Abstract
Depression is a leading cause of burden of disease among young people. Current treatments are not uniformly effective, in part due to the heterogeneous nature of major depressive disorder (MDD). Refining MDD into more homogeneous subtypes is an important step towards identifying underlying pathophysiological mechanisms and improving treatment of young people. In adults, symptom-based subtypes of depression identified using data-driven methods mainly differed in patterns of neurovegetative symptoms (sleep and appetite/weight). These subtypes have been associated with differential biological mechanisms, including immuno-metabolic markers, genetics and brain alterations (mainly in the ventral striatum, medial orbitofrontal cortex, insular cortex, anterior cingulate cortex amygdala and hippocampus). K-means clustering was applied to individual depressive symptoms from the Quick Inventory of Depressive Symptoms (QIDS) in 275 young people (15–25 years old) with MDD to identify symptom-based subtypes, and in 244 young people from an independent dataset (a subsample of the STAR*D dataset). Cortical surface area and thickness and subcortical volume were compared between the subtypes and 100 healthy controls using structural MRI. Three subtypes were identified in the discovery dataset and replicated in the independent dataset; severe depression with increased appetite, severe depression with decreased appetite and severe insomnia, and moderate depression. The severe increased appetite subtype showed lower surface area in the anterior insula compared to both healthy controls. Our findings in young people replicate the previously identified symptom-based depression subtypes in adults. The structural alterations of the anterior insular cortex add to the existing evidence of different pathophysiological mechanisms involved in this subtype.
Highlights
Introduction Approximately322 million people worldwide (5% of the world’s population) suffer from Major Depressive Disorder (MDD); a disease characterized by a depressed mood and associated symptoms[1]
The current study aims to replicate the data-driven symptom profiles based on neurovegetative symptoms, previously identified in adults, in young people with MDD
The Scott and Friedman partitioning measure showed that the index number for this number of clusters was higher than the cluster indices of an empirical null distribution, meaning that 3 clusters described the data better than data with no underlying clusters (Scott: p < 0.001, Friedman: p = 0.01, Supplementary Fig. S3)
Summary
Participants were recruited from youth mental health centers in Australia as part of the YoDA-A and YoDA-C (Youth Depression Alleviation) studies[32,33]. All participants were aged between 15 and 25 years old and the participants in the MDD group were diagnosed with a primary diagnosis of MDD. For the MDD participants, a score of 20 or higher on the Montgomery–Åsberg Depression Rating Scale (MADRS), indicating at least moderate severity of symptoms, was required. To match the YoDA sample, the sample was restricted to young people between 18 and 25 years old with an MDD diagnosis, resulting in a sample size of 244. The STAR*D sample is comparable to the YoDA sample in age, gender and depression severity. In both studies, only participants with moderate to severe depression based on either the Montgomery–Åsberg Depression Rating Scale or the Hamilton Depression Rating Scale were included. The participants in STAR*D were not using antidepressants at time of inclusion and only a small proportion (7%) of the YoDA participants were using antidepressants at time of inclusion
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