Early Exposure to Nonnutritive Sweeteners and Long-term Metabolic Health: A Systematic Review
Bibliographic record
Abstract
CONTEXT: Nonnutritive sweetener (NNS) consumption is increasing among children, yet its long-term health impact is unclear, particularly when exposure occurs during early life. OBJECTIVE: To synthesize evidence from prospective studies evaluating the association of early-life NNS exposure and long-term metabolic health. DATA SOURCES: Medline, Embase, and Cochrane Library (inception to July 2015). STUDY SELECTION: We aimed to include randomized controlled trials (RCTs) evaluating NNS-based interventions and prospective cohort studies reporting NNS exposure among pregnant women, infants, or children (<12 years of age), with a minimum study duration of 6 months. DATA EXTRACTION: The primary outcome was BMI; secondary outcomes included growth velocity, overweight/obesity, adiposity, and adverse metabolic effects. Study quality and risk of bias were evaluated using validated assessment tools. RESULTS: We identified 6 eligible cohort studies and 2 RCTs (n = 15,641 children). Half of the cohorts reported increasing weight gain or fat mass accumulation with increasing NNS intake, and pooled data from 2 cohorts showed a significant correlation with BMI gain (weighted mean correlation 0.023, 95% confidence interval 0.006 to 0.041). RCTs reported contradictory effects on weight change in children receiving NNSs. No eligible studies evaluated prenatal or infant NNS exposure. LIMITATIONS: Meta-analysis was limited because of the small number of eligible studies and heterogeneity of populations and outcomes. CONCLUSIONS: There is limited and inconsistent evidence of the long-term metabolic effects of NNS exposure during gestation, infancy, and childhood. Further research is needed to inform recommendations for the use of NNSs in this sensitive population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".