Systematic Review and Meta-Substitution Analysis of Non-Sugar Beverages as Replacements for Sugar-Sweetened Beverages: Evidence from Prospective Mega-Cohort Studies.
Bibliographic record
Abstract
Sugar-sweetened beverages (SSBs) are a major contributor to excess calorie intake and are associated with cardiometabolic disease risk. While non-sugar beverages (NSBs) containing non-nutritive sweeteners (NNS) are promoted as alternatives, evidence regarding their health effects is mixed. Randomized controlled trials generally show benefits for weight management when NSBs replace SSBs, but traditional prospective cohort studies using baseline analysis show increased cardiometabolic risk which lead to the the latest WHO Guidance on Non-Sugar Sweeteners recommending against the use of non-sugar sweeteners as means of achieving weight control or reducing the risk of noncommunicable diseases. This systematic review and meta-substitution analysis aims to quantify the effect of substituting SSBs with NSBs on cardiometabolic outcomes using advanced substitution modelling techniques applied to prospective mega-cohort studies. The literature search will be conducted in MEDLINE, EMBASE, and the Cochrane CENTRAL Library. Eligible studies will include prospective mega-cohorts (N≥100,000) reporting on SSB and/or NSB intake and cardiometabolic outcomes. Outcomes are prioritized clinical outcomes identified by WHO and these include overweight and obesity, type 2 diabetes, coronary heart disease, stroke, cardiovascular disease (CVD), all-cause mortality, hypertension, cancer, and chronic kidney disease (CKD). Two reviewers will independently extract data and assess bias using the Newcastle-Ottawa Scale. Substitution effects will be quantified by comparing beta coefficients for SSB and NSB exposures in both linear and non-linear dose-response models. Where possible, multivariate meta-analysis will be used to account for within-study correlations. The results from this study will help inform dietary guidelines, improve health outcomes by supporting healthcare providers and patients, and guide future research design.
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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 | 0.026 | 0.070 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.047 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".