A Metagenomics Investigation of Intergenerational Effects of Non-nutritive Sweeteners on Gut Microbiome
Bibliographic record
Abstract
To identify possible mechanisms by which maternal consumption of non-nutritive sweeteners increases obesity risk in offspring, we reconstructed the major alterations in the cecal microbiome of 3-week-old offspring of obese dams consuming high fat/sucrose (HFS) diet with or without aspartame (5–7 mg/kg/day) or stevia (2–3 mg/kg/day) by shotgun metagenomic sequencing ( n = 36). High throughput 16S rRNA gene sequencing ( n = 105) was performed for dams, 3- and 18-week-old offspring. Maternal consumption of sweeteners altered cecal microbial composition and metabolism of propionate/lactate in their offspring. Offspring daily body weight gain, liver weight and body fat were positively correlated to the relative abundance of key microbes and enzymes involved in succinate/propionate production while negatively correlated to that of lactose degradation and lactate production. The altered propionate/lactate production in the cecum of weanlings from aspartame and stevia consuming dams implicates an altered ratio of dietary carbohydrate digestion, mainly lactose, in the small intestine vs. microbial fermentation in the large intestine. The reconstructed microbiome alterations could explain increased offspring body weight and body fat. This study demonstrates that intense sweet tastants have a lasting and intergenerational effect on gut microbiota, microbial metabolites and host health.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".