Proteins in our blood could in future help provide a comprehensive way to assess our health status and predict the likelihood that we will develop a range of diseases, according to research published in Nature Medicine. The study, by an international team of researchers led by scientists at the University of California, San Francisco, Cambridge University in the UK and SomaLogic, shows that large-scale measurement of proteins in a single blood test can provide important information about a wide range of health factors and can help to predict a range of disease risks.
Our bodies contain 20,000 to 30,000 different proteins, which are coded for by our DNA and regulate biological processes. Some of these proteins enter the blood stream by purposeful secretion to orchestrate processes in health or in disease, for example hormones and growth factors. Others enter the blood through leakage from cell damage and cell death. Both secreted and leaked proteins can inform health status and disease risk.
In a proof-of-concept study based on five observational cohorts in almost 17,000 participants, researchers scanned 5,000 proteins in a plasma sample taken from each participant. Plasma is the single largest component of blood and is the clear liquid that remains after the removal of red and white blood cells and platelets. The study resulted in around 85 million protein targets being measured.
The researchers analyzed the results using statistical methods and machine learning techniques to develop predictive models — for example, that an individual whose blood contains a certain pattern of proteins is at increased risk of developing diabetes. The models covered a number of health states, including levels of liver fat, kidney function and visceral fat, alcohol consumption, physical activity and smoking behavior, as well as risk of developing type 2 diabetes and cardiovascular disease.
The accuracy of the models varied, with some showing high predictive powers, such as for percentage body fat, while others had only modest prognostic power, such as for cardiovascular risk. However, the researchers report that their protein-based models were all either better predictors than models based on traditional risk factors or would constitute more convenient and less expensive alternatives to traditional testing.
“It’s remarkable that plasma protein patterns alone can faithfully represent such a wide variety of common and important health issues, and we think that this is just the tip of the iceberg,” said Stephen Williams, MD, PhD, Chief Medical Officer of SomaLogic, who led the study. “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 — a “Liquid Health Check” — for individualized health assessments.”
While this study shows a proof of principle, the researchers say that as the technology improves and becomes more affordable, it is feasible that a comprehensive health evaluation using a battery of protein models derived from a single blood sample could be offered as routine 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 health care providers. 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,” said Peter Ganz, MD, co-leader of this study and the Maurice Eliaser Distinguished Professor of Medicine at the UCSF and Director of the Center of Excellence in Vascular Research at Zuckerberg San Francisco General Hospital and Trauma Center.
