Blood DNA methylation profiling identifies candidate diabetic kidney disease risk signatures in the PROPHECY Aboriginal Diabetes Study
Abstract
Background: Diabetic kidney disease is a leading cause of kidney failure, yet molecular predictors of risk remain poorly defined, particularly among Indigenous Australians who experience a disproportionate burden. We aimed to identify blood-derived candidate DNA methylation signatures associated with Kidney Disease: Improving Global Outcomes (KDIGO) risk categories in the PROPHECY Aboriginal Diabetes Study.
Methods: We applied methyl-CpG binding domain sequencing to blood-derived DNA from 95 participants stratified as non-diabetic (n = 23), low-risk (n = 21), moderate-risk (n = 26), high-risk (n = 14) or very-high-risk (n = 11). Differential MBD-enriched methylated regions were assessed using covariate-adjusted models incorporating clinical, leukocyte-marker and CpG-density covariates. Candidate regions were prioritised using a nominal discovery threshold and explored using annotation, random forest modelling and human podocyte experiments.
Results: For the primary diabetic kidney disease risk contrast, 1,317 regions met the nominal discovery threshold of P < 0.001, but no region reached FDR < 0.10. Across 16 predefined analysis terms - 11 pairwise/grouped contrasts and 5 single-group terms - 22,392 nominal DMR entries met P < 0.001, corresponding to 13,024 unique candidate regions. Exploratory random forest models suggested that methylation features may contribute discriminatory information beyond clinical covariates, although estimates require independent validation. Podocyte analyses provided locus-level functional context but not genome-wide cross-tissue replication.
Conclusions: This cross-sectional discovery study nominates research-grade candidate methylation biomarkers and pathways for future longitudinal validation in PROPHECY and external cohorts.
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BibTeX
@article{maxwell2026blood,
title = {{Blood DNA methylation profiling identifies candidate diabetic kidney disease risk signatures in the PROPHECY Aboriginal Diabetes Study}},
author = {Scott Maxwell and Ishant Khurana and Sam Buckberry and Harikrishnan Kaipananickal and James Breen and Natasha J. Howard and Jun Okabe and Sorine Birkelund and Moshe Olshansky and Paul Z. Zimmet and Mark E. Cooper and Alex Brown and Assam El-Osta},
journal = {Communications Medicine},
year = {2026},
month = oct,
doi = {10.1038/s43856-026-01959-y},
url = {https://www.nature.com/articles/s43856-026-01959-y},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Blood DNA methylation profiling identifies candidate diabetic kidney disease risk signatures in the PROPHECY Aboriginal Diabetes Study
AU - Scott Maxwell
AU - Ishant Khurana
AU - Sam Buckberry
AU - Harikrishnan Kaipananickal
AU - James Breen
AU - Natasha J. Howard
AU - Jun Okabe
AU - Sorine Birkelund
AU - Moshe Olshansky
AU - Paul Z. Zimmet
AU - Mark E. Cooper
AU - Alex Brown
AU - Assam El-Osta
PY - 2026
DA - 2026/10/13
JO - Communications Medicine
DO - 10.1038/s43856-026-01959-y
UR - https://www.nature.com/articles/s43856-026-01959-y
AB - Background: Diabetic kidney disease is a leading cause of kidney failure, yet molecular predictors of risk remain poorly defined, particularly among Indigenous Australians who experience a disproportionate burden. We aimed to identify blood-derived candidate DNA methylation signatures associated with Kidney Disease: Improving Global Outcomes (KDIGO) risk categories in the PROPHECY Aboriginal Diabetes Study. Methods: We applied methyl-CpG binding domain sequencing to blood-derived DNA from 95 participants stratified as non-diabetic (n = 23), low-risk (n = 21), moderate-risk (n = 26), high-risk (n = 14) or very-high-risk (n = 11). Differential MBD-enriched methylated regions were assessed using covariate-adjusted models incorporating clinical, leukocyte-marker and CpG-density covariates. Candidate regions were prioritised using a nominal discovery threshold and explored using annotation, random forest modelling and human podocyte experiments. Results: For the primary diabetic kidney disease risk contrast, 1,317 regions met the nominal discovery threshold of P < 0.001, but no region reached FDR < 0.10. Across 16 predefined analysis terms - 11 pairwise/grouped contrasts and 5 single-group terms - 22,392 nominal DMR entries met P < 0.001, corresponding to 13,024 unique candidate regions. Exploratory random forest models suggested that methylation features may contribute discriminatory information beyond clinical covariates, although estimates require independent validation. Podocyte analyses provided locus-level functional context but not genome-wide cross-tissue replication. Conclusions: This cross-sectional discovery study nominates research-grade candidate methylation biomarkers and pathways for future longitudinal validation in PROPHECY and external cohorts.
SN - 2730-664X
ER -