There is an increasing need for new biomarkers improving prediction of chronic kidney disease (CKD) in individuals with type 2 diabetes (T2D). We aimed to identify blood-based epigenetic biomarkers associated with incident CKD and develop a methylation risk score (MRS) predicting CKD in newlydiagnosed individuals with T2D.
DNA methylation was analysed epigenome-wide in blood from 487 newly-diagnosed individuals with T2D, of whom 88 developed CKD during 11.5-year follow-up. Weighted Cox regression was used to associate methylation with incident CKD. Weighted logistic models and cross-validation (k=5) were performed to test if the MRS could predict CKD.
Methylation at 37 sites was associated with CKD development, based on FDR<5% and absolute methylation differences ≥5% between individuals with incident CKD and those free of CKD during follow-up. Notably, 15 genes annotated to these sites, e.g., TGFBI, SHISA3, and SLC43A2 (encoding LAT4), have been linked to CKD or related risk factors including blood pressure, BMI, or eGFR. Using a MRS including 37 sites and cross-validation for prediction of CKD, we generated ROC curves with AUC=0.82 for the MRS and AUC=0.87 for the combination of MRS and clinical factors. Importantly, ROC curves including the MRS had significantly better AUCs versus the one only including clinical factors (AUC=0.72). The combined epigenetic biomarker had high accuracy in identifying individuals free of future CKD (negative predictive value=94.6%).
We discovered a high-performance epigenetic biomarker for predicting CKD, encouraging its potential role in precision medicine, risk stratification, and targeted prevention in T2D.
This article contains supplementary material online at https://doi.org/10.2337/figshare.28062917.
