Approved Research

A model for health risk prediction based on inherited DNA variation and clinical data

Lay summary

Aging is commonly associated with an increase in the risk of complex phenotypes onset, such as cardiovascular diseases, diabetes, cancer, etc. One can consider healthy aging or mortality as complex phenotypes shaped by susceptibilities to many underlying diseases and with significant contribution of environmental factors. In this context, the outcomes could be defined as age of major disease onset, age of critical health deterioration or age of death. We will use such interpretations to build a predictor for the individual risks of various aging outcomes. Specifically, we will start with most common diseases of aging - cardiovascular disease (and incidents) and cancer. Further, the mathematical design of the predictor will be investigated in other phenotypes - diabetes, autoimmune disorders, to understand specific features required for each disease type.

Main deliverables are: first, information about feature importance, such as, selection of data points that are most informative for health outcome. Second, average health endpoints for individuals with similar input parameters, found in training data. And finally, data-based individual longevity recommendations for managing parameters with the highest contribution to age/mortality prediction. This work is in line with the UK Biobank's aim of enabling research to improve prevention, diagnosis and treatment of illness and the promotion of health throughout society.