- Email: [email protected]
Early changes of serum BDNF and SSRI response in adolescents with major depressive disorder
Journal of Affective Disorders 265 (2020) 325–332
Contents lists available at ScienceDirect
Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad
Research paper
Early changes of serum BDNF and SSRI response in adolescents with major depressive disorder
T
Jung Leea, Kyung Hwa Leeb, Seong Hae Kimb, Ji Youn Hanb, Soon-Beom Hongb, Soo-Churl Choc, ⁎ Jae-Won Kimb, , David Brentd a
Integrative Care Hub, Children's Hospital, Seoul National University Hospital, Seoul, South Korea Division of Child and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea c Department of Psychiatry, Korea Armed Forces Capital Hospital, Gyenggi-do, South Korea d Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States b
ARTICLE INFO
ABSTRACT
Keywords: Major depressive disorder Adolescents Brain-derived neurotrophic factor Biomarker Antidepressant response
Background: Recent evidence suggests that brain-derived neurotrophic factor (BDNF) levels and their early changes may predict antidepressant response in adults with major depressive disorder (MDD). However, in adolescents, BDNF levels in depression and their changes during antidepressant treatment are relatively unknown. We aimed to investigate whether pre-treatment BDNF levels and their early changes predict antidepressant response in depressed adolescents. Methods: The study included 83 MDD adolescents and 52 healthy controls aged 12 to 17 years. All depressed adolescents were treated with escitalopram in an 8 week, open-label trial. Depression severity and serum BDNF level at baseline, and weeks 2 and 8 were measured with the Children's Depression Rating Scale-Revised (CDRSR) and ELISA, respectively. Results: Responders showed a significant decrease in BDNF levels at week 2 but non-responders and healthy controls had no changes in BDNF levels at week 2. The early decrease (baseline – week 2) of BDNF levels predicted SSRI response with moderate sensitivity and specificity. Logistic regression analysis revealed that early BDNF decrease predicted SSRI response at week 8 after controlling for other demographic and clinical variables. Limitations: The follow-up duration of the study was limited in 8 weeks. It remains possible that serum BDNF levels would have changed with longer treatment. Conclusions: This is the first longitudinal study to investigate the effect of antidepressants on BDNF levels in adolescents with MDD. Our findings suggest that a decrease of serum BDNF levels in early phase of SSRI treatment may be associated later SSRI response in adolescents with MDD.
1. Introduction Major Depressive disorder (MDD) is a common and impairing mental health problem in adolescents. The lifetime and 12-month prevalence of major depression in adolescence in a recent survey in the United States are 11.0% and 7.5%, respectively (Avenevoli et al., 2015). Depressed adolescents are at high risk for suicidal behaviors and impairments in social and educational functioning (Bridge et al., 2006; Fergusson and Woodward, 2002). Adolescent depression is often a gateway to a chronic or recurrent depressive disorder and heightened risk for suicide and suicidal behavior that persists into adulthood. (Weissman et al., 1999).
There are some challenges with respect to the treatment of depression with antidepressants in adolescence. Even with the combination of medication and cognitive behavioral therapy (CBT), remission rates of adolescents with MDD are only 60% (Kennard et al., 2009). Moreover, it takes at least 6–8 weeks to determine if a given medication will be efficacious. (Birmaher et al., 2007; March et al., 2007; Vitiello et al., 2011). Additionally, the risk of suicidal adverse events on antidepressants has been a particular concern in pediatric population and suicidal events are common in those who do not respond to antidepressant treatment (Bridge et al., 2007; Hammad et al., 2006; Vitiello et al., 2009). In the Treatment of SSRI-Resistant Depression in Adolescents (TORDIA) study, 58.5 percent of adolescents with
⁎ Corresponding author at: Division of Child and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, 103, Daehak-ro, Jongno-gu, Seoul 03080, South Korea. E-mail address: [email protected] (J.-W. Kim).
https://doi.org/10.1016/j.jad.2020.01.045 Received 29 June 2019; Received in revised form 11 December 2019; Accepted 12 January 2020 Available online 13 January 2020 0165-0327/ © 2020 Elsevier B.V. All rights reserved.
Journal of Affective Disorders 265 (2020) 325–332
J. Lee, et al.
with primary diagnosis of MDD for at least 4 weeks with a score of at least 40 on the Children's Depression Rating Scale-Revised (CDRS-R) (Goo et al., 2013; Kim et al., 2018; Poznanski and Mokros, 1996), and Clinical Global Impressions-Severity (CGI-S) (Guy, 1976) ≥ 4 at baseline were enrolled. MDD was diagnosed according to DSM-5 criteria (American Psychiatric Association, 2013) using the Kiddie-Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL) (Kaufman et al., 2016, 1997; Kim et al., 2004). Depressed participants were recruited from an outpatient psychiatric clinic of the Seoul National University Hospital. Individuals with psychotic features or first-degree relatives with a history of bipolar I disorder were excluded. Fifty-seven adolescents (14.3 ± 1.4 years, 33 girls, ages 12–17) without history of any psychiatric illness were enrolled as healthy controls, who were recruited from local schools via flyers. Control participants were excluded if their first-degree relatives had a history of any psychiatric disorders. Exclusion criteria for both groups included the intelligence quotient (IQ) lower than 70, alcohol or substance abuse within the past 6 months, a history of schizophrenia, bipolar disorder, eating disorder, or autism, concurrent medications with psychotropic effects (other than stimulants for ADHD), chronic medical conditions (e.g., asthma, inflammatory bowel disease, diabetes) and/or prolonged medication with psychotropic effects (e.g., anticonvulsants). The present study included 83 depressed adolescents and 52 healthy controls of these participants, who had analyzable data for serum BDNF levels at baseline and week 2 (for participant flow chart, see Figure S1).
treatment-resistant depression had clinically significant levels of suicidal ideation at baseline and suicidal adverse event during the study was associated with a poorer response to treatment (Brent et al., 2008, 2009). Current predictors of non-response and/or suicidal events – clinical severity, comorbidity, alcohol/substance use, anhedonia, subsyndromal manic symptoms and family conflict – are non-specific and do not provide guidance on optimal pharmacotherapy (Asarnow et al., 2009; Curry et al., 2006; Maalouf et al., 2012; McMakin et al., 2012; Wilkinson et al., 2011). Thus, the most effective antidepressant medication for each patient can only be identified through trial and error, which may result in a prolonged sequence of several trials. Therefore, biomarkers predicting a priori whether an individual patient will respond to medication or allowing early distinction of responders and non-responders during pharmacotherapy could help to greatly improve the risk-benefit ratio and speed up the process of matching patient to an effective treatment. There is some evidence in adult depression that changes in gene expression and serum levels of neurotrophic markers may predict treatment response (Frodl, 2017). Brain-derived neurotrophic factor (BDNF) is the most studied neurotrophin, which regulates neuronal survival, synaptic signaling, and synaptic consolidation. The neurotrophin hypothesis of depression postulates that depression results from stress-induced decreases in central BDNF expression and that antidepressants are efficacious via the restoration and upregulation of central BDNF expression (Duman and Monteggia, 2006; Levy et al., 2018). In support of the neurotrophin hypothesis, several meta-analyses have found that hippocampal and serum BDNF levels are low in unmedicated depressed patients, and that these levels increase with antidepressant treatment (Brunoni et al., 2008; Kishi et al., 2017; Molendijk et al., 2014; Polyakova et al., 2015; Sen et al., 2008). Some, but not all, studies have shown that the early increase of serum or plasma level of BDNF after 1 or 2 weeks of antidepressant intervention may predict the antidepressant response (Dreimuller et al., 2012; Mikoteit et al., 2014; Tadic et al., 2011). Yet, relatively few studies have examined the neurotrophin profile in pediatric depression and reported inconsistent results. Sasaki et al. (2011) observed low serum BDNF levels in depressed boys, but not in depressed girls compared to healthy controls. Tsuchimine et al. (2015) also found that serum BDNF levels did not differ between adolescent girls with depression and healthy controls. However, Pallavi et al. (2013) observed low serum BDNF levels in depressed adolescents compared to healthy controls, regardless of gender. All above three studies in pediatric population were crosssectional and two of them included medicated patients. Furthermore, no longitudinal study investigated the change of BDNF levels in adolescents during antidepressant treatment. Therefore, we performed this study to explore the above-noted unresolved questions about depression and antidepressant effects on serum BDNF levels in adolescents. The aims of the study were to examine whether serum BDNF levels are altered in adolescents with MDD, to assess changes in serum BDNF levels during the course of SSRI treatment of depressed adolescents, and to explore whether pre-treatment serum BDNF levels and their early changes predict SSRI response in adolescents with MDD. Based on the literature, we established our hypotheses as follows: (1) pre-treatment serum BDNF levels in adolescents with MDD would be lower than those in healthy controls; (2) serum BDNF levels would increase in parallel with improvement of depressive symptoms, that is, patients who respond to SSRI would show more increase in serum BDNF levels at the end of study than patients with SSRI resistant depression; and (3) either baseline serum BDNF levels or their early changes or both would predict therapeutic outcome.
2.2. Procedures The present study was approved by the Institutional Review Board for Human Subjects at the Seoul National University Hospital. Detailed information about the study was provided to the parents and adolescents, and written informed consent was obtained before study entry. All depressed participants were treated with escitalopram in an 8 week, open-label trial. Escitalopram was initiated at 5 mg/day for 1 week, followed by an increase to 10 mg at week 2. Thereafter the dose could be flexibly titrated upward to a maximum dose of 30 mg/day for a satisfactory clinical response. Concomitant psychotropic medication was not permitted other than stable doses of stimulants for ADHD during the trial. Although we permitted stimulant use for ADHD, there was no one among the participants who used stimulants during the 8week trial. Specific, indicated psychotherapy for depression (e.g., CBT or interpersonal therapy) was not allowed during the study. Patients could be prematurely terminated from the study at an investigator (J.W.K.)’s discretion for reasons such as adverse events and/or failure to improve despite increases in the study drug dose. Medication compliance was monitored by pill counts (Woldu et al., 2011). Subjects were discontinued if they were non-compliant (<60% of pills taken) on 2 consecutive visits. 2.3. Behavioral ratings and schedule of assessments All participants were assessed at baseline using the Children's Depression Rating Scale–Revised (CDRS-R), Columbia Suicide Severity Rating Scale (C-SSRS) (Pai et al., 2015; Posner et al., 2011), Screening for Childhood Anxiety Related Emotional Disorders (SCARED) (Birmaher et al., 1997), ADHD Rating Scale-IV (ADHD-RS) (DuPaul et al., 1998; So et al., 2002), and Disruptive Behavior Disorder Scale (DBDS) (Silva et al., 2005). History of childhood adversity and family environment were measured with Early Trauma Inventory (ETI) (Bremner et al., 2007; Jeon et al., 2012) and Family Adaptability and Cohesion Evaluation Scales-IV (FACES-IV) (Olson and Gorall, 2006) respectively . Depressed participants were followed up at weeks 1, 2, 4, 6 and 8 during treatment. Depression severity and suicidal ideation was assessed using CDRS-R, CGI-S and C-SSRS at each visit. The outcome
2. Methods 2.1. Participants Ninety-five adolescents (14.8 ± 1.6 years, 65 girls, ages 12–17) 326
Journal of Affective Disorders 265 (2020) 325–332
J. Lee, et al.
measure in this study was the change from baseline in CDRS-R at week 8 or upon termination. Patients who had at least a 40% decrease in the adjusted CDRS-R total score (CDRS-R total minus 17, the minimum possible total score) were defined as responders; others were non-responders (Wagner et al., 2003). Adverse events were spontaneously reported by patients or observed by investigators using the Side Effects for Children and Adolescents (SEFCA) (Klein, 1998). Healthy control participants’ depression severity was also assessed again at weeks 2 and 8 of the study using the CDRS-R and CGI-S.
the simple linear mixed model (p < 0.001 from the likelihood ratio test between models). Group, time (week), time by group interaction, spline transformation, spline by group interaction and the covariates (including age, gender, and BMI) were kept in the model as fixed effects, while intercept and slope was set as random effects. The spline transformation was
spline =
0 week
2
ifweek ifweek > 2.
2,
We performed ROC analysis to calculate the area under the curve (AUC) and to determine the most appropriate cut-off for the predictive value of the early BDNF changes between baseline – week 2 on treatment response at week 8. We also conducted multivariable logistic regression analysis to assess the relationship between early BDNF change and treatment response adjusting for age, gender, BMI, and potential predictors of treatment response including clinical characteristics with between-group significant difference. Early BDNF change was categorized as a binary variable using the cut-off value from ROC analysis and all continuous variables except time were standardized with respect to their means and standard deviations to facilitate interpretation of odds ratios (ORs) from the logistic regression model. All analyses were performed using R, version 3.5.0 (R Core Team, 2018). We used the R package lme4 (Bates et al., 2007) to fit the piecewise linear mixed model. We then performed contrasts over the fixed effects coefficients to test the significance of change within groups using the esticon procedure in the R package doBy (Højsgaard, 2012). The ROC analyses were done using the R package pROC (Robin et al., 2011).
2.4. Blood sample collection and serum BDNF assay methods At baseline and weeks 2 and 8, all participants underwent venipuncture to obtain blood for BDNF assays. Blood was drawn in the morning (9 am – 12 pm) following an overnight fast and collected into serum separator tubes. After sitting at room temperature for 30 min to allow clotting, blood was centrifuged at 1000 × g for 15 min, and serum was separated and stored at −70 °C until assay. Serum was assayed for BDNF using a commercial BDNF ELISA assay kit (Quantikine, R&D Systems, Minneapolis, MN, USA) by a technician blind to the clinical situation. In brief, the samples and standards were added to each well of BDNF microplate which was coated with a mouse monoclonal antibody against BDNF and the plate was incubated for 2 h at room temperature. BDNF conjugate, a mouse monoclonal antibody against BDNF conjugated to horseradish peroxidase (concentration provided by the manufacturer), was added and the plate was incubated for 1 h at room temperature. After washing the plate three times, the mixture of color reagents (concentration provided by the manufacturer) was added to each well and the plate was incubated in the dark for 30 min. The reaction was stopped with the addition of 2 N sulfuric acid to each well. The absorbance was read on a plate reader at 450 nm wavelengths. The concentrations of the sample in each plate were calculated according to a standard curve. Each assay was performed in duplicate. The intra- and inter-assay coefficients of variation were below 10%. All participants’ baseline, week 2 and 8 samples were run in the same assay batch. The serum probes of each patient were analyzed on one ELISA plate.
3. Results Forty-six (55.4%) of 83 adolescents with MDD were defined as responders. The mean daily dose of escitalopram over the 8-week trial period did not differ between responders and non-responders (14.0 ± 2.5 mg/day vs. 13.5 ± 3.3 mg/day). 3.1. Demographical and clinical characteristics
2.5. Statistical analysis
There were no significant differences between the responders, nonresponders and healthy controls with respect to age, gender, IQ and BMI. In the post hoc analysis, no baseline differences were found between responders and non-responders in CDRS-R scores and other clinical characteristics with the exception of oppositional defiant disorder (ODD) scores on the DBDS. Responders showed significantly lower ODD symptoms than non-responders (p = 0.019). Detailed demographical and clinical characteristics of the entire sample are presented in Table 1.
Demographic and clinical characteristics at baseline were compared between responders, non-responders and healthy controls by using oneway analysis of variance followed by Tukey HSD post hoc tests for continuous variables and Chi-square tests for categorical variables. One-way analysis for covariance was used to compare the serum BDNF levels at baseline controlling for age, gender and body mass index (BMI). The relationships between the changes of BDNF levels and CDRS-R scores in adolescents with MDD were investigated using multivariable linear regression models adjusted for age, gender and BMI. The models were also used to examine the relationships between the changes of BDNF levels and C-SSRS scores. For 71 of the 83 adolescents with MDD, the CDRS-R, C-SSRS scores and serum BDNF levels at week 2 and 8 were available and their data were used in the models. The level of significance after Bonferroni correction for multiple comparison was adjusted to P < 0.004. A piecewise linear mixed model (Long, 2011) was used to examine changes of BDNF levels over 8 weeks in responders, non-responders and healthy controls with a change point at week 2. We chose the model instead of a simple linear mixed model because of following reasons: (1) we needed to assess the early changes (baseline – week 2) of BDNF levels separately because of our special interest in finding early biomarkers for treatment response; (2) some previous studies with serial BDNF measurements reported non-linear changes during antidepressant treatments (Mikoteit et al., 2014; Nase et al., 2016); and (3) the piecewise linear mixed model showed substantially better fit than
3.2. Relationships between BDNF levels and clinical symptoms Serum BDNF levels at baseline did not differ between the responders, non-responders and healthy controls after adjusting the age, gender, and BMI (30.1 ± 10.1 ng/ml, 27.6 ± 9.5 ng/ml, and 27.7 ± 9.0 ng/ml, p = 0.282). In depressed adolescents, serum BDNF levels at baseline were not significantly associated with baseline CDRS-R scores, C-SSRS scores, and changes in those scores at week 2 and week 8 after adjusting for age, gender and BMI. The early decrease of BDNF levels at week 2 was not associated with improvement in CDRS-R scores at week 2 but associated with improvement in CDRS-R scores at week 8 (β = 4.72; 95% CI: 1.72, 7.72, p = 0.003). Similarly, the early decrease of BDNF levels at week 2 was not related with the reduction of C-SSRS scores at week 2 but related with the reduction of C-SSRS scores at week 8 (β = 0.72; 95% CI: 0.26, 1.19, p = 0.003). The changes of BDNF levels at week 8 was not 327
Journal of Affective Disorders 265 (2020) 325–332
J. Lee, et al.
Table 1 Demographic and clinical characteristics at baseline of the responders, non-responders and healthy controls. N (%) or mean ± SD
Age (years) Female BMI (kg/m2) BDNF (ng/ml) Intelligence (IQ) CDRS-R CGI-S C-SSRS SCARED ADHD-RS DBDS: ODD DBDS: CD ETI FACES-IV
Responders (n = 46)
Non-responders (n = 37)
Healthy controls (n = 52)
14.8 ± 1.7 29 (63.0%) 21.4 ± 4.4 30.1 ± 10.1 105.1 ± 13.1 61.0 ± 12.0 4.9 ± 0.4 3.0 ± 1.9 40.4 ± 15.5 9.8 ± 10.0 6.2 ± 5.0 2.2 ± 3.4 5.2 ± 3.5 37.3 ± 15.0
15.1 ± 1.5 26 (70.3%) 22.2 ± 4.5 27.6 ± 9.5 105.7 ± 15.0 57.7 ± 12.2 4.9 ± 0.6 2.3 ± 1.9 35.0 ± 15.3 10.5 ± 9.6 8.9 ± 5.4 1.7 ± 2.4 4.4 ± 2.7 37.9 ± 15.1
14.4 ± 1.4 29 (55.8%) 21.1 ± 2.9 27.7 ± 9.0 108.9 ± 11.0 22.9 ± 4.1 1 ± 0 0.1 ± 0.4 10.5 ± 9.9 3.8 ± 5.3 2.4 ± 3.0 0.5 ± 0.9 1.7 ± 2.6 50.0 ± 13.0
Test
p-value
post hoc
F = 2.17 X2 = 1.95 F = 0.88 F = 1.28* F = 1.21 F = 223.22 F = 8.73 F = 47.82 F = 65.87 F = 9.11 F = 23.55 F = 6.62 F = 17.59 F = 11.71
0.118 0.377 0.416 0.282 0.301 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.002 < 0.001 < 0.001
R = NR R = NR R = NR R = NR R = NR NR > R R = NR R = NR R = NR
> > > > > > > > <
HC HC HC HC HC HC HC HC HC
Adjusted for age, gender and BMI BMI, Body mean index; BDNF, Brain-derived neurotrophic factor; CDRS-R, Children's depression rating scale-revised; CGI-S, Clinical global impression – severity scale; C-SSRS, Columbia suicide severity rating scale; SCARED, Screening of childhood anxiety related emotional disorder; ADHD-RS, Attention deficit hyperactivity disorder rating scale; DBDS, Disruptive behavior disorder rating scale; ODD, Oppositional defiant disorder; CD, conduct disorder; ETI, Early trauma inventory; FACES-IV, Family adaptability and cohesion evaluation scale-IV. ⁎
associated with the changes of CDRS-R and C-SSRS scores at week 8.
3.4. Prediction of treatment response ROC-analysis determined a decrease of 1.23 ng/ml in the BDNF level between baseline and week 2 as an optimal cut-off value for the prediction of SSRI response at week 8. The early BDNF decrease predicted SSRI response with a sensitivity of 67.4% and a specificity of 73.0% (AUC: 72.6%; 95% CI: 61.6%, 83.5%) (Fig. 2). The depressed adolescents with an early BDNF decrease by 1.23 ng/ ml or more had significantly higher odds of SSRI response (odds ratio = 5.96, 95% CI: 1.96, 20.78, p = 0.003) after controlling for other demographic and clinical variables (Table 2).
3.3. Changes of BDNF levels during antidepressant treatment in Responders showed significant decrease in BDNF levels at week 2 (mean estimated change: −3.1 ± 0.8, p = 0.001) but non-responders (mean estimated change: 0.9 ± 0.9, p = 0.3) and healthy controls (mean estimated change: 0.5 ± 0.8, p = 0.5) did not (p < 0.001 for group-time interaction) (Fig. 1; see also Table S1). Serum BDNF levels at week 8 did not differ from the baseline levels significantly within each group.
Fig. 1. Early changes between baseline and week 2 of BDNF levels in adolescents with MDD. Early changes of serum BDNF levels between baseline and week 2 in Non-responders (n = 37) vs. Responders (n = 46) to SSRI treatment for adolescents with MDD, where “response” is defined as a ≥ 40% decreased in CDRS-R scores from baseline to week 8 or upon termination. (a) Mean curves (thick line) and individual curves of serum BDNF levels between baseline and visit 1 in responders and non-responders. In the piecewise linear mixed model, responders showed significant decrease in BDNF levels at week 2 (mean estimated change: −3.1 ± 0.8, p = 0.001) but non-responders did not (mean estimated change: 0.9 ± 0.9, p = 0.3). (b) Boxplot diagram showed the changes of BDNF levels between and week 2 in Non-responder (median = 0.6; range: −20.1, 21.2) and Responder (median = −2.0, range: −18.9, 6.6).
328
Journal of Affective Disorders 265 (2020) 325–332
J. Lee, et al.
Fig. 2. ROC curve of SSRI response and early BDNF change (baseline - week 2). ROC-analysis determined a decreased of 1.23 ng/ml in serum BDNF level at week 2 as an optimal cut-off for the prediction of SSRI response at week 8 with a sensitivity of 67.4% and a specificity of 73.0%.
various classes of antidepressants (Zhou et al., 2017). Lastly, BDNF levels were not only assessed at the final visit to determine treatment response but also in the middle of the treatment, which enables the examination of the differences between early and late effects of SSRI on BDNF levels. Therefore, comparing the results from this study with the findings in depressed adults may shed more light on developmental perspectives in the association between BDNF, depression and antidepressant treatment. Contrary to our hypothesis based on the findings in adult studies, the pre-treatment BDNF levels in depressed adolescents were not lower than healthy controls and the levels were found to be higher in depressed adolescents who responded to SSRI than healthy controls although not reaching statistical significance (see Table 1). Ever since Karege and colleagues (2002a) demonstrated lower serum BDNF levels in depressed adults, the relationship between decreased peripheral BDNF levels and pathogenesis of MDD remained quite consistent in several following studies with adult population (Brunoni et al., 2008; Molendijk et al., 2014; Polyakova et al., 2015; Sen et al., 2008). This discrepancy between our study and prior adult studies might be accounted for by the developmental differences of BDNF expression between adolescents and adults. Age-dependent reduction of BDNF expression from adolescence to adulthood was found in several animal studies, as well as in one human postmortem brain study (Bath et al., 2013; Oh et al., 2016). Also, stress effects on BDNF expression in adolescence were found to differ from those in adulthood in rodents (Bath et al., 2013). For instance, BDNF expression in the hippocampus of rats was elevated following social defeat in adolescence, but not in adulthood (Coppens et al., 2011). Early postnatal maternal separation stress led to a transient increase in BDNF expression of the cerebral cortex during preadolescence and adolescence, followed by reduced BDNF levels in adulthood (Lee et al., 2012). Therefore, our findings with depressed adolescents could be in line with these studies, suggesting the different features of BDNF expression between adolescence and adulthood that could lead to differences in the relationship between depression and BDNF levels. With respect to antidepressant effects on peripheral BDNF levels in depressed adolescents, although there were no significant overall changes of BDNF levels from baseline to 8-week SSRI treatment, the patients who responded to SSRI demonstrated greater reduction of
Table 2 Results of the multivariate logistic regression model predicting SSRI response at week 8. OR
Early BDNF decrease at week 2 (≥ 1.23 ng/ ml) Age* Gender (Male) BMI* Intelligence (IQ)* CDRS-R scores at baseline* DBDS ODD scores at baseline* BDNF levels at baseline*
95% CI for OR
p-value
Lower
Upper
5.96
1.96
20.78
0.003
0.86 1.37 0.82 0.96 1.31 0.46 0.95
0.48 0.46 0.45 0.56 0.75 0.25 0.53
1.53 4.24 1.42 1.64 2.36 0.08 1.69
0.611 0.574 0.479 0.884 0.349 0.008 0.857
OR per 1 SD increase BMI, Body mean index; BDNF, Brain-derived neurotrophic factor; CDRS-R, Children's depression rating scale-revised; DBDS, Disruptive behavior disorder rating scale; ODD, Oppositional defiant disorder. ⁎
4. Discussion To our knowledge, this is the first longitudinal study to investigate the effect of antidepressants on BDNF levels and the predictive value of early BDNF changes for antidepressant response in adolescents with MDD. In depressed adolescents, the early BDNF decrease (baseline – week 2) predicted SSRI response at week 8. Pre-treatment BDNF levels were not different between depressed adolescents and controls and were not associated with SSRI response in adolescents with MDD. In both responders and non-responders, the BDNF levels at week 8 did not differ from pre-treatment levels. The present study has several advantages in examining the relevance of BDNF to depression and antidepressant treatments in adolescent population. First of all, this study included 83 medication free/naïve depressed adolescents with MDD, which the number of subjects is larger than previous studies assessing BDNF levels in depressed youth and comparable with the studies with adult population (Pallavi et al., 2013; Sasaki et al., 2011; Tsuchimine et al., 2015; Zhou et al., 2017). Second, all patients were treated with a uniform antidepressant, escitalopram, minimizing the heterogeneity of potential differential effects on BDNF levels between 329
Journal of Affective Disorders 265 (2020) 325–332
J. Lee, et al.
BDNF levels at week 2 as compared with patients who did not respond to SSRI. Furthermore, the early decrease of BDNF levels predicted SSRI response with moderate sensitivity and specificity. These findings are in opposition to the neurotrophin hypothesis of depression which suggests that a deficiency in neurotrophic factor synthesis and signaling could underlie depression and that antidepressant drugs would act by increasing the levels of these neurotrophic factors (Duman and Monteggia, 2006; Huang and Reichardt, 2001). However, the focus on the physiological role of BDNF has shifted recently to a critical mediator of activity-dependent neuronal plasticity in the developing and adult central nervous system via balancing between anabolic (BDNF-TrkB signaling) and catabolic (BDNF-p75 signaling) effects on plasticity (Deppmann et al., 2008; Lu et al., 2014). According to this complex scenario for BDNF-driven neuronal plasticity, BDNF has apparently opposite effects on the plasticity in the visual cortex during the critical developing period and in adulthood, which has been extensively studied and characterized by the monocular deprivation technique in animal models (Berardi et al., 2003; Hensch, 2005; Vetencourt et al., 2008; Wiesel and Hubel, 1965). As for affective brain network, adolescence is a critical period in development characterized by significant changes in affective regulation and its underlying hormonal and neural correlates including the hypothalamic-pituitary-adrenal axis, amygdala, and prefrontal cortex (Tottenham and Galvan, 2016). Effects of antidepressants on BDNF levels related to therapeutic response in depressed adolescents, who are experiencing the critical period with dynamic changes in the affective network, may have different features from those in depressed adults, whose critical periods are already terminated with a relatively fixed network. Since circulating BDNF arises from a number of peripheral tissues, as well as from cerebral sources, it is uncertain what extent peripheral BDNF level reflects cerebral BDNF level (Nockher and Renz, 2005; Sen et al., 2008). There are some evidences supporting that a substantial part of peripheral BDNF might derive from cerebral sources and peripheral BDNF levels could reflect BDNF activity in the brain. The evidences include findings that BDNF crosses the blood-brain barrier bidirectionally (Pan et al., 1998; Sartorius et al., 2009) and positive correlations of peripheral BDNF with brain levels as well as brain phenomena found by animal and human studies (Erickson et al., 2010; Hwang et al., 2015; Karege et al., 2002b; Klein et al., 2011; Lang et al., 2007; Pillai et al., 2010; Sartorius et al., 2009). However other studies have reported an absence of correlations between the peripheral and CSF BDNF levels (Chiaretti et al., 2004; Laske et al., 2007). In fact, ninety percent or more of blood BDNF is contained in platelets. Platelets do not synthesize but store BDNF derived from brain or body tissues and they may be actively taking part in the regulation of homeostasis by storing BDNF for later release in case of increased demand (Fujimura et al., 2002; Pliego-Rivero et al., 1997). In brief, serum BDNF levels probably represent an integrative composite from different sources and may also be modulated by platelet function (Gass and Hellweg, 2010; Serra-Millas, 2016). Therefore, the unique features with relationships among serum BDNF levels, depression and antidepressant treatment in adolescents could be associated with developmental differences of peripheral BDNF production or platelet modulation between adolescents and adults. Further preclinical and clinical studies are needed to investigate these issues. There are several limitations to this study. First, the strength of this study that all patients were medicated with a single SSRI can also be considered as a weakness in terms of generalizability across other classes of antidepressants. However, since SSRIs are the only class of antidepressants approved by the FDA for children and adolescents with MDD (Martin et al., 2017), this limitation may not be an important issue in adolescent population. The second limitation is the lack of a double-blind design. While we measured the changes of BDNF levels in healthy controls to rule out natural changes in BDNF levels in adolescence, without a placebo-controlled group, we cannot fully exclude “placebo effects” on BDNF levels. Third, there are several nonspecific
confounders which were not measured in this study but may affect serum BDNF levels, such as platelet number and menstrual cycle (Lommatzsch et al., 2005). Finally, the follow-up duration of the study was limited in 8 weeks. Although serum BDNF levels after 8 weeks of treatment did not differ from baseline levels, it remains possible that serum BDNF levels would have changed with longer treatment. In summary, the results of this study demonstrate that a decrease in serum BDNF levels in the early phase of SSRI treatment may be associated with later treatment response in adolescents with MDD. In the adolescent population, lower serum BDNF levels do not necessarily underlie depression and a rise in serum BDNF levels may not indicate the therapeutic effect of antidepressants. These findings were in the opposite direction of most adult studies, suggesting possibilities of a different underlying pathophysiology of depression and mechanism of action of antidepressants between adolescents and adults. From a clinical perspective, our results provide preliminary findings suggesting that adolescents with MDD may have different features with biomarkers predicting SSRI response from adults with MDD. Further longitudinal studies for pediatric depression with extended treatment duration are warranted to investigate unique features of pathophysiology of pediatric depression and biomarkers for antidepressant response in the pediatric population. Funding This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF2015R1A2A2A01004501) and was supported by Promising-Pioneering Research Program through Seoul National University (SNU) in 2015. Declaration of Competing Interest Drs. Lee, Lee, Hong, Cho, Kim, Ms. Kim, and Ms. Han report no biomedical financial interests or potential conflicts of interest. Dr. Brent receives research support from NIMH, AFSP, the Once Upon a Time Foundation, and the Beckwith Foundation, receives royalties from Guilford Press, from the electronic self-rated version of Title Page including all author information the C-SSRS from eRT, Inc., and from performing duties as an UptoDate Psychiatry Section Editor, and receives consulting fees from Healthwise. Acknowledgments None. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jad.2020.01.045. References American Psychiatric Association, 2013. Diagnostic and statistical manual of mental disorders (5th ed.), Washington, DC: author. Asarnow, J.R., Emslie, G., Clarke, G., Wagner, K.D., Spirito, A., Vitiello, B., Iyengar, S., Shamseddeen, W., Ritz, L., McCracken, J., Strober, M., Suddath, R., Leonard, H., Porta, G., Keller, M., Brent, D., 2009. Treatment of selective serotonin reuptake inhibitor-resistant depression in adolescents: predictors and moderators of treatment response. J. Am. Acad. Child Adolesc. Psychiatry 48, 330–339. Avenevoli, S., Swendsen, J., He, J.P., Burstein, M., Merikangas, K.R., 2015. Major depression in the national comorbidity survey-adolescent supplement: prevalence, correlates, and treatment. J. Am. Acad. Child Adolesc. Psychiatry 54 37-44 e32. Bates, D., Sarkar, D., Bates, M.D., Matrix, L., 2007. The lme4 package. R. Package Version 2, 74. Bath, K.G., Schilit, A., Lee, F.S., 2013. Stress effects on BDNF expression: effects of age, sex, and form of stress. Neuroscience 239, 149–156. Berardi, N., Pizzorusso, T., Ratto, G.M., Maffei, L., 2003. Molecular basis of plasticity in the visual cortex. J. Trend Neurosci. 26, 369–378.
330
Journal of Affective Disorders 265 (2020) 325–332
J. Lee, et al. Birmaher, B., Brent, D., Issues, A.W.G., Bernet, W., Bukstein, O., Walter, H., Benson, R.S., Chrisman, A., Farchione, T., Greenhill, L., Hamilton, J., Keable, H., Kinlan, J., Schoettle, U., Stock, S., Ptakowski, K.K., Medicus, J., 2007. Practice parameter for the assessment and treatment of children and adolescents with depressive disorders. J. Am. Acad. Child Adolesc. Psychiatry 46, 1503–1526. Birmaher, B., Khetarpal, S., Brent, D., Cully, M., Balach, L., Kaufman, J., Neer, S.M., 1997. The screen for child anxiety related emotional disorders (SCARED): scale construction and psychometric characteristics. J. Am. Acad. Child Adolesc. Psychiatry 36, 545–553. Bremner, J.D., Bolus, R., Mayer, E.A., 2007. Psychometric properties of the early trauma inventory-self report. J. Nerv. Ment. Dis. 195, 211–218. Brent, D., Emslie, G., Clarke, G., Wagner, K.D., Asarnow, J.R., Keller, M., Vitiello, B., Ritz, L., Iyengar, S., Abebe, K., Birmaher, B., Ryan, N., Kennard, B., Hughes, C., DeBar, L., McCracken, J., Strober, M., Suddath, R., Spirito, A., Leonard, H., Melhem, N., Porta, G., Onorato, M., Zelazny, J., 2008. Switching to another SSRI or to venlafaxine with or without cognitive behavioral therapy for adolescents with SSRI-resistant depression: the TORDIA randomized controlled trial. JAMA 299, 901–913. Brent, D.A., Emslie, G.J., Clarke, G.N., Asarnow, J., Spirito, A., Ritz, L., Vitiello, B., Iyengar, S., Birmaher, B., Ryan, N.D., Zelazny, J., Onorato, M., Kennard, B., Mayes, T.L., Debar, L.L., McCracken, J.T., Strober, M., Suddath, R., Leonard, H., Porta, G., Keller, M.B., 2009. Predictors of spontaneous and systematically assessed suicidal adverse events in the treatment of SSRI-resistant depression in adolescents (TORDIA) study. Am. J. Psychiatry 166, 418–426. Bridge, J.A., Goldstein, T.R., Brent, D.A., 2006. Adolescent suicide and suicidal behavior. J. Child Psychol. Psychiatry 47, 372–394. Bridge, J.A., Iyengar, S., Salary, C.B., Barbe, R.P., Birmaher, B., Pincus, H.A., Ren, L., Brent, D.A., 2007. Clinical response and risk for reported suicidal ideation and suicide attempts in pediatric antidepressant treatment: a meta-analysis of randomized controlled trials. JAMA 297, 1683–1696. Brunoni, A.R., Lopes, M., Fregni, F., 2008. A systematic review and meta-analysis of clinical studies on major depression and BDNF levels: implications for the role of neuroplasticity in depression. Int. J. Neuropsychopharmacol. 11, 1169–1180. Chiaretti, A., Antonelli, A., Piastra, M., Genovese, O., Polidori, G., Aloe, L., 2004. Expression of neurotrophic factors in cerebrospinal fluid and plasma of children with viral and bacterial meningoencephalitis. Acta Paediatr. 93, 1178–1184. Coppens, C.M., Siripornmongcolchai, T., Wibrand, K., Alme, M.N., Buwalda, B., de Boer, S.F., Koolhaas, J.M., Bramham, C.R., 2011. Social defeat during adolescence and adulthood differentially induce BDNF-regulated immediate early genes. Front. Behav. Neurosci. 5, 72. Curry, J., Rohde, P., Simons, A., Silva, S., Vitiello, B., Kratochvil, C., Reinecke, M., Feeny, N., Wells, K., Pathak, S., Weller, E., Rosenberg, D., Kennard, B., Robins, M., Ginsburg, G., March, J., Team, T., 2006. Predictors and moderators of acute outcome in the treatment for adolescents with depression study (TADS). J. Am. Acad. Child Adolesc. Psychiatry 45, 1427–1439. Deppmann, C.D., Mihalas, S., Sharma, N., Lonze, B.E., Niebur, E., Ginty, D.D., 2008. A model for neuronal competition during development. Science 320, 369–373. Dreimuller, N., Schlicht, K.F., Wagner, S., Peetz, D., Borysenko, L., Hiemke, C., Lieb, K., Tadic, A., 2012. Early reactions of brain-derived neurotrophic factor in plasma (pBDNF) and outcome to acute antidepressant treatment in patients with major depression. Neuropharmacology 62, 264–269. Duman, R.S., Monteggia, L.M., 2006. A neurotrophic model for stress-related mood disorders. Biol. Psychiatry 59, 1116–1127. DuPaul, G.J., Power, T.J., Anastopoulos, A.D., Reid, R., 1998. ADHD Rating Scale—IV: Checklists, norms, and Clinical Interpretation. Guilford Press. Erickson, K.I., Prakash, R.S., Voss, M.W., Chaddock, L., Heo, S., McLaren, M., Pence, B.D., Martin, S.A., Vieira, V.J., Woods, J.A., McAuley, E., Kramer, A.F., 2010. Brain-derived neurotrophic factor is associated with age-related decline in hippocampal volume. J. Neurosci. 30, 5368–5375. Fergusson, D.M., Woodward, L.J., 2002. Mental health, educational, and social role outcomes of adolescents with depression. Arch. Gen. Psychiatry 59, 225–231. Frodl, T., 2017. Recent advances in predicting responses to antidepressant treatment. F1000Res 6. Fujimura, H., Altar, C.A., Chen, R., Nakamura, T., Nakahashi, T., Kambayashi, J., Sun, B., Tandon, N.N., 2002. Brain-derived neurotrophic factor is stored in human platelets and released by agonist stimulation. Thromb. Haemost. 87, 728–734. Gass, P., Hellweg, R., 2010. Peripheral brain-derived neurotrophic factor (BDNF) as a biomarker for affective disorders? Int. J. Neuropsychopharmacol. 13, 1–4. Goo, A.-K., Won, E.-K., Lee, C., Lee, J.-I., Jung, H.-Y., Y, K., 2013. Long-term tolerability of escitalopram in korean adolescents. J. Korean Acad. Child Adolesc. Psychiatry 24, 207–212. Guy, W., 1976. The clinician global severity and impression scales, ECDEU assessment manual for psychopharmacology. National Institute of Mental Health, Rockville, MD, 218–222. Hammad, T.A., Laughren, T., Racoosin, J., 2006. Suicidality in pediatric patients treated with antidepressant drugs. Arch. Gen. Psychiatry 63, 332–339. Hensch, T.K., 2005. Critical period plasticity in local cortical circuits. J. Nature Rev. Neurosci. 6, 877. Huang, E.J., Reichardt, L.F., 2001. Neurotrophins: roles in neuronal development and function. Annu. Rev. Neurosci. 24, 677–736. Højsgaard, S., 2012. The doBy package. R package version 4. Hwang, K.S., Lazaris, A.S., Eastman, J.A., Teng, E., Thompson, P.M., Gylys, K.H., Cole, G.M., Apostolova, L.G., Alzheimer’s Disease Neuroimaging, I., 2015. Plasma BDNF levels associate with Pittsburgh compound B binding in the brain. Alzheimers Dement (Amst) 1, 187–193. Jeon, J.R., Lee, E.H., Lee, S.W., Jeong, E.G., Kim, J.H., Lee, D., Jeon, H.J., 2012. The early trauma inventory self report-short form: psychometric properties of the korean
version. Psychiatry Investig. 9, 229–235. Karege, F., Perret, G., Bondolfi, G., Schwald, M., Bertschy, G., Aubry, J.-M.J.P.r., 2002a. Decreased serum brain-derived neurotrophic factor levels in major depressed patients. 109, 143–148. Karege, F., Schwald, M., Cisse, M., 2002b. Postnatal developmental profile of brain-derived neurotrophic factor in rat brain and platelets. Neurosci. Lett. 328, 261–264. Kaufman, J., Birmaher, B., Axelson, D., Perepletchikova, F., Brent, D., Ryan, N., 2016. Schedule for Affective Disorders and Schizophrenia for School Aged Children: Present and Lifetime Version for DSM-5 (K-SADS-PL DSM-5). Western Psychiatric Institute and Yale University. Kaufman, J., Birmaher, B., Brent, D., Rao, U., Flynn, C., Moreci, P., Williamson, D., Ryan, N., 1997. Schedule for affective disorders and schizophrenia for school-age childrenpresent and lifetime version (K-SADS-PL): initial reliability and validity data. J. Am. Acad. Child Adolesc. Psychiatry 36, 980–988. Kennard, B.D., Silva, S.G., Tonev, S., Rohde, P., Hughes, J.L., Vitiello, B., Kratochvil, C.J., Curry, J.F., Emslie, G.J., Reinecke, M., March, J., 2009. Remission and recovery in the treatment for adolescents with depression study (TADS): acute and long-term outcomes. J. Am. Acad. Child Adolesc. Psychiatry 48, 186–195. Kim, K.M., Nam, S., Choi, J.W., Jung, A.H., Hong, S.B., Kim, J.W., Kim, S.Y., Kim, E., Kim, J.W., 2018. Psychometric properties and factor structures of the Korean version of children’s depression rating scale-revised. J. Child Adolesc. Psychopharmacol. 28, 285–292. Kim, Y.S., Cheon, K.A., Kim, B.N., Chang, S.A., Yoo, H.J., Kim, J.W., Cho, S.C., Seo, D.H., Bae, M.O., So, Y.K., Noh, J.S., Koh, Y.J., McBurnett, K., Leventhal, B., 2004. The reliability and validity of kiddie-schedule for affective disorders and schizophreniapresent and lifetime Version- Korean version (K-SADS-PL-K). Yonsei Med. J. 45, 81–89. Kishi, T., Yoshimura, R., Ikuta, T., Iwata, N., 2017. Brain-derived neurotrophic factor and major depressive disorder: evidence from meta-analyses. Front. Psychiatry 8, 308. Klein, A.B., Williamson, R., Santini, M.A., Clemmensen, C., Ettrup, A., Rios, M., Knudsen, G.M., Aznar, S., 2011. Blood BDNF concentrations reflect brain-tissue bdnf levels across species. Int. J. Neuropsychopharmacol. 14, 347–353. Klein, R.G., 1998. Side Effects Form for Children and Adolescents. New York State Psychiatric Institute, New York. Lang, U.E., Hellweg, R., Seifert, F., Schubert, F., Gallinat, J., 2007. Correlation between serum brain-derived neurotrophic factor level and an in vivo marker of cortical integrity. Biol. Psychiatry 62, 530–535. Laske, C., Stransky, E., Leyhe, T., Eschweiler, G.W., Maetzler, W., Wittorf, A., Soekadar, S., Richartz, E., Koehler, N., Bartels, M., Buchkremer, G., Schott, K., 2007. BDNF serum and CSF concentrations in alzheimer’s disease, normal pressure hydrocephalus and healthy controls. J. Psychiatr. Res. 41, 387–394. Lee, K.Y., Miki, T., Yokoyama, T., Ueki, M., Warita, K., Suzuki, S., Ohta, K., Wang, Z.Y., Jamal, M., Yakura, T., Liu, J.Q., Hosomi, N., Takeuchi, Y., 2012. Neonatal repetitive maternal separation causes long-lasting alterations in various neurotrophic factor expression in the cerebral cortex of rats. Life Sci. 90, 578–584. Levy, M.J.F., Boulle, F., Steinbusch, H.W., van den Hove, D.L.A., Kenis, G., Lanfumey, L., 2018. Neurotrophic factors and neuroplasticity pathways in the pathophysiology and treatment of depression. Psychopharmacology (Berl) 235, 2195–2220. Lommatzsch, M., Zingler, D., Schuhbaeck, K., Schloetcke, K., Zingler, C., Schuff-Werner, P., Virchow, J.C., 2005. The impact of age, weight and gender on BDNF levels in human platelets and plasma. Neurobiol. Aging 26, 115–123. Long, J.D., 2011. Modeling nonlinear change. Longitudinal Data Analysis for the Behavioral Sciences Using R. SAGE Publications, pp. 604–608. Lu, B., Nagappan, G., Lu, Y., 2014. BDNF and synaptic plasticity, cognitive function, and dysfunction. Handb. Exp. Pharmacol. 220, 223–250. Maalouf, F.T., Porta, G., Vitiello, B., Emslie, G., Mayes, T., Clarke, G., Wagner, K.D., Asarnow, J.R., Spirito, A., Keller, M., Birmaher, B., Ryan, N., Shamseddeen, W., Iyengar, S., Brent, D., 2012. Do sub-syndromal manic symptoms influence outcome in treatment resistant depression in adolescents? A latent class analysis from the Tordia study. J. Affect. Disord. 138, 86–95. March, J.S., Silva, S., Petrycki, S., Curry, J., Wells, K., Fairbank, J., Burns, B., Domino, M., McNulty, S., Vitiello, B., Severe, J., 2007. The treatment for adolescents with depression study (TADS): long-term effectiveness and safety outcomes. Arch. Gen. Psychiatry 64, 1132–1143. Martin, A., Volkmar, F.R., Bloch, M.H., 2017. Lewis's Child and Adolescent Psychiatry: A Comprehensive Textbook. Wolters Kluwer Health. McMakin, D.L., Olino, T.M., Porta, G., Dietz, L.J., Emslie, G., Clarke, G., Wagner, K.D., Asarnow, J.R., Ryan, N.D., Birmaher, B., Shamseddeen, W., Mayes, T., Kennard, B., Spirito, A., Keller, M., Lynch, F.L., Dickerson, J.F., Brent, D.A., 2012. Anhedonia predicts poorer recovery among youth with selective serotonin reuptake inhibitor treatment-resistant depression. J. Am. Acad. Child Adolesc. Psychiatry 51, 404–411. Mikoteit, T., Beck, J., Eckert, A., Hemmeter, U., Brand, S., Bischof, R., Holsboer-Trachsler, E., Delini-Stula, A., 2014. High baseline BDNF serum levels and early psychopathological improvement are predictive of treatment outcome in major depression. Psychopharmacology (Berl) 231, 2955–2965. Molendijk, M.L., Spinhoven, P., Polak, M., Bus, B.A., Penninx, B.W., Elzinga, B.M., 2014. Serum BDNF concentrations as peripheral manifestations of depression: evidence from a systematic review and meta-analyses on 179 associations (N=9484). Mol. Psychiatry 19, 791–800. Nase, S., Kohler, S., Jennebach, J., Eckert, A., Schweinfurth, N., Gallinat, J., Lang, U.E., Kuhn, S., 2016. Role of serum brain derived neurotrophic factor and central NAcetylaspartate for clinical response under antidepressive pharmacotherapy. Neurosignals 24, 1–14. Nockher, W.A., Renz, H., 2005. Neurotrophins in clinical diagnostics: pathophysiology and laboratory investigation. Clin. Chim. Acta 352, 49–74. Oh, H., Lewis, D.A., Sibille, E., 2016. The role of BDNF in age-dependent changes of
331
Journal of Affective Disorders 265 (2020) 325–332
J. Lee, et al. excitatory and inhibitory synaptic markers in the human prefrontal cortex. Neuropsychopharmacology 41, 3080–3091. Olson, D.H., Gorall, D.M., 2006. Faces IV and the Circumplex model. Life Innovations Minneapolis, MN. Pai, D., Woo, J.M., Son, M.H., Lee, C., 2015. The reliability and validity of the Korean version of Columbia-suicide severity rating scale in alcohol dependent patients. J. Korean Neuropsychiatr. Assoc. 54, 222–227. Pallavi, P., Sagar, R., Mehta, M., Sharma, S., Subramanium, A., Shamshi, F., Sengupta, U., Qadri, R., Pandey, R.M., Mukhopadhyay, A.K., 2013. Serum neurotrophic factors in adolescent depression: gender difference and correlation with clinical severity. J. Affect. Disord. 150, 415–423. Pan, W., Banks, W.A., Fasold, M.B., Bluth, J., Kastin, A.J., 1998. Transport of brain-derived neurotrophic factor across the blood-brain barrier. Neuropharmacology 37, 1553–1561. Pillai, A., Kale, A., Joshi, S., Naphade, N., Raju, M.S., Nasrallah, H., Mahadik, S.P., 2010. Decreased BDNF levels in CSF of drug-naive first-episode psychotic subjects: correlation with plasma BDNF and psychopathology. Int. J. Neuropsychopharmacol. 13, 535–539. Pliego-Rivero, F.B., Bayatti, N., Giannakoulopoulos, X., Glover, V., Bradford, H.F., Stern, G., Sandler, M., 1997. Brain-derived neurotrophic factor in human platelets. Biochem. Pharmacol. 54, 207–209. Polyakova, M., Stuke, K., Schuemberg, K., Mueller, K., Schoenknecht, P., Schroeter, M.L., 2015. BDNF as a biomarker for successful treatment of mood disorders: a systematic & quantitative meta-analysis. J. Affect. Disord. 174, 432–440. Posner, K., Brown, G.K., Stanley, B., Brent, D.A., Yershova, K.V., Oquendo, M.A., Currier, G.W., Melvin, G.A., Greenhill, L., Shen, S., Mann, J.J., 2011. The Columbia-suicide severity rating scale: initial validity and internal consistency findings from three multisite studies with adolescents and adults. Am. J. Psychiatry 168, 1266–1277. Poznanski, E.O., Mokros, H.B., 1996. Manual: Children's Depression Rating Scale Revised. Western Psychological Services, Los Angeles. R Core Team, 2018. R: A language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.C., Muller, M., 2011. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 12, 77. Sartorius, A., Hellweg, R., Litzke, J., Vogt, M., Dormann, C., Vollmayr, B., Danker-Hopfe, H., Gass, P., 2009. Correlations and discrepancies between serum and brain tissue levels of neurotrophins after electroconvulsive treatment in rats. Pharmacopsychiatry 42, 270–276. Sasaki, T., Niitsu, T., Hashimoto, T., Kanahara, N., Shiina, A., Hasegawa, T., Kimura, H., Ishikawa, M., Tone, J., Yamauchi, A., Hosoda, Y., Kunou, M., Takahashi, J., Ishima, T., Fujita, Y., Nakazato, M., Hashimoto, K., Iyo, M., 2011. Decreased levels of serum brain-derived neurotrophic factor in male pediatric patients with depression. The Open Clin. Chem. J. 4, 28–33. Sen, S., Duman, R., Sanacora, G., 2008. Serum brain-derived neurotrophic factor, depression, and antidepressant medications: meta-analyses and implications. Biol. Psychiatry 64, 527–532. Serra-Millas, M., 2016. Are the changes in the peripheral brain-derived neurotrophic factor levels due to platelet activation? World J. Psychiatry 6, 84–101. Silva, R.R., Alpert, M., Pouget, E., Silva, V., Trosper, S., Reyes, K., Dummit, S., 2005. A
rating scale for disruptive behavior disorders, based on the DSM-IV item pool. Psychiatr. Q. 76, 327–339. So, Y.K., Noh, J.S., Kim, Y.S., Ko, S.G., Koh, Y.J., 2002. The reliability and validity of Korean parent and teacher ADHD rating scale. J. Korean Neuropsychiatr. Assoc. 41, 283–289. Tadic, A., Wagner, S., Schlicht, K.F., Peetz, D., Borysenko, L., Dreimuller, N., Hiemke, C., Lieb, K., 2011. The early non-increase of serum BDNF predicts failure of antidepressant treatment in patients with major depression: a pilot study. Prog. Neuropsychopharmacol. Biol. Psychiatry 35, 415–420. Tottenham, N., Galvan, A., 2016. Stress and the adolescent brain: amygdala-prefrontal cortex circuitry and ventral striatum as developmental targets. Neurosci. Biobehav. Rev. 70, 217–227. Tsuchimine, S., Saito, M., Kaneko, S., Yasui-Furukori, N., 2015. Decreased serum levels of polyunsaturated fatty acids and folate, but not brain-derived neurotrophic factor, in childhood and adolescent females with depression. Psychiatry Res. 225, 187–190. Vetencourt, J.F.M., Sale, A., Viegi, A., Baroncelli, L., De Pasquale, R., O’leary, O.F., Castrén, E., Maffei, L.J.S., 2008. The antidepressant fluoxetine restores plasticity in the adult visual cortex. Science 320, 385–388. Vitiello, B., Emslie, G., Clarke, G., Wagner, K.D., Asarnow, J.R., Keller, M.B., Birmaher, B., Ryan, N.D., Kennard, B., Mayes, T.L., DeBar, L., Lynch, F., Dickerson, J., Strober, M., Suddath, R., McCracken, J.T., Spirito, A., Onorato, M., Zelazny, J., Porta, G., Iyengar, S., Brent, D.A., 2011. Long-term outcome of adolescent depression initially resistant to selective serotonin reuptake inhibitor treatment: a follow-up study of the Tordia sample. J. Clin. Psychiatry 72, 388–396. Vitiello, B., Silva, S.G., Rohde, P., Kratochvil, C.J., Kennard, B.D., Reinecke, M.A., Mayes, T.L., Posner, K., May, D.E., March, J.S., 2009. Suicidal events in the treatment for adolescents with depression study (TADS). J. Clin. Psychiatry 70, 741–747. Wagner, K.D., Ambrosini, P., Rynn, M., Wohlberg, C., Yang, R., Greenbaum, M.S., Childress, A., Donnelly, C., Deas, D., Sertraline Pediatric Depression Study, G., 2003. Efficacy of sertraline in the treatment of children and adolescents with major depressive disorder: two randomized controlled trials. JAMA 290, 1033–1041. Weissman, M.M., Wolk, S., Goldstein, R.B., Moreau, D., Adams, P., Greenwald, S., Klier, C.M., Ryan, N.D., Dahl, R.E., Wickramaratne, P., 1999. Depressed adolescents grown up. JAMA 281, 1707–1713. Wiesel, T.N., Hubel, D.H., 1965. Comparison of the effects of unilateral and bilateral eye closure on cortical unit responses in kittens. J. Neurophysiol. 28, 1029–1040. Wilkinson, P., Kelvin, R., Roberts, C., Dubicka, B., Goodyer, I., 2011. Clinical and psychosocial predictors of suicide attempts and nonsuicidal self-injury in the adolescent depression antidepressants and psychotherapy trial (ADAPT). Am. J. Psychiatry 168, 495–501. Woldu, H., Porta, G., Goldstein, T., Sakolsky, D., Perel, J., Emslie, G., Mayes, T., Clarke, G., Ryan, N.D., Birmaher, B., Wagner, K.D., Asarnow, J.R., Keller, M.B., Brent, D., 2011. Pharmacokinetically and clinician-determined adherence to an antidepressant regimen and clinical outcome in the Tordia trial. J. Am. Acad. Child Adolesc. Psychiatry 50, 490–498. Zhou, C., Zhong, J., Zou, B., Fang, L., Chen, J., Deng, X., Zhang, L., Zhao, X., Qu, Z., Lei, Y., Lei, T., 2017. Meta-analyses of comparative efficacy of antidepressant medications on peripheral BDNF concentration in patients with depression. PLoS ONE 12, e0172270.
332
Contents lists available at ScienceDirect
Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad
Research paper
Early changes of serum BDNF and SSRI response in adolescents with major depressive disorder
T
Jung Leea, Kyung Hwa Leeb, Seong Hae Kimb, Ji Youn Hanb, Soon-Beom Hongb, Soo-Churl Choc, ⁎ Jae-Won Kimb, , David Brentd a
Integrative Care Hub, Children's Hospital, Seoul National University Hospital, Seoul, South Korea Division of Child and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea c Department of Psychiatry, Korea Armed Forces Capital Hospital, Gyenggi-do, South Korea d Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States b
ARTICLE INFO
ABSTRACT
Keywords: Major depressive disorder Adolescents Brain-derived neurotrophic factor Biomarker Antidepressant response
Background: Recent evidence suggests that brain-derived neurotrophic factor (BDNF) levels and their early changes may predict antidepressant response in adults with major depressive disorder (MDD). However, in adolescents, BDNF levels in depression and their changes during antidepressant treatment are relatively unknown. We aimed to investigate whether pre-treatment BDNF levels and their early changes predict antidepressant response in depressed adolescents. Methods: The study included 83 MDD adolescents and 52 healthy controls aged 12 to 17 years. All depressed adolescents were treated with escitalopram in an 8 week, open-label trial. Depression severity and serum BDNF level at baseline, and weeks 2 and 8 were measured with the Children's Depression Rating Scale-Revised (CDRSR) and ELISA, respectively. Results: Responders showed a significant decrease in BDNF levels at week 2 but non-responders and healthy controls had no changes in BDNF levels at week 2. The early decrease (baseline – week 2) of BDNF levels predicted SSRI response with moderate sensitivity and specificity. Logistic regression analysis revealed that early BDNF decrease predicted SSRI response at week 8 after controlling for other demographic and clinical variables. Limitations: The follow-up duration of the study was limited in 8 weeks. It remains possible that serum BDNF levels would have changed with longer treatment. Conclusions: This is the first longitudinal study to investigate the effect of antidepressants on BDNF levels in adolescents with MDD. Our findings suggest that a decrease of serum BDNF levels in early phase of SSRI treatment may be associated later SSRI response in adolescents with MDD.
1. Introduction Major Depressive disorder (MDD) is a common and impairing mental health problem in adolescents. The lifetime and 12-month prevalence of major depression in adolescence in a recent survey in the United States are 11.0% and 7.5%, respectively (Avenevoli et al., 2015). Depressed adolescents are at high risk for suicidal behaviors and impairments in social and educational functioning (Bridge et al., 2006; Fergusson and Woodward, 2002). Adolescent depression is often a gateway to a chronic or recurrent depressive disorder and heightened risk for suicide and suicidal behavior that persists into adulthood. (Weissman et al., 1999).
There are some challenges with respect to the treatment of depression with antidepressants in adolescence. Even with the combination of medication and cognitive behavioral therapy (CBT), remission rates of adolescents with MDD are only 60% (Kennard et al., 2009). Moreover, it takes at least 6–8 weeks to determine if a given medication will be efficacious. (Birmaher et al., 2007; March et al., 2007; Vitiello et al., 2011). Additionally, the risk of suicidal adverse events on antidepressants has been a particular concern in pediatric population and suicidal events are common in those who do not respond to antidepressant treatment (Bridge et al., 2007; Hammad et al., 2006; Vitiello et al., 2009). In the Treatment of SSRI-Resistant Depression in Adolescents (TORDIA) study, 58.5 percent of adolescents with
⁎ Corresponding author at: Division of Child and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, 103, Daehak-ro, Jongno-gu, Seoul 03080, South Korea. E-mail address: [email protected] (J.-W. Kim).
https://doi.org/10.1016/j.jad.2020.01.045 Received 29 June 2019; Received in revised form 11 December 2019; Accepted 12 January 2020 Available online 13 January 2020 0165-0327/ © 2020 Elsevier B.V. All rights reserved.
Journal of Affective Disorders 265 (2020) 325–332
J. Lee, et al.
with primary diagnosis of MDD for at least 4 weeks with a score of at least 40 on the Children's Depression Rating Scale-Revised (CDRS-R) (Goo et al., 2013; Kim et al., 2018; Poznanski and Mokros, 1996), and Clinical Global Impressions-Severity (CGI-S) (Guy, 1976) ≥ 4 at baseline were enrolled. MDD was diagnosed according to DSM-5 criteria (American Psychiatric Association, 2013) using the Kiddie-Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL) (Kaufman et al., 2016, 1997; Kim et al., 2004). Depressed participants were recruited from an outpatient psychiatric clinic of the Seoul National University Hospital. Individuals with psychotic features or first-degree relatives with a history of bipolar I disorder were excluded. Fifty-seven adolescents (14.3 ± 1.4 years, 33 girls, ages 12–17) without history of any psychiatric illness were enrolled as healthy controls, who were recruited from local schools via flyers. Control participants were excluded if their first-degree relatives had a history of any psychiatric disorders. Exclusion criteria for both groups included the intelligence quotient (IQ) lower than 70, alcohol or substance abuse within the past 6 months, a history of schizophrenia, bipolar disorder, eating disorder, or autism, concurrent medications with psychotropic effects (other than stimulants for ADHD), chronic medical conditions (e.g., asthma, inflammatory bowel disease, diabetes) and/or prolonged medication with psychotropic effects (e.g., anticonvulsants). The present study included 83 depressed adolescents and 52 healthy controls of these participants, who had analyzable data for serum BDNF levels at baseline and week 2 (for participant flow chart, see Figure S1).
treatment-resistant depression had clinically significant levels of suicidal ideation at baseline and suicidal adverse event during the study was associated with a poorer response to treatment (Brent et al., 2008, 2009). Current predictors of non-response and/or suicidal events – clinical severity, comorbidity, alcohol/substance use, anhedonia, subsyndromal manic symptoms and family conflict – are non-specific and do not provide guidance on optimal pharmacotherapy (Asarnow et al., 2009; Curry et al., 2006; Maalouf et al., 2012; McMakin et al., 2012; Wilkinson et al., 2011). Thus, the most effective antidepressant medication for each patient can only be identified through trial and error, which may result in a prolonged sequence of several trials. Therefore, biomarkers predicting a priori whether an individual patient will respond to medication or allowing early distinction of responders and non-responders during pharmacotherapy could help to greatly improve the risk-benefit ratio and speed up the process of matching patient to an effective treatment. There is some evidence in adult depression that changes in gene expression and serum levels of neurotrophic markers may predict treatment response (Frodl, 2017). Brain-derived neurotrophic factor (BDNF) is the most studied neurotrophin, which regulates neuronal survival, synaptic signaling, and synaptic consolidation. The neurotrophin hypothesis of depression postulates that depression results from stress-induced decreases in central BDNF expression and that antidepressants are efficacious via the restoration and upregulation of central BDNF expression (Duman and Monteggia, 2006; Levy et al., 2018). In support of the neurotrophin hypothesis, several meta-analyses have found that hippocampal and serum BDNF levels are low in unmedicated depressed patients, and that these levels increase with antidepressant treatment (Brunoni et al., 2008; Kishi et al., 2017; Molendijk et al., 2014; Polyakova et al., 2015; Sen et al., 2008). Some, but not all, studies have shown that the early increase of serum or plasma level of BDNF after 1 or 2 weeks of antidepressant intervention may predict the antidepressant response (Dreimuller et al., 2012; Mikoteit et al., 2014; Tadic et al., 2011). Yet, relatively few studies have examined the neurotrophin profile in pediatric depression and reported inconsistent results. Sasaki et al. (2011) observed low serum BDNF levels in depressed boys, but not in depressed girls compared to healthy controls. Tsuchimine et al. (2015) also found that serum BDNF levels did not differ between adolescent girls with depression and healthy controls. However, Pallavi et al. (2013) observed low serum BDNF levels in depressed adolescents compared to healthy controls, regardless of gender. All above three studies in pediatric population were crosssectional and two of them included medicated patients. Furthermore, no longitudinal study investigated the change of BDNF levels in adolescents during antidepressant treatment. Therefore, we performed this study to explore the above-noted unresolved questions about depression and antidepressant effects on serum BDNF levels in adolescents. The aims of the study were to examine whether serum BDNF levels are altered in adolescents with MDD, to assess changes in serum BDNF levels during the course of SSRI treatment of depressed adolescents, and to explore whether pre-treatment serum BDNF levels and their early changes predict SSRI response in adolescents with MDD. Based on the literature, we established our hypotheses as follows: (1) pre-treatment serum BDNF levels in adolescents with MDD would be lower than those in healthy controls; (2) serum BDNF levels would increase in parallel with improvement of depressive symptoms, that is, patients who respond to SSRI would show more increase in serum BDNF levels at the end of study than patients with SSRI resistant depression; and (3) either baseline serum BDNF levels or their early changes or both would predict therapeutic outcome.
2.2. Procedures The present study was approved by the Institutional Review Board for Human Subjects at the Seoul National University Hospital. Detailed information about the study was provided to the parents and adolescents, and written informed consent was obtained before study entry. All depressed participants were treated with escitalopram in an 8 week, open-label trial. Escitalopram was initiated at 5 mg/day for 1 week, followed by an increase to 10 mg at week 2. Thereafter the dose could be flexibly titrated upward to a maximum dose of 30 mg/day for a satisfactory clinical response. Concomitant psychotropic medication was not permitted other than stable doses of stimulants for ADHD during the trial. Although we permitted stimulant use for ADHD, there was no one among the participants who used stimulants during the 8week trial. Specific, indicated psychotherapy for depression (e.g., CBT or interpersonal therapy) was not allowed during the study. Patients could be prematurely terminated from the study at an investigator (J.W.K.)’s discretion for reasons such as adverse events and/or failure to improve despite increases in the study drug dose. Medication compliance was monitored by pill counts (Woldu et al., 2011). Subjects were discontinued if they were non-compliant (<60% of pills taken) on 2 consecutive visits. 2.3. Behavioral ratings and schedule of assessments All participants were assessed at baseline using the Children's Depression Rating Scale–Revised (CDRS-R), Columbia Suicide Severity Rating Scale (C-SSRS) (Pai et al., 2015; Posner et al., 2011), Screening for Childhood Anxiety Related Emotional Disorders (SCARED) (Birmaher et al., 1997), ADHD Rating Scale-IV (ADHD-RS) (DuPaul et al., 1998; So et al., 2002), and Disruptive Behavior Disorder Scale (DBDS) (Silva et al., 2005). History of childhood adversity and family environment were measured with Early Trauma Inventory (ETI) (Bremner et al., 2007; Jeon et al., 2012) and Family Adaptability and Cohesion Evaluation Scales-IV (FACES-IV) (Olson and Gorall, 2006) respectively . Depressed participants were followed up at weeks 1, 2, 4, 6 and 8 during treatment. Depression severity and suicidal ideation was assessed using CDRS-R, CGI-S and C-SSRS at each visit. The outcome
2. Methods 2.1. Participants Ninety-five adolescents (14.8 ± 1.6 years, 65 girls, ages 12–17) 326
Journal of Affective Disorders 265 (2020) 325–332
J. Lee, et al.
measure in this study was the change from baseline in CDRS-R at week 8 or upon termination. Patients who had at least a 40% decrease in the adjusted CDRS-R total score (CDRS-R total minus 17, the minimum possible total score) were defined as responders; others were non-responders (Wagner et al., 2003). Adverse events were spontaneously reported by patients or observed by investigators using the Side Effects for Children and Adolescents (SEFCA) (Klein, 1998). Healthy control participants’ depression severity was also assessed again at weeks 2 and 8 of the study using the CDRS-R and CGI-S.
the simple linear mixed model (p < 0.001 from the likelihood ratio test between models). Group, time (week), time by group interaction, spline transformation, spline by group interaction and the covariates (including age, gender, and BMI) were kept in the model as fixed effects, while intercept and slope was set as random effects. The spline transformation was
spline =
0 week
2
ifweek ifweek > 2.
2,
We performed ROC analysis to calculate the area under the curve (AUC) and to determine the most appropriate cut-off for the predictive value of the early BDNF changes between baseline – week 2 on treatment response at week 8. We also conducted multivariable logistic regression analysis to assess the relationship between early BDNF change and treatment response adjusting for age, gender, BMI, and potential predictors of treatment response including clinical characteristics with between-group significant difference. Early BDNF change was categorized as a binary variable using the cut-off value from ROC analysis and all continuous variables except time were standardized with respect to their means and standard deviations to facilitate interpretation of odds ratios (ORs) from the logistic regression model. All analyses were performed using R, version 3.5.0 (R Core Team, 2018). We used the R package lme4 (Bates et al., 2007) to fit the piecewise linear mixed model. We then performed contrasts over the fixed effects coefficients to test the significance of change within groups using the esticon procedure in the R package doBy (Højsgaard, 2012). The ROC analyses were done using the R package pROC (Robin et al., 2011).
2.4. Blood sample collection and serum BDNF assay methods At baseline and weeks 2 and 8, all participants underwent venipuncture to obtain blood for BDNF assays. Blood was drawn in the morning (9 am – 12 pm) following an overnight fast and collected into serum separator tubes. After sitting at room temperature for 30 min to allow clotting, blood was centrifuged at 1000 × g for 15 min, and serum was separated and stored at −70 °C until assay. Serum was assayed for BDNF using a commercial BDNF ELISA assay kit (Quantikine, R&D Systems, Minneapolis, MN, USA) by a technician blind to the clinical situation. In brief, the samples and standards were added to each well of BDNF microplate which was coated with a mouse monoclonal antibody against BDNF and the plate was incubated for 2 h at room temperature. BDNF conjugate, a mouse monoclonal antibody against BDNF conjugated to horseradish peroxidase (concentration provided by the manufacturer), was added and the plate was incubated for 1 h at room temperature. After washing the plate three times, the mixture of color reagents (concentration provided by the manufacturer) was added to each well and the plate was incubated in the dark for 30 min. The reaction was stopped with the addition of 2 N sulfuric acid to each well. The absorbance was read on a plate reader at 450 nm wavelengths. The concentrations of the sample in each plate were calculated according to a standard curve. Each assay was performed in duplicate. The intra- and inter-assay coefficients of variation were below 10%. All participants’ baseline, week 2 and 8 samples were run in the same assay batch. The serum probes of each patient were analyzed on one ELISA plate.
3. Results Forty-six (55.4%) of 83 adolescents with MDD were defined as responders. The mean daily dose of escitalopram over the 8-week trial period did not differ between responders and non-responders (14.0 ± 2.5 mg/day vs. 13.5 ± 3.3 mg/day). 3.1. Demographical and clinical characteristics
2.5. Statistical analysis
There were no significant differences between the responders, nonresponders and healthy controls with respect to age, gender, IQ and BMI. In the post hoc analysis, no baseline differences were found between responders and non-responders in CDRS-R scores and other clinical characteristics with the exception of oppositional defiant disorder (ODD) scores on the DBDS. Responders showed significantly lower ODD symptoms than non-responders (p = 0.019). Detailed demographical and clinical characteristics of the entire sample are presented in Table 1.
Demographic and clinical characteristics at baseline were compared between responders, non-responders and healthy controls by using oneway analysis of variance followed by Tukey HSD post hoc tests for continuous variables and Chi-square tests for categorical variables. One-way analysis for covariance was used to compare the serum BDNF levels at baseline controlling for age, gender and body mass index (BMI). The relationships between the changes of BDNF levels and CDRS-R scores in adolescents with MDD were investigated using multivariable linear regression models adjusted for age, gender and BMI. The models were also used to examine the relationships between the changes of BDNF levels and C-SSRS scores. For 71 of the 83 adolescents with MDD, the CDRS-R, C-SSRS scores and serum BDNF levels at week 2 and 8 were available and their data were used in the models. The level of significance after Bonferroni correction for multiple comparison was adjusted to P < 0.004. A piecewise linear mixed model (Long, 2011) was used to examine changes of BDNF levels over 8 weeks in responders, non-responders and healthy controls with a change point at week 2. We chose the model instead of a simple linear mixed model because of following reasons: (1) we needed to assess the early changes (baseline – week 2) of BDNF levels separately because of our special interest in finding early biomarkers for treatment response; (2) some previous studies with serial BDNF measurements reported non-linear changes during antidepressant treatments (Mikoteit et al., 2014; Nase et al., 2016); and (3) the piecewise linear mixed model showed substantially better fit than
3.2. Relationships between BDNF levels and clinical symptoms Serum BDNF levels at baseline did not differ between the responders, non-responders and healthy controls after adjusting the age, gender, and BMI (30.1 ± 10.1 ng/ml, 27.6 ± 9.5 ng/ml, and 27.7 ± 9.0 ng/ml, p = 0.282). In depressed adolescents, serum BDNF levels at baseline were not significantly associated with baseline CDRS-R scores, C-SSRS scores, and changes in those scores at week 2 and week 8 after adjusting for age, gender and BMI. The early decrease of BDNF levels at week 2 was not associated with improvement in CDRS-R scores at week 2 but associated with improvement in CDRS-R scores at week 8 (β = 4.72; 95% CI: 1.72, 7.72, p = 0.003). Similarly, the early decrease of BDNF levels at week 2 was not related with the reduction of C-SSRS scores at week 2 but related with the reduction of C-SSRS scores at week 8 (β = 0.72; 95% CI: 0.26, 1.19, p = 0.003). The changes of BDNF levels at week 8 was not 327
Journal of Affective Disorders 265 (2020) 325–332
J. Lee, et al.
Table 1 Demographic and clinical characteristics at baseline of the responders, non-responders and healthy controls. N (%) or mean ± SD
Age (years) Female BMI (kg/m2) BDNF (ng/ml) Intelligence (IQ) CDRS-R CGI-S C-SSRS SCARED ADHD-RS DBDS: ODD DBDS: CD ETI FACES-IV
Responders (n = 46)
Non-responders (n = 37)
Healthy controls (n = 52)
14.8 ± 1.7 29 (63.0%) 21.4 ± 4.4 30.1 ± 10.1 105.1 ± 13.1 61.0 ± 12.0 4.9 ± 0.4 3.0 ± 1.9 40.4 ± 15.5 9.8 ± 10.0 6.2 ± 5.0 2.2 ± 3.4 5.2 ± 3.5 37.3 ± 15.0
15.1 ± 1.5 26 (70.3%) 22.2 ± 4.5 27.6 ± 9.5 105.7 ± 15.0 57.7 ± 12.2 4.9 ± 0.6 2.3 ± 1.9 35.0 ± 15.3 10.5 ± 9.6 8.9 ± 5.4 1.7 ± 2.4 4.4 ± 2.7 37.9 ± 15.1
14.4 ± 1.4 29 (55.8%) 21.1 ± 2.9 27.7 ± 9.0 108.9 ± 11.0 22.9 ± 4.1 1 ± 0 0.1 ± 0.4 10.5 ± 9.9 3.8 ± 5.3 2.4 ± 3.0 0.5 ± 0.9 1.7 ± 2.6 50.0 ± 13.0
Test
p-value
post hoc
F = 2.17 X2 = 1.95 F = 0.88 F = 1.28* F = 1.21 F = 223.22 F = 8.73 F = 47.82 F = 65.87 F = 9.11 F = 23.55 F = 6.62 F = 17.59 F = 11.71
0.118 0.377 0.416 0.282 0.301 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.002 < 0.001 < 0.001
R = NR R = NR R = NR R = NR R = NR NR > R R = NR R = NR R = NR
> > > > > > > > <
HC HC HC HC HC HC HC HC HC
Adjusted for age, gender and BMI BMI, Body mean index; BDNF, Brain-derived neurotrophic factor; CDRS-R, Children's depression rating scale-revised; CGI-S, Clinical global impression – severity scale; C-SSRS, Columbia suicide severity rating scale; SCARED, Screening of childhood anxiety related emotional disorder; ADHD-RS, Attention deficit hyperactivity disorder rating scale; DBDS, Disruptive behavior disorder rating scale; ODD, Oppositional defiant disorder; CD, conduct disorder; ETI, Early trauma inventory; FACES-IV, Family adaptability and cohesion evaluation scale-IV. ⁎
associated with the changes of CDRS-R and C-SSRS scores at week 8.
3.4. Prediction of treatment response ROC-analysis determined a decrease of 1.23 ng/ml in the BDNF level between baseline and week 2 as an optimal cut-off value for the prediction of SSRI response at week 8. The early BDNF decrease predicted SSRI response with a sensitivity of 67.4% and a specificity of 73.0% (AUC: 72.6%; 95% CI: 61.6%, 83.5%) (Fig. 2). The depressed adolescents with an early BDNF decrease by 1.23 ng/ ml or more had significantly higher odds of SSRI response (odds ratio = 5.96, 95% CI: 1.96, 20.78, p = 0.003) after controlling for other demographic and clinical variables (Table 2).
3.3. Changes of BDNF levels during antidepressant treatment in Responders showed significant decrease in BDNF levels at week 2 (mean estimated change: −3.1 ± 0.8, p = 0.001) but non-responders (mean estimated change: 0.9 ± 0.9, p = 0.3) and healthy controls (mean estimated change: 0.5 ± 0.8, p = 0.5) did not (p < 0.001 for group-time interaction) (Fig. 1; see also Table S1). Serum BDNF levels at week 8 did not differ from the baseline levels significantly within each group.
Fig. 1. Early changes between baseline and week 2 of BDNF levels in adolescents with MDD. Early changes of serum BDNF levels between baseline and week 2 in Non-responders (n = 37) vs. Responders (n = 46) to SSRI treatment for adolescents with MDD, where “response” is defined as a ≥ 40% decreased in CDRS-R scores from baseline to week 8 or upon termination. (a) Mean curves (thick line) and individual curves of serum BDNF levels between baseline and visit 1 in responders and non-responders. In the piecewise linear mixed model, responders showed significant decrease in BDNF levels at week 2 (mean estimated change: −3.1 ± 0.8, p = 0.001) but non-responders did not (mean estimated change: 0.9 ± 0.9, p = 0.3). (b) Boxplot diagram showed the changes of BDNF levels between and week 2 in Non-responder (median = 0.6; range: −20.1, 21.2) and Responder (median = −2.0, range: −18.9, 6.6).
328
Journal of Affective Disorders 265 (2020) 325–332
J. Lee, et al.
Fig. 2. ROC curve of SSRI response and early BDNF change (baseline - week 2). ROC-analysis determined a decreased of 1.23 ng/ml in serum BDNF level at week 2 as an optimal cut-off for the prediction of SSRI response at week 8 with a sensitivity of 67.4% and a specificity of 73.0%.
various classes of antidepressants (Zhou et al., 2017). Lastly, BDNF levels were not only assessed at the final visit to determine treatment response but also in the middle of the treatment, which enables the examination of the differences between early and late effects of SSRI on BDNF levels. Therefore, comparing the results from this study with the findings in depressed adults may shed more light on developmental perspectives in the association between BDNF, depression and antidepressant treatment. Contrary to our hypothesis based on the findings in adult studies, the pre-treatment BDNF levels in depressed adolescents were not lower than healthy controls and the levels were found to be higher in depressed adolescents who responded to SSRI than healthy controls although not reaching statistical significance (see Table 1). Ever since Karege and colleagues (2002a) demonstrated lower serum BDNF levels in depressed adults, the relationship between decreased peripheral BDNF levels and pathogenesis of MDD remained quite consistent in several following studies with adult population (Brunoni et al., 2008; Molendijk et al., 2014; Polyakova et al., 2015; Sen et al., 2008). This discrepancy between our study and prior adult studies might be accounted for by the developmental differences of BDNF expression between adolescents and adults. Age-dependent reduction of BDNF expression from adolescence to adulthood was found in several animal studies, as well as in one human postmortem brain study (Bath et al., 2013; Oh et al., 2016). Also, stress effects on BDNF expression in adolescence were found to differ from those in adulthood in rodents (Bath et al., 2013). For instance, BDNF expression in the hippocampus of rats was elevated following social defeat in adolescence, but not in adulthood (Coppens et al., 2011). Early postnatal maternal separation stress led to a transient increase in BDNF expression of the cerebral cortex during preadolescence and adolescence, followed by reduced BDNF levels in adulthood (Lee et al., 2012). Therefore, our findings with depressed adolescents could be in line with these studies, suggesting the different features of BDNF expression between adolescence and adulthood that could lead to differences in the relationship between depression and BDNF levels. With respect to antidepressant effects on peripheral BDNF levels in depressed adolescents, although there were no significant overall changes of BDNF levels from baseline to 8-week SSRI treatment, the patients who responded to SSRI demonstrated greater reduction of
Table 2 Results of the multivariate logistic regression model predicting SSRI response at week 8. OR
Early BDNF decrease at week 2 (≥ 1.23 ng/ ml) Age* Gender (Male) BMI* Intelligence (IQ)* CDRS-R scores at baseline* DBDS ODD scores at baseline* BDNF levels at baseline*
95% CI for OR
p-value
Lower
Upper
5.96
1.96
20.78
0.003
0.86 1.37 0.82 0.96 1.31 0.46 0.95
0.48 0.46 0.45 0.56 0.75 0.25 0.53
1.53 4.24 1.42 1.64 2.36 0.08 1.69
0.611 0.574 0.479 0.884 0.349 0.008 0.857
OR per 1 SD increase BMI, Body mean index; BDNF, Brain-derived neurotrophic factor; CDRS-R, Children's depression rating scale-revised; DBDS, Disruptive behavior disorder rating scale; ODD, Oppositional defiant disorder. ⁎
4. Discussion To our knowledge, this is the first longitudinal study to investigate the effect of antidepressants on BDNF levels and the predictive value of early BDNF changes for antidepressant response in adolescents with MDD. In depressed adolescents, the early BDNF decrease (baseline – week 2) predicted SSRI response at week 8. Pre-treatment BDNF levels were not different between depressed adolescents and controls and were not associated with SSRI response in adolescents with MDD. In both responders and non-responders, the BDNF levels at week 8 did not differ from pre-treatment levels. The present study has several advantages in examining the relevance of BDNF to depression and antidepressant treatments in adolescent population. First of all, this study included 83 medication free/naïve depressed adolescents with MDD, which the number of subjects is larger than previous studies assessing BDNF levels in depressed youth and comparable with the studies with adult population (Pallavi et al., 2013; Sasaki et al., 2011; Tsuchimine et al., 2015; Zhou et al., 2017). Second, all patients were treated with a uniform antidepressant, escitalopram, minimizing the heterogeneity of potential differential effects on BDNF levels between 329
Journal of Affective Disorders 265 (2020) 325–332
J. Lee, et al.
BDNF levels at week 2 as compared with patients who did not respond to SSRI. Furthermore, the early decrease of BDNF levels predicted SSRI response with moderate sensitivity and specificity. These findings are in opposition to the neurotrophin hypothesis of depression which suggests that a deficiency in neurotrophic factor synthesis and signaling could underlie depression and that antidepressant drugs would act by increasing the levels of these neurotrophic factors (Duman and Monteggia, 2006; Huang and Reichardt, 2001). However, the focus on the physiological role of BDNF has shifted recently to a critical mediator of activity-dependent neuronal plasticity in the developing and adult central nervous system via balancing between anabolic (BDNF-TrkB signaling) and catabolic (BDNF-p75 signaling) effects on plasticity (Deppmann et al., 2008; Lu et al., 2014). According to this complex scenario for BDNF-driven neuronal plasticity, BDNF has apparently opposite effects on the plasticity in the visual cortex during the critical developing period and in adulthood, which has been extensively studied and characterized by the monocular deprivation technique in animal models (Berardi et al., 2003; Hensch, 2005; Vetencourt et al., 2008; Wiesel and Hubel, 1965). As for affective brain network, adolescence is a critical period in development characterized by significant changes in affective regulation and its underlying hormonal and neural correlates including the hypothalamic-pituitary-adrenal axis, amygdala, and prefrontal cortex (Tottenham and Galvan, 2016). Effects of antidepressants on BDNF levels related to therapeutic response in depressed adolescents, who are experiencing the critical period with dynamic changes in the affective network, may have different features from those in depressed adults, whose critical periods are already terminated with a relatively fixed network. Since circulating BDNF arises from a number of peripheral tissues, as well as from cerebral sources, it is uncertain what extent peripheral BDNF level reflects cerebral BDNF level (Nockher and Renz, 2005; Sen et al., 2008). There are some evidences supporting that a substantial part of peripheral BDNF might derive from cerebral sources and peripheral BDNF levels could reflect BDNF activity in the brain. The evidences include findings that BDNF crosses the blood-brain barrier bidirectionally (Pan et al., 1998; Sartorius et al., 2009) and positive correlations of peripheral BDNF with brain levels as well as brain phenomena found by animal and human studies (Erickson et al., 2010; Hwang et al., 2015; Karege et al., 2002b; Klein et al., 2011; Lang et al., 2007; Pillai et al., 2010; Sartorius et al., 2009). However other studies have reported an absence of correlations between the peripheral and CSF BDNF levels (Chiaretti et al., 2004; Laske et al., 2007). In fact, ninety percent or more of blood BDNF is contained in platelets. Platelets do not synthesize but store BDNF derived from brain or body tissues and they may be actively taking part in the regulation of homeostasis by storing BDNF for later release in case of increased demand (Fujimura et al., 2002; Pliego-Rivero et al., 1997). In brief, serum BDNF levels probably represent an integrative composite from different sources and may also be modulated by platelet function (Gass and Hellweg, 2010; Serra-Millas, 2016). Therefore, the unique features with relationships among serum BDNF levels, depression and antidepressant treatment in adolescents could be associated with developmental differences of peripheral BDNF production or platelet modulation between adolescents and adults. Further preclinical and clinical studies are needed to investigate these issues. There are several limitations to this study. First, the strength of this study that all patients were medicated with a single SSRI can also be considered as a weakness in terms of generalizability across other classes of antidepressants. However, since SSRIs are the only class of antidepressants approved by the FDA for children and adolescents with MDD (Martin et al., 2017), this limitation may not be an important issue in adolescent population. The second limitation is the lack of a double-blind design. While we measured the changes of BDNF levels in healthy controls to rule out natural changes in BDNF levels in adolescence, without a placebo-controlled group, we cannot fully exclude “placebo effects” on BDNF levels. Third, there are several nonspecific
confounders which were not measured in this study but may affect serum BDNF levels, such as platelet number and menstrual cycle (Lommatzsch et al., 2005). Finally, the follow-up duration of the study was limited in 8 weeks. Although serum BDNF levels after 8 weeks of treatment did not differ from baseline levels, it remains possible that serum BDNF levels would have changed with longer treatment. In summary, the results of this study demonstrate that a decrease in serum BDNF levels in the early phase of SSRI treatment may be associated with later treatment response in adolescents with MDD. In the adolescent population, lower serum BDNF levels do not necessarily underlie depression and a rise in serum BDNF levels may not indicate the therapeutic effect of antidepressants. These findings were in the opposite direction of most adult studies, suggesting possibilities of a different underlying pathophysiology of depression and mechanism of action of antidepressants between adolescents and adults. From a clinical perspective, our results provide preliminary findings suggesting that adolescents with MDD may have different features with biomarkers predicting SSRI response from adults with MDD. Further longitudinal studies for pediatric depression with extended treatment duration are warranted to investigate unique features of pathophysiology of pediatric depression and biomarkers for antidepressant response in the pediatric population. Funding This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF2015R1A2A2A01004501) and was supported by Promising-Pioneering Research Program through Seoul National University (SNU) in 2015. Declaration of Competing Interest Drs. Lee, Lee, Hong, Cho, Kim, Ms. Kim, and Ms. Han report no biomedical financial interests or potential conflicts of interest. Dr. Brent receives research support from NIMH, AFSP, the Once Upon a Time Foundation, and the Beckwith Foundation, receives royalties from Guilford Press, from the electronic self-rated version of Title Page including all author information the C-SSRS from eRT, Inc., and from performing duties as an UptoDate Psychiatry Section Editor, and receives consulting fees from Healthwise. Acknowledgments None. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jad.2020.01.045. References American Psychiatric Association, 2013. Diagnostic and statistical manual of mental disorders (5th ed.), Washington, DC: author. Asarnow, J.R., Emslie, G., Clarke, G., Wagner, K.D., Spirito, A., Vitiello, B., Iyengar, S., Shamseddeen, W., Ritz, L., McCracken, J., Strober, M., Suddath, R., Leonard, H., Porta, G., Keller, M., Brent, D., 2009. Treatment of selective serotonin reuptake inhibitor-resistant depression in adolescents: predictors and moderators of treatment response. J. Am. Acad. Child Adolesc. Psychiatry 48, 330–339. Avenevoli, S., Swendsen, J., He, J.P., Burstein, M., Merikangas, K.R., 2015. Major depression in the national comorbidity survey-adolescent supplement: prevalence, correlates, and treatment. J. Am. Acad. Child Adolesc. Psychiatry 54 37-44 e32. Bates, D., Sarkar, D., Bates, M.D., Matrix, L., 2007. The lme4 package. R. Package Version 2, 74. Bath, K.G., Schilit, A., Lee, F.S., 2013. Stress effects on BDNF expression: effects of age, sex, and form of stress. Neuroscience 239, 149–156. Berardi, N., Pizzorusso, T., Ratto, G.M., Maffei, L., 2003. Molecular basis of plasticity in the visual cortex. J. Trend Neurosci. 26, 369–378.
330
Journal of Affective Disorders 265 (2020) 325–332
J. Lee, et al. Birmaher, B., Brent, D., Issues, A.W.G., Bernet, W., Bukstein, O., Walter, H., Benson, R.S., Chrisman, A., Farchione, T., Greenhill, L., Hamilton, J., Keable, H., Kinlan, J., Schoettle, U., Stock, S., Ptakowski, K.K., Medicus, J., 2007. Practice parameter for the assessment and treatment of children and adolescents with depressive disorders. J. Am. Acad. Child Adolesc. Psychiatry 46, 1503–1526. Birmaher, B., Khetarpal, S., Brent, D., Cully, M., Balach, L., Kaufman, J., Neer, S.M., 1997. The screen for child anxiety related emotional disorders (SCARED): scale construction and psychometric characteristics. J. Am. Acad. Child Adolesc. Psychiatry 36, 545–553. Bremner, J.D., Bolus, R., Mayer, E.A., 2007. Psychometric properties of the early trauma inventory-self report. J. Nerv. Ment. Dis. 195, 211–218. Brent, D., Emslie, G., Clarke, G., Wagner, K.D., Asarnow, J.R., Keller, M., Vitiello, B., Ritz, L., Iyengar, S., Abebe, K., Birmaher, B., Ryan, N., Kennard, B., Hughes, C., DeBar, L., McCracken, J., Strober, M., Suddath, R., Spirito, A., Leonard, H., Melhem, N., Porta, G., Onorato, M., Zelazny, J., 2008. Switching to another SSRI or to venlafaxine with or without cognitive behavioral therapy for adolescents with SSRI-resistant depression: the TORDIA randomized controlled trial. JAMA 299, 901–913. Brent, D.A., Emslie, G.J., Clarke, G.N., Asarnow, J., Spirito, A., Ritz, L., Vitiello, B., Iyengar, S., Birmaher, B., Ryan, N.D., Zelazny, J., Onorato, M., Kennard, B., Mayes, T.L., Debar, L.L., McCracken, J.T., Strober, M., Suddath, R., Leonard, H., Porta, G., Keller, M.B., 2009. Predictors of spontaneous and systematically assessed suicidal adverse events in the treatment of SSRI-resistant depression in adolescents (TORDIA) study. Am. J. Psychiatry 166, 418–426. Bridge, J.A., Goldstein, T.R., Brent, D.A., 2006. Adolescent suicide and suicidal behavior. J. Child Psychol. Psychiatry 47, 372–394. Bridge, J.A., Iyengar, S., Salary, C.B., Barbe, R.P., Birmaher, B., Pincus, H.A., Ren, L., Brent, D.A., 2007. Clinical response and risk for reported suicidal ideation and suicide attempts in pediatric antidepressant treatment: a meta-analysis of randomized controlled trials. JAMA 297, 1683–1696. Brunoni, A.R., Lopes, M., Fregni, F., 2008. A systematic review and meta-analysis of clinical studies on major depression and BDNF levels: implications for the role of neuroplasticity in depression. Int. J. Neuropsychopharmacol. 11, 1169–1180. Chiaretti, A., Antonelli, A., Piastra, M., Genovese, O., Polidori, G., Aloe, L., 2004. Expression of neurotrophic factors in cerebrospinal fluid and plasma of children with viral and bacterial meningoencephalitis. Acta Paediatr. 93, 1178–1184. Coppens, C.M., Siripornmongcolchai, T., Wibrand, K., Alme, M.N., Buwalda, B., de Boer, S.F., Koolhaas, J.M., Bramham, C.R., 2011. Social defeat during adolescence and adulthood differentially induce BDNF-regulated immediate early genes. Front. Behav. Neurosci. 5, 72. Curry, J., Rohde, P., Simons, A., Silva, S., Vitiello, B., Kratochvil, C., Reinecke, M., Feeny, N., Wells, K., Pathak, S., Weller, E., Rosenberg, D., Kennard, B., Robins, M., Ginsburg, G., March, J., Team, T., 2006. Predictors and moderators of acute outcome in the treatment for adolescents with depression study (TADS). J. Am. Acad. Child Adolesc. Psychiatry 45, 1427–1439. Deppmann, C.D., Mihalas, S., Sharma, N., Lonze, B.E., Niebur, E., Ginty, D.D., 2008. A model for neuronal competition during development. Science 320, 369–373. Dreimuller, N., Schlicht, K.F., Wagner, S., Peetz, D., Borysenko, L., Hiemke, C., Lieb, K., Tadic, A., 2012. Early reactions of brain-derived neurotrophic factor in plasma (pBDNF) and outcome to acute antidepressant treatment in patients with major depression. Neuropharmacology 62, 264–269. Duman, R.S., Monteggia, L.M., 2006. A neurotrophic model for stress-related mood disorders. Biol. Psychiatry 59, 1116–1127. DuPaul, G.J., Power, T.J., Anastopoulos, A.D., Reid, R., 1998. ADHD Rating Scale—IV: Checklists, norms, and Clinical Interpretation. Guilford Press. Erickson, K.I., Prakash, R.S., Voss, M.W., Chaddock, L., Heo, S., McLaren, M., Pence, B.D., Martin, S.A., Vieira, V.J., Woods, J.A., McAuley, E., Kramer, A.F., 2010. Brain-derived neurotrophic factor is associated with age-related decline in hippocampal volume. J. Neurosci. 30, 5368–5375. Fergusson, D.M., Woodward, L.J., 2002. Mental health, educational, and social role outcomes of adolescents with depression. Arch. Gen. Psychiatry 59, 225–231. Frodl, T., 2017. Recent advances in predicting responses to antidepressant treatment. F1000Res 6. Fujimura, H., Altar, C.A., Chen, R., Nakamura, T., Nakahashi, T., Kambayashi, J., Sun, B., Tandon, N.N., 2002. Brain-derived neurotrophic factor is stored in human platelets and released by agonist stimulation. Thromb. Haemost. 87, 728–734. Gass, P., Hellweg, R., 2010. Peripheral brain-derived neurotrophic factor (BDNF) as a biomarker for affective disorders? Int. J. Neuropsychopharmacol. 13, 1–4. Goo, A.-K., Won, E.-K., Lee, C., Lee, J.-I., Jung, H.-Y., Y, K., 2013. Long-term tolerability of escitalopram in korean adolescents. J. Korean Acad. Child Adolesc. Psychiatry 24, 207–212. Guy, W., 1976. The clinician global severity and impression scales, ECDEU assessment manual for psychopharmacology. National Institute of Mental Health, Rockville, MD, 218–222. Hammad, T.A., Laughren, T., Racoosin, J., 2006. Suicidality in pediatric patients treated with antidepressant drugs. Arch. Gen. Psychiatry 63, 332–339. Hensch, T.K., 2005. Critical period plasticity in local cortical circuits. J. Nature Rev. Neurosci. 6, 877. Huang, E.J., Reichardt, L.F., 2001. Neurotrophins: roles in neuronal development and function. Annu. Rev. Neurosci. 24, 677–736. Højsgaard, S., 2012. The doBy package. R package version 4. Hwang, K.S., Lazaris, A.S., Eastman, J.A., Teng, E., Thompson, P.M., Gylys, K.H., Cole, G.M., Apostolova, L.G., Alzheimer’s Disease Neuroimaging, I., 2015. Plasma BDNF levels associate with Pittsburgh compound B binding in the brain. Alzheimers Dement (Amst) 1, 187–193. Jeon, J.R., Lee, E.H., Lee, S.W., Jeong, E.G., Kim, J.H., Lee, D., Jeon, H.J., 2012. The early trauma inventory self report-short form: psychometric properties of the korean
version. Psychiatry Investig. 9, 229–235. Karege, F., Perret, G., Bondolfi, G., Schwald, M., Bertschy, G., Aubry, J.-M.J.P.r., 2002a. Decreased serum brain-derived neurotrophic factor levels in major depressed patients. 109, 143–148. Karege, F., Schwald, M., Cisse, M., 2002b. Postnatal developmental profile of brain-derived neurotrophic factor in rat brain and platelets. Neurosci. Lett. 328, 261–264. Kaufman, J., Birmaher, B., Axelson, D., Perepletchikova, F., Brent, D., Ryan, N., 2016. Schedule for Affective Disorders and Schizophrenia for School Aged Children: Present and Lifetime Version for DSM-5 (K-SADS-PL DSM-5). Western Psychiatric Institute and Yale University. Kaufman, J., Birmaher, B., Brent, D., Rao, U., Flynn, C., Moreci, P., Williamson, D., Ryan, N., 1997. Schedule for affective disorders and schizophrenia for school-age childrenpresent and lifetime version (K-SADS-PL): initial reliability and validity data. J. Am. Acad. Child Adolesc. Psychiatry 36, 980–988. Kennard, B.D., Silva, S.G., Tonev, S., Rohde, P., Hughes, J.L., Vitiello, B., Kratochvil, C.J., Curry, J.F., Emslie, G.J., Reinecke, M., March, J., 2009. Remission and recovery in the treatment for adolescents with depression study (TADS): acute and long-term outcomes. J. Am. Acad. Child Adolesc. Psychiatry 48, 186–195. Kim, K.M., Nam, S., Choi, J.W., Jung, A.H., Hong, S.B., Kim, J.W., Kim, S.Y., Kim, E., Kim, J.W., 2018. Psychometric properties and factor structures of the Korean version of children’s depression rating scale-revised. J. Child Adolesc. Psychopharmacol. 28, 285–292. Kim, Y.S., Cheon, K.A., Kim, B.N., Chang, S.A., Yoo, H.J., Kim, J.W., Cho, S.C., Seo, D.H., Bae, M.O., So, Y.K., Noh, J.S., Koh, Y.J., McBurnett, K., Leventhal, B., 2004. The reliability and validity of kiddie-schedule for affective disorders and schizophreniapresent and lifetime Version- Korean version (K-SADS-PL-K). Yonsei Med. J. 45, 81–89. Kishi, T., Yoshimura, R., Ikuta, T., Iwata, N., 2017. Brain-derived neurotrophic factor and major depressive disorder: evidence from meta-analyses. Front. Psychiatry 8, 308. Klein, A.B., Williamson, R., Santini, M.A., Clemmensen, C., Ettrup, A., Rios, M., Knudsen, G.M., Aznar, S., 2011. Blood BDNF concentrations reflect brain-tissue bdnf levels across species. Int. J. Neuropsychopharmacol. 14, 347–353. Klein, R.G., 1998. Side Effects Form for Children and Adolescents. New York State Psychiatric Institute, New York. Lang, U.E., Hellweg, R., Seifert, F., Schubert, F., Gallinat, J., 2007. Correlation between serum brain-derived neurotrophic factor level and an in vivo marker of cortical integrity. Biol. Psychiatry 62, 530–535. Laske, C., Stransky, E., Leyhe, T., Eschweiler, G.W., Maetzler, W., Wittorf, A., Soekadar, S., Richartz, E., Koehler, N., Bartels, M., Buchkremer, G., Schott, K., 2007. BDNF serum and CSF concentrations in alzheimer’s disease, normal pressure hydrocephalus and healthy controls. J. Psychiatr. Res. 41, 387–394. Lee, K.Y., Miki, T., Yokoyama, T., Ueki, M., Warita, K., Suzuki, S., Ohta, K., Wang, Z.Y., Jamal, M., Yakura, T., Liu, J.Q., Hosomi, N., Takeuchi, Y., 2012. Neonatal repetitive maternal separation causes long-lasting alterations in various neurotrophic factor expression in the cerebral cortex of rats. Life Sci. 90, 578–584. Levy, M.J.F., Boulle, F., Steinbusch, H.W., van den Hove, D.L.A., Kenis, G., Lanfumey, L., 2018. Neurotrophic factors and neuroplasticity pathways in the pathophysiology and treatment of depression. Psychopharmacology (Berl) 235, 2195–2220. Lommatzsch, M., Zingler, D., Schuhbaeck, K., Schloetcke, K., Zingler, C., Schuff-Werner, P., Virchow, J.C., 2005. The impact of age, weight and gender on BDNF levels in human platelets and plasma. Neurobiol. Aging 26, 115–123. Long, J.D., 2011. Modeling nonlinear change. Longitudinal Data Analysis for the Behavioral Sciences Using R. SAGE Publications, pp. 604–608. Lu, B., Nagappan, G., Lu, Y., 2014. BDNF and synaptic plasticity, cognitive function, and dysfunction. Handb. Exp. Pharmacol. 220, 223–250. Maalouf, F.T., Porta, G., Vitiello, B., Emslie, G., Mayes, T., Clarke, G., Wagner, K.D., Asarnow, J.R., Spirito, A., Keller, M., Birmaher, B., Ryan, N., Shamseddeen, W., Iyengar, S., Brent, D., 2012. Do sub-syndromal manic symptoms influence outcome in treatment resistant depression in adolescents? A latent class analysis from the Tordia study. J. Affect. Disord. 138, 86–95. March, J.S., Silva, S., Petrycki, S., Curry, J., Wells, K., Fairbank, J., Burns, B., Domino, M., McNulty, S., Vitiello, B., Severe, J., 2007. The treatment for adolescents with depression study (TADS): long-term effectiveness and safety outcomes. Arch. Gen. Psychiatry 64, 1132–1143. Martin, A., Volkmar, F.R., Bloch, M.H., 2017. Lewis's Child and Adolescent Psychiatry: A Comprehensive Textbook. Wolters Kluwer Health. McMakin, D.L., Olino, T.M., Porta, G., Dietz, L.J., Emslie, G., Clarke, G., Wagner, K.D., Asarnow, J.R., Ryan, N.D., Birmaher, B., Shamseddeen, W., Mayes, T., Kennard, B., Spirito, A., Keller, M., Lynch, F.L., Dickerson, J.F., Brent, D.A., 2012. Anhedonia predicts poorer recovery among youth with selective serotonin reuptake inhibitor treatment-resistant depression. J. Am. Acad. Child Adolesc. Psychiatry 51, 404–411. Mikoteit, T., Beck, J., Eckert, A., Hemmeter, U., Brand, S., Bischof, R., Holsboer-Trachsler, E., Delini-Stula, A., 2014. High baseline BDNF serum levels and early psychopathological improvement are predictive of treatment outcome in major depression. Psychopharmacology (Berl) 231, 2955–2965. Molendijk, M.L., Spinhoven, P., Polak, M., Bus, B.A., Penninx, B.W., Elzinga, B.M., 2014. Serum BDNF concentrations as peripheral manifestations of depression: evidence from a systematic review and meta-analyses on 179 associations (N=9484). Mol. Psychiatry 19, 791–800. Nase, S., Kohler, S., Jennebach, J., Eckert, A., Schweinfurth, N., Gallinat, J., Lang, U.E., Kuhn, S., 2016. Role of serum brain derived neurotrophic factor and central NAcetylaspartate for clinical response under antidepressive pharmacotherapy. Neurosignals 24, 1–14. Nockher, W.A., Renz, H., 2005. Neurotrophins in clinical diagnostics: pathophysiology and laboratory investigation. Clin. Chim. Acta 352, 49–74. Oh, H., Lewis, D.A., Sibille, E., 2016. The role of BDNF in age-dependent changes of
331
Journal of Affective Disorders 265 (2020) 325–332
J. Lee, et al. excitatory and inhibitory synaptic markers in the human prefrontal cortex. Neuropsychopharmacology 41, 3080–3091. Olson, D.H., Gorall, D.M., 2006. Faces IV and the Circumplex model. Life Innovations Minneapolis, MN. Pai, D., Woo, J.M., Son, M.H., Lee, C., 2015. The reliability and validity of the Korean version of Columbia-suicide severity rating scale in alcohol dependent patients. J. Korean Neuropsychiatr. Assoc. 54, 222–227. Pallavi, P., Sagar, R., Mehta, M., Sharma, S., Subramanium, A., Shamshi, F., Sengupta, U., Qadri, R., Pandey, R.M., Mukhopadhyay, A.K., 2013. Serum neurotrophic factors in adolescent depression: gender difference and correlation with clinical severity. J. Affect. Disord. 150, 415–423. Pan, W., Banks, W.A., Fasold, M.B., Bluth, J., Kastin, A.J., 1998. Transport of brain-derived neurotrophic factor across the blood-brain barrier. Neuropharmacology 37, 1553–1561. Pillai, A., Kale, A., Joshi, S., Naphade, N., Raju, M.S., Nasrallah, H., Mahadik, S.P., 2010. Decreased BDNF levels in CSF of drug-naive first-episode psychotic subjects: correlation with plasma BDNF and psychopathology. Int. J. Neuropsychopharmacol. 13, 535–539. Pliego-Rivero, F.B., Bayatti, N., Giannakoulopoulos, X., Glover, V., Bradford, H.F., Stern, G., Sandler, M., 1997. Brain-derived neurotrophic factor in human platelets. Biochem. Pharmacol. 54, 207–209. Polyakova, M., Stuke, K., Schuemberg, K., Mueller, K., Schoenknecht, P., Schroeter, M.L., 2015. BDNF as a biomarker for successful treatment of mood disorders: a systematic & quantitative meta-analysis. J. Affect. Disord. 174, 432–440. Posner, K., Brown, G.K., Stanley, B., Brent, D.A., Yershova, K.V., Oquendo, M.A., Currier, G.W., Melvin, G.A., Greenhill, L., Shen, S., Mann, J.J., 2011. The Columbia-suicide severity rating scale: initial validity and internal consistency findings from three multisite studies with adolescents and adults. Am. J. Psychiatry 168, 1266–1277. Poznanski, E.O., Mokros, H.B., 1996. Manual: Children's Depression Rating Scale Revised. Western Psychological Services, Los Angeles. R Core Team, 2018. R: A language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.C., Muller, M., 2011. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 12, 77. Sartorius, A., Hellweg, R., Litzke, J., Vogt, M., Dormann, C., Vollmayr, B., Danker-Hopfe, H., Gass, P., 2009. Correlations and discrepancies between serum and brain tissue levels of neurotrophins after electroconvulsive treatment in rats. Pharmacopsychiatry 42, 270–276. Sasaki, T., Niitsu, T., Hashimoto, T., Kanahara, N., Shiina, A., Hasegawa, T., Kimura, H., Ishikawa, M., Tone, J., Yamauchi, A., Hosoda, Y., Kunou, M., Takahashi, J., Ishima, T., Fujita, Y., Nakazato, M., Hashimoto, K., Iyo, M., 2011. Decreased levels of serum brain-derived neurotrophic factor in male pediatric patients with depression. The Open Clin. Chem. J. 4, 28–33. Sen, S., Duman, R., Sanacora, G., 2008. Serum brain-derived neurotrophic factor, depression, and antidepressant medications: meta-analyses and implications. Biol. Psychiatry 64, 527–532. Serra-Millas, M., 2016. Are the changes in the peripheral brain-derived neurotrophic factor levels due to platelet activation? World J. Psychiatry 6, 84–101. Silva, R.R., Alpert, M., Pouget, E., Silva, V., Trosper, S., Reyes, K., Dummit, S., 2005. A
rating scale for disruptive behavior disorders, based on the DSM-IV item pool. Psychiatr. Q. 76, 327–339. So, Y.K., Noh, J.S., Kim, Y.S., Ko, S.G., Koh, Y.J., 2002. The reliability and validity of Korean parent and teacher ADHD rating scale. J. Korean Neuropsychiatr. Assoc. 41, 283–289. Tadic, A., Wagner, S., Schlicht, K.F., Peetz, D., Borysenko, L., Dreimuller, N., Hiemke, C., Lieb, K., 2011. The early non-increase of serum BDNF predicts failure of antidepressant treatment in patients with major depression: a pilot study. Prog. Neuropsychopharmacol. Biol. Psychiatry 35, 415–420. Tottenham, N., Galvan, A., 2016. Stress and the adolescent brain: amygdala-prefrontal cortex circuitry and ventral striatum as developmental targets. Neurosci. Biobehav. Rev. 70, 217–227. Tsuchimine, S., Saito, M., Kaneko, S., Yasui-Furukori, N., 2015. Decreased serum levels of polyunsaturated fatty acids and folate, but not brain-derived neurotrophic factor, in childhood and adolescent females with depression. Psychiatry Res. 225, 187–190. Vetencourt, J.F.M., Sale, A., Viegi, A., Baroncelli, L., De Pasquale, R., O’leary, O.F., Castrén, E., Maffei, L.J.S., 2008. The antidepressant fluoxetine restores plasticity in the adult visual cortex. Science 320, 385–388. Vitiello, B., Emslie, G., Clarke, G., Wagner, K.D., Asarnow, J.R., Keller, M.B., Birmaher, B., Ryan, N.D., Kennard, B., Mayes, T.L., DeBar, L., Lynch, F., Dickerson, J., Strober, M., Suddath, R., McCracken, J.T., Spirito, A., Onorato, M., Zelazny, J., Porta, G., Iyengar, S., Brent, D.A., 2011. Long-term outcome of adolescent depression initially resistant to selective serotonin reuptake inhibitor treatment: a follow-up study of the Tordia sample. J. Clin. Psychiatry 72, 388–396. Vitiello, B., Silva, S.G., Rohde, P., Kratochvil, C.J., Kennard, B.D., Reinecke, M.A., Mayes, T.L., Posner, K., May, D.E., March, J.S., 2009. Suicidal events in the treatment for adolescents with depression study (TADS). J. Clin. Psychiatry 70, 741–747. Wagner, K.D., Ambrosini, P., Rynn, M., Wohlberg, C., Yang, R., Greenbaum, M.S., Childress, A., Donnelly, C., Deas, D., Sertraline Pediatric Depression Study, G., 2003. Efficacy of sertraline in the treatment of children and adolescents with major depressive disorder: two randomized controlled trials. JAMA 290, 1033–1041. Weissman, M.M., Wolk, S., Goldstein, R.B., Moreau, D., Adams, P., Greenwald, S., Klier, C.M., Ryan, N.D., Dahl, R.E., Wickramaratne, P., 1999. Depressed adolescents grown up. JAMA 281, 1707–1713. Wiesel, T.N., Hubel, D.H., 1965. Comparison of the effects of unilateral and bilateral eye closure on cortical unit responses in kittens. J. Neurophysiol. 28, 1029–1040. Wilkinson, P., Kelvin, R., Roberts, C., Dubicka, B., Goodyer, I., 2011. Clinical and psychosocial predictors of suicide attempts and nonsuicidal self-injury in the adolescent depression antidepressants and psychotherapy trial (ADAPT). Am. J. Psychiatry 168, 495–501. Woldu, H., Porta, G., Goldstein, T., Sakolsky, D., Perel, J., Emslie, G., Mayes, T., Clarke, G., Ryan, N.D., Birmaher, B., Wagner, K.D., Asarnow, J.R., Keller, M.B., Brent, D., 2011. Pharmacokinetically and clinician-determined adherence to an antidepressant regimen and clinical outcome in the Tordia trial. J. Am. Acad. Child Adolesc. Psychiatry 50, 490–498. Zhou, C., Zhong, J., Zou, B., Fang, L., Chen, J., Deng, X., Zhang, L., Zhao, X., Qu, Z., Lei, Y., Lei, T., 2017. Meta-analyses of comparative efficacy of antidepressant medications on peripheral BDNF concentration in patients with depression. PLoS ONE 12, e0172270.
332