Associations between maternal preconception and pregnancy adiposity and neuropsychiatric and behavioral outcomes in the offspring: A systematic review and meta-analysis
Bibliographic record
Abstract
Maternal adiposity (overweight or obesity) has been associated with adverse perinatal outcomes, although the potential risks of long-term neuropsychiatric and behavioral outcomes in the offspring remain unclear. Using the PRISMA guidelines, we searched PubMed, EMBASE, Scopus, and Web of Science to identify studies on maternal adiposity and offspring neuropsychiatric outcomes. Inverse variance-weighted random-effects meta-analyses were used to pool effect estimates with 95 % confidence intervals (95 % CIs) from adjusted odds ratios (OR) and hazard ratios (HR). Estimates were computed separately for preconception and pregnancy maternal overweight and obesity, with outcomes stratified by the type of neuropsychiatric outcome. In our meta-analyses of 42 epidemiological studies involving 3,680,937 mother-offspring pairs, we found increased risks of ADHD [OR=1.57, 95 % CI: 1.42-1.74], autism spectrum disorder [OR=1.42, 95 % CI: 1.22-1.65], conduct disorder [OR=1.16, 95 % CI: 1.00-1.35], Psychotic disorder [HR=1.61, 95 % CI: 1.41-1.83], externalizing behaviors [OR=1.30, 95 % CI: 1.07-1.56] and peer relationship problems [OR=1.25, 95 % CI: 1.04-1.27] in the offspring of preconception obese mothers. Similar increased risks were found in the offspring of preconception overweight mothers and those exposed to maternal adiposity during pregnancy. However, no association was found with offspring mood, anxiety, personality, eating, sleep disorders or prosocial problems. Preconception weight management may mitigate such adverse effects in the 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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".