Does exposure to hyperglycaemia <i>in utero</i> increase the risk of obesity and diabetes in the offspring? A critical reappraisal
Bibliographic record
Abstract
BACKGROUND: The idea that exposure to hyperglycaemia in utero is an important factor in the development of obesity and diabetes in the offspring has become entrenched as popular belief. AIM: To appraise the literature supporting this hypothesis in the light of recent studies that have clarified the main drivers of obesity in children and adolescents. METHODS: A review of published evidence from animal studies, human observational studies, systematic reviews and experimental trials that address the impact of diabetes (Types 1 and 2, genetic or gestational) on the future risk of obesity and/or glucose intolerance in the offspring. RESULTS: Some animal studies support a relationship between exposure to hyperglycaemia in utero and future development of obesity and diabetes, but the results are inconsistent. Most of the human studies claiming to show a relationship have not taken into account important known confounders, such as maternal and paternal BMI. Evidence supporting a dose-response relationship between maternal hyperglycaemia exposure and obesity and diabetes in the offspring is weak, and there is no convincing evidence that treating gestational diabetes reduces the later risk of offspring obesity or glucose intolerance. CONCLUSIONS: Exposure to hyperglycaemia in utero has minimal direct effect on the later risk of obesity and Type 2 diabetes. The increased risk of obesity in the offspring of women with Type 2 or gestational diabetes can be explained by confounding factors, such as parental obesity.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".