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

Insa M Schmidt; Steele Myrick; Jing Liu; Ashish Verma; Anand Srivastava; Ragnar Palsson; Ingrid F Onul; Isaac E Stillman; Claire Avillach; Prasad Patil; Sushrut S Waikar

doi:10.1093/ckj/sfac202

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

Clin Kidney J. 2022 Sep 9;16(1):90-99. doi: 10.1093/ckj/sfac202. eCollection 2023 Jan.

Authors

Affiliations

¹ Boston University School of Medicine and Boston Medical Center, Department of Medicine, Section of Nephrology, Boston, MA, USA.
² Boston University School of Public Health, Department of Biostatistics, Boston, MA, USA.
³ Division of Nephrology and National Clinical Research Center for Geriatrics, Kidney Research Institute, West China Hospital of Sichuan University, Chengdu, China.
⁴ Center for Translational Metabolism and Health, Institute for Public Health and Medicine, Division of Nephrology and Hypertension, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
⁵ Division of Nephrology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁶ Beth Israel Deaconess Medical Center, Harvard Medical School, Department of Pathology, Boston, MA, USA.
⁷ Boston Medical Center, Department of Pathology, Boston, MA, USA.

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.

Grants and funding

K23 DK120811/DK/NIDDK NIH HHS/United States