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Abstract: FR-OR35

Whole Blood DNA Methylation Signature, Circulating Proteins, and Risk of Progression to ESKD in Type 1 Diabetes (T1D)

Session Information

Category: Diabetic Kidney Disease

  • 701 Diabetic Kidney Disease: Basic

Authors

  • Chen, Zhuo, City of HopeDepartment of Diabetes Complications and Metabolism, Arthur Riggs Diabetes & Metabolism Research Institute and Beckman Research Institute, Duarte, California, United States
  • Satake, Eiichiro, Section on Genetics and Epidemiology, Research Division, Joslin Diabetes Center, Boston, Massachusetts, United States
  • Pezzolesi, Marcus G., Department of Internal Medicine, Division of Nephrology and Hypertension, University of Utah School of Medicine, Salt Lake City, Utah, United States
  • Md Dom, Zaipul, Section on Genetics and Epidemiology, Research Division, Joslin Diabetes Center, Boston, Massachusetts, United States
  • Stucki, Devorah Olivia, Department of Internal Medicine, Division of Nephrology and Hypertension, University of Utah School of Medicine, Salt Lake City, Utah, United States
  • Wu, Xiwei, Department of Computational and Quantitative Medicine, Beckman Research Institute of City of Hope, Duarte, California, United States
  • Krolewski, Andrzej S., Section on Genetics and Epidemiology, Research Division, Joslin Diabetes Center, Boston, Massachusetts, United States
  • Natarajan, Rama, City of HopeDepartment of Diabetes Complications and Metabolism, Arthur Riggs Diabetes & Metabolism Research Institute and Beckman Research Institute, Duarte, California, United States
Background

Diabetic Kidney Disease (DKD) can progress to end stage kidney disease (ESKD) which increases morbidity and mortality. Since commonly utilized clinical variables do not adequately predict ESKD onset, there is an unmet need to update the current ESKD risk prediction model with more sensitive biomarkers. We examined whether DNA methylation (DNAme) can fulfil this need.

Methods

Using human EPIC DNAme arrays, we profiled DNAme in whole blood DNAs of 277 well characterized T1D participants with DKD (median eGFR 52.2mL/min/1.73m2 and ACR 728.9mg/g) from the Joslin Kidney Study; 51% of our cohort developed ESKD during follow-up (7-20 years). We then performed epigenome-wide analysis followed by integration with genetics and circulating proteins data (Olink) from the same cohort, and developed statistical models including DNAme for ESKD-risk prediction.

Results

We identified DNAme at 17 CpGs associated with ESKD risk (ESKD-associated CpGs) independent of major demographic/clinical risk factors. These CpGs were located in/near genes related to pathogenesis of DKD and/or ESKD, including inflammation. Notably, 7 of these ESKD-associated CpGs had methylation quantitative trait loci SNPs. Some of these SNPs could affect binding sites for key transcription factors with functions related to DKD, suggesting novel links between genetic variants and ESKD via DNAme. Moreover, the impact of 5 of these CpGs on ESKD-risk were partially mediated by several circulating proteins previously reported to be associated with ESKD e.g. TNF-R27 and KIM1, suggesting downstream functions of these CpGs on target cells/tissues related to inflammation and renal injury. Notably, using our ESKD-associated CpGs, we developed two updated ESKD prediction models by adding DNAme at selected CpGs, or a DNAme score (imputed using selected CpGs) to the Clinical Model (using only clinical predictors). Both updated models markedly improved ESKD prediction (by 20%) compared to the Clinical Model alone.

Conclusion

Our results identified novel putative epigenetic DNAme prognostic biomarkers to significantly improve ESKD risk prediction in T1D, and uncovered molecular mechanisms of DNAme involvement in ESKD, both important for early detection and prevention.

Funding

  • NIDDK Support