Gender Differences in Major Depressive Disorder and Bipolar Disorder

CNS Spectrums ◽  
1999 ◽  
Vol 4 (10) ◽  
pp. 25-33 ◽  
Author(s):  
Ellen Leibenluft
Keyword(s):  
Gender Differences ◽  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Gonadal Steroids ◽  
Gene Interactions ◽  
Emotional Stimuli ◽  
Major Depressive ◽  
Men And Women ◽  
Psychotropic Effects

AbstractThis paper reviews the literature on gender differences in major depressive disorder (MDD) and bipolar disorder (BPD). Beginning in adolescence, women are at a higher risk than men of becoming depressed. Avenues of investigation that might ultimately help to explain this phenomenon include studies of gender differences in the processing of emotional stimuli, the psychotropic effects of gonadal steroids, and environment/gene interactions in men and women. With the exception of the elevated suicide rate among men, consistent gender differences in the course and symptoms of MDD have not been found. In BPD, women are more likely than men to develop a rapid-cycling course. Gender differences in treatment response, particularly in regard to mood stabilizing medications, warrant further study.

scholarly journals Gender Differences in Brain Serotonin Synthesis in Individuals with Major Depressive Disorder: A A-[11C]MT Pet Imaging Study

2009 ◽  
Vol 24 (S1) ◽  
pp. 1-1 ◽  
Author(s):  
B.N. Frey ◽  
I. Skelin ◽  
Y. Sakai ◽  
M. Nishikawa ◽  
M. Diksic
Keyword(s):  
Gender Differences ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Inferior Frontal Gyrus ◽  
Anterior Cingulate ◽  
Brain Morphology ◽  
Brain Serotonin ◽  
Serotonin Synthesis ◽  
Major Depressive ◽  
Men And Women

Objective:Women are at higher risk than men to develop major depressive disorder (MDD), but the mechanisms underlying the higher risk for MDD in women are unknown. There is a wealth of data showing gender differences in brain morphology and function. In addition, preclinical studies have demonstrated reciprocal relationships between ovarian hormones and serotonin neurotransmission. Thus, gender differences in brain serotonin neurotransmission are potential underlying mechanisms. In the present study, we compared normalized α-[11C]methyl-L-tryptophan brain trapping constant (α-[11C]MTrp K*; ml/g/min), an index of serotonin synthesis, between men and women with MDD.Method:α-[11C]MTrp K* was measured in 25 medication-free individuals with MDD (13 females and 12 males) using positron emission tomography. Comparisons of normalized α-[11C]MTrp K* values between men and women were conducted at the voxel level using Statistical Parametric Mapping 2 (SPM2) analysis.Results:Women with MDD displayed significantly higher (p< 0.005) normalized α-[11C]MTrp K* than men in the inferior frontal gyrus, anterior cingulate cortex (ACC), parahippocampal gyrus, precuneus and superior parietal lobule, and occipital lingual gyrus.Conclusions:This finding suggests that depressive women have higher serotonin synthesis in multiple regions of the prefrontal cortex and limbic system involved with mood regulation. Gender differences in brain serotonin synthesis may be associated with higher risk for MDD in women because extra levels of tissue 5-HT could create non-physiological connections influencing changes in mood.

scholarly journals Modality-specific alterations in the perception of emotional stimuli in Bipolar Disorder compared to Healthy Controls and Major Depressive Disorder

Cortex ◽  
2012 ◽  
Vol 48 (8) ◽  
pp. 1027-1034 ◽  
Author(s):  
Aaron C. Vederman ◽  
Sara L. Weisenbach ◽  
Lisa J. Rapport ◽  
Hadia M. Leon ◽  
Brennan D. Haase ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Healthy Controls ◽  
Emotional Stimuli ◽  
Major Depressive

Gender differences in major depressive disorder: Results from the Netherlands study of depression and anxiety

2014 ◽  
Vol 156 ◽  
pp. 156-163 ◽  
Author(s):  
Jérôme J.J. Schuch ◽  
Annelieke M. Roest ◽  
Willem A. Nolen ◽  
Brenda W.J.H. Penninx ◽  
Peter de Jonge
Keyword(s):  
Gender Differences ◽  
Major Depressive Disorder ◽  
The Netherlands ◽  
Depressive Disorder ◽  
Depression And Anxiety ◽  
Major Depressive

scholarly journals The overlap of genetic susceptibility to schizophrenia and cardiometabolic disease can be used to identify metabolically different groups of individuals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rona J. Strawbridge ◽  
Keira J. A. Johnston ◽  
Mark E. S. Bailey ◽  
Damiano Baldassarre ◽  
Breda Cullen ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
European Ancestry ◽  
Cardiometabolic Disease ◽  
Metabolic Profiles ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Major Depressive ◽  
Increased Risk

AbstractUnderstanding why individuals with severe mental illness (Schizophrenia, Bipolar Disorder and Major Depressive Disorder) have increased risk of cardiometabolic disease (including obesity, type 2 diabetes and cardiovascular disease), and identifying those at highest risk of cardiometabolic disease are important priority areas for researchers. For individuals with European ancestry we explored whether genetic variation could identify sub-groups with different metabolic profiles. Loci associated with schizophrenia, bipolar disorder and major depressive disorder from previous genome-wide association studies and loci that were also implicated in cardiometabolic processes and diseases were selected. In the IMPROVE study (a high cardiovascular risk sample) and UK Biobank (general population sample) multidimensional scaling was applied to genetic variants implicated in both psychiatric and cardiometabolic disorders. Visual inspection of the resulting plots used to identify distinct clusters. Differences between these clusters were assessed using chi-squared and Kruskall-Wallis tests. In IMPROVE, genetic loci associated with both schizophrenia and cardiometabolic disease (but not bipolar disorder or major depressive disorder) identified three groups of individuals with distinct metabolic profiles. This grouping was replicated within UK Biobank, with somewhat less distinction between metabolic profiles. This work focused on individuals of European ancestry and is unlikely to apply to more genetically diverse populations. Overall, this study provides proof of concept that common biology underlying mental and physical illness may help to stratify subsets of individuals with different cardiometabolic profiles.

Predictors of conversion of major depressive disorder to bipolar disorder

2021 ◽  
pp. 113939
Author(s):  
Satish Suhas ◽  
Abha Thakurdesai ◽  
Amal Jolly Joseph ◽  
Chittaranjan Andrade
Keyword(s):  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Major Depressive

scholarly journals A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jakub Tomasik ◽  
Sung Yeon Sarah Han ◽  
Giles Barton-Owen ◽  
Dan-Mircea Mirea ◽  
Nayra A. Martin-Key ◽  
...  
Keyword(s):  
Mental Health ◽  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Diagnostic Algorithm ◽  
Health Questionnaire ◽  
Major Depressive ◽  
Blood Biomarker ◽  
Mental Health Questionnaire ◽  
Biomarker Data

AbstractThe vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18–45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86–0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86–0.91) and 0.90 (0.87–0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57–0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD.

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