Increased loss‐of‐function filaggrin gene mutation prevalence in atopic dermatitis patients across northern latitudes indicates genetic fitness: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Loss‐of‐function (LoF) mutations in the filaggrin gene (FLG) constitute the strongest genetic risk for atopic dermatitis (AD). A latitude‐dependent difference in the prevalence of LoF FLG mutations was systematically evaluated. A systematic review and meta‐analysis were performed to estimate the prevalence of LoF FLG mutations in AD patients and the general population by geography and ethnicity. Risk of bias was assessed by Newcastle‐Ottawa Scale and Jadad score. StatsDirect, version 3 software was used to calculate all outcomes. PubMed and EMBASE were searched until 9th December 2021. Studies were included if they contained data on the prevalence of LoF FLG mutations in AD patients or from the general population or associations between AD and LoF FLG mutations and were authored in English. Overall, 248 studies and 229 310 AD patients and individuals of the general population were included in the quantitative analysis. The prevalence of LoF FLG mutations was 19.1% (95% CI, 17.3–21.0) in AD patients and 5.8% (95% CI, 5.3–6.2) in the general population. There was a significant positive association between AD and LoF FLG mutations in all latitudes in the Northern hemisphere, but not in all ethnicities. The prevalence of LoF FLG mutations became gradually more prevalent in populations residing farther north of the Equator but was negligible in Middle Easterners and absent in most African populations. FLG LoF mutations are common and tend to increase with northern latitude, suggesting potential clinical implications for future AD management. The existence of possible genetic fitness from FLG LoF mutations remains unknown.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.023 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".