Maternal Smoking and Metabolic Health Biomarkers in Newborns
Bibliographic record
Abstract
BACKGROUND: Maternal smoking has been associated with elevated risk of type 2 diabetes among the offspring in adulthood. The mechanisms underlying this fetal "programming" effect remain unclear. The present study sought to explore whether maternal smoking affects metabolic health biomarkers in fetuses/newborns. METHODS: In a prospective singleton pregnancy cohort (n = 248), we compared metabolic health biomarkers in the newborns of smoking and non-smoking mothers. Outcomes included cord plasma insulin, proinsulin, insulin-like growth factor I (IGF-I), IGF-II, leptin and adiponectin concentrations, glucose-to-insulin ratio (an indicator of insulin sensitivity) and proinsulin-to-insulin ratio (an indicator of β-cell function). RESULTS: Independent of maternal (glucose tolerance, age, ethnicity, parity, education, body mass index, alcohol use) and infant (sex, gestational age, birth weight z score, mode of delivery, cord blood glucose concentration) characteristics, the newborns of smoking mothers had lower IGF-I concentrations (mean: 6.7 vs. 8.4 nmol/L, adjusted p = 0.006), and marginally higher proinsulin-to-insulin ratios (0.94 vs. 0.72, adjusted p = 0.06) than the newborns of non-smoking mothers. Cord plasma insulin, proinsulin, IGF-II, leptin and adiponectin concentrations and glucose-to-insulin ratios were similar in the newborns of smoking and non-smoking mothers. CONCLUSIONS: Maternal smoking was associated with decreased fetal IGF-I levels, and borderline lower fetal β-cell function. Larger cohort studies are required to confirm the latter finding. The preliminary findings prompt the hypothesis that these early life metabolic changes may be involved in the impact of maternal smoking on future risk of metabolic syndrome related disorders in the offspring.
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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 | 0.002 |
| 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 | 0.001 | 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".