Machine learning and proteomics predict cardiovascular risk
A new study describes an innovative proteomics-based model that predicts the risk of cardiovascular events within the next four years with higher accuracy than current clinical models. the researchers created a proteomics-based prognostic score that would be predictive of actual cardiovascular outcomes within a relatively short time frame, while also including all known mechanisms and allowing the model to be responsive to changes in the outcome. If successful, this score would be useful for Phase II studies for drugs used in the prevention and treatment of CVD and diabetes, as well as an endpoint for the accelerated approval of breakthrough drugs.

The researchers measured 5,000 proteins in each sample of plasma and applied machine learning to the results to develop a prognostic model. The model used 27 proteins and predicted the absolute risk that any of the multiple components that made up the composite endpoint, some of which included heart attack, stroke, hospitalization for heart failure, and mortality from any cause, would occur within the next four years. This was tested on multiple cohorts with several comorbidities and changes in the parameters were measured over time. Overall, over 11,600 participants with a four-year outcome were included in the study. At this point, 22% of the population had experienced one or more of these events for an event number of 2,500. These events consisted of 622 hospitalizations for cardiac failure, 601 heart attacks, and 345 strokes. Of the proteins used in this model, 14 showed a positive correlation, and 13 a negative correlation. These proteins correspond to ten or more biological processes, such as those involved in maintaining blood volume and sodium excretion, the formation of vesicles, angiogenesis, and the glomerular filtration rate. The model also responded in the right direction to adverse and beneficial changes in the protein-predicted risk.