Loss-of-function <i>FLG</i> mutations are associated with reduced history of acne vulgaris in a cohort of patients with atopic eczema of Bangladeshi ancestry in East London
Bibliographic record
Abstract
BACKGROUND: Acne vulgaris (AV) is the eighth most common nonfatal disease globally. Previous work identified an association between AV and increased filaggrin (FLG) protein expression in the follicular epidermis, but further work did not find a clear link between loss-of-function (LoF) FLG gene mutations and protection from AV. OBJECTIVES: To explore any association between AV and FLG LoF mutations in a cohort of genotyped patients of Bangladeshi ancestry with atopic eczema (AE) in East London. METHODS: A retrospective notes review was performed on 245 patients who had been genotyped for FLG LoF mutations and undergone -clinical assessment. A χ2-test or Fisher's exact test was used to determine differences in AV history between FLG LoF genotype groups. RESULTS: We found a significant reduction in history of AV in patients with AE with FLG LoF mutations (19 of 82) relative to those without FLG mutations (47 of 129) (23% vs. 36.4%; P = 0.02). We showed a nonsignificant reduction in AV diagnosis in patients with impaired barrier function (measured by transepidermal water loss) and palmar hyperlinearity. We found that patients with severe AE were less likely to have a history of AV only if they had an existing FLG LoF mutation (P = 0.02). CONCLUSIONS: In the context of AE, our work suggests that FLG LoF mutations protect patients from developing AV.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".