Evidence map›Paper›PMID 42389435›Full record

ArticleJournal of translational internal medicine2026

Subphenotyping of prediabetes with a phenotypic tree model and its correlations with outcomes: Insights from two Chinese cohorts.

Jia Zhang, Jing Ma, Zijian Tian, Yan Zhao, Jian Shao, Guogang Xu, Kaixin Zhou, Qi Pan, Weihao Wang, Lixin Guo

Abstract read
In one paragraph

Article in Journal of translational internal medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Jia ZhangDepartment of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Jing MaHealth Management Institute, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.
Zijian TianGuangzhou Laboratory, Guangzhou International Bio Island, Guangzhou, Guangdong Province, China.
Yan ZhaoDepartment of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Jian ShaoGuangzhou Laboratory, Guangzhou International Bio Island, Guangzhou, Guangdong Province, China.
Guogang XuHealth Management Institute, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.
Kaixin ZhouGuangzhou Laboratory, Guangzhou International Bio Island, Guangzhou, Guangdong Province, China.
Qi PanDepartment of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.ORCID https://orcid.org/0000-0002-2567-1439
Weihao WangDepartment of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.ORCID https://orcid.org/0000-0002-5896-2793
Lixin GuoDepartment of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.ORCID https://orcid.org/0000-0003-2609-8387

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Objectives: To investigate whether stratifying participants with prediabetes using the Scottish tree model could identify their risks of developing diabetes and related outcomes. Methods: The current study enrolled subjects with prediabetes at baseline from the China Health and Retirement Longitudinal Study and the Chinese follow-up cohort, comprising 10,165 and 4,090 participants, respectively. We assigned individuals to the Scottish tree using the mapping function based on nine phenotypic variables, including HbA1c, BMI, blood lipids, liver enzyme, creatinine, and blood pressure. Cox proportional hazards regression models were utilized to assess the relationships between dimensions on the tree and future diabetes, renal and cardiovascular diseases, and death from all causes. Results: Our findings showed that individuals in the upper-right branch of the tree exhibited the increased risk of progression to subclinical atherosclerosis, cardiovascular disease, microalbuminuria, and all-cause mortality, potentially linked to elevated blood pressure, dyslipidemia, and body fat percentage. Individuals in the right part of the tree were more likely to progress to the onset of type 2 diabetes and chronic kidney disease, which was possibly associated with insulin resistance, triglycerides, and blood pressure. Conclusions: The phenotypic tree model could be incorporated into future risk stratification and treatment plans to better benefit individuals with prediabetes.

Indexed as

outcomesprediabetesprogressionsubphenotypingtype 2 diabetes

Identifiers

PMID42389435
PMCPMC13320526

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