies using a standardized data collection form, and any disagreements were adjudicated by Ji HY; An XD and Jiang LL contributed to data interpretation, critical revision, and improvement of the manuscript; Kang XM and Jiang LL provided technical support for literature organization, reference management, and data synthesis; and all authors approved the final version of the manuscript and agreed to be accountable for all aspects of the work, ensuring its integrity and accuracy. Zhao XF and Tang SS contributed equally to this work.
AI contribution statement: Portions of this manuscript were edited using AI tools solely for language refinement. The authors carefully reviewed and verified all AI-assisted outputs and take full responsibility for the scientific content of the manuscript.
Supported by the National Natural Science Foundation of China, No. T2341018; and the Science and Technology Innovation Project of the Chinese Academy of Traditional Chinese Medicine, No. CI2023C049YLL.
Conflict-of-interest statement: The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. No specific financial interests, affiliations, or other relevant conflicts exist for any of the authors.
Corresponding author: Feng-Mei Lian, MD, Professor, Institute of Metabolic Diseases, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, No. 5 Beixiange Xicheng District, Beijing 100053, China. lfm565@sohu.com
Received: June 1, 2026 Revised: August 2, 2026 Accepted: September 23, 2026 Published online: November 15, 2026 Processing time: 159 Days and 15.4 Hours
Abstract
BACKGROUND
Type 2 diabetes mellitus (T2DM) prediction models are increasingly used to identify undiagnosed disease and estimate future T2DM risk, but the clinical relevance of potentially modifiable predictors remains unclear.
AIM
To systematically review diagnostic and prognostic T2DM prediction models, focusing on potentially modifiable predictors, model performance, validation, and methodological quality.
METHODS
PubMed, Embase, and Web of Science were searched for studies published from January 2016 to February 2026 that developed or substantially updated multivariable models for prevalent undiagnosed or incident T2DM and included at least 1 potentially modifiable predictor. Study characteristics, modeling methods, predictors, validation, and performance were extracted. Risk of bias and applicability were assessed using the Prediction Model Risk of Bias Assessment Tool. Findings were synthesized descriptively because of substantial heterogeneity.
RESULTS
Eighty-two studies were included. Most were conducted in China or Japan, and 47.6% used prospective cohort designs. Only 28 studies performed external validation. Overall risk of bias was high in 67 studies (81.7%), mainly because of analytical limitations, including data-driven predictor selection and insufficient calibration assessment. Common predictors included age, body mass index, glucose measures, sex, family history, and triglycerides. Potentially modifiable domains included adiposity, smoking, physical inactivity, diet, blood pressure, and glycemic and lipid biomarkers.
CONCLUSION
T2DM prediction models increasingly incorporate potentially modifiable predictors, but clinical translation is limited by high risk of bias, inadequate calibration, and insufficient external validation. More rigorous development and prospective evaluation are needed before routine implementation.
Core Tip: This systematic review of 82 studies developing type 2 diabetes mellitus prediction models (2016–2025) shows that while modifiable risk factors (e.g., obesity, smoking, and physical inactivity) are increasingly included, most models have a high risk of bias, use inappropriate variable selection methods, and lack external validation. Future models should prioritize rigorous methodology and clinically actionable predictors to improve early detection and prevention.
