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癌症风险评估模型:生化流行病学的预期贡献。

Cancer risk-assessment models: anticipated contributions from biochemical epidemiology.

作者信息

Alavanja M, Aron J, Brown C, Chandler J

出版信息

J Natl Cancer Inst. 1987 Apr;78(4):633-43.

PMID:3470540
Abstract

Uncertainties in quantitative assessment of cancer risk limit the scientific role of this activity in the policy and regulatory debate on cancer control. Risk-assessment strategies and models are highly dependent on the nature and quality of the data available. However, accumulating laboratory and epidemiologic studies that are shedding new light on mechanisms of carcinogenesis have not yet been integrated into current risk-assessment models. Future developments in risk assessment may, therefore, be anticipated by considering the type of epidemiologic data that may soon be generated. Three important areas of epidemiologic and biochemical research should reduce some of the uncertainty in quantitative risk assessment by making it possible to: identify stages of the disease process through precursor lesions, biochemical markers, and to determine at which stage the carcinogen acts; identify subpopulations that are at enhanced or reduced susceptibility to carcinogenic influences; and obtain additional and refined indices of dose. Much of this research is being called "biochemical epidemiology." Strategies for incorporating these newly generated data into regulatory decisions vary from developing new mechanistically based mathematical models to applying existing models to biologically distinct subpopulations.

摘要

癌症风险定量评估中的不确定性限制了这一活动在癌症控制政策与监管辩论中的科学作用。风险评估策略和模型高度依赖于现有数据的性质和质量。然而,越来越多的实验室研究和流行病学研究为致癌机制带来了新的见解,但尚未被纳入当前的风险评估模型。因此,通过考虑可能很快产生的流行病学数据类型,可以预期风险评估的未来发展。流行病学和生化研究的三个重要领域应通过以下方式减少定量风险评估中的一些不确定性:通过前驱病变、生化标志物识别疾病过程的阶段,并确定致癌物在哪个阶段起作用;识别对致癌影响易感性增强或降低的亚人群;以及获得额外的和更精确的剂量指标。这项研究中的许多被称为“生化流行病学”。将这些新生成的数据纳入监管决策的策略各不相同,从开发新的基于机制的数学模型到将现有模型应用于生物学上不同的亚人群。

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