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Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

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ADHD latent class clusters: DSM-IV subtypes and comorbidity.

Josephine Elia¹, Mauricio Arcos-Burgos, Kelly L Bolton

¹The Children's Hospital of Philadelphia, Philadelphia, PA, USA. elia@email.chop.edu

Psychiatry Research

|November 11, 2009

Summary

Latent class analysis identified six distinct Attention Deficit Hyperactivity Disorder (ADHD) phenotypes, offering more homogenous groups for genetic studies. These ADHD clusters showed varied age and gender distributions, with similar comorbidity patterns across groups.

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Published on: April 1, 2018

Area of Science:

Psychiatry
Genetics
Developmental Psychology

Background:

Attention Deficit Hyperactivity Disorder (ADHD) presents a complex, heterogeneous phenotype not fully encompassed by current diagnostic criteria.
Existing diagnostic frameworks may not adequately capture the full spectrum of ADHD presentations.

Purpose of the Study:

To identify distinct ADHD phenotypes using latent class analysis (LCA) in a clinical sample.
To explore the utility of identified ADHD clusters in genetic association studies.

Main Methods:

Latent class analyses (LCA) were applied to symptom and severity data from 500 children and adolescents (ages 6-18) diagnosed with ADHD.
Data were collected using the Schedule for Affective Disorders & Schizophrenia for School Age Children-IV-Revised (K-SADS-IVR).

Main Results:

LCA revealed six distinct ADHD clusters, some aligning with DSM-IV subtypes and others encompassing multiple subtypes.
Comorbid anxiety and mood disorders showed similar prevalence across clusters.
Age and gender distributions varied among the identified ADHD clusters.

Conclusions:

The identified ADHD clusters offer more homogenous groupings for genetic research, potentially aiding in the study of gene expression variations across developmental phases.
Comorbid mood and anxiety disorders did not significantly increase heterogeneity within the identified ADHD clusters.
Further longitudinal studies are recommended to investigate these findings over time.