- Predicting cardiovascular risk is important for clinicians providing patient care and for scientists developing new drugs.
- Scientists can use biomarker surrogates as signs that cardiovascular disease (CVD) risk may increase or decrease.
- In the new study, scientists have developed a blood test that offers an accurate and personalized prediction of CVD.
In a new study, scientists have reported findings that show a blood test can be used to predict CVD.
The study, published in the journal Science Translational Medicine, opens the door to more individualized treatment plans for CVD. It may also improve the speed at which new CVD drugs can be identified and developed.
Surrogate biomarkers
When a new drug is developed, scientists have to make sure that it is both effective and safe. This is a rigorous process that can often take many years. While important, this significantly slows down the speed at which new drugs can be developed, and also increases the costs.
One way of increasing the speed and reducing the cost of drug development without sacrificing efficacy or safety is to use a surrogate biomarker as a predictor of risk.
If a surrogate can reliably predict risk, then some stages of clinical trials can be streamlined.
Finding a surrogate that can accurately predict the risk of certain diseases can also benefit patients directly. If a clinician can measure a reliable surrogate they can potentially prevent a disease before it has developed, reducing the risks to the patient.
Medical News Today spoke with Dr. Stephen Williams — Chief Medical Officer at SomaLogic, and the corresponding author of the present study — who stressed the importance of surrogates, particularly for CVD risk.
“Surrogates are the ‘holy grail’ in drug development and personalized medicine.”
“For situations where clinical cardiovascular outcomes studies are required today, a surrogate enables unsafe or ineffective candidate drugs to be terminated early and cheaply and supports the acceleration of safe and effective drugs. Participants in the trials do not have to have events or die in order to contribute to the signal.”
“In personalized medicine, a surrogate enables cost-effective allocation of treatments to the people who need them the most, and potentially increases the uptake of newer more effective drugs so that outcomes are improved,” said Dr. Williams.
FDA recommended
In 2004 the United States Food and Drug Administration (FDA) published a report recommending that researchers identify biomarker surrogates that could help in CVD drug development and improve individualized patient care.
However, in the years that followed this has not happened. Dr. Williams explained to MNT why it has taken so long to realize the FDA’s recommendation.
“Surrogates are highly valuable, but they also have high consequences for errors — for example, the approval of an unsafe or ineffective drug. The weight of evidence needed is therefore very high, in order to have confidence that their value in patient care exceeds the potential consequence for errors.”
Dr. Williams highlighted that a surrogate needs to be able to do three things: first, accurately predict likely clinical outcomes; second, be able to change as potential risks change; and third, work no matter what the driver of risk was.
“It is quite common to do item 1 — for example, risk prediction from artificial intelligence approaches to medical records, or combinations of risk factors or genetic factors. But commonly those factors cannot also do 2 — be faithful in response to change in risk.”
“This is either because they are immutable — for example, genetic, demographic, or medical history — or are reverse causal — for example, artificial intelligence applied to medical records commonly selects the number of drugs a patient is on as a risk predictor, so taking someone off all their drugs would paradoxically and wrongly create a prediction of lower risk.”
Dr. Williams said he and his team were able to realize the FDA’s recommendation because SomaLogic’s business model supported the research necessary to develop the CVD surrogate, building on frameworks that the FDA proposed.
5,000 proteins analyzed
To develop the test, Dr. Williams and his colleagues analyzed blood plasma samples from 22,849 people.
They studied 5,000 proteins from these samples, and using machine learning identified 27 proteins that together could predict the risk of stroke, myocardial infarction, heart failure, or death over a 4-year period.
Speaking to MNT, Dr. Rebekah Gundry — professor and vice-chair of the Department of Cellular and Integrative Physiology at the University of Nebraska Medical Center — said that “clinically, these findings are potentially very important.” Dr. Gundry, who was not involved with the study, is also the director of the CardiOmics Program of the Center for Heart & Vascular Research at the University of Nebraska.
“Having a panel of 27 markers that could be used to predict cardiovascular risk would be an improvement over current risk score calculators like high cholesterol, which can serve as an indicator for all of us as an average but is a poor predictor for the individual and does not give good information on the timing of when a cardiovascular event is likely to occur,” said Dr. Gundry.
Dr. Gundry said the findings would help in the goal of preventing disease before it requires significant treatment.
“A major goal of cardiovascular research is to find new ways to predict patient outcomes as soon as possible after disease starts, because prevention is always easier than reversal. Basically, we want to know what is going to happen with enough advance notice to be able to change the outcome.”
“Having a simple blood test provide information on all major cardiovascular outcomes and deaths would have a tremendous impact on clinical decisions regarding timing and nature of interventions to prevent or delay further progression of cardiovascular disease.”
– Dr. Gundry
“Proteins play critical roles, which is why they have been used so effectively as indicators. The study by Williams and colleagues provides evidence that simultaneously measuring a panel of circulating proteins, including proteins previously associated with cardiovascular disease and those without prior known associations, could provide a pathway to predict cardiovascular outcomes,” said Dr. Gundry.
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