Unsupervised clustering to differentiate rheumatoid arthritis patients based on proteomic signatures

• Tipo: Artigo em Revista Científica Internacional
• Data: 2023

Autores

Ferreira, MB (Autor) Outra; Kobayashi, M (Autor) Outra; Costa, RQ (Autor) Outra; Fonseca, T (Autor) Outra; Brandao, M (Autor) Outra; Oliveira, JC (Autor) Outra; Marinho, A (Autor) Outra; Carvalho, HC (Autor) ICBAS; Rodrigues, P (Autor) Outra; Zannad, F (Autor) Outra; Rossignol, P (Autor) Outra; Barros, AS (Autor) FMUP; Ferreira, JP (Autor) FMUP

Revista

A Revista está pendente de validação pelos Serviços Administrativos.

Título: Scandinavian Journal of Rheumatology

Vol. 52

Páginas: 619-626

ISSN: 0300-9742

Indexação

ISI Web of Knowledge - 4 Citações

Scopus - 4 Citações

Outras Informações

ID Authenticus: P-00Y-8SX

DOI: 10.1080/03009742.2023.2196781

Abstract (EN): ObjectivePatients 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.MethodThis was a cohort study including 399 RA patients. Clustering was performed on 94 circulating proteins (92 CVDII Olink (R), high-sensitivity troponin T, and C-reactive protein). Unsupervised clustering was performed using a partitioning cluster algorithm.ResultsThe 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.ConclusionUsing 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8