Non-nutritive sweetener consumption during pregnancy affects adiposity in mouse and human offspring
Bibliographic record
Abstract
Abstract Overweight and obesity affect over 20% of children worldwide. Emerging evidence shows that nonnutritive sweeteners (NNS) could adversely influence weight gain and metabolic health, particularly during critical periods of development. Thus, we aimed to investigate the impact of prenatal NNS exposure on postnatal growth and adiposity. Among 2298 families participating in the CHILD cohort study, children born to mothers who regularly consumed NNS during pregnancy had elevated body mass index and adiposity at 3 years of age. In a complementary study designed to eliminate confounding by human lifestyle factors and investigate causal mechanisms, we exposed pregnant mice and cultured adipocytes to NNS (aspartame or sucralose) at doses relevant to human consumption. In mice, maternal NNS exposure caused elevated body weight, adiposity and insulin resistance in offspring, especially in males. Further, in 3T3-L1 pre-adipocyte cells, sucralose exposure during early stages of differentiation caused increased lipid accumulation and expression of adipocyte differentiation genes (e.g. C/EBP-α, FABP4, FAS). The same genes were upregulated in the adipose tissue of male mouse offspring born to sucralose-fed dams. Together, these clinical and experimental findings provide evidence suggesting that maternal NNS consumption induces obesity risk in the offspring through effects on adiposity and adipocyte differentiation. One Sentence Summary Maternal consumption of non-nutritive sweeteners during pregnancy stimulates adipocyte differentiation, insulin resistance, weight gain, and adiposity in mouse and human offspring.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".