THU0323 MYELOID BIOMARKERS IN GIANT CELL ARTERITIS AND POLYMYALGIA RHEUMATICA – TWO INDEPENDENT COHORTS
Authors: Y. Van Sleen, P. Therkildsen, B. Dalsgaard Nielsen, M. G. Huitema, E. Toonen, A. Boots, P. Heeringa, E. M. Hauge, E. Brouwer
Journal: Annals of the Rheumatic Diseases (2020)

Background: Giant cell arteritis (GCA) commonly overlaps with polymyalgia rheumatica (PMR). The incidence of GCA among PMR patients is between 16 and 21%, and both diseases are treated with long-term glucocorticoids (GCs). Patients with GCA suffer from inflammation of their large-sized arteries, whereas PMR is characterized by synovial inflammation. A key question for every physician dealing with a PMR patient is whether or not the patient also has GCA. Symptoms of GCA are often non-specific and difficult to identify. Compared to healthy controls (HCs), newly-diagnosed GCA and PMR patients display a change in leukocyte composition with a shift towards the myeloid lineage, evidenced by elevated monocyte and neutrophil counts. Persistence of this myeloid bias (during and after treatment) is in congruence with mounting evidence that GCs do not sufficiently suppress the vascular/synovial inflammation, contributing to a relapsing disease course. Yet, it may be difficult to readily identify the myeloid bias in the blood, and therefore easily detectable biomarkers are required to monitor this myeloid bias in GCA and PMR patients. Objectives: The first objective of this study is to identify disease specific biomarkers for GCA and for PMR using myeloid serum/plasma markers. Next, we assessed whether (a profile of) these markers could be used to reflect the persistent myeloid profile. Methods: Biomarkers were measured in two independent cohorts: Groningen, the Netherlands (GPS cohort) and Aarhus Denmark (Aarhus cohort). Both cohorts included treatment-naive GCA and PMR patients, supplemented with age- and sex matched HCs (Table 1). Along with the GPS cohort, age-matched inflammatory controls were included. GCA-lookalike patients were added in the measurements for the Aarhus cohort. All patients started treatment with GCs, and follow-up samples were measured at 8 weeks for GCA patients. All measurements were performed by ELISA: sCD206, calprotectin, A1AT and elastase in serum samples, whereas PR3 and MPO were measured in plasma samples. Table 1. Baseline characteristics of the two cohorts GPS cohort Aarhus Cohort GCA PMR HC Infection GCA PMR HC GCA-lookalike N 48 39 50 16 52 25 20 18 PET-CT: positive/ negative/ not done 32 5 11 0 29 10 NA NA 48 4 0 0 25 0 NA 0 13 5 CRP, median (mg/L) 52 35 2 70 74 35 1 54 Results: Analyses of biomarkers in the two cohorts had mostly equivalent results. Compared to HCs, levels of sCD206, calprotectin, PR3 and A1AT were elevated in GCA, PMR and infection/GCA-lookalike patients. GCA patients had higher levels of sCD206 than PMR patients, but only in the Aarhus cohort this reached statistical significance (Aarhus: p=0.02, GPS: p=0.17). Treatment with GCs substantially affected the biomarker levels: in GCA patients of both cohorts, calprotectin and A1AT levels dropped, sCD206 levels remained high (unchanged), and elastase and PR3 levels increased. Next, we assessed whether the biomarkers correlated with inflammation and the myeloid bias in the GPS cohort. Particularly in GCA patients, A1AT levels correlated with inflammatory marker CRP. Elastase correlated significantly with neutrophil counts in both GCA (R=0.42) and PMR (R=0.57). Calprotectin correlated with neutrophil counts in both GCA and PMR, and with monocyte counts in GCA. Conclusion: This is one of the first studies in GCA and PMR patients to study biomarkers in two independent cohorts. We consistently showed elevated levels of monocyte/macrophage and neutrophil products in both cohorts. Levels of sCD206 may help in discriminating GCA from PMR patients. The myeloid bias may be monitored using a combination of calprotectin and elastase levels. Additionally, sCD206 or calprotectin may serve as tissue inflammation markers under the cover of GC treatment, a notion to be further investigated using follow-up imaging data. Disclosure of Interests: Yannick van Sleen: None declared, Philip Therkildsen: None declared, Berit Dalsgaard NIelsen: None declared, Minke G. Huitema: None declared, Erik Toonen Employee of: Employed by Hycult Biotech, Annemieke Boots Consultant of: Grünenthal Gmbh until 2017, Peter Heeringa: None declared, Ellen-Margrethe Hauge Speakers bureau: Fees for speaking/consulting: MSD, AbbVie, UCB and Sobi; research funding to Aarhus University Hospital: Roche and Novartis (not related to the submitted work)., Elisabeth Brouwer Consultant of: Roche (consultancy fee 2017 and 2018 paid to the UMCG), Speakers bureau: Roche (2017 and 2018 paid to the UMCG)