Associations of maternal glucose markers in pregnancy with cord blood glucocorticoids and child hair cortisol levels
Bibliographic record
Abstract
has been associated with adverse metabolic outcomes in offspring. However, few studies have investigated the relationship between maternal hyperglycemia and offspring cortisol levels. We assessed associations of gestational diabetes mellitus (GDM) with cortisol biomarkers in two longitudinal prebirth cohorts: Project Viva included 928 mother-child pairs and Gen3G included 313 mother-child pairs. In Project Viva, GDM was diagnosed in N = 48 (5.2%) women using a two-step procedure (50 g glucose challenge test, if abnormal followed by 100 g oral glucose tolerance test [OGTT]), and in N = 29 (9.3%) women participating in Gen3G using one-step 75 g OGTT. In Project Viva, we measured cord blood glucocorticoids and child hair cortisol levels during mid-childhood (mean (SD) age: 7.8 (0.8) years) and early adolescence (mean (SD) age: 13.2 (0.9) years). In Gen3G, we measured hair cortisol at 5.4 (0.3) years. We used multivariable linear regression to examine associations of GDM with offspring cortisol, adjusting for child age and sex, maternal prepregnancy body mass index, education, and socioeconomic status. We additionally adjusted for child race/ethnicity in the cord blood analyses. In both Project Viva and Gen3G, we observed null associations of GDM and maternal glucose markers in pregnancy with cortisol biomarkers in cord blood at birth (β = 16.6 nmol/L, 95% CI -60.7, 94.0 in Project Viva) and in hair samples during childhood (β = -0.56 pg/mg, 95% CI -1.16, 0.04 in Project Viva; β = 0.09 pg/mg, 95% CI -0.38, 0.57 in Gen3G). Our findings do not support the hypothesis that maternal hyperglycemia is related to hypothalamic-pituitary-adrenal axis activity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".