Exposome: Epigenetics and autoimmune diseases.

Systemic autoimmune diseases are complex conditions characterized by an immune system dysregulation and an aberrant activation against self-antigens, leading to tissue and organ damage. Even though genetic predisposition plays a role, it cannot fully explain the onset of these diseases, highlighting the significant impact of non-heritable influences such as environment, hormones and infections. The exposome represents all those factors, ranging from chemical pollutants and dietary components to psychological stressors and infectious agents. Epigenetics, which studies changes in gene expression without altering the DNA sequence, is a crucial link between exposome and the development of autoimmune diseases. Key epigenetic mechanisms include DNA methylation, histone modifications, and non-coding RNAs. These epigenetic modifications could provide a potential piece of the puzzle in understanding systemic autoimmune diseases and their connection with the exposome. In this work we have collected the most important and recent evidence in epigenetic changes linked to systemic autoimmune diseases (systemic lupus erythematosus, idiopathic inflammatory myopathies, ANCA-associated vasculitis, and rheumatoid arthritis), emphasizing the roles these changes may play in disease pathogenesis, their potential as diagnostic biomarkers and their prospective in the development of targeted therapies.

A machine learning analysis to evaluate the outcome measures in inflammatory myopathies.

OBJECTIVE: To assess the long-term outcome in patients with Idiopathic Inflammatory Myopathies (IIM), focusing on damage and activity disease indexes using artificial intelligence (AI). BACKGROUND: IIM are a group of rare diseases characterized by involvement of different organs in addition to the musculoskeletal. Machine Learning analyses large amounts of information, using different algorithms, decision-making processes and self-learning neural networks. METHODS: We evaluate the long-term outcome of 103 patients with IIM, diagnosed on 2017 EULAR/ACR criteria. We considered different parameters, including clinical manifestations and organ involvement, number and type of treatments, serum creatine kinase levels, muscle strength (MMT8 score), disease activity (MITAX score), disability (HAQ-DI score), disease damage (MDI score), and physician and patient global assessment (PGA). The data collected were analysed, applying, with R, supervised ML algorithms such as lasso, ridge, elastic net, classification, and regression trees (CART), random forest and support vector machines (SVM) to find the factors that best predict disease outcome. RESULTS AND CONCLUSION: Using artificial intelligence algorithms we identified the parameters that best correlate with the disease outcome in IIM. The best result was on MMT8 at follow-up, predicted by a CART regression tree algorithm. MITAX was predicted based on clinical features such as the presence of RP-ILD and skin involvement. A good predictive capacity was also demonstrated on damage scores: MDI and HAQ-DI. In the future Machine Learning will allow us to identify the strengths or weaknesses of the composite disease activity and damage scores, to validate new criteria or to implement classification criteria.

A machine learning analysis to predict the response to intravenous and subcutaneous immunoglobulin in inflammatory myopathies. A proposal for a future multi-omics approach in autoimmune diseases.

OBJECTIVE: To evaluate the response to treatment with intravenous (IVIg) and subcutaneous (20%SCIg) immunoglobulin in our series of patients with Inflammatory idiopathic myopathies (IIM) by the means of artificial intelligence. BACKGROUND: IIM are rare diseases mainly involving the skeletal muscle with particular clinical, laboratory and radiological characteristics. Artificial intelligence (AI) represents computer processes which allows to perform complex calculations and data analyses, with the least human intervention. Recently, the use an AI in medicine significantly expanded, especially through machine learning (ML) which analyses huge amounts of information and accordingly makes decisions, and deep learning (DL) which uses artificial neural networks to analyse data and automatically learn. METHODS: In this study, we employed AI in the evaluation of the response to treatment with IVIg and 20%SCIg in our series of patients with IIM. The diagnoses were determined on the established EULAR/ACR criteria. The treatment response was evaluated employing the following: serum creatine kinase levels, muscle strength (MMT8 score), disease activity (MITAX score) and disability (HAQ-DI score). We evaluated all the above parameters, applying, with R, different supervised ML algorithms, including Least Absolute Shrinkage and Selection Operator, Ridge, Elastic Net, Classification and Regression Trees and Random Forest to estimate the most important predictors for a good response to IVIg and 20%SCIg treatment. RESULTS AND CONCLUSION: By the means of AI we have been able to identify the scores that best predict a good response to IVIg and 20%SCIg treatment. The muscle strength as evaluated by MMT8 score at the follow-up is predicted by the presence of dysphagia and of skin disorders, and the myositis activity index (MITAX) at the beginning of the treatment. The relationship between muscle strength and MITAX indicates a better action of IVIg therapy in patients with more active systemic disease. Considering our results, Elastic Net and similar approaches were seen to be the most viable, efficient, and effective ML methods for predicting the clinical outcome (MMT8 and MITAX at most) in myositis.

Intravenous immunoglobulin as an important adjunct in the prevention and therapy of coronavirus 2019 disease.

The coronavirus disease-19 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) challenged globally with its morbidity and mortality. A small percentage of affected patients (20%) progress into the second stage of the disease clinically presenting with severe or fatal involvement of lung, heart and vascular system, all contributing to multiple-organ failure. The so-called 'cytokines storm' is considered the pathogenic basis of severe disease and it is a target for treatment with corticosteroids, immunotherapies and intravenous immunoglobulin (IVIg). We provide an overview of the role of IVIg in the therapy of adult patients with COVID-19 disease. After discussing the possible underlying mechanisms of IVIg immunomodulation in COVID-19 disease, we review the studies in which IVIg was employed. Considering the latest evidence that show a link between new coronavirus and autoimmunity, we also discuss the use of IVIg in COVID-19 and anti-SARS-CoV-2 vaccination related autoimmune diseases and the post-COVID-19 syndrome. The benefit of high-dose IVIg is evident in almost all studies with a rapid response, a reduction in mortality and improved pulmonary function in critically ill COVID-19 patients. It seems that an early administration of IVIg is crucial for a successful outcome. Studies' limitations are represented by the small number of patients, the lack of control groups in some and the heterogeneity of included patients. IVIg treatment can reduce the stay in ICU and the demand for mechanical ventilation, thus contributing to attenuate the burden of the disease.