Psychotherapy and Psychosomatics最新文献

Introduction: Depression frequently co-occurs with psychosis and is associated with poor outcomes. Early identification of patients at risk of persistent depression remains challenging, limiting opportunities for stratified treatment planning. This study aimed to evaluate the transdiagnostic generalizability of machine learning (ML) models predicting depressive episodes across the affective-psychotic spectrum and whether model-informed predictions could identify patients who may benefit from antidepressant treatment.

Methods: Support vector machine models were trained to predict depressive episodes within 6 months using clinical and physiological data from two large, multisite first-episode psychosis (FEP) trials: EUFEST (n=447) and RAISE-ETP (n=288), totalling 735 participants. A nested cross-validation framework was used to evaluate model performance. Generalizability was tested in psychotic depression (PD) patients from the STOP-PD trial (n=142), which compared olanzapine plus sertraline versus olanzapine plus placebo.

Results: Models predicted depressive episodes in the FEP sample with a balanced accuracy (BAC) of 69% (sensitivity: 65.7%, specificity: 72.4%). When applied to STOP-PD patients treated with olanzapine plus placebo, FEP-trained models achieved a BAC of 65.2% (sensitivity: 58.3%, specificity: 72.0%) in predicting 3-month non-remission. In the olanzapine plus sertraline group, predictions were at chance levels (BAC: 47.2%, sensitivity: 48.4%, specificity: 46.0%), reflecting sertraline's therapeutic effects.

Conclusion: ML models can identify shared risk signatures for depression across the psychosis-affective spectrum. Patterns of depressive episodes in FEP patients share predictive features with PD patients not receiving antidepressants, while adjunctive antidepressant treatment improves remission outcomes beyond model expectations. These findings support ML-informed treatment stratification to identify patients unlikely to benefit from antipsychotic monotherapy.

Introduction: Research has consistently shown associations between elevated impulsivity and adverse physical and mental health outcomes. Specifically, impulsivity has been associated with higher body mass index and cardiometabolic issues. However, evidence from randomized clinical trials (RCTs) on whether impulsivity is responsive to a multidomain lifestyle intervention in older adults with overweight or obesity who are at high cardiovascular risk is limited.

Methods: Participants from the PREDIMED-Plus-Cognition sub-study, followed for 3 years as part of the PREDIMED-Plus RCT, were included. Eligible participants had overweight or obesity and metabolic syndrome at baseline. Participants were randomly allocated to either an intensive intervention group (IIG), with an energy-restricted Mediterranean diet (MedDiet), physical activity, and behavioral support, or to a control group (CG), advised to follow an ad libitum MedDiet. The primary outcome of the current study was the evaluation of impulsivity, which was only assessed in participants from the PREDIMED-Plus-Cognition sub-study. Trait impulsivity was assessed using the UPPS-P Impulsive Behavior Scale and behavioral impulsivity through 4 neuropsychological evaluations at baseline, 1-year, and 3-year follow-ups. Z-score composites for Global, Trait, and Behavioral Impulsivity were estimated. Multivariate linear mixed models were used to assess the effect of the PREDIMED-Plus intervention on impulsivity among the participants who participated in the PREDIMED-Plus-Cognition sub-study.

Results: A total of 306 participants (CG: n=156; IIG: n=150; mean age [SD]: 65.0 [4.7] years; 45.7% female) had impulsivity data at baseline. Compared to the CG, participants in the IIG exhibited decreases in Trait Impulsivity at 1 year (mean z-score change [95%CI]: -0.26 [-0.49, -0.03]), and in Global (-0.28 [-0.52, -0.03]) and Behavioral (-0.28 [-0.54, -0.02]) Impulsivity at 3-year follow-up.

Conclusion: An intensive lifestyle intervention combining MedDiet, physical activity, and behavioral support resulted in a long-term reduction of impulsivity among older adults at high cardiometabolic risk. Public health guidelines may consider multidomain lifestyle intervention strategies to decrease impulsivity.

Introduction: Many patients at risk for suicide present in primary care where insomnia and other suicide risk factors are prevalent. Addressing insomnia in this milieu may augment suicide prevention strategies.

Methods: A randomized clinical trial was conducted to test the effectiveness of brief cognitive behavioral therapy for insomnia (bCBTi) for primary care patients at risk for suicide recruited from three U.S. Veterans Health Administration practices. The 194 participants (77% male; mean age 51.5+/-13.6 years) with insomnia, co-occurring depression and/or posttraumatic stress disorder (PTSD) and recent death or suicide ideation were randomized to receive four 30-minute sessions of bCBTi or Sleep Hygiene over six weeks. Blinded assessments occurred every 6 weeks from baseline to 30 weeks. Change in Insomnia Severity Index (ISI) across 30 weeks was the primary outcome with co-secondary outcomes measured by the Scale for Suicidal Ideation (SSI), Patient Health Questionnaire-9 (PHQ-9) and PTSD Checklist (PCL).

Results: Mixed level modeling showed a significant condition effect at posttreatment (6-weeks) for ISI favoring bCBTi (t(df) = -4.58(153); p < .0001) which was maintained through 30-weeks (t(df) = 0.35(141); p >.05). PHQ-9 was lower at 6 weeks in the bCBTi condition (t(df) = -2.07(155); p = .04) maintained through 30 weeks (t(df) = -0.47(137); p > .05). No PCL differences and no difference in SSI at posttreatment. However, a significant timexcondition effect from 6 to 30 weeks existed for SSI favoring bCBTi (t(df) = -2.60(118); p = 0.01).

Conclusion: The bCBTi intervention designed for primary care settings effectively reduces insomnia severity with modest effects for depression and suicidal thoughts, offering an adjuvant to suicide prevention strategies.

Introduction: Depression and diabetes often co-occur and worsen clinical outcomes of both conditions. However, mortality risk among depression patients with diabetes exposed to antidepressant is understudied. We investigated whether antidepressant would decrease mortality risk in people with depression and incident diabetes.

Methods: This population-based cohort study identified 11,137 depression patients with incident type 2 diabetes between 2002 and 2021 in Hong Kong who were exposed to antidepressants, using territory-wide electronic medical-record database. Association between antidepressant exposure and mortality risk was analyzed by Cox proportional-hazards models for any antidepressant, specific drug classes, and individual agents, with stratified analysis by HbA1c level. A comprehensive array of covariates, including age, sex, calendar-year period, catchment-area, preexisting physical comorbidities, diabetic complications, substance/alcohol use disorders, cardiovascular/antidiabetes medications, and presence of antidepressants other than the specified drug was adjusted. Three sets of sensitivity analyses were conducted by restricting to patients (a) with cumulative drug exposure ≥90 days and ≥180 days, (b) with medication-possession ratio ≥80%, and (c) monotherapy.

Results: Lower risk of all-cause mortality was associated with exposure to any antidepressant (hazard ratio 0.79, 95% confidence interval 0.70-0.90) compared with no antidepressant in depression patients with incident diabetes. Lower mortality risk was associated with exposure to noradrenergic and specific-serotonergic antidepressants (0.77 [0.66-0.90]) compared with no antidepressant, and to mirtazapine (0.76 [0.65-0.88]) and trazodone (0.75 [0.63-0.90]). Sensitivity analyses affirmed that lower mortality risk was associated with mirtazapine.

Conclusion: Depression patients with comorbid type 2 diabetes with exposure to several antidepressant are at decreased mortality risk. Further research is warranted to confirm our findings and clarify the mortality-reducing mechanisms of antidepressant in this vulnerable population.