DNA methylation markers for kidney function and progression of diabetic kidney disease

Abstract

Epigenetic markers are potential biomarkers for diabetes and related complications. Using a prospective cohort from the Hong Kong Diabetes Register, we perform two independent epigenome-wide association studies to identify methylation markers associated with baseline estimated glomerular filtration rate (eGFR) and subsequent decline in kidney function (eGFR slope), respectively, in 1,271 type 2 diabetes subjects. Here we show 40 (30 previously unidentified) and eight (all previously unidentified) CpG sites individually reach epigenome-wide significance for baseline eGFR and eGFR slope, respectively. We also develop a multisite analysis method, which selects 64 and 37 CpG sites for baseline eGFR and eGFR slope, respectively. These models are validated in an independent cohort of Native Americans with type 2 diabetes. Our identified CpG sites are near genes enriched for functional roles in kidney diseases, and some show association with renal damage. This study highlights the potential of methylation markers in risk stratification of kidney disease among type 2 diabetes individuals.

Fig. 1. Association between CpG methylation and renal function.

The methylation level of each CpG site was tested for its association with baseline eGFR (a–c) and eGFR slope (d–f). The results of all the 434,908 CpG sites analyzed in this study are shown using Manhattan plots (a, d), quantile–quantile (QQ) plots (b, e), and volcano plots (c, f). P values were computed using two-sided Student’s t test. In the Manhattan plots, CpG sites with a Bonferroni-corrected P value <0.05 are shown in red. The horizontal red lines show the cutoff above which all sites are significant at FDR = 0.05. In the QQ plots, the diagonal straight line is the expectation under the null hypothesis. λ is the inflation factor. In the volcano plots, CpG sites with a Bonferroni-corrected P value <0.05 are shown in red.

Fig. 2. Performance of the multisite models.

Scatter plots of inferred baseline eGFR and eGFR slope against their corresponding actual measurements using selected CpG sites based on the models constructed from the primary cohort and applied to the primary cohort (a–d) or the Native American cohort (trained using CpG sites available to both cohorts) (e–h). In each panel, the black lines mark the best fit lines of linear regression. Source data are provided as a Source Data file.

Fig. 3. Support for the functional significance of genes near the CpG sites identified in our single-site and multisite analyses.

Each row corresponds to a CpG site and all genes within 1 kb from it. The “Single-site” and “Multi-site” columns show whether a site is significant at FDR = 0·05 in our single-site analysis and whether it is included in the final multisite model, respectively. The “DNAm” and “DEGs” columns show whether at least one of the nearby genes is differentially methylated or differentially expressed in samples with and without kidney function decline in one or more previous methylation^,– or gene expression studies^,,, respectively. The “eQTL” column shows whether at least one of the nearby genes is associated with an expression quantitative trait locus identified in human kidney samples in a previous study. The “MarkerGenes” column shows whether at least one of the nearby genes is a cell-type-specific marker of a major kidney cell type as identified previously. The “GWAS” column shows whether at least one of the nearby genes is prioritized by GWAS results in two recent studies^,. Only CpG sites where the nearby genes have at least 3 and 1 functional supports, respectively, for baseline eGFR and eGFR slope, are shown.

Fig. 4. Performance of risk scores by risk equations and the multisite models.

AUROC and AUPR of the risk scores from simple negative value of baseline eGFR, JADE risk model, UKPDS-OM2, and our multisite models with or without covariates. The risk scores of the JADE model and UKPDS-OM2 were calculated with the risk equations in the original paper. The risk scores of the multisite models were calculated using the inferred eGFR slope with 5-fold cross-validation. Source data are provided as a Source Data file.