Exploring the potential of cluster analysis in identifying disease phenotypes in CPPD: Moving from intuition to insight
Abstract
OBJECTIVE: Calcium pyrophosphate deposition (CPPD) disease is a heterogeneous condition and is frequently misdiagnosed due to the different clinical presentations and variable disease progression. Aim of this study is to identify clinical phenotypes of CPPD disease by integrating real-world data from two data sets. METHODS: Data from the COLCHICORT trial and CHRONIC CPPD European observational cohort were analyzed. Multiple correspondence analysis (MCA) was employed to evaluate and visualize the association between modalities of quantitative variables. Hierarchical clustering on principal components were obtained from MCA-identified clusters based on Ward agglomeration method with Euclidean distance. A bivariate analysis between clusters was then performed to characterize the clusters numerically. RESULTS: A total of 134 patients were included in the analysis, resulting in four distinct clusters. Cluster 1 had predominantly monoarticular involvement, frequent recurrent flares, older age at onset, female prevalence, and high C-reactive protein (CRP) levels. Cluster 2 also involved monoarticular cases but with persistent arthritis and onset after 60 years, with about half exhibiting elevated CRP. Cluster 3 included polyarticular cases, featuring persistent arthritis and lower CRP levels, whereas cluster 4 highlighted polyarticular involvement (spine and shoulder included) and primarily recurrent flares. CONCLUSION: In conclusion, by using for the first time a robust, data-driven methodology based on real-world data, two clusters similar to the EULAR phenotypes "acute CPP crystal arthritis," and "chronic CPP crystal inflammatory arthritis" were confirmed, but in addition, two more clusters that do not correspond to any existing EULAR phenotypes were also identified. Due to the characteristics of the cohorts, the osteoarthritis plus CPPD cluster has not been explored.
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BibTeX
@article{pellegrino2026exploring,
title = {{Exploring the potential of cluster analysis in identifying disease phenotypes in CPPD: Moving from intuition to insight}},
author = {Greta Pellegrino and Laurène Norberciak and Silvia Sirotti and Abhishek Abhishek and Mariano Andrès and Edoardo Cipolletta and Julien Damart and Renaud Desbarbieux and Pilar Diez and Vincent Ducoulombier and Hang‐korng Ea and Emilio Filippucci and Charlotte Jauffret and Augustin Latourte and Jean‐guillaume Letarouilly and Sébastien Ottaviani and Laura Pezzoni and Pascal Richette and Pierre Robinet and Georgios Filippou and Tristan Pascart},
journal = {Arthritis \& rheumatology},
year = {2026},
month = sep,
doi = {10.1002/art.70345},
url = {https://hal.science/hal-05777965v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - Exploring the potential of cluster analysis in identifying disease phenotypes in CPPD: Moving from intuition to insight
AU - Greta Pellegrino
AU - Laurène Norberciak
AU - Silvia Sirotti
AU - Abhishek Abhishek
AU - Mariano Andrès
AU - Edoardo Cipolletta
AU - Julien Damart
AU - Renaud Desbarbieux
AU - Pilar Diez
AU - Vincent Ducoulombier
AU - Hang‐korng Ea
AU - Emilio Filippucci
AU - Charlotte Jauffret
AU - Augustin Latourte
AU - Jean‐guillaume Letarouilly
AU - Sébastien Ottaviani
AU - Laura Pezzoni
AU - Pascal Richette
AU - Pierre Robinet
AU - Georgios Filippou
AU - Tristan Pascart
PY - 2026
DA - 2026/09/18
JO - Arthritis & rheumatology
DO - 10.1002/art.70345
UR - https://hal.science/hal-05777965v1
AB - OBJECTIVE: Calcium pyrophosphate deposition (CPPD) disease is a heterogeneous condition and is frequently misdiagnosed due to the different clinical presentations and variable disease progression. Aim of this study is to identify clinical phenotypes of CPPD disease by integrating real-world data from two data sets. METHODS: Data from the COLCHICORT trial and CHRONIC CPPD European observational cohort were analyzed. Multiple correspondence analysis (MCA) was employed to evaluate and visualize the association between modalities of quantitative variables. Hierarchical clustering on principal components were obtained from MCA-identified clusters based on Ward agglomeration method with Euclidean distance. A bivariate analysis between clusters was then performed to characterize the clusters numerically. RESULTS: A total of 134 patients were included in the analysis, resulting in four distinct clusters. Cluster 1 had predominantly monoarticular involvement, frequent recurrent flares, older age at onset, female prevalence, and high C-reactive protein (CRP) levels. Cluster 2 also involved monoarticular cases but with persistent arthritis and onset after 60 years, with about half exhibiting elevated CRP. Cluster 3 included polyarticular cases, featuring persistent arthritis and lower CRP levels, whereas cluster 4 highlighted polyarticular involvement (spine and shoulder included) and primarily recurrent flares. CONCLUSION: In conclusion, by using for the first time a robust, data-driven methodology based on real-world data, two clusters similar to the EULAR phenotypes "acute CPP crystal arthritis," and "chronic CPP crystal inflammatory arthritis" were confirmed, but in addition, two more clusters that do not correspond to any existing EULAR phenotypes were also identified. Due to the characteristics of the cohorts, the osteoarthritis plus CPPD cluster has not been explored.
SN - 2326-5205
SN - 2326-5191
ER -