Maternal Body Mass Index, Gestational Weight Gain, and Risk of Cancer in Offspring: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Mounting evidence suggests that maternal obesity and gestational weight gain (GWG) may increase the risk of cancer in their offspring; however, results are inconsistent. The purpose of this research is to determine the association between maternal body mass index (BMI) and GWG and the risk of cancer in offspring through a systematic and comprehensive meta-analysis. Methods: A systematic literature search of several databases was conducted on 1 October 2022 to identify relevant studies. The quality of the included studies was evaluated using the Newcastle–Ottawa scale. The overall risk estimates were pooled using a random-effects meta-analysis. Results: Twenty-two studies with more than 8 million participants were included. An increased risk of total cancer was found in offspring whose mothers had a high GWG (odds ratio [OR]: 1.10; 95% CI: 1.01–1.19; p: 0.040) but not in offspring whose mothers had a low GWG (OR: 1.06; 95% CI: 0.96–1.17; p: 0.030), when compared with offspring whose mothers had a suitable GWG. In addition, no statistically significant association was found between maternal underweight (OR: 1.05; 95% CI: 0.97–1.13; p: 0.630), overweight/obesity (OR: 1.07; 95% CI: 0.99–1.16; p: 0.020), and risk of total cancer in offspring. Conclusions: Our study proposes evidence that maternal BMI and GWG may be associated with the risk of cancer in offspring, although statistical significance was found only for high GWG. Further well-designed research is required to clarify the potential relevance of maternal BMI and GWG on offspring cancer, especially for specific cancers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 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".