Early improvement and response to antidepressant medications in adults with major depressive disorder. Meta-analysis and study of a sample with treatment-resistant depression

Prediction of Early Antidepressant Efficacy in Patients with Major Depressive Disorder Based on Multidimensional Features of rs-fMRI and P11 Gene DNA Methylation: Prédiction de l'efficacité précoce d'un antidépresseur chez des patients souffrant du trouble dépressif majeur d'après les caractéristiques multidimensionnelles de la méthylation de l'ADN du gène P11 et de la IRMf-rs

OBJECTIVE

This study established a machine learning model based on the multidimensional data of resting-state functional activity of the brain and P11 gene DNA methylation to predict the early efficacy of antidepressant treatment in patients with major depressive disorder (MDD).

METHODS

A total of 98 Han Chinese MDD were analysed in this study. Patients were divided into 51 responders and 47 nonresponders according to whether the Hamilton Depression Rating Scale-17 items (HAMD-17) reduction rate was ≥50% after 2 weeks of antidepressant treatment. At baseline, the Illumina HiSeq Platform was used to detect the methylation of 74 CpG sites of the P11 gene in peripheral blood samples. Resting-state functional magnetic resonance imaging (rs-fMRI) scan detected the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity (FC) in 116 brain regions. The least absolute shrinkage and selection operator analysis method was used to perform feature reduction and feature selection. Four typical machine learning methods were used to establish support vector machine (SVM), random forest (RF), Naïve Bayes (NB), and logistic regression (LR) prediction models based on different combinations of functional activity of the brain, P11 gene DNA methylation and clinical/demographic features after screening.

RESULTS

The SVM model based on ALFF, ReHo, FC, P11 methylation, and clinical/demographic features showed the best performance, with 95.92% predictive accuracy and 0.9967 area under the receiver operating characteristic curve, which was better than RF, NB, and LR models.

CONCLUSION

The multidimensional data features combining rs-fMRI, DNA methylation, and clinical/demographic features can predict the early antidepressant efficacy in MDD.

Differences in bacterial taxa between treatment-naive patients with major depressive disorder and non-affected controls may be related to a proinflammatory profile

BACKGROUND

Major depressive disorder (MDD) is characterized by sadness and anhedonia, but also physical symptoms such as changes in appetite and weight. Gut microbiota has been hypothesized to be involved in MDD through gut-brain axis signaling. Moreover, antidepressants display antibacterial properties in the gastrointestinal tract. The aim of this study was to compare the gut microbiota and systemic inflammatory profile of young patients with MDD before and after initiation of antidepressant treatment and/or psychotherapy in comparison with a non-depressed control group (nonMDD).

METHODS

Fecal and blood samples were collected at baseline and at follow-up after four and twelve weeks, respectively. Patients started treatment immediately after collection of the baseline samples. The gut microbiota was characterized by 16 S rRNA gene sequencing targeting the hypervariable V4 region. Plasma levels of 49 unique immune markers were assessed using Mesoscale.

RESULTS

In total, 27 MDD patients and 32 nonMDD controls were included in the study. The gut microbiota in the baseline samples of MDD versus nonMDD participants did not differ regarding α- or β-diversity. However, there was a higher relative abundance of the genera Ruminococcus gnavus group, and a lower relative abundance of the genera Desulfovibrio, Tyzzerella, Megamonas, Olsenella, Gordonibacter, Allisonella and Rothia in the MDD group compared to the nonMDD group. In the MDD group, there was an increase in the genera Rothia, Desulfovibrio, Gordinobacteer and Lactobacillus, while genera belonging to the Firmicutes phylum were found depleted at twelve weeks follow-up compared to baseline. In the MDD group, IL-7, IL-8 and IL-17b levels were elevated compared to the nonMDD group at baseline. Furthermore, MDI score in the MDD group was found to correlate with Bray-Curtis dissimilarity at baseline, and several inflammatory markers at both baseline and after initiation of antidepressant treatment.

CONCLUSION

Several bacterial taxa differed between the MDD group and the nonMDD group at baseline and changed in relative abundance during antidepressant treatment and/or psychotherapy. The MDD group was furthermore found to have a pro-inflammatory profile compared to the nonMDD group at baseline. Further studies are required to investigate the gut microbiota and pro-inflammatory profile of patients with MDD.

Changes in Multiple microRNA Levels with Antidepressant Treatment Are Associated with Remission and Interact with Key Pathways: A Comprehensive microRNA Analysis

Individual treatment outcomes to antidepressants varies widely, yet the determinants to this difference remain elusive. MicroRNA (miRNA) gene expression regulation in major depressive disorder (MDD) has attracted interest as a biomarker. This 4-week randomized controlled trial examined changes in the plasma miRNAs that correlated with the treatment outcomes of mirtazapine (MIR) and selective serotonin reuptake inhibitor (SSRI) monotherapy. Pre- and post- treatment, we comprehensively analyzed the miRNA levels in MDD patients, and identified the gene pathways linked to these miRNAs in 46 patients. Overall, 141 miRNA levels significantly demonstrated correlations with treatment remission after 4 weeks of MIR, with miR-1237-5p showing the most robust and significant correlation after Bonferroni correction. These 141 miRNAs displayed a negative correlation with remission, indicating a decreasing trend. These miRNAs were associated with 15 pathways, including TGF-β and MAPK. Through database searches, the genes targeted by these miRNAs with the identified pathways were compared, and it was found that MAPK1, IGF1, IGF1R, and BRAF matched. Alterations in specific miRNAs levels before and after MIR treatment correlated with remission. The miRNAs mentioned in this study have not been previously reported. No other studies have investigated treatment with MIR. The identified miRNAs also correlated with depression-related genes and pathways.

Factors Affecting Antidepressant Response Trajectories: A Veterans Affairs Augmentation and Switching Treatments for Improving Depression Outcomes Trial Report

Background

In this secondary analysis of the VA Augmentation and Switching Treatments for Improving Depression Outcomes (VAST-D) study we used antidepressant response trajectories to assess the association of treatment and multiple clinical/demographic factors with the probability of response.

Methods

Using data from VAST-D, a multi-site, randomized, single-blind trial with parallel-assignment to one of three treatment interventions in 1522 Veterans whose major depressive disorder was unresponsive to at least one antidepressant trial, we evaluated response patterns using group-based trajectory modeling (GBTM). A weighted multinomial logistic regression analysis with backward elimination and additional exploratory analyses were performed to evaluate the association of multiple clinical/demographic factors with the probability of inclusion into specific trajectories. Additional exploratory analyses were used to identify factors associated with trajectory group membership that could have been missed in the primary analysis.

Results

GBTM showed the best fit for depression symptom change was comprised of six trajectories, with some trajectories demonstrating minimal improvement and others showing a high probability of remission. High baseline depression and anxiety severity scores decreased, and early improvement increased, the likelihood of inclusion into the most responsive trajectory in both the GBTM and exploratory analyses.

Conclusion

While multiple factors influence responsiveness, the probability of inclusion into a specific depression symptom trajectory is most strongly influenced by three factors: baseline depression, baseline anxiety, and the presence of early improvement.

The Korean Medication Algorithm Project for Depressive Disorder (KMAP-DD): Changes in Preferred Treatment Strategies and Medications over 20 Years and Five Editions

BACKGROUND

The Korean Medication Algorithm Project for Depressive Disorder (KMAP-DD) is an expert consensus guideline for depressive disorder created in 2002, and since then, four revisions (2006, 2012, 2017, 2021) have been published. In this study, changes in the content of the KMAP-DD survey and recommendations for each period were examined.

METHODS

The development process of the KMAP-DD was composed of two stages. First, opinions from experts with abundant clinical experience were gathered through surveys. Next, a final guideline was prepared through discussion within the working committee regarding the suitability of the results with reference to recent clinical studies or other guidelines.

RESULTS

In mild depressive symptoms, antidepressant (AD) monotherapy was preferred, but when severe depression or when psychotic features were present, a combination of AD and atypical antipsychotics (AD + AAP) was preferred. AD monotherapy was preferred in most clinical subtypes. AD monotherapy was preferred for mild depressive symptoms, and AD + AAP was preferred for severe depression and depression with psychotic features in children, adolescents, and the elderly.

CONCLUSIONS

This study identified the changes in the KMAP-DD treatment strategies and drug preferences in each period over the past 20 years. This work is expected to aid clinicians in establishing effective treatment strategies.

Early Response to Antidepressant Medications in Adults With Major Depressive Disorder: A Naturalistic Study and Odds of Remission at 14 Weeks

PURPOSE/BACKGROUND

Early response after 2 to 4 weeks of antidepressant therapy has been shown to predict remission by 8 to 12 weeks. Most of the work to date on early response has been done using data from randomized controlled trials.

METHODS/PROCEDURES

This naturalistic study uses archival data from a national tele-mental health company. The positive and negative predictive values as well as sensitivity and specificity were calculated using different drops in baseline Patient Health Questionnaire 9 scores at various periods. Demographic and clinical characteristics were compared between early responders versus those lacking early response. Binary logistic regression analyses determined if early response was predictive of remission, response, and greater than minimal improvement at 14 weeks. For those who do not show early improvement, treatments were investigated using binary logistic regression to see if changes predicted later outcomes.

FINDINGS/RESULTS

Positive predictive values for all endpoints improved with the strength of early response but did not improve much with the time allowed for that response to occur. In contrast, negative predictive values increased substantially with time. Using a definition of 30% drop in Patient Health Questionnaire 9 score at week 4, 56.5% of patients were early responders. Early responders were ~3.2 times more likely to achieve remission than those lacking early response. Of nonresponders by week 4, those prescribed atypical antipsychotics (+SSRI) had significantly reduced odds of response at week 14, whereas those prescribed a norepinephrine and dopamine reuptake inhibitor had increased odds.

IMPLICATIONS/CONCLUSIONS

Early response may be associated with better outcomes at 14 weeks. In those with lack of response by week 4, patients prescribed a norepinephrine and dopamine reuptake inhibitor may achieve superior outcomes.

Venlafaxine XR treatment for older patients with major depressive disorder: decision trees for when to change treatment

BACKGROUND

Predictors of antidepressant response in older patients with major depressive disorder (MDD) need to be confirmed before they can guide treatment.

OBJECTIVE

To create decision trees for early identification of older patients with MDD who are unlikely to respond to 12 weeks of antidepressant treatment, we analysed data from 454 older participants treated with venlafaxine XR (150-300 mg/day) for up to 12 weeks in the Incomplete Response in Late-Life Depression: Getting to Remission study.

METHODS

We selected the earliest decision point when we could detect participants who had not yet responded (defined as >50% symptom improvement) but would do so after 12 weeks of treatment. Using receiver operating characteristic models, we created two decision trees to minimise either false identification of future responders (false positives) or false identification of future non-responders (false negatives). These decision trees integrated baseline characteristics and treatment response at the early decision point as predictors.

FINDING

We selected week 4 as the optimal early decision point. Both decision trees shared minimal symptom reduction at week 4, longer episode duration and not having responded to an antidepressant previously as predictors of non-response. Test negative predictive values of the leftmost terminal node of the two trees were 77.4% and 76.6%, respectively.

CONCLUSION

Our decision trees have the potential to guide treatment in older patients with MDD but they require to be validated in other larger samples.

CLINICAL IMPLICATIONS

Once confirmed, our findings may be used to guide changes in antidepressant treatment in older patients with poor early response.

Concordance of the treatment patterns for major depressive disorders between the Canadian Network for Mood and Anxiety Treatments (CANMAT) algorithm and real-world practice in China

Background: Antidepressant (AD) algorithm is an important tool to support treatment decision-making and improve management of major depressive disorder (MDD). However, little is known about its concordance with real-world practice. This study aimed to assess the concordance between the longitudinal treatment patterns and AD algorithm recommended by a clinical practice guideline in China. Methods: Data were obtained from the electronic medical records of Shanghai Mental Health Center (SMHC), one of the largest mental health institutions in China. We examined the concordance between clinical practice and the Canadian Network for Mood and Anxiety Treatments (CANMAT) algorithm among a cohort composed of 19,955 MDD patients. The longitudinal characteristics of treatment regimen and duration were described to identify the specific inconsistencies. Demographics and health utilizations of the algorithm-concordant and -discordant subgroups with optimized treatment were measured separately. Results: The overall proportion of algorithm-concordant treatment significantly increased from 84.45% to 86.03% during the year of 2015-2017. Among the patients who received recommended first-line drugs with subsequent optimized treatment (n = 2977), the concordance proportion was 27.24%. Mirtazapine and trazodone were the most used drugs for adjunctive strategy. Inadequate or extended duration before optimized treatment are common inconsistency. The median length of follow-up for algorithm-concordant (n = 811) and algorithm-discordant patients (n = 2166) were 153 days (Q1-Q3 = 79-328) and 368 days (Q1-Q3 = 181-577) respectively, and the average number of clinical visits per person-year was 13.07 and 13.08 respectively. Conclusion: Gap existed between clinical practice and AD algorithm. Improved access to evidence-based treatment is required, especially for optimized strategies during outpatient follow-up.

Obsessive-compulsive symptoms in major depressive disorder correlate with clinical severity and mixed features

Obsessive-compulsive symptoms (OCS) are often reported in patients with bipolar disorder. The aim of this study was to investigate OCS and their related clinical features in major depressive disorder (MDD). The analysis involved 482 outpatients with MDD collected within the Combining Medications to Enhance Depression outcomes trial, who were assessed with scales for depression, suicidality, irritability, hypomanic symptomatology, and other comorbid psychiatric manifestations. OCS were reported in 27% of the sample. Patients with MDD experiencing OCS were found to differ from those not experiencing OCS by a greater severity of depression (d = 0.41, P = 0.0001), more hypomanic symptoms (d = 0.48, P < 0.0001) and mixed features (22% vs. 10%, P = 0.001), increased levels of suicidal thoughts (d = 0.40, P = 0.0001), a lower likelihood of achieving remission after antidepressant treatment (19% vs. 33%, P = 0.0109), as well as more comorbid anxiety disorders (i.e. panic disorder: d = 0.98, P < 0.0001; generalized anxiety disorder: d = 0.74, P < 0.0001; social phobia: d = 0.71, P < 0.0001), and post-traumatic stress disorder (d = 0.81, P < 0.0001). In light of these findings, clinicians should pay more attention to the occurrence of OCS in MDD, as these symptoms may reflect greater clinical severity, poorer treatment outcome, and increased risk for bipolarity.

Combination of spontaneous regional brain activity and HTR1A/1B DNA methylation to predict early responses to antidepressant treatments in MDD

BACKGROUND

Antidepressant medications are suggested as the first-line treatment in patients with major depressive disorder (MDD). However, the drug therapy outcomes vary from person to person. The functional activity of the brain and DNA methylation levels correlate with the antidepressant efficacy. To predict the early antidepressant responses in MDD and establish the prediction framework, we aimed to apply multidimensional data based on the resting-state activity of the brain and HTR1A/1B methylation.

METHODS

The values of Amplitude of Low-Frequency Fluctuations (ALFF) and regional homogeneity (ReHo) were measured as variables in 116 brain regions along with 181 CpG sites in the promoter region of HTR1A/1B and 11 clinical characteristics. After performing the feature reduction step using the least absolute shrinkage and selection operator (LASSO) method, the selected variables were put into Support Vector Machines (SVM), Random Forest (RF), Naïve Bayes (NB), and logistic regression (LR), consecutively, to construct the prediction models. The models' performance was evaluated by the Leave-One-Out Cross-Validation.

RESULTS

The LR model composed of the selected multidimensional features reached a maximum performance of 78.57% accuracy and 0.8340 area under the ROC curve (AUC). The prediction accuracies based on multidimensional datasets were found to be higher than those obtained from the data based only on fMRI or methylation.

LIMITATIONS

A relatively small sample size potentially restricted the usage of our prediction framework in clinical applications.

CONCLUSION

Our study revealed that combining the data of brain imaging and DNA methylation could provide a complementary effect in predicting early-stage antidepressant outcomes.

Predictors of Remission in Acute and Continuation Treatment of Depressive Disorders

Objective

To identify factors predicting remission of depression during acute (12 weeks) and continuation treatment (12 months) using a 1-year, naturalistic prospective study design.

Methods

Patients with depressive disorders were recruited from Chonnam National University Hospital in South Korea from March 2012 to April 2017. At baseline, 1,262 patients received outpatient therapy, and sociodemographic and clinical data were obtained. Clinical visits took place every 3 weeks during the acute treatment phase (at 3, 6, 9, and 12 weeks; n = 1,246), and every 3 months during the continuation treatment phase (at 6, 9, and 12 months; n = 1,015). Remission was defined as a Hamilton Depression Rating Scale score ≤ 7.

Results

The remission rate was 43.3% at 12 weeks and 70.4% at 12 months. In multivariate analyses, remission during the acute treatment phase was more likely in patients with a shorter-duration present episode, higher functioning, and good social support. Remission during the continuation treatment phase was more likely in patients with fewer previous depressive episodes and/or a lower baseline stress score.

Conclusion

Factors predicting depressive disorder remission may differ between the acute and continuation treatment phases.

The use of sequential pharmacotherapy for the treatment of acute major depression: a scoping review

INTRODUCTION

Major Depressive Disorder (MDD) is a chronic, relapsing, and remitting disorder affecting over 250 million persons each year worldwide. More than 50% of the patients do not respond to their initial antidepressant treatment and may benefit from sequential pharmacotherapy for the acute treatment of their MDD. Although guidelines outline options for next-step treatments, there is a paucity of evidence to select specific second- or third-step treatments.

AREAS COVERED

This scoping review synthesizes and discusses available evidence for sequential pharmacotherapy for MDD. MEDLINE was searched from inception to 7 July 2020; 4490 studies were identified. We selected meta-analyses and reports on clinical trials that were judged to inform the sequential selection of pharmacotherapy for MDD.

EXPERT OPINION

Most relevant published trials are focused on, and support, the use of augmentation pharmacotherapy. There is also some support for other strategies such as combining or switching antidepressants. In the future, more studies need to directly compare these sequential options. To provide more personalized treatment within the framework of precision psychiatry, these studies should include an assessment of moderators and mediators ('mechanism') of antidepressant response.

Prevalence, median time, and associated factors with the likelihood of initial antidepressant change: a cross-sectional study in Qatar

BACKGROUND

Major Depressive Disorder (MDD) requires therapeutic interventions during the initial month after being diagnosed for better disease outcomes. International guidelines recommend a duration of 4-12 weeks for an initial antidepressant (IAD) trial at an optimized dose to get a response. If depressive symptoms persist after this duration, guidelines recommend switching, augmenting, or combining strategies as the next step. Premature discontinuation of IAD due to ineffectiveness can cause unfavorable consequences. We aimed to determine the prevalence and the patterns of strategies applied after an IAD was changed because of a suboptimal response as a primary outcome. Secondary outcomes included the median survival time on IAD before any change; and the predictors that were associated with IAD change.

METHODS

This was a retrospective study conducted in Mental Health Services in Qatar. A dataset between January 1, 2018, and December 31, 2019, was extracted from the electronic health records. Inclusion and exclusion criteria were defined and applied. The sample size was calculated to be at least 379 patients. Descriptive statistics were reported as frequencies and percentages, in addition, to mean and standard deviation. The median time of IAD to any change strategy was calculated using survival analysis. Associated predictors were examined using several cox regression models.

RESULTS

A total of 487 patients met the inclusion criteria of the study, 431 (88%) of them had an occurrence of IAD change to any strategy before end of the study. Almost half of the sample (212 (49%); 95% CI [44-53%]) had their IAD changed less than or equal to 30 days. The median time to IAD change was 43 days with 95% CI [33.2-52.7]. The factors statistically associated with higher hazard of IAD change were: younger age, un-optimization of the IAD dose before any change, and comorbid anxiety.

CONCLUSIONS

Because almost half of the patients in this study changed their IAD as early as within the first month, efforts to avoid treatment failure are needed to ensure patient-treatment targets are met. Our findings offered some clues to help clinicians identify the high-risk predictors of short survival and subsequent failure of IAD.

Small-molecule control of neurotransmitter sulfonation

Controlling unmodified serotonin levels in brain synapses is a primary objective when treating major depressive disorder-a disease that afflicts ∼20% of the world's population. Roughly 60% of patients respond poorly to first-line treatments and thus new therapeutic strategies are sought. To this end, we have constructed isoform-specific inhibitors of the human cytosolic sulfotransferase 1A3 (SULT1A3)-the isoform responsible for sulfonating ∼80% of the serotonin in the extracellular brain fluid. The inhibitor design includes a core ring structure, which anchors the inhibitor into a SULT1A3-specific binding pocket located outside the active site, and a side chain crafted to act as a latch to inhibit turnover by fastening down the SULT1A3 active-site cap. The inhibitors are allosteric, they bind with nanomolar affinity and are highly specific for the 1A3 isoform. The cap-stabilizing effects of the latch can be accurately calculated and are predicted to extend throughout the cap and into the surrounding protein. A free-energy correlation demonstrates that the percent inhibition at saturating inhibitor varies linearly with cap stabilization - the correlation is linear because the rate-limiting step of the catalytic cycle, nucleotide release, scales linearly with the fraction of enzyme in the cap-open form. Inhibitor efficacy in cultured cells was studied using a human mammary epithelial cell line that expresses SULT1A3 at levels comparable with those found in neurons. The inhibitors perform similarly in ex vivo and in vitro studies; consequently, SULT1A3 turnover can now be potently suppressed in an isoform-specific manner in human cells.

Clinical, behavioral, and neural measures of reward processing correlate with escitalopram response in depression: a Canadian Biomarker Integration Network in Depression (CAN-BIND-1) Report

Anhedonia is thought to reflect deficits in reward processing that are associated with abnormal activity in mesocorticolimbic brain regions. It is expressed clinically as a deficit in the interest or pleasure in daily activities. More severe anhedonia in major depressive disorder (MDD) is a negative predictor of antidepressant response. It is unknown, however, whether the pathophysiology of anhedonia represents a viable avenue for identifying biological markers of antidepressant treatment response. Therefore, this study aimed to examine the relationships between reward processing and response to antidepressant treatment using clinical, behavioral, and functional neuroimaging measures. Eighty-seven participants in the first Canadian Biomarker Integration Network in Depression (CAN-BIND-1) protocol received 8 weeks of open-label escitalopram. Clinical correlates of reward processing were assessed at baseline using validated scales to measure anhedonia, and a monetary incentive delay (MID) task during functional neuroimaging was completed at baseline and after 2 weeks of treatment. Response to escitalopram was associated with significantly lower self-reported deficits in reward processing at baseline. Activity during the reward anticipation, but not the reward consumption, phase of the MID task was correlated with clinical response to escitalopram at week 8. Early (baseline to week 2) increases in frontostriatal connectivity during reward anticipation significantly correlated with reduction in depressive symptoms after 8 weeks of treatment. Escitalopram response is associated with clinical and neuroimaging correlates of reward processing. These results represent an important contribution towards identifying and integrating biological, behavioral, and clinical correlates of treatment response. ClinicalTrials.gov: NCT01655706.

Prognosis and Improved Outcomes in Major Depression: A Review

(Reprinted from Transl Psychiatry. 2019 Apr 3; 9(1):127. Open access; is licensed under a Creative Commons Attribution 4.0 International License).

Predictability of Nonremitting Depression After First 2 Weeks of Antidepressant Treatment: A VAST‐D Trial Report

Objective:

In this secondary analysis of data from the Veterans Affairs Augmentation and Switching Treatments for Improving Depression Outcomes (VAST‐D) study, the authors sought to determine the effectiveness of early improvement (or lack thereof) for predicting remission from depression with antidepressant therapy.

Methods:

This study used data from the VAST‐D study, a multisite, randomized, single‐blind trial with parallel assignment to one of three medication interventions for 1,522 veterans whose major depressive disorder was unresponsive to at least one course of antidepressant treatment meeting minimal standards for dosage and duration. The authors calculated the positive predictive value (PPV) and negative predictive value (NPV) of early improvement on remission, response, or greater than minimal improvement from depression for various degrees of improvement (10%–50%) on the Quick Inventory of Depressive Symptomatology–Clinician Rated (QIDS‐C) at 1, 2, 4, and 6 weeks.

Results:

The end of week 2 of treatment was identified as the best time to evaluate early improvement. The presence of a ≥20% drop from the baseline QIDS‐C score by the end of week 2 resulted in a PPV for remission of 38% and an NPV of 93% by week 12. Extending the observational window to week 6 minimally improved NPV (97%). This association did not differ across treatment groups.

Conclusions:

A lack of early improvement at the end of week 2 of antidepressant therapy can be used to inform clinical decisions on the likelihood of nonremission of depression during the subsequent 10 weeks, even when dosage optimization is incomplete.

Results of the European Group for the Study of Resistant Depression (GSRD) - basis for further research and clinical practice

Objectives: The overview outlines two decades of research from the European Group for the Study of Resistant Depression (GSRD) that fundamentally impacted evidence-based algorithms for diagnostics and psychopharmacotherapy of treatment-resistant depression (TRD). Methods: The GSRD staging model characterising response, non-response and resistance to antidepressant (AD) treatment was applied to 2762 patients in eight European countries. Results: In case of non-response, dose escalation and switching between different AD classes did not show superiority over continuation of original AD treatment. Predictors for TRD were symptom severity, duration of the current major depressive episode (MDE), suicidality, psychotic and melancholic features, comorbid anxiety and personality disorders, add-on treatment, non-response to the first AD, adverse effects, high occupational level, recurrent disease course, previous hospitalisations, positive family history of MDD, early age of onset and novel associations of single nucleoid polymorphisms (SNPs) within the PPP3CC, ST8SIA2, CHL1, GAP43 and ITGB3 genes and gene pathways associated with neuroplasticity, intracellular signalling and chromatin silencing. A prediction model reaching accuracy of above 0.7 highlighted symptom severity, suicidality, comorbid anxiety and lifetime MDEs as the most informative predictors for TRD. Applying machine-learning algorithms, a signature of three SNPs of the BDNF, PPP3CC and HTR2A genes and lacking melancholia predicted treatment response. Conclusions: The GSRD findings offer a unique and balanced perspective on TRD representing foundation for further research elaborating on specific clinical and genetic hypotheses and treatment strategies within appropriate study-designs, especially interaction-based models and randomized controlled trials.

Attrition in treatment-resistant depression: predictors and clinical impact

The aim of this study was to investigate attrition (dropout) during a second antidepressant trial in treatment-resistant depression. Three hundred forty-two outpatients with major depressive disorder and lack of response to a prior antidepressant were treated with venlafaxine for 6 weeks. Sociodemographic and clinical characteristics were compared between the attrition and non-attrition groups. Attrition was reported in 65 patients (19%), of whom 30 patients (46%) dropped out within week 4. The characteristics of dropout patients included a longer duration of depressive episode (P = 0.011) and lower antidepressant doses (P < 0.0001) as a consequence of a faster decrease (week 2) in depressive symptoms (P = 0.028). However, by controlling for early improvement, dropout subjects were associated with a smaller probability of antidepressant response (odds ratio = 0.16▪.83). A decrease of at least 30% in Montgomery Asberg Depression Rating Scale on day 14 predicted subsequent dropout with high specificity (81.9%▪1.0%) but lower sensitivity (19.6%▪2.8%) for clinical use. Patients who have been depressed for a longer period and show an initial improvement of symptoms after changing their antidepressant may be at increased risk for drop out. Further studies are necessary to ascertain the usefulness of these characteristics for predicting attrition.

Duloxetine plasma level and antidepressant response

BACKGROUND

Major Depressive Disorder (MDD) is associated with a high rate of inadequate treatment response, which is mainly due to the large inter-individual genetic variability in pharmacokinetic and pharmacodynamic targets of antidepressant drugs. Little is still known about the exact association between plasma level of first-line antidepressants and clinical response. This is particularly true for duloxetine, a dual serotonin and norepinephrine reuptake inhibitor recommended as first-line treatment for MDD. The aim of this study was to investigate the association between serum concentration of duloxetine (SCD) and antidepressant response (AR).

METHODS

66 MDD patients treated with duloxetine 60 mg/day monotherapy were recruited in an outpatient setting and followed for three months. Hamilton Depression Rating Scale - 21 (HAMD-21) was administrated at baseline, at month 1, and at month 3 to assess AR. SCD was measured at steady state. Linear regression analysis and nonlinear least-squares regression were used to estimate association between SCD and AR.

RESULTS

SCD showed a high inter-individual variability in our sample, despite the duloxetine fixed oral dosage. We found a strong association between SCD and AR following a bell-shaped function at month 1 and at month 3. Nonetheless, within the recommended SCD range of 30-120 ng/mL a more linear correlation between SCD and AR was observed.

DISCUSSION

Our results suggest that for duloxetine the association between SCD and AR likely follows a bell-shaped quadratic function with poor AR at subtherapeutic SCD and progressive decrease of AR at higher SCD. The maximum antidepressant efficacy seems to require SCD values next to the highest recommended SCD (30-120 ng/mL), probably because of the optimal saturation of both serotonin and norepinephrine transporters. Thus, taking into account the observed high interindividual variability of SCD, our findings suggest that for MDD patients treated with duloxetine, SCD could be a useful tool to guide the treatment by optimizing the oral dosage in order to increase the AR rate.

Predictors of response and remission in a naturalistic inpatient sample undergoing multimodal treatment for depression

BACKGROUND

Many depressed patients do not achieve response or remission despite adequate treatment. Identifying predictors of outcome can contribute to developing therapeutic algorithms for difficult-to-treat depression. Therefore, we examined clinical predictors of response and remission in a naturalistic inpatient sample undergoing multimodal treatment for depression.

METHODS

Three hundred and fifty-one consecutive inpatients admitted to a tertiary care university hospital (specialized psychiatry unit for treatment of unipolar and bipolar depression) between January 2014 and December 2016 were characterized by a set of sociodemographic and clinical variables. The predictive value of these variables for response (≥ 50% decrease from baseline Montgomery-Åsberg Depression Rating Scale (MADRS) score) and remission (MADRS score at discharge < 10) were explored using bivariate analysis and logistic regression.

RESULTS

Greater symptom severity and fewer psychotropic medications at the time of admission predicted response. Remission rates were higher for patients with non-chronic depression, higher number of previous depressive episodes, fewer psychotropic medications and less severe depression at admission.

LIMITATIONS

This was a retrospective study without a control group. The sample was drawn from a single inpatient ward specialized for difficult-to-treat depression.

CONCLUSIONS

Greater baseline depression severity might be a proxy for a less chronic course of depression thereby explaining its association with greater response rates. Fewer episodes in the past and polypharmacy could indicate treatment-resistance and chronicity, contributing to lower remission rates. Therefore, preventing chronicity should be a central aim of depression treatment.

CA, Kasper S. Prognosis and improved outcomes in major depression: a review

Treatment outcomes for major depressive disorder (MDD) need to be improved. Presently, no clinically relevant tools have been established for stratifying subgroups or predicting outcomes. This literature review sought to investigate factors closely linked to outcome and summarize existing and novel strategies for improvement. The results show that early recognition and treatment are crucial, as duration of untreated depression correlates with worse outcomes. Early improvement is associated with response and remission, while comorbidities prolong course of illness. Potential biomarkers have been explored, including hippocampal volumes, neuronal activity of the anterior cingulate cortex, and levels of brain-derived neurotrophic factor (BDNF) and central and peripheral inflammatory markers (e.g., translocator protein (TSPO), interleukin-6 (IL-6), C-reactive protein (CRP), tumor necrosis factor alpha (TNFα)). However, their integration into routine clinical care has not yet been fully elucidated, and more research is needed in this regard. Genetic findings suggest that testing for CYP450 isoenzyme activity may improve treatment outcomes. Strategies such as managing risk factors, improving clinical trial methodology, and designing structured step-by-step treatments are also beneficial. Finally, drawing on existing guidelines, we outline a sequential treatment optimization paradigm for selecting first-, second-, and third-line treatments for acute and chronically ill patients. Well-established treatments such as electroconvulsive therapy (ECT) are clinically relevant for treatment-resistant populations, and novel transcranial stimulation methods such as theta-burst stimulation (TBS) and magnetic seizure therapy (MST) have shown promising results. Novel rapid-acting antidepressants, such as ketamine, may also constitute a paradigm shift in treatment optimization for MDD.

Allosteres to regulate neurotransmitter sulfonation

Catecholamine neurotransmitter levels in the synapses of the brain shape human disposition-cognitive flexibility, aggression, depression, and reward seeking-and manipulating these levels is a major objective of the pharmaceutical industry. Certain neurotransmitters are extensively sulfonated and inactivated by human sulfotransferase 1A3 (SULT1A3). To our knowledge, sulfonation as a therapeutic means of regulating transmitter activity has not been explored. Here, we describe the discovery of a SULT1A3 allosteric site that can be used to inhibit the enzyme. The structure of the new site is determined using spin-label-triangulation NMR. The site forms a cleft at the edge of a conserved ∼30-residue active-site cap that must open and close during the catalytic cycle. Allosteres anchor into the site via π-stacking interactions with two residues that sandwich the planar core of the allostere and inhibit the enzyme through cap-stabilizing interactions with substituents attached to the core. Changes in cap free energy were calculated ab initio as a function of core substituents and used to design and synthesize a series of inhibitors intended to progressively stabilize the cap and slow turnover. The inhibitors bound tightly (34 nm to 7.4 μm) and exhibited progressive inhibition. The cap-stabilizing effects of the inhibitors were experimentally determined and agreed remarkably well with the theoretical predictions. These studies establish a reliable heuristic for the design of SULT1A3 allosteric inhibitors and demonstrate that the free-energy changes of a small, dynamic loop that is critical for SULT substrate selection and turnover can be calculated accurately.

Approaching to the Essence of Major Depressive Disorder

Major Depressive Disorder (MDD) is a serious neuropsychic disease. It destroys person’s family relationship and social connections seriously. Latest WHO investigation disclosed nearly 4.4% of the population worldwide (approximately 322 million people) were being affected by MDD extensively [1]. While in China, Dong M, et al. reported the occurrence rate of suicide attempt during hospitalization and after the onset of MDD were 17.3% (95% CI: 12.4-23.7%) and 42.1% (95% CI: 26.1-60.0%) respectively [2]. Another research made by Grupta S, et al. announced MDD in urban China might be under-diagnosed and untreated [3].