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Stannah J, Flores Anato JL, Pickles M, Larmarange J, Mitchell KM, Artenie A, Dumchev K, Niangoran S, Platt L, Terris-Prestholt F, Singh A, Stone J, Vickerman P, Phillips A, Johnson L, Maheu-Giroux M, Boily MC. From conceptualising to modelling structural determinants and interventions in HIV transmission dynamics models: a scoping review and methodological framework for evidence-based analyses. BMC Med 2024; 22:404. [PMID: 39300441 PMCID: PMC11414142 DOI: 10.1186/s12916-024-03580-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 08/22/2024] [Indexed: 09/22/2024] Open
Abstract
BACKGROUND Including structural determinants (e.g. criminalisation, stigma, inequitable gender norms) in dynamic HIV transmission models is important to help quantify their population-level impacts and guide implementation of effective interventions that reduce the burden of HIV and inequalities thereof. However, evidence-based modelling of structural determinants is challenging partly due to a limited understanding of their causal pathways and few empirical estimates of their effects on HIV acquisition and transmission. METHODS We conducted a scoping review of dynamic HIV transmission modelling studies that evaluated the impacts of structural determinants, published up to August 28, 2023, using Ovid Embase and Medline online databases. We appraised studies on how models represented exposure to structural determinants and causal pathways. Building on this, we developed a new methodological framework and recommendations to support the incorporation of structural determinants in transmission dynamics models and their analyses. We discuss the data and analyses that could strengthen the evidence used to inform these models. RESULTS We identified 17 HIV modelling studies that represented structural determinants and/or interventions, including incarceration of people who inject drugs (number of studies [n] = 5), violence against women (n = 3), HIV stigma (n = 1), and housing instability (n = 1), among others (n = 7). Most studies (n = 10) modelled exposures dynamically. Almost half (8/17 studies) represented multiple exposure histories (e.g. current, recent, non-recent exposure). Structural determinants were often assumed to influence HIV indirectly by influencing mediators such as contact patterns, condom use, and antiretroviral therapy use. However, causal pathways' assumptions were sometimes simple, with few mediators explicitly represented in the model, and largely based on cross-sectional associations. Although most studies calibrated models using HIV epidemiological data, less than half (7/17) also fitted or cross-validated to data on the prevalence, frequency, or effects of exposure to structural determinants. CONCLUSIONS Mathematical models can play a crucial role in elucidating the population-level impacts of structural determinants and interventions on HIV. We recommend the next generation of models reflect exposure to structural determinants dynamically and mechanistically, and reproduce the key causal pathways, based on longitudinal evidence of links between structural determinants, mediators, and HIV. This would improve the validity and usefulness of predictions of the impacts of structural determinants and interventions.
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Affiliation(s)
- James Stannah
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, Canada
| | - Jorge Luis Flores Anato
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, Canada
| | - Michael Pickles
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
- HPTN Modelling Centre, Imperial College London, London, UK
| | - Joseph Larmarange
- Centre Population et Développement, Institut de Recherche pour le Développement, Université Paris Cité, Inserm, Paris, France
| | - Kate M Mitchell
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
- Department of Nursing and Community Health, Glasgow Caledonian University, London, UK
| | - Adelina Artenie
- Population Health Sciences, University of Bristol, Bristol, UK
| | | | - Serge Niangoran
- Programme PAC-CI, CHU de Treichville, Site ANRS, Abidjan, Côte d'Ivoire
| | - Lucy Platt
- Department of Social and Environmental Health Research, London School of Hygiene and Tropical Medicine Faculty of Public Health and Policy, London, UK
| | | | - Aditya Singh
- The Johns Hopkins University School of Medicine, Delhi, India
| | - Jack Stone
- Population Health Sciences, University of Bristol, Bristol, UK
| | - Peter Vickerman
- Population Health Sciences, University of Bristol, Bristol, UK
| | - Andrew Phillips
- Institute for Global Health, University College London, London, UK
| | - Leigh Johnson
- Centre for Infectious Disease Epidemiology and Research, University of Cape Town, Cape Town, South Africa
| | - Mathieu Maheu-Giroux
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, Canada
| | - Marie-Claude Boily
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK.
- HPTN Modelling Centre, Imperial College London, London, UK.
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A new trajectory approach for investigating the association between an environmental or occupational exposure over lifetime and the risk of chronic disease: Application to smoking, asbestos, and lung cancer. PLoS One 2020; 15:e0236736. [PMID: 32785269 PMCID: PMC7423115 DOI: 10.1371/journal.pone.0236736] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 07/12/2020] [Indexed: 11/19/2022] Open
Abstract
Quantifying the association between lifetime exposures and the risk of developing a chronic disease is a recurrent challenge in epidemiology. Individual exposure trajectories are often heterogeneous and studying their associations with the risk of disease is not straightforward. We propose to use a latent class mixed model (LCMM) to identify profiles (latent classes) of exposure trajectories and estimate their association with the risk of disease. The methodology is applied to study the association between lifetime trajectories of smoking or occupational exposure to asbestos and the risk of lung cancer in males of the ICARE population-based case-control study. Asbestos exposure was assessed using a job exposure matrix. The classes of exposure trajectories were identified using two separate LCMM for smoking and asbestos, and the association between the identified classes and the risk of lung cancer was estimated in a second stage using weighted logistic regression and all subjects. A total of 2026/2610 cases/controls had complete information on both smoking and asbestos exposure, including 1938/1837 cases/controls ever smokers, and 1417/1520 cases/controls ever exposed to asbestos. The LCMM identified four latent classes of smoking trajectories which had different risks of lung cancer, all much stronger than never smokers. The most frequent class had moderate constant intensity over lifetime while the three others had either long-term, distant or recent high intensity. The latter had the strongest risk of lung cancer. We identified five classes of asbestos exposure trajectories which all had higher risk of lung cancer compared to men never occupationally exposed to asbestos, whatever the dose and the timing of exposure. The proposed approach opens new perspectives for the analyses of dose-time-response relationships between protracted exposures and the risk of developing a chronic disease, by providing a complete picture of exposure history in terms of intensity, duration, and timing of exposure.
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Jia P, Lakerveld J, Wu J, Stein A, Root ED, Sabel CE, Vermeulen R, Remais JV, Chen X, Brownson RC, Amer S, Xiao Q, Wang L, Verschuren WMM, Wu T, Wang Y, James P. Top 10 Research Priorities in Spatial Lifecourse Epidemiology. ENVIRONMENTAL HEALTH PERSPECTIVES 2019; 127:74501. [PMID: 31271296 PMCID: PMC6791465 DOI: 10.1289/ehp4868] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 06/07/2019] [Accepted: 06/14/2019] [Indexed: 05/21/2023]
Abstract
The International Initiative on Spatial Lifecourse Epidemiology (ISLE) convened its first International Symposium on Lifecourse Epidemiology and Spatial Science at the Lorentz Center in Leiden, Netherlands, 16–20 July 2018. Its aim was to further an emerging transdisciplinary field: Spatial Lifecourse Epidemiology. This field draws from a broad perspective of scientific disciplines including lifecourse epidemiology, environmental epidemiology, community health, spatial science, health geography, biostatistics, spatial statistics, environmental science, climate change, exposure science, health economics, evidence-based public health, and landscape ecology. The participants, spanning 30 institutions in 10 countries, sought to identify the key issues and research priorities in spatial lifecourse epidemiology. The results published here are a synthesis of the top 10 list that emerged out of the discussion by a panel of leading experts, reflecting a set of grand challenges for spatial lifecourse epidemiology in the coming years. https://doi.org/10.1289/EHP4868.
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Affiliation(s)
- Peng Jia
- GeoHealth Initiative, Department of Earth Observation Science, Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, Enschede, Netherlands
- International Initiative on Spatial Lifecourse Epidemiology (ISLE)
| | - Jeroen Lakerveld
- International Initiative on Spatial Lifecourse Epidemiology (ISLE)
- Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands
- Global Geo Health Data Center, Utrecht University, Utrecht, Netherlands
| | - Jianguo Wu
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
- School of Sustainability and Julie A. Wrigley Global Institute of Sustainability, Arizona State University, Tempe, Arizona, USA
- Center for Human-Environment System Sustainability, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China
| | - Alfred Stein
- GeoHealth Initiative, Department of Earth Observation Science, Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, Enschede, Netherlands
- International Initiative on Spatial Lifecourse Epidemiology (ISLE)
| | - Elisabeth D. Root
- Department of Geography, Ohio State University, Columbus, Ohio, USA
- Division of Epidemiology, Ohio State University, Columbus, Ohio, USA
| | - Clive E. Sabel
- Department of Environmental Science, Aarhus University, Aarhus, Denmark
- Big Data Center for Environment and Health, Aarhus University, Aarhus, Denmark
| | - Roel Vermeulen
- International Initiative on Spatial Lifecourse Epidemiology (ISLE)
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands
| | - Justin V. Remais
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, California, USA
| | - Xi Chen
- International Initiative on Spatial Lifecourse Epidemiology (ISLE)
- Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut, USA
- Yale Climate Change and Health Initiative, New Haven, Connecticut, USA
| | - Ross C. Brownson
- Prevention Research Center in St. Louis, Brown School at Washington University in St. Louis, St. Louis, Missouri, USA
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, Missouri, USA
- Alvin J. Siteman Cancer Center, Barnes-Jewish Hospital and Washington University School of Medicine, Missouri, USA
| | - Sherif Amer
- International Initiative on Spatial Lifecourse Epidemiology (ISLE)
- Department of Urban and Regional Planning and Geo-information Management, ITC, University of Twente, Enschede, Netherlands
| | - Qian Xiao
- International Initiative on Spatial Lifecourse Epidemiology (ISLE)
- Department of Health and Human Physiology, University of Iowa, Iowa City, Iowa, USA
- Department of Epidemiology, University of Iowa, Iowa City, Iowa, USA
| | - Limin Wang
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - W. M. Monique Verschuren
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands
- Centre for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment, Bilthoven, Netherlands
| | - Tong Wu
- International Initiative on Spatial Lifecourse Epidemiology (ISLE)
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China
| | - Youfa Wang
- International Initiative on Spatial Lifecourse Epidemiology (ISLE)
- Fisher Institute of Health and Well-Being, College of Health, Ball State University, Muncie, Indiana, USA
- Department of Nutrition and Health Sciences, College of Health, Ball State University, Muncie, Indiana, USA
- Global Health Institute; Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, China
| | - Peter James
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA
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Lévêque E, Lacourt A, Luce D, Sylvestre MP, Guénel P, Stücker I, Leffondré K. Time-dependent effect of intensity of smoking and of occupational exposure to asbestos on the risk of lung cancer: results from the ICARE case-control study. Occup Environ Med 2018; 75:586-592. [PMID: 29777039 DOI: 10.1136/oemed-2017-104953] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Revised: 03/28/2018] [Accepted: 04/27/2018] [Indexed: 11/03/2022]
Abstract
OBJECTIVE To estimate the impact of intensity of both smoking and occupational exposure to asbestos on the risk of lung cancer throughout the whole exposure history. METHODS Data on 2026 male cases and 2610 male controls came from the French ICARE (Investigation of occupational and environmental causes of respiratory cancers) population-based, case-control study. Lifetime smoking history and occupational history were collected from standardised questionnaires and face-to-face interviews. Occupational exposure to asbestos was assessed using a job exposure matrix. The effects of annual average daily intensity of smoking (reported average number of cigarettes smoked per day) and asbestos exposure (estimated average daily air concentration of asbestos fibres at work) were estimated using a flexible weighted cumulative index of exposure in logistic regression models. RESULTS Intensity of smoking in the 10 years preceding diagnosis had a much stronger association with the risk of lung cancer than more distant intensity. By contrast, intensity of asbestos exposure that occurred more than 40 years before diagnosis had a stronger association with the risk of lung cancer than more recent intensity, even if intensity in the 10 years preceding diagnosis also had a significant effect. CONCLUSION Our results illustrate the dynamic of the effect of intensity of both smoking and occupational exposure to asbestos on the risk of lung cancer. They confirm that the timing of exposure plays an important role, and suggest that standard analytical methods assuming equal weights of intensity over the whole exposure history may be questionable.
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Affiliation(s)
- Emilie Lévêque
- Université de Bordeaux, ISPED, INSERM, Bordeaux Population Health Research Center, Team Biostatistics, UMR 1219, Bordeaux, France.,Université de Bordeaux, INSERM, Bordeaux Population Health Research Center, Team EPICENE, UMR 1219, Bordeaux, France
| | - Aude Lacourt
- Université de Bordeaux, INSERM, Bordeaux Population Health Research Center, Team EPICENE, UMR 1219, Bordeaux, France
| | - Danièle Luce
- Université de Rennes, INSERM, EHESP, IRSET (Institut de recherche en santé, environnement et travail), UMR_S 1085, Pointe-à-Pitre, France
| | - Marie-Pierre Sylvestre
- Department of Social and Preventive Medicine, Montreal School of Public Health (ESPUM), University of Montreal, Montreal, Quebec, Canada.,Research Center, University of Montreal Health Center (CRCHUM), Montreal, Quebec, Canada
| | - Pascal Guénel
- INSERM, CESP, Cancer and Environment Team, Université Paris Saclay, Université de Paris-Sud, UVSQ, Villejuif, France
| | - Isabelle Stücker
- INSERM, CESP, Cancer and Environment Team, Université Paris Saclay, Université de Paris-Sud, UVSQ, Villejuif, France
| | - Karen Leffondré
- Université de Bordeaux, ISPED, INSERM, Bordeaux Population Health Research Center, Team Biostatistics, UMR 1219, Bordeaux, France
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5
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Peters S, Vermeulen R, Portengen L, Olsson A, Kendzia B, Vincent R, Savary B, Lavoué J, Cavallo D, Cattaneo A, Mirabelli D, Plato N, Fevotte J, Pesch B, Brüning T, Straif K, Kromhout H. SYN-JEM: A Quantitative Job-Exposure Matrix for Five Lung Carcinogens. THE ANNALS OF OCCUPATIONAL HYGIENE 2016; 60:795-811. [PMID: 27286764 DOI: 10.1093/annhyg/mew034] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2015] [Accepted: 05/12/2016] [Indexed: 03/25/2024]
Abstract
OBJECTIVE The use of measurement data in occupational exposure assessment allows more quantitative analyses of possible exposure-response relations. We describe a quantitative exposure assessment approach for five lung carcinogens (i.e. asbestos, chromium-VI, nickel, polycyclic aromatic hydrocarbons (by its proxy benzo(a)pyrene (BaP)) and respirable crystalline silica). A quantitative job-exposure matrix (JEM) was developed based on statistical modeling of large quantities of personal measurements. METHODS Empirical linear models were developed using personal occupational exposure measurements (n = 102306) from Europe and Canada, as well as auxiliary information like job (industry), year of sampling, region, an a priori exposure rating of each job (none, low, and high exposed), sampling and analytical methods, and sampling duration. The model outcomes were used to create a JEM with a quantitative estimate of the level of exposure by job, year, and region. RESULTS Decreasing time trends were observed for all agents between the 1970s and 2009, ranging from -1.2% per year for personal BaP and nickel exposures to -10.7% for asbestos (in the time period before an asbestos ban was implemented). Regional differences in exposure concentrations (adjusted for measured jobs, years of measurement, and sampling method and duration) varied by agent, ranging from a factor 3.3 for chromium-VI up to a factor 10.5 for asbestos. CONCLUSION We estimated time-, job-, and region-specific exposure levels for four (asbestos, chromium-VI, nickel, and RCS) out of five considered lung carcinogens. Through statistical modeling of large amounts of personal occupational exposure measurement data we were able to derive a quantitative JEM to be used in community-based studies.
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Affiliation(s)
- Susan Peters
- 1.Environmental Epidemiology Division, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands; 2.Occupational Respiratory Epidemiology, School of Population Health, University of Western Australia, Perth, Australia;
| | - Roel Vermeulen
- 1.Environmental Epidemiology Division, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands; 3.Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands
| | - Lützen Portengen
- 1.Environmental Epidemiology Division, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
| | - Ann Olsson
- 4.International Agency for Research on Cancer, Lyon, France
| | - Benjamin Kendzia
- 5.Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Rurh-Universität Bochum, Bochum, Germany
| | - Raymond Vincent
- 6.Institut National de Recherche et de Sécurité, Vandoeuvre lès Nancy, France
| | - Barbara Savary
- 6.Institut National de Recherche et de Sécurité, Vandoeuvre lès Nancy, France
| | - Jérôme Lavoué
- 7.Research Centre of University of Montreal Hospital Research Centre, Canada
| | - Domenico Cavallo
- 8.Department of Science and High Technology, Università degli Studi dell'Insubria, Como, Italy
| | - Andrea Cattaneo
- 8.Department of Science and High Technology, Università degli Studi dell'Insubria, Como, Italy
| | - Dario Mirabelli
- 9.Cancer Epidemiology Unit, CPO-Piemonte and University of Turin, Turin, Italy
| | - Nils Plato
- 10.The Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Joelle Fevotte
- 11.Département santé travail, Institut de veille sanitaire, St Maurice, France
| | - Beate Pesch
- 5.Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Rurh-Universität Bochum, Bochum, Germany
| | - Thomas Brüning
- 5.Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Rurh-Universität Bochum, Bochum, Germany
| | - Kurt Straif
- 4.International Agency for Research on Cancer, Lyon, France
| | - Hans Kromhout
- 1.Environmental Epidemiology Division, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
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Fasanelli F, Baglietto L, Ponzi E, Guida F, Campanella G, Johansson M, Grankvist K, Johansson M, Assumma MB, Naccarati A, Chadeau-Hyam M, Ala U, Faltus C, Kaaks R, Risch A, De Stavola B, Hodge A, Giles GG, Southey MC, Relton CL, Haycock PC, Lund E, Polidoro S, Sandanger TM, Severi G, Vineis P. Hypomethylation of smoking-related genes is associated with future lung cancer in four prospective cohorts. Nat Commun 2015; 6:10192. [PMID: 26667048 PMCID: PMC4682166 DOI: 10.1038/ncomms10192] [Citation(s) in RCA: 168] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2015] [Accepted: 11/16/2015] [Indexed: 01/10/2023] Open
Abstract
DNA hypomethylation in certain genes is associated with tobacco exposure but it is unknown whether these methylation changes translate into increased lung cancer risk. In an epigenome-wide study of DNA from pre-diagnostic blood samples from 132 case-control pairs in the NOWAC cohort, we observe that the most significant associations with lung cancer risk are for cg05575921 in AHRR (OR for 1 s.d.=0.37, 95% CI: 0.31-0.54, P-value=3.3 × 10(-11)) and cg03636183 in F2RL3 (OR for 1 s.d.=0.40, 95% CI: 0.31-0.56, P-value=3.9 × 10(-10)), previously shown to be strongly hypomethylated in smokers. These associations remain significant after adjustment for smoking and are confirmed in additional 664 case-control pairs tightly matched for smoking from the MCCS, NSHDS and EPIC HD cohorts. The replication and mediation analyses suggest that residual confounding is unlikely to explain the observed associations and that hypomethylation of these CpG sites may mediate the effect of tobacco on lung cancer risk.
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Affiliation(s)
- Francesca Fasanelli
- Molecular end Epidemiology Unit, HuGeF, Human Genetics Foundation, Torino 10126, Italy
- Unit of Cancer Epidemiology, Citta' della Salute e della Scienza Hospital-University of Turin, Center for Cancer Prevention, Via Santena 7, Torino 10126, Italy
| | - Laura Baglietto
- Inserm (Institut National de la Santé et de la Recherche Médicale), Centre for Research in Epidemiology and Population Health, U1018, Team 9, 114 rue Edouard Vaillant, Villejuif 94805, France
- Paris-South University, Villejuif 91450, France
- Department of Genetic Epidemiology, Cancer Epidemiology Centre, Cancer Council of Victoria, Melbourne, Victoria 3004, Australia
- School of Population and Global Health, Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Erica Ponzi
- Molecular end Epidemiology Unit, HuGeF, Human Genetics Foundation, Torino 10126, Italy
| | - Florence Guida
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK
| | - Gianluca Campanella
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK
| | - Mattias Johansson
- Genetic Epidemiology Division, International Agency for Research on Cancer, Lyon 69008, France
- Department of Biobank Research, Umeå University, Umeå SE—90187, Sweden
| | - Kjell Grankvist
- Department of Biobank Research, Umeå University, Umeå SE—90187, Sweden
| | - Mikael Johansson
- Department of Radiation Sciences, Umeå University, Umeå SE—90187, Sweden
| | | | - Alessio Naccarati
- Molecular end Epidemiology Unit, HuGeF, Human Genetics Foundation, Torino 10126, Italy
| | - Marc Chadeau-Hyam
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK
| | - Ugo Ala
- Department of Molecular Biotechnology and Health Sciences, Università di Torino, Torino 10126, Italy
| | - Christian Faltus
- Division of Epigenomics and Cancer Risk Factors, DKFZ—German Cancer Research Center, Heidelberg 69121, Germany
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, DKFZ—German Cancer Research Center, Heidelberg 69121, Germany
- Translational Lung Research Center Heidelberg, Member of the German Center for Lung Research (DZL), Heidelberg 69120, Germany
| | - Angela Risch
- Division of Epigenomics and Cancer Risk Factors, DKFZ—German Cancer Research Center, Heidelberg 69121, Germany
- Translational Lung Research Center Heidelberg, Member of the German Center for Lung Research (DZL), Heidelberg 69120, Germany
- Division of Cancer Research and Epigenetics, Department of Molecular Biology, University of Salzburg, Salzburg 5020, Austria
| | - Bianca De Stavola
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Allison Hodge
- Department of Genetic Epidemiology, Cancer Epidemiology Centre, Cancer Council of Victoria, Melbourne, Victoria 3004, Australia
| | - Graham G. Giles
- Department of Genetic Epidemiology, Cancer Epidemiology Centre, Cancer Council of Victoria, Melbourne, Victoria 3004, Australia
- School of Population and Global Health, Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Melissa C. Southey
- Genetic Epidemiology Laboratory, University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Caroline L. Relton
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK
| | - Philip C. Haycock
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK
| | - Eiliv Lund
- Department of Community Medicine UiT–The Arctic University of Norway, Tromso 9019, Norway
| | - Silvia Polidoro
- Molecular end Epidemiology Unit, HuGeF, Human Genetics Foundation, Torino 10126, Italy
| | - Torkjel M. Sandanger
- Department of Community Medicine UiT–The Arctic University of Norway, Tromso 9019, Norway
| | - Gianluca Severi
- Molecular end Epidemiology Unit, HuGeF, Human Genetics Foundation, Torino 10126, Italy
- Inserm (Institut National de la Santé et de la Recherche Médicale), Centre for Research in Epidemiology and Population Health, U1018, Team 9, 114 rue Edouard Vaillant, Villejuif 94805, France
- Paris-South University, Villejuif 91450, France
- Department of Genetic Epidemiology, Cancer Epidemiology Centre, Cancer Council of Victoria, Melbourne, Victoria 3004, Australia
- School of Population and Global Health, Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Paolo Vineis
- Molecular end Epidemiology Unit, HuGeF, Human Genetics Foundation, Torino 10126, Italy
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK
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7
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van Oyen SC, Peters S, Alfonso H, Fritschi L, de Klerk NH, Reid A, Franklin P, Gordon L, Benke G, Musk AW. Development of a Job-Exposure Matrix (AsbJEM) to Estimate Occupational Exposure to Asbestos in Australia. ANNALS OF OCCUPATIONAL HYGIENE 2015; 59:737-48. [DOI: 10.1093/annhyg/mev017] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2014] [Accepted: 02/16/2015] [Indexed: 12/30/2022]
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8
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Guida F, Sandanger TM, Castagné R, Campanella G, Polidoro S, Palli D, Krogh V, Tumino R, Sacerdote C, Panico S, Severi G, Kyrtopoulos SA, Georgiadis P, Vermeulen RCH, Lund E, Vineis P, Chadeau-Hyam M. Dynamics of smoking-induced genome-wide methylation changes with time since smoking cessation. Hum Mol Genet 2015; 24:2349-59. [PMID: 25556184 DOI: 10.1093/hmg/ddu751] [Citation(s) in RCA: 234] [Impact Index Per Article: 23.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Several studies have recently identified strong epigenetic signals related to tobacco smoking. However, an aspect that did not receive much attention is the evolution of epigenetic changes with time since smoking cessation. We conducted a series of epigenome-wide association studies to capture the dynamics of smoking-induced epigenetic changes after smoking cessation, using genome-wide methylation profiles obtained from blood samples in 745 women from 2 European populations. Two distinct classes of CpG sites were identified: sites whose methylation reverts to levels typical of never smokers within decades after smoking cessation, and sites remaining differentially methylated, even more than 35 years after smoking cessation. Our results suggest that the dynamics of methylation changes following smoking cessation are driven by a differential and site-specific magnitude of the smoking-induced alterations (with persistent sites being most affected) irrespective of the intensity and duration of smoking. Analyses of the link between methylation and expression levels revealed that methylation predominantly and remotely down-regulates gene expression. Among genes whose expression was associated with our candidate CpG sites, LRRN3 appeared to be particularly interesting as it was one of the few genes whose methylation and expression were directly associated, and the only gene in which both methylation and gene expression were found associated with smoking. Our study highlights persistent epigenetic markers of smoking, which can potentially be detected decades after cessation. Such historical signatures are promising biomarkers to refine individual risk profiling of smoking-induced chronic disease such as lung cancer.
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Affiliation(s)
- Florence Guida
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK
| | - Torkjel M Sandanger
- Department of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway
| | - Raphaële Castagné
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK
| | - Gianluca Campanella
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK
| | | | - Domenico Palli
- Molecular and Nutritional Epidemiology Unit, Cancer Research and Prevention Institute-ISPO, Florence, Italy
| | - Vittorio Krogh
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Rosario Tumino
- Ragusa Cancer Registry Azienda Ospedaliera "Civile M.P. Arezzo," Ragusa, Italy
| | | | - Salvatore Panico
- Department of Clinical Medicine and Surgery, Federico II University, Naples, Italy
| | - Gianluca Severi
- HuGeF, Human Genetics Foundation, Torino, Italy, Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia, Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Australia
| | - Soterios A Kyrtopoulos
- Institute of Biology, Pharmaceutical Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece
| | - Panagiotis Georgiadis
- Institute of Biology, Pharmaceutical Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece
| | - Roel C H Vermeulen
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands and Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Eiliv Lund
- Department of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway
| | - Paolo Vineis
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK, HuGeF, Human Genetics Foundation, Torino, Italy
| | - Marc Chadeau-Hyam
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands and
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Vineis P, van Veldhoven K, Chadeau-Hyam M, Athersuch TJ. Advancing the application of omics-based biomarkers in environmental epidemiology. ENVIRONMENTAL AND MOLECULAR MUTAGENESIS 2013; 54:461-7. [PMID: 23519765 DOI: 10.1002/em.21764] [Citation(s) in RCA: 66] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2012] [Revised: 01/15/2013] [Accepted: 01/14/2013] [Indexed: 05/20/2023]
Abstract
The use of omics represents a shift in approach for environmental epidemiology and exposure science. In this article, the aspects of the use of omics that will require further development in the near future are discussed, including (a) the underlying causal interpretation and models; (b) the "meet-in-the-middle" concept, with examples; (c) the role of "calibration" of measurements; and (d) the role of life-course epidemiology and the related development of adequate biostatistical models.
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Affiliation(s)
- Paolo Vineis
- MRC-HPA Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.
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