The use of plasma biomarker-derived clusters for clinicopathologic phenotyping: results from the Boston Kidney Biopsy Cohort

Background Protein biomarkers may provide insight into kidney disease pathology but their use for the identification of phenotypically distinct kidney diseases has not been evaluated. Methods We used unsupervised hierarchical clustering on 225 plasma biomarkers in 541 individuals enrolled into the Boston Kidney Biopsy Cohort, a prospective cohort study of individuals undergoing kidney biopsy with adjudicated histopathology. Using principal component analysis, we studied biomarker levels by cluster and examined differences in clinicopathologic diagnoses and histopathologic lesions across clusters. Cox proportional hazards models tested associations of clusters with kidney failure and death. Results We identified three biomarker-derived clusters. The mean estimated glomerular filtration rate was 72.9 +/- 28.7, 72.9 +/- 33.4 and 39.9 +/- 30.4 mL/min/1.73 m(2) in Clusters 1, 2 and 3, respectively. The top-contributing biomarker in Cluster 1 was AXIN, a negative regulator of the Wnt signaling pathway. The top-contributing biomarker in Clusters 2 and 3 was Placental Growth Factor, a member of the vascular endothelial growth factor family. Compared with Cluster 1, individuals in Cluster 3 were more likely to have tubulointerstitial disease (P < .001) and diabetic kidney disease (P < .001) and had more severe mesangial expansion [odds ratio (OR) 2.44, 95% confidence interval (CI) 1.29, 4.64] and inflammation in the fibrosed interstitium (OR 2.49 95% CI 1.02, 6.10). After multivariable adjustment, Cluster 3 was associated with higher risks of kidney failure (hazard ratio 3.29, 95% CI 1.37, 7.90) compared with Cluster 1. Conclusion Plasma biomarkers may identify clusters of individuals with kidney disease that associate with different clinicopathologic diagnoses, histopathologic lesions and adverse outcomes, and may uncover biomarker candidates and relevant pathways for further study. Lay Summary In this study, we used a computational algorithm to explore whether different subgroups of patients with chronic kidney disease can be identified based on measurements of 225 blood proteins. We found that three subgroups, or clusters, existed. These clusters associated with different types of underlying kidney diseases, different findings as seen on kidney biopsies and differing risk of progression of the disease. Our study uncovered important plasma proteins for future study and points towards the involvement of different biological pathways that may play an important role in kidney disease. These could potentially be targeted for future drug development.