Predictors Of Remission In Depression Research Articles

Functional connectivity of salience and affective networks among remitted depressed patients predicts episode recurrence.

Recurrent episodes in major depressive disorder (MDD) are common but the neuroimaging features predictive of recurrence are not established. Participants in the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study who achieved remission after 12 weeks of treatment withcognitive behavior therapy, duloxetine, or escitalopram were prospectively monitored for up to 21 months for recurrence. Neuroimaging markers predictive of recurrence were identified from week 12 functional magnetic resonance imaging scans by analyzing whole-brain resting state functional connectivity (RSFC) using seeds for four brain networks that are altered in MDD. Neuroimaging correlates of established clinical predictors of recurrence, including the magnitude of depressive (Hamilton Depression Rating Scale), anxiety (Hamilton Anxiety Rating Scale) symptom severity at time of remission, and a comorbid anxiety disorder were examined for their similarity to the neuroimaging predictors of recurrence. Of the 344 patients randomized in PReDICT, 61 achieved remission and had usable scans for analysis, 9 of whom experienced recurrence during follow-up. Recurrence was predicted by: 1) increased RSFC between subcallosal cingulate cortex (SCC) and right anterior insula, 2) decreased RSFC between SCC and bilateral primary visual cortex, and 3) decreased RSFC between insula and bilateral caudate. Week 12 depression and anxiety scores were negatively correlated with RSFC strength between executive control and default mode networks, but they were not correlated with the three RSFC patterns predicting recurrence. We conclude that altered RSFC in SCC and anterior insula networks are prospective risk factors associated with MDD recurrence, reflecting additional sources of risk beyond clinical measures.

Brain Scans May Offer Clues About Depression Remission

Brain Scans May Offer Clues About Depression Remission

Shared and Unique Changes in Brain Connectivity Among Depressed Patients After Remission With Pharmacotherapy Versus Psychotherapy.

The authors sought to determine the shared and unique changes in brain resting-state functional connectivity (rsFC) between patients with major depressive disorder who achieved remission with cognitive-behavioral therapy (CBT) or with antidepressant medication. The Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) trial randomized adults with treatment-naive major depressive disorder to 12 weeks of treatment with CBT (16 1-hour sessions) or medication (duloxetine 30-60 mg/day or escitalopram 10-20 mg/day). Resting-state functional MRI scans were performed at baseline and at week 12. The primary outcome was change in the whole-brain rsFC of four seeded brain networks among participants who achieved remission. Of the 131 completers with usable MRI data (74 female; mean age, 39.8 years), remission was achieved by 19 of 40 CBT-treated and 45 of 91 medication-treated patients. Three patterns of connectivity changes were observed. First, those who remitted with either treatment shared a pattern of reduction in rsFC between the subcallosal cingulate cortex and the motor cortex. Second, reciprocal rsFC changes were observed across multiple networks, primarily increases in CBT remitters and decreases in medication remitters. And third, in CBT remitters only, rsFC increased within the executive control network and between the executive control network and parietal attention regions. Remission from major depression via treatment with CBT or medication is associated with changes in rsFC that are mostly specific to the treatment modality, providing biological support for the clinical practice of switching between or combining these treatment approaches. Medication is associated with broadly inhibitory effects. In CBT remitters, the increase in rsFC strength between networks involved in cognitive control and attention provides biological support for the theorized mechanism of CBT. Reducing affective network connectivity with motor systems is a shared process important for remission with both CBT and medication.

Pilot Study of Metabolomic Clusters as State Markers of Major Depression and Outcomes to CBT Treatment.

Major depressive disorder (MDD) is a common and disabling syndrome with multiple etiologies that is defined by clinically elicited signs and symptoms. In hopes of developing a list of candidate biological measures that reflect and relate closely to the severity of depressive symptoms, so-called “state-dependent” biomarkers of depression, this pilot study explored the biochemical underpinnings of treatment response to cognitive behavior therapy (CBT) in medication-free MDD outpatients. Plasma samples were collected at baseline and week 12 from a subset of MDD patients (N = 26) who completed a course of CBT treatment as part of the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study. Targeted metabolomic profiling using the AbsoluteIDQ® p180 Kit and LC-MS identified eight “co-expressed” metabolomic modules. Of these eight, three were significantly associated with change in depressive symptoms over the course of the 12-weeks. Metabolites found to be most strongly correlated with change in depressive symptoms were branched chain amino acids, acylcarnitines, methionine sulfoxide, and α-aminoadipic acid (negative correlations with symptom change) as well as several lipids, particularly the phosphatidlylcholines (positive correlation). These results implicate disturbed bioenergetics as an important state marker in the pathobiology of MDD. Exploratory analyses contrasting remitters to CBT versus those who failed the treatment further suggest these metabolites may serve as mediators of recovery during CBT treatment. Larger studies examining metabolomic change patterns in patients treated with pharmacotherapy or psychotherapy will be necessary to elucidate the biological underpinnings of MDD and the -specific biologies of treatment response.

Recall accuracy for the symptoms of a major depressive episode among clinical trial participants

For patients with major depressive disorder (MDD), approaches to treatment differ for those with a single versus recurrent episodes. Based on studies of community samples, however, accuracy is low for identifying past episodes. Recall accuracy among clinical samples with a well-defined major depressive episode (MDE) has not been examined previously. We evaluated episode recall accuracy in 79 MDD patients in follow-up of the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study at 12- and 24-month time-points after starting treatment. Using the Structured Clinical Interview for DSM-IV, patients were asked to recall whether they had been experiencing the nine criterion symptoms of an MDE at the time of their intake assessment. Accuracy of recall for the index MDE was high, with 95% of patients at month 12 and 85% at month 24 recalling sufficient symptoms to meet the diagnostic criteria. Recall accuracy for specific symptoms varied considerably, from >90% for dysphoria and anhedonia, to 55% for psychomotor and weight/appetite changes. For the thoughts of death/suicide criterion, patients with erroneous recall were significantly more likely to recall having had the symptom at the intake evaluation (though they had denied it at the time) than vice versa (p < .007). Patients who have participated in a clinical trial are likely to recall accurately a past MDE up to two years prior. Optimal vigilance for suicidal ideation for treatment-naïve patients should include a combination of self-report and clinician assessments.

Temporal Profiles and Dose-Responsiveness of Side Effects with Escitalopram and Duloxetine in Treatment-Naïve Depressed Adults.

Side effect profiles of antidepressants are relevant to treatment selection and adherence among patients with major depressive disorder (MDD), but several clinically-relevant characteristics of side effects are poorly understood. We aimed to compare the side effect profiles of escitalopram and duloxetine, including frequencies, time to onset, duration, dose responsiveness, and impact on treatment outcomes. Side effects occurring in 211 treatment-naïve patients with MDD randomized to 12 weeks of treatment with flexibly-dosed escitalopram (10–20 mg/day) or duloxetine (30–60 mg/day) as part of the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study were evaluated. Escitalopram- and duloxetine-treated patients experienced a similar mean number of overall side effects and did not differ in terms of the specific side effects observed or their temporal profile. Experiencing any side effect during the first 2 weeks of treatment was associated with increased likelihood of trial completion (86.7% vs. 73.7%, p = 0.045). Duloxetine-treated patients who experienced dry mouth were significantly more likely to achieve remission than those who did not (73.7% vs. 44.8%, p = 0.026). Side effects that resolved prior to a dose increase were unlikely to recur after the increase, but only about 45% of intolerable side effects that required a dose reduction resolved within 30 days of the reduction. At the doses used in this study, escitalopram and duloxetine have similar side effect profiles. Understanding characteristics of side effects beyond simple frequency rates may help prescribers make more informed medication decisions and support conversations with patients to improve treatment adherence.

Antidepressant Outcomes Predicted by Genetic Variation in Corticotropin-Releasing Hormone Binding Protein.

Genetic variation within the hypothalamic-pituitary-adrenal (HPA) axis has been linked to risk for depression and antidepressant response. However, these associations have yet to produce clinical gains that inform treatment decisions. The authors investigated whether variation within HPA axis genes predicts antidepressant outcomes within two large clinical trials. The test sample comprised 636 patients from the International Study to Predict Optimized Treatment in Depression (iSPOT-D) who completed baseline and 8-week follow-up visits and for whom complete genotyping data were available. The authors tested the relationship between genotype at 16 candidate HPA axis single-nucleotide polymorphisms (SNPs) and treatment outcomes for three commonly used antidepressants (escitalopram, sertraline, and extended-release venlafaxine), using multivariable linear and logistic regression with Bonferroni correction. Response and remission were defined using the Hamilton Depression Rating Scale. Findings were then validated using the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study of outcome predictors in treatment-naive patients with major depression. The authors found that the rs28365143 variant within the corticotropin-releasing hormone binding protein (CRHBP) gene predicted antidepressant outcomes for remission, response, and symptom change. Patients homozygous for the G allele of rs28365143 had greater remission rates, response rates, and symptom reductions. These effects were specific to drug class. Patients homozygous for the G allele responded significantly better to the selective serotonin reuptake inhibitors escitalopram and sertraline than did A allele carriers. In contrast, rs28365143 genotype was not associated with treatment outcomes for the serotonin norepinephrine reuptake inhibitor venlafaxine. When patients were stratified by race, the overall effect of genotype on treatment response remained. In the validation sample, the GG genotype was again associated with favorable antidepressant outcomes, with comparable effect sizes. These findings suggest that a specific CRHBP SNP, rs28365143, may have a role in predicting which patients will improve with antidepressants and which type of antidepressant may be most effective. The results add to the foundational knowledge needed to advance a precision approach to personalized antidepressant choices.

CBT, Antidepressants Are Equally Effective in Treatment-Naïve Patients

Back to table of contents Previous article Next article Clinical and Research NewsFull AccessCBT, Antidepressants Are Equally Effective in Treatment-Naïve PatientsVabren WattsVabren WattsSearch for more papers by this authorPublished Online:19 May 2017https://doi.org/10.1176/appi.pn.2017.4b15AbstractPatients with depression who are given their preferred treatment may be more likely to stick with the therapy.Current guidelines for treating major depressive disorder (MDD) recommend that clinicians consider both the clinical features of the disorder and patient preference when choosing the initial form of treatment. A study published in AJP in Advance in March suggests that while antidepressants and psychotherapy appear to be equally effective in treatment-naïve MDD patients, those matched with their preferred treatment may be more likely to complete therapy.“These results suggest that patients who receive their preferred treatment may invest more fully in their treatment, which makes the finding that preference does not affect improvement all the more striking,” Boadie Dunlop, M.D., director of the Mood and Anxiety Disorders Program at Emory University School of Medicine, and colleagues wrote. “This discrepancy implies that biological or psychosocial factors are stronger determinants of treatment efficacy than patient preference.”The study was a part of the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) trial that aimed to identify biological and psychological factors predictive of treatment outcomes in major depressive disorder in adults who had never previously received treatment for a mood disorder (see story above). The researchers randomly assigned 344 adults aged 18 to 65 with moderate MDD (mean baseline Hamilton Depression Rating Scale [HAM-D] score of 19.8) to 12 weeks of escitalopram (10-20 mg/day), duloxetine (30-60 mg/day), or cognitive-behavioral therapy (CBT, 16 50-minute sessions). Prior to randomization, patients indicated whether they preferred pharmacotherapy, CBT, or had no preference. Patients were evaluated at weeks 1 through 6, and again at weeks 8, 10, and 12. The authors found that the mean estimated overall decreases in HAM-D score from baseline to week 12 did not significantly differ between treatments (CBT: 10.2; escitalopram: 11.1; duloxetine: 11.2). Additionally, remission rates did not significantly differ between treatment arms (CBT: 41.9 percent; escitalopram: 46.7 percent; duloxetine: 54.7 percent). Of the 225 patients who expressed a treatment preference, 107 were matched to their preferred treatment and 118 were mismatched. Patients who were matched with their preferred treatment were significantly more likely to complete the trial than those who were not matched with preferred treatment (82.2 percent vs. 67.8 percent, respectively). However, patients matched to their preferred treatment were not found to be more likely to achieve remission.“Asking patients about their treatment preference, and exploring the basis for their preference, can help build the therapeutic alliance, even if the clinician’s final treatment recommendation does not align with the patient’s preference,” Dunlop told Psychiatric News. While the “findings about patients’ preferences do not warrant a change in current practice,” he said he believes advances in biomarkers will allow mental health professionals to “take a more definitive stance when recommending a specific form of treatment for an individual patient.” The study was supported by the National Institutes of Health. ■“Effects of Patient Preferences on Outcomes in the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) Study” can be accessed here. ISSUES NewArchived

The structure of personality disorders within a depressed sample: Implications for personalizing treatment

BackgroundPersonality disorders (PDs) and major depressive disorder (MDD) are both significant public health burdens. They are frequently comorbid, and this comorbidity predicts poorer treatment outcomes and lower maintenance of treatment effects. Although there is growing consensus on the structure of personality pathology in non-depressed individuals, there is limited research on the structure of personality pathology in individuals experiencing MDD. MethodAs part of the Predictors of Remission in Depression to Individual and Combined Treatment (PReDICT) randomized controlled trial, 192 treatment-naïve subjects meeting DSM-IV-TR criteria for MDD completed the International Personality Disorder Examination (IPDE). Using this sample, a principal components analysis explored the factor structure of the IPDE. ResultsA three-factor model comprised three factors labeled “NADA” (Negative Affectivity, Disinhibition, and Antagonism),” “Social Anxiety,” and “Antisociality.” Factor intercorrelations were small-to-moderate, and the sum score of the three factors was highly correlated (r=0.94) with the total IPDE score. LimitationsPersonality pathology was assessed with one instrument, and sample size was smaller than ideal for factor analytic research. ConclusionsConsistent with prior factor-analytic findings, a three-factor solution provided the most clinically and theoretically useful model. This finding lends support for the personality disorders retained in DSM-5 and some support for a model of personality pathology aligned with the personality traits found in the leading nonclinical models of personality. The obtained factors are potential moderators of clinical interventions and may serve as an avenue to personalizing treatments.

A depression network of functionally connected regions discovered via multi-attribute canonical correlation graphs

To establish brain network properties associated with major depressive disorder (MDD) using resting-state functional magnetic resonance imaging (Rs-fMRI) data, we develop a multi-attribute graph model to construct a region-level functional connectivity network that uses all voxel level information. For each region pair, we define the strength of the connectivity as the kernel canonical correlation coefficient between voxels in the two regions; and we develop a permutation test to assess the statistical significance. We also construct a network based classifier for making predictions on the risk of MDD. We apply our method to Rs-fMRI data from 20 MDD patients and 20 healthy control subjects in the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study. Using this method, MDD patients can be distinguished from healthy control subjects based on significant differences in the strength of regional connectivity. We also demonstrate the performance of the proposed method using simulationstudies.

ENHANCING HISPANIC PARTICIPATION IN MENTAL HEALTH CLINICAL RESEARCH: DEVELOPMENT OF A SPANISH-SPEAKING DEPRESSION RESEARCH SITE

Hispanics, particularly those with limited English proficiency, are underrepresented in psychiatric clinical research studies. We developed a bilingual and bicultural research clinic dedicated to the recruitment and treatment of Spanish-speaking subjects in the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study, a large clinical trial of treatment-naïve subjects with major depressive disorder (MDD). Demographic and clinical data derived from screening evaluations of the first 1,174 subjects presenting for participation were compared between the Spanish-speaking site (N = 275) and the primary English-speaking site (N = 899). Reasons for ineligibility (N = 888) for the PReDICT study were tallied for each site. Compared to English speakers, Spanish speakers had a lower level of education and were more likely to be female, uninsured, and have uncontrolled medical conditions. Clinically, Spanish speakers demonstrated greater depression severity, with higher mean symptom severity scores, and a greater number of previous suicide attempts. Among the subjects who were not randomized into the PReDICT study, Spanish-speaking subjects were more likely to have an uncontrolled medical condition or refuse participation, whereas English-speaking subjects were more likely to have bipolar disorder or a non-MDD depressive disorder. Recruitment of Hispanic subjects with MDD is feasible and may enhance efforts at signal detection, given the higher severity of depression among Spanish-speaking participants presenting for clinical trials. Specific approaches for the recruitment and retention of Spanish-speaking participants are required.

Predictors of remission in depression to individual and combined treatments (PReDICT): study protocol for a randomized controlled trial.

BackgroundLimited controlled data exist to guide treatment choices for clinicians caring for patients with major depressive disorder (MDD). Although many putative predictors of treatment response have been reported, most were identified through retrospective analyses of existing datasets and very few have been replicated in a manner that can impact clinical practice. One major confound in previous studies examining predictors of treatment response is the patient’s treatment history, which may affect both the predictor of interest and treatment outcomes. Moreover, prior treatment history provides an important source of selection bias, thereby limiting generalizability. Consequently, we initiated a randomized clinical trial designed to identify factors that moderate response to three treatments for MDD among patients never treated previously for the condition.Methods/designTreatment-naïve adults aged 18 to 65 years with moderate-to-severe, non-psychotic MDD are randomized equally to one of three 12-week treatment arms: (1) cognitive behavior therapy (CBT, 16 sessions); (2) duloxetine (30–60 mg/d); or (3) escitalopram (10–20 mg/d). Prior to randomization, patients undergo multiple assessments, including resting state functional magnetic resonance imaging (fMRI), immune markers, DNA and gene expression products, and dexamethasone-corticotropin-releasing hormone (Dex/CRH) testing. Prior to or shortly after randomization, patients also complete a comprehensive personality assessment. Repeat assessment of the biological measures (fMRI, immune markers, and gene expression products) occurs at an early time-point in treatment, and upon completion of 12-week treatment, when a second Dex/CRH test is also conducted. Patients remitting by the end of this acute treatment phase are then eligible to enter a 21-month follow-up phase, with quarterly visits to monitor for recurrence. Non-remitters are offered augmentation treatment for a second 12-week course of treatment, during which they receive a combination of CBT and antidepressant medication. Predictors of the primary outcome, remission, will be identified for overall and treatment-specific effects, and a statistical model incorporating multiple predictors will be developed to predict outcomes.DiscussionThe PReDICT study’s evaluation of biological, psychological, and clinical factors that may differentially impact treatment outcomes represents a sizeable step toward developing personalized treatments for MDD. Identified predictors should help guide the selection of initial treatments, and identify those patients most vulnerable to recurrence, who thus warrant maintenance or combination treatments to achieve and maintain wellness.Trial registrationClinicaltrials.gov Identifier: NCT00360399. Registered 02 AUG 2006. First patient randomized 09 FEB 2007.