NIOSHTIC-2 Publications Search

A Bayesian network model for biomarker-based dose response.

Hack CE; Haber LT; Maier A; Schulte P; Fowler B; Lotz WG; Savage RE Jr.

Risk Anal 2010 Jul; 30(7):1037-1051

https://doi.org/10.1111/j.1539-6924.2010.01413.x

20037221

A Bayesian network model was developed to integrate diverse types of data to conduct an exposure-dose-response assessment for benzene-induced acute myeloid leukemia (AML). The network approach was used to evaluate and compare individual biomarkers and quantitatively link the biomarkers along the exposure-disease continuum. The network was used to perform the biomarker-based dose-response analysis, and various other approaches to the dose-response analysis were conducted for comparison. The network-derived benchmark concentration was approximately an order of magnitude lower than that from the usual exposure concentration versus response approach, which suggests that the presence of more information in the low-dose region (where changes in biomarkers are detectable but effects on AML mortality are not) helps inform the description of the AML response at lower exposures. This work provides a quantitative approach for linking changes in biomarkers of effect both to exposure information and to changes in disease response. Such linkage can provide a scientifically valid point of departure that incorporates precursor dose-response information without being dependent on the difficult issue of a definition of adversity for precursors.

Mathematical-models; Risk-analysis; Risk-factors; Statistical-analysis; Biomarkers; Exposure-assessment; Occupational-diseases; Benzenes; Leukemogenesis; Myeloid-tissue; Dose-response; Author Keywords: Bayesian; Benzene; Biomarker; Dose-response

C. Eric Hack, AFRL RHPB, 2729 R Street, Building 837, Wright Patterson AFB, OH 45433-5707

RIANDF

71-43-2

20100701

Journal Article

charles.hack@wpafb.af.mil

2010

7

0272-4332

DART; EID

Risk Analysis

OH; GA