Adding prebiotic fiber to a maternal high fat, sucrose diet during pregnancy and lactation reduces offspring body fat in Sprague‐Dawley rats
Bibliographic record
Abstract
The prebiotic fiber oligofructose (OFS) may help reduce the negative effects associated with intake of a high‐fat/high sucrose (HFS) diet during pregnancy and lactation. Obesity was induced in female SD rats (n=90) with a HFS diet for 10 wk. Top weight gainers were randomized to 1 of 3 groups for pregnancy and lactation (n=14 per group): 1) HFS; 2) HFS + 10% OFS; 3) Weight matched (WM) to group 2. A 4th lean control (LC) group was kept on standard AIN‐93G diet. Primary outcomes included maternal and offspring body weight (BW) and composition (via DXA). Secondary outcomes included an oral glucose tolerance test, satiety hormone analysis and fecal microbiota profiles. At baseline, obesity induced dams had significantly higher BW (p<0.001) and fasting glucose (p<0.05) than LC. During gestation, BW was lower in LC than all other groups (p=0.001) up to 1 day before due date, then was only lower than the HFS dams; OFS and WM dams were not different than LC. At weaning, HFS dams had higher % body fat than all other groups (p<0.015). Gestational glycemic response was highest in HFS dams (p<0.05). Offspring of HFS dams had higher % body fat than all other groups (p<0.001); offspring of OFS dams were no different then LC or WM pups. Gut microbiota and satiety hormone analysis is ongoing. In conclusion, consumption of OFS during pregnancy and lactation normalizes the high body fat found in HFS‐fed dams and their offspring. Funded by CIHR, AIHS, NSERC. Grant Funding Source : CIHR, NSERC, AIHS
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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".