{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zheng Z"],"funding":["National Center for Advancing Translational Sciences","Clinical and Translational Science Collaborative of Cleveland","Tulane COBRE for Clinical and Translational Research in Cardiometabolic Diseases","National Center for Research Resources","NCATS NIH HHS","NIDDK NIH HHS","National Institute of Diabetes and Digestive and Kidney Diseases","NCRR NIH HHS","American Philosophical Society","NIGMS NIH HHS"],"pagination":["639-653"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7920178"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["32(3)"],"pubmed_abstract":["<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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 (<i>n</i>=1203). Patients with higher prevalence of diabetes and obesity and who used more medications formed cluster 2 (<i>n</i>=1098). Patients with less favorable levels of bone mineral density, poor cardiac and kidney function markers, and inflammation delineated cluster 3 (<i>n</i>=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.<h4>Conclusions</h4>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."],"journal":["Journal of the American Society of Nephrology : JASN"],"pubmed_title":["Subtyping CKD Patients by Consensus Clustering: The Chronic Renal Insufficiency Cohort (CRIC) Study."],"pmcid":["PMC7920178"],"funding_grant_id":["P20 GM109036","U01 DK061021","U01DK061028","U01 DK060990","U01 DK061022","UL1RR029879","UL1TR000433","U01DK061021","U01 DK061028","UL1 RR029879","U01DK060990","UL1 TR000433","U01DK061022","UL1 TR001863","Daland Fellowship in Clinical Investigation","UL1 TR000439","U24 DK060990","M01 RR016500","M01 RR-16500","U01 DK060902","UL1TR-000424","UL1 RR-024131","UL1 RR024131","R01 DK119199","U01DK060963","UL1TR000003","U01DK060984","UL1 TR000003","U01DK060902","UL1 TR000424","U01 DK060963","U01 DK060984","U24DK060990","U01DK060980","U01 DK060980","P30 DK079310","UL1 TR001878","UL1TR000439","R01DK119199"],"pubmed_authors":["Waikar SS","Schmidt IM","Hsu CY","Isakova T","Saab G","Xie D","Go AS","Feldman HI","CRIC Study Investigators","Townsend RR","Rao PS","Wilson FP","Lash JP","Fink JC","Rincon-Choles H","Anderson AH","Zheng Z","Landis JR","Chen J","He J","Shafi T","Rahman M","Kallem R","Yang W","Appel LJ","Ricardo AC"],"additional_accession":[]},"is_claimable":false,"name":"Subtyping CKD Patients by Consensus Clustering: The Chronic Renal Insufficiency Cohort (CRIC) Study.","description":"<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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 (<i>n</i>=1203). Patients with higher prevalence of diabetes and obesity and who used more medications formed cluster 2 (<i>n</i>=1098). Patients with less favorable levels of bone mineral density, poor cardiac and kidney function markers, and inflammation delineated cluster 3 (<i>n</i>=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.<h4>Conclusions</h4>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.","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Mar","modification":"2026-03-18T13:17:15.728Z","creation":"2025-04-04T14:59:09.075Z"},"accession":"S-EPMC7920178","cross_references":{"pubmed":["33462081"],"doi":["10.1681/ASN.2020030239","10.1681/asn.2020030239"]}}