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

Abstract

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 m² 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.

Keywords: biomarkers; cluster; histopathology; kidney biopsy; kidney disease; proteomics.

Graphical Abstract

Figure 1:

Hierarchical cluster analysis. (A) Dendrogram showing the results from hierarchical clustering on 225 plasma biomarkers using the Euclidean distance measure and Ward's Minimum Variance. Rectangles show the split into three clusters. (B) Shown is the cluster formation in the first two PCs. PC1 and PC2 explain 21.5% and 10.2% of the total variance, respectively. PC, principal component.

Figure 2:

Clinicopathologic diagnoses by cluster. Shown are differences in clinicopathologic diagnostic categories by cluster membership. P-values obtained from Chi-square tests: proliferative GN, P = .30; non-proliferative glomerulopathy, P = .93; paraprotein disease, P = .07; *diabetic kidney disease, P < .001; vascular disease, P = .53; *tubulointerstitial disease, P < .001; advanced chronic changes, P = .28; *other (comprised of individuals with minor abnormalities or relatively preserved parenchyma), P = .003.

Figure 3:

Associations of future kidney failure and death with cluster membership. Kaplan–Meier survival curves show associations between time-to-kidney failure (A) and death (B) by cluster membership. P-values obtained from log rank test: P < .001 (A), P < .001 (B).