Work

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

Year: 2026

Type: article

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.

Source: Frontiers in Endocrinology

DOI: 10.3389/fendo.2026.1833972