Folic acid in a high multivitamin diet during pregnancy increases post‐weaning weight gain and PPAR gene expression in adipose and liver of Wistar rat dams (135.2)
Bibliographic record
Abstract
High multivitamin (10‐fold, HV) or folic acid (Fol) intake alone during pregnancy leads to an obesogenic phenotype in the offspring but its effects on the later post‐weaning (PW) weight gain of the dams has not been reported. This study investigated whether Fol alone as a component of a HV gestational diet affects PW weight gain and associates with expression of peroxisome‐proliferator activated receptor isoforms (PPAR‐α/β/γ) involved in lipid metabolism and adipocyte differentiation. Wistar rat dams were fed an AIN‐93G diet during pregnancy containing either: 1) Recommended vitamin (RV); 2) HV; or 3) HV with recommended Fol (HVNF) content (n=15/group). Dams were fed a RV diet during lactation and then switched to a high fat (RV with 60% fat) diet for 16 weeks. Dams fed the HV diet during pregnancy gained 30% more weight over time from weaning (p<0.05) and had increased PPAR‐γ (3.8‐fold, p<0.05) and PPAR‐α (1.2‐fold, p<0.05) mRNA expression in visceral adipose and liver respectively compared to RV dams. Reducing Fol to recommended quantities modulated these effects as HVNF dams did not gain weight over time and had similar PPAR‐γ and ‐α mRNA expression relative to RV dams. Food intake and muscle PPAR‐α /β expression was not affected by any dietary treatment during pregnancy. In conclusion, high vitamin content, perhaps due to folate alone, in the gestational diet increases the obesogenic phenotype of the mothers in later life. Grant Funding Source : Supported by Canadian Institutes of Health Research, Institue of Nutrition, Metabolism and Diabetes
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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 | 0.001 |
| 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".