Maternal diabetes promotes offspring lung dysfunction and inflammation in a sex-dependent manner
Bibliographic record
Abstract
Exposure to maternal diabetes is increasingly recognized as a risk factor for chronic respiratory disease in children. It is currently unclear; however, whether maternal diabetes affects the lung health of male and female offspring equally. This study characterizes the sex-specific impact of a murine model of diet-induced gestational diabetes (GDM) on offspring lung function and airway inflammation. Female adult mice are fed a high-fat (45% kcal) diet for 6 wk prior to mating. Control offspring are from mothers fed a low-fat (10% kcal) diet. Offspring were weaned and fed a chow diet until 10 wk of age, at which point lung function was measured and lung lavage was collected. Male, but not female, offspring exposed to GDM had increased lung compliance and reduced lung resistance at baseline. Female offspring exposed to GDM displayed increased methacholine reactivity and elevated levels of proinflammatory cytokines [e.g., interleukin (IL)-1β, IL-5, and CXCL1] in lung lavage. Female GDM offspring also displayed elevated abundance of matrix metalloproteinases (MMP) within their airways, namely, MMP-3 and MMP-8. These results indicate disparate effects of maternal diabetes on lung health and airway inflammation of male and female offspring exposed to GDM. Female mice may be at greater risk of inflammatory lung conditions, such as asthma, whereas male offspring display changes that more closely align with models of chronic obstructive pulmonary disease. In conclusion, there are important sex-based differences in the impact of maternal diabetes on offspring lung health that could signal differences in future disease risk.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".