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; CRIC Study Investigators

doi:10.1681/ASN.2020030239

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Visual Abstract

Significance Statement

Staging CKD by eGFR and urine albumin-creatinine ratio does not fully capture underlying patient heterogeneity. Applying machine learning consensus clustering to multidimensional patient data, including demographics, biomarkers from blood and urine, health status and behaviors, and medication use, enables subtyping of patients with CKD into three distinct subgroups defined by 72 baseline characteristics. These subgroups are strongly associated with future risks of kidney disease, cardiovascular events, and death, independent of established CKD risk factors. Identification of clinically meaningful subgroups among patients with CKD provides an important step toward patient classification and precision medicine in nephrology.

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 ±0.3 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 (n=1203). Patients with higher prevalence of diabetes and obesity and who used more medications formed cluster 2 (n=1098). Patients with less favorable levels of bone mineral density, poor cardiac and kidney function markers, and inflammation delineated cluster 3 (n=395). 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.

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