Unbiased kidney-centric molecular categorization of chronic kidney disease as a step towards precision medicine.


Current classification of chronic kidney disease (CKD) into stages using indirect systemic measures (estimated glomerular filtration rate (eGFR) and albuminuria) is agnostic to the heterogeneity of underlying molecular processes in the kidney thereby limiting precision medicine approaches. To generate a novel CKD categorization that directly reflects within kidney disease drivers we analyzed publicly available transcriptomic data from kidney biopsy tissue. A Self-Organizing Maps unsupervised artificial neural network machine-learning algorithm was used to stratify a total of 369 patients with CKD and 46 living kidney donors as healthy controls. Unbiased stratification of the discovery cohort resulted in identification of four novel molecular categories of disease termed CKD-Blue, CKD-Gold, CKD-Olive, CKD-Plum that were replicated in independent CKD and diabetic kidney disease datasets and can be further tested on any external data at kidneyclass.org. Each molecular category spanned across CKD stages and histopathological diagnoses and represented transcriptional activation of distinct biological pathways. Disease progression rates were highly significantly different between the molecular categories. CKD-Gold displayed rapid progression, with significant eGFR-adjusted Cox regression hazard ratio of 5.6 [1.01-31.3] for kidney failure and hazard ratio of 4.7 [1.3-16.5] for composite of kidney failure or a 40% or more eGFR decline. Urine proteomics revealed distinct patterns between the molecular categories, and a 25-protein signature was identified to distinguish CKD-Gold from other molecular categories. Thus, patient stratification based on kidney tissue omics offers a gateway to non-invasive biomarker-driven categorization and the potential for future clinical implementation, as a key step towards precision medicine in CKD.





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Publication Info

Reznichenko, Anna, Viji Nair, Sean Eddy, Damian Fermin, Mark Tomilo, Timothy Slidel, Wenjun Ju, Ian Henry, et al. (2024). Unbiased kidney-centric molecular categorization of chronic kidney disease as a step towards precision medicine. Kidney international, 105(6). p. S0085-2538(24)00068-1. 10.1016/j.kint.2024.01.012 Retrieved from https://hdl.handle.net/10161/30737.

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Laura Barisoni

Professor of Pathology

Uptal Dinesh Patel

Adjunct Professor in the Department of Medicine

Uptal Patel, MD is an Adjunct Professor interested in population health with a broad range of clinical and research experience. As an adult and pediatric nephrologist with training in health services and epidemiology, his work seeks to improve population health for patients with  kidney diseases through improvements in prevention, diagnosis and treatment.

Prior efforts focused on four inter-related areas that are essential to improving kidney health: i) reducing the progression of chronic kidney disease by improving its detection and management, particularly by leveraging technology to facilitate engagement and self-management; ii) elucidating the inter-relationships between kidney disease and cardiovascular disease, which together amplify the risk of death; iii) improving the evidence in nephrology through comparative effectiveness research, including clinical trials, observational studies, and meta-analyses; and iv) promoting more optimal clinical health policy for all patients with kidney disease. These inter-disciplinary projects have been funded by a variety of public and private sources including the Robert Wood Johnson Foundation, Veterans Affairs, National Institutes of Health, Agency for Healthcare Research & Quality, Food and Drug Administration, Centers for Medicare & Medicaid Services, Renal Physicians Association, and the American Society of Nephrology. 

Current efforts seek to advance novel therapies for kidney diseases through early clinical development that he leads at AstraZeneca.

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