Treatment Outcome In Major Depressive Disorder Research Articles

Genetic factors and symptom dimensions associated with antidepressant treatment outcomes: clues for new potential therapeutic targets?

Treatment response and resistance in major depressive disorder (MDD) show a significant genetic component, but previous studies had limited power also due to MDD heterogeneity. This literature review focuses on the genetic factors associated with treatment outcomes in MDD, exploring their overlap with those associated with clinically relevant symptom dimensions. We searched PubMed for: (1) genome-wide association studies (GWASs) or whole exome sequencing studies (WESs) that investigated efficacy outcomes in MDD; (2) studies examining the association between MDD treatment outcomes and specific depressive symptom dimensions; and (3) GWASs of the identified symptom dimensions. We identified 13 GWASs and one WES of treatment outcomes in MDD, reporting several significant loci, genes, and gene sets involved in gene expression, immune system regulation, synaptic transmission and plasticity, neurogenesis and differentiation. Nine symptom dimensions were associated with poor treatment outcomes and studied by previous GWASs (anxiety, neuroticism, anhedonia, cognitive functioning, melancholia, suicide attempt, psychosis, sleep, sociability). Four genes were associated with both treatment outcomes and these symptom dimensions: CGREF1 (anxiety); MCHR1 (neuroticism); FTO and NRXN3 (sleep). Other overlapping signals were found when considering genes suggestively associated with treatment outcomes. Genetic studies of treatment outcomes showed convergence at the level of biological processes, despite no replication at gene or variant level. The genetic signals overlapping with symptom dimensions of interest may point to shared biological mechanisms and potential targets for new treatments tailored to the individual patient's clinical profile.

Predicting treatment outcomes in major depressive disorder using brain magnetic resonance imaging: a meta-analysis.

Recent studies have provided promising evidence that neuroimaging data can predict treatment outcomes for patients with major depressive disorder (MDD). As most of these studies had small sample sizes, a meta-analysis is warranted to identify the most robust findings and imaging modalities, and to compare predictive outcomes obtained in magnetic resonance imaging (MRI) and studies using clinical and demographic features. We conducted a literature search from database inception to July 22, 2023, to identify studies using pretreatment clinical or brain MRI features to predict treatment outcomes in patients with MDD. Two meta-analyses were conducted on clinical and MRI studies, respectively. The meta-regression was employed to explore the effects of covariates and compare the predictive performance between clinical and MRI groups, as well as across MRI modalities and intervention subgroups. Meta-analysis of 13 clinical studies yielded an area under the curve (AUC) of 0.73, while in 44 MRI studies, the AUC was 0.89. MRI studies showed a higher sensitivity than clinical studies (0.78 vs. 0.62, Z = 3.42, P = 0.001). In MRI studies, resting-state functional MRI (rsfMRI) exhibited a higher specificity than task-based fMRI (tbfMRI) (0.79 vs. 0.69, Z = -2.86, P = 0.004). No significant differences in predictive performance were found between structural and functional MRI, nor between different interventions. Of note, predictive MRI features for treatment outcomes in studies using antidepressants were predominantly located in the limbic and default mode networks, while studies of electroconvulsive therapy (ECT) were restricted mainly to the limbic network. Our findings suggest a promise for pretreatment brain MRI features to predict MDD treatment outcomes, outperforming clinical features. While tasks in tbfMRI studies differed, those studies overall had less predictive utility than rsfMRI data. Overlapping but distinct network-level measures predicted antidepressants and ECT outcomes. Future studies are needed to predict outcomes using multiple MRI features, and to clarify whether imaging features predict outcomes generally or differ depending on treatments.

Leveraging DNA methylation to predict treatment response in major depressive disorder: A critical review.

Major depressive disorder (MDD) is a debilitating and prevalent mental disorder with a high disease burden. Despite a wide array of different treatment options, many patients do not respond to initial treatment attempts. Selection of the most appropriate treatment remains a significant clinical challenge in psychiatry, highlighting the need for the development of biomarkers with predictive utility. Recently, the epigenetic modification DNA methylation (DNAm) has emerged to be of great interest as a potential predictor of MDD treatment outcomes. Here, we review efforts to date that seek to identify DNAm signatures associated with treatment response in individuals with MDD. Searches were conducted in the databases PubMed, Scopus, and Web of Science with the concepts and keywords MDD, DNAm, antidepressants, psychotherapy, cognitive behavior therapy, electroconvulsive therapy, transcranial magnetic stimulation, and brain stimulation therapies. We identified 32 studies implicating DNAm patterns associated with MDD treatment outcomes. The majority of studies (N = 25) are focused on selected target genes exploring treatment outcomes in pharmacological treatments (N = 22) with a few studies assessing treatment response to electroconvulsive therapy (N = 3). Additionally, there are few genome-scale efforts (N = 7) to characterize DNAm patterns associated with treatment outcomes. There is a relative dearth of studies investigating DNAm patterns in relation to psychotherapy, electroconvulsive therapy, or transcranial magnetic stimulation; importantly, most existing studies have limited sample sizes. Given the heterogeneity in both methods and results of studies to date, there is a need for additional studies before existing findings can inform clinical decisions.

A distinctive subcortical functional connectivity pattern linking negative affect and treatment outcome in major depressive disorder

Major depressive disorder (MDD) is associated with functional disturbances in subcortical regions. In this naturalistic prospective study (NCT03294525), we aimed to investigate relationships among subcortical functional connectivity (FC), mood symptom profiles and treatment outcome in MDD using multivariate methods. Medication-free participants with MDD (n = 135) underwent a functional magnetic resonance imaging scan at baseline and completed posttreatment clinical assessment after 8 weeks of antidepressant monotherapy. We used partial least squares (PLS) correlation analysis to explore the association between subcortical FC and mood symptom profiles. FC score, reflecting the weighted representation of each individual in this association, was computed. Replication analysis was undertaken in an independent sample (n = 74). We also investigated the relationship between FC score and treatment outcome in the main sample. A distinctive subcortical connectivity pattern was found to be associated with negative affect. In general, higher FC between the caudate, putamen and thalamus was associated with greater negative affect. This association was partly replicated in the independent sample (similarity between the two samples: r = 0.66 for subcortical connectivity, r = 0.75 for mood symptom profile). Lower FC score predicted both remission and response to treatment after 8 weeks of antidepressant monotherapy. The emphasis here on the role of dorsal striatum and thalamus consolidates prior work of subcortical connectivity in MDD. The findings provide insight into the pathogenesis of MDD, linking subcortical FC with negative affect. However, while the FC score significantly predicted treatment outcome, the low odds ratio suggests that finding predictive biomarkers for depression remains an aspiration.

Polygenic scores of subcortical brain volumes as possible modulators of treatment response in depression

A significant proportion of patients with major depressive disorder (MDD) do not experience remission after one or more pharmacological treatments. Research has explored brain structural measures, particularly hippocampal volume, as potential predictors of treatment response, as well as genetic factors.This study investigated the association of polygenic scores (PGSs) for seven subcortical brain volumes (including the hippocampus, nucleus accumbens, amygdala, and caudate nucleus) with treatment non-response and non-remission in MDD.Patients with MDD were recruited in the context of five clinical studies, including a total of 3637 individuals. PGSs were estimated using a Bayesian framework and continuous shrinkage priors (PRS-CS-auto) after standard genotype quality control and imputation. Logistic regressions were performed between PGSs and non-response or non-remission in each sample, adjusting for age, sex, baseline symptom severity, recruitment sites, and population stratification. Results were meta-analysed across samples, using a random-effect model.No association was significant in the meta-analysis after Bonferroni correction. The top finding was found for the caudate volume PGS and non-remission (OR = 1.09, 95% CI = 1.01–1.19, p = 0.036), with no evidence of heterogeneity. Leave-one-out sensitivity analyses showed that this result was influenced by the two largest samples in the meta-analysis.This result should be considered as preliminary as it did not reach the Bonferroni-adjusted significance threshold. Future studies with greater statistical power may enhance the predictive performance of PGSs and contribute to the identification of polygenic predictors of treatment outcomes in MDD, contributing to precision psychiatry.

The Role of Daily Steps in the Treatment of Major Depressive Disorder: Secondary Analysis of a Randomized Controlled Trial of a 6-Month Internet-Based, Mindfulness-Based Cognitive Behavioral Therapy Intervention for Youth.

Current evidence supports physical activity (PA) as an adjunctive treatment for major depressive disorder (MDD). Few studies, however, have examined the relationship between objectively measured PA and MDD treatment outcomes using prospective data. This study is a secondary analysis of data from a 24-week internet-based, mindfulness-based cognitive behavioral therapy program for MDD. The purpose of this analysis was twofold: (1) to examine average daily step counts in relation to MDD symptom improvement, and whether pain moderated this relationship; and (2) to examine whether changes in step activity (ie, step trajectories) during treatment were associated with baseline symptoms and symptom improvement. Patients from the Centre for Addiction and Mental Health were part of a randomized controlled trial evaluating the effects of internet-based, mindfulness-based cognitive behavioral therapy for young adults (aged 18-30 years old) with MDD. Data from 20 participants who had completed the intervention were analyzed. PA, in the form of objectively measured steps, was measured using the Fitbit-HR Charge 2 (Fitbit Inc), and self-reported depression severity was measured with the Beck Depression Inventory-II (BDI-II). Linear regression analysis was used to test PA's relationship with depression improvement and the moderating effect of pain severity and pain interference. Growth curve and multivariable regression models were used to test longitudinal associations. Participants walked an average of 8269 steps per day, and each additional +1000-step difference between participants was significantly associated with a 2.66-point greater improvement (reduction) in BDI-II, controlling for anxiety, pain interference, and adherence to Fitbit monitoring (P=.02). Pain severity appeared to moderate (reduce) the positive effect of average daily steps on BDI-II improvement (P=.03). Higher baseline depression and anxiety symptoms predicted less positive step trajectories throughout treatment (Ps≤.001), and more positive step trajectories early in the trial predicted greater MDD improvement at the end of the trial (Ps<.04). However, step trajectories across the full duration of the trial did not significantly predict MDD improvement (Ps=.40). This study used objective measurements to demonstrate positive associations between PA and depression improvement in the context of cognitive behavioral treatment. Pain appeared to moderate this relationship, and baseline symptoms of anxiety and depression predicted PA trajectories. The findings inform future interventions for major depression. Future research with larger samples should consider additional moderators of PA-related treatment success and the extent to which outcomes are related to PA change in multimodal interventions. Clinical Trials.gov NCT03406052; https://www.clinicaltrials.gov/ct2/show/NCT03406052. RR2-10.2196/11591.

Relationship between Baseline Physical Activity and Generalized Anxiety Disorder and Major Depressive Disorder Treatment Outcomes.

Generalized anxiety disorder and major depressive disorder often benefit from medication and psychotherapy. Our aim was to determine whether a correlation exists between patient baseline physical activity and response to treatment. This was a retrospective study that included adult patients with anxiety and depression who received outpatient care for their conditions by providers in the Department of Psychiatry and Psychology of the Mayo Clinic in Jacksonville, Florida. Statistical analyses were used to analyze whether Rapid Assessment of Physical Activity scores as a measure of baseline exercise correlated to changes in Patient Health Questionnaire-9 (PHQ-9) scores or Generalized Anxiety Disorder-7-item scale (GAD-7) scores during treatment for anxiety or depression. Factors including age, sex, smoking status, and caffeine intake also were analyzed. When comparing change in GAD-7 or PHQ-9 scores from baseline to follow-up during treatment for anxiety or depression, there was no significant difference based on Rapid Assessment of Physical Activity scores. Caffeine intake had a direct correlation with PHQ-9 scores from baseline to 12 to 24 weeks but no correlation with GAD-7 scores. Overall, the amount of physical activity a patient participates in before anxiety or depression treatment does not appear to affect improvement outcomes. Caffeine intake may improve depression severity scores; however, further research is needed to assess whether this could be a part of future treatment plans.

A posterior-alpha ageing network is differentially associated with antidepressant effects of venlafaxine and rTMS

Major depressive disorder (MDD) is a highly prevalent psychiatric disorder, but chances for remission largely decrease with each failed treatment attempt. It is therefore desirable to assign a given patient to the most promising individual treatment option as early as possible. We used a polygenic score (PGS) informed electroencephalography (EEG) data-driven approach to identify potential predictors for MDD treatment outcome. Post-hoc we conducted exploratory analyses in order to understand the results in depth. First, an EEG independent component analysis produced 54 functional brain networks in a large heterogeneous cohort of psychiatric patients (n = 4,045; 5–84 yrs.). Next, the network that was associated to PGS for antidepressant-response (PRS-AR) in an independent sample (n = 722) was selected: an age-related posterior alpha network that explained >60 % of EEG variance, and was highly stable over recording time. Translational analyses were performed in two other independent datasets to examine if the network was predictive of psychopharmacotherapy (n = 535) and/or repetitive transcranial magnetic stimulation (rTMS) and concomitant psychotherapy (PT; n = 186) outcome. The network predicted remission to venlafaxine (p = 0.015), resulting in a normalized positive predicted value (nPPV) of 138 %, and rTMS + PT – but in opposite direction for women (p = 0.002) relative to men (p = 0.018) – yielding a nPPV of 131 %. Blinded out-of-sample validations for venlafaxine (n = 29) and rTMS + PT (n = 36) confirmed the findings for venlafaxine, while results for rTMS + PT could not be replicated. These data suggest the existence of a relatively stable EEG posterior alpha aging network related to PGS-AR that has potential as MDD treatment predictor.

The Intersection of Insomnia Disorder and Major Depressive Disorder: A Clinical Perspective

Major depressive disorder (MDD) is associated with profound sleep architectural alterations including reduced slow wave activity, increased delta sleep ratio, REM sleep disinhibition, shortened REM onset latency, and increased REM density. Antidepressants, though mitigating depressive symptoms, often disrupt sleep patterns, highlighting the delicate balance between treatment benefits and sleep-related impacts. Comprehensive assessment and management of insomnia is pivotal due to its significant implications for effective clinical management and treatment outcomes in MDD. Nonpharmacological treatments, notably cognitive-behavioral therapy for insomnia (CBTi), and digital CBTi (dCBTi) should be considered. CBTi unravels maladaptive sleep beliefs and behaviors, yielding improvements in both insomnia and MDD symptoms. Digital CBTi platforms extend accessibility, offering potential relief to a broader demographic. Sedating antidepressants, nonbenzodiazepine hypnotics, and atypical antipsychotics address insomnia within MDD, with cautious consideration for side effects. A combination of pharmacotherapy with CBTi might yield augmented outcomes in patients with MDD and comorbid insomnia. Ketamine, electroconvulsive therapy (ECT), repetitive transcranial magnetic stimulation (rTMS), vagal nerve stimulation (VNS), and deep brain stimulation (DBS) explore novel dimensions in treating treatment-resistant depression, potentially influencing sleep disruptions. [ Psychiatr Ann . 2023;53(11):502–507.]

Exome-wide association study of treatment-resistant depression suggests novel treatment targets

Treatment-resistant depression (TRD) is a severe form of major depressive disorder (MDD) with substantial public health impact and poor treatment outcome. Treatment outcome in MDD is significantly heritable, but genome-wide association studies have failed to identify replicable common marker alleles, suggesting a potential role for uncommon variants. Here we investigated the hypothesis that uncommon, putatively functional genetic variants are associated with TRD. Whole-exome sequencing data was obtained from 182 TRD cases and 2021 psychiatrically healthy controls. After quality control, the remaining 149 TRD cases and 1976 controls were analyzed with tests designed to detect excess burdens of uncommon variants. At the gene level, 5 genes, ZNF248, PRKRA, PYHIN1, SLC7A8, and STK19 each carried exome-wide significant excess burdens of variants in TRD cases (q < 0.05). Analysis of 41 pre-selected gene sets suggested an excess of uncommon, functional variants among genes involved in lithium response. Among the genes identified in previous TRD studies, ZDHHC3 was also significant in this sample after multiple test correction. ZNF248 and STK19 are involved in transcriptional regulation, PHYIN1 and PRKRA are involved in immune response, SLC7A8 is associated with thyroid hormone transporter activity, and ZDHHC3 regulates synaptic clustering of GABA and glutamate receptors. These results implicate uncommon, functional alleles in TRD and suggest promising novel targets for future research.

Treatment with the second-generation antipsychotic quetiapine is associated with increased subgenual ACC activation during reward processing in major depressive disorder

BackgroundThe second-generation antipsychotic (SGA) quetiapine is an essential option for antidepressant augmentation therapy in major depressive disorder (MDD), yet neurobiological mechanisms behind its antidepressant properties remain unclear. As SGAs interfere with activity in reward-related brain areas, including the anterior cingulate cortex (ACC) — a key brain region in antidepressant interventions, this study examined whether quetiapine treatment affects ACC activity during reward processing in MDD patients. MethodsUsing the ACC as region of interest, an independent t-test comparing reward-related BOLD response of 51 quetiapine-taking and 51 antipsychotic-free MDD patients was conducted. Monetary reward outcome feedback was measured in a card-guessing paradigm using pseudorandom blocks. Participants were matched for age, sex, and depression severity and analyses were controlled for confounding variables, including total antidepressant medication load, illness chronicity and acute depression severity. Potential dosage effects were examined in a 3 × 1 ANOVA. Differences in ACC-related functional connectivity were assessed in psycho-physiological interaction (PPI) analyses. ResultsLeft subgenual ACC activity was significantly higher in the quetiapine group compared to antipsychotic-free participants and dependent on high-dose quetiapine intake. Results remained significant after controlling for confounding variables. The PPI analysis did not yield significant group differences in ACC-related functional connectivity. LimitationsCausal interpretation is limited due to cross-sectional findings. ConclusionElevated subgenual ACC activity to rewarding stimuli may represent a neurobiological marker and potential key interface of quetiapine's antidepressant effects in MDD. These results underline ACC activity during reward processing as an investigative avenue for future research and therapeutic interventions to improve MDD treatment outcomes.

Efficacy of Adjunctive D-Cycloserine to Intermittent Theta-Burst Stimulation for Major Depressive Disorder

The antidepressant effects of transcranial magnetic stimulation protocols for major depressive disorder (MDD) are thought to depend on synaptic plasticity. The theta-burst stimulation (TBS) protocol synaptic plasticity is known to be N-methyl-D-aspartate (NMDA)-receptor dependent, yet it is unknown whether enhancing NMDA-receptor signaling improves treatment outcomes in MDD. To test whether low doses of the NMDA-receptor partial-agonist, D-cycloserine, would enhance intermittent TBS (iTBS) treatment outcomes in MDD. This was a single-site 4-week, double-blind, placebo-controlled, randomized clinical trial conducted from November 6, 2019, to December 24, 2020, including 50 participants with MDD. Participants were recruited via advertisements and referral. Inclusion criteria were as follows: age 18 to 65 years with a primary diagnosis of MDD, a major depressive episode with score of 18 or more on the 17-item Hamilton Depression Rating Scale, a Young Mania Rating Scale score of 8 or less, and normal blood work (including complete blood cell count, electrolytes, liver function tests, and creatinine level). Participants were randomly assigned 1:1 to either iTBS plus placebo or iTBS plus D-cycloserine (100 mg) for the first 2 weeks followed by iTBS without an adjunct for weeks 3 and 4. The primary outcome was change in depressive symptoms as measured by the Montgomery-Åsberg Depression Rating Scale (MADRS) at the conclusion of treatment. Secondary outcomes included clinical response, clinical remission, and Clinical Global Impression (CGI) scores. A total of 50 participants (mean [SD] age, 40.8 [13.4] years; 31 female [62%]) were randomly assigned to treatment groups: iTBS plus placebo (mean [SD] baseline score, 30.3 [4.2]) and iTBS plus D-cycloserine (mean [SD] baseline score, 30.4 [4.5]). The iTBS plus D-cycloserine group had greater improvements in MADRS scores compared with the iTBS plus placebo group (mean difference, -6.15; 95% CI, -2.43 to -9.88; Hedges g = 0.99; 95% CI, 0.34-1.62). Rates of clinical response were higher in the iTBS plus D-cycloserine group than in the iTBS plus placebo group (73.9% vs 29.3%), as were rates of clinical remission (39.1% vs 4.2%). This was reflected in lower CGI-severity ratings and greater CGI-improvement ratings. No serious adverse events occurred. Findings from this clinical trial indicate that adjunctive D-cycloserine may be a promising strategy for enhancing transcranial magnetic stimulation treatment outcomes in MDD using iTBS requiring further investigation. ClinicalTrials.gov Identifier: NCT03937596.

Modulation of resting-state functional connectivity in default mode network is associated with the long-term treatment outcome in major depressive disorder.

Treatment non-response and recurrence are the main sources of disease burden in major depressive disorder (MDD). However, little is known about its neurobiological mechanism concerning the brain network changes accompanying pharmacotherapy. The present study investigated the changes in the intrinsic brain networks during 6-month antidepressant treatment phase associated with the treatment response and recurrence in MDD. Resting-state functional magnetic resonance imaging was acquired from untreated patients with MDD and healthy controls at baseline. The patients' depressive symptoms were monitored by using the Hamilton Rating Scale for Depression (HAMD). After 6 months of antidepressant treatment, patients were re-scanned and followed up every 6 months over 2 years. Traditional statistical analysis as well as machine learning approaches were conducted to investigate the longitudinal changes in macro-scale resting-state functional network connectivity (rsFNC) strength and micro-scale resting-state functional connectivity (rsFC) associated with long-term treatment outcome in MDD. Repeated measures of the general linear model demonstrated a significant difference in the default mode network (DMN) rsFNC change before and after the 6-month antidepressant treatment between remitters and non-remitters. The difference in the rsFNC change over the 6-month antidepressant treatment between recurring and stable MDD was also specific to DMN. Machine learning analysis results revealed that only the DMN rsFC change successfully distinguished non-remitters from the remitters at 6 months and recurring from stable MDD during the 2-year follow-up. Our findings demonstrated that the intrinsic DMN connectivity could be a unique and important target for treatment and recurrence prevention in MDD.

Psychotherapy employed additionally to Psychopharmacotherapy is not related to Better Treatment Outcome in Major Depressive Disorder

IntroductionAlthough numerous effective antidepressant (AD) strategies are available for the treatment of major depressive disorder (MDD), many patients do not achieve satisfactory treatment response.ObjectivesThe aims of the present European, cross-sectional, multicenter, naturalistic study were (1) to determine the proportion of patients suffering from primary MDD who received additional psychotherapy to their ongoing psychopharmacotherapy and (2) to identify the associated socio-demographic and clinical patterns.MethodsPatients receiving both treatments were compared to those lacking concomitant additional psychotherapy that was manual-driven psychotherapy (MDP) in all cases.ResultsWhile 68.8% of a total of 1279 MDD patients received exclusively psychopharmacotherapy, 31.2% underwent a psychopharmacotherapy-MDP combination. The latter patient population was rather younger, higher educated, employed, exhibited an earlier mean age of MDD onset, lower severity of current depressive symptoms with lower odds of suicidality and higher rates of melancholic features, and comorbid asthma and migraine, and was generally treated with lower daily doses of their first-line ADs. Whereas agomelatine was more commonly dispensed in these patients, selective serotonin reuptake inhibitors were more often prescribed in MDD patients lacking additional MDP. No significant between-group differences were detected in terms of treatment outcome.ConclusionsThe fact that the employment of additional MDP was not related to better treatment outcome in MDD represents our major and clinically most relevant finding. Generally, MDP was employed in a minority of our patients who experienced rather beneficial socio-demographic and clinical characteristics. This might reflect an inferior accessibility of these psychotherapeutic techniques for patients who are more severely ill and less socio-economically privileged.DisclosureReferences Bartova L, Fugger G, Dold M, Swoboda MMM, Zohar J, Mendlewicz J, Souery D, Montgomery S, Fabbri C, Serretti A, Kasper S. Combining psychopharmacotherapy and psychotherapy is not associated with better treatment outcome in major depressive disor

Machine Learning and Deep Learning for the Pharmacogenomics of Antidepressant Treatments.

A growing body of evidence now proposes that machine learning and deep learning techniques can serve as a vital foundation for the pharmacogenomics of antidepressant treatments in patients with major depressive disorder (MDD). In this review, we focus on the latest developments for pharmacogenomics research using machine learning and deep learning approaches together with neuroimaging and multi-omics data. First, we review relevant pharmacogenomics studies that leverage numerous machine learning and deep learning techniques to determine treatment prediction and potential biomarkers for antidepressant treatments in MDD. In addition, we depict some neuroimaging pharmacogenomics studies that utilize various machine learning approaches to predict antidepressant treatment outcomes in MDD based on the integration of research on pharmacogenomics and neuroimaging. Moreover, we summarize the limitations in regard to the past pharmacogenomics studies of antidepressant treatments in MDD. Finally, we outline a discussion of challenges and directions for future research. In light of latest advancements in neuroimaging and multi-omics, various genomic variants and biomarkers associated with antidepressant treatments in MDD are being identified in pharmacogenomics research by employing machine learning and deep learning algorithms.

Periodontal status and the efficacy of the first-line treatment of major depressive disorder.

ObjectivesThe efficacy of treatment of major depressive disorder (MDD) is not satisfactory. Systemic inflammation may play an important role in MDD pathogenesis and treatment outcomes. Periodontal disease is the systemic inflammatory condition. Its prevalence may be as high as 45%. We aimed to assess the association of periodontal status with the outcome of 3‐month first‐line treatment of MDD with selective serotonin reuptake inhibitors.Material and MethodsWe performed the prospective cohort study during 2018/2019 at Psychiatric Hospital “Sveti Ivan,” Croatia, on a consecutive sample of 43 patients. The outcome was the MDD symptoms severity measured using the Hamilton Depression Rating Scale‐17. The periodontal status was indicated by the clinical attachment loss (CAL).ResultsBaseline periodontal status had a nonlinear significant and clinically relevant association with the MDD treatment outcome (R 2 change of the quadratic term = 0.12; p = 0.027). In patients with good baseline periodontal status the severity of MDD symptoms was significantly improved. When the value of CAL was ≥4.44 mm, indicating the worse periodontal status, further increase in baseline CAL was associated with the worsening of MDD treatment outcomes independently of the baseline depression severity and 14 sociodemographic and clinical predictors of treatment outcome.ConclusionsPeriodontal healthcare is accessible, and should be utilize in an integrative, multidisciplinary approach not only for the sake of psychiatric patients' quality of life and prevention of periodontal disease, but for the sake of the outcomes of psychiatric treatment as well.

Combining psychopharmacotherapy and psychotherapy is not associated with better treatment outcome in major depressive disorder - evidence from the European Group for the Study of Resistant Depression

Despite plenty of effective antidepressant (AD) treatments, the outcome of major depressive disorder (MDD) is often unsatisfactory, probably due to improvable exploitation of available therapies. This European, cross-sectional, naturalistic multicenter study investigated the frequency of additional psychotherapy in terms of a manual-driven psychotherapy (MDP) in 1410 adult in- and outpatients with MDD, who were primarily treated with AD psychopharmacotherapy. Socio-demographic and clinical patterns were compared between patients receiving both treatments and those lacking concomitant MDP. In a total of 1279 MDD patients (90.7%) with known status of additional MDP, those undergoing a psychopharmacotherapy-MDP combination (31.2%) were younger, higher educated, more often employed and less severely ill with lower odds for suicidality as compared to patients receiving exclusively psychopharmacotherapy (68.8%). They experienced an earlier mean age of MDD onset, melancholic features, comorbid asthma and migraine and received lower daily doses of their first-line ADs. While agomelatine was more often established in these patients, MDD patients without MDP received selective serotonin reuptake inhibitors more frequently. These two patient groups did not differ in terms of response, non-response and treatment resistant depression (TRD). Accordingly, the employment of additional MDP could not be related to better treatment outcomes in MDD. The fact that MDP was applied in a minority of patients with rather beneficial socio-demographic and clinical characteristics might reflect inferior accessibility of these psychotherapeutic techniques for socially and economically disadvantaged populations.

Ketamine’s modulation of cerebro-cerebellar circuitry during response inhibition in major depression

Patients with major depressive disorder (MDD) exhibit impaired control of cognitive and emotional systems, including deficient response selection and inhibition. Though these deficits are typically attributed to abnormal communication between macro-scale cortical networks, altered communication with the cerebellum also plays an important role. Yet, how the circuitry between the cerebellum and large-scale functional networks impact treatment outcome in MDD is not understood. We thus examined how ketamine, which elicits rapid therapeutic effects in MDD, modulates cerebro-cerebellar circuitry during response-inhibition using a functional imaging NoGo/Go task in MDD patients (N = 46, mean age: 39.2, 38.1% female) receiving four ketamine infusions, and healthy controls (N = 32, mean age:35.2, 71.4% female). We fitted psychophysiological-interaction (PPI) models for a functionally-derived cerebellar-seed and extracted average PPI in three target functional networks, frontoparietal (FPN), sensory-motor (SMN) and salience (SN) networks. Time and remission status were then evaluated for each of the networks and their network-nodes. Follow-up tests examined whether PPI-connectivity differed between patient remitter/non-remitters and controls. Results showed significant decreases in PPI-connectivity after ketamine between the cerebellum and FPN (p < 0.001) and SMN networks (p = 0.008) in remitters only (N = 20). However, ketamine-related changes in PPI-connectivity between the cerebellum and the SN (p = 0.003) did not vary with remitter status. Cerebellar-FPN, -SN PPI values at baseline were also associated with treatment outcome. Using novel methodology to quantify the functional coupling of cerebro-cerebellar circuitry during response-inhibition, our findings highlight that these loops play distinct roles in treatment response and could potentially serve as novel biomarkers for fast-acting antidepressant therapies in MDD.

Single Channel EEG Classification: A Case Study on Prediction of Major Depressive Disorder Treatment Outcome

In multichannel EEG, several electrodes are attached to the head that may be annoying for patients and troublesome for operators. Moreover, the number of electrodes is the main reason of the infeasibility of developing EEG based wearable and point of care devices. To address this problem, recently, the concept of single-channel EEG (SCEEG) is presented. The spatial resolution of SCEEG is lower than the multichannel one, but it is easy to use, cost-effective, ubiquitous, and wearable. In this paper, for the first time, we have developed the concept of SCEEG for the classification of responders and nonresponders to repetitive transcranial magnetic stimulation (rTMS) treatment in major depressive disorder (MDD). We also compared the performance of SCEEG and multichannel EEG with the different number of channels in the prediction of responding to rTMS treatment. 19-electrode EEG is recorded from 46 MDD patients before rTMS treatment. Among participants, 23 individuals responded to treatment. The dataset is partitioned into the training (36 subjects) and testing (10 subjects) datasets. Linear and nonlinear features were extracted from every channel of EEG. In training, to select informative features, the minimal-redundancy-maximal-relevance (mRMR) algorithm was applied. The selected features were classified by k-nearest neighbors (KNN) classifier, which is evaluated by leave-one-out cross-validation. Then the obtained classifier is applied to the testing dataset. The results demonstrated that the F8 channel classifies responders and nonresponders with an accuracy of 80%. Moreover, our results revealed that SCEEG could perform as multichannel EEG in the prediction of rTMS treatment outcome in MDD patients. The obtained accuracy indicates that our proposed method based on SCEEG has a high potential for predicting rTMS treatment outcome in MDD patients.

The association of body mass index (BMI) with treatment outcomes in patients with major depressive disorder

BackgroundIn patients with major depressive disorder (MDD), poor antidepressant treatment response might be associated with an excessive body mass index (BMI). However, the impact of underweight on treatment response is unclear. Moreover, it has not been studied whether a continuous or categorical BMI measure should be used to predict of treatment response. MethodsPost-hoc analysis of data collected in a clinical trial including adults with MDD (n=202) reporting outcomes of antidepressant medication, i.e. paroxetine, mirtazapine or paroxetine+mirtazapine. Measures included baseline BMI (underweight=BMI <18.5, normal weight:=BMI:18.5-23.9, overweight=BMI≥24) and symptom severity (17-item-Hamilton Depression scale; HAMD-17) assessed at weeks 0, 2, 3, 4, 6 and 8.Univariate analyses were used to explore the effect of baseline BMI on HAMD-17 reduction, response (defined as ≥50% HAMD-17 reduction) and remission (defined as HAMD-17 ≤7) at endpoint. Pearson correlation were used to explore the relationship between body weight, BMI as continuous measure and HAMD-17 reduction. Logistic regression was used to determine the predictors for remission. Multiple linear regression was used to establish the correlation of BMI with change of HAMD-17. Results111 (55.0%) patients were normal weight, 20 (9.9%) were underweight, 71 (35.1%) were overweight. Underweight patients showed the best improvement to antidepressant treatment. Non-remitters had greater body weight and BMI than remitters (P<0.05). The reduction of HAMD-17 was correlated with baseline body weight (r=-0.16, P=0.032) and BMI (r=-0.19, P=0.012). Logistic regression found patients with BMI<24 to be 2 times (OR=1.958, 95%CI: 1.015, 3.774) remitters (P=0.045) than overweight patients. The multiple linear regression showed that the change of HAMD-17 total score decreased with increasing BMI (β=-0.32, P = 0.016). ConclusionWe confirmed that BMI can predict treatment outcomes in MDD. For the first time we found that underweight patients benefit most from antidepressant treatment. The findings may be useful to physicians in their decision regarding the choice of antidepressants according to BMI.