Evidence map›Paper›PMID 42312201›Full record

ArticleFrontiers in endocrinology2026

Longitudinal trajectories of urinary albumin-to-creatinine ratio and risk of proteinuria among Chinese patients with type 2 diabetes: a single-center retrospective cohort study.

Qian Chen, Tieqiao Wang, Xiaoqing Tian, Qiankai Jin, Li Li, Yushan Mao, Guoqing Huang

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

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Qian ChenDepartment of Endocrinology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.
Tieqiao WangDepartment of Endocrinology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.
Xiaoqing TianDepartment of Gastroenterology, Zhenhai District People's Hospital, Ningbo, China.
Qiankai JinDepartment of Endocrinology, Beilun District People's Hospital, Ningbo, China.
Li LiDepartment of Endocrinology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.
Yushan MaoDepartment of Endocrinology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.
Guoqing HuangDepartment of Endocrinology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Urinary albumin-to-creatinine ratio (UACR) is a key marker for monitoring proteinuria progression in type 2 diabetes mellitus (T2DM). However, UACR trajectory patterns and their association with proteinuria risk remain underexplored. Methods: This retrospective cohort study included 3,101 T2DM patients (baseline UACR <30 mg/g) with regular follow-up from March 2018 to October 2024 at the Ningbo Metabolic Management Center (MMC) subcenter. Clinical data were obtained from electronic medical records. Group-based trajectory modeling (GBTM) identified UACR trajectory patterns. Partial Least Squares Discriminant Analysis (PLS-DA) with Boruta algorithm selected key variables associated with trajectories. Additionally, we used the Light Gradient Boosting Machine (LightGBM) algorithm for multi-class classification modeling and Shapley Additive exPlanations (SHAP) values to quantify individual feature contributions to distinct trajectories. Multivariable Cox regression evaluated proteinuria risk by trajectory. Results: GBTM analysis identified three distinct UACR trajectories: low-normal, mid-range normal, and rising with fluctuation groups. PLS-DA with Boruta algorithm selected 11 significant features, including baseline UACR, sex, height, Hb, HCT, BMI, waist circumference, RBC, FCP, SCR, and FINS. Meanwhile, we explained the prediction results of a multi-class LightGBM model by assigning SHAP values to 11 features. Multivariable Cox regression showed significantly increased proteinuria risk in both mid-range normal (HR = 48.40, 95% CI: 14.67-159.67) and rising with fluctuation groups (HR = 509.56, 95% CI: 157.01-1653.70) compared to low-normal group. Conclusions: Identifying distinct UACR trajectories in Chinese T2DM patients, particularly the strong association between rising with fluctuation pattern and proteinuria risk, indicates that trajectory-informed patient classification could enhance early-stage diabetic kidney disease screening and preventive strategies.

Indexed as

AlbuminuriaCreatinineDiabetes Mellitus, Type 2Diabetic NephropathiesProteinuriaAgedBiomarkersChinaEast Asian PeopleFemaleHumansLongitudinal StudiesMaleMiddle AgedRetrospective StudiesRisk FactorsBiomarkersCreatininegroup-based trajectory modelingproteinuriaretrospective cohorttype 2 diabetes mellitusurinary albumin-to-creatinine ratio

Identifiers

PMID42312201
PMCPMC13268924

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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.