Evidence map›Paper›PMID 41535268›Full record

ArticleNature communications2026

Precision phenotyping of type 2 diabetes in chinese populations using a variational autoencoder-informed tree model.

Tong Yue, Wenhao Zhang, Yu Ding, Xueying Zheng, Yunjie Ma, Juliana C N Chan, Eric S H Lau, Juliana N M Lui, Guoxi Jin, Wen Xu and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Tong Yue *Department of Endocrinology and Metabolism, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, 230026, Hefei, China.
Wenhao Zhang *Department of Endocrinology and Metabolism, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, 230026, Hefei, China.
Yu Ding *Department of Endocrinology and Metabolism, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, 230026, Hefei, China.ORCID http://orcid.org/0000-0003-1617-2125
Xueying ZhengDepartment of Endocrinology and Metabolism, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, 230026, Hefei, China.
Yunjie MaInstitute of Dataspace, Hefei Comprehensive National Science Center, 230088, Hefei, China.
Juliana C N ChanDepartment of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China.ORCID http://orcid.org/0000-0003-1325-1194
Eric S H LauDepartment of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China.ORCID http://orcid.org/0000-0003-1581-5643
Juliana N M LuiDepartment of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China.
Guoxi JinDepartment of Endocrinology, the First Affiliated Hospital of Bengbu Medical University, 233004, Bengbu, China.
Wen XuDepartment of Endocrinology and Metabolism, the Third Affiliated Hospital of Sun Yat-sen University, 510630, Guangzhou, China.
Yan BiDepartment of Endocrinology, Nanjing Drum Tower Hospital, 210008, Nanjing, China.
Zuocheng WangInstitute of Dataspace, Hefei Comprehensive National Science Center, 230088, Hefei, China.
Sheng NieDivision of Nephrology, National Clinical Research Center for Kidney Disease, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, 510510, Guangzhou, China.
Mengchun GongInstitute of Health Management, Southern Medical University, 510510, Guangzhou, China.ORCID http://orcid.org/0000-0001-8197-6643
Ewan R PearsonDivision of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, UK.ORCID http://orcid.org/0000-0001-9237-8585
Sihui LuoDepartment of Endocrinology and Metabolism, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, 230026, Hefei, China. luosihui@ustc.edu.cn.ORCID http://orcid.org/0000-0001-8503-0310
Jianping WengDepartment of Endocrinology and Metabolism, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, 230026, Hefei, China. wengjp@ustc.edu.cn.ORCID http://orcid.org/0000-0002-7889-1697

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Type 2 diabetes (T2D) exhibits clinical heterogeneity, yet most existing classification models are derived from European populations and face challenges in clinical application. Here, we evaluate the generalizability of a tree-like graph structure from Scottish data to 32,501 newly diagnosed T2D patients from a multi-center Chinese cohort comprising over 8.6 million individuals. We observe similar distribution between the Scottish and Chinese individuals in heart and kidney outcomes, but diabetic retinopathy varies across ancestries even within similar phenotypes. To capture T2D Chinese-specific heterogeneity, we apply a variational autoencoder (VAE) framework to identify key clinical features and construct a tree structure using the Discriminative Dimensionality Reduction Tree (DDRTree) algorithm. This Chinese tree model is validated in two independent external cohorts and revealed longitudinal phenotypic shifts trending toward higher-risk branches. Our findings emphasize the need for population-specific classification frameworks to advance precision diabetology through individualized risk prediction and specialized treatment guidelines.

Indexed as

Diabetes Mellitus, Type 2AlgorithmsAsian PeopleAutoencoderChinaDiabetic RetinopathyDimensionality ReductionFemaleHumansMiddle AgedPhenotypeScotland

Identifiers

PMID41535268
PMCPMC12886989

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.