Antidepressant Treatment Outcome Research Articles

Heterogeneity in Antidepressant Treatment and Major Depressive Disorder Outcomes Among Clinicians

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

Heterogeneity in Antidepressant Treatment and Major Depressive Disorder Outcomes Among Clinicians.

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

The bidirectional interaction between antidepressants and the gut microbiota: are there implications for treatment response?

This review synthesizes the evidence on associations between antidepressant use and gut microbiota composition and function, exploring the microbiota's possible role in modulating antidepressant treatment outcomes. Antidepressants exert an influence on measures of gut microbial diversity. The most consistently reported differences were in β-diversity between those exposed to antidepressants and those not exposed, with longitudinal studies supporting a potential causal association. Compositional alterations in antidepressant users include an increase in the Bacteroidetes phylum, Christensenellaceae family, and Bacteroides and Clostridium genera, while a decrease was found in the Firmicutes phylum, Ruminococcaceae family, and Ruminococcus genus. In addition, antidepressants attenuate gut microbial differences between depressed and healthy individuals, modulate microbial serotonin transport, and influence microbiota's metabolic functions. These include lyxose degradation, peptidoglycan maturation, membrane transport, and methylerythritol phosphate pathways, alongside gamma-aminobutyric acid metabolism. Importantly, baseline increased α-diversity and abundance of the Roseburia and Faecalibacterium genera, in the Firmicutes phylum, are associated with antidepressant response, emerging as promising biomarkers. This review highlights the potential for gut microbiota as a predictor of treatment response and emphasizes the need for further research to elucidate the mechanisms underlying antidepressant-microbiota interactions. More homogeneous studies and standardized techniques are required to confirm these initial findings.

Subjective experience of the environment determines serotoninergic antidepressant treatment outcome in male mice

BackgroundThe effects of the selective serotonin reuptake inhibitors (SSRIs), the first-line antidepressant treatment, have been proposed to be affected, at least in part, by the living environment. Since the quality of the environment depends not only on its objective features, but also on the subjective experience, we hypothesized that the latter plays a key role in determining SSRI treatment outcome. MethodsWe chronically administered the SSRI fluoxetine to two groups of adult CD-1 male mice that reportedly show distinct subjective experiences of the environment measured as consistent and significantly different responses to the same emotional and social stimuli. These distinct socioemotional profiles were generated by rearing mice either in standard laboratory conditions (SN) or in a communal nest (CN) where three dams breed together their offspring, sharing caregiving behavior. ResultsAt adulthood, CN mice displayed higher levels of agonistic and anxiety-like behaviors than SN mice, indicating that they experience the environment as more socially challenging and potentially dangerous. We then administered fluoxetine, which increased offensive and anxious response in SN, while producing opposite effects in CN mice. BDNF regulation was modified by the treatment accordingly. LimitationsSubjective experience in mice was assessed as behavioral response to the environment. ConclusionsThese results show that the subjective experience of the environment determines fluoxetine outcome. In a translational perspective, our findings suggest considering not only the objective quality, but also the subjective appraisal, of the patient's living environment for developing effective personalized therapeutic approaches in psychiatry.

Psychoneuroendocrine profiles of unmedicated men with major depressive disorder and associations to treatment effects and sexual side-effects

Sex hormones are potentially involved in major depressive disorder (MDD) as there are sex differences in risk for depression, symptomatology and treatment efficacy. Such hormonal contributions to MDD have mainly been studied in women and scarcely in men. We, therefore, investigate whether testosterone and estradiol levels in unmedicated men with MDD are associated with depressive symptom profiles, SSRI treatment response, and sexual side-effects. Pretreatment plasma testosterone and estradiol were available from 26 unmedicated men (age 18–49). The patients were treated with 10–20 mg escitalopram for 12 weeks. Depression severity was measured by Hamilton Depression Rating Scale 17 (HAMD17), and treatment response was the change in HAMD6 from baseline. Sexual side-effects were assessed with a standardized clinician-rated scale. Pretreatment testosterone was positively associated with depression severity (p = 0.016), the association was primarily driven by vegetative symptoms: weight loss, gastrointestinal symptoms, and insomnia (p ≤ 0.027). Pretreatment sex hormone levels were not associated with antidepressant treatment outcome. However, pretreatment sex hormones were lower in patients who experienced sexual side-effects after 8–12 weeks of escitalopram treatment; low estradiol was associated with erectile and ejaculatory dysfunction (p = 0.039) and low testosterone and low estradiol were both associated with decreased libido (p ≤ 0.004). In conclusion, we find low testosterone in men with MDD coupled with depression severity, particularly vegetative symptoms. However, pretreatment sex hormone levels were not associated with treatment efficacy but with sexual side-effects. Taken together, our findings highlight sex hormone profiles as potential prognostic biomarkers of SSRI-induced sexual dysfunction. Data fromClinical study (NCT02869035).

EEG abnormalities are not associated with poor antidepressant treatment outcome - A NeuroPharm study

EEG brain abnormalities, such as slowing and isolated epileptiform discharges (IEDs), has previously been associated with non-response to antidepressant treatment with escitalopram and venlafaxine, suggesting a potential need for treatment with anticonvulsant property in some patients. The current study aims to replicate the reported association of EEG abnormality and treatment outcomes in an open-label trial of escitalopram for major depressive disorder (MDD) and explore its relationship to mood and cognition. Pretreatment, 6 min eyes-closed resting-state 256-channel EEG was recorded in 91 patients with MDD (age 18–57) who were treated with 10–20 mg escitalopram for 12 weeks; patients could switch to duloxetine after four weeks. A certified clinical neurophysiologist rated the EEGs.IED and EEG slowing was seen in 13.2%, and in 6.6% there were findings with unclear significance (i.e., Wicket spikes and theta activity). We saw no group-difference in remission or response rates after 8 and 12 weeks of treatment or switching to duloxetine. Patients with EEG abnormalities had higher pretreatment mood disturbances driven by greater anger (p=.039) and poorer verbal memory (p=.012). However, EEG abnormality was not associated with improved mood or verbal memory after treatment. Our findings should be interpreted in light of the rarity of EEG abnormalities and the sample size. While we cannot confirm that EEG abnormalities are associated with non-response to treatment, including escitalopram, abnormal EEG activity is associated with poor mood and verbal memory. The clinical utility of EEG abnormality in antidepressant treatment selection needs careful evaluation before deciding if useful for clinical implementation.

Predicting Outcomes of Antidepressant Treatment in Community Practice Settings.

The authors examined whether machine-learning models could be used to analyze data from electronic health records (EHRs) to predict patients' responses to antidepressant medications. EHR data from a Washington State health system identified patients ages ≥13 years who started an antidepressant medication in 2016 in a community practice setting and had a baseline Patient Health Questionnaire-9 (PHQ-9) score of ≥10 and at least one PHQ-9 score recorded 14-180 days later. Potential predictors of a response to antidepressants were extracted from the EHR and included demographic characteristics, psychiatric and substance use diagnoses, past psychiatric medication use, mental health service use, and past PHQ-9 scores. Random-forest and penalized regression analyses were used to build models predicting follow-up PHQ-9 score and a favorable treatment response (≥50% improvement in score). Among 2,469 patients starting antidepressant medication treatment, the mean±SD baseline PHQ-9 score was 17.3±4.5, and the mean lowest follow-up score was 9.2±5.9. Outcome data were available for 72% of the patients. About 48% of the patients had a favorable treatment response. The best-fitting random-forest models yielded a correlation between predicted and observed follow-up scores of 0.38 (95% CI=0.32-0.45) and an area under the receiver operating characteristic curve for a favorable response of 0.57 (95% CI=0.52-0.61). Results were similar for penalized regression models and for models predicting last PHQ-9 score during follow-up. Prediction models using EHR data were not accurate enough to inform recommendations for or against starting antidepressant medication. Personalization of depression treatment should instead rely on systematic assessment of early outcomes.

Links among genetic variants and hierarchical brain structural and functional networks for antidepressant treatment: A multivariate study

BackgroundAntidepressant treatment effects are strongly heritable and have substantial effects on brain function and structure, but the underlying mechanisms are still poorly understood. In this research, we aimed to evaluate the factors of single nucleotide polymorphisms (SNPs) and hierarchical brain structural and functional networks that were associated with antidepressant treatment. Moreover, we further explored the correlations and mediation pattern among “brain structure-brain function-gene” in major depressive disorder (MDD). MethodsWe analysed 405 SNPs and rich club/feeder/local connections of hierarchical structural and functional networks with three-way parallel independent component analysis in 179 MDD patients. The group-discriminative independent components of the three modalities between responders and non-responders of antidepressant treatment were identified. Pearson correlations and mediation analysis were further utilized to investigate the associations among SNPs and connections of the structural and functional networks. ResultsNotably, correlations with antidepressant treatment outcomes were found in structural, functional and SNP modalities simultaneously. The features of group-discriminative independent components included the shared feeder connections of hub regions with the inferior frontal orbital gyrus and amygdala in structural and functional modalities and genes enriched in circadian rhythmic processes and dopaminergic synapse pathways. The structural feeder network displayed close correlations with SNPs and the functional feeder network. Furthermore, the structural feeder network could mediate the association between SNPs and the functional feeder network, implying that genetic variants might influence brain function by affecting brain structure in MDD. ConclusionsThese findings provide potential biomarkers for antidepressant therapy and provide a better grasp of the associations among SNPs and hierarchical structural and functional networks in MDD.

Program of algorithm for pharmacological treatment of major depressive disorder in China: Benefits or not?

BackgroundThis research was designed to investigate Algorithm Guided Treatment (AGT) and clinical traits for the prediction of antidepressant treatment outcomes in Chinese patients with major depressive disorder (MDD). MethodsThis study included 581 patients who had reached treatment response and 406 patients remained non-responded observed after three months of treatment. Sociodemographic factors, clinical traits, and psychiatric rating scales for evaluating therapeutic responses between the two groups were compared. Logistic regression analysis was adopted to determine the risk factors of unresponsive to antidepressant (URA) in MDD. Kaplan-Meier survival analysis was utilized to compare the therapeutic response between AGT and treatment as usual (TAU). ResultsCompared to the MDD responsive to antidepressant (RA) group, the URA group had significantly lower rates of the following clinical traits: married status, anxious distress, moderate to severe depressive symptoms, and higher rates of comorbidity (p-value < 0.05). Logistic Regression Analysis showed that eight clinical traits from psychiatric rating scales, such as anxious characteristics, were correlated positively with URA, while the other eight symptoms, such as autonomic symptoms, were negatively correlated. Time to symptomatic remission was longer in TAU without statistically significant (p-value = 0.11) by log-rank testing. ConclusionsThe factors may affect the therapeutic responses and compliance of patients, increasing the non-response risk for antidepressants. Therapeutic responses might be improved by increasing the clarification and elucidation of different symptom clusters of patients. Benefits on treatment response to AGT were not found in our study, indicating a one-size-fits-all approach may not work.Trial Registration: We registered as a clinical trial at the International Clinical Trials Registry Platform (No. NCT01764867) and obtained ethical approval 2012-42 from SMHC.

Impact of antidepressant medication on the analgetic effect of repetitive transcranial magnetic stimulation treatment of neuropathic pain. Preliminary findings from a registry study.

Repetitive transcranial magnetic stimulation (rTMS) has been found to be effective in chronic neuropathic pain conditions. However, information about the combined effects of rTMS and antidepressant treatment is scarce. We studied the outcome of rTMS and concurrent antidepressant treatment in patients with neuropathic pain. In this retrospective, real-world study, 34 patients with neuropathic pain, who were considered resistant or not benefitting from conventional treatment, received rTMS treatment between 2017 and 2020. Pain-related factors were measured using the Numerical Rating Scale (NRS), Global Impression of Change (GIC), and Beck Depression Inventory. A decrease in pain intensity and pain interference assessed with NRS was observed after 10 treatment sessions in 16 patients. The impression of change was positive in 20 patients. Half of the patients (n=17) used antidepressant medication, while half (n=17) did not. A concurrent use of antidepressants with therapeutic rTMS was significantly linked with less pain intensity relief when compared with the nonuse of antidepressants (p=0.019). The impression of change was significantly in favor of the antidepressant nonuser group (p=0.002). No group differences in pain interference were found between the groups. Therapeutic rTMS for neuropathic pain is plausibly sensitive to interference with antidepressant medication. The exact mechanism of our findings remains to be elucidated; confirmatory studies are warranted.

Relationship between circulating mitochondrial DNA and microRNA in patients with major depression

BackgroundMicroRNAs (miRNAs) and circulating cell-free mitochondrial DNA (ccf-mtDNA) have attracted interest as biological markers of affective disorders. In response to stress, it is known that miRNAs in mitochondria diffuse out of the cytoplasm alongside mtDNA; however, this process has not yet been identified. We hypothesized that miRNAs derived from specific cell nuclei cause mitochondrial damage and mtDNA fragmentation under MDD-associated stress conditions. MethodsA comprehensive analysis of the plasma miRNA levels and quantification of the plasma ccf-mtDNA copy number were performed in 69 patients with depression to determine correlations and identify genes and pathways interacting with miRNAs. The patients were randomly assigned to receive either selective serotonin reuptake inhibitors (SSRI) or mirtazapine. Their therapeutic efficacy over four weeks was evaluated in relation to miRNAs correlated with ccf-mtDNA copy number. ResultsThe expression levels of the five miRNAs showed a significant positive correlation with the ccf-mtDNA copy number after correcting for multiple testing. These miRNAs are involved in gene expression related to thyroid hormone synthesis, the Hippo signaling pathway, vasopressin-regulated water reabsorption, and lysine degradation. Of these five miRNAs, miR-6068 and miR-4708-3p were significantly associated with the SSRI and mirtazapine treatment outcomes, respectively. LimitationsThis study did not show comparison with a healthy group. ConclusionsThe expression levels of specific miRNAs were associated with ccf-mtDNA copy number in untreated depressed patients; moreover, these miRNAs were linked to antidepressant treatment outcomes. These findings are expected to lead to the elucidation of new pathological mechanism of depression.

Antidepressant emergent mood switch in major depressive disorder: onset, clinical correlates and impact on suicidality.

Antidepressant (AD)- emergent mood switch (AEMS) is a common complication of bipolar depression. This study aimed to investigate the prevalence and clinical correlates of subthreshold AEMS (i.e. not fulfilling DSM criteria for hypomanic episodes) in major depressive disorder (MDD) and, prognostically, its impact on AD treatment outcome and suicidality. The study involved 425 outpatients with MDD followed during the acute phase (12 weeks) and continuation (weeks 13-28) AD treatment. AEMS was assessed through the Altman Self-Rating Mania scale (ASRM ≥ 6). Several clinical features differentiated individuals with or without subthreshold AEMS (n = 204 vs. 221): negative self-perception [odds ratio (OR) 1.017-1.565]; panic disorder (OR 1.000-1.091); subthreshold hypomanic episodes (OR 1.466-13.352); childhood emotional abuse (OR 1.053-2.447); lifetime suicidal behaviour (OR 1.027-1.236); AD-related remission (χ 2 = 22.903 P < 0.0001) and suicide ideation (χ 2 = 16.701 P < 0.0001). In AEMS earlier onset showed a strong correlation with bipolar spectrum disorder (overall score: P = 0.0053; mixed depression: P = 0.0154; subthreshold hypomania: P = 0.0150) whereas late-onset was associated with more severe suicidal behaviour ( P < 0.001). In conclusion, our results demonstrate that subthreshold mood switches occur frequently in unipolar depression during acute AD treatment as well as in continuation phase. Time of switch onset seems to have the greatest diagnostic and prognostic value.

Mapping Neurophysiological Subtypes of Major Depressive Disorder Using Normative Models of the Functional Connectome

BackgroundMajor depressive disorder (MDD) is a highly heterogeneous disorder that typically emerges in adolescence and can occur throughout adulthood. Studies aimed at quantitatively uncovering the heterogeneity of individual functional connectome abnormalities in MDD and identifying reproducibly distinct neurophysiological MDD subtypes across the lifespan, which could provide promising insights for precise diagnosis and treatment prediction, are still lacking. MethodsLeveraging resting-state functional magnetic resonance imaging data from 1148 patients with MDD and 1079 healthy control participants (ages 11–93), we conducted the largest multisite analysis to date for neurophysiological MDD subtyping. First, we characterized typical lifespan trajectories of functional connectivity strength based on the normative model and quantitatively mapped the heterogeneous individual deviations among patients with MDD. Then, we identified neurobiological MDD subtypes using an unsupervised clustering algorithm and evaluated intersite reproducibility. Finally, we validated the subtype differences in baseline clinical variables and longitudinal treatment predictive capacity. ResultsOur findings indicated great intersubject heterogeneity in the spatial distribution and severity of functional connectome deviations among patients with MDD, which inspired the identification of 2 reproducible neurophysiological subtypes. Subtype 1 showed severe deviations, with positive deviations in the default mode, limbic, and subcortical areas and negative deviations in the sensorimotor and attention areas. Subtype 2 showed a moderate but converse deviation pattern. More importantly, subtype differences were observed in depressive item scores and the predictive ability of baseline deviations for antidepressant treatment outcomes. ConclusionsThese findings shed light on our understanding of different neurobiological mechanisms underlying the clinical heterogeneity of MDD and are essential for developing personalized treatments for this disorder.

Serum cytokines-based biomarkers in the diagnosis and monitoring of therapeutic response in patients with major depressive disorder

Several lines of evidence have suggested that cytokines are implicated in the pathophysiology of depression and antidepressant treatment outcome. However, the results are not always congruent and partly contradictory. We therefore examined the serum levels of multiple cytokines in patients with major depressive disorder (MDD), with the aim to identify serum cytokines-based biomarkers for MDD diagnosis and antidepressant response. Fifty-nine patients with MDD and 61 healthy controls were included. The baseline levels of serum cytokines between MDD group and control group were compared, and the discriminative ability of different cytokines in predicting MDD patients from healthy controls was investigated using the receiver operating characteristic (ROC) curve method. The baseline levels of serum cytokines between antidepressant nonresponders and responders were compared, and the discriminative ability of different cytokines in predicting nonresponders from responders was evaluated using the ROC curve method. Compared to controls, 15 of the 37 serum cytokines were increased, while 8 cytokines were decreased in MDD patients (all P<0.05). The results of ROC curve showed that the Area Under Curve (AUC) values of 15 cytokines including IL-2, IL-5, IL-6, IL-8, IL-12, IL-13, IL-16, CCL3, CCL4, CCL17, CXCL10, TNF-α, TNF-β, VEGF-C, and FGF basic were greater than 0.7 in discriminating MDD patients from healthy control. Moreover, after 4-week treatment, levels of the 2 cytokines (IL-12 and TSLP) elevated at baseline significantly down-regulated, and levels of the 6 cytokines (IL-5, IL-16, CCL17, CXCL10, TNF-β, and PIGF) decreased at baseline significantly up-regulated (all P<0.05). Furthermore, a positive relationship was found between TNF-α levels and Hamilton Depression Rating Scale-24 (HAMD-24) scores in patients with MDD at baseline (r=0.302, P=0.019). Additionally, compared to responders, nonresponders exhibited decreased levels of IL-1α, IL-5, IL-13, IL-15, VEGF, and ICAM-1 (all P<0.05). The ROC curve analysis demonstrated that a combined panel of IL-1α, IL-5, and ICAM-1 achieved a high accuracy in discriminating antidepressant nonresponders from responders (AUC=0.850, sensitivity=83.3%, specificity=81.8%). These results suggested that alterations in peripheral cytokines levels hold significant promise as biomarkers for MDD diagnosis and antidepressant response.

Using Machine Learning to Predict Antidepressant Treatment Outcome From Electronic Health Records.

To evaluate if a machine learning approach can accurately predict antidepressant treatment outcome using electronic health records (EHRs) from patients with depression. This study examined 808 patients with depression at a New York City-based outpatient mental health clinic between June 13, 2016 and June 22, 2020. Antidepressant treatment outcome was defined based on trend in depression symptom severity over time and was categorized as either "Recovering" or "Worsening" (i.e., non-Recovering), measured by the slope of individual-level Patient Health Questionnaire-9 (PHQ-9) score trajectory spanning 6 months following treatment initiation. A patient was designated as "Recovering" if the slope is less than 0 and as "Worsening" if the slope was no less than 0. Multiple machine learning (ML) models including L2 norm regularized Logistic Regression, Naive Bayes, Random Forest, and Gradient Boosting Decision Tree (GBDT) were used to predict treatment outcome based on additional data from EHRs, including demographics and diagnoses. Shapley Additive Explanations were applied to identify the most important predictors. The GBDT achieved the best results of predicting "Recovering" (AUC: 0.7654±0.0227; precision: 0.6002± 0.0215; recall: 0.5131±0.0336). When excluding patients with low PHQ-9 scores (<10) at baseline, the results of predicting "Recovering" (AUC: 0.7254±0.0218; precision: 0.5392±0.0437; recall: 0.4431±0.0513) were obtained. Prior diagnosis of anxiety, psychotherapy, recurrent depression, and baseline depression symptom severity were strong predictors. The results demonstrate the potential utility of using ML in longitudinal EHRs to predict antidepressant treatment outcome. Our predictive tool holds the promise to accelerate personalized medical management in patients with psychiatric illnesses.

ABCB1 Gene Variants and Antidepressant Treatment Outcomes: A Systematic Review and Meta-Analysis Including Results from the CAN-BIND-1 Study.

The P-glycoprotein efflux pump, encoded by the ABCB1 gene, has been shown to alter concentrations of various antidepressants in the brain. In this study, we conducted a systematic review and meta-analysis to investigate the association between six ABCB1 single-nucleotide polymorphisms (SNPs; rs1045642, rs2032582, rs1128503, rs2032583, rs2235015, and rs2235040) and antidepressant treatment outcomes in individuals with major depressive disorder (MDD), including new data from the Canadian Biomarker and Integration Network for Depression (CAN-BIND-1) cohort. For the CAN-BIND-1 sample, we applied regression models to investigate the association between ABCB1 SNPs and antidepressant treatment response, remission, tolerability, and antidepressant serum levels. For the meta-analysis, we systematically summarized pharmacogenetic evidence of the association between ABCB1 SNPs and antidepressant treatment outcomes. Studies were included in the meta-analysis if they investigated at least one ABCB1 SNP in individuals with MDD treated with at least one antidepressant. We did not find a significant association between ABCB1 SNPs and antidepressant treatment outcomes in the CAN-BIND-1 sample. A total of 39 studies were included in the systematic review. In the meta-analysis, we observed a significant association between rs1128503 and treatment response (T vs. C-allele, odds ratio=1.30, 95% confidence interval=1.15-1.48, P value (adjusted)=0.024, n=2,526). We did not find associations among the six SNPs and treatment remission nor tolerability. Our findings provide limited evidence for an association between common ABCB1 SNPs and antidepressant outcomes, which do not support the implementation of ABCB1 genotyping to inform antidepressant treatment at this time. Future research, especially on rs1128503, is recommended.

Immuno-metabolic depression features predicting antidepressant treatment outcomes: a pooled analysis of iSPOT-D, CO-MED, EMBARC and Genome-based therapeutic drugs for depression clinical trials

Immuno-metabolic depression features predicting antidepressant treatment outcomes: a pooled analysis of iSPOT-D, CO-MED, EMBARC and Genome-based therapeutic drugs for depression clinical trials

Pharmacogenomic-guided therapy for the improvement of antidepressant treatment outcomes: systematic review and meta-analysis of current evidence from randomised controlled trials

Pharmacogenomic-guided therapy for the improvement of antidepressant treatment outcomes: systematic review and meta-analysis of current evidence from randomised controlled trials

A viewpoint on aldosterone and BMI related brain morphology in relation to treatment outcome in patients with major depression.

An abundance of knowledge has been collected describing the involvement of neuroendocrine parameters in major depression. The hypothalamic-pituitary-adrenocortical (HPA) axis regulating cortisol release has been extensively studied; however, attempts to target the HPA axis pharmacologically to treat major depression have failed. This review focuses on the importance of the adrenocortical stress hormone aldosterone, which is released by adrenocorticotropic hormone and angiotensin, and the mineralocorticoid receptor (MR) in depression. Depressed patients, in particular those with atypical depression, have signs of central hyperactivation of the aldosterone sensitive MR, potentially as a consequence of a reactive aldosterone release induced by low blood pressure and as a result of low sensitivity of peripheral MR. This is reflected in reduced heart rate variability, increased salt appetite and sleep changes in this group of patients. In addition, enlarged brain ventricles, compressed corpus callosum and changes of the choroid plexus are associated with increased aldosterone (in relation to cortisol). Furthermore, subjects with these features often show obesity. These characteristics are related to a worse antidepressant treatment outcome. Alterations in choroid plexus function as a consequence of increased aldosterone levels, autonomic dysregulation, metabolic changes and/or inflammation may be involved. The characterization of this regulatory system is in its early days but may identify new targets for therapeutic interventions.

Different depression: motivational anhedonia governs antidepressant efficacy in Huntington's disease.

Depression is more common in neurodegenerative diseases such as Huntington's disease than the general population. Antidepressant efficacy is well-established for depression within the general population: a recent meta-analysis showed serotonin norepinephrine reuptake inhibitors, tricyclic antidepressants and mirtazapine outperformed other antidepressants. Despite the severe morbidity, antidepressant choice in Huntington's disease is based on Class IV evidence. We used complementary approaches to determine treatment choice for depression in Huntington's disease: propensity score analyses of antidepressant treatment outcome using the ENROLL-HD data set, and a dissection of the cognitive mechanisms underlying depression in Huntington's disease using a cognitive battery based on the Research Domain Criteria for Depression. Study 1 included ENROLL-HD 5486 gene-positive adult patients started on an antidepressant medication for depression. Our outcome measures were depression (Hospital Anxiety and Depression Scale or Problem Behaviours Assessment 'Depressed Mood' item) at first follow-up (primary outcome) and all follow-ups (secondary outcome). The intervention was antidepressant class. We used Svyglm&Twang in R to perform propensity scoring, using known variables (disease progression, medical comorbidity, psychiatric morbidity, sedatives, number of antidepressants, demographics and antidepressant contraindications) to determine the probability of receiving different antidepressants (propensity score) and then included the propensity score in a model of treatment efficacy. Study 2 recruited 51 gene-positive adult patients and 26 controls from the South Wales Huntington's Disease Management Service. Participants completed a motor assessment, in addition to measures of depression and apathy, followed by tasks measuring consummatory anhedonia, motivational anhedonia, learning from reward and punishment and reaction to negative outcome. We used generalised linear models to determine the association between task performance and depression scores. Study 1 showed selective serotonin reuptake inhibitors outperformed serotonin norepinephrine reuptake inhibitors on the primary outcome (P = 0.048), whilst both selective serotonin reuptake inhibitors (P = 0.00069) and bupropion (P = 0.0045) were superior to serotonin norepinephrine reuptake inhibitors on the secondary outcome. Study 2 demonstrated an association between depression score and effort for reward that was not explained by apathy. No other mechanisms were associated with depression score. We found that selective serotonin reuptake inhibitors and bupropion outperform serotonin norepinephrine reuptake inhibitors at alleviating depression in Huntington's disease. Moreover, motivational anhedonia appears the most significant mechanism underlying depression in Huntington's disease. Bupropion is improves motivational anhedonia and has a synergistic effect with selective serotonin reuptake inhibitors. This work provides the first large-scale, objective evidence to determine treatment choice for depression in Huntington's disease, and provides a model for determining antidepressant efficacy in other neurodegenerative diseases.