Beijers L, Wardenaar KJ, Bosker FJ, Lamers F, van Grootheest G, de Boer MK, Penninx BW, Schoevers RA. Biomarker-based subtyping of depression and anxiety disorders using Latent Class Analysis. A NESDA study.
Psychol Med 2019;
49:617-627. [PMID:
29860945 PMCID:
PMC6393228 DOI:
10.1017/s0033291718001307]
[Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Revised: 04/18/2018] [Accepted: 04/19/2018] [Indexed: 12/11/2022]
Abstract
BACKGROUND
Etiological research of depression and anxiety disorders has been hampered by diagnostic heterogeneity. In order to address this, researchers have tried to identify more homogeneous patient subgroups. This work has predominantly focused on explaining interpersonal heterogeneity based on clinical features (i.e. symptom profiles). However, to explain interpersonal variations in underlying pathophysiological mechanisms, it might be more effective to take biological heterogeneity as the point of departure when trying to identify subgroups. Therefore, this study aimed to identify data-driven subgroups of patients based on biomarker profiles.
METHODS
Data of patients with a current depressive and/or anxiety disorder came from the Netherlands Study of Depression and Anxiety, a large, multi-site naturalistic cohort study (n = 1460). Thirty-six biomarkers (e.g. leptin, brain-derived neurotrophic factor, tryptophan) were measured, as well as sociodemographic and clinical characteristics. Latent class analysis of the discretized (lower 10%, middle, upper 10%) biomarkers were used to identify different patient clusters.
RESULTS
The analyses resulted in three classes, which were primarily characterized by different levels of metabolic health: 'lean' (21.6%), 'average' (62.2%) and 'overweight' (16.2%). Inspection of the classes' clinical features showed the highest levels of psychopathology, severity and medication use in the overweight class.
CONCLUSIONS
The identified classes were strongly tied to general (metabolic) health, and did not reflect any natural cutoffs along the lines of the traditional diagnostic classifications. Our analyses suggested that especially poor metabolic health could be seen as a distal marker for depression and anxiety, suggesting a relationship between the 'overweight' subtype and internalizing psychopathology.
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