Characterisation and Genetic Architecture of Major Depressive Disorder Subgroups Defined by Weight and Sleep Changes
Marshall, S.; Adams, M. J.; Evans, K. L.; Strawbridge, R. J.; McIntosh, A. M.; Thomson, P.
Show abstract
Major depressive disorder, MDD, is highly heterogeneous and thus subgroups with different underlying aetiologies have been postulated. The aim of this work is to further characterise depression subgroups defined using sleep and weight changes. Probable lifetime MDD cases (n = 26,662) from the UK Biobank were stratified into three subgroups defined by self-reported weight and sleep changes during worst episode: (i) increased weight and sleep ({uparrow}WS), (ii) decreased weight and sleep ({downarrow}WS) and (iii) the remaining uncategorised individuals. Analyses compared the depression characteristics, mental health scores, neurological and inflammatory comorbidities and genetic architecture between subgroups and with 50,147 controls from UK Biobank. In contrast to {uparrow}WS depression, {downarrow}WS depression had a higher age of onset and lower proportion reporting countless or continuous episodes compared to uncategorised individuals. The {downarrow}WS depression also had a higher wellbeing score than the other subgroups. Analyses of subgroup comorbidities identified a novel association between {uparrow}WS depression and epilepsy. Subgroup-specific GWAS identified three genome-wide significant loci associated with {uparrow}WS in genes previously associated with immunometabolic traits and response to anticonvulsants. The effect of BMI adjustment in the genetic analyses of the subgroups and using broader weight-only definitions were also examined. The findings provide further evidence for differences in the characteristics and genetic architecture of depression subgroups defined by sleep and weight change and highlight the importance of dividing non-{uparrow}WS individuals into {downarrow}WS and uncategorised subgroups in analyses, as {downarrow}WS symptoms may identify a more acute depression subgroup.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Acylcarnitines metabolism in depression: association with diagnostic status, depression severity and symptom profile in the NESDA cohort 96%
- Role of Inflammation in Depressive and Anxiety Disorders, Affect, and Cognition: Genetic and Non-Genetic Findings in the Lifelines Cohort Study 95%
- Differential and spatial expression meta-analysis of genes identified in genome-wide association studies of depression 94%
Similar papers in this journal
Similar papers in this journal
- Sociodemographic, clinical, and genetic factors associated with self-reported antidepressant response outcomes in the UK Biobank 94%
- A computational approach to understanding effort-based decision-making in depression 93%
- Major depression symptom severity associations with willingness to exert effort and patch foraging strategy 93%
Similar papers in this journal
- What’s in a diagnosis? A genetic decomposition of major depression 97%
- Polygenic prediction of major depressive disorder and related traits in African ancestries UK Biobank participants 96%
- Association of Inflammation with Depression and Anxiety: Evidence for Symptom-Specificity and Potential Causality from UK Biobank and NESDA Cohorts 96%
Similar papers in this journal
- Novel polygenic risk score links depression-related cortical transcriptomic changes to brain morphology and depressive symptoms in men 95%
- Sensitive period-regulating genetic pathways and exposure to adversity shape risk for depression 95%
- Estimating the direct effects of the genetic liabilities to bipolar disorder, schizophrenia, and behavioral traits on suicide attempt using a multivariable Mendelian randomization approach 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.