Gender differences in major depressive disorder at different ages: a REST-meta-MDD project-based study
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ABSTRACT:
SUBMITTER: Tian X
PROVIDER: S-EPMC11342488 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:We examined the genetic profile of postmortem brain (hippocampal) samples; 15 brains from patients diagnosed with MDD were matched to brains from healthy subjects based on gender, race and age. Gene expression profiles in the dentate gyrus and CA1 subregions of the hippocampus were assessed by cDNA hybridization to 48K human HEEBO whole genome microarrays (Microarray, Inc). Two-group comparison: MDD (13 male, 8 female) vs. Control (11 male, 7 female). Dentate Gyrus (DG): 15 pairs of samples (1 array per pair); CA1: 15 pairs of samples (1 array per pair). Biological replicates. We can not provide a list of normalized values for each individual hybridization (per chip), but rather have a list of average expression values for all hybridizations used in the experiment (n=15). See supplementary files below. CODES: AAm, African American; C, Caucasian; CO, carbon monoxide; CVD, cardiovascular disease; ETOH, ethanol; F, female; Hx, history of alcohol abuse but not currently active; M, male; MDD, Major Depressive disorder, ND, no psychotropic medication detected; NOS, not otherwise specified, OD, drug overdose; PE, prior episode of major depression with psychotic features; PMI, postmortem interval (hours); SIGSW, self-inflicted gunshot wound; [1], Psychotrophic prescriptions within last month; [2], MDD in remission; [3], prescriptions for six days prior to death; *, samples present only in array sets for the dentate gyrus; **, samples present only in array sets for CA1.
2010-10-17 | E-GEOD-24095 | biostudies-arrayexpress
Project description:We examined the genetic profile of postmortem brain (hippocampal) samples; 15 brains from patients diagnosed with MDD were matched to brains from healthy subjects based on gender, race and age. Gene expression profiles in the dentate gyrus and CA1 subregions of the hippocampus were assessed by cDNA hybridization to 48K human HEEBO whole genome microarrays (Microarray, Inc).
2010-10-17 | GSE24095 | GEO
Project description:BackgroundDysfunctional attitudes play a key role in the development and prognosis of depression. Gender also plays an important role in many clinical features of major depressive disorder (MDD). This study is aimed at investigating the gender differences in dysfunctional attitudes in patients with MDD.MethodsOne hundred and seventy-two patients with MDD and 159 healthy controls (HCs) were enrolled in this study. Dysfunctional attitudes were assessed by the Chinese version of the dysfunctional attitude scale-form A (C-DAS-A) and depression severity was assessed by the 24-item Hamilton rating scale for depression (HAMD24). The 14-item Hamilton Anxiety Rating Scale (HAMA14) was used to measure anxiety. Factorial analysis of variance (ANOVA) of gender and diagnosis on C-DAS-A total and factor scores was adopted with age, education, and body mass index (BMI) controlled. Multiple linear regression analyses of DAS were performed in the MDD group.ResultsFirst, the C-DAS-A score in the MDD group was increased significantly than HCs. Second, female patients with MDD showed significantly higher scores in C-DAS-A total and three-factor scores (seeking applause, dependence, and self-determination attitude), while no significant difference between female HCs and male HCs was detected. Third, five variables (age, gender, smoking history, HAMD24, and HAMA14) had predictive effects on and gender showed the greatest contributions to C-DAS-A total and three-factor scores (seeking applause, dependence, and self-determination attitude).ConclusionFemales with MDD may be linked to more severe cognitive distortion than their male counterparts in seeking applause, dependence, and self-determination attitude, supporting the reasonableness for gender-specific psychosocial interventions.
| S-EPMC7057763 | biostudies-literature
Project description:BackgroundMajor depressive disorder (MDD) is known to be characterized by altered brain functional connectivity (FC) patterns. However, whether and how the features of dynamic FC would change in patients with MDD are unclear. In this study, we aimed to characterize dynamic FC in MDD using a large multi-site sample and a novel dynamic network-based approach.MethodsResting-state functional magnetic resonance imaging (fMRI) data were acquired from a total of 460 MDD patients and 473 healthy controls, as a part of the REST-meta-MDD consortium. Resting-state dynamic functional brain networks were constructed for each subject by a sliding-window approach. Multiple spatio-temporal features of dynamic brain networks, including temporal variability, temporal clustering and temporal efficiency, were then compared between patients and healthy subjects at both global and local levels.ResultsThe group of MDD patients showed significantly higher temporal variability, lower temporal correlation coefficient (indicating decreased temporal clustering) and shorter characteristic temporal path length (indicating increased temporal efficiency) compared with healthy controls (corrected p < 3.14×10-3). Corresponding local changes in MDD were mainly found in the default-mode, sensorimotor and subcortical areas. Measures of temporal variability and characteristic temporal path length were significantly correlated with depression severity in patients (corrected p < 0.05). Moreover, the observed between-group differences were robustly present in both first-episode, drug-naïve (FEDN) and non-FEDN patients.ConclusionsOur findings suggest that excessive temporal variations of brain FC, reflecting abnormal communications between large-scale bran networks over time, may underlie the neuropathology of MDD.
| S-EPMC7229351 | biostudies-literature
Project description:The identification of biomarkers associated with major depressive disorder (MDD) holds great promise to develop an objective laboratory test. However, current biomarkers lack discriminative power due to the complex biological background, and not much is known about the influence of potential modifiers such as gender. We first performed a cross-sectional study on the discriminative power of biomarkers for MDD by investigating gender differences in biomarker levels. Out of 28 biomarkers, 21 biomarkers were significantly different between genders. Second, a novel statistical approach was applied to investigate the effect of gender on MDD disease classification using a panel of biomarkers. Eleven biomarkers were identified in men and eight in women, three of which were active in both genders. Gender stratification caused a (non-significant) increase of Area Under Curve (AUC) for men (AUC = 0.806) and women (AUC = 0.807) compared to non-stratification (AUC = 0.739). In conclusion, we have shown that there are differences in biomarker levels between men and women which may impact accurate disease classification of MDD when gender is not taken into account.
| S-EPMC7246841 | biostudies-literature
Project description:Overall risk for Major Depressive Disorder (MDD) is determined by complex interactions between genetic and environmental factors that influence epigenetic regulation of neuroplasticity and stress pathways1,2. These mechanisms are specific to the distinct regulatory context within regionally-defined brain cell-types3,4. Here, we employed genome-wide chromatin accessibility profiling of neuronal vs. non-neuronal cells in orbitofrontal cortex (OFC) to capture regulatory signatures of MDD. We mapped genetic risk for MDD to active promoters of non-neuronal cell-types in OFC and identified MDD-specific open chromatin regions, which were differentially accessible exclusively in non-neuronal cells. Characterization of these loci revealed a key role for astrocyte dysfunction, and implicated the chromatin remodeling protein ZBTB7A, which coordinates wide-ranging cellular activation programs, including NF-kB inflammatory transcription5. In mice, astrocyte-specific knockdown of Zbtb7a reversed chromatin remodeling, reactive astrocyte transcription, and behavioral deficits associated with chronic stress. Conversely, ZBTB7A overexpression in OFC astrocytes induced stress-related behavioral deficits, promoted widespread inflammatory gene expression, and drove pathophysiological OFC neuronal hyperactivity in response to a mild subthreshold stressor. Our data highlight a critical role for OFC astrocytes in the bidirectional regulation of stress vulnerability and pinpoint ZBTB7A as a key factor mediating maladaptive astrocyte plasticity and OFC neuronal hyperexcitability in MDD.
2023-08-08 | GSE214921 | GEO
Project description:We do this study to identify and validate the differentially expressed circRNAs in MDD patients and to evaluate their potential as diagnostic biomarkers and as novel therapeutic targets for MDD, to explore the potential clinical value and possible mechanism of circRNAs in MDD.
2022-03-21 | GSE190518 | GEO
Project description:Depressive symptoms are related to abnormalities in the autonomic nervous system (ANS), and physiological signals that can be used to measure and evaluate such abnormalities have previously been used as indicators for diagnosing mental disorder, such as major depressive disorder (MDD). In this study, we investigate the feasibility of developing an objective measure of depressive symptoms that is based on examining physiological abnormalities in individuals when they are experiencing mental stress. To perform this, we recruited 30 patients with MDD and 31 healthy controls. Then, skin conductance (SC) was measured during five 5-min experimental phases, comprising baseline, mental stress, recovery from the stress, relaxation, and recovery from the relaxation, respectively. For each phase, the mean amplitude of the skin conductance level (MSCL), standard deviations of the SCL (SDSCL), slope of the SCL (SSCL), mean amplitude of the non-specific skin conductance responses (MSCR), number of non-specific skin conductance responses (NSCR), and power spectral density (PSD) were evaluated from the SC signals, producing 30 parameters overall (six features for each phase). These features were used as input data for a support vector machine (SVM) algorithm designed to distinguish MDD patients from healthy controls based on their physiological responses. Statistical tests showed that the main effect of task was significant in all SC features, and the main effect of group was significant in MSCL, SDSCL, SSCL, and PSD. In addition, the proposed algorithm achieved 70% accuracy, 70% sensitivity, 71% specificity, 70% positive predictive value, 71% negative predictive value in classifying MDD patients and healthy controls. These results demonstrated that it is possible to extract meaningful features that reflect changes in ANS responses to various stimuli. Using these features, detection of MDD was feasible, suggesting that SC analysis has great potential for future diagnostics and prediction of depression based on objective interpretation of depressive states.
| S-EPMC6447153 | biostudies-literature
Project description:Objective: To examine the dose-dependent relationship of different types of statins with the occurrence of major depressive disorder (MDD) and prescription of antidepressant medication. Methods: This cross-sectional study used medical claims data for the general Austrian population (n = 7,481,168) to identify all statin-treated patients. We analyzed all patients with MDD undergoing statin treatment and calculated the average defined daily dose for six different types of statins. In a sub-analysis conducted independently of inpatient care, we investigated all patients on antidepressant medication (statin-treated patients: n = 98,913; non-statin-treated patients: n = 789,683). Multivariate logistic regression analyses were conducted to calculate the risk of diagnosed MDD and prescription of antidepressant medication in patients treated with different types of statins and dosages compared to non-statin-treated patients. Results: In this study, there was an overrepresentation of MDD in statin-treated patients when compared to non-statin-treated patients (OR: 1.22, 95% CI: 1.20-1.25). However, there was a dose dependent relationship between statins and diagnosis of MDD. Compared to controls, the ORs of MDD were lower for low-dose statin-treated patients (simvastatin>0- < =10 mg:OR: 0.59, 95% CI: 0.54-0.64; atorvastatin>0- < =10 mg:OR:0.65, 95%CI: 0.59-0.70; rosuvastatin>0- < =10 mg:OR: 0.68, 95% CI: 0.53-0.85). In higher statin dosages there was an overrepresentation of MDD (simvastatin>40- < =60 mg:OR: 2.42, 95% CI: 2.18-2.70, >60-80 mg:OR: 5.27, 95% CI: 4.21-6.60; atorvastatin>40- < =60 mg:OR: 2.71, 95% CI: 1.98-3.72, >60- < =80 mg:OR: 3.73, 95% CI: 2.22-6.28; rosuvastatin>20- < =40 mg:OR: 2.09, 95% CI: 1.31-3.34). The results were confirmed in a sex-specific analysis and in a cohort of patients taking antidepressants, prescribed independently of inpatient care. Conclusions: This study shows that it is important to carefully re-investigate the relationship between statins and MDD. High-dose statin treatment was related to an overrepresentation, low-dose statin treatment to an underrepresentation of MDD.
| S-EPMC7904887 | biostudies-literature
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.
2011-10-04 | E-GEOD-19738 | biostudies-arrayexpress