Treatment Outcome In Major Depressive Disorder Research Articles

Peripheral cytokine levels and response to antidepressant treatment in depression: a systematic review and meta-analysis.

Predicting antidepressant treatment response has been a clinical challenge for major depressive disorder (MDD). The inflammation hypothesis of depression suggests that cytokines play a key role in the pathophysiology of MDD and alterations in peripheral cytokine levels are associated with antidepressant treatment outcome. Present meta-analysis aimed to examine the association between baseline peripheral cytokine levels and the response to antidepressant treatment and to evaluate whether changes of cytokine levels were associated with the response to antidepressant treatment in patients with MDD. Human-based studies published in any language in peer-reviewed journals were systematically searched from the PubMed, Embase and Web of Science databases, from inception up to October 2018. The search terms included cytokine, depressive disorder and antidepressant and their synonyms. Case-control or case-case studies reporting on levels of IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12, CRP, TNF-α, IFN-γ, GM-CSF, MIP-1α, and Eotaxin-1 in patients with MDD based on validated depression scales both before and after antidepressant treatment were included. Of 7408 identified records, 44 studies met inclusion. Standardized mean differences in each cytokine were evaluated, and random-effects meta-analyses were performed. MDD patients who responded to antidepressant treatment had lower baseline IL-8 levels than the nonresponders (Hedge's g = -0.28; 95%CI, -0.43 to -0.13; P = 0.0003; FDR = 0.004). Antidepressant treatment significantly decreased levels of TNF-α (Hedge's g = 0.60; 95%CI, 0.26-0.94; P = 0.0006; FDR = 0.004) only in responders, and responders showed significantly more decreased TNF-α levels compared with nonresponders (P = 0.046). These findings suggested that alterations in peripheral cytokine levels were associated with antidepressant treatment outcomes in MDD. Further investigations are warranted to elucidate sources of heterogeneity and examine the potentiality of using inflammatory cytokines as novel predictive markers for the pharmacological treatment of MDD.

High occupational level is associated with poor response to the treatment of depression: A replication study

Major depressive disorder (MDD) is a leading cause of disability and inability to work. There is evidence that occupational factors may precipitate a MDD episode and interfere with the recovery process. In a previous investigation, we found that those employed in high occupational levels had a worse outcome after treatment for depression (Mandelli et al., 2016). The aim of the present study was to further investigate response to treatments for depression according to occupational status on an independent sample of MDD patients.Six hundred and forty-seven (647) subjects with a stable working occupation were taken from a larger independent sample of MDD patients evaluated for response and resistance to treatment for depression, after at least one adequate treatment trial. Three broad occupational categories were considered: ‘manager’, ‘white-collar’, ‘blue-collar’ and ‘self-employed’.Managers had the highest rate of non-response and resistance to treatments. White-collar workers also had high non-response and resistance rates. At the opposite, Blue-collar workers had significantly lower rates of non-response and resistance. Self-employed were in between White- and Blue-collar workers and did not significantly differ from the other occupational categories.The findings of this replication study substantially support our previous observations. MDD patients employed in high-middle occupations may have a less favorable outcome after standard treatments of depression. Working stressful condition and other psychosocial factors at work should be investigated more closely in relation to treatment outcomes in MDD.

The effect of chronic physical illnesses on psychiatric hospital admission in patients with recurrent major depression

People with major depressive disorder (MDD) have an increased burden of chronic physical illnesses (CPI). However, information about the effect of CPIs on recurrent MDD treatment outcome is limited. The objective of this study was to explore whether the number of CPIs in patients with recurrent MDD was associated with higher rate of psychiatric admissions. Data were collected for a consecutive sample of 190 patients diagnosed with recurrent MDD. The key outcome was the number of psychiatric admissions following psychiatric diagnosis. The independent variable was the number of CPIs. The effects of different clinical, sociodemographic, and lifestyle confounding factors were controlled using robust regression. The patients with CPI had significantly more psychiatric admissions than the patients without CPI, and the number of CPIs was significantly associated with the number of psychiatric admissions. The results of our study largely confirmed that more than two CPIs in patients diagnosed with recurrent MDD are associated with higher rates of psychiatric admission, independent of psychiatric comorbidities and other clinical and sociodemographic factors. These findings indicate that to improve treatment outcome and to reduce recurrence, it is crucial to enhance early recognition and treatment of physical comorbidity.

PR265: Gingival bleeding is associated with the major depressive disorder treatment outcome; A prospective cohort study

PR265: Gingival bleeding is associated with the major depressive disorder treatment outcome; A prospective cohort study

Collaborative Care Management Associated With Improved Depression Outcomes in Patients With Personality Disorders, Compared to Usual Primary Care.

Background: The use of a collaborative care management (CCM) model can dramatically improve short- and long-term treatment outcomes for patients with major depressive disorder (MDD). Patients with comorbid personality disorder (PD) may experience poorer treatment outcomes for MDD. Our study seeks to examine the differences in MDD treatment outcomes for patients with comorbid PD when using a CCM approach rather than usual care (UC). Methods: In our retrospective cohort study, we reviewed the records of 9614 adult patients enrolled in our depression registry with the clinical diagnosis MDD and the diagnosis of PD (Yes/No). Clinical outcomes for depression were measured with Patient Health Questionnaire–9 (PHQ-9) scores at 6 months. Results: In our study cohort, 59.4% of patients (7.1% of which had comorbid PD) were treated with CCM, as compared with 40.6% (6.8% with PD) treated with UC. We found that the presence of a PD adversely affected clinical outcomes of remission within both groups, however, at 6 months patients with PD had significantly lower MDD remission rates when treated with UC as compared with those treated with CCM (11.5% vs 25.2%, P = .002). Patients with PD in the UC group were also noted to have an increased rate of persistent depressive symptoms (PHQ-9 score ≥10) at 6 months as compared with those in the CCM group (67.7% vs 51.7%, P = .004). Conclusions: In patients with comorbid MDD and PD, clinical outcomes at 6 months were significantly improved when treated with CCM compared with UC. This finding is encouraging and supports the idea that CCM is an effective model for caring for patients with behavioral concerns, and it may be of even greater benefit for those patients being treated for comorbid behavioral health conditions.

Change in Quantitative EEG Theta Cordance as a Potential Predictor of Repetitive Transcranial Magnetic Stimulation Clinical Outcome in Major Depressive Disorder.

Repetitive transcranial magnetic stimulation (rTMS) has demonstrated efficacy in major depressive disorder (MDD), although clinical outcome is variable. Change in the resting-state quantitative electroencephalogram (qEEG), particularly in theta cordance early in the course of treatment, has been linked to antidepressant medication outcomes but has not been examined extensively in clinical rTMS. This study examined change in theta cordance over the first week of clinical rTMS and sought to identify a biomarker that would predict outcome at the end of 6 weeks of treatment. Clinically stable outpatients (n = 18) received nonblinded rTMS treatment administered to the dorsolateral prefrontal cortex (DLPFC). Treatment parameters (site, intensity, number of pulses) were adjusted on an ongoing basis guided by changes in symptom severity rating scale scores. qEEGs were recorded at pretreatment baseline and after 1 week of left DLPFC (L-DLPFC) rTMS using a 21-channel dry-electrode headset. Analyses examined the association between week 1 regional changes in theta band (4-8 Hz) cordance, and week 6 patient- and physician-rated outcomes. Theta cordance change in the central brain region predicted percent change in Inventory of Depressive Symptomology-Self-Report (IDS-SR) score, and improvement versus nonimprovement on the Clinical Global Impression-Improvement Inventory (CGI-I) ( R2 = .38, P = .007; and Nagelkerke R2 = .78, P = .0001, respectively). The cordance biomarker remained significant when controlling for age, gender, and baseline severity. Treatment-emergent change in EEG theta cordance in the first week of rTMS may predict acute (6-week) treatment outcome in MDD. This oscillatory synchrony biomarker merits further study in independent samples.

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

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. 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. 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. 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.

Diffusion tensor imaging predictors of treatment outcomes in major depressive disorder.

Functional neuroimaging studies implicate anterior cingulate and limbic dysfunction in major depressive disorder (MDD) and responsiveness to antidepressants. Diffusion tensor imaging (DTI) enables characterisation of white matter tracts that relate to these regions. To examine whether DTI measures of anterior cingulate and limbic white matter are useful prognostic biomarkers for MDD. Of the 102 MDD out-patients from the International Study to Predict Optimized Treatment for Depression (iSPOT-D) who provided baseline magnetic resonance imaging (MRI) data, 74 completed an 8-week course of antidepressant medication (randomised to escitalopram, sertraline or extended-release venlafaxine) and were included in the present analyses. Thirty-four matched controls also provided DTI data. Fractional anisotropy was measured for five anterior cingulate-limbic white matter tracts: cingulum cingulate and hippocampus bundle, fornix, stria terminalis and uncinate fasciculus. (Trial registered at ClinicalTrials.gov: NCT00693849.) A cross-validated logistic regression model demonstrated that altered connectivity for the cingulum part of the cingulate and stria terminalis tracts significantly predicted remission independent of demographic and clinical measures with 62% accuracy. Prediction improved to 74% when age was added to this model. Anterior cingulate-limbic white matter is a useful predictor of antidepressant treatment outcome in MDD.

Heart rate variability and treatment outcome in major depression: A pilot study

Variations in heart rate variability (HRV) have been associated with major depressive disorder (MDD), but the relationship of baseline HRV to treatment outcome in MDD is unclear. We conducted a pilot study to examine associations between resting baseline HRV and MDD treatment outcome. We retrospectively tested several parameters of HRV in an MDD treatment study with escitalopram (ESC, N=26) to generate a model of how baseline HRV related to treatment outcome, and cross-validated the model in a separate trial of MDD treatment with Iyengar yoga (IY, N=16). Lower relative power of very low frequency (rVLF) HRV at baseline predicted improvement in depressive symptoms when adjusted for age and gender (R2>.43 and p<0.05 for both trials). Although vagal parasympathetic measures were correlated with antidepressant treatment outcome, their predictive power was not significant after adjusting for age and gender. In conclusion, baseline resting rVLF was associated with depression treatment outcome in two independent MDD treatment studies. These results should be interpreted with caution due to limited sample size, but a strength of this study is its validation of the rVLF predictor in an independent sample. rVLF merits prospective confirmation as a candidate biomarker.

Non-linear EEG analyses predict non-response to rTMS treatment in major depressive disorder

ObjectiveSeveral linear electroencephalographic (EEG) measures at baseline have been demonstrated to be associated with treatment outcome after antidepressant treatment. In this study we investigated the added value of non-linear EEG metrics in the alpha band in predicting treatment outcome to repetitive transcranial magnetic stimulation (rTMS). MethodsSubjects were 90 patients with major depressive disorder (MDD) and a group of 17 healthy controls (HC). MDD patients were treated with rTMS and psychotherapy for on average 21 sessions. Three non-linear EEG metrics (Lempel–Ziv Complexity (LZC); False Nearest Neighbors and Largest Lyapunov Exponent) were applied to the alpha band (7–13Hz) for two 1-min epochs EEG and the association with treatment outcome was investigated. ResultsNo differences were found between a subgroup of unmedicated MDD patients and the HC. Non-responders showed a significant decrease in LZC from minute 1 to minute 2, whereas the responders and HC showed an increase in LZC. ConclusionsThere is no difference in EEG complexity between MDD and HC and the change in LZC across time demonstrated value in predicting outcome to rTMS. SignificanceThis is the first study demonstrating utility of non-linear EEG metrics in predicting treatment outcome in MDD.

FMRI response to negative words and SSRI treatment outcome in major depressive disorder: A preliminary study

Clinically useful predictors of treatment outcome in major depressive disorder (MDD) remain elusive. We examined associations between functional magnetic resonance imaging (fMRI) blood oxygen level dependent (BOLD) signal during active negative word processing and subsequent selective serotonin reuptake inhibitor (SSRI) treatment outcome in MDD. Unmedicated MDD subjects (n=17) performed an emotional word processing fMRI task, and then received eight weeks of standardized antidepressant treatment with escitalopram. Lower pre-treatment BOLD responses to negative words in midbrain, dorsolateral prefrontal cortex, paracingulate, anterior cingulate, thalamus and caudate nuclei correlated significantly with greater improvement following escitalopram treatment. Activation of these regions in response to negative words correlated significantly with reaction time for rating word relevance. Maximally predictive clusters of voxels identified using a cross-validation approach predicted 48% of the variance in response to treatment. This study provides preliminary evidence that SSRIs may be most beneficial in patients who are less able to engage cognitive control networks while processing negative stimuli. Differences between these findings and previous fMRI studies of SSRI treatment outcome may relate to differences in task design. Regional BOLD responses to negative words predictive of SSRI outcome in this study were both overlapping and distinct from those predictive of outcome with cognitive behavioral therapy (CBT) in previous studies using the same task. Future studies may examine prediction of differential outcome across treatments in the context of a randomized controlled trial.

Using Standardized fMRI Protocols to Identify Patterns of Prefrontal Circuit Dysregulation that are Common and Specific to Cognitive and Emotional Tasks in Major Depressive Disorder: First Wave Results from the iSPOT-D Study

Functional neuroimaging studies have implicated dysregulation of prefrontal circuits in major depressive disorder (MDD), and these circuits are a viable target for predicting treatment outcomes. However, because of the heterogeneity of tasks and samples used in studies to date, it is unclear whether the central dysfunction is one of prefrontal hyperreactivity or hyporeactivity. We used a standardized battery of tasks and protocols for functional magnetic resonance imaging, to identify the common vs the specific prefrontal circuits engaged by these tasks in the same 30 outpatients with MDD compared with 30 matched, healthy control participants, recruited as part of the International Study to Predict Optimized Treatment in Depression (iSPOT-D). Reflecting cognitive neuroscience theory and established evidence, the battery included cognitive tasks designed to assess functions of selective attention, sustained attention-working memory and response inhibition, and emotion tasks to assess explicit conscious and implicit nonconscious viewing of facial emotion. MDD participants were distinguished by a distinctive biosignature of: hypoactivation of the dorsolateral prefrontal cortex during working memory updating and during conscious negative emotion processing; hyperactivation of the dorsomedial prefrontal cortex during working memory and response inhibition cognitive tasks and hypoactivation of the dorsomedial prefrontal during conscious processing of positive emotion. These results show that the use of standardized tasks in the same participants provides a way to tease out prefrontal circuitry dysfunction related to cognitive and emotional functions, and not to methodological or sample variations. These findings provide the frame of reference for identifying prefrontal biomarker predictors of treatment outcomes in MDD.

Cross-Cultural Aspects of Depression Management in Primary Care

Major depressive disorder (MDD) is a prevalent illness in minority populations. Minority patients with MDD are often unrecognized and untreated. This review examines promising interventions to address MDD in primary care settings, where minority groups are more likely to seek care. Since 2010, eleven interventions have been developed to address patient-specific and provider-specific barriers, many of which are adaptations of the collaborative care model. Other promising interventions include cultural tailoring of the collaborative care model, as well as the addition of telepsychiatry, motivational interviewing, cultural consultation, and innovations in interpreting. Overall, collaborative care was found feasible and improved satisfaction and treatment engagement of depressed minority patients in primary care. It remains inconclusive whether these newer intervention models improve MDD treatment outcomes. Future research will be needed to establish the effectiveness of these intervention models in improving the treatment outcomes of minority populations with MDD.

Glutamate normalization with ECT treatment response in major depression

Though psychotherapy, medications and brain stimulation are used to treat major depressive disorder (MDD), treatment response depends on several individualized clinical factors and frequently requires multiple treatment trials. To guide more effective personalized interventions, there is thus a need to identify biological markers to elucidate the mechanisms of response to predict treatment outcomes in MDD. Prior evidence suggests neurochemical disturbances in mood regulating networks, including the anterior cingulate cortex (ACC) and connected prefrontal and subcortical centers, contribute to MDD and play a role in treatment response1–4. To identify treatment-related changes in neurochemical markers implicated in MDD2, 4, 5, we applied single-voxel Proton Magnetic Resonance Spectroscopy (1HMRS) for cross sectional and longitudinal analysis of ACC glutamate (Glu), choline (Cho) and N-acetyl aspartate (NAA) in 10 patients with DSM-IVR diagnoses of MDD (mean age: 44.0 years, 7.93 SD; 6 females) followed prospectively during Index treatment and 10 demographically similar controls (mean age: 39.03 years, 9.55 SD; 6 females). Electroconvulsive therapy (ECT) was used as the therapeutic modality since it is a highly effective procedure for treating severe MDD that elicits rapid response in eligible individuals6. Patients were assessed at three time points: baseline, and after the 2nd and 6th ECT sessions, corresponding to ~48 hrs and 2 weeks post treatment initiation (see Supplementary Information (SI) for clinical details). The Hamilton (HAMD) and Montgomery-Asberg (MADRS) Depression Rating Scales recorded therapeutic response. Controls were assessed twice, ~2-weeks apart, to determine disease effects, normative values and to estimate the variance of serial scanning. All subjects, without neurological disorders, alcohol/substance abuse or dementia, provided UCLA Institutional Review Board-approved informed consent. High-resolution T1-weighted MPRAGE structural (FOV: 256; voxel size: 1 mm3; TR/TE: 2200/5.16 ms; flip angle: 12°) and single-voxel Point Resolved Spectroscopy (PRESS) acquisitions (TR/TE: 2000/30 ms; spectral width 2000 Hz; 1024 points) with (128 averages) and without water suppression (8 averages) were collected on a Siemens 3T Allegra system. Automatic and manual 3D shimming was applied to reduce B0 inhomogeneity for voxels (20×18×12 mm3) placed in midline anterior ACC gray matter [Fig 1A; see SI]. LCModel computed the levels of Glu, Cho and NAA relative to the unsuppressed water signal. Derived metabolites levels were corrected for voxel CSF content using corresponding tissue classified T1-weighted images7. Figure 1 A) Voxel placement and 1HMRS spectra in a single subject, B) Mean CSF-adjusted glutamate (left), NAA (middle) and choline (right) levels at each time point in patients and controls indicating significant disease effects (purple), group by time interactions ... Metabolites (with variances 0.50). For baseline comparisons, including age and gender as covariates, main effects of diagnosis were observed for Glu only, F(1,19)=8.61, p<.01 (lower in patients). General Linear Mixed Models (GLMMs) including metabolites from each time point and a subject variable to control for within-subject correlations, also showed a significant diagnosis by time interaction for Glu, F(1, 21.98)=4.94, p<.037. Glu levels increased significantly over time in patients approximating normal levels, F(2, 16.56)=4.05, p<.037, but remained stable in controls (p=.47). In patients, subsequent pairwise comparisons showed significant Glu elevations between baseline and the 6th ECT, p<.018. In the absence of overall effects of time, pairwise comparisons in patients also showed significant decreases in NAA, p<.048, and increases in Cho, p<.047 between baseline and the 6th ECT [Fig 1B]. HAMD and MADRS scores improved significantly with treatment, F(2, 16.67)=29.48 and F(2, 13.12)=19.79, both p<.0001. Variations in Glu correlated with MADRS scores across time, F(1, 15.78)=4.49, p<.05. Baseline Cho levels correlated significantly with overall change in HAMD ratings, r=.85, p<.0001; baseline Glu levels also showed a trend for predicting HAMD change, r=.59, p<.08 [Fig 1C]. MDD-related reductions in ACC glutamate normalize with ECT treatment and associate with clinically determined therapeutic response. Results are in line with prior reports indicating glutamate deficits in MDD2, 4 and extend earlier observations of increased glutamate/glutamine (Glx) in the left ACC in patients treated with ECT8, shown here to also mirror clinical response. NAA and Choline, though not discriminating diagnostic groups at baseline, also showed treatment effects in patients. Together, these neurochemical changes suggest treatment-induced neuroplasticity in the structure, density and integrity of mature neurons9 and/or may reflect changes relating to adult neurogenesis in connected hippocampal and subventricular regions10. Results also support that neurochemical signatures in the ACC may predict future therapeutic response through mechanisms potentially overlapping with other forms of treatment. Studies including additional treatment modalities, larger samples, and longer follow-ups with relapse rates may confirm these observations.

CORRELATIONS AMONG ELECTROENCEPHALOGRAPHY AND SERUM DRUG LEVEL IN MAJOR DEPRESSION: PREDICTION OF RESPONSIVENESS TO ANTIDEPRESSANT TREATMENT

Objective: To examine the correlations among electroencephalography and serum drug level parameters to predict the responsiveness to antidepressant treatment in patients with major depression. Methods: Subjects received sertraline, 50 mg daily, for four weeks. On the first day before the initiation of treatment, 1 hour and 3 hour afterwards oral sertraline ingestion, on the 7th day and on the 4th week of treatment, all patients underwent assessments using electroencephalography. One hour and 3 hours after oral ingestion of 50 mg oral sertraline, serum drug level was measured. Serum drug level measurement was repeated on the 7th day and on the 4th week of the treatment. Depression was rated using Hamilton Depression Rating Scale (HAMD) while anxiety was rated using Beck Anxiety Scale (BAS) obtained before treatment (at baseline), on the 7th day and on the 4th week of the treatment. Results: A significant decrease in theta activity at the 1st hour electroencephalography after single dose of sertraline ingestion was examined. A significant alpha decrease after 7 days of sertraline treatment and beta activity decrease after 4 weeks of sertraline treatment were observed on electroencephalography measures. Conclusion: These findings support that the use of electroencephalography measures have the value of predicting treatment outcome in depression. If these results will be replicated in other studies, usage of electroencephalography and serum drug levels may become a useful biomarker for predicting treatment outcome in major depressive disorder. Key words: electroencephalography, major depressive disorder, predictors of treatment response, serum drug level

Effects of heart disease on depression treatment: results from the COMED study

Objective To determine the impact of self-reported heart disease (HD) on major depressive disorder (MDD) treatment outcomes. Method This single-blind, 7-month prospective randomized trial enrolled 665 participants, 18–75 years old, from six primary and nine psychiatric care sites across the USA. Participants had at least moderately severe (baseline 17-item Hamilton Rating Scale of Depression ≥16), nonpsychotic chronic and/or recurrent MDD. Participants with and without self-reported HD were randomized into three treatment groups (1:1:1 ratio): escitalopram plus placebo, bupropion sustained-release plus escitalopram or venlafaxine extended-release plus mirtazapine. The primary outcome (remission) was defined by the last two consecutive 16-item Quick Inventory of Depressive Symptomatology–Self-Report (QIDS-SR 16) ratings: one had to be <8 and one <6. Secondary outcomes included response (reduction in QIDS-SR 16 >50%) side-effect burden, quality of life and functioning. A P value <.05 indicated statistical significance. Result Participants with HD were less depressed at baseline and demonstrated fewer side effects at Treatment Weeks 12 and 28. The HD groups did not differ regarding remission [40.0% (16/40) vs. 38.2% (239/625), P=.5566] or response [50% (20/40) vs. 52.1% (314/625), P=.8055]. Conclusions Despite apparent baseline and side-effect differences between participants with and without HD, the two groups did not differ regarding MDD treatment outcomes.

Socio-demographic features of participants in randomized con-trolled trials for major depression: generalizability and individu-alization.

Rationale, Aims and Objectives: It is important for clinicians to know to what extent the results of randomized controlled trials (RCTs) are generalizable to their psychiatric practice, since RCTs are considered to be the most reliable source of evidence for treatment guideline development. Furthermore, it is important to know whether results from individual randomised controlled trials (RCTs) can be directly compared to each other. Socio-demographic and socio-economic (SES) features influence treatment-outcome in major depressive disorder (MDD). Differences in (reporting of) SES features of participants in RCTs will hamper comparison and jeopardise the external validity (generalizability) of their results. We explored the reporting of SES features in RCTs for depression. Methods: We selected 45 antidepressant efficacy trials (AETs) and 19 psychotherapy efficacy trials (PETs). We listed the reported socio-demographic and -economic features. Results: Reporting on SES-features was very diverse and often limited. Especially important SES features like educational level, socio-economic status and income were reported insufficiently. The mean age of RCT participants in MDD trials was 41 years. Participants are predominantly female (62%) and white (89%). Of the participants 61% were employed and 45% of the participants were married/cohabitating. Conclusions: Standardisation of reporting on socio-demographic and socio-economic status is needed to adequately judge the generalizability of RCTs to daily practice and to facilitate comparisons within the body of RCTs in order to move increasingly towards more individualized and person-centered models of clinical practice.

Peripheral blood and neuropsychological markers for the onset of action of antidepressant drugs in patients with Major Depressive Disorder

BackgroundIn Major Depressive Disorder (MDD), treatment outcomes with currently available strategies are often disappointing. Therefore, it is sensible to develop new strategies to increase remission rates in acutely depressed patients. Many studies reported that true drug response can be observed within 14 days (early improvement) of antidepressant treatment. The identical time course of symptom amelioration after early improvement in patients treated with antidepressants of all classes or with placebo strongly suggests a common biological mechanism, which is not specific for a particular antidepressant medication. However, the biology underlying early improvement and final treatment response is not understood and there is no established biological marker as yet, which can predict treatment response for the individual patient before initiation or during the course of antidepressant treatment. Peripheral blood markers and executive functions are particularly promising candidates as markers for the onset of action and thus the prediction of final treatment outcome in MDD.Methods/DesignThe present paper presents the rationales, objectives and methods of a multi-centre study applying close-meshed repetitive measurements of peripheral blood and neuropsychological parameters in patients with MDD and healthy controls during a study period of eight weeks for the identification of biomarkers for the onset of antidepressants' action in patients with MDD. Peripheral blood parameters and depression severity are assessed in weekly intervals from baseline to week 8, executive performance in bi-weekly intervals. Patients are participating in a randomized controlled multi-level clinical trial, healthy controls are matched according to mean age, sex and general intelligence.DiscussionThis investigation will help to identify a biomarker or a set of biomarkers with decision-making quality in the treatment of MDD in order to increase the currently disappointing remission rates of antidepressant treatment.Trial RegistrationClinicalTrials.gov: NCT00974155

Biomarkers to Predict Antidepressant Response

During the past several years, we have achieved a deeper understanding of the etiology/pathophysiology of major depressive disorder (MDD). However, this improved understanding has not translated to improved treatment outcome. Treatment often results in symptomatic improvement, but not full recovery. Clinical approaches are largely trial-and-error, and when the first treatment does not result in recovery for the patient, there is little proven scientific basis for choosing the next. One approach to enhancing treatment outcomes in MDD has been the use of standardized sequential treatment algorithms and measurement-based care. Such treatment algorithms stand in contrast to the personalized medicine approach, in which biomarkers would guide decision making. Incorporation of biomarker measurements into treatment algorithms could speed recovery from MDD by shortening or eliminating lengthy and ineffective trials. Recent research results suggest several classes of physiologic biomarkers may be useful for predicting response. These include brain structural or functional findings, as well as genomic, proteomic, and metabolomic measures. Recent data indicate that such measures, at baseline or early in the course of treatment, may constitute useful predictors of treatment outcome. Once such biomarkers are validated, they could form the basis of new paradigms for antidepressant treatment selection.

The Effect of Pain on Outcomes in a Trial of Duloxetine Treatment of Major Depressive Disorder

Patients with major depressive disorder (MDD) frequently report one or more pain symptoms. To explore the relationship between improvement in pain symptoms and MDD treatment outcomes, we conducted a secondary analysis of an approximately 12-week, open label trial of duloxetine in MDD. The primary objective was to test the hypothesis that a greater reduction in pain was associated with a higher probability of MDD remission. Adults with DSM-IV MDD were enrolled in the study if they had a Hamilton Depression Scale (HAMD-17) total score of 15 or more and a Clinical Global Impression of Severity (CGI-S) score of 4 or more. The duloxetine dose of patients could be titrated on the basis of the degree of response within the range from 60 to 120 mg given QD, with 90 mg QD as an intermediate dose. Remission of major depressive disorder was defined as a HAMD-17 total score of < or = 7. Core emotional symptoms of depression were determined by the HAMD-17 Maier subscale. Pain was assessed using a 100 mm visual analog scale (VAS) of overall pain severity over the last week (0 = no pain, 100 = pain as severe as I can imagine). For the primary analysis, mean change in VAS overall pain score over time was compared between remitters and non-remitters at endpoint using a mixed model repeated measures approach. Two hundred forty nine patients were included in the analysis. A greater reduction in pain was associated with a significantly higher probability of remission of MDD, after accounting for changes in the core emotional symptoms. Greater pain reduction was associated with significant improvement in MDD core emotional symptoms. A greater improvement in pain was also associated with improvements in patient and clinician-rated global assessments. The effective treatment of pain symptoms in patients with major depressive disorder was associated with higher remission rates. The results underscore the importance of effectively treating painful symptoms associated with depression.