How to choose an antidepressant medication

Poor muscle health and cardiometabolic risks associated with antidepressant treatment

OBJECTIVE

This study aims to investigate whether antidepressant users display differences in fat distribution and muscle composition relative to non-users and to explore risk factors for developing cardiovascular disease (CVD) and type 2 diabetes.

METHODS

The study used quantitative adipose and muscle tissue measures derived from magnetic resonance imaging data from UK Biobank (N = 40,174). Fat distribution and muscle composition of selective serotonin reuptake inhibitor (SSRI) and tricyclic antidepressant (TCA) users were compared with sex-, age-, and BMI-matched control individuals. Cox regression models were used to test for increased risk of developing CVD and type 2 diabetes.

RESULTS

SSRI users had more visceral fat, smaller muscle volume, and higher muscle fat infiltration compared with matched control individuals. Female users showed a larger increase in BMI over time compared with male users. However, male users displayed an unhealthier body composition profile. Male SSRI users also had an increased risk of developing CVD. Both male and female TCA users showed lower muscle volume and an increased risk of developing type 2 diabetes.

CONCLUSIONS

Adverse changes in body composition of antidepressant users are not captured by tracking the body weight or the BMI of the patients. These changes may lead to a worsened cardiometabolic risk profile.

The Relationship of Early Sleep Improvement With Response to Pharmacotherapy in Unipolar Psychotic Depression

BACKGROUND

Since insomnia and depression are interrelated, improved sleep early in antidepressant pharmacotherapy may predict a positive treatment outcome. We investigated whether early insomnia improvement (EII) predicted treatment outcome in psychotic depression (PD) and examined if there was an interaction effect between EII and treatment type to assess if findings were treatment-specific.

METHODS

This study is a secondary analysis of a randomized trial comparing 7 weeks treatment with the antidepressants venlafaxine, imipramine and venlafaxine plus the antipsychotic quetiapine in PD ( n = 114). Early insomnia improvement, defined as ≥20% reduced insomnia after 2 weeks, was assessed by the Hamilton Rating Scale for Depression (HAM-D-17). Associations between EII and treatment outcome were examined using logistic regressions. Subsequently, we added interaction terms between EII and treatment type to assess interaction effects. The predictive value of EII was compared with early response on overall depression (≥20% reduced HAM-D-17 score after 2 weeks).

RESULTS

EII was associated with response (odds ratio [OR], 7.9; 95% confidence interval [CI], 2.7-23.4; P = <0.001), remission of depression (OR, 6.1; 95% CI, 1.6-22.3; P = 0.009), and remission of psychosis (OR, 4.1; 95% CI, 1.6-10.9; P = 0.004). We found no interaction effects between EII and treatment type on depression outcome. Early insomnia improvement and early response on overall depression had a comparable predictive ability for treatment outcome.

CONCLUSIONS

Early insomnia improvement was associated with a positive outcome in pharmacotherapy of PD, regardless of the medication type. Future studies are needed to confirm our findings and to examine the generalizability of EII as predictor in treatment of depression.

Epigenetic regulation in major depression and other stress-related disorders: molecular mechanisms, clinical relevance and therapeutic potential

Major depressive disorder (MDD) is a chronic, generally episodic and debilitating disease that affects an estimated 300 million people worldwide, but its pathogenesis is poorly understood. The heritability estimate of MDD is 30-40%, suggesting that genetics alone do not account for most of the risk of major depression. Another factor known to associate with MDD involves environmental stressors such as childhood adversity and recent life stress. Recent studies have emerged to show that the biological impact of environmental factors in MDD and other stress-related disorders is mediated by a variety of epigenetic modifications. These epigenetic modification alterations contribute to abnormal neuroendocrine responses, neuroplasticity impairment, neurotransmission and neuroglia dysfunction, which are involved in the pathophysiology of MDD. Furthermore, epigenetic marks have been associated with the diagnosis and treatment of MDD. The evaluation of epigenetic modifications holds promise for further understanding of the heterogeneous etiology and complex phenotypes of MDD, and may identify new therapeutic targets. Here, we review preclinical and clinical epigenetic findings, including DNA methylation, histone modification, noncoding RNA, RNA modification, and chromatin remodeling factor in MDD. In addition, we elaborate on the contribution of these epigenetic mechanisms to the pathological trait variability in depression and discuss how such mechanisms can be exploited for therapeutic purposes.

Bayesian network analysis of antidepressant treatment trajectories

It is currently difficult to successfully choose the correct type of antidepressant for individual patients. To discover patterns in patient characteristics, treatment choices and outcomes, we performed retrospective Bayesian network analysis combined with natural language processing (NLP). This study was conducted at two mental healthcare facilities in the Netherlands. Adult patients admitted and treated with antidepressants between 2014 and 2020 were included. Outcome measures were antidepressant continuation, prescription duration and four treatment outcome topics: core complaints, social functioning, general well-being and patient experience, extracted through NLP of clinical notes. Combined with patient and treatment characteristics, Bayesian networks were constructed at both facilities and compared. Antidepressant choices were continued in 66% and 89% of antidepressant trajectories. Score-based network analysis revealed 28 dependencies between treatment choices, patient characteristics and outcomes. Treatment outcomes and prescription duration were tightly intertwined and interacted with antipsychotics and benzodiazepine co-medication. Tricyclic antidepressant prescription and depressive disorder were important predictors for antidepressant continuation. We show a feasible way of pattern discovery in psychiatry data, through combining network analysis with NLP. Further research should explore the found patterns in patient characteristics, treatment choices and outcomes prospectively, and the possibility of translating these into a tool for clinical decision support.

Interactions Between Antidepressants and Intestinal Microbiota

The microbiota-gut-brain axis has been shown to influence human health and diseases, including depression. The interactions between drugs and intestinal microbiota are complex and highly relevant to treat diseases. Studies have shown an interaction between antidepressants and intestinal microbiota. Antidepressants may alter the abundance and composition of intestinal microbiota, which are closely related to the treatment outcomes of depression. Intestinal microbiota can influence the metabolism of antidepressants to change their availability (e.g., tryptophan can be metabolized to kynurenine by intestinal microbiota) and regulate their absorption by affecting intestinal permeability. In addition, the permeability of the blood-brain barrier can be altered by intestinal microbiota, influencing antidepressants to reach the central nervous system. Bioaccumulation is also a type of drug-microbiota interaction, which means bacteria accumulate drugs without biotransformation. These findings imply that it is important to consider intestinal microbiota when evaluating antidepressant therapy regimens and that intestinal microbiota can be a potential target for depression treatment.

Effects of Tibetan medicine metacinnabar (β-HgS) combined with imipramine or sertraline on depression-like symptoms in mice

Depression is a common mood disorder that has exhibited an increased incidence rate worldwide, but the overall clinical efficacy of antidepressants remains unsatisfactory. In traditional Ayurveda and Tibetan medicines, β-HgS-containing medicines have been used to treat neurological diseases for thousands of years, and our previous study found that β-HgS ameliorated depression-like behaviors in chronic restraint stress (CRS)-treated or chronic unpredictable mild stress (CUMS)-treated mice. Hence, present study investigated the effects of β-HgS combined with the clinical first-line antidepressants, imipramine (IMI) and sertraline (SER), on depression-like symptoms in CRS- and CUMS-co-treated mice. Our results revealed that β-HgS promoted the antidepressant effect of SER on depression-like behavior in mice, and enhanced its effects on promoting glucocorticoid receptor (GR) expression and neuronal proliferation in key hippocampal subregions, as well as increasing interleukin 10 (IL-10) levels and decreasing malondialdehyde levels in the sera of stress-stimulated mice. As for IMI, β-HgS enhanced its effects on preventing atrophy and severe structural damage in the hippocampus, as well as in promoting hippocampal GR levels and neuronal proliferation and serum IL-10 and superoxide dismutase (SOD) levels. Additionally, combination therapy resulted in the increased diversity of important intestinal microbiota compared to that of monotherapy, which may help sustain the health of the digestive tract and reduce inflammation to further enhance the antidepressant effects of IMI and SER in mice.

Quality of clinical practice guidelines for inadequate response to first-line treatment for depression according to AGREE II checklist and comparison of recommendations: a systematic review

OBJECTIVE

To assess similarities and differences in the recommended sequence of strategies among the most relevant clinical practice guidelines (CPGs) for the treatment of depression in adults with inadequate response to first-line treatment.

DATA SOURCES

We performed a systematic review of the literature spanning January 2011 to August 2020 in Medline, Embase, Cochrane Library and 12 databases recognised as CPGs repositories. CPGs quality was assessed using the Appraisal of Guidelines for Research and Evaluation II (AGREE II).

STUDY SELECTION

The eligibility criteria were CPGs that described pharmacological recommendations for treating depression for individuals aged 18 years or older in outpatient care setting. We included CPGs considered of high-quality (≥80% in domain 3 of AGREE II) or recognised as clinically relevant.

DATA EXTRACTION

Two independent researchers extracted recommendations for patients who did not respond to first-line pharmacological treatment from the selected CPGs.

RESULTS

We included 46 CPGs and selected 8, of which 5 were considered high quality (≥80% in domain 3 of AGREE II) and 3 were recognised as clinically relevant. Three CPGs did not define inadequate response to treatment and 3 did not establish a clear sequence of strategies. The duration of treatment needed to determine that a patient had not responded was not explicit in 3 CPGs and was discordant in 5 CPGs. Most CPGs agree in reassessing the diagnosis, assessing the presence of comorbidities, adherence to treatment, and increase dosage as first steps. All CPGs recommend psychotherapy, switching antidepressants, and considering augmentation/combining antidepressants.

CONCLUSION

Relevant CPGs present shortcomings in recommendations for non-responders to first-line antidepressant treatment including absence and divergencies in definition of inadequate response and sequence of recommended strategies. Overall, most relevant CPGs recommend reassessing the diagnosis, evaluate comorbidities, adherence to treatment, increase dosage of antidepressants, and psychotherapy as first steps.

PROSPERO REGISTRATION NUMBER

CRD42016043364.

Tricyclic antidepressants for major depressive disorder: a comprehensive evaluation of current practice in the Netherlands

BACKGROUND

Traditionally tricyclic antidepressants (TCAs) have an important place in treatment of major depressive disorder (MDD). Today, often other antidepressant medications are considered as first step in the pharmacological treatment of MDD, mainly because they are associated with less adverse effects, whereby the position of TCAs appears unclear. In this study we aimed to examine the current practice of TCAs in treatment of unipolar MDD.

METHODS

A mixed methods approach was applied. First, a selection of leading international and national guidelines was reviewed. Second, actual TCA prescription was examined by analyzing health records of 75 MDD patients treated with the TCAs nortriptyline, clomipramine or imipramine in different centers in the Netherlands. Third, promotors and barriers influencing the choice for TCAs and dosing strategies were explored using semi-structured interviews with 24 Dutch psychiatrists.

RESULTS

Clinical practice guidelines were sometimes indirective and inconsistent with each other. Health records revealed that most patients (71%) attained therapeutic plasma concentrations within two months of TCA use. Patients who achieved therapeutic plasma concentrations reached them on average after 19.6 days (SD 10.9). Both health records and interviews indicated that therapeutic nortriptyline concentrations were attained faster compared to other TCAs. Various factors were identified influencing the choice for TCAs and dosing by psychiatrists.

CONCLUSIONS

Guideline recommendations and clinical practice regarding TCA prescription for MDD vary. To increase consistency in clinical practice we recommend development of an up-to-date guideline integrating selection and dosing of TCAs, including the roles of therapeutic drug monitoring and pharmacogenetics. Such a guideline is currently lacking and would contribute to optimal TCA treatment, whereby efficacy and tolerability may be increased.

Association of the neutrophil to lymphocyte ratio and white blood cell count with response to pharmacotherapy in unipolar psychotic depression: An exploratory analysis

Background

Low-grade inflammation occurs in a subgroup of patients with Major Depressive Disorder (MDD) and may be associated with response to antidepressant medications. The Neutrophil to Lymphocyte Ratio (NLR) and total White Blood cell Count (WBC) are markers of systemic inflammation which have not been investigated as predictors for outcome to pharmacotherapy in unipolar depression yet. Moreover, the association between inflammation and treatment response has not been studied in unipolar Psychotic Depression (PD). We conducted an exploratory analysis to examine the prognostic significance of NLR and WBC in pharmacotherapy of PD.

Methods

Baseline NLR and WBC were examined in their association with response to seven weeks of treatment with antidepressants (venlafaxine or imipramine) and the combination of an antidepressant with an antipsychotic (venlafaxine plus quetiapine) in 87 patients with PD. Logistic regression models were adjusted for age, gender, Body Mass Index (BMI), depression severity, duration of the current episode and number of previous depressive episodes. Secondary outcomes were remission of depression and disappearance of psychotic symptoms.

Results

Higher NLR was associated with increased response to pharmacotherapy (Exp(B) 1.66, 95 ​% CI 1.03–2.66, p ​= ​0.036), but not with remission of depression or disappearance of psychotic symptoms. WBC was not associated with any of the outcome measures.

Conclusion

NLR may be a novel, inexpensive and widely available biomarker associated with response to pharmacotherapy in PD. The association between white blood cell measures and treatment outcome should be further investigated for different types of antidepressants in PD and in non-psychotic MDD.

Symptom Cluster-Matching Antidepressant Treatment: A Case Series Pilot Study

Despite treating depression with antidepressants, their effectiveness is often insufficient. Comparative effectiveness studies and meta-analyses show the effectiveness of antidepressants; however, they do not provide clear indications as to the choice of a specific antidepressant. The rational choice of antidepressants may be based on matching their mechanisms of action to the symptomatic profiles of depression, reflecting the heterogeneity of symptoms in different patients. The authors presented a series of cases of patients diagnosed with depression in whom at least one previous antidepressant treatment was shown to be ineffective before drug targeted symptom cluster-matching treatment (SCMT). The presented pilot study shows for the first time the effectiveness of SCMT in the different clusters of depressive symptoms. All the described patients obtained recovery from depressive symptoms after introducing drug-targeted SCMT. Once validated in clinical trials, SCMT might become an effective and rational method of selecting an antidepressant according to the individual profile of depressive symptoms, the mechanism of their formation, and the mechanism of drug action. Although the study results are preliminary, SCMT can be a way to personalize treatment, increasing the likelihood of improvement even in patients who meet criteria for treatment-resistant depression.

Depression From a Precision Mental Health Perspective: Utilizing Personalized Conceptualizations to Guide Personalized Treatments

Modern research has proven that the "typical patient" requiring standardized treatments does not exist, reflecting the need for more personalized approaches for managing individual clinical profiles rather than broad diagnoses. In this regard, precision psychiatry has emerged focusing on enhancing prevention, diagnosis, and treatment of psychiatric disorders through identifying clinical subgroups, suggesting personalized evidence-based interventions, assessing the effectiveness of different interventions, and identifying risk and protective factors for remission, relapse, and vulnerability. Literature shows that recent advances in the field of precision psychiatry are rapidly becoming more data-driven reflecting both the significance and the continuous need for translational research in mental health. Different etiologies underlying depression have been theorized and some factors have been identified including neural circuitry, biotypes, biopsychosocial markers, genetics, and metabolomics which have shown to explain individual differences in pathology and response to treatment. Although the precision approach may prove to enhance diagnosis and treatment decisions, major challenges are hindering its clinical translation. These include the clinical diversity of psychiatric disorders, the technical complexity and costs of multiomics data, and the need for specialized training in precision health for healthcare staff, besides ethical concerns such as protecting the privacy and security of patients' data and maintaining health equity. The aim of this review is to provide an overview of recent findings in the conceptualization and treatment of depression from a precision mental health perspective and to discuss potential challenges and future directions in the application of precision psychiatry for the treatment of depression.

Antidepressant drugs as emerging contaminants: Occurrence in urban and non-urban waters and analytical methods for their detection

Antidepressants are drugs with a direct action on the brain's biochemistry through their interaction with the neurotransmitters, such as dopamine, norepinephrine, and serotonin. The increasing worldwide contamination from these drugs may be witnessed through their increasing presence in the urban water cycle. Furthermore, their occurrence has been detected in non-urban water, such as rivers and oceans. Some endemic aquatic animals, such as certain fish and mollusks, have bioaccumulated different antidepressant drugs in their tissues. This problem will increase in the years to come because the present COVID-19 pandemic has increased the general worldwide occurrence of depression and anxiety, triggering the consumption of antidepressants and, consequently, their presence in the environment. This work provides information on the occurrence of the most administrated antidepressants in urban waters, wastewater treatment plants, rivers, and oceans. Furthermore, it provides an overview of the analytical approaches currently used to detect each antidepressant presented. Finally, the ecotoxicological effect of antidepressants on several in vivo models are listed. Considering the information provided in this review, there is an urgent need to test the presence of antidepressant members of the MAOI and TCA groups. Furthermore, incorporating new degradation/immobilization technologies in WWTPs will be useful to stop the increasing occurrence of these drugs in the environment.

Clustering patients by depression symptoms to predict venlafaxine ER antidepressant efficacy: Individual patient data analysis

OBJECTIVE

To identify clusters of patients with major depressive disorder (MDD) based on the baseline 17-item Hamilton Rating Scale for Depression (HAM-D₁₇) items and to evaluate the efficacy of venlafaxine extended release (VEN) vs placebo, and the potential effect of dose on efficacy, in each cluster.

METHODS

Cluster analysis was performed to identify clusters based on standardized HAM-D₁₇ item scores of individual patient data at baseline from 9 double-blind, placebo-controlled studies of VEN for MDD. Change from baseline in HAM-D₁₇ total score was analyzed using a mixed-effects model for repeated measures for each cluster; response and remission rates at week 8 were analyzed using logistic regression. Discontinuation rates were also evaluated in each cluster.

RESULTS

In 2599 patients, 3 patient clusters were identified, characterized as High modified Core (mCore) Symptoms/High Anxiety (cluster 1), High mCore Symptoms/Medium Anxiety (cluster 2), and Medium mCore Symptoms/Medium Anxiety (cluster 3). Significant effects of VEN vs placebo were observed on change from baseline in HAM-D₁₇ total score at week 8 for both clusters 1 and 2 (both P < 0.001), but not for cluster 3. In cluster 3, a significant treatment effect of VEN was observed at week 8 in the lower-dose subgroup but not in the higher-dose subgroup. All-cause discontinuation rates were significantly higher in placebo than VEN in each cluster.

CONCLUSIONS

Three unique clusters of patients were identified differing in baseline mCore symptoms and anxiety. Cluster membership may predict efficacy outcomes and contribute to dose effects in patients treated with VEN.

CLINICAL TRIALS REGISTRATION

NCT01441440; other studies included in this analysis were conducted before the requirement to register clinical studies took effect.

Vortioxetine versus other antidepressants in the treatment of burning mouth syndrome: An open-label randomized trial

OBJECTIVES

This randomized open-label trial compared the efficacy and tolerability of vortioxetine (15 mg/daily) with different antidepressants in the treatment of patients with burning mouth syndrome (BMS).

METHODS

One and hundred fifty BMS patients were randomized into five groups and treated with either vortioxetine, paroxetine (20 mg/daily), sertraline (50 mg/daily), escitalopram (10 mg/daily) or duloxetine (60 mg/daily). The Visual Analogue Scale (VAS), Total Pain Rating Index (T-PRI), Hamilton Rating Scales for Depression (HAM-D) and Anxiety (HAM-A), and Clinical Global Impression Improvement (CGI-I) and Efficacy scales (CGI-E) were performed at baseline and after 2, 4, 6, and 12 months of treatment. Any adverse events (AEs) were tabulated for each group. Descriptive statistics, including the Kruskal-Wallis non-parametric test and the Friedman non-parametric test for median comparisons between different times, were used.

RESULTS

All the antidepressants (AD) were associated with a significant decrease in the VAS, T-PRI, HAM-A, HAM-D, CGI-I, and CGI-E scores in the long-term (p < .001). However, the response rate of the vortioxetine group showed a significant reduction after six months. The medians, after 6 months, were as follows: VAS 0.0; T-PRI 2.0; HAM-A 7.0; HAM-D 7.0; CGI-I 1.0; and CGI-E 1.0 with a lower incidence of AEs (p < .019).

CONCLUSION

Vortioxetine was efficacious with a shorter latency of action and fewer AEs compared with other ADs.

Machine learning and big data in psychiatry: toward clinical applications

Psychiatry is a medical field concerned with the treatment of mental illness. Psychiatric disorders broadly relate to higher functions of the brain, and as such are richly intertwined with social, cultural, and experiential factors. This makes them exquisitely complex phenomena that depend on and interact with a large number of variables. Computational psychiatry provides two ways of approaching this complexity. Theory-driven computational approaches employ mechanistic models to make explicit hypotheses at multiple levels of analysis. Data-driven machine-learning approaches can make predictions from high-dimensional data and are generally agnostic as to the underlying mechanisms. Here, we review recent advances in the use of big data and machine-learning approaches toward the aim of alleviating the suffering that arises from psychiatric disorders.