Project description
Mechanistic insight into attention-deficit/hyperactivity disorder comorbid conditions
Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental condition that has a high co-occurrence with anxiety, depression, substance use and obesity across generations. Shared genetic risk factors, particularly related to the dopamine system, suggest a genetic link among these psychiatric disorders. The EU-funded CoCA project aims to investigate the underlying mechanisms of comorbidities using epidemiologic and genetic approaches. Researchers will assess the burden and socioeconomic impact, identify risk factors, study biological pathways and develop biomarkers. They will also conduct a clinical trial on non-pharmacological treatments with the help of mobile health technology. Key project objectives include early detection, prevention and affordable treatment with immediate societal benefits.
Objective
Understanding mechanisms underlying comorbid disorders poses a challenge for developing precision medicine tools. Psychiatric disorders are highly comorbid, and are among the last areas of medicine, where classification is driven by phenomenology rather than pathophysiology. We will study comorbidity between the most frequent psychiatric conditions, ADHD, mood/anxiety, and substance use disorders, and a highly prevalent somatic disease, obesity. ADHD, a childhood-onset disorder, forms the entry into a lifelong negative trajectory characterized by these comorbidities. Common mechanisms underlying this course are unknown, despite their relevance for early detection, prevention, and treatment. Our interdisciplinary team of experts will integrate epidemiologic/genetic approaches with experimental designs to address those issues. We will determine disease burden of comorbidity, calculate its socioeconomic impact, and reveal risk factors. We will study biological pathways of comorbidity and derive biomarkers, prioritizing two candidate mechanisms (circadian rhythm and dopaminergic neurotransmission), but also leveraging large existing data sets to identify new ones. A pilot clinical trial to study non-pharmacologic, dopamine-based and chronobiological treatments will be performed, employing innovative mHealth to monitor and support patients’ daily life. Integration of findings will lead to prediction algorithms enhancing early diagnosis and prevention of comorbidity. Finally, we will screen to repurpose existing pharmacological compounds. Integrating complementary approaches based on large-scale, existing data and innovative data collection, we maximize value for money in this project, leading to insight into the mechanisms underlying this comorbidity triad with its huge burden for healthcare, economy, and society. This will facilitate early detection and non-invasive, scalable, and low-cost treatment, creating opportunities for substantial and immediate societal impact.
Fields of science
Programme(s)
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Funding Scheme
RIA - Research and Innovation actionCoordinator
60323 Frankfurt Am Main
Germany
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Participants (17)
Participation ended
6525 XZ Nijmegen
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9713 GZ Groningen
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17177 Stockholm
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08007 Barcelona
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08035 Barcelona
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18055 Rostock
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WC2R 2LS London
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69120 Heidelberg
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51005 Tartu
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76131 Karlsruhe
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18071 Granada
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5020 Bergen
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12201 Albany Ny
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8000 Aarhus C
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40213 DUSSELDORF
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
82256 Furstenfeldbruck
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
6525 GA Nijmegen
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