Identifying sickle cell disease patients at high risk of complications could lead to personalized treatment and better prognosis but despite many advances prediction of the clinical course of these patients remains elusive. We propose a system-type approach to discover profiles of multiple, common biomarkers that correlate with morbidity and mortality in sickle cell disease. We used cluster analysis to discover 17 signatures of 17 common circulating biomarkers in 2320 participants of the Cooperative Study of Sickle Cell Disease, and evaluated the association of these signatures with risk for stroke, pain, leg ulceration, acute chest syndrome, avascular necrosis, seizure, death, and trend of fetal hemoglobin and hemolysis using longitudinally collected data. The analysis shows that some of the signatures are associated with reduced risk for complications, while others are associated with increased risk for complications. We also show that these signatures repeat in two more contemporary studies of sickle cell disease and correlate with recently discovered biomarkers of pulmonary vascular disease. With replication and further study, these biomarker signatures could become an important and affordable precision medicine tool to aid treatment and management of the disease.
Keywords: Clustering; Disease severity; Fetal hemoglobin; Hemolytic anemia; Molecular signature; Sickle cell disease.
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