Combining Blood Biomarkers and Genomic Information Provides More Accurate and Cost-Effective Prediction of Chronic Disease Risk

A combination of genomic information and blood biomarkers can more accurately identify people at high risk of developing chronic diseases, according to research conducted by Finnish firm Nightingale Health. Using machine learning, data from 300,000 UK Biobank participants and 200,000 from an Estonian biobank were analysed to build predictive models for individuals’ chances of developing heart disease, stroke, lung cancer, diabetes, chronic obstructive pulmonary disease, Alzheimer’s and other dementias, depression, liver disease and colon cancer. The biomarkers provided better indications of risk in nearly all cases, with stronger results sometimes seen for near-term risk.