• Omics;
  • molecular epidemiology;
  • human population studies;
  • environmental risks;
  • individual susceptibility


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  2. Abstract

The convergence of striking developments in (bio)-technology, increasing availability of biobanked samples, and advances in biostatistics and bio-informatics allow an optimistic outlook for epidemiological research. In this special issue on Omics in Population Studies: A Molecular Epidemiology Perspective we explore and reflect on the potential of these new developments in both exposure science and clinical research since they provide the essential link between exposure and disease and may enable scientists to improve their understanding of disease origin and progression. As noted in this special issue, this is an exciting time for epidemiology. While cancer and other noncommunicable diseases rise in number worldwide, various new tools can be applied effectively to increase understanding of the underlying causes and potential for progression to improve their prevention and treatment. Environ. Mol. Mutagen. 54:455-460, 2013. © 2013 Wiley Periodicals, Inc.


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  2. Abstract

The advent of molecular epidemiology in the 1980s fueled the expectation that with the application of new molecular techniques we would improve our ability to detect environmental risk factors by more accurate quantification of the exposure, integration of susceptibility factors, and identification of early disease states. Certainly, there have been success stories, for example: the human papillomavirus-cervical cancer association [Schiffman et al., 2007], the underpinning of the mechanism of action of certain chemical carcinogens (e.g., benzene, formaldehyde) [Zhang et al., 2010; IARC Monographs, 1995; Lan et al., 2004], the reduction in exposure misclassification (e.g., aflatoxin exposure and hepatocellular carcinoma) [Ross et al., 1992], and the identification of susceptibility factors through functional assays (e.g., chromosomal aberration, micronucleus assay) [Hagmar et al., 1998; Bonassi et al., 2007]. However, even with these advances many of the environmental factors that modify disease susceptibility remain elusive.

The early years of molecular epidemiology can be broadly characterized by: (i) hypothesis-driven biomarker research, and (ii) studies based on single markers, generally designed to identify and quantify exposures and internal doses, intermediate endpoints, and individual susceptibility. Here we provide some illustrative examples.

The first exposure biomarkers that were successfully employed in epidemiological research were derived predominantly from chemical carcinogenesis studies. This led to an emphasis on the measurement of carcinogen-DNA adducts, carcinogen-protein adducts, or carcinogen metabolites in biological fluids [Wild, 2005]. Classical examples of improved exposure assessment by incorporation of exposure biomarkers in epidemiological research are the use of aflatoxin-DNA adducts in the etiological studies of hepatocellular carcinoma [Ross et al., 1992], and the evaluation of polycyclic aromatic hydrocarbon-DNA adducts in lung cancer [Phillips et al., 1988]. Currently, a wide range of exposures can be measured biologically, including environmental factors (e.g., dioxins, polychloro-biphenyls, polycyclic aromatic hydrocarbons, aflatoxin, heavy metals), nutrients (e.g., β-carotene, phytoestrogens, folate), infectious agents (e.g., EBV, HIV, HBV, HCV, H. pylori, SV40) and endogenous compounds (e.g., hormones, growth factors) [Coggon and Friesen, 1997; Kaaks et al., 1997b; Munoz and Bosch, 1997; Rothman et al., 1997; Wild and Pisani, 1997; Ketchum et al., 1999; Wild et al., 2001; Adlercreutz, 2002; Gammon et al., 2002; Krajcik et al., 2002; Lan et al., 2004; Lamar et al., 2003; Pavuk et al., 2003; Riboli et al., 2002; Starek, 2003]. However, to a certain extent their application has been limited in epidemiological research - especially of chronic diseases - because of the relatively short half-life of many of these markers. As such, if exposure is not constant they are unlikely to reflect accurately the long-term exposure status.

A second line of research focused on the identification of intermediate markers that would underpin the exposure disease association. Examples of such markers, which are generally early, nonclonal and measure nonpersistent effects, include various measures of cellular toxicity, alterations to DNA and chromosomes, changes in gene expression and protein levels, and early non-neoplastic alterations in cell function (e.g., altered DNA repair, altered immune function). However, for maximum utility, an intermediate biomarker must be shown to be predictive of developing a disease, preferably in prospective cohort studies [Schatzkin et al., 1990]. Chromosomal aberration frequencies in peripheral blood lymphocytes have been extensively evaluated in cross-sectional studies of populations exposed to a wide variety of potential carcinogens, and are a classic biomarker of early biologic effect [McHale et al., 2008]. Higher levels of chromosomal aberrations subsequently have been shown to predict overall cancer risk [Bonassi et al., 2008] and are one of the few validated intermediate biomarkers used in environmental research to date. Validation of intermediate (early effect) biomarkers should, however, become easier in the future as many human biological samples have been biobanked worldwide. These resources enable the nested case-control studies that examine the intersection of potential disease and exposure signals [Vineis et al., 2009].

A third line of research has focused on the integration of individual susceptibility in the exposure disease association. Individual susceptibility can be measured by functional assays such as the comet, micronucleus, and the γH2AX assays [Fenech, 2007; Azqueta and Collins, 2013; Valdiglesias et al., 2013]. More recently, other assays have been used. For example, the telomere length assay that has been shown to be related to cancer at several sites, including prostate, esophagus, lymphoma, breast, lung, head and neck, bladder, and kidney cancer [Codd et al., 2013] and to be influenced by environmental exposures [Spitz and Bondy, 2010]. Individual susceptibility has also been assessed through candidate gene studies. This approach has in general been disappointing with very few gene-environmental interactions being identified. Successful examples have focused mostly on interactions with genes in the metabolic pathways of carcinogens [Marcus et al., 2000; Cantor et al., 2010]. For example, Cantor et al. conducted a large case-control study and showed that polymorphisms in key metabolizing enzymes modified the association between disinfection by-products and bladder cancer [Cantor et al., 2010].

Most recently, genetics has moved away from candidate gene-approaches to agnostic searches through genome-wide-association-studies. This has been facilitated by-and-large through rapid development in the technology to measure genetic polymorphisms by ever more dense microarrays and recently by whole genome sequencing. These studies have revealed several new associations between genes and diseases (for an overview see However, the search for gene-environment interactions based on GWAS data (GEWIS; Gene-environment-wide-interaction-studies) is still complicated because of the limited power to detect such interactions due to: (i) relatively small marginal effects of genes and exposures, (ii) misclassification of the exposures, and (iii) the large number of possible interactions to be explored leading to inherent problems in classical statistical inference.

Recently, a similar shift from hypothesis-driven single marker analyses to agnostic analyses of multiple markers can be observed in the analyses of metabolite, adduct, protein, RNA, and DNA-based assays [Vlaanderen et al., 2009]. Similar to genomic analyses, rapid biotechnological development has enabled the measurement of large sets of metabolites (metabolomics), adducts (adductomics), proteins (proteomics), transcription factors (transcriptomics), and epigenetic regulation of the genome (epigenomics) [Saberi Hosnijeh et al., 2013; McHale et al., 2013; Matullo et al., 2013; Langevin and Kelsey, 2013]. Application of these omics techniques in both exposure science and clinical research may provide the essential link between exposure and disease, and possibly enable researchers to find new environmental factors associated with chronic disease. Of course, the switch from single marker analyses to high-dimensional marker assays also brings challenges with regard to study designs, laboratory procedures to minimize variance, statistical inferences, and biological interpretation. This special issue presents a collection of commentaries, reviews and research articles that describes the state of the art in this field with a special emphasis on the identification of environmental factors of noncommunicable disease. In addition, future perspectives on the use of omics biomarkers in population studies are discussed.


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The publications included in this special issue raise several interesting points that should guide future research plans on the inclusion of omics technologies in human population studies. Several commentaries indicate that recent results in the field show the existence of underlying molecular networks that enable the study of phenomes instead of individual disease-exposure associations [Vineis et al, 2013; Wild et al., 2013]. Such profiles may be predictive of the occurrence of groups of diseases linked by common exposure profiles [Kyrtopoulos, 2013]. The example of smoking is one of the most studied and self-evident, thanks to the wealth of data generated by over 50 years of research.

Characterization of the exposome may present unprecedented research opportunities in the study of disease causation and outcome, by allowing the consideration of temporal variation while integrating several biomarkers at the same time [Wild et al., 2013], and external exposure with internal measures on what reaches the cells and tissues [Brunekreef, 2013]. It has been pointed out that new strategies should be defined by epidemiologists in order to drive the basic science work towards meaningful steps geared to the understanding of exposure-disease associations, disease progression and outcome [Wild et al., 2013]. A return to exposure-disease studies using new technologies to assess exposure effects is a critical step, while whole genome studies conducted on samples where exposure is known and well characterized are a missing opportunity in this direction. Another aspect that should be studied in more depth is how a change in exposure may induce changes in some omes (e.g., the transcriptome), and how these changes may allow the identification of associations between gene expression profiles and specific exposures [Wild et al., 2013]. Furthermore, the characterization of omics profiles of healthy tissues may help to attribute the perturbation of these profiles to environmental changes [Langevin and Kelsey, 2013].

The strategy of revisiting the initial steps of the disease-exposure process requires the exploitation of existing data bases and tissue/sample banks; this is a very cost-efficient approach that is recommended worldwide, although it opens the door to a series of ethical problems that are discussed in detail in this special issue [Vähäkangas, 2013]. It is evident that the purposes of secondary studies are sometimes questionable and not always well described to the participants. Re-analysis and testing of existing cohorts builds on the premises of standardization of the information across studies; in addition, it can be predicted that large data bases and large numbers of samples will be shared around the world for testing and analyses purposes. This makes it hard to fulfil the promise we make to each participant of being able to withdraw from a study if he/she decides so. Thus, informed consent should take into account new avenues of epidemiologic research such as the one we just described.

Another aspect to keep in mind is that the linkage of large data sets and testing of multiple samples to create omics profiles makes it possible to create a detailed picture of an individual and of a population. The integration of omics data would produce much more information at a population level, and this may contribute to the identification of certain diseases/conditions of that population, thus stigmatizing not just the single individual but the group at large.


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Many of the manuscripts in the special issue address the question of what is around the corner, discussing changes and innovations that should be expected for the years to come and that can modify the use and the perspectives of omics assays applied to human population studies. Research breakthroughs are certainly expected to come from a better understanding of the complex interactions linking the exposome to health effects, and from the availability of specific omics profiles in people exposed to well-defined factors (e.g., tobacco, diet, occupational exposures, environmental pollutants) [Moore et al., 2013; Wild et al., 2013]. The importance of reconstructing the complex pattern of exposures in critical periods of life is also emphasized by Vineis and colleagues. These authors recognize the improvement of methodological approaches as the key issue for the future, and the input and collaboration of researchers across many disciplines as the requirement for exposome studies to succeed [Vineis et al., 2013]. However, the exposome may express a cascade of events that are not completely intercepted by omics biomarkers [Brunekreef, 2013] and therefore the improvement in the assessment of external exposures remains a priority, considering also that some external exposures may not leave a specific traceable signal in the body.

The use of omics techniques in human population studies is in its infancy. Nevertheless, a number of exposures have been already investigated with these new tools. The first results are contrasting showing the limitations, but also the great potential of this approach. A few examples are reported in this issue, including studies on common occupational (e.g., benzene, TCDD) [Saberi Hosnijeh et al., 2013; Thomas et al., 2013] or environmental exposures (e.g., arsenic, mixed) [De Coster et al., 2013; Moore et al., 2013].

Emphasis on new methodologies in this issue also addresses the evolution of cost-effective, high-throughput epigenomic assays that are starting to be used to implement the candidate gene approach to molecular epidemiology. Epigenome-wide association studies (EWAS) are presented as a promising opportunities for disentangling the complexity of most chronic, noncommunicable diseases [Langevin and Kelsey, 2013]. Among these technologies, the ones that have the potential to significantly improve our knowledge of diseases are certainly new sequencing techniques. The decrease of sequencing costs and the increased availability of instruments and technologies will bring the discovery of rare and private variants of many complex traits [Matullo et al., 2013]. The constant evolution of techniques creates new ethical dilemmas, such as incidental findings that can be easily generated by the management of the bulk information that can be retrieved from massive sequencing studies. Research on ethics will play a growing role in the next few years, when personalized profiles of susceptibility to disease will be identified and their link to disease validated [Matullo et al, 2013; Vähäkangas, 2013]. The main responsibility for the transfer of research tradition to the next generation of scientists through education—setting an example by their behavior—is in the hand of senior scientists, as well as in the ability of our universities to create qualified and easily accessible tools for learning molecular epidemiology [Arts and Weijenberg, 2013; Vähäkangas, 2013]. The final and arguably among the most important technological improvements that are expected in the next few years are those dealing with the ability to interpret the large databases generated by the high-throughput assays. Research on statistical methods is quite active, with several methods derived from other fields or specifically designed for mining large datasets [Chadeau et al., 2013]. In addition to the expected development of statistical/computational methods necessary to extract the information concealed by the massive nature of epidemiological omics data, a systematic effort should be put forth to implement prior knowledge of the multistage evolution of disease, and disease and phenome pathways to better understand networks of complex biological systems [Kyrtopoulos, 2013].

Understanding the link between the molecular framework of complex chronic non-communicable diseases, and the clinical occurrence of new clinical phenotypes, characterized by the simultaneous occurrence of different noncommunicable diseases, is the real challenge for the future years. Systems approaches provide the tools for the so called meet-in-the-middle approach that tries to find a common ground between disease clinical features and preclinical characteristics [Vineis et al., 2013]. Systems biology tools will surely pave the way to this approach, although only the development of systems medicine platforms with extensively characterized patients, and integrating clinical features with molecular and cellular biology studies, may provide a basis for a personalized preventive intervention.

Systems medicine applies the perspective of systems biology to the study of disease mechanisms, with the aim of improving diagnosis, treatment, and prognosis, and the power of mathematical and computational modelling [Mardinoglu and Nielsen, 2012]. This approach, which extends and completes the intuition of Margaret Spitz who first described the concept of integrative epidemiology [Spitz et al. 2005], fuels the transition to a new medicine that is predictive, preventive, personalized and participatory (P4 medicine) [Hood and Flores, 2012; Hood et al., 2012]. The P4, or proactive medicine, is mostly oriented towards health maintenance and well-being rather than to disease treatment, and is characterized by the use of large datasets of integrated information coming from clinical, epidemiological and biological (e.g., multi-omics) platforms to identify the best prevention and therapeutic approach based on an individual-centered approach.

As Wild and colleagues commented in their contribution to the special issue, this is an exciting time for epidemiology. While cancer and other noncommunicable diseases rise in number worldwide, several tools can be applied effectively to increase understanding their causes and progression to improve their prevention and treatment [Wild et al., 2013].


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