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Whereas genetic susceptibility increases the risk for major depressive disorder (MDD), non-genetic protective factors may mitigate this risk. In a large-scale prospective study of US Army soldiers, we examined whether trait resilience and/or unit cohesion could protect against the onset of MDD following combat deployment, even in soldiers at high polygenic risk.
Data were analyzed from 3079 soldiers of European ancestry assessed before and after their deployment to Afghanistan. Incident MDD was defined as no MDD episode at pre-deployment, followed by a MDD episode following deployment. Polygenic risk scores were constructed from a large-scale genome-wide association study of major depression. We first examined the main effects of the MDD PRS and each protective factor on incident MDD. We then tested the effects of each protective factor on incident MDD across strata of polygenic risk.
Polygenic risk showed a dose–response relationship to depression, such that soldiers at high polygenic risk had greatest odds for incident MDD. Both unit cohesion and trait resilience were prospectively associated with reduced risk for incident MDD. Notably, the protective effect of unit cohesion persisted even in soldiers at highest polygenic risk.
Polygenic risk was associated with new-onset MDD in deployed soldiers. However, unit cohesion – an index of perceived support and morale – was protective against incident MDD even among those at highest genetic risk, and may represent a potent target for promoting resilience in vulnerable soldiers. Findings illustrate the value of combining genomic and environmental data in a prospective design to identify robust protective factors for mental health.
Retrospective reports of lifetime experience with mental disorders greatly underestimate the actual experiences of disorder because recall error biases reporting of earlier life symptoms downward. This fundamental obstacle to accurate reporting has many adverse consequences for the study and treatment of mental disorders. Better tools for accurate retrospective reporting of mental disorder symptoms have the potential for broad scientific benefits.
We designed a life history calendar (LHC) to support this task, and randomized more than 1000 individuals to each arm of a retrospective diagnostic interview with and without the LHC. We also conducted a careful validation with the Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition.
Results demonstrate that—just as with frequent measurement longitudinal studies—use of an LHC in retrospective measurement can more than double reports of lifetime experience of some mental disorders.
The LHC significantly improves retrospective reporting of mental disorders. This tool is practical for application in both large cross-sectional surveys of the general population and clinical intake of new patients.
Axis IV is for reporting ‘psychosocial and environmental problems that may affect the diagnosis, treatment and prognosis of mental disorders’. No studies have examined the prognostic value of Axis IV in DSM-IV.
We analyzed data from 2497 participants in the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) with major depressive episode (MDE). We hypothesized that psychosocial stressors predict a poor prognosis of MDE. Secondarily, we hypothesized that psychosocial stressors predict a poor prognosis of anxiety and substance use disorders. Stressors were defined according to DSM-IV's taxonomy, and empirically using latent class analysis (LCA).
Primary support group problems, occupational problems and childhood adversity increased the risks of depressive episodes and suicidal ideation by 20–30%. Associations of the empirically derived classes of stressors with depression were larger in magnitude. Economic stressors conferred a 1.5-fold increase in risk for a depressive episode [95% confidence interval (CI) 1.2–1.9]; financial and interpersonal instability conferred a 1.3-fold increased risk of recurrent depression (95% CI 1.1–1.6). These two classes of stressors also predicted the recurrence of anxiety and substance use disorders. Stressors were not related to suicidal ideation independent from depression severity.
Psychosocial and environmental problems are associated with the prognosis of MDE and other Axis I disorders. Although DSM-IV's taxonomy of stressors stands to be improved, these results provide empirical support for the prognostic value of Axis IV. Future work is needed to determine the reliability of Axis IV assessments in clinical practice, and the usefulness of this information to improving the clinical course of mental disorders.
Electronic medical records (EMR) provide a unique opportunity for efficient, large-scale clinical investigation in psychiatry. However, such studies will require development of tools to define treatment outcome.
Natural language processing (NLP) was applied to classify notes from 127 504 patients with a billing diagnosis of major depressive disorder, drawn from out-patient psychiatry practices affiliated with multiple, large New England hospitals. Classifications were compared with results using billing data (ICD-9 codes) alone and to a clinical gold standard based on chart review by a panel of senior clinicians. These cross-sectional classifications were then used to define longitudinal treatment outcomes, which were compared with a clinician-rated gold standard.
Models incorporating NLP were superior to those relying on billing data alone for classifying current mood state (area under receiver operating characteristic curve of 0.85–0.88 v. 0.54–0.55). When these cross-sectional visits were integrated to define longitudinal outcomes and incorporate treatment data, 15% of the cohort remitted with a single antidepressant treatment, while 13% were identified as failing to remit despite at least two antidepressant trials. Non-remitting patients were more likely to be non-Caucasian (p<0.001).
The application of bioinformatics tools such as NLP should enable accurate and efficient determination of longitudinal outcomes, enabling existing EMR data to be applied to clinical research, including biomarker investigations. Continued development will be required to better address moderators of outcome such as adherence and co-morbidity.
Some personality characteristics have previously been associated with an increased risk for psychiatric disorder. Longitudinal studies are required in order to tease apart temporary (state) and enduring (trait) differences in personality among individuals with bipolar disorder (BD). This study aimed to determine whether there is a characteristic personality profile in BD, and whether associations between BD and personality are best explained by state or trait effects.
A total of 2247 participants in the Systematic Treatment Enhancement Program for Bipolar Disorder study completed the NEO Five-Factor Inventory administered at study entry, and at 1 and 2 years.
Personality in BD was characterized by high neuroticism (N) and openness (O), and low agreeableness (A), conscientiousness (C) and extraversion (E). This profile was replicated in two independent samples, and openness was found to distinguish BD from major depressive disorder. Latent growth modeling demonstrated that manic symptoms were associated with increased E and decreased A, and depressed symptoms with higher N and lower E, A, C and O. During euthymic phases, high N and low E scores predicted a future depression-prone course.
While there are clear state effects of mood on self-reported personality, personality variables during euthymia predict future course of illness. Personality disturbances in extraversion, neuroticism and openness may be enduring characteristics of patients with BD.
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