Literature DB >> 21807641

Epidemiology, epigenetics and the 'Gloomy Prospect': embracing randomness in population health research and practice.

George Davey Smith1.   

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.

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Year:  2011        PMID: 21807641     DOI: 10.1093/ije/dyr117

Source DB:  PubMed          Journal:  Int J Epidemiol        ISSN: 0300-5771            Impact factor:   7.196


  73 in total

1.  Commentary: a gerontological perspective on Klaus Gärtner's discovery that phenotypic variability of mammals is driven by stochastic events.

Authors:  George M Martin
Journal:  Int J Epidemiol       Date:  2012-01-20       Impact factor: 7.196

2.  Is epidemiology ready for epigenetics?

Authors:  Caroline L Relton; George Davey Smith
Journal:  Int J Epidemiol       Date:  2012-02       Impact factor: 7.196

Review 3.  From promises to practical strategies in epigenetic epidemiology.

Authors:  Jonathan Mill; Bastiaan T Heijmans
Journal:  Nat Rev Genet       Date:  2013-07-02       Impact factor: 53.242

4.  The mathematical limits of genetic prediction for complex chronic disease.

Authors:  Katherine M Keyes; George Davey Smith; Karestan C Koenen; Sandro Galea
Journal:  J Epidemiol Community Health       Date:  2015-02-03       Impact factor: 3.710

5.  Child psychiatric epidemiology: stars and hypes.

Authors:  Frank C Verhulst; Henning Tiemeier
Journal:  Eur Child Adolesc Psychiatry       Date:  2015-06       Impact factor: 4.785

6.  The current deconstruction of paradoxes: one sign of the ongoing methodological "revolution".

Authors:  Miquel Porta; Paolo Vineis; Francisco Bolúmar
Journal:  Eur J Epidemiol       Date:  2015-07-12       Impact factor: 8.082

Review 7.  Stochastic developmental variation, an epigenetic source of phenotypic diversity with far-reaching biological consequences.

Authors:  Günter Vogt
Journal:  J Biosci       Date:  2015-03       Impact factor: 1.826

Review 8.  Origins of human disease: the chrono-epigenetic perspective.

Authors:  Edward Saehong Oh; Art Petronis
Journal:  Nat Rev Genet       Date:  2021-04-26       Impact factor: 53.242

9.  Health Equity and the Fallacy of Treating Causes of Population Health as if They Sum to 100.

Authors:  Nancy Krieger
Journal:  Am J Public Health       Date:  2017-04       Impact factor: 9.308

10.  Commentary: The Limits of Risk Factors Revisited: Is It Time for a Causal Architecture Approach?

Authors:  Katherine M Keyes; Sandro Galea
Journal:  Epidemiology       Date:  2017-01       Impact factor: 4.822

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