Psychiatry - Research Publications
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ItemThe association between major depressive disorder, use of antidepressants and bone mineral density (BMD) in men(JMNI, 2015-06)OBJECTIVE: Both depression and use of antidepressants have been negatively associated with bone mineral density (BMD) but mainly in studies among postmenopausal women. Therefore, the aim of this study was to investigate these relationships in men. METHODS: Between 2006 and 2011, 928 men (aged 24-98 years) from the Geelong Osteoporosis Study completed a comprehensive questionnaire, clinical measurements and had BMD assessments at the forearm, spine, total hip and total body. Major depressive disorder (MDD) was identified using a structured clinical interview (SCID-I/NP). The cross-sectional associations between BMD and both MDD and antidepressant use were analyzed using multivariable linear regression. RESULTS: Of the study population, 84 (9.1%) men had a single MDD episode, 50 (5.4%) had recurrent episodes and 65 (7.0%) were using antidepressants at the time of assessment. Following adjustments, recurrent MDD was associated with lower BMD at the forearm and total body (-6.5%, P=0.033 and -2.5%, P=0.033, respectively compared to men with no history of MDD), while single MDD episodes were associated with higher BMD at the total hip (+3.4%, P=0.030). Antidepressant use was associated with lower BMD only in lower-weight men (<75-110 kg depending on bone site). CONCLUSIONS: Both depression and use of antidepressants should be taken into account as possible risk factors for osteoporosis in men.
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ItemGender-specific risk factors for low bone mineral density in patients taking antipsychotics for psychosis(WILEY, 2018-01)OBJECTIVE: This study examined clinical and gender-specific risk factors for low bone mineral density (BMD) in adult patients with psychotic disorders. METHODS: The study included 285 community-dwelling patients with psychotic disorders. Dual-energy X-ray absorptiometry was used to measure BMD. Clinical characteristics associated with low BMD were identified with logistic regression analysis in total population and each gender. RESULTS: Fifty-eight (20.4%) subjects had low BMD. Low BMD was more common in men and in patients with low body mass indices (BMIs), as well as in those with shorter treatment durations, those on Medicaid, and patients using serotonergic antidepressants. Logistic regression analysis revealed that low BMD was negatively associated with BMI and treatment duration and positively with gender (male) and serotonergic antidepressants use in the overall population. In men, low BMD was associated with treatment duration and BMI; in women, low BMD was associated with BMI, prolactin level, vitamin D, and serotonergic antidepressant use. CONCLUSION: Managing the risk factors associated with low BMD among patients with psychotic disorder should be done gender-specifically. Psychotropic agents should be prescribed mindful of their effects on bone, as use of these medications is a modifiable risk factor for osteoporosis in women with psychotic disorders.
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ItemStatin use and risk of depression: a Swedish national cohort study(BMC, 2014-12-04)BACKGROUND: Statin medications, used to prevent heart disease by reducing cholesterol, also reduce inflammation and protect against oxidative damage. As inflammation and oxidative stress occur in depression, there is interest in their potential to reduce depression risk. We investigated whether use of statin medications was associated with a change in the risk of developing depression in a very large Swedish national cohort (n = 4,607,990). METHODS: National register data for adults ≥40yr was analyzed to obtain information about depression diagnoses and prescriptions of statin medications between 2006 and 2008. Associations were tested using logistic regression. RESULTS: Use of any statin was shown to reduce the odds of depression by 8% compared to individuals not using statin medications (OR = 0.92, 95% CI, 0.89-0.96; p < 0.001). Simvastatin had a protective effect (OR = 0.93, 95% CI, 0.89-0.97; p = 0.001), whereas atorvastatin was associated with increased risk of depression (OR = 1.11, 95% CI, 1.01-1.22; p = 0.032). There was a stepwise decrease in odds ratio with increasing age (OR ≥ 40 years = 0.95, OR ≥ 50 years = 0.91, OR ≥ 60 years = 0.85, OR ≥ 70 years = 0.81). CONCLUSIONS: The use of any statin was associated with a reduction in risk of depression in individuals over the age of 40. Clarification of the strength of these protective effects, the clinical relevance of these effects and determination of which statins are most effective is needed.
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ItemPop, heavy metal and the blues: secondary analysis of persistent organic pollutants (POP), heavy metals and depressive symptoms in the NHANES National Epidemiological Survey(BMJ PUBLISHING GROUP, 2014)OBJECTIVES: Persistent environmental pollutants, including heavy metals and persistent organic pollutants (POPs), have a ubiquitous presence. Many of these pollutants affect neurobiological processes, either accidentally or by design. The aim of this study was to explore the associations between assayed measures of POPs and heavy metals and depressive symptoms. We hypothesised that higher levels of pollutants and metals would be associated with depressive symptoms. SETTING: National Health and Nutrition Examination Survey (NHANES). PARTICIPANTS: A total of 15 140 eligible people were included across the three examined waves of NHANES. PRIMARY AND SECONDARY OUTCOME MEASURES: Depressive symptoms were assessed using the nine-item version of the Patient Health Questionnaire (PHQ-9), using a cut-off point of 9/10 as likely depression cases. Organic pollutants and heavy metals, including cadmium, lead and mercury, as well as polyfluorinated compounds (PFCs), pesticides, phenols and phthalates, were measured in blood or urine. RESULTS: Higher cadmium was positively associated with depression (adjusted Prevalence Ratios (PR)=1.48, 95% CI 1.16 to 1.90). Higher levels of mercury were negatively associated with depression (adjusted PR=0.62, 95% CI 0.50 to 0.78), and mercury was associated with increased fish consumption (n=5500, r=0.366, p<0.001). In addition, several PFCs (perfluorooctanoic acid, perfluorohexane sulfonic acid, perfluorodecanoic acid and perfluorononanoic acid) were negatively associated with the prevalence of depression. CONCLUSIONS: Cadmium was associated with an increased likelihood of depression. Contrary to hypotheses, many of persistent environmental pollutants were not associated or negatively associated with depression. While the inverse association between mercury and depressive symptoms may be explained by a protective role for fish consumption, the negative associations with other pollutants remains unclear. This exploratory study suggests the need for further investigation of the role of various agents and classes of agents in the pathophysiology of depression.
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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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ItemNo Preview AvailableGetting RID of the blues: Formulating a Risk Index for Depression (RID) using structural equation modeling(SAGE PUBLICATIONS LTD, 2017-11)OBJECTIVE: While risk factors for depression are increasingly known, there is no widely utilised depression risk index. Our objective was to develop a method for a flexible, modular, Risk Index for Depression using structural equation models of key determinants identified from previous published research that blended machine-learning with traditional statistical techniques. METHODS: Demographic, clinical and laboratory variables from the National Health and Nutrition Examination Study (2009-2010, N = 5546) were utilised. Data were split 50:50 into training:validation datasets. Generalised structural equation models, using logistic regression, were developed with a binary outcome depression measure (Patient Health Questionnaire-9 score ⩾ 10) and previously identified determinants of depression: demographics, lifestyle-environs, diet, biomarkers and somatic symptoms. Indicative goodness-of-fit statistics and Areas Under the Receiver Operator Characteristic Curves were calculated and probit regression checked model consistency. RESULTS: The generalised structural equation model was built from a systematic process. Relative importance of the depression determinants were diet (odds ratio: 4.09; 95% confidence interval: [2.01, 8.35]), lifestyle-environs (odds ratio: 2.15; 95% CI: [1.57, 2.94]), somatic symptoms (odds ratio: 2.10; 95% CI: [1.58, 2.80]), demographics (odds ratio:1.46; 95% CI: [0.72, 2.95]) and biomarkers (odds ratio:1.39; 95% CI: [1.00, 1.93]). The relationships between demographics and lifestyle-environs and depression indicated a potential indirect path via somatic symptoms and biomarkers. The path from diet was direct to depression. The Areas under the Receiver Operator Characteristic Curves were good (logistic:training = 0.850, validation = 0.813; probit:training = 0.849, validation = 0.809). CONCLUSION: The novel Risk Index for Depression modular methodology developed has the flexibility to add/remove direct/indirect risk determinants paths to depression using a structural equation model on datasets that take account of a wide range of known risks. Risk Index for Depression shows promise for future clinical use by providing indications of main determinant(s) associated with a patient's predisposition to depression and has the ability to be translated for the development of risk indices for other affective disorders.
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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.
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ItemStatin and Aspirin Use and the Risk of Mood Disorders among Men(OXFORD UNIV PRESS, 2016-06)BACKGROUND: There is a growing understanding that depression is associated with systemic inflammation. Statins and aspirin have anti-inflammatory properties. Given these agents have been shown to reduce the risk of a number of diseases characterized by inflammation, we aimed to determine whether a similar relationship exists for mood disorders (MD). METHODS: This study examined data collected from 961 men (24-98 years) participating in the Geelong Osteoporosis Study. MD were identified using a semistructured clinical interview (SCID-I/NP). Anthropometry was measured and information on medication use and lifestyle factors was obtained via questionnaire. Two study designs were utilized: a nested case-control and a retrospective cohort study. RESULTS: In the nested case-control study, exposure to statin and aspirin was documented for 9 of 142 (6.3%) cases and 234 of 795 (29.4%) controls (P < .001); after adjustment for age, exposure to these anti-inflammatory agents was associated with reduced likelihood of MD (OR 0.2, 95%CI 0.1-0.5). No effect modifiers or other confounders were identified. In the retrospective cohort study of 836 men, among the 210 exposed to statins or aspirin, 6 (2.9%) developed de novo MD during 1000 person-years of observation, whereas among 626 nonexposed, 34 (5.4%) developed de novo MD during 3071 person-years of observation. The hazard ratio for de novo MD associated with exposure to anti-inflammatory agents was 0.55 (95%CI 0.23-1.32). CONCLUSIONS: This study provides both cross-sectional and longitudinal evidence consistent with the hypothesis that statin and aspirin use is associated with a reduced risk of MD.
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ItemPsychiatric disorders, psychotropic medication use and falls among women: an observational study(BMC, 2015-04-08)BACKGROUND: Psychotropic agents known to cause sedation are associated with an increased risk of falls, but the role of psychiatric illness as an independent risk factor for falls is not clear. Thus, this study aimed to investigate the association between psychiatric disorders, psychotropic medication use and falls risk. METHODS: This study examined data collected from 1062 women aged 20-93 yr (median 50 yr) participating in the Geelong Osteoporosis Study, a large, ongoing, population-based study. Depressive and anxiety disorders for the preceding 12-month period were ascertained by clinical interview. Current medication use and falls history were self-reported. Participants were classified as fallers if they had fallen to the ground at least twice during the same 12-month period. Anthropometry, demographic, medical and lifestyle factors were determined. Logistic regression was used to test the associations, after adjusting for potential confounders. RESULTS: Fifty-six women (5.3%) were classified as fallers. Those meeting criteria for depression within the past 12 months had a 2.4-fold increased odds of falling (unadjusted OR = 2.4, 95% CI 1.2-4.5). Adjustment for age and mobility strengthened the relationship (adjusted OR = 2.7, 95% CI 1.4-5.2) between depression and falling, with results remaining unchanged following further adjustment for psychotropic medication use (adjusted OR = 2.7, 95% CI 1.3-5.6). In contrast, past (prior to 12-month) depression were not associated with falls. No association was observed between anxiety and falls risk. Falling was associated with psychotropic medication use (unadjusted OR = 2.8, 95% CI 1.5-5.2), as well as antidepressant (unadjusted OR = 2.4, 95% CI 1.2-4.8) and benzodiazepine use (unadjusted OR = 3.4, 95% CI 1.6-7.3); associations remained unchanged following adjustment for potential confounders. CONCLUSION: The likelihood of falls was increased among those with depression within the past 12 months, independent of psychotropic medication use and other recognised confounders, suggesting an independent effect of depression on falls risk. Psychotropic drug use was also confirmed as an independent risk factor for falls, but anxiety disorders were not. Further research into the underlying mechanisms is warranted.