scholarly journals Gender Differences in the Relationships Among Major Depressive Disorder, Heavy Alcohol Use, and Mental Health Treatment Engagement Among College Students

2016 ◽  
Vol 77 (4) ◽  
pp. 620-628 ◽  
Author(s):  
Paola Pedrelli ◽  
Brian Borsari ◽  
Sarah Ketchen Lipson ◽  
Justin E. Heinze ◽  
Daniel Eisenberg
Keyword(s):  
Mental Health ◽  
Gender Differences ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Mental Health Treatment ◽  
Treatment Engagement ◽  
Major Depressive ◽  
Health Treatment ◽  
Heavy Alcohol ◽  
Heavy Alcohol Use

scholarly journals Gender differences in major depressive disorder: findings from the Singapore Mental Health Study

2017 ◽  
Vol 58 (11) ◽  
pp. 649-655 ◽  
Author(s):  
L Picco ◽  
M Subramaniam ◽  
E Abdin ◽  
JA Vaingankar ◽  
SA Chong
Keyword(s):  
Mental Health ◽  
Gender Differences ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Health Study ◽  
Major Depressive ◽  
Mental Health Study

Pilot data of a brief veteran peer intervention and its relationship to mental health treatment engagement.

2018 ◽  
Vol 15 (4) ◽  
pp. 453-456 ◽  
Author(s):  
Elizabeth M. Goetter ◽  
Eric Bui ◽  
Travis P. Weiner ◽  
Laura Lakin ◽  
Thomas Furlong ◽  
...  
Keyword(s):  
Mental Health ◽  
Mental Health Treatment ◽  
Treatment Engagement ◽  
Health Treatment ◽  
Pilot Data ◽  
Peer Intervention

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 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.

scholarly journals Exploring the Role and Potential of Probiotics in the Field of Mental Health: Major Depressive Disorder

Nutrients ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 1728
Author(s):  
Dinyadarshini Johnson ◽  
Sivakumar Thurairajasingam ◽  
Vengadesh Letchumanan ◽  
Kok-Gan Chan ◽  
Learn-Han Lee
Keyword(s):  
Mental Health ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Epigenetic Mechanisms ◽  
Preclinical Models ◽  
Major Depressive ◽  
Health Domains ◽  
Probiotic Intervention

The field of probiotic has been exponentially expanding over the recent decades with a more therapeutic-centered research. Probiotics mediated microbiota modulation within the microbiota–gut–brain axis (MGBA) have been proven to be beneficial in various health domains through pre-clinical and clinical studies. In the context of mental health, although probiotic research is still in its infancy stage, the promising role and potential of probiotics in various mental disorders demonstrated via in-vivo and in-vitro studies have laid a strong foundation for translating preclinical models to humans. The exploration of the therapeutic role and potential of probiotics in major depressive disorder (MDD) is an extremely noteworthy field of research. The possible etio-pathological mechanisms of depression involving inflammation, neurotransmitters, the hypothalamic–pituitary–adrenal (HPA) axis and epigenetic mechanisms potentially benefit from probiotic intervention. Probiotics, both as an adjunct to antidepressants or a stand-alone intervention, have a beneficial role and potential in mitigating anti-depressive effects, and confers some advantages compared to conventional treatments of depression using anti-depressants.

The sunshine induced placebo effect in major depressive disorder patients exhibits gender differences

2016 ◽  
Vol 33 (S1) ◽  
pp. S411-S412
Author(s):  
J. Gailledreau ◽  
B. Gailledreau ◽  
P. Desbonnet ◽  
P. Khalifa Soussan ◽  
N. Desbonnet ◽  
...  
Keyword(s):  
Gender Differences ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Placebo Effect ◽  
Microsoft Excel ◽  
Major Depressive ◽  
Double Blind ◽  
Sunny Weather ◽  
Competing Interest ◽  
Variable Weather

RationaleSunshine increases placebo effect in major depressive disorder (MDD) patients (Gailledreau et al., 2015). Kokras et al. (2014) showed that sunshine induces different responses in female than male mice in preclinical models of depression.ObjectiveTo determine whetehr the sunshine induced placebo effect exhibits gender differences in human.Materiel and methodsData from 9 double-blind, randomized, placebo-controlled studies of antidepressants conducted by the French GICIPI network were reviewed. MADRS (5) or HAM-D 17 (4) were used as the main efficacy tool. For each patient, variation of scores (Delta MADRS/Delta HAM-D) between two consecutive visits were correlated with the average sunshine index observed at noon between these visits. Sunshine indexes were provided by Météo-France. Correlations were computed with Microsoft Excel.ResultsAnalysis of both genders (n = 52) showed no statistically significant (NS) correlation (r2 = 0.0064) between sunshine and score variations. Analysis of males (n = 8) failed to demonstrate any significant correlation in cloudy (< 1000 Joules/cm2), variable (1000–2000 Joules/cm2) or sunny (> 2000 Joules/cm2) weather. Analysis of females (n = 44) showed NS correlation as well for cloudy or variable weather (r2 = 0.0016), but a strong correlation was observed for females exposed to sunny weather: r2 = 0, 315, n = 20, P < 0.01. This correlation was even stronger in the subpopulation of females aged less than 50 years: r2 = 0.6398, n = 12, P < 0.001.DiscussionThe hypothesis underlying this correlation between sunshine index and variations of MADRS/HAMD scales will be discussed.ConclusionSunshine increases placebo effect in female patients aged less than 50. This insufficiently known effect may be responsible for failure of a number of double-blind, randomized, studies of antidepressant compounds.Disclosure of interestThe authors have not supplied their declaration of competing interest.

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