Clinical and genetic determinants of cardiomyopathy risk among hematopoietic cell transplantation survivors.

Publication Type:

Journal Article


Biology of blood and marrow transplantation : journal of the American Society for Blood and Marrow Transplantation (2016)


Cardiomyopathy has been recognized as a complication after hematopoietic cell transplantation (HCT). Using a nested case-cohort design, we examined the relationships between demographic, therapeutic, and selected cardiovascular disease risk factors among 1-year HCT survivors who developed cardiomyopathy before (n=43) or after (n=89) one year from HCT as compared to a randomly selected subcohort of survivors without cardiomyopathy (n=444). Genomic data were available for 79 cases and 267 non-cases. Clinical and genetic covariates were examined for association with the risk of early or late cardiomyopathy. Clinical risk factors associated with both early and late-onset cardiomyopathy included anthracycline exposure ≥250 mg/m(2) and pre-existing hypertension. Among late-onset cardiomyopathy cases, the development of diabetes and ischemic heart disease further increased risk. We replicated several previously reported genetic associations among early-onset cardiomyopathy cases, including rs1786814 in CELF4, rs2232228 in HAS3, and rs17863783 in UGT1A6. None of these markers were associated with risk of late-onset cardiomyopathy. A combination of demographic, treatment, and clinical covariates predicted early-onset cardiomyopathy with reasonable accuracy (AUC 0.76, 95% CI 0.68-0.83), but prediction of late cardiomyopathy was poor (AUC 0.59, 95% CI 0.53-0.67). The addition of replicated genetic polymorphisms did not enhance prediction for either early or late-onset cardiomyopathy. Conventional cardiovascular risk factors influence the risk of both early and late-onset cardiomyopathy in HCT survivors. While certain genetic markers may influence the risk of early-onset disease, further work is required to validate previously reported findings and to determine how genetic information should be incorporated into clinically useful risk prediction models.