Epidemiology, epigenetics and the ‘Gloomy Prospect’: embracing randomness in population health research and practice

George Davey Smith

+Author Affiliations

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

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Abstract

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.

We cannot imagine these diseases, they are called idiopathic, spontaneous in origin, but we know instinctively there must be something more, some invisible weakness they are exploiting. It is impossible to think they fall at random, it is unbearable to think it. James Salter, Light Years, 1975

Epidemiology is concerned with the identification of modifiable causes of disease, which is often a prerequisite for the application of epidemiological findings in public health programmes, health service planning and clinical medicine.¹ Despite many successes, even with respect to the most celebrated—such as the identification of cigarette smoking as a major cause of lung cancer and other chronic diseases—it can appear that much remains to be done. Consider Winnie, lighting a cigarette from the candles on her centenary birthday cake, who, after 93 years of smoking, is not envisaging giving up the habit (Figure 1). Such people, who survive to a ripe old age despite transgressing every code of healthy living, loom large in the popular imagination² and are reflected in the low positive predictive values and C statistics in many formal epidemiological prediction models. In general, epidemiologists do a rather poor job of predicting who is and who is not going to develop disease.