Outcomes 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.

The mediating role of family functioning between childhood trauma and depression severity in major depressive disorder and bipolar disorder

BackgroundChildhood trauma (CT) and family functioning exert significant influences on the course and long-term outcome of major depressive disorder (MDD) and bipolar disorder (BD) patients. Hence, we examined the intricate relationship between CT, family function, and the severity of depressive episodes in MDD and BD patients. Methods562 patients with depressive episodes (336 MDD and 226 BD) and 204 healthy controls (HCs) were included in this retrospective study. The 17-item Hamilton Depression Rating Scale (HAMD-17), Childhood Trauma Questionnaire (CTQ), and Family Adaptability and Cohesion Evaluation Scale (FACES II-CV) were assessed. Pearson correlation analysis and mediation analysis were performed. ResultsCT had both a direct and indirect impact on depression severity in MDD and BD groups. In MDD, family adaptability mediated the impact of all CT subtypes on depression severity (Effect = 0.113, [0.030, 0.208]). In BD, family cohesion played a mediating role between emotional neglect (EN) and HAMD-17 scores (Effect = 0.169, [0.008, 0.344]). Notable differences were observed in onset age, illness duration, episode frequency, family history, and CT subtypes between MDD and BD (P < 0.05). LimitationsThis study has several limitations including recall bias, lack of objective family functioning measures, small sample size, and cross-sectional design. ConclusionsFamily functioning mediated the impact of CT on depressive symptoms severity in MDD and BD patients. MDD patients with a history of CT exhibited reduced family adaptability, while BD patients with a history of EN had weaker familial emotional bonds. Our findings highlighted the importance of family-focused preventive interventions in mitigating the long-term effects of CT.

Antidepressant class and concurrent rTMS outcomes in major depressive disorder: a systematic review and meta-analysis

Repetitive transcranial magnetic stimulation (rTMS) is frequently used as an adjunctive treatment with antidepressants for depression. We aimed to evaluate the clinical efficacy and safety of antidepressant classes when administered concurrently with rTMS for the management of major depressive disorder (MDD). In this systematic review and meta-analysis, MEDLINE, Embase, PsycINFO, and the Cochrane Library were searched from inception to April 12th 2024 for terms relating to medication, depression, and rTMS and appraised by 2 independent screeners. All randomized clinical trials that prospectively evaluated a specific antidepressant adjunctively with sham rTMS as a control in MDD were included. The study was registered with PROSPERO (CRD42023418435). The primary outcome measure assessed symptomatic improvement measured by formal depression scales. We used a random-effects model with pooled Standardized Mean Differences (SMDs) and log odds ratios (OR). All studies were assessed for their methodological quality and bias using the Cochrane Collaboration Risk of Bias tool version 2 (RoB2). 14 articles from 5376 identified studies were included in the systematic review and meta-analysis. There was only sufficient trial data to evaluate the effects of rTMS and combination therapy with selective serotonin reuptake inhibitors (SSRIs) and selective norepinephrine reuptake inhibitors (SNRIs). Across studies, 848 participants (mean [SD] age:41.1 [18.7] years for SSRIs, 51.8 [3.8] years for SNRIs) prospectively examined the efficacy of antidepressant medication with rTMS. Combining rTMS with SSRIs led to significantly lower depression scores, (SMD [CI] of-0.65 [-0.98,-0.31], p=0.0002, I2=66.1%), higher response (OR=0.97 [0.50, 1.44], p<0.0001, I2=25.33%) and remission rates (OR=1.04 [0.55, 1.52], p<0.0001, I2=0.00%) than medication with sham rTMS. No additive benefit was found for SNRIs with rTMS (SMD of 0.10 [-0.14, 0.34], p=0.42, I2=0.00%; OR=0.12 [-0.39, 0.62], p=0.64, I2=0.00%; OR=-0.31 [-0.90, 0.28], p=0.86, I2=39.9%). The overall risk of bias for the included studies ranged from low to high, with 1 study having a high risk of bias. The combination of rTMS with SSRIs, but not SNRIs, significantly reduced depression severity, increasing response and remission rates. Some analyses demonstrated high heterogeneity, which was influenced by an SSRI trial with a high effect size. Overall, these results suggest that not all antidepressant combination therapies are alike, and SSRIs should be considered when initiating rTMS. Donald T. Stuss Young Investigator Research Innovation Award from the Sandra Black Centre for Brain Resilience & Recovery and the Harquail Centre for Neuromodulation through the Sunnybrook Foundation.

Heterogeneity in Antidepressant Treatment and Major Depressive Disorder Outcomes Among Clinicians

While abundant work has examined patient-level differences in antidepressant treatment outcomes, little is known about the extent of clinician-level differences. Understanding these differences may be important in the development of risk models, precision treatment strategies, and more efficient systems of care. To characterize differences between outpatient clinicians in treatment selection and outcomes for their patients diagnosed with major depressive disorder across academic medical centers, community hospitals, and affiliated clinics. This was a longitudinal cohort study using data derived from electronic health records at 2 large academic medical centers and 6 community hospitals, and their affiliated outpatient networks, in eastern Massachusetts. Participants were deidentified clinicians who billed at least 10 International Classification of Diseases, Ninth Revision (ICD-9) or Tenth Revision (ICD-10) diagnoses of major depressive disorder per year between 2008 and 2022. Data analysis occurred between September 2023 and January 2024. Heterogeneity of prescribing, defined as the number of distinct antidepressants accounting for 75% of prescriptions by a given clinician; proportion of patients who did not return for follow-up after an index prescription; and proportion of patients receiving stable, ongoing antidepressant treatment. Among 11 934 clinicians treating major depressive disorder, unsupervised learning identified 10 distinct clusters on the basis of ICD codes, corresponding to outpatient psychiatry as well as oncology, obstetrics, and primary care. Between these clusters, substantial variability was identified in the proportion of selective serotonin reuptake inhibitors, selective norepinephrine reuptake inhibitors, and tricyclic antidepressants prescribed, as well as in the number of distinct antidepressants prescribed. Variability was also detected between clinician clusters in loss to follow-up and achievement of stable treatment, with the former ranging from 27% to 69% and the latter from 22% to 42%. Clinician clusters were significantly associated with treatment outcomes. Groups of clinicians treating individuals diagnosed with major depressive disorder exhibit marked differences in prescribing patterns as well as longitudinal patient outcomes defined by electronic health records. Incorporating these group identifiers yielded similar prediction to more complex models incorporating individual codes, suggesting the importance of considering treatment context in efforts at risk stratification.

MSR58 Comparing Predictive Models: Traditional Versus Modern Statistical Approaches for Economic Outcomes in Major Depressive Disorder

MSR58 Comparing Predictive Models: Traditional Versus Modern Statistical Approaches for Economic Outcomes in Major Depressive Disorder

Causes and consequences of major depressive disorder: An encompassing Mendelian randomization study.

Major depressive disorder (MDD) is a prevalent and debilitating disorder that has been associated with a range of risk factors and outcomes. Causal pathways between MDD and other traits can be studied using genetic variants as instrumental variables. A literature review was conducted to identify 201 MDD-associated traits. For 115 traits, there were well-powered genome-wide association study (GWAS) results available that could be used to assess the genetic correlation with MDD. Of these, there were 89 meeting criteria for investigating causal associations in both directions using two-sample Mendelian randomization (TSMR). Of the traits that were not captured by GWAS, 43 could be included as outcomes of MDD using one-sample MR (OSMR). A range of methods and sensitivity tests was applied to gauge robustness of results, together with statistical power analyses to aid interpretation. Moderate to strong genetic overlap was found between MDD and most traits. Support for causal effects of MDD liability were found for circadian, cognitive, diet, medical disease, endocrine, functional, inflammatory, metabolic, mortality, physical activity, reproduction, risk behavior, social, socioeconomic, and suicide outcomes. Most associations were bidirectional, although there was less evidence for diet, disease, and endocrine traits causing MDD risk. Results were robust across sensitivity analyses. This study provides a systematic overview of traits putatively causally related to MDD, confirming previous findings as well as identifying new associations. Our results highlight the importance of MDD as a risk factor cross-cutting across medical, functional, and psychosocial domains and emphasize the need for concerted efforts at reducing this highly prevalent disorder.

Real-world evidence from a retrospective study on suicide during depression: clinical characteristics, treatment patterns and disease burden

BackgroundSuicide stands as both a primary symptom and the direst outcome of major depressive disorder (MDD). The scarcity of effective treatment strategies makes managing MDD patients with suicide especially challenging. Hence, it is crucial to investigate disease characteristics and efficacious therapeutic strategies for these patients, drawing insights from disease databases and real-world data.MethodsIn this retrospective study, MDD patients hospitalized between January 2013 and December 2020 were investigated using Electronic Health Records (EHR) data from Beijing Anding Hospital. The study enrolled 4138 MDD patients with suicidal ideation or behavior (MDS) and 3848 without (MDNS). Demographic data, clinical attributes, treatment approaches, disease burden, and re-hospitalization within one year of discharge were extracted and compared.ResultsPatients in the MDS group were predominantly younger and female, exhibiting a higher prevalence of alcohol consumption, experiencing frequent life stress events, and having an earlier onset age. Re-hospitalizations within six months post-discharge in the MDS group were significantly higher than in the MDNS group (11.36% vs. 8.91%, p < 0.001). Moreover, a more considerable fraction of MDS patients underwent combined electroconvulsive therapy treatment (56.72% vs. 43.71%, p < 0.001). Approximately 38% of patients in both groups were prescribed two or more therapeutic regimes, and over 90% used antidepressants, either alone or combined. Selective serotonin reuptake inhibitors (SSRIs) were the predominant choice in both groups. Furthermore, antidepressants were often prescribed with antipsychotics or mood stabilizers. When medication alterations were necessary, the favoured options involved combination with antipsychotics or transitioning to alternative antidepressants. Yet, in the MDS group, following these initial modifications, the addition of mood stabilizers tended to be the more prioritized alternative.ConclusionsMDD patients with suicidal ideation or behaviour displayed distinctive demographic and clinical features. They exhibited intricate treatment patterns, a pronounced burden of illness, and an increased likelihood of relapse.

Adjunctive cariprazine for major depressive disorder: a systematic review and meta-analysis.

Converging evidence has suggested that treatment augmentation with a second-generation atypical antipsychotic (SGA) may improve treatment outcomes in major depressive disorder (MDD) patients after an incomplete response to a first-line antidepressant. Cariprazine is a recently approved SGA for MDD augmentation. Herein, we evaluate both continuous (ie, change in depressive symptom severity scores over time) and categorical (ie, remission and response rates) outcomes. Following a full-text review, four randomized controlled trials (RCTs) were included in our meta-analysis, while five studies were included for a qualitative review. Risk ratios (RRs) were calculated for all included randomized controlled studies to determine the relative response and remission rates of cariprazine compared to placebo augmentation. The RR for all-cause dropout was also determined as a proxy for overall acceptability. Two studies found a statistically significant treatment response using cariprazine augmentation. One study observed depressive symptom remission for cariprazine compared to placebo. Our random-effects model revealed moderate antidepressant effects of cariprazine, with a standardized mean difference (SMD) in Montgomery-Åsberg Depression Rating Scale (MADRS) scores of -1.79 (95% CI): -2.89, -0.69). Our pooled response RR and remission RR were calculated as 1.21 (95% CI: 1.05, 1.39, P=0.008) and 0.99 (95% CI: 0.84, 1.17, P=0.91), respectively. The RR for response was statistically significant (P<0.05). However, the RR for remission was not statistically significant. The findings from our meta-analysis include a variable magnitude of effects. Evidence suggests cariprazine may be an effective treatment for MDD; however, further results are needed to clarify this relation.

Brain magnetic resonance imaging outperforms clinical severity ratings in the prediction of treatment outcomes in major depressive disorder

IntroductionMajor depressive disorder (MDD) is a prevalent and disabling condition. Approximately 30-50% of patients do not respond to first-line medication or psychotherapy. Therefore, several studies have investigated the predictive potential of pretreatment severity rating or neuroimaging features to guide clinical approaches that can speed optimal treatment selection.ObjectivesTo evaluate the performance of 1) severity ratings (scores of Hamilton Depression/Anxiety Scale, illness duration, and sleep quality, etc.) and demographic characteristic and 2) brain magnetic resonance imaging (MRI) features in predicting treatment outcomes for MDD. Second, to assess performance variations among varied modalities and interventions in MRI studies.MethodsWe searched studies in PubMed, Embase, Web of Science, and Science Direct databases before March 22, 2023. We extracted a confusion matrix for prediction in each study. Separate meta-analyses were performed for clinical and MRI studies. The logarithm of diagnostic odds ratio [log(DOR)], sensitivity, and specificity were conducted using Reitsma’s random effect model. The area under curve (AUC) of summary receiver operating characteristic (SROC) curve was calculated.Subgroup analyses were conducted in MRI studies based on modalities: resting-state functional MRI (rsfMRI), task-based fMRI (tbfMRI), and structural MRI (sMRI), and interventions: antidepressant (including selective serotonin reuptake inhibitors [SSRI]) and electroconvulsive therapy (ECT). Meta-regression was conducted 1) between clinical and MRI studies and 2) among modality or intervention subgroups in MRI studies.ResultsWe included ten studies used clinical features covering 6494 patients, yielded a log(DOR) of 1.42, AUC of 0.71, sensitivity of 0.61, and specificity of 0.74. In terms of MRI, 44 studies with 2623 patients were included, revealing an overall log(DOR) of 2.53. The AUC, sensitivity, and specificity were 0.89, 0.78, and 0.75.Studies using MRI features had a higher sensitivity (0.89 vs. 0.61) in predicting treatment outcomes than clinical features (P &lt; 0.001). RsfMRI had higher specificity (0.79 vs. 0.69) than tbfMRI subgroup (P = 0.01). No significant differences were found between sMRI and other modalities, nor between antidepressants (SSRIs and others) and ECT. Antidepressant studies primarily identified predictive imaging features in limbic and default mode networks, while ECT mainly focused on limbic network.ConclusionsOur findings suggest a robust promise for pretreatment brain MRI features in predicting treatment outcomes in MDD, offering higher accuracy than clinical studies. While tasks in tbfMRI studies differed, those studies overall had less predictive utility than rsfMRI data. For MRI studies, overlapping but distinct network level measures predicted outcomes for antidepressants and ECT.Disclosure of InterestNone Declared

The Role of Subgenual Resting-State Connectivity Networks in Predicting Prognosis in Major Depressive Disorder

BackgroundA seminal study found higher subgenual frontal cortex resting-state connectivity with 2 left ventral frontal regions and the dorsal midbrain to predict better response to psychotherapy versus medication in individuals with treatment-naïve major depressive disorder (MDD). Here, we examined whether these subgenual networks also play a role in the pathophysiology of clinical outcomes in MDD with early treatment resistance in primary care. MethodsForty-five people with current MDD who had not responded to ≥2 serotonergic antidepressants (n = 43, meeting predefined functional magnetic resonance imaging minimum quality thresholds) were enrolled and followed over 4 months of standard care. Functional magnetic resonance imaging resting-state connectivity between the preregistered subgenual frontal cortex seed and 3 previously identified left ventromedial, ventrolateral prefrontal/insula, and dorsal midbrain regions was extracted. The clinical outcome was the percentage change on the self-reported 16-item Quick Inventory of Depressive Symptomatology. ResultsWe observed a reversal of our preregistered hypothesis in that higher resting-state connectivity between the subgenual cortex and the a priori ventrolateral prefrontal/insula region predicted favorable rather than unfavorable clinical outcomes (rs39 = −0.43, p = .006). This generalized to the sample including participants with suboptimal functional magnetic resonance imaging quality (rs43 = −0.35, p = .02). In contrast, no effects (rs39 = 0.12, rs39 = −0.01) were found for connectivity with the other 2 preregistered regions or in a whole-brain analysis (voxel-based familywise error–corrected p < .05). ConclusionsSubgenual connectivity with the ventrolateral prefrontal cortex/insula is relevant for subsequent clinical outcomes in current MDD with early treatment resistance. Its positive association with favorable outcomes could be explained primarily by psychosocial rather than the expected pharmacological changes during the follow-up period.

Depression and cognition are associated with lipid dysregulation in both a multigenerational study of depression and the National Health and Nutrition Examination Survey

Chronic dysregulation of peripheral lipids has been found to be associated with depression and cognition, but their interaction has not been investigated. Growing evidence has highlighted the association between peripheral lipoprotein levels with depression and cognition with inconsistent results. We assessed the association between peripheral lipids, depression, and cognition while evaluating their potential interactions using robust clinically relevant predictors such as lipoprotein levels and chronic medical disorders that dysregulate lipoproteins. We report an association between peripheral lipids, depression, and cognition, suggesting a common underlying biological mechanism driven by lipid dysregulation in two independent studies. Analysis of a longitudinal study of a cohort at high or low familial risk for major depressive disorder (MDD) (n = 526) found metabolic diseases, including diabetes, hypertension, and other cardiovascular diseases, were associated with MDD and cognitive outcomes. Investigating a cross-sectional population survey of adults in the National Health and Nutrition Examination Survey 2011–2014 (NHANES) (n = 2377), depression was found to be associated with high density lipoprotein (HDL) and cognitive assessments. In the familial risk study, medical conditions were found to be associated with chronic lipid dysregulation and were significantly associated with MDD using the structural equation model. A positive association between chronic lipid dysregulation and cognitive scores was found in an exploratory analysis of the familial risk study. In a complementary study, analysis of NHANES revealed a positive association of HDL levels with cognition. Further analysis of the NHANES cohort indicated that depression status mediated the interaction between HDL levels and cognitive tests. Importantly, the protective effect of HDL on cognition was absent in those with depressive symptoms, which may ultimately result in worse outcomes leading to cognitive decline. These findings highlight the potential for the early predictive value of medical conditions with chronic lipid dyshomeostasis for the risk of depression and cognitive decline.

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.

Clinical features and treatment outcomes of major depressive disorder with genital symptoms.

Clinical features and treatment outcomes of major depressive disorder with genital symptoms.

Altered regional brain activity moderating the relationship between childhood trauma and depression severity

ObjectiveChildhood trauma (CT) is a major environmental risk factor for an adverse course and treatment outcome of major depressive disorder (MDD). Evidence suggests that an altered regional brain activity may play a crucial role in the relationship between CT and MDD. This study aimed to clarify the relationship between CT, regional brain activity, and depression severity. MethodsIn this study, 96 patients with MDD and 82 healthy controls (HCs) participated. Regional brain activity was measured using the fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo). These measures were compared between the MDD and HC groups, and the values of different brain regions were extracted as moderators. ResultsIncreased fALFF and ReHo values were observed in the left middle temporal gyrus in the MDD group compared with the HC group (p < 0.001). Furthermore, the fALFF and ReHo values moderated the positive correlation between the Childhood Trauma Questionnaire (CTQ) score, 17-item Hamilton Depression Rating Scale (HAMD-17) total score, and retardation factor score in the MDD group (all, p < 0.05). Finally, as the fALFF and ReHo values increased, the positive correlations between CTQ, HAMD-17 total, and retardation dimension scores became stronger. ConclusionOur study highlighted the crucial role of altered brain function in connecting childhood maltreatment with depressive symptoms. Our findings indicate that an altered regional brain activity could explain the potential neurobiological mechanisms of MDD symptoms, offering the opportunity to function as a powerful diagnostic biomarker.

Prediction and Validation of Hub Genes Related to Major Depressive Disorder Based on Co-expression Network Analysis.

Major depressive disorder (MDD) is generally among the most prevalent psychiatric illnesses. Significant advances have occurred in comprehension of the MDD biology. However, it is still essential to recognize new biomarkers for potential targeted treatment of patients with MDD. The present work deals with in-depth comparative computational analyses to obtain new insights, such as gene ontology and pathway enrichment analyses and weighted gene co-expression network analysis (WGCNA) through gene expression dataset. The expression of selected hub-genes was validated in MDD patients using quantitative real-time PCR (RT-qPCR). We found that MDD progression includes the turquoise module genes (p-value = 1e-18, r = 0.97). According to gene enrichment analysis, the cytokine-mediated signaling pathway mostly involves genes in this module. By selection of four candidate hub-genes (IL6, NRG1, TNF, and BDNF), RT-qPCR validation was performed. A significant NRG1 downregulation was revealed by the RT-qPCR outcomes in MDD. In MDD patients, TNF and IL6 expression were considerably higher, and no considerable differences were found in the BDNF expression. Ultimately, based on ROC analyses, IL6, NRG1, and TNF had a higher MDD diagnostic performance. Therefore, our study presents information on the intricate association between MDD development and cytokine-mediated signaling, thus providing new rationales to develop new therapeutic approaches.

Biomarker-Guided Tailored Therapy in Major Depression.

This chapter provides a comprehensive examination of a broad range of biomarkers used for the diagnosis and prediction of treatment outcomes in major depressive disorder (MDD). Genetic, epigenetic, serum, cerebrospinal fluid (CSF), and neuroimaging biomarkers are analyzed in depth, as well as the integration of new technologies such as digital phenotyping and machine learning. The intricate interplay between biological and psychological elements is emphasized as essential for tailoring MDD management strategies. In addition, the evolving link between psychotherapy and biomarkers is explored to uncover potential associations that shed light on treatment response. This analysis underscores the importance of individualized approaches in the treatment of MDD that integrate advanced biological insights into clinical practice to improve patient outcomes.

Care pathways, prescribing practices and treatment outcomes in major depressive disorder and treatment-resistant depression: retrospective, population-based cohort study.

Despite the availability of effective therapies, many patients with major depressive disorder (MDD) develop treatment-resistant depression (TRD). To evaluate and compare prescribing patterns, contact with specialist services and treatment outcomes in patients with MDD and TRD. This was a retrospective analysis of linked primary and secondary care National Health Service data in the north-west London Discover-NOW data-set. Eligible patients were adults who had diagnostic codes for depression and had been prescribed at least one antidepressant between 2015 and 2020. A total of 110 406 patients were included, comprising 101 333 (92%) with MDD and 9073 (8%) with TRD. Patients with TRD had significantly higher risks of suicidal behaviour and comorbidities such as anxiety, asthma, and alcohol or substance misuse (all P < 0.0001). Citalopram, sertraline, fluoxetine and mirtazapine accounted for 83% of MDD and 71% of TRD prescriptions. Use of antidepressant switching (1% MDD, 7% TRD) and combination therapy (1%, 5%) was rare, whereas augmentation occurred more frequently in the TRD group (4%, 35%). Remission was recorded in 42 348 (42%) patients with MDD and 1188 (13%) with TRD (P < 0.0001), whereas relapse was seen in 20 970 (21%) and 4923 (54%), respectively (P < 0.0001). Mean times from diagnosis to first contact with mental health services were 38.9 (s.d. 33.6) months for MDD and 41.5 (s.d. 32.0) months for TRD (P < 0.0001). There appears to be a considerable difference between treatment guidelines for depression and TRD and the reality of clinical practice. Long-term treatment with single antidepressants, poor remission, and high relapse rates among patients in primary care highlight the need to optimise treatment pathways and access to newer therapies.

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.

Neuroplasticity: Pathophysiology and Role in Major Depressive Disorder.

Neuroplasticity is characterized by the brain's ability to change its activity in response to extrinsic and intrinsic factors and is thought to be the mechanism behind all brain functions. Neuroplasticity causes structural and functional changes on a molecular level, specifically the growth of different regions in the brain and changes in synaptic and post-synaptic activities. The four types of neuroplasticity are homologous area adaption, compensatory masquerade, cross-modal reassignment, and map expansion. All of these help the brain work around injuries or new information inputs. In addition to baseline physical functions, neuroplasticity is thought to be the basis of emotional and mental regulations and the impairment of it can cause various mental illnesses. Concurrently, these mental illnesses further the damage of synaptic plasticity in the brain. Major depressive disorder (MDD) is one of the most common mental illnesses. It is affected by and accelerates the impairment of neuroplasticity. It is characterized by a chronically depressed state of mind that can impact the patient's daily life, including work life and interests. This review will focus on highlighting the physiological aspects of the disease and the role of neuroplasticity in the pathogenesis and pathology of the disorder. Moreover, the role of monoamine regulation and ketamine uptake will be discussed in terms of their antidepressant effects on the outcomes of MDD.