human psychopharmacology Hum. Psychopharmacol Clin Exp 2013; 28: 576–585. Published online 7 October 2013 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/hup.2347
Prediction and structural equation model of sertraline treatment response in Japanese patients with major depressive disorder†,‡ Gentaro Nishioka1,2,3*, Hideaki Yashima4,5, Yuji Kiuchi8, Sumiko Nakamura4,6, Hideto Oyamada7, Masakazu Ishii4 and Ikuo Kudo2 1
Department of Psychiatry, Tokyo Metropolitan Police Hospital, Tokyo, Japan Department of Psychiatry, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa Prefecture Japan 3 Kawaguchi Hospital, Kawaguchi, Saitama Prefecture Japan 4 Department of Pathophysiology, School of Pharmaceutical Sciences, Showa University, Tokyo, Japan 5 Pharmaceutical Department, Gunma University Hospital, Maebashi, Gunma Prefecture Japan 6 Pharmaceutical Department, Tokyo Kosei Nenkin Hospital, Tokyo, Japan 7 Department of Pharmacology, School of Medicine, Showa University, Tokyo, Japan 8 Center of Pharmaceutical Education, School of Pharmaceutical Sciences, Showa University, Tokyo, Japan 2
Objective The aim of this study was to extract the factors possibly associated with sertraline treatment response and elucidate their interactions and extent of influence. Methods Demographic state, stress state, personality, and eight genetic polymorphisms at baseline and clinical symptoms at baseline and 8 weeks were analyzed and examined by logistic regression and a structural equation model in sertraline treatment study of 96 Japanese patients with major depressive disorder. Results Non-responders were associated with higher scores of harm avoidance in Temperament and Character Inventory, higher scores (≥24) of 17-item Hamilton Rating Scale for Depression at baseline, recurrence, and 12/12 genotype of the serotonin transporter variable number of tandem repeat polymorphism in intron 2 (5HTTSTin2). When we calculated the response index using four factors extracted, the mean response index value of non-responders was significantly higher than that of responders. The symptoms at baseline, personality, recurrence, and polymorphism of 5HTTSTin2 showed significantly direct and positive influences on the symptoms at 8 weeks in our final structural equation model with a good model fit. Conclusion Considering the combination of four factors extracted may be useful for predicting a worse response to sertraline treatment and selecting different treatment other than sertraline. Copyright © 2013 John Wiley & Sons, Ltd. key words—major depressive disorder; sertraline treatment response; 5HTTSTin2; personality; recurrence; structural equation model
INTRODUCTION Major depressive disorder (MDD) is a common mental disorder that presents with depressed mood, loss of interest or pleasure, decreased energy, feelings of guilt or low self-worth, disturbed sleep or appetite, and poor concentration. MDD is a significant contributor to the global burden of disease and affects people in all communities across the world. MDD often recurs, reduces social or occupational functioning, and leads to suicide in the worst case (Moussavi et al., 2007).
*Correspondence to: G. Nishioka MD, PhD, Department of Psychiatry, Tokyo Metropolitan Police Hospital, 4-22-1 Nakano, Nakano-ku, Tokyo 164–8541, Japan. Tel: +81-3-5343-5611; Fax: +81-3-5343-5612. E-mail: [email protected] † The research was conducted at Showa University Northern Yokohama Hospital in Kanagawa Kawaguchi Hospital in Saitama Prefecture, Japan. ‡ There was no source of support in equipment and drugs.
Copyright © 2013 John Wiley & Sons, Ltd.
The diagnosis of “Major Depressive Disorder (MDD)” is based on the symptomatic criteria set in the Diagnostic and Statistical Manual, 4th Edition, Text Revision. MDD should be viewed as a heterogeneous syndrome comprising numerous diseases of distinct causes and pathophysiologies (Nestler et al., 2002). Antidepressants are important in MDD treatment. Selective serotonin reuptake inhibitors (SSRI) are the first-line pharmacological options and the most commonly prescribed antidepressants because of their efficacy, tolerability, and general safety in overdose. Compared with the old generations of antidepressants, the SSRIs present a therapeutic option that is attractive to the majority of primary care physicians, psychiatrists, and patients (Schatzberg and Nemeroff, 2009). Sertraline might be the best choice when starting treatment for moderate–severe major depression in adults because it has the most favorable balance between benefits, Received 1 December 2012 Accepted 22 July 2013
a 8 week naturalistic sertraline treatment study
acceptability, and acquisition cost (Cipriani et al., 2009). Large majority of individuals are first-time responders to antidepressant treatment and approximately 50% patients respond to the first treatment in acute phase (Santaguida et al., 2012). On the other hand, however, 30–40% patients do not remit after continuous treatment over a year (Rush et al., 2006, Trivedi et al., 2006). However, patients must continue their prescribed medications for at least 4 weeks before knowing whether antidepressants will be effective. If it is possible to predict treatment response with fewer factors, time and cost in MDD treatment may be lowered. Although the reasons why some patients do not respond to antidepressant treatment are not clear, several predictors of treatment response have been suggested. They include clinical symptoms or severity (Rush et al., 2006, Trivedi et al., 2006, Katon et al., 2010), demographic state such as age and sex (Pfohl et al., 1984, Keitner et al., 1992, Perlis et al., 2003, Mulder et al., 2006, Olfson et al., 2006, Warden et al., 2007), stress state (Mazure et al., 2000, Caspi et al., 2003, Heim et al., 2006, Bulmash et al., 2009), personality (Mandelli et al., 2009, Hruby et al., 2010), and genetic polymorphism of serotonin transporter (Mrazek et al., 2009), 5-HT receptor 2A(Kato et al., 2006), tryptophan hydroxylase 1(TPH-1) (Serretti et al., 2001), brainderived neurotrophic factor (BDNF) (Duman, 2004), angiotensin converting enzyme (ACE) (Baghai et al., 2001), and monoamine oxidase A (Yu et al., 2005). However, each factor has varying degrees of success, and nearly all predictors have poor prognostic sensitivity and specificity (Kemp et al., 2008). Each factor may be involved in antidepressant response, but the weight of each factor may not be clear. In order to elucidate weight of each factor and overall interactions of the factors at the same time in the treatment response, it is necessary to consider and analyze simultaneously these factors. (Keitner et al., 1992) investigated various factors by multivariate analysis about recovery of major depression and concluded that shorter length of hospital stay, older age at onset of depression, better family functioning, fewer than two previous hospitalizations, and absence of comorbid illness related to recovery of depression. (Mulder et al., 2006) also found by multivariate analysis that personality, demographic features, depression characteristics, depression subtypes, and comorbidity were predictors in treatment outcome for depression. These findings were very suggestive and useful, but genetic polymorphisms recently reported in many studies had not been considered simultaneously. Previously, we studied retrospectively about the relationship between antidepressant treatment response and various factors from medical records of patients with MDD. Some factors including Copyright © 2013 John Wiley & Sons, Ltd.
577
personality and genetic polymorphism could be associated with antidepressant treatment response in a multivariate analysis (unpublished data). Therefore, we investigated clinical symptoms, demographic variables, stress state, personality, and genetic polymorphisms in patients treated with the SSRI sertraline and performed a multivariate analysis (logistic regression analysis) to extract the predicting factors possibly associated with sertraline treatment response. Furthermore, several factors are presumably interrelated with certain weights in antidepressant treatment response. Structural Equation Model (SEM) can appropriately determine the weight of significantly related factors and their interactions or pathways with correlation coefficients between the variables. This model shows factor weighting, pathway, and correlation coefficients in a simple figure and it is easy to understand visually the relation network of variables. Therefore, we investigated the weights and interactions of the factors involved in sertraline treatment response using SEM.
METHODS AND MATERIALS Clinical assessments Patients were recruited from the general population from June 2008 to March 2010. Ninety-six Japanese patients [inpatients, six; outpatients, 90; age, ≥20 years; 40 men and 56 women (not pregnant or breastfeeding)] participated in this study. Patients meeting Diagnostic and Statistical Manual, 4th Edition, Text Revision axis I MDD diagnoses, but not axis II diagnoses, were recruited at Showa University Northern Yokohama Hospital in Kanagawa and Kawaguchi Hospital in Saitama Prefecture, Japan. Psychiatric clinicians assessed and defined the psychiatric states of all patients as the first or recurrent episodes. Patients were not treated with antidepressants for >12 weeks before registration. Patients with scores of ≥14 on the 17-item Hamilton Rating Scale of Depression (HAM-D17) were included in this study. Patients ≥75 years (Osborn et al., 2003, Schwarzbach et al., 2013) and demonstrating scores of 24 ≥ on the mini-mental state exam (Woodford and George, 2007) were excluded. Furthermore, patients with bipolar or psychotic disorders, anxiety disorders, obsessive–compulsive disorder, eating disorders, and general medical conditions that contraindicated protocol treatment or substance abuse/ dependence were excluded from this study. This study was approved by the Showa University Ethics Committee. Patients provided written informed consent for participation. Open-label sertraline was administered once daily (after dinner or at bedtime) for 8 weeks. Antidepressant Hum. Psychopharmacol Clin Exp 2013; 28: 576–585. DOI: 10.1002/hup
578
g. nishioka
medication was comprised of sertraline monotherapy; the dose was based on clinical judgment. The initial dose was 25 mg/day; it was adequately adjusted after ≥1 week. The final dose ranged from 25–100 mg/day based on individual patient conditions. Clinical judgments whether to increase sertraline dose or not were as follows. For patients with scores of >7 on the HAM-D17 at clinical visit, we increased sertraline dose gradually up to 100 mg at maximum and aimed at scores of 7 ≥ on the HAM-D17 as soon as possible, to reach remission. Patients with scores of 7 ≥ on the HAM-D17 at clinical visit were maintained the same dose. Clinicians assessed the clinical global impressionseverity of illness (CGI-S) and HAM-D17 at baseline and at 8 weeks. All patients completed self-report questionnaires, including the Beck depression inventory-II (BDI-II) at baseline and at 8 weeks. Other self-report questionnaires assessed demographic factors (onset age, recurrence, psychiatric family history, adverse childhood events, number of stressful life events, education periods, educational background, spouse, residential family members, somatic complications, smoking habits, and drinking habits); clinical information was collected from medical records at baseline. A responder to sertraline treatment was defined as a patient with a ≥50% decrease in their score on HAM-D17 at 8 weeks compared with that at baseline. The perceived stress scale (PSS) was used to assess the degree to which participants appraised their daily life as unpredictable, uncontrollable, and overwhelming (Cohen et al., 1983). The number of stressful life events and adverse childhood events were determined by clinician interview (Caspi et al., 2003). Neuroticism extraversion openness–five factor inventory, which is the 60-item version of the revised NEO personality inventory (NEO-FFI) (Costa and Mccrae, 1992), was used to assess the five domains of adult personality: neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness. Temperament and character inventory (TCI), which is a 240-item self-reporting true/false questionnaire, assesses four temperaments (harm avoidance, novelty seeking, persistence, and reward dependence), and three character dimensions (self-directedness, cooperativeness, and self-transcendence) (Cloninger et al., 1993). Laboratory analysis In this study, we examined the following eight genetic polymorphisms including serotonin-related polymorphisms, which may be related to antidepressant treatment response (Kato and Serretti, 2010): genetic polymorphism of serotonin transporter linked promoter region (LPR) variation, variable number of tandem Copyright © 2013 John Wiley & Sons, Ltd.
ET AL.
repeats polymorphisms in intron 2 (5HTTSTin2), 5HTR2A 1438A/G and 102T/C, TPH-1 218A/C, BDNF Val66Met, intronic insertion (I)/deletion (D) in ACE, and MAO-A 941T/G. The Val66Met variant of BDNF is a single nucleotide polymorphism of G to A at nucleotide 196 in the 5′ pro-BDNF sequence that results in a valine to methionine change. DNA was extracted from a 2 mL blood sample using the NucleoSpin Blood Quick Pure kit (MACHEREYNAGEL GmbH & Co. KG, Düren, Germany). Polymerase chain reaction (PCR) amplification mixtures and PCR conditions are shown in Supplementary Table 1 and 2. Polymerase chain reaction products were separated by electrophoresis on a 3% agarose gel (BioWhittaker Molecular Applications, Inc., Rockland, ME, USA) and visualized with ethidium bromide staining under ultraviolet fluorescence. Statistical methods We evaluated the differences in the continuous and categorical variables between responders and nonresponders. Clinical symptoms, demographics, stress state, personality, and the eight genetic polymorphisms at baseline were set as variables. We performed univariate analyses (t-tests and chi-square tests). Considering the results of the previous studies and our data of the p-values of t-tests and chi-square tests in the univariate analysis, we began with a fully saturated model and used one approach to produce a model with the optimal balance of explanatory power. Next, we performed a multivariate analysis (logistic regression analysis) using SPSS, version 17.0 (SPSS, Tokyo, Japan). Potential predictors that were defined with a liberal alpha p < 0.10 in t-tests and chi-square tests were entered into the logistic regression analysis; p-values less than 0.05 were considered significant. We calculated a response index (RI) using the factors extracted. Integer approximation was conducted for calculating RI using β (regression coefficient) in the logistic regression analysis. We devised the following formula for RI: RI ¼ A1 x1 þ A2 x2 þ e þ An xn where, x1–xn: extracted factors and A1–An: integers determined based on coefficient β. RI value was calculated for each patient, and RIs of each responder were compared with those of each non-responder. Structural equation modeling was performed using the AMOS7.0 SEM software (SPSS, Tokyo, Japan). Next, we confirmed the structural model that comprised the pathway and correlation coefficients connecting the latent and observed variables of the model proper. Hum. Psychopharmacol Clin Exp 2013; 28: 576–585. DOI: 10.1002/hup
579
a 8 week naturalistic sertraline treatment study Table 1.
Demographic, stress state, and background Responders
age education(years) age at onset PSS number of stressful life events
HAM-D17 at baseline gender adverse life event in childhood spouse residential family member somatic complications educational background smoking habits drinking habits psychiatric family history (within first relative) recurrence
≥24
Prediction and structural equation model of sertraline treatment response in Japanese patients with major depressive disorder†,‡ Gentaro Nishioka1,2,3*, Hideaki Yashima4,5, Yuji Kiuchi8, Sumiko Nakamura4,6, Hideto Oyamada7, Masakazu Ishii4 and Ikuo Kudo2 1
Department of Psychiatry, Tokyo Metropolitan Police Hospital, Tokyo, Japan Department of Psychiatry, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa Prefecture Japan 3 Kawaguchi Hospital, Kawaguchi, Saitama Prefecture Japan 4 Department of Pathophysiology, School of Pharmaceutical Sciences, Showa University, Tokyo, Japan 5 Pharmaceutical Department, Gunma University Hospital, Maebashi, Gunma Prefecture Japan 6 Pharmaceutical Department, Tokyo Kosei Nenkin Hospital, Tokyo, Japan 7 Department of Pharmacology, School of Medicine, Showa University, Tokyo, Japan 8 Center of Pharmaceutical Education, School of Pharmaceutical Sciences, Showa University, Tokyo, Japan 2
Objective The aim of this study was to extract the factors possibly associated with sertraline treatment response and elucidate their interactions and extent of influence. Methods Demographic state, stress state, personality, and eight genetic polymorphisms at baseline and clinical symptoms at baseline and 8 weeks were analyzed and examined by logistic regression and a structural equation model in sertraline treatment study of 96 Japanese patients with major depressive disorder. Results Non-responders were associated with higher scores of harm avoidance in Temperament and Character Inventory, higher scores (≥24) of 17-item Hamilton Rating Scale for Depression at baseline, recurrence, and 12/12 genotype of the serotonin transporter variable number of tandem repeat polymorphism in intron 2 (5HTTSTin2). When we calculated the response index using four factors extracted, the mean response index value of non-responders was significantly higher than that of responders. The symptoms at baseline, personality, recurrence, and polymorphism of 5HTTSTin2 showed significantly direct and positive influences on the symptoms at 8 weeks in our final structural equation model with a good model fit. Conclusion Considering the combination of four factors extracted may be useful for predicting a worse response to sertraline treatment and selecting different treatment other than sertraline. Copyright © 2013 John Wiley & Sons, Ltd. key words—major depressive disorder; sertraline treatment response; 5HTTSTin2; personality; recurrence; structural equation model
INTRODUCTION Major depressive disorder (MDD) is a common mental disorder that presents with depressed mood, loss of interest or pleasure, decreased energy, feelings of guilt or low self-worth, disturbed sleep or appetite, and poor concentration. MDD is a significant contributor to the global burden of disease and affects people in all communities across the world. MDD often recurs, reduces social or occupational functioning, and leads to suicide in the worst case (Moussavi et al., 2007).
*Correspondence to: G. Nishioka MD, PhD, Department of Psychiatry, Tokyo Metropolitan Police Hospital, 4-22-1 Nakano, Nakano-ku, Tokyo 164–8541, Japan. Tel: +81-3-5343-5611; Fax: +81-3-5343-5612. E-mail: [email protected] † The research was conducted at Showa University Northern Yokohama Hospital in Kanagawa Kawaguchi Hospital in Saitama Prefecture, Japan. ‡ There was no source of support in equipment and drugs.
Copyright © 2013 John Wiley & Sons, Ltd.
The diagnosis of “Major Depressive Disorder (MDD)” is based on the symptomatic criteria set in the Diagnostic and Statistical Manual, 4th Edition, Text Revision. MDD should be viewed as a heterogeneous syndrome comprising numerous diseases of distinct causes and pathophysiologies (Nestler et al., 2002). Antidepressants are important in MDD treatment. Selective serotonin reuptake inhibitors (SSRI) are the first-line pharmacological options and the most commonly prescribed antidepressants because of their efficacy, tolerability, and general safety in overdose. Compared with the old generations of antidepressants, the SSRIs present a therapeutic option that is attractive to the majority of primary care physicians, psychiatrists, and patients (Schatzberg and Nemeroff, 2009). Sertraline might be the best choice when starting treatment for moderate–severe major depression in adults because it has the most favorable balance between benefits, Received 1 December 2012 Accepted 22 July 2013
a 8 week naturalistic sertraline treatment study
acceptability, and acquisition cost (Cipriani et al., 2009). Large majority of individuals are first-time responders to antidepressant treatment and approximately 50% patients respond to the first treatment in acute phase (Santaguida et al., 2012). On the other hand, however, 30–40% patients do not remit after continuous treatment over a year (Rush et al., 2006, Trivedi et al., 2006). However, patients must continue their prescribed medications for at least 4 weeks before knowing whether antidepressants will be effective. If it is possible to predict treatment response with fewer factors, time and cost in MDD treatment may be lowered. Although the reasons why some patients do not respond to antidepressant treatment are not clear, several predictors of treatment response have been suggested. They include clinical symptoms or severity (Rush et al., 2006, Trivedi et al., 2006, Katon et al., 2010), demographic state such as age and sex (Pfohl et al., 1984, Keitner et al., 1992, Perlis et al., 2003, Mulder et al., 2006, Olfson et al., 2006, Warden et al., 2007), stress state (Mazure et al., 2000, Caspi et al., 2003, Heim et al., 2006, Bulmash et al., 2009), personality (Mandelli et al., 2009, Hruby et al., 2010), and genetic polymorphism of serotonin transporter (Mrazek et al., 2009), 5-HT receptor 2A(Kato et al., 2006), tryptophan hydroxylase 1(TPH-1) (Serretti et al., 2001), brainderived neurotrophic factor (BDNF) (Duman, 2004), angiotensin converting enzyme (ACE) (Baghai et al., 2001), and monoamine oxidase A (Yu et al., 2005). However, each factor has varying degrees of success, and nearly all predictors have poor prognostic sensitivity and specificity (Kemp et al., 2008). Each factor may be involved in antidepressant response, but the weight of each factor may not be clear. In order to elucidate weight of each factor and overall interactions of the factors at the same time in the treatment response, it is necessary to consider and analyze simultaneously these factors. (Keitner et al., 1992) investigated various factors by multivariate analysis about recovery of major depression and concluded that shorter length of hospital stay, older age at onset of depression, better family functioning, fewer than two previous hospitalizations, and absence of comorbid illness related to recovery of depression. (Mulder et al., 2006) also found by multivariate analysis that personality, demographic features, depression characteristics, depression subtypes, and comorbidity were predictors in treatment outcome for depression. These findings were very suggestive and useful, but genetic polymorphisms recently reported in many studies had not been considered simultaneously. Previously, we studied retrospectively about the relationship between antidepressant treatment response and various factors from medical records of patients with MDD. Some factors including Copyright © 2013 John Wiley & Sons, Ltd.
577
personality and genetic polymorphism could be associated with antidepressant treatment response in a multivariate analysis (unpublished data). Therefore, we investigated clinical symptoms, demographic variables, stress state, personality, and genetic polymorphisms in patients treated with the SSRI sertraline and performed a multivariate analysis (logistic regression analysis) to extract the predicting factors possibly associated with sertraline treatment response. Furthermore, several factors are presumably interrelated with certain weights in antidepressant treatment response. Structural Equation Model (SEM) can appropriately determine the weight of significantly related factors and their interactions or pathways with correlation coefficients between the variables. This model shows factor weighting, pathway, and correlation coefficients in a simple figure and it is easy to understand visually the relation network of variables. Therefore, we investigated the weights and interactions of the factors involved in sertraline treatment response using SEM.
METHODS AND MATERIALS Clinical assessments Patients were recruited from the general population from June 2008 to March 2010. Ninety-six Japanese patients [inpatients, six; outpatients, 90; age, ≥20 years; 40 men and 56 women (not pregnant or breastfeeding)] participated in this study. Patients meeting Diagnostic and Statistical Manual, 4th Edition, Text Revision axis I MDD diagnoses, but not axis II diagnoses, were recruited at Showa University Northern Yokohama Hospital in Kanagawa and Kawaguchi Hospital in Saitama Prefecture, Japan. Psychiatric clinicians assessed and defined the psychiatric states of all patients as the first or recurrent episodes. Patients were not treated with antidepressants for >12 weeks before registration. Patients with scores of ≥14 on the 17-item Hamilton Rating Scale of Depression (HAM-D17) were included in this study. Patients ≥75 years (Osborn et al., 2003, Schwarzbach et al., 2013) and demonstrating scores of 24 ≥ on the mini-mental state exam (Woodford and George, 2007) were excluded. Furthermore, patients with bipolar or psychotic disorders, anxiety disorders, obsessive–compulsive disorder, eating disorders, and general medical conditions that contraindicated protocol treatment or substance abuse/ dependence were excluded from this study. This study was approved by the Showa University Ethics Committee. Patients provided written informed consent for participation. Open-label sertraline was administered once daily (after dinner or at bedtime) for 8 weeks. Antidepressant Hum. Psychopharmacol Clin Exp 2013; 28: 576–585. DOI: 10.1002/hup
578
g. nishioka
medication was comprised of sertraline monotherapy; the dose was based on clinical judgment. The initial dose was 25 mg/day; it was adequately adjusted after ≥1 week. The final dose ranged from 25–100 mg/day based on individual patient conditions. Clinical judgments whether to increase sertraline dose or not were as follows. For patients with scores of >7 on the HAM-D17 at clinical visit, we increased sertraline dose gradually up to 100 mg at maximum and aimed at scores of 7 ≥ on the HAM-D17 as soon as possible, to reach remission. Patients with scores of 7 ≥ on the HAM-D17 at clinical visit were maintained the same dose. Clinicians assessed the clinical global impressionseverity of illness (CGI-S) and HAM-D17 at baseline and at 8 weeks. All patients completed self-report questionnaires, including the Beck depression inventory-II (BDI-II) at baseline and at 8 weeks. Other self-report questionnaires assessed demographic factors (onset age, recurrence, psychiatric family history, adverse childhood events, number of stressful life events, education periods, educational background, spouse, residential family members, somatic complications, smoking habits, and drinking habits); clinical information was collected from medical records at baseline. A responder to sertraline treatment was defined as a patient with a ≥50% decrease in their score on HAM-D17 at 8 weeks compared with that at baseline. The perceived stress scale (PSS) was used to assess the degree to which participants appraised their daily life as unpredictable, uncontrollable, and overwhelming (Cohen et al., 1983). The number of stressful life events and adverse childhood events were determined by clinician interview (Caspi et al., 2003). Neuroticism extraversion openness–five factor inventory, which is the 60-item version of the revised NEO personality inventory (NEO-FFI) (Costa and Mccrae, 1992), was used to assess the five domains of adult personality: neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness. Temperament and character inventory (TCI), which is a 240-item self-reporting true/false questionnaire, assesses four temperaments (harm avoidance, novelty seeking, persistence, and reward dependence), and three character dimensions (self-directedness, cooperativeness, and self-transcendence) (Cloninger et al., 1993). Laboratory analysis In this study, we examined the following eight genetic polymorphisms including serotonin-related polymorphisms, which may be related to antidepressant treatment response (Kato and Serretti, 2010): genetic polymorphism of serotonin transporter linked promoter region (LPR) variation, variable number of tandem Copyright © 2013 John Wiley & Sons, Ltd.
ET AL.
repeats polymorphisms in intron 2 (5HTTSTin2), 5HTR2A 1438A/G and 102T/C, TPH-1 218A/C, BDNF Val66Met, intronic insertion (I)/deletion (D) in ACE, and MAO-A 941T/G. The Val66Met variant of BDNF is a single nucleotide polymorphism of G to A at nucleotide 196 in the 5′ pro-BDNF sequence that results in a valine to methionine change. DNA was extracted from a 2 mL blood sample using the NucleoSpin Blood Quick Pure kit (MACHEREYNAGEL GmbH & Co. KG, Düren, Germany). Polymerase chain reaction (PCR) amplification mixtures and PCR conditions are shown in Supplementary Table 1 and 2. Polymerase chain reaction products were separated by electrophoresis on a 3% agarose gel (BioWhittaker Molecular Applications, Inc., Rockland, ME, USA) and visualized with ethidium bromide staining under ultraviolet fluorescence. Statistical methods We evaluated the differences in the continuous and categorical variables between responders and nonresponders. Clinical symptoms, demographics, stress state, personality, and the eight genetic polymorphisms at baseline were set as variables. We performed univariate analyses (t-tests and chi-square tests). Considering the results of the previous studies and our data of the p-values of t-tests and chi-square tests in the univariate analysis, we began with a fully saturated model and used one approach to produce a model with the optimal balance of explanatory power. Next, we performed a multivariate analysis (logistic regression analysis) using SPSS, version 17.0 (SPSS, Tokyo, Japan). Potential predictors that were defined with a liberal alpha p < 0.10 in t-tests and chi-square tests were entered into the logistic regression analysis; p-values less than 0.05 were considered significant. We calculated a response index (RI) using the factors extracted. Integer approximation was conducted for calculating RI using β (regression coefficient) in the logistic regression analysis. We devised the following formula for RI: RI ¼ A1 x1 þ A2 x2 þ e þ An xn where, x1–xn: extracted factors and A1–An: integers determined based on coefficient β. RI value was calculated for each patient, and RIs of each responder were compared with those of each non-responder. Structural equation modeling was performed using the AMOS7.0 SEM software (SPSS, Tokyo, Japan). Next, we confirmed the structural model that comprised the pathway and correlation coefficients connecting the latent and observed variables of the model proper. Hum. Psychopharmacol Clin Exp 2013; 28: 576–585. DOI: 10.1002/hup
579
a 8 week naturalistic sertraline treatment study Table 1.
Demographic, stress state, and background Responders
age education(years) age at onset PSS number of stressful life events
HAM-D17 at baseline gender adverse life event in childhood spouse residential family member somatic complications educational background smoking habits drinking habits psychiatric family history (within first relative) recurrence
≥24
