Optimizing Early Detection of Diabetic Kidney Disease through Synergistic Biomarkers and Serum Metabolites in Humans

Diabetic kidney disease (DKD) often progresses to end-stage renal disease more rapidly than nondiabetic kidney disease because of persistent hyperglycemia and early activation of multiple pathogenic pathways. Early detection of DKD is crucial for identifying subtle kidney damage before clinical symptoms appear.

This study combined human serum proteomics with public single-cell RNA sequencing and spatial transcriptomics data from diabetic kidneys to identify key biomarkers for DKD diagnosis. These biomarkers were validated in multiple organs of db/db mice at early and advanced stages. In a discovery cohort, sera from 173 healthy adults and 444 patients with type 2 diabetes mellitus (T2DM), with or without kidney disease, were analyzed using metabolomics and enzyme-linked immunosorbent assay (ELISA). Multiple machine learning algorithms were developed to integrate synergistic biomarkers and serum metabolites for early DKD detection, with results validated in 435 participants from four independent clinical cohorts.

Metalloproteinase-7 (MMP-7) and tenascin C (TNC) were elevated in human diabetic kidneys at the single-cell and spatial levels. Proteomics indicated upregulation of serum amyloid A1 (SAA1) and TNC in the serum of patients with DKD. In db/db mice, all three biomarkers increased in multiple organs by 18 weeks of age. In sera from patients with DKD, MMP-7 and TNC levels were consistently elevated across cohorts. The new algorithms combining MMP-7, SAA1, and TNC enhanced early-stage DKD detection, with approximately 13% improvements in accuracy when serum metabolites were included to distinguish progression from early to advanced DKD stages.

Integrating synergistic biomarkers with serum metabolomics enhances the early detection of DKD, potentially improving outcomes by slowing disease progression in patients with T2DM.