Stratifying major depressive disorder by polygenic risk for schizophrenia in relation to structural brain measures

Substantial clinical heterogeneity of major depressive disorder (MDD) suggests it may group together individuals with diverse aetiologies. Identifying distinct subtypes should lead to more effective diagnosis and treatment, while providing more useful targets for further research. Genetic and clinic...

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Veröffentlicht in:Psychological medicine 2020-07, Vol.50 (10), p.1653-1662
Hauptverfasser: Harris, Mathew A., Shen, Xueyi, Cox, Simon R., Gibson, Jude, Adams, Mark J., Clarke, Toni-Kim, Deary, Ian J., Lawrie, Stephen M., McIntosh, Andrew M., Whalley, Heather C.
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container_end_page 1662
container_issue 10
container_start_page 1653
container_title Psychological medicine
container_volume 50
creator Harris, Mathew A.
Shen, Xueyi
Cox, Simon R.
Gibson, Jude
Adams, Mark J.
Clarke, Toni-Kim
Deary, Ian J.
Lawrie, Stephen M.
McIntosh, Andrew M.
Whalley, Heather C.
description Substantial clinical heterogeneity of major depressive disorder (MDD) suggests it may group together individuals with diverse aetiologies. Identifying distinct subtypes should lead to more effective diagnosis and treatment, while providing more useful targets for further research. Genetic and clinical overlap between MDD and schizophrenia (SCZ) suggests an MDD subtype may share underlying mechanisms with SCZ. The present study investigated whether a neurobiologically distinct subtype of MDD could be identified by SCZ polygenic risk score (PRS). We explored interactive effects between SCZ PRS and MDD case/control status on a range of cortical, subcortical and white matter metrics among 2370 male and 2574 female UK Biobank participants. There was a significant SCZ PRS by MDD interaction for rostral anterior cingulate cortex (RACC) thickness (β = 0.191, q = 0.043). This was driven by a positive association between SCZ PRS and RACC thickness among MDD cases (β = 0.098, p = 0.026), compared to a negative association among controls (β = -0.087, p = 0.002). MDD cases with low SCZ PRS showed thinner RACC, although the opposite difference for high-SCZ-PRS cases was not significant. There were nominal interactions for other brain metrics, but none remained significant after correcting for multiple comparisons. Our significant results indicate that MDD case-control differences in RACC thickness vary as a function of SCZ PRS. Although this was not the case for most other brain measures assessed, our specific findings still provide some further evidence that MDD in the presence of high genetic risk for SCZ is subtly neurobiologically distinct from MDD in general.
doi_str_mv 10.1017/S003329171900165X
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Med</addtitle><date>2020-07</date><risdate>2020</risdate><volume>50</volume><issue>10</issue><spage>1653</spage><epage>1662</epage><pages>1653-1662</pages><issn>0033-2917</issn><eissn>1469-8978</eissn><abstract>Substantial clinical heterogeneity of major depressive disorder (MDD) suggests it may group together individuals with diverse aetiologies. Identifying distinct subtypes should lead to more effective diagnosis and treatment, while providing more useful targets for further research. Genetic and clinical overlap between MDD and schizophrenia (SCZ) suggests an MDD subtype may share underlying mechanisms with SCZ. The present study investigated whether a neurobiologically distinct subtype of MDD could be identified by SCZ polygenic risk score (PRS). We explored interactive effects between SCZ PRS and MDD case/control status on a range of cortical, subcortical and white matter metrics among 2370 male and 2574 female UK Biobank participants. 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source Applied Social Sciences Index & Abstracts (ASSIA); Cambridge Journals
subjects Biobanks
Brain
Clinical research
Consortia
Cortex
Cortex (cingulate)
Depressive personality disorders
Genetic susceptibility
Genetics
Medical diagnosis
Medical imaging
Mental depression
Mental disorders
Neurobiology
Original Articles
Questionnaires
Schizophrenia
Substantia alba
Subtypes
title Stratifying major depressive disorder by polygenic risk for schizophrenia in relation to structural brain measures
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