The course of attention-deficit/hyperactivity disorder through midlife.
Eugenio Horacio GrevetCibele Edom BandeiraEduardo Schneider VitolaMaria Eduarda de Araujo TavaresVitor BredaGregory ZeniStefania Pigatto TecheFelipe Almeida PiconCarlos Alberto Iglesias SalgadoRafael Gomes KaramBruna Santos da SilvaMargaret H SibleyLuis Augusto RohdeRenata Basso CupertinoDiego Luiz RovarisClaiton Henrique Dotto BauPublished in: European archives of psychiatry and clinical neuroscience (2022)
The course of ADHD from childhood up to young adulthood has been characterized in several studies. However, little is known about the course of symptoms into middle age and beyond. This study aims to evaluate predictors of ADHD trajectories in midlife based on three assessments. The follow-up sample comprised 323 adults with ADHD, evaluated at baseline and seven and thirteen years later, from the average ages of 34 up to 47 years old. ADHD status at reassessments was used to characterize trajectories. Demographics, ADHD features, comorbidities, and polygenic scores for ADHD and genetically correlated psychiatric disorders were evaluated to predict ADHD trajectories. Study retention rate was 67% at T2 (n = 216) and 62% at T3 (n = 199). Data from patients evaluated three times showed that 68.8% coursed stable, 25.5% unstable, and 5.7% remission trajectory of ADHD. Women, individuals with more severe syndromes, higher frequency of comorbidities at reassessments, and genetic liability to depression present a higher probability of a stable trajectory. Our findings shed light on midlife ADHD trajectories and their gender, genomic and clinical correlates.
Keyphrases
- attention deficit hyperactivity disorder
- autism spectrum disorder
- working memory
- depressive symptoms
- end stage renal disease
- type diabetes
- gene expression
- sleep quality
- dna methylation
- systemic lupus erythematosus
- chronic kidney disease
- physical activity
- metabolic syndrome
- machine learning
- prognostic factors
- rheumatoid arthritis
- mental health
- insulin resistance
- drug induced
- big data