Paradoxical Psoriasis in Patients Receiving Therapy with Tumor Necrosis Factor Inhibitors: Potential Pathogenic Mechanisms and the Role of Genetic Factors.

TNF inhibitors (TNFi) have revolutionized the therapeutic management of various chronic immune-mediated inflammatory diseases. Despite their known benefits, these therapies are related to paradoxical adverse effects (PAEs), including paradoxical psoriasis (PP). Although the underlying mechanism remains somewhat unclear, some theories suggest that genetic factors, particularly certain single-nucleotide polymorphisms (SNPs), may play an important role. The present review aimed to research and analyze recent findings regarding the pathomechanisms involved in the appearance of PP and the association between various genetic factors and PP in individuals treated with TNFi. We performed a literature search and found that certain genes (IL23R, TNF, FBXL19, CTLA4, SLC12A8, TAP1) are strongly associated with the occurrence of PP in pediatric and adult patients during therapy with TNFi. The identification of the specific SNPs involved in the appearance of PP and other PAEs in patients treated with TNFi for various diseases and in different populations may later favor the recognition of those patients at a high risk of developing such adverse effects and could guide personalized therapeutic strategies in future years.

Interstitial Lung Disease in Systemic Lupus Erythematosus and Systemic Sclerosis: How Can We Manage the Challenge?

Interstitial lung disease (ILD) is a severe and frequent manifestation of connective tissue diseases (CTD). Due to its debilitating potential, it requires serious evaluation and treatment. The prevalence of ILD in systemic lupus erythematosus (SLE) is still controversial. Therefore, in order to establish the diagnosis of ILD, an overlap syndrome must be excluded. Increasing the identification of SLE-associated ILD cases should become a target. To treat this complication, various therapies are now being proposed. To date, no placebo-controlled studies were conducted. Regarding another CTD, systemic sclerosis (SSc), SSc-associated ILD is considered one of the leading causes of mortality. The incidence of ILD varies among disease subtypes, being influenced by diagnostic method, but also by disease duration. Due to the high prevalence of this complication, all SSc patients should be investigated for ILD at the time of SSc diagnosis and during the course of the disease. Fortunately, progress was made in terms of treatment. Nintedanib, a tyrosine kinases inhibitor, showed promising results. It appeared to decrease the rate of progression of ILD compared to placebo. This review aimed to provide up-to-date findings related to SLE-associated ILD and SSc-associated ILD, in order to raise awareness of their diagnosis and management.

Non-Invasive Retinal Vessel Analysis as a Predictor for Cardiovascular Disease.

Cardiovascular disease (CVD) is the most frequent cause of death worldwide. The alterations in the microcirculation may predict the cardiovascular mortality. The retinal vasculature can be used as a model to study vascular alterations associated with cardiovascular disease. In order to quantify microvascular changes in a non-invasive way, fundus images can be taken and analysed. The central retinal arteriolar (CRAE), the venular (CRVE) diameter and the arteriolar-to-venular diameter ratio (AVR) can be used as biomarkers to predict the cardiovascular mortality. A narrower CRAE, wider CRVE and a lower AVR have been associated with increased cardiovascular events. Dynamic retinal vessel analysis (DRVA) allows the quantification of retinal changes using digital image sequences in response to visual stimulation with flicker light. This article is not just a review of the current literature, it also aims to discuss the methodological benefits and to identify research gaps. It highlights the potential use of microvascular biomarkers for screening and treatment monitoring of cardiovascular disease. Artificial intelligence (AI), such as Quantitative Analysis of Retinal vessel Topology and size (QUARTZ), and SIVA-deep learning system (SIVA-DLS), seems efficient in extracting information from fundus photographs and has the advantage of increasing diagnosis accuracy and improving patient care by complementing the role of physicians. Retinal vascular imaging using AI may help identify the cardiovascular risk, and is an important tool in primary cardiovascular disease prevention. Further research should explore the potential clinical application of retinal microvascular biomarkers, in order to assess systemic vascular health status, and to predict cardiovascular events.