Using a diet and drug combination treatment prior to pregnancy to improve maternal health in rats
Bibliographic record
Abstract
Maternal obesity, before and during pregnancy, can program obesity risk in offspring. Glucagon‐like peptide‐1 (GLP‐1) is a satiety hormone stimulated by the prebiotic fiber, oligofructose (OFS). GLP‐1 is protected against degradation by the diabetes drug, sitagliptin. Our objective was to determine if treating maternal obesity in the pre‐pregnancy period with OFS and sitagliptin is more effective at reducing adiposity and improving gut microbiota profiles than either alone. Obese female rats (n=52) were randomized to 1 of 4 treatments for 8wk: 1) AIN‐93M; 2) OFS; 3) AIN‐93M+Sitagliptin; 4) OFS+ Sitagliptin. Reference groups were: high fat, sucrose (HFS, obese control), lean control, and caloric restriction (matched weight to gp 4). Rats were mated following treatment. Results showed: 1) Consumption of the HFS diet throughout pregnancy and lactation deteriorated maternal health with significantly increased adiposity and impaired glucose tolerance (P<0.05). 2) Whereas pre‐treating obesity with OFS and sitagliptin reduced maternal weight gain during pregnancy, rats that were calorically restricted showed excessive gestational weight gain. 3) The beneficial effects of OFS consumption on bifidobacteria and C. leptum numbers persisted throughout pregnancy and lactation. The significance of this work lies in identification of novel obesity treatments that could be used prior to conception and reduce the transmission of obesity risk to offspring. Funded by CIHR.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".