Klaas J. Wardenaar

Dimensions of Depression and Anxiety and the Hypothalamo-Pituitary-Adrenal Axis

BACKGROUND Results on the association between depression and the hypothalamo-pituitary-adrenal (HPA) axis have been inconsistent, possibly due to heterogeneity of the DSM-IV category of depression. Specific symptom-dimensions could be used as a more homogenous phenotype in HPA-axis research. METHODS Subjects (n = 1029) with a lifetime depression and/or anxiety disorder from the NESDA study (Netherlands Study of Depression and Anxiety) (mean age: 43.0 ± 12.7 years, 67.4% women) provided seven saliva samples to yield the cortisol awakening response (CAR), evening cortisol, and dexamethasone suppression data. The dimensions of the tripartite model (General Distress, Anhedonic Depression, and Anxious Arousal) were measured with the 30-item adapted Mood and Anxiety Symptoms Questionnaire (MASQ-D30) and analyzed in association with the cortisol measures with linear and nonlinear regression. RESULTS Median (interquartile range) scores of General Distress, Anhedonic Depression, and Anxious Arousal were 20 (14-27), 36 (28-44), and 15 (12-19), respectively, indicating large variability. Nonlinear associations with the shape of an inverted U were found between General Distress, Anhedonic Depression, and Anxious Arousal on one hand and total morning secretion and the dynamic of the CAR by contrast. Both high and low severity levels were associated with a lower CAR, compared with intermediate levels of severity. Most of the associations remained significant when adjusted for covariates and the presence of DSM-IV diagnoses. CONCLUSIONS Nonlinear associations were found between the CAR and the dimensions of the tripartite model. This could explain previous inconsistent findings regarding HPA-axis activity in depressed patients and illustrates the added value of symptom-dimensions for HPA-axis research.

Development and validation of a 30-item short adaptation of the Mood and Anxiety Symptoms Questionnaire (MASQ)

The original Mood and Anxiety Symptoms Questionnaire (MASQ) is a 90-item self-report, designed to measure the dimensions of Clark and Watsons tripartite model. We developed and validated a 30-item short adaptation of the MASQ: the MASQ-D30, which is more suitable for large-scale psychopathology research and has a clearer factor structure. The MASQ-D30 was developed through a process of item reduction and grouping of the appropriate subscales in a sample of 489 psychiatric outpatients, using a validated Dutch translation, based on the original English MASQ, as a starting point. Validation was done in two other large samples of 1461 and 2471 subjects, respectively, with an anxiety, somatoform and/or depression diagnosis or no psychiatric diagnosis. Psychometric properties were investigated and compared between the MASQ-D30 and the full (adapted) MASQ. A three-dimensional model (negative affect, positive affect and somatic arousal) was found to represent the data well, indicating good construct validity. The scales of the MASQ-D30 showed good internal consistency (all alphas>0.87) in patient samples. Correlations of the subscales with other instruments indicated acceptable convergent validity. Psychometric properties were similar for the MASQ-D30 and the full questionnaire. In conclusion, the MASQ-D30 is a valid instrument to assess dimensional aspects of depression and anxiety and can easily be implemented in psychopathology studies.

Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports

Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. Although efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine-learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared with observed scores assessed 10–12 years after baseline. ML model prediction accuracy was also compared with that of conventional logistic regression models. Area under the receiver operating characteristic curve based on ML (0.63 for high chronicity and 0.71–0.76 for the other prospective outcomes) was consistently higher than for the logistic models (0.62–0.70) despite the latter models including more predictors. A total of 34.6–38.1% of respondents with subsequent high persistence chronicity and 40.8–55.8% with the severity indicators were in the top 20% of the baseline ML-predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML-predicted risk distribution. These results confirm that clinically useful MDD risk-stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models.

Diagnostic heterogeneity in psychiatry: towards an empirical solution

The launch of the 5th version of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) has sparked a debate about the current approach to psychiatric classification. The most basic and enduring problem of the DSM is that its classifications are heterogeneous clinical descriptions rather than valid diagnoses, which hampers scientific progress. Therefore, more homogeneous evidence-based diagnostic entities should be developed. To this end, data-driven techniques, such as latent class- and factor analyses, have already been widely applied. However, these techniques are insufficient to account for all relevant levels of heterogeneity, among real-life individuals. There is heterogeneity across persons (p:for example, subgroups), across symptoms (s:for example, symptom dimensions) and over time (t:for example, course-trajectories) and these cannot be regarded separately. Psychiatry should upgrade to techniques that can analyze multi-mode (p-by-s-by-t) data and can incorporate all of these levels at the same time to identify optimal homogeneous subgroups (for example, groups with similar profiles/connectivity of symptomatology and similar course). For these purposes, Multimode Principal Component Analysis and (Mixture)-Graphical Modeling may be promising techniques.

Symptom dimensions as predictors of the two-year course of depressive and anxiety disorders

BACKGROUND Because of the heterogeneity of known predictive factors, course-predictions for depression and anxiety are often unspecific. Therefore, it was investigated whether symptom-dimensions could be used as more specific course-predictors, on top of already known predictors, such as diagnosis and overall severity. METHODS A sample of 992 subjects with depressive and/or anxiety disorders was followed in a 2-year prospective cohort study. Dimensions of the tripartite model (general distress, anhedonic depression and anxious arousal) were assessed at baseline. Diagnostic and course information were assessed at baseline and 2-year follow-up. RESULTS Dimensional scores at baseline predicted diagnosis after two years and course-trajectories during follow-up. Increased general distress at baseline was associated with comorbid depression-anxiety at follow-up, increased anhedonic depression was associated with single depression and anxious arousal was associated with (comorbid) panic disorders at follow-up. Baseline general distress was associated with an unfavorable course in all patients. All associations were independent and added prognostic information on top of diagnosis and other predictive factors at baseline. LIMITATIONS Only prevalent patients were included at baseline and only three dimensions were measured CONCLUSIONS Symptom dimensions predict the future 2-year course of depression and anxiety. Importantly, the dimensions yield predictive information on top of diagnosis and other prognostic factors at baseline.

Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder.

BACKGROUNDS Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful. METHOD We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments. RESULTS Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials. CONCLUSIONS Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.

Major depressive disorder subtypes to predict long-term course

Variation in the course of major depressive disorder (MDD) is not strongly predicted by existing subtype distinctions. A new subtyping approach is considered here.

Utility of the Brief Symptom Inventory (BSI) in Psychiatric Outpatients with Intellectual Disabilities.

BACKGROUND Diagnostics and care for people with intellectual disabilities (ID) and psychiatric disorders need to be improved. This can be done by using assessment instruments to routinely measure the nature and severity of psychiatric symptoms. Up until now, in the Netherlands, assessment measures are seldom used in the psychiatric care for this population. The objective of the present paper is to evaluate the use of the Brief Symptom Inventory (BSI), a widely used standardised questionnaire in general psychiatry, in a well-defined sample of people with borderline intellectual functioning or mild ID diagnosed with one or more psychiatric disorders. METHODS A total of 224 psychiatric outpatients with either borderline intellectual functioning or mild ID participated in this study. All participants were new patients of Kristal, Centre for Psychiatry and Intellectual Disability in the Netherlands, in the period between 1 April 2008 and 1 October 2009. All participants were assessed by a multidisciplinary team, including a certified psychiatrist. Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) criteria were applied. The mean total intelligence quotient was measured with the Wechsler Adult Intelligence Scale (WAIS-III). The BSI was administered in an assisted fashion. Utility and psychometric properties of the BSI were investigated. Internal consistency coefficients (Cronbachs alphas) were computed. Bivariate correlations between the sub-scales were computed to assess differentiation between the scales. Mean sub-scale scores were compared between different DSM-IV-TR subgroups to investigate the discriminant abilities of the scales. A confirmatory factor analysis was conducted. RESULTS The results suggest that the BSI is practically useful. Internal consistencies ranged from 0.70 to 0.96 and thus are considered good to adequate. Sub-scale inter-correlations showed there is a degree of differentiation between the sub-scales. Discriminant validity was shown for the sub-scales depression, anxiety and phobic anxiety. Confirmatory factor analysis showed that the underlying structure of the BSI could be described by the same nine-factor model as reported in previous studies. CONCLUSIONS As a result of the psychometric properties illustrated, this study supports the use of the BSI as a screener for psychopathology and a general outcome measure in people with ID.

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.

Different gene sets contribute to different symptom dimensions of depression and anxiety

Although many genetic association studies have been carried out, it remains unclear which genes contribute to depression. This may be due to heterogeneity of the DSM‐IV category of depression. Specific symptom‐dimensions provide a more homogenous phenotype. Furthermore, as effects of individual genes are small, analysis of genetic data at the pathway‐level provides more power to detect associations and yield valuable biological insight. In 1,398 individuals with a Major Depressive Disorder, the symptom dimensions of the tripartite model of anxiety and depression, General Distress, Anhedonic Depression, and Anxious Arousal, were measured with the Mood and Anxiety Symptoms Questionnaire (30‐item Dutch adaptation; MASQ‐D30). Association of these symptom dimensions with candidate gene sets and gene sets from two public pathway databases was tested using the Global test. One pathway was associated with General Distress, and concerned molecules expressed in the endoplasmatic reticulum lumen. Seven pathways were associated with Anhedonic Depression. Important themes were neurodevelopment, neurodegeneration, and cytoskeleton. Furthermore, three gene sets associated with Anxious Arousal regarded development, morphology, and genetic recombination. The individual pathways explained up to 1.7% of the variance. These data demonstrate mechanisms that influence the specific dimensions. Moreover, they show the value of using dimensional phenotypes on one hand and gene sets on the other hand.