Symptom-specific course trajectories and their determinants in primary care patients with Major Depressive Disorder: Evidence for two etiologically distinct prototypes

Response trajectories during escitalopram treatment of patients with major depressive disorder

Depression is a leading global cause of disability, yet about half of patients do not respond to initial antidepressant treatment. This treatment difficulty may be in part due to the heterogeneity of depression and corresponding response to treatment. Unsupervised machine learning allows underlying patterns to be uncovered, and can be used to understand this heterogeneity by finding groups of patients with similar response trajectories. Prior studies attempting this have clustered patients using a narrow range of data primarily from depression scales. In this work, we used unsupervised machine learning to cluster patients receiving escitalopram therapy using a wide variety of subjective and objective clinical features from the first eight weeks of the Canadian Biomarker Integration Network in Depression-1 trial. We investigated how these clusters responded to treatment by comparing changes in symptoms and symptom categories, and by using Principal Component Analysis (PCA). Our algorithm found three clusters, which broadly represented non-responders, responders, and remitters. Most categories of features followed this response pattern except for objective cognitive features. Using PCA with our clusters, we found that subjective mood state/anhedonia is the core feature of response with escitalopram, but there exists other distinct patterns of response around neurovegetative symptoms, activation, and cognition.

Staging of unipolar depression: systematic review and discussion of clinical implications

Staging has been increasingly used in unipolar depression since its introduction in the nineties. Several models are available, but their differential features and implications are not completely clear. We systematically reviewed: (a) staging models of longitudinal development of unipolar depression; (b) staging models of treatment-resistant unipolar depression; (c) their applications. MEDLINE, PsycINFO, EMBASE, and Web of Science were examined according to PRISMA guidelines from inception to December 2021. Search terms were: 'stage/staging', combined using the Boolean 'AND' operator with 'psychiatric disorder/mental disorder/depressive/mood disorder'. A total of 169 studies were identified for inclusion: 18 described staging models or applications, 151 described treatment-resistant staging models or applications. Staging models of longitudinal development were found to play a key role in formulating sequential treatment, with particular reference to the use of psychotherapy after pharmacotherapy. Staging methods based on treatment resistance played a crucial role in setting entry criteria for randomized clinical trials and neurobiological investigations. Staging is part of clinimetrics, the science of clinical measurements, and its role can be enhanced by its association with other clinimetric strategies, such as repeated assessments, organization of problematic areas, and evaluation of phenomena that may affect responsiveness. In research, it may allow to identify more homogeneous populations in terms of treatment history that may diminish the likelihood of spurious results in comparisons. In clinical practice, the use of staging in a clinimetric perspective allows clinicians to make full use of the information that is available for an individual patient at a specific time.

ConNEcT: A Novel Network Approach for Investigating the Co-occurrence of Binary Psychopathological Symptoms Over Time

Network analysis is an increasingly popular approach to study mental disorders in all their complexity. Multiple methods have been developed to extract networks from cross-sectional data, with these data being either continuous or binary. However, when it comes to time series data, most efforts have focused on continuous data. We therefore propose ConNEcT, a network approach for binary symptom data across time. ConNEcT allows to visualize and study the prevalence of different symptoms as well as their co-occurrence, measured by means of a contingency measure in one single network picture. ConNEcT can be complemented with a significance test that accounts for the serial dependence in the data. To illustrate the usefulness of ConNEcT, we re-analyze data from a study in which patients diagnosed with major depressive disorder weekly reported the absence or presence of eight depression symptoms. We first extract ConNEcTs for all patients that provided data during at least 104 weeks, revealing strong inter-individual differences in which symptom pairs co-occur significantly. Second, to gain insight into these differences, we apply Hierarchical Classes Analysis on the co-occurrence patterns of all patients, showing that they can be grouped into meaningful clusters. Core depression symptoms (i.e., depressed mood and/or diminished interest), cognitive problems and loss of energy seem to co-occur universally, but preoccupation with death, psychomotor problems or eating problems only co-occur with other symptoms for specific patient subgroups.

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 (class-probability), 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 (ρ_pred) were calculated.

RESULTS

Low to high prediction correlations (ρ_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.

A dimensional approach to understanding the relationship between self-reported hearing loss and depression over 12 years: the Three-City study

Objectives: To examine the relationship between hearing loss and depression in older adults longitudinally. This paper uses a dimensional approach to conceptualising depression, with the aim of further enhancing understanding of this relationship.Method: 8344 community-dwelling adults aged 65 years and above enrolled in the Three-City prospective cohort study were included. Relationships between baseline self-reported hearing loss (HL) with the trajectory of different dimensions of depression symptoms over 12 years were examined using linear mixed models. Depression dimensions were determined using the four-factor structure of the Centre for Epidemiology Studies-Depression Scale (CESD): depressed affect, positive affect, somatic symptoms and interpersonal problems.Results: HL was associated with somatic symptoms of depression both at baseline (b = .07, p = .04) and over 12 years (b = .01, p = .04). HL was associated with poorer depressed affect and interpersonal problems at baseline (b = .05, p = .001, b = .35, p < .001; respectively), but not over follow-up. HL was associated with poorer positive affect symptoms over time (b = -.01, p = .01).Conclusion: HL had varied relationships with different dimensions of depression symptoms, and there were different patterns of adjustment for the dimensions. HL was primarily associated with somatic symptoms, suggesting that shared disease processes might partly underlie the relationship between HL and depression. Targeted assessment and treatment of somatic and positive affect symptoms in older adults with HL might facilitate better wellbeing in this population.

Patterns, predictors, and outcome of the trajectories of depressive symptoms from adolescence to adulthood

BACKGROUND

The long-term trajectory of depressive symptoms has a heterogeneous pattern. Identifying factors associated with different trajectories and outcomes may have important theoretical and clinical implications. This study explored patterns of depressive symptom trajectory from adolescence to adulthood, and their relationship with subsequent psychiatric disorders.

METHOD

A sample of 816 participants (58.8% girls; M = 16.58 years old at baseline, SD = 1.21) from a large community sample were interviewed four times during adolescence and adulthood. Depressive symptoms were also assessed. Symptom trajectory identification was based on latent class mixed modeling. Logistic regression was used for predicting emotional and drug use disorder over age 30.

RESULTS

Three trajectories of depressive symptoms were identified: "decreasing symptom" (decreasing trajectory of symptoms; 15.1% of participants), "increasing symptom" (initially decreasing pattern of symptoms and then increasing; 6.1% of participants), and "normative symptom" (consistently low symptom levels; 78.8% of participants). Predictors of the increasing symptom trajectory were high level of loneliness and state anxiety, presence of an emotional disorder, and low involvement in physical exercise at baseline. This trajectory membership predicted the development of anxiety disorders over age 30. Predictors of the decreasing symptom class were being female and high level of worry at baseline.

CONCLUSIONS

Long-term trajectories of depressive symptoms are heterogeneous, with each trajectory having different predictors and are associated with different outcomes during adulthood.

Connectivity between the anterior insula and dorsolateral prefrontal cortex links early symptom improvement to treatment response

BACKGROUND

Early improvement (EI) following treatment with antidepressants is a widely reported predictor to the treatment response. This study aimed to identify the resting-state functional connectivity (rs-FC) and its related clinical features that link the treatment response at the time of EI.

METHODS

This study included 23 first-episode treatment-naive patients with MDD. After 2 weeks of antidepressant treatment, these patients received 3.0 Tesla resting-state functional magnetic resonance imaging scanning and were subgrouped into an EI group (N = 13) and a non-EI group (N = 10). Using the anterior insula (rAI) as a seed region, this study identified the rs-FC that were associated with both EI and the treatment response at week 12, and further tested the associations of the identified rs-FC with either the clinical features or the early symptom improvement.

RESULTS

Rs-FC between rAI and the left dorsolateral prefrontal cortex (dlPFC) was associated with EI (t₂₁ = -6.091, p = 0.022 after FDR correction for multiple comparisons). This rs-FC was also associated with an interaction between EI and the treatment response at the week 12 (t₂₁ = -5.361, p = 6.37e-5). Moreover, among the clinical features, this rs-FC was associated with the early symptom improvement in the insomnia, somatic symptoms, and anxiety symptoms, and these early symptom improvements were associated with the treatment response.

CONCLUSION

Rs-FC between the rAI and the left dlPFC played a crucial role in the early antidepressant effect, which linked the treatment response. The early treatment effect relating to rAI may represent an early symptom improvement in self-perceptual anxiety, somatic symptoms and insomnia.

Treatment response classes in major depressive disorder identified by model-based clustering and validated by clinical prediction models

The identification of generalizable treatment response classes (TRC[s]) in major depressive disorder (MDD) would facilitate comparisons across studies and the development of treatment prediction algorithms. Here, we investigated whether such stable TRCs can be identified and predicted by clinical baseline items. We analyzed data from an observational MDD cohort (Munich Antidepressant Response Signature [MARS] study, N = 1017), treated individually by psychopharmacological and psychotherapeutic means, and a multicenter, partially randomized clinical/pharmacogenomic study (Genome-based Therapeutic Drugs for Depression [GENDEP], N = 809). Symptoms were evaluated up to week 16 (or discharge) in MARS and week 12 in GENDEP. Clustering was performed on 809 MARS patients (discovery sample) using a mixed model with the integrated completed likelihood criterion for the assessment of cluster stability, and validated through a distinct MARS validation sample and GENDEP. A random forest algorithm was used to identify prediction patterns based on 50 clinical baseline items. From the clustering of the MARS discovery sample, seven TRCs emerged ranging from fast and complete response (average 4.9 weeks until discharge, 94% remitted patients) to slow and incomplete response (10% remitted patients at week 16). These proved stable representations of treatment response dynamics in both the MARS and the GENDEP validation sample. TRCs were strongly associated with established response markers, particularly the rate of remitted patients at discharge. TRCs were predictable from clinical items, particularly personality items, life events, episode duration, and specific psychopathological features. Prediction accuracy improved significantly when cluster-derived slopes were modelled instead of individual slopes. In conclusion, model-based clustering identified distinct and clinically meaningful treatment response classes in MDD that proved robust with regard to capturing response profiles of differently designed studies. Response classes were predictable from clinical baseline characteristics. Conceptually, model-based clustering is translatable to any outcome measure and could advance the large-scale integration of studies on treatment efficacy or the neurobiology of treatment response.

Severity, course trajectory, and within-person variability of individual symptoms in patients with major depressive disorder

BACKGROUND

Depression shows a large heterogeneity of symptoms between and within persons over time. However, most outcome studies have assessed depression as a single underlying latent construct, using the sum score on psychometric scales as an indicator for severity. This study assesses longitudinal symptom-specific trajectories and within-person variability of major depressive disorder over a 9-year period.

METHODS

Data were derived from the Netherlands Study of Depression and Anxiety (NESDA). This study included 783 participants with a current major depressive disorder at baseline. The Inventory Depressive Symptomatology-Self-Report (IDS-SR) was used to analyze 28 depressive symptoms at up to six time points during the 9-year follow-up.

RESULTS

The highest baseline severity scores were found for the items regarding energy and mood states. The core symptoms depressed mood and anhedonia had the most favorable course, whereas sleeping problems and (psycho-)somatic symptoms were more persistent over 9-year follow-up. Within-person variability was highest for symptoms related to energy and lowest for suicidal ideation.

CONCLUSIONS

The severity, course, and within-person variability differed markedly between depressive symptoms. Our findings strengthen the idea that employing a symptom-focused approach in both clinical care and research is of value.

The association of vision loss and dimensions of depression over 12 years in older adults: Findings from the Three City study

BACKGROUND

The established relationship between vision impairment and depression is limited by the examination of depression only as a unidimensional construct. The present study explores the vision-depression relationship using a dimensional approach.

METHODS

9036 participants aged 65 years and above enrolled in the Three-City study were included. Relationships between baseline near Vision Impairment (VI) or self-reported distance Visual Function (VF) loss with trajectory of four dimensions of depression - depressed affect, positive affect, somatic symptoms and interpersonal problems - over 12 years were examined using mixed-effects models. Depression dimensions were determined using the four-factor structure of the Centre for Epidemiology Studies-Depression Scale (CESD).

RESULTS

In the fully adjustment models, mild near VI predicted poorer depressed affect (b = 0.04, p = .002) and positive affect (b = -0.06, p < 0.001) over time, with evidence of longer term adjustment. Distance VF loss was associated with poorer depressed affect (b = 0.27, p ≤ .001), positive affect (b = -0.15, p = .002), and somatic symptoms (b = 0.18, p ≤ .001) at baseline, although only the association with depressed affect was significant longitudinally (b = 0.01, p = .001). Neither near VI nor distance VF loss was associated with interpersonal problems.

LIMITATIONS

This paper uses a well-supported model of depression dimensions, however, there remains no definite depression dimension model. Distance VF loss was self-reported, which can be influenced by depression symptoms.

CONCLUSIONS

Vision impairment in older adults is primarily associated with affective dimensions of depression. A reduction in social connectedness and ability to engage in pleasurable activities may underlie the depression-vision relationship. Older adults with vision impairment may benefit from targeted treatment of affective symptoms, and pleasant event scheduling.

Response rate profiles for major depressive disorder: Characterizing early response and longitudinal nonresponse

BACKGROUND

Definition of response is critical when seeking to establish valid predictors of treatment success. However, response at the end of study or endpoint only provides one view of the overall clinical picture that is relevant in testing for predictors. The current study employed a classification technique designed to group subjects based on their rate of change over time, while simultaneously addressing the issue of controlling for baseline severity.

METHODS

A set of latent class trajectory analyses, incorporating baseline level of symptoms, were performed on a sample of 344 depressed patients from a clinical trial evaluating the efficacy of cognitive behavior therapy and two antidepressant medications (escitalopram and duloxetine) in patients with major depressive disorder.

RESULTS

Although very few demographic and illness-related features were associated with response rate profiles, the aggregated effect of candidate genetic variants previously identified in large pharmacogenetic studies and meta-analyses showed a significant association with early remission as well as nonresponse. These same genetic scores showed a less compelling relationship with endpoint response categories. In addition, consistent nonresponse throughout the study treatment period was shown to occur in different subjects than endpoint nonresponse, which was verified by follow-up augmentation treatment outcomes.

CONCLUSIONS

When defining groups based on the rate of change, controlling for baseline depression severity may help to identify the clinically relevant distinctions of early response on one end and consistent nonresponse on the other.

Major Depression in Chinese Medicine Outpatients with Stagnation Syndrome: Prevalence and the Impairments in Well-Being

Stagnation syndrome, a diagnostic entity in traditional Chinese medicine (TCM), has been long regarded as the TCM counterpart of major depression in Western medicine. The study investigated the prevalence of major depression among stagnation syndrome patients and evaluated their well-being and functioning outcomes. In total, 117 patients diagnosed with stagnation syndrome were measured using Stagnation Scale, the Patient Health Questionnaire-9 (PHQ-9), and the Body-Mind-Spirit Well-Being Inventory. Results indicate major depression among stagnation syndrome patients was characterized by a high co-occurrence rate and worse physical, mental, and functional outcomes. More than one-quarter (26.5%) of the patients met the DSM-V diagnostic criteria for major depression and over half (53%) exceeded the PHQ-9 cutoff (score above 10) for moderate/severe depression symptoms. The wellness of the stagnation syndrome patients was worse (M = 298.2, SD = 66.5) than that of the general population (M = 360.9, SD = 79.9), with a large Cohen's d value of 0.9. The “wellness outlook” of the depressed stagnation syndrome patients appeared grimmer (M = 252.3, SD = 52.2). The correlation between stagnation and depression was higher for affective symptoms than somatic symptoms. Physical distress did not mediate the relationship between stagnation and daily functioning. These might suggest that stagnation syndrome and major depression may share some similar psychological mechanisms.

The predictive specificity of psychological vulnerability markers for the course of affective disorders

High scores on markers of psychological vulnerability have been associated with a worse course of affective disorders. However, little is known about the specificity of those associations in predicting the course of different depressive and anxiety disorders. We examined the impact of psychological vulnerability on the short- and long-term course of depressive and anxiety disorders. Participants from the Netherlands Study of Depression and Anxiety with a current diagnosis of depression or anxiety (n = 1256) were reassessed after 2 and 6 years. Diagnostic status and chronic duration (>85% of the time) of symptoms were the outcomes. Predictors were neuroticism, extraversion, locus of control, cognitive reactivity (rumination and hopelessness reactivity), worry and anxiety sensitivity. High neuroticism, low extraversion and external locus of control predicted chronicity of various affective disorders. Rumination, however, predicted chronicity of depressive but not anxiety disorders. Worry specifically predicted chronicity of GAD and anxiety sensitivity predicted chronicity of panic disorder and social anxiety disorder. These patterns were present both at short-term and at long-term, without losing predictive accuracy. Psychological vulnerabilities that are theoretically specific to certain disorders indeed selectively predict the course of these disorders. General markers of vulnerability predicted the course of multiple affective disorders. This pattern of results supports the notion of specific as well as transdiagnostic predictors of the course of affective disorders and is consistent with hierarchical models of psychopathology.

Seeing the signs: Using the course of residual depressive symptomatology to predict patterns of relapse and recurrence of major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is characterized by high relapse/recurrence rates. Predicting individual patients' relapse/recurrence risk has proven hard, possibly due to course heterogeneity among patients. This study aimed to (1) identify homogeneous data-driven subgroups with different patterns of relapse/recurrence and (2) identify associated predictors.

METHODS

For a year, we collected weekly depressive symptom ratings in 213 primary care MDD patients. Latent class growth analyses (LCGA), based on symptom-severity during the 24 weeks after no longer fulfilling criteria for the initial major depressive episode (MDE), were used to identify groups with different patterns of relapse/recurrence. Associations of baseline predictors with these groups were investigated, as were the groups' associations with 3- and 11-year follow-up depression outcomes.

RESULTS

LCGA showed that heterogeneity in relapse/recurrence after no longer fulfilling criteria for the initial MDE was best described by four classes: "quick symptom decline" (14.0%), "slow symptom decline" (23.3%), "steady residual symptoms" (38.7%), and "high residual symptoms" (24.1%). The latter two classes showed lower self-esteem at baseline, and more recurrences and higher severity at 3-year follow-up than the first two classes. Moreover, the high residual symptom class scored higher on neuroticism and lower on extraversion and self-esteem at baseline. Interestingly, the steady residual symptoms and high residual symptoms classes still showed higher severity of depressive symptoms after 11 years.

CONCLUSION

Some measures were associated with specific patterns of relapse/recurrence. Moreover, the data-driven relapse/recurrence groups were predictive of long-term outcomes, suggesting that patterns of residual symptoms could be of prognostic value in clinical practice.

Gender similarities in somatic depression and in DSM depression secondary symptom profiles within the context of severity and bereavement

BACKGROUND

Most population studies report higher rates of depression among women than men, and some researchers have observed gender differences in depression symptoms overall, or in sub-groupings (e.g. somatic depression). However, gender symptom differences have been inconsistent, prompting this investigation of gender differences in secondary DSM symptom profiles in the context of bereavement status, age, and depression severity.

METHODS

Individuals with symptoms of core depression (flat affect or anhedonia) were selected from a large survey of adults in the Alberta, Canada workforce. Analyses involved the comparison of gender profiles across the seven DSM-IV secondary depressive symptoms plus a MANOVA of sex, bereavement, and age, with secondary symptoms comprising the dependent variable.

RESULTS

Gender profiles were very similar, irrespective of depression severity or bereavement. Secondary symptoms were marginally more common among women and more frequent among bereaved young adults, but there was no evidence for a gender-related somatic factor.

LIMITATIONS

First, data were gathered only for persons in the workforce and thus may not be generalizable to, for example, stay-at-home parents or those with employment issues. Second, the focus here is restricted to DSM symptoms, leaving risk factors, social roles, and brain functioning for separate investigation. Third, inferences were drawn from associations between groups of persons, rather than between individuals, requiring caution when speculating about individual attributes.

CONCLUSIONS

Gender differences in depression represent a difference in amount, not kind, suggesting that the range of depressive experiences is similar for men and women. There was no gender difference ascribable to somatic depression.

A precision medicine approach for psychiatric disease based on repeated symptom scores

For psychiatric diseases, rich information exists in the serial measurement of mental health symptom scores. We present a precision medicine framework for using the trajectories of multiple symptoms to make personalized predictions about future symptoms and related psychiatric events. Our approach fits a Bayesian hierarchical model that estimates a population-average trajectory for all symptoms and individual deviations from the average trajectory, then fits a second model that uses individual symptom trajectories to estimate the risk of experiencing an event. The fitted models are used to make clinically relevant predictions for new individuals. We demonstrate this approach on data from a study of antipsychotic therapy for schizophrenia, predicting future scores for positive, negative, and general symptoms, and the risk of treatment failure in 522 schizophrenic patients with observations over 8 weeks. While precision medicine has focused largely on genetic and molecular data, the complementary approach we present illustrates that innovative analytic methods for existing data can extend its reach more broadly. The systematic use of repeated measurements of psychiatric symptoms offers the promise of precision medicine in the field of mental health.

Moving Forward: Challenges and Directions for Psychopathological Network Theory and Methodology

Since the introduction of mental disorders as networks of causally interacting symptoms, this novel framework has received considerable attention. The past years have resulted in over 40 scientific publications and numerous conference symposia and workshops. Now is an excellent moment to take stock of the network approach: What are its most fundamental challenges, and what are potential ways forward in addressing them? After a brief conceptual introduction, we first discuss challenges to network theory: (1) What is the validity of the network approach beyond some commonly investigated disorders such as major depression? (2) How do we best define psychopathological networks and their constituent elements? And (3) how can we gain a better understanding of the causal nature and real-life underpinnings of associations among symptoms? Next, after a short technical introduction to network modeling, we discuss challenges to network methodology: (4) heterogeneity of samples studied with network analytic models, and (5) a lurking replicability crisis in this strongly data-driven and exploratory field. Addressing these challenges may propel the network approach from its adolescence into adulthood and promises advances in understanding psychopathology both at the nomothetic and idiographic level.

Longitudinal Trajectories of Depression Symptoms in Adolescence: Psychosocial Risk Factors and Outcomes

Variations in symptom trajectories within a population may represent distinct groups with different etiologies and outcomes. This study aimed to identify subgroups of depression symptom trajectories in a sample of adolescents, and to describe psychosocial attributes of the different groups. In a longitudinal study, 243 adolescents (121 males and 122 females), were assessed using a battery of measures of temperament, psychopathology, and psychological and behavioral functioning. Four phases of data collection over 7 years spanned average ages of the participants from 12 to 18 years old. Depressive symptoms from each phase were used to model latent class growth trajectories. A 4-group solution was selected as the best-fitting model: (1) ongoing stable low levels of depression; (2) very high depressive symptoms initially, but a steep decrease in symptoms over time; (3) moderately high depressive symptoms initially, but symptoms decreased over time; and (4) initially low levels of symptoms that increased over time. Trajectory group membership was associated with a range of psychosocial variables including temperament, childhood maltreatment, and young adult quality of life. Characterising these subgroups allows for a better understanding of how the interaction of risk factors increases the likelihood of depression and other poor outcomes, and highlights the importance of early interventions to prevent and treat adolescent depression.

Using a hybrid subtyping model to capture patterns and dimensionality of depressive and anxiety symptomatology in the general population

BACKGROUND

Researchers have tried to identify more homogeneous subtypes of major depressive disorder (MDD) with latent class analyses (LCA). However, this approach does no justice to the dimensional nature of psychopathology. In addition, anxiety and functioning-levels have seldom been integrated in subtyping efforts. Therefore, this study used a hybrid discrete-dimensional approach to identify subgroups with shared patterns of depressive and anxiety symptomatology, while accounting for functioning-levels.

METHODS

The Comprehensive International Diagnostic Interview (CIDI) 1.1 was used to assess previous-year depressive and anxiety symptoms in the Netherlands Mental Health Survey and Incidence Study-1 (NEMESIS-1; n=5583). The data were analyzed with factor analyses, LCA and hybrid mixed-measurement item response theory (MM-IRT) with and without functioning covariates. Finally, the classes' predictors (measured one year earlier) and outcomes (measured two years later) were investigated.

RESULTS

A 3-class MM-IRT model with functioning covariates best described the data and consisted of a 'healthy class' (74.2%) and two symptomatic classes ('sleep/energy' [13.4%]; 'mood/anhedonia' [12.4%]). Factors including older age, urbanicity, higher severity and presence of 1-year MDD predicted membership of either symptomatic class vs. the healthy class. Both symptomatic classes showed poorer 2-year outcomes (i.e. disorders, poor functioning) than the healthy class. The odds of MDD after two years were especially increased in the mood/anhedonia class.

LIMITATIONS

Symptoms were assessed for the past year whereas current functioning was assessed.

CONCLUSIONS

Heterogeneity of depression and anxiety symptomatology are optimally captured by a hybrid discrete-dimensional subtyping model. Importantly, accounting for functioning-levels helps to capture clinically relevant interpersonal differences.

Dual Trajectories of Reactive and Proactive Aggression from Mid-childhood to Early Adolescence: Relations to Sensation Seeking, Risk Taking, and Moral Reasoning

We examined the roles of sensation seeking, risk taking, and moral reasoning in the development of reactive and proactive aggression. Data were drawn from a multiethnic, longitudinal study of children from Switzerland (N = 1571; 52 % male; assessed annually over 6 years; 7-years-old at Time 1). At all 6 time points, teachers reported children's reactive and proactive aggression via questionnaire. Children's sensation seeking (at Time 1) and risk taking (at Time 2) were assessed with two interactive computer tasks and their moral reasoning was assessed at Time 2 in response to four hypothetical vignettes depicting moral transgressions. Parallel process Latent Class Growth Analysis (PP-LCGA) identified six dual trajectories of reactive and proactive aggression. Children with either childhood-limited or adolescent-onset aggression showed high sensation seeking. Children with persistent, high levels of both reactive and proactive aggression across time showed high levels of sensation seeking and risk taking, as well as low levels of moral reasoning. Children with only high risk taking were more likely to display moderate levels of aggression across time. These findings highlight the shared and differential roles of sensation seeking, risk taking, and moral reasoning in the dual development of reactive and proactive aggression from mid-childhood to early adolescence. We discuss implications for common and tailored strategies to combat these aggression subtypes.

Changing depressive symptoms following percutaneous coronary intervention, clustering and effect on adherence - The THORESCI study

BACKGROUND

Depressive symptom dimensions may have a differential effect on cardiac prognosis. It is yet unknown whether and how depressive symptoms change together over time and how this may affect disease progression. We examined the clustering of changing depressive symptoms over the first 6 months after percutaneous coronary intervention (PCI), and examined the influence of the change profile on the predictive value of depression for treatment adherence at 6 months post-PCI.

METHODS

PCI patients (N=219, age: 62±15, 20% women) reported on depressive symptoms (PHQ-9, BDI; 30 symptoms) and adherence (MOS-GAS) at 1 and 6 months post-PCI. Principal component analysis (PCA) was performed on the individual symptom change scores. Multivariable linear regression examined the role of change profiles in predicting general treatment adherence, while adjusting for demographic and clinical characteristics.

RESULTS

Four change-factors emerged from PCA. One somatic-affective change-factor (10 symptoms), two cognitive-affective change-factors (6 general cognitive-affective and 7 severe cognitive symptoms) and one mixed factor were identified. We extracted 5 symptom change profiles. Linear regression showed the moderating role of the change profiles. In patients reporting a net increase in depressive symptoms, higher cognitive affective symptoms (β=-.46, p=.001) and higher somatic-affective symptoms (β=-.29; p=.044) were associated with worse general adherence.

DISCUSSION

Four distinct depressive symptom change-factors were identified that moderated the association of somatic-affective and cognitive-affective depressive symptom levels with general treatment adherence. This is of clinical importance as not only current symptoms, but also symptom change over the preceding months may be important to consider in screening and risk prediction.

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.