Innovative data mining for biological age
Aging has emerged as a major public health issue in the U.S., which necessitates the need for identifying biological measures and biomarkers of aging and age-related chronic diseases. More recently, DNA methylation has been identified as a useful tool for defining biological age. However, there are several issues in the original approaches. We propose a more accurate epigenetic age model using ultra-high dimensional DNA methylation markers. We also consider an integrated combination of different types of biomarkers, e.g., epigenetics, cardiovascular risk factors, other aging-related factors, and telomere length. We will develop and disseminate a user-friendly statistical software package that will enable researchers to implement these methods with ease. Our discoveries may illustrate the biological mechanisms underlying traditional and innovative risk factors for mortality and discover more accurate and reliable markers for biological aging.