Abstract
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.
Highlights
The heterogeneity of Major Depressive Disorder (MDD) is one of the great challenges in psychiatry [1]
A review that summarized 91 Factor Analysis (FA)/Principal Component Analysis (PCA) studies showed that the items of a range of widely used depression self-report questionnaires can be decomposed into different factors, with the most consistent factor distinction being found between somatic symptoms of depression (e.g. ‘energy loss’, ‘sleeping problems’) and symptoms of depressive mood (e.g. ‘feeling sad’) and/or cognitions (‘feeling guilty’, ‘feeling worthless’) [13]
The current study was conceived to investigate the use of 3MPCA to get better insight into the heterogeneity of depression
Summary
The heterogeneity of Major Depressive Disorder (MDD) is one of the great challenges in psychiatry [1]. Two main approaches have been taken to identify more homogeneous entities of depression, using either ‘clinical categories’ or ‘data driven’ approaches The former approach uses a priori combinations of symptoms, course trajectories (e.g. chronic, recurrent) and/or severity, which are anchored in clinical theories or experience, to differentiate between patients [7]. To investigate heterogeneity on the symptom level, Principal Component Analysis (PCA) and Factor Analysis (FA) have been used to identify more homogeneous subdomains (dimensions) underlying depression symptomatology [11,12,13,14,15,16,17] Some of these studies have looked at the structure of the nine MDD criterion symptoms. The results have been inconsistent, in part due to the diversity in study designs, samples, analytical methods and input-variables [17]
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