Maternal low-dose aspartame and stevia consumption with an obesogenic diet alters metabolism, gut microbiota and mesolimbic reward system in rat dams and their offspring
Bibliographic record
Abstract
OBJECTIVE: We examined the impact of maternal low-dose aspartame and stevia consumption on adiposity, glucose tolerance, gut microbiota and mesolimbic pathway in obese dams and their offspring. DESIGN: Following obesity induction, female Sprague-Dawley rats were allocated during pregnancy and lactation to: (1) high fat/sucrose diet (HFS) +water (obese-WTR); (2) HFS +aspartame (obese-APM; 5-7 mg/kg/day); (3) HFS +stevia (obese-STV; 2-3 mg/kg/day). Offspring were weaned onto control diet and water and followed until 18 weeks. Gut microbiota and metabolic outcomes were measured in dams and offspring. Cecal matter from offspring at weaning was used for faecal microbiota transplant (FMT) into germ-free (GF) mice. RESULTS: Maternal APM and STV intake with a HFS diet increased body fat in offspring at weaning and body weight long-term with APM. Maternal APM/HFS consumption impaired glucose tolerance in male offspring at age 8 weeks and both APM and STV altered faecal microbiota in dams and offspring. Maternal obesity/HFS diet affected offspring adiposity and glucose tolerance more so than maternal LCS consumption at age 12 and 18 weeks. APM and STV altered expression of genes in the mesolimbic reward system that may promote consumption of a palatable diet. GF mice receiving an FMT from obese-APM and obese-STV offspring had greater weight gain and body fat and impaired glucose tolerance compared with obese-WTR. CONCLUSION: Maternal low-calorie sweetener consumption alongside HFS may disrupt weight regulation, glucose control and gut microbiota in dams and their offspring most notably in early life despite no direct low-calorie sweetener consumption by offspring.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".