Tissue and systemic inflammation in dystrophic epidermolysis bullosa: a systematic review and meta-analysis.

BACKGROUND

Dystrophic epidermolysis bullosa (DEB) is a rare inherited skin disorder caused by mutations in the type VII collagen gene, leading to mucocutaneous blistering. Subsequent inflammation contributes to chronic wounds, scarring, and systemic complications. There is controversy over whether and how inflammation should be therapeutically targeted.

OBJECTIVE

This systematic review and meta-analysis aim to question tissue and systemic inflammation in DEB and identify inflammatory patterns and research gaps to improve patient management.

METHODS

A comprehensive search of MEDLINE via PubMed was conducted to identify studies examining "DEB and tissue or systemic inflammation". Out of 663 studies identified, 37 met the inclusion criteria. Data for synthesis were extracted from studies assessing systemic inflammatory parameter levels in DEB patients. For outcomes with multiple available studies, we performed an exploratory network meta-analysis to compare the standardized mean difference in systemic inflammatory parameters across three patient groups: DEB patients, healthy controls, and patients with other types of epidermolysis bullosa (EB).

RESULTS

The point estimate results for IL-4, IL-6, tumor necrosis factor-alpha, C-reactive protein, immunoglobulin (Ig) A, IgG, and IgM, as well as anti-collagen VII, anti-BP230, anti-BP180 autoantibodies suggested elevated values in DEB patients compared to healthy patients or other EB patients. The estimated standardized mean differences showed lower values of interleukin (IL)-10, hemoglobin and serum albumin in DEB patients compared to controls or other EB patients.

CONCLUSION

Current evidence is limited by small and heterogeneous patient cohorts, variability in study designs and reporting methods, and a predominant reliance on observational and retrospective descriptive studies. Well-designed clinical trials and prospective studies are necessary to further investigate inflammatory pathways and assess the efficacy of (targeted) anti-inflammatory therapies but are difficult to perform and cost-intensive. AI tools for small-data may support research in this field. PROSPERO Registration Number CRD42024535352.