Dataset Information

Neurovegetative symptom subtypes in young people with major depressive disorder and their structural brain correlates.

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.

SUBMITTER: Toenders YJ

PROVIDER: S-EPMC7170873 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Neurovegetative symptom subtypes in young people with major depressive disorder and their structural brain correlates.

Toenders Yara J YJ Schmaal Lianne L Harrison Ben J BJ Dinga Richard R Berk Michael M Davey Christopher G CG

Translational psychiatry 20200420 1

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 sympto ...[more]

PMID: 32312958

Similar Datasets

Project description:BackgroundCognitive dysfunction and brain structural connectivity alterations have been observed in major depressive disorder (MDD). However, little is known about their interrelation. The present study follows a network approach to evaluate alterations in cognition-related brain structural networks.MethodsCognitive performance of n = 805 healthy and n = 679 acutely depressed or remitted individuals was assessed using 14 cognitive tests aggregated into cognitive factors. The structural connectome was reconstructed from structural and diffusion-weighted magnetic resonance imaging. Associations between global connectivity strength and cognitive factors were established using linear regressions. Network-based statistics were applied to identify subnetworks of connections underlying these global-level associations. In exploratory analyses, effects of depression were assessed by evaluating remission status-related group differences in subnetwork-specific connectivity. Partial correlations were employed to directly test the complete triad of cognitive factors, depressive symptom severity, and subnetwork-specific connectivity strength.ResultsAll cognitive factors were associated with global connectivity strength. For each cognitive factor, network-based statistics identified a subnetwork of connections, revealing, for example, a subnetwork positively associated with processing speed. Within that subnetwork, acutely depressed patients showed significantly reduced connectivity strength compared to healthy controls. Moreover, connectivity strength in that subnetwork was associated to current depressive symptom severity independent of the previous disease course.ConclusionsOur study is the first to identify cognition-related structural brain networks in MDD patients, thereby revealing associations between cognitive deficits, depressive symptoms, and reduced structural connectivity. This supports the hypothesis that structural connectome alterations may mediate the association of cognitive deficits and depression severity.

Project description:Background: Major Depressive Disorder (MDD) is a moderately heritable disorder with a high lifetime prevalence. At present, laboratory blood tests to support MDD diagnosis are not available. Methods: We used a classifier approach on blood gene expression profiles of a unique set of non-medicated subjects (MDD patients and controls) to select genes of which expression is predictive for disease status. To reveal blood gene expression changes related to MDD disease, we applied a powerful ex vivo stimulus to the blood, i.e. incubation with lipopolysaccharide (LPS; 10 ng/ml blood). Results: Based on LPS-stimulated blood gene expression using whole-genome microarrays in 42 subjects (primary cohort; 21 MDD patients (mean age 42.3 years), 21 healthy controls (mean age 41.9 years)), we identified a set of genes (CAPRIN1, CLEC4A, KRT23, MLC1, PLSCR1, PROK2, ZBTB16) that serves as a molecular signature of MDD. These findings were validated for the primary cohort using an independent quantitative PCR method (P = 0.007). The difference between depressive patients and controls was confirmed (P = 0.019) in a replication cohort of 13 patients with MDD (mean age 42.8 years) and 14 controls (mean age 45.6 years). The MDD-signature score comprised of expression levels of 7 genes could discriminate depressive patients from controls with sensitivity of 76.9% and specificity of 71.8%. Conclusions: We show for the first time that molecular analysis of stimulated blood cells can be used as an endophenotype for MDD diagnosis, which is a milestone in establishing biomarkers for neuropsychiatric disorders with moderate heritability in general. Our results may provide a new entry point for following and predicting treatment outcome, as well as prediction of severity and recurrence of MDD. In total, 33 MDD patients and 34 healthy controls were analyzed using basal gene expression in whole blood, and gene expression from whole blood that was stimulated with LPS for 5-6 h, using microarrays. Patients were arbitrarily selected from all patients to serve as primary cohort (nMDD = 21 (MDD01-MDD21); nControls = 21 (Con01-Con21)), or replication cohort (nMDD = 12 (MDD22-MDD35); nControls = 13 (Con22-Con37)) using microarrays. This submission does not include Samples CON21_LPS or CON30_LPS.

Project description:Imaging techniques are a potentially powerful method of identifying phenotypes that are associated with, or are indicative of, a vulnerability to developing major depressive disorder (MDD). Here we identify seven promising MDD-associated traits identified by magnetic resonance imaging (MRI) or positron emission tomography (PET). We evaluate whether these traits are state-independent, heritable endophenotypes, or state-dependent phenotypes that may be useful markers of treatment efficacy. In MDD, increased activity of the amygdala in response to negative stimuli appears to be a mood-congruent phenomenon, and is likely moderated by the 5-HT transporter gene (SLC6A4) promoter polymorphism (5-HTTLPR). Hippocampal volume loss is characteristic of elderly or chronically-ill samples and may be impacted by the val66met brain-derived neurotrophic factor (BDNF) gene variant and the 5-HTTLPR SLC6A4 polymorphism. White matter pathology is salient in elderly MDD cohorts but is associated with cerebrovascular disease, and is unlikely to be a useful marker of a latent MDD diathesis. Increased blood flow or metabolism of the subgenual anterior cingulate cortex (sgACC), together with gray matter volume loss in this region, is a well-replicated finding in MDD. An attenuation of the usual pattern of fronto-limbic connectivity, particularly a decreased temporal correlation in amygdala-anterior cingulate cortex (ACC) activity, is another MDD-associated trait. Concerning neuroreceptor PET imaging, decreased 5-HT(1A) binding potential in the raphe, medial temporal lobe, and medial prefrontal cortex (mPFC) has been strongly associated with MDD, and may be impacted by a functional single nucleotide polymorphism in the promoter region of the 5-HT(1A) gene (HTR1A: -1019 C/G; rs6295). Potentially indicative of inter-study variation in MDD etiology or mood state, both increased and decreased binding potential of the 5-HT transporter has been reported. Challenges facing the field include the problem of phenotypic and etiological heterogeneity, technological limitations, the confounding effects of medication, and non-disease related inter-individual variation in brain morphology and function. Further advances are likely as epigenetic, copy-number variant, gene-gene interaction, and genome-wide association (GWA) approaches are brought to bear on imaging data.

Dataset Information

Neurovegetative symptom subtypes in young people with major depressive disorder and their structural brain correlates.

Publications

Neurovegetative symptom subtypes in young people with major depressive disorder and their structural brain correlates.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets