Die Epidemiologie ist zunehmend angewiesen auf laborgenerierte Biomarker-Daten, und das Verst ndnis von Krankheitsmechanismen ist von steigender Bedeutung f r die Erl uterungen der tiologie. Deshalb bedarf es eines Buches, das die disziplin ren Grenzen berbr ckt und allen relevanten Forschungsgruppen (Laborwissenschaftlern, Epidemiologen, klinischen Forschern und Statistikern) zug nglich ist. Zurzeit gibt es kein Werk mit einem expliziten Praxisschwerpunkt, das diese Bandbreite bietet. Diese L cke f llt das vorliegende Handbuch. Es gibt Epidemiologen Einblick in die Forschung, insbesondere zu Biomarkern, und erm glicht es Laborwissenschaftlern, die Kernpunkte epidemiologischer Studien zu verstehen. Au erdem stellt es ein Tool f r einen Kurs in molekularer Epidemiologie bereit. Einzelne Kapitel widmen sich neuen Technologien (Genomforschung, Proteomik, Metabonomik) sowie Herangehensweisen der Statistik und der Bioinformatik, die aufgrund der zu bew ltigenden Datenmenge unerl sslich sind.
Contributors.
Artist statement.
Acknowledgements.
1. Introduction: why molecular epidemiology (Chris Wild, Seymour Garte and Paolo Vineis).
References.
2. Study design (Paolo Vineis).
2.1. Introduction: study design at square one.
2.2. Epidemiological measures.
2.3. Bias.
Bias in screening practices.
2.4. More on confounding.
2.5. Specificities of molecular epidemiology design.
Special designs in molecular epidemiology.
Bias.
Selection bias related to sample collection.
Confounding and population admixture.
Mendelian randomization.
2.6. Conclusions.
References.
Essential reading.
3. Molecular epidemiological studies that can be nested within cohorts (Andrew Rundle and Habibul Ahsan).
3.1. Introduction.
3.2. Case-cohort studies.
Design and calculable measures of effect.
Case-cohort designs offer flexibility.
Analytical complexity.
3.3. Nested case-control studies.
Design and calculable measures of effect.
Matching.
Counter-matching.
Individuals may be included in the analyses multiple times.
3.4. Considerations regarding biomarker analyses in case-cohort and nested case-control studies.
Batch effects.
Batch effects and case-cohort studies.
Batch effects and nested case-control studies.
Storage effects.
Storage effects and case-cohort studies.
Storage effects and nested case-control studies.
Freeze-thaw cycles.
Freeze-thaw cycles and case-cohort studies.
Freeze-thaw cycles and nested case-control studies.
3.5. Conclusion.
References.
4. Family studies, haplotypes and gene association studies (Jennifer H Barrett, D. Timothy Bishop and Mark M. Iles).
4.1. Introduction.
4.2. Family studies.
Is there an increased risk of disease in relatives of cases?.
Is the familial aggregation due to genes or environment?.
What is the genetic mechanism?.
Where is the gene?.
Linkage analysis.
Example.
4.3. Genetic association studies.
Genetic case-control studies.
Bias and confounding.
Family-based study designs.
Haplotypes.
Reconstructing haplotypes.
Association studies with haplotypes.
SNP selection.
Whole-genome association studies.
4.4. Discussion.
References.
5. Individual susceptibility and gene-environment interaction (Seymour Garte).
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 (Paolo Vineis and Seymour Garte).
6.1. Validity and reliability.
6.2. Biomarker variability.
6.3. Measurement of variation.
6.4. Other issues of validation.
6.5. Measurement error.
Sources of laboratory measurement error.
6.6. Blood collection for biomarkers.
6.7. Validation of high-throughput techniques.
References.
7. Exposure assessment (Mark J Nieuwenhuijsen).
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 (Stephen S. Hecht).
8.1. Introduction.
8.2. Overview of carcinogen metabolism.
8.3. Examples of carcinogen metabolite biomarkers.
Total NNAL (NNAL plus its glucuronides): an established biomarker of exposure to the tobacco-specific lung carcinogen NNK.
Phenanthrene metabolites: developing biomarkers of PAH exposure and metabolism.
Other examples of carcinogen metabolite biomarkers.
8.4. Summary.
Acknowledgement.
References.
9. Biomarkers of exposure: adducts (David H. Phillips).
9.1. Introduction.
9.2. Methods for adduct detection.
9.3. Adducts identified in human tissue.
Sources of DNA for biomonitoring.
9.4. Adducts as biomarkers of occupational and environmental exposure to carcinogens.
9.5. Smoking-related adducts.
9.6. DNA adducts in prospective studies.
DNA adducts in human DNA repair studies.
Correlations between DNA and protein adducts.
9.7. Conclusions.
References.
10. Biomarkers of mutation and DNA repair capacity (Marianne Berwick and Richard Albertini).
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.
Cellular biomarkers for DNA repair.
Assays based on induced DNA damage.
Mutagen sensitivity assay.
Comet assay.
Unscheduled DNA synthesis (UDS).
Host cell reactivation assay.
Other assays.
OGG activity.
Combination studies.
10.7. Integration of DNA repair assays into epidemiological studies.
Study design.
Assay variability.
Biological plausibility.
10.8. Genetic markers for DNA repair capacity.
References.
11. High-throughput techniques - genotyping and genomics (Alison M. Dunning and Craig Luccarini).
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.
DNA extraction and normalization.
DNA arraying.
Robotics and plate sealing.
QC steps.
11.8. After the association has been proved - what next?.
References.
12. Proteomics and molecular epidemiology (Jeff N. Keen and John B. C. Findlay).
12.1. Introduction.
12.2. General considerations.
12.3. Sample selection.
12.4. Proteomics technologies.
Protein identification using mass spectrometry.
Sample fractionation.
Sample enrichment.
Sample depletion.
Quantification of proteins and peptides.
Validation.
12.5. Illustrative applications.
12.6. Final considerations.
References.
13. Exploring the contribution of metabolic profiling to epidemiological studies (M. Bictash, E. Holmes, H. Keun, P. Elliott and J. K. Nicholson).
13.1. Background.
13.2. Cancer.
13.3. Cardiovascular disease.
13.4. Neurodegenerative disorders.
13.5. The way forward.
Acknowledgements.
References.
14. Univariate and multivariate data analysis (Yu-Kang Tu and Mark S Gilthorpe).
14.1. Introduction.
Overview.
Terminology and definitions.
A priori model assumptions.
Initial data exploration.
14.2. Univariate analysis.
Simple linear regression.
Multiple linear regression.
Path diagram.
Simple linear regression.
Multiple linear regression.
14.3. Generalized linear modelS.
14.4. Multivariate methods.
Multilevel modelling (MLM).
Structural equation modelling (SEM).
Latent growth curve modelling (LGCM).
Flexibility of LGCM.
14.5. Conclusions.
Acknowledgements.
References.
15. Meta-analysis and pooled analysis - genetic and environmental data (Camille Ragin and Emanuela Taioli).
15.1. Introduction.
15.2. Meta analysis.
Database searching, eligibility criteria and data extraction.
Graphical summaries.
Summary estimates and assessment of heterogeneity.
Assessing publication bias.
15.3. Pooled analysis.
15.4. Issues in pooled analysis of epidemiological studies involving molecular markers.
Choice of study design.
Planning of the study.
Selection of studies.
Data request.
Evaluation of the validity of the study.
Data standardization.
Heterogeneity among studies.
Publication bias.
Ethical issues.
References.
16. Analysis of complex datasets (Jason H. Moore, Margaret R. Karagas and Angeline S. Andrew).
16.1. Introduction.
16.2. Gene-environment interaction.
16.3. Gene-gene interaction.
16.4. Statistical interaction.
Detecting statistical patterns of interaction.
Decision trees, classification trees and random forests.
Multifactor dimensionality reduction (MDR).
Statistical interpretation of interaction models.
16.5. Case study: bladder cancer.
16.6. Genome-wide analysis.
A filter strategy for genome-wide analysis.
A wrapper strategy for genome-wide analysis.
16.7. Summary.
Acknowledgements.
References.
17. Some implications of random exposure measurement errors in occupational and environmental epidemiology (. S. M. Rappaport and L. L. Kupper).
17.1. Introduction.
17.2. Individual-based study.
Regression analysis.
Estimating sample sizes for a specified bias.
17.3. Group-based studies.
Exposure model with a random group effect.
Health-outcome model.
Regression analysis.
Estimating sample sizes.
Adjusting estimated regression coefficients for attenuation bias.
17.4. Comparing biases for individual-based and group-based studies.
17.5. Conclusions.
Acknowledgement.
References.
18. Bioinformatics (Jason H. Moore).
18.1. Introduction.
18.2. Database resources.
18.3. Data analysis.
Data mining using R.
Data mining using Weka.
Data mining using Orange.
Data mining using multifactor dimensionality reduction.
Interpreting data mining results.
18.4. The future.
Acknowledgements.
References.
19. Biomarkers, disease mechanisms and their role in regulatory decisions (Pier Alberto Bertazzi and Antonio Mutti).
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 (Lynnette R. Ferguson).
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.
Oxidative stress.
Inflammation.
Cancer.
Cardiovascular disease.
Diabetes.
20.6. Biomarkers that predict the optimization of health or performance.
20.7. Conclusions.
References.
21. Biological resource centres in molecular epidemiology: collecting, storing and analysing biospecimens (Elodie Caboux, Pierre Hainaut and Emmanuelle Gormally).
21.1. Introduction.
21.2. Obtaining and collecting biospecimens.
Planning a collection.
Types of biospecimens.
Collecting biospecimens.
21.3. Annotating, storing and processing biospecimens.
Identifying biospecimens.
Storage facilities.
Labelling.
Conditions of storage.
Laboratory processing and shipping.
BRC database.
Quality assurance and quality control.
21.4. Analysing biomarkers.
21.5. Conclusions.
References.
22. Molecular epidemiology and ethics: biomarkers for disease susceptibility (Kirsi Vähäkangas).
22.1. Introduction.
22.2. Ethical aspects in biomarker development for disease susceptibility.
Variation of biomarkers.
Ethical aspects of genetic biomarkers for susceptibility.
Quality of research.
22.3. Ethical aspects of biobanking.
Management of biobanks.
Consent practice.
Storage and distribution of samples and data.
Populations, individuals and autonomy.
22.4. Molecular epidemiology and society.
Science, money and public trust.
Communication.
Education in ethics.
22.5. Conclusions.
Acknowledgements.
References.
23. Biomarkers for dietary carcinogens: the example of heterocyclic amines in epidemiological studies (Rashmi Sinha, Amanda Cross and Robert J. Turesky).
23.1. Introduction.
23.2. Intake assessment of HCAs.
23.3. HCA metabolism.
Urinary biomarkers as a measure of internal exposure.
Adducts as a measure of biologically effective dose.
23.4. Conclusions and future research.
References.
24. Practical examples 2: hormones (Sabina Rinaldi and Rudolf Kaaks).
24.1. Introduction.
24.2. Hormone measurements for large-scale epidemiological studies.
24.3. Laboratory methods.
Reference methods.
Direct immunoassays.
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 (J. D. Groopman, T. W. Kensler and C. P. Wild).
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.
Early aetiological studies of aflatoxin, HBV and HCC.
Development of methodologies for measuring biomarkers.
Relationship of aflatoxin biomarkers to exposure and disease in experimental animals.
Modulation of biomarkers and disease in animal chemoprevention studies.
Validation of aflatoxin biomarkers in cross-sectional studies in human populations.
Longitudinal study of biomarkers in humans.
Case-control and cohort studies.
Clinical trials for reducing aflatoxin exposure and internal dose.
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.
Acknowledgements.
References.
26. Complex exposures - air pollution (Steffen Loft, Elvira Vaclavik Bräuner, Lykke Forchhammer, Marie Pedersen, Lisbeth E. Knudsen and Peter Møller)).
26.1. Introduction.
26.2. Personal monitoring of external dose.
26.3. Biomarkers of internal dose and air pollutants.
PAH metabolites.
Benzene and metabolites.
26.4. Biomarkers of biologically effective dose.
Mechanisms of oxidative stress induced by particles and other air pollutants.
Oxidative stress-induced DNA damage and repair.
Air pollution exposure and biomarkers of oxidative stress and DNA damage.
26.5. Biomarkers of biological effects.
Inflammation.
Cell damage.
Gene expression.
Cytogenetic markers.
Mutations.
26.6. Genetic susceptibility and oxidative stress related to air pollution.
26.7. Conclusion.
Acknowledgements.
References.
Index.