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Objective Trained immunity (TI) is a de facto memory program of innate immune cells, characterized by immunometabolic and epigenetic changes sustaining enhanced production of cytokines. TI evolved as a protective mechanism against infections; however, inappropriate activation can cause detrimental inflammation and might be implicated in the pathogenesis of chronic inflammatory diseases. In this study, we investigated the role of TI in the pathogenesis of giant cell arteritis (GCA), a large-vessel vasculitis characterized by aberrant macrophage activation and excess cytokine production. Methods Monocytes from GCA patients and from age- and sex-matched healthy donors were subjected to polyfunctional studies, including cytokine production assays at baseline and following stimulation, intracellular metabolomics, chromatin immunoprecipitation-qPCR, and combined ATAC/RNA sequencing. Immunometabolic activation (i.e. glycolysis) was assessed in inflamed vessels of GCA patients with FDG-PET and immunohistochemistry (IHC), and the role of this pathway in sustaining cytokine production was confirmed with selective pharmacologic inhibition in GCA monocytes. Results GCA monocytes exhibited hallmark molecular features of TI. Specifically, these included enhanced IL-6 production upon stimulation, typical immunometabolic changes (e.g. increased glycolysis and glutaminolysis) and epigenetic changes promoting enhanced transcription of genes governing pro-inflammatory activation. Immunometabolic changes of TI (i.e. glycolysis) were a feature of myelomonocytic cells in GCA lesions and were required for enhanced cytokine production. Conclusions Myelomonocytic cells in GCA activate TI programs sustaining enhanced inflammatory activation with excess cytokine production. [ABSTRACT FROM AUTHOR]

Background: Host inflammation contributes to determine whether SARS-CoV-2 infection causes mild or life-threatening disease. Tools are needed for early risk assessment. Methods: We studied in 111 COVID-19 patients prospectively followed at a single reference Hospital fifty-three potential biomarkers including alarmins, cytokines, adipocytokines and growth factors, humoral innate immune and neuroendocrine molecules and regulators of iron metabolism. Biomarkers at hospital admission together with age, degree of hypoxia, neutrophil to lymphocyte ratio (NLR), lactate dehydrogenase (LDH), C-reactive protein (CRP) and creatinine were analysed within a data-driven approach to classify patients with respect to survival and ICU outcomes. Classification and regression tree (CART) models were used to identify prognostic biomarkers. Results: Among the fifty-three potential biomarkers, the classification tree analysis selected CXCL10 at hospital admission, in combination with NLR and time from onset, as the best predictor of ICU transfer (AUC [95% CI] = 0.8374 [0.6233–0.8435]), while it was selected alone to predict death (AUC [95% CI] = 0.7334 [0.7547–0.9201]). CXCL10 concentration abated in COVID-19 survivors after healing and discharge from the hospital. Conclusions: CXCL10 results from a data-driven analysis, that accounts for presence of confounding factors, as the most robust predictive biomarker of patient outcome in COVID-19. [ABSTRACT FROM AUTHOR]