Early-Life Exposure to Non-Nutritive Sweeteners and the Developmental Origins of Childhood Obesity: Global Evidence from Human and Rodent Studies
Bibliographic record
Abstract
Non-nutritive sweeteners (NNS) are increasingly consumed by children and pregnant women around the world, yet their long-term health impact is unclear. Here, we review an emerging body of evidence suggesting that early-life exposure to NNS may adversely affect body composition and cardio-metabolic health. Some observational studies suggest that children consuming NNS are at increased risk for obesity-related outcomes; however, others find no association or provide evidence of confounding. Fewer studies have examined prenatal NNS exposure, with mixed results from different analytical approaches. There is a paucity of RCTs evaluating NNS in children, yielding inconsistent results that can be difficult to interpret due to study design limitations (e.g., choice of comparator, multifaceted interventions). The majority of this research has been conducted in high-income countries. Some rodent studies demonstrate adverse metabolic effects from NNS, but most have used extreme doses that are not relevant to humans, and few have distinguished prenatal from postnatal exposure. Most studies focus on synthetic NNS in beverages, with few examining plant-derived NNS or NNS in foods. Overall, there is limited and inconsistent evidence regarding the impact of early-life NNS exposure on the developmental programming of obesity and cardio-metabolic health. Further research and mechanistic studies are needed to elucidate these effects and inform dietary recommendations for expectant mothers and children worldwide.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".