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

@article{aberger2026datadriven,
  title = {{Data-Driven Clustering of Secretory Bone and Inflammatory Markers to Subphenotype Patients Undergoing Hemodialysis.}},
  author = {Simon Aberger and Ameen Abu-Hanna and Vianda Stel and Nicholas Chesnaye and Emanuel Zitt and Daniel Cejka and Thomas B Dschietzig and Franz P Armbruster and Hildegard Hafner-Gießauf and Maximilian Waller and Corinna Schabhüttl and Alexander H Kirsch and Alexander R Rosenkranz and Kathrin Eller},
  journal = {Kidney international reports},
  year = {2026},
  month = aug,
  doi = {10.1016/j.ekir.2026.107019},
  url = {https://europepmc.org/article/MED/42834924},
  note = {CC BY},
}

TY  - JOUR
TI  - Data-Driven Clustering of Secretory Bone and Inflammatory Markers to Subphenotype Patients Undergoing Hemodialysis.
AU  - Simon Aberger
AU  - Ameen Abu-Hanna
AU  - Vianda Stel
AU  - Nicholas Chesnaye
AU  - Emanuel Zitt
AU  - Daniel Cejka
AU  - Thomas B Dschietzig
AU  - Franz P Armbruster
AU  - Hildegard Hafner-Gießauf
AU  - Maximilian Waller
AU  - Corinna Schabhüttl
AU  - Alexander H Kirsch
AU  - Alexander R Rosenkranz
AU  - Kathrin Eller
PY  - 2026
DA  - 2026/08/14
JO  - Kidney international reports
DO  - 10.1016/j.ekir.2026.107019
UR  - https://europepmc.org/article/MED/42834924
AB  - Introduction  Parathyroid hormone (PTH) remains the primary biomarker used to classify and monitor chronic kidney disease-mineral and bone disorder (CKD-MBD); nonetheless, 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 patients undergoing hemodialysis. The Partitioning Around Medoids algorithm was used to cluster the cohort by a pathophysiology-based biomarker panel (noxPTH, intact parathyroid hormone [iPTH], osteoprotegerin [OPG], soluble receptor activator of nuclear factor-κB ligand [sRANKL], antioxidative capacity [ImAnOx], oxidative capacity [PerOx], β-Crosslaps [β-CL], and high-sensitivity C-reactive protein [hsCRP]) at baseline. The cluster phenotypes were characterized by anthropometrics, imaging, and clinical data. Cluster-specific outcomes and event patterns were assessed after 1 year.Results  A total of 4 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 and the highest 1-year mortality; 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 1 year.Conclusion  Multidimensional biomarker clustering captures heterogeneity in bone and vascular disease phenotypes of CKD-MBD, exhibiting differential mortality risk and event patterns and highlighting inflammation as an important pathway for further studies.
SN  - 2468-0249
ER  -