Biomarker Profiles of Sickle Cell Disease Sickle cell disease is caused by mutations in the ?-hemoglobin gene (HBB). Some individuals have mild disease that can be clinically unapparent while others can have most of the known severe complications such as pulmonary hypertension, priapism, stroke, leg ulceration, acute painful episodes, acute chest syndrome, and avascular necrosis of bone. This phenotypic heterogeneity has been linked to different genetic backgrounds and substantial work in the last few years has focused on discovering those genetic modifiers of sickle cell disease that can explain the variability of the clinical phenotype. Parallel research has been conducted to identify blood and urine biomarkers that correlate with complications of sickle cell disease, but the predictive value of these biomarkers is still unclear. Despite these advances, prediction of the clinical course of patients with sickle cell disease remains elusive. Our main hypothesis is that profiles comprising several biomarkers can capture a large proportion of the clinical severity of sickle cell disease patients at any point in time and together with genetic data can be useful to predict the chance for complications of the disease. To test this hypothesis, we propose to conduct secondary analyses of multiple biomarkers in the largest longitudinal study of sickle cell disease, the CSSCD, and leverage the longitudinal data in this cohort to discover profiles of biomarkers that predict short and long term incidence of complications of the disease. We established collaboration with the Pulmonary Hypertension and the Hypoxic Response in SCD (PUSH) study to replicate the CSSCD findings in an independent, more contemporary cohort and generate robust data for future studies. We propose 3 specific Aims.
In Specific Aim 1 we will discover profiles of multiple biomarkers predictive of complications of sickle cell disease in longitudinal follow up. We will use advanced analysis to generate profiles of blood and urine biomarkers measured at the time of CSSCD enrollment and test the predictive value of these profiles prospectively. Replication of the findings will be conducted in the PUSH study cohort.
In Specific Aim 2 we will extend the analysis of Aim 1 and correlate longitudinal changes of biomarkers with risk for disease complications. We will generate profiles of multiple biomarker changes, and examine their correlation with complications of sickle cell disease and death in short and long term follow up. Finally, in Specific Aim 3 we will analyze the added value of genetic data to biomarker data for prediction of disease complications. We will build genetic risk prediction models and compare the added values of biomarker data to known genetic variants at different ages of the patients. Our analysis will identify biomarkers that are predictive of sickle cell disease complications; it will also establish a catalogue of biomarker profiles that future investigators can use for patients stratification in clinical studies and as a bench-mark to evaluate the added value of new, yet to be discovered, biomarkers in sickle cell disease.
. We propose to conduct secondary analyses of multiple biomarker data in the largest longitudinal study of sickle cell disease, the Cooperative Study of Sickle Cell Disease, and leverage the longitudinal data in this cohort to discover profiles of biomarkers that predict short and long term incidence of complications of the disease. We established collaboration with the Pulmonary Hypertension and the Hypoxic Response in SCD (PUSH) study to replicate the findings in an independent, more contemporary cohort and generate robust data for future studies. Our analysis will establish a catalogue of biomarker profiles that future investigators can use as bench- mark to evaluate the added value of new, yet to be discovered, biomarkers in sickle cell disease. This collection of biomarker profiles can also be used for patients? stratification.
| Du, Mengtian; Van Ness, Sarah; Gordeuk, Victor et al. (2018) Biomarker signatures of sickle cell disease severity. Blood Cells Mol Dis 72:1-9 |
| Sebastiani, Paola; Thyagarajan, Bharat; Sun, Fangui et al. (2017) Biomarker signatures of aging. Aging Cell 16:329-338 |
