Impact of Food Ingredients (Aspartame, Stevia, Prebiotic Oligofructose) on Fertility and Reproductive Outcomes in Obese Rats
Bibliographic record
Abstract
OBJECTIVE: This study aimed to investigate the interaction between obesity, low-calorie sweeteners, and prebiotic oligofructose on reproductive parameters in rats. METHODS: Data were derived from two separate studies of female Sprague-Dawley rats with (1) Lean (n = 24), (2) Obese (n = 27), (3) Obese+Aspartame (n = 14), (4) Obese+Stevia (n = 15), and (5) Obese+Prebiotic (n = 15) groups. Obesity was induced with a high-fat/high-sucrose diet prior to pregnancy. In one study, human-approved doses of aspartame (5-7 mg/kg/d) and stevia (2-3 mg/kg/d) in drinking water were examined, and in the second, 10% prebiotics (oligofructose) in the diet was examined. Reproductive parameters, including fertility, pregnancy, and delivery indexes, were analyzed. RESULTS: Obesity significantly reduced pregnancy index in Obese dams (60.7% successful pregnancies) compared with lean (100%). Obesity also reduced the number of pups born alive and pup survival percentage compared with those of Lean dams (P < 0.001). Only 53.3% of rats were able to conceive in the Obese+Stevia group, but if rats did become pregnant, they had 100% pregnancy and delivery index. While prebiotic administration rescued the pregnancy index, it could not remediate pup survival percentage (P = 0.025) in Obese dams. CONCLUSIONS: Both obesity status and dietary ingredients affect the ability to conceive. Future rigorously controlled studies designed to examine reproductive outcomes in depth are needed to confirm these findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".