The E.YU Lab integrates multidisciplinary approaches that encompass epidemiology, health statistics & bioinformatics, multi-omic techniques, wet-lab experiments, and clinical practice to combat the development and progression of chronic diseases (e.g., urinary cancers and metabolic disorders).
Fight for bladder cancer
Bladder cancer, the 4th most common cancer in men and 17th most common in women, represents a major health problem. It is therefore, of utmost importance to establish risk factors associated with the development of this disease. Through large-scale population cohorts (e.g., BLEND, BCPP) and trans-ethnic (e.g., BCPET) comparisons, we aim to find out what cause bladder cancer occurred, how to prevent and inhibit its progression, and why different races needs more precise strategies, based on multi-aspects. Down to molecular underpinnings, the potential mechanisms and therapeutic targets are also crucial for tackling this malignancy, where we will introduce and collaborate with other research teams and medical facilities in verifying our observational findings and provide individuals with accurate evidence-based practical recommendations.
Live well with(out) diabetes
Diabetes contributes enormously to global burdens of mortality and disability, which has been reported to affect millions of people worldwide in the past decade with an increased tendency of occurring in adolescent and young adults. In addition, more than 470 million people worldwide are estimated to suffer from prediabetes, a high-risk state of diabetes development, whereof 5% to 10% will progress to diabetes within a year. Identifying high-risk individuals and manage glucose metabolism status before manifest is essential. The aetiology of glucose metabolism disorders (i.e., prediabetes and diabetes) is multi-factorial, with obesity, physical inactivity and genetic factors being some of the driving forces. Nevertheless, certain hidden and emerging characters, e.g., extracellular vesicles and exosomes, has yet to be well recognized and investigated. In the sooner future, our lab would take efforts to dig in deeper, mainly based on extracellular vesicles, for revealing novel insights of diabetes.
Build-up “LEGO” of Omics
Similar environmental and lifestyle patterns may cause different health effects in different populations. A key aim of modern-epidemiological research therefore is to determine the role of molecular landscape in regulation and to identify factors that influences an individual’s response to environment and lifestyle. Multiple new opportunities regarding this aim have recently been created by remarkable advances in omics technologies, including genomics, metabolomics, proteomics, gut microbiome, etc.. These novel omics technologies hold great potential to get deeper insights in the complex relation between environmental/lifestyle and chronic diseases.
Novel and advanced analysis approach
With the rise of the omics data, epidemiology is entering the “big data” era, as such, the potential for advanced analysis grows significantly. The use of traditional regression and/or survival analysis will likely be replaced by machine learning techniques. Particularly in contexts where prediction or hypothesis generation rather than hypothesis testing is the analytic goal. Machine learning technique applied in the field of epidemiology could help to identifying patterns that relate variables to diseases and maximize accuracy when predicting those outcomes. In addition, algorithmic identification of patterns/items associated with a disease of interest allows epidemiologists to focus on independent validation and interpretation of these associations in subsequent studies.
Therefore, our lab will integrate relative new approaches of hypothesis generation，yet in a very early stage, which enable epidemiologists to move to analysing more complex notions and characterizing how the quantity and timing of exposure influence small molecular weight cellular constituents. However, although the use of big data in epidemiology calls for new skills and collaboration between different research fields (i.e.，knowledge engineering), traditional epidemiological and expert skills will not be removed due to their bona fide multidisciplinary features in understanding how political, social and scientific factors intersect to disease risk.