Association of Mental Disorders With Subsequent Chronic Physical Conditions: World Mental Health Surveys From 17 Countries

IMPORTANCE

It is clear that mental disorders in treatment settings are associated with a higher incidence of chronic physical conditions, but whether this is true of mental disorders in the community, and how generalized (across a range of physical health outcomes) these associations are, is less clear. This information has important implications for mental health care and the primary prevention of chronic physical disease.

OBJECTIVE

To investigate associations of 16 temporally prior DSM-IV mental disorders with the subsequent onset or diagnosis of 10 chronic physical conditions.

DESIGN, SETTING, AND PARTICIPANTS

Eighteen face-to-face, cross-sectional household surveys of community-dwelling adults were conducted in 17 countries (47,609 individuals; 2,032,942 person-years) from January 1, 2001, to December 31, 2011. The Composite International Diagnostic Interview was used to retrospectively assess the lifetime prevalence and age at onset of DSM-IV-identified mental disorders. Data analysis was performed from January 3, 2012, to September 30, 2015.

MAIN OUTCOMES AND MEASURES

Lifetime history of physical conditions was ascertained via self-report of physician's diagnosis and year of onset or diagnosis. Survival analyses estimated the associations of temporally prior first onset of mental disorders with subsequent onset or diagnosis of physical conditions.

RESULTS

Most associations between 16 mental disorders and subsequent onset or diagnosis of 10 physical conditions were statistically significant, with odds ratios (ORs) (95% CIs) ranging from 1.2 (1.0-1.5) to 3.6 (2.0-6.6). The associations were attenuated after adjustment for mental disorder comorbidity, but mood, anxiety, substance use, and impulse control disorders remained significantly associated with onset of between 7 and all 10 of the physical conditions (ORs [95% CIs] from 1.2 [1.1-1.3] to 2.0 [1.4-2.8]). An increasing number of mental disorders experienced over the life course was significantly associated with increasing odds of onset or diagnosis of all 10 types of physical conditions, with ORs (95% CIs) for 1 mental disorder ranging from 1.3 (1.1-1.6) to 1.8 (1.4-2.2) and ORs (95% CIs) for 5 or more mental disorders ranging from 1.9 (1.4-2.7) to 4.0 (2.5-6.5). In population-attributable risk estimates, specific mental disorders were associated with 1.5% to 13.3% of physical condition onsets.

CONCLUSIONS AND RELEVANCE

These findings suggest that mental disorders of all kinds are associated with an increased risk of onset of a wide range of chronic physical conditions. Current efforts to improve the physical health of individuals with mental disorders may be too narrowly focused on the small group with the most severe mental disorders. Interventions aimed at the primary prevention of chronic physical diseases should optimally be integrated into treatment of all mental disorders in primary and secondary care from early in the disorder course.

The identification of symptom-based subtypes of depression: A nationally representative cohort study

BACKGROUND

In recent years, researchers have used various techniques to elucidate the heterogeneity in depressive symptoms. This study seeks to resolve the extent to which variations in depression reflect qualitative differences between symptom categories and/or quantitative differences in severity.

METHODS

Data were used from the Netherlands Mental Health Survey and Incidence Study-2, a nationally representative face-to-face survey of the adult general population. In a subsample of respondents with a lifetime key symptom of depression at baseline and who participated in the first two waves (n=1388), symptom profiles at baseline were based on symptoms reported during their worst lifetime depressive episode. Depressive symptoms and DSM-IV diagnoses were assessed with the Composite International Diagnostic Interview 3.0. Three latent variable techniques (latent class analysis, factor analysis, factor mixture modelling) were used to identify the best subtyping model.

RESULTS

A latent class analysis, adjusted for local dependence between weight change and appetite change, described the data best and resulted in four distinct depressive subtypes: severe depression with anxiety (28.0%), moderate depression with anxiety (29.3%), moderate depression without anxiety (23.6%) and mild depression (19.0%). These classes showed corresponding clinical correlates at baseline and corresponding course and outcome indicators at follow-up (i.e., class severity was linked to lifetime mental disorders at baseline, and service use for mental health problems and current disability at follow-up).

LIMITATIONS

Although the sample was representative of the population on most parameters, the findings are not generalisable to the most severely affected depressed patients.

CONCLUSIONS

Depression could best be described in terms of both qualitative differences between symptom categories and quantitative differences in severity. In particular anxiety was a distinguishing feature within moderate depression. This study stresses the central position anxiety occupies in the concept of depression.

Predicting long-term depression outcome using a three-mode principal component model for depression heterogeneity

BACKGROUND

Depression heterogeneity has hampered development of adequate prognostic models. Therefore, more homogeneous clinical entities (e.g. dimensions, subtypes) have been developed, but their differentiating potential is limited because neither captures all relevant variation across persons, symptoms and time. To address this, three-mode Principal Component Analysis (3MPCA) was previously applied to capture person-, symptom- and time-level variation in a single model (Monden et al., 2015). This study evaluated the added prognostic value of such an integrated model for longer-term depression outcomes.

METHODS

The Beck Depression Inventory (BDI) was administered quarterly for two years to major depressive disorder outpatients participating in a randomized controlled trial. A previously developed 3MPCA model decomposed the data into 2 symptom-components ('somatic-affective', 'cognitive'), 2 time-components ('recovering', 'persisting') and 3 person-components ('severe non-persisting depression', 'somatic depression' and 'cognitive depression'). The predictive value of the 3MPCA model for BDI scores at 3-year (n=136) and 11-year follow-up (n=145) was compared with traditional latent variable models and traditional prognostic factors (e.g. baseline BDI component scores, personality).

RESULTS

3MPCA components predicted 41% and 36% of the BDI variance at 3- and 11-year follow-up, respectively. A latent class model, growth mixture model and other known prognostic variables predicted 4-32% and 3-24% of the BDI variance at 3- and 11-year follow-up, respectively.

LIMITATIONS

Only primary care patients were included. There was no independent validation sample.

CONCLUSION

Accounting for depression heterogeneity at the person-, symptom- and time-level improves longer-term predictions of depression severity, underlining the potential of this approach for developing better prognostic models.

Using State Space Methods to Reveal Dynamical Associations Between Cortisol and Depression

Despite extensive research, the link between etiological factors and depression remains poorly understood. This may in part be due to a focus on strictly linear definitions of causality, derived at the group level. However, etiological relations in depression are likely to be dynamical, nonlinear and potentially unquantifiable with traditional statistics. Therefore the aim of this study was to evaluate the use of the convergent cross-mapping (CCM) method in investigating possible nonlinear relationships between supposed etiological factors and depressive symptomatology. Time series data from six healthy individuals were used to model the relationship between 24-h urinary free cortisol and negative affect using CCM and dewdrop embeddings. CCM is a nonlinear measure of causality, based on state space reconstruction with lagged coordinate embeddings. The results showed that nonlinear dynamical relationships between cortisol and negative affect may be present within participants, as demonstrated by a positive cross-map convergence from negative affect to cortisol. However, analyses also showed that noise and influential points had considerable impact on the results. Convergent crossmapping can be used to reveal possible nonlinear dynamical relationships between etiological factors and psychopathology that may remain undetected with traditional linear causality measures.

The association between cardiac rehabilitation and mortality risk for myocardial infarction patients with and without depressive symptoms

BACKGROUND

Post-myocardial infarction (MI) depression is associated with reduced adherence to cardiac rehabilitation (CR) and increased mortality risk. The present study investigated whether all-cause mortality reduction associated with CR is different for MI-patients with and without depressive symptoms.

METHODS

Data of 2198 post-MI patients from the Depression after Myocardial Infarction (DepreMI) study and Myocardial Infarction and Depression Intervention Trial (MIND-IT) was used. Depression was assessed at hospitalization, defined as a score≥10 on the Beck Depression Inventory (BDI). Participation in CR was assessed with a self-report questionnaire, 12 months post-MI. Cox regression was used to estimate hazard ratios (HR) for all-cause mortality, up till 10 years post-MI. Missing data was imputed, using multiple imputation.

RESULTS

878 (52%) Patients attended CR, 517 (26%) patients had a BDI score ≥10, and 379 (18%) patients died during the follow-up period. Overall, CR was not associated with a lower mortality risk (HR: 0.83; 0.54-1.30; p=0.41), adjusted for age, sex, left ventricle ejection fraction, previous MI, and past or current heart failure. However, there was a significant interaction between depression and CR on mortality (HR: 0.49; 0.27-0.90; p=0.02). CR was significantly associated with reduced mortality in depressed patients (HR: 0.48; 0.28-0.84; p=0.01), but not in non-depressed patients (HR: 1.09; 0.63-1.89; p=0.74).

LIMITATIONS

Patients were not randomized to CR. We had no information about the specific reasons of clinicians to offer CR and about the patients' motives to participate.

CONCLUSIONS

CR was associated with reduced mortality risk only for MI-patients with depression. Clinicians should therefore particularly encourage MI-patients with depression to participate in CR.

Decomposing the heterogeneity of depression at the person-, symptom-, and time-level: latent variable models versus multimode principal component analysis

BACKGROUND

Heterogeneity of psychopathological concepts such as depression hampers progress in research and clinical practice. Latent Variable Models (LVMs) have been widely used to reduce this problem by identification of more homogeneous factors or subgroups. However, heterogeneity exists at multiple levels (persons, symptoms, time) and LVMs cannot capture all these levels and their interactions simultaneously, which leads to incomplete models. Our objective is to briefly review the most widely used LVMs in depression research, illustrating their use and incompatibility in real data, and to consider an alternative, statistical approach, namely multimode principal component analysis (MPCA).

METHODS

We applied LVMs to data from 147 patients, who filled out the Quick Inventory of Depressive Symptomatology (QIDS) at 9 time points. Compatibility of the results and suitability of the LVMs to capture the heterogeneity of the data were evaluated. Alternatively, MPCA was used to simultaneously decompose depression on the person-, symptom- and time-level and to investigate the interactions between these levels.

RESULTS

QIDS-data could be decomposed on the person-level (2 classes), symptom-level (2 factors) and time-level (2 trajectory-classes). However, these results could not be integrated into a single model. Instead, MPCA allowed for decomposition of the data at the person- (3 components), symptom- (2 components) and time-level (2 components) and for the investigation of these components' interactions.

CONCLUSIONS

Traditional LVMs have limited use when trying to define an integrated model of depression heterogeneity at the person, symptom and time level. More integrative statistical techniques such as MPCA can be used to address these relatively complex data patterns and could be used in future attempts to identify empirically-based subtypes/phenotypes of depression.

Major depressive disorder as a nonlinear dynamic system: bimodality in the frequency distribution of depressive symptoms over time

BACKGROUND

A defining characteristic of Major Depressive Disorder (MDD) is its episodic course, which might indicate that MDD is a nonlinear dynamic phenomenon with two discrete states. We investigated this hypothesis using the symptom time series of individual patients.

METHODS

In 178 primary care patients with MDD, the presence of the nine DSM-IV symptoms of depression was recorded weekly for two years. For each patient, the time-series plots as well as the frequency distributions of the symptoms over 104 weeks were inspected. Furthermore, two indicators of bimodality were obtained: the bimodality coefficient (BC) and the fit of a 1- and a 2-state Hidden Markov Model (HMM).

RESULTS

In 66% of the sample, high bimodality coefficients (BC>.55) were found. These corresponded to relatively sudden jumps in the symptom curves and to highly skewed or bimodal frequency distributions. The results of the HMM analyses classified 90% of the symptom distributions as bimodal.

CONCLUSIONS

A two-state pattern can be used to describe the course of depression symptoms in many patients. The BC seems useful in differentiating between subgroups of MDD patients based on their life course data.

Ecological Momentary Assessments and Automated Time Series Analysis to Promote Tailored Health Care: A Proof-of-Principle Study

BACKGROUND

Health promotion can be tailored by combining ecological momentary assessments (EMA) with time series analysis. This combined method allows for studying the temporal order of dynamic relationships among variables, which may provide concrete indications for intervention. However, application of this method in health care practice is hampered because analyses are conducted manually and advanced statistical expertise is required.

OBJECTIVE

This study aims to show how this limitation can be overcome by introducing automated vector autoregressive modeling (VAR) of EMA data and to evaluate its feasibility through comparisons with results of previously published manual analyses.

METHODS

We developed a Web-based open source application, called AutoVAR, which automates time series analyses of EMA data and provides output that is intended to be interpretable by nonexperts. The statistical technique we used was VAR. AutoVAR tests and evaluates all possible VAR models within a given combinatorial search space and summarizes their results, thereby replacing the researcher's tasks of conducting the analysis, making an informed selection of models, and choosing the best model. We compared the output of AutoVAR to the output of a previously published manual analysis (n=4).

RESULTS

An illustrative example consisting of 4 analyses was provided. Compared to the manual output, the AutoVAR output presents similar model characteristics and statistical results in terms of the Akaike information criterion, the Bayesian information criterion, and the test statistic of the Granger causality test.

CONCLUSIONS

Results suggest that automated analysis and interpretation of times series is feasible. Compared to a manual procedure, the automated procedure is more robust and can save days of time. These findings may pave the way for using time series analysis for health promotion on a larger scale. AutoVAR was evaluated using the results of a previously conducted manual analysis. Analysis of additional datasets is needed in order to validate and refine the application for general use.

Associations between DSM-IV mental disorders and subsequent non-fatal, self-reported stroke

OBJECTIVES

To examine the associations between a wide range of mental disorders and subsequent onset of stroke. Lifecourse timing of stroke was examined using retrospectively reconstructed data from cross-sectional surveys.

METHODS

Data from the World Mental Health Surveys were accessed. This data was collected from general population surveys over 17 countries of 87,250 adults. The Composite International Diagnostic Interview retrospectively assessed lifetime prevalence and age at onset of DSM-IV mental disorders. A weighted subsample (n=45,288), was used for analysis in the present study. Survival analyses estimated associations between first onset of mental disorders and subsequent stroke onset.

RESULTS

Bivariate models showed that 12/16 mental disorders were associated with subsequent stroke onset (ORs ranging from 1.6 to 3.8). However, after adjustment for mental disorder comorbidity and smoking, only significant relationships between depression and stroke (OR 1.3) and alcohol abuse and stroke (OR 1.5) remained. Among females, having a bipolar disorder was also associated with increased stroke incidence (OR 2.1). Increasing number of mental disorders was associated with stroke onset in a dose-response fashion (OR 3.3 for 5+ disorders).

CONCLUSIONS

Depression and alcohol abuse may have specific associations with incidence of non-fatal stroke. General severity of psychopathology may be a more important predictor of non-fatal stroke onset. Mental health treatment should be considered as part of stroke risk prevention. Limitations of retrospectively gathered cross sectional surveys design mean further research on the links between mental health and stroke incidence is warranted.

Simultaneous Decomposition of Depression Heterogeneity on the Person-, Symptom- and Time-Level: The Use of Three-Mode Principal Component Analysis

Although heterogeneity of depression hinders research and clinical practice, attempts to reduce it with latent variable models have yielded inconsistent results, probably because these techniques cannot account for all interacting sources of heterogeneity at the same time. Therefore, to simultaneously decompose depression heterogeneity on the person-, symptom and time-level, three-mode Principal Component Analysis (3MPCA) was applied to data of 219 Major Depression patients, who provided Beck Depression Inventory assessments every three months for two years. The resulting person-level components were correlated with external baseline clinical and demographic variables. The 3MPCA extracted two symptom-level components (‘cognitive’, ‘somatic-affective’), two time-level components (‘improving’, ‘persisting’) and three person-level components, characterized by different interaction-patterns between the symptom- and time-components (‘severe non-persisting’, ‘somatic depression’ and ‘cognitive depression’). This model explained 28% of the total variance and 65% when also incorporating the general trend in the data). Correlations with external variables illustrated the content differentiation between the person-components. Severe non-persisting depression was positively correlated with psychopathology (r=0.60) and negatively with quality of life (r=-0.50). Somatic depression was negatively correlated with physical functioning (r=-0.45). Cognitive depression was positively correlated with neuroticism (r=0.38) and negatively with self-esteem (r=-0.47). In conclusion, 3MPCA decomposes depression into homogeneous entities, while accounting for the interactions between different sources of heterogeneity, which shows the utility of the technique to investigate the underlying structure of complex psychopathology data and could help future development of better empirical depression subtypes.

Cortisol and α-Amylase Secretion Patterns between and within Depressed and Non-Depressed Individuals

OBJECTIVES

Associations between biological stress markers and depression are inconsistent across studies. We assessed whether inter- and intra-individual variability explain these inconsistencies.

METHODS

Pair-matched depressed and non-depressed participants (N = 30) collected saliva thrice a day for 30 days, resulting in 90 measurements per individual. The relationships between measures of stress-system function and depression were examined at the group level by means of mixed model analyses, and at the individual level by means of pair-matched comparisons. The analyses were repeated after adjusting for time-varying lifestyle factors by means of time-series regression analyses.

RESULTS

Cortisol and α-amylase levels were higher, the α-amylase/cortisol ratio larger, and the daily cortisol slope steeper in the depressed compared to the non-depressed group. Adjusting for lifestyle factors and antidepressant use reduced the associations under study. In 40%-60% of the matched comparisons, depressed individuals had higher cortisol and α-amylase levels, a larger α-amylase/cortisol ratio, and a steeper daily slope than their non-depressed match, regardless of adjustment.

CONCLUSIONS

Our group-level findings were mostly in line with the literature but generalization to individuals appeared troublesome. Findings of studies on this topic should be interpreted with care, because in clinical practice the focus is on individuals instead of groups.

Psychotic Experiences in the General Population: A Cross-National Analysis Based on 31,261 Respondents From 18 Countries

IMPORTANCE

Community-based surveys find that many otherwise healthy individuals report histories of hallucinations and delusions. To date, most studies have focused on the overall lifetime prevalence of any of these psychotic experiences (PEs), which might mask important features related to the types and frequencies of PEs.

OBJECTIVE

To explore detailed epidemiologic information about PEs in a large multinational sample.

DESIGN, SETTING, AND PARTICIPANTS

We obtained data from the World Health Organization World Mental Health Surveys, a coordinated set of community epidemiologic surveys of the prevalence and correlates of mental disorders in representative household samples from 18 countries throughout the world, from 2001 through 2009. Respondents included 31,261 adults (18 years and older) who were asked about lifetime and 12-month prevalence and frequency of 6 types of PEs (2 hallucinatory experiences and 4 delusional experiences). We analyzed the data from March 2014 through January 2015.

MAIN OUTCOMES AND MEASURES

Prevalence, frequency, and correlates of PEs.

RESULTS

Mean lifetime prevalence (SE) of ever having a PE was 5.8% (0.2%), with hallucinatory experiences (5.2% [0.2%]) much more common than delusional experiences (1.3% [0.1%]). More than two-thirds (72.0%) of respondents with lifetime PEs reported experiencing only 1 type. Psychotic experiences were typically infrequent, with 32.2% of respondents with lifetime PEs reporting only 1 occurrence and 31.8% reporting only 2 to 5 occurrences. We found a significant relationship between having more than 1 type of PE and having more frequent PE episodes (Cochran-Armitage z = -10.0; P < .001). Lifetime prevalence estimates (SEs) were significantly higher among respondents in middle- and high-income countries than among those in low-income countries (7.2% [0.4%], 6.8% [0.3%], and 3.2% [0.3%], respectively; χ²₂ range, 7.1-58.2; P < .001 for each) and among women than among men (6.6% [0.2%] vs 5.0% [0.3%]; χ²₁ = 16.0; P < .001). We found significant associations with lifetime prevalence of PEs in the multivariate model among nonmarried compared with married respondents (χ²₂ = 23.2; P < .001) and among respondents who were not employed (χ²₄= 10.6; P < .001) and who had low family incomes (χ²₃ = 16.9; P < .001).

CONCLUSIONS AND RELEVANCE

The epidemiologic features of PEs are more nuanced than previously thought. Research is needed that focuses on similarities and differences in the predictors of the onset, course, and consequences of distinct PEs.

Pediatric-Onset and Adult-Onset Separation Anxiety Disorder Across Countries in the World Mental Health Survey

OBJECTIVE

The age-at-onset criterion for separation anxiety disorder was removed in DSM-5, making it timely to examine the epidemiology of separation anxiety disorder as a disorder with onsets spanning the life course, using cross-country data.

METHOD

The sample included 38,993 adults in 18 countries in the World Health Organization (WHO) World Mental Health Surveys. The WHO Composite International Diagnostic Interview was used to assess a range of DSM-IV disorders that included an expanded definition of separation anxiety disorder allowing onsets in adulthood. Analyses focused on prevalence, age at onset, comorbidity, predictors of onset and persistence, and separation anxiety-related role impairment.

RESULTS

Lifetime separation anxiety disorder prevalence averaged 4.8% across countries (interquartile range [25th-75th percentiles]=1.4%-6.4%), with 43.1% of lifetime onsets occurring after age 18. Significant time-lagged associations were found between earlier separation anxiety disorder and subsequent onset of internalizing and externalizing DSM-IV disorders and conversely between these disorders and subsequent onset of separation anxiety disorder. Other consistently significant predictors of lifetime separation anxiety disorder included female gender, retrospectively reported childhood adversities, and lifetime traumatic events. These predictors were largely comparable for separation anxiety disorder onsets in childhood, adolescence, and adulthood and across country income groups. Twelve-month separation anxiety disorder prevalence was considerably lower than lifetime prevalence (1.0% of the total sample; interquartile range=0.2%-1.2%). Severe separation anxiety-related 12-month role impairment was significantly more common in the presence (42.4%) than absence (18.3%) of 12-month comorbidity.

CONCLUSIONS

Separation anxiety disorder is a common and highly comorbid disorder that can have onset across the lifespan. Childhood adversity and lifetime trauma are important antecedents, and adverse effects on role function make it a significant target for treatment.

Reporting Bias in Clinical Trials Investigating the Efficacy of Second-Generation Antidepressants in the Treatment of Anxiety Disorders: A Report of 2 Meta-analyses

IMPORTANCE

Studies have shown that the scientific literature has overestimated the efficacy of antidepressants for depression, but other indications for these drugs have not been considered.

OBJECTIVE

To examine reporting biases in double-blind, placebo-controlled trials on the pharmacologic treatment of anxiety disorders and quantify the extent to which these biases inflate estimates of drug efficacy.

DATA SOURCES AND STUDY SELECTION

We included reviews obtained from the US Food and Drug Administration (FDA) for premarketing trials of 9 second-generation antidepressants in the treatment of anxiety disorders. A systematic search for matching publications (until December 19, 2012) was performed using PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials.

DATA EXTRACTION AND SYNTHESIS

Double data extraction was performed for the FDA reviews and the journal articles. The Hedges g value was calculated as the measure of effect size.

MAIN OUTCOMES AND MEASURES

Reporting bias was examined and classified as study publication bias, outcome reporting bias, or spin (abstract conclusion not consistent with published results on primary end point). Separate meta-analyses were conducted for the 2 sources, and the effect of publication status on the effect estimates was examined using meta-regression.

RESULTS

The findings of 41 of the 57 trials (72%) were positive according to the FDA, but 43 of the 45 published article conclusions (96%) were positive (P < .001). Trials that the FDA determined as positive were 5 times more likely to be published in agreement with that determination compared with trials determined as not positive (risk ratio, 5.20; 95% CI, 1.87 to 14.45; P < .001). We found evidence for study publication bias (P < .001), outcome reporting bias (P = .02), and spin (P = .02). The pooled effect size based on the published literature (Hedges g, 0.38; 95% CI, 0.33 to 0.42; P < .001) was 15% higher than the effect size based on the FDA data (Hedges g, 0.33; 95% CI, 0.29 to 0.38; P < .001), but this difference was not statistically significant (β = 0.04; 95% CI, -0.02 to 0.10; P = .18).

CONCLUSIONS AND RELEVANCE

Various reporting biases were present for trials on the efficacy of FDA-approved second-generation antidepressants for anxiety disorders. Although these biases did not significantly inflate estimates of drug efficacy, reporting biases led to significant increases in the number of positive findings in the literature.

The associations of depression and hypertension with brain volumes: Independent or interactive?

Independent studies on major depressive disorder (MDD) and hypertension, suggest overlapping abnormalities in brain regions associated with emotional and autonomic processing. However, the unique and interactive effects of MDD and hypertension have never been studied in a single sample. Brain volume in these areas may be an explanatory link in the comorbidity between MDD and hypertension. Voxel-based morphometry was used to test for main effects of MDD (N = 152) and hypertension (N = 82) and their interactions on gray and white matter volumes. Voxel-wise results are reported at p < .05 FWE corrected for the spatial extent of the whole brain and a-priori regions of interest (ROIs: hippocampus, anterior cingulate cortex (ACC) and inferior frontal gyrus (IFG)). In addition, analyses on the extracted total volumes of our ROIs were performed. Interactive effects in the mid-cingulate cortex (MCC) (p FWE = .01), cerebellum (p FWE = .01) and in the ACC total ROI volume (p = .02) were found. MDD in the presence, but not in the absence of hypertension was associated with lower volumes in the ACC and MCC, and with a trend towards larger gray matter volume in the cerebellum. No associations with white matter volumes were observed. Results suggest that the combination of MDD and hypertension has a unique effect on brain volumes in areas implicated in the regulation of emotional and autonomic functions. Brain volume in these regulatory areas may be an explanatory link in the comorbidity between hypertension and MDD.

Differential reporting of depressive symptoms across distinct clinical subpopulations: what DIFference does it make?

OBJECTIVE

To investigate the impact of differences in depressive symptom reporting across clinical groups (healthcare setting, chronic illness, depression diagnosis and anxiety diagnosis) on clinical interpretability and comparability of depression scores.

METHODS

Participants from the Netherlands Study of Depression and Anxiety (n=2981) completed the self-report Inventory of Depressive Symptomatology (IDS-SR). Differences in depressive symptom reporting between distinct clinical subpopulations were assessed using a Differential Item Functioning (DIF) analysis. The effects of DIF on symptom level were evaluated by examining whether DIF-adjustment had clinically relevant effects.

RESULTS

Significant DIF was detected across all tested clinical subpopulation groupings. Clinically relevant DIF was found on the symptom level for 13 IDS-SR items. However, impact of DIF on the aggregate level ranged from small to negligible: adjustment for DIF only led to salient changes in aggregate scores for 0.2-12.7% of individuals across tested sources of DIF.

CONCLUSION

Differences in endorsement patterns of depressive symptoms were observed across clinical populations, challenging the assumptions regarding the measurement properties of self-reported depression. However, effects of DIF on the aggregate level of IDS-SR total scores were found to be minimal and not clinically important. The IDS-SR thus seems robust against DIF across clinical populations.

Pregnancy Outcomes After a Maternity Intervention for Stressful Emotions (PROMISES): A Randomised Controlled Trial

There is ample evidence from observational prospective studies that maternal depression or anxiety during pregnancy is a risk factor for adverse psychosocial outcomes in the offspring. However, to date no previous study has demonstrated that treatment of depressive or anxious symptoms in pregnancy actually could prevent psychosocial problems in children. Preventing psychosocial problems in children will eventually bring down the huge public health burden of mental disease. The main objective of this study is to assess the effects of cognitive behavioural therapy in pregnant women with symptoms of anxiety or depression on the child's development as well as behavioural and emotional problems. In addition, we aim to study its effects on the child's development, maternal mental health, and neonatal outcomes, as well as the cost-effectiveness of cognitive behavioural therapy relative to usual care.We will include 300 women with at least moderate levels of anxiety or depression at the end of the first trimester of pregnancy. By including 300 women, we will be able to demonstrate effect sizes of 0.35 or more on the total problems scale of the Child Behaviour Checklist 1.5-5 with alpha 5 % and power (1-beta) 80 %.Women in the intervention arm are offered 10-14 individual cognitive behavioural therapy sessions, 6-10 sessions during pregnancy and 4-8 sessions after delivery (once a week). Women in the control group receive care as usual.Primary outcome is behavioural/emotional problems at 1.5 years of age as assessed by the total problems scale of the Child Behaviour Checklist 1.5-5 years.Secondary outcomes are mental, psychomotor and behavioural development of the child at age 18 months according to the Bayley scales; maternal anxiety and depression during pregnancy and postpartum; and neonatal outcomes such as birth weight, gestational age and Apgar score, health-care consumption and general health status (economic evaluation).

TRIAL REGISTRATION

NTR2242.

Sex dependent risk factors for mortality after myocardial infarction: individual patient data meta-analysis

BACKGROUND

Although a number of risk factors are known to predict mortality within the first years after myocardial infarction, little is known about interactions between risk factors, whereas these could contribute to accurate differentiation of patients with higher and lower risk for mortality. This study explored the effect of interactions of risk factors on all-cause mortality in patients with myocardial infarction based on individual patient data meta-analysis.

METHODS

Prospective data for 10,512 patients hospitalized for myocardial infarction were derived from 16 observational studies (MINDMAPS). Baseline measures included a broad set of risk factors for mortality such as age, sex, heart failure, diabetes, depression, and smoking. All two-way and three-way interactions of these risk factors were included in Lasso regression analyses to predict time-to-event related all-cause mortality. The effect of selected interactions was investigated with multilevel Cox regression models.

RESULTS

Lasso regression selected five two-way interactions, of which four included sex. The addition of these interactions to multilevel Cox models suggested differential risk patterns for males and females. Younger women (age<50) had a higher risk for all-cause mortality than men in the same age group (HR 0.7 vs. 0.4), while men had a higher risk than women if they had depression (HR 1.4 vs. 1.1) or a low left ventricular ejection fraction (HR 1.7 vs. 1.3). Predictive accuracy of the Cox model was better for men than for women (area under the curves: 0.770 vs. 0.754).

CONCLUSIONS

Interactions of well-known risk factors for all-cause mortality after myocardial infarction suggested important sex differences. This study gives rise to a further exploration of prediction models to improve risk assessment for men and women after myocardial infarction.

Association between mental disorders and subsequent adult onset asthma

BACKGROUND AND OBJECTIVES

Associations between asthma and anxiety and mood disorders are well established, but little is known about their temporal sequence. We examined associations between a wide range of DSM-IV mental disorders with adult onset of asthma and whether observed associations remain after mental comorbidity adjustments.

METHODS

During face-to-face household surveys in community-dwelling adults (n = 52,095) of 19 countries, the WHO Composite International Diagnostic Interview retrospectively assessed lifetime prevalence and age at onset of 16 DSM-IV mental disorders. Asthma was assessed by self-report of physician's diagnosis together with age of onset. Survival analyses estimated associations between first onset of mental disorders and subsequent adult onset asthma, without and with comorbidity adjustment.

RESULTS

1860 adult onset (21 years+) asthma cases were identified, representing a total of 2,096,486 person-years of follow up. After adjustment for comorbid mental disorders several mental disorders were associated with subsequent adult asthma onset: bipolar (OR = 1.8; 95%CI 1.3-2.5), panic (OR = 1.4; 95%CI 1.0-2.0), generalized anxiety (OR = 1.3; 95%CI 1.1-1.7), specific phobia (OR = 1.3; 95%CI 1.1-1.6); post-traumatic stress (OR = 1.5; 95%CI 1.1-1.9); binge eating (OR = 1.8; 95%CI 1.2-2.9) and alcohol abuse (OR = 1.5; 95%CI 1.1-2.0). Mental comorbidity linearly increased the association with adult asthma. The association with subsequent asthma was stronger for mental disorders with an early onset (before age 21).

CONCLUSIONS

A wide range of temporally prior mental disorders are significantly associated with subsequent onset of asthma in adulthood. The extent to which asthma can be avoided or improved among those with early mental disorders deserves study.

How well can post-traumatic stress disorder be predicted from pre-trauma risk factors? An exploratory study in the WHO World Mental Health Surveys

Post-traumatic stress disorder (PTSD) should be one of the most preventable mental disorders, since many people exposed to traumatic experiences (TEs) could be targeted in first response settings in the immediate aftermath of exposure for preventive intervention. However, these interventions are costly and the proportion of TE-exposed people who develop PTSD is small. To be cost-effective, risk prediction rules are needed to target high-risk people in the immediate aftermath of a TE. Although a number of studies have been carried out to examine prospective predictors of PTSD among people recently exposed to TEs, most were either small or focused on a narrow sample, making it unclear how well PTSD can be predicted in the total population of people exposed to TEs. The current report investigates this issue in a large sample based on the World Health Organization (WHO)'s World Mental Health Surveys. Retrospective reports were obtained on the predictors of PTSD associated with 47,466 TE exposures in representative community surveys carried out in 24 countries. Machine learning methods (random forests, penalized regression, super learner) were used to develop a model predicting PTSD from information about TE type, socio-demographics, and prior histories of cumulative TE exposure and DSM-IV disorders. DSM-IV PTSD prevalence was 4.0% across the 47,466 TE exposures. 95.6% of these PTSD cases were associated with the 10.0% of exposures (i.e., 4,747) classified by machine learning algorithm as having highest predicted PTSD risk. The 47,466 exposures were divided into 20 ventiles (20 groups of equal size) ranked by predicted PTSD risk. PTSD occurred after 56.3% of the TEs in the highest-risk ventile, 20.0% of the TEs in the second highest ventile, and 0.0-1.3% of the TEs in the 18 remaining ventiles. These patterns of differential risk were quite stable across demographic-geographic sub-samples. These results demonstrate that a sensitive risk algorithm can be created using data collected in the immediate aftermath of TE exposure to target people at highest risk of PTSD. However, validation of the algorithm is needed in prospective samples, and additional work is warranted to refine the algorithm both in terms of determining a minimum required predictor set and developing a practical administration and scoring protocol that can be used in routine clinical practice.

Markers of stress and inflammation as potential mediators of the relationship between exercise and depressive symptoms: findings from the TRAILS study

The hypothalamic-pituitary-adrenal axis, autonomic nervous system, and immune system have been proposed to underlie the antidepressant effect of exercise. Using a population sample of 715 adolescents, we examined whether pathways from exercise to affective and somatic symptoms of depression were mediated by these putative mechanisms. Exercise (hours/week) and depressive symptoms were assessed at age 13.5 (± 0.5) and 16.1 (± 0.6). Cortisol and heart rate responses to a standardized social stress test and C-reactive protein levels were measured at age 16. Exercise was prospectively and inversely related to affective (B = -0.16, 95% CI = -0.30 to -0.03) but not somatic symptoms (B = -0.04, 95% CI = -0.21 to 0.13). Heart rate during social stress partially mediated this relationship (B = -0.03, 95% CI = -0.07 to -0.01). No other mediating effects were found. Hence, the autonomic stress system may play a role in the relationship between exercise and depressive symptoms.

Personality modulates the efficacy of treatment in patients with major depressive disorder

OBJECTIVE

Effects of depression treatment are obscured by heterogeneity among patients. Personality types could be one source of heterogeneity that explains variability in treatment response. Clinically meaningful variations in personality patterns could be captured with data-driven subgroups. The aim of this study was to identify such personality types and to explore their predictive value for treatment efficacy.

METHOD

Participants (N = 146) in the current exploratory study came from a randomized controlled trial in primary care depressed patients, conducted between January 1998 and June 2003, comparing different treatments. All participants were diagnosed with a major depressive disorder (MDD) according to the DSM-IV. Primary (care as usual [CAU] or CAU plus a psychoeducational prevention program [PEP]) and specialized (CAU + PEP + psychiatric consultation or cognitive-behavioral therapy) treatment were compared. Personality was assessed with the Neuroticism-Extraversion-Openness Five-Factor Inventory (NEO-FFI). Personality classes were identified with latent profile analysis (LPA). During 1 year, weekly depression ratings were obtained by trimonthly assessment with the Composite International Diagnostic Interview. Mixed models were used to analyze the effects of personality on treatment efficacy.

RESULTS

A 2-class LPA solution fit best to the NEO-FFI data: Class 1 (vulnerable, n = 94) was characterized by high neuroticism, low extraversion, and low conscientiousness, and Class 2 (resilient, n = 52) by medium neuroticism and extraversion and higher agreeableness and conscientiousness. Recovery was quicker in the resilient class (class × time: P < .001). Importantly, specialized treatment had added value only in the vulnerable class, in which it was associated with quicker recovery than primary treatment (class × time × treatment: P < .001).

CONCLUSIONS

Personality profile may predict whether specialized clinical efforts have added value, showing potential implications for planning of treatments.