Our current research interest includes the remote effects of oral microbiota, predictive system, and the triad of caries, obesity and eating patterns in children.
The oral cavity has a myriad of resident microbiota and periodontal microbiota is the causative agent for periodontitis, which is one of the most prevalent diseases in humans and almost every adult is affected by some form of periodontitis during his or her life. It has been shown that specific combinations of periodontal microbiota are associated with risk for the various forms of periodontitis. In addition to the intra-oral inflammation, the chronic trickling of periodontal microbiota into the bloodstream also elicits low-grade systemic inflammation. This periodontal microbiota-related systemic inflammation has been suggested to increase the risk for several extra-oral diseases, such as atherosclerosis, rheumatoid arthritis, metabolic disorders, and neurodegenerative diseases, such as age-related macular degeneration and Alzheimer’s disease. To characterize the interactions between specific compositions of periodontal microbiota and human immune system, we chose antibodies as a surrogate to reflect the immune responses to specific combinations of periodontal microbiota. Therefore, characterization of serum immunoglobulin G (IgG) patterns may provide an opportunity to understand the relationship between specific combinations of periodontal microbiota and human diseases and could lead to new therapeutic and preventative strategies.
There are two challenges in the traditional approach for developing predictive systems. The first one is whether to incorporate new predictive information derived from various sources of biological data (e.g. various omics data), and the other is how to adapt dynamic, heterogeneous target populations. Based on this premise, we are developing a new generation of predictive systems by incorporating several cutting-edge technologies, including big data approach, machine learning, cloud technology, mobile application software (App) into the traditional approach, which will allow us to build interactive and self-updated predictive analytics. Most importantly, such a predictive framework can support the creation of a wide range of custom predictive models for different outcomes of interest, including caries and periodontitis.
Dietary factors have long been considered important risk factors for dental caries. However, the focus was mainly on the ingredients of one’s diet. Using the techniques of dietary pattern analysis to examine the eating patterns in children with high prevalence of overweight/obesity, we found that all the features of eating patterns, including what, when, and how to eat, have some effects on caries development. In addition, our analysis on the triad of caries, obesity, and eating patterns indicated the importance of primary prevention strategies in the prevention of caries initiation, such as parental care. Other risk factors, such as low birth weight, overweight/obesity, eating characteristics, lower household income, etc. play important roles in the promotion and progression of caries. Our future direction aims to clarify the inter-relationships and mechanisms of these risk factors in order to develop personalized strategies to help children keep away from caries and related morbidities.
