Potential shared mechanisms in atopic dermatitis and type 2 diabetes identified via transcriptomic and machine learning approaches.
Journal:
Scientific reports
PMID:
39681684
Abstract
Although atopic dermatitis (AD) and type 2 diabetes mellitus (T2DM) may appear clinically and pathophysiologically unrelated, AD is a common skin disease characterized by chronic inflammation and skin barrier dysfunction, whereas T2DM is a metabolic disorder marked by hyperglycemia and chronic inflammation, which further exacerbates insulin resistance (IR) through the release of systemic inflammatory factors. Despite their apparent differences, the molecular mechanisms shared between AD and T2DM remain relatively unexplored. In this study, we integrated transcriptomic data from both AD and T2DM using differential gene expression analyses (DEGs), gene set variation analysis (GSVA), and machine learning algorithms to uncover common features of these diseases. We identified several characteristic genes, including LTF, LTB4R, and CCR1, which are significantly upregulated in both conditions and may serve as potential biomarkers. Furthermore, virtual screening revealed that Dioscin, Camptothecin, and Albamycin exhibit strong affinity for the CCR1 binding site, indicating their potential as therapeutic candidates. In summary, this study elucidates the shared molecular mechanisms of AD and T2DM and introduces new potential targets and drugs for the diagnosis and treatment of these diseases.