Higher maternal parathyroid hormone concentration at delivery is not associated with smaller newborn size
Bibliographic record
Abstract
Intrauterine growth restriction (IUGR) reflects inadequate growth in-utero and is prevalent in low resource settings. This study aimed to assess the association of maternal delivery parathyroid hormone (PTH) - a regulator of bone turnover and calcium homeostasis - with newborn anthropometry, to identify regulators of PTH, and to delineate pathways by which maternal PTH regulates birth size using path analysis. This was a cross-sectional analysis of data from participants (n = 537) enrolled in the Maternal Vitamin D for Infant Growth trial in Dhaka, Bangladesh. Primary exposures were maternal delivery intact PTH (iPTH) or whole PTH (wPTH) and outcomes were gestational age- and sex-standardized z-scores for birth length (LAZ), weight (WAZ), and head circumference (HCAZ). Hypothesized regulators of PTH included calcium and protein intake, vitamin D, magnesium, fibroblast-like growth factor-23 (FGF23), and C-reactive protein. Maternal iPTH was not associated with birth size in linear regression analyses; however, in path analysis models, every SD increase in log(iPTH) was associated with 0.08SD (95% CI: 0.002, 0.162) higher LAZ. In linear regression and path analysis models, wPTH was positively associated with WAZ. Vitamin D suppressed PTH, while FGF23 was positively associated with PTH. In path analysis models, higher magnesium was negatively associated with LAZ; FGF23 was positively associated and protein intake was negatively associated with LAZ, WAZ, and HCAZ. Higher maternal PTH in late pregnancy is unlikely to contribute to IUGR. Future studies should investigate maternal FGF23, magnesium and protein intake as regulators of fetal growth, particularly in settings where food insecurity and IUGR are public health problems.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".