Dataset Information

Biomarker-based subtyping of depression and anxiety disorders using Latent Class Analysis. A NESDA study.

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.

SUBMITTER: Beijers L

PROVIDER: S-EPMC6393228 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Publications

Biomarker-based subtyping of depression and anxiety disorders using Latent Class Analysis. A NESDA study.

Beijers Lian L Wardenaar Klaas J KJ Bosker Fokko J FJ Lamers Femke F van Grootheest Gerard G de Boer Marrit K MK Penninx Brenda W J H BWJH Schoevers Robert A RA

Psychological medicine 20180604 4

<h4>Background</h4>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 ...[more]

PMID: 29860945

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Project description:BackgroundMaternal depression in the postpartum period confers substantial morbidity and mortality, but the definition of postpartum depression remains controversial. We investigated the heterogeneity of symptoms with the aim of identifying clinical subtypes of postpartum depression.MethodsData were aggregated from the international perinatal psychiatry consortium Postpartum Depression: Action Towards Causes and Treatment, which represents 19 institutions in seven countries. 17,912 unique subject records with phenotypic data were submitted. We applied latent class analyses in a two-tiered approach to assess the validity of empirically defined subtypes of postpartum depression. Tier one assessed heterogeneity in women with complete data on the Edinburgh postnatal depression scale (EPDS) and tier two in those with postpartum depression case status.Findings6556 individuals were assessed in tier one and 4245 in tier two. A final model with three latent classes was optimum for both tiers. The most striking characteristics associated with postpartum depression were severity, timing of onset, comorbid anxiety, and suicidal ideation. Women in class 1 had the least severe symptoms (mean EPDS score 10·5), followed by those in class 2 (mean EPDS score 14·8) and those in class 3 (mean EPDS score 20·1). The most severe symptoms of postpartum depression were significantly associated with poor mood (mean EPDS score 20·1), increased anxiety, onset of symptoms during pregnancy, obstetric complications, and suicidal ideation. In class 2, most women (62%) reported symptom onset within 4 weeks postpartum and had more pregnancy complications than in other two classes (69% vs 67% in class 1 and 29% in class 3).InterpretationPPD seems to have several distinct phenotypes. Further assessment of PPD heterogeneity to identify more precise phenotypes will be important for future biological and genetic investigations.FundingSources of funding are listed at the end of the article.

Dataset Information

Biomarker-based subtyping of depression and anxiety disorders using Latent Class Analysis. A NESDA study.

Background

Methods

Results

Conclusions

Publications

Biomarker-based subtyping of depression and anxiety disorders using Latent Class Analysis. A NESDA study.

Similar Datasets

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