Psychiatry - Research Publications
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ItemThe association between self-reported diet quality and health-related quality of life in rural and urban Australian adolescents(WILEY, 2016-10)OBJECTIVE: This study examines the relationship between diet quality and health-related quality of life (HRQoL) in rural and urban Australian adolescents, and gender differences. DESIGN: Cross-sectional. SETTING: Secondary schools. PARTICIPANTS: 722 rural and 422 urban students from 19 secondary schools. MAIN OUTCOME MEASURES: Self-report dietary-related behaviours, demographic information, HRQoL (AQoL-6D) were collected. Healthy and unhealthy diet quality scores were calculated; multiple linear regression investigated associations between diet quality and HRQoL. RESULTS: Compared to urban students, rural students had higher HRQoL, higher healthy diet score, lower unhealthy diet score, consumed less soft drink and less frequently, less takeaway and a higher proportion consumed breakfast (P < 0.05). Overall, males had higher unhealthy diet score, poorer dietary behaviours but a higher HRQoL score compared to females (P < 0.05). In all students, final regression models indicated: a unit increase in healthy diet score was associated with an increase in HRQoL (unstandardised coefficient(B)±standard error(SE); B = 0.02 ± 0.01(SE); P < 0.02); and a unit increase in unhealthy diet scores was associated with a decrease in HRQoL (-0.01 ± 0.00; P < 0.05). In rural students alone, a unit increase in unhealthy diet score was associated with a decrease in HRQoL (B = -0.01 ± 0.00; P = 0.002), and in urban students a unit increase in healthy diet score was associated with an increase in HRQoL (B = 0.02 ± 0.00; P < 0.001). CONCLUSIONS: Cross-sectional associations between diet quality and HRQoL were observed. Dietary modification may offer a target to improve HRQoL and general well-being; and consequently the prevention and treatment of adolescent health problems. Such interventions should consider gender and locality.
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ItemThe Microbiome and Mental Health: Looking Back, Moving Forward with Lessons from Allergic Diseases(KOREAN COLL NEUROPSYCHOPHARMACOLOGY, 2016-05)Relationships between gastrointestinal viscera and human emotions have been documented by virtually all medical traditions known to date. The focus on this relationship has waxed and waned through the centuries, with noted surges in interest driven by cultural forces. Here we explore some of this history and the emerging trends in experimental and clinical research. In particular, we pay specific attention to how the hygiene hypothesis and emerging research on traditional dietary patterns has helped re-ignite interest in the use of microbes to support mental health. At present, the application of microbes and their structural parts as a means to positively influence mental health is an area filled with promise. However, there are many limitations within this new paradigm shift in neuropsychiatry. Impediments that could block translation of encouraging experimental studies include environmental forces that work toward dysbiosis, perhaps none more important than westernized dietary patterns. On the other hand, it is likely that specific dietary choices may amplify the value of future microbial-based therapeutics. Pre-clinical and clinical research involving microbiota and allergic disorders has predated recent work in psychiatry, an early start that provides valuable lessons. The microbiome is intimately connected to diet, nutrition, and other lifestyle variables; microbial-based psychopharmacology will need to consider this contextual application, otherwise the ceiling of clinical expectations will likely need to be lowered.
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ItemInto the Bowels of Depression: Unravelling Medical Symptoms Associated with Depression by Applying Machine-Learning Techniques to a Community Based Population Sample(PUBLIC LIBRARY SCIENCE, 2016-12-09)BACKGROUND: Depression is commonly comorbid with many other somatic diseases and symptoms. Identification of individuals in clusters with comorbid symptoms may reveal new pathophysiological mechanisms and treatment targets. The aim of this research was to combine machine-learning (ML) algorithms with traditional regression techniques by utilising self-reported medical symptoms to identify and describe clusters of individuals with increased rates of depression from a large cross-sectional community based population epidemiological study. METHODS: A multi-staged methodology utilising ML and traditional statistical techniques was performed using the community based population National Health and Nutrition Examination Study (2009-2010) (N = 3,922). A Self-organised Mapping (SOM) ML algorithm, combined with hierarchical clustering, was performed to create participant clusters based on 68 medical symptoms. Binary logistic regression, controlling for sociodemographic confounders, was used to then identify the key clusters of participants with higher levels of depression (PHQ-9≥10, n = 377). Finally, a Multiple Additive Regression Tree boosted ML algorithm was run to identify the important medical symptoms for each key cluster within 17 broad categories: heart, liver, thyroid, respiratory, diabetes, arthritis, fractures and osteoporosis, skeletal pain, blood pressure, blood transfusion, cholesterol, vision, hearing, psoriasis, weight, bowels and urinary. RESULTS: Five clusters of participants, based on medical symptoms, were identified to have significantly increased rates of depression compared to the cluster with the lowest rate: odds ratios ranged from 2.24 (95% CI 1.56, 3.24) to 6.33 (95% CI 1.67, 24.02). The ML boosted regression algorithm identified three key medical condition categories as being significantly more common in these clusters: bowel, pain and urinary symptoms. Bowel-related symptoms was found to dominate the relative importance of symptoms within the five key clusters. CONCLUSION: This methodology shows promise for the identification of conditions in general populations and supports the current focus on the potential importance of bowel symptoms and the gut in mental health research.
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ItemThe Cross-Sectional Association between Diet Quality and Depressive Symptomology amongst Fijian Adolescents(PUBLIC LIBRARY SCIENCE, 2016-08-25)OBJECTIVE: To examine the relationship between diet quality and depressive symptomology amongst a community-based sample of Fijian adolescents. METHODS: Participants included 7,237 adolescents (52.6% girls; mean age 15.6 years) at baseline (2005) and 2,948 (56% girls; mean age 17.4 years) at follow-up (2007/2008), from the Pacific Obesity Prevention in Communities Project. Intervention schools (n = 7) were selected from Nasinu, near Suva on the main Fijian island Viti Levu, and comparison schools (n = 11) were chosen from towns on the opposite, west side of the island. A dietary questionnaire was used to measure diet quality. Factor analysis clustered dietary variables into two unique and independent factors, referred to as healthy diet quality and unhealthy diet quality. Depressive symptomology was assessed via the emotional subscale of the Paediatric Quality of Life Inventory. Both measures were self-reported and self-administered. Multiple linear regression was used to test cross-sectional associations (at baseline and follow-up) between diet quality and depressive symptomology. Variables controlled for included gender, age, ethnicity, study condition, BMI-z scores, and physical activity. FINDINGS: Strong, positive dose-response associations between healthy diet and high emotional scores (lower depressive symptomology) were found in cross-sectional analyses at baseline and follow-up, among boys and girls. No association was found between emotional health and unhealthy diet. CONCLUSIONS: This study suggests that cross-sectional relationships exist between a high quality diet during adolescence and less depressive symptoms, however more evidence is required to determine if these two variables are linked causally. Trial population health strategies that use dietary interventions as a mechanism for mental health promotion provide an opportunity to further test these associations. If this is indeed a true relationship, these forms of interventions have the potential to be inexpensive and have substantial reach, especially in Low and Middle Income Countries. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12608000345381.
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ItemStudy protocol for a systematic review of evidence for lifestyle interventions targeting smoking, sleep, alcohol/other drug use, physical activity, and healthy diet in people with bipolar disorder(BMC, 2016)BACKGROUND: People with bipolar disorder (BD) have a mortality gap of up to 20 years compared to the general population. Physical conditions, such as cardiovascular disease (CVD) and cancer, cause the majority of excess deaths in psychiatric populations and are the leading causes of mortality in people with BD. However, comparatively little attention has been paid to reducing the risk of physical conditions in psychiatric populations. Unhealthy lifestyle behaviors are among the potentially modifiable risk factors for a range of commonly comorbid chronic medical conditions, including CVD, diabetes, and obesity. This systematic review will identify and evaluate the available evidence for effective interventions to reduce risk and promote healthy lifestyle behaviors in BD. METHODS/DESIGN: We will search MEDLINE, Embase, PsychINFO, Cochrane Database of Systematic Reviews, and CINAHL for published research studies (with at least an abstract published in English) that evaluate behavioral or psychosocial interventions to address the following lifestyle factors in people with BD: tobacco use, physical inactivity, unhealthy diet, overweight or obesity, sleep-wake disturbance, and alcohol/other drug use. Primary outcomes for the review will be changes in tobacco use, level of physical activity, diet quality, sleep quality, alcohol use, and illicit drug use. Data on each primary outcome will be synthesized across available studies in that lifestyle area (e.g., tobacco abstinence, cigarettes smoked per day), and panel of research and clinical experts in each of the target lifestyle behaviors and those experienced with clinical and research with individuals with BD will determine how best to represent data related to that primary outcome. Seven members of the systematic review team will extract data, synthesize the evidence, and rate it for quality. Evidence will be synthesized via a narrative description of the behavioral interventions and their effectiveness in improving the healthy lifestyle behaviors in people with BD. DISCUSSION: The planned review will synthesize and evaluate the available evidence regarding the behavioral or psychosocial treatment of lifestyle-related behaviors in people with BD. From this review, we will identify gaps in our existing knowledge and research evidence about the management of unhealthy lifestyle behaviors in people with BD. We will also identify potential opportunities to address lifestyle behaviors in BD, with a view to reducing the burden of physical ill-health in this population. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42015019993.
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ItemHealthy together Victoria and childhood obesity-a methodology for measuring changes in childhood obesity in response to a community-based, whole of system cluster randomized control trial(BMC, 2016-04-25)BACKGROUND: Healthy Together Victoria (HTV) - a complex 'whole of system' intervention, including an embedded cluster randomized control trial, to reduce chronic disease by addressing risk factors (physical inactivity, poor diet quality, smoking and harmful alcohol use) among children and adults in selected communities in Victoria, Australia (Healthy Together Communities). OBJECTIVES: To describe the methodology for: 1) assessing changes in the prevalence of measured childhood obesity and associated risks between primary and secondary school students in HTV communities, compared with comparison communities; and 2) assessing community-level system changes that influence childhood obesity in HTC and comparison communities. METHODS: Twenty-four geographically bounded areas were randomized to either prevention or comparison (2012). A repeat cross-sectional study utilising opt-out consent will collect objectively measured height, weight, waist and self-reported behavioral data among primary [Grade 4 (aged 9-10y) and Grade 6 (aged 11-12y)] and secondary [Grade 8 (aged 13-14y) and Grade 10 (aged 15-16y)] school students (2014 to 2018). Relationships between measured childhood obesity and system causes, as defined in the Foresight obesity systems map, will be assessed using a range of routine and customised data. CONCLUSION: This research methodology describes the beginnings of a state-wide childhood obesity monitoring system that can evolve to regularly inform progress on reducing obesity, and situate these changes in the context of broader community-level system change.
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ItemFusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression(PUBLIC LIBRARY SCIENCE, 2016-02-05)BACKGROUND: Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. METHODS: The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. RESULTS: After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001). CONCLUSION: The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin.
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ItemDiet and Common Mental Disorders: The imperative to Translate Evidence into Action(FRONTIERS MEDIA SA, 2016-04-29)
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ItemNo Preview AvailableDepression is a risk factor for incident coronary heart disease in women: An 18-year longitudinal study(ELSEVIER SCIENCE BV, 2016-05-15)BACKGROUND: According to a recent position paper by the American Heart Association, it remains unclear whether depression is a risk factor for incident Coronary Heart Disease (CHD). We assessed whether a depressive disorder independently predicts 18-year incident CHD in women. METHOD: A prospective longitudinal study of 860 women enrolled in the Geelong Osteoporosis Study (1993-2011) was conducted. Participants were derived from an age-stratified, representative sample of women (20-94 years) randomly selected from electoral rolls in South-Eastern Australia. The exposure was a diagnosis of a depressive disorder using the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders. Outcomes data were collected from hospital medical records: (1) PRIMARY OUTCOME: a composite measure of cardiac death, non-fatal Myocardial Infarction or coronary intervention. (2) Secondary outcome: any cardiac event (un/stable angina, cardiac event not otherwise defined) occurring over the study period. RESULTS: Seven participants were excluded based on CHD history. Eighty-three participants (9.6%) recorded ≥1 cardiac event over the study period; 47 had a diagnosis that met criteria for inclusion in the primary analysis. Baseline depression predicted 18-year incidence, adjusting for (1) anxiety (adj. OR:2.39; 95% CIs:1.19-4.82), plus (2) typical risk factors (adj. OR:3.22; 95% CIs:1.45-6.93), plus (3) atypical risk factors (adj. OR:3.28; 95% CIs:1.36-7.90). This relationship held when including all cardiac events. No relationship was observed between depression and recurrent cardiac events. CONCLUSION: The results of this study support the contention that depression is an independent risk factor for CHD incidence in women. Moreover, the strength of association between depression and CHD incidence was of a greater magnitude than any typical and atypical risk factor.