Kidney Week

Abstract: FR-PO0520

Urinary Transcriptomic Analysis to Identify New Markers of Kidney Injury in Patients with Diabetes

Session Information

• CKM: Clinical - Trials, Epidemiology, and Biomarkers
  October 23, 2026 | Location: Exhibit Hall A, Convention Center
  Abstract Time: 10:00 AM - 12:00 PM

Category: Cardiovascular-Kidney-Metabolic Health

• 602 Cardiovascular-Kidney-Metabolic Health: Clinical

Authors

• Atchison, Douglas K., Henry Ford Health System, Detroit, Michigan, United States

Douglas K. Atchison, MD, PhD, FASN

• Meng, Ze, Henry Ford Health System, Detroit, Michigan, United States

Ze Meng

• Wu, Andrew, Henry Ford Health System, Detroit, Michigan, United States

Andrew Wu

• Mendez, Mariela, Henry Ford Health System, Detroit, Michigan, United States

Mariela Mendez, MS, PhD

• Ortiz, Pablo A., Henry Ford Health System, Detroit, Michigan, United States

Pablo A. Ortiz, PhD

Background

Patients who develop Type 2 diabetic kidney disease (DKD) have a higher risk of progression to end-stage kidney disease. Albuminuria remains a major predictor of progressive DKD and no other urinary markers are used in clinical practice to identify progressive DKD. Identifying new markers could help treat patients at risk for DKD and help discover new mechanisms of disease progression. The goal of this pilot study is to identify new kidney-specific genes using bulk RNAseq of cells shed in fresh urine that are associated with albuminuria in diabetic patients.

Methods

Non-diabetic control patients, normoalbuminuric type 2 diabetic patients (albumin/creatinine ratio < 30 mg/g, eGFR > 45 ml/min/1.73m² ) and albuminuric type 2 diabetic patients (albumin/creatinine ratio > 30 mg/g, eGFR > 45 ml/min/1.73m² ) were recruited from Nephrology, Endocrinology, Primary Care Clinics and Inpatient Wards at Henry Ford Health. First-morning urine was collected and processed within 3 hours for intact cell isolation followed by bulk RNA extraction as we previously reported (PMID: 36892045). Full-coverage transcriptomes were obtained by RNAseq. Analysis of differentially expressed genes (DEGs) was conducted using a negative binomial generalized linear model as implemented in DESeq2 R/Bioconductor. All genes with an average normalized count >10 and a false discovery rate (FDR)–adjusted P value <0.05 were analyzed using Ingenuity Pathway Analysis (IPA) software and CyberSort.

Results

Preliminary urinary RNAseq results of are available for 6 control patients and 15 diabetic patients. We identified 4,128 DEGs (1,750 down and 2,378 up) in cells shed in urine between control and diabetic groups. CyberSort analysis indicates that 35% of cells shed in urine identified as kidney cells (podocytes and nephron). The top 5 upregulated signaling pathways in diabetic patients identified via IPA were S100, IL-10, GPCR signaling, phagosome formation and oxidative stress-induced senescence. In this small cohort, we also identified some DEGs that were unique to the diabetic albuminuric population compared to control.

Conclusion

Our data demonstrates that this approach can identify DE kidney-specific genes in diabetic patients, that could be used in larger cohorts to predict GFR decline or albuminuria.

Funding

• Private Foundation Support