- Email: [email protected]
P.2.b.036 Attachment style, alexithymia and social support affect emotional states and experiences
P.2.b. Mood disorders and treatment − Affective disorders (clinical) BDNF and PDYN (p < 0.05, Spearman r = 0.2017) and an inverse correlation with two PDYN SNPs (rs1997794 and rs2281285). Conclusions: Our results are consistent with the epigenetic theory of psychosis, supporting the importance of alterations of epigenetic mechanisms in the etiology of BD and MDD. They also provide a new and clear correlation between changes in the epigenetic level of the BDNF and PDYN gene, suggesting their interaction in the development of BD. We also confirm that the use of PBMCs could be exploited as a reliable model of the complex epigenetic mechanisms leading to the discovery of biomarkers of diseases. Finally, we further suggest the relevance of integrating data on genetic variants and DNA methylation. References [1] Labrie V, Pai S, Petronis A. Epigenetics of major psychosis: progress, problems and perspectives. Trends Genet 2012; 28: 427–435. [2] D’Addario C, Dell’Osso B, Palazzo MC et al. Selective DNA methylation of BDNF promoter in bipolar disorder: differences among patients with BDI and BDII. Neuropsychopharmacology 2012; 37: 1647–1655. [3] D’Addario C, Dell’Osso B, Galimberti D, Palazzo MC, Benatti B, Di Francesco A, Scarpini E, Altamura AC, Maccarrone M. Biol Psych Epigenetic modulation of BDNF gene in patients with major depressive disorder. Biol Psychiatry 2013; 73: e6−7.
P.2.b.035 The combined effect of genetic polymorphisms and clinical parameters on treatment response and resistance in major depressive disorder A. Kautzky1 ° , R. Calati2 , P. Baldinger3 , D. Souery4 , S. Montgomery5 , J. Mendlewicz6 , J. Zohar7 , A. Serretti8 , R. Lanzenberger1 , S. Kasper1 1 Medical University of Vienna, Department for Psychiatry and Psychotherapy, Vienna, Austria; 2 Institute of Psychiatry, Department for Psychiatry and Psychotherapy, Bologna, Italy; 3 Medical University of Vienna, Department for Psychiatry and Psychotherapy, Veinna, Austria; 4 Universit´e Libre de Bruxelles and Psy Pluriel Centre Europe en de Psychologie Medicale, Laboratoire de Psychologie Medicale, Bruxelles, Belgium; 5 University of London, Imperial College, London, United Kingdom; 6 Free University of Brussels, School of Medicine, Bruxelles, Belgium; 7 Chaim Sheba Medical Center, Psychiatric Division, Tel-Hashomer, Israel; 8 University of Bologna, Institute of Psychiatry, Bologna, Italy Introduction: The treatment resistant depression (TRD) group, embracing many European medical centers and researchers, has studied the role of various single nucleotide polymorphisms (SNP) and clinical factors on treatment resistance and response for nearly a decade [1]. However, a model combining all these SNPs and clinical parameters and analyzing their putative interactions has not been established yet. Regarding the proposed low single effect of SNPs, a combined model showing the synergy of many variables seems auspicious. Objective: To investigate the interactive role of 12 SNPs and 8 clinical parameters in TRD. 4 SNPs covering the BDNF (rs11030101, rs11030104, rs6265, rs12273363) and 2 each of the COMT (rs174696, rs4680), ST8SIA2 (rs3784723, rs8035760), PPP3CC (rs10108011, rs7430) and HTR2A (rs643627, rs6313) genes as well as the clinical parameters comorbid anxiety and panic disorder, social phobia, melancholic depression, suicidality, severity of the depressive episode, early disease onset and response to the first antidepressant administered were investigated using the machine learning algorithm random forests (RF) and clustering.
S399
Methods: Patients derive from the sample of the TRD1 European multicenter study on treatment resistant depression [2]. 299 patients featured in previous work of the TRD group enrolled in this investigation. Statistical tests were performed using the ‘R’ software. RF was used for variable shrinkage, filtering the most important variables and accounting for their interactions. Subsequently clustering was applied using the k-means algorithm. Results: RF showed high variable importance for rs6313 (HTR2A), rs6265 (BDNF) and rs7430 (PPP3CC) as well as the clinical variable melancholic depression. These four factors showed the highest importance values, surpassing the threshold for significance for the model in all RF runs. K-means clustering revealed a cluster comprising 46 patients which showed better treatment response (54% responders vs a mean of 30% responders in all other clusters). When investigating the variable characteristics of this cluster, the most frequent set was homozygocity for the G-allele of rs6265 and rs7430, the T-allele of rs6313 and no melancholic features. 15 patients demonstrated this set of variable characteristics; of those 10 were responders, 2 non-responders and 3 resistant, deviating significantly from the response rates in the whole sample (66% vs 34%). Conclusion: All of the factors studied in this investigation have been analyzed by the TRD group and others before, showing sometimes inconclusive results. However, to our knowledge, this is the first study comprising a set of SNPs and clinical parameters in TRD in an interaction based machine learning and pattern analysis model. Using RF and clustering, we were able to select a set of clinical and genetic characteristics that was associated with treatment response. Thereby we could confirm previous findings of the TRD group and gain new information as well. Thus, although limited by the sample size as only patients genotyped for all SNPs and exhibiting values at all clinical parameters could be enrolled, in this study we demonstrate the strength of new, interaction based models and further strengthen the combined role of BDNF, HTR2A and PPP3CC in TRD and treatment phenotypes. References [1] Schosser A. et al. 2012; European Group for the Study of Resistant Depression (GSRD) − where have we gone so far: review of clinical and genetic findings. Eur Neuropsychopharmacol. 22(7):453−68. [2] Souery D. et al. 2007; Clinical factors associated with treatment resistance in major depressive disorder: results from a European multicenter study. J Clin Psychiatry. Jul; 68(7): 1062−70.
P.2.b.036 Attachment style, alexithymia and social support affect emotional states and experiences K. Meskanen1 ° , E. Komulainen1 , J. Lipsanen2 , J. Lahti2 , J. Ekelund1 , E. Isomets¨a1 1 University of Helsinki, Department of Psychiatry, Helsinki, Finland; 2 University of Helsinki, Institute of Behavioral Sciences, Helsinki, Finland Background: Identification of sensitive and specific markers for early detection of psychotropic, e.g. antidepressant, effects of new candidate molecules would aid in drug development. One potential methodology is assessment of real-time mood changes by experience sampling methods (ESM) [1], a method which can assess affective reactivity and emotional states with less influence from negative memory bias. The benefit of ESM method is that it is applicable in the real-life context in which the individual lives. Yet in such studies there are various psychological factors that
S400
P.2.b. Mood disorders and treatment − Affective disorders (clinical)
should be taken into consideration, affecting emotional states and appraisals of daily life situations and social contexts in a variety of ways. Within the framework of our study on early treatment response to antidepressants, we explored how attachment style, alexithymia and social support influence the emotional states and daily experiences in healthy individuals. Methods: The Experience Sampling Method was used to collect repeated reports of daily affect and experiences from 104 healthy university students during one week of their normal lives. Affective states were assessed using words adapted from the circumplex model of affect [2]. Daily life experiences were assessed by asking the subjects to report their current activities, recent events, social context and appraisals of them. Attachment styles, alexithymia and perceived social support were assessed using self-report questionnaires (ECR, TAS and PSSS-R, respectively). Hierarchical linear modeling was used to analyze their effect on daily emotional processes, e.g. on daily life level of affect and on daily life contexts. Results: Effect on level of affect: Both anxious and avoidant attachment style were associated with lower total level of positive affect, as well as with higher total level of negative affect. Alexithymia and its subscales ‘difficulty describing feelings’ and ‘difficulty identifying feelings’ predicted higher total level of negative affect, whereas perceived social support predicted lower level of negative affect, higher level of positive and low arousal affect. Effect on daily life contexts: Both anxious and avoidant attachment style were associated with more negatively appraised daily life activities and social interaction. Anxious attachment style was also associated with more negatively appraised daily life events. Alexithymia and its subscales’ difficulty describing feelings’ and ‘difficulty identifying feelings’ predicted more negatively appraised daily life activities, whereas perceived social support predicted more positive appraisals of daily life events, activities, social situations and interaction. Conclusions: The association of attachment style, social support and alexithymia on daily emotional states and experiences was investigated in healthy individuals. Insecure attachment style and alexithymia were found to predict higher levels of experienced negative affect and more negatively appraised daily life activities, whereas perceived social support predicted lower level of negative affect and higher positive affect as well as more positive appraisals of daily life events, activities, social situations and interaction. The results indicate that these psychological factors have an impact on daily life emotional states and experiences and should therefore be taken into account in experience sampling studies of every day mood experiences. This could have implications for future intervention studies of affective disorders. References [1] Csikszentmihalyi, M. & Larson, R. (1987) Validity and Reliability of the Experience-Sampling Method. Journal of Nervous and Mental Disease, 175, 526−36. [2] Russell, J.A. (1980) A circumplex model of affect. Journal of Personality & Social Psychology, 39, 1161–1178.
P.2.b.037 Increase of alertness correlates with early brain-derived neurotrophic factor serum level rise and treatment outcome in major depression T. Mikoteit1 ° , J. Beck1 , U. Hemmeter2 , A. Eckert3 , S. Brand1 , R. Bischof4 , A. Delini-Stula4 , E. Holsboer-Trachsler1 1 Psychiatric Clinics of the University of Basel, Center for Affective Stress and Sleep Disorders, Basel, Switzerland; 2 Psychiatric Service Canton of St. Gallen, Center of Education and Research (COEUR), Wil, Switzerland; 3 Psychiatric Clinics of the University of Basel, Neurobiology Laboratory for Brain Aging and Mental Health, Basel, Switzerland; 4 Pharma R&D Consulting, Basel, Switzerland Background: Brain-derived neurotrophic factor (BDNF) has been associated with neuroplasticity and neurocognitive functioning. In major depressive disorder (MDD) serum BDNF levels are often decreased. Some studies could show that improvement of depression went along with an increase of BDNF levels; others could not [1]. While normalization of decreased serum BDNF levels did not seem to be mandatory for favourable antidepressant treatment outcome, even less is known about the timely associations of serum BDNF changes and improvement of neuropsychological functions. Therefore the aim of this study was to explore (i) the predictive value of neuropsychological functioning to therapy outcome and (ii) the association between neuropsychological functioning and serum BDNF levels over the course of treatment. Our hypothesis was that higher baseline neuropsychological functioning was predictive to favourable treatment outcome and that improvement of impaired neuropsychological functioning was associated with normalization of serum BDNF levels. Methods: Twenty-five patients (mean age: 43.7±12.5 years; 68% males) with MDD underwent standardized treatment with duloxetine. Severity of depression, measured by the Hamilton Depression Rating Scale (HDRS), and BDNF serum concentrations were assessed at baseline, and after one, two and six weeks of treatment. Alertness, working memory and divided attention were assessed with a standardized test battery [‘Testbatterie zur Aufmerksamkeitspr¨ufung’ (TAP), version 2.1, Zimmermann P., Fimm B.] at baseline, after one week and at the end of treatment after six weeks. Results: At baseline neither alertness, nor working memory, nor divided attention correlated with baseline HDRS. Higher alertness (i.e., shorter reaction time) and greater divided attention (i.e., higher number of correct responses) at baseline correlated with a lower HDRS score at week 6 (r = 0.46, p = 0.02; r = −0.47, p = 0.02; resp.). There was a transient increase of mean BDNF serum concentrations between week 1/week 2, and baseline, respectively (mean: 3.65±6.46 ng/ml, 6.27±8.13 ng/ml), while BDNF levels at week 6 were similar to baseline again. A greater early increase of serum BDNF at week 1 correlated with a greater improvement of attention functioning at week 1 (alertness: r = −0.74; p = 0.000). A greater early increase of serum BDNF at week 2 was also predictive to a greater improvement of attention functioning at week 6 (alertness: r = −0.56, p = 0.005; working memory: r = −0.46, p = 0.03; divided attention: r = −0.57, p = 0.006). Conclusion: The results of the present study suggest that greater alertness at baseline was associated with greater symptom improvement six weeks later. Moreover, the early increase of BDNF serum levels during the first two weeks of treatment
P.2.b.035 The combined effect of genetic polymorphisms and clinical parameters on treatment response and resistance in major depressive disorder A. Kautzky1 ° , R. Calati2 , P. Baldinger3 , D. Souery4 , S. Montgomery5 , J. Mendlewicz6 , J. Zohar7 , A. Serretti8 , R. Lanzenberger1 , S. Kasper1 1 Medical University of Vienna, Department for Psychiatry and Psychotherapy, Vienna, Austria; 2 Institute of Psychiatry, Department for Psychiatry and Psychotherapy, Bologna, Italy; 3 Medical University of Vienna, Department for Psychiatry and Psychotherapy, Veinna, Austria; 4 Universit´e Libre de Bruxelles and Psy Pluriel Centre Europe en de Psychologie Medicale, Laboratoire de Psychologie Medicale, Bruxelles, Belgium; 5 University of London, Imperial College, London, United Kingdom; 6 Free University of Brussels, School of Medicine, Bruxelles, Belgium; 7 Chaim Sheba Medical Center, Psychiatric Division, Tel-Hashomer, Israel; 8 University of Bologna, Institute of Psychiatry, Bologna, Italy Introduction: The treatment resistant depression (TRD) group, embracing many European medical centers and researchers, has studied the role of various single nucleotide polymorphisms (SNP) and clinical factors on treatment resistance and response for nearly a decade [1]. However, a model combining all these SNPs and clinical parameters and analyzing their putative interactions has not been established yet. Regarding the proposed low single effect of SNPs, a combined model showing the synergy of many variables seems auspicious. Objective: To investigate the interactive role of 12 SNPs and 8 clinical parameters in TRD. 4 SNPs covering the BDNF (rs11030101, rs11030104, rs6265, rs12273363) and 2 each of the COMT (rs174696, rs4680), ST8SIA2 (rs3784723, rs8035760), PPP3CC (rs10108011, rs7430) and HTR2A (rs643627, rs6313) genes as well as the clinical parameters comorbid anxiety and panic disorder, social phobia, melancholic depression, suicidality, severity of the depressive episode, early disease onset and response to the first antidepressant administered were investigated using the machine learning algorithm random forests (RF) and clustering.
S399
Methods: Patients derive from the sample of the TRD1 European multicenter study on treatment resistant depression [2]. 299 patients featured in previous work of the TRD group enrolled in this investigation. Statistical tests were performed using the ‘R’ software. RF was used for variable shrinkage, filtering the most important variables and accounting for their interactions. Subsequently clustering was applied using the k-means algorithm. Results: RF showed high variable importance for rs6313 (HTR2A), rs6265 (BDNF) and rs7430 (PPP3CC) as well as the clinical variable melancholic depression. These four factors showed the highest importance values, surpassing the threshold for significance for the model in all RF runs. K-means clustering revealed a cluster comprising 46 patients which showed better treatment response (54% responders vs a mean of 30% responders in all other clusters). When investigating the variable characteristics of this cluster, the most frequent set was homozygocity for the G-allele of rs6265 and rs7430, the T-allele of rs6313 and no melancholic features. 15 patients demonstrated this set of variable characteristics; of those 10 were responders, 2 non-responders and 3 resistant, deviating significantly from the response rates in the whole sample (66% vs 34%). Conclusion: All of the factors studied in this investigation have been analyzed by the TRD group and others before, showing sometimes inconclusive results. However, to our knowledge, this is the first study comprising a set of SNPs and clinical parameters in TRD in an interaction based machine learning and pattern analysis model. Using RF and clustering, we were able to select a set of clinical and genetic characteristics that was associated with treatment response. Thereby we could confirm previous findings of the TRD group and gain new information as well. Thus, although limited by the sample size as only patients genotyped for all SNPs and exhibiting values at all clinical parameters could be enrolled, in this study we demonstrate the strength of new, interaction based models and further strengthen the combined role of BDNF, HTR2A and PPP3CC in TRD and treatment phenotypes. References [1] Schosser A. et al. 2012; European Group for the Study of Resistant Depression (GSRD) − where have we gone so far: review of clinical and genetic findings. Eur Neuropsychopharmacol. 22(7):453−68. [2] Souery D. et al. 2007; Clinical factors associated with treatment resistance in major depressive disorder: results from a European multicenter study. J Clin Psychiatry. Jul; 68(7): 1062−70.
P.2.b.036 Attachment style, alexithymia and social support affect emotional states and experiences K. Meskanen1 ° , E. Komulainen1 , J. Lipsanen2 , J. Lahti2 , J. Ekelund1 , E. Isomets¨a1 1 University of Helsinki, Department of Psychiatry, Helsinki, Finland; 2 University of Helsinki, Institute of Behavioral Sciences, Helsinki, Finland Background: Identification of sensitive and specific markers for early detection of psychotropic, e.g. antidepressant, effects of new candidate molecules would aid in drug development. One potential methodology is assessment of real-time mood changes by experience sampling methods (ESM) [1], a method which can assess affective reactivity and emotional states with less influence from negative memory bias. The benefit of ESM method is that it is applicable in the real-life context in which the individual lives. Yet in such studies there are various psychological factors that
S400
P.2.b. Mood disorders and treatment − Affective disorders (clinical)
should be taken into consideration, affecting emotional states and appraisals of daily life situations and social contexts in a variety of ways. Within the framework of our study on early treatment response to antidepressants, we explored how attachment style, alexithymia and social support influence the emotional states and daily experiences in healthy individuals. Methods: The Experience Sampling Method was used to collect repeated reports of daily affect and experiences from 104 healthy university students during one week of their normal lives. Affective states were assessed using words adapted from the circumplex model of affect [2]. Daily life experiences were assessed by asking the subjects to report their current activities, recent events, social context and appraisals of them. Attachment styles, alexithymia and perceived social support were assessed using self-report questionnaires (ECR, TAS and PSSS-R, respectively). Hierarchical linear modeling was used to analyze their effect on daily emotional processes, e.g. on daily life level of affect and on daily life contexts. Results: Effect on level of affect: Both anxious and avoidant attachment style were associated with lower total level of positive affect, as well as with higher total level of negative affect. Alexithymia and its subscales ‘difficulty describing feelings’ and ‘difficulty identifying feelings’ predicted higher total level of negative affect, whereas perceived social support predicted lower level of negative affect, higher level of positive and low arousal affect. Effect on daily life contexts: Both anxious and avoidant attachment style were associated with more negatively appraised daily life activities and social interaction. Anxious attachment style was also associated with more negatively appraised daily life events. Alexithymia and its subscales’ difficulty describing feelings’ and ‘difficulty identifying feelings’ predicted more negatively appraised daily life activities, whereas perceived social support predicted more positive appraisals of daily life events, activities, social situations and interaction. Conclusions: The association of attachment style, social support and alexithymia on daily emotional states and experiences was investigated in healthy individuals. Insecure attachment style and alexithymia were found to predict higher levels of experienced negative affect and more negatively appraised daily life activities, whereas perceived social support predicted lower level of negative affect and higher positive affect as well as more positive appraisals of daily life events, activities, social situations and interaction. The results indicate that these psychological factors have an impact on daily life emotional states and experiences and should therefore be taken into account in experience sampling studies of every day mood experiences. This could have implications for future intervention studies of affective disorders. References [1] Csikszentmihalyi, M. & Larson, R. (1987) Validity and Reliability of the Experience-Sampling Method. Journal of Nervous and Mental Disease, 175, 526−36. [2] Russell, J.A. (1980) A circumplex model of affect. Journal of Personality & Social Psychology, 39, 1161–1178.
P.2.b.037 Increase of alertness correlates with early brain-derived neurotrophic factor serum level rise and treatment outcome in major depression T. Mikoteit1 ° , J. Beck1 , U. Hemmeter2 , A. Eckert3 , S. Brand1 , R. Bischof4 , A. Delini-Stula4 , E. Holsboer-Trachsler1 1 Psychiatric Clinics of the University of Basel, Center for Affective Stress and Sleep Disorders, Basel, Switzerland; 2 Psychiatric Service Canton of St. Gallen, Center of Education and Research (COEUR), Wil, Switzerland; 3 Psychiatric Clinics of the University of Basel, Neurobiology Laboratory for Brain Aging and Mental Health, Basel, Switzerland; 4 Pharma R&D Consulting, Basel, Switzerland Background: Brain-derived neurotrophic factor (BDNF) has been associated with neuroplasticity and neurocognitive functioning. In major depressive disorder (MDD) serum BDNF levels are often decreased. Some studies could show that improvement of depression went along with an increase of BDNF levels; others could not [1]. While normalization of decreased serum BDNF levels did not seem to be mandatory for favourable antidepressant treatment outcome, even less is known about the timely associations of serum BDNF changes and improvement of neuropsychological functions. Therefore the aim of this study was to explore (i) the predictive value of neuropsychological functioning to therapy outcome and (ii) the association between neuropsychological functioning and serum BDNF levels over the course of treatment. Our hypothesis was that higher baseline neuropsychological functioning was predictive to favourable treatment outcome and that improvement of impaired neuropsychological functioning was associated with normalization of serum BDNF levels. Methods: Twenty-five patients (mean age: 43.7±12.5 years; 68% males) with MDD underwent standardized treatment with duloxetine. Severity of depression, measured by the Hamilton Depression Rating Scale (HDRS), and BDNF serum concentrations were assessed at baseline, and after one, two and six weeks of treatment. Alertness, working memory and divided attention were assessed with a standardized test battery [‘Testbatterie zur Aufmerksamkeitspr¨ufung’ (TAP), version 2.1, Zimmermann P., Fimm B.] at baseline, after one week and at the end of treatment after six weeks. Results: At baseline neither alertness, nor working memory, nor divided attention correlated with baseline HDRS. Higher alertness (i.e., shorter reaction time) and greater divided attention (i.e., higher number of correct responses) at baseline correlated with a lower HDRS score at week 6 (r = 0.46, p = 0.02; r = −0.47, p = 0.02; resp.). There was a transient increase of mean BDNF serum concentrations between week 1/week 2, and baseline, respectively (mean: 3.65±6.46 ng/ml, 6.27±8.13 ng/ml), while BDNF levels at week 6 were similar to baseline again. A greater early increase of serum BDNF at week 1 correlated with a greater improvement of attention functioning at week 1 (alertness: r = −0.74; p = 0.000). A greater early increase of serum BDNF at week 2 was also predictive to a greater improvement of attention functioning at week 6 (alertness: r = −0.56, p = 0.005; working memory: r = −0.46, p = 0.03; divided attention: r = −0.57, p = 0.006). Conclusion: The results of the present study suggest that greater alertness at baseline was associated with greater symptom improvement six weeks later. Moreover, the early increase of BDNF serum levels during the first two weeks of treatment