Predictors of antidepressant response: A selective review

A common symptom geometry of mood improvement under sertraline and placebo associated with distinct neural patterns

Importance

Understanding the mechanisms of major depressive disorder (MDD) improvement is a key challenge to determine effective personalized treatments.

Objective

To perform a secondary analysis quantifying neural-to-symptom relationships in MDD as a function of antidepressant treatment.

Design

Double blind randomized controlled trial.

Setting

Multicenter.

Participants

Patients with early onset recurrent depression from the public Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study.

Interventions

Either sertraline or placebo during 8 weeks (stage 1), and according to response a second line of treatment for 8 additional weeks (stage 2).

Main Outcomes and Measures

To identify a data-driven pattern of symptom variations during these two stages, we performed a Principal Component Analysis (PCA) on the variations of individual items of four clinical scales measuring depression, anxiety, suicidal ideas and manic-like symptoms, resulting in a univariate measure of clinical improvement. We then investigated how initial clinical and neural factors predicted this measure during stage 1. To do so, we extracted resting-state global brain connectivity (GBC) at baseline at the individual level using a whole-brain functional network parcellation. In turn, we computed a linear model for each brain parcel with individual data-driven clinical improvement scores during stage 1 for each group.

Results

192 patients (127 women), age 37.7 years old (standard deviation: 13.5), were included. The first PC (PC1) capturing 20% of clinical variation was similar across treatment groups at stage 1 and stage 2, suggesting a reproducible pattern of symptom improvement. PC1 patients' scores significantly differed according to treatment during stage 1, whereas no difference of response was evidenced between groups with the Clinical Global Impressions (CGI). Baseline GBC correlated to stage 1 PC1 scores in the sertraline, but not in the placebo group.

Conclusions and Relevance

Using data-driven reduction of symptoms scales, we identified a common profile of symptom improvement across placebo and sertraline. However, the neural patterns of baseline that mapped onto symptom improvement distinguished between treatment and placebo. Our results underscore that mapping from data-driven symptom improvement onto neural circuits is vital to detect treatment-responsive neural profiles that may aid in optimal patient selection for future trials.

Changes in insulin resistance following antidepressant treatment mediate response in major depressive disorder

BACKGROUND

Insulin resistance (IR) is a potential predictor of antidepressant treatment response.

AIMS

We assess changes in IR after antidepressant treatment and whether these changes have any effect on treatment response. Also, to see whether changes in IR mediates relationship between C-reactive protein (CRP) and antidepressant efficacy.

METHODS

This is a secondary analysis of an 8-week, open-label clinical trial with 95 adults experiencing a major depressive episode. Response to vortioxetine was measured using the Montgomery-Åsberg Depression Rating Scale (MADRS). Generalized estimating equation models were utilized for this intent-to-treat analysis.

RESULTS

When adjusted for age, sex, and body mass index, there was a significant increase in IR following treatment in the overall sample (p = 0.035). This finding was detected in treatment non-responders (p = 0.019), whereas it was not observed in responders (p = 0.329). Mediation analysis revealed that change in IR during treatment was responsible for change in MADRS as well as the relationship between baseline CRP and treatment response.

CONCLUSIONS

Exacerbation of IR during antidepressant treatment mediated non-response. Conversely in treatment responders IR reduced. Like previous studies, baseline CRP moderated treatment response. This relationship was also mediated by changes in IR. These findings further elucidate the role of IR in terms of antidepressant response as well as potentially explain inflammation's relationship with the latter.

CYP2C19 slow metabolizer phenotype is associated with lower antidepressant efficacy and tolerability

The inter-individual variability in CYP2C19-mediated metabolism may affect the antidepressant treatment. The aim of this study is to evaluate differences in antidepressant efficacy and tolerability between different CYP2C19 metabolizer categories in inpatients suffering from major depressive disorder. The cohort was divided into experimental groups based on CYP2C19 genotype and it contained 24 slow (SMs), 41 normal (NMs), and 37 fast metabolizers (FMs). Efficacy and tolerability were assessed at baseline, and after two and four weeks as a follow-up. The primary efficacy measurement was the change from baseline in Hamilton's Depression Rating Scale (HAMD), while the primary tolerability measurement was the Toronto Side-Effects Scale (TSES) intensity scores at the last visit. The reduction in HAMD score was 36% less pronounced and response rate was exceedingly less prevalent (75% lower) in SMs, compared with NMs. The TSES intensity score was increased in SMs, compared with NMs, by 43% for central nervous system and by 22% for gastrointestinal adverse drug reactions. No significant differences in measured parameters were observed between NMs and FMs. Compared with NM and RM, lower antidepressant efficacy and tolerability was observed in SMs; this association is likely connected with the lower SM capacity to metabolize antidepressant drugs.

A clinical approach to treatment resistance in depressed patients: What to do when the usual treatments don't work well enough?

BACKGROUND

Major depressive disorder is a common, recurrent, disabling and costly disorder that is often severe and/or chronic, and for which non-remission on guideline concordant first-line antidepressant treatment is the norm. A sizeable percentage of patients diagnosed with MDD do not achieve full remission after receiving antidepressant treatment. How to understand or approach these 'refractory', 'TRD' or 'difficult to treat' patients need to be revisited. Treatment resistant depression (TRD) has been described elsewhere as failure to respond to adequate treatment by two different antidepressants. This definition is problematic as it suggests that TRD is a subtype of major depressive disorder (MDD), inferring a boundary between TRD and depression that is not treatment resistant. However, there is scant evidence to suggest that a discrete TRD entity exists as a distinct subtype of MDD, which itself is not a discrete or homogeneous entity. Similarly, the boundary between TRD and other forms of depression is predicated at least in part on regulatory and research requirements rather than biological evidence or clinical utility.

AIM

This paper aims to investigate the notion of treatment failure in order to understand (i) what is TRD in the context of a broader formulation based on the understanding of depression, (ii) what factors make an individual patient difficult to treat, and (iii) what is the appropriate and individualised treatment strategy, predicated on an individual with refractory forms of depression?

METHOD

Expert contributors to this paper were sought internationally by contacting representatives of key professional societies in the treatment of MDD - World Federation of Societies for Biological Psychiatry, Australasian Society for Bipolar and Depressive Disorders, International Society for Affective Disorders, Collegium Internationale Neuro-Psychopharmacologium and the Canadian Network for Mood and Anxiety Treatments. The manuscript was prepared through iterative editing.

OUTCOMES

The concept of TRD as a discrete subtype of MDD, defined by failure to respond to pharmacotherapy, is not supported by evidence. Between 15 and 30% of depressive episodes fail to respond to adequate trials of 2 antidepressants, and 68% of individuals do not achieve remission from depression after a first-line course of antidepressant treatment. Failure to respond to antidepressant treatment, somatic therapies or psychotherapies may often reflect other factors including; biological resistance, diagnostic error, limitations of current therapies, psychosocial variables, a past history of exposure to childhood maltreatment or abuse, job satisfaction, personality disorders, co-morbid mental and physical disorders, substance use or non-adherence to treatment. Only a subset of patients not responding to antidepressant treatment can be explained through pharmacokinetic or pharmacodynamics mechanisms. We propose that non remitting MDD should be personalised, and propose a strategy of 'deconstructing depression'. By this approach, the clinician considers which factors contribute to making this individual both depressed and 'resistant' to previous therapeutic approaches. Clinical formulation is required to understand the nature of the depression. Many predictors of response are not biological, and reflect a confluence of biological, psychological, and sociocultural factors, which may influence the illness in a particular individual. After deconstructing depression at a personalised level, a personalised treatment plan can be constructed. The treatment plan needs to address the factors that have contributed to the individual's hard to treat depression. In addition, an individual with a history of illness may have a lot of accumulated life issues due to consequences of their illness, and these should be addressed in a recovery plan.

LIMITATIONS

A 'deconstructing depression' qualitative rubric does not easily provide clear inclusion and exclusion criteria for researchers wanting to investigate TRD.

CONCLUSIONS

MDD is a polymorphic disorder and many individuals who fail to respond to standard pharmacotherapy and are considered hard to treat. These patients are best served by personalised approaches that deconstruct the factors that have contributed to the patient's depression and implementing a treatment plan that adequately addresses these factors. The existence of TRD as a discrete and distinct subtype of MDD, defined by two treatment failures, is not supported by evidence.

Objective Assessments in Diagnoses and Treatment: A Proposed Change in Paradigm

For patients with psychiatric disorders, current diagnostic and treatment approaches are far from optimal. The clinical interview drives the standard approach-matching symptoms to diagnostic criteria-and results in standardized pharmacological and behavioral treatments, often, with inadequate outcome; but now, recent imaging advances can correlate behavioral assessments with brain function and measure them against normative databases to provide data critical for the reevaluation of patient diagnosis and treatment. This article addresses the data that support a redefinition of our current paradigm. We believe a neurobehavioral approach provides for more personalized treatment approaches unbound from classically defined diagnostic biases.

Insulin resistance is associated with deficits in hedonic, self-reported cognitive, and psychosocial functional response to antidepressant treatment in individuals with major depressive disorder

BACKGROUND

To assess the effect of insulin resistance (IR) on treatment response to the antidepressant, vortioxetine, in patients with Major Depressive Disorder (MDD).

METHODS

This is a secondary analysis of an 8-week, open-label clinical trial. Ninety-five adults in a primary care setting experiencing a major depressive episode were included. Response to vortioxetine was measured using the THINC-integrated tool, Montgomery Åsberg Depression Rating Scale (MADRS), the Snaith-Hamilton Pleasure Scale (SHAPS), the Perceived Deficits Questionnaire (PDQ-5), and the Sheehan Disability Scale (SDS). Generalized estimating equation models were utilized for data analysis.

RESULTS

When adjusted for age, gender, dose, and BMI, there was a significant baseline IR by time interaction for SHAPS (p = 0.022), PDQ-5 (p = 0.037), and SDS (p = 0.013). Higher baseline IR predicted decreased early improvements in anhedonia. It also predicted poorer subjective assessments of cognition and increased functional impairment at the endpoint of treatment. For functional capacity (i.e. SDS) other covariates including severity of symptoms, illness course, other metabolic factors (e.g. cholesterol), and physical activity were included with no changes to the moderating effect of baseline IR.

LIMITATIONS

This was a post-hoc analysis of a primarily non-diabetic sample. Also, only one agent was assessed.

CONCLUSIONS

IR was a predictor of response to vortioxetine. This persisted after controlling for other factors including, but not limited to, BMI. These findings strengthen the link between depression and IR and may point to another novel metabolic predictor of response. These findings should be replicated using other antidepressants.

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

INTRODUCTION

The heterogeneity of symptoms and complex etiology of depression pose a significant challenge to the personalization of treatment. Meanwhile, the current application of generic treatment approaches to patients with vastly differing biological and clinical profiles is far from optimal. Here, we conduct a meta-review to identify predictors of response to antidepressant therapy in order to select robust input features for machine learning models of treatment response. These machine learning models will allow us to learn associations between patient features and treatment response which have predictive value at the individual patient level; this learning can be optimized by selecting high-quality input features for the model. While current research is difficult to directly apply to the clinic, machine learning models built using knowledge gleaned from current research may become useful clinical tools.

METHODS

The EMBASE and MEDLINE/PubMed online databases were searched from January 1996 to August 2017, using a combination of MeSH terms and keywords to identify relevant literature reviews. We identified a total of 1909 articles, wherein 199 articles met our inclusion criteria.

RESULTS

An array of genetic, immune, endocrine, neuroimaging, sociodemographic, and symptom-based predictors of treatment response were extracted, varying widely in clinical utility.

LIMITATIONS

Due to heterogeneous sample sizes, effect sizes, publication biases, and methodological disparities across reviews, we could not accurately assess the strength and directionality of every predictor.

CONCLUSION

Notwithstanding our cautious interpretation of the results, we have identified a multitude of predictors that can be used to formulate a priori hypotheses regarding the input features for a computational model. We highlight the importance of large-scale research initiatives and clinically accessible biomarkers, as well as the need for replication studies of current findings. In addition, we provide recommendations for future improvement and standardization of research efforts in this field.

Pharmacogenetic guidelines and decision support tools for depression treatment: application to late-life

Late-life depression (LLD) is a major depressive disorder that affects someone after the age of 60 years. LLD is frequently associated with inadequate response and remission from antidepressants, in addition to polypharmacy. Pharmacogenetics offers a promising approach to improve clinical outcomes in LLD via new discoveries determining the genetic basis of response rates and side effects, as well as the development of tailored pharmacogenetic-based decision support tools. This invited review evaluates the LLD pharmacogenetic evidence base and the extent to which this was incorporated into existing commercial decision support tools and clinical pharmacogenetic guidelines.

Monitoring for antidepressant-associated adverse events in the treatment of patients with major depressive disorder: An international consensus statement

OBJECTIVES

These recommendations were designed to ensure safety for patients with major depressive disorder (MDD) and to aid monitoring and management of adverse effects during treatment with approved antidepressant medications. The recommendations aim to inform prescribers about both the risks associated with these treatments and approaches for mitigating such risks.

METHODS

Expert contributors were sought internationally by contacting representatives of key stakeholder professional societies in the treatment of MDD (ASBDD, CANMAT, WFSBP and ISAD). The manuscript was drafted through iterative editing to ensure consensus.

RESULTS

Adequate risk assessment prior to commencing pharmacotherapy, and safety monitoring during pharmacotherapy are essential to mitigate adverse events, optimise the benefits of treatment, and detect and assess adverse events when they occur. Risk factors for pharmacotherapy vary with individual patient characteristics and medication regimens. Risk factors for each patient need to be carefully assessed prior to initiating pharmacotherapy, and appropriate individualised treatment choices need to be selected. Some antidepressants are associated with specific safety concerns which were addressed.

CONCLUSIONS

Risks of adverse outcomes with antidepressant treatment can be managed through appropriate assessment and monitoring to improve the risk benefit ratio and improve clinical outcomes.

Tobacco use does not influence the response to non-pharmacologic depression treatment: A secondary analysis of the Regassa randomized controlled trial

Behavioural interventions show promising results among patients with mild- to moderate depression. However, whether tobacco use moderates the effects of these interventions is not known. In the present study, we examined whether patients suffering from mild-to-moderate depression differed in their response to prescribed physical exercise or internet-based cognitive behavioural therapy (ICBT) according to their current tobacco use. We conducted a secondary analysis of data from 740 participants in a multicentre randomised controlled trial comparing physical exercise, internet-based cognitive behavioural therapy and treatment as usual (Regassa study). Information on current daily tobacco use was self-reported at baseline. Linear and logistic regression were used to examine the treatments' effect (reduction in depression score) in the subgroups of tobacco users (n=154) and non-users (n=586). We found that baseline tobacco use did not significantly moderate the association between treatment type and post-treatment depression severity. Both interventions (exercise and ICBT) resulted in a reduction of depression scores that was similar among non-users and users of tobacco, albeit formally statistically significant only among non-users. Physical exercise on prescription and ICBT can be used in the clinical management of depressed patients, with similar prognostic advantage among tobacco users and non-users.

Association of the Polygenic Scores for Personality Traits and Response to Selective Serotonin Reuptake Inhibitors in Patients with Major Depressive Disorder

Studies reported a strong genetic correlation between the Big Five personality traits and major depressive disorder (MDD). Moreover, personality traits are thought to be associated with response to antidepressants treatment that might partly be mediated by genetic factors. In this study, we examined whether polygenic scores (PGSs) derived from the Big Five personality traits predict treatment response and remission in patients with MDD who were prescribed selective serotonin reuptake inhibitors (SSRIs). In addition, we performed meta-analyses of genome-wide association studies (GWASs) on these traits to identify genetic variants underpinning the cross-trait polygenic association. The PGS analysis was performed using data from two cohorts: the Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS, n = 529) and the International SSRI Pharmacogenomics Consortium (ISPC, n = 865). The cross-trait GWAS meta-analyses were conducted by combining GWAS summary statistics on SSRIs treatment outcome and on the personality traits. The results showed that the PGS for openness and neuroticism were associated with SSRIs treatment outcomes at p < 0.05 across P_T thresholds in both cohorts. A significant association was also found between the PGS for conscientiousness and SSRIs treatment response in the PGRN-AMPS sample. In the cross-trait GWAS meta-analyses, we identified eight loci associated with (a) SSRIs response and conscientiousness near YEATS4 gene and (b) SSRI remission and neuroticism eight loci near PRAG1, MSRA, XKR6, ELAVL2, PLXNC1, PLEKHM1, and BRUNOL4 genes. An assessment of a polygenic load for personality traits may assist in conjunction with clinical data to predict whether MDD patients might respond favorably to SSRIs.

Concordance between actual and pharmacogenetic predicted desvenlafaxine dose needed to achieve remission in major depressive disorder: a 10-week open-label study

Supplemental Digital Content is available in the text.

Background

Pharmacogenetic-based dosing support tools have been developed to personalize antidepressant-prescribing practice. However, the clinical validity of these tools has not been adequately tested, particularly for specific antidepressants.

Objective

To examine the concordance between the actual dose and a polygene pharmacogenetic predicted dose of desvenlafaxine needed to achieve symptom remission.

Materials and methods

A 10-week, open-label, prospective trial of desvenlafaxine among Caucasian adults with major depressive disorder (n=119) was conducted. Dose was clinically adjusted and at the completion of the trial, the clinical dose needed to achieve remission was compared with the predicted dose needed to achieve remission.

Results

Among remitters (n=95), there was a strong concordance (Kendall’s τ-b=0.84, P=0.0001; Cohen’s κ=0.82, P=0.0001) between the actual and the predicted dose need to achieve symptom remission, showing high sensitivity (≥85%), specificity (≥86%), and accuracy (≥89%) of the tool.

Conclusion

Findings provide initial evidence for the clinical validity of a polygene pharmacogenetic-based tool for desvenlafaxine dosing.

Antidepressant prescribing in the precision medicine era: a prescriber's primer on pharmacogenetic tools

About half of people who take antidepressants do not respond and many experience adverse effects. These detrimental outcomes are in part a result of the impact of an individual's genetic profile on pharmacokinetics and pharmcodynamics. If known and made available to clinicians, this could improve decision-making and antidepressant therapy outcomes. This has spurred the development of numerous pharmacogenetic-based decision support tools. In this article, we provide an overview of pharmacogenetic decision support tools, with particular focus on tools relevant to antidepressants. We briefly describe the evolution and current state of antidepressant pharmacogenetic decision support tools in clinical practice, followed by the evidence-base for their use. Finally, we present a series of considerations for clinicians contemplating use of these tools and discuss the future of antidepressant pharmacogenetic decision support tools.

Royal Australian and New Zealand College of Psychiatrists clinical practice guidelines for mood disorders

OBJECTIVES

To provide guidance for the management of mood disorders, based on scientific evidence supplemented by expert clinical consensus and formulate recommendations to maximise clinical salience and utility.

METHODS

Articles and information sourced from search engines including PubMed and EMBASE, MEDLINE, PsycINFO and Google Scholar were supplemented by literature known to the mood disorders committee (MDC) (e.g., books, book chapters and government reports) and from published depression and bipolar disorder guidelines. Information was reviewed and discussed by members of the MDC and findings were then formulated into consensus-based recommendations and clinical guidance. The guidelines were subjected to rigorous successive consultation and external review involving: expert and clinical advisors, the public, key stakeholders, professional bodies and specialist groups with interest in mood disorders.

RESULTS

The Royal Australian and New Zealand College of Psychiatrists clinical practice guidelines for mood disorders (Mood Disorders CPG) provide up-to-date guidance and advice regarding the management of mood disorders that is informed by evidence and clinical experience. The Mood Disorders CPG is intended for clinical use by psychiatrists, psychologists, physicians and others with an interest in mental health care.

CONCLUSIONS

The Mood Disorder CPG is the first Clinical Practice Guideline to address both depressive and bipolar disorders. It provides up-to-date recommendations and guidance within an evidence-based framework, supplemented by expert clinical consensus.

MOOD DISORDERS COMMITTEE

Professor Gin Malhi (Chair), Professor Darryl Bassett, Professor Philip Boyce, Professor Richard Bryant, Professor Paul Fitzgerald, Dr Kristina Fritz, Professor Malcolm Hopwood, Dr Bill Lyndon, Professor Roger Mulder, Professor Greg Murray, Professor Richard Porter and Associate Professor Ajeet Singh.

INTERNATIONAL EXPERT ADVISORS

Professor Carlo Altamura, Dr Francesco Colom, Professor Mark George, Professor Guy Goodwin, Professor Roger McIntyre, Dr Roger Ng, Professor John O'Brien, Professor Harold Sackeim, Professor Jan Scott, Dr Nobuhiro Sugiyama, Professor Eduard Vieta, Professor Lakshmi Yatham.

AUSTRALIAN AND NEW ZEALAND EXPERT ADVISORS

Professor Marie-Paule Austin, Professor Michael Berk, Dr Yulisha Byrow, Professor Helen Christensen, Dr Nick De Felice, A/Professor Seetal Dodd, A/Professor Megan Galbally, Dr Josh Geffen, Professor Philip Hazell, A/Professor David Horgan, A/Professor Felice Jacka, Professor Gordon Johnson, Professor Anthony Jorm, Dr Jon-Paul Khoo, Professor Jayashri Kulkarni, Dr Cameron Lacey, Dr Noeline Latt, Professor Florence Levy, A/Professor Andrew Lewis, Professor Colleen Loo, Dr Thomas Mayze, Dr Linton Meagher, Professor Philip Mitchell, Professor Daniel O'Connor, Dr Nick O'Connor, Dr Tim Outhred, Dr Mark Rowe, Dr Narelle Shadbolt, Dr Martien Snellen, Professor John Tiller, Dr Bill Watkins, Dr Raymond Wu.

Effects of persisting emotional impact from child abuse and norepinephrine transporter genetic variation on antidepressant efficacy in major depression: a pilot study

Objective

Previous studies suggest child abuse and serotonergic polymorphism influence depression susceptibility and anti-depressant efficacy. Polymorphisms of the norepinephrine transporter (NET) may also be involved. Research in the area is possibly clouded by under reporting of abuse in researcher trials.

Methods

Adults (n=51) with major depressive disorder has 8 weeks treatment with escitalopram or venlafaxine. Abuse history was obtained, the ongoing emotional impact of which was measured with the 15-item impact of event scale (IES-15). The 17-item Hamilton Depression Rating Scale (HDRS) was applied serially. Two NET polymorphisms (rs2242446 and rs5569) were assayed, blinded to HDRS ratings and abuse history.

Results

No subjects reporting abuse with high impact in adulthood (IES-15 ≥26, n=12) remitted; whereas 77% reporting low impact (IES-15 <26; n=26) remitted (p<0.001). Subjects reporting high impact abuse (n=12) had a 50-fold (95% confidence interval=4.85–514.6) greater odds of carrying rs2242446-TT genotype, but the small sample size leaves this finding vulnerable to type I error.

Conclusion

The level of persisting impact of child abuse appears relevant to antidepressant efficacy, with susceptibility to such possibly being influence by NET rs2242446 polymorphism. Larger studies may be merited to expand on this pilot level finding given potential for biomarker utility.

Application of the Gradient Boosted method in randomised clinical trials: Participant variables that contribute to depression treatment efficacy of duloxetine, SSRIs or placebo

BACKGROUND

Randomised, placebo-controlled trials of treatments for depression typically collect outcomes data but traditionally only analyse data to demonstrate efficacy and safety. Additional post-hoc statistical techniques may reveal important insights about treatment variables useful when considering inter-individual differences amongst depressed patients. This paper aims to examine the Gradient Boosted Model (GBM), a statistical technique that uses regression tree analyses and can be applied to clinical trial data to identify and measure variables that may influence treatment outcomes.

METHODS

GBM was applied to pooled data from 12 randomised clinical trials of 4987 participants experiencing an acute depressive episode who were treated with duloxetine, an SSRI or placebo to predict treatment remission. Additional analyses were conducted on the same dataset using the logistic regression model for comparison between these two methods.

RESULTS

With GBM, there were noticeable differences between treatments when identifying which and to what extent variables were associated with remission. A single logistic regression only revealed a decreasing or increasing relationship between predictors and remission while GBM was able to reveal a complex relationship between predictors and remission.

LIMITATIONS

These analyses were conducted post-hoc utilising clinical trials databases. The criteria for constructing the analyses data were based on the characteristics of the clinical trials.

CONCLUSIONS

GBM can be used to identify and quantify patient variables that predict remission with specific treatments and has greater flexibility than the logistic regression model. GBM may provide new insights into inter-individual differences in treatment response that may be useful for selecting individualised treatments.

TRIAL REGISTRATION

IMPACT clinical trial number 3327; IMPACT clinical trial number 4091; IMPACT clinical trial number 4689; IMPACT clinical trial number 4298; NCT00071695; NCT00062673; NCT00036335; NCT00067912; NCT00073411; NCT00489775; NCT00536471; NCT00666757 (note that trials with IMPACT numbers predate mandatory clinical trial registration requirements).

Stage managing bipolar disorder

OBJECTIVES

Clinical staging is widespread in medicine - it informs prognosis, clinical course, and treatment, and assists individualized care. Staging places an individual on a probabilistic continuum of increasing potential disease severity, ranging from clinically at-risk or latency stage through first threshold episode of illness or recurrence, and, finally, to late or end-stage disease. The aim of the present paper was to examine and update the evidence regarding staging in bipolar disorder, and how this might inform targeted and individualized intervention approaches.

METHODS

We provide a narrative review of the relevant information.

RESULTS

In bipolar disorder, the validity of staging is informed by a range of findings that accompany illness progression, including neuroimaging data suggesting incremental volume loss, cognitive changes, and a declining likelihood of response to pharmacological and psychosocial treatments. Staging informs the adoption of a number of approaches, including the active promotion of both indicated prevention for at-risk individuals and early intervention strategies for newly diagnosed individuals, and the tailored implementation of treatments according to the stage of illness.

CONCLUSIONS

The nature of bipolar disorder implies the presence of an active process of neuroprogression that is considered to be at least partly mediated by inflammation, oxidative stress, apoptosis, and changes in neurogenesis. It further supports the concept of neuroprotection, in that a diversity of agents have putative effects against these molecular targets. Clinically, staging suggests that the at-risk state or first episode is a period that requires particularly active and broad-based treatment, consistent with the hope that the temporal trajectory of the illness can be altered. Prompt treatment may be potentially neuroprotective and attenuate the neurostructural and neurocognitive changes that emerge with chronicity. Staging highlights the need for interventions at a service delivery level and implementing treatments at the earliest stage of illness possible.

Treatment response for acute depression is not associated with number of previous episodes: lack of evidence for a clinical staging model for major depressive disorder

Mental illness has been observed to follow a neuroprogressive course, commencing with prodrome, then onset, recurrence and finally chronic illness. In bipolar disorder and schizophrenia responsiveness to treatment mirrors these stages of illness progression, with greater response to treatment in the earlier stages of illness and greater treatment resistance in chronic late stage illness. Using data from 5627 participants in 15 controlled trials of duloxetine, comparator arm (paroxetine, venlafaxine, escitalopram) or placebo for the treatment of an acute depressive episode, the relationship between treatment response and number of previous depressive episodes was determined. Data was dichotomised for comparisons between participants who had >3 previous episodes (n=1697) or ≤3 previous episodes (n=3930), and additionally for no previous episodes (n=1381) or at least one previous episode (n=4246). Analyses were conducted by study arm for each clinical trial, and results were then pooled. There was no significant difference between treatment response and number of previous depressive episodes. This unexpected finding suggests that treatments to reduce symptoms of depression during acute illness do not lose efficacy for patients with a longer history of illness.

Ketamine for depression: where do we go from here?

Since publication of the first randomized controlled trial describing rapid antidepressant effects of ketamine, several reports have confirmed the potential utility of this dissociative anesthetic medication for treatment of major depressive episodes, including those associated with bipolar disorder and resistant to other medications and electroconvulsive therapy. These reports have generated several questions with respect to who might respond to ketamine, how, and for how long. To start answering these questions. We used PubMed.gov and ClinicalTrials.gov to perform a systematic review of all available published data on the antidepressant effects of ketamine and of all recently completed, ongoing, and planned studies. To date, 163 patients, primarily with treatment-resistant depression, have participated in case studies, open-label investigations, or controlled trials. All controlled trials have used a within-subject, crossover design with an inactive placebo as the control. Ketamine administration has usually involved an anaesthesiologist infusing a single, subanesthetic, intravenous dose, and required hospitalization for at least 24 hours postinfusion. Response rates in the open-label investigations and controlled trials have ranged from 25% to 85% at 24 hours postinfusion and from 14% to 70% at 72 hours postinfusion. Although adverse effects have generally been mild, some patients have experienced brief changes in blood pressure, heart rate, or respiratory rate. Risk-benefit analyses support further research of ketamine for individuals with severe mood disorders. However, given the paucity of randomized controlled trials, lack of an active placebo, limited data on long-term outcomes, and potential risks, ketamine administration is not recommended outside of the hospital setting.

Pathways underlying neuroprogression in bipolar disorder: focus on inflammation, oxidative stress and neurotrophic factors

There is now strong evidence of progressive neuropathological processes in bipolar disorder (BD). On this basis, the current understanding of the neurobiology of BD has shifted from an initial focus on monoamines, subsequently including evidence of changes in intracellular second messenger systems and more recently to, incorporating changes in inflammatory cytokines, corticosteroids, neurotrophins, mitochondrial energy generation, oxidative stress and neurogenesis into a more comprehensive model capable of explaining some of the clinical features of BD. These features include progressive shortening of the inter-episode interval with each recurrence, occurring in consort with reduced probability of treatment response as the illness progresses. To this end, emerging data shows that these biomarkers may differ between early and late stages of BD in parallel with stage-related structural and neurocognitive alterations. This understanding facilitates identification of rational therapeutic targets, and the development of novel treatment classes. Additionally, these pathways provide a cogent explanation for the efficacy of seemingly diverse therapies used in BD, that appear to share common effects on oxidative, inflammatory and neurotrophic pathways.

Changes in QEEG prefrontal cordance as a predictor of response to antidepressants in patients with treatment resistant depressive disorder: a pilot study

INTRODUCTION

Previous studies of patients with unipolar depression have shown that early decreases of EEG cordance (a new quantitative EEG method) can predict clinical response. We examined whether early QEEG decrease represents a phenomenon associated with response to treatment with different antidepressants in patients with treatment resistant depression.

METHOD

The subjects were 17 inpatients with treatment resistant depression. EEG data and response to treatment were monitored at baseline and after 1 and 4 weeks on an antidepressant treatment. QEEG cordance was computed at three frontal electrodes in theta frequency band. The prefrontal cordance combines complementary information from absolute and relative power of EEG spectra. Recent studies have shown that cordance correlates with cortical perfusion. Depressive symptoms were assessed using Montgomery-Asberg Depression Rating Scale (MADRS).

RESULTS

All 17 patients completed the 4-week study. All five responders showed decreases in prefrontal cordance after the first week of treatment. Only 2 of the 12 nonresponders showed early prefrontal cordance decrease. The decrease of prefrontal QEEG cordance after week 1 in responders as well as the increase in nonresponders were both statistically significant (p-value 0.03 and 0.01, respectively) and the changes of prefrontal cordance values were different between both groups (p-value 0.001).

CONCLUSION

Our results suggest that decrease in prefrontal cordance may indicate early changes of prefrontal activity in responders to antidepressants. QEEG cordance may become a useful tool in the prediction of response to antidepressants.

'Bipolar missed states': the diagnosis and clinical salience of bipolar mixed states

OBJECTIVE

To explore diagnostic and treatment issues concerning bipolar mixed states.

METHOD

Bipolar mixed states are described and concerns about diagnostic and treatment difficulties are summarized and discussed.

RESULT

Mixed states can present with equal admixtures of depressive or manic symptoms, or more commonly one component predominates. There is fair consensus, although little data, regarding the management of manic mixed states. However depressive mixed states are far more complex both in terms of recognition and management. People suffering from mixed states characteristically present with complaints of depression.

CONCLUSIONS

The boundaries between depressive mixed states and agitated depression are vague, yet carry substantial therapeutic implications. Bipolar mixed states are often difficult to treat, and tend to take much longer to settle than either pure mania or depression. Furthermore there is data that treatment with antidepressants can worsen the course of mixed states. Hence missed diagnoses can potentially have negative clinical implications. Therefore in this paper the clinical presentation, diagnosis and therapy of mixed states is reviewed with a view to improving management.