Biomarker Signatures of Sickle Cell Disease Severity

Phenotypically and genetically, sickle cell disease is heterogeneous. Genetic variants have been identified that correlate with fetal hemoglobin (HbF) levels and some disease complications. Scores reflecting disease severity have been proposed to stratify patients by risk of complications and typically they include a combination of laboratory data and patient medical history. We used hierarchical cluster analysis of standardized blood biomarkers coupled with a new method to identify statistically significant clusters as a new approach to identify patients with various risks for common complications. We selected 17 uncorrelated blood biomarkers measured in 2,320 patients and generated 17 clusters characterized by specific patterns of the biomarkers to see if patients stratified in 8 clusters differed in the distribution of β-globin genotypes, and risk of painful episodes, seizure, stroke, and mortality using longitudinally data. Compared with the largest cluster of 675 patients we identified a cluster of 437 patients with elevated HbF, hemoglobin, reduced bilirubin, LDH and other biomarkers who had reduced risk for stroke and mortality. Another cluster of 341 patients was characterized by elevated HbF, LDH and other markers of hemolysis and had reduced risk for number of seizures and pain severity. One cluster was characterized by 47% HbSC disease patients and approximately 16% HbS homozygotes who, by their inclusion in this cluster, were likely to have milder disease. Another was characterized by 79% HbS homozygotes and 18% HbS homozygotes with α thalassemia. We implemented a Bayesian classification rule to predict the cluster membership of patients enrolled in two other studies that grouped into clusters with profiles of biomarkers similar to those discovered in the primary study. A subset of patients with blood biomarker signatures associated with a better prognosis was identified and we hypothesize that the small number of HbS homozygotes with a positive prognosis characterized by reduced morbidity and mortality could carry rare protective variants. Identifying these variants could lead to the discovery of new therapeutic targets. With this method , commonly available laboratory data can be used to stratify patients by risk of complications in time for preventive therapy or for enrollment in clinical trials.