The older section of the population increases at a steady pace especially in the western world including Denmark. Identifying risk factors that would allow improvements in population health and life expectancy is challenging issue in promoting healthy aging and longevity.
In the literature, a substantial diversity of biomarkers, reflecting possible dysregulation in multiple biological systems has been used to predict mortality in the elderly.
Although interesting, current biomarkers for predictions are closely related to specific disease endpoints, instead of reflecting general health. Taking advantage of the genomic data, we aim at developing highly efficient models for predicting late-life mortality in the elderly by identifying prognostic epigenetic biomarkers using bioinformatics and statistics tools.
A unique epigenomic data measured longitudinally on large Scottish old birth cohorts will be used for training and testing the models through collaboration in combination with a longitudinal Danish old birth cohort as an external validation.
The developed prediction models for mortality risk prediction will provide important indicator of overall health status for the elderly useful for designing individualized healthcare program while the selected genomic sites will serve as promising targets for functional analysis and for drug intervention, all aimed at promoting healthy aging in the elderly population.
- Professor Qihua Tan
- Associate Professor Jan Baumbach
Grant from the Velux Foundation.