Kidney Week

Abstract: TH-PO385

Discovery of CKD Patient Subgroups by Consensus Clustering: The CRIC Study

Session Information

• CKD: Risk Scores and Translational Epidemiology
  November 07, 2019 | Location: Exhibit Hall, Walter E. Washington Convention Center
  Abstract Time: 10:00 AM - 12:00 PM

Category: CKD (Non-Dialysis)

• 2101 CKD (Non-Dialysis): Epidemiology, Risk Factors, and Prevention

Authors

• Zheng, Zihe, University of Pennsylvania, Philadelphia, Pennsylvania, United States

Zihe Zheng,

• Waikar, Sushrut S., Harvard Medical School, Boston, Massachusetts, United States

Sushrut S. Waikar,

• Yang, Wei, University of Pennsylvania, Philadelphia, Pennsylvania, United States

Wei Yang,

• Shafi, Tariq, University of Mississippi Medical Center, Jackson, Mississippi, United States

Tariq Shafi,

• Hsu, Chi-yuan, University of California San Francisco, San Francisco, California, United States

Chi-yuan Hsu,

• Wilson, Francis Perry, Yale School of Medicine, New Haven, Connecticut, United States

Francis Perry Wilson,

• Chen, Jing, Tulane School of Medicine, New Orleans, Louisiana, United States

Jing Chen,

• Anderson, Amanda Hyre, Tulane University, New Orleans, Louisiana, United States

Amanda Hyre Anderson,

• Ricardo, Ana C., University of Illinois at Chicago, Chicago, Illinois, United States

Ana C. Ricardo,

• Isakova, Tamara, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States

Tamara Isakova,

• Rincon-Choles, Hernan, Cleveland Clinic, Cleveland, Ohio, United States

Hernan Rincon-Choles,

• Kallem, Radhakrishna Reddy, University of Pennsylvania, Philadelphia, Pennsylvania, United States

Radhakrishna Reddy Kallem,

• Feldman, Harold I., University of Pennsylvania, Philadelphia, Pennsylvania, United States

Harold I. Feldman,

• Landis, J. Richard, University of Pennsylvania, Philadelphia, Pennsylvania, United States

J. Richard Landis,

Background

CKD is a heterogeneous condition with multiple underlying causes, risk factors, and outcomes. Consensus clustering may reveal CKD subgroups with different risk profiles of adverse outcomes.

Methods

Among 2696 participants in the prospective CRIC Study, we performed unsupervised consensus clustering with K-means, without using outcome information, on 72 baseline characteristics of traditional and novel factors to discover patient subgroups. We calculated the standardized difference of all predictors across subgroups, and used the cut-off of ±0.3 to show key features. We examined the associations of each subgroup with ESRD, cardiovascular diseases (composite of heart failure, MI, stroke and PAD), and death.

Results

Three unique CKD subgroups, identified using only baseline factors, were associated with low (Cluster1, N=1203), medium (Cluster2, N=1098), and high (Cluster3, N=395) risks of the outcomes (Fig 1). Patients in cluster 1 (lowest risk) had lower bone & mineral, diabetes, cardiac, and obesity markers, greater eGFR, and used fewer medications. Patients in cluster 2 had higher diabetes and obesity markers, and used more medications. Patients in cluster 3 (highest risk) had higher bone & mineral (except for lower serum calcium), cardiac (except for lower serum CO₂), inflammation (except for lower serum albumin), and kidney markers (except for lower eGFR) (Fig 2).

Conclusion

Consensus clustering discovered distinct subgroups of CKD patients with distinguishing patterns of baseline clinical and laboratory factors yielding markedly different risks of important clinical outcomes. Specific biomarkers featuring high-risk CKD subgroup could provide potential treatment targets.

Funding

• NIDDK Support