MRC Centre for Causal Analyses in Translational Epidemiology, School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK.
Int J Epidemiol. 2011 Jun;40(3):537-62. doi: 10.1093/ije/dyr117.
Epidemiologists aim to identify modifiable causes of disease, this often being a prerequisite for the application of epidemiological findings in public health programmes, health service planning and clinical medicine. Despite successes in identifying causes, it is often claimed that there are missing additional causes for even reasonably well-understood conditions such as lung cancer and coronary heart disease. Several lines of evidence suggest that largely chance events, from the biographical down to the sub-cellular, contribute an important stochastic element to disease risk that is not epidemiologically tractable at the individual level. Epigenetic influences provide a fashionable contemporary explanation for such seemingly random processes. Chance events-such as a particular lifelong smoker living unharmed to 100 years-are averaged out at the group level. As a consequence population-level differences (for example, secular trends or differences between administrative areas) can be entirely explicable by causal factors that appear to account for only a small proportion of individual-level risk. In public health terms, a modifiable cause of the large majority of cases of a disease may have been identified, with a wild goose chase continuing in an attempt to discipline the random nature of the world with respect to which particular individuals will succumb. The quest for personalized medicine is a contemporary manifestation of this dream. An evolutionary explanation of why randomness exists in the development of organisms has long been articulated, in terms of offering a survival advantage in changing environments. Further, the basic notion that what is near-random at one level may be almost entirely predictable at a higher level is an emergent property of many systems, from particle physics to the social sciences. These considerations suggest that epidemiological approaches will remain fruitful as we enter the decade of the epigenome.
流行病学家旨在确定疾病的可改变原因,这通常是将流行病学发现应用于公共卫生计划、卫生服务规划和临床医学的前提。尽管在确定病因方面取得了成功,但人们常常声称,即使是肺癌和冠心病等相对了解的疾病,也存在其他缺失的病因。有几条证据表明,从传记到亚细胞水平的大量偶然事件,对疾病风险产生了重要的随机因素,而这种因素在个体水平上是无法用流行病学方法来处理的。表观遗传影响为这种看似随机的过程提供了一种时髦的现代解释。偶然事件——例如,某个终生吸烟的人活到 100 岁而没有受到伤害——在群体水平上被平均化了。因此,人群水平上的差异(例如,长期趋势或行政区域之间的差异)可以完全用似乎只占个体风险很小比例的因果因素来解释。从公共卫生的角度来看,一种疾病的绝大多数病例的可改变病因可能已经被确定,而对于试图用因果关系来规范世界的随机性的盲目追求仍在继续,而这种随机性与哪些特定个体将屈服有关。个性化医疗的探索就是这种梦想的当代表现。从生物体发展中存在随机性的角度,人们早就提出了一种进化解释,即这种随机性为在变化的环境中提供了生存优势。此外,一个基本的概念是,在一个层面上接近随机的东西,在更高的层面上可能几乎完全可以预测,这是许多系统的一个突现特性,从粒子物理学到社会科学。这些考虑表明,随着我们进入表观基因组学的十年,流行病学方法仍将富有成效。