Attention-Deficit/Hyperactivity Disorder as a Potential Risk Factor for Dementia and Other Neurocognitive Disorders: A Systematic Review
Bibliographic record
Abstract
Background: Attention-deficit/hyperactivity disorder (ADHD), a common neurodevelopmental condition now recognized to persist into older adulthood, has been postulated to be a risk factor for neurocognitive disorders given the overlap in clinical features and neurobiology, as well as the complex interplay between ADHD and known risk factors for dementia. Studies have emerged assessing this relationship, but there has not yet been a comprehensive systematic review addressing this topic. Objective: To assess whether ADHD is a risk factor for neurocognitive disorders and to explore possible mechanisms for such an association. Methods: A systematic review of the literature was conducted using Medline, Embase, and PsycINFO from inception until June 4, 2023. Studies were included if they assessed whether or how ADHD may be a risk factor for neurocognitive disorders. Studies were excluded if they were not primary literature, not published in a peer-reviewed journal, not in English, and/or used non-human subjects. Study quality was assessed using the QualSyst tool. Results: Sixteen studies met inclusion criteria. Seven studies found a positive association between ADHD and neurocognitive disorders (all-cause dementia in four studies, Alzheimer's disease in three studies, Lewy body dementia in two studies, and mild cognitive impairment in one study). Four studies did not find an association. Five studies pertained to possible mechanisms for an association, including genetics, with minimal significant findings. Conclusions: ADHD may be a risk factor for certain neurocognitive disorders, although the evidence base is limited, and the absolute risk is small. Possible explanations include genetic and lifestyle factors.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".