Alemtuzumab‐induced autoimmune thyroid events in patients with relapsing‐remitting multiple sclerosis: A real‐life and monocentric experience at a tertiary‐level centre. (1st November 2021) - Manso, Jacopo; Zhu, Yi Hang; Margoni, Monica; Rinaldi, Francesca; Censi, Simona; Carducci, Sofia; Cosma, Chiara; Plebani, Mario; Gallo, Paolo; Mian, Caterina
Abstract: Objective: Alemtuzumab‐induced autoimmune thyroid events (AIATEs) are the most common adverse effects observed in relapsing‐remitting multiple sclerosis (RRMS) patients. This study aims to explore the clinical and biochemical characteristics of such AIATEs, and to examine the risk factors for their occurrence, particularly for the worst clinical phenotype of fluctuating Graves' disease (GD). Design, Patients, Measurements: We retrospectively analysed a real‐life single‐centre consecutive series of 57 RRMS patients treated with alemtuzumab whose clinical and biochemical parameters were collected before starting the treatment and then monthly during their follow‐up. Results: AIATEs developed in 39% of patients a mean 17 months ± 11 after the first cycle of alemtuzumab. The most common AIATEs were GD (64%), followed by Hashimoto's thyroiditis with hypothyroidism (23%), TSH‐receptor‐antibody (TRAb)‐positive hypothyroidism (9%), and silent thyroiditis (4%). GD showed a fluctuating course in 57% of cases. Baseline positivity for anti‐thyroperoxidase antibodies, and higher absolute titers of anti‐thyroglobulin and anti‐thyroperoxidase antibodies correlated significantly with the risk of developing AIATEs, but TRAb positivity did not. Higher TRAb titers at the time of GD being diagnosed correlated strongly with a greater risk of the fluctuating GD phenotype. On ROC curve analysis, we found that a cut‐off of 7.3 IU/L could be used to predict the risk of developing a fluctuating GD, with a positive predictive value of 100%. Conclusions: TRAb levels measured with commercial automatic methods at the time of a patient being diagnosed with alemtuzumab‐induced GD emerged as a novel biomarker for predicting a fluctuating disease phenotype, with an influence on subsequent therapeutic decisions and patients' follow‐up.