Development of a predictive model for pediatric atopic dermatitis: A retrospective cross-sectional nationwide database study.

BACKGROUND: The rise in prevalence of atopic dermatitis (AD) has been correlated with numerous elements of the exposome, modern-day lifestyle, and familial history. The combined analysis of familial history and other risk elements may allow us to understand the driving factors behind the development of AD. OBJECTIVE: To develop prediction models to assess the risk of developing AD using a large and diverse cohort (N = 77,525) and easily assessed risk factors. METHODS: We analyzed electronic medical record data from Leumit Health System. Documented predictive factors include sex, season of birth, environment (urban/rural), socioeconomic status, household smoking, diagnosed skin conditions, number of siblings, a paternal, maternal, or sibling history of an atopic condition, and antibiotic prescriptions during pregnancy or after birth. Predictive models were trained and validated on the data set. RESULTS: Medium (odds ratio [OR] 2.04, CI 1.92-2.17, P < .001) and high (OR 2.13, CI 1.95-2.34, P < .001) socioeconomic status, a previous diagnosis of contact dermatitis (OR 2.57, CI 2.37-2.78, P < .001), presence of siblings with an AD diagnosis (OR 2.21, CI 2.04-2.40, P < .001), and the percentage of siblings with any atopic condition (OR 2.58, CI 2.09-3.17, P < .001) drove risk for AD in a logistic regression model. A random forest prediction model with a sensitivity of 61% and a specificity of 84% was developed. Generalized mixed models accounting for the random effect of familial relationships boasted an area under the curve of 0.98. CONCLUSION: Predictive modeling using noninvasive and accessible inputs is a powerful tool to stratify risk for developing AD.

Bacterial Skin Dysbiosis in Darier Disease.

INTRODUCTION: Darier disease is a rare inherited disease with dominant skin manifestations including keratotic papules and plaques on sebaceous and flexural areas. Secondary infection of skin lesions is common, and Staphylococcus aureus commonly colonizes these lesions. The aim of the study was to characterize the bacterial microbiome of cutaneous Darier lesions compared to normal-looking skin and disease severity. METHODS: All patients with a history of Darier followed up at Emek Medical Center were invited to participate in the study. Patients that did not use antibiotics in the past month and signed informed consent had four skin sites sampled with swabs: scalp, chest, axilla, and palm. All samples were analyzed for bacterial microbiome using 16S rDNA sequencing. RESULTS: Two hundred and eighty microbiome samples obtained from lesional and non-lesional skin of the scalp, chest, axilla, and palm of 42 Darier patients were included in the analysis. The most abundant bacterial genera across all skin sites were Propionibacterium, Corynebacterium, Paracoccus, Micrococcus, and Anaerococcus. Scalp and chest lesions featured a distinct microbiome configuration that was mainly driven by an overabundance of Staphylococci species. Patients with more severe disease exhibited microbiome alterations in the chest, axilla, and palm compared with patients with only mild disease, driven by Peptoniphilus and Moryella genera in scalp and palmar lesions, respectively. CONCLUSION: Staphylococci were significantly associated with Darier lesions and drove Darier-associated dysbiosis. Severity of the disease was associated with two other bacterial genera. Whether these associations also hold a causative role and may serve as a therapeutic target remains to be determined and requires further investigation.