Proteomic Blood Test Predicts Cardiovascular Risk

A blood test has been developed that can predict whether someone is at high risk of a heart attack, stroke, heart failure or dying from one of these conditions within the next four years. The test, which relies of measurements of proteins in the blood, has roughly twice the accuracy of existing risk scores. It could enable doctors to determine whether patients’ existing medications are working or whether they need additional drugs to reduce their risk.

Scientist at SomaLogic Inc (Boulder, CO, USA) and their medical colleagues used highly multiplexed proteomics to address their objectives, measuring about 5,000 proteins in each of 32,130 archived plasma samples from 22,849 participants in nine clinical studies. They used machine learning to derive a 27-protein model predicting 4-year likelihood of myocardial infarction, stroke, heart failure, or death. The 27 proteins encompassed 10 biologic systems, and 12 were associated with relevant causal genetic traits. They independently validated results in 11,609 participants.

The SomaScan assay (SomaLogic Inc) uses DNA-based binding reagents (modified aptamers) to quantify the availability of binding epitopes on plasma proteins for about 5,000 proteins, with high specificity and limits of detection largely comparable to antibody-based assays. Briefly, the SomaScan assay started as a mix of thousands of fluorophore-labeled SOMAmer reagents immobilized on streptavidin-coated beads and incubated with 55 μL of EDTA plasma. SOMAmer reagents were hybridized to complementary sequences on a DNA microarray chip and quantified by fluorescence, which was related to the relative availability of the three-dimensional shape-charge epitope on each protein in the original sample. This integrates each protein’s abundance, shape, charge, and availability of the binding epitope.

The investigators reported that compared to a clinical model, the ratio of observed events in quintile 5 to quintile 1 was 6.7 for proteins versus 2.9 for the clinical model, AUCs were 0.73 versus 0.64, c-statistics were 0.71 versus 0.62, and the net reclassification index was +0.43. Adding the clinical model to the proteins only improved discrimination metrics by 0.01 to 0.02. Event rates in four predefined protein risk categories were 5.6%, 11.2%, 20.0%, and 43.4% within 4 years; median time to event was 1.71 years. Protein predictions were directionally concordant with changed outcomes. Adverse risks were predicted for aging, approaching an event, anthracycline chemotherapy, diabetes, smoking, rheumatoid arthritis, cancer history, cardiovascular disease, high systolic blood pressure, and lipids. Reduced risks were predicted for weight loss and exenatide. The 27-protein model has potential as a “universal” surrogate end point for cardiovascular risk.

Manuel Mayr, MD, PhD, a Professor of cardiovascular proteomics, said, “Proteins are the building blocks of our body. This study provides measurements for a quarter of all proteins that are encoded by our genes, which has become possible because of emerging, new technologies that allow measurement of thousands of proteins and offers new opportunities to assess risk in patients. While this study uncovers new associations between proteins in blood and death by all causes, more studies are needed to assess the potential clinical impact of using these 27 proteins, compared to current risk prediction tools for cardiovascular disease.” The study was published on April 6, 2022 in the journal Science Translation Medicine.

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