New research shared at the European Society of Human Genetics suggests that applying predictive machine learning models to genetic data and blood biomarkers can provide information about an individual’s risk of developing certain diseases up to ten years into the future. The study found that genomic data analysis combined with blood biomarker analysis is more accurate and cost-effective than analyzing genomic data alone.
The research was carried out by Jeffrey Barrett, the chief scientific officer at Nightingale Health in Helsinki, and his colleagues, who measured over 200 biomarkers in blood samples from 300,000 participants in the UK Biobank and 200,000 people in the Estonian biobank. The team used machine learning models to predict individuals’ future risks of developing certain diseases based on their genetic information and biomarkers, including ischemic heart disease, stroke, lung cancer, diabetes, chronic obstructive pulmonary disease, Alzheimer’s and other dementias, depression, liver disease, and colon cancer.
The findings showed that blood biomarkers provide better predictions in nearly all cases. For instance, the top 10% of individuals based on genetics had 1.8 times greater risk of lung cancer than an average person, while the 10% of people with the highest risk of lung cancer based on biomarkers had four times the risk of average. For liver disease, the increased risk based on biomarkers was 10 times, compared to two times for genetics. Blood biomarkers were particularly predictive of some diseases in the next two to four years.
The researchers believe that if the findings are validated, healthcare systems may be able to use blood tests to find patients who would benefit from preventative health actions. By identifying individuals with the greatest risk of developing diseases, healthcare systems can offer guidance to help them reduce their risk, promoting healthier lifestyles and reducing the financial burden on healthcare systems.
These findings demonstrate that it is relatively easy to find 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, said Barrett.
Frequently Asked Questions (FAQs) Related to the Above News
What is the focus of the new research shared at the European Society of Human Genetics?
The research focuses on how applying predictive machine learning models to genetic data and blood biomarkers can provide information about an individual's risk of developing certain diseases up to ten years into the future.
Who conducted the research and where was it carried out?
The research was carried out by Jeffrey Barrett, the chief scientific officer at Nightingale Health in Helsinki, and his colleagues. The study measured over 200 biomarkers in blood samples from 300,000 participants in the UK Biobank and 200,000 people in the Estonian biobank.
What diseases did the machine learning models predict future risks for?
The machine learning models predicted future risks for several diseases, including ischemic heart disease, stroke, lung cancer, diabetes, chronic obstructive pulmonary disease, Alzheimer's and other dementias, depression, liver disease, and colon cancer.
Which method of analysis is more accurate and cost-effective in predicting future disease risks?
The study found that genomic data analysis combined with blood biomarker analysis is more accurate and cost-effective than analyzing genomic data alone.
What were the findings related to blood biomarkers and disease prediction?
The findings showed that blood biomarkers provided better predictions in almost all cases. For instance, blood biomarkers were particularly predictive of some diseases in the next two to four years.
How can healthcare systems benefit from these findings?
Healthcare systems can use blood tests to find patients who would benefit from preventative health actions, based on their risk of developing certain diseases. By identifying individuals with the greatest risk of developing diseases, healthcare systems can offer guidance to help them reduce their risk, promoting healthier lifestyles, and thereby reducing the financial burden on healthcare systems.
What did Jeffrey Barrett say about the research findings?
Jeffrey Barrett said that these findings demonstrate that it is relatively easy to find individuals at the 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.
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