- Email: [email protected]
P.2.b.017 Structural brain alterations in major depression: findings from the ENIGMA Major Depressive Disorder Working Group
S394
P.2.b. Mood disorders and treatment − Affective disorders (clinical)
References [1] Martinotti, G., et al, 2012. Agomelatine versus venlafaxine XR in the treatment of anhedonia in MDD: a pilot study. J Clin Psychopharmacol 32(4):487–491. [2] Llorca, P.M., Gourion, D., 2014. Eur Neuropsychopharmacol. [3] Demyttenaere, et al, 2011. Do general practitioners and psychiatrists agree about defining cure from depression? The DEsCRIBE™ survey. BMC Psychiatry 11:169 Disclosure statement: This abstract is financially supported by an educational grant from Servier
P.2.b.017 Structural brain alterations in major depression: findings from the ENIGMA Major Depressive Disorder Working Group L. Schmaal1 ° , D.J. Veltman1 , T.G. Van Erp2 , P.G. S¨amann3 , T. Frodl4 , N. Jahanshad5 , E. Loehrer6 , H. Tiemeier6 , A. Hofman6 , W.J. Niessen7 , M.W. Vernooij6 , M.A. Ikram6 , K. Wittfeld8 , H.J. Grabe9 , A. Block9 , K. Hegenscheid10 , H. V¨olzke11 , D. Hoehn3 , M. Czisch3 , J. Lagopoulos12 , S.N. Hatton12 , I.B. Hickie12 , R. Goya-Maldonado13 , B. Kr¨amer13 , O. Gruber13 , B. Couvy-Duchesne14 , M.E. Renter´ıa14 , L.T. Strike14 , N.T. Mills14 , G.I. De Zubicaray15 , K.L. McMahon16 , S.E. Medland14 , N.G. Martin14 , N.A. Gillespie17 , M.J. Wright14 , G.B. Hall18 , G.M. MacQueen19 , E.M. Frey4 , A. Carballedo20 , L.S. Van Velzen1 , M.J. Van Tol21 , N.J. Van der Wee22 , I.M. Veer23 , H. Walter23 , K. Schnell24 , E. Schramm25 , C. Normann25 , D. Schoepf26 , C. Konrad27 , B. Zurowski28 , T. Nickson29 , A.M. McIntosh29 , M. Papmeyer29 , H.C. Whalley29 , J.E. Sussmann29 , B.R. Godlewska30 , P.J. Cowen30 , F.H. Fischer31 , M. Rose31 , B.W. Penninx1 , P.M. Thompson5 , D.P. Hibar5 , for the ENIGMA-Major Depressive Disorder Working Group32 1 VU University medical center, Department of Psychiatry, Amsterdam, The Netherlands; 2 University of California, Department of Psychiatry and Human Behavior, Irvine, USA; 3 Max Planck Institute of Psychiatry, Neuroimaging Research Group, Munich, Germany; 4 University of Dublin − Trinity College, Department of Psychiatry, Dublin, Ireland; 5 Keck School of Medicine − University of Southern California, Imaging Genetics Center − Department of Neurology, Marina del Rey, USA; 6 Erasmus MC University Medical Center, Department of Epidemiology, Rotterdam, The Netherlands; 7 Erasmus MC University Medical Center, Departments of Radiology and Medical Informatics, Rotterdam, The Netherlands; 8 German Center for Neurodegenerative Diseases DZNE, Department of Psychiatry and Psychotherapy, Greifswald, Germany; 9 University Medicine Greifswald, Department of Psychiatry and Psychotherapy, Greifswald, Germany; 10 University Medicine Greifswald, Institute of Diagnostic Radiology and Neuroradiology, Greifswald, Germany; 11 University Medicine Greifswald, Institute for Community Medicine, Greifswald, Germany; 12 University of Sydney, Brain and Mind Research Institute, Camperdown, Australia; 13 University Medical Center UMG − Georg-AugustUniversity, Center for Translational Research in Systems Neuroscience and Psychiatry − Department of Psychiatry and Psychotherapy, Goettingen, Germany; 14 QIMR Berghofer Medical Research Institute, QIMR Berghofer Medical Research Institute, Brisbane, Australia; 15 University of Queensland, School of Psychology, Brisbane, Australia; 16 University of Queensland, Center for Advanced Imaging, Brisbane, Australia; 17 Virginia Commonwealth University, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond,
USA; 18 McMaster University, Department of Psychology − Neuroscience and Behaviour, Hamilton, Canada; 19 Cumming School of Medicine − University of Calgary, Department of Psychiatry − Mathison Centre for Mental Health Research and Education − Hotchkiss Brain Institute, Calgary, Canada; 20 Trinity College Dublin, Trinity College Institute of Neuroscience, Dublin, Ireland; 21 University Medical Center Groningen − University of Groningen, Neuroimaging Center, Groningen, The Netherlands; 22 Leiden University Medical Center − Leiden University, Department of Psychiatry, Leiden, The Netherlands; 23 Charit´e Universit¨atsmedizin Berlin, Department of Psychiatry and Psychotherapy − Division of Mind and Brain Research, Berlin, Germany; 24 University Hospital Heidelberg, Department of General Psychiatry, Heidelberg, Germany; 25 University Medical Center Freiburg, Department of Psychiatry and Psychotherapy, Freiburg, Germany; 26 University of Bonn, Department of Psychiatry, Bonn, Germany; 27 Philipps-University Marburg, Department of Psychiatry and Psychotherapy, Marburg, Germany; 28 University of L¨ubeck, Center for Integrative Psychiatry, Lubeck, Germany; 29 University of Edinburgh, Division of Psychiatry, Edinburgh, United Kingdom; 30 Warneford Hospital, University Department of Psychiatry, Oxford, United Kingdom; 31 Charit´e Universit¨atsmedizin, Department of Psychosomatic Medicine − Center for Internal Medicine and Dermatology, Berlin, Germany; 32 VU University medical center, http://enigma.ini.usc.edu/ongoing/enigma-mdd-working-group/, Amsterdam, The Netherlands Background: Patterns of structural brain alterations in Major Depressive Disorder (MDD) remain unresolved. This is in part due to small sample sizes of neuroimaging studies resulting in limited statistical power, disease heterogeneity and the complex interactions between clinical characteristics and brain morphology. Therefore, we initiated the ENIGMA-MDD Working Group to identify robust imaging markers of MDD using coordinated standardized image processing and statistical analysis protocols. Here, we investigated subcortical volume alterations in MDD in the largest sample to date using an individual participant data (IPD) based meta-analysis approach. Methods: Structural T1-weighted MRI scans from 1,728 MDD patients and 7,199 controls from 15 research samples worldwide were analyzed locally using FreeSurfer. Segmentations of subcortical regions, lateral ventricles and total intracranial volume were visually inspected for accuracy and compared between patients and controls using regression models controlling for age, sex, and intracranial volume locally following standardized protocols designed to facilitate harmonized image analysis across multiple sites. Separate stratified analyses comparing age of onset, stage of illness (first versus recurrent episode patients), and symptom severity were performed. Results were combined in random-effect meta-analysis models. Meta-regression analyses were used to test whether mean age of each sample, field strength of MR images, FreeSurfer version, percentage of patients acutely depressed, percentage of patients taking antidepressants, and percentage of patients taking antipsychotics explained a significant proportion of the variance in effect sizes across sites in the meta-analysis. Results were considered significant if they exceeded a Bonferroni corrected P-value threshold (P = 0.05/9 regions=5.6×10−3 ). Results: Relative to controls, patients had significantly lower hippocampal volumes (Cohen’s d = −0.14) − an effect driven by recurrent MDD patients (d = −0.17). Age of onset 21 was associated with a smaller hippocampus (d = −0.20) and a
P.2.b. Mood disorders and treatment − Affective disorders (clinical) trend towards smaller amygdala (d = −0.12) and larger lateral ventricles (d = 0.14). Symptom severity was not associated with regional brain volumes. Sample characteristics including mean age, proportion of antidepressant users and proportion of remitted patients did not moderate brain volume alterations. Samples with a higher proportion of antipsychotic medication users showed larger caudate volumes in MDD patients compared to controls. Conclusions: Results of this first initiative of the ENIGMAMDD working group clearly indicate a key role of the hippocampus in the pathophysiology of MDD, showing robust hippocampal volume reductions particularly in recurrent patients and patients with an age of onset of MDD 21. Brain changes in other subcortical regions in MDD were less evident. Our findings suggest that the hippocampus is a prime target region for future research aimed at further unravelling the pathophysiology of MDD and improving treatment. The important next step within our consortium will be examining cortical brain alterations associated with MDD and we are in the process of applying similar methods to cortical surface thickness and surface area measures. P.2.b.018 Effect of accumulation of 5-HTTLPR, BDNF Vall66Met and COMT Val158Met polymorphisms on brain morphology in patients with major depressive disorder M. Kostic1 ° , E. Canu2 , A. Munjiza3 , F. Agosta2 , I. Novakovic4 , V. Dobricic4 , V. Jerkovic5 , M. Jerkovic5 , T. Pekmezovic6 , D. Lecic Tosevski7 , M. Filippi8 1 Institute of Mental Health, Belgrade, Serbia; 2 Institute of Experimental Neurology − Division of Neuroscience − Vita-Salute University and San Raffaele Scientific Institute, Neuroimaging Research Unit, Milan, Italy; 3 Institute of Mental Health, Department for affective disorders, Belgrade, Serbia; 4 Neurology Clinic − Clinical Center of Serbia, Genetic research laboratory, Belgrade, Serbia; 5 School of Electrical Engineering − University of Belgrade, Department of statistics, Belgrade, Serbia; 6 School of Medicine − University of Belgrade, Department for epidemiology, Belgrade, Serbia; 7 Institute of Mental Health and School of Medicine − University of Belgrade, Department for affective disorders, Belgrade, Serbia; 8 Institute of Experimental Neurology − Division of Neuroscience − Vita-Salute University and San Raffaele Scientific Institute, Neuroimaging Research Unit and Department of Neurology, Milan, Italy Purpose of the study: One of the dominant theories for the etiology of complex human diseases, such as Major depressive disorder (MDD), is the “common disease, common variant hypothesis” [1]. This theory suggests that a large number of genes exert a small on the etiology and pathology of a disease. In MDD, this could explain the lack of positive findings in genome-wide association studies [2] and the need to study effects of interaction or accumulation of multiple genes. Our aim was to assess the effect of accumulation of specific SERT, BDNF and COMT genes functional polymorphisms, that have already been implicated in depression by previous research, in patients and to understand whether it affects the patient clinical features and brain structure. Methods: Seventy-seven MDD patients and 66 healthy controls underwent a comprehensive clinical assessment, genetic testing for serotonin transporter 5-HTTLPR, BDNF Val66Met and COMT Val158Met polymorphisms, and an MRI scan. Compared with controls, patients were more BDNF-Val homozygotes (MDD=53; controls=32; p = 0.013), COMT Met carriers (MDD=63; controls=47; p = 0.058) and SERT L’ carriers (MDD=65; controls=47;
S395
p = 0.056). Based on these results, all patients and controls were separated into three groups: 1. High risk group, i.e., patients or controls with all three susceptibility polymorphisms (SP); 2. Intermediate risk group (two SPs); and 3. Low risk group (one or none SPs). Regional gray matter (GM) volumes, cortical thickness and white matter (WM) tract integrity were assessed. Results: The High risk group was larger among the patients than the controls (MDD=40; controls=17; p = 0.001). High risk patients showed decreased GM volume and thickness in several frontal and temporal regions compared to Low risk controls; and decreased cortical thickness in the right medial orbitofrontal cortex and the frontal pole when compared with Low risk patients. On the other hand, High risk controls showed greater WM integrity of the corpus callosum, the superior and inferior longitudinal fasciculi, and the cingulum, with a prevalent involvement of the right side, compared to both High risk patients and Low risk controls. In the entire sample, the GM and WM integrity of the Intermediate risk group was in between the High risk and Low risk groups. Conclusions: This study showed that the accumulation of SPs in High risk patients is associated with reduced GM integrity of frontal and temporal cortices. These results suggest that brain structure of MDD patients is modulated by accumulation of the three gene we studied. On the other hand, High risk controls may have some protective factors leading to higher integrity of crucial WM connections. Investigating the effect of multiple genes within an accumulation model is promising and may help get greater understanding of MDD pathological mechanisms, define specific phenotypes of the disorder, discover risk and protective factors, and improve patient treatment. References [1] Singleton A., Hardy J., 2011. A generalizable hypothesis for the genetic architecture of disease: pleomorphic risk loci. Human Molecular Genetics 20(R2):R158−62. [2] Levinson D.F., Mostafavi S., Milaneschi Y., Rivera M., Ripke S., Wray N.R., Sullivan P.F., 2014. Genetic studies of major depressive disorder: why are there no genome-wide association study findings and what can we do about it. Biological Psychiatry 76(7):510−2.
P.2.b.019 Association between clinical response to duloxetine treatment and brain-derived neurotrophic factor or catecholamine metabolites K. Atake1 ° , Y. Reiji1 , H. Hikaru1 , K. Asuka1 , N. Jun1 of Occupational and Environmental Health − Japan, Psychiatry, Kitakyushu, Japan
1 University
Purpose of the study: Duloxetine, a serotonin noradrenaline reuptake inhibitor (SNRI), is an effective first-line treatment for patients with major depressive disorder (MDD). Previously, we reported that milnacipran, another SNRI, increased the plasma levels of 3-methoxy-4-hydroxyphenylglycol (MHPG), a major metabolite of noradrenalin, in patients with MDD. Therefore, we hypothesized that duloxetine also increases plasma MHPG levels in patients with MDD. Whereas, brain-derived neurotrophic factor (BDNF) plays an important role in the pathophysiology of depression. In addition, blood levels of BDNF is a biomarker for depressive state. So this study investigated the relationships among the plasma levels of catecholamine metabolites, the clinical response to duloxetine treatment, and BDNF. Methods used: The present study utilized an open-label and non-fixed dose design. All of the patients were administered
P.2.b. Mood disorders and treatment − Affective disorders (clinical)
References [1] Martinotti, G., et al, 2012. Agomelatine versus venlafaxine XR in the treatment of anhedonia in MDD: a pilot study. J Clin Psychopharmacol 32(4):487–491. [2] Llorca, P.M., Gourion, D., 2014. Eur Neuropsychopharmacol. [3] Demyttenaere, et al, 2011. Do general practitioners and psychiatrists agree about defining cure from depression? The DEsCRIBE™ survey. BMC Psychiatry 11:169 Disclosure statement: This abstract is financially supported by an educational grant from Servier
P.2.b.017 Structural brain alterations in major depression: findings from the ENIGMA Major Depressive Disorder Working Group L. Schmaal1 ° , D.J. Veltman1 , T.G. Van Erp2 , P.G. S¨amann3 , T. Frodl4 , N. Jahanshad5 , E. Loehrer6 , H. Tiemeier6 , A. Hofman6 , W.J. Niessen7 , M.W. Vernooij6 , M.A. Ikram6 , K. Wittfeld8 , H.J. Grabe9 , A. Block9 , K. Hegenscheid10 , H. V¨olzke11 , D. Hoehn3 , M. Czisch3 , J. Lagopoulos12 , S.N. Hatton12 , I.B. Hickie12 , R. Goya-Maldonado13 , B. Kr¨amer13 , O. Gruber13 , B. Couvy-Duchesne14 , M.E. Renter´ıa14 , L.T. Strike14 , N.T. Mills14 , G.I. De Zubicaray15 , K.L. McMahon16 , S.E. Medland14 , N.G. Martin14 , N.A. Gillespie17 , M.J. Wright14 , G.B. Hall18 , G.M. MacQueen19 , E.M. Frey4 , A. Carballedo20 , L.S. Van Velzen1 , M.J. Van Tol21 , N.J. Van der Wee22 , I.M. Veer23 , H. Walter23 , K. Schnell24 , E. Schramm25 , C. Normann25 , D. Schoepf26 , C. Konrad27 , B. Zurowski28 , T. Nickson29 , A.M. McIntosh29 , M. Papmeyer29 , H.C. Whalley29 , J.E. Sussmann29 , B.R. Godlewska30 , P.J. Cowen30 , F.H. Fischer31 , M. Rose31 , B.W. Penninx1 , P.M. Thompson5 , D.P. Hibar5 , for the ENIGMA-Major Depressive Disorder Working Group32 1 VU University medical center, Department of Psychiatry, Amsterdam, The Netherlands; 2 University of California, Department of Psychiatry and Human Behavior, Irvine, USA; 3 Max Planck Institute of Psychiatry, Neuroimaging Research Group, Munich, Germany; 4 University of Dublin − Trinity College, Department of Psychiatry, Dublin, Ireland; 5 Keck School of Medicine − University of Southern California, Imaging Genetics Center − Department of Neurology, Marina del Rey, USA; 6 Erasmus MC University Medical Center, Department of Epidemiology, Rotterdam, The Netherlands; 7 Erasmus MC University Medical Center, Departments of Radiology and Medical Informatics, Rotterdam, The Netherlands; 8 German Center for Neurodegenerative Diseases DZNE, Department of Psychiatry and Psychotherapy, Greifswald, Germany; 9 University Medicine Greifswald, Department of Psychiatry and Psychotherapy, Greifswald, Germany; 10 University Medicine Greifswald, Institute of Diagnostic Radiology and Neuroradiology, Greifswald, Germany; 11 University Medicine Greifswald, Institute for Community Medicine, Greifswald, Germany; 12 University of Sydney, Brain and Mind Research Institute, Camperdown, Australia; 13 University Medical Center UMG − Georg-AugustUniversity, Center for Translational Research in Systems Neuroscience and Psychiatry − Department of Psychiatry and Psychotherapy, Goettingen, Germany; 14 QIMR Berghofer Medical Research Institute, QIMR Berghofer Medical Research Institute, Brisbane, Australia; 15 University of Queensland, School of Psychology, Brisbane, Australia; 16 University of Queensland, Center for Advanced Imaging, Brisbane, Australia; 17 Virginia Commonwealth University, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond,
USA; 18 McMaster University, Department of Psychology − Neuroscience and Behaviour, Hamilton, Canada; 19 Cumming School of Medicine − University of Calgary, Department of Psychiatry − Mathison Centre for Mental Health Research and Education − Hotchkiss Brain Institute, Calgary, Canada; 20 Trinity College Dublin, Trinity College Institute of Neuroscience, Dublin, Ireland; 21 University Medical Center Groningen − University of Groningen, Neuroimaging Center, Groningen, The Netherlands; 22 Leiden University Medical Center − Leiden University, Department of Psychiatry, Leiden, The Netherlands; 23 Charit´e Universit¨atsmedizin Berlin, Department of Psychiatry and Psychotherapy − Division of Mind and Brain Research, Berlin, Germany; 24 University Hospital Heidelberg, Department of General Psychiatry, Heidelberg, Germany; 25 University Medical Center Freiburg, Department of Psychiatry and Psychotherapy, Freiburg, Germany; 26 University of Bonn, Department of Psychiatry, Bonn, Germany; 27 Philipps-University Marburg, Department of Psychiatry and Psychotherapy, Marburg, Germany; 28 University of L¨ubeck, Center for Integrative Psychiatry, Lubeck, Germany; 29 University of Edinburgh, Division of Psychiatry, Edinburgh, United Kingdom; 30 Warneford Hospital, University Department of Psychiatry, Oxford, United Kingdom; 31 Charit´e Universit¨atsmedizin, Department of Psychosomatic Medicine − Center for Internal Medicine and Dermatology, Berlin, Germany; 32 VU University medical center, http://enigma.ini.usc.edu/ongoing/enigma-mdd-working-group/, Amsterdam, The Netherlands Background: Patterns of structural brain alterations in Major Depressive Disorder (MDD) remain unresolved. This is in part due to small sample sizes of neuroimaging studies resulting in limited statistical power, disease heterogeneity and the complex interactions between clinical characteristics and brain morphology. Therefore, we initiated the ENIGMA-MDD Working Group to identify robust imaging markers of MDD using coordinated standardized image processing and statistical analysis protocols. Here, we investigated subcortical volume alterations in MDD in the largest sample to date using an individual participant data (IPD) based meta-analysis approach. Methods: Structural T1-weighted MRI scans from 1,728 MDD patients and 7,199 controls from 15 research samples worldwide were analyzed locally using FreeSurfer. Segmentations of subcortical regions, lateral ventricles and total intracranial volume were visually inspected for accuracy and compared between patients and controls using regression models controlling for age, sex, and intracranial volume locally following standardized protocols designed to facilitate harmonized image analysis across multiple sites. Separate stratified analyses comparing age of onset, stage of illness (first versus recurrent episode patients), and symptom severity were performed. Results were combined in random-effect meta-analysis models. Meta-regression analyses were used to test whether mean age of each sample, field strength of MR images, FreeSurfer version, percentage of patients acutely depressed, percentage of patients taking antidepressants, and percentage of patients taking antipsychotics explained a significant proportion of the variance in effect sizes across sites in the meta-analysis. Results were considered significant if they exceeded a Bonferroni corrected P-value threshold (P = 0.05/9 regions=5.6×10−3 ). Results: Relative to controls, patients had significantly lower hippocampal volumes (Cohen’s d = −0.14) − an effect driven by recurrent MDD patients (d = −0.17). Age of onset 21 was associated with a smaller hippocampus (d = −0.20) and a
P.2.b. Mood disorders and treatment − Affective disorders (clinical) trend towards smaller amygdala (d = −0.12) and larger lateral ventricles (d = 0.14). Symptom severity was not associated with regional brain volumes. Sample characteristics including mean age, proportion of antidepressant users and proportion of remitted patients did not moderate brain volume alterations. Samples with a higher proportion of antipsychotic medication users showed larger caudate volumes in MDD patients compared to controls. Conclusions: Results of this first initiative of the ENIGMAMDD working group clearly indicate a key role of the hippocampus in the pathophysiology of MDD, showing robust hippocampal volume reductions particularly in recurrent patients and patients with an age of onset of MDD 21. Brain changes in other subcortical regions in MDD were less evident. Our findings suggest that the hippocampus is a prime target region for future research aimed at further unravelling the pathophysiology of MDD and improving treatment. The important next step within our consortium will be examining cortical brain alterations associated with MDD and we are in the process of applying similar methods to cortical surface thickness and surface area measures. P.2.b.018 Effect of accumulation of 5-HTTLPR, BDNF Vall66Met and COMT Val158Met polymorphisms on brain morphology in patients with major depressive disorder M. Kostic1 ° , E. Canu2 , A. Munjiza3 , F. Agosta2 , I. Novakovic4 , V. Dobricic4 , V. Jerkovic5 , M. Jerkovic5 , T. Pekmezovic6 , D. Lecic Tosevski7 , M. Filippi8 1 Institute of Mental Health, Belgrade, Serbia; 2 Institute of Experimental Neurology − Division of Neuroscience − Vita-Salute University and San Raffaele Scientific Institute, Neuroimaging Research Unit, Milan, Italy; 3 Institute of Mental Health, Department for affective disorders, Belgrade, Serbia; 4 Neurology Clinic − Clinical Center of Serbia, Genetic research laboratory, Belgrade, Serbia; 5 School of Electrical Engineering − University of Belgrade, Department of statistics, Belgrade, Serbia; 6 School of Medicine − University of Belgrade, Department for epidemiology, Belgrade, Serbia; 7 Institute of Mental Health and School of Medicine − University of Belgrade, Department for affective disorders, Belgrade, Serbia; 8 Institute of Experimental Neurology − Division of Neuroscience − Vita-Salute University and San Raffaele Scientific Institute, Neuroimaging Research Unit and Department of Neurology, Milan, Italy Purpose of the study: One of the dominant theories for the etiology of complex human diseases, such as Major depressive disorder (MDD), is the “common disease, common variant hypothesis” [1]. This theory suggests that a large number of genes exert a small on the etiology and pathology of a disease. In MDD, this could explain the lack of positive findings in genome-wide association studies [2] and the need to study effects of interaction or accumulation of multiple genes. Our aim was to assess the effect of accumulation of specific SERT, BDNF and COMT genes functional polymorphisms, that have already been implicated in depression by previous research, in patients and to understand whether it affects the patient clinical features and brain structure. Methods: Seventy-seven MDD patients and 66 healthy controls underwent a comprehensive clinical assessment, genetic testing for serotonin transporter 5-HTTLPR, BDNF Val66Met and COMT Val158Met polymorphisms, and an MRI scan. Compared with controls, patients were more BDNF-Val homozygotes (MDD=53; controls=32; p = 0.013), COMT Met carriers (MDD=63; controls=47; p = 0.058) and SERT L’ carriers (MDD=65; controls=47;
S395
p = 0.056). Based on these results, all patients and controls were separated into three groups: 1. High risk group, i.e., patients or controls with all three susceptibility polymorphisms (SP); 2. Intermediate risk group (two SPs); and 3. Low risk group (one or none SPs). Regional gray matter (GM) volumes, cortical thickness and white matter (WM) tract integrity were assessed. Results: The High risk group was larger among the patients than the controls (MDD=40; controls=17; p = 0.001). High risk patients showed decreased GM volume and thickness in several frontal and temporal regions compared to Low risk controls; and decreased cortical thickness in the right medial orbitofrontal cortex and the frontal pole when compared with Low risk patients. On the other hand, High risk controls showed greater WM integrity of the corpus callosum, the superior and inferior longitudinal fasciculi, and the cingulum, with a prevalent involvement of the right side, compared to both High risk patients and Low risk controls. In the entire sample, the GM and WM integrity of the Intermediate risk group was in between the High risk and Low risk groups. Conclusions: This study showed that the accumulation of SPs in High risk patients is associated with reduced GM integrity of frontal and temporal cortices. These results suggest that brain structure of MDD patients is modulated by accumulation of the three gene we studied. On the other hand, High risk controls may have some protective factors leading to higher integrity of crucial WM connections. Investigating the effect of multiple genes within an accumulation model is promising and may help get greater understanding of MDD pathological mechanisms, define specific phenotypes of the disorder, discover risk and protective factors, and improve patient treatment. References [1] Singleton A., Hardy J., 2011. A generalizable hypothesis for the genetic architecture of disease: pleomorphic risk loci. Human Molecular Genetics 20(R2):R158−62. [2] Levinson D.F., Mostafavi S., Milaneschi Y., Rivera M., Ripke S., Wray N.R., Sullivan P.F., 2014. Genetic studies of major depressive disorder: why are there no genome-wide association study findings and what can we do about it. Biological Psychiatry 76(7):510−2.
P.2.b.019 Association between clinical response to duloxetine treatment and brain-derived neurotrophic factor or catecholamine metabolites K. Atake1 ° , Y. Reiji1 , H. Hikaru1 , K. Asuka1 , N. Jun1 of Occupational and Environmental Health − Japan, Psychiatry, Kitakyushu, Japan
1 University
Purpose of the study: Duloxetine, a serotonin noradrenaline reuptake inhibitor (SNRI), is an effective first-line treatment for patients with major depressive disorder (MDD). Previously, we reported that milnacipran, another SNRI, increased the plasma levels of 3-methoxy-4-hydroxyphenylglycol (MHPG), a major metabolite of noradrenalin, in patients with MDD. Therefore, we hypothesized that duloxetine also increases plasma MHPG levels in patients with MDD. Whereas, brain-derived neurotrophic factor (BDNF) plays an important role in the pathophysiology of depression. In addition, blood levels of BDNF is a biomarker for depressive state. So this study investigated the relationships among the plasma levels of catecholamine metabolites, the clinical response to duloxetine treatment, and BDNF. Methods used: The present study utilized an open-label and non-fixed dose design. All of the patients were administered