Novel insights into the puberty switch mechanisms of the sex bias in human asthma
Bibliographic record
Abstract
Abstract The gender bias in human asthma prevalence changes with puberty, with males having a higher asthma incidence before puberty and females having higher rates after. The mechanisms involved in this switch are unknown. Our previous research showed that multiple hormonal systems are dysregulated in allergic disease. Because sex hormones interact with all endocrine system axes, we hypothesized that non-sex hormones and their downstream functions in allergic disease are driven by sex related changes in puberty. Measuring non-sex hormone protein levels from human serum samples by multiplex ELISAs, we found a striking sexual dimorphism in endocrine regulation. Pre-puberty asthmatic males had higher T3, T4, and GH levels compared to healthy controls, and pre-puberty asthmatic females had lower C-peptide and insulin levels. Interestingly, we saw the opposite pattern post-puberty. In a kinetic murine ovalbumin asthma model, adult male mice had higher C-peptide and insulin baseline levels, which were maintained during inflammation. Males also had higher GIP levels in the acute phase and higher PP, PYY, and resistin levels in the model’s late phase. Since hormones regulate tissue homeostasis, we examined the puberty switch in key epithelial remodeling markers in the inflamed mouse lung. Thbs2, Nes, Postn, Tnc, and Wnt5a expression was different relative to baseline in both sexes pre-puberty, but only females had altered expression of these markers post-puberty. Interestingly, though the Th2 immune response was higher in females post-puberty, there was no sexual dimorphism pre-puberty. These results implicate that sex hormones exert systemic level regulation determining the age and gender dependent progression of asthma pathogenesis.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".