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Kidney Week

Abstract: PO2363

The Use of Plasma Biomarker-Derived Clusters for Clinicopathologic Phenotyping: Results from the Boston Kidney Biopsy Cohort

Session Information

Category: CKD (Non-Dialysis)

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

Authors

  • Schmidt, Insa Marie, Boston Medical Center, Boston, Massachusetts, United States
  • Patil, Prasad, Boston University School of Public Health, Boston, Massachusetts, United States
  • Myrick, Steele, Boston University School of Public Health, Boston, Massachusetts, United States
  • Onul, Ingrid Fara, Boston Medical Center, Boston, Massachusetts, United States
  • Srivastava, Anand, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States
  • Palsson, Ragnar, Landspitali, Reykjavik, Capital, Iceland
  • Stillman, Isaac Ely, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States
  • Rennke, Helmut G., Brigham and Women's Hospital, Boston, Massachusetts, United States
  • Waikar, Sushrut S., Boston Medical Center, Boston, Massachusetts, United States
Background

Protein biomarkers may provide non-invasive insight into kidney disease pathology. Prior studies have not evaluated whether unsupervised clustering analyses of multiple plasma protein biomarkers may identify phenotypically distinct kidney diseases.

Methods

We performed unsupervised hierarchical clustering on 225 plasma biomarkers measured in 541 individuals enrolled into the Boston Kidney Biopsy Cohort, a prospective cohort study of individuals undergoing clinically indicated native kidney biopsy with adjudicated clinicopathologic diagnoses and semiquantitative scores of histopathology. Chi-square tests compared differences in proportions of clinicopathologic diagnoses by cluster membership. We examined contributions of biomarkers to each cluster and explored cluster-specific pathways using principal component analysis and pathway enrichment analysis, respectively.

Results

The biomarker-derived clusters partitioned subjects into 3 groups. The mean eGFR was 71.4±29.2, 72.5±34.3, and 39.3±31.3 ml/min/1.73m2 in Cluster 1, 2, and 3, respectively. Compared to Cluster 1, individuals in Cluster 3 were more likely to have tubulointerstitial disease (p<0.001) and diabetic nephropathy (p<0.001), (Figure 1). The top-contributing biomarker in Cluster 1 was AXIN, a negative regulator of the Wnt signaling pathway. The top-contributing biomarker in Cluster 2 and 3 was Placental Growth Factor, a member of the VEGF family. The top ranked pathways were tumor-necrosis factor receptor-related signaling and interleukin and cytokine signaling in Cluster 1, 2, and 3, respectively.

Conclusion

Clusters of plasma biomarkers may identify individuals with distinct forms of CKD, which may uncover relevant pathways and biomarker candidates for clinicopathologic phenotyping of kidney diseases.

Figure 1

Funding

  • NIDDK Support