Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease – published online 28/06/2022

Niina Sandholm, Joanne B. Cole, Viji Nair, Xin Sheng, Hongbo Liu, Emma Ahlqvist, Natalie van Zuydam, Emma H. Dahlström, Damian Fermin, Laura J. Smyth, Rany M. Salem, Carol Forsblom, Erkka Valo, Valma Harjutsalo, Eoin P. Brennan, Gareth J. McKay, Darrell Andrews, Ross Doyle, Helen C. Looker, Robert G. Nelson, Colin Palmer, Amy Jayne McKnight, Catherine Godson, Alexander P. Maxwell, Leif Groop, Mark I. McCarthy, Matthias Kretzler, Katalin Susztak, Joel N. Hirschhorn, Jose C. Florez

Diabetic kidney disease is the leading cause of kidney disease. In this issue, Sandholm, Cole et al (https://doi.org/10.1007/s00125-022-05735-0) analysed genetic data from nearly 27,000 individuals with diabetes. These were combined with multiple omics datasets including gene expression, chromatin accessibility and DNA methylation as well as careful morphological characterisation of kidney tissue from nephrectomies and biopsies to identify novel genetic factors and genes that contribute to the risk of diabetic kidney disease. The authors report that several genes—TENM2, DCLK1, AKIRIN2, SNX30 and LSM14A in particular—contribute to the biological processes that lead to diabetic kidney disease and suggest that these genes could be putative therapeutic targets. They also provide evidence that genetic factors for chronic kidney disease in the general population are correlated with those for diabetic kidney disease in type 2 diabetes, but less in type 1 diabetes. The authors also report that the data further confirms the role of obesity in the pathogenesis of diabetic kidney disease.

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