Problems with latent class analysis to detect data-driven subtypes of depression

FSW – Publicaties zonder aanstelling Universiteit Leide

Childhood trauma and dysregulation of multiple biological stress systems in adulthood: results from the Netherlands Study of Depression and Anxiety (NESDA)

Background: Childhood trauma (CT) is a risk factor for depressive and anxiety disorders. Although dysregulated biological stress systems may underlie the enduring effect of CT, the relation between CT and separate and cumulative activity of the major stress systems, namely, the hypothalamic-pituitary-adrenal (HPA)-axis, the immune-inflammatory system, and the autonomic nervous system (ANS), remains inconclusive.Methods: In the Netherlands Study of Depression and Anxiety (NESDA, n = 2778), we determined whether self-reported CT (as assessed by the Childhood Trauma Interview) was associated with separate and cumulative markers of the HPA-axis (cortisol awakening response, evening cortisol, dexamethasone suppression test cortisol), the immune-inflammatory system (C-reactive protein, interleukin-6, tumor necrosis factor-alpha), and the ANS (heart rate, respiratory sinus arrhythmia, pre-ejection period) in adulthood.Results: Almost all individuals with CT (n = 1330) had either current or remitted depressive and/or anxiety disorder (88.6%). Total-sample analyses showed little evidence for CT being significantly associated with the separate or cumulative stress systems' activity in adulthood. These findings were true for individuals with and without depressive and/or anxiety disorders. To maximize contrast, individuals with severe CT were compared to healthy controls without CT. This yielded slight, but significantly higher levels of cortisol awakening response (AUCg, beta =.088, p =.007; AUCi, beta =.084, p =.010), cumulative HPA-axis markers (beta =.115, p =.001), Creactive protein (beta =.055, p =.032), interleukin-6 (beta =.053, p =.038), cumulative inflammation (beta =.060, p =.020), and cumulative markers across all systems (beta =.125, p =.0003) for those with severe CT, partially explained by higher rates of smoking, body mass index, and chronic diseases.Conclusion: While our findings do not provide conclusive evidence on CT directly dysregulating stress systems, individuals with severe CT showed slight indications of dysregulations, partially explained by an unhealthy lifestyle and poorer health.Stress-related psychiatric disorders across the life spa

The 9-year clinical course of depressive and anxiety disorders: new NESDA findings

Background: In longitudinal research, switching between diagnoses should be considered when examining patients with depression and anxiety. We investigated course trajectories of affective disorders over a nine-year period, comparing a categorical approach using diagnoses to a dimensional approach using symptom severity.Method: Patients with a current depressive and/or anxiety disorder at baseline (N = 1701) were selected from the Netherlands Study of Depression and Anxiety (NESDA). Using psychiatric diagnoses, we described 'consistently recovered,' 'intermittently recovered,' 'intermittently recurrent', and 'consistently chronic' at two-, four-, six-, and nine-year follow-up. Additionally, latent class growth analysis (LCGA) using depressive, anxiety, fear, and worry symptom severity scores was used to identify distinct classes.Results: Considering the categorical approach, 8.5% were chronic, 32.9% were intermittently recurrent, 37.6% were intermittently recovered, and 21.0% remained consistently recovered from any affective disorder at nine-year follow-up. In the dimensional approach, 66.6% were chronic, 25.9% showed partial recovery, and 7.6% had recovered.Limitations: 30.6% of patients were lost to follow-up. Diagnoses were rated by the interviewer and questionnaires were completed by the participant.Conclusions: Using diagnoses alone as discrete categories to describe clinical course fails to fully capture the persistence of affective symptoms that were observed when using a dimensional approach. The enduring, fluctuating presence of sub-threshold affective symptoms likely predisposes patients to frequent relapse. The commonness of subthreshold symptoms and their adverse impact on long-term prognoses deserve continuous clinical attention in mental health care as well further research.Stress-related psychiatric disorders across the life spa

Corrigendum to “Identifying mismatch and match between clinical needs and mental healthcare use trajectories in people with anxiety and depression: Results of a longitudinal study”. [Journal of affective disorders. 297 (2022) 657-670]

Stress-related psychiatric disorders across the life spa

Common and specific determinants of 9-year depression and anxiety course-trajectories: a machine-learning investigation in the Netherlands Study of Depression and Anxiety (NESDA)

Background: Given the strong relationship between depression and anxiety, there is an urge to investigate their shared and specific long-term course determinants. The current study aimed to identify and compare the main determinants of the 9-year trajectories of combined and pure depression and anxiety symptom severity. Methods: Respondents with a 6-month depression and/or anxiety diagnosis (n=1,701) provided baseline data on 152 sociodemographic, clinical and biological variables. Depression and anxiety symptom severity assessed at baseline, 2-, 4-, 6- and 9-year follow-up, were used to identify data-driven course-trajectory subgroups for general psychological distress, pure depression, and pure anxiety severity scores. For each outcome (classprobability), a Superlearner (SL) algorithm identified an optimally weighted (minimum mean squared error) combination of machine-learning prediction algorithms. For each outcome, the top determinants in the SL were identified by determining variable-importance and correlations between each SL-predicted and observed outcome (rho pred) were calculated. Results: Low to high prediction correlations (rho pred: 0.41-0.91, median=0.73) were found. In the SL, important determinants of psychological distress were age, young age of onset, respiratory rate, participation disability, somatic disease, low income, minor depressive disorder and mastery score. For course of pure depression and anxiety symptom severity, similar determinants were found. Specific determinants of pure depression included several types of healthcare-use, and of pure-anxiety course included somatic arousal and psychological distress. Limitations: Limited sample size for machine learning. Conclusions: The determinants of depression- and anxiety-severity course are mostly shared. Domain-specific exceptions are healthcare use for depression and somatic arousal and distress for anxiety-severity course.Stress-related psychiatric disorders across the life spa

Predicting the 9-year course of mood and anxiety disorders with automated machine learning: a comparison between auto-sklearn, naïve Bayes classifier, and traditional logistic regression

Background: Predicting the onset and course of mood and anxiety disorders is of clinical importance but remains difficult. We compared the predictive performances of traditional logistic regression, basic probabilistic machine learning (ML) methods, and automated ML (Auto-sklearn).Methods: Data were derived from the Netherlands Study of Depression and Anxiety. We compared how well multinomial logistic regression, a na?ve Bayes classifier, and Auto-sklearn predicted depression and anxiety diagnoses at a 2-, 4-, 6-, and 9-year follow up, operationalized as binary or categorical variables. Predictor sets included demographic and self-report data, which can be easily collected in clinical practice at two initial time points (baseline and 1-year follow up).Results: At baseline, participants were 42.2 years old, 66.5% were women, and 53.6% had a current mood or anxiety disorder. The three methods were similarly successful in predicting (mental) health status, with correct predictions for up to 79% (95% CI 75?81%). However, Auto-sklearn was superior when assessing a more complex dataset with individual item scores.Conclusions: Automated ML methods added only limited value, compared to traditional data modelling when predicting the onset and course of depression and anxiety. However, they hold potential for automatization and may be better suited for complex datasets.Algorithms and the Foundations of Software technolog

Performance of methods to conduct mediation analysis with time-to-event outcomes

Previous studies have discouraged the use of the Cox proportional hazards (PH) model for traditional mediation analysis as it might provide biased results. Accelerated failure time (AFT) models have been proposed as an alternative for Cox PH models. In addition, the use of the potential outcomes framework has been proposed for mediation models with time-to-event outcomes. The aim of this paper is to investigate the performance of traditional mediation analysis and potential outcomes mediation analysis based on both the Cox PH and the AFT model. This is done by means of a Monte Carlo simulation study and the illustration of the methods using an empirical data set. Both the product-of-coefficients method of the traditional mediation analysis and the potential outcomes framework yield unbiased estimates with respect to their own underlying indirect effect value for simple mediation models with a time-to-event outcome and estimated based on Cox PH or AFT

Syndromes versus symptoms: towards validation of a dimensional approach of depression and anxiety

There is a growing awareness that research of the etiology of depressive and anxiety disorders has been hampered by their strictly categorical definition in the Diagnostic and Statistical Manual (DSM). The DSM uses a syndrome approach, which __ although beneficial for standardization - has inherent problems that make it suboptimal for research: high rates of (artificial) comorbidity, diagnostic heterogeneity and the unrealistic assumption of discontinuity between ill and healthy. A dimensional approach that focusses on the relative severity of continuous symptom domains could be more optimal but measurement and the added value of such dimensions has been debated. Therefore, this dissertation was aimed to investigate (1) the internal validity and possibility to measure dimensions and (2) their added value in etiological and clinical research. The results showed that measurement of dimensions can be optimized using self-report questionnaires. In addition, dimensions were shown to have added value in etiological and clinical research. Because of their specific and continuous nature, dimensions could be used to uncover symptom-specific and/or non-linear association. Together, the results suggest that dimensions of depression and anxiety have internal and external validity and have the potential to improve the psychiatric research

Data-driven course trajectories in primary care patients with major depressive disorder

Background: The chronic nature of MDD has been acknowledged as one of the key determinants of the burden associated with depression. Unfortunately, so far described prognostic factors have been inconsistent, possibly due to used course outcomes that are often based on arbitrary criteria/cut-offs. Therefore, the aim of the current study was to use data-driven trajectory groups based on closely spaced weekly severity ratings, as outcomes in prognostic research. Methods: The sample consisted of primary care patients with MDD (n = 153), who were followed up for a year with 52 consecutive weekly ratings of the nine DSM-IV MDD criterion symptoms. Growth Mixture Modeling (GMM) was used to reduce the interpersonal growth variation to an optimal set of clinically interpretable trajectory groups. Next, baseline course predictors were investigated and the prognostic (added) value of course-group membership was investigated for clinical outcomes after 1, 2, and 3 years. Results: GMM resulted in four trajectory groups: "early remission" (40.2%), "late remission" (9.8%), "remission and recurrence" (17.0%), and "chronic" (33.0%). Multivariate predictors of "chronic" group membership were a prior suicide attempt, comorbid dysthymia, and lower levels of somatic depressive symptoms. Group membership predicted differences in depression severity and/or quality of life after 1, 2, and 3 years. Conclusions: The used data-driven approach provided a parsimonious and clinically informative way to describe course variation across MDD patients. Using the trajectory groups to investigate prognostic factors of MDD provided insight in potentially useful prognostic factors. Importantly, trajectory-group membership was itself a strong predictor of future mental well-being