Metabolic feature-based clustering for subtype identification and longitudinal outcome predictions in the Chinese prediabetic population

doi:10.4239/wjd.119604

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World Journal of Diabetes

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1948-9358

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Diabetes. Jul 15, 2026; 17(7): 119604
Published online Jul 15, 2026. doi: 10.4239/wjd.119604

Metabolic feature-based clustering for subtype identification and longitudinal outcome predictions in the Chinese prediabetic population

Shu-Han Zhang, Jin-Ping Zhang, Lu-Lu Song, Li-Li Wu, Zhao-Qin Li, Yi-Fan He, Rui-Fen Deng, Wan-Lu Ma, Cong Zhang, Bo Zhang, Li-Ping Yu

Shu-Han Zhang, Department of Endocrinology and Metabolism, Peking University Health Science Center, Beijing 100191, China

Shu-Han Zhang, Jin-Ping Zhang, Lu-Lu Song, Li-Li Wu, Zhao-Qin Li, Yi-Fan He, Rui-Fen Deng, Wan-Lu Ma, Cong Zhang, Bo Zhang, Li-Ping Yu, Department of Endocrinology, China-Japan Friendship Hospital, Beijing 100029, China

Co-corresponding authors: Bo Zhang and Li-Ping Yu.

Author contributions: Zhang SH and Yu LP designed the research study, contributed to the discussion, and wrote and edited the manuscript; Zhang SH, Yu LP, and Zhang B reviewed the manuscript; Zhang JP, Song LL, Wu LL, Li ZQ, He YF, Deng RF, Ma WL, and Zhang C provided suggestions for the revision of the manuscript; Zhang B is the guarantor of this work and, as such, has full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis; Zhang B and Yu LP made equal contributions as co-corresponding authors. All authors approved the final version of the manuscript.

AI contribution statement: The study design, scientific content, data, statistical analyses, interpretation of the results, and conclusions were all developed independently by our research team. During manuscript preparation, we used AI assistance for language support in parts of the Abstract, based on the research content and Chinese draft prepared by the authors. Therefore, some wording in the English text may reflect AI-assisted language editing. However, the scientific content, logic, and final wording were carefully reviewed, revised, and approved by our team before submission.

Supported by National High Level Hospital Clinical Research Funding, No. 2025-NHLHCRF-JBGS-B-WZ-01; and National Key Research and Development Program of China, No. 2018YFC1313902.

Institutional review board statement: The study was reviewed and approved by the Clinical Research Ethics Committee of China-Japan Friendship Hospital, No. 2019-63-K43.

Clinical trial registration statement: This study has been registered at https://clinicaltrials.gov/study/NCT03987438, No. NCT03987438.

Informed consent statement: All study participants or their legal guardians provided informed written consent prior to enrollment in the study.

Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.

CONSORT 2010 statement: The authors have read the CONSORT 2010 Statement, and the manuscript was prepared and revised according to the CONSORT 2010 Statement.

Data sharing statement: The datasets generated during and/or analyzed in the current study are available from the corresponding author upon reasonable request.

Corresponding author: Bo Zhang, MD, Professor, Department of Endocrinology, China-Japan Friendship Hospital, No. 2 Yinghua East Street, Chaoyang District, Beijing 100029, China. zhangbo@zryhyy.com.cn

Received: February 7, 2026
Revised: March 13, 2026
Accepted: June 3, 2026
Published online: July 15, 2026
Processing time: 152 Days and 13.8 Hours

Abstract

BACKGROUND

In China, the prevalence of prediabetes is alarmingly high, affecting approximately 35.7% of adults. Although lifestyle interventions can reduce diabetes onset, the manifestations of prediabetes vary across individuals with age, body composition, insulin resistance, and beta-cell function. Therefore, current diagnostic approaches based solely on glucose thresholds are not sufficient, necessitating improved risk stratification through data-driven, unsupervised clustering methods.

AIM

To investigate the metabolic heterogeneity of prediabetes in Chinese individuals and its association with lifestyle intervention outcomes.

METHODS

A prospective, multicenter cohort study was conducted in China with 2527 adults aged 18-70 years, at high risk for diabetes. Centers were assigned to either enhanced lifestyle management or standard health education. Enhanced management included individualized dietary energy prescriptions, wearable activity trackers, a mobile co-management app, and structured follow-up. Unsupervised K-means clustering identified four metabolic subtypes. Longitudinal outcomes, including incident diabetes and reversion to normoglycemia, were analyzed using Cox models adjusted for intervention and demographic covariates.

RESULTS

Four distinct prediabetes subtypes were identified: Mild obesity-related dysmetabolism (MOD, n = 177), mild age-related dysmetabolism (MARD, n = 190), severe insulin resistance (n = 95), and severe insulin deficiency (n = 159). Of 621 participants, 367 (59.1%) contributed longitudinal data; although attrition differed significantly across subtypes (P < 0.001), inverse probability of censoring weighting confirmed the robustness of all estimates. After a median follow-up of 735 days, MOD had a lower risk of diabetes progression than MARD [adjusted hazard ratio (aHR) = 0.52, P = 0.028] and a greater likelihood of reversion to normoglycemia (aHR = 2.08, P = 0.049). Adjusted for subtype and sex, enhanced lifestyle management reduced diabetes progression risk (aHR = 0.52, 95% confidence interval: 0.30-0.89; P = 0.017), but not reversion to normoglycemia (P = 0.159).

CONCLUSION

Data-driven prediabetes subtyping improved risk stratification. The MOD subtype showed more favorable metabolic trajectories than the MARD subtype. Enhanced lifestyle management was associated with reduced diabetes progression risk.

Keywords: Prediabetes; Metabolic subtypes; Longitudinal outcomes; Lifestyle intervention; Risk stratification

Core Tip: Using unsupervised k-means clustering of core metabolic features, we identified four clinically meaningful prediabetes subtypes: Mild obesity-related dysmetabolism, mild age-related dysmetabolism, severe insulin resistance, and severe insulin deficiency. Among them, mild obesity-related dysmetabolism showed a clear metabolic advantage over mild age-related dysmetabolism, with a lower risk of progression to diabetes and a greater chance of reverting to normal glucose tolerance. Although enhanced lifestyle management was associated with reduced progression risk, metabolic subtype membership, not intervention exposure, was the main driver of outcome heterogeneity, highlighting its value for baseline risk stratification and precision prevention.