- Email: [email protected]
The promises and challenges of epigenetic epidemiology
Experimental Gerontology 45 (2010) 297–301
Contents lists available at ScienceDirect
Experimental Gerontology journal homepage: www.elsevier.com/locate/expgero
The promises and challenges of epigenetic epidemiology Karin B. Michels * Obstetrics and Gynecology Epidemiology Center, Department of Obstetrics, Gynecology and Reproductive Biology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States Department of Epidemiology, Harvard School of Public Health, Boston, MA, United States
a r t i c l e
i n f o
Article history: Received 26 September 2009 Received in revised form 11 December 2009 Accepted 15 December 2009 Available online 23 December 2009 Keywords: Epigenetics Epidemiology DNA methylation Genomic imprinting Histone modification Tissue-specificity Confounding Study design
a b s t r a c t The union between epidemiology and epigenetics creates a new science that combines the strengths of both fields: the appropriate study design with adequate sample size to identify disease biomarkers and to uncover mechanistic pathways for environmental exposure and disease associations while controlling for confounding variables. Realization of the promises of epigenetic epidemiology requires overcoming some challenges in the design, conduct, and interpretation of epigenetic studies in human populations. These challenges include the choice of the appropriate tissue, confounding, misclassification, and effect modification. In particular, the tissue-specificity of epigenetic marks restricts epidemiologic studies of adequate size to easily accessible tissues. Ó 2010 Elsevier Inc. All rights reserved.
1. A union creates a new science: epigenetic epidemiology Epidemiology, the study of the frequency, distribution, and determinants of disease in humans, has its roots with the Greek physician Hippocrates but has garnered increased awareness over the past several decades. As a fundamental science in public health, epidemiology is concerned with the prevention or effective control of disease. Epigenetics is still a young science, evolved over the past three decades, and has only recently attracted a rapid rise in interest. Epigenetics can be defined as the mitotically heritable state of the gene expression potential. The most important mechanisms defining epigenetics are DNA methylation and histone modification, ultimately aimed at governing gene expression. Many fundamental questions in epigenetics remain unanswered, providing abundant opportunities for discovery. Both ‘‘epi” sciences (epidemiology: upon the people; epigenetics: above genetics) connect at the intersection of epigenetic variation and the distribution of disease. Epigenetic epidemiology is defined as the study of the association between epigenetic variation and the risk of disease in humans (Waterland and Michels, 2007). Combing the two fields creates a new science that supports
* Address: Obstetrics and Gynecology Epidemiology Center, Department of Obstetrics, Gynecology and Reproductive Biology, Brigham and Women’s Hospital, Harvard Medical School, 221 Longwood Avenue, Boston, MA 02115, United States. Tel.: +1 617 732 8496; fax: +1 617 732 4899. E-mail address: [email protected]. 0531-5565/$ - see front matter Ó 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.exger.2009.12.011
the study of the role of epigenetic modifications in human disease etiology and understanding epigenetics as a possible mechanism underlying the link between an exposure (such as diet) and disease outcome. Epidemiology and epigenetics share the element of time and variability: Epidemics are time-dependent and fluctuate; the epigenetic code – unlike the genetic code – while fairly stable (Cruz-Correa et al., 2009), is modifiable (Jaenisch and Bird, 2003) and can change with age (Wilson and Jones, 1983; Issa et al., 1994; Bjornsson et al., 2008) and as a result of environmental stressors (Jaenisch and Bird, 2003; Whitelaw and Whitelaw, 2006; Christensen et al., 2009). Environmental exposures play an important role in epidemiologic studies on disease causation and in shaping the epigenetic signature. One of the challenges of epigenetic epidemiology is to differentiate environmental influences on the epigenome from ‘‘normal” aging of the epigenome, which in itself can be associated with age-related illness. Epigenetic traits such as CpG methylation and genomic imprinting can be used as markers in epidemiologic studies because they are fairly stable in samples if properly processed and stored. Comparisons can be made of the degree of global methylation, DNA methylation patterns, or gene-specific methylation between population groups defined by the presence or absence of a specific exposure or disease. One specific characteristic of epigenetic marks is their tissue-specificity. Tissue-specificity is perhaps the primary conceptual difference in the evaluation of conventional biomarkers and genetic disease susceptibility markers such as germline muta-
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tions compared to epimutations. The choice of the most appropriate tissue for epigenetic analyses is therefore imperative to successfully answering the research question. 2. Selecting the most appropriate study design Population-based association studies are an excellent way to study epigenetic variation in DNA methylation or chromatin structure and disease frequency. Epidemiology employs a number of different study designs that can be used to study the role of epigenetics in health and disease (Gordis, 2009). 2.1. Cross-sectional studies In a cross-sectional study all factors of interest are assessed at one point in time. The prevalence of methylation of a CpG island in the promoter of a specific gene in the population or a subgroup of the population defined by a special characteristic, such as lefthanded females, or the prevalence of loss of imprinting of a particular gene, say IGF-2, in the population or a subsample such as newborns, can be easiest studied in a cross-sectional study. Similarly, comparing the degree of global DNA methylation in two groups, e.g., children and adults, can be accomplished effectively with a cross-sectional design. 2.2. Retrospective case-control study In a case-control study, individuals with a disease and appropriately selected individuals free of the disease are sampled from the same source population. To study epigenetic variation, biospecimens would be obtained and DNA methylation or loss of imprinting assessed. In the context of epigenetics, this study design bears similarities to a cross-sectional study, except for the choice of the controls whose selection cannot in any way be related to exposure status, e.g., methylation. The purpose of the controls is to provide information on the methylation status of cases if they had not contracted the disease under study; if this information is distorted, selection bias results. Neither in the cross-sectional study nor in the retrospective case-control study can it be determined whether the level of the biomarker, for instance, a low global DNA methylation status among cases, preceded (and possibly caused) the disease or whether it may be a consequence of (and possibly have been caused by) the disease. 2.3. Cohort study In a cohort study healthy individuals are recruited to participate in a longitudinal study over a certain period of follow-up time (weeks, months, years). At baseline, biospecimens from all participants would be obtained and stored. During follow-up, additional biospecimens may be collected. Such a biorepository provides the opportunity to study changes of DNA methylation or other epigenetic markers over time. During follow-up, any disease outcome of interest is recorded and participants censored from the study once they reach this endpoint. Cohort studies are usually large and often include many thousands of participants. Because it is not cost-effective to analyze the samples from all participants obtained at baseline and during follow-up, a nested case-control study is embedded in the cohort. 2.4. Nested case-control study A nested or prospective case-control study is embedded in a cohort study. All individuals who develop the disease of interest throughout follow-up are selected and appropriate controls (often
2 controls per case) are selected from those who remained free of the disease throughout follow-up. Since the biospecimens of the cases and controls were obtained prior to the diagnosis of disease (and were stored since), this study design allows relating the prediagnostic status of DNA methylation or other biomarkers to the disease outcome, i.e., it is clear that the methylation status assessed preceded the diagnosis of disease. The nested case-control study is a cost-effective study design. 2.5. Intervention studies The effect of folate and other supplements that affect the one carbon metabolism or the effect of demethylating agents can be studied in intervention studies (crossover studies or randomized trials). In a crossover study the effect of supplements on DNA methylation can be explored by assessing the methylation profile before and after supplement use. In a randomized clinical trial, a demethylating agent is randomly assigned to half of the study participants, while the other half receives a different dose, a different drug, or a placebo. The outcome is recorded in both groups. 2.6. Family-based studies Maintenance and loss of imprinting (mono-allelic and biallelic gene expression) and their role in disease causation can also be studied in population-based studies; family-based studies, including triads of mother, father, and child, may allow discrimination between inheritance (incomplete erasure) and acquisition of loss of imprinting. 2.7. Birth cohorts In a birth cohort, pre-conceptual and prenatal exposures can be assessed and related to DNA methylation and imprinting profiles of the offspring at birth. Additional follow-up of the birth cohort permits tracking changes of epigenetic marks over time. 3. Challenges of epigenetic epidemiology Besides choosing a suitable study design, the choices of the population and the sample size have important implications for the results of an epigenetic epidemiology study. The tissue-specificity of methylation patterns requires careful considerations of the choice of the appropriate tissue to adequately address the goal of the study. Furthermore, confounding – a common problem in observational research – needs to be avoided, since it can invalidate the study findings. 3.1. Variation The epigenetic signature is a useful tool to characterize population subgroups with certain traits, if the between-person variation exceeds the within-person variation. Significant interindividual epigenetic variation at specific loci has been established and linked to the risk of disease. 3.2. Choice of the study population For all study designs, the choice of the population may influence the associations observed. A convenience sample for a birth cohort may focus on patients with uncomplicated deliveries because of the logistical ease in obtaining the samples. However, the epigenetic profile among those babies may differ from babies born prematurely or whose mothers suffered from preeclampsia. Recruitment for a cohort study may only include participants
K.B. Michels / Experimental Gerontology 45 (2010) 297–301
responding to an initial invitation, which may be a less than 30% response. Such a population and their epigenetic profiles may not be representative of the general population.
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surrogate markers if the goal is to identify a risk marker for disease. E.g., variation in global DNA methylation of PBLs may be an overall indicator of the effect of a particular exposure or the health status of a group of individuals.
3.3. Sample size 3.5. Confounding One of the most important shortcomings of basic science research is the often small sample size. A sufficiently large sample size is a fundamental requirement of a high-quality study in epigenetic epidemiology: It increases the likelihood of a valid study result and is necessary to achieve adequate precision of the findings. Moderate to modest differences in epigenetic patterns between two groups cannot be detected with a limited sample size, however, they may be of interest. Depending on the factor under study, only modest changes may be expected: diet and dietary supplements may exert only limited changes to the epigenetic signature. Accordingly, a sufficiently large sample size is necessary to detect such differences. Demethylating agents like 5-azacytidine may have a more profound impact on DNA methylation. Power calculations conducted during the planning phase of a study inform about the number of participants necessary to detect a specific difference, but assumptions have to be made about the expected difference. Even if the expected level of change is large, the number of participants has to be sufficient to ensure precision of the results reflected in a narrow confidence interval. 3.4. Choice of the tissue Epigenetic mechanisms play an important role in cell differentiation. Hence DNA methylation is tissue-specific and may be cell type-specific and a certain DNA methylation pattern found in one specific tissue does not permit inferences about its variation across different tissues and possibly not even across different cell types in the same tissue. The research question at hand determines the most appropriate selection of the tissue(s) for analysis. Tissue harvested from invasive tumors in the intestinal wall of the colon displays characteristic epimutations when compared to colon mucosa from a cancer-free patient (Ahuja et al., 1998). Other cancers show different distinct DNA methylation patterns. When studying cancer, it is important to microdissect tumor tissue to ensure a homogeneous cancer cell population. Comparisons should optimally be made with tissue from the same organ obtained from a healthy individual. Evaluation of the DNA methylation profile in breast cancer tissue requires comparison with the methylation pattern in ‘‘normal” mammary tissue. Most researchers use adjacent mammary tissue from the same patient that according to histology was declared tumor-free. Using control tissue samples from the same person controls for confounding by age or other between-person-differences. Many studies have shown, however, that morphologically normal tissues from diseased breasts harbor a number of genetic anomalies (Deng et al., 1996; Kurose et al., 2001; Ellsworth et al., 2004), and the same epigenetic changes identified in cancer tissue have been found in adjacent cancer-free tissue and extended as far as 4 cm from primary tumors (Cui et al., 2003; Shen et al., 2005; Yan et al., 2006). This field effect renders the use of adjacent tissue obsolete when the goal is to identify epimutations specific for a particular tissue and phenotype. Potential DNA methylation markers of tumorigenesis may be missed due to the use of inappropriate control tissue (Riazalhosseini and Hoheisel, 2008). Conversely, tissue samples from healthy controls may be difficult to obtain depending on the organ of interest and comparisons may be confounded by age and other interindividual differences. The DNA methylation pattern and imprinting profile in peripheral blood lymphocytes (PBLs) or other blood cells may be useful as
Confounding is one of the most important threats to the validity of an epidemiologic study. A confounder is a third variable that is correlated with both the epigenetic mark and the disease of interest. For example, a study of the influence of a specific DNA methylation pattern on a disease outcome could be confounded by a variety of variables that affect methylation and are also risk factors for the disease. Age is a likely confounder of a study in epigenetic epidemiology, since the DNA methylation profile changes with age and age increases the risk for most diseases. Another potential confounder could be an environmental factor that affects DNA methylation and also predicts the risk of disease. Confounding can be overcome by assessing confounding factors and appropriately adjusting for them in the statistical analysis. Unfortunately, confounding factors are often not recognized or not measured which results in uncontrolled confounding. Poor assessment of a confounder does not allow complete statistical correction resulting in residual confounding. For example, if alcohol consumption affects the epigenetic profile, it may be difficult to quantify alcohol intake in individuals precisely. Such measurement error makes it difficult to eliminate the confounding influence of alcohol on the epigenetics-disease association. 3.6. Misclassification As the field of epigenetics matures, so do its methods. Methylation microarrays now allow assessing the methylation status of individual CpGs outside of CpG islands. The assessment of loss of imprinting by pyrosequencing is more quantitative than previously used methods, which relied on gel electrophoresis and radioactive labeling. Each of these improvements in technology decreases misclassification of the epigenetic state and improves precision. Misclassification will be further reduced by the anticipated next generation of microarrays that allow genome-wide CpG methylation assessment. Correspondingly, increasingly sophisticated bioinformatics tools allow differentiation of true signals from noise. 3.7. Effect modification A third variable can be an effect modifier, if, depending on the level of this third variable, the association between the exposure and the disease outcome differs. The effect modifier ‘‘interacts” with the exposure of interest. For example, the association between aberrant DNA methylation and a cancer type may be stronger among postmenopausal than among premenopausal women. Or, loss of imprinting of IGF-2 among macrosomic babies may be more common among males than females. Stratification by the effect modifier allows obtaining information about the association between the exposure and the disease for each level of the effect modifier. Each of the smaller population subsets created by stratification needs to be sufficiently large to generate valid and reliable stratum-specific estimates. 4. Promises and opportunities of epigenetic epidemiology To date, a large proportion of epigenetic research has focused on plant and animal models. While intriguing and important observations have emerged from this research, the implications for hu-
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mans remain unclear. Leads obtained from non-human organisms can inform research efforts in humans. Epigenetic epidemiology provides the best framework to conduct these studies. The best studied area in humans is cancer epigenetics. Other diseases including cardiovascular disease, asthma, obesity, and neurologic disorders will likely be targets of epigenetic epidemiology in the future. Environmental factors may exert their effects via epigenetic pathways. Feeding pregnant Agouti mice methyl-supplemented diets shifted the coat color of their offspring from their normal yellow to brown in a dose-dependent fashion. This phenomenon is controlled by methylation of a retrotransposon within the Agouti gene: If hypomethylated, the gene is expressed and the offspring yellow, if hypermethylated due to the effect of methyl-supplementation, the gene is increasingly silenced and the coat color migrates toward brown (Wolff et al., 1998; Waterland and Jirtle, 2003). Whether a similar mechanism might explain the developmental origins of health and disease in humans is currently being explored. The importance for the pre-conceptual and post-fertilization period for the establishment of imprinting and methylation pattern – indicated by animal models – suggests a role of epigenetics in regulating developmental plasticity. Environmental exposures during perinatal life may establish interindividual epigenetic variation that persists and can be traced to the incidence of chronic disease later in life (Waterland and Michels, 2007). The effect of many stressors gametes and embryo are exposed to on their epigenetic signature remain to be studied. For example, the effects of pre-conceptual and prenatal folate intake recommended for all women of child bearing age on the epigenetic profile of the offspring are not well understood. The establishment of a benchmark of what represents the ‘‘normal” or ‘‘disease free” state is fundamental in genetics and epigenetics alike. The recent sequencing of the human methylome is a giant step towards this goal (Lister et al., 2009). Departures from this state may be linked to diseases of interest in order to establish a phenotypic epigenetic profile. If this profile is already manifest prior to diagnosis of disease, it can be used as a biomarker for early detection. If it manifests before disease development, it may indicate the risk of disease. Unlike genetic marks, however, DNA methylation is tissue-specific. Epigenetic studies are currently most amenable in easily accessible tissues. It may be difficult to study epigenetic contributions to diseases in organs and tissues for which it is ethically impossible to obtain tissues from healthy controls. Prognostic markers are identified in casecontrol studies, early detection or risk markers preferably in nested case-control studies with a prediagnostic biorepository. Insights into phenotypic epigenetic profiles allow creation of preventive and treatment strategies. Since the epigenetic signature is amenable to changes by environmental factors, identifying factors that create or correct disease-specific patterns is essential. Epigene-environment interactions may be gene-specific or involve larger components of the genome. There are also gene-environment interactions that affect the epigenetic profile. The effect of folate, choline, and methionine on the risk of several cancers is currently explored in epidemiologic and intervention studies (Figueiredo et al., 2009). In one study, plasma folate levels affected global DNA methylation only among individuals with the T/T genotype of the common polymorphism (C677T) in 5,10-methylenetetrahydrofolate reductase (Friso et al., 2002). The influences of endocrine-disrupting chemicals (Crews and McLachlan, 2006), alcohol consumption (Platek et al., 2009), and smoking habits (Figueiredo et al., 2009) on DNA methylation are also under study. Demethylating agents for the treatment of cancer have been tested in randomized clinical trials (Oki and Issa, 2006; Garcia-Manero, 2008; Fenaux et al., 2009).
Epigenetic epidemiology provides new opportunities to identify disease biomarkers, to discover pathways for links between environmental exposures and disease outcomes, and to explore new avenues in disease prevention and treatment. This new science may also provide the mechanistic underpinning of the developmental origins of health and disease.
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Cruz-Correa, M., Zhao, R., Oviedo, M., Bernabe, R.D., Lacourt, M., Cardona, A., LopezEnriquez, R., Wexner, S., Cuffari, C., Hylind, L., Platz, E., Cui, H., Feinberg, A.P., Giardiello, F.M., 2009. Temporal stability and age-related prevalence of loss of imprinting of the insulin-like growth factor-2 gene. Epigenetics 4, 114–118. Cui, H., Cruz-Correa, M., Giardiello, F.M., Hutcheon, D.F., Kafonek, D.R., Brandenburg, S., et al., 2003. Loss of IGF2 imprinting: a potential marker of colorectal cancer risk. Science 299, 1753–1755. Deng, G., Lu, Y., Zlotnikov, G., Thor, A.D., Smith, H.S., 1996. Loss of heterozygosity in normal tissue adjacent to breast carcinomas. Science 274, 2057–2059. Ellsworth, D.L., Ellsworth, R.E., Love, B., Deyarmin, B., Lubert, S.M., Mittal, V., et al., 2004. Genomic patterns of allelic imbalance in disease free tissue adjacent to primary breast carcinomas. Breast Cancer Res. Treat. 88, 131–139. Fenaux, P., Mufti, G.J., Hellstrom-Lindberg, E., Santini, V., Finelli, C., Giagounidis, A., Schoch, R., Gattermann, N., Sanz, G., List, A., Gore, S.D., Seymour, J.F., Bennett, J.M., Byrd, J., Backstrom, J., Zimmerman, L., McKenzie, D., Beach, C., Silverman, L.R., 2009. International vidaza high-risk MDS survival study group. Efficacy of azacitidine compared with that of conventional care regimens in the treatment of higher-risk myelodysplastic syndromes: a randomised, open-label, phase III study. Lancet Oncol. 10, 223–232. Figueiredo, J.C., Grau, M.V., Wallace, K., Levine, A.J., Shen, L., Hamdan, R., Chen, X., Bresalier, R.S., McKeown-Eyssen, G., Haile, R.W., Baron, J.A., Issa, J.P., 2009. Global DNA hypomethylation (LINE-1) in the normal colon and lifestyle characteristics and dietary and genetic factors. Cancer Epidemiol. Biomarkers Prev. 18, 1041–1049. Friso, S., Choi, S.W., Girelli, D., Mason, J.B., Dolnikowski, G.G., et al., 2002. A common mutation in the 5,10-methylenetetrahydrofolate reductase gene affects genomic DNA methylation through an interaction with folate status. Proc. Natl. Acad. Sci. USA 99, 5606–5611. Garcia-Manero, G., 2008. Demethylating agents in myeloid malignancies. Curr. Opin. Oncol. 20, 705–710. Gordis, L., 2009. Epidemiology, fourth ed. Saunders, Elsevier, Philadelphia. Issa, J.P., Ottaviano, Y.L., Celano, P., Hamilton, S.R., Davidson, N.E., Baylin, S.B., 1994. Methylation of the oestrogen receptor CpG island links ageing and neoplasia in human colon. Nat. Genet. 7, 536–540. Jaenisch, R., Bird, A., 2003. Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals. Nat. Genet. 33 (Suppl.), 245–254. Kurose, K., Hoshaw-Woodard, S., Adeyinka, A., Lemeshow, S., Watson, P.H., Eng, C., 2001. Genetic model of multi-step breast carcinogenesis involving the epithelium and stroma: clues to tumour-microenvironment interactions. Hum. Mol. Genet. 10, 1907–1913. Lister, R., Pelizzola, M., Dowen, R.H., Hawkins, R.D., Hon, G., Tonti-Filippini, J., Nery, J.R., Lee, L., Ye, Z., Ngo, Q.M., Edsall, L., Antosiewicz-Bourget, J., Stewart, R., Ruotti, V., Millar, A.H., Thomson, J.A., Ren, B., Ecker, J.R., 2009. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462, 315–322. Oki, Y., Issa, J.P., 2006. Review: recent clinical trials in epigenetic therapy. Rev. Recent Clin. Trials 1, 169–182. Platek, M.E., Shields, P.G., Marian, C., McCann, S.E., Bonner, M.R., Nie, J., Ambrosone, C.B., Millen, A.E., Ochs-Balcom, H.M., Quick, S.K., Trevisan, M., Russell, M., Nochajski, T.H., Edge, S.B., Freudenheim, J.L., 2009. Alcohol consumption and genetic variation in methylenetetrahydrofolate reductase and 5methyltetrahydrofolate-homocysteine methyltransferase in relation to breast cancer risk. Cancer Epidemiol Biomarkers Prev. [Epub ahead of print]. Riazalhosseini, Y., Hoheisel, J.D., 2008. 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K.B. Michels / Experimental Gerontology 45 (2010) 297–301 Waterland, R.A., Michels, K.B., 2007. Epigenetic epidemiology of the developmental origins hypothesis. Annu. Rev. Nut. 27, 363–388. Waterland, R.A., Jirtle, R.L., 2003. Transposable elements: targets for early nutritional effects on epigenetic gene regulation. Mol. Cell. Biol. 23, 5293– 5300. Whitelaw, N.C., Whitelaw, E., 2006. How lifetimes shape epigenotype within and across generations. Hum Mol Genet 15, 2R131–7.
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Contents lists available at ScienceDirect
Experimental Gerontology journal homepage: www.elsevier.com/locate/expgero
The promises and challenges of epigenetic epidemiology Karin B. Michels * Obstetrics and Gynecology Epidemiology Center, Department of Obstetrics, Gynecology and Reproductive Biology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States Department of Epidemiology, Harvard School of Public Health, Boston, MA, United States
a r t i c l e
i n f o
Article history: Received 26 September 2009 Received in revised form 11 December 2009 Accepted 15 December 2009 Available online 23 December 2009 Keywords: Epigenetics Epidemiology DNA methylation Genomic imprinting Histone modification Tissue-specificity Confounding Study design
a b s t r a c t The union between epidemiology and epigenetics creates a new science that combines the strengths of both fields: the appropriate study design with adequate sample size to identify disease biomarkers and to uncover mechanistic pathways for environmental exposure and disease associations while controlling for confounding variables. Realization of the promises of epigenetic epidemiology requires overcoming some challenges in the design, conduct, and interpretation of epigenetic studies in human populations. These challenges include the choice of the appropriate tissue, confounding, misclassification, and effect modification. In particular, the tissue-specificity of epigenetic marks restricts epidemiologic studies of adequate size to easily accessible tissues. Ó 2010 Elsevier Inc. All rights reserved.
1. A union creates a new science: epigenetic epidemiology Epidemiology, the study of the frequency, distribution, and determinants of disease in humans, has its roots with the Greek physician Hippocrates but has garnered increased awareness over the past several decades. As a fundamental science in public health, epidemiology is concerned with the prevention or effective control of disease. Epigenetics is still a young science, evolved over the past three decades, and has only recently attracted a rapid rise in interest. Epigenetics can be defined as the mitotically heritable state of the gene expression potential. The most important mechanisms defining epigenetics are DNA methylation and histone modification, ultimately aimed at governing gene expression. Many fundamental questions in epigenetics remain unanswered, providing abundant opportunities for discovery. Both ‘‘epi” sciences (epidemiology: upon the people; epigenetics: above genetics) connect at the intersection of epigenetic variation and the distribution of disease. Epigenetic epidemiology is defined as the study of the association between epigenetic variation and the risk of disease in humans (Waterland and Michels, 2007). Combing the two fields creates a new science that supports
* Address: Obstetrics and Gynecology Epidemiology Center, Department of Obstetrics, Gynecology and Reproductive Biology, Brigham and Women’s Hospital, Harvard Medical School, 221 Longwood Avenue, Boston, MA 02115, United States. Tel.: +1 617 732 8496; fax: +1 617 732 4899. E-mail address: [email protected]. 0531-5565/$ - see front matter Ó 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.exger.2009.12.011
the study of the role of epigenetic modifications in human disease etiology and understanding epigenetics as a possible mechanism underlying the link between an exposure (such as diet) and disease outcome. Epidemiology and epigenetics share the element of time and variability: Epidemics are time-dependent and fluctuate; the epigenetic code – unlike the genetic code – while fairly stable (Cruz-Correa et al., 2009), is modifiable (Jaenisch and Bird, 2003) and can change with age (Wilson and Jones, 1983; Issa et al., 1994; Bjornsson et al., 2008) and as a result of environmental stressors (Jaenisch and Bird, 2003; Whitelaw and Whitelaw, 2006; Christensen et al., 2009). Environmental exposures play an important role in epidemiologic studies on disease causation and in shaping the epigenetic signature. One of the challenges of epigenetic epidemiology is to differentiate environmental influences on the epigenome from ‘‘normal” aging of the epigenome, which in itself can be associated with age-related illness. Epigenetic traits such as CpG methylation and genomic imprinting can be used as markers in epidemiologic studies because they are fairly stable in samples if properly processed and stored. Comparisons can be made of the degree of global methylation, DNA methylation patterns, or gene-specific methylation between population groups defined by the presence or absence of a specific exposure or disease. One specific characteristic of epigenetic marks is their tissue-specificity. Tissue-specificity is perhaps the primary conceptual difference in the evaluation of conventional biomarkers and genetic disease susceptibility markers such as germline muta-
298
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tions compared to epimutations. The choice of the most appropriate tissue for epigenetic analyses is therefore imperative to successfully answering the research question. 2. Selecting the most appropriate study design Population-based association studies are an excellent way to study epigenetic variation in DNA methylation or chromatin structure and disease frequency. Epidemiology employs a number of different study designs that can be used to study the role of epigenetics in health and disease (Gordis, 2009). 2.1. Cross-sectional studies In a cross-sectional study all factors of interest are assessed at one point in time. The prevalence of methylation of a CpG island in the promoter of a specific gene in the population or a subgroup of the population defined by a special characteristic, such as lefthanded females, or the prevalence of loss of imprinting of a particular gene, say IGF-2, in the population or a subsample such as newborns, can be easiest studied in a cross-sectional study. Similarly, comparing the degree of global DNA methylation in two groups, e.g., children and adults, can be accomplished effectively with a cross-sectional design. 2.2. Retrospective case-control study In a case-control study, individuals with a disease and appropriately selected individuals free of the disease are sampled from the same source population. To study epigenetic variation, biospecimens would be obtained and DNA methylation or loss of imprinting assessed. In the context of epigenetics, this study design bears similarities to a cross-sectional study, except for the choice of the controls whose selection cannot in any way be related to exposure status, e.g., methylation. The purpose of the controls is to provide information on the methylation status of cases if they had not contracted the disease under study; if this information is distorted, selection bias results. Neither in the cross-sectional study nor in the retrospective case-control study can it be determined whether the level of the biomarker, for instance, a low global DNA methylation status among cases, preceded (and possibly caused) the disease or whether it may be a consequence of (and possibly have been caused by) the disease. 2.3. Cohort study In a cohort study healthy individuals are recruited to participate in a longitudinal study over a certain period of follow-up time (weeks, months, years). At baseline, biospecimens from all participants would be obtained and stored. During follow-up, additional biospecimens may be collected. Such a biorepository provides the opportunity to study changes of DNA methylation or other epigenetic markers over time. During follow-up, any disease outcome of interest is recorded and participants censored from the study once they reach this endpoint. Cohort studies are usually large and often include many thousands of participants. Because it is not cost-effective to analyze the samples from all participants obtained at baseline and during follow-up, a nested case-control study is embedded in the cohort. 2.4. Nested case-control study A nested or prospective case-control study is embedded in a cohort study. All individuals who develop the disease of interest throughout follow-up are selected and appropriate controls (often
2 controls per case) are selected from those who remained free of the disease throughout follow-up. Since the biospecimens of the cases and controls were obtained prior to the diagnosis of disease (and were stored since), this study design allows relating the prediagnostic status of DNA methylation or other biomarkers to the disease outcome, i.e., it is clear that the methylation status assessed preceded the diagnosis of disease. The nested case-control study is a cost-effective study design. 2.5. Intervention studies The effect of folate and other supplements that affect the one carbon metabolism or the effect of demethylating agents can be studied in intervention studies (crossover studies or randomized trials). In a crossover study the effect of supplements on DNA methylation can be explored by assessing the methylation profile before and after supplement use. In a randomized clinical trial, a demethylating agent is randomly assigned to half of the study participants, while the other half receives a different dose, a different drug, or a placebo. The outcome is recorded in both groups. 2.6. Family-based studies Maintenance and loss of imprinting (mono-allelic and biallelic gene expression) and their role in disease causation can also be studied in population-based studies; family-based studies, including triads of mother, father, and child, may allow discrimination between inheritance (incomplete erasure) and acquisition of loss of imprinting. 2.7. Birth cohorts In a birth cohort, pre-conceptual and prenatal exposures can be assessed and related to DNA methylation and imprinting profiles of the offspring at birth. Additional follow-up of the birth cohort permits tracking changes of epigenetic marks over time. 3. Challenges of epigenetic epidemiology Besides choosing a suitable study design, the choices of the population and the sample size have important implications for the results of an epigenetic epidemiology study. The tissue-specificity of methylation patterns requires careful considerations of the choice of the appropriate tissue to adequately address the goal of the study. Furthermore, confounding – a common problem in observational research – needs to be avoided, since it can invalidate the study findings. 3.1. Variation The epigenetic signature is a useful tool to characterize population subgroups with certain traits, if the between-person variation exceeds the within-person variation. Significant interindividual epigenetic variation at specific loci has been established and linked to the risk of disease. 3.2. Choice of the study population For all study designs, the choice of the population may influence the associations observed. A convenience sample for a birth cohort may focus on patients with uncomplicated deliveries because of the logistical ease in obtaining the samples. However, the epigenetic profile among those babies may differ from babies born prematurely or whose mothers suffered from preeclampsia. Recruitment for a cohort study may only include participants
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responding to an initial invitation, which may be a less than 30% response. Such a population and their epigenetic profiles may not be representative of the general population.
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surrogate markers if the goal is to identify a risk marker for disease. E.g., variation in global DNA methylation of PBLs may be an overall indicator of the effect of a particular exposure or the health status of a group of individuals.
3.3. Sample size 3.5. Confounding One of the most important shortcomings of basic science research is the often small sample size. A sufficiently large sample size is a fundamental requirement of a high-quality study in epigenetic epidemiology: It increases the likelihood of a valid study result and is necessary to achieve adequate precision of the findings. Moderate to modest differences in epigenetic patterns between two groups cannot be detected with a limited sample size, however, they may be of interest. Depending on the factor under study, only modest changes may be expected: diet and dietary supplements may exert only limited changes to the epigenetic signature. Accordingly, a sufficiently large sample size is necessary to detect such differences. Demethylating agents like 5-azacytidine may have a more profound impact on DNA methylation. Power calculations conducted during the planning phase of a study inform about the number of participants necessary to detect a specific difference, but assumptions have to be made about the expected difference. Even if the expected level of change is large, the number of participants has to be sufficient to ensure precision of the results reflected in a narrow confidence interval. 3.4. Choice of the tissue Epigenetic mechanisms play an important role in cell differentiation. Hence DNA methylation is tissue-specific and may be cell type-specific and a certain DNA methylation pattern found in one specific tissue does not permit inferences about its variation across different tissues and possibly not even across different cell types in the same tissue. The research question at hand determines the most appropriate selection of the tissue(s) for analysis. Tissue harvested from invasive tumors in the intestinal wall of the colon displays characteristic epimutations when compared to colon mucosa from a cancer-free patient (Ahuja et al., 1998). Other cancers show different distinct DNA methylation patterns. When studying cancer, it is important to microdissect tumor tissue to ensure a homogeneous cancer cell population. Comparisons should optimally be made with tissue from the same organ obtained from a healthy individual. Evaluation of the DNA methylation profile in breast cancer tissue requires comparison with the methylation pattern in ‘‘normal” mammary tissue. Most researchers use adjacent mammary tissue from the same patient that according to histology was declared tumor-free. Using control tissue samples from the same person controls for confounding by age or other between-person-differences. Many studies have shown, however, that morphologically normal tissues from diseased breasts harbor a number of genetic anomalies (Deng et al., 1996; Kurose et al., 2001; Ellsworth et al., 2004), and the same epigenetic changes identified in cancer tissue have been found in adjacent cancer-free tissue and extended as far as 4 cm from primary tumors (Cui et al., 2003; Shen et al., 2005; Yan et al., 2006). This field effect renders the use of adjacent tissue obsolete when the goal is to identify epimutations specific for a particular tissue and phenotype. Potential DNA methylation markers of tumorigenesis may be missed due to the use of inappropriate control tissue (Riazalhosseini and Hoheisel, 2008). Conversely, tissue samples from healthy controls may be difficult to obtain depending on the organ of interest and comparisons may be confounded by age and other interindividual differences. The DNA methylation pattern and imprinting profile in peripheral blood lymphocytes (PBLs) or other blood cells may be useful as
Confounding is one of the most important threats to the validity of an epidemiologic study. A confounder is a third variable that is correlated with both the epigenetic mark and the disease of interest. For example, a study of the influence of a specific DNA methylation pattern on a disease outcome could be confounded by a variety of variables that affect methylation and are also risk factors for the disease. Age is a likely confounder of a study in epigenetic epidemiology, since the DNA methylation profile changes with age and age increases the risk for most diseases. Another potential confounder could be an environmental factor that affects DNA methylation and also predicts the risk of disease. Confounding can be overcome by assessing confounding factors and appropriately adjusting for them in the statistical analysis. Unfortunately, confounding factors are often not recognized or not measured which results in uncontrolled confounding. Poor assessment of a confounder does not allow complete statistical correction resulting in residual confounding. For example, if alcohol consumption affects the epigenetic profile, it may be difficult to quantify alcohol intake in individuals precisely. Such measurement error makes it difficult to eliminate the confounding influence of alcohol on the epigenetics-disease association. 3.6. Misclassification As the field of epigenetics matures, so do its methods. Methylation microarrays now allow assessing the methylation status of individual CpGs outside of CpG islands. The assessment of loss of imprinting by pyrosequencing is more quantitative than previously used methods, which relied on gel electrophoresis and radioactive labeling. Each of these improvements in technology decreases misclassification of the epigenetic state and improves precision. Misclassification will be further reduced by the anticipated next generation of microarrays that allow genome-wide CpG methylation assessment. Correspondingly, increasingly sophisticated bioinformatics tools allow differentiation of true signals from noise. 3.7. Effect modification A third variable can be an effect modifier, if, depending on the level of this third variable, the association between the exposure and the disease outcome differs. The effect modifier ‘‘interacts” with the exposure of interest. For example, the association between aberrant DNA methylation and a cancer type may be stronger among postmenopausal than among premenopausal women. Or, loss of imprinting of IGF-2 among macrosomic babies may be more common among males than females. Stratification by the effect modifier allows obtaining information about the association between the exposure and the disease for each level of the effect modifier. Each of the smaller population subsets created by stratification needs to be sufficiently large to generate valid and reliable stratum-specific estimates. 4. Promises and opportunities of epigenetic epidemiology To date, a large proportion of epigenetic research has focused on plant and animal models. While intriguing and important observations have emerged from this research, the implications for hu-
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mans remain unclear. Leads obtained from non-human organisms can inform research efforts in humans. Epigenetic epidemiology provides the best framework to conduct these studies. The best studied area in humans is cancer epigenetics. Other diseases including cardiovascular disease, asthma, obesity, and neurologic disorders will likely be targets of epigenetic epidemiology in the future. Environmental factors may exert their effects via epigenetic pathways. Feeding pregnant Agouti mice methyl-supplemented diets shifted the coat color of their offspring from their normal yellow to brown in a dose-dependent fashion. This phenomenon is controlled by methylation of a retrotransposon within the Agouti gene: If hypomethylated, the gene is expressed and the offspring yellow, if hypermethylated due to the effect of methyl-supplementation, the gene is increasingly silenced and the coat color migrates toward brown (Wolff et al., 1998; Waterland and Jirtle, 2003). Whether a similar mechanism might explain the developmental origins of health and disease in humans is currently being explored. The importance for the pre-conceptual and post-fertilization period for the establishment of imprinting and methylation pattern – indicated by animal models – suggests a role of epigenetics in regulating developmental plasticity. Environmental exposures during perinatal life may establish interindividual epigenetic variation that persists and can be traced to the incidence of chronic disease later in life (Waterland and Michels, 2007). The effect of many stressors gametes and embryo are exposed to on their epigenetic signature remain to be studied. For example, the effects of pre-conceptual and prenatal folate intake recommended for all women of child bearing age on the epigenetic profile of the offspring are not well understood. The establishment of a benchmark of what represents the ‘‘normal” or ‘‘disease free” state is fundamental in genetics and epigenetics alike. The recent sequencing of the human methylome is a giant step towards this goal (Lister et al., 2009). Departures from this state may be linked to diseases of interest in order to establish a phenotypic epigenetic profile. If this profile is already manifest prior to diagnosis of disease, it can be used as a biomarker for early detection. If it manifests before disease development, it may indicate the risk of disease. Unlike genetic marks, however, DNA methylation is tissue-specific. Epigenetic studies are currently most amenable in easily accessible tissues. It may be difficult to study epigenetic contributions to diseases in organs and tissues for which it is ethically impossible to obtain tissues from healthy controls. Prognostic markers are identified in casecontrol studies, early detection or risk markers preferably in nested case-control studies with a prediagnostic biorepository. Insights into phenotypic epigenetic profiles allow creation of preventive and treatment strategies. Since the epigenetic signature is amenable to changes by environmental factors, identifying factors that create or correct disease-specific patterns is essential. Epigene-environment interactions may be gene-specific or involve larger components of the genome. There are also gene-environment interactions that affect the epigenetic profile. The effect of folate, choline, and methionine on the risk of several cancers is currently explored in epidemiologic and intervention studies (Figueiredo et al., 2009). In one study, plasma folate levels affected global DNA methylation only among individuals with the T/T genotype of the common polymorphism (C677T) in 5,10-methylenetetrahydrofolate reductase (Friso et al., 2002). The influences of endocrine-disrupting chemicals (Crews and McLachlan, 2006), alcohol consumption (Platek et al., 2009), and smoking habits (Figueiredo et al., 2009) on DNA methylation are also under study. Demethylating agents for the treatment of cancer have been tested in randomized clinical trials (Oki and Issa, 2006; Garcia-Manero, 2008; Fenaux et al., 2009).
Epigenetic epidemiology provides new opportunities to identify disease biomarkers, to discover pathways for links between environmental exposures and disease outcomes, and to explore new avenues in disease prevention and treatment. This new science may also provide the mechanistic underpinning of the developmental origins of health and disease.
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