The effect of tobacco exposure on the fetal hypothalamic–pituitary–adrenal axis
Bibliographic record
Abstract
OBJECTIVE: Our objective was to determine if maternal smoking is associated with programming of the fetal hypothalamic-pituitary-adrenal (HPA) axis. Cigarette smoking, which induces a state of hypoxia in the fetus, may promote in utero'programming' of the HPA axis. In utero, adaptations to the HPA axis, which become maladaptive later in life, have been hypothesised to contribute to the development of adult cardiovascular disease and metabolic disorders. DESIGN: This was a prospective cohort study of term infants. POPULATION AND SETTING: The study involved 104 infants born by elective caesarean section, 21 of whom were exposed to in utero tobacco and 83 were nonexposed. METHODS: Healthy women with healthy pregnancies were recruited if they were undergoing elective caesarean section. Maternal blood was drawn for cortisol and cotinine in the morning, and the umbilical blood was drawn immediately after delivery of the baby. MAIN OUTCOME MEASURES: Umbilical arterial cortisol and adrenocorticotropin hormone (ACTH) levels. RESULTS: ACTH levels were significantly elevated in smoke-exposed infants [17 (4-22) pmol/l versus 4 (2-11) pmol/l, respectively, P= 0.005], while cortisol levels were similar [182 (130-240) nmol/l versus 192 (127-265) nmol/l, respectively, P= 0.541]. CONCLUSIONS: For the first time, it was shown that infants exposed to in utero tobacco smoke have significantly elevated ACTH levels compared with nonexposed infants. The results of this study warrant further exploration of the effect of smoking on the neonatal HPA axis as a potential set up for 'programming'.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".