High fructose consumption in pregnancy alters the perinatal environment without increasing metabolic disease in the offspring
Bibliographic record
Abstract
Maternal carbohydrate intake is one important determinant of fetal body composition, but whether increased exposure to individual sugars has long-term adverse effects on the offspring is not well established. Therefore, we examined the effect of fructose feeding on the mother, placenta, fetus and her offspring up to 6 months of life when they had been weaned onto a standard rodent diet and not exposed to additional fructose. Dams fed fructose were fatter, had raised plasma insulin and triglycerides from mid-gestation and higher glucose near term. Maternal resistance arteries showed changes in function that could negatively affect regulation of blood pressure and tissue perfusion in the mother and development of the fetus. Fructose feeding had no effect on placental weight or fetal metabolic profiles, but placental gene expression for the glucose transporter GLUT1 was reduced, whereas the abundance of sodium-dependent neutral amino acid transporter-2 was raised. Offspring born to fructose-fed and control dams were similar at birth and had similar post-weaning growth rates, and neither fat mass nor metabolic profiles were affected. In conclusion, raised fructose consumption during reproduction results in pronounced maternal metabolic and vascular effects, but no major detrimental metabolic effects were observed in offspring up to 6 months of age.
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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 | 0.000 | 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) | 0.001 | 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".