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Record W4391610449 · doi:10.1093/bjd/ljad498.059

563 - Dupilumab improves skin lipid composition in atopic dermatitis irrespective of patient filaggrin (<i>FLG</i>) mutation status

2024· article· en· W4391610449 on OpenAlexaff
Evgeny Berdyshev, Elena Goleva, Anna-Sofia Bronoff, Brittany Richers, Shannon Garcia, Marco Ramírez-Gama, Patricia A. Taylor, Robert Bissonnette, Joseph Zahn, Inoncent Agueusop, Shantanu Bafna, Mark Boguniewicz, Annie Zhang

Bibliographic record

VenueBritish Journal of Dermatology · 2024
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsInnovaderm (Canada)
Fundersnot available
KeywordsFilaggrinAtopic dermatitisDupilumabDermatologyMedicine

Abstract

fetched live from OpenAlex

Abstract Introduction/Background Type 2 inflammatory cytokines interleukin 4 (IL-4) and IL-13 play an important role in skin barrier disruption in atopic dermatitis (AD). Filaggrin and ceramides play a crucial role in skin barrier integrity. Loss of function mutations in the FLG gene are associated with the impaired skin barrier function and more severe AD.1,2 FLG mutations only affect a minority of AD patients; however, increased IL-4 and IL-13 cytokines are a common cause of reduced filaggrin expression in patients with AD, independent of FLG genotype. Objectives To explore whether dupilumab treatment improves skin barrier function in patients with or without FLG mutations. Methods In the BArrier function and LIpidomics STudy in Atopic Dermatitis (BALISTAD; NCT04447417), a 16-week study in patients with AD aged 12 to 65 years, adult patients with AD received dupilumab 300 mg every 2 weeks; adolescent patients with AD received dupilumab 200 mg every 2 weeks if their baseline weight was <60 kg and 300 mg if ≥60 kg. FLG mutations were evaluated in DNA from blood samples of consenting patients with AD and healthy volunteers. Transepidermal water loss (TEWL) was assessed longitudinally after 5 skin tape strippings (STS) from AD lesions (n = 26) and from the skin of healthy participants (n =26) (age: 12 to 63 years) over a 16-week course of dupilumab treatment. Quantitative N(C18)S-ceramide analysis of STS samples collected on Days 1, 15, 29, 57, and 85, and at Week 16 from AD lesions and from the skin of healthy participants was performed using liquid chromatography tandem mass spectrometry. Results At baseline, mean TEWL after 5 STS (TEWL5) was significantly higher in AD lesional skin than healthy skin (p<0.0001). The mean TEWL5 in AD lesions in subjects with FLG mutations (n = 6/19) was significantly higher at baseline than in AD subjects without mutations (P < 0.0001). Dupilumab treatment significantly reduced TEWL5 in AD lesional skin as early as Week 2 with a progressive decrease through Week 16 (P < 0.0001). Reduction in mean TEWL5 was similar from Week 2 to Week 16 in AD patients with and without FLG mutations. At Week 16, TEWL5 was comparable to healthy skin in the lesional skin of AD patients with and without FLG mutations (P > 0.05). AD skin lesions had increased levels of N(C18)S-ceramides at baseline (P < 0.0001); but no differences were noted in subjects with or without FLG mutations (P > 0.05). Dupilumab treatment significantly reduced levels of N(C18)S-ceramides in AD lesional skin as early as Week 2 with a progressive decrease through Week 16 (P < 0.0001). Dupilumab treatment decreased levels of N(C18)S-ceramides in STS samples similarly in subjects with and without FLG mutations from Week 2 to Week 16. Conclusions Dupilumab treatment normalizes TEWL5 and decreases levels of N(C18)S-ceramides in AD lesional skin of subjects with and without FLG mutations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.005
GPT teacher head0.247
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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