Medicine (Western Health) - Research Publications
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ItemThe Role of Health Literacy in the Treatment of Osteoporosis(WILEY, 2016-10)
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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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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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ItemBone health in bipolar disorder: a study protocol for a case-control study in Australia(BMJ PUBLISHING GROUP, 2020-02)INTRODUCTION: Little is known about the bone health of adults with bipolar disorder, aside from evidence purporting bone deficits among individuals with other mental illnesses, or those taking medications commonly used in bipolar disorder. In this paper, we present the methodology of a case-control study which aims to examine the role of bipolar disorder as a risk factor for bone fragility. METHODS AND ANALYSIS: Men and women with bipolar disorder (~200 cases) will be recruited and compared with participants with no history of bipolar disorder (~1500 controls) from the Geelong Osteoporosis Study. Both cases and controls will be drawn from the Barwon Statistical Division, south-eastern Australia. The Structured Clinical Interview for DSM-IV-TR Research Version, Non-patient edition is the primary diagnostic instrument, and psychiatric symptomatology will be assessed using validated rating scales. Demographic information and detailed lifestyle data and medical history will be collected via comprehensive questionnaires. Participants will undergo dual energy X-ray absorptiometry scans and other clinical measures to determine bone and body composition. Blood samples will be provided after an overnight fast and stored for batch analysis. ETHICS AND DISSEMINATION: Ethics approval has been granted from Barwon Health Research Ethics Committee. Participation in the study is voluntary. The study findings will be disseminated via peer-reviewed publications, conference presentations and reports to the funding body.
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ItemFractures in indigenous compared to non-indigenous populations: A systematic review of rates and aetiology.(Elsevier BV, 2017-06)BACKGROUND: Compared to non-indigenous populations, indigenous populations experience disproportionately greater morbidity, and a reduced life expectancy; however, conflicting data exist regarding whether a higher risk of fracture is experienced by either population. We systematically evaluate evidence for whether differences in fracture rates at any skeletal site exist between indigenous and non-indigenous populations of any age, and to identify potential risk factors that might explain these differences. METHODS: On 31 August 2016 we conducted a comprehensive computer-aided search of peer-reviewed literature without date limits. We searched PubMed, OVID, MEDLINE, CINAHL, EMBASE, and reference lists of relevant publications. The protocol for this systematic review is registered in PROSPERO, the International Prospective Register of systematic reviews (CRD42016043215). Using the World Health Organization reference population as standard, hip fracture incidence rates were re-standardized for comparability between countries. RESULTS: Our search yielded 3227 articles; 283 potentially eligible articles were cross-referenced against predetermined criteria, leaving 27 articles for final inclusion. Differences in hip fracture rates appeared as continent-specific, with lower rates observed for indigenous persons in all countries except for Canada and Australia where the opposite was observed. Indigenous persons consistently had higher rates of trauma-related fractures; the highest were observed in Australia where craniofacial fracture rates were 22-times greater for indigenous compared to non-indigenous women. After adjustment for socio-demographic and clinical risk factors, approximately a three-fold greater risk of osteoporotic fracture and five-fold greater risk of craniofacial fractures was observed for indigenous compared to non-indigenous persons; diabetes, substance abuse, comorbidity, lower income, locality, and fracture history were independently associated with an increased risk of fracture. CONCLUSIONS: The observed paucity of data and suggestion of continent-specific differences indicate an urgent need for further research regarding indigenous status and fracture epidemiology and aetiology. Our findings also have implications for communities, governments and healthcare professionals to enhance the prevention of trauma-related fractures in indigenous persons, and an increased focus on modifiable lifestyle behaviours to prevent osteoporotic fractures in all populations.