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Record W7117245925 · doi:10.1002/alz70857_103431

Are Lewy Body Disease and Attention‐Deficit/Hyperactivity Disorder linked?

2025· article· en· W7117245925 on OpenAlexaffabout
Sara Becker, Baeleigh VanderZwaag, Hawra Al‐Khaz'Alya, Alexandra K. Wall, Brandy L. Callahan

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsHotchkiss Brain InstituteInstitute of AgingOntario Brain InstituteUniversity of VictoriaUniversity of Calgary
Fundersnot available
KeywordsDiseaseLewy body diseaseRisk factorLewy bodyProdromal StageStage (stratigraphy)

Abstract

fetched live from OpenAlex

Abstract Background Recent research suggests that, relative to controls, adults with attention‐deficit/hyperactivity disorder (ADHD) are more often diagnosed with Lewy Body Disease (LBD) in old age and people with LBD are more likely to report features of ADHD earlier in life. We examined the prevalence of childhood ADHD symptoms in older adults with Parkinson's disease (PD) and known early LBD markers in older adults with ADHD. Method Participants aged 40+ were recruited from the local community in Calgary, Alberta and the Calgary Parkinson Research Initiative Registry. The following measures were administered: Montreal Cognitive Assessment, Barkley Adult ADHD Rating Scale‐IV Childhood symptoms, Beck Depression Inventory (BDI), a 5‐item self‐report questionnaire for autonomic dysfunction, Innsbruck REM Sleep Behavior Disorder (RBD) Inventory, and University of Pennsylvania Smell Identification Test. Kruskal‐Wallis H or Chi‐square tests analyzed group differences. Result The sample consisted of ADHD ( n = 40), PD ( n = 35), and control ( n = 37) participants and was largely female (61.6%). Mean age of the sample was 64.34 years (SD=11.03). There was no difference in cognition between groups, H (2,108)=4.42, p = .11. The ADHD group had significantly worse childhood ADHD symptoms than PD and control groups on inattention, hyperactive‐impulsive, and total ADHD symptom percentiles, H (2,111)>38.28, all p < 0.001). The ADHD group also had significantly more depressive symptoms than control participants ( F (3,103)=8.34, p = .001). People with PD (44.1%) reported more problems with salivation than ADHD (30.8%) and control (10.8%) groups ( χ 2 (2,110)=9.91, p = .007), and more issues with constipation (67.6%) than ADHD (46.2%) and control (13.5%) groups ( χ 2 (2,110)=21.81, p < .001). Based on the RBD inventory cut‐off (≥0.25), 22.9% of ADHD, 53.1% of PD, and 12.5% of controls were classified as having probable RBD ( χ 2 (2,112)=13.82, p < .001). Participants with PD had worse olfaction than ADHD and control groups ( H (2,112)=25.44, p < .001); 86% of participants with PD had moderate microsmia or worse, compared to 7.5% and 30% in the ADHD and control groups, respectively. Conclusion Our data did not show a higher prevalence of childhood ADHD symptoms in people with PD, or that people with ADHD had increased LB markers. Our findings do not suggest that ADHD is an early stage or risk factor for LBD and there is no evidence that they are pathophysiologically linked.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.030
GPT teacher head0.315
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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