Unsupervised clustering to differentiate rheumatoid arthritis patients based on proteomic signatures - Université de Lorraine Accéder directement au contenu
Article Dans Une Revue Scandinavian Journal of Rheumatology Année : 2023

Unsupervised clustering to differentiate rheumatoid arthritis patients based on proteomic signatures

Résumé

Objective: Patients with rheumatoid arthritis (RA) have different presentations and prognoses. Cluster analysis based on proteomic signatures creates independent phenogroups of patients with different pathophysiological backgrounds. We aimed to identify distinct pathophysiological clusters of RA patients based on circulating proteomic biomarkers. Method: This was a cohort study including 399 RA patients. Clustering was performed on 94 circulating proteins (92 CVDII Olink®, high-sensitivity troponin T, and C-reactive protein). Unsupervised clustering was performed using a partitioning cluster algorithm. Results: The clustering algorithm identified two distinct clusters: cluster 1 (n = 223) and cluster 2 (n = 176). Compared with cluster 1, cluster 2 included older patients with a higher burden of comorbidities (cardiovascular and RA related), more erosive and longer RA duration, more dyspnoea and fatigue, walking a shorter distance in the Six-Minute Walk Test, with more severe diastolic dysfunction, and a 4.5-fold higher risk of death or hospitalization for cardiovascular reasons. Tumour necrosis factor (TNF) receptor superfamily-related pathways were mainly responsible for the model's discriminative ability. Conclusion: Using unsupervised cluster analysis based on proteomic phenotypes, we identified two clusters of RA patients with distinct biomarkers profiles, clinical characteristics, and different outcomes that could reflect different pathophysiological backgrounds. TNF receptor superfamily-related proteins may be used to distinguish subgroups.
Fichier principal
Vignette du fichier
RA Cluster Paper.pdf (531.56 Ko) Télécharger le fichier
RA_Cluster_Supp.pdf (552.2 Ko) Télécharger le fichier

Dates et versions

hal-04089213 , version 1 (04-05-2023)

Licence

Identifiants

Citer

Maria Betânia Ferreira, Masatake Kobayashi, Rita Quelhas Costa, Tomás Fonseca, Mariana Brandão, et al.. Unsupervised clustering to differentiate rheumatoid arthritis patients based on proteomic signatures. Scandinavian Journal of Rheumatology, 2023, pp.1-8. ⟨10.1080/03009742.2023.2196781⟩. ⟨hal-04089213⟩
71 Consultations
34 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More