Perinatal Oxidative Stress May Affect Fetal Ghrelin Levels in Humans
Bibliographic record
Abstract
In vitro cell model studies have shown that oxidative stress may affect beta-cell function. It is unknown whether oxidative stress may affect metabolic health in human fetuses/newborns. In a singleton pregnancy cohort (n = 248), we studied maternal (24-28 weeks gestation) and cord plasma biomarkers of oxidative stress [malondialdehyde (MDA), F2-isoprostanes] in relation to fetal metabolic health biomarkers including cord plasma glucose-to-insulin ratio (an indicator of insulin sensitivity), proinsulin-to-insulin ratio (an indicator of beta-cell function), insulin, IGF-I, IGF-II, leptin, adiponectin and ghrelin concentrations. Strong positive correlations were observed between maternal and cord plasma biomarkers of oxidative stress (r = 0.33 for MDA, r = 0.74 for total F2-isoprostanes, all p < 0.0001). Adjusting for gestational age at blood sampling, cord plasma ghrelin concentrations were consistently negatively correlated to oxidative stress biomarkers in maternal (r = -0.32, p < 0.0001 for MDA; r = -0.31, p < 0.0001 for F2-isoprostanes) or cord plasma (r = -0.13, p = 0.04 for MDA; r = -0.32, p < 0.0001 for F2-isoprostanes). Other fetal metabolic health biomarkers were not correlated to oxidative stress. Adjusting for maternal and pregnancy characteristics, similar associations were observed. Our study provides the first preliminary evidence suggesting that oxidative stress may affect fetal ghrelin levels in humans. The implications in developmental "programming" the vulnerability to metabolic syndrome related disorders remain to be elucidated.
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 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".