Maternal diabetes and lung health: an unexplored risk factor for COPD?
Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD), the fourth leading cause of death worldwide, is traditionally considered a disease of smoking. However, <20% of people who smoke develop COPD, indicating the disease is complex, resulting from the interplay of genetic and environmental factors. Emerging evidence highlights the importance of exposure in early life to environmental irritants that impair fetal lung development and subsequent lung function trajectories, increasing risk for future COPD. Specifically, childhood asthma, preterm birth, and surfactant deficiency have been associated with lung function impairments and an increased COPD risk later in life. Furthermore, prenatal exposure to cigarettes influences sensitivity of individuals to smoking in their later life. A mounting body of evidence now indicates that diabetes exposure during pregnancy increases the risk for several childhood conditions linked to COPD risk, suggesting that maternal diabetes may be an unexplored risk factor for COPD. This article reviews the current literature on the influence of maternal diabetes on known early-life COPD risk factors (asthma and preterm birth), and identifies knowledge gaps that need to be addressed to pindown a potential association with COPD. Specifically, whether exposure to maternal diabetes influences offspring risk for COPD through already identified risk modifiers, or directly by altering lung function trajectories or sensitivity to cigarettes. Maternal diabetes rates are rising worldwide, with type 2 diabetes mellitus (T2DM) during pregnancy and gestational diabetes mellitus (GDM) nearly doubling over the last 15 years. Understanding how prenatal diabetes influences COPD risk is imperative to establishing whether intervening early can prevent COPD in this population.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".