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Record W4294990854 · doi:10.1002/wps.21020

Attention‐deficit/hyperactivity disorder as a risk factor for cardiovascular diseases: a nationwide population‐based cohort study

2022· article· en· W4294990854 on OpenAlexaff
Lin Li, Zheng Chang, Jiangwei Sun, Miguel Garcia‐Argibay, Ebba Du Rietz, Maja Dobrosavljevic, Isabell Brikell, Tomas Jernberg, Marco Solmi, Samuele Cortese, Henrik Larsson

Bibliographic record

VenueWorld Psychiatry · 2022
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineHazard ratioAttention deficit hyperactivity disorderProportional hazards modelDyslipidemiaPopulationCohortProspective cohort studyDiabetes mellitusCohort studyRisk factorInternal medicineDiseaseConfidence intervalPsychiatryPediatricsEndocrinologyEnvironmental health

Abstract

fetched live from OpenAlex

Accumulating evidence suggests a higher risk for cardiovascular diseases among individuals with mental disorders, but very little is known about the risk for overall and specific groups of cardiovascular diseases in people with attention-deficit/hyperactivity disorder (ADHD). To fill this knowledge gap, we investigated the prospective associations between ADHD and a wide range of cardiovascular diseases in adults. In a nationwide population-based cohort study, we identified 5,389,519 adults born between 1941 and 1983, without pre-existing cardiovascular diseases, from Swedish registers. The study period was from January 1, 2001 to December 31, 2013. Incident cardiovascular disease events were identified according to ICD codes. Hazard ratios (HR) with 95% confidence intervals (CI) were calculated using Cox proportional hazards regression model, with ADHD as a time-varying exposure. After an average 11.80 years of follow-up, 38.05% of individuals with ADHD versus 23.57% of those without ADHD had at least one diagnosis of cardiovascular disease (p<0.0001). ADHD was significantly associated with increased risk of any cardiovascular disease (HR=2.05, 95% CI: 1.98-2.13) after adjusting for sex and year of birth. Further adjustments for education level, birth country, type 2 diabetes mellitus, obesity, dyslipidemia, sleep problems and heavy smoking attenuated the association, which however remained significant (HR=1.84, 95% CI: 1.77-1.91). Further adjustment for psychiatric comorbidities attenuated but could not fully explain the association (HR=1.65, 95% CI: 1.59-1.71). The strongest associations were found for cardiac arrest (HR=2.28, 95% CI: 1.81-2.87), hemorrhagic stroke (HR=2.16, 95% CI: 1.68-2.77), and peripheral vascular disease/arteriosclerosis (HR=2.05, 95% CI: 1.76-2.38). Stronger associations were observed in males and younger adults, while comparable associations were found among individuals with or without psychotropic medications and family history of cardiovascular diseases. These data suggest that ADHD is an independent risk factor for a wide range of cardiovascular diseases. They highlight the importance of carefully monitoring cardiovascular health and developing age-appropriate and individualized strategies to reduce the cardiovascular risk in individuals with ADHD.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.305
Teacher spread0.289 · 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

Citations102
Published2022
Admission routes1
Has abstractyes

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