The pathophysiology of atopic dermatitis (AD) is highly complex and understanding of disease endotypes may improve disease management. Immunoglobulins E (IgE) against human skin epitopes (IgE autoantibodies) are thought to play a role in disease progression and prolongation. These antibodies have been described in patients with severe and chronic AD, suggesting a progression from allergic inflammation to severe autoimmune processes against the skin. This review provides a summary of the current knowledge and gaps on IgE autoreactivity and self-reactive T cells in children and adults with AD based on a systematic search. Currently, the clinical relevance and the pathomechanism of IgE autoantibodies in AD needs to be further investigated. Additionally, it is unknown whether the presence of IgE autoantibodies in patients with AD is an epiphenomenon or a disease endotype. However, increased knowledge on the clinical relevance and the pathophysiologic role of IgE autoantibodies and self-reactive T cells in AD can have consequences for diagnosis and treatment. Responses to the current available treatments can be used for better understanding of the pathways and may shed new lights on the treatment options for patients with AD and autoreactivity against skin epitopes. To conclude, IgE autoantibodies and self-reactive T cells can contribute to the pathophysiology of AD based on the body of evidence in literature. However, many questions remain open. Future studies on autoreactivity in AD should especially focus on the clinical relevance, the contribution to the disease progression and chronicity on cellular level, the onset and therapeutic strategies.

Aims/hypothesis The aims of this study were to identify all published prognostic models predicting retinopathy risk applicable to people with type 2 diabetes, to assess their quality and accuracy, and to validate their predictive accuracy in a head-to-head comparison using an independent type 2 diabetes cohort. Methods A systematic search was performed in PubMed and Embase in December 2019. Studies that met the following criteria were included: (1) the model was applicable in type 2 diabetes; (2) the outcome was retinopathy; and (3) follow-up was more than 1 year. Screening, data extraction (using the checklist for critical appraisal and data extraction for systemic reviews of prediction modelling studies [CHARMS]) and risk of bias assessment (by prediction model risk of bias assessment tool [PROBAST]) were performed independently by two reviewers. Selected models were externally validated in the large Hoorn Diabetes Care System (DCS) cohort in the Netherlands. Retinopathy risk was calculated using baseline data and compared with retinopathy incidence over 5 years. Calibration after intercept adjustment and discrimination (Harrell's C statistic) were assessed. Results Twelve studies were included in the systematic review, reporting on 16 models. Outcomes ranged from referable retinopathy to blindness. Discrimination was reported in seven studies with C statistics ranging from 0.55 (95% CI 0.54, 0.56) to 0.84 (95% CI 0.78, 0.88). Five studies reported on calibration. Eight models could be compared head-to-head in the DCS cohort (N = 10,715). Most of the models underestimated retinopathy risk. Validating the models against different severities of retinopathy, C statistics ranged from 0.51 (95% CI 0.49, 0.53) to 0.89 (95% CI 0.88, 0.91). Conclusions/interpretation Several prognostic models can accurately predict retinopathy risk in a population-based type 2 diabetes cohort. Most of the models include easy-to-measure predictors enhancing their applicability. Tailoring retinopathy screening frequency based on accurate risk predictions may increase the efficiency and cost-effectiveness of diabetic retinopathy care. Registration PROSPERO registration ID CRD42018089122

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