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

BACKGROUND: Diabetic kidney disease (DKD) progresses to end-stage renal disease more rapidly than chronic kidney disease due to persistent hyperglycemia and early activation of multiple pathways. Early detection of DKD is crucial to identify subtle kidney damage before clinical symptoms appear. METHODS: This study combined human serum proteomics and 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 type 2 diabetes mellitus (T2DM) patients, 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 DKD early detection, with results validated in 435 participants from four independent clinical cohorts. RESULTS: 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 DKD patients' serum. In db/db mice, all three biomarkers increased in multiple organs by 18 weeks of age. In DKD patient sera, 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 about 13% improvements in accuracy when serum metabolites were included to distinguish the progression from early to advanced stages after DKD. CONCLUSION: Integrating synergistic biomarkers with serum metabolomics enhances early detection of DKD, potentially improving outcomes by slowing disease progression in T2DM patients.