Estimation of the Exposure–Response Relation between Benzene and Acute Myeloid Leukemia by Combining Epidemiologic, Human Biomarker, and Animal Data

Background: Chemical risk assessment can benefit from integrating data across multiple evidence bases, especially in exposure–response curve (ERC) modeling when data across the exposure range are sparse. Methods: We estimated the ERC for benzene and acute myeloid leukemia (AML), by fitting linear and spline-based Bayesian meta-regression models that included summary risk estimates from non-AML and nonhuman studies as prior information. Our complete dataset included six human AML studies, three human leukemia studies, 10 human biomarker studies, and four experimental animal studies. Results: A linear meta-regression model with intercept best predicted AML risks after cross-validation, both for the full dataset and AML studies only. Risk estimates in the low exposure range [<40 parts per million (ppm)-years] from this model were comparable, but more precise when the ERC was derived using all available data than when using AML data only. Allowing for between-study heterogeneity, RRs and 95% prediction intervals (95% PI) at 5 ppm-years were 1.58 (95% PI, 1.01–3.22) and 1.44 (95% PI, 0.85–3.42), respectively. Conclusions: Integrating the available epidemiologic, biomarker, and animal data resulted in more precise risk estimates for benzene exposure and AML, although the large between-study heterogeneity hampers interpretation of these results. The harmonization steps required to fit the Bayesian meta-regression model involve a range of assumptions that need to be critically evaluated, as they seem crucial for successful implementation. Impact: By describing a framework for data integration and explicitly describing the necessary data harmonization steps, we hope to enable risk assessors to better understand the advantages and assumptions underlying a data integration approach.