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81.
The purpose of this paper is to systematically analyse the design and results of epidemiological studies on the association between various types of cancer (lung, bladder, breast, colon, stomach) and four genetically-based metabolic polymorphisms, involved in the metabolism of several carcinogens (glutathione-S-transferase M1, debrisoquine hydroxylase, N acetyltransferase, aryl hydrocarbon hydroxylase). These inherited polymorphisms usually cause modifications in the quality or quantity of the relevant enzymes. Such enzymes are involved in the activation/inactivation of known carcinogens and seem to modify the extent to which carcinogens interact with DNA in target tissues. Two enzymes, debrisoquine hydroxylase and aryl hydrocarbon hydroxylase, activate procarcinogens to carcinogens (phase I enzymes). The other two, glutathione-S-transferase M1 and N-acetyltransferase, mainly detoxity carcinogenic substances (phase II enzymes). Because of their role as host factors (modulating the action of carcinogens), it has been hypothesized that subjects presenting a specific phenotype for such polymorphisms could be at a greater risk of developing various types of cancer. A number of epidemiological studies have investigated such associations, often with discordant results. We examine and discuss the design of the studies, and present a meta-analysis of the available data.  相似文献   
82.
Existing risk assessment procedures for carcinogens are intended to be “conservative” in the uncertainty dimension—giving estimates that are expected to be higher than true risks for typical people. However, these procedures do not consider the likely variability in susceptibility among individual people. This paper updates previous estimates of the likely extent of this variability for metabolically activated, genetically-acting carcinogens based on recent information on human interindividual variability in metabolic activation, detoxification, and DNA repair. The resulting expected skewness of cancer risk distributions is estimated using Monte Carlo simulations of both variability and uncertainty.

Some risk management implications are:

  1. When evaluating the fairness of a particular risk distribution, managers need to gain familiarity with a three-dimensional characterization—X level of risk, for the Yth percentile individual (addressing variability) with Z degree of confidence (addressing uncertainty).

  2. To the extent that variability distributions are skewed (e.g., with a long tail extending to high values) population mean risks will tend to exceed risks for median individuals. Together with the skewness in uncertainty distributions, this implies that “expected value” estimates of aggregate population risks—the estimates of interest for cost benefit analyses—are likely to be closer to traditional upper confidence limit risk estimates than has often been assumed in the past.

 

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