Novel phenotypic clusters of adult-onset diabetic kidney disease based on static and dynamic clustering algorithms - a data-driven multi-center study.

ABSTRACT:

Objective

This study aimed to identify clinically interpretable DKD sub-phenotypes for personalized prognosis and intervention.

Methods

We enrolled participants with type 2 diabetes mellitus (T2DM) from three hospitals in China. Clinically interpretable sub-phenotypes were identified using two complementary clustering frameworks: a static model (k-means clustering) capturing the metabolic-renal profile at DKD diagnosis, and a dynamic model (group-based multi-trajectory model, GBMTM) reconstructing longitudinal trajectories preceding diagnosis. Associations between DKD clusters and post-diagnosis DKD progression were estimated using cox regression.

Results

A total of 1520 participants were included for the analysis of static clustering, 32.53% were females with a mean

SUBMITTER: Li K 

PROVIDER: S-EPMC13643975 | biostudies-literature | 2026 Jul

REPOSITORIES: biostudies-literature