Unsupervised clustering to differentiate rheumatoid arthritis patients based on proteomic signatures
Authors' 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.
Study context
- Population
- Rheumatoid arthritis (RA) patients; clustering on 94 circulating proteins (92 CVDII Olink®, high-sensitivity troponin T, CRP)
- Participants
- 399 patients
- Exposure
- Cluster 2 vs cluster 1, identified by unsupervised clustering on 94 circulating proteins
- Cluster 2n = 176
- Cluster 1n = 223reference
- Outcome
- Distinct pathophysiological clusters of RA patients based on circulating proteomic biomarkers
Methodological detailsunsupervised clustering, partitioning cluster algorithm
- Unsupervised clustering
- Partitioning cluster algorithm
Main finding
As reported in the abstract
Clusters identified
Cluster 2 vs cluster 1: higher risk of death or hospitalization for cardiovascular reasons (fold)
4.5
Observational data: associations, not effects.
Original publication
Unsupervised clustering to differentiate rheumatoid arthritis patients based on proteomic signatures. Scand J Rheumatol. Published 21 April 2023. doi:10.
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