18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Large-Vessel Vasculitis During Active and Inactive Disease Stages Is Associated with the Metabolic Profile, but Not the Macrophage-Related Cytokines: A Proof-of-Concept Study

Giant cell arteritis (GCA) is an autoimmune/autoinflammatory disease affecting large vessels in patients over 50 years old. The disease presents as an acute inflammatory response with two phenotypes, cranial GCA and large-vessel vasculitis (LV)-GCA, involving the thoracic aorta and its branches. 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET-CT) is among the imaging techniques contributing to diagnosing patients with systemic disease. However, its association with soluble inflammatory markers is still elusive. This proof-of-concept study aims to identify novel soluble serum biomarkers in PET/CT-positive patients with LV-GCA and associate them with active (0 months) and inactive disease (6 months following treatment), in sequential samples. The most-diseased-segment target-to-background ratio (TBR_MDS) was calculated for 13 LV-GCA patients, while 14 cranial GCA and 14 Polymyalgia Rheumatica patients with negative initial PET/CT scans served as disease controls. Serum macrophage-related cytokines were evaluated by cytometric bead array (CBA). Finally, previously published NMR/metabolomics data acquired from the same blood sampling were analyzed along with PET/CT findings. TBR_MDS was significantly increased in active versus inactive disease (3.32 vs. 2.65, <i>p</i> = 0.006). The analysis identified nine serum metabolites as more sensitive to change from the active to inactive state. Among them, choline levels were exclusively altered in the LV-GCA group but not in the disease controls. Cytokine levels were not associated with PET/CT activity. Combining CRP, ESR, and TBR_MDS with choline levels, a composite index was generated to distinguish active and inactive LV-GCA (20.4 vs. 11.62, <i>p</i> = 0.001). These preliminary results could pave the way for more extensive studies integrating serum metabolomic parameters with PET/CT imaging data to extract sensitive composite disease indexes useful for everyday clinical practice.