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Some data suggest that antipsychotics may adversely affect brain structure. We examined the relationship among olanzapine exposure, relapse, and changes in brain structure in patients with major depressive disorder with psychotic features. We analyzed data from the Study of the Pharmacotherapy of Psychotic Depression II trial (STOP-PD II), a randomized, placebo-controlled trial in patients with psychotic depression who attained remission on sertraline and olanzapine and were randomized to continue sertraline plus olanzapine or placebo for 36 weeks. Olanzapine steady state concentration (SSC) were calculated based on sparsely-sampled levels. Rates of relapse and changes in brain structure were assessed as outcomes. There were significant associations between dosage and relapse rates (N = 118; HR = 0.94, 95% CI [0.897, 0.977], p = 0.002) or changes in left cortical thickness (N = 44; B = -2.0 × 10-3, 95% CI [-3.1 × 10-3, -9.6 × 10-4], p < 0.001) and between SSC and changes in left cortical thickness (N = 44; B = -8.7 × 10-4, 95% CI [-1.4 × 10-3, -3.6 × 10-4], p = 0.001). Similar results were found for the right cortex. These associations were no longer significant when the analysis was restricted to participants treated with olanzapine. Our findings suggest that, within its therapeutic range, the effect of olanzapine on relapse or cortical thickness does not depend on its dosage or SSC. Further research is needed on the effect of olanzapine and other antipsychotics on mood symptoms and brain structure.
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BACKGROUND: The neurobiology of psychotic depression is not well understood and can be confounded by antipsychotics. Magnetic resonance spectroscopy (MRS) is an ideal tool to measure brain metabolites non-invasively. We cross-sectionally assessed brain metabolites in patients with remitted psychotic depression and controls. We also longitudinally assessed the effects of olanzapine versus placebo on brain metabolites. METHODS: Following remission, patients with psychotic depression were randomized to continue sertraline + olanzapine (n = 15) or switched to sertraline + placebo (n = 18), at which point they completed an MRS scan. Patients completed a second scan either 36 weeks later, relapse, or discontinuation. Where water-scaled metabolite levels were obtained and a Point-RESolved Spectroscopy sequence was utilized, choline, myo-inositol, glutamate + glutamine (Glx), N-acetylaspartate, and creatine were measured in the left dorsolateral prefrontal cortex (L-DLPFC) and dorsal anterior cingulate cortex (dACC). An ANCOVA was used to compare metabolites between patients (n = 40) and controls (n = 46). A linear mixed-model was used to compare olanzapine versus placebo groups. RESULTS: Cross-sectionally, patients (compared to controls) had higher myo-inositol (standardized mean difference [SMD] = 0.84; 95%CI = 0.25-1.44; p = 0.005) in the dACC but not different Glx, choline, N-acetylaspartate, and creatine. Longitudinally, patients randomized to placebo (compared to olanzapine) showed a significantly greater change with a reduction of creatine (SMD = 1.51; 95%CI = 0.71-2.31; p = 0.0002) in the dACC but not glutamate + glutamine, choline, myo-inositol, and N-acetylaspartate. CONCLUSIONS: Patients with remitted psychotic depression have higher myo-inositol than controls. Olanzapine may maintain creatine levels. Future studies are needed to further disentangle the mechanisms of action of olanzapine.
Subject(s)
Antipsychotic Agents , Brain , Depression , Humans , Antipsychotic Agents/pharmacology , Aspartic Acid , Brain/diagnostic imaging , Brain/metabolism , Choline/metabolism , Creatine/metabolism , Depression/drug therapy , Glutamine/metabolism , Inositol/metabolism , Magnetic Resonance Imaging , Olanzapine/pharmacology , Sertraline/pharmacologyABSTRACT
BACKGROUND: Remitted psychotic depression (MDDPsy) has heterogeneity of outcome. The study's aims were to identify subgroups of persons with remitted MDDPsy with distinct trajectories of depression severity during continuation treatment and to detect predictors of membership to the worsening trajectory. METHOD: One hundred and twenty-six persons aged 18-85 years participated in a 36-week randomized placebo-controlled trial (RCT) that examined the clinical effects of continuing olanzapine once an episode of MDDPsy had remitted with sertraline plus olanzapine. Latent class mixed modeling was used to identify subgroups of participants with distinct trajectories of depression severity during the RCT. Machine learning was used to predict membership to the trajectories based on participant pre-trajectory characteristics. RESULTS: Seventy-one (56.3%) participants belonged to a subgroup with a stable trajectory of depression scores and 55 (43.7%) belonged to a subgroup with a worsening trajectory. A random forest model with high prediction accuracy (AUC of 0.812) found that the strongest predictors of membership to the worsening subgroup were residual depression symptoms at onset of remission, followed by anxiety score at RCT baseline and age of onset of the first lifetime depressive episode. In a logistic regression model that examined depression score at onset of remission as the only predictor variable, the AUC (0.778) was close to that of the machine learning model. CONCLUSIONS: Residual depression at onset of remission has high accuracy in predicting membership to worsening outcome of remitted MDDPsy. Research is needed to determine how best to optimize the outcome of psychotic MDDPsy with residual symptoms.
Subject(s)
Depressive Disorder, Major , Psychotic Disorders , Humans , Olanzapine/therapeutic use , Depression , Depressive Disorder, Major/diagnosis , Depressive Disorder, Major/drug therapy , Psychotic Disorders/drug therapy , Sertraline/therapeutic useABSTRACT
Objective: To evaluate if a machine learning approach can accurately predict antidepressant treatment outcome using electronic health records (EHRs) from patients with depression. Method: This study examined 808 patients with depression at a New York City-based outpatient mental health clinic between June 13, 2016 and June 22, 2020. Antidepressant treatment outcome was defined based on trend in depression symptom severity over time and was categorized as either "Recovering" or "Worsening" (i.e., non-Recovering), measured by the slope of individual-level Patient Health Questionnaire-9 (PHQ-9) score trajectory spanning 6 months following treatment initiation. A patient was designated as "Recovering" if the slope is less than 0 and as "Worsening" if the slope was no less than 0. Multiple machine learning (ML) models including L2 norm regularized Logistic Regression, Naive Bayes, Random Forest, and Gradient Boosting Decision Tree (GBDT) were used to predict treatment outcome based on additional data from EHRs, including demographics and diagnoses. Shapley Additive Explanations were applied to identify the most important predictors. Results: The GBDT achieved the best results of predicting "Recovering" (AUC: 0.7654 ± 0.0227; precision: 0.6002 ± 0.0215; recall: 0.5131 ± 0.0336). When excluding patients with low PHQ-9 scores (<10) at baseline, the results of predicting "Recovering" (AUC: 0.7254 ± 0.0218; precision: 0.5392 ± 0.0437; recall: 0.4431 ± 0.0513) were obtained. Prior diagnosis of anxiety, psychotherapy, recurrent depression, and baseline depression symptom severity were strong predictors. Conclusions: The results demonstrate the potential utility of using ML in longitudinal EHRs to predict antidepressant treatment outcome. Our predictive tool holds the promise to accelerate personalized medical management in patients with psychiatric illnesses.
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Objective: Geography may influence the relationships of predictors for suicidal ideation (SI) and suicide attempts (SA) in children and youth. Method: This is a nationwide retrospective cohort study of 124,424 individuals less than 25 years of age using commercial claims data (2011-2015) from the Health Care Cost Institute. Outcomes were time to SI or SA within 3 months after the indexed mental health or substance use disorder (MH/SUD) outpatient visit. Predictors included sociodemographic and clinical characteristics up to 3 years before the index event. Results: At each follow-up time period, rates of SI and SA varied by the US geographic division (p < .001), and the Mountain Division consistently had the highest rates for both SI and SA (5.44%-10.26% for SI; 0.70%-2.82% for SA). Having MH emergency department (ED) visits in the past year increased the risk of SI by 28% to 65% for individuals residing in the New England, Mid-Atlantic, East North Central, West North Central, and East South Central Divisions. The main effects of geographic divisions were significant for SA (p<0.001). Risk of SA was lower in New England, Mid-Atlantic, South Atlantic, and Pacific (hazard ratios = 0.57, 0.51, 0.67, and 0.79, respectively) and higher in the Mountain Division (hazard ratio = 1.46). Conclusion: To understand the underlying mechanisms driving the high prevalence of SI and SA in the Mountain Division and the elevated risk of SI after having MH ED visits, future research examining regional differences in risks for SI and SA should include indicators of access to MH ED care and other social determinants of health.
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Depression and anxiety are highly correlated, yet little is known about the course of each condition when presenting concurrently. This study aimed to identify longitudinal patterns and changes in depression and anxiety symptoms during antidepressant treatment, and evaluate clinical factors associated with each response pattern. Self-report Patient Health Questionnaire-9 (PHQ-9) and General Anxiety Disorder-7 (GAD-7) scores were used to track the courses of depression and anxiety respectively over a three-month window, and group-based trajectory modeling was used to derive subgroups of patients who have similar response patterns. Multinomial regression was used to associate various clinical variables with trajectory subgroup membership. Of the 577 included adults, 373 (64.6%) were women, and the mean age was 39.3 (SD: 12.9) years. Six depression and six anxiety trajectory subgroups were computationally derived; three depression subgroups demonstrated symptom improvement, and three exhibited nonresponse. Similar patterns were observed in the six anxiety subgroups. Factors associated with treatment nonresponse included higher pretreatment depression and anxiety severity and poorer sleep quality, while better overall health and younger age were associated with higher rates of remission. Synchronous and asynchronous paths to improvement were also observed between depression and anxiety. High baseline depression or anxiety severity alone may be an insufficient predictor of treatment nonresponse. These findings have the potential to motivate clinical strategies aimed at treating depression and anxiety simultaneously.
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BACKGROUND: Social rewards (e.g., social feedback, praise, and social interactions) are fundamental to social learning and relationships across the life span. Exposure to social rewards is linked to activation in key brain regions, that are impaired in major depression. This is the first summary of neuroimaging literature on social reward processing in depressed and healthy individuals. METHOD: We screened 409 studies and identified 25 investigating task-based fMRI activation during exposure to social stimuli in depressed and healthy populations across the lifespan. We conducted a systematic review followed by an Activation Likelihood Estimation (ALE) analysis of three main contrasts: a) positive social feedback vs. neutral stimuli; b) negative social feedback vs. neutral stimuli; c) positive vs. negative social feedback. We also compared activation patterns in depressed versus healthy controls. RESULTS: Systematic review revealed that social rewards elicit increased activation in subcortical reward regions (NAcc, amygdala, ventral striatum, thalamus) in healthy and depressed individuals; and decreased activation in prefrontal reward regions (medial prefrontal cortex, orbitofrontal cortex) among depressed persons. Our meta-analysis showed, in both depressed and healthy individuals, increased cluster activation of the putamen and caudate in response to negative social stimuli vs. positive stimuli. We also found increased cluster activation in the inferior frontal gyrus (IFG) and the medial frontal gyrus (MFG) in healthy controls vs. depressed individuals, in response to negative social stimuli. CONCLUSIONS: Processing of social stimuli elicits activation of key brain regions involved in affective and social information processing. Interventions for depression can increase social reward responsivity to improve outcomes.
Subject(s)
Depressive Disorder, Major , Longevity , Humans , Magnetic Resonance Imaging , Neuroimaging , Depressive Disorder, Major/diagnostic imaging , RewardABSTRACT
Positive valence systems are disrupted in late-life depression and in individuals at risk for suicide. The reward positivity (RewP) is an event-related potential measure of positive valence system function that relates to depression and anhedonia in children and young adults. However, it is unclear whether a reliable RewP signal can be elicited in middle-aged and older adults at high risk for suicide and, if so, whether this signal is similarly associated with clinical symptoms. In the current study, a RewP was elicited with a standard gambling task in middle-aged and older adults (N = 31) at discharge from a hospitalization for suicidal thought or behaviors. The resulting electrocortical response differed significantly for monetary wins compared to losses. Internal reliability of the RewP and the feedback negativity (FN) to monetary loss was good to excellent. Internal reliability of difference measures was lower but still largely acceptable, with residualized differences scores demonstrating stronger reliability than subtraction-based scores. A smaller residualized RewP, after accounting for the influence of the FN, was associated with greater severity of lassitude, an index of appetitive anhedonia. These findings set the groundwork for future studies of positive valence system function and depression in middle-aged and older adults at high risk for suicide.
Subject(s)
Electroencephalography , Suicide , Child , Young Adult , Middle Aged , Humans , Aged , Anhedonia/physiology , Reproducibility of Results , Evoked Potentials/physiology , RewardABSTRACT
BACKGROUND: A significant number of late middle-aged adults with depression have a high illness burden resulting from chronic conditions which put them at high risk of hospitalization. Many late middle-aged adults are covered by commercial health insurance, but such insurance claims have not been used to identify the risk of hospitalization in individuals with depression. In the present study, we developed and validated a non-proprietary model to identify late middle-aged adults with depression at risk for hospitalization, using machine learning methods. METHODS: This retrospective cohort study involved 71,682 commercially insured older adults aged 55-64 years diagnosed with depression. National health insurance claims were used to capture demographics, health care utilization, and health status during the base year. Health status was captured using 70 chronic health conditions, and 46 mental health conditions. The outcomes were 1- and 2-year preventable hospitalization. For each of our two outcomes, we evaluated seven modelling approaches: four prediction models utilized logistic regression with different combinations of predictors to evaluate the relative contribution of each group of variables, and three prediction models utilized machine learning approaches - logistic regression with LASSO penalty, random forests (RF), and gradient boosting machine (GBM). RESULTS: Our predictive model for 1-year hospitalization achieved an AUC of 0.803, with a sensitivity of 72% and a specificity of 76% under the optimum threshold of 0.463, and our predictive model for 2-year hospitalization achieved an AUC of 0.793, with a sensitivity of 76% and a specificity of 71% under the optimum threshold of 0.452. For predicting both 1-year and 2-year risk of preventable hospitalization, our best performing models utilized the machine learning approach of logistic regression with LASSO penalty which outperformed more black-box machine learning models like RF and GBM. CONCLUSIONS: Our study demonstrates the feasibility of identifying depressed middle-aged adults at higher risk of future hospitalization due to burden of chronic illnesses using basic demographic information and diagnosis codes recorded in health insurance claims. Identifying this population may assist health care planners in developing effective screening strategies and management approaches and in efficient allocation of public healthcare resources as this population transitions to publicly funded healthcare programs, e.g., Medicare in the US.
Subject(s)
Depression , Medicare , United States/epidemiology , Middle Aged , Humans , Aged , Depression/diagnosis , Depression/epidemiology , Retrospective Studies , Hospitalization , Risk AssessmentSubject(s)
Knowledge , Mental Disorders , Humans , Aged , Psychopathology , Mental Disorders/diagnostic imagingABSTRACT
BACKGROUND: Psychomotor disturbance is common in psychotic depression and is associated with relapse. In this analysis, we examined whether white matter microstructure is associated with relapse probability in psychotic depression and, if so, whether white matter microstructure accounts for the association between psychomotor disturbance and relapse. METHODS: We used tractography to characterize diffusion-weighted MRI data in 80 participants enrolled in a randomized clinical trial that compared efficacy and tolerability of sertraline plus olanzapine with sertraline plus placebo in the continuation treatment of remitted psychotic depression. Cox proportional hazard models tested the relationships between psychomotor disturbance (processing speed and CORE score) at baseline, white matter microstructure (fractional anisotropy [FA] and mean diffusivity [MD]) in 15 selected tracts at baseline, and relapse probability. RESULTS: CORE was significantly associated with relapse. Higher mean MD was significantly associated with relapse in the each of the following tracts: corpus callosum, left striato-frontal, left thalamo-frontal, and right thalamo-frontal. CORE and MD were each associated with relapse in the final models. LIMITATIONS: As a secondary analysis with a small sample size, this study was not powered for its aims, and is vulnerable to types I and II statistical errors. Further, the sample size was not sufficient to test the interaction of the independent variables and randomized treatment group with relapse probability. CONCLUSIONS: While both psychomotor disturbance and MD were associated with psychotic depression relapse, MD did not account for the relationship between psychomotor disturbance and relapse. The mechanism by which of psychomotor disturbance increases the risk of relapse requires further investigation. CLINICAL TRIAL REGISTRATION: Study of the Pharmacotherapy of Psychotic Depression II (STOP-PD II); NCT01427608. URL: https://clinicaltrials.gov/ct2/show/NCT01427608.
Subject(s)
Depressive Disorder, Major , Psychotic Disorders , White Matter , Humans , White Matter/diagnostic imaging , Sertraline/therapeutic use , Depression , Depressive Disorder, Major/diagnostic imaging , Depressive Disorder, Major/drug therapy , Psychotic Disorders/diagnostic imaging , Psychotic Disorders/drug therapy , Brain , AnisotropyABSTRACT
Importance: Approximately half of older adults with depression remain symptomatic at treatment end. Identifying discrete clinical profiles associated with treatment outcomes may guide development of personalized psychosocial interventions. Objective: To identify clinical subtypes of late-life depression and examine their depression trajectory during psychosocial interventions in older adults with depression. Design, Setting, and Participants: This prognostic study included older adults aged 60 years or older who had major depression and participated in 1 of 4 randomized clinical trials of psychosocial interventions for late-life depression. Participants were recruited from the community and outpatient services of Weill Cornell Medicine and the University of California, San Francisco, between March 2002 and April 2013. Data were analyzed from February 2019 to February 2023. Interventions: Participants received 8 to 14 sessions of (1) personalized intervention for patients with major depression and chronic obstructive pulmonary disease, (2) problem-solving therapy, (3) supportive therapy, or (4) active comparison conditions (treatment as usual or case management). Main Outcomes and Measures: The main outcome was the trajectory of depression severity, assessed using the Hamilton Depression Rating Scale (HAM-D). A data-driven, unsupervised, hierarchical clustering of HAM-D items at baseline was conducted to detect clusters of depressive symptoms. A bipartite network analysis was used to identify clinical subtypes at baseline, accounting for both between- and within-patient variability across domains of psychopathology, social support, cognitive impairment, and disability. The trajectories of depression severity in the identified subtypes were compared using mixed-effects models, and time to remission (HAM-D score ≤10) was compared using survival analysis. Results: The bipartite network analysis, which included 535 older adults with major depression (mean [SD] age, 72.7 [8.7] years; 70.7% female), identified 3 clinical subtypes: (1) individuals with severe depression and a large social network; (2) older, educated individuals experiencing strong social support and social interactions; and (3) individuals with disability. There was a significant difference in depression trajectories (F2,2976.9 = 9.4; P < .001) and remission rate (log-rank χ22 = 18.2; P < .001) across clinical subtypes. Subtype 2 had the steepest depression trajectory and highest likelihood of remission regardless of the intervention, while subtype 1 had the poorest depression trajectory. Conclusions and Relevance: In this prognostic study, bipartite network clustering identified 3 subtypes of late-life depression. Knowledge of patients' clinical characteristics may inform treatment selection. Identification of discrete subtypes of late-life depression may stimulate the development of novel, streamlined interventions targeting the clinical vulnerabilities of each subtype.
Subject(s)
Depression , Psychosocial Intervention , Humans , Female , Aged , Male , Depression/therapy , Psychotherapy , Treatment Outcome , PrognosisABSTRACT
The effect of antipsychotic medication on resting state functional connectivity in major depressive disorder (MDD) is currently unknown. To address this gap, we examined patients with MDD with psychotic features (MDDPsy) participating in the Study of the Pharmacotherapy of Psychotic Depression II. All participants were treated with sertraline plus olanzapine and were subsequently randomized to continue sertraline plus olanzapine or be switched to sertraline plus placebo. Participants completed an MRI at randomization and at study endpoint (study completion at Week 36, relapse, or early termination). The primary outcome was change in functional connectivity measured within and between specified networks and the rest of the brain. The secondary outcome was change in network topology measured by graph metrics. Eighty-eight participants completed a baseline scan; 73 completed a follow-up scan, of which 58 were usable for analyses. There was a significant treatment X time interaction for functional connectivity between the secondary visual network and rest of the brain (t = -3.684; p = 0.0004; pFDR = 0.0111). There was no significant treatment X time interaction for graph metrics. Overall, functional connectivity between the secondary visual network and the rest of the brain did not change in participants who stayed on olanzapine but decreased in those switched to placebo. There were no differences in changes in network topology measures when patients stayed on olanzapine or switched to placebo. This suggests that olanzapine may stabilize functional connectivity, particularly between the secondary visual network and the rest of the brain.
Subject(s)
Antipsychotic Agents , Depressive Disorder, Major , Humans , Antipsychotic Agents/therapeutic use , Depressive Disorder, Major/drug therapy , Olanzapine/therapeutic use , Sertraline/therapeutic use , Benzodiazepines , Drug Therapy, Combination , Magnetic Resonance ImagingABSTRACT
INTRODUCTION: Little is known regarding genetic factors associated with treatment outcome of psychotic depression. We explored genomic associations of remission and relapse of psychotic depression treated with pharmacotherapy. METHODS: Genomic analyses were performed in 171 men and women aged 18-85 years with an episode of psychotic depression who participated in the Study of the Pharmacotherapy of Psychotic Depression II (STOP-PD II). Participants were treated with open-label sertraline plus olanzapine for up to 12 weeks; those who achieved remission or near-remission and maintained it following 8 weeks of stabilization were eligible to participate in a 36-week randomized controlled trial that compared sertraline plus olanzapine with sertraline plus placebo in preventing relapse. RESULTS: There were no genome-wide significant associations with either remission or relapse. However, at a suggestive threshold, SNP rs1026501 (31 kb from SYNPO2) in the whole sample and rs6844137 (within the intronic region of SYNPO2) in the European ancestry subsample were associated with a decreased likelihood of remission. In polygenic risk analyses, participants who had greater improvement after antidepressant treatments showed a higher likelihood of reaching remission. Those who achieved remission and had a higher polygenic risk for Alzheimer's disease had a significantly decreased likelihood of relapse. CONCLUSION: Our analyses provide preliminary insights into the genetic architecture of remission and relapse in a well-characterized group of patients with psychotic depression.
Subject(s)
Antipsychotic Agents , Sertraline , Male , Humans , Female , Olanzapine/therapeutic use , Sertraline/therapeutic use , Depression , Antipsychotic Agents/therapeutic use , Benzodiazepines/therapeutic use , Drug Therapy, Combination , Treatment Outcome , Genomics , Double-Blind MethodABSTRACT
We used US nationwide commercial insurance claims data (2011-2015) to study the effect of social deprivation on clinical and demographic risk factors for suicidal ideation (SI) and suicide attempts (SA) among US youth and adults < 65 years, after having a mental health or substance use disorder-related outpatient encounter. Neighborhood social deprivation level was summarized by the quintile of social deprivation index (SDI) at individuals' zip code level. Cox proportional hazard models were used to evaluate the effect of social deprivation on demographic and clinical risk factors for SI and SA. The study cohort consisted of 317,383 individuals < 65 years, with 124,424 aged < 25 (youth) and 192,959 aged between 25 and 64 (adults). Neighborhood social deprivation impacted risk factors for SI and SA differently for youth and adults. Among youth, SDI interacted with multiple risk factors for both SI and SA. The effects of the risk factors were larger on youth from middle socioeconomic neighborhoods. Among adults, risk of SI was the strongest in the most deprived neighborhoods, but risk of SA did not vary by neighborhood deprivation level. Our findings suggest community-based suicide prevention initiatives should be tailored according to neighborhood deprivation level and the targeted individual's age to maximize the impact.