Blood biomarkers and genomics combo predicts common disease risk more accurately than only genomic information

Polygenic risk scores, where genomic information alone is used to assess the risk of developing diseases, have been receiving a lot of attention recently, but research to be presented at the annual conference of the European Society of Human Genetics on 10 June suggests that combining blood biomarkers with genomic information gives more accurate, cost-effective results.

The researchers used machine learning to build predictive models for individuals’ future risks of nine common diseases, based on their genetic information and the biomarkers that they measured. The diseases studied were ischaemic heart disease, stroke, lung cancer, diabetes, chronic obstructive pulmonary disease, Alzheimer’s and other dementias, depression, liver disease, and colon cancer.

“We found that in all the diseases, both genetics and biomarkers could provide useful information about disease risk, even ten years into the future. And the blood biomarkers provided better prediction in nearly all cases – for example, the 10% of individuals with the highest risk of lung cancer based on the biomarkers had four times the risk of an average person, whereas the top 10% based on genetics had only 1.8 times the risk,” says Dr Jeffrey Barrett, Chief Scientific Officer, Nightingale Health.“And for liver disease the same numbers are 10 times and two times respectively.”

The team also found that the prediction using blood biomarkers was sometimes even stronger for near-term risk (for example in two or four years), which they believe may reflect direct links between some of what the biomarkers measure and the pre-symptomatic phase of the disease.

Reference:

Dr Jeffrey Barrett, et al,EUROPEAN SOCIETY OF HUMAN GENETICS