Association Between ADHD and Obesity: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: Impulsivity and inattention related to attention deficit hyperactivity disorder (ADHD) may increase food intake and, consequently, weight gain. However, findings on the association between obesity/overweight and ADHD are mixed. The authors conducted a meta-analysis to estimate this association. METHOD: A broad range of databases was searched through Aug. 31, 2014. Unpublished studies were also obtained. Study quality was rated with the Newcastle-Ottawa Scale. Random-effects models were used. RESULTS: Forty-two studies that included a total of 728,136 individuals (48,161 ADHD subjects; 679,975 comparison subjects) were retained. A significant association between obesity and ADHD was found for both children (odds ratio=1.20, 95% CI=1.05-1.37) and adults (odds ratio=1.55, 95% CI=1.32-1.81). The pooled prevalence of obesity was increased by about 70% in adults with ADHD (28.2%, 95% CI=22.8-34.4) compared with those without ADHD (16.4%, 95% CI=13.4-19.9), and by about 40% in children with ADHD (10.3%, 95% CI=7.9-13.3) compared with those without ADHD (7.4%, 95% CI=5.4-10.1). The significant association between ADHD and obesity remained when limited to studies 1) reporting odds ratios adjusted for possible confounding factors; 2) diagnosing ADHD by direct interview; and 3) using directly measured height and weight. Gender, study setting, study country, and study quality did not moderate the association between obesity and ADHD. ADHD was also significantly associated with overweight. Individuals medicated for ADHD were not at higher risk of obesity. CONCLUSIONS: This study provides meta-analytic evidence for a significant association between ADHD and obesity/overweight. Further research should address possible underlying mechanisms and the long-term effects of ADHD treatments on weight in individuals with both ADHD and 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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.010 | 0.010 |
| 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".