Longitudinal integrated proteomic and metabolomic skin changes in patients with atopic dermatitis treated with dupilumab
Bibliographic record
Abstract
BACKGROUND: Inhibition of IL-4/IL-13-driven inflammation by dupilumab has shown significant clinical benefits in treatment of atopic dermatitis (AD). OBJECTIVE: Our aim was to assess longitudinal protein and metabolite composition in AD skin during dupilumab treatment. METHODS: Skin tape strips (STSs) were collected from lesional/nonlesional skin of 20 patients with AD during a 16-week dupilumab treatment course and from 20 healthy volunteers (HVs) followed for 16 weeks. STS extracts were examined by liquid chromatography-mass spectrometry proteomic analysis and targeted metabolomics. RESULTS: Approximately 2500 individual proteins were identified in the STS extracts. Of those proteins, 490 were present in at least 80% of the AD and HV skin samples and differentially expressed in the AD skin; the levels of 249 proteins were significantly reduced (cluster 1), and the levels of 136 were significantly increased (cluster 2) in the AD skin versus in the HV skin (both P < .0001). Functionally, cluster 1 included proteins involved in epidermal barrier formation, lysosomal enzymes required for lamellae assembly, and oxidative response. Cluster 2 was enriched for markers of epidermal hyperplasia, glycolytic enzymes, and actin filament proteins. A significant increase in cluster 1 and a significant inhibition of cluster 2 proteins expression were achieved in AD skin by 16 weeks of dupilumab treatment (P < .0001 for both vs baseline), approaching the levels in HV skin. These improvements were also revealed in differential metabolite changes in the STS extracts, including amino acids, nucleotide breakdown products, and antioxidants. CONCLUSION: Longitudinal integrated assessment of the skin proteome and metabolome in patients with AD who were treated with dupilumab established significant inhibition of epidermal hyperplasia and improvement in epidermal differentiation. The identified changes were linked to improvements in clinical AD skin assessments, including improvements in transepidermal water loss and disease severity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".