Researchers from Children’s Hospital Boston, Boston University School of Public Health (BUSPH), Boston University School of Medicine (BUSM), Boston Medical Center (BMC) and Harvard Medical School have developed a novel approach to predict the risk of stroke in these patients using their genetic variations. This approach is based on a data mining method called Bayesian networks, and the study showed that these networks allow the integration of clinical information with the genetic profile of a patient to quantify his or her risk of developing a disease.
Using this method, the researchers analyzed the genetic differences in genes that might protect from or increase the chance of stroke in sickle cell anemia. The analysis showed that genetic differences in 12 genes interact with fetal hemoglobin level to modulate the risk of stroke.
The model includes one gene already associated with stroke in the general population and the researchers theorized that the presence in the model of genes already associated with stroke suggests that some genetic factors predisposing patients to stroke are shared by both sickle cell anemia patients and stroke victims in the general population. Their model predicted the correct outcome with an overall predictive accuracy of 98.2 percent.
According to Martin Steinberg, M.D., director of the Center of Excellence in Sickle Cell Disease at BMC and professor of medicine at BUSM, the ultimate outcome of sickle cell anemia is likely to be determined by the actions of many genes that all modify the abnormalities triggered by the sickle cell mutation.