Predictors Of Major Depressive Disorder Research Articles

Neuroimaging Genomics a Predictor of Major Depressive Disorder (MDD).

Depression is a complex psychiatric disorder influenced by various genetic and environmental factors. Strong evidence has established the contribution of genetic factors in depression through twin studies and the heritability rate for depression has been reported to be 37%. Genetic studies have identified genetic variations associated with an increased risk of developing depression. Imaging genetics is an integrated approach where imaging measures are combined with genetic information to explore how specific genetic variants contribute to brain abnormalities. Neuroimaging studies allow us to examine both structural and functional abnormalities in individuals with depression. This review has been designed to study the correlation of the significant genetic variants with different regions of neural activity, connectivity, and structural alteration in the brain as detected by imaging techniques to understand the scope of biomarkers in depression. This might help in developing novel therapeutic interventions targeting specific genetic pathways or brain circuits and the underlying pathophysiology of depression based on this integrated approach can be established at length.

Predictors of time to first symptomatic recovery of major depressive disordered patients: a case study at Jimma University Medical Center

BackgroundMajor Depressive Disorder is one of the most common mental disorders, and it is the main cause of disability worldwide with a prevalence ranging from 7 to 21%.ObjectiveThe goal of this study was to predict the time it took for patients with severe depressive disorders at Jimma University Medical Center to experience their initial symptomatic recovery.Study designThe researchers utilized a prospective study design.MethodsPatients with major depressive disorder were followed up on at Jimma University Medical Center from September 2018 to August 2020 for this study. The Gamma and Inverse Gaussian frailty distributions were employed with Weibull, Log-logistic, and Log-normal as baseline hazard functions. Akaike Information Criteria were used to choose the best model for describing the data.ResultsThis study comprised 366 patients, with 54.1% of them experiencing their first symptomatic recovery from a severe depressive disorder. The median time from the onset of symptoms to symptomatic recovery was 7 months. In the study area, there was a clustering effect in terms of time to first symptomatic recovery from major depressive disorder. According to the Log-normal Inverse-Gaussian frailty model, marital status, chewing khat, educational status, work status, substance addiction, and other co-variables were significant predictors of major depressive disorder (p-value < 0.05).ConclusionThe best model for describing the time to the first symptomatic recovery of major depressive disorder is the log-normal Inverse-Gaussian frailty model. Being educated and working considerably were the variables that reduces the time to first symptomatic recovery from major depressive disorder; whereas being divorced, chewing khat, substance abused and other co-factors were the variables that significantly extends the time to first symptomatic recovery.

Prevalence of major depressive disorder and its associated factors among students of private higher secondary schools of science stream, Rajkot city, Gujarat: A cross-sectional study

Background: Major Depressive Disorder (MDD) is a mood disorder characterized by persistent feelings of sadness, hopelessness, worthlessness, and a loss of interest in activities. There is an emerging concern about the prevalence of MDD among high school students. Aims and Objectives: The objective of the study was to estimate the prevalence of MDD among students of private higher secondary schools and to find association between MDD with sociodemographic variables and other predictors of MDD. Materials and Methods: A cross-sectional study was conducted among the students of private higher secondary school of science stream in Rajkot city of Gujarat. A total of 1219 students were enrolled in the study. The students were screened for MDD by Patient Health Questionnaire (PHQ-9) modified for teens. Severity of the depression according to PHQ-9 scale had been described as Minimal Depression, Mild Depression, Moderate Depression, Moderately Severe Depression, and Severe Depression. Data were entered and analyzed using Microsoft excel 2007 and Epi info version 7.1.5.2. The Chi-square test was used to check association between MDD and its various predictors. Results: The prevalence of MDD was found to be 3.20% with highest proportion (9.21%) in Muslim students. Significant association found between MDD and its predictors such as physical illness, flow of ongoing study, and performance in study and addiction. Conclusion: It is recommended that the students should be assessed periodically to know their depression status and academic performance-related problems. Responsible factors should be identified and addressed with the help of parents, teachers, psychologists, and psychiatrists for prompt resolvement.

Disturbed hippocampal intra-network in first-episode of drug-naïve major depressive disorder.

Complex networks inside the hippocampus could provide new insights into hippocampal abnormalities in various psychiatric disorders and dementia. However, evaluating intra-networks in the hippocampus using MRI is challenging. Here, we employed a high spatial resolution of conventional structural imaging and independent component analysis to investigate intra-networks structural covariance in the hippocampus. We extracted the intra-networks based on the intrinsic connectivity of each 0.9 mm isotropic voxel to every other voxel using a data-driven approach. With a total volume of 3 cc, the hippocampus contains 4115 voxels for a 0.9 mm isotropic voxel size or 375 voxels for a 2 mm isotropic voxel of high-resolution functional or diffusion tensor imaging. Therefore, the novel method presented in the current study could evaluate the hippocampal intra-networks in detail. Furthermore, we investigated the abnormality of the intra-networks in major depressive disorders. A total of 77 patients with first-episode drug-naïve major depressive disorder and 79 healthy subjects were recruited. The independent component analysis extracted seven intra-networks from hippocampal structural images, which were divided into four bilateral networks and three networks along the longitudinal axis. A significant difference was observed in the bilateral hippocampal tail network between patients with major depressive disorder and healthy subjects. In the logistic regression analysis, two bilateral networks were significant predictors of major depressive disorder, with an accuracy of 78.1%. In conclusion, we present a novel method for evaluating intra-networks in the hippocampus. One advantage of this method is that a detailed network can be estimated using conventional structural imaging. In addition, we found novel bilateral networks in the hippocampus that were disturbed in patients with major depressive disorders, and these bilateral networks could predict major depressive disorders.

Intrinsic brain abnormalities in female major depressive disorder patients with childhood trauma: A resting-state functional magnetic resonance imaging study.

ObjectiveChildhood trauma is a strong predictor of major depressive disorder (MDD). Women are more likely to develop MDD than men. However, the neural basis of female MDD patients with childhood trauma remains unclear. We aimed to identify the specific brain regions that are associated with female MDD patients with childhood trauma.MethodsWe recruited 16 female MDD patients with childhood trauma, 16 female MDD patients without childhood trauma, and 20 age- and education level-matched healthy controls. All participants underwent resting-state functional magnetic resonance imaging (MRI). Regional brain activity was evaluated as the amplitude of low-frequency fluctuation (ALFF). Furthermore, functional connectivity (FC) analyses were performed on areas with altered ALFF to explore alterations in FC patterns.ResultsThere was increased ALFF in the left middle frontal gyrus (MFG) and the right postcentral gyrus (PoCG) in MDD with childhood trauma compared with MDD without childhood trauma. The areas with significant ALFF discrepancies were selected as seeds for the FC analyses. There was increased FC between the left MFG and the bilateral putamen gyrus. Moreover, ALFF values were correlated with childhood trauma severity.ConclusionOur findings revealed abnormal intrinsic brain activity and FC patterns in female MDD patients with childhood trauma, which provides new possibilities for exploring the pathophysiology of this disorder in women.

Mental Health in Engineering Education: Identifying Population and Intersectional Variation

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contribution:</i> Screening rates for engineering students for several major and moderate mental health issues are reported, including unspecified psychological distress as captured by the Kessler 6 screening instrument; screening rates for depressive, anxiety, and eating disorders as measured by the patient health questionnaire (PHQ); and screening rates for post-traumatic stress disorder (PTSD) as measured by the primary care post-traumatic stress disorder (PC-PTSD) instrument. This work also explores how mental health issues affect different student demographic groups within engineering. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background:</i> Anecdotal evidence has long suggested that stress and certain mental health issues are particularly acute in the field of Engineering, and some recent research has shown elevated rates of mental health issues at different institutions around the country. This article presents the results of a previously validated mental health survey conducted with first- and second-year students at eight universities. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Intended Outcomes:</i> A better understanding of which mental health issues affect engineering students as a population, and an understanding of what mental health disparities exist among different demographics in engineering. This information is intended to allow engineering programs, student groups, and other stakeholders to better target mental health resources for all engineering students. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Application Design:</i> This work combines several widely used population-scale mental health diagnostic tools into a single comprehensive survey instrument that was deployed to first- and second-year engineering students at eight universities nationwide. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Findings:</i> This study finds that 50% of respondents screening positive for a major mental health condition—including depression, anxiety, PTSD, an eating disorder, or major psychological distress—while only 16% of respondents report having ever received a diagnosis for a mental health condition. Women respondents are more likely to screen positive for anxiety disorders ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.4\times $ </tex-math></inline-formula> for panic disorder, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.2\times $ </tex-math></inline-formula> for other anxiety, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.9\times $ </tex-math></inline-formula> for PTSD) and major depressive disorder ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.3\times $ </tex-math></inline-formula> ) relative to men. Respondents reporting physical disabilities have significantly higher likelihoods of suffering from mental health issues than peers with no reported physical disabilities and are <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.9\times $ </tex-math></inline-formula> more likely to screen positive for PTSD. Identifying as Hispanic was also a significant predictor of major depressive disorder ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.2\times $ </tex-math></inline-formula> more likely) and PTSD ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.5\times $ </tex-math></inline-formula> more likely).

Assessing the Evidence for Causal Associations Between Body Mass Index, C-Reactive Protein, Depression, and Reported Trauma Using Mendelian Randomization

BackgroundTraumatic experiences are described as the strongest predictors of major depressive disorder (MDD), with inflammation potentially mediating the association between trauma and symptom onset. However, several studies indicate that body mass index (BMI) exerts a large confounding effect on both inflammation and MDD. MethodsFirst, we sought to replicate previously reported associations between these traits in a large subset of the UK Biobank, using regression models with C-reactive protein (CRP) and MDD and as the outcome variables in 113,481 and 30,137 individuals, respectively. Second, we ran bidirectional Mendelian randomization analyses between these traits to establish a potential causal framework between BMI, MDD, reported childhood trauma, and inflammation. ResultsOur phenotypic analyses revealed no association between CRP and MDD but did suggest a strong effect of BMI and reported trauma on both CRP (BMI: β = 0.43, 95% CI = 0.43–0.43, p ≤ .001; childhood trauma: β = 0.02, 95% CI = 0.00–0.03, p = .006) and MDD (BMI: odds ratio [OR] = 1.16, 95% CI = 1.14–1.19, p ≤ .001; childhood trauma: OR = 1.99, 95% CI = 1.88–2.11, p ≤ .001). Our Mendelian randomization analyses confirmed a lack of causal relationship between CRP and MDD but showed evidence consistent with a strong causal influence of higher BMI on increased CRP (β = 0.37, 95% CI = 0.36–0.39, p ≤ .001) and a bidirectional influence between reported trauma and MDD (OR trauma-MDD = 1.75, 95% CI = 1.49–2.07, p ≤ .001; OR MDD-trauma = 1.22, 95% CI = 1.18–1.27, p ≤ .001). ConclusionsOur findings highlight the importance of controlling for both BMI and trauma when studying MDD in the context of inflammation. They also suggest that the experience of traumatic events can increase the risk for MDD and that MDD can increase the experience of traumatic events.

Predictors of Major Depressive Disorder in Older People.

Major depressive disorder (MDD) is one of the most common mental disorders in older people. There are several biological, psychological, and social factors associated with this disorder. This study aimed to describe the depressive state to identify the associated factors and potential predictors of MDD in a population of community-dwelling older people with probable MDD. The sample consisted of 378 participants with probable dementia, with 47.3% of them presenting MDD. The factors that were found to be associated with MDD were sex, living status, mobility, and nutritional status. Knowing the factors that can predict a condition such as MDD is extremely important, both for prevention and for the customization of interventions.

The Changes of qEEG Approximate Entropy during Test of Variables of Attention as a Predictor of Major Depressive Disorder.

Evaluating brain function through biosignals remains challenging. Quantitative electroencephalography (qEEG) outcomes have emerged as a potential intermediate biomarker for diagnostic clarification in psychological disorders. The Test of Variables of Attention (TOVA) was combined with qEEG to evaluate biomarkers such as absolute power, relative power, cordance, and approximate entropy from covariance matrix images to predict major depressive disorder (MDD). EEG data from 18 healthy control and 18 MDD patients were monitored during the resting state and TOVA. TOVA was found to provide aspects for the evaluation of MDD beyond resting electroencephalography. The results showed that the prefrontal qEEG theta cordance of the control and MDD groups were significantly different. For comparison, the changes in qEEG approximate entropy (ApEn) patterns observed during TOVA provided features to distinguish between participants with or without MDD. Moreover, ApEn scores during TOVA were a strong predictor of MDD, and the ApEn scores correlated with the Beck Depression Inventory (BDI) scores. Between-group differences in ApEn were more significant for the testing state than for the resting state. Our results provide further understanding for MDD treatment selection and response prediction during TOVA.

Predictors of Major Depressive Disorder among Pakistani Women

Social and cultural pressures play a significant role in the development of depressive disorders. Women are at a greater risk of experiencing depressive disorders because they always become the subject of social and cultural pressure. This paper presents the case study of a 23-year-old female who developed symptoms of Major Depressive Disorder (MDD) due to cultural constraints. Initial pre-testing and therapist observation of the client’s verbal and non-verbal attitude / behavior confirmed the diagnosis of MDD. Diagnosis was made according to DSM-5. After initial intake interview, psychological tests including House-tree-person (HTP), Thematic Apprehension Test (TAT), Rotter Incomplete Sentence Blank (RISB), Beck Depression Inventory (BDI), Raven Standard Progressive Matrices (SPM) and Clinical Structured Interview (CIS-R) were administered on the client. After exploring the causal factors of her problem and conducting diagnostic sessions, therapeutic sessions were also conducted. Some techniques of cognitive behavioral therapy were selected for the client based on the strong empirical evidence available that advocates for their use in effectively treating depressive disorders. After achieving the goals of psychotherapy set at the first session with the help of the client as well as gaining satisfactory results of post-testing and therapist observation of the client’s progress, psychotherapy was terminated. Follow up sessions were conducted to monitor the progress of the client. Our research study has clinical and community implications.

Classification Between Major Depressive Disorder and Healthy Controls Using Functional Brain Network Topology

Diagnosis for major depressive disorder (MDD) largely rely on self-report. The ability to identify MDD without self-report is greatly needed. Graph theory studies have reported aberrant brain network organization among MDD patients. Implementing graph theoretical analysis on resting state fMRI (rsfMRI), we tested whether network topology can be used as predictors of MDD using machine learning.

Predictors of Major Depressive Disorder: The Need for Social Approval and Self-Esteem.

This study aimed to investigate self-esteem and the need for social approval as predictors of depression in patients with a diagnosis of major depressive disorder (MDD). It also aimed to compare depression, self-esteem, and the need for social approval in relation to some variables. The Beck Depression Scale, Rosenberg Self-Esteem Scale, and the need for social approval scale (NSAS) were administered to 117 patients diagnosed with MDD and who applied to the Psychiatry Outpatient Clinic in Sabuncuoğlu Şerefeddin Training and Research Hospital of Amasya University, Turkey. In the analysis of the collected data, the t-test, one-way analysis of variance, Pearson's moment-product correlation coefficient, and multiple regression analysis were used. Self-esteem and NSAS subdimensions were found to explain 33% of the total variance in depression. Self-esteem was found to be the variable making the greatest contribution. Two of the subdimensions of the NSAS-sensitivity to others' judgments and leaving a positive impression-were found to have no contribution to the model. The mean score taken by the male participants from leaving a positive impression subdimension of the NSAS was found to be significantly higher than that taken by the female participants. The patients with depression who are single were found to have a significantly higher mean score from leaving a positive impression subdimension than the married patients. No significant difference was found between the variables depending on the age and education level variables. As a result, it should be noted that while evaluating patients diagnosed with MDD, the need for social approval in male patients and patients who are single can be higher than leaving a positive impression and that culture may cause people to hide the symptoms of depression. Given that there are fewer male patients applying to psychiatry clinics than female patients, it would be appropriate to refer male patients to individual counseling activities to reduce their need for approval in relation to leaving a positive impression. It is also suggested that research on anxiety, obsessive-compulsive disorder, and somatization as well as to investigate the intermediate models that address various variables between depression and the need for social approval be conducted.

Biological and psychological components of depression in patients receiving IFN-alpha therapy for hepatitis C.

Depressive symptoms are frequent side effects of interferon α therapy (IFN-α). Both biological and psychological processes may occur concomitantly during hepatitis C treatment. This study was carried out to determine the impact of biological (immune response) and psychological factors on formation of depressive symptoms and major depressive disorder (MDD) during hepatitis C treatment. A total of 99 patients receiving pegylated IFN-α and ribavirin for chronic C type hepatitis participated in the prospective cohort study. Symptoms of depression were assessed with the MontgomeryÅsberg Depression Rating Scale (MADRS) during treatment and 24 weeks after treatment. Neuroticism was measured with the Eysenck Personality Questionnaire - Revised (EPQ-R/N). Diagnosis of MDD was made using the Present State Examination (PSE-10) and DSM-IV-TR criteria. Factor analysis was used to detect factors adding up to total severity of depressive symptoms. Predictors of MDD were investigated using logistic regression analysis. Factor analysis returned 3 factors: 1st - MADRS scores at weeks 0-12, 2nd - MADRS and N scores before treatment, 3rd - MADRS at the 24th week of treatment and 24 weeks after treatment. The total severity of depressive symptoms consisted of 3 components: personality-related before treatment, IFN-α-related during treatment and dependent on the effect of treatment. Regression analysis showed that a history of psychiatric disorders (OR = 4.8) and MADRS scores before treatment (OR = 1.25) were predictors of MDD, as opposed to level of neuroticism. The severity of depressive symptoms and MDD during the hepatitis C treatment was related to general depressive vulnerability, not to psychological factors related to neuroticism.

The review of the probable individually determined alpha EEG predictors of major depressive disorder

The review of the probable individually determined alpha EEG predictors of major depressive disorder

Comorbidities and Consequences in Hospitalized Heart Failure Patients with Depression.

ObjectiveTo evaluate the demographic predictors of major depressive disorder (MDD) in hospitalized congestive heart failure (CHF) patients and measure the differences in hospital stay and cost per comorbidities and the associated risk of in-hospital mortality.MethodsThis retrospective cross-sectional study used nationwide inpatient data from the healthcare cost and utilization project (HCUP). We identified patients with CHF as the primary diagnosis and MDD as the secondary diagnosis using ICD-9-CM codes and compared with the CHF patient without MDD. The differences in comorbidities were quantified using chi-square tests and the logistic regression model was used to evaluate mortality risk among comorbidities using odds ratio (OR).ResultsElder CHF patients, 36–50-year-old (OR: 1.324) and whites (OR: 1.673), have a higher likelihood of a co-diagnosis of MDD. Females with heart failure have two-fold higher odds of MDD (OR: 2.332). Majority of the medical comorbidities were seen in a higher proportion of CHF patients without MDD. Hypothyroidism (10.2%) and drug abuse (15.2%) were seen more in depressed patients comparatively. Among substance use disorder, patients with drug abuse stayed longer and had a higher hospitalization total cost ($51,828). And, hypothyroidism was associated with longer inpatient stay (5.6 days) and cost ($64,726), and four-fold higher odds of in-hospital mortality (OR: 4.405). Though alcohol abuse was seen only in 7.4% of CHF patients with MDD, it was associated with the three-fold higher likelihood of deaths during hospitalization (OR: 3.195).ConclusionA middle-aged, white female with comorbid depression has a higher risk of hospitalization for heart failure. Depressed CHF patients with comorbid hypothyroidism were hospitalized for a longer duration with higher inpatient cost and four times higher risk of mortality during hospitalization stay. Further studies are required to evaluate the underlying cause of worse hospital outcomes in depressed CHF patients with alcohol abuse and hypothyroidism. An integrated healthcare model is required for early diagnosis and treatment of depression and associated comorbidities in CHF patients to reduce mortality and improve post-CHF outcomes.

Predictors of Major Depressive Disorder following Intensive Care of Chronically Critically Ill Patients.

Objective Major depressive disorder (MDD) is a common condition following treatment in the Intensive Care Unit (ICU). Long-term data on MDD in chronically critically ill (CCI) patients are scarce. Hence, the primary aim of the present study was to investigate the frequency and predictors of MDD after intensive care of CCI patients. Materials and Methods In a prospective cohort study, patients with long-term mechanical ventilation requirements (n=131) were assessed with respect to a diagnosis of MDD, using the Structured Clinical Interview for DSM-IV, three and six months after the transfer from acute ICU to post-acute ICU. Sociodemographic, psychological, and clinical risk factors with p values ≤ 0.1 were identified in a univariate logistic regression analysis and entered in a multivariable logistic regression model. A mediator analysis was run using the bootstrapping method, testing the mediating effect of perceived helplessness during the ICU stay, between the recalled traumatic experience from the ICU and a post-ICU MDD. Results 17.6% (n=23) of the patients showed a full- or subsyndromal MDD. Perceived helplessness, recalled experiences of a traumatic event from the ICU, symptoms of acute stress disorder, and the diagnosis of posttraumatic stress disorder (PTSD) after ICU could be identified as significant predictors of MDD. In a mediator analysis, perceived helplessness could be proved as a mediator. Conclusions Every fifth CCI patient suffers from MDD up to six months after being discharged from ICU. Particularly, perceived helplessness during the ICU stay seems to mainly affect the long-term evolvement of MDD. CCI patients with symptoms of acute stress disorder/PTSD should also be screened for MDD.

Prevalence and predictors of major depressive disorder in bereaved caregivers at 6 and 13 months.

Depressive symptoms are common in bereaved caregivers; however, there have been few prospective studies using a structured interview. This study investigated the prevalence and preloss predictors of major depressive disorder (MDD) in bereaved caregivers of patients in a palliative care unit. This prospective cohort study collected caregiver sociodemographic and psychological data before the death of a palliative care unit patient, including MDD, care-burden, coping style, and hopeful attitude. Postloss MDD was assessed 6 and 13 months after death, and a multivariate logistic regression analysis was conducted to identify its predictors.ResultOf 305 caregivers contacted, 92 participated in this study. The prevalence of preloss MDD was 21.8%; the prevalences of postloss MDD were 34.8% and 24.7% at 6 and 13 months, respectively. Preloss MDD predicted postloss MDD at 6 months (odds ratio [OR] = 5.38, 95% confidence interval [CI95%] = 1.29, 22.43); preloss nonhopeful attitude and unemployment status of caregivers predicted postloss MDD at 13 months (OR = 8.77, CI95% = 1.87, 41.13 and OR = 7.10, CI95% = 1.28, 39.36, respectively).Significance of resultsApproximately 35% of caregivers suffered from MDD at 6 months postloss, but the prevalence of MDD decreased to about 25% at 13 months. Preloss MDD significantly predicted postloss MDD at 6 months, whereas hopeful attitude and unemployment at baseline were significantly associated with postloss MDD at 13 months.

A screening algorithm for early detection of major depressive disorder in head and neck cancer patients post-treatment: Longitudinal study.

The primary purpose of this study was to identify predictors of Major Depressive Disorder in head and neck cancer (HNC) patients in the immediate post-treatment period (ie, at 3months post-diagnosis), with a focus on previously unexamined historical and contextual factors. Prospective longitudinal study of 223 consecutive adults (72% participation) newly diagnosed with a first occurrence of primary HNC, including validated psychometric measures, Structured Clinical Interviews for DSM Disorders, and medical chart reviews. The 3-month period prevalence of Major Depressive Disorder was 20.4%; with point prevalences of 6.8% upon HNC diagnosis, 14.2% at 3months, and 22.6% lifetime. Patients most susceptible to developing Major Depressive Disorder in the immediate post-treatment period: were diagnosed with advanced-stage cancer rather than early-stage cancer (O.R.=4.94, P=0.04), received surgery only (O.R.=8.73, P=0.04), presented a lifetime history of Anxiety Disorder on SCID-I (O.R.=6.62; P=0.01), and indicated higher pre-treatment levels of anxiety on the HADS (O.R.=0.45, P=0.05). Our results outline the predominant role of anxiety upon diagnosis as a precursor to post-treatment Major Depressive Disorder, suggesting the need for identification and prophylactic treatment of anxiety upon diagnosis in head and neck cancer patients. Further investigation into pathways by which pre-treatment anxiety predisposes to post-treatment Major Depressive Disorder in this population is warranted.

Evaluation of Endocrine Parameters as Predictor of Major Depressive Disorder.

Background:The diagnosis of the disease, major depressive disorder (MDD), entirely depends on the presence of some symptoms without any biochemical parameter to support it. Depletion of dopamine though is an established feature, is not the sole causative factor of MDD. Moreover, it has very little diagnostic value due to a short half-life. Other chemical messengers like hormones have also been found to get altered due to significant over activity of hypothalamo-pituitary axis. Literature review suggests that cortisol, thyroid-stimulating hormone (TSH), and prolactin (PRL) are mostly altered in MDD, which can be utilized to diagnose the condition.Materials and Methods:A total of 101 patients suffering from MDD along with 106 age- and sex-matched controls were included in this study. Cortisol, TSH, and PRL were assayed in all the study participants by enzyme immunoassay. Student's t-test and linear discriminant analysis were used for statistical analysis.Results:All the three hormones were found to be significantly high in cases with MDD. When applied for classification purpose, the errors in training group were found to be 15% and 15.74% from test set. None of the normal population was wrongly diagnosed as a patient of depression.Conclusion:To the best of our knowledge, this is the first attempt to evaluate multiple biochemical parameters as diagnostic marker of MDD. The study is in progress to find out a cutoff value of the responsible parameter so that they can be optimally used to diagnose a case of MDD.

The role of peri-traumatic stress and disruption distress in predicting symptoms of major depression following exposure to a natural disaster.

Few studies have examined the contribution of specific disaster-related experiences to symptoms of depression. The aims of this study were to do this by examining the roles of peri-traumatic stress and distress due to lingering disaster-related disruption in explaining linkages between disaster exposure and major depressive disorder symptoms among a cohort exposed to the 2010-2011 Canterbury (New Zealand) earthquakes. Structural equation models were fitted to data obtained from the Christchurch Health and Development Study at age 35 ( n = 495), 20-24 months following the onset of the disaster. Measures included earthquake exposure, peri-traumatic stress, disruption distress and symptoms of major depressive disorder. The associations between earthquake exposure and major depression were explained largely by the experience of peri-traumatic stress during the earthquakes (β = 0.180, p < 0.01) and not by disruption distress following the earthquakes (β = 0.048, p = 0.47). The results suggest that peri-traumatic stress has been under-recognised as a predictor of major depressive disorder.