Psychiatry - Research Publications
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ItemPrevalence of mood and anxiety disorder in self reported irritable bowel syndrome (IBS). An epidemiological population based study of women(BMC, 2010-08-05)BACKGROUND: Irritable bowel syndrome (IBS) is commonly regarded as a functional disorder, and is hypothesized to be associated with anxiety and depression. This evidence mainly rests on population-based studies utilising self-report screening instruments for psychopathology. Other studies applying structured clinical interviews are generally based on small clinical samples, which are vulnerable to biases. The extant evidence base for an association between IBS and psychopathology is hence not conclusive. The aim of this study was therefore to re-examine the hypothesis using population-based data and psychiatric morbidity established with a structured clinical interview. METHODS: Data were derived from a population-based epidemiological study (n = 1077). Anxiety and mood disorders were established using the Structured Clinical Interview for DSM-IV-TR (SCID-I/NP) and the General Health Questionnaire (GHQ-12). Current and lifetime IBS was self-reported. Hypertension and diabetes were employed as comparison groups as they are expected to be unrelated to mental health. RESULTS: Current IBS (n = 69, 6.4%) was associated with an increased likelihood of current mood and/or anxiety disorders (OR = 2.62, 95%CI 1.49 - 4.60). Half the population reporting a lifetime IBS diagnosis also had a lifetime mood or anxiety disorder. Exploratory analyses demonstrated an increased prevalence of IBS across most common anxiety and mood disorders, the exception being bipolar disorder. The association with IBS and symptoms load (GHQ-12) followed a curved dose response pattern. In contrast, hypertension and diabetes were consistently unrelated to psychiatric morbidity. CONCLUSIONS: IBS is significantly associated with anxiety and mood disorders. This study provides indicative evidence for IBS as a disorder with a psychosomatic aspect.
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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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ItemThe impact of statins on psychological wellbeing: a systematic review and meta-analysis(BMC, 2012-12-03)BACKGROUND: Cholesterol-lowering medications such as statins have anti-inflammatory and antioxidant properties, which may be beneficial for treating depression and improving mood. However, evidence regarding their effects remains inconsistent, with some studies reporting links to mood disturbances. We aimed to conduct a meta-analysis to determine the impact of statins on psychological wellbeing of individuals with or without hypercholesterolemia. METHODS: Articles were identified using medical, health, psychiatric and social science databases, evaluated for quality, and data were synthesized and analyzed in RevMan-5 software using a random effects model. RESULTS: The 7 randomized controlled trials included in the analysis represented 2,105 participants. A test for overall effect demonstrated no statistically significant differences in psychological wellbeing between participants receiving statins or a placebo (standardized mean difference (SMD) = -0.08, 95% CI -0.29 to 0.12; P = 0.42). Sensitivity analyses were conducted to separately analyze depression (n = 5) and mood (n = 2) outcomes; statins were associated with statistically significant improvements in mood scores (SMD = -0.43, 95% CI -0.61 to -0.24). CONCLUSIONS: Our findings refute evidence of negative effects of statins on psychological outcomes, providing some support for mood-related benefits. Future studies could examine the effects of statins in depressed populations.
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ItemSo depression is an inflammatory disease, but where does the inflammation come from?(BMC, 2013-09-12)BACKGROUND: We now know that depression is associated with a chronic, low-grade inflammatory response and activation of cell-mediated immunity, as well as activation of the compensatory anti-inflammatory reflex system. It is similarly accompanied by increased oxidative and nitrosative stress (O&NS), which contribute to neuroprogression in the disorder. The obvious question this poses is 'what is the source of this chronic low-grade inflammation?' DISCUSSION: This review explores the role of inflammation and oxidative and nitrosative stress as possible mediators of known environmental risk factors in depression, and discusses potential implications of these findings. A range of factors appear to increase the risk for the development of depression, and seem to be associated with systemic inflammation; these include psychosocial stressors, poor diet, physical inactivity, obesity, smoking, altered gut permeability, atopy, dental cares, sleep and vitamin D deficiency. SUMMARY: The identification of known sources of inflammation provides support for inflammation as a mediating pathway to both risk and neuroprogression in depression. Critically, most of these factors are plastic, and potentially amenable to therapeutic and preventative interventions. Most, but not all, of the above mentioned sources of inflammation may play a role in other psychiatric disorders, such as bipolar disorder, schizophrenia, autism and post-traumatic stress disorder.
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ItemThe association between diet quality, dietary patterns and depression in adults: a systematic review(BMC, 2013-06-27)BACKGROUND: Recent evidence suggests that diet modifies key biological factors associated with the development of depression; however, associations between diet quality and depression are not fully understood. We performed a systematic review to evaluate existing evidence regarding the association between diet quality and depression. METHOD: A computer-aided literature search was conducted using Medline, CINAHL, and PsycINFO, January 1965 to October 2011, and a best-evidence analysis performed. RESULTS: Twenty-five studies from nine countries met eligibility criteria. Our best-evidence analyses found limited evidence to support an association between traditional diets (Mediterranean or Norwegian diets) and depression. We also observed a conflicting level of evidence for associations between (i) a traditional Japanese diet and depression, (ii) a "healthy" diet and depression, (iii) a Western diet and depression, and (iv) individuals with depression and the likelihood of eating a less healthy diet. CONCLUSION: To our knowledge, this is the first review to synthesize and critically analyze evidence regarding diet quality, dietary patterns and depression. Further studies are urgently required to elucidate whether a true causal association exists.
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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.