Outcomes In Major Depressive Disorder Research Articles

Cost-effectiveness of genetic and clinical predictors for choosing combined psychotherapy and pharmacotherapy in major depression

Background: Predictors of treatment outcome in major depressive disorder (MDD) could contribute to evidence-based therapeutic choices. Combined pharmacotherapy and psychotherapy show increased efficacy but higher cost compared with antidepressant pharmacotherapy; baseline predictors of pharmacotherapy resistance could be used to identify patients more likely to benefit from combined treatment. Methods: We performed a proof-of-principle study of the cost-effectiveness of using previously identified pharmacogenetic and clinical risk factors (PGx-CL-R) of antidepressant resistance or clinical risk factors alone (CL-R) to guide the prescription of combined pharmacotherapy and psychotherapy vs pharmacotherapy. The cost-effectiveness of these two strategies was compared with standard care (ST, pharmacotherapy to all subjects) using a three-year Markov model. Model parameters were literature-based estimates of response to pharmacotherapy and combined treatment, costs (UK National Health System) and benefits (quality-adjusted life years [QALYs], one QALY=one year lived in perfect health). Results: CL-R was more cost-effective than PGx-CL-R: the cost of one-QALY improvement was £2341 for CL-R and £3937 for PGx-CL-R compared to ST. PGx-CL-R had similar or better cost-effectiveness compared to CL-R when 1) the cost of genotyping was £100 per subject or less or 2) the PGx-CL-R test had sensitivity ≥ 0.90 and specificity ≥ 0.85. The cost of one-QALY improvement for CL-R was £3664 and of £4110 in two independent samples. Limitations: lack of validation in large samples from the general population. Conclusions: Using clinical risk factors to predict pharmacotherapy resistance and guide the prescription of pharmacotherapy combined with psychotherapy could be a cost-effective strategy.

Clinical Correlates and Outcome of Major Depressive Disorder and Comorbid Migraine: A Report of the European Group for the Study of Resistant Depression.

BackgroundThe present multicenter study aimed at defining the clinical profile of patients with major depressive disorder (MDD) and comorbid migraine.MethodsDemographic and clinical information for 1410 MDD patients with vs without concurrent migraine were compared by descriptive statistics, analyses of covariance, and binary logistic regression analyses.ResultsThe point prevalence rate for comorbid migraine was 13.5% for female and 6.2% for male patients. MDD + migraine patients were significantly younger, heavier, more likely female, of non-Caucasian origin, outpatient, and suffering from asthma. The presence of MDD + migraine resulted in a significantly higher functional disability. First-line antidepressant treatment strategy revealed a trend towards agomelatine. Second-generation antipsychotics were significantly less often administered for augmentation treatment in migraineurs. Overall, MDD + migraine patients tended to respond worse to their pharmacotherapy.ConclusionTreatment guidelines for comorbid depression and migraine are warranted to ensure optimal efficacy and avoid possible pitfalls in psychopharmacotherapy, including serotonin syndrome.

Clinical outcomes in a large registry of patients with major depressive disorder treated with Transcranial Magnetic Stimulation

BackgroundRandomized clinical trials have demonstrated that Transcranial Magnetic Stimulation (TMS) is an effective treatment for episodes of major depressive disorder (MDD). However, characterization of outcomes in routine clinical practice is needed, as well as identification of patient- and treatment-related outcome predictors. This study documented patient-rated (PHQ-9) and clinician-rated (CGI-S) clinical outcomes in the NeuroStar® Advanced Therapy System Clinical Outcomes Registry. MethodsRegistry data were collected at 103 practice sites. Of 7759 participants, 5010 patients were included in an intent-to-treat (ITT) sample, defined as a primary MDD diagnosis, age ≥ 18, and completion of the PHQ-9 before TMS and with at least one PHQ-9 assessment after baseline. Completers (N = 3,814) were responders or had received ≥ 20 sessions and had an end of acute treatment PHQ-9 assessment. CGI-S ratings were obtained in smaller samples. ResultsIn the total ITT and Completer samples, response (58–83%) and remission (28–62%) rates were notably high across self-report and clinician-administered assessments. Female patients and those treated with a larger number of pulses per session had superior clinical outcomes. LimitationsSite participation in the registry was voluntary and treatment was open label. ConclusionsThe extent of clinical benefit reported by patients and clinicians following TMS in routine practice compares favorably with alternative interventions for treatment-resistant depression. Strong efficacy and the low side effect and medical risk profile suggest that TMS be evaluated as a first-line treatment for MDD. The findings derive from the largest registry of clinical outcomes in MDD for any treatment.

Patient-reported outcomes in major depressive disorder with suicidal ideation: a real-world data analysis using PatientsLikeMe platform

BackgroundThe current analysis utilized data collected via an online patient community platform, PatientsLikeMe (PLM) to compare patient-reported experiences in patients with major depressive disorder (MDD) with suicidal ideation (MDSI) to those with MDD but without suicidal ideation.MethodsPLM members who joined PLM between May-2007 and February-2018 and reported a diagnosis of MDD were included. The MDSI cohort included patients with MDD who reported at least one suicide-related symptom at a severity greater than “none”. Demographics, comorbidities, symptoms, and side-effects were compared between MDSI and MDD cohorts. Factors correlated with suicidal ideation (SI) were determined by a random forest procedure.ResultsPatients in the MDSI cohort (n = 266) were younger (median age, 36 vs 44 years) with an earlier disease onset (before 30 years, 83% vs 71%), and a longer diagnosis latency (median, 4 vs 2 years) vs patients in the MDD cohort (n = 11,963). Majority of patients were women in both cohorts (73% vs 83%). Median number of psychiatric comorbidities was higher in the MDSI cohort (4 vs 3). Unprompted symptoms (e.g., loneliness, feeling of hopelessness, social anxiety, impulsivity, and self-hating thoughts) were more frequent in the MDSI cohort. Hopelessness, loneliness, anhedonia, social anxiety, and younger age were highly correlated with suicidal ideation.ConclusionsThis analysis utilized patient-reported data to better understand symptoms, experiences, and characteristics of patients with MDSI compared to patients with MDD. The results identified various risk factors correlated with suicidal ideation that may help guide clinical judgement for patients with MDD who may not voluntarily report suicidal ideation.

Predicting Treatment Outcome in Major Depressive Disorder Using Serotonin 4 Receptor PET Brain Imaging, Functional MRI, Cognitive-, EEG-Based, and Peripheral Biomarkers: A NeuroPharm Open Label Clinical Trial Protocol.

BackgroundBetween 30 and 50% of patients with major depressive disorder (MDD) do not respond sufficiently to antidepressant regimens. The conventional pharmacological treatments predominantly target serotonergic brain signaling but better tools to predict treatment response and identify relevant subgroups of MDD are needed to support individualized and mechanistically targeted treatment strategies. The aim of this study is to investigate antidepressant-free patients with MDD using neuroimaging, electrophysiological, molecular, cognitive, and clinical examinations and evaluate their ability to predict clinical response to SSRI treatment as individual or combined predictors.MethodsWe will include 100 untreated patients with moderate to severe depression (>17 on the Hamilton Depression Rating Scale 17) in a non-randomized open clinical trial. We will collect data from serotonin 4 receptor positron emission tomography (PET) brain scans, functional magnetic resonance imaging (fMRI), electroencephalogram (EEG), cognitive tests, psychometry, and peripheral biomarkers, before (at baseline), during, and after 12 weeks of standard antidepressant treatment. Patients will be treated with escitalopram, and in case of non-response at week 4 or intolerable side effects, offered to switch to a second line treatment with duloxetine. Our primary outcome (treatment response) is assessed using the Hamilton depression rating subscale 6-item scores at week 8, compared to baseline. In a subset of the patients (n = ~40), we will re-assess the neurobiological response (using PET, fMRI, and EEG) 8 weeks after initiated pharmacological antidepressant treatment, to map neurobiological signatures of treatment responses. Data from matched controls will either be collected or is already available from other cohorts.DiscussionThe extensive investigational program with follow-up in this large cohort of participants provides a unique possibility to (a) uncover potential biomarkers for antidepressant treatment response, (b) apply the findings for future stratification of MDD, (c) advance the understanding of pathophysiological underpinnings of MDD, and (d) uncover how putative biomarkers change in response to 8 weeks of pharmacological antidepressant treatment. Our data can pave the way for a precision medicine approach for optimized treatment of MDD and also provides a resource for future research and data sharing.Clinical Trial RegistrationThe study was registered at clinicaltrials.gov prior to initiation (NCT02869035; 08.16.2016, URL: https://clinicaltrials.gov/ct2/results?cond=&term=NCT02869035&cntry=&state=&city=&dist=)

Efficacy outcomes in the MDD-1 India study: First episode major depressive disorder outcomes in antidepressant-treated real-world patients in India

Little is known about the outcome of major depressive disorder in India, especially in primary care contexts not usually associated with research. This large, prospective, naturalistic observational study examined the feasibility of studying first-episode major depressive disorder outcomes in such contexts, and examined outcomes after antidepressant drug treatment. A convenience sample of 205 psychiatrists across India each recruited five adults with first-episode major depressive disorder. No patient had clinically significant suicidal ideation, psychotic symptoms, or significant medical or psychiatric comorbidity. Patients were naturalistically treated with antidepressant medication and rated using the Montgomery-Asberg Depression Rating Scale and other instruments, including patient-reported outcome measures. Assessments were obtained at baseline and at a 6-week follow-up. Of 900 patients recruited, 92 did not meet selection criteria, and there were 10 dropouts, leaving 798 evaluable patients. The most common antidepressants received were desvenlafaxine, vilazodone, and escitalopram. The primary outcome, response (50% attenuation of Montgomery-Asberg Depression Rating Scale score), was identified in 66.1% of the sample. The patient-reported outcome measures-defined response rate was 53.1%. Remission (endpoint Montgomery-Asberg Depression Rating Scale score of 10 or less) was observed in 36.7% of the sample. Response (but not remission) was very slightly but significantly better in women than in men on some but not all measures. It is feasible to conduct simple but meaningful observational studies in primary care contexts that are not usually associated with research. About two-thirds of real-world adults in primary care in India diagnosed with uncomplicated first-episode major depressive disorder respond to 6 weeks of antidepressant medications; about one-third of the adults remit.

Impact of Comorbid PTSD on Outcome of Repetitive Transcranial Magnetic Stimulation (TMS) for Veterans With Depression.

A recent randomized controlled trial of repetitive transcranial magnetic stimulation (TMS) for major depressive disorder (MDD) in veterans raised the question of whether comorbid posttraumatic stress disorder (PTSD) negatively impacted the outcome of TMS in veterans. To address this, a quality database was analyzed to compare outcomes of MDD treated with TMS in veterans with and without comorbid PTSD. The clinical outcomes of all consecutive veterans with MDD treated with TMS at the James A. Haley Veterans' Hospital as outpatients from October 2013 through September 2018 were included. Patients were initially evaluated by an experienced psychiatrist, and the diagnosis of MDD was made by clinical evaluation per DSM-IV-TR/DSM-5 criteria. At the start of treatment, after every 5 treatments, and at the end of treatment, patients were assessed with self-report and clinician-rated scales of depression. All data were abstracted from an existing quality database. Among the 118 patients treated with TMS for depression, 55 (47%) had comorbid PTSD and 63 (53%) had no comorbid PTSD. Response and remission rates by score on the Montgomery-Asberg Depression Rating Scale were similar between patients with PTSD (52.5% and 40.9%, respectively) and without PTSD (53.8% and 35.6%, respectively). No seizures or persistent adverse effects were observed or reported in either group. Comorbid PTSD did not impact the outcome of TMS for depression in this sample of veterans. Future studies should include formal ratings of PTSD to determine if the severity of PTSD affects the outcome.

Antidepressant efficacy and immune effects of bilateral theta burst stimulation monotherapy in major depression: A randomized, double-blind, sham-controlled study

Inflammation theory has been consolidated by accumulating evidence, and many studies have suggested that the peripheral cytokine levels could be biomarkers for disease status and treatment outcome in major depressive disorder (MDD). Theta burst stimulation (TBS), a new form of repetitive transcranial magnetic stimulation (TMS) for MDD, has been demonstrated to improve depression via modulating dysfunctional neural network or hypothalamic–pituitary–adrenal axis hyperactivities in MDD. However, there is lack of exploratory studies investigating its effect on serum inflammatory cytokines. Here, we aimed to investigate the antidepressant efficacy of bilateral TBS monotherapy and its effects on the serum cytokine levels in MDD. We conducted a double-blind, randomized, sham-controlled trial, with 53 MDD patients who exhibited no responses to at least one adequate antidepressant treatment for the prevailing episode assigned randomly to one of two groups: bilateral TBS monotherapy (n = 27) or sham stimulation (n = 26). The TBS treatment period was 22 days. Blood samples from 31 study subjects were obtained for analyses. The bilateral TBS group exhibited significantly greater decreases in depression scores than the sham group at week 4 (56.5% vs. 33.1%; p < 0.001 [effect size (Cohen ’ s d) = 1.00]) and during the 20-week follow-up periods. Significantly more responders were also found at week 4 (70.3% vs. 23.1%, p = 0.001) and during the 20-week follow-up periods. However, we did not detect any significant effects of TBS on the cytokine panels or any correlations between improvement in depressive symptoms and changes in serum inflammatory markers. Our findings provided the first evidence that the antidepressant efficacy of bilateral TBS monotherapy might not work via immune-modulating mechanisms.

Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With Depression

Despite the high prevalence and potential outcomes of major depressive disorder, whether and how patients will respond to antidepressant medications is not easily predicted. To identify the extent to which a machine learning approach, using gradient-boosted decision trees, can predict acute improvement for individual depressive symptoms with antidepressants based on pretreatment symptom scores and electroencephalographic (EEG) measures. This prognostic study analyzed data collected as part of the International Study to Predict Optimized Treatment in Depression, a randomized, prospective open-label trial to identify clinically useful predictors and moderators of response to commonly used first-line antidepressant medications. Data collection was conducted at 20 sites spanning 5 countries and including 518 adult outpatients (18-65 years of age) from primary care or specialty care practices who received a diagnosis of current major depressive disorder between December 1, 2008, and September 30, 2013. Patients were antidepressant medication naive or willing to undergo a 1-week washout period of any nonprotocol antidepressant medication. Statistical analysis was conducted from January 5 to June 30, 2019. Participants with major depressive disorder were randomized in a 1:1:1 ratio to undergo 8 weeks of treatment with escitalopram oxalate (n = 162), sertraline hydrochloride (n = 176), or extended-release venlafaxine hydrochloride (n = 180). The primary objective was to predict improvement in individual symptoms, defined as the difference in score for each of the symptoms on the 21-item Hamilton Rating Scale for Depression from baseline to week 8, evaluated using the C index. The resulting data set contained 518 patients (274 women; mean [SD] age, 39.0 [12.6] years; mean [SD] 21-item Hamilton Rating Scale for Depression score improvement, 13.0 [7.0]). With the use of 5-fold cross-validation for evaluation, the machine learning model achieved C index scores of 0.8 or higher on 12 of 21 clinician-rated symptoms, with the highest C index score of 0.963 (95% CI, 0.939-1.000) for loss of insight. The importance of any single EEG feature was higher than 5% for prediction of 7 symptoms, with the most important EEG features being the absolute delta band power at the occipital electrode sites (O1, 18.8%; Oz, 6.7%) for loss of insight. Over and above the use of baseline symptom scores alone, the use of both EEG and baseline symptom features was associated with a significant increase in the C index for improvement in 4 symptoms: loss of insight (C index increase, 0.012 [95% CI, 0.001-0.020]), energy loss (C index increase, 0.035 [95% CI, 0.011-0.059]), appetite changes (C index increase, 0.017 [95% CI, 0.003-0.030]), and psychomotor retardation (C index increase, 0.020 [95% CI, 0.008-0.032]). This study suggests that machine learning may be used to identify independent associations of symptoms and EEG features to predict antidepressant-associated improvements in specific symptoms of depression. The approach should next be prospectively validated in clinical trials and settings. ClinicalTrials.gov Identifier: NCT00693849.

Associations among serum markers of inflammation, life stress and suicide risk in patients with major depressive disorder

BackgroundPatients with major depressive disorder (MDD) are at high risk for suicide. As the worst outcome of MDD and common self-concealment in patients with suicide risk, studies of biomarkers may provide useful tools for suicide prevention and treatment. MethodsThis study recruited 168 patients with MDD from the Objective Diagnostic Markers and Personalized Intervention in MDD patients (ODMPIM), including 50 patients with suicide risk. Based on previous evidence and hypothesis, 23 targeted serum biomarkers involving immune-inflammation, neurotrophins, hypothalamic–pituitary–adrenal (HPA) axis and metabolism, were measured. We used path analysis and principal components analysis (PCA) to clarify the associations among serum biomarkers, childhood adversities, adulthood life events, severity of depression and suicide risk. ResultsWe identified that patients with suicide risk had a higher level of inflammatory markers in serum than patients without suicide risk (P < 0.001), especially chemokine (C-X-C motif) ligand 1 (CXCL-1). After using the Bonferroni correction, there were no differences in biomarkers related to neurotrophins, HPA-axis and metabolism. In addition, a higher proportion of patients with suicide risk had adulthood adversity (assessed by Life Events Scale) (P = 0.003). Intriguingly, path analysis demonstrated that the association between adulthood adversity and suicide risk mainly depended on severity of depression and inflammatory index. ConclusionThis study highlights the possible role of inflammation involved in suicide risk of MDD patients. Inflammatory markers have the potential for early identification and then reducing suicidal behaviors or becoming novel treatment targets in suicide risk management.

Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group

Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18–75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted “brain age” and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen’s d = 0.14, 95% CI: 0.08–0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates.

Exploring the mechanisms of action of the antidepressant effect of the ketogenic diet.

The ketogenic diet (KD) is characterized by a diet ratio of 4:1 fat to non-fat energy sources. For decades KD has been successfully used to control seizures in epilepsy patients. Investigations into its mechanism of action suggest that it may have an effect on the metabolic, nervous, immune, and digestive systems. In this review, we postulate that KD may also improve depressive symptoms - for that, we highlight the similarities between depression and epilepsy, describe the extent to which body systems involved in both conditions are affected by the KD, and ultimately hypothesize how KD could improve MDD outcomes. Research into animal models and human patients have reported that KD can increase mitochondrial biogenesis and increase cellular resistance to oxidative stress both at the mitochondrial and genetic levels. Its effect on neurotransmitters alters cell-to-cell communication in the brain and may decrease hyperexcitability by increasing Gamma Aminobutyric Acid (GABA) and decreasing excitatory neurotransmitter levels. Its anti-inflammatory effects are mediated by decreasing chemo- and cytokine levels, including TNF-alpha and IL-1 levels. Finally, KD can alter gut microbiota (GM). Certain strains of microbiota predominate in major depressive disorder (MDD) when compared to healthy individuals. Recent evidence points to Bacteroidetes as a potential treatment predictor as it seems to increase in KD treatment responders for epilepsy. Each of these observations contributes to the presumed modulatory effects of KD on mood and supports its potential role as antidepressant.

Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables.

Despite several pharmacological options, the clinical outcomes of major depressive disorder (MDD) are often unsatisfactory. Personalized psychiatry attempts to tailor therapeutic interventions according to each patient’s unique profile and characteristics. This approach can be a crucial strategy in improving pharmacological outcomes in MDD and overcoming trial-and-error treatment choices. In this narrative review, we evaluate whether sociodemographic (i.e., gender, age, race/ethnicity, and socioeconomic status) and clinical [i.e., body mass index (BMI), severity of depressive symptoms, and symptom profiles] variables that are easily assessable in clinical practice may help clinicians to optimize the selection of antidepressant treatment for each patient with MDD at the early stages of the disorder. We found that several variables were associated with poorer outcomes for all antidepressants. However, only preliminary associations were found between some clinical variables (i.e., BMI, anhedonia, and MDD with melancholic/atypical features) and possible benefits with some specific antidepressants. Finally, in clinical practice, the assessment of sociodemographic and clinical variables considered in our review can be valuable for early identification of depressed individuals at high risk for poor responses to antidepressants, but there are not enough data on which to ground any reliable selection of specific antidepressant class or compounds. Recent advances in computational resources, such as machine learning techniques, which are able to integrate multiple potential predictors, such as individual/ clinical variables, biomarkers, and genetic factors, may offer future reliable tools to guide personalized antidepressant choice for each patient with MDD.

Cognitive functioning in ultra-high risk for psychosis individuals with and without depression: Secondary analysis of findings from the NEURAPRO randomized clinical trial

Neurocognitive impairments are well established in both ultra-high risk (UHR) for psychosis and major depressive disorder (MDD). Despite this understanding, investigation of neurocognitive deficits in UHR individuals with MDD and its association with MDD within this population, has been scarce. Hence, this study aimed to examine any differences in neurocognition at baseline between those with MDD at baseline and those with no history of MDD, as well as determine whether neurocognitive variables are significantly associated with meeting criteria for MDD at follow-up, while controlling for relevant clinical variables, within a UHR cohort. Data analysis was conducted on 207 participants whose baseline neurocognition was assessed using Brief Assessment of Cognition for Schizophrenia, as part of a trial of omega-3 fatty acids (NEURAPRO) for UHR individuals. While baseline MDD was the strongest predictor, poorer verbal memory and higher verbal fluency were significantly associated with MDD at 12 months (p = .04 and 0.026, respectively). Further, higher processing speed was significantly associated with MDD at medium-term follow-up (p = .047). These findings outline that neurocognitive skills were independently associated with meeting criteria for MDD at follow-up within UHR individuals, with novel findings of better verbal fluency and processing speed being linked to MDD outcomes. Hence, neurocognitive performance should be considered as a marker of risk for MDD outcomes and a target for management of MDD in UHR.

Sex differences on the relation between major depressive disorder and labor market outcomes: A national prospective study

To ascertain the direction of causality and differences by sex between major depressive disorder (MDD) and labor market outcomes in the US population, we used structural equation models separately for males and females to assess prospectively the interdependency of depression and labor market outcomes at Waves 1 (2001–2002) and 2 (2004–2005) of the National Epidemiologic Survey on Alcohol and Related Conditions. Diagnosis of MDD used DSM-IV criteria. We found that MDD at Wave 1 predicted being out of the labor force for males at Wave 2 (p = 0.006) and being in the labor force at Wave 1 was associated with lower probability of MDD at Wave 2 (p = 0.049). Among males in the labor force, MDD at Wave 1 was negatively associated with employment at Wave 2 (p = 0.047), and employment at Wave 1 was negatively associated with MDD at Wave 2 (p < 0.001). For women, there was no association between MDD and labor force participation. However, among women in the labor force, MDD at Wave 1 was negatively associated with employment at Wave 2 (p = 0.013) and being employed at Wave 1 was negatively associated with MDD at Wave 2 (p < 0.0001). These results indicate that MDD and negative labor market outcomes are associated with one another at both time points, but the effects differ by sex. To reduce the economic and social burden of MDD, these differences should be considered in clinical practice, vocational rehabilitation, and in the design of social policies.

Genetics of Treatment Outcomes in Major Depressive Disorder: Present and Future.

Pharmacogenetic testing is a useful and increasingly widespread tool to assist in antidepressant prescription. More than ten antidepressants (including tricyclics, selective serotonin reuptake inhibitors and venlafaxine) have already genetic biomarkers of response/side effects in clinical guidelines and drug labels. These are represented by functional genetic variants in genes coding for cytochrome enzymes (CYP2D6 and CYP2C19). Depending on the predicted metabolic activity, guidelines provide recommendations on drug choice and dosing. Despite not conclusive, the current evidence suggests that testing can be useful in patients who did not respond or tolerate at least one previous pharmacotherapy. However, the current recommendations are based on pharmacokinetic genes only (CYP450 enzymes), while pharmacodynamic genes (modulating antidepressant mechanisms of action in the brain) are still being studied because of their greater complexity. This may be captured by polygenic risk scores, which reflect the cumulative contribution of many genetic variants to a trait, and they may provide future clinical applications of pharmacogenetics. A more extensive use of genotyping in clinical practice may lead to improvement in treatment outcomes thanks to personalized treatments, but possible ethical issues and disparities should be taken into account and prevented.

Electroencephalogram Source Connectivity in the Prediction of Electroconvulsive Therapy Outcome in Major Depressive Disorder.

Objectives. Major depressive disorder (MDD) is a common and potentially lethal disorder affecting up to 14% of all persons worldwide. However, one-third to thwo-thirds of patients are nonresponders to first-line therapy. Even the electroconvulsive therapy (ECT) as the option of choice in therapy-resistant MDD still shows a high proportion of nonresponders. In case of a predicted nonresponse to ECT, for example, by means of electrophysiological electroencephalogram (EEG) parameters, other therapies of MDD (eg, augmentation, polypharmacy etc) could be chosen. Methods. In this study, we retrospectively analyzed 2-minute resting state EEGs from patients with MDD who underwent ECT (6-12 sessions with 3 sessions per week) between 2006 and 2015 at the University Hospital of Zurich. Following several lines of evidence, we hypothesized altered linear EEG connectivity within the alpha band being predictive for treatment outcome. We used a network-based statistics (NBS) approach to compare connectivity measures between responders and nonresponders. Source estimates and connectivity measures were mapped using low-resolution brain tomography (LORETA). As the main outcome parameter served the retrospectively assessed efficacy index (CGI-E) from the Clinical Global Impression (CGI) rating scale. Results. Responders in comparison with non-responders showed a significant lower linear lagged connectivity in widespread cortical areas within the EEG alpha 2 band. Additionally, there were strong correlations between CGI ratings and connectivity strength mainly within frontal cortices. Conclusions. Pretreatment EEG-connectivity within the alpha 2 band has a predictive value for the efficacy of ECT treatment.

Genes Involved in Neurodevelopment, Neuroplasticity and Major Depression: No Association for CACNA1C, CHRNA7 and MAPK1.

ObjectiveGenetics factors are likely to play a role in the risk, clinical presentation and treatment outcome in major depressive disorder (MDD). In this study, we investigated the role of three candidate genes for MDD; calcium voltage-gated channel subunit alpha1 C (CACNA1C ), cholinergic receptor nicotinic alpha 7 subunit (CHRNA7 ), and mitogen-activated protein kinase 1 (MAPK1).MethodsTwo-hundred forty-two MDD patients and 326 healthy controls of Korean ancestry served as samples for the analyses. Thirty-nine single nucleotide polymorphisms (SNPs) within CACNA1C, CHRNA7, and MAPK1 genes were genotyped and subsequently tested for association with MDD (primary analysis) and other clinical features (symptoms’ severity, age of onset, history of suicide attempt, treatment outcome) (secondary analyses). Single SNPs, haplotypes and epistatic analyses were performed.ResultsSingle SNPs were not associated with disease risk and clinical features. However, a combination of alleles (haplotype) within MAPK1 was found associated with MDD-status. Secondary analyses detected a possible involvement of CACNA1C haplotype in resistance to antidepressant treatment.ConclusionThese data suggest a role for MAPK1 and CACNA1C in MDD risk and treatment resistance, respectively. However, since many limitations characterize the analysis, the results must be considered with great caution and verified.

Peripheral cytokine levels and response to antidepressant treatment in depression: a systematic review and meta-analysis.

Predicting antidepressant treatment response has been a clinical challenge for major depressive disorder (MDD). The inflammation hypothesis of depression suggests that cytokines play a key role in the pathophysiology of MDD and alterations in peripheral cytokine levels are associated with antidepressant treatment outcome. Present meta-analysis aimed to examine the association between baseline peripheral cytokine levels and the response to antidepressant treatment and to evaluate whether changes of cytokine levels were associated with the response to antidepressant treatment in patients with MDD. Human-based studies published in any language in peer-reviewed journals were systematically searched from the PubMed, Embase and Web of Science databases, from inception up to October 2018. The search terms included cytokine, depressive disorder and antidepressant and their synonyms. Case-control or case-case studies reporting on levels of IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12, CRP, TNF-α, IFN-γ, GM-CSF, MIP-1α, and Eotaxin-1 in patients with MDD based on validated depression scales both before and after antidepressant treatment were included. Of 7408 identified records, 44 studies met inclusion. Standardized mean differences in each cytokine were evaluated, and random-effects meta-analyses were performed. MDD patients who responded to antidepressant treatment had lower baseline IL-8 levels than the nonresponders (Hedge's g = -0.28; 95%CI, -0.43 to -0.13; P = 0.0003; FDR = 0.004). Antidepressant treatment significantly decreased levels of TNF-α (Hedge's g = 0.60; 95%CI, 0.26-0.94; P = 0.0006; FDR = 0.004) only in responders, and responders showed significantly more decreased TNF-α levels compared with nonresponders (P = 0.046). These findings suggested that alterations in peripheral cytokine levels were associated with antidepressant treatment outcomes in MDD. Further investigations are warranted to elucidate sources of heterogeneity and examine the potentiality of using inflammatory cytokines as novel predictive markers for the pharmacological treatment of MDD.

Major Depressive Disorder (MDD) and Schizophrenia- Addressing Unmet Needs With Partial Agonists at the D2 Receptor: A Review.

Second-generation antipsychotics are common candidates for the adjunctive treatment of major depressive disorder and for the treatment of schizophrenia. However, unmet needs remain in the treatment of both disorders. Considering schizophrenia, antipsychotics are the most common treatment and have demonstrated good efficacy. Still, side effects of these treatments are commonly reported and may impact adherence to the medication and functioning in patients with schizophrenia. Regarding major depressive disorder, despite the availability of several classes of antidepressants, many patients do not achieve remission. Adjunctive treatment with antipsychotics may improve clinical and functional outcomes. Compared with dopamine D2 receptor antagonism that is exhibited by most antipsychotics, partial agonism may result in improved outcomes in major depressive disorder and in schizophrenia. Aripiprazole, cariprazine, and brexpiprazole have partial agonism at the dopamine D2 receptor and could potentially overcome limitations associated with D2 antagonism. The objectives of this review were (1) to discuss the goal of treatment with second-generation antipsychotics in major depressive disorder and schizophrenia, and the clinical factors that should be considered, and (2) to examine the short- and long-term existing data on the efficacy and safety of D2 receptor partial agonists (aripiprazole, cariprazine, and brexpiprazole) in the adjunctive treatment of major depressive disorder and in the treatment of schizophrenia.