| ISSN |
1948-9358 (online) |
| 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: http://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
|
| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Endocrinology & Metabolism |
| Manuscript Type |
Clinical Trials Study |
| Article Title |
Metabolic feature-based clustering for subtype identification and longitudinal outcome predictions in the Chinese prediabetic population
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
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 and Li-Ping Yu |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| National High Level Hospital Clinical Research Funding |
2025-NHLHCRF-JBGS-B-WZ-01 |
| National Key Research and Development Program of China |
2018YFC1313902 |
|
| 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 |
| Key Words |
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. |
| Publish Date |
2026-07-09 06:58 |
| Citation |
Zhang SH, Zhang JP, Song LL, Wu LL, Li ZQ, He YF, Deng RF, Ma WL, Zhang C, Zhang B, Yu LP. Metabolic feature-based clustering for subtype identification and longitudinal outcome predictions in the Chinese prediabetic population. World J Diabetes 2026; 17(7): 119604
|
| URL |
https://www.wjgnet.com/1948-9358/full/v17/i7/119604.htm |
| DOI |
https://doi.org/10.4239/wjd.119604 |
