Treatment effects on residual cognitive symptoms among partially or fully remitted patients with major depressive disorder: A randomized, double-blinded, exploratory study with vortioxetine.

BACKGROUND: Residual cognitive symptoms in major depressive disorder (MDD) are common, yet poorly investigated. We explored the effectiveness of vortioxetine as adjunctive treatment to selective serotonin reuptake inhibitors (SSRI) and as monotherapy versus continued SSRI, in patients with MDD who achieved full or partial remission with SSRI, but report residual cognitive symptoms. METHODS: Patients (18-65 years old, N =151) diagnosed with MDD, with a Hamilton Depression Rating Scale 17-items total score ≤10 and a Perceived Deficits Questionnaire-Depression total score >25, were randomized 1:1:1 to 8 weeks of double-blind treatment with current SSRI + placebo, SSRI + vortioxetine (10-20 mg/day), or vortioxetine (10-20 mg/day) + placebo. The primary efficacy measure was the Digit Symbol Substitution Test (DSST), analyzed using a mixed model for repeated measurements. Secondary outcomes comprised cognitive functioning, subjectively-rated cognitive symptoms, patient functioning, and mood symptoms. RESULTS: From baseline to week 8, all treatment groups improved DSST performance, with statistically nonsignificant treatment differences. Similar results were seen for secondary endpoints. Improvement in cognitive performance tended to be numerically larger with vortioxetine monotherapy than with SSRI monotherapy, while vortioxetine as adjunctive treatment tended to perform numerically better in further improving depressive symptoms. Most adverse events were mild or moderate. Nausea was the most common adverse event for vortioxetine. LIMITATIONS: Small sample sizes limited statistical power. CONCLUSION: In this explorative study, remitted patients with MDD improved their cognitive performance with no treatment differences. Secondary results indicate numerical benefits for cognitive performance with vortioxetine monotherapy, and for depressive symptoms with vortioxetine augmentation.

Face and predictive validity of the ClockΔ19 mouse as an animal model for bipolar disorder: a systematic review.

Mice carrying the circadian locomotor output cycles Kaput delta 19 N-ethyl-N-nitrosoure (ENU) mutation (ClockΔ19) are used as an animal model for bipolar disorder (BD). We aimed to systematically review the face validity (phenotypical and pathophysiological resemblance with BD) and predictive validity (responsiveness to treatments used in BD) of this model in adherence with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. We carried out a systematic search of the databases PubMed and Embase, combining search terms covering BD and ClockΔ19. The 22 studies included in the review (from a total of 1281 identified records) show that the behavioral phenotype of the ClockΔ19 mouse is characterized by hyperactivity, decreased anxiety-like behavior, decreased depression-like behavior and increased preference for rewarding stimuli. This is highly consistent with mania in humans. Moreover, the ClockΔ19 mouse exhibits rapid mood cycling (a manic-like phenotype during the day followed by euthymia at night), which is consistent with BD. Chronic administration of lithium, a drug with well established mood-stabilizing effect in humans with BD, reverses the majority of the bipolar-like traits and most of the neurobiological abnormalities observed in the ClockΔ19 mouse. In conclusion, the ClockΔ19 mouse has substantial face validity as an animal model for BD. The predictive validity of the ClockΔ19 mouse has primarily been investigated via studies using lithium challenge. Therefore, further studies are needed to determine how the ClockΔ19 mouse responds to other mood-stabilizing treatments of BD such as valproate, lamotrigine, carbamazepine, oxcarbazepine, antipsychotics, electroconvulsive therapy and various light interventions.

Increased illness burden in women with comorbid bipolar and premenstrual dysphoric disorder: data from 1 099 women from STEP-BD study.

BACKGROUND: The impact of comorbid premenstrual dysphoric disorder (PMDD) in women with bipolar disorder (BD) is largely unknown. AIMS: We compared illness characteristics and female-specific mental health problems between women with BD with and without PMDD. MATERIALS & METHODS: A total of 1 099 women with BD who participated in the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) were studied. Psychiatric diagnoses and illness characteristics were assessed using the Mini International Neuropsychiatric Interview. Female-specific mental health was assessed using a self-report questionnaire developed for STEP-BD. PMDD diagnosis was based on DSM-5 criteria. RESULTS: Women with comorbid BD and PMDD had an earlier onset of bipolar illness (P < 0.001) and higher rates of rapid cycling (P = 0.039), and increased number of past-year hypo/manic (P = 0.003), and lifetime/past-year depressive episodes (P < 0.05). Comorbid PMDD was also associated with higher proportion of panic disorder, post-traumatic stress disorder, generalized anxiety disorder, bulimia nervosa, substance abuse, and adult attention deficit disorder (all P < 0.05). There was a closer gap between BD onset and age of menarche in women with comorbid PMDD (P = 0.003). Women with comorbid PMDD reported more severe mood symptoms during the perinatal period and while taking oral contraceptives (P < 0.001). DISCUSSION: The results from this study is consistent with research suggesting that sensitivity to endogenous hormones may impact the onset and the clinical course of BD. CONCLUSIONS: The comorbidity between PMDD and BD is associated with worse clinical outcomes and increased illness burden.

Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder.

BACKGROUNDS: Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful. METHOD: We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments. RESULTS: Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials. CONCLUSIONS: Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.

Predictive analytics in mental health: applications, guidelines, challenges and perspectives.

The emerging field of 'predictive analytics in mental health' has recently generated tremendous interest with the bold promise to revolutionize clinical practice in psychiatry paralleling similar developments in personalized and precision medicine. Here, we provide an overview of the key questions and challenges in the field, aiming to (1) propose general guidelines for predictive analytics projects in psychiatry, (2) provide a conceptual introduction to core aspects of predictive modeling technology, and (3) foster a broad and informed discussion involving all stakeholders including researchers, clinicians, patients, funding bodies and policymakers.

Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports.

Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. Although efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine-learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared with observed scores assessed 10-12 years after baseline. ML model prediction accuracy was also compared with that of conventional logistic regression models. Area under the receiver operating characteristic curve based on ML (0.63 for high chronicity and 0.71-0.76 for the other prospective outcomes) was consistently higher than for the logistic models (0.62-0.70) despite the latter models including more predictors. A total of 34.6-38.1% of respondents with subsequent high persistence chronicity and 40.8-55.8% with the severity indicators were in the top 20% of the baseline ML-predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML-predicted risk distribution. These results confirm that clinically useful MDD risk-stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models.

Physical Exercise for Treatment of Mood Disorders: A Critical Review.

PURPOSE OF THE REVIEW: The purpose of this review is to critically assess the evidence for exercise as an adjunct intervention for major depressive disorder and bipolar disorder, chronic conditions characterized by frequent comorbid conditions as well as interepisodic symptoms with poor quality of life and impaired functioning. Individuals with these mood disorders are at higher risk of cardiovascular disease and premature death in part because of increased rates of obesity, inactivity, and diabetes mellitus compared to the general population. Exercise may not only mitigate the increased risk of cardiovascular disease, but could also potentially improve the long term outcomes of mood disorders. RECENT FINDINGS: We conducted a literature review on the impact of exercise on mood disorders and associated comorbid conditions as well as possible biological mechanisms. We found that exercise impacts both the physical health parameters of mood disorders as well as mental health outcomes. Exercise also positively impacts conditions frequently comorbid with mood disorders (i.e. anxiety, pain, and insomnia). There are multiple candidate biomarkers for exercise, with brain-derived neurotrophic factor and oxidative stress as two main promising components of exercise's anti-depressant effect. SUMMARY: Exercise appears to be a promising adjunct treatment for mood disorders. We conclude with recommendations for future research of exercise as an adjunct intervention for mood disorders.

Obesity, but not metabolic syndrome, negatively affects outcome in bipolar disorder.

OBJECTIVE: Examine the effects of obesity and metabolic syndrome on outcome in bipolar disorder. METHOD: The Comparative Effectiveness of a Second Generation Antipsychotic Mood Stabilizer and a Classic Mood Stabilizer for Bipolar Disorder (Bipolar CHOICE) study randomized 482 participants with bipolar disorder in a 6-month trial comparing lithium- and quetiapine-based treatment. Baseline variables were compared between groups with and without obesity, with and without abdominal obesity, and with and without metabolic syndrome respectively. The effects of baseline obesity, abdominal obesity, and metabolic syndrome on outcomes were examined using mixed effects linear regression models. RESULTS: At baseline, 44.4% of participants had obesity, 48.0% had abdominal obesity, and 27.3% had metabolic syndrome; neither obesity, nor abdominal obesity, nor metabolic syndrome were associated with increased global severity, mood symptoms, or suicidality, or with poorer functioning or life satisfaction. Treatment groups did not differ on prevalence of obesity, abdominal obesity, or metabolic syndrome. By contrast, among the entire cohort, obesity was associated with less global improvement and less improvement in total mood and depressive symptoms, suicidality, functioning, and life satisfaction after 6 months of treatment. Abdominal obesity was associated with similar findings. Metabolic syndrome had no effect on outcome. CONCLUSION: Obesity and abdominal obesity, but not metabolic syndrome, were associated with less improvement after 6 months of lithium- or quetiapine-based treatment.

Inflammation as a predictive biomarker for response to omega-3 fatty acids in major depressive disorder: a proof-of-concept study.

This study explores whether inflammatory biomarkers act as moderators of clinical response to omega-3 (n-3) fatty acids in subjects with major depressive disorder (MDD). One hundred fifty-five subjects with Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) MDD, a baseline 17-item Hamilton Depression Rating Scale (HAM-D-17) score ⩾ 15 and baseline biomarker data (interleukin (IL)-1ra, IL-6, high-sensitivity C-reactive protein (hs-CRP), leptin and adiponectin) were randomized between 18 May 2006 and 30 June 2011 to 8 weeks of double-blind treatment with eicosapentaenoic acid (EPA)-enriched n-3 1060 mg day(-1), docosahexaenoic acid (DHA)-enriched n-3 900 mg day(-1) or placebo. Outcomes were determined using mixed model repeated measures analysis for 'high' and 'low' inflammation groups based on individual and combined biomarkers. Results are presented in terms of standardized treatment effect size (ES) for change in HAM-D-17 from baseline to treatment week 8. Although overall treatment group differences were negligible (ES=-0.13 to +0.04), subjects with any 'high' inflammation improved more on EPA than placebo (ES=-0.39) or DHA (ES=-0.60) and less on DHA than placebo (ES=+0.21); furthermore, EPA-placebo separation increased with increasing numbers of markers of high inflammation. Subjects randomized to EPA with 'high' IL-1ra or hs-CRP or low adiponectin ('high' inflammation) had medium ES decreases in HAM-D-17 scores vs subjects 'low' on these biomarkers. Subjects with 'high' hs-CRP, IL-6 or leptin were less placebo-responsive than subjects with low levels of these biomarkers (medium to large ES differences). Employing multiple markers of inflammation facilitated identification of a more homogeneous cohort of subjects with MDD responding to EPA vs placebo in our cohort. Studies are needed to replicate and extend this proof-of-concept work.

The effects of co-morbidity in defining major depression subtypes associated with long-term course and severity.

BACKGROUND: Although variation in the long-term course of major depressive disorder (MDD) is not strongly predicted by existing symptom subtype distinctions, recent research suggests that prediction can be improved by using machine learning methods. However, it is not known whether these distinctions can be refined by added information about co-morbid conditions. The current report presents results on this question. METHOD: Data came from 8261 respondents with lifetime DSM-IV MDD in the World Health Organization (WHO) World Mental Health (WMH) Surveys. Outcomes included four retrospectively reported measures of persistence/severity of course (years in episode; years in chronic episodes; hospitalization for MDD; disability due to MDD). Machine learning methods (regression tree analysis; lasso, ridge and elastic net penalized regression) followed by k-means cluster analysis were used to augment previously detected subtypes with information about prior co-morbidity to predict these outcomes. RESULTS: Predicted values were strongly correlated across outcomes. Cluster analysis of predicted values found three clusters with consistently high, intermediate or low values. The high-risk cluster (32.4% of cases) accounted for 56.6-72.9% of high persistence, high chronicity, hospitalization and disability. This high-risk cluster had both higher sensitivity and likelihood ratio positive (LR+; relative proportions of cases in the high-risk cluster versus other clusters having the adverse outcomes) than in a parallel analysis that excluded measures of co-morbidity as predictors. CONCLUSIONS: Although the results using the retrospective data reported here suggest that useful MDD subtyping distinctions can be made with machine learning and clustering across multiple indicators of illness persistence/severity, replication with prospective data is needed to confirm this preliminary conclusion.

Age at onset, course of illness and response to psychotherapy in bipolar disorder: results from the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD).

BACKGROUND: The course of bipolar disorder progressively worsens in some patients. Although responses to pharmacotherapy appear to diminish with greater chronicity, less is known about whether patients' prior courses of illness are related to responses to psychotherapy. METHOD: Embedded in the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) was a randomized controlled trial of psychotherapy for bipolar depression comparing the efficacy of intensive psychotherapy with collaborative care (a three-session psycho-educational intervention). We assessed whether the number of previous mood episodes, age of illness onset, and illness duration predicted or moderated the likelihood of recovery and time until recovery from a depressive episode in patients in the two treatments. RESULTS: Independently of treatment condition, participants with one to nine prior depressive episodes were more likely to recover and had faster time to recovery than those with 20 or more prior depressive episodes. Participants with fewer than 20 prior manic episodes had faster time to recovery than those with 20 or more episodes. Longer illness duration predicted a longer time to recovery. Participants were more likely to recover in intensive psychotherapy than collaborative care if they had 10-20 prior episodes of depression [number needed to treat (NNT) = 2.0], but equally likely to respond to psychotherapy and collaborative care if they had one to nine (NNT = 32.0) or >20 (NNT = 9.0) depressive episodes. CONCLUSIONS: Number of previous mood episodes and illness duration are associated with the likelihood and speed of recovery among bipolar patients receiving psychosocial treatments for depression.

Medication adherence in a comparative effectiveness trial for bipolar disorder.

OBJECTIVE: Psychopharmacology remains the foundation of treatment for bipolar disorder, but medication adherence in this population is low (range 20-64%). We examined medication adherence in a multisite, comparative effectiveness study of lithium. METHOD: The Lithium Moderate Dose Use Study (LiTMUS) was a 6-month, six-site, randomized effectiveness trial of adjunctive moderate dose lithium therapy compared with optimized treatment in adult out-patients with bipolar I or II disorder (N=283). Medication adherence was measured at each study visit with the Tablet Routine Questionnaire. RESULTS: We found that 4.50% of participants reported missing at least 30% of their medications in the past week at baseline and non-adherence remained low throughout the trial (<7%). Poor medication adherence was associated with more manic symptoms and side-effects as well as lower lithium serum levels at mid- and post-treatment, but not with poor quality of life, overall severity of illness, or depressive symptoms. CONCLUSION: Participants in LiTMUS were highly adherent with taking their medications. The lack of association with possible predictors of adherence, such as depression and quality of life, could be explained by the limited variance or other factors as well as by not using an objective measure of adherence.

General medical burden in bipolar disorder: findings from the LiTMUS comparative effectiveness trial.

OBJECTIVE: This study examined general medical illnesses and their association with clinical features of bipolar disorder. METHOD: Data were cross-sectional and derived from the Lithium Treatment - Moderate Dose Use Study (LiTMUS), which randomized symptomatic adults (n = 264 with available medical comorbidity scores) with bipolar disorder to moderate doses of lithium plus optimized treatment (OPT) or to OPT alone. Clinically significant high and low medical comorbidity burden were defined as a Cumulative Illness Rating Scale (CIRS) score ≥4 and <4 respectively. RESULTS: The baseline prevalence of significant medical comorbidity was 53% (n = 139). Patients with high medical burden were more likely to present in a major depressive episode (P = .04), meet criteria for obsessive-compulsive disorder (P = .02), and experience a greater number of lifetime mood episodes (P = 0.02). They were also more likely to be prescribed a greater number of psychotropic medications (P = .002). Sixty-nine per cent of the sample was overweight or obese as defined by body mass index (BMI), with African Americans representing the racial group with the highest proportion of stage II obesity (BMI ≥35; 31%, n = 14). CONCLUSION: The burden of comorbid medical illnesses was high in this generalizable sample of treatment-seeking patients and appears associated with worsened course of illness and psychotropic medication patterns.

Lithium tremor revisited: pathophysiology and treatment.

OBJECTIVE: Tremor occurs frequently as a side-effect of lithium, and it is, however, easily overlooked in the clinical setting. In this article, we attempt to review the pathophysiology and the clinical approach of lithium tremor. METHOD: We searched the Pubmed and Cochrane Library for relevant articles up to the year 2012. Sixty-four articles including 10 review papers, 3 clinical trials, and 12 case reports were reviewed. RESULTS: Lithium tremor is classified as a postural tremor and subcategorized as an exaggerated physiologic tremor. Differential diagnosis includes metabolic abnormalities, benign essential tremor, Parkinson's disease, and lithium toxicity. Various methods of evaluating lithium tremor and treatment options are discussed. CONCLUSION: When lithium tremor has developed, thorough history taking, physical examination, and blood examination including serum lithium level are needed. Pharmacotherapy is indicated only in patients with disabling tremor.

Using comparative effectiveness design to improve the generalizability of bipolar treatment trials data: contrasting LiTMUS baseline data with pre-existing placebo controlled trials.

BACKGROUND: Efficacy-based double-blind placebo controlled trials were conducted to establish efficacy and safety for FDA approval. Such designs allowed and encouraged the use of exclusion criteria to improve assay sensitivity and internal validity. The LiTMUS trial increased the representation of real-world individuals with bipolar disorder despite the acknowledgment that this compromises assay sensitivity. METHOD: To maximize generalizability, LiTMUS used broad inclusion and narrow exclusion criteria: participants experiencing mood symptoms of sufficient intensity (at least with a CGI-BP ≥ 3) that would warrant a change in treatment, and that lithium treatment would be a reasonable therapeutic option if they were randomized to it. At baseline demographic, illness, clinical, and treatment characteristics were collected. The LiTMUS study design and baseline sociodemographic data were compared to previous efficacy studies. RESULTS: As compared to the previous bipolar disorder efficacy studies, LiTMUS participants were of similar age, gender, weight and illness severity; however LiTMUS participants were more racially and ethnically representative of the general population, had a greater number of mood episodes in the past 12 months, more Axis I/II comorbidity, a greater number of prior suicide attempts, and higher functional capacity. CONCLUSIONS: LiTMUS was a comparative effectiveness trial that had broad inclusion and minimal exclusion criteria that produced a more representative sample comprised of real-world participants. This design enables the results of the LiTMUS study to be a more representative of real world pharmacotherapuetic outcomes. LIMITATIONS: Limitations include possible selection bias, paucity of sociodemographic data in efficacy trials, and lack of a placebo.

Composite International Diagnostic Interview screening scales for DSM-IV anxiety and mood disorders.

BACKGROUND: Lack of coordination between screening studies for common mental disorders in primary care and community epidemiological samples impedes progress in clinical epidemiology. Short screening scales based on the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI), the diagnostic interview used in community epidemiological surveys throughout the world, were developed to address this problem. METHOD: Expert reviews and cognitive interviews generated CIDI screening scale (CIDI-SC) item pools for 30-day DSM-IV-TR major depressive episode (MDE), generalized anxiety disorder (GAD), panic disorder (PD) and bipolar disorder (BPD). These items were administered to 3058 unselected patients in 29 US primary care offices. Blinded SCID clinical reinterviews were administered to 206 of these patients, oversampling screened positives. RESULTS: Stepwise regression selected optimal screening items to predict clinical diagnoses. Excellent concordance [area under the receiver operating characteristic curve (AUC)] was found between continuous CIDI-SC and DSM-IV/SCID diagnoses of 30-day MDE (0.93), GAD (0.88), PD (0.90) and BPD (0.97), with only 9-38 questions needed to administer all scales. CIDI-SC versus SCID prevalence differences are insignificant at the optimal CIDI-SC diagnostic thresholds (χ2 1 = 0.0-2.9, p = 0.09-0.94). Individual-level diagnostic concordance at these thresholds is substantial (AUC 0.81-0.86, sensitivity 68.0-80.2%, specificity 90.1-98.8%). Likelihood ratio positive (LR+) exceeds 10 and LR- is 0.1 or less at informative thresholds for all diagnoses. CONCLUSIONS: CIDI-SC operating characteristics are equivalent (MDE, GAD) or superior (PD, BPD) to those of the best alternative screening scales. CIDI-SC results can be compared directly to general population CIDI survey results or used to target and streamline second-stage CIDIs.