Dupilumab Decreases Staphylococcus aureus Colonization and Increases Microbial Diversity in Patients With Atopic Dermatitis
Bibliographic record
Abstract
Atopic dermatitis (AD) is a chronic inflammatory skin disease associated with Staphylococcus aureus (SA) colonization. Dupilumab (DPL), an anti-IL-4Ru03b1 mAb that inhibits signaling of IL-4/IL-13, key drivers of Type 2/Th2 inflammation, is approved for patients aged u226512 years in the USA with moderate-to-severe AD inadequately controlled by topical prescription treatments or when those therapies are not advisable, adult AD patients in Japan not adequately controlled with existing therapies, and adults with moderate-to-severe AD in Europe who are candidates for systemic therapy. We investigated DPL treatment in AD influences skin colonization by SA and skin microbiome diversity. In a phase 2 trial (NCT01979016), 54 adults with moderate-to-severe AD were randomized (1:1) to weekly subcutaneous injections with DPL 200mg or placebo (PBO) for 16 weeks. SA absolute abundance was assessed by qPCR and relative microbial composition by 16S rRNA sequencing. AD severity was assessed by the Eczema Area and Severity Index (EASI). Serum biomarkers (CCL17, CCL18, and IgE) were measured. At baseline, SA had higher absolute abundance on lesional vs non-lesional skin (P<0.05). Lesional skin showed a distinct cluster in microbial u03b2-diversity (P<0.001) and lower u03b1-diversity (P<0.05). DPL decreased SA absolute and relative abundance while overall microbial diversity increased. DPL improved EASI scores and decreased serum biomarkers. No major changes were noted with PBO. Improved EASI and serum biomarkers strongly correlated with decreased SA abundance (P<0.001). These results reveal strong links between SA, the skin microbial community, serum biomarkers, and AD severity, and demonstrate that targeted suppression of Type 2 immunity by anti-IL-4Ru03b1 mAb improves the skin microbial community.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".