Depression, diet and exercise

Unhealthy lifestyle behaviour is driving an increase in the burden of chronic non-communicable diseases worldwide.

Recent evidence suggests that poor diet and a lack of exercise contribute to the genesis and course of depression.

While studies examining dietary improvement as a treatment strategy in depression are lacking, epidemiological evidence clearly points to diet quality being of importance to the risk of depression.

Exercise has been shown to be an effective treatment strategy for depression, but this is not reflected in treatment guidelines, and increased physical activity is not routinely encouraged when managing depression in clinical practice.

Recommendations regarding dietary improvement, increases in physical activity and smoking cessation should be routinely given to patients with depression.

Specialised and detailed advice may not be necessary. Recommendations should focus on following national guidelines for healthy eating and physical activity.

Computer-aided detection of depression from magnetic resonance images

Magnetic resonance imaging (MRI) of the brain is used to detect depression disorder. However, a large number of MRI scans needs to be analyzed for such detection. Manual segmentation of the biomarkers in MRI scans by clinical experts can become time consuming and sometimes erroneous. This paper presents a study on computer-aided detection of depression from MRI scans. These systems have not yet been identified, categorized and compared in the literature. The paper covers fully automated to semi-automated detection systems. It also presents performance comparison for the considered systems.

Evaluation of a training program to assist care staff to better recognize and manage depression among palliative care patients and their families

This study was designed to evaluate the effectiveness of a training program to improve the knowledge, attitudes, and self-efficacy of palliative care staff and thus enable them to better detect and manage depression among palliative care patients and their families. Participants were 90 professional carers who completed a four-session training program. Knowledge, attitudes, self-efficacy, and barriers to working with depressed patients were assessed preintervention, post-intervention, and at a three-month follow-up. The results demonstrated that compared to the control group, the intervention group had improved in all of these areas. Improvements were maintained at the three-month follow-up in all areas except attitudes. The results of this study indicate the importance of training in managing depression among palliative care staff. Booster sessions will likely be needed to ensure that training program gains are maintained.

ABCB1 polymorphism predicts escitalopram dose needed for remission in major depression

The ATP-binding cassette family of transporter proteins, subfamily B (MDR/TAP), member 1 (ABCB1) (P-glycoprotein) transporter is a key component of the blood–brain barrier. Many antidepressants are subject to ABCB1 efflux. Functional polymorphisms of ABCB1 may influence central nervous system bioavailability of antidepressants subject to efflux. Single-nucleotide polymorphisms (SNPs) at rs1045642 (C3435T) of ABCB1 have been associated with efflux pump efficiency. This may explain part of the interindividual variation in antidepressant dose needed to remit. Individuals (N=113) with DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition) major depressive disorder (MDD) were treated with escitalopram (ESC) or venlafaxine (VEN) over 8 weeks. The17-item Hamilton Depression Rating Scale was assessed serially, blind to genotype. SNP rs1045642 of ABCB1 along with two SNPs previously reported to be in linkage disequilibrium with it (rs2032582 and rs1128503) were genotyped. Demographic features, clinical features, P450 metabolizer status and 5-HTTLPR (serotonin-transporter-linked promoter region) genotype were controlled for. Carriers of rs1045642 TT needed on average 11 mg of ESC to remit, whereas TC and CC carriers required 24 and 19 mg, respectively (P=0.0001). This equates to a 2.0- (95% confidence interval=1.5–3.4; P<0.001) fold greater ESC dose needed to remit for C carriers compared with TT carriers at rs1045642. Of VEN-treated subjects carrying TT genotype at rs1045642, 73.3% remitted compared with 12.5% for CC genotype (odds ratio=6.69; 95% confidence interval=1.72–25.9, P=0.006). These data suggest that antidepressant dose needed to remit can be predicted by an ABCB1 SNP. This has the potential clinical translation implications for dose selection and remission from MDD.

Preventive strategies in depression : gathering evidence for risk factors and potential interventions

This editorial critiques the recent literature concerning both vitamin D deficiency in major depression and supplementation as a treatment strategy, and contextualises it within a broader approach to the prevention of depression, based on the recent evidence for lifestyle as a risk factor for depression and anxiety.

Towards automated detection of depression from brain structural magnetic resonance images

Introduction : Depression is a major issue worldwide and is seen as a significant health problem. Stigma and patient denial, clinical experience, time limitations, and reliability of psychometrics are barriers to the clinical diagnoses of depression. Thus, the establishment of an automated system that could detect such abnormalities would assist medical experts in their decision-making process. This paper reviews existing methods for the automated detection of depression from brain structural magnetic resonance images (sMRI).Methods : Relevant sources were identified from various databases and online sites using a combination of keywords and terms including depression, major depressive disorder, detection, classification, and MRI databases. Reference lists of chosen articles were further reviewed for associated publications.Results : The paper introduces a generic structure for representing and describing the methods developed for the detection of depression from sMRI of the brain. It consists of a number of components including acquisition and preprocessing, feature extraction, feature selection, and classification.Conclusion : Automated sMRI-based detection methods have the potential to provide an objective measure of depression, hence improving the confidence level in the diagnosis and prognosis of depression.

Barriers to the detection and management of depression by palliative care professional carers among their patients : perspectives from professional carers and patients' family members

Introduction: Clinical depression is highly prevalent yet underdetected and poorly managed within palliative care settings.

Objectives: This qualitative study explored the identification, monitoring, and management of symptoms of depression in patients receiving palliative care from 2 juxtaposed perspectives that are of care providers and care recipients' family members. Examining the barriers that restrict professional carers detecting and managing depression in their patients was a central focus of the study.

Methods: Focus groups were held with 18 professional carers, including 8 holding managerial positions, across 2 palliative care services, 1 regional and 1 metropolitan, which provided both inpatient and community-based care. Individual interviews were conducted with 10 family members of patients who had received or were receiving palliative care through these services.

Results: Thematic analysis of these data identified that both professional carers and family members perceived that depression is a wide-spread concern for patients receiving palliative care; however, numerous barriers were identified that affect professional carers’ ability to identify depression. These included knowledge and training deficits, low self-efficacy, prioritization of physical concerns and time constraints, patient/family characteristics, and system/process issues. These themes (and related subthemes) are discussed in this article.

Conclusions: Specialized training in depression is recommended for professional carers in order to improve their depression-related knowledge, detection skills, and self-efficacy. The ultimate goal of such training is to increase the rate of recognition of depression that in turn will lead to appropriate treatment for depressed patients.

Validity of the Beck Depression Inventory as a screening tool for a clinical mood disorder in Bariatric surgery candidates

Background : The Beck Depression Inventory (BDI) is one of the most commonly used instruments to assess depression in persons with obesity. While it has been validated in normal and psychiatric populations, in obese populations, its validity remains uncertain. This study aimed to investigate the validity and reliability of the BDI-IA and BDI-II in severely obese bariatric surgery candidates.

Methods : Consecutive new candidates at a bariatric surgery clinic were invited to participate in the study by their consulting surgeon. All candidates were assessed using the Structured Clinical Interview for DSM-IV Disorders (SCID-I); 118 completed the BDI-IA and 83 completed the BDI-II. Two hundred one patients (response rate, 88 %) participated in the study. The current sample (82 % female) had an average body mass index of 42.83 ± 6.34 and an average age of 45 ± 12 years.

Results : Based on the SCID-I, 54 candidates (26.9 %) met the criteria for a mood disorder, with 37 meeting the criteria for current major depressive disorder. Individuals diagnosed with a clinical mood disorder had significantly higher scores on the BDI (BDI-IA, 23.59 ± 9.69 vs. 12.76 ± 8.29; BDI-II, 22.93 ± 5.22 vs. 11.25 ± 8.44). Our results indicated that, as a screening tool for a clinical mood disorder, the BDI-II had an optimal cutoff of 13, with a sensitivity of 100 and specificity of 67.75.

Conclusions : Results indicated that the BDI-IA should not be used as a tool to measure depressive symptomatology in obese bariatric surgery candidates. No cutoff was identified with adequate sensitivity and specificity, and over 20 % of patients were misclassified. As a screening tool for a clinical mood disorder, the BDI-II was adequate; however, prevalence rates were significantly overestimated.

Confirmatory factor analysis of the Beck Depression inventory in obese individuals seeking surgery

Background : The Beck Depression Inventory (BDI) is frequently employed as measure of depression in studies of obesity. The aim of the study was to assess the factorial structure of the BDI in obese patients prior to bariatric surgery.

Methods : Confirmatory factor analysis was conducted on the current published factor analyses of the BDI. Three published models were initially analysed with two additional modified models subsequently included. A sample of 285 patients presenting for Lap-Band® surgery was used.

Results : The published bariatric model by Munoz et al. was not an adequate fit to the data. The general model by Shafer et al. was a good fit to the data but had substantial limitations. The weight loss item did not significantly load on any factor in either model. A modified Shafer model and a proposed model were tested, and both were found to be a good fit to the data with minimal differences between the two. A proposed model, in which two items, weight loss and appetite, were omitted, was suggested to be the better model with good reliability.

Conclusions : The previously published factor analysis in bariatric candidates by Munoz et al. was a poor fit to the data, and use of this factor structure should be seriously reconsidered within the obese population. The hypothesised model was the best fit to the data. The findings of the study suggest that the existing published models are not adequate for investigating depression in obese patients seeking surgery.