Kidney cytosine methylation changes improve renal function decline estimation in patients with diabetic kidney disease

Caroline Gluck, Chengxiang Qiu, Sang Youb Han, Matthew Palmer, Jihwan Park, Yi An Ko, Yuting Guan, Xin Sheng, Robert L. Hanson, Jing Huang, Yong Chen, Ae Seo Deok Park, Maria Concepcion Izquierdo, Ioannis Mantzaris, Amit Verma, James Pullman, Hongzhe Li, Katalin Susztak

Research output: Contribution to journalArticlepeer-review

52 Scopus citations

Abstract

Epigenetic changes might provide the biological explanation for the long-lasting impact of metabolic alterations of diabetic kidney disease development. Here we examined cytosine methylation of human kidney tubules using Illumina Infinium 450 K arrays from 91 subjects with and without diabetes and varying degrees of kidney disease using a cross-sectional design. We identify cytosine methylation changes associated with kidney structural damage and build a model for kidney function decline. We find that the methylation levels of 65 probes are associated with the degree of kidney fibrosis at genome wide significance. In total 471 probes improve the model for kidney function decline. Methylation probes associated with kidney damage and functional decline enrich on kidney regulatory regions and associate with gene expression changes, including epidermal growth factor (EGF). Altogether, our work shows that kidney methylation differences can be detected in patients with diabetic kidney disease and improve kidney function decline models indicating that they are potentially functionally important.

Original languageEnglish (US)
Article number2461
JournalNature communications
Volume10
Issue number1
DOIs
StatePublished - Dec 1 2019

ASJC Scopus subject areas

  • General Chemistry
  • General Biochemistry, Genetics and Molecular Biology
  • General Physics and Astronomy

Fingerprint

Dive into the research topics of 'Kidney cytosine methylation changes improve renal function decline estimation in patients with diabetic kidney disease'. Together they form a unique fingerprint.

Cite this