Researchers using cutting-edge machine learning to analyse vast amounts of medical data have been able to predict a person's risk of disease up to ten years before diagnosis.
Scientists from Edinburgh University, working with industry partners, used artificial intelligence (AI) to scour blood samples collected from almost 50,000 individuals for protein signatures associated with a range of conditions, including heart disease, Alzheimer's, and Type 2 diabetes.
The study - in collaboration with Optima Partners and Biogen - suggests that "early warning signs" could be detected much earlier, before patients have developed symptoms, potentially preventing disease onset.
The study used machine learning - a form of AI - to analyse data from a set of 50,000 randomised individuals held by the UK Biobank, a database containing blood samples from half a million volunteers aged 40 to 69 who were recruited between 2006 and 2010 and are being followed up over 30 years.
It was able to identify protein patterns, or "signatures", linked to increased risk of disease.
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The research also showed how these newly discovered protein patterns from the Biobank data can be compared against results from currently used patient blood tests.
In theory this would allow clinicians to detect the possibility of a particular disease developing later in life.
If tests show a patient is at higher risk, there will be more time to proactively plan and take preventative measures to improve the eventual patient outcome.
Among the proteins under investigation was GDF15, a marker of inflammation.
It was found to be linked with almost half (11 out of 23) of the diseases being studied, including both Alzheimer’s and vascular dementia, heart disease, liver disease, type 2 diabetes and all-cause mortality.
Dr Danni Gadd, the first author of the study, said: “Our research represents a promising step forward in risk prediction.
"It’s encouraging to see how much potential there is from a single blood sample that allow us to predict a range of disease outcomes.
"Being able to detect early warning signs for a broad set of conditions may lead to opportunities for early intervention and prevention, marking a significant moment for the healthcare industry.”
Dr Chris Foley, managing director and chief scientist a Edinburgh-based, Optima Partners, said: “More work is still needed to convert these findings for practical use in clinical settings.
"However, our discoveries set strong foundations for the inclusion of new risk prediction signatures to shed a light on possible pathways and mechanisms that underlie diseases.
"Pattern recognition like this would not be possible without modern machine learning technology and its capacity to analyse data at this scale and will in turn allow us to address some of the most pressing healthcare challenges of our time.”
The study is published in the journal Nature Aging.
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