The association between maternal diabetes and the risk of attention deficit hyperactivity disorder in offspring: an updated systematic review and meta-analysis
Bibliographic record
Abstract
Mixed results have been reported regarding the link between different types of maternal diabetes and attention deficit hyperactivity disorder (ADHD) in offspring. Hence, we conducted a systematic review and meta-analysis to explore these associations. Relevant studies on the subject were retrieved from six major databases, including PubMed, Medline, Embase, Scopus, CINAHL, and PsychINFO. The methodological quality of the included studies was evaluated using the Newcastle-Ottawa Scale, and between-study heterogeneity was assessed using the I2 statistic. Subgroup, sensitivity, and meta-regression analyses were conducted to identify the sources of heterogeneity between studies. In total, seventeen observational studies (five case-control and twelve cohort studies) with 18,063,336 study participants were included in the final analysis. Our random-effects meta-analysis revealed that exposure to any form of maternal diabetes was associated with an increased risk of ADHD in children. Specifically, we observed a heightened risk of ADHD in children exposed to gestational diabetes mellitus, any pre-existing diabetes, pre-existing type 1 diabetes mellitus, and type 2 diabetes mellitus. Our study suggests that children exposed to diabetes during prenatal development are at a higher risk of developing ADHD. These findings underscore the critical importance of early screening and timely interventions for exposed 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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.027 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".