Attention‐deficit/hyperactivity disorder is associated with increased risk of cardiovascular diseases: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Attention‐deficit/hyperactivity disorder (ADHD) often co‐occurs with other psychiatric and physical diseases. However, available evidence on associations between ADHD and cardiovascular diseases (CVDs) is mixed. To systematically review, quantitatively synthesize, and appraise available evidence on the link between ADHD with CVDs, we searched relevant articles in PubMed, Embase, PsycINFO, and Web of Science from inception to May 1, 2022. Study quality was assessed by using the Newcastle‐Ottawa Scale, and random‐effects model meta‐analyses were performed. A total of 18,391,169 (ADHD: n = 421,224) individuals from 11 studies were included in our systematic review and 8,196,648 (ADHD = 332,619) individuals from five studies were included in the main meta‐analysis of adjusted estimates. Pooled estimates showed that ADHD was significantly associated with an increased risk of CVDs in analyses based on adjusted effect size (odds ratio (OR) = 1.96; 95% confidence interval (CI) = 1.19–2.23, Q = 140.74, PQ < 0.001, I2 = 97.2%). When restricted among adults, the heterogeneity declined to null (OR = 1.73; 95% CI = 1.14–2.62, Q = 6.28, PQ = 0.10, I2 = 6.28%), suggesting age might be the main source of heterogeneity. In subgroup analyses, we found increased risk of CVDs associated with ADHD across age groups, type of CVDs, and data sources. This systematic review and meta‐analyses indicate that ADHD is associated with increased risk for CVDs, but further studies with various study designs are warranted to advance the understanding of the underlying mechanisms for the observed association between ADHD and CVDs. Additional research is also needed to resolve the role of ADHD medications which remains unclear due to the limited number of primary studies exploring this issue.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.007 | 0.008 |
| 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".