Maternal selenium, copper and zinc concentrations in pregnancy associated with small‐for‐gestational‐age infants
Bibliographic record
Abstract
Pregnancy during adolescence increases the risk of adverse pregnancy outcome, especially small-for-gestational-age (SGA) birth, which has been linked to micronutrient deficiencies. Smoking has been shown to be related to lower micronutrient concentrations. Different ethnicities have not been examined. We used a subset from a prospective observational study, the About Teenage Eating study consisting of 126 pregnant adolescents (14-18-year-olds) between 28 and 32 weeks gestation. Micronutrient status was assessed by inductively coupled mass spectrometry. Smoking was assessed by self-report and plasma cotinine, and SGA was defined as infants born <10th corrected birthweight centile. The main outcome measures were as follows: (1) maternal plasma selenium, copper and zinc concentrations in adolescent mothers giving birth to SGA vs. appropriate-for-gestational-age (AGA) infants; and (2) comparison of micronutrient concentrations between women of different ethnicities and smoking habits. The plasma selenium {mean ± standard deviation (SD) [95% confidence interval (CI)]} concentration was lower in the SGA [n = 19: 49.4 ± 7.3 (CI: 45.9, 52.9) µg L(-1)] compared with the AGA [n = 107: 65.1 ± 12.5 (CI: 62.7, 67.5) µg L(-1); P < 0.0001] group. Smoking mothers had a lower selenium concentration compared with non-smokers (P = 0.01) and Afro-Caribbean women had higher selenium concentrations compared with White Europeans (P = 0.02). Neither copper nor zinc concentrations varied between groups. Low plasma selenium concentration in adolescent mothers could contribute to the risk of delivering an SGA infant, possibly through lowering placental antioxidant defence, thus directly affecting fetal growth. Differences in plasma selenium between ethnicities may relate to variation in nutritional intake, requiring further investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".