Psychological Medicine最新文献

Background: To evaluate the clinical efficacy of COMPASS, a therapist-supported digital therapeutic for reducing psychological distress (anxiety/depression) in people living with long-term physical health conditions (LTCs).

Methods: A two-armed randomized-controlled trial recruiting from LTC charities. Participants with anxiety and/or depression symptoms related to their LTC(s) were randomized (concealed allocation via independent administrator) to COMPASS (access to 11 tailored modules plus five thirty-minute therapist support sessions) or standard charity support (SCS). Assessments were completed online pre-randomization, at 6- and 12-weeks post-randomization. Primary outcome was Patient Health Questionnaire Anxiety and Depression Scale; PHQ-ADS measured at 12-weeks. Analysis used intention-to-treat principles with adjusted mean differences estimated using linear mixed-effects models. Data-analyst was blinded to group allocation.

Results: 194 participants were randomized to COMPASS (N = 94) or SCS (N = 100). At 12-weeks, mean level of psychological distress was 6.82 (95% confidence interval; CI 4.55-9.10) points lower (p < 0.001) in the COMPASS arm compared with SCS (standardized mean difference of 0.71 (95% CI 0.48-0.95)). The COMPASS arm also showed moderate significant treatment effects on secondary outcomes including depression, anxiety and illness-related distress and small significant effects on functioning and quality-of-life. Rates of adverse events were comparable across the arms. Deterioration in distress at 12-weeks was observed in 2.2% of the SCS arm, and no participants in the COMPASS arm.

Conclusion: Compared with SCS, COMPASS digital therapeutic with minimal therapist input reduces psychological distress at post-treatment (12-weeks). COMPASS offers a potentially scalable implementation model for health services but its translation to these contexts needs further evaluating.

Trial registration: NCT04535778.

Precision psychiatry is an emerging field that aims to provide individualized approaches to mental health care. An important strategy to achieve this precision is to reduce uncertainty about prognosis and treatment response. Multivariate analysis and machine learning are used to create outcome prediction models based on clinical data such as demographics, symptom assessments, genetic information, and brain imaging. While much emphasis has been placed on technical innovation, the complex and varied nature of mental health presents significant challenges to the successful implementation of these models. From this perspective, I review ten challenges in the field of precision psychiatry, including the need for studies on real-world populations and realistic clinical outcome definitions, and consideration of treatment-related factors such as placebo effects and non-adherence to prescriptions. Fairness, prospective validation in comparison to current practice and implementation studies of prediction models are other key issues that are currently understudied. A shift is proposed from retrospective studies based on linear and static concepts of disease towards prospective research that considers the importance of contextual factors and the dynamic and complex nature of mental health.

Background: Elucidation of the interaction of biological and psychosocial/environmental factors on opioid dependence (OD) risk can inform our understanding of the etiology of OD. We examined the role of psychosocial/environmental factors in moderating polygenic risk for opioid use disorder (OUD).

Methods: Data from 1958 European ancestry adults who participated in the Yale-Penn 3 study were analyzed. Polygenic risk scores (PRS) were based on a large-scale multi-trait analysis of genome-wide association studies (MTAG) of OUD.

Results: A total of 420 (21.1%) individuals had a lifetime diagnosis of OD. OUD PRS were positively associated with OD (odds ratio [OR] 1.42, 95% confidence interval [CI] 1.21-1.66). Household income and education were the strongest correlates of OD. Among individuals with higher OUD PRS, those with higher education level had lower odds of OD (OR 0.92, 95% CI 0.85-0.98); and those with posttraumatic stress disorder (PTSD) were more likely to have OD relative to those without PTSD (OR 1.56, 95% CI 1.04-2.35).

Conclusions: Results suggest an interplay between genetics and psychosocial environment in contributing to OD risk. While PRS alone do not yet have useful clinical predictive utility, psychosocial factors may help enhance prediction. These findings could inform more targeted clinical and policy interventions to help address this public health crisis.

Background: Obsessive-compulsive disorder (OCD) is thought to arise from dysconnectivity among interlinked brain regions resulting in a wide spectrum of clinical manifestations. Cortical gyrification, a key morphological feature of human cerebral cortex, has been considered associated with developmental connectivity in early life. Monitoring cortical gyrification alterations may provide new insights into the developmental pathogenesis of OCD.

Methods: Sixty-two medication-naive patients with OCD and 59 healthy controls (HCs) were included in this study. Local gyrification index (LGI) was extracted from T1-weighted MRI data to identify the gyrification changes in OCD. Total distortion (splay, bend, or twist of fibers) was calculated using diffusion-weighted MRI data to examine the changes in white matter microstructure in patients with OCD.

Results: Compared with HCs, patients with OCD showed significantly increased LGI in bilateral medial frontal gyrus and the right precuneus, where the mean LGI was positively correlated with anxiety score. Patients with OCD also showed significantly decreased total distortion in the body, genu, and splenium of the corpus callosum (CC), where the average distortion was negatively correlated with anxiety scores. Intriguingly, the mean LGI of the affected cortical regions was significantly correlated with the mean distortion of the affected white matter tracts in patients with OCD.

Conclusions: We demonstrated associations among increased LGI, aberrant white matter geometry, and higher anxiety in patients with OCD. Our findings indicate that developmental dysconnectivity-driven alterations in cortical folding are one of the neural substrates underlying the clinical manifestations of OCD.

Background: Fatigue is a central feature of myalgic encephalomyelitis or chronic fatigue syndrome (ME/CFS), but many ME/CFS patients also report comorbid pain symptoms. It remains unclear whether these symptoms are related to similar or dissociable brain networks. This study used resting-state fMRI to disentangle networks associated with fatigue and pain symptoms in ME/CFS patients, and to link changes in those networks to clinical improvements following cognitive behavioral therapy (CBT).

Methods: Relationships between pain and fatigue symptoms and cortico-cortical connectivity were assessed within ME/CFS patients at baseline (N = 72) and after CBT (N = 33) and waiting list (WL, N = 18) and compared to healthy controls (HC, N = 29). The analyses focused on four networks previously associated with pain and/or fatigue, i.e. the fronto-parietal network (FPN), premotor network (PMN), somatomotor network (SMN), and default mode network (DMN).

Results: At baseline, variation in pain and fatigue symptoms related to partially dissociable brain networks. Fatigue was associated with higher SMN-PMN connectivity and lower SMN-DMN connectivity. Pain was associated with lower PMN-DMN connectivity. CBT improved SMN-DMN connectivity, compared to WL. Larger clinical improvements were associated with larger increases in frontal SMN-DMN connectivity. No CBT effects were observed for PMN-DMN or SMN-PMN connectivity.

Conclusions: These results provide insight into the dissociable neural mechanisms underlying fatigue and pain symptoms in ME/CFS and how they are affected by CBT in successfully treated patients. Further investigation of how and in whom behavioral and biomedical treatments affect these networks is warranted to improve and individualize existing or new treatments for ME/CFS.

Background: Depression is a common mental health disorder that often starts during adolescence, with potentially important future consequences including 'Not in Education, Employment or Training' (NEET) status.

Methods: We took a structured life course modeling approach to examine how depressive symptoms during adolescence might be associated with later NEET status, using a high-quality longitudinal data resource. We considered four plausible life course models: (1) an early adolescent sensitive period model where depressive symptoms in early adolescence are more associated with later NEET status relative to exposure at other stages; (2) a mid adolescent sensitive period model where depressive symptoms during the transition from compulsory education to adult life might be more deleterious regarding NEET status; (3) a late adolescent sensitive period model, meaning that depressive symptoms around the time when most adults have completed their education and started their careers are the most strongly associated with NEET status; and (4) an accumulation of risk model which highlights the importance of chronicity of symptoms.

Results: Our analysis sample included participants with full information on NEET status (N = 3951), and the results supported the accumulation of risk model, showing that the odds of NEET increase by 1.015 (95% CI 1.012-1.019) for an increase of 1 unit in depression at any age between 11 and 24 years.

Conclusions: Given the adverse implications of NEET status, our results emphasize the importance of supporting mental health during adolescence and early adulthood, as well as considering specific needs of young people with re-occurring depressed mood.

Background: Bipolar disorder (BD) shows heterogeneous illness presentation both cross-sectionally and longitudinally. This phenotypic heterogeneity might reflect underlying genetic heterogeneity. At the same time, overlapping characteristics between BD and other psychiatric illnesses are observed at clinical and biomarker levels, which implies a shared biological mechanism between them. Incorporating these two issues in a single study design, this study investigated whether phenotypically heterogeneous subtypes of BD have a distinct polygenic basis shared with other psychiatric illnesses.

Methods: Six lifetime phenotype dimensions of BD identified in our previous study were used as target phenotypes. Associations between these phenotype dimensions and polygenic risk scores (PRSs) of major psychiatric illnesses from East Asian (EA) and other available populations were analyzed.

Results: Each phenotype dimension showed a different association pattern with PRSs of mental illnesses. PRS for EA schizophrenia showed a significant negative association with the cyclicity dimension (p = 0.044) but a significant positive association with the psychotic/irritable mania dimension (p = 0.001). PRS of EA major depressive disorder demonstrated a significant negative association with the elation dimension (p = 0.003) but a significant positive association with the comorbidity dimension (p = 0.028).

Conclusion: This study demonstrates that well-defined phenotype dimensions of lifetime-basis in BD have distinct genetic risks shared with other major mental illnesses. This finding supports genetic heterogeneity in BD and suggests a pleiotropy among BD subtypes and other psychiatric disorders beyond BD. Further genomic analyses adopting deep phenotyping across mental illnesses in ancestrally diverse populations are warranted to clarify intra-diagnosis heterogeneity and inter-diagnoses commonality issues in psychiatry.

Background: Evidence suggests a possible relationship between exposure to childhood adversity (CA) and functional impairment in psychosis. However, the impact of CA on long-term outcomes of psychotic disorders remains poorly understood.

Methods: Two hundred and forty-three patients were assessed at their first episode of psychosis for CA and re-assessed after a mean of 21 years of follow-up for several outcome domains, including symptoms, functioning, quality of life, cognitive performance, neurological dysfunction, and comorbidity. The unique predictive ability of CA exposure for outcomes was examined using linear regression analysis controlling for relevant confounders, including socioeconomic status, family risk of schizophrenia, and obstetric complications.

Results: There were 54% of the patients with a documented history of CA at mild or higher levels. CA experiences were more prevalent and severe in schizophrenia than in other psychotic disorders (p < 0.001). Large to very large effect sizes were observed for CA predicting most role functioning variables and negative symptoms (ΔR² between 0.105 and 0.181). Moderate effect sizes were observed for positive symptoms, personal functioning, impaired social cognition, impaired immediate verbal learning, poor global cognition, internalized stigma, poor personal recovery, and drug abuse severity (ΔR² between 0.040 and 0.066). A dose-response relationship was observed between levels of CA and severity of outcome domains.

Conclusion: Our results suggest a strong and widespread link between early adversity exposure and outcomes of psychotic disorders. Awareness of the serious long-term consequences of CA should encourage better identification of those at risk and the development of effective interventions.

Background: Mood disorders are characterized by great heterogeneity in clinical manifestation. Uncovering such heterogeneity using neuroimaging-based individual biomarkers, clinical behaviors, and genetic risks, might contribute to elucidating the etiology of these diseases and support precision medicine.

Methods: We recruited 174 drug-naïve and drug-free patients with major depressive disorder and bipolar disorder, as well as 404 healthy controls. T1 MRI imaging data, clinical symptoms, and neurocognitive assessments, and genetics were obtained and analyzed. We applied regional gray matter volumes (GMV) and quantile normative modeling to create maturation curves, and then calculated individual deviations to identify subtypes within the patients using hierarchical clustering. We compared the between-subtype differences in GMV deviations, clinical behaviors, cell-specific transcriptomic associations, and polygenic risk scores. We also validated the GMV deviations based subtyping analysis in a replication cohort.

Results: Two subtypes emerged: subtype 1, characterized by increased GMV deviations in the frontal cortex, cognitive impairment, a higher genetic risk for Alzheimer's disease, and transcriptionally associated with Alzheimer's disease pathways, oligodendrocytes, and endothelial cells; and subtype 2, displaying globally decreased GMV deviations, more severe depressive symptoms, increased genetic vulnerability to major depressive disorder and transcriptionally related to microglia and inhibitory neurons. The distinct patterns of GMV deviations in the frontal, cingulate, and primary motor cortices between subtypes were shown to be replicable.

Conclusions: Our current results provide vital links between MRI-derived phenotypes, spatial transcriptome, genetic vulnerability, and clinical manifestation, and uncover the heterogeneity of mood disorders in biological and behavioral terms.