A systematic meta-review of predictors of antidepressant treatment outcome in major depressive disorder

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.

The efficacy and safety of Anyu Peibo Capsule in the treatment of patients with major depressive disorder in China: study protocol for a randomized placebo-controlled trial

BACKGROUND

Major depressive disorder is the second leading cause of years lost to disability worldwide. Anyu Peibo Capsule has been shown to be effective and safe in phase II trials.

METHODS

This clinical study is a multi-center, randomized, double-blinded, placebo-controlled, parallel-group, phase III trial of Anyu Peibo Capsule in China. The aim is to test whether the administration of Anyu Peibo Capsule compared to placebo improves clinical outcomes in adults (aged 18 to 65 years) with MDD. Patients will receive an 8-week treatment of Anyu Peibo Capsule 1.6 g per day or placebo. The primary outcome will be the change from baseline in the total score for the Montgomery-Asberg Depression Rating Scale at the end of the 8-week treatment.

DISCUSSION

The trial aims to provide pivotal evidence for the efficacy and safety of Anyu Peibo Capsule in patients with major depressive disorder.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04210973 . Registered on December 26, 2019.

Depression symptom profiles and long-term response to cognitive behavioral therapy plus contingency management for smoking cessation

BACKGROUND

Depression is heterogeneous in nature and using diagnostic categories limits insight into understanding psychopathology and its impact on treatment efficacy. This secondary analysis sought to: 1) identify distinct subpopulations of cigarette users with depression, and 2) examine their response to cognitive-behavioral treatment (CBT) + contingency management (CM) for smoking cessation at one year.

METHOD

The sample comprised 238 (74 % females) adults who smoke receiving CBT only or CBT + CM. A latent class analysis was conducted on baseline depressive symptoms measured using the Beck Depression Inventory-II. Generalized estimating equations assessed the main and interactive effects of class, time, treatment, and sex on smoking abstinence.

RESULTS

Three distinct classes were identified: C1 (n= 76/238), characterized by mild depression, loss of energy, pessimism, and criticism, C2 (n= 100/238) presenting moderate severity and decreased appetite, and C3 (n= 62/238) showing severe depression, increased appetite, and feelings of punishment. There was a significant cluster × treatment interaction, which indicated additive effects of CM over CBT alone for Class 1 and 2. Persons in Class 1 and 2 were 3.60 [95 % CI: 1.62, 7.97] and 2.65 [95 % CI: 1.19, 5.91] times more likely to be abstinent if CBT + CM was delivered rather than CBT only. No differential sex effects were observed on treatment response according to cluster.

CONCLUSIONS

Profiling depression symptom subtypes of cigarette users may be more informative to improve CM treatment response than merely focusing on total scores.

Minocycline as adjunctive treatment for major depressive disorder: Pooled data from two randomized controlled trials

BACKGROUND

Randomized controlled clinical trials that have investigated minocycline as an adjunctive treatment for major depressive disorder have proved promising. Data from two studies were pooled to evaluate more definitively whether the addition of minocycline to standard treatment for major depressive disorder leads to an improvement of depressive symptoms when compared with placebo.

METHODS

Both studies were multi-site, double-blinded, placebo-controlled trials of minocycline 200 mg/day added to treatment as usual during a 12-week period. The primary outcome measure was change in depressive symptoms (Montgomery-Asberg Depression Rating Scale in Dean et al. and Hamilton Depression Rating Scale in Husain et al.). Secondary outcomes were change in depression severity (Montgomery-Asberg Depression Rating Scale for Dean et al. and 9-item Patient Health Questionnaire in Husain et al.), anxiety severity (Hamilton Anxiety Rating Scale in Dean et al. and Generalized Anxiety Disorder 7-item scale in Husain et al.) and functional status, which were also evaluated as potential mediators on the primary outcome.

RESULTS

A total of 112 participants were included in the pooled data (Dean et al., n = 71; Husain et al., n = 41). A significant change from baseline to week 12 was noted in depressive symptoms - differential change (Placebo vs Minocycline): 9.0, 95% confidence interval = [4.2, 13.9], Cohen's D (95% confidence interval): 0.71 [0.29, 1.14], p < 0.001 - anxiety severity - differential change (Placebo vs Minocycline): 0.38, confidence interval = [0.00, 0.75], Cohen's D (95% confidence interval): 0.41 [0.00, 0.82], p = 0.050) and functional status - differential change (Placebo vs Minocycline): 1.0, 95% confidence interval = [0.4, 1.5], Cohen's D (95% confidence interval): 0.76 [0.34, 1.19], p = 0.001). Duration of illness, current use of benzodiazepine and pain medication were identified as moderators, whereas functional status as a mediator/predictor.

CONCLUSION

The improvement of depressive symptoms, anxiety severity and functional status is promising and suggests that minocycline has potential as an adjunctive treatment for major depressive disorder. However, further studies are warranted to confirm therapeutic effects of minocycline in major depressive disorder.

TRIAL REGISTRATIONS

NCT02263872, registered October 2014, and ACTRN12612000283875, registered March 2012.

Effect of Ketamine on Rumination in Treatment-Resistant Depressive Patients

BACKGROUND

A rapid antidepressant effect of ketamine has repeatedly been documented in the literature, and identifying clinical features associated with a better response to this treatment is currently an essential question. Considering the relationship between rumination and depression and the need to identify potential predictors of response to ketamine, we analyzed the effect of a single injection of ketamine 0.5 mg/kg on rumination in treatment-resistant depressive (TRD) patients and explored whether baseline ruminative style and early improvements of rumination would predict a greater antidepressant effect of ketamine.

METHODS

Ten TRD outpatients who participated in a 4-week open study on the antidepressant effect of ketamine also completed the Ruminative Response Scale the day before, the day after, and a week after ketamine administration.

RESULTS

We found that in our patients, a single rapid 1-minute intravenous injection of ketamine 0.5 mg/kg was efficacious in reducing rumination, but neither severity of rumination at baseline nor early improvements of rumination after ketamine injection predicted antidepressant response.

CONCLUSIONS

Our preliminary data suggest that a single injection of ketamine 0.5 mg/kg can be efficacious in reducing rumination in TRD patients but rumination does not seem to be a useful clinical predictor of response to ketamine. Larger studies are necessary to confirm these results.

Optimizing prediction of response to antidepressant medications using machine learning and integrated genetic, clinical, and demographic data

Major depressive disorder (MDD) is complex and multifactorial, posing a major challenge of tailoring the optimal medication for each patient. Current practice for MDD treatment mainly relies on trial and error, with an estimated 42-53% response rates for antidepressant use. Here, we sought to generate an accurate predictor of response to a panel of antidepressants and optimize treatment selection using a data-driven approach analyzing combinations of genetic, clinical, and demographic factors. We analyzed the response patterns of patients to three antidepressant medications in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, and employed state-of-the-art machine learning (ML) tools to generate a predictive algorithm. To validate our results, we assessed the algorithm's capacity to predict individualized antidepressant responses on a separate set of 530 patients in STAR*D, consisting of 271 patients in a validation set and 259 patients in the final test set. This assessment yielded an average balanced accuracy rate of 72.3% (SD 8.1) and 70.1% (SD 6.8) across the different medications in the validation and test set, respectively (p < 0.01 for all models). To further validate our design scheme, we obtained data from the Pharmacogenomic Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) of patients treated with citalopram, and applied the algorithm's citalopram model. This external validation yielded highly similar results for STAR*D and PGRN-AMPS test sets, with a balanced accuracy of 60.5% and 61.3%, respectively (both p's < 0.01). These findings support the feasibility of using ML algorithms applied to large datasets with genetic, clinical, and demographic features to improve accuracy in antidepressant prescription.

Clinical laboratory tests and five-year incidence of major depressive disorder: a prospective cohort study of 433,890 participants from the UK Biobank

Prevention of major depressive disorder (MDD) is a public health priority. Identifying biomarkers of underlying biological processes that contribute to MDD onset may help address this public health need. This prospective cohort study encompassed 383,131 white British participants from the UK Biobank with no prior history of MDD, with replication in 50,759 participants of other ancestries. Leveraging linked inpatient and primary care records, we computed adjusted odds ratios for 5-year MDD incidence among individuals with values below or above the 95% confidence interval (<2.5th or >97.5th percentile) on each of 57 laboratory measures. Sensitivity analyses were performed across multiple percentile thresholds and in comparison to established reference ranges. We found that indicators of liver dysfunction were associated with increased 5-year MDD incidence (even after correction for alcohol use and body mass index): elevated alanine aminotransferase (AOR = 1.35, 95% confidence interval [1.16, 1.58]), aspartate aminotransferase (AOR = 1.39 [1.19, 1.62]), and gamma glutamyltransferase (AOR = 1.52 [1.31, 1.76]) as well as low albumin (AOR = 1.28 [1.09, 1.50]). Similar observations were made with respect to endocrine dysregulation, specifically low insulin-like growth factor 1 (AOR = 1.34 [1.16, 1.55]), low testosterone among males (AOR = 1.60 [1.27, 2.00]), and elevated glycated hemoglobin (HbA1C; AOR = 1.23 [1.05, 1.43]). Markers of renal impairment (i.e. elevated cystatin C, phosphate, and urea) and indicators of anemia and macrocytosis (i.e. red blood cell enlargement) were also associated with MDD incidence. While some immune markers, like elevated white blood cell and neutrophil count, were associated with MDD (AOR = 1.23 [1.07, 1.42]), others, like elevated C-reactive protein, were not (AOR = 1.04 [0.89, 1.22]). The 30 significant associations validated as a group in the multi-ancestry replication cohort (Wilcoxon p = 0.0005), with a median AOR of 1.235. Importantly, all 30 significant associations with extreme laboratory test results were directionally consistent with an increased MDD risk. In sum, markers of liver and kidney dysfunction, growth hormone and testosterone deficiency, innate immunity, anemia, macrocytosis, and insulin resistance were associated with MDD incidence in a large community-based cohort. Our results support a contributory role of diverse biological processes to MDD onset.

Dynamic Functional Connectivity Predicts Treatment Response to Electroconvulsive Therapy in Major Depressive Disorder

Background: Electroconvulsive therapy (ECT) is one of the most effective treatments for major depressive disorder. Recently, there has been increasing attention to evaluate the effect of ECT on resting-state functional magnetic resonance imaging (rs-fMRI). This study aims to compare rs-fMRI of depressive disorder (DEP) patients with healthy participants, investigate whether pre-ECT dynamic functional network connectivity network (dFNC) estimated from patients rs-fMRI is associated with an eventual ECT outcome, and explore the effect of ECT on brain network states.

Method: Resting-state functional magnetic resonance imaging (fMRI) data were collected from 119 patients with depression or depressive disorder (DEP) (76 females), and 61 healthy (HC) participants (34 females), with an age mean of 52.25 (N = 180) years old. The pre-ECT and post-ECT Hamilton Depression Rating Scale (HDRS) were 25.59 ± 6.14 and 11.48 ± 9.07, respectively. Twenty-four independent components from default mode (DMN) and cognitive control network (CCN) were extracted, using group-independent component analysis from pre-ECT and post-ECT rs-fMRI. Then, the sliding window approach was used to estimate the pre-and post-ECT dFNC of each subject. Next, k-means clustering was separately applied to pre-ECT dFNC and post-ECT dFNC to assess three distinct states from each participant. We calculated the amount of time each subject spends in each state, which is called “occupancy rate” or OCR. Next, we compared OCR values between HC and DEP participants. We also calculated the partial correlation between pre-ECT OCRs and HDRS change while controlling for age, gender, and site. Finally, we evaluated the effectiveness of ECT by comparing pre- and post-ECT OCR of DEP and HC participants.

Results: The main findings include (1) depressive disorder (DEP) patients had significantly lower OCR values than the HC group in state 2, where connectivity between cognitive control network (CCN) and default mode network (DMN) was relatively higher than other states (corrected p = 0.015), (2) Pre-ECT OCR of state, with more negative connectivity between CCN and DMN components, is linked with the HDRS changes (R = 0.23 corrected p = 0.03). This means that those DEP patients who spent less time in this state showed more HDRS change, and (3) The post-ECT OCR analysis suggested that ECT increased the amount of time DEP patients spent in state 2 (corrected p = 0.03).

Conclusion: Our finding suggests that dynamic functional network connectivity (dFNC) features, estimated from CCN and DMN, show promise as a predictive biomarker of the ECT outcome of DEP patients. Also, this study identifies a possible underlying mechanism associated with the ECT effect on DEP patients.

Can the Risk of Severe Depression-Related Outcomes Be Reduced by Tailoring the Antidepressant Therapy to Patient Characteristics?

Selective serotonin reuptake inhibitors (SSRIs) are the first-line treatment for patients with unipolar depression, yet there is little guidance on which SSRI provides the most benefit to a patient, based on personal characteristics. In this work, we explore whether an individualized treatment strategy can be used by health-care providers to adapt their prescription pattern to reduce the risk of a severe depression-related outcome (SDO) when choosing between citalopram and fluoxetine, 2 commonly prescribed SSRIs. Our population-based cohort study used data from the Clinical Practice Research Datalink, the Hospital Episode Statistics repository, and the Office for National Statistics database in the United Kingdom to create a cohort of individuals diagnosed with depression who were prescribed citalopram or fluoxetine between April 1998 and December 2017. Patients were followed from treatment initiation until occurrence of the SDO outcome, treatment discontinuation, or end of study. To find an optimal treatment strategy, we used dynamic weighted survival modeling, considering patient features such as age, sex, body mass index, previous psychiatric diagnoses, and medications. Our findings suggest that using patient characteristics to tailor the antidepressant drug therapy is associated with an increase of 4 days in the median time to SDO (95% confidence interval: 2, 10 days).

The clinical effectiveness of using a predictive algorithm to guide antidepressant treatment in primary care (PReDicT): an open-label, randomised controlled trial

Depressed patients often do not respond to the first antidepressant prescribed, resulting in sequential trials of different medications. Personalised medicine offers a means of reducing this delay; however, the clinical effectiveness of personalised approaches to antidepressant treatment has not previously been tested. We assessed the clinical effectiveness of using a predictive algorithm, based on behavioural tests of affective cognition and subjective symptoms, to guide antidepressant treatment. We conducted a multicentre, open-label, randomised controlled trial in 913 medication-free depressed patients. Patients were randomly assigned to have their antidepressant treatment guided by a predictive algorithm or treatment as usual (TaU). The primary outcome was the response of depression symptoms, defined as a 50% or greater reduction in baseline score of the QIDS-SR-16 scale, at week 8. Additional prespecified outcomes included symptoms of anxiety at week 8, and symptoms of depression and functional outcome at weeks 8, 24 and 48. The response rate of depressive symptoms at week 8 in the PReDicT (55.9%) and TaU (51.8%) arms did not differ significantly (odds ratio: 1.18 (95% CI: 0.89-1.56), P = 0.25). However, there was a significantly greater reduction of anxiety in week 8 and a greater improvement in functional outcome at week 24 in the PReDicT arm. Use of the PReDicT test did not increase the rate of response to antidepressant treatment estimated by depressive symptoms but did improve symptoms of anxiety at week 8 and functional outcome at week 24. Our findings indicate that personalisation of antidepressant treatment may improve outcomes in depressed patients.

Does the Loudness Dependence of Auditory Evoked Potential Predict Response to Selective Serotonin Reuptake Inhibitors?: A Meta-analysis

Objective

Loudness of dependence of the auditory evoked potential (LDAEP) is an electroencephalogram-based measure that represents amplitude changes of auditory evoked potentials in primary auditory cortex. Several narrative reviews argued that pre-treatment LDAEP values predict responses to selective serotonin reuptake inhibitors (SSRIs). This study aims to quantify the overall relationship between baseline LDAEP values and treatment response to SSRIs in patients with depression and generalized anxiety disorders, evidenced by clinical symptoms reductions, across multiple studies.

Methods

In our meta-analysis, seven articles with a total sample of 241 patients were included.

Results

Our results showed that stronger baseline LDAEP values predicted favorable response to SSRIs for depression and anxiety, with a moderate effect size.

Conclusion

The current results support the idea that LDAEP is a promising biomarker for SSRIs treatment prediction in patients with depression and generalized anxiety disorder.

Does Culture Influence Antidepressant Response? A Preliminary Investigation of Randomized Controlled Trials of Fluoxetine

Background

Contemporary models of depression view the disorder as arising from an interaction between genetic vulnerability and adverse life experiences. The nature of these experiences is strongly influenced by social-cultural factors, and there is preliminary evidence that these factors may influence the response to treatment.

Methods

In this pilot study, pooled response rates obtained from 56 randomized controlled trials of fluoxetine for major depression, conducted across 21 countries, were analyzed in relation to Hofstede’s six dimensions of culture in these countries, while controlling for methodological quality.

Results

The cultural dimensions of power distance (r = .62, p = .002), masculinity (r = .45, p = .04) and indulgence (r = -.52, p = .016) were significantly correlated with antidepressant response rates, though only the first of these remained significant after correction for multiple comparisons. On linear regression analysis, the association between power distance and antidepressant response remained significant (β = .62, p = .002).

Conclusions

These preliminary results suggest that certain cultural factors may be significantly associated with cross-national variations in antidepressant response rates during clinical trials.

Machine learning to predict clinical remission in depressed patients after acute phase selective serotonin reuptake inhibitor treatment

OBJECTIVE

Selective serotonin reuptake inhibitors (SSRIs) are suggested as the first-line treatment for patients with major depressive disorder (MDD), but the remission rate is unsatisfactory. We aimed to establish machine learning models and explore variables available at baseline to predict the 8-week outcome among patients taking SSRIs.

METHODS

Data from 400 patients were used to build machine learnings. The last observation carried forward approach was used to determine the remitter/non-remitter status of the patients at week 8. Using least absolute shrinkage and selection operator (LASSO) to select features, we built 4 different machine learning algorithms including gradient boosting decision tree, support vector machine (SVM), random forests, and logistic regression with five-fold cross-validation. Then, we adopted Shapley additive explanations (SHAP) values to interpret the model output.

RESULTS

The remission rate is 67.8%. We obtained 78 features from the baseline characteristics, including 25 sociodemographic characteristics, 31 clinical features, 15 psychological traits and 7 neurocognitive functions, and 13 of these features were selected to establish SVM. The accuracy of the SVM prediction is 74.49%, reaching an average area under the curve of 0.734±0.043. The sensitivity is 0.899±0.038 with a positive predictive value of 0.776±0.028. The specificity is 0.422±0.091 with a negative predictive value of 0.674±0.086. According to the SHAP values, neurocognitive functions and anxiety and hypochondriasis symptoms were important predictors.

CONCLUSION

Our study supports the utilization of machine learning approaches with inexpensive and highly accessible variables to accurately predict the 8-week treatment outcome of SSRIs in patients with MDD.

Synthesizing the Evidence for Ketamine and Esketamine in Treatment-Resistant Depression: An International Expert Opinion on the Available Evidence and Implementation

Replicated international studies have underscored the human and societal costs associated with major depressive disorder. Despite the proven efficacy of monoamine-based antidepressants in major depression, the majority of treated individuals fail to achieve full syndromal and functional recovery with the index and subsequent pharmacological treatments. Ketamine and esketamine represent pharmacologically novel treatment avenues for adults with treatment-resistant depression. In addition to providing hope to affected persons, these agents represent the first non-monoaminergic agents with proven rapid-onset efficacy in major depressive disorder. Nevertheless, concerns remain about the safety and tolerability of ketamine and esketamine in mood disorders. Moreover, there is uncertainty about the appropriate position of these agents in treatment algorithms, their comparative effectiveness, and the appropriate setting, infrastructure, and personnel required for their competent and safe implementation. In this article, an international group of mood disorder experts provides a synthesis of the literature with respect to the efficacy, safety, and tolerability of ketamine and esketamine in adults with treatment-resistant depression. The authors also provide guidance for the implementation of these agents in clinical practice, with particular attention to practice parameters at point of care. Areas of consensus and future research vistas are discussed.

Efficacy of hyperbaric oxygen combined with escitalopram in depression and its effect on cognitive function

Objective:

To investigate the efficacy of hyperbaric oxygen (HBO) combined with escitalopram in patients with depression and its effect on cognitive function.

Methods:

From 2016 to 2018, seventy patients with depression aged 18-65 years treated in Affiliated Hospital of Hebei University were selected. Seventy patients with depression meeting the diagnostic criteria of ICD-10 were selected and randomly divided into control group and observation group using a random number table, with 35 patients in each group. The control group was treated with escitalopram, while the observation group was additionally treated with HBO on this basis. The patients were assessed using the Hamilton Depression Scale (HAMD) and Montreal Cognitive Assessment Scale (MoCA) before treatment and two, four and six weeks after treatment.

Results:

Two weeks after treatment, HAMD score showed a statistically significant difference between the two groups (P < 0.05). No statistically significant differences were found in HAMD score between the two groups four and six weeks after treatment (P > 0.05). Four and six weeks after treatment, MoCA score presented statistically significant differences between the two groups (P < 0.05).

Conclusion:

Escitalopram combined with HBO in the treatment of depression presents rapid efficacy and a certain effect in improving cognitive function.

Association between psychomotor disturbance and treatment outcome in psychotic depression: a STOP-PD II report

BACKGROUND

Little is known about the relationship between psychomotor disturbance (PMD) and treatment outcome of psychotic depression. This study examined the association between PMD and subsequent remission and relapse of treated psychotic depression.

METHODS

Two hundred and sixty-nine men and women aged 18-85 years with an episode of psychotic depression were treated with open-label sertraline plus olanzapine for up to 12 weeks. Participants who remained in remission or near-remission following an 8-week stabilization phase were eligible to participate in a 36-week randomized controlled trial (RCT) that compared the efficacy and tolerability of sertraline plus olanzapine (n = 64) with sertraline plus placebo (n = 62). PMD was measured with the psychiatrist-rated sign-based CORE at acute phase baseline and at RCT baseline. Spearman's correlations and logistic regression analyses were used to analyze the association between CORE total score at acute phase baseline and remission/near-remission and CORE total score at RCT baseline and relapse.

RESULTS

Higher CORE total score at acute phase baseline was associated with lower frequency of remission/near-remission. Higher CORE total score at RCT baseline was associated with higher frequency of relapse, in the RCT sample as a whole, as well as in each of the two randomized groups.

CONCLUSIONS

PMD is associated with poorer outcome of psychotic depression treated with sertraline plus olanzapine. Future research needs to examine the neurobiology of PMD in psychotic depression in relation to treatment outcome.

Serotonin 2A receptor polymorphism rs3803189 mediated by dynamics of default mode network: a potential biomarker for antidepressant early response

BACKGROUND

Serotonin 2A receptors (HTR2A) play a crucial role in the therapeutic response to antidepressant. The activity of serotonergic system could modulate the connectivity of the default mode network (DMN) in human brain. Our research investigated the influence of the single nucleotide polymorphism (SNP) of HTR2A on the early treatment response of antidepressant and their relation to dynamic changes of DMN for the first time.

METHODS

A total of 134 major depressive disorder patients and 95 healthy controls from two independent datasets were enrolled. All subjects have genotyped candidate HTR2A polymorphisms, dynamic brain parameters flexibility and integration were calculated according to the resting-state functional magnetic resonance imaging (rs-fMRI) at baseline. Patients received selective serotonin reuptake inhibitors (SSRIs) treatment with conventional dose in the next two weeks.

RESULTS

We found the correlation of the risk-associated variant belonged to HTR2A polymorphism rs3803189 with the achievements of antidepressant early response, and also with the stronger dynamic changes of DMN. Further mediation analysis indicated that the bond between rs3803189 and antidepressant early response was mediated by the integration between the right angular gyrus (AG.R) and the subcortical network (SCN), which were validated over both the main and replication datasets.

LIMITATIONS

Except the AG.R-SCN circuit, other factors which influence the relationship between rs3803189 and antidepressant therapy deserve to be explored further. Besides, heterogeneity of samples limited the power of the current result.

CONCLUSIONS

Our findings provided a potential biomarker for individual treatment sensitivity and produced positive effects on revealing the complicated gene-brain-disorder relationship.

Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis

No tools are currently available to predict whether a patient suffering from major depressive disorder (MDD) will respond to a certain treatment. Machine learning analysis of magnetic resonance imaging (MRI) data has shown potential in predicting response for individual patients, which may enable personalized treatment decisions and increase treatment efficacy. Here, we evaluated the accuracy of MRI-guided response prediction in MDD. We conducted a systematic review and meta-analysis of all studies using MRI to predict single-subject response to antidepressant treatment in patients with MDD. Classification performance was calculated using a bivariate model and expressed as area under the curve, sensitivity, and specificity. In addition, we analyzed differences in classification performance between different interventions and MRI modalities. Meta-analysis of 22 samples including 957 patients showed an overall area under the bivariate summary receiver operating curve of 0.84 (95% CI 0.81-0.87), sensitivity of 77% (95% CI 71-82), and specificity of 79% (95% CI 73-84). Although classification performance was higher for electroconvulsive therapy outcome prediction (n = 285, 80% sensitivity, 83% specificity) than medication outcome prediction (n = 283, 75% sensitivity, 72% specificity), there was no significant difference in classification performance between treatments or MRI modalities. Prediction of treatment response using machine learning analysis of MRI data is promising but should not yet be implemented into clinical practice. Future studies with more generalizable samples and external validation are needed to establish the potential of MRI to realize individualized patient care in MDD.

Circadian depression: A mood disorder phenotype

Major mood syndromes are among the most common and disabling mental disorders. However, a lack of clear delineation of their underlying pathophysiological mechanisms is a major barrier to prevention and optimised treatments. Dysfunction of the 24-h circadian system is a candidate mechanism that has genetic, behavioural, and neurobiological links to mood syndromes. Here, we outline evidence for a new clinical phenotype, which we have called 'circadian depression'. We propose that key clinical characteristics of circadian depression include disrupted 24-h sleep-wake cycles, reduced motor activity, low subjective energy, and weight gain. The illness course includes early age-of-onset, phenomena suggestive of bipolarity (defined by bidirectional associations between objective motor and subjective energy/mood states), poor response to conventional antidepressant medications, and concurrent cardiometabolic and inflammatory disturbances. Identifying this phenotype could be clinically valuable, as circadian-targeted strategies show promise for reducing depressive symptoms and stabilising illness course. Further investigation of underlying circadian disturbances in mood syndromes is needed to evaluate the clinical utility of this phenotype and guide the optimal use of circadian-targeted interventions.

Benefits of animal models to understand the pathophysiology of depressive disorders

Major depressive disorder (MDD) is a potentially life-threatening mental disorder imposing severe social and economic burden worldwide. Despite the existence of effective antidepressant treatment strategies the exact pathophysiology of the disease is still unknown. Large number of animal models of MDD have been developed over the years, but all of them suffer from significant shortcomings. Despite their limitations these models have been extensively used in academic research and drug development. The aim of this review is to highlight the benefits of animal models of MDD. We focus here on recent experimental data where animal models were used to examine current theories of this complex disease. We argue, that despite their evident imperfections, these models provide invaluable help to understand cellular and molecular mechanisms contributing to the development of MDD. Furthermore, animal models are utilized in research to find clinically useful biomarkers. We discuss recent neuroimaging and microRNA studies since these investigations yielded promising candidates for biomarkers. Finally, we briefly summarize recent progresses in drug development, i.e. the FDA approval of two novel antidepressant drugs: S-ketamine and brexanolone (allopregnanolone). Deeper understanding of the exact molecular and cellular mechanisms of action responsible for the antidepressant efficacy of these rapid acting drugs could aid us to design further compounds with similar effectiveness, but less side effects. Animal studies are likely to provide valuable help in this endeavor.

Precision psychiatry in clinical practice

The treatment of depression represents a major challenge for healthcare systems and choosing among the many available drugs without objective guidance criteria is an error-prone process. Recently, pharmacogenetic biomarkers entered in prescribing guidelines, giving clinicians the possibility to use this additional tool to guide prescription and improve therapeutic outcomes. This marked an important step towards precision psychiatry, which aim is to integrate biological and environmental information to personalise treatments. Only genetic variants in cytochrome enzymes are endorsed by prescribing guidelines, but in the future polygenic predictors of treatment outcomes may be translated into the clinic. The integration of genetics with other relevant information (e.g., concomitant diseases and treatments, drug plasma levels) could be managed in a standardised way through ad hoc software. The overcoming of the current obstacles (e.g., staff training, genotyping and informatics facilities) can lead to a broad implementation of precision psychiatry and represent a revolution for psychiatric care.Key pointsPrecision psychiatry aims to integrate biological and environmental information to personalise treatments and complement clinical judgementPharmacogenetic biomarkers in cytochrome genes were included in prescribing guidelines and represented an important step towards precision psychiatryTherapeutic drug monitoring is an important and cost-effective tool which should be integrated with genetic testing and clinical evaluation in order to optimise pharmacotherapyOther individual factors relevant to pharmacotherapy response (e.g., individual's symptom profile, concomitant diseases) can be integrated with genetic information through artificial intelligence to provide treatment recommendationsThe creation of pharmacogenetic services within healthcare systems is a challenging and multi-step process, education of health professionals, promotion by institutions and regulatory bodies, economic and ethical barriers are the main issues.

Predicting Neuroimaging Biomarkers for Antidepressant Selection in Early Treatment of Depression

BACKGROUND

Due to the biological heterogeneity, 60%-70% of patients with major depressive disorder (MDD) do not respond to or achieve remission from first-line antidepressants. Predicting neuroimaging biomarkers for early antidepressant treatment could guide initial antidepressant therapy.

PURPOSE

To assess for neuroimaging biomarkers for antidepressant selection in early antidepressant treatment.

STUDY TYPE

Prospective.

SUBJECTS

A total of 85 MDD patients from the major site and 33 MDD patients from an out-of-sample test site.

FIELD STRENGTH/SEQUENCE

A 3.0 T, T1-weighted imaging using a magnetization-prepared rapid acquisition gradient-echo sequence and diffusion tensor imaging (DTI) using an echo-planar sequence.

ASSESSMENT

Baseline DTI data of patients who achieved early improvement after 2-weeks of antidepressant treatment (selective serotonin reuptake inhibitors [SSRI] or serotonin-norepinephrine reuptake inhibitors [SNRI]) were analyzed. An ensemble model was constructed using data from the major site and then applied to assess the early response of patients at the out-of-sample test site.

STATISTICAL TESTS

Support vector machine combined with leave-one-out cross-validation were applied to construct the whole model from individual base models from different brain regions. Discriminative biomarkers were evaluated by calculating the changes in sensitivity and specificity obtained when removing a single base model from the whole model, the base model being removed changing in each run.

RESULTS

Training performance over MDD patients at the major site achieved 75% accuracy while performance with accuracy of 70% was achieved in the out-of-sample test site. Assessing sensitivity and specificity changes following the removal of single base models from the prominent model highlighted the functions of two neural circuitries: SSRI-related emotion regulation circuitry, centered on the hippocampus (sensitivity changes: 10%) and amygdala (sensitivity changes: 11%); and SNRI-related emotion and reward circuitry, centered on the putamen (specificity changes: 8%) and orbital part of superior frontal gyrus (specificity changes: 12%).

DATA CONCLUSION

These findings support future research on clinical antidepressant selection for MDD.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 2.

Neuregulin signaling mediates the acute and sustained antidepressant effects of subanesthetic ketamine

Subanesthetic ketamine evokes rapid antidepressant effects in human patients that persist long past ketamine's chemical half-life of ~2 h. Ketamine's sustained antidepressant action may be due to modulation of cortical plasticity. We find that ketamine ameliorates depression-like behavior in the forced swim test in adult mice, and this depends on parvalbumin-expressing (PV) neuron-directed neuregulin-1 (NRG1)/ErbB4 signaling. Ketamine rapidly downregulates NRG1 expression in PV inhibitory neurons in mouse medial prefrontal cortex (mPFC) following a single low-dose ketamine treatment. This NRG1 downregulation in PV neurons co-tracks with the decreases in synaptic inhibition to mPFC excitatory neurons for up to a week. This results from reduced synaptic excitation to PV neurons, and is blocked by exogenous NRG1 as well as by PV targeted ErbB4 receptor knockout. Thus, we conceptualize that ketamine's effects are mediated through rapid and sustained cortical disinhibition via PV-specific NRG1 signaling. Our findings reveal a novel neural plasticity-based mechanism for ketamine's acute and long-lasting antidepressant effects.

Functional connectivity between the amygdala and subgenual cingulate gyrus predicts the antidepressant effects of ketamine in patients with treatment-resistant depression

Aim

Approximately one‐third of patients with major depressive disorder develop treatment‐resistant depression. One‐third of patients with treatment‐resistant depression demonstrate resistance to ketamine, which is a novel antidepressant effective for this disorder. The objective of this study was to examine the utility of resting‐state functional magnetic resonance imaging for the prediction of treatment response to ketamine in treatment‐resistant depression.

Methods

An exploratory seed‐based resting‐state functional magnetic resonance imaging analysis was performed to examine baseline resting‐state functional connectivity differences between ketamine responders and nonresponders before treatment with multiple intravenous ketamine infusions.

Results

Fifteen patients with treatment‐resistant depression received multiple intravenous subanesthetic (0.5 mg/kg/40 minutes) ketamine infusions, and nine were identified as responders. The exploratory resting‐state functional magnetic resonance imaging analysis identified a cluster of significant baseline resting‐state functional connectivity differences associating ketamine response between the amygdala and subgenual anterior cingulate gyrus in the right hemisphere. Using anatomical region of interest analysis of the resting‐state functional connectivity, ketamine response was predicted with 88.9% sensitivity and 100% specificity. The resting‐state functional connectivity of significant group differences between responders and nonresponders retained throughout the treatment were considered a trait‐like feature of heterogeneity in treatment‐resistant depression.

Conclusion

This study suggests the possible clinical utility of resting‐state functional magnetic resonance imaging for predicting the antidepressant effects of ketamine in treatment‐resistant depression patients and implicated resting‐state functional connectivity alterations to determine the trait‐like pathophysiology underlying treatment response heterogeneity in treatment‐resistant depression.

This study illustrates that the alteration in the RSFC within the right AN in TRD patients reflects the antidepressant response to ketamine at baseline. The alteration remained throughout the 2‐week treatment with multiple ketamine infusions and seemed to reflect the trait‐like features underlying treatment heterogeneity in TRD. By employing an anatomical ROI of the sc/sgACC, the present study also suggests the possible clinical utility of the rsfMRI to predict the treatment response to ketamine in TRD patients.

Treatment-Resistant Depression Revisited: A Glimmer of Hope

Major Depressive Disorder (MDD) is a highly prevalent psychiatric disorder worldwide. It causes individual suffering, loss of productivity, increased health care costs and high suicide risk. Current pharmacologic interventions fail to produce at least partial response to approximately one third of these patients, and remission is obtained in approximately 30% of patients. This is known as Treatment-Resistant Depression (TRD). The burden of TRD exponentially increases the longer it persists, with a higher risk of impaired functional and social functioning, vast losses in quality of life and significant risk of somatic morbidity and suicidality. Different approaches have been suggested and utilized, but the results have not been encouraging. In this review article, we present new approaches to identify and correct potential causes of TRD, thereby reducing its prevalence and with it the overall burden of this disease entity. We will address potential contributory factors to TRD, most of which can be investigated in many laboratories as routine tests. We discuss endocrinological aberrations, notably, hypothalamic-pituitary-adrenal (HPA) axis dysregulation and thyroid and gonadal dysfunction. We address the role of Vitamin D in contributing to depression. Pharmacogenomic testing is being increasingly used to determine Single Nucleotide Polymorphisms in Cytochrome P450, Serotonin Transporter, COMT, folic acid conversion (MTHFR). As the role of immune system dysregulation is being recognized as potentially a major contributory factor to TRD, the measurement of C-reactive protein (CRP) and select immune biomarkers, where testing is available, can guide combination treatments with anti-inflammatory agents (e.g., selective COX-2 inhibitors) reversing treatment resistance. We focus on established and emerging test procedures, potential biomarkers and non-biologic assessments and interventions to apply personalized medicine to effectively manage treatment resistance in general and TRD specifically.

Potential Biomarkers for Diagnosing Major Depressive Disorder Patients with Suicidal Ideation

Background

Major depressive disorder (MDD) and suicide are two major health problems, but there are still no objective methods to diagnose MDD or suicidal ideation (SI). This study was conducted to identify potential biomarkers for diagnosing MDD patients with SI.

Methods

First-episode drug-naïve MDD patients with SI and demographics-matched healthy controls (HCs) were recruited. First-episode drug-naïve MDD patients without SI were also included. The serum lipids, C-reactive protein (CRP), transferring (TRSF), homocysteine (HCY) and alpha 1-antitrypsin (AAT) in serum were detected. The univariate and multivariate statistical analyses were used to identify and validate the potential biomarkers.

Results

The 86 HCs, 53 MDD patients with SI and 20 MDD patients without SI were included in this study. Four potential biomarkers were identified: AAT, TRSF, high-density lipoprotein cholesterol (HDLC), and apolipoprotein A1 (APOA1). After one month treatment, the levels of AAT and APOA1 were significantly improved. The panel consisting of these potential biomarkers had an excellent diagnostic performance, yielding an area under the ROC curve (AUC) of 0.994 and 0.990 in the training and testing set, respectively. Moreover, this panel could effectively distinguish MDD patients with SI from MDD patients without SI (AUC=0.928).

Conclusion

These results showed that these potential biomarkers could facilitate the development of an objective method for diagnosing MDD patients with SI, and the decreased AAT levels in MDD patients might lead to the appearance of SI by resulting in the elevated inflammation.

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

INTRODUCTION

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

AREAS COVERED

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

EXPERT OPINION

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

Post-weaning Social Isolated Flinders Sensitive Line Rats Display Bio-Behavioural Manifestations Resistant to Fluoxetine: A Model of Treatment-Resistant Depression

Treatment-resistant depression (TRD) complicates the management of major depression (MD). The underlying biology of TRD involves interplay between genetic propensity and chronic and/or early life adversity. By combining a genetic animal model of MD and post-weaning social isolation rearing (SIR), we sought to produce an animal that displays more severe depressive- and social anxiety-like manifestations resistant to standard antidepressant treatment. Flinders Sensitive Line (FSL) pups were social or isolation reared from weaning [postnatal day (PND) 21], receiving fluoxetine (FLX) from PND 63 (10 mg/kg × 14 days), and compared to Sprague Dawley (SD) controls. Depressive-, anxiety-like, and social behaviour were assessed from PND 72 in the forced swim test (FST) and social interaction test (SIT). Post-mortem cortico-hippocampal norepinephrine (NE), serotonin (5-HT), and dopamine (DA), as well as plasma interleukin 6 (IL-6), tumour necrosis factor alpha (TNF-α), corticosterone (CORT), and dopamine-beta-hydroxylase (DBH) levels were assayed. FSL rats displayed significant cortico-hippocampal monoamine disturbances, and depressive- and social anxiety-like behaviour, the latter two reversed by FLX. SIR-exposed FSL rats exhibited significant immobility in the FST and social impairment which were, respectively, worsened by or resistant to FLX. In SIR-exposed FSL rats, FLX significantly raised depleted NE and 5-HT, significantly decreased DBH and caused a large effect size increase in DA and decrease in CORT and TNF-α. Concluding, SIR-exposed FSL rats display depressive- and social anxiety-like symptoms that are resistant to, or worsened by, FLX, with reduced plasma DBH and suppressed cortico-hippocampal 5-HT, NE and DA, all variably altered by FLX. Exposure of a genetic animal model of MD to post-weaning SIR results in a more intractable depressive-like phenotype as well as changes in TRD-related biomarkers, that are resistant to traditional antidepressant treatment. Given the relative absence of validated animal models of TRD, these findings are especially promising and warrant study, especially further predictive validation.

Development of Neuroimaging-Based Biomarkers in Major Depression

A leading goal in the field of biological psychiatry for depression is to find a promising diagnostic biomarker and selection of specific psychiatric treatment mode that is most likely to benefit patients with depression. Recent neuroimaging studies have characterized the pathophysiology of major depressive disorder (MDD) with functional and structural alterations in the neural circuitry involved in emotion or reward processing. Particularly, structural and functional magnetic resonance imaging (MRI) studies have reported that the brain structures deeply involved in emotion regulation or reward processing including the amygdala, prefrontal cortex (PFC), anterior cingulate cortex (ACC), ventral striatum, and hippocampus are key regions that provide useful information about diagnosis and treatment outcome prediction in MDD. For example, it has been consistently reported that elevated activity of the ACC is associated with better antidepressant response in patients with MDD. This chapter will discuss a growing body of evidence that suggests that diagnosis or prediction of outcome for specific treatment can be assisted by a neuroimaging-based biomarker in MDD.

The alteration of cognitive function networks in remitted patients with major depressive disorder: an independent component analysis

INTRODUCTION

Dysfunctional connectivity of resting-state functional networks has been observed in patients with major depressive disorder (MDD), particularly in cognitive function networks including the central executive network (CEN), default mode network (DMN) and salience network (SN). Findings from studies examining how aberrant functional connectivity (FC) changed after antidepressant treatment, however, have been inconsistent. Thus, the purpose of the present study was to explore potential mechanisms of altered cognitive function networks during resting-state between remitted major depressive disorder (rMDD) patients and healthy controls (HCs) and furthermore, the relationship between dysfunctional connectivity patterns in rMDD and clinical symptoms.

METHODOLOGY

In this study, 19 HCs and 19 rMDD patients were recruited for resting-state functional magnetic resonance imaging (fMRI) scanning. FC was evaluated with independent component analysis for CEN, DMN and SN. Two sample t tests were conducted to compare differences between rMDD and HCs. A Pearson correlation analysis was also performed to examine the relationship between connectivity of networks and cognitive function scores and clinical symptoms.

RESULTS

Compared to healthy controls, remitted patients showed lower connectivity in CEN, mostly in the superior frontal gyrus (SFG), middle frontal gyrus (MFG), inferior parietal lobule (IPL) and part of the supramarginal gyrus (SMG). Conversely, the bilateral insula, part of the SMG (a key node of the CEN) and dorsal anterior cingulate cortex (dACC) of the DMN showed higher connectivity in rMDD patients. Pearson correlation results demonstrated that connectivity of the right IPL in CEN was positively correlated with cognitive function scores, and connectivity of the left insula was negatively correlated with BDI scores.

CONCLUSIONS

Though rMDD patients reached the standard of clinal remission, unique impairments of FC in cognitive function networks remained. Aberrant FC between cognitive function networks responsible for executive control was observed in rMDD and may be associated with residual clinical symptoms.

Schema therapy versus cognitive behavioral therapy versus individual supportive therapy for depression in an inpatient and day clinic setting: study protocol of the OPTIMA-RCT

BACKGROUND

Major depressive disorder represents (MDD) a major cause of disability and disease burden. Beside antidepressant medication, psychotherapy is a key approach of treatment. Schema therapy has been shown to be effective in the treatment of psychiatric disorders, especially personality disorders, in a variety of settings and patient groups. Nevertheless, there is no evidence on its effectiveness for MDD in an inpatient nor day clinic setting and little is known about the factors that drive treatment response in such a target group.

METHODS

In the current protocol, we outline OPTIMA (OPtimized Treatment Identification at the MAx Planck Institute): a single-center randomized controlled trial of schema therapy as a treatment approach for MDD in an inpatient and day clinic setting. Over the course of 7 weeks, we compare schema therapy with cognitive behavioral therapy and individual supportive therapy, conducted in individual and group sessions and with no restrictions regarding concurrent antidepressant medication, thus approximating real-life treatment conditions. N = 300 depressed patients are included. All study therapists undergo a specific training and supervision and therapy adherence is assessed. Primary outcome is depressive symptom severity as self-assessment (Beck Depression Inventory-II) and secondary outcomes are clinical ratings of MDD (Montgomery-Asberg Depression Rating Scale), recovery rates after 7 weeks according to the Munich-Composite International Diagnostic Interview, general psychopathology (Brief Symptom Inventory), global functioning (World Health Organization Disability Assessment Schedule), and clinical parameters such as dropout rates. Further parameters on a behavioral, cognitive, psychophysiological, and biological level are measured before, during and after treatment and in 2 follow-up assessments after 6 and 24 months after end of treatment.

DISCUSSION

To our knowledge, the OPTIMA-Trial is the first to investigate the effectiveness of schema therapy as a treatment approach of MDD, to investigate mechanisms of change, and explore predictors of treatment response in an inpatient and day clinic setting by using such a wide range of parameters. Insights from OPTIMA will allow more integrative approaches of psychotherapy of MDD. Especially, the identification of intervention-specific markers of treatment response can improve evidence-based clinical decision for individualizing treatment.

TRIAL REGISTRATION

Identifier on clinicaltrials.gov : NCT03287362 ; September, 12, 2017.

Genome-wide association study and polygenic risk score analysis of esketamine treatment response

To elucidate the genetic underpinnings of the antidepressant efficacy of S-ketamine (esketamine) nasal spray in major depressive disorder (MDD), we performed a genome-wide association study (GWAS) in cohorts of European ancestry (n = 527). This analysis was followed by a polygenic risk score approach to test for associations between genetic loading for psychiatric conditions, symptom profiles and esketamine efficacy. We identified a genome-wide significant locus in IRAK3 (p = 3.57 × 10^–8, rs11465988, β = − 51.6, SE = 9.2) and a genome-wide significant gene-level association in NME7 (p = 1.73 × 10^–6) for esketamine efficacy (i.e. percentage change in symptom severity score compared to baseline). Additionally, the strongest association with esketamine efficacy identified in the polygenic score analysis was from the genetic loading for depressive symptoms (p = 0.001, standardized coefficient β = − 3.1, SE = 0.9), which did not reach study-wide significance. Pathways relevant to neuronal and synaptic function, immune signaling, and glucocorticoid receptor/stress response showed enrichment among the suggestive GWAS signals.

The Predictive Validity of Machine Learning Models in the Classification and Treatment of Major Depressive Disorder: State of the Art and Future Directions

Major depressive disorder imposes a substantial disease burden worldwide, ranking as the third leading contributor to global disability. In spite of its ubiquity, classifying and treating depression has proven troublesome. One argument put forward to explain this predicament is the heterogeneity of patients diagnosed with the disorder. Recently, many areas of daily life have witnessed the surge of machine learning techniques, computational approaches to elucidate complex patterns in large datasets, which can be employed to make predictions and detect relevant clusters. Due to the multidimensionality at play in the pathogenesis of depression, it is suggested that machine learning could contribute to improving classification and treatment. In this paper, we investigated literature focusing on the use of machine learning models on datasets with clinical variables of patients diagnosed with depression to predict treatment outcomes or find more homogeneous subgroups. Identified studies based on best practices in the field are evaluated. We found 16 studies predicting outcomes (such as remission) and identifying clusters in patients with depression. The identified studies are mostly still in proof-of-concept phase, with small datasets, lack of external validation, and providing single performance metrics. Larger datasets, and models with similar variables present across these datasets, are needed to develop accurate and generalizable models. We hypothesize that harnessing natural language processing to obtain data 'hidden' in clinical texts might prove useful in improving prediction models. Besides, researchers will need to focus on the conditions to feasibly implement these models to support psychiatrists and patients in their decision-making in practice. Only then we can enter the realm of precision psychiatry.

Predicting treatment effects in unipolar depression: A meta-review

There is increasing interest in clinical prediction models in psychiatry, which focus on developing multivariate algorithms to guide personalized diagnostic or management decisions. The main target of these models is the prediction of treatment response to different antidepressant therapies. This is because the ability to predict response based on patients' personal data may allow clinicians to make improved treatment decisions, and to provide more efficacious or more tolerable medications to the right patient. We searched the literature for systematic reviews about treatment prediction in the context of existing treatment modalities for adult unipolar depression, until July 2019. Treatment effect is defined broadly to include efficacy, safety, tolerability and acceptability outcomes. We first focused on the identification of individual predictor variables that might predict treatment response, and second, we considered multivariate clinical prediction models. Our meta-review included a total of 10 systematic reviews; seven (from 2014 to 2018) focusing on individual predictor variables and three focusing on clinical prediction models. These identified a number of sociodemographic, phenomenological, clinical, neuroimaging, remote monitoring, genetic and serum marker variables as possible predictor variables for treatment response, alongside statistical and machine-learning approaches to clinical prediction model development. Effect sizes for individual predictor variables were generally small and clinical prediction models had generally not been validated in external populations. There is a need for rigorous model validation in large external data-sets to prove the clinical utility of models. We also discuss potential future avenues in the field of personalized psychiatry, particularly the combination of multiple sources of data and the emerging field of artificial intelligence and digital mental health to identify new individual predictor variables.

Reduction of depression-like behavior in rat model induced by ShRNA targeting norepinephrine transporter in locus coeruleus

Depression may be associated with reduced monoamine neurotransmission, particularly serotonin and norepinephrine (NE). Reuptake of NE by the norepinephrine transporter (NET) is the primary mechanism by which many of the antidepressants are high-affinity substrates for NET. This study aimed to examine the effect of lentivirus-mediated shRNA targeting NET in locus coeruleus (LC) on depression-like behaviors of rats. We randomly assigned 60 male Wistar rats to 6 experimental groups: (1) Control group: without chronic unpredictable mild stress (CUMS) and without NET-shRNA treatment; (2) shRNA group: without CUMS + NET-shRNA; (3) CUMS group: 3-week CUMS without NET-shRNA; (4) CUMS + nonsense shRNA group; (5) CUMS + amygdala (Amy)-shRNA group; (6) CUMS+ locus coeruleus (LC)-shRNA group. First, recombinant lentiviral vector expressing shRNA (ShRNA-629, ShRNA-330, ShRNA-1222, ShRNA-1146 or ShRNA- negative control) against NET were produced, and their efficiency in knocking down of NET in PC12 cells were assessed by Q-PCR and western blot analysis. Second, shRNA was injected into the rat LC bilaterally to investigate whether it could prevent the depressive-like behavior induced by 3-week CUMS. Third, we tested the depressive-like behavior of the rats in the forced swimming test, the open field test, the sucrose preference test, as well as the body weight gain at the end of the seventh week. Finally, the protein expressions of NET was measured by western blot and the NE levels were measured by high performance liquid chromatography. Q-PCR and western blot showed that the ShRNA-1146 had the best interference efficiency targeting on NET in PC12 cells (p < 0.01). Compared to the depression model group, the immobility time in the forced swimming test was significantly reduced (p < 0.01), but the sucrose preference and the total scores in the open field test were significantly increased (all p < 0.01) in the group treated with shRNA in LC. Furthermore, compared with the depression model group, NET levels were significantly decreased (p < 0.01), but NE levels were significantly increased in the group treated with shRNA in LC (p < 0.05). Our findings suggest that Lentivirus-mediated shRNA targeting NET in LC downregulated NET both in vitro and in vivo, resulting in a significant decrease in depressive-like behavior of rats.

Current challenges and possible future developments in personalized psychiatry with an emphasis on psychotic disorders

A personalized medicine approach seems to be particularly applicable to psychiatry. Indeed, considering mental illness as deregulation, unique to each patient, of molecular pathways, governing the development and functioning of the brain, seems to be the most justified way to understand and treat disorders of this medical category. In order to extract correct information about the implicated molecular pathways, data can be drawn from sampling phenotypic and genetic biomarkers and then analyzed by a machine learning algorithm. This review describes current difficulties in the field of personalized psychiatry and gives several examples of possibly actionable biomarkers of psychotic and other psychiatric disorders, including several examples of genetic studies relevant to personalized psychiatry. Most of these biomarkers are not yet ready to be introduced in clinical practice. In a next step, a perspective on the path personalized psychiatry may take in the future is given, paying particular attention to machine learning algorithms that can be used with the goal of handling multidimensional datasets.

The identification, assessment and management of difficult-to-treat depression: An international consensus statement

BACKGROUND

Many depressed patients are not able to achieve or sustain symptom remission despite serial treatment trials - often termed "treatment resistant depression". A broader, perhaps more empathic concept of "difficult-to-treat depression" (DTD) was considered.

METHODS

A consensus group discussed the definition, clinical recognition, assessment and management implications of the DTD heuristic.

RESULTS

The group proposed that DTD be defined as "depression that continues to cause significant burden despite usual treatment efforts". All depression management should include a thorough initial assessment. When DTD is recognized, a regular reassessment that employs a multi-dimensional framework to identify addressable barriers to successful treatment (including patient-, illness- and treatment-related factors) is advised, along with specific recommendations for addressing these factors. The emphasis of treatment, in the first instance, shifts from a goal of remission to optimal symptom control, daily psychosocial functional and quality of life, based on a patient-centred approach with shared decision-making to enhance the timely consideration of all treatment options (including pharmacotherapy, psychotherapy, neurostimulation, etc.) to optimize outcomes when sustained remission is elusive.

LIMITATIONS

The recommended definition and management of DTD is based largely on expert consensus. While DTD would seem to have clinical utility, its specificity and objectivity may be insufficient to define clinical populations for regulatory trial purposes, though DTD could define populations for service provision or phase 4 trials.

CONCLUSIONS

DTD provides a clinically useful conceptualization that implies a search for and remediation of specific patient-, illness- and treatment obstacles to optimizing outcomes of relevance to patients.

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

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

Neuroimaging as a Tool for Individualized Treatment Choice in Depression: the Past, the Present and the Future

Purpose of Review

This paper aims to review the findings on neuroimaging as a tool for facilitating individualized treatment choice in depression.

Recent Findings

Neuroimaging has allowed the exploration of neural candidates for response biomarkers. In less than two decades, the field has expanded from small single drug studies to large multisite initiatives testing multiple interventions; from simple analytical methods to employing artificial intelligence, with an aim of establishing models based on a variety of data, such as neuroimaging, biological, psychological and clinical measures.

Summary

Neural biomarkers of response may play an important role in treatment response prediction. It seems likely that they will need to be considered together with other types of data in complex models in order to achieve the high accuracy and generalizability of results necessary for clinical use.

Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches

A growing body of evidence now suggests that precision psychiatry, an interdisciplinary field of psychiatry, precision medicine, and pharmacogenomics, serves as an indispensable foundation of medical practices by offering the accurate medication with the accurate dose at the accurate time to patients with psychiatric disorders. In light of the latest advancements in artificial intelligence and machine learning techniques, numerous biomarkers and genetic loci associated with psychiatric diseases and relevant treatments are being discovered in precision psychiatry research by employing neuroimaging and multi-omics. In this review, we focus on the latest developments for precision psychiatry research using artificial intelligence and machine learning approaches, such as deep learning and neural network algorithms, together with multi-omics and neuroimaging data. Firstly, we review precision psychiatry and pharmacogenomics studies that leverage various artificial intelligence and machine learning techniques to assess treatment prediction, prognosis prediction, diagnosis prediction, and the detection of potential biomarkers. In addition, we describe potential biomarkers and genetic loci that have been discovered to be associated with psychiatric diseases and relevant treatments. Moreover, we outline the limitations in regard to the previous precision psychiatry and pharmacogenomics studies. Finally, we present a discussion of directions and challenges for future research.

Analysis of Features Selected by a Deep Learning Model for Differential Treatment Selection in Depression

Background: Deep learning has utility in predicting differential antidepressant treatment response among patients with major depressive disorder, yet there remains a paucity of research describing how to interpret deep learning models in a clinically or etiologically meaningful way. In this paper, we describe methods for analyzing deep learning models of clinical and demographic psychiatric data, using our recent work on a deep learning model of STAR*D and CO-MED remission prediction.

Methods: Our deep learning analysis with STAR*D and CO-MED yielded four models that predicted response to the four treatments used across the two datasets. Here, we use classical statistics and simple data representations to improve interpretability of the features output by our deep learning model and provide finer grained understanding of their clinical and etiological significance. Specifically, we use representations derived from our model to yield features predicting both treatment non-response and differential treatment response to four standard antidepressants, and use linear regression and t-tests to address questions about the contribution of trauma, education, and somatic symptoms to our models.

Results: Traditional statistics were able to probe the input features of our deep learning models, reproducing results from previous research, while providing novel insights into depression causes and treatments. We found that specific features were predictive of treatment response, and were able to break these down by treatment and non-response categories; that specific trauma indices were differentially predictive of baseline depression severity; that somatic symptoms were significantly different between males and females, and that education and low income proved important psycho-social stressors associated with depression.

Conclusion: Traditional statistics can augment interpretation of deep learning models. Such interpretation can lend us new hypotheses about depression and contribute to building causal models of etiology and prognosis. We discuss dataset-specific effects and ideal clinical samples for machine learning analysis aimed at improving tools to assist in optimizing treatment.

Vasopressin Surrogate Marker Copeptin as a Potential Novel Endocrine Biomarker for Antidepressant Treatment Response in Major Depression: A Pilot Study

BACKGROUND

Major depressive disorder (MDD) constitutes the leading cause of disability worldwide. Although efficacious antidepressant pharmacotherapies exist for MDD, only about 40-60% of the patients respond to initial treatment. However, there is still a lack of robustly established and applicable biomarkers for antidepressant response in everyday clinical practice.

OBJECTIVE

This study targets the assessment of the vasopressin (AVP) surrogate marker Copeptin (CoP), as a potential peripheral hypothalamic-level biomarker of antidepressant treatment response in MDD.

METHODS

We measured baseline and dynamic levels of plasma CoP along with plasma ACTH and cortisol (CORT) in drug-naive outpatients with MDD before and after overnight manipulation of the hypothalamic-pituitary-adrenal (HPA) axis [i.e., stimulation (metyrapone) and suppression (dexamethasone)] on three consecutive days and their association with treatment response to 4 weeks of escitalopram treatment.

RESULTS

Our findings suggest significantly higher baseline and post-metyrapone plasma CoP levels in future non-responders, a statistically significant invert association between baseline CoP levels and probability of treatment response and a potential baseline plasma CoP cut-off level of above 2.9 pmol/L for future non-response screening. Baseline and dynamic plasma ACTH and CORT levels showed no association with treatment response.

CONCLUSIONS

This pilot study provide first evidence in humans that CoP may represent a novel, clinically easily applicable, endocrine biomarker of antidepressant response, based on a single-measurement, cut-off level. These findings, underline the role of the vasopressinergic system in the pathophysiology of MDD and may represent a significant new tool in the clinical and biological phenotyping of MDD enhancing individual-tailored therapies.

What are the chances for personalised treatment with antidepressants? Detection of patient-by-treatment interaction with a variance ratio meta-analysis

OBJECTIVES

To investigate if the treatment effect of antidepressants in patients with depression substantially varies in each patient (patient-by-treatment interaction or treatment heterogeneity), a necessary but largely unexplored prerequisite of personalised antidepressant treatment.

DESIGN

Meta-analytic variance comparison of treatment outcome between drug arms and placebo arms of clinical trials, based on the assumption that patient-by-treatment interaction should lead to larger variances in drug arms than placebo arms. To put the results into context, we run simple simulations, assuming different definitions and rates of those who respond especially well to antidepressants.

DATA SOURCES

163 randomised, placebo-controlled trials (51 396 patients) with complete results for pre-post differences, selected from a recently published systematic review.

ANALYSIS

Variance ratios (VRs) and coefficients of variance ratios (CVRs) of individual trials were meta-analytically combined. The analysis was repeated for classes of antidepressants and specific antidepressants.

RESULTS

VRs (VR=1.01, CI 0.99 to 1.02) and CVRs (CVR=0.82, CI 0.80 to 0.84) of the antidepressant-treatment arms were comparable or smaller than in placebo arms. Similar results were observed for classes of antidepressants and for specific antidepressants. Our simulation analysis confirmed that equal VRs can only be obtained if they are not more than a few patients who respond slightly above average.

CONCLUSIONS

The lack of increased treatment-outcome variance in the antidepressants versus placebo groups in randomised controlled trials indicates that no or only very small subgroups of patients respond particularly well to antidepressants. Thus, the scope for personalised treatment with antidepressants seems to be limited.

Predictors of response to augmentation treatment in patients with treatment-resistant depression: A systematic review

BACKGROUND

Treatment-resistant depression is an important contributor to the global burden of depression. Antidepressant augmentation is a recommended treatment strategy for treatment-resistant patients, but outcomes remain poor. Identifying factors that are predictive of response to augmentation treatments may improve outcomes.

AIMS

This review aimed to synthesise the existing literature examining predictors of response to augmentation treatments in patients who had insufficiently responded to initial treatment.

METHODS

A systematic search was conducted identifying 2241 unique manuscripts. 24 examining predictors of outcome to pharmacological or psychological augmentation treatment were included in this review.

RESULTS

Atypical antipsychotics were the most frequently assessed treatment class (nine studies), closely followed by mood stabilisers (eight studies). Only one eligible psychological augmentation study was identified. Early response to treatment (week 2) was the best-supported predictor of subsequent treatment outcome, reported by six studies. Many predictor variables were only assessed by one report and others such as pre-treatment severity yielded contradictory results, both within and across treatment classes.

CONCLUSIONS

This review highlights the importance of early response as a predictor of pharmacological augmentation outcome, with implications for both the monitoring and treatment of resistant unipolar patients. Further replication is needed across specific interventions to fully assess the generalisability of this finding. However, the clear lack of consistent evidence for other predictive factors both within and across treatments, and the scarce examination of psychological augmentation, demonstrates the need for much more research of a high quality if response prediction is to improve outcomes for patients with treatment-resistant depression.

Low-Grade Inflammation as a Predictor of Antidepressant and Anti-Inflammatory Therapy Response in MDD Patients: A Systematic Review of the Literature in Combination With an Analysis of Experimental Data Collected in the EU-MOODINFLAME Consortium

Low-grade inflammation plays a role not only in the pathogenesis of major depressive disorder (MDD) but probably also in the poor responsiveness to regular antidepressants. There are also indications that anti-inflammatory agents improve the outcomes of antidepressants. Aim: To study whether the presence of low-grade inflammation predicts the outcome of antidepressants, anti-inflammatory agents, or combinations thereof. Methods: We carried out a systematic review of the literature on the prediction capability of the serum levels of inflammatory compounds and/or the inflammatory state of circulating leukocytes for the outcome of antidepressant/anti-inflammatory treatment in MDD. We compared outcomes of the review with original data (collected in two limited trials carried out in the EU project MOODINFLAME) on the prediction capability of the inflammatory state of monocytes (as measured by inflammatory gene expression) for the outcome of venlafaxine, imipramine, or sertraline treatment, the latter with and without celecoxib added. Results: Collectively, the literature and original data showed that: 1) raised serum levels of pro-inflammatory compounds (in particular of CRP/IL-6) characterize an inflammatory form of MDD with poor responsiveness to predominately serotonergic agents, but a better responsiveness to antidepressant regimens with a) (add-on) noradrenergic, dopaminergic, or glutamatergic action or b) (add-on) anti-inflammatory agents such as infliximab, minocycline, or eicosapentaenoic acid, showing-next to anti-inflammatory-dopaminergic or lipid corrective action; 2) these successful anti-inflammatory (add-on) agents, when used in patients with low serum levels of CRP/IL-6, decreased response rates in comparison to placebo. Add-on aspirin, in contrast, improved responsiveness in such "non-inflammatory" patients; 3) patients with increased inflammatory gene expression in circulating leukocytes had a poor responsiveness to serotonergic/noradrenergic agents. Conclusions: The presence of inflammation in patients with MDD heralds a poor outcome of first-line antidepressant therapies. Immediate step-ups to dopaminergic or glutamatergic regimens or to (add-on) anti-inflammatory agents are most likely indicated. However, at present, insufficient data exist to design protocols with reliable inflammation parameter cutoff points to guide such therapies, the more since detrimental outcomes are possible of anti-inflammatory agents in "non-inflamed" patients.