Effects of nonnutritive sweeteners on body weight and BMI in diverse clinical contexts: Systematic review and meta‐analysis
Bibliographic record
Abstract
There is an ongoing debate about the possible influences of nonnutritive sweeteners (NNS) on body weight. We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) with NNS to assess their impact on body weight. We systematically searched for RCTs at least 4 weeks in duration, evaluating the effect of NNS on body weight, both in subjects with healthy weight and in subjects with overweight/obesity at any age, and compared the effects of NNS vs caloric and noncaloric comparators. The primary outcome was the difference in body weight between NNS and comparators. Twenty studies were eligible (n = 2914). Participants consuming NNS showed significant weight/BMI differences favouring NNS compared with nonusers. Grouping by nature of comparator revealed that NNS vs placebo/no intervention and NNS vs water produced no effect. When comparing NNS vs sucrose, significant weight/BMI differences appeared favouring NNS. Consumption of NNS led to significantly negative weight/BMI differences in unrestricted energy diets, but not in weight-reduction diets. Participants with overweight/obesity and adults showed significant favourable weight/BMI differences with NNS. Data suggest that replacing sugar with NNS leads to weight reduction, particularly in participants with overweight/obesity under an unrestricted diet, information that could be utilized for evidence-based public policy decisions.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.022 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".