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Ever heard MDD/BD?
Your cloest neighbors!
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“Mood disorders” are heritable and serious psychiatric disorders, for which we focus on “major depressive disorder (MDD)” and “bipolar disorder (BD)”. In the past decade, with cooperation with hospitals across the nation, we have recruited participants and collected data from hundreds of mood disorder patients, including demographic data, clinical symptoms, neurocognitive performance, sleep questions, and a sort of self-administered questionnaires, as well as bio-samples, etc. Combining with genotyping and next generational sequencing techniques, we conduct a series of genomics and bioinformatics analyses to integrate the genomic and phenotypic data for mood disorders for building genetic maps for MDD and BD in Taiwanese population. Moreover, with the completion of literature review and data prioritization procedures, we have built a candidate gene database for mood disorders for the next step to evaluate the relevance of these candidate genes in Taiwanese samples. In addition, previous genomic studies of mood disorders are often restricted to diagnosis per se without considering its episodic feature in nature. We aim to follow patients from their acute state to remission. Through comprehensive data collection and systematic analyses, we anticipate that our findings in this regard would shed light on the understanding about the pathogenesis of mood disorders, and can eventually translate for clinical use in the near future to assist for diagnosis, monitoring disease course and treatment response.
“Microbiota” is one of the new research fields in recent years. In human body, the microbiota genomics (microbiome) information is about 200 times larger than a human genome. Many human complex traits and diseases are influenced by the interaction between host factors and environments, for which microbiota may contribute significantly. Microbiota composition in humans not only reflect their metabolism ability, but also affect host gene expression, which makes a unique focus in studying gene-environment interactions for health related topics. For instance, subjects with healthy diets are associated with a reduced risk of developing depression or anxiety in both animal studies and clinical trials. This is partially through the influence of microbiota and metabolism ability. To investigate the roles of microbiota on mood disorders, we applied next generation sequencing for stool samples from mood disorder patients and healthy controls. Combining with data collection in diet and behaviors, as well as additional genomic information, we are conducting study to explore the effects of microbiota composition on human mood regulation. We hope that study findings would benefit developing novel intervention or therapy for mood disorders.
“Youth mental health” is highly correlated with the development of adult mental health. Promoting mental health is an important health policy, and we all agree that ‘An ounce of prevention is worth a pound of cure’. In the past few years, we recruited thousands of school students from a number of elementary and junior high schools in big Taipei area to build several nested cohorts across different grades of students. We repeatedly measure students’ emotion and behavioral changes in consecutive years, including demographic variables, mental health related traits, mood and sleep, attentions, etc, to get a better sense of adolescents’ healthy developmental trajectories. Parents of students also fill out questionnaires regarding themselves, and their observations to evaluate students’ traits from parental perspective. We hope to provide evidence-based information to aid with improving mental health in school children through systematically investigating issues related to youth mental health.
“Metabolic syndrome and obesity” is one of the most concerned health related issues in the recent decade. Obesity causes abnormal metabolic and physical symptoms, such as insulin resistance, diabetes, dyslipidemia, hypertension or cardiovascular disease clinically. However, among the obese population, there is a proportion of individuals has normal metabolism and has low risk of developing chronic diseases or metabolic symptoms. With extensive clinical data collection and bio-sample collection, we utilize different analytical tools and technical platforms to study individuals of normal versus abnormal metabolism, to explore the effects of specific genetic variants, hormone secretion, and metabolomics on metabolism status. This study aims to better understand the relationships between metabolism status and a series of biomarkers and metabolic indexes, and potential usage of study findings in shaping a better metabolism profile and treatment response.
Among psychiatric disorders, “substance use disorders” are one of the devastating disorders that cause tremendous social and disease burden. Patients with substance use disorders are often comorbid with other physical and mental disorders, which further increase medical burden in clinical settings. Our past research in different ethnic populations has led to several important findings in terms of identifying relevant clinical and genetic factors using different study designs, such as genetic linkage and association studies. We also used casual-pie model to quantify specific effects of several personal and family risk factors on mood disorders, for which paternal substance use disorder has substantial influence. Recently, a research team that we’re involved develops APPs for assisting effectiveness evaluation of the rehabilitation therapies on substance use patients. We anticipate that these results would translate to clinical use and benefit for developing efficient strategies for substance use prevention and patient care.
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