“Liquid health check” could one day predict disease risk

“Liquid health check” could one day predict disease risk

Measuring proteins present in the blood could one day serve as a "liquid health check" that could predict people's risk of developing diseases, according to a new study recently published in the journal Nature Medicine.

The researchers say that as technology advances, it is feasible that such a test could one day be offered routinely by health services.

Improving preventive medicine programs

The goal of preventive medicine programs such as "Healthier You" and the "UK National Health Service's Health Check" is to improve people's health and reduce their risk of developing diseases.
While such programs are cost-effective and scalable, they could be significantly improved by using information about an individual's health status and risk for disease.

The application of "big data" in healthcare, which analyses large-scale datasets, increases the likelihood that it will one day be possible to make predictions about an individual's health and disease outcomes and to employ stratified prevention and clinical management approaches.

Now, researchers from the UK and USA collaborating with the biotechnology company SomaLogic have shown that large-scale measurement of the proteins present in a single blood sample could provide important information about a person's health and help to predict their risk of developing a range of different diseases.

About the proteins in our blood

Our blood contains approximately 30,000 different proteins encoded for by our DNA that are involved in biological processes. Some proteins, such as hormones, are secreted into the bloodstream to regulate these processes, while others leak into the blood as a result of cell damage or cell death. In either case, the concentration of these proteins can serve as an indicator of health and disease risk.

For the current research, lead author Stephen Williams (SomaLogic) and colleagues conducted a proof-of-concept study based on five observational cohorts covering nearly 17,000 individuals.
They measured the concentrations of 5,000 proteins in a single plasma sample taken from each participant. The technique uses specific fragments of DNA called aptamers that only bind to particular proteins. Genetic sequencing can then be used to search for the aptamers to determine which proteins are present and at what concentration.

Developing predictive models

By using machine learning and statistical methods, the team analyzed the results and developed predictive models for whether a person with a certain battery of proteins was at an increased risk for a particular disease. The models covered various health states, including kidney function, liver fat, smoking behavior, and physical activity.
Although the predictive accuracy of the models varied, the team found that all of them were either better predictors than models based on traditional risk factors or would provide more convenient and cost-effective alternatives to conventional testing.

Many of the proteins were associated with various health states. For example, the concentration of leptin, which regulates appetite and metabolism, was predictive of body fat, physical activity, visceral fat, and fitness level.

One important difference between genome sequencing and studying proteins (proteomics) is that the proteome may change as a person becomes more obese or less active, for example, whereas the genome is fixed. Therefore, measuring proteins informs on changes in a person's health status over time.

"Proteins circulating in our blood are a manifestation of our genetic make-up as well as many other factors, such as behaviors or the presence of disease, even if not yet diagnosed," says Claudia Langenberg from the University of Cambridge. "This is one of the reasons why proteins are such good indicators of our current and future health state and have the potential to improve clinical prediction across different and diverse diseases."

"Just the tip of the iceberg"

Williams says it is "remarkable" that plasma protein patterns alone can faithfully represent such a wide variety of common and important health issues. Furthermore, he thinks this is just the tip of the iceberg: "We have more than a hundred tests in our SomaSignal pipeline and believe that large-scale protein scanning has the potential to become a sole information source for individualized health assessments."

While this study shows a proof-of-principle, the team says that as technology advances and becomes less expensive, it is feasible that a comprehensive health evaluation based on protein models derived from a single blood sample could be offered routinely by health services.

This proof of concept study demonstrates a new paradigm that measurement of blood proteins can accurately deliver health information that spans across numerous medical specialties and that should be actionable for patients and their healthcare providers,"
Co-study leader Peter Ganz, MD, Director of the Center of Excellence in Vascular Research at Zuckerberg San Francisco General Hospital and Trauma Center

"I expect that in the future we will look back at this Nature Medicine proteomic study as a critical milestone in personalizing and thus improving the care of our patients," he concludes.
The study highlights the potential for 'liquid health check' to predict disease risk. Eurekalert. Available from: https://www.eurekalert.org/emb_releases/2019-12/uoc-shp112719.php.

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