Multicentre Study Validates Interpretable Machine Learning Model to Distinguish Diabetic from Non-Diabetic Kidney Disease Without Biopsy — Implications for Nephrologists
247 Health News Editorial
·15 July 2026
·4 min read
·clinical-trialsA completed multicentre Chinese cohort study involving 2,201 biopsy-confirmed patients has developed and externally validated an interpretable machine learning model to noninvasively distinguish diabetic kidney disease from non-diabetic kidney disease in type 2 diabetes. The web-based clinical decision support tool could help nephrologists stratify biopsy necessity and accelerate appropriate therapy — with significant implications for Australian practice where DKD drives the highest burden of end-stage kidney disease.
Originally reported by ClinicalTrials.gov
References
- 1.Beijing Tongren Hospital. (2022). Development and validation of an interpretable machine learning model for noninvasive differentiation of diabetic kidney disease and non-diabetic kidney disease in type 2 diabetes: A multicenter retrospective cohort study. ClinicalTrials.gov. https://clinicaltrials.gov/study/NCT07672639
- 2.Sharma, S. G., Bomback, A. S., Radhakrishnan, J., Herlitz, L. C., Stokes, M. B., Markowitz, G. S., & D'Agati, V. D. (2013). The modern spectrum of renal biopsy findings in patients with diabetes. Clinical Journal of the American Society of Nephrology, 8(10), 1718–1724. https://doi.org/10.2215/CJN.06920712
- 3.Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30. https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html
- 4.Tuttle, K. R., Bakris, G. L., Bilous, R. W., Chiang, J. L., de Boer, I. H., Goldstein-Fuchs, J., Hirsch, I. B., Kalantar-Zadeh, K., Narva, A. S., Navaneethan, S. D., Neumiller, J. J., Patel, U. D., Ratner, R. E., Whaley-Connell, A. T., & Molitch, M. E. (2014). Diabetic kidney disease: A report from an ADA Consensus Conference. Diabetes Care, 37(10), 2864–2883. https://doi.org/10.2337/dc14-1296
- 5.Heerspink, H. J. L., Stefánsson, B. V., Correa-Rotter, R., Chertow, G. M., Greene, T., Hou, F. F., Mann, J. F. E., McMurray, J. J. V., Lindberg, M., Rossing, P., Sjöström, C. D., Toto, R. D., Langkilde, A. M., & Wheeler, D. C. (2020). Dapagliflozin in patients with chronic kidney disease. New England Journal of Medicine, 383(15), 1436–1446. https://doi.org/10.1056/NEJMoa2024816
- 6.Australia and New Zealand Dialysis and Transplant Registry. (2023). ANZDATA Registry 46th Annual Report 2023. ANZDATA Registry. https://www.anzdata.org.au/report/anzdata-46th-annual-report-2023/
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