Epigenetic aging and social adversity as synergistic predictors of oral health in older Canadians living with multimorbidity: a machine learning approach

Project Summary

Oral health is an essential determinant of overall quality of life. As individuals age, a multitude of health conditions manifest due to accumulated environmental and behavioural exposures throughout the life course. However, very few have explored how these exposures affect biological gene expression and its potential influence on oral health. Factors that influence oral health have traditionally been thought of as solely biological or solely social. However, the growing field of biologic aging, a process in which one’s apparent age is older than their chronological age, presents a promising link between the genome and the environment, with numerous studies highlighting its relationship with an increased risk of several age-related chronic conditions. Therefore, our study aims to assess the extent to which social and biological factors can predict the risk of developing poor oral health in older Canadians living with chronic conditions using Machine Learning algorithms.