Major Depressive Disorder Response Research Articles

Serum brain-derived neurotrophic factor, Val66Met polymorphism and open-label SSRI treatment response in Major Depressive Disorder

BackgroundBrain-derived neurotrophic factor (BDNF) has been implicated in the therapeutic action of antidepressants and possibly in the pathophysiology of Major Depressive Disorder (MDD). Clinical studies of peripheral blood levels of BDNF in MDD have provided conflicting results, and there are also conflicting reports regarding the predictive value of peripheral BDNF levels for antidepressant treatment response. The present study investigated the association between serum BDNF levels, the BDNF Val66Met polymorphism (rs6265), clinical characteristics and SSRI treatment response. MethodsThis open-label clinical trial included 99 physically healthy, unmedicated MDD participants and 70 healthy controls. Following a baseline assessment, 53 of the MDD participants completed an eight-week, open-label course of SSRI antidepressant treatment. Serum BDNF levels and Hamilton Rating Scale for Depression (HDRS) ratings were examined at baseline and after eight weeks of treatment. Antidepressant response was defined as a decrease in HDRS ratings of > 50% from baseline to the end-of-treatment. Finally, serum BDNF levels and SSRI treatment response were compared between MDD participants who were heterozygous or homozygous for the Met allele (“Met-carriers”) and individuals homozygous for the Val allele. ResultsSerum BDNF levels at baseline were significantly higher in the unmedicated MDD participants compared to healthy controls (15.90ng/ml vs 13.75ng/ml, t (167) = -2.041, p = 0.043). In a post-hoc analysis, this difference was seen in the female but not male participants (16.85ng/ml vs 14.06ng/ml, t (91) = -2.067, p = 0.042; 14.86ng/ml vs 13.31ng/ml, t (74) = -0.923, p = 0.359). Baseline serum BDNF levels were not associated with treatment responder status or with absolute change in depression ratings over the course of 8-week SSRI treatment (p = 0.599). In both Responders and Non-responders, no significant changes in serum BDNF levels were found over the 8-week period of SSRI-treatment (16.32ng/ml vs 16.23ng/ml, t (18) = 0.060, p = 0.953; 16.04ng/ml vs 15.61ng/ml, t (29) = 0.438, p = 0.665, respectively). Further, no differences were found in serum BDNF levels prior to treatment between MDD Met-carriers and MDD Val/Val homozygotes (15.32ng/ml vs 16.36ng/ml, t (85) = 0.747, p = 0.457), and no differences were found in post-treatment serum BDNF (F1,42= 0.031, p = 0.862). However, MDD Val/Val homozygotes showed significantly greater antidepressant responses at week 8 than did MDD Met-carriers (F1,46 = 4.366, p = 0.043). ConclusionOur results do not support sufficient reliability of using peripheral BDNF to characterize depression or to predict antidepressant response in clinical use. The role of sex in moderating BDNF differences in depression, and the role of BDNF gene polymorphisms in predicting antidepressant response, remain to be further investigated. We conclude that, while central nervous system BDNF is likely involved in antidepressant efficacy and in aspects of MDD pathophysiology, its reflection in serum BDNF levels is of limited diagnostic or prognostic utility.

MicroRNAs as Diagnostic Biomarkers and Predictors of Antidepressant Response in Major Depressive Disorder: A Systematic Review

MicroRNAs as Diagnostic Biomarkers and Predictors of Antidepressant Response in Major Depressive Disorder: A Systematic Review

Prediction of treatment response in major depressive disorder using a hybrid of convolutional recurrent deep neural networks and effective connectivity based on EEG signal.

In this study, we have developed a novel method based on deep learning and brain effective connectivity to classify responders and non-responders to selective serotonin reuptake inhibitors (SSRIs) antidepressants in major depressive disorder (MDD) patients prior to the treatment using EEG signal. The effective connectivity of 30 MDD patients was determined by analyzing their pretreatment EEG signals, which were then concatenated into delta, theta, alpha, and beta bands and transformed into images. Using these images, we then fine tuned a hybrid Convolutional Neural Network that is enhanced with bidirectional Long Short-Term Memory cells based on transfer learning. The Inception-v3, ResNet18, DenseNet121, and EfficientNet-B0 models are implemented as base models. Finally, the models are followed by BiLSTM and dense layers in order to classify responders and non-responders to SSRI treatment. Results showed that the EfficiencyNet-B0 has the highest accuracy of 98.33, followed by DensNet121, ResNet18 and Inception-v3. Therefore, a new method was proposed in this study that uses deep learning models to extract both spatial and temporal features automatically, which will improve classification results. The proposed method provides accurate identification of MDD patients who are responding, thereby reducing the cost of medical facilities and patient care.

EEG-vigilance regulation is associated with and predicts ketamine response in major depressive disorder

Ketamine offers promising new therapeutic options for difficult-to-treat depression. The efficacy of treatment response, including ketamine, has been intricately linked to EEG measures of vigilance. This research investigated the interplay between intravenous ketamine and alterations in brain arousal, quantified through EEG vigilance assessments in two distinct cohorts of depressed patients (original dataset: n = 24; testing dataset: n = 24). Clinical response was defined as a decrease from baseline of >33% on the Montgomery–Åsberg Depression Rating Scale (MADRS) 24 h after infusion. EEG recordings were obtained pre-, start-, end- and 24 h post- infusion, and the resting EEG was automatically scored using the Vigilance Algorithm Leipzig (VIGALL). Relative to placebo (sodium chloride 0.9%), ketamine increased the amount of low-vigilance stage B1 at end-infusion. This increase in B1 was positively related to serum concentrations of ketamine, but not to norketamine, and was independent of clinical response. In contrast, treatment responders showed a distinct EEG pattern characterized by a decrease in high-vigilance stage A1 and an increase in low-vigilance B2/3, regardless of whether placebo or ketamine had been given. Furthermore, pretreatment EEG differed between responders and non-responders with responders showing a higher percentage of stage A1 (53% vs. 21%). The logistic regression fitted on the percent of A1 stages was able to predict treatment outcomes in the testing dataset with an area under the ROC curve of 0.7. Ketamine affects EEG vigilance in a distinct pattern observed only in responders. Consequently, the percentage of pretreatment stage A1 shows significant potential as a predictive biomarker of treatment response.Clinical Trials Registration:https://www.clinicaltrialsregister.eu/ctr-search/trial/2013-000952-17/CZRegistration number: EudraCT Number: 2013-000952-17.

Predictive Biomarkers of Treatment Response in Major Depressive Disorder.

Major depressive disorder (MDD) is a highly prevalent, debilitating disorder with a high rate of treatment resistance. One strategy to improve treatment outcomes is to identify patient-specific, pre-intervention factors that can predict treatment success. Neurophysiological measures such as electroencephalography (EEG), which measures the brain's electrical activity from sensors on the scalp, offer one promising approach for predicting treatment response for psychiatric illnesses, including MDD. In this study, a secondary data analysis was conducted on the publicly available Two Decades Brainclinics Research Archive for Insights in Neurophysiology (TDBRAIN) database. Logistic regression modeling was used to predict treatment response, defined as at least a 50% improvement on the Beck's Depression Inventory, in 119 MDD patients receiving repetitive transcranial magnetic stimulation (rTMS). The results show that both age and baseline symptom severity were significant predictors of rTMS treatment response, with older individuals and more severe depression scores associated with decreased odds of a positive treatment response. EEG measures contributed predictive power to these models; however, these improvements in outcome predictability only trended towards statistical significance. These findings provide confirmation of previous demographic and clinical predictors, while pointing to EEG metrics that may provide predictive information in future studies.

Rostral Anterior Cingulate Cortex Oscillatory Power Indexes Treatment-Resistance to Multiple Therapies in Major Depressive Disorder

Introduction: High rostral anterior cingulate cortex (rACC) activity is proposed as a nonspecific prognostic marker for treatment response in major depressive disorder, independent of treatment modality. However, other studies report a negative association between baseline high rACC activation and treatment response. Interestingly, these contradictory findings were also found when focusing on oscillatory markers, specifically rACC-theta power. An explanation could be that rACC-theta activity dynamically changes according to number of previous treatment attempts and thus is mediated by level of treatment-resistance. Methods: Primarily, we analyzed differences in rACC- and frontal-theta activity in large national cross-sectional samples representing various levels of treatment-resistance and resistance to multimodal treatments in depressed patients (psychotherapy [n = 175], antidepressant medication [AD; n = 106], repetitive transcranial magnetic stimulation [rTMS; n = 196], and electroconvulsive therapy [ECT; n = 41]), and the respective difference between remitters and non-remitters. For exploratory purposes, we also investigated other frequency bands (delta, alpha, beta, gamma). Results: rACC-theta activity was higher (p < 0.001) in the more resistant rTMS and ECT patients relative to the less resistant psychotherapy and AD patients (psychotherapy-rTMS: d = 0.315; AD-rTMS: d = 0.320; psychotherapy-ECT: d = 1.031; AD-ECT: d = 1.034), with no difference between psychotherapy and AD patients. This association was even more pronounced after controlling for frontal-theta. Post hoc analyses also yielded effects for delta, beta, and gamma bands. Conclusion: Our findings suggest that by factoring in degree of treatment-resistance during interpretation of the rACC-theta biomarker, its usefulness in treatment selection and prognosis could potentially be improved substantially in future real-world practice. Future research should however also investigate specificity of the theta band.

Brain-Derived Neurotrophic Factor (BDNF) as a Predictor of Treatment Response in Major Depressive Disorder (MDD): A Systematic Review.

Brain-derived neurotrophic factor (BDNF) has been studied as a biomarker of major depressive disorder (MDD). Besides diagnostic biomarkers, clinically useful biomarkers can inform response to treatment. We aimed to review all studies that sought to relate BDNF baseline levels, or BDNF polymorphisms, with response to treatment in MDD. In order to achieve this, we performed a systematic review of studies that explored the relation of BDNF with both pharmacological and non-pharmacological treatment. Finally, we reviewed the evidence that relates peripheral levels of BDNF and BDNF polymorphisms with the development and management of treatment-resistant depression.

Plasma Brain-Derived Neurotrophic Factor Levels in First-Episode and Recurrent Major Depression and before and after Bright Light Therapy in Treatment-Resistant Depression.

Brain-derived neurotrophic factor (BDNF) is implicated in the etiology and treatment response in major depressive disorder (MDD). However, peripheral BDNF concentrations have not been compared across different MDD stages. Bright light therapy (BLT) offers some potential in treatment-resistant depression (TRD), but its effects on BDNF levels are unknown. This study included a cross-sectional analysis of plasma BDNF concentration in females with TRD, unmedicated MDD patients, and healthy controls (HC), and measurements of longitudinal BLT effects on plasma BDNF levels in TRD patients. The present study included 55 drug-naïve, first-episode patients, 25 drug-free recurrent-episode MDD patients, 71 HC participants, and 54 TRD patients. Patients were rated by Hamilton Depression Rating Scale (HAMD)-17 and the Montgomery-Åsberg Depression Rating Scale (MADRS). Patients with TRD received BLT during 4 weeks. The total HAMD-17 and MADRS scores decreased following BLT. All patient groups had lower plasma BDNF than HC, but BDNF levels did not differ between first- and recurrent-episode BDNF patients and TRD patients before or after BLT. However, responders and remitters to BLT had higher post-treatment plasma BDNF concentrations than patients who did not achieve response or remission. The changes in plasma BDNF levels may be candidates for biomarkers of treatment response to BLT in TRD patients.

Different roles of microbiota and genetics in the prediction of treatment response in major depressive disorder

The roles of gut microbiota and susceptibility genes in patients with major depression disorder (MDD) are not well understood. Examining the microbiome and host genetics might be helpful for clinical decision-making. Patients with MDD were recruited in this study and subsequently treated for eight weeks. We identified the differences between the population with a response after two weeks and those with a response after eight weeks. The factors that were significantly correlated with efficacy were used to predict the treatment response. The differences in the importance of microbiota and genetics in prediction were analyzed. Our study identified rs58010457 as a potentially key locus affecting the treatment effect. Different microbiota and enriched pathways might play different roles in the response after two and eight weeks. We found that the area under the curve (AUC) value was greater than 0.8 for both random forest models. The contribution of different components to the AUC was evaluated by removing genetic information, microbiota abundance, and pathway data. The gut microbiome was an important predictor of the response after eight weeks, while genetics was an important predictor of the response after two weeks. These results suggested a dynamic effect of interaction among genetics and gut microbes on treatment. Furthermore, these results provide new guidance for clinical decisions: in cases of inadequate treatment effects after two weeks, the composition of the intestinal flora can be improved by diet therapy, which could ultimately affect the efficacy.

Importance of achieving rapid treatment response in major depressive disorder.

Major depressive disorder (MDD) is a leading contributor to disability worldwide and is associated with increased morbidity and mortality. Current pharmacologic treatment options may be ineffective for some patients and can pose several limitations and challenges, including suboptimal response and slow onset of action. Many of these therapies can take 6 to 8 weeks for patients to achieve response and 12 weeks or longer to demonstrate full clinical benefit. Delays in depressive symptom resolution are associated with poor symptomatic and functional outcomes, decreased quality of life, and increased burden on the healthcare system. Achieving response and remission of symptoms soon after diagnosis and treatment is associated with lower rates of relapse and a greater likelihood of functional recovery. An unmet need exists for innovative treatments that offer rapid and sustained effects. This editorial discusses the benefits of rapid improvement in depressive symptoms with available and investigational agents for patients with MDD.

Machine learning approaches and non-linear processing of extracted components in frontal region to predict rTMS treatment response in major depressive disorder.

Predicting the therapeutic result of repetitive transcranial magnetic stimulation (rTMS) treatment could save time and costs as ineffective treatment can be avoided. To this end, we presented a machine-learning-based strategy for classifying patients with major depression disorder (MDD) into responders (R) and nonresponders (NR) to rTMS treatment. Resting state EEG data were recorded using 32 electrodes from 88 MDD patients before treatment. Then, patients underwent 7 weeks of rTMS, and 46 of them responded to treatment. By applying Independent Component Analysis (ICA) on EEG, we identified the relevant brain sources as possible indicators of neural activity in the dorsolateral prefrontal cortex (DLPFC). This was served through estimating the generators of activity in the sensor domain. Subsequently, we added physiological information and placed certain terms and conditions to offer a far more realistic estimation than the classic EEG. Ultimately, those components mapped in accordance with the region of the DLPFC in the sensor domain were chosen. Features extracted from the relevant ICs time series included permutation entropy (PE), fractal dimension (FD), Lempel-Ziv Complexity (LZC), power spectral density, correlation dimension (CD), features based on bispectrum, frontal and prefrontal cordance, and a combination of them. The most relevant features were selected by a Genetic Algorithm (GA). For classifying two groups of R and NR, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Multilayer Perceptron (MLP) were applied to predict rTMS treatment response. To evaluate the performance of classifiers, a 10-fold cross-validation method was employed. A statistical test was used to assess the capability of features in differentiating R and NR for further research. EEG characteristics that can predict rTMS treatment response were discovered. The strongest discriminative indicators were EEG beta power, the sum of bispectrum diagonal elements in delta and beta bands, and CD. The Combined feature vector classified R and NR with a high performance of 94.31% accuracy, 92.85% specificity, 95.65% sensitivity, and 92.85% precision using SVM. This result indicates that our proposed method with power and nonlinear and bispectral features from relevant ICs time-series can predict the treatment outcome of rTMS for MDD patients only by one session pretreatment EEG recording. The obtained results show that the proposed method outperforms previous methods.

Respuesta inadecuada al tratamiento antidepresivo; recomendaciones de expertos

Introduction. Major depressive disorder is common worldwide and one of the pathologies with the highest global burden. The dysfunction, the frequency of relapses and the high rate of suicide make this disorder a serious health problem. There is a delay for patients to receive diagnosis and optimal treatment, which decreases the response rate, increases dysfunction, and presents residual symptoms. One of the best predictors of early response is receiving treatment as soon as possible. While early treatment is associated with high remission rates if started before the first month, treatment after the third month is associated with low remission rates. It is called inadequate response to antidepressant treatment when the patient does not achieve 50 % improvement with a standardized treatment and implies a high risk for the patient of symptomatic chronification and a brutal negative impact on their functioning. Methodology. A group of experts analyzed the evidence from specific bibliographic searches and agreed on the pertinent recommendations. Recommendations. Treatment based on measurements is necessary to determine the degree of response and guide effective and timely treatment to achieve the best results in the management of depression. Periodic measurements must be made to determine the result and make decisions. Antidepressant pharmacological optimization, combination and adjuvant strategies are described for better patient management and algorithms for decision making are included. How to overcome the challenges of treating inappropriate response in major depressive disorder is discussed.

Cingulate prediction of response to antidepressant and cognitive behavioral therapies for depression: Meta-analysis and empirical application.

We sought to identify baseline (pre-treatment) neural markers associated with treatment response in major depressive disorder (MDD), specific to treatment type, Cognitive Behavioral Therapy (CBT) or pharmacotherapy (selective serotonin reuptake inhibitors; SSRI). We conducted a meta-analysis of functional magnetic resonance imaging (fMRI) studies to identify neural prognostic indicators of response to CBT or SSRI. To verify the regions derived from literature, the meta-analytic regions were used to predict clinical change in a verification sample of participants with MDD who received either CBT (n = 60) or an SSRI (n = 19) as part of prior clinical trials. The meta-analysis consisted of 21 fMRI studies that used emotion-related tasks. It yielded prognostic regions of the perigenual (meta pgACC) and subgenual anterior cingulate cortex (meta sgACC), associated with SSRI and CBT response, respectively. When applying the meta-analytic regions to predict treatment response in the verification sample, reactivity of the meta pgACC was prognostic for SSRI response, yet the effect direction was opposite of most prior studies. Meta sgACC reactivity failed to be prognostic for CBT response. Results confirm the prognostic potential of neural reactivity of ACC subregions in MDD but further research is necessary for clinical translation.

QEEG Predictors of Treatment Response in Major Depressive Disorder- A Replication Study from Northwest India.

Background: Predicting treatment response with antidepressant is a challenging task for clinicians and researchers. An important limitation of an antidepressant trial is the increased time spent before an adequacy of trial can be decided. Quantitative Electroencephalography has shown some evidence in identifying early changes seen with antidepressants. No data has been reported from Indian population on its predictive capabilities. Aim: To examine whether early changes in frontal and prefrontal theta value in QEEG could predict antidepressant treatment response. Methods: Structured clinical assessments were conducted at baseline and after one week in a sample of treatment-seeking adults with major depressive disorder (n = 50). Patients were started on SSRI (Escitalopram, fluoxetine, paroxetine or sertraline) and followed for 8 weeks. QEEG recordings were carried out at baseline and week 1 and its parameters (relative theta power and cordance) were assessed to identify its predictive value for treatment response. Treatment response was assessed using Hamilton depression rating scale with 50% reduction after 8 weeks being considered as response. Results: Mean age of the sample was 39 ± 10 years and majority of them were females (64%). A significant reduction was found in relative frontal theta value (p = 0.021) from baseline to one week in responders. However, linear regression revealed that this change could not predict the treatment response (p = 0.37). Conclusions: QEEG changes are observed in initial phase of antidepressant treatment but these changes can't predict the treatment response.

Changes in insulin resistance following antidepressant treatment mediate response in major depressive disorder.

Insulin resistance (IR) is a potential predictor of antidepressant treatment response. We assess changes in IR after antidepressant treatment and whether these changes have any effect on treatment response. Also, to see whether changes in IR mediates relationship between C-reactive protein (CRP) and antidepressant efficacy. This is a secondary analysis of an 8-week, open-label clinical trial with 95 adults experiencing a major depressive episode. Response to vortioxetine was measured using the Montgomery-Åsberg Depression Rating Scale (MADRS). Generalized estimating equation models were utilized for this intent-to-treat analysis. When adjusted for age, sex, and body mass index, there was a significant increase in IR following treatment in the overall sample (p = 0.035). This finding was detected in treatment non-responders (p = 0.019), whereas it was not observed in responders (p = 0.329). Mediation analysis revealed that change in IR during treatment was responsible for change in MADRS as well as the relationship between baseline CRP and treatment response. Exacerbation of IR during antidepressant treatment mediated non-response. Conversely in treatment responders IR reduced. Like previous studies, baseline CRP moderated treatment response. This relationship was also mediated by changes in IR. These findings further elucidate the role of IR in terms of antidepressant response as well as potentially explain inflammation's relationship with the latter.

Cloninger’in Mizaç ve Karakter Boyutları’nın Depresyon Tedavisi ile İlişkisi

Objective: The aim of our study is to investigate the relationship between Temperament and Character Inventory (TCI) dimensions and treatment response in Major Depressive Disorder (MDD) patients.Materials and Methods: 88 patients diagnosed with MDD were included in the study. Patients were given Sociodemographic Data Form, Beck Depression Inventory (BDI) and TCI before the treatment. According to the BDI, which was applied 8 weeks after the treatment, the patients were divided into two groups as those who responded to the treatment and those who did not (responders and non-responders).Results: The education level of the patients was statistically higher in the responders. The number of unemployed (92.3%) patients in the non-responders was statistically higher than the number of unemployed (41.3%) patients in the responders. The number of patients with severe depression was statistically significantly higher in the non-responders. The mean harm avoidence score of the patients in the non-responders were found to be statistically higher than in the responders. The mean persistence score of the patients in the responders was found to be statistically higher than the non-responders.Conclusion: Our study shows that some sociodemographic characteristics and TCI dimensions may be associated with early treatment response in MDD patients.

Retinal markers of therapeutic responses in major depressive disorder: Effects of antidepressants on retinal function

BackgroundOne goal of research into major depressive disorder (MDD) is to develop markers to predict and monitor the response to psychotropic treatments. The retina is endowed with a complex neurotransmission system, composed of the main neurotransmitters involved in the pathophysiology of MDD. The retina is therefore a relevant site of investigation for the identification of reliable and robust markers. However, the effects of antidepressants on the human retina are poorly studied. Here, we seek to study the potential specific effects of various antidepressants on retinal function in MDD patients. MethodsWe assessed retinal function using flash (fERG), pattern (PERG) and multifocal (mfERG) electroretinogram in 19 MDD patients treated using antidepressants at baseline and at weeks 4, 8 and 12. ResultsWe observed reduced b-wave amplitude of photopic fERG 3.0 in patients treated with Selective Serotonin Reuptake Inhibitor (SSRI) in comparison with patients treated with Serotonin-Norepinephrine Reuptake Inhibitor (SNRI) or Tricyclic Antidepressant (TCAD). We also showed that SNRIs were associated both with a decrease in PERG P50 implicit time and an increase in fERG 3.0 b-wave amplitude. TCADs were associated with an increase in fERG flicker 3.0 a- and b-wave amplitude. ConclusionsThis is the first study in real-life conditions to show a specific effect of various antidepressants on retinal function evaluated by electroretinogram. Further investigations should be led to specify the effects of antidepressants on ERG in order to isolate reliable and reproducible markers for predicting and monitoring the response to antidepressants.

D-amino Acids as Novel Blood-based Biomarkers.

D-amino acids are present in the human body originating from diet, bacterial flora, and endogenous synthesis (at least for D-serine and, probably, D-aspartate). D-amino acids are involved in important physiological processes (e.g., D-serine and D-aspartate act on the N-methyl-D-aspartate receptor as co-agonist and agonist, respectively) and increasing evidence links D-amino acids to different pathological states. Determination of D-amino acids levels in blood is mainly based on enantiomeric separations by high performance liquid chromatography. Because of the low amount of D-enantiomers compared to the corresponding L-amino acids and the high background noise associated with biological matrices, positive and negative controls are absolutely required to obtain reliable values. Altered levels of D-serine in blood have been reported in several neurological and psychiatric disorders: it has been proposed as promising biomarker in schizophrenia, Alzheimer's disease, and amyotrophic lateral sclerosis. Indeed, D-serine levels seem an appropriate predictor of anti-depressant response in major depressive disorder and posttraumatic stress disorder, as well as a prognostic biomarker of early cognitive decline, especially when considering D-serine and D-proline levels simultaneously. Furthermore, D-amino acids seem useful biomarkers for pathologies not related to the central nervous system, such as pancreatic cancer and chronic kidney diseases. This is the first review focusing on the determination of blood levels of Damino acids as diagnostic and prognostic biomarkers. The experimental evidence of involvement of D-amino acids in various physiological pathways suggest investigating their levels in additional pathologies too, such as diabetes mellitus. In conclusion, the levels of D-amino acids in blood may represent novel diagnostic peripheral biomarkers for various disorders. Further studies are required to standardize/automatize the determinations and for confirming their clinical effectiveness.

Less basal thyrotropin levels predict antidepressant response in patients with major depression

IntroductionThe close association among thyroid metabolism, mood disorders and behavior has long been known. The role of basal thyroid axis in antidepressant treatment response is less known.ObjectivesThe aim of the present study was to study the association of basal serum thyrotropin (TSH) levels, with antidepressant treatment response in major depressive disorder.MethodsThirty-one depressed adult outpatients were included. Major depressive episode was diagnosed through the MINI (DSM-IV-TR) interview. Clinical symptomatology and blood samples were assessed at baseline, and at 4- and 8-weeks of either escitalopram or sertraline. Treatment response was defined by an improvement ≥50% in MADRS scores at 4-, and 8-weeks. Basal TSH levels were included in a linear regression model as predictor of treatment response.ResultsTwenty-seven patients finished 8-weeks of treatment. Response to treatment was of 74% at 4-weeks, and 63% at 8-weeks of antidepressant treatment. Basal median TSH levels were between normal ranges (M+SD=1.85+1,02 mlU/L). Basal TSH levels not correlated with basal MADRS scores, but with higher MADRS scores at week-4 (r=0,415, p=0,031) and at week-8 (r=0,392, p=0,043). Moreover, less baseline TSH levels trend to be a significant good predictor for treatment response at 4-weeks (R2=.116, p=.083); and a good predictor at 8-weeks treatment (R2=.147, p=.049).ConclusionsBaseline TSH levels even within the normal range may play a role in predicting antidepressant response.DisclosureNo significant relationships.

Changes in Emotion Regulation Following Transcranial Magnetic Stimulation (TMS) and the Relationship to Treatment Response in Major Depressive Disorder

Deficits in emotion regulation (ER) have been repeatedly demonstrated in MDD, linked to symptom severity. In two cohorts, we examined changes in ER over 30 sessions of TMS and the relationship to treatment response.