Attention on inattention: Dissecting the causal mechanisms of adult ADHD features
- Funded by Estonian Research Council
- Total publications:0 publications
Grant number: 101222412
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Key facts
Disease
COVID-19Start & end year
20262031Known Financial Commitments (USD)
$178,755,202.5Funder
Estonian Research CouncilPrincipal Investigator
Lehto, KelliResearch Location
EstoniaLead Research Institution
University of TartuResearch Priority Alignment
N/A
Research Category
N/A
Research Subcategory
N/A
Special Interest Tags
N/A
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Not Applicable
Vulnerable Population
Not applicable
Occupations of Interest
Not applicable
Abstract
The world is witnessing a sharp rise in adult attention deficit hyperactivity disorder (ADHD) diagnoses, especially after the COVID-19 pandemic. While historically considered a childhood neurodevelopmental disorder, an increasing number of adults are now seeking help for impairing ADHD-like features like inattention, disorganization, restlessness, impulsivity, forgetfulness and emotional dysregulation. Strikingly little is known about the mechanisms causing specific adult ADHD features, raising a key question: Are adult ADHD features truly the same as childhood ADHD, or do they rather reflect other mental conditions with overlapping symptoms? Substantial phenotypic overlap of mental health conditions and lack of precise screening tools make accurate detection of adult ADHD extremely challenging and error prone. Modern mental health care critically needs advanced, biology-informed methods to differentiate mental conditions with greater accuracy. AT-TENSION combines psychology, genomics and data science to dissect the causal mechanisms underlying adult ADHD features and to propose an innovative framework for genomics-informed screening tool development. We will (i) determine diverse genetic mechanisms and causal links with other mental conditions by conducting a pioneering genome-wide investigation of specific adult ADHD features across five large biobanks; (ii) quantify the impact of diverse environmental factors like stress, lifestyle and COVID-19 pandemic on adult ADHD features across life stages and genders; (iii) develop and test a novel genomics-informed screening tool for more accurate ADHD detection in adults by using machine learning and genomics. The goals are achieved by innovatively combining three large-scale data layers: self-reported, electronic health records and genetic data. AT-TENSION aims to redefine our current conceptualization of adult ADHD and revolutionize mental health psychometrics by integrating genomics into screening tool development