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Enhanced neurobiological biomarker differentiation for attention-deficit/hyperactivity disorder through a risk-informed design.

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Title: Enhanced neurobiological biomarker differentiation for attention-deficit/hyperactivity disorder through a risk-informed design.
Authors: Duarte, Igor, Hoffmann, Mauricio Scopel, Salum, Giovanni A., Leffa, Douglas Teixeira, Belangero, Sintia, Santoro, Marcos, Ota, Vanessa Kiyomi, Ito, Lucas Toshio, Pan, Pedro M., Farhat, Luis C., Murray, Aja Louise, Miguel, Euripedes C., Kieling, Christian, Rohde, Luis Augusto, Caye, Arthur
Source: European Child & Adolescent Psychiatry. Jul2025, Vol. 34 Issue 7, p2107-2117. 11p.
Subjects: Cross-sectional method, Attention-deficit hyperactivity disorder, Prediction models, Research funding, Magnetic resonance imaging, Genetic risk score, Machine learning, Biomarkers
Abstract: Translation of biomarkers to clinical practice is hindered by the significant overlap in neurobiological measures between ADHD cases and controls. A risk-informed design can enhance the utility and validation of ADHD biomarkers by highlighting differences between individuals with ADHD and those without at differential risk. Participants were 2511 children and adolescents (aged 6 to 14 years) from the Brazilian High Risk Cohort for Mental Conditions. We calculated risk for ADHD among unaffected individuals using a multivariable clinical and sociodemographic risk model. We compared measures of three proposed ADHD biomarkers (polygenic scores, subcortical volumes, and executive function) between participants with vs. without ADHD, and ADHD vs. without ADHD with a high- vs. low-risk loading for ADHD. Compared to the unaffected group, children and adolescents with ADHD had higher ADHD polygenic scores (cohen's d = 0.17), smaller subcortical volumes (d = − 0.25), and poorer executive function (d = − 0.22). Separating the unaffected group into low- and high-risk subgroups revealed more pronounced differences (Cohen's d = 0.20 to 0.60) and nearly doubled the overlap-free area for these three neurobiological measures between the low-risk group and the other two groups. Upon adjustment for the number of ADHD symptoms, simple ADHD vs. without ADHD differences vanished, while the risk-informed analyses remained significant. Here, we demonstrate that a risk-based design increases effect sizes when comparing candidate biomarkers for ADHD. Our study provides a model that may hold promise for evaluating similar contrasts in other mental disorders and samples. [ABSTRACT FROM AUTHOR]
Copyright of European Child & Adolescent Psychiatry is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Enhanced neurobiological biomarker differentiation for attention-deficit/hyperactivity disorder through a risk-informed design.
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  Data: <searchLink fieldCode="AR" term="%22Duarte%2C+Igor%22">Duarte, Igor</searchLink><br /><searchLink fieldCode="AR" term="%22Hoffmann%2C+Mauricio+Scopel%22">Hoffmann, Mauricio Scopel</searchLink><br /><searchLink fieldCode="AR" term="%22Salum%2C+Giovanni+A%2E%22">Salum, Giovanni A.</searchLink><br /><searchLink fieldCode="AR" term="%22Leffa%2C+Douglas+Teixeira%22">Leffa, Douglas Teixeira</searchLink><br /><searchLink fieldCode="AR" term="%22Belangero%2C+Sintia%22">Belangero, Sintia</searchLink><br /><searchLink fieldCode="AR" term="%22Santoro%2C+Marcos%22">Santoro, Marcos</searchLink><br /><searchLink fieldCode="AR" term="%22Ota%2C+Vanessa+Kiyomi%22">Ota, Vanessa Kiyomi</searchLink><br /><searchLink fieldCode="AR" term="%22Ito%2C+Lucas+Toshio%22">Ito, Lucas Toshio</searchLink><br /><searchLink fieldCode="AR" term="%22Pan%2C+Pedro+M%2E%22">Pan, Pedro M.</searchLink><br /><searchLink fieldCode="AR" term="%22Farhat%2C+Luis+C%2E%22">Farhat, Luis C.</searchLink><br /><searchLink fieldCode="AR" term="%22Murray%2C+Aja+Louise%22">Murray, Aja Louise</searchLink><br /><searchLink fieldCode="AR" term="%22Miguel%2C+Euripedes+C%2E%22">Miguel, Euripedes C.</searchLink><br /><searchLink fieldCode="AR" term="%22Kieling%2C+Christian%22">Kieling, Christian</searchLink><br /><searchLink fieldCode="AR" term="%22Rohde%2C+Luis+Augusto%22">Rohde, Luis Augusto</searchLink><br /><searchLink fieldCode="AR" term="%22Caye%2C+Arthur%22">Caye, Arthur</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22European+Child+%26+Adolescent+Psychiatry%22">European Child & Adolescent Psychiatry</searchLink>. Jul2025, Vol. 34 Issue 7, p2107-2117. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Cross-sectional+method%22">Cross-sectional method</searchLink><br /><searchLink fieldCode="DE" term="%22Attention-deficit+hyperactivity+disorder%22">Attention-deficit hyperactivity disorder</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging%22">Magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+risk+score%22">Genetic risk score</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Biomarkers%22">Biomarkers</searchLink>
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  Data: Translation of biomarkers to clinical practice is hindered by the significant overlap in neurobiological measures between ADHD cases and controls. A risk-informed design can enhance the utility and validation of ADHD biomarkers by highlighting differences between individuals with ADHD and those without at differential risk. Participants were 2511 children and adolescents (aged 6 to 14 years) from the Brazilian High Risk Cohort for Mental Conditions. We calculated risk for ADHD among unaffected individuals using a multivariable clinical and sociodemographic risk model. We compared measures of three proposed ADHD biomarkers (polygenic scores, subcortical volumes, and executive function) between participants with vs. without ADHD, and ADHD vs. without ADHD with a high- vs. low-risk loading for ADHD. Compared to the unaffected group, children and adolescents with ADHD had higher ADHD polygenic scores (cohen's d = 0.17), smaller subcortical volumes (d = − 0.25), and poorer executive function (d = − 0.22). Separating the unaffected group into low- and high-risk subgroups revealed more pronounced differences (Cohen's d = 0.20 to 0.60) and nearly doubled the overlap-free area for these three neurobiological measures between the low-risk group and the other two groups. Upon adjustment for the number of ADHD symptoms, simple ADHD vs. without ADHD differences vanished, while the risk-informed analyses remained significant. Here, we demonstrate that a risk-based design increases effect sizes when comparing candidate biomarkers for ADHD. Our study provides a model that may hold promise for evaluating similar contrasts in other mental disorders and samples. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of European Child & Adolescent Psychiatry is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00787-024-02622-4
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      – Code: eng
        Text: English
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        PageCount: 11
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      – SubjectFull: Cross-sectional method
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      – SubjectFull: Attention-deficit hyperactivity disorder
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      – SubjectFull: Prediction models
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      – SubjectFull: Machine learning
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              Text: Jul2025
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