Urinary Sediment Transcriptomic and Longitudinal Data to Investigate Renal Function Decline in Type 1 Diabetes

Area of Science:

• Nephrology
• Genetics
• Endocrinology

Background:

• Diabetic kidney disease (DKD) is a major complication of type 1 diabetes (T1D).
• Predicting estimated glomerular filtration rate (eGFR) decline in T1D patients is crucial for timely intervention.
• Urinary biomarkers offer a non-invasive approach to monitor kidney function.

Purpose of the Study:

• To identify and validate urinary genes associated with eGFR decline in T1D patients.
• To assess the utility of these genes in predicting longitudinal eGFR changes.

Main Methods:

• Discovery/validation approach using urinary sediment transcriptomics in T1D patients.
• Selection of differentially expressed genes between rapid (decliners) and stable (non-decliners) eGFR groups.
• Validation of candidate genes using quantitative reverse transcription PCR (qRT-PCR) in a longitudinal T1D cohort.

Main Results:

• Transcriptomic analysis identified 158 differentially expressed genes.
• CYP4F22 and PMP22 were confirmed as differentially expressed between decliners and non-decliners.
• Eight genes (CYP4F22, LYPD3, PMP22, MAP1LC3C, HS3ST2, GPNMB, CDH6, PKD2L1) significantly modified eGFR slope over time.

Conclusions:

• The identified eight urinary genes significantly improved the accuracy of predicting renal function in T1D patients.
• These genes may represent novel mechanisms underlying diabetic kidney disease progression.
• Urinary gene expression profiling holds promise for early detection and monitoring of DKD.