A non-invasive urinary diagnostic signature for diabetic kidney disease revealed by machine learning and single-cell analysis.

ABSTRACT:

Background

Diabetic kidney disease (DKD) poses a significant health burden with inadequate diagnostic sensitivity. This study develops non-invasive biomarkers by integrating urinary and renal single-cell sequencing with machine learning.

Methods

This study analyzed DKD single-cell and bulk transcriptomic data from public repositories. We established a computational pipeline to distinguish kidney-originating cells in urinary sediments, enabling the identification of injury-associated gene signatures. These signatures were refined using machine learning to develop a diagnostic model, which was validated in independent cohorts. The biomarkers were further verified in DKD renal tissues at single-cell resolution and across multiple nephropathies. Functional and spatial analyses c

SUBMITTER: Chen Y 

PROVIDER: S-EPMC12758759 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature