Combining blood biomarkers with genomic information yields improved identification of people at high risk of chronic disease, years before symptoms emerge, a conference will hear today.
Dr Jeffrey Barrett, chief scientific officer of Finland-based Nightingale Health, will tell delegates at the annual conference of the European Society of Human Genetics in Glasgow, Scotland, the combination could also prove to be cost effective and easy for healthcare sectors across the world.
His research team measured more than 200 biomarkers in blood samples from 300,000 participants in the UK Biobank, and 200,000 in the Estonian biobank to collect the biggest dataset of its kind.
They then used machine learning to build predictive models for individuals’ future risks of nine common diseases – were ischaemic heart disease, stroke, lung cancer, diabetes, chronic obstructive pulmonary disease, Alzheimer’s and other dementias, depression, liver disease, and colon cancer – based on their genetic information and the biomarkers they measured.
“We found that in all the diseases, both genetics and biomarkers could provide useful information about disease risk, even ten years into the future,” he said.
“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. And for liver disease the same numbers are 10 times and two times respectively.”
The team found the prediction using blood biomarkers was sometimes even stronger for near-term risk – between two and four years, which they believe may reflect direct links between some of what the biomarkers measure and the pre-symptomatic phase of the disease.
Dr Barrett said the findings could lead to easy-to-measure blood tests being used in population preventive health.
“It means that it is relatively easy to find the individuals at greatest risk of many diseases and offer them ways to reduce their risk, keeping them healthier and at the same time reducing the financial burden on healthcare systems,” he added.
He said their predictions were highly consistent across multiple biobanks, while other studies had access to just one.
“This suggests that these biomarker scores are not a narrow research finding, and could be used effectively in general practice,” he added.
Abstract no. C02.6 Early prevention for 9 common diseases via combined genomic and metabolomic prediction
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