Chia K S, Shi C Y, Lee J, Seow A, Lee H P
Department of Community, Occupational and Family Medicine, National University of Singapore, Singapore.
Ann Acad Med Singap. 1996 Jan;25(1):55-63.
Traditional analytical epidemiology is directed at identifying the association between risk factors and occurrence of disease by using crude exposure data derived from questionnaires or clinical measures, and taking clinical disease as the end point. With the rapid development in molecular biology and laboratory methods, it is now possible to use biomarkers which are capable of identifying molecular events for epidemiologic research. This improved sensitivity enables us to develop a mechanistic understanding of disease causation: a step closer to the unravelling of the "black box" of traditional epidemiology. Biomarkers may be classified as internal indicators of exposure (biomarkers of exposure), indicators of preclinical adverse effect (biomarkers of effect) or indicators of an intrinsic or acquired susceptibility to disease (biomarkers of susceptibility). Biomarkers provide a better definition of exposure and disease status and consequently they could help to reduce misclassification bias in both exposure and disease, reduce the follow-up time in prospective studies, as well as identify possible interactions between risk factors on disease occurrence. However, a biomarker needs to be validated and its distribution in large populations described before it can be used profitably for aetiologic research. Also, the use of biomarkers in epidemiologic research raises other interesting epidemiological and statistical issues like confounding, effect modification and the analysis of repeated measurements. Molecular epidemiology is a multidisciplinary endeavour which comprises molecular biology, epidemiology and biostatistics. Clearly then, to carry out research in this field profitably, the molecular biologist, epidemiologist and biostatistician must acquire not only expertise in their respective fields, but also an integrated understanding of all three fields. The molecular biologist is not merely a laboratory bench worker; the epidemiologist, a field data-collector and the biostatistician, a number cruncher. They must work together to pry open the "black box" to gain a greater insight into how risk factors operate to initiate disease onset and ultimately to make use of this knowledge base to implement preventive measures.
传统分析性流行病学旨在通过使用从问卷或临床测量中获得的粗略暴露数据，并将临床疾病作为终点，来确定风险因素与疾病发生之间的关联。随着分子生物学和实验室方法的迅速发展，现在有可能使用能够识别分子事件的生物标志物进行流行病学研究。这种提高的敏感性使我们能够对疾病病因形成一种机制性的理解：向解开传统流行病学的“黑匣子”又迈进了一步。生物标志物可分为暴露的内部指标（暴露生物标志物）、临床前不良反应的指标（效应生物标志物）或对疾病的内在或获得性易感性指标（易感性生物标志物）。生物标志物能更好地界定暴露和疾病状态，因此有助于减少暴露和疾病方面的错误分类偏差，缩短前瞻性研究的随访时间，并识别风险因素在疾病发生上可能存在的相互作用。然而，一种生物标志物在能够有效地用于病因学研究之前，需要进行验证并描述其在大人群中的分布情况。此外，在流行病学研究中使用生物标志物还引发了其他有趣的流行病学和统计学问题，如混杂、效应修饰以及重复测量的分析。分子流行病学是一项多学科的工作，包括分子生物学、流行病学和生物统计学。显然，为了有效地开展该领域的研究，分子生物学家、流行病学家和生物统计学家不仅必须在各自领域具备专业知识，而且还需要对这三个领域有综合的理解。分子生物学家不仅仅是实验室的实验人员；流行病学家不仅仅是现场数据收集者；生物统计学家也不仅仅是数字运算者。他们必须共同努力撬开“黑匣子”，以更深入地了解风险因素如何引发疾病发作，最终利用这一知识库实施预防措施。
