Psychiatry - Research Publications
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ItemA systematic review of gut microbiota composition in observational studies of major depressive disorder, bipolar disorder and schizophrenia(SPRINGERNATURE, 2022-04)The emerging understanding of gut microbiota as 'metabolic machinery' influencing many aspects of physiology has gained substantial attention in the field of psychiatry. This is largely due to the many overlapping pathophysiological mechanisms associated with both the potential functionality of the gut microbiota and the biological mechanisms thought to be underpinning mental disorders. In this systematic review, we synthesised the current literature investigating differences in gut microbiota composition in people with the major psychiatric disorders, major depressive disorder (MDD), bipolar disorder (BD) and schizophrenia (SZ), compared to 'healthy' controls. We also explored gut microbiota composition across disorders in an attempt to elucidate potential commonalities in the microbial signatures associated with these mental disorders. Following the PRISMA guidelines, databases were searched from inception through to December 2021. We identified 44 studies (including a total of 2510 psychiatric cases and 2407 controls) that met inclusion criteria, of which 24 investigated gut microbiota composition in MDD, seven investigated gut microbiota composition in BD, and 15 investigated gut microbiota composition in SZ. Our syntheses provide no strong evidence for a difference in the number or distribution (α-diversity) of bacteria in those with a mental disorder compared to controls. However, studies were relatively consistent in reporting differences in overall community composition (β-diversity) in people with and without mental disorders. Our syntheses also identified specific bacterial taxa commonly associated with mental disorders, including lower levels of bacterial genera that produce short-chain fatty acids (e.g. butyrate), higher levels of lactic acid-producing bacteria, and higher levels of bacteria associated with glutamate and GABA metabolism. We also observed substantial heterogeneity across studies with regards to methodologies and reporting. Further prospective and experimental research using new tools and robust guidelines hold promise for improving our understanding of the role of the gut microbiota in mental and brain health and the development of interventions based on modification of gut microbiota.
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ItemHow cigarette smoking may increase the risk of anxiety symptoms and anxiety disorders: a critical review of biological pathways(WILEY, 2013-05)Multiple studies have demonstrated an association between cigarette smoking and increased anxiety symptoms or disorders, with early life exposures potentially predisposing to enhanced anxiety responses in later life. Explanatory models support a potential role for neurotransmitter systems, inflammation, oxidative and nitrosative stress, mitochondrial dysfunction, neurotrophins and neurogenesis, and epigenetic effects, in anxiety pathogenesis. All of these pathways are affected by exposure to cigarette smoke components, including nicotine and free radicals. This review critically examines and summarizes the literature exploring the role of these systems in increased anxiety and how exposure to cigarette smoke may contribute to this pathology at a biological level. Further, this review explores the effects of cigarette smoke on normal neurodevelopment and anxiety control, suggesting how exposure in early life (prenatal, infancy, and adolescence) may predispose to higher anxiety in later life. A large heterogenous literature was reviewed that detailed the association between cigarette smoking and anxiety symptoms and disorders with structural brain changes, inflammation, and cell-mediated immune markers, markers of oxidative and nitrosative stress, mitochondrial function, neurotransmitter systems, neurotrophins and neurogenesis. Some preliminary data were found for potential epigenetic effects. The literature provides some support for a potential interaction between cigarette smoking, anxiety symptoms and disorders, and the above pathways; however, limitations exist particularly in delineating causative effects. The literature also provides insight into potential effects of cigarette smoke, in particular nicotine, on neurodevelopment. The potential treatment implications of these findings are discussed in regards to future therapeutic targets for anxiety. The aforementioned pathways may help mediate increased anxiety seen in people who smoke. Further research into the specific actions of nicotine and other cigarette components on these pathways, and how these pathways interact, may provide insights that lead to new treatment for anxiety and a greater understanding of anxiety pathogenesis.
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ItemFood policies for physical and mental health(BMC, 2014-05-09)Noncommunicable diseases (NCDs) account for the largest burden of early mortality and are predicted to cost the global community more than US $30 trillion over the next 20 years. Unhealthy dietary habits, in large part driven by substantial changes to global food systems, are recognised as major contributors to many of the common NCDs, including cardiovascular disease, cancer and diabetes. Recent evidence now indicates that unhealthy diets are also risk factors for mental disorders, particularly depression and dementia. This affords substantial scope to leverage on the established and developing approaches to the nutrition-related NCDs to address the large global burden of these mental disorders and reinforces the imperative for governments take substantial actions in regards to improving the food environment and consequent population health via policy initiatives.
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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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ItemThe impact of maternal smoking during pregnancy on depressive and anxiety behaviors in children: the Norwegian Mother and Child Cohort Study(BMC, 2015-02-03)BACKGROUND: Maternal smoking during pregnancy (MSDP) is associated with multiple adverse childhood outcomes including externalizing behaviors. However, the association between MSDP and internalizing (anxiety and depressive) behaviors in offspring has received less investigation. We aimed to assess the association between MSDP and childhood internalizing (anxiety and depressive) behaviors in a very large, well-characterized cohort study. METHODS: We assessed the association between MSDP and internalizing behaviors in offspring utilizing information drawn from 90,040 mother-child pairs enrolled in the Norwegian Mother and Child Cohort Study. Mothers reported smoking information, including status and frequency of smoking, twice during pregnancy. Mothers also reported their child's internalizing behaviors at 18 months, 36 months, and 5 years. Associations between MSDP and childhood internalizing behaviors, including dose-response and timing of smoking in pregnancy, were assessed at each time point. RESULTS: MSDP was associated with increased internalizing behaviors when offspring were aged 18 months (B = 0.11, P <0.001) and 36 months (B = 0.06, P <0.01), adjusting for numerous potential confounders. Higher rates of smoking (e.g., >20 cigarettes per day) were associated with higher levels of internalizing behaviors. Maternal smoking during early pregnancy appeared to be the critical period for exposure. CONCLUSIONS: We found evidence supporting a potential role for MSDP in increasing internalizing (anxiety and depressive) behaviors in offspring. We also found evidence supportive of a possible causal relationship, including dose-dependency and support for a predominant role of early pregnancy exposure. Further investigation utilizing genetically informed designs are warranted to assess this association.
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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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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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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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ItemThe Associations between Dairy Product Consumption and Biomarkers of Inflammation, Adipocytokines, and Oxidative Stress in Children: A Cross-Sectional Study(MDPI, 2020-10)The association between dairy product consumption and biomarkers of inflammation, adipocytokines, and oxidative stress is poorly studied in children. Therefore, these associations were examined in a representative subsample of 1338 schoolchildren with a mean age of 11.5 (±0.7) years in the Healthy Growth Study. Information on dairy product consumption was collected by dietary recalls. Total dairy consumption was calculated by summing the intake of milk, yogurt, and cheese. Inflammatory markers, i.e., high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), and adipocytokines, i.e., leptin, adiponectin, and the antioxidant enzyme glutathione peroxidase (GPx) were analysed. Due to the skewed distribution hs-CRP, IL-6, and leptin were log transformed. Multivariable regression analyses adjusted for age, sex, energy intake, physical activity, parental education, Tanner stage, and fat mass were used to assess the associations between consumption of total dairy, milk, yogurt, cheese, and markers of inflammation, adipocytokines, oxidative stress, and adiponectin-leptin ratio. Our results showed that milk consumption was inversely associated with leptin (β: -0.101; 95% CI: -0.177, -0.025, p = 0.009) and positively associated with the adiponectin-leptin ratio (β: 0.116; 95% CI: 0.020, 0.211; p = 0.018), while total dairy, cheese, and yogurt consumption were not associated with inflammatory, adipocytokine, or antioxidant markers. Further prospective studies are needed to confirm these results.
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ItemEfficacy of adjunctive Garcinia mangostana Linn (mangosteen) pericarp for bipolar depression: study protocol for a proof-of-concept trial(ASSOC BRASILEIRA PSIQUIATRIA, 2019)OBJECTIVE: Bipolar depression is characterized by neurobiological features including perturbed oxidative biology, reduction in antioxidant levels, and a concomitant rise in oxidative stress markers. Bipolar depression manifests systemic inflammation, mitochondrial dysfunction, and changes in brain growth factors. The depressive phase of the disorder is the most common and responds the least to conventional treatments. Garcinia mangostana Linn, commonly known as mangosteen, is a tropical fruit. The pericarp's properties may reduce oxidative stress and inflammation and improve neurogenesis, making mangosteen pericarp a promising add-on therapy for bipolar depression. METHODS: Participants will receive 24 weeks of either 1,000 mg mangosteen pericarp or placebo per day, in addition to their usual treatment. The primary outcome is change in severity of mood symptoms, measured using the Montgomery-Åsberg Depression Rating Scale (MADRS), over the treatment phase. Secondary outcomes include global psychopathology, quality of life, functioning, substance use, cognition, safety, biological data, and cost-effectiveness. A follow-up interview will be conducted 4 weeks post-treatment. CONCLUSION: The findings of this study may have implications for improving treatment outcomes for those with bipolar disorder and may contribute to our understanding of the pathophysiology of bipolar depression. CLINICAL TRIAL REGISTRATION: Australian and New Zealand Clinical Trial Registry, ACTRN12616000028404.