Unsupervised clustering to differentiate rheumatoid arthritis patients based on proteomic signatures

Abstract

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.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethics

This study was conducted following the principles of the Declaration of Helsinki and approved by the Hospital de Santo António, Porto, Portugal ethics committee under number #2016-023 (020-DEFI/020-CES). All participants provided their written informed consent before enrolment in the study.

Data availability

The authors state they have full control of all primary data and agree to allow the journal to review their data upon reasonable request.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/03009742.2023.2196781

Additional information

Funding

This work was supported by national funds through FCT Fundação para a Ciência e a Technology, IP, within the scope of the Cardiovascular R&D Center [UIDB/00051/2020 and UIDP/00051/2020] and RISE [LA/P/0053/2020].