Subtyping CKD patients by consensus clustering: The chronic renal insufficiency cohort (CRIC) study

Zihe Zheng*, Sushrut S. Waikar, Insa M. Schmidt, J. Richard Landis, Chi Yuan Hsu, Tariq Shafi, Harold I. Feldman, Amanda H. Anderson, Francis P. Wilson, Jing Chen, Hernan Rincon-Choles, Ana C. Ricardo, Georges Saab, Tamara Isakova, Radhakrishna Kallem, Jeffrey C. Fink, Panduranga S. Rao, Dawei Xie, Wei Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

Background CKD is a heterogeneous condition with multiple underlying causes, risk factors, and outcomes. Subtyping CKD with multidimensional patient data holds the key to precision medicine. Consensus clustering may reveal CKD subgroups with different risk profiles of adverse outcomes. Methods We used unsupervised consensus clustering on 72 baseline characteristics among 2696 participants in the prospective Chronic Renal Insufficiency Cohort (CRIC) study to identify novel CKD subgroups that best represent the data pattern. Calculation of the standardized difference of each parameter used the cutoff of 60.3 to show subgroup features. CKD subgroup associations were examined with the clinical end points of kidney failure, the composite outcome of cardiovascular diseases, and death. Results The algorithm revealed three unique CKD subgroups that best represented patients’ baseline characteristics. Patients with relatively favorable levels of bone density and cardiac and kidney function markers, with lower prevalence of diabetes and obesity, and who used fewer medications formed cluster 1 (n51203). Patients with higher prevalence of diabetes and obesity and who used more medications formed cluster 2 (n51098). Patients with less favorable levels of bone mineral density, poor cardiac and kidney function markers, and inflammation delineated cluster 3 (n5395). These three subgroups, when linked with future clinical end points, were associated with different risks of CKD progression, cardiovascular disease, and death. Furthermore, patient heterogeneity among predefined subgroups with similar baseline kidney function emerged. Conclusions Consensus clustering synthesized the patterns of baseline clinical and laboratory measures and revealed distinct CKD subgroups, which were associated with markedly different risks of important clinical outcomes. Further examination of patient subgroups and associated biomarkers may provide next steps toward precision medicine.

Original languageEnglish (US)
Pages (from-to)639-653
Number of pages15
JournalJournal of the American Society of Nephrology
Volume32
Issue number3
DOIs
StatePublished - Mar 2021

Funding

A.H. Anderson reports fees from Kyowa Hakko Kirin, outside the submitted work. H.I. Feldman reports consultancy agreements with DLA Piper, LLP, InMed, Inc., Kyowa Hakko Kirin Co, Ltd. (ongoing), and the National Kidney Foundation; research funding from Regeneron; honoraria from Rogosin Institute (invited speaker); and scientific advisor or membership as a member of the Steering Committee for the CRIC Study and Editor-in-Chief of the National Kidney Foundation (member of advisory board). C.-y. Hsu reports personal fees from EcoR1 Capital Fund, Health Advances, Ice Miller LLP, Reata, Satellite Healthcare, and UpToDate and grants from Satellite Healthcare, outside the submitted work. T. Isakova reports personal fees from Akebia Therapeutics, Kyowa Kirin Co, and LifeSci Capital, outside the submitted work. P.S. Rao reports honoraria from AstraZeneca and scientific advisor or membership with the AstraZeneca Nephrology Fellowship Advisory Board and the Renal Research Institute. G. Saab serves as a hemodialysis unit medical Funding for the CRIC study was obtained under a cooperative agreement with National Institute of Diabetes and Digestive and Kidney Diseases grants U01DK060990, U01DK060984, U01DK061022, U01DK061021, U01DK061028, U01DK060980, U01DK060963, U01DK060902, and U24DK060990. In addition, this work was supported in part by Perelman School of Medicine at the University of Pennsylvania Clinical and Translational Science Award National Institutes of Health (NIH)/National Center for Advancing Translational Sciences grant UL1TR000003; Johns Hopkins University National Center for Advancing Translational Sciences grant UL1TR-000424; University of Maryland General Clinical Research Center National Center for Advancing Translational Sciences grant M01 RR-16500; the Clinical and Translational Science Collaborative of Cleveland; National Center for Advancing Translational Sciences component of the NIH and NIH roadmap for Medical Research grant UL1TR000439; Michigan Institute for Clinical and Health Research National Center for Advancing Translational Sciences grant UL1TR000433; University of Illinois at Chicago Clinical and Translational Science Award National Center for Research Resources grant UL1RR029879; Tulane Center of Biomedical Research Excellence for Clinical and Translational Research in Cardiometabolic Diseases grant P20 GM109036; Kaiser Permanente NIH/National Center for Research Resources University of California San Francisco Clinical and Translational Science Institute grant UL1 RR-024131; and Department of Internal Medicine, University of New Mexico School of Medicine Albuquerque, New Mexico National Institute of Diabetes and Digestive and Kidney Diseases grant R01DK119199. I. M. Schmidt is supported by the American Philosophical Society Daland Fellowship in Clinical Investigation.

ASJC Scopus subject areas

  • General Medicine

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