Fetal programming of metabolic health through a combined dietary and pharmacological pre‐pregnancy intervention in rats
Bibliographic record
Abstract
Maternal obesity is associated with low birth weight, type 2 diabetes and obesity risk in offspring. Diets high in oligofructose (OFS) improve glucose control through glucagon‐like‐peptide‐1 (GLP‐1). Sitagliptin inhibits the degradation of GLP‐1in vivo. The aim of this study was to determine if a combined OFS and sitagliptin pre‐pregnancy intervention in obese female Sprague‐Dawley rats can improve the metabolic health of female offspring. Obese female Sprague‐Dawley rats were randomized to 1 of 6 groups: 1) AIN‐93; 2) 10% OFS; 3) Sitagliptin; 4) AIN‐93+OFS+Sitagliptin; 5) High fat & sucrose (HFS); 6) Caloric restriction (CR). A lean reference group was also included. Pups consumed AIN‐93 until 11wk when they were given HFS diet for 6 wk. Body weight and blood glucose were assessed. At birth the offspring of combination‐treated dams were significantly heavier than offspring in all other groups (p<0.04). Following the HFS diet challenge the blood glucose area under the curve for offspring of the combination group was less than the HFS and CR groups (p<0.05). Despite higher birth weight, the combined treatment significantly improved offspring glycemic control in adulthood. Funded by CIHR and ACHRI. Grant Funding Source : CIHR and ACHRI
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".