Effect of sucralose and aspartame on glucose metabolism and gut hormones
Bibliographic record
Abstract
Non-nutritive sweeteners are thought to be useful replacements for caloric sweeteners in sweet food and beverages, since the reduction in energy and carbohydrate intake may lead to health benefits stemming from weight management and glycemic control. However, the potential effects of non-nutritive sweeteners on glucose metabolism and gut hormones have not been determined definitively. Here, the available evidence of the effects of aspartame and sucralose consumption on glucose metabolism and gut hormones is reviewed. A majority of studies have found that consumption of aspartame or sucralose has no effect on concentrations of blood glucose, insulin, or gut hormones; however, 2 trials have shown that aspartame consumption affects glucose, insulin, and glucagon-like peptide 1 concentrations, while only a few trials have shown that sucralose consumption affects glucose, insulin, and glucagon-like peptide 1 concentrations. One study found higher glucose concentrations after sucralose consumption, while 3 studies found lower concentrations and 33 studies found no change in glucose concentrations. Moreover, only 4 studies reported increased concentrations of glucagon-like peptide 1. Three studies reported decreased insulin sensitivity following sucralose consumption, while 1 trial reported an increase in insulin sensitivity. In summary, the evidence from the clinical trials conducted to date is contradictory because of the different protocols used.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".