Recent evidence for the effects of nonnutritive sweeteners on glycaemic control
Bibliographic record
Abstract
PURPOSE OF REVIEW: By replacing sugar, nonnutritive sweeteners (NNSs) are thought to aid in weight management and decrease insulin resistance. We reviewed the latest randomized clinical trials (RCTs) investigating the effects NNSs on glycaemic control. RECENT FINDINGS: Six RCTs addressed this topic between 2017 and 2018; the majority tested artificial NNS (sucralose or aspartame), with only one testing natural NNS (stevia and monk fruit extract). Most found no effect of NNS on blood glucose, insulin, gastric inhibitory polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) levels; however, two trials showed an effect of sucralose on the acute insulin response. SUMMARY: We are still incapable of reaching a definite judgement on which types of NNS, if any, impact glycaemic control. There is a need for more research to overcome the limitations of recent RCTs, related to sample size, intervention duration, dose, form of NNSs used, and inclusion of males or female participants only. Future studies should also compare different NNS types with each other, and include the increasingly popular 'natural' NNS.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".