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

https://doi.org/10.1016/j.jad.2015.03.029Get rights and content

Abstract

Background

The course-heterogeneity of Major Depressive Disorder (MDD) hampers development of better prognostic models. Although latent class growth analyses (LCGA) have been used to explain course-heterogeneity, such analyses have failed to also account for symptom-heterogeneity of depressive symptoms. Therefore, the aim was to identify more specific data-driven subgroups based on patterns of course-trajectories on different depressive symptom domains.

Methods

In primary care MDD patients (n=205), the presence of the MDD criterion symptoms was determined for each week during a year. Weekly ‘mood/cognition’ (MC) and ‘somatic’ (SOM) scores were computed and parallel processes-LCGA (PP-LCGA) was used to identify subgroups based on the course on these domains. The classes׳ associations with baseline predictors and 2-/3-year outcomes were investigated.

Results

PP-LCGA identified four classes: quick recovery, persisting SOM, persisting MC, and persisting SOM+MC (chronic). Persisting SOM was specifically predicted by higher baseline somatic symptomatology and somatization, and was associated with more somatic depressive symptomatology at long-term follow-up. Persisting MC was specifically predicted by higher depressive severity, thinking insufficiencies, neuroticism, loneliness and lower self-esteem, and was associated with lower mental health related quality of life and more mood/cognitive depressive symptomatology at follow-up.

Limitations

The sample was small and contained only primary care MDD patients. The weekly depression assessments were collected retrospectively at 3-month intervals.

Conclusions

The results indicate that there are two specific prototypes of depression, characterized by either persisting MC or persisting SOM, which have different sets of associated prognostic factors and long-term outcomes, and could have different etiological mechanisms.

Introduction

Major Depressive Disorder (MDD) has a strongly recurrent nature with many patients having multiple episodes during their lifetime or developing chronic symptoms. Given the adverse effects of these course-trajectories on quality of life and psychosocial functioning (Ormel et al., 1993), it is important to develop better models to predict which patients are at risk of recurrence/chronicity and which patients are not. Unfortunately, accurate course-prediction in MDD has so far proven difficult. Although predictors of chronicity have been reported (e.g. high baseline severity (van Beljouw et al., 2010), comorbidity (Penninx et al., 2011, Patten et al., 2010), young age-of-onset (Karlsson et al., 2008) and long episode duration (Conradi et al., 2008a, Conradi et al., 2008b)), most of these are general severity indicators that can differentiate between those with a chronic and non-chronic course, but not between patients with qualitatively different course-trajectories (e.g. quick remission vs. remission followed by recurrence; Wardenaar et al., 2014). This lack of specific predictors could be due to the fact that traditional prognostic studies have (1) used suboptimal course-trajectory outcomes and (2) have not addressed the issue of depressive symptom heterogeneity.

Most prognostic studies in depression have used course outcomes based on diagnostic criteria (e.g. Diagnostic and Statistical Manual [DSM]) or depression severity scales (e.g. Penninx et al., 2011). Although pragmatic and clinically appealing, these outcomes are suboptimal for research because the used diagnostic concepts are arbitrary (Widiger and Samuel, 2005) and impose artificial dichotomies between healthy and ill (Kendell, 1989, Kendell and Jablensky, 2003). In addition, the reliance on scale cutoffs or relevant change scores on depression severity scales can be problematic as their reproducibility is seldom considered (Wise, 2004).

To overcome abovementioned problems, researchers have used data-driven techniques to identify empirically-based course groups for use as outcomes in prognostic research. These studies have provided insights into the naturally occurring course patterns of depression and their determinants. However, the identified prognostic factors in these studies have mainly been general severity indicators that are associated with chronicity (e.g. severity, co-morbidity; (Wardenaar et al., 2014; Murphy et al., 2008; Rhebergen et al., 2012)) but do not differentiate between patients with similar severity levels but qualitatively different future course-trajectories (e.g. ‘early remission’ vs. ‘remission and recurrence’).

The failure to find more specific course-predictors could be due to the fact that the heterogeneity of depressive symptomatology is seldom addressed when looking at depressive course-trajectories (Wardenaar and de Jonge, 2013). Previous work has shown that depression is not a homogeneous, unidimensional construct, but can be decomposed into ‘mood/cognitive’ (MC; e.g. sadness, feelings of guilt) and ‘somatic/vegetative’ (SOM; e.g. energy loss) subdomains (Shafer, 2006, Wardenaar et al., 2010). These domains have been shown to be differently associated with a range of clinically important factors. MC has been shown to be more strongly associated with increased neuroticism, decreased extraversion, psychiatric comorbidity (Lux and Kendler, 2010) and cognitive vulnerability (Struijs et al., 2013), whereas SOM has been shown to be more strongly associated with somatic risk factors (Luppino et al., 2011), and somatic diseases (de Jonge et al., 2006). Together, these findings indicate that SOM and MC represent entities with different underlying mechanisms and clinical characteristics, which also suggests that the two domains could follow different course-trajectories in the same patient.

Describing two separate course-trajectories of SOM and MC instead of one overall severity trajectory would decrease the heterogeneity of the resulting course-groups and allow researchers to capture a larger range of possible phenotypes. For example, some patients may only persist on SOM, others only on MC, and some on both domains. Each group would be likely to have different predictors associated with them. Data-driven parallel processes LCGA (PP-LCGA) could be used to identify such subgroups based on contemporaneous growth on two symptom domains. This approach has been successfully used in developmental psychopathology research (e.g. Jester et al., 2005, Wiesner and Kim, 2006), but to our knowledge not to identify more homogeneous depression subgroups.

The current study was intended to bridge above described research lines on course- and symptom-heterogeneity and used PP-LCGA to identify clinically meaningful subgroups of MDD patients with different combinations of SOM and MC course-trajectories. Data came from primary care MDD patients (n=205), who provided weekly ratings of the 9 DSM-IV MDD criterion symptoms during a 1-year follow-up period. Associations of the identified subgroups with baseline prognostic factors (general, SOM-specific or MC-specific) and outcomes (e.g. depression severity) at 24- and 36-month follow-up were investigated.

Section snippets

Participants and procedures

Data came from a randomized-controlled trial in primary care MDD patients conducted between 1998 and 2003 (Conradi et al., 2007, Conradi et al., 2008a, Conradi et al., 2008b). At baseline, patients were stratified for antidepressant use and randomly allocated to one of four treatment conditions by use of a computer-generated random list, with each next allocation kept blinded. Patients either received care as usual (CAU), CAU plus a psychoeducation (CAU+PEP), CAU plus psychoeducation and

Demographic and clinical characteristics

The sample descriptives (Table 1, left column) showed that the mean age was 43.3 years and the majority was female (65.7%). The sample had a mean 12.7 years of education; the majority was employed (61.9%) and living together (66.7%). The median number of previous MDD episodes was 2 (IQR=0–4). Antidepressants were used by 75.6%. Comorbid social phobia was most common (14.1%), followed by panic disorder (11.2%).

The MC and SOM scales were moderately correlated at baseline (ρ=0.55), and the scale

Discussion

The development of more specific prognostic and etiological models of depression has been hampered by the lack of empirically-based, homogeneous diagnostic and course-descriptions that capture sufficient inter-individual symptom variation. Therefore, this study aimed to identify data-driven depression course-groups, addressing symptom heterogeneity by using a parallel processes approach (PP-LCGA), which can model latent classes (subgroups) based on the course-trajectories of SOM and MC

Role of funding source

The funding sources had no involvement in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

Conflict of interest

None.

Acknowledgments

This study was supported by a VICI grant (No. 91812607) received by Peter de Jonge from the Netherlands organization for Scientific research (NWO-ZonMW). The trial from which the data were sourced was supported by grants from NWO Medical Sciences Program and Chronic Diseases Program, Research Foundations of Health Insurance Company ‘Het Groene Land’, Regional Health Insurance Company (RZG), Netherlands Foundation for Mental Health, and the University Medical Center Groningen.

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