Genetic modifiers of AKI in SCA

The National Heart, Lung, and Blood Institute awarded the University of Tennessee Health Science Center $253,395 on September 1, 2026, under the Cardiovascular Diseases Research program (CFDA 93.837) to identify genetic modifiers of acute kidney injury in sickle cell disease.

The award funds research evaluating candidate genetic variants—apolipoprotein L1 (APOL1), hemeoxygenase-1 (HMOX1), BCL11A, and alpha thalassemia—for their association with acute kidney injury (AKI) in a well-genotyped and phenotyped cohort of children and adults with sickle cell disease. The project will develop a machine learning model integrating genetic variables and clinical electronic health record data to predict AKI risk during hospitalization, with the long-term goal of enabling incorporation of AKI prediction into the electronic medical record to institute protective measures.

The period of performance runs from September 1, 2026, through May 31, 2028. Work is performed in Memphis, Tennessee. The research addresses how genetic modifiers contribute to chronic kidney disease development in sickle cell disease, an independent risk factor for increased morbidity and earlier mortality in this patient population.

Abstract

Chronic kidney disease (CKD) is associated with an increased risk of morbidity and earlier mortality in individuals with sickle cell disease (SCD). Our long-term goal is to understand how certain genetic modifiers are associated with the development of CKD in SCD. We have specifically examined the role of the apolipoprotein L1 gene (APOL1) in the development of CKD. In addition to this gene variant, three additional modifiers of SCD will be examined: allelic variants in HMOX1, BCL11A, and alpha thalassemia. Our objectives here are the following: 1) evaluate the association of these candidate genetic modifiers of kidney disease with acute kidney injury (AKI), an independent risk factor for CKD, by utilizing a well-genotyped and phenotyped cohort of children and adults with SCD; and 2) predict the development of AKI by utilizing a machine learning algorithm, a subset of artificial intelligence, which will incorporate genetic modifiers of kidney disease. We will test two hypotheses: 1) candidate gene variants for SCD-related CKD are associated with increased risk for developing of AKI; and 2) a machine learning model utilizing genetic variables and clinical data from the electronic health record will predict AKI during hospitalization. This proposal will clarify the association between AKI and gene variants of kidney disease in SCD and for the first time develop a machine learning model to predict AKI risk by integrating genetic modifiers of kidney disease. Developing a model to predict risk for AKI will allow for future incorporation in the electronic medical record to institute protective measures to prevent AKI.