Consumption of Non-Nutritive Sweetener during Pregnancy and Weight Gain in Offspring: Evidence from Human Studies
Bibliographic record
Abstract
The relationship between the consumption of maternal non-nutritive sweeteners (NNS) during pregnancy and the risk of obesity in offspring remains inconsistent. We aimed to systematically evaluate and clarify the relationship between NNS intake during pregnancy and weight gain in offspring based on evidence from population and clinical research. Databases including PubMed (via Medline), EMBASE, and the Cochrane Library were systematically searched for eligible human studies. The primary outcome was the differences in body mass index (BMI) z-scores between offspring at 1 year of age who were with and without NNS intake during pregnancy or between offspring with different NNS intake levels during pregnancy. A random-effects meta-analysis was conducted for data synthesis to calculate the weighted mean difference (WMD). A total of six prospective cohort studies were eligible for inclusion, among which three were used for pooled analysis of the BMI z-score. A significant increase was found in an offspring’s weight at 1 year of age in the NNS group when compared with the control group: WMD in BMI z-score = 0.19 (95% CI: 0.07, 0.31), p-value = 0.002. Results from the dose-response analysis showed a linear relationship between NNS intake during pregnancy and WMD at 1 year of age: beta = 0.02 (95% CI: 0.001, 0.04) for per serving/week increase in NNS consumption. The whole body of evidence for the review was rated as low quality. In summary, maternal NNS intake during pregnancy was found to be associated with increased weight gain in offspring based on evidence from human studies. Further well-designed and adequately powered studies are needed to confirm this relationship.
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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 | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".