The Effects of Non-Nutritive Artificial Sweeteners, Aspartame and Sucralose, on the Gut Microbiome in Healthy Adults: Secondary Outcomes of a Randomized Double-Blinded Crossover Clinical Trial
Bibliographic record
Abstract
Non-nutritive artificial sweeteners (NNSs) may have the ability to change the gut microbiota, which could potentially alter glucose metabolism. This study aimed to determine the effect of sucralose and aspartame consumption on gut microbiota composition using realistic doses of NNSs. Seventeen healthy participants between the ages of 18 and 45 years who had a body mass index (BMI) of 20-25 were selected. They undertook two 14-day treatment periods separated by a four-week washout period. The sweeteners consumed by each participant consisted of a standardized dose of 14% (0.425 g) of the acceptable daily intake (ADI) for aspartame and 20% (0.136 g) of the ADI for sucralose. Faecal samples collected before and after treatments were analysed for microbiome and short-chain fatty acids (SCFAs). There were no differences in the median relative proportions of the most abundant bacterial taxa (family and genus) before and after treatments with both NNSs. The microbiota community structure also did not show any obvious differences. There were no differences in faecal SCFAs following the consumption of the NNSs. These findings suggest that daily repeated consumption of pure aspartame or sucralose in doses reflective of typical high consumption have minimal effect on gut microbiota composition or SCFA production.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".