Prognosis and Improved Outcomes in Major Depression: A Review

Therapeutic potential of COX-2 inhibitors in neuropsychiatric disorders

Neuropsychiatric disorders such as bipolar disorder, migraine, major depressive disorder, epilepsy, attention-deficit/hyperactivity disorder, autism spectrum disorder and schizophrenia, are a huge burden on global health, impacting millions of individuals worldwide and posing significant barriers to effective treatment. Despite advancements in medication and psychotherapy, many patients continue to suffer from severe symptoms and receive little alleviation. All of these conditions are quite frequent, yet they affect people in a way that is exceedingly detrimental. The increasing evidence suggests the connection between these disorders and inflammation. Therefore, the use of anti-inflammatory agents, namely cyclooxygenase-2 (COX-2) inhibitors, offers a new approach to prevent and treat neuropsychiatric disorders. This review discusses about the COX pathway and the role of COX-2 in the neuroinflammation. Furthermore, this review highlights the COX-2 inhibitors as a promising therapeutic agent in these neuropsychiatric disorders, however, further studies are required to assess appropriate illness stage-related indication.

The role of MED22 and its transcriptional interactions with childhood trauma and trauma-focused psychotherapy in patients with major depressive disorder

The biological mechanisms linking childhood trauma (CT) to major depressive disorder (MDD) remain unclear. Expanding on previous research linking MED22 to CT in MDD, we examined three research questions: (1) MED22's role in the relationship between CT and MDD, considering genetic (GReX) and environmental (EReX) components of gene expression regulation; (2) associations between MED22 genetic variations and personality traits like neuroticism in 177 healthy volunteers; and (3) changes in MED22 expression over time in 22 MDD patients with CT undergoing trauma-focused psychotherapy, with clinical and blood assessments at baseline (T0), 4 (T4), 8 (T8), 12 (T12), and 24 weeks (T24). For the first question, in MDD patients, MED22 GReX was associated with neglect, sexual, and emotional abuse, while EReX was associated with neglect. For the second question, in healthy volunteers, MED22 SNPs were associated with higher neuroticism (Beta=0.2284; p-value=0.008), indicating reduced stress resilience. Finally, for the third question, psychotherapy improved depressive symptoms (p < 0.001) and decreased MED22 expression (F=3.05; p = 0.03), with a treatment response effect (F= 3.44; p = 0.02) at T12. Differences in MED22 expression between responders and non-responders were observed at T4 (z_value= -2.13; p = 0.040), T8 (z_value=-3.85; p = 0.0004), and T12 (z_value= -2.93; p = 0.007). Baseline transcript levels were positively associated with relapse (τ=0.390; p = 0.037) and were higher (p = 0.026) in non-remitters, suggesting potential for detecting relapse. MED22 reductions from T0 to T8 were associated with improved cognitive symptoms (τ= 0.345; p = 0.040). Transcript reductions at T12 were associated with improvements in neurovegetative (τ=0.362; p = 0.027) and anxiety symptoms (τ= 0.324; p = 0.040). Genetic and environmental factors may influence stress responses.

Towards an expanded neurocognitive account of ketamine's rapid antidepressant effects

Ketamine is an N-methyl-D-aspartate receptor antagonist that has shown effectiveness as a rapidly acting treatment for depression. Although advances have been made in understanding ketamine's antidepressant pharmacological and molecular mechanisms of action, the large-scale neurocognitive mechanisms driving its therapeutic effects are less clearly understood. To help provide such a framework, we provide a synthesis of current evidence linking ketamine treatment to the modulation of brain systems supporting reward processing, interoception, and self-related cognition. We suggest that ketamine's antidepressant effects are, at least in part, driven by dynamic multi-level influences across these key functional domains.

Clinical associations with treatment resistance in depression: An electronic health record study

Treatment resistance is common in major depressive disorder (MDD), yet clinical risk factors are not well understood. Using a discovery-replication design, we conducted phenome-wide association studies (PheWASs) of MDD treatment resistance in two electronic health record (EHR)-linked biobanks. The PheWAS included participants with an MDD diagnosis in the EHR and at least one antidepressant (AD) prescription. Participant lifetime diagnoses were mapped to phecodes. PheWASs were conducted for three treatment resistance outcomes based on AD prescription data: number of unique ADs prescribed, ≥1 and ≥2 CE switches. Of the 180 phecodes significantly associated with these outcomes in the discovery cohort (n = 12,558), 71 replicated (n = 8,206). In addition to identifying known clinical factors for treatment resistance in MDD, the total unique AD prescriptions was associated with additional clinical variables including irritable bowel syndrome, gastroesophageal reflux disease, symptomatic menopause, and spondylosis. We calculated polygenic risk of specific-associated conditions and tested their association with AD outcomes revealing that genetic risk for many of these conditions is also associated with the total unique AD prescriptions. The number of unique ADs prescribed, which is easily assessed in EHRs, provides a more nuanced measure of treatment resistance, and may facilitate future research and clinical application in this area.

Effectiveness of non-invasive brain stimulation on depressive symptoms targeting prefrontal cortex in functional magnetic resonance imaging studies: a combined systematic review and meta-analysis

The prefrontal cortex (PFC) is a critical non-invasive brain stimulation (NIBS) target for treating depression. However, the alterations of brain activations post-intervention remain inconsistent and the clinical moderators that could improve symptomatic effectiveness are unclear. The study aim was to systematically review the effectiveness of NIBS on depressive symptoms targeting PFC in functional magnetic resonance imaging (fMRI) studies. In our study, we delivered a combined activation likelihood estimation (ALE) meta-analysis and meta-regression. Until November 2020, three databases (PubMed, Web of Science, EMBASE) were searched and 14 studies with a total sample size of 584 were included in the ALE meta-analysis; after NIBS, four clusters in left cerebrum revealed significant activation while two clusters in right cerebrum revealed significant deactivation (P < 0.001, cluster size >150 mm3). Eleven studies were statistically reanalyzed for depressive symptoms pre-post active-NIBS and the pooled effect size was very large [(d = 1.82, 95%CI (1.23, 2.40)]; significant moderators causing substantial heterogeneity (Chi squared = 75.25, P < 0.01; I 2 = 87%) were detected through subgroup analysis and univariate meta-regression. Multivariate meta-regression was then conducted accordingly and the model suggested good fitness (Q = 42.32, P < 0.01). In all, NIBS targeting PFC balanced three core depressive-related neurocognitive networks (the salience network, the default mode network, and the central executive network); the striatum played a central role and might serve as a candidate treatment biomarker; gender difference, treatment-resistant condition, comorbidity, treatment duration, and localization all contributed to moderating depressive symptoms during NIBS. More high-quality, multi-center randomized controlled trails delivering personalized NIBS are needed for clinical practice in the future.

Deconstructing depression by machine learning: the POKAL-PSY study

Unipolar depression is a prevalent and disabling condition, often left untreated. In the outpatient setting, general practitioners fail to recognize depression in about 50% of cases mainly due to somatic comorbidities. Given the significant economic, social, and interpersonal impact of depression and its increasing prevalence, there is a need to improve its diagnosis and treatment in outpatient care. Various efforts have been made to isolate individual biological markers for depression to streamline diagnostic and therapeutic approaches. However, the intricate and dynamic interplay between neuroinflammation, metabolic abnormalities, and relevant neurobiological correlates of depression is not yet fully understood. To address this issue, we propose a naturalistic prospective study involving outpatients with unipolar depression, individuals without depression or comorbidities, and healthy controls. In addition to clinical assessments, cardiovascular parameters, metabolic factors, and inflammatory parameters are collected. For analysis we will use conventional statistics as well as machine learning algorithms. We aim to detect relevant participant subgroups by data-driven cluster algorithms and their impact on the subjects' long-term prognosis. The POKAL-PSY study is a subproject of the research network POKAL (Predictors and Clinical Outcomes in Depressive Disorders; GRK 2621).

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

Background

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.

Methods

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.

Outcomes

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.

Interpretation

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.

Evaluating Machine Learning Stability in Predicting Depression and Anxiety Amidst Subjective Response Errors

Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) pose significant burdens on individuals and society, necessitating accurate prediction methods. Machine learning (ML) algorithms utilizing electronic health records and survey data offer promising tools for forecasting these conditions. However, potential bias and inaccuracies inherent in subjective survey responses can undermine the precision of such predictions. This research investigates the reliability of five prominent ML algorithms-a Convolutional Neural Network (CNN), Random Forest, XGBoost, Logistic Regression, and Naive Bayes-in predicting MDD and GAD. A dataset rich in biomedical, demographic, and self-reported survey information is used to assess the algorithms' performance under different levels of subjective response inaccuracies. These inaccuracies simulate scenarios with potential memory recall bias and subjective interpretations. While all algorithms demonstrate commendable accuracy with high-quality survey data, their performance diverges significantly when encountering erroneous or biased responses. Notably, the CNN exhibits superior resilience in this context, maintaining performance and even achieving enhanced accuracy, Cohen's kappa score, and positive precision for both MDD and GAD. This highlights the CNN's superior ability to handle data unreliability, making it a potentially advantageous choice for predicting mental health conditions based on self-reported data. These findings underscore the critical importance of algorithmic resilience in mental health prediction, particularly when relying on subjective data. They emphasize the need for careful algorithm selection in such contexts, with the CNN emerging as a promising candidate due to its robustness and improved performance under data uncertainties.

Regional decreases of cortical thickness in major depressive disorder and their correlation with illness duration: a case-control study

Background

Alterations in brain structure and function in major depressive disorder (MDD) have been identified in a number of studies, but findings regarding cortical thickness were various and inconsistent. Our current study aims to explore the differences in cortical thickness between individuals with MDD and healthy controls (HC) in a Chinese population.

Methods

We investigated T1-weighted brain magnetic resonance imaging data from 61 participants (31 MDD and 30 HC). The cortical thickness between the two groups and analyzed correlations between cortical thickness and demographic variables in the MDD group for regions with significant between-group differences were conducted.

Results

Compared with the HC group, patients with MDD had significantly decreased cortical thickness, in left pars triangularis, left pars orbitalis, left rostral middle frontal gyrus, left supramarginal gyrus, right parahippocampal gyrus, right lingual gyrus, right fusiform and right inferior parietal gyrus. The cortical thickness of left rostral middle frontal gyrus was negatively correlated (r = -0.47, p = 0.028) with the illness duration in patients with MDD.

Conclusion

Our study distinguished that cortical thickness decreases in numerous brain regions both in the left and right hemisphere in individuals with MDD, and the negative correlation between the cortical thickness of left rostral middle frontal gyrus illness duration. Our current findings are valuable in providing neural markers to identify MDD and understanding the potential pathophysiology of mood disorders.

Predicting acupuncture efficacy for major depressive disorder using baseline clinical variables: A machine learning study

Acupuncture is a viable treatment option for major depressive disorder (MDD). However, its effectiveness varies among patients. This study aimed to develop a model to predict the efficacy of acupuncture therapy for MDD using machine learning and baseline clinical variables. A total of 124 patients with MDD from five research centers were included in our machine learning study. All patients underwent acupuncture treatment for 6 weeks and the efficacy of the treatment was evaluated using the Hamilton Depression Scale-17 (HAMD-17). The max-relevance and min-redundancy (mRMR) algorithm and Pearson correlation analysis were used for selecting 11 significant features from 26 baseline clinical variables for model training. We compared the performance of five machine learning models, including logistic regression, support vector machine, K-nearest neighbor, random forest, and XgBoost, in predicting the effect of acupuncture in relieving major depression. Among the five models, XgBoost performed the best with an area under the receiver operating characteristic curve (AUC) of 0.835, an accuracy of 0.730, a sensitivity of 0.670, a specificity of 0.774, and an F1 score of 0.751. The key predictive variables identified were anxiety score in the self-rating depression scale (SDS), the traditional Chinese medicine syndrome of deficiency in both heart and spleen, and body mass index (BMI). The study demonstrates that the developed model can help physicians predict the patients who will benefit from acupuncture treatment, which is of positive significance for improving the clinical efficacy of acupuncture on MDD.

Evaluating Implementation and Outcomes of the Achieving Depression and Anxiety Patient-Centered Treatment Collaborative Care Program in a Large, Integrated Health Care System: A Mixed Methods Observational Study Protocol

Background Collaborative care is an evidence-based multidisciplinary model shown to improve patient depression and anxiety outcomes. Although there is robust literature showing the effectiveness of collaborative care on depression and anxiety symptoms, there is little published on outcomes of collaborative care implementation or the efficacy of collaborative care compared with psychiatric referrals. Reported here is a study protocol examining a novel depression and anxiety collaborative care program in a large, integrated health care system. Methods This is a mixed methods study of the Achieving Depression and Anxiety Patient-Centered Treatment (ADAPT) program as compared to outpatient psychiatric care at Kaiser Permanente Northern California, a large, integrated health care delivery system. The ADAPT program was designed using collaborative care principles, including measurement-based care, accurate diagnosis, and population management. Eligible participants will be ≥ 18 years old with mild to moderate-severe depressive symptoms as measured by the Patient Health Questionnaire-9. Exclusion criteria include acute suicide risk and serious mental health comorbidities. Implementation is examined using the reach, effectiveness, adoption, implementation, and maintenance (RE-AIM) framework and interviews with program stakeholders. Results Pending. Conclusion Study data will help inform future collaborative care efforts while expanding the literature base. The Achieving Depression and Anxiety Patient-Centered Treatment program may improve patient outcomes and access to quality depression and/or anxiety care.

Epigenetic signatures in antidepressant treatment response: a methylome-wide association study in the EMC trial

Although the currently available antidepressants are well established in the treatment of the major depressive disorder (MDD), there is strong variability in the response of individual patients. Reliable predictors to guide treatment decisions before or in an early stage of treatment are needed. DNA-methylation has been proven a useful biomarker in different clinical conditions, but its importance for mechanisms of antidepressant response has not yet been determined. 80 MDD patients were selected out of >500 participants from the Early Medication Change (EMC) cohort with available genetic material based on their antidepressant response after four weeks and stratified into clear responders and age- and sex-matched non-responders (N = 40, each). Early improvement after two weeks was analyzed as a secondary outcome. DNA-methylation was determined using the Illumina EPIC BeadChip. Epigenome-wide association studies were performed and differentially methylated regions (DMRs) identified using the comb-p algorithm. Enrichment was tested for hallmark gene-sets and in genome-wide association studies of depression and antidepressant response. No epigenome-wide significant differentially methylated positions were found for treatment response or early improvement. Twenty DMRs were associated with response; the strongest in an enhancer region in SORBS2, which has been related to cardiovascular diseases and type II diabetes. Another DMR was located in CYP2C18, a gene previously linked to antidepressant response. Results pointed towards differential methylation in genes associated with cardiac function, neuroticism, and depression. Linking differential methylation to antidepressant treatment response is an emerging topic and represents a step towards personalized medicine, potentially facilitating the prediction of patients' response before treatment.

The evaluation of reporting of patient-reported outcomes in MDD: A meta-epidemiological study of clinical trials

Major depressive disorder (MDD) is a multifaceted disease that profoundly affects quality of life. Patient reported outcomes (PROs) are used in randomized controlled trials (RCTs) to better understand patient perspectives on interventions. Therefore, we sought to assess the completeness of reporting PROs in RCTs addressing MDD. We identified RCTs evaluating MDD containing a PRO measure published between 2016 and 2020 from MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials. Inclusion of studies was performed in duplicate. The completion of reporting of RCTs was assessed using the Consolidated Standards of Reporting Trials (CONSORT-PRO) adaptation. Bivariate regression analyses were used to evaluate reporting completeness and trial characteristics. A total of 49 RCTs were included in our analysis, with a mean CONSORT-PRO completion score of 56.7% (SD = 17.3).Our findings show a significant association with completeness of reporting and the following: secondary PRO trials were less completely reported as compared to primary PRO trials (t = -3.19, p = .003); studies with a follow-up period between six months and year were more completely reported as compared to three months or less (6 months to a year, t = 2.34, p = .024); and increased trial sample size was associated with more completeness of reporting (t = 3.17, p = .003). As compared to brain stimulation, the intervention types classified as combination, other, and psychotherapy had greater completeness of reporting (combination, t = 2.35, p = .024; other, t = 3.13, p = .003; psychotherapy, t = 3.41, p = .001). There were no other significant findings. Our study found the completeness of PRO reporting to be inconsistent in RCTs regarding MDD. Moreover, we advocate for the need to establish a core outcome set relevant to the management of adults diagnosed with MDD and facilitate training on the application of PRO data.

Molecular Mechanisms Associated with Antidepressant Treatment on Major Depression

Major depressive disorder (MDD) is a complex and multifactorial psychiatric disorder that causes serious health, social, and economic concerns worldwide. The main treatment of the symptoms is through antidepressant (AD) drugs. However, not all patients respond properly to these drugs. Omic sciences are widely used to analyze not only biomarkers for the AD response but also their molecular mechanism. In this review, we aimed to focus on omics data to better understand the molecular mechanisms involving AD effects on MDD. We consistently found, from preclinical to clinical data, that glutamatergic transmission, immune/inflammatory processes, energy metabolism, oxidative stress, and lipid metabolism were associated with traditional and potential new ADs. Despite efforts of studies investigating biomarkers of response to ADs, which could contribute to personalized treatment, there is no biomarker panel available for clinical application. From clinical genomic studies, we found that the main findings contribute to the development of pharmacogenomic tests for AD efficacy for each patient. Several studies pointed at DRD2, PXDNL, CACNA1E, and CACNA2D1 genes as potential targets for MDD treatment and the efficacy and rapid-antidepressant effect of ketamine. Finally, more in-depth studies of the molecular targets pointed here are needed to determine the clinical relevance and provide further evidence for precision MDD treatment.

Depression and mortality among survivors of acute respiratory distress syndrome in South Korea: A nationwide cohort study conducted from 2010 to 2018

It has been reported that survivors of acute respiratory distress syndrome (ARDS) experience depression. However, the relationship between newly developed depression and mortality among survivors of ARDS has not been elucidated. Thus, the aim of this study was to investigate the prevalence and associated factors of depression among survivors of ARDS and determine whether newly developed depression is associated with long-term mortality among them. The data used for this study were extracted from the national health insurance database of South Korea. All adult patients who were admitted into intensive care units for ARDS between January 1, 2010, and December 31, 2018, and survived for ≥1 year after treatment were defined as ARDS survivors and included in this study. A total of 4,452 ARDS survivors were included in the final analysis. Of these, 581 (13.4%) were newly diagnosed with depression within 1 year after ARDS diagnosis. Multivariable logistic regression showed that underlying anxiety disorder (odds ratio [OR], 2.35; 95% confidence interval [CI], 1.93-2.86; P < 0.001), substance abuse (OR, 2.18; 95% CI, 1.48-3.20; P < 0.001), and post-traumatic stress disorder (OR, 15.52; 95% CI, 2.57-93.51; P = 0.003) were associated with a higher prevalence of post-ARDS depression among survivors. Multivariable Cox regression analysis showed that post-ARDS depression was associated with a 1.49-fold increased risk of 2-year all-cause mortality (hazard ratio, 1.49; 95% CI, 1.18-1.88; P = 0.001). Underlying psychiatric illness is a potential risk factor for the development of depression among ARDS survivors. Post-ARDS depression is associated with an increased risk of 2-year all-cause mortality.

MicroRNAs as Critical Biomarkers of Major Depressive Disorder: A Comprehensive Perspective

Major Depressive Disorder (MDD) represents a major global health concern, a body-mind malady of rising prevalence worldwide nowadays. The complex network of mechanisms involved in MDD pathophysiology is subjected to epigenetic changes modulated by microRNAs (miRNAs). Serum free or vesicles loaded miRNAs have starred numerous publications, denoting a key role in cell-cell communication, systematically and in brain structure and neuronal morphogenesis, activity and plasticity. Upregulated or downregulated expression of these signaling molecules may imply the impairment of genes implicated in pathways of MDD etiopathogenesis (neuroinflammation, brain-derived neurotrophic factor (BDNF), neurotransmitters, hypothalamic-pituitary-adrenal (HPA) axis, oxidative stress, circadian rhythms...). In addition, these miRNAs could serve as potential biomarkers with diagnostic, prognostic and predictive value, allowing to classify severity of the disease or to make decisions in clinical management. They have been considered as promising therapy targets as well and may interfere with available antidepressant treatments. As epigenetic malleable regulators, we also conclude emphasizing lifestyle interventions with physical activity, mindfulness and diet, opening the door to new clinical management considerations.

REL-1017 (Esmethadone) Increases Circulating BDNF Levels in Healthy Subjects of a Phase 1 Clinical Study

Brain-derived neurotrophic factor (BDNF), a neurotrophin widely expressed in the central nervous system, exhibits important effects on neural plasticity. BDNF has been implicated in the mechanism of action of ketamine, a N-methyl-d-aspartic acid receptor (NMDAR) antagonist with rapid anti-depressant effects in humans. REL-1017 (esmethadone), the d-optical isomer of the racemic mixture d-l-methadone, is devoid of clinically relevant opioid activity at doses expected to exert therapeutic NMDAR antagonistic activity in humans. The present study was conducted to ascertain the effects of oral administration of 25 mg of REL-1017 for 10 days on plasma BDNF in healthy subjects confined to an inpatient unit for a phase 1 clinical trial. We observed an increase in post-treatment BDNF plasma levels compared to pre-treatment levels. Post-treatment, Day 10 BDNF plasma levels ranged from 2 to 17 times pre-treatment levels in the 25 mg REL-1017 treatment group, whereas in the placebo group, BDNF plasma levels remained unchanged (p = 0.028). Diastolic blood pressure decreased significantly in subjects treated with REL-1017, while no effect could be observed in the placebo group. In conclusion, the administration of 25 mg REL-1017 significantly increased BDNF plasma levels and significantly decreased diastolic blood pressure in healthy subjects confined to an inpatient unit for a phase 1 clinical trial.