Molecular Epidemiology of Chronic Diseases E-Book

Chris Wild

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 994

Veröffentlichungsjahr: 2011

Beschreibung

"I think this is an excellent book–I recommend it to anyone involved in molecular epidemiology... The 26 chapters are written by topic specialists, in an explanatory, east to read style." –BTS Newsletter, Summer 2009

"This text provides an accessible and useful handbook for the epidemiologist who wants to survey the field, to become better informed, to look at recent developments and get some background on these or simply to appreciate further the relatively rapid changes in informatic and analytical technologies which increasingly will serve and underpin future epidemiological studies.  One of the strengths in this book is the extensive array of practical illustrative examples, and it would also in my opinion have useful potential as a teaching text." –American Journal of Human Biology, March 2009

With the sequencing of the human genome and the mapping of millions of single nucleotide polymorphisms, epidemiology has moved into the molecular domain. Scientists can now use molecular markers to track disease-associated genes in populations, enabling them to study complex chronic diseases that might result from the weak interactions of many genes with the environment. Use of these laboratory generated biomarker data and an understanding of disease mechanisms are increasingly important in elucidating disease aetiology. 

Molecular Epidemiology of Disease crosses the disciplinary boundaries between laboratory scientists, epidemiologists, clinical researchers and biostatisticians and is accessible to all these relevant research communities in focusing on practical issues of application, rather than reviews of current areas of research. 

• Covers categories of biomarkers of exposure, susceptibility and disease
• Includes chapters on novel technologies: genomics, transcriptomics, proteomics and metabonomics, which are increasingly finding application in population studies
• Emphasizes new statistical and bioinformatics approaches necessitated by the large data sets generated using these new methodologies
• Demonstrates the potential applications of laboratory techniques in tackling epidemiological problems while considering their limitations, including the sources of uncertainty and inaccuracy
• Discusses issues such as reliability (compared to traditional epidemiological methods) and the timing of exposure
• Explores practical elements of conducting population studies, including biological repositories and ethics

Molecular Epidemiology of Disease provides an easy-to-use, clearly presented handbook that allows epidemiologists to understand the specifics of research involving biomarkers, and laboratory scientists to understand the main issues of epidemiological study design and analysis. It also provides a useful tool for courses on molecular epidemiology, using many examples from population studies to illustrate key concepts and principles.

Leseprobe

Contents

List of Contributors

Acknowledgements

1: Introduction: Why Molecular Epidemiology?

2: Study Design

2.1. INTRODUCTION: STUDY DESIGN AT SQUARE ONE1

2.2. EPIDEMIOLOGICAL MEASURES

2.3. BIAS

2.4. MORE ON CONFOUNDING

2.5. SPECIFICITIES OF MOLECULAR EPIDEMIOLOGY DESIGN

2.6. CONCLUSIONS

References

Essential reading

3: Molecular Epidemiological Studies that can be Nested within Cohorts

3.1. INTRODUCTION

3.2. CASE-COHORT STUDIES

3.3. NESTED CASE-CONTROL STUDIES

3.4. CONSIDERATIONS REGARDING BIOMARKER ANALYSES IN CASECOHORT AND NESTED CASE-CONTROL STUDIES

3.5. CONCLUSION

References

4: Family Studies, Haplotypes and Gene Association Studies

4.1. INTRODUCTION

4.2. FAMILY STUDIES

4.3. GENETIC ASSOCIATION STUDIES

4.4. DISCUSSION

References

5: Individual Susceptibility and GeneEnvironment Interaction

5.1. INDIVIDUAL SUSCEPTIBILITY

5.2. GENETIC SUSCEPTIBILITY

5.3. METABOLIC SUSCEPTIBILITY GENES

5.4. STUDY DESIGNS

5.5. GENE-ENVIRONMENT INTERACTION

5.6. EXPOSURE DOSE EFFECTS IN GENE-ENVIRONMENT INTERACTIONS

5.7. MUTATIONAL EFFECTS OF GENE-ENVIRONMENT INTERACTIONS

5.8. CONCLUSIONS

References

6: Biomarker Validation

6.1. VALIDITY AND RELIABILITY

6.2. BIOMARKER VARIABILITY

6.3. MEASUREMENT OF VARIATION

6.4. OTHER ISSUES OF VALIDATION

6.5. MEASUREMENT ERROR

6.6. BLOOD COLLECTION FOR BIOMARKERS

6.7. VALIDATION OF HIGHTHROUGHPUT TECHNIQUES

References

7: Exposure Assessment

7.1. INTRODUCTION

7.2. INITIAL CONSIDERATIONS OF AN EXPOSURE ASSESSMENT STRATEGY

7.3. EXPOSURE PATHWAYS AND ROUTES

7.4. EXPOSURE DIMENSIONS

7.5. EXPOSURE CLASSIFICATION, MEASUREMENT OR MODELLING

7.6. RETROSPECTIVE EXPOSURE ASSESSMENT

7.7. VALIDATION STUDIES

7.8. QUALITY CONTROL ISSUES

References

8: Carcinogen Metabolites as Biomarkers

8.1. INTRODUCTION

8.2. OVERVIEW OF CARCINOGEN METABOLISM

8.3. EXAMPLES OF CARCINOGEN METABOLITE BIOMARKERS

8.4. SUMMARY

References

9: Biomarkers of Exposure: Adducts

9.1. INTRODUCTION

9.2. METHODS FOR ADDUCT DETECTION

9.3. ADDUCTS IDENTIFIED IN HUMAN TISSUE

9.4. ADDUCTS AS BIOMARKERS OF OCCUPATIONAL AND ENVIRONMENTAL EXPOSURE TO CARCINOGENS

9.5. SMOKING-RELATED ADDUCTS

9.6. DNA ADDUCTS IN PROSPECTIVE STUDIES

9.7. CONCLUSIONS

References

10: Biomarkers of Mutation and DNA Repair Capacity

10.1. INTRODUCTION

10.2. CLASSIFICATION OF MUTATIONS

10.3. MUTATIONS IN MOLECULAR EPIDEMIOLOGY

10.4. DNA REPAIR

10.5. CLASSES OF DNA REPAIR

10.6. COMMON ASSAYS TO MEASURE DNA REPAIR CAPACITY

10.7. INTEGRATION OF DNA REPAIR ASSAYS INTO EPIDEMIOLOGICAL STUDIES

10.8. GENETIC MARKERS FOR DNA REPAIR CAPACITY

References

11: High-Throughput Techniques Genotyping and Genomics

11.1. INTRODUCTION

11.2. BACKGROUND

11.3. SNP DATABASES

11.4. STUDY TYPES

11.5. STUDY DESIGN

11.6. GENOTYPING TECHNOLOGIES

11.7. SAMPLE AND STUDY MANAGEMENT AND QC

11.8. AFTER THE ASSOCIATION HAS BEEN PROVED - WHAT NEXT?

References

12: Proteomics and Molecular Epidemiology

12.1. INTRODUCTION

12.2. GENERAL CONSIDERATIONS

12.3. SAMPLE SELECTION

12.4. PROTEOMICS TECHNOLOGIES

12.5. ILLUSTRATIVE APPLICATIONS

12.6. FINAL CONSIDERATIONS

References

13: Exploring the Contribution of Metabolic Profiling to Epidemiological Studies

13.1. BACKGROUND

13.2. CANCER

13.3. CARDIOVASCULAR DISEASE

13.4. NEURODEGENERATIVE DISORDERS

13.5. THE WAY FORWARD

References

14: Univariate and Multivariate Data Analysis

14.1. INTRODUCTION

14.2. UNIVARIATE ANALYSIS

14.3. GENERALIZED LINEAR MODELS

14.4. MULTIVARIATE METHODS

14.5. CONCLUSIONS

References

15: Meta-Analysis and Pooled Analysis Genetic and Environmental Data

15.1. INTRODUCTION

15.2. META-ANALYSIS

15.3. POOLED ANALYSIS

15.4. ISSUES IN POOLED ANALYSIS OF EPIDEMIOLOGICAL STUDIES INVOLVING MOLECULAR MARKERS

References

16: Analysis of Complex Datasets

16.1. INTRODUCTION

16.2. GENE—ENVIRONMENT INTERACTION

16.3. GENE—GENE INTERACTION

16.4. STATISTICAL INTERACTION

16.5. CASE STUDY: BLADDER CANCER

16.6. GENOME-WIDE ANALYSIS

16.7. SUMMARY

References

17: Some Implications of Random Exposure Measurement Errors in Occupational and Environmental Epidemiology

17.1. INTRODUCTION

17.2. INDIVIDUAL-BASED STUDY

17.3. GROUP-BASED STUDIES

17.4. COMPARING BIASES FOR INDIVIDUAL-BASED AND GROUP-BASED STUDIES

17.5. CONCLUSIONS

References

18: Bioinformatics

18.1. INTRODUCTION

18.2. DATABASE RESOURCES

18.3. DATA ANALYSIS

18.4. THE FUTURE

References

19: Biomarkers, Disease Mechanisms and their Role in Regulatory Decisions

19.1. INTRODUCTION

19.2. HAZARD IDENTIFICATION AND STANDARD SETTING

19.3. RISK CHARACTERIZATION: INDIVIDUALS AND POPULATIONS

19.4. MONITORING AND SURVEILLANCE

19.5. WHAT TO REGULATE: EXPOSURES OR PEOPLE’S ACCESS TO THEM?

19.6. CONCLUSION

References

20: Biomarkers as Endpoints in Intervention Studies

20.1. INTRODUCTION: WHY ARE BIOMARKERS NEEDED IN INTERVENTION STUDIES?

20.2. IDENTIFICATION AND VALIDATION OF BIOMARKERS

20.3. USE OF BIOMARKERS IN MAKING HEALTH CLAIMS

20.4. BIOMARKERS OF STUDY COMPLIANCE

20.5. BIOMARKERS THAT PREDICT THE RISK OF DISEASE

20.6. BIOMARKERS RELEVANT TO MORE THAN ONE DISEASE

20.7. BIOMARKERS THAT PREDICT THE OPTIMIZATION OF HEALTH OR PERFORMANCE

20.8. CONCLUSIONS

References

21: Biological Resource Centres in Molecular Epidemiology: Collecting, Storing and Analysing Biospecimens

21.1. INTRODUCTION

21.2. OBTAINING AND COLLECTING BIOSPECIMENS

21.3. ANNOTATING, STORING AND PROCESSING BIOSPECIMENS

21.4. ANALYSING BIOMARKERS

21.5. CONCLUSIONS

References

22: Molecular Epidemiology and Ethics: Biomarkers for Disease Susceptibility

22.1. INTRODUCTION

22.2. ETHICAL ASPECTS IN BIOMARKER DEVELOPMENT FOR DISEASE SUSCEPTIBILITY

22.3. ETHICAL ASPECTS OF BIOBANKING

22.4. MOLECULAR EPIDEMIOLOGY AND SOCIETY

22.5. CONCLUSIONS

References

23: Biomarkers for Dietary Carcinogens: The Example of Heterocyclic Amines in Epidemiological Studies

23.1. INTRODUCTION

23.2. INTAKE ASSESSMENT OF HCAS

23.3. HCA METABOLISM

23.4. CONCLUSIONS AND FUTURE RESEARCH

References

24: Practical Examples: Hormones

24.1. INTRODUCTION

24.2. HORMONE MEASUREMENTS FOR LARGE-SCALE EPIDEMIOLOGICAL STUDIES

24.3. LABORATORY METHODS

24.4. VALIDATION AND REPRODUCIBILITY OF HORMONE MEASUREMENTS

24.5. SAMPLE COLLECTION AND LONG-TIME STORAGE

24.6. DOES A SINGLE HORMONE MEASUREMENT REPRESENT LONG-TERM EXPOSURE?

24.7. INTERPRETATION OF MEASUREMENTS OF CIRCULATING HORMONES

24.8. CONCLUSIONS

References

25: Aflatoxin, Hepatitis B Virus and Liver Cancer: A Paradigm for Molecular Epidemiology

25.1. INTRODUCTION

25.2. DEFINING MOLECULAR BIOMARKERS

25.3. VALIDATION STRATEGY FOR MOLECULAR BIOMARKERS

25.4. DEVELOPMENT AND VALIDATION OF BIOMARKERS FOR HUMAN HEPATOCELLULAR CARCINOMA

25.5. SUSCEPTIBILITY

25.6. BIOMARKERS TO ELUCIDATE MECHANISMS OF INTERACTION

25.7. EARLY DETECTION BIOMARKERS FOR HCC

25.8. SUMMARY AND PERSPECTIVES FOR THE FUTURE

References

26: Complex Exposures - Air Pollution

26.1. INTRODUCTION

26.2. PERSONAL MONITORING OF EXTERNAL DOSE

26.3. BIOMARKERS OF INTERNAL DOSE AND AIR POLLUTANTS

26.4. BIOMARKERS OF BIOLOGICALLY EFFECTIVE DOSE

26.5. BIOMARKERS OF BIOLOGICAL EFFECTS

26.6. GENETIC SUSCEPTIBILITY AND OXIDATIVE STRESS RELATED TO AIR POLLUTION

26.7. CONCLUSION

References

Index

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Library of Congress Cataloging-in-Publication Data

Molecular epidemiology of chronic diseases/edited by Chris Wild, Paolo

Vineis, and Seymour Garte.

p.; cm.

Includes bibliographical references and index.

ISBN 978-0-470-02743-1 (cloth: alk. paper)

1. Molecular epidemiology. 2. Chronic diseases-Epidemiology. I. Wild,

Chris, 1959- II. Vineis, Paolo. III. Garte, Seymour J.

[DNLM: 1. Chronic Disease-epidemiology. 2. Biological Markers.

3. Epidemiologic Methods. 4. Epidemiology, Molecular. WT 500 M719 2008]

RA652.5.M648 2008

614.4-dc22

2008003725

British Library Cataloguing in Publication Data

A catalogue record for this book is available from the British Library

ISBN 978-0-470-02743-1

List of Contributors

Habibul Ahsan

Center for Genetics in Epidemiology Columbia University Mailman School of Public Health New York, NY, USA

Richard J. Albertini

Cell and Molecular Biology University of Vermont Burlington, VT, USA

Angeline S. Andrew

Department of Genetics Norris Cotton Cancer Center Dartmouth Medical School Lebanon, NH, USA

Jennifer H. Barrett

Section of Epidemiology and Biostatistics Leeds Institute of Molecular Medicine St James’s University Hospital Leeds, UK

Pier Alberto Bertazzi

EPOCA, Epidemiology Research Center Department of Occupational and

Environmental Health University of Milan Milano, Italy

Marianne Berwick

Division of Epidemiology UNM School of Medicine University of New Mexico Albuquerque, NM, USA

M. Bictash

Biological Chemistry Division of Biomedical Sciences Imperial College London London, UK

Timothy D. Bishop

Section of Epidemiology and Biostatistics Leeds Institute of Molecular Medicine St James’s University Hospital Leeds, UK

Elodie Caboux

Department of Biostatistics Université Catholique de Lyon Lyon, France

Amanda Cross

Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda, MD, USA

Alison M. Dunning

Cancer Research UK Department of Oncology Strangeway’s Research Laboratory Cambridge, UK

P. Elliott

Biological Chemistry Division of Biomedical Sciences Imperial College London London, UK

Lynnette R. Ferguson

Discipline of Nutrition Faculty of Medical & Health Sciences The University of Auckland Auckland, New Zealand

John B.C. Findlay

Institute of Membrane and Systems Biology LIGHT Laboratories University of Leeds Leeds, UK

Lykke Forchhammer

Institute of Public Health University of Copenhagen Department of Occupational and Environmental Health Copenhagen, Denmark

Seymour Garte

Graduate School of Public Health University of Pittsburgh Cancer Institute Pittsburgh, PA, USA

Mark S. Gilthorpe

Centre for Epidemiology and Biostatistics University of Leeds Leeds, UK

Emmanuelle Gormally

Department of Biostatistics Université Catholique de Lyon Lyon, France

John D. Groopman

Department of Environmental Health Sciences Johns Hopkins Bloomberg School of Public Health Baltimore, MD, USA

Pierre Hainaut

Department of Biostatistics Université Catholique de Lyon Lyon, France

Stephens S. Hecht

University of Minnesota Cancer Center Minneapolis, MN, USA

Elaine Holmes

Biological Chemistry Division of Biomedical Sciences Imperial College London London, UK

Mark M. Iles

Section of Epidemiology and Biostatistics Leeds Institute of Molecular Medicine St James’s University Hospital Leeds, UK

Rudolf Kaaks

Division of Cancer Epidemiology German Cancer Research Center Heidelberg, Germany

Margaret R. Karagas

Department of Genetics Norris Cotton Cancer Center Dartmouth Medical School Lebanon, NH, USA

Jeff N. Keen

Institute of Membrane and Systems Biology LIGHT Laboratories University of Leeds Leeds, UK

Thomas N. Kensler

Department of Environmental Health Sciences Johns Hopkins Bloomberg School of

Public Health Baltimore MD, USA

H. Keun

Biological Chemistry Division of Biomedical Sciences Imperial College London London, UK

Lisbeth E. Knudsen

Institute of Public Health University of Copenhagen Department of Occupational and Environmental Health Copenhagen, Denmark

Lawrence Kupper

School of Public Health University of North Carolina at Chapel Hill Chapel Hill, NC, USA

Steffen Loft

Institute of Public Health University of Copenhagen Department of Occupational and Environmental Health Copenhagen, Denmark

Craig Luccarini

Cancer Research UK Department of Oncology Strangeway’s Research Laboratory Cambridge, UK

Peter Møller

Institute of Public Health University of Copenhagen Department of Occupational and Environmental Health Copenhagen, Denmark

Jason H. Moore

Department of Genetics Norris Cotton Cancer Center Dartmouth Medical School Lebanon, NH, USA

Antonio Mutti

University of Parma Department of Clinical Medicine Nephrology and Health Sciences Parma, Italy

Mark J. Nieuwenhuijsen

Department of Environmental Science and Technology Health and Environment Imperial College London London, UK

J. K. Nicholson

Biological Chemistry Division of Biomedical Sciences Imperial College London, UK

Marie Pedersen

Institute of Public Health University of Copenhagen Department of Occupational and Environmental Health Copenhagen, Denmark

David H. Philips

Institute of Cancer Research Brookes Lawley Building Sutton, UK

Camille Ragin

University of Pittsburgh Cancer Institute UPMC Cancer Pavillion Pittsburgh, PA, USA

Stephen Rappaport

School of Public Health University of North Carolina at Chapel Hill Chapel Hill, NC, USA

Sabina Rinaldi

International Agency for Research on Cancer Lyon, France

Andrew Rundle

Department of Epidemiology Mailman School of Public Health Columbia University New York, NY, USA

Rashmi Sinha

Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda, MD, USA

Emanuela Taioli

University of Pittsburgh Cancer Institute UPMC Cancer Pavillion Pittsburgh, PA, USA

Yu-Kang Tu

Biostatistics Unit Centre for Epidemiology and Biostatistics Leeds Institute of Genetics and Health Therapeutics University of Leeds Leeds, UK

Robert J. Turesky

Department of Health Albany, New York, USA

Elvira Vaclavik Bräuner

Institute of Public Health University of Copenhagen Department of Occupational and Environmental Health Copenhagen, Denmark

Kirsi Vähäkangas

Department of Pharmacology and Toxicology Kuopio, Finland

Paolo Vineis

Division Epidemiology Public Health and Primary Care Imperial College London London, UK

Chris Wild

Molecular Epidemiology Unit Centre for Epidemiology and Biostatistics The LIGHT Laboratories University of Leeds Leeds, UK

Acknowledgements

The Editors would like to thank all the authors for providing excellent manuscripts in a timely fashion and to the following: David Forman, Rudolph Kaaks, Soterios Kyrtopoulos, Stephen Rappaport, Alan Boobis, Julie Fisher, Tony Fletcher, John Molitor, Duccio Cavalieri, Ann Daly, David Phillips, Miriam Poirier, Giuseppe Matullo, Paul Schulte, Thomas Kensler, Emanuella Taioli, Marja Sorsa, Lenore Arab, Regina Santella, and Peter Farmer reviewers who played a critical role in ensuring the quality of the manuscripts:

Special thanks are due to Margaret Jones for collating all the manuscripts and keeping everyone on schedule and for doing all of this with tremendous commitment and good grace.

PV was supported by ECNIS (Environmental Cancer Risk, Nutrition and Individual Susceptibility), a network of excellence operating within the European Union 6th Framework Program, Priority 5: “Food Quality and Safety” (Contract No 513943) and by Compagnia di San Paolo, Torino.

CPW was supported by the NIEHS, USA ES06052 and by the EU NewGeneris (Newborns and Genotoxic Exposure Risks), an integrated project operating within the European Union 6th Framework Program, Priority 5: “Food Quality and Safety” (Contract No 016320-2).

1

Introduction: Why Molecular Epidemiology?

Chris Wild,1 Seymour Garte2 and Paolo Vineis3

1University of Leeds, UK,2University of Pittsburgh Cancer Institute, USA, and3Imperial College London, UK

Physicians, public health workers, the press and the public at large are increasingly preoccupied with ‘environmental risks’ of disease. What are the causes of Alzheimer’s disease? Are the causes environmental or genetic, or a mixture of the two? Does exposure to particles from incinerators or traffic exhausts cause cancer? Epidemiology has traditionally tried to answer such important questions. For example, the associations between tobacco smoking and lung cancer, between chronic hepatitis B virus infection and liver cancer, and between aromatic amines and bladder cancer are now considered to be ‘causal’, i.e. there is no doubt about the causal nature of these relationships. This is because the same positive observations have been made in a large number of settings, the association is very strong, it is biologically plausible, there is a dose-response relationship, and we cannot explain away the association in terms of bias or confounding. In the case of aromatic amines, strong animal evidence was available before observations in humans.

But not all issues of causality in human disease from environmental exposures are so clear. Consider two examples. Is there a ‘causal’ association between dietary exposure to acrylamide and cancer in humans? Are polycyclic aromatic hydrocarbons (PAHs) a cause of lung cancer? These examples are obviously much more difficult to resolve than the previous ones. In the case of cigarettes, a simple questionnaire proved accurate enough to allow a reasonable estimation of exposure, while in the case of aromatic amines there were rosters in the chemical industries that allowed unequivocal identification of exposure to single agents for all workers. In contrast, estimation of the intake of acrylamide from French fries and other sources, using dietary questionnaires, is an almost desperate enterprise. For PAHs, the sources of exposure are multiple (e.g. diet, air pollution from car exhaust, heating, industrial pollution, specific occupations), making it almost impossible to have an estimate of total PAH exposure based on a questionnaire. One can measure PAHs in ambient air through air sampling (in the work environment or with a personal monitor); however, the level of PAHs in the air is only indirectly associated to the amount of PAHs that actually enter the body and end up binding to DNA. The ability of PAHs to reach DNA and bind to it depends on individual metabolic capabilities (which are in part genetically determined), involving a number of different enzymes and pathways. Finally, the ability of PAHs to lead to heritable changes in DNA (mutations) is additionally related to the individual’s ability to repair DNA damage.

For these reasons, starting at least in 1982 with a paper by Perera and Weinstein but probably before with a paper by Lower (Vineis 2007), ‘molecular epidemiology’ was introduced into the practice of cancer research. A simple definition is that:

’#x2026; it entails the inclusion in epidemiologic research of biologic measurements made at the molecular level - and is thus an extension of the increasing use of biologically based measures in epidemiologic research’ (McMichael 1994).

This corresponds to one of the first (if not the first) definition:

Advanced laboratory methods are used in combination with analytic epidemiology to identify at the biochemical or molecular level specific exogenous and/or host factors that play a role in human cancer causation (Perera and Weinstein 1982).

However, the terminology has been criticized by some:

The term ‘molecular epidemiology’ may suggest the existence of a sub-discipline with substantive new research content. Molecular techniques, however, are directed principally at enhancing the measurement of exposure, effect, or susceptibility, and not at formulating new etiologic hypotheses. As techniques of refinement and elaboration, the integration of molecular measures into mainstream epidemiologic research can offer higher resolution answers in relation to disease causation’ (McMichael 1994).

In this book, many examples are drawn from cancer epidemiology but there is also reference to other chronic degenerative conditions, including vascular disease, diabetes and neurodegenerative disease, which share some of the challenges with cancer in terms of establishing causality. In contrast, we have not included specific emphasis on infectious disease, where the term ‘molecular epidemiology’ was introduced many years earlier than in the cancer epidemiology field.

The goals of the new discipline of molecular epidemiology are the same as those suggested by the few examples above. First of all, to contribute to better estimation of exposure, including ‘internal’ exposure, through the measurement of end-points, such as chemical metabolites and adducts (e.g. haemoglobin adducts for acrylamide, DNA adducts for PAHs). Second, genetic susceptibility, emerged as became an important subject for enquiry, since it became clear that between exposure and effect there was a layer of metabolic reactions, including activation, deactivation and DNA repair, which affected the dose-response relationship in a fashion analogous to other susceptibility factors, such as age, sex and nutritional status. A further goal of epidemiology is to reduce disease burden by identification of risk factors for disease. It took a long time to discover the association between aromatic amines and bladder cancer, and thus hundreds of workers died from causes that could have been avoided. One limitation therefore of cancer epidemiology is the long latency period (decades) after exposure starts and before the disease is clinically diagnosed. For this reason, epidemiologists have been searching for early lesions that could be reasonably used as surrogates of the risk of cancer. Chromosome aberrations, gene mutations and, more recently, gene expression and epigenetics have been introduced as intermediate markers in the pathway that leads from exposure to overt disease, thus adding to the categories of exposure and susceptibility biomarkers mentioned above. The goal of all these efforts in biomarker development is to allow faster and earlier detection of disease in individuals, as well as to shorten the time needed to identify possible human carcinogens.

Molecular epidemiology studies will normally employ a number of tools for the measurement of exposure, susceptibility and disease, e.g. questionnaires, job-exposure matrices, data from environmental monitoring, routinely collected health data and biomarkers. The biomarkers have certain properties which influence their application. For example, some biomarkers of exposure may only relate to the recent past, whilst the level of others may be affected by the presence of disease (reverse causation). Consequently, careful consideration of the properties of the biomarkers is needed in relation to how they are to be applied from the point of study design through to data analysis. This book therefore begins (Chapters 2-5) by discussing study design, particularly in light of the complex interplay between the environment and genes, thus laying a foundation for the subsequent parts of the book.

Molecular epidemiology has had several success stories (see Table 1.1) in all three categories of biomarkers - exposure, susceptibility and early response. Biomarkers of exposure can make a significant contribution to establishing disease aetiology. For example, aflatoxins were structurally identified in 1963 and shown to be liver carcinogens in animals shortly afterwards. However, despite evidence from ecological studies, it was only with the development of biomarkers of individual exposure that convincing evidence of the association with human liver cancer risk was obtained in a prospective cohort study in China, published in 1992 (IARC 1993). In the case of Helicobacter pylori and gastric cancer, the time between identification of the pathogen and evaluation as a human carcinogen by IARC was around 10 years; this was in no small part due to the early availability of serum markers (antibodies to bacterial antigens) to establish exposure status (IARC 1994). The latter example also demonstrates the value of being able to establish long-term past exposure to putative risk factors, something which has been easier with infectious agents than with chemical exposures. The development and validation of biomarkers of exposure to environmental risk factors therefore remains an outstanding challenge to molecular epidemiology (Vineis 2004; Wild 2005). The principles of biomarker development, validation and application are discussed by Vineis and Garte and by Nieuwenhuijsen in Chapters 6 and 7, followed by a number of examples of categories of biomarker described in chapters by Hecht, Phillips, and Berwick and Albertini (Chapters 8-10).

Table 1.1 Discoveries that support the original model of molecular epidemiology1

Marker linked to exposure or diseaseExposureReferenceExposure/biologically effective doseDNA adductsPAHs, aromatic compoundsTang et al. 2001 AFB1Ross et al. 1992Albumin adductsAFB1Wang et al. 1996 Gong et al. 2002Haemoglobin adductsAcrylamide Styrene 1,3-ButadieneHagmar et al. 2005 Vodicka et al. 2003 Albertini et al. 2001Preclinical effect (exposure and/or cancer)Chromosome aberrationsLung Leukaemia BenzeneBonassi et al. 2004 Smith et al. 2005 Holeckova et al. 2004HPRTPAHs 1,3-ButadienePerera et al. 2002 Ammenheuser et al. 2001Glycophorin APAHsLee et al. 2002Gene expressionCisplatinGwosdz et al. 2005Genetic susceptibilityPhenotypic markersSeveral cancersBerwick and Vineis 2000 Wei, Spitz et al. 2000SNPs NAT2, GSTM1 CYP1A1Bladder LungGracia Closas 2005 Vineis et al. 2003

1Perera and Weinstein 1982; NRC 1987.

Identification of genetic susceptibility to environmental exposures due to low penetrance alleles continues to occupy the attention of many researchers in the field of molecular epidemiology. Indeed, with the arrival of the polymerase chain reaction (PCR), the facile genotyping for single nucleotide polymorphisms (SNPs) and the ease of application to case-control studies, there has at times appeared to be an imbalance of effort between the development of biomarkers for environmental exposure assessment and genotyping (Wild 2005). The problems of genetic association studies comprising small subject numbers and focusing on single polymorphisms in genes involved in complex pathways has been discussed (Rebbeck et al. 2004). These limitations are partially overcome by systematic reviews and meta-analyses (see Ragin and Taioli, Chapter 15). However, the Human Genome Project and subsequent programmes to comprehensively catalogue SNPs have spawned a new generation of large genome-wide association studies with the statistical power to identify at-risk genotypes in a more reliable manner (Scott et al. 2007; Zeggini et al. 2007). The ability to study the role of genetic susceptibility to environmental risk factors may help to reveal those underlying environmental factors that only entail a risk in a subset of the population.

Approaches to large-scale genotyping, together with the potential pitfalls, are addressed by Dunning and Luccarini in Chapter 11. At the same time, other ‘-omics’ technologies are beginning to find application in population studies and to generate novel hypotheses about disease mechanisms as well as candidate biomarkers. Bictash and colleagues in Chapter 13 describe the contribution of metabonomics in elucidating key pathways affected by both exposure and disease development, whilst Keen and Findlay in Chapter 12 provide a similar introduction to the application of proteomics to epidemiological studies.

The introduction of genomics, proteomics and metabonomics represents one dimension in which the volume of data from molecular epidemiology studies is growing at a startling pace. In addition, in order to unravel the complex interplay between environmental and genetic factors in disease aetiology, often involving modest elevated risks (1.5-fold or less) associated with a particular risk factor, epidemiologists are turning to studies encompassing larger and larger numbers of subjects. This may be, for example, through consortium-led multicentre case-control studies or large prospective cohort studies entailing biological banks of samples, such as EPIC or UK Biobank. The complexity of these datasets demands increasingly sophisticated biostatistical approaches to derive meaningful conclusions. Several chapters in this book therefore address the analysis of molecular epidemiological studies (Chapters 14-17). An equally important element to the future exploitation of these large datasets is the ability to access and process information through bioinformatics (Moore, Chapter 18).

Epidemiology ultimately aims to lead to a reduced health burden through disease prevention. In Chapters 19 (Bertazzi and Mutti) and 20 (Ferguson) the application of molecular epidemiology to regulatory decision making and in the evaluation of intervention strategies are considered. Some practical issues of sample collection and processing are covered in Chapter 21 (Caboux and colleagues). The collection of biological specimens and the derived genetic and other molecular or biochemical measurements represent sensitive information, with concerns over exploitation of genetic data, e.g. in terms of access to insurance or employment. These issues become even more complex when studies are multicentre and involve collaborations across international borders, where ethical regulations may differ significantly. Some of the ethical considerations are discussed in this volume by Vähäkangas (Chapter 22).

The purpose of this handbook is both to show the potential applications of laboratory techniques to tackle epidemiological problems and to consider the limitations of laboratory work, including the sources of uncertainty and inaccuracy. Issues such as reliability (compared to traditional epidemiological methods, involving questionnaires) and the timing of exposures are also explored. The latter part of the book comprises four chapters (Chapters 23-26) which take individual examples of research areas where molecular epidemiology has led to advances in understanding as well as serving to illustrate some of the limitations of the approach. These final chapters emphasise the main focus of the book, which is on practical and applied aspects of molecular epidemiology, as actually used in the study of human exposure and disease.

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2

Study Design

Paolo Vineis

Imperial College of Science, Technology and Medicine, London, and University of Torino, Italy

2.1. INTRODUCTION: STUDY DESIGN AT SQUARE ONE1

Usually when we design an epidemiological study, we start with an aetiological hypothesis. The hypothesis can come from the observation of a cluster of disease, or a trend, or a peculiar geographic distribution, or observations made by clinicians, or experimental data in animals. For example, case series documenting ‘larger-than-expected’ numbers of haematopoietic cancers among shoe workers in Italy (Vigliani and Saita 1964) and Turkey (Aksoy 1974) provided important clues on the role of benzene in leukaemogenesis. However, due to the lack of information on the population at risk and disease rates in a comparison population, the magnitude of association between benzene exposure and haematopoietic cancers could not be assessed on this basis only.

The study design aims essentially at answering two types of questions:

1. Contributing to the identification of causeeffect relationships between exposure to putative risk/preventive factors and disease (although causality can be inferred only with a complex reasoning that usually involves also other types of evidence).

2. Measuring the exposure-disease association (strength, dose-response, population impact).

A first crucial distinction is between observational studies, in which the exposure is not manipulated by the investigator, and experimental studies, in which it is. It is usually thought that experimental studies (e.g. randomized controlled trials) are a better study design than simply observational studies, because they allow better control of confounding, thanks to random allocation of potential confounders. The philosopher Popper thought that only experiments allow us to make scientific causal statements, and therefore astronomy would not be a science, certainly an extreme point of view.

Confounders are variables other than the one under study that can provide an alternative explanation for the observed association. For example, exposure to benzene of shoe workers might be associated with other chemical exposures that could turn out to be the real cause of leukaemia. Although the randomized trial may be the strongest design to show causal links, for ethical reasons we cannot randomize exposure to suspect carcinogens, and we have to live with observational epidemiology.