What do the genetic association data say about the high risk of suicide in people with depression? A novel network-based approach to find common molecular basis for depression and suicidal behavior and related therapeutic targets

Bozorgmehr, Ali; Alizadeh, Fatemeh; Ofogh, Sattar Norouzi; Hamzekalayi, Mohammad Reza Abdollahzadeh; Herati, Sara; Moradkhani, Atefeh; Shahbazi, Ali; Ghadirivasfi, Mohammad

doi:10.1016/j.jad.2017.12.079

Cited by 13 publications

(5 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The psychosocial and physical tolls of depression are enormous. Individuals with depression are more likely to experience social isolation, decreased work performance, anxiety, and substance use disorder, and are more likely to attempt suicide [13][14][15]. They are also more likely to develop cardiovascular disease and diabetes, and have higher all-cause mortality than individuals without depression [19,[48][49][50][51].…”

Section: Discussionmentioning

confidence: 99%

“…The onset of depression in PLWH is multifactorial and includes neurobiological changes, HIV-related stigma, life stressors, substance use and functional health status [11,12]. Once present, depression significantly affects health and wellbeing and is associated with poor behavioral and psychosocial outcomes such as social isolation, decreased work performance, anxiety, substance use disorder, and suicide [13][14][15], as well as with poor outcomes for medical comorbidities such as diabetes, cardiovascular disease and cancer [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Depression Prevalence, Antidepressant Treatment Status, and Association with Sustained HIV Viral Suppression Among Adults Living with HIV in Care in the United States, 2009–2014

et al. 2019

View full text Add to dashboard Cite

Previous research indicates a high burden of depression among adults living with HIV and an association between depression and poor HIV clinical outcomes. National estimates of diagnosed depression, depression treatment status, and association with HIV clinical outcomes are lacking. We used 2009-2014 data from the Medical Monitoring Project to estimate diagnosed depression, antidepressant treatment status, and associations with sustained viral suppression (all viral loads in past year < 200 copies/mL). Data were obtained through interview and medical record abstraction and were weighted to account for unequal selection probabilities and non-response. Of adults receiving HIV medical care in the U.S. and prescribed ART, 27% (95% confidence interval [CI] 25-29%) had diagnosed depression during the surveillance period; the majority (65%) were prescribed antidepressants. The percentage with sustained viral suppression was highest among those without depression (72%, CI 71-73%) and lowest among those with untreated depression (66%, CI 64-69%). Compared to those without depression, those with a depression diagnosis were less likely to achieve sustained viral suppression (aPR 0.95, CI 0.93-0.97); this association held for persons with treated depression compared to no depression (aPR 0.96, CI 0.94-0.99) and untreated depression compared to no depression (aPR 0.92, CI 0.89-0.96). The burden of depression among adults living with HIV in care is high. While in our study depression was only minimally associated with a lower prevalence of sustained viral suppression, diagnosing and treating depression in persons living with HIV remains crucial in order to improve mental health and avoid other poor health outcomes.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Depression Prevalence, Antidepressant Treatment Status, and Association with Sustained HIV Viral Suppression Among Adults Living with HIV in Care in the United States, 2009–2014

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The detection and treatment of depression has long been recognized as a major priority for improving the health and well-being of millions of individuals. It has an adverse impact on patient feelings, thoughts, behavior and body, and in more severe cases, depression is considered one of the leading causes of suicide and substance abuse [2]. Normally, medications and psychotherapy are effective treatments for depression, however errors in clinical assessment of depression are frequent [3].…”

Section: Introductionmentioning

confidence: 99%

Combining Global and Local Convolutional 3D Networks for Detecting Depression from Facial Expressions

Melo

Granger

Hadid

2019

2019 14th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2019)

View full text Add to dashboard Cite

Deep learning architectures have been successfully applied in video-based health monitoring, to recognize distinctive variations in the facial appearance of subjects. To detect patterns of variation linked to depressive behavior, deep neural networks (NNs) typically exploit spatial and temporal information separately by, e.g., cascading a 2D convolutional NN (CNN) with a recurrent NN (RNN), although the intrinsic spatio-temporal relationships can deteriorate. With the recent advent of 3D CNNs like the convolutional 3D (C3D) network, these spatio-temporal relationships can be modeled to improve performance. However, the accuracy of C3D networks remain an issue when applied to depression detection. In this paper, the fusion of diverse C3D predictions are proposed to improve accuracy, where spatio-temporal features are extracted from global (full-face) and local (eyes) regions of subject. This allows to increasingly focus on a local facial region that is highly relevant for analyzing depression. Additionally, the proposed network integrates 3D Global Average Pooling in order to efficiently summarize spatio-temporal features without using fully-connected layers, and thereby reduce the number of model parameters and potential over-fitting. Experimental results on the Audio Visual Emotion Challenge (AVEC 2013 and AVEC 2014) depression datasets indicates that combining the responses of global and local C3D networks achieves a higher level of accuracy than state-of-the-art systems.

show abstract

“…Impaired serotonergic function is involved in the development of suicide behavior and was proven decades ago [43]. Some studies on genes showed that depression and suicide were at the same gene locus[6, [44][45][46], other than a causal relationship. They found that the S allele in the serotonin transporterlinked polymorphic region (5-HTTLPR) may associate with affective temperament, especially cyclothymic temperament [47].…”

Section: Discussionmentioning

confidence: 99%

The Association Between Suicide Risk and Affective Temperaments in First-Episode and Neuroleptic-Naïve Major Depressive Disorder

Yin

Wang

Wei

et al. 2021

Preprint

View full text Add to dashboard Cite

Background Suicide risk is associated with depression. Affective temperaments may play a role in this risk. We explored the relationship between affective temperaments and suicide and identified some traits that can predict suicide risk in depression. Methods We analyzed the results of Temperament Evaluation of the Memphis, Pisa, Paris, and San Diego Auto-questionnaire (TEMPS-A) in 284 participants recruited from a psychiatric clinic and the community in Beijing and compared the subscale scores (cyclothymic, dysthymic, anxious, irritable and hyperthymic) among major depressive disorder (MDD) versus the general population as well as depressive patients with versus without suicide risk, using student’s test, Chi-square test, rank sum test, multivariable regression modeling and receiver operating characteristic (ROC) analysis. Results The incidence of suicidal risk in depressive subjects was 47.62% (80/168). Being unmarried (P<0.001), unemployment (P=0.007) and temperaments of dysthymic, cyclothymic, anxious, and irritable scores (all P<0.05) were significantly more prevalent in depressive patients than in the general population. A young age (P<0.001), female sex (P=0.037), being unmarried (P=0.001), more severe depression (P<0.001), and dysthymic, anxious and cyclothymic temperament (all P<0.05) were significantly more prevalent in depressive disorder patients with suicide risk than in those without suicide risk. The logistic regression analysis showed that younger age (OR=0.937, 95% CI 0.905~0.970), female sex (OR=2.606, 95% CI 1.142~5.948), more severe depression (OR=1.145, 95% CI 1.063~1.234), cyclothymic temperament (OR=1.275, 95% CI 1.102~1.475) and dysthymic temperament (OR=1.265, 95% CI 1.037~1.542) were all independently associated with high suicidal risk in first-episode major depressive patients (P<0.05). By ROC analysis, the area under the compound factor (age, sex, HAMD score without the 3rd item, cyclothymic and dysthymic temperament) was 0.853 (95% CI 0.790~0.903). Conclusion The suicide rate in first-episode neuroleptic-naïve major depressive disorder (MDD) subjects was higher than we thought. Temperament traits differ between the general population and those with major depressive disorder. Major depressive disorder subjects with much more severe depressive symptoms and cyclothymic or dysthymic temperament were at high risk of suicide. Compound factors (age, sex, HAMD score without the 3rd item, cyclothymic and dysthymic temperament score) could be predictors of suicide risk in the clinic.

show abstract

What do the genetic association data say about the high risk of suicide in people with depression? A novel network-based approach to find common molecular basis for depression and suicidal behavior and related therapeutic targets

Cited by 13 publications

References 57 publications

Depression Prevalence, Antidepressant Treatment Status, and Association with Sustained HIV Viral Suppression Among Adults Living with HIV in Care in the United States, 2009–2014

Depression Prevalence, Antidepressant Treatment Status, and Association with Sustained HIV Viral Suppression Among Adults Living with HIV in Care in the United States, 2009–2014

Combining Global and Local Convolutional 3D Networks for Detecting Depression from Facial Expressions

The Association Between Suicide Risk and Affective Temperaments in First-Episode and Neuroleptic-Naïve Major Depressive Disorder

Contact Info

Product

Resources

About