Objective: To assess the correlation of serum protein biomarkers with disease activity across different domains of psoriatic arthritis (PsA).Material and methods: A cross-sectional cohort of 45 adult patients with PsA fulfilling the classification for psoriatic arthritis (CASPAR) criteria was recruited from University of California San Diego (UCSD) Arthritis Clinics. Clinical data and serum samples were collected and serum was analyzed for protein biomarkers hypothesized to be relevant to disease activity in PsA. Correlations were evaluated for clinical disease activity measures across disease domains. Results: Biomarkers with the highest correlation to the composite indices and disease domains were SAA, IL-6, YKL-40, and ICAM-1. In addition, several biomarkers were moderately correlated with individual composite indices and/or disease domains. Low or no correlation was observed with some biomarkers, e.g. MMP-3, MMP-1, EGF, VEGF, and IL-6R. In contrast, the correlation of all biomarkers with certain disease domains was low; specifically, pain, percent body surface area of psoriasis, and patient global assessment. The multi-biomarker disease activity score (MBDA) developed for rheumatoid arthritis (RA) showed high correlations with most composite indices and some disease domains in PsA.Conclusions: These data suggest biomarker analysis can reflect disease activity across disease domains in PsA. Certain domains would likely benefit from the evaluation of additional biomarkers.
Objective: To assess the correlation of serum protein biomarkers with disease activity across different domains of psoriatic arthritis (PsA).Material and methods: A cross-sectional cohort of 45 adult patients with PsA fulfilling the classification for psoriatic arthritis (CASPAR) criteria was recruited from University of California San Diego (UCSD) Arthritis Clinics. Clinical data and serum samples were collected and serum was analyzed for protein biomarkers hypothesized to be relevant to disease activity in PsA. Correlations were evaluated for clinical disease activity measures across disease domains. Results: Biomarkers with the highest correlation to the composite indices and disease domains were SAA, IL-6, YKL-40, and ICAM-1. In addition, several biomarkers were moderately correlated with individual composite indices and/or disease domains. Low or no correlation was observed with some biomarkers, e.g. MMP-3, MMP-1, EGF, VEGF, and IL-6R. In contrast, the correlation of all biomarkers with certain disease domains was low; specifically, pain, percent body surface area of psoriasis, and patient global assessment. The multi-biomarker disease activity score (MBDA) developed for rheumatoid arthritis (RA) showed high correlations with most composite indices and some disease domains in PsA.Conclusions: These data suggest biomarker analysis can reflect disease activity across disease domains in PsA. Certain domains would likely benefit from the evaluation of additional biomarkers.
Authors: James T Rosenbaum; Christina A Harrington; Robert P Searles; Suzanne S Fei; Amr Zaki; Sruthi Arepalli; Michael A Paley; Lynn M Hassman; Albert T Vitale; Christopher D Conrady; Puthyda Keath; Claire Mitchell; Lindsey Watson; Stephen R Planck; Tammy M Martin; Dongseok Choi Journal: Am J Ophthalmol Date: 2021-01-24 Impact factor: 5.488