The association between maternal diabetes and the risk of attention deficit/hyperactivity disorder in offspring: Updated systematic review and meta-analysis.
Bibliographic record
Abstract
Introduction The existing body of evidence on the association between maternal diabetes and attention deficit/hyperactivity disorder (ADHD) in offspring is inconsistent and inconclusive. Thus, we need to synthesise the available evidence to examine the association between maternal diabetes and risk of ADHD in offspring. Objectives The aim of this meta-analysis was to examine the association between maternal diabetes and the risk of ADHD in offspring. Methods We conducted a comprehensive search across PubMed, MEDLINE, EMBASE, Scopus, CINAHL and PsychINFO databases from their inception to September 8th, 2023. The methodological quality of the included studies was evaluated using Joanna Briggs Institute (JBI) and Newcastle-Ottawa Scale (NOS). Between-study heterogeneity was assessed using I2 statistic and potential publication bias was checked using both funnel plot and Egger’s test. Randomeffect model was used to calculate the pooled effect estimates and subgroup, sensitivity, and meta-regression were further performed to support our findings Results Twenty observational studies (two cross-sectional, five case-control and thirteen cohort studies) were included in this systematic review and meta-analysis. Our meta-analysis indicated that intra-uterine exposure to any type of maternal diabetes was associated with an increased risk ADHD in offspring [RR=1.33: 95 % CI: 1.23–1.43, I2=79.9%]. When we stratified the analysis by maternal diabetes type, we found 17%, and 37% higher risk of ADHD in offspring exposed to maternal gestational [RR=1.17: 95 % CI: 1.07–1.29] and pre-existing diabetes [RR=1.37: 95 % CI: 1.27–1.48] compared to unexposed offspring respectively. Results of subgroup and sensitivity analysis further supported the robustness of our main finding. Conclusions Our review suggested that exposure to maternal diabetes increased the risk of ADHD in offspring. These findings underscore the need for early screening and prompt interventions for exposed offspring. Disclosure of Interest None Declared
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".