Data-Driven Clustering of Secretory Bone and Inflammatory Markers to Subphenotype Patients Undergoing Hemodialysis

Introduction Parathyroid hormone remains the primary biomarker used to classify and monitor CKD-MBD, yet its reliability for risk stratification and treatment guidance is limited. Multidimensional biomarker clustering that integrates indicators of bone turnover, vascular calcification, inflammatory and oxidative stress, may better capture the heterogeneity of clinical phenotypes, improve risk stratification, and inform on new therapeutic approaches in dialysis care. Methods We conducted a computational analysis of a multicentric cohort study including 471 hemodialysis patients. The Partitioning Around Medoids algorithm was used to cluster the cohort by a pathophysiology-based biomarker panel (noxPTH®, iPTH, OPG, sRANKL, ImAnOx®, PerOx®, β-Crosslaps and hsCRP) at baseline. The cluster phenotypes were characterized by anthropometrics, imaging and clinical data. Cluster-specific outcomes and event patterns were assessed after one year. Results Four distinct clusters were identified – Cluster 1 reflected a high bone turnover state with high antioxidative capacity and high bone-specific alkaline phosphatase. Cluster 2 showed low bone turnover with high antioxidative capacity and high sclerostin levels. Cluster 3 exhibited an inflammatory–oxidative phenotype, the highest one-year mortality and cluster membership improved prediction of events beyond clinical covariates. This phenotype was marked by high aortic calcium burden, vascular disease, markers of vitamin D degradation, low vaccine response, and hepatic steatosis. Cluster 4 showed a balanced biomarker profile and the highest rate of reclassification after one year. Conclusions Multidimensional biomarker clustering captures heterogeneity in bone and vascular disease phenotypes of CKD-MBD exhibiting differential mortality risk and event patterns, highlighting inflammation as an important pathway for further studies.