Reconciling conflicting evidence on low- and no-calorie sweeteners and cardiometabolic outcomes: an umbrella review using naïve and bias-adjusted methods
Bibliographic record
Abstract
Inconsistency among evidence syntheses has led to opposing guidelines and public confusion regarding low- and no-calorie sweeteners (LNCS) in noncommunicable diseases. To understand the role of different analytical approaches in assessing LNCS and cardiometabolic outcomes, we conducted an umbrella review of systematic reviews and meta-analyses. MEDLINE, EMBASE, and Cochrane were searched for systematic reviews and meta-analyses of trials or cohorts that had at least two analytical approaches: naïve (LNCS vs. all-comparators (trials) and prevalent (cohorts)) and bias-adjusted (LNCS vs. intended or reference substitution (trials) and LNCS change or intended or reference substitution (cohorts)). Grading of Recommendations Assessment, Development, and Evaluation assessed certainty of evidence. We included six trial- and five cohort-based analyses. In trials, LNCS reduced energy, body weight, and body fat in both analyses and body mass index and systolic blood pressure in bias-adjusted only, while glycosylated hemoglobin showed smaller reductions than water in bias-adjusted only. In analyses of cohorts, LNCS was associated with higher obesity, diabetes, stroke, and cardiovascular and all-cause mortality in naïve analyses but lower body weight, waist circumference, obesity, coronary heart disease, and cardiovascular and all-cause mortality in bias-adjusted analyses. The certainty of evidence was generally moderate for trials and very low for cohorts. LNCS show benefits across analytical approaches in both analyses of trials. These results agree with bias-adjusted analyses of cohorts, in which LNCS are associated benefits across cardiometabolic outcomes, but not naïve analyses of cohorts. Systematic reviews and meta-analyses using bias-reduction methods support the use of LNCS as a sugar-reduction strategy. Protocol registration: https://doi.org/10.17605/OSF.IO/TSEQM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".