Dataset Information

Multiple-Network Alterations in Major Depressive Disorder With Gastrointestinal Symptoms at Rest Revealed by Global Functional Connectivity Analysis.

ABSTRACT:

Objective

Gastrointestinal (GI) symptoms are prominent in major depressive disorder (MDD) and bring patients lots of complaints and troubles. We aimed to explore whether there were some distinctive brain image alterations in MDD with GI symptoms, which could be used to distinguish MDD with GI symptoms from those without GI symptoms and healthy controls (HCs).

Methods

A total of 35 outpatients with GI symptoms, 17 outpatients without GI symptoms, and 28 HCs were recruited. All the participants were scanned by a resting-state functional magnetic resonance imaging. Imaging data were analyzed with the global functional connectivity (GFC) and support vector machine methods.

Results

MDD with GI symptoms showed decreased GFC in the left superior medial prefrontal cortex (MPFC) compared with MDD without GI symptoms. Compared with HCs, MDD with GI symptoms showed decreased GFC in the bilateral middle temporal pole (MTP) and left posterior cingulate cortex/precuneus (PCC/Pcu), and increased GFC in the right insula and bilateral thalamus. SVM analysis showed that an accuracy was 78.85% in differentiating MDD with GI symptoms from MDD without GI symptoms by using the GFC of the left superior MPFC. A combination of GFC of the left PCC/Pcu and bilateral MTP exhibited the highest accuracy (87.30%) in differentiating patients with MDD with GI symptoms from HCs.

Conclusion

MDD with GI symptoms showed abnormal GFC in multiple networks, including the default mode network and cortico-limbic mood-regulating circuit. Using abnormal GFC might work well to discriminate MDD with GI symptoms from MDD without GI symptoms and HCs.

SUBMITTER: Yan M

PROVIDER: S-EPMC9263397 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.