Prediction models for development of retinopathy in people with type 2 diabetes:systematic review and external validation in a Dutch primary care setting

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

Immunoglobulin E autoantibodies in atopic dermatitis associate with Type‐2 comorbidities and the atopic march

Background Autoreactive immunoglobulin E (IgE) antibodies to self-peptides within the epidermis have been identified in patients with atopic dermatitis (AD). Prevalence, concomitant diseases, patient characteristics, and risk factors of IgE autoantibody development remain elusive. We aimed to determine IgE autoantibodies in serum samples (n = 672) from well-characterized patients with AD and controls (1.2–88.9 years). Methods Atopic dermatitis patients were sub-grouped in AD with comorbid Type-2 diseases (“AD + Type 2”; asthma, allergic rhinitis, food allergy, n = 431) or “solely AD” (n = 115). Also, subjects without AD but with Type-2 diseases (“atopic controls,” n = 52) and non-atopic “healthy controls” (n = 74) were included. Total proteins from primary human keratinocytes were used for the immunoassay to detect IgE autoantibodies. Values were compared to already known positive and negative serum samples. Results Immunoglobulin E autoantibodies were found in 15.0% (82/546) of all analyzed AD-patients. “AD + Type 2” showed a higher prevalence (16.4%) than “solely AD” (9.6%). “Atopic controls” (9.6%) were comparable with “solely AD” patients, while 2.7% of healthy controls showed IgE autoantibodies. Of those with high levels of IgE autoantibodies, 15 out of 16 were patients with “AD + Type 2”. AD patients with IgE autoantibodies were younger than those without. Patients with IgE autoreactivity also displayed higher total serum IgE levels. Factors that affected IgE autoantibody development were as follows: birth between January and June, cesarean-section and diversity of domestic pets. Conclusions Immunoglobulin E autoantibodies in AD seem to associate with the presence of atopic comorbidities and environmental factors. The potential value of IgE autoantibodies as a predictive biomarker for the course of AD, including the atopic march, needs further exploration