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Title: Biomarker-based subtyping of depression and anxiety disorders using Latent Class Analysis. A NESDA study
Author(s): Lian Beijers, Klaas J. Wardenaar, Fokko J. Bosker, Femke Lamers, Gerard van Grootheest, Marrit K. de Boer, Brenda W.J.H. Penninx and Robert A. Schoevers
Journal: Psychological Medicine
Year: 2018
Month: April
Day: 19
Volume: 49
Pages: 617-627
Affiliation: Department of Psychiatry, University of Groningen, University Medical Center Groningen, Interdisciplinary Center Psychopathology and Emotion regulation (ICPE), Groningen, The Netherlands
DOI: 10.1017/ S0033291718001307
File URL: /vuams-pubs/Biomarker-based_subtyping_of_depression_and_anxiety_disorders_using_Latent_Class_Analysis._A_NESDA_study.pdf
Keywords: Anxiety; biomarkers; depression; latent class analysis; subtyping
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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