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Boogaard H, Crouse DL, Tanner E, Mantus E, van Erp AM, Vedal S, Samet J. Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution: The HEI Experience and What's Next? ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024. [PMID: 38991107 DOI: 10.1021/acs.est.3c09745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/13/2024]
Abstract
Although concentrations of ambient air pollution continue to decline in high-income regions, epidemiological studies document adverse health effects at levels below current standards in many countries. The Health Effects Institute (HEI) recently completed a comprehensive research initiative to investigate the health effects of long-term exposure to low levels of air pollution in the United States (U.S.), Canada, and Europe. We provide an overview and synthesis of the results of this initiative along with other key research, the strengths and limitations of the research, and remaining research needs. The three studies funded through the HEI initiative estimated the effects of long-term ambient exposure to fine particulate matter (PM2.5), nitrogen dioxide, ozone, and other pollutants on a broad range of health outcomes, including cause-specific mortality and cardiovascular and respiratory morbidity. To ensure high quality research and comparability across studies, HEI worked actively with the study teams and engaged independent expert panels for project oversight and review. All three studies documented positive associations between mortality and exposure to PM2.5 below the U.S. National Ambient Air Quality Standards and current and proposed European Union limit values. Furthermore, the studies observed nonthreshold linear (U.S.), or supra-linear (Canada and Europe) exposure-response functions for PM2.5 and mortality. Heterogeneity was found in both the magnitude and shape of this association within and across studies. Strengths of the studies included the large populations (7-69 million), state-of-the-art exposure assessment methods, and thorough statistical analyses that applied novel methods. Future work is needed to better understand potential sources of heterogeneity in the findings across studies and regions. Other areas of future work include the changing and evolving nature of PM components and sources, including wildfires, and the role of indoor environments. This research initiative provided important new evidence of the adverse effects of long-term exposures to low levels of air pollution at and below current standards, suggesting that further reductions could yield larger benefits than previously anticipated.
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Affiliation(s)
- Hanna Boogaard
- Health Effects Institute, 75 Federal Street, Boston, Massachusetts 02110-1940, United States
| | - Dan L Crouse
- Health Effects Institute, 75 Federal Street, Boston, Massachusetts 02110-1940, United States
| | - Eva Tanner
- Health Effects Institute, 75 Federal Street, Boston, Massachusetts 02110-1940, United States
| | - Ellen Mantus
- Health Effects Institute, 75 Federal Street, Boston, Massachusetts 02110-1940, United States
| | - Annemoon M van Erp
- Health Effects Institute, 75 Federal Street, Boston, Massachusetts 02110-1940, United States
| | - Sverre Vedal
- Department of Environmental & Occupational Health Sciences, University of Washington, 4225 Roosevelt Way N.E., Seattle, Washington 98105, United States
| | - Jonathan Samet
- Department of Environmental & Occupational Health, Department of Epidemiology, Colorado School of Public Health, 13001 East 17th Place, Aurora, Colorado 80045, United States
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2
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Mahboubifar M, Zidorn C, Farag MA, Zayed A, Jassbi AR. Chemometric-based drug discovery approaches from natural origins using hyphenated chromatographic techniques. PHYTOCHEMICAL ANALYSIS : PCA 2024; 35:990-1016. [PMID: 38806406 DOI: 10.1002/pca.3382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 05/02/2024] [Accepted: 05/02/2024] [Indexed: 05/30/2024]
Abstract
INTRODUCTION Isolation and characterization of bioactive components from complex matrices of marine or terrestrial biological origins are the most challenging issues for natural product chemists. Biochemometric is a new potential scope in natural product analytical science, and it is a methodology to find the compound's correlation to their bioactivity with the help of hyphenated chromatographic techniques and chemometric tools. OBJECTIVES The present review aims to evaluate the application of chemometric tools coupled to chromatographic techniques for drug discovery from natural resources. METHODS The searching keywords "biochemometric," "chemometric," "chromatography," "natural products bioassay," and "bioassay" were selected to search the published articles between 2010-2023 using different search engines including "Pubmed", "Web of Science," "ScienceDirect," and "Google scholar." RESULTS An initial stage in natural product analysis is applying the chromatographic hyphenated techniques in conjunction with biochemometric approaches. Among the applied chromatographic techniques, liquid chromatography (LC) techniques, have taken up more than half (53%) and also, mass spectroscopy (MS)-based chromatographic techniques such as LC-MS are the most widely used techniques applied in combination with chemometric methods for natural products bioassay. Considering the complexity of dataset achieved from chromatographic hyphenated techniques, chemometric tools have been increasingly employed for phytochemical studies in the context of determining botanicals geographical origin, quality control, and detection of bioactive compounds. CONCLUSION Biochemometric application is expected to be further improved with advancing in data acquisition methods, new efficient preprocessing, model validation and variable selection methods which would guarantee that the applied model to have good prediction ability in compound relation to its bioactivity.
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Affiliation(s)
- Marjan Mahboubifar
- Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Christian Zidorn
- Pharmazeutisches Institut, Abteilung Pharmazeutische Biologie, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Mohamed A Farag
- Pharmacognosy Department, College of Pharmacy, Cairo University, Cairo, Egypt
| | - Ahmed Zayed
- Pharmacognosy Department, College of Pharmacy, Tanta University, Tanta, Egypt
| | - Amir Reza Jassbi
- Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
- Pharmazeutisches Institut, Abteilung Pharmazeutische Biologie, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
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3
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Cheng SL, Hedges M, Keski-Rahkonen P, Chatziioannou AC, Scalbert A, Chung KF, Sinharay R, Green DC, de Kok TMCM, Vlaanderen J, Kyrtopoulos SA, Kelly F, Portengen L, Vineis P, Vermeulen RCH, Chadeau-Hyam M, Dagnino S. Multiomic Signatures of Traffic-Related Air Pollution in London Reveal Potential Short-Term Perturbations in Gut Microbiome-Related Pathways. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:8771-8782. [PMID: 38728551 PMCID: PMC11112755 DOI: 10.1021/acs.est.3c09148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 04/25/2024] [Accepted: 04/26/2024] [Indexed: 05/12/2024]
Abstract
This randomized crossover study investigated the metabolic and mRNA alterations associated with exposure to high and low traffic-related air pollution (TRAP) in 50 participants who were either healthy or were diagnosed with chronic pulmonary obstructive disease (COPD) or ischemic heart disease (IHD). For the first time, this study combined transcriptomics and serum metabolomics measured in the same participants over multiple time points (2 h before, and 2 and 24 h after exposure) and over two contrasted exposure regimes to identify potential multiomic modifications linked to TRAP exposure. With a multivariate normal model, we identified 78 metabolic features and 53 mRNA features associated with at least one TRAP exposure. Nitrogen dioxide (NO2) emerged as the dominant pollutant, with 67 unique associated metabolomic features. Pathway analysis and annotation of metabolic features consistently indicated perturbations in the tryptophan metabolism associated with NO2 exposure, particularly in the gut-microbiome-associated indole pathway. Conditional multiomics networks revealed complex and intricate mechanisms associated with TRAP exposure, with some effects persisting 24 h after exposure. Our findings indicate that exposure to TRAP can alter important physiological mechanisms even after a short-term exposure of a 2 h walk. We describe for the first time a potential link between NO2 exposure and perturbation of the microbiome-related pathways.
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Affiliation(s)
- Sibo Lucas Cheng
- NIHR
HPRU in Environmental Exposures and Health, Imperial College London, London W12 0BZ, U.K.
- MRC
Centre for Environment and Health, Department of Epidemiology and
Biostatistics, School of Public Health, Imperial College London, London W12 7TA, U.K.
| | - Michael Hedges
- MRC
Centre for Environment and Health, Environmental Research Group, Imperial College London, London W12 0BZ, U.K.
| | | | | | - Augustin Scalbert
- International
Agency for Research on Cancer (IARC), Lyon 69366 Cedex, France
| | - Kian Fan Chung
- National
Heart & Lung Institute, Imperial College
London, London SW7 2AZ, U.K.
- Royal Brompton
& Harefield NHS Trust, London SW3 6NP, U.K.
| | - Rudy Sinharay
- National
Heart & Lung Institute, Imperial College
London, London SW7 2AZ, U.K.
- Imperial
College Healthcare NHS Trust, London W2 1NY, U.K.
| | - David C. Green
- NIHR
HPRU in Environmental Exposures and Health, Imperial College London, London W12 0BZ, U.K.
- MRC
Centre for Environment and Health, Environmental Research Group, Imperial College London, London W12 0BZ, U.K.
| | - Theo M. C. M. de Kok
- Department
of Toxicogenomics, GROW School for Oncology and Reproduction, Maastricht University, Maastricht 6229 ER, The Netherlands
| | - Jelle Vlaanderen
- Division
of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, Utrecht 3584 CS, The Netherlands
| | | | - Frank Kelly
- NIHR
HPRU in Environmental Exposures and Health, Imperial College London, London W12 0BZ, U.K.
- MRC
Centre for Environment and Health, Environmental Research Group, Imperial College London, London W12 0BZ, U.K.
| | - Lützen Portengen
- Division
of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, Utrecht 3584 CS, The Netherlands
| | - Paolo Vineis
- MRC
Centre for Environment and Health, Department of Epidemiology and
Biostatistics, School of Public Health, Imperial College London, London W12 7TA, U.K.
| | - Roel C. H. Vermeulen
- Division
of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, Utrecht 3584 CS, The Netherlands
- Julius Centre for Health Sciences and Primary Care, University
Medical
Centre, Utrecht University, Utrecht 3584 CG, The Netherlands
| | - Marc Chadeau-Hyam
- NIHR
HPRU in Environmental Exposures and Health, Imperial College London, London W12 0BZ, U.K.
- MRC
Centre for Environment and Health, Department of Epidemiology and
Biostatistics, School of Public Health, Imperial College London, London W12 7TA, U.K.
| | - Sonia Dagnino
- MRC
Centre for Environment and Health, Department of Epidemiology and
Biostatistics, School of Public Health, Imperial College London, London W12 7TA, U.K.
- Transporters
in Imaging and Radiotherapy in Oncology (TIRO), School
of Medicine, Direction de la Recherche Fondamentale (DRF), Institut
des Sciences du Vivant Fréderic Joliot, Commissariat à
l’Energie Atomique et aux Énergies Alternatives (CEA), Université Côte d’Azur (UniCA), Nice 06107, France
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Harewood R, Rothwell JA, Bešević J, Viallon V, Achaintre D, Gicquiau A, Rinaldi S, Wedekind R, Prehn C, Adamski J, Schmidt JA, Jacobs I, Tjønneland A, Olsen A, Severi G, Kaaks R, Katzke V, Schulze MB, Prada M, Masala G, Agnoli C, Panico S, Sacerdote C, Jakszyn PG, Sánchez MJ, Castilla J, Chirlaque MD, Atxega AA, van Guelpen B, Heath AK, Papier K, Tong TYN, Summers SA, Playdon M, Cross AJ, Keski-Rahkonen P, Chajès V, Murphy N, Gunter MJ. Association between pre-diagnostic circulating lipid metabolites and colorectal cancer risk: a nested case-control study in the European Prospective Investigation into Cancer and Nutrition (EPIC). EBioMedicine 2024; 101:105024. [PMID: 38412638 PMCID: PMC10907191 DOI: 10.1016/j.ebiom.2024.105024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 01/26/2024] [Accepted: 02/05/2024] [Indexed: 02/29/2024] Open
Abstract
BACKGROUND Altered lipid metabolism is a hallmark of cancer development. However, the role of specific lipid metabolites in colorectal cancer development is uncertain. METHODS In a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC), we examined associations between pre-diagnostic circulating concentrations of 97 lipid metabolites (acylcarnitines, glycerophospholipids and sphingolipids) and colorectal cancer risk. Circulating lipids were measured using targeted mass spectrometry in 1591 incident colorectal cancer cases (55% women) and 1591 matched controls. Multivariable conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations between concentrations of individual lipid metabolites and metabolite patterns with colorectal cancer risk. FINDINGS Of the 97 assayed lipids, 24 were inversely associated (nominally p < 0.05) with colorectal cancer risk. Hydroxysphingomyelin (SM (OH)) C22:2 (ORper doubling 0.60, 95% CI 0.47-0.77) and acylakyl-phosphatidylcholine (PC ae) C34:3 (ORper doubling 0.71, 95% CI 0.59-0.87) remained associated after multiple comparisons correction. These associations were unaltered after excluding the first 5 years of follow-up after blood collection and were consistent according to sex, age at diagnosis, BMI, and colorectal subsite. Two lipid patterns, one including 26 phosphatidylcholines and all sphingolipids, and another 30 phosphatidylcholines, were weakly inversely associated with colorectal cancer. INTERPRETATION Elevated pre-diagnostic circulating levels of SM (OH) C22:2 and PC ae C34:3 and lipid patterns including phosphatidylcholines and sphingolipids were associated with lower colorectal cancer risk. This study may provide insight into potential links between specific lipids and colorectal cancer development. Additional prospective studies are needed to validate the observed associations. FUNDING World Cancer Research Fund (reference: 2013/1002); European Commission (FP7: BBMRI-LPC; reference: 313010).
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Affiliation(s)
- Rhea Harewood
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France.
| | - Joseph A Rothwell
- Centre for Epidemiology and Population Health (U1018), Exposome and Heredity Team, Faculté de Médecine, Université Paris-Saclay, UVSQ, INSERM, Gustave Roussy, F-94805, Villejuif, France
| | - Jelena Bešević
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Vivian Viallon
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France
| | - David Achaintre
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France; School of Plant Sciences and Food Security, Faculty of Biology, Tel-Aviv University, Tel Aviv-Yafo, Israel
| | - Audrey Gicquiau
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France
| | - Sabina Rinaldi
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France
| | - Roland Wedekind
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France
| | - Cornelia Prehn
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, 85764, Neuherberg, Germany
| | - Jerzy Adamski
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore, 117597; Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany; Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000, Ljubljana, Slovenia
| | - Julie A Schmidt
- Department of Clinical Medicine, Department of Clinical Epidemiology, Aarhus University and Aarhus University Hospital, Olof Palmes Allé 43-45, 8200 Aarhus N, Denmark
| | - Inarie Jacobs
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France
| | - Anne Tjønneland
- Danish Cancer Society Research Center, Diet, Cancer and Health, Strandboulevarden 49, DK-2100, Copenhagen, Denmark; Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Anja Olsen
- Danish Cancer Society Research Center, Diet, Cancer and Health, Strandboulevarden 49, DK-2100, Copenhagen, Denmark; The Department of Public Health, University of Aarhus, Aarhus, Denmark
| | - Gianluca Severi
- Centre for Epidemiology and Population Health (U1018), Exposome and Heredity Team, Faculté de Médecine, Université Paris-Saclay, UVSQ, INSERM, Gustave Roussy, F-94805, Villejuif, France; Department of Statistics, Computer Science, Applications "G. Parenti", University of Florence, Florence, Italy
| | - Rudolf Kaaks
- German Cancer Research Center (DKFZ), Division of Cancer Epidemiology, Heidelberg, Germany
| | - Verena Katzke
- German Cancer Research Center (DKFZ), Division of Cancer Epidemiology, Heidelberg, Germany
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Arthur-Scheunert-Allee 114-116, 14558, Nuthetal, Germany; Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany
| | - Marcela Prada
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Arthur-Scheunert-Allee 114-116, 14558, Nuthetal, Germany
| | - Giovanna Masala
- Institute for Cancer Research, Prevention and Clinical Network (ISPRO), Florence, Italy
| | - Claudia Agnoli
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Venezian, 1, 20133, Milan, Italy
| | - Salvatore Panico
- Dipartimento Di Medicina Clinica E Chirurgia Federico Ii University, Naples, Italy
| | - Carlotta Sacerdote
- Unit of Cancer Epidemiology, Città della Salute e della Scienza University-Hospital and Center for Cancer Prevention (CPO), Via Santena 7, 10126, Turin, Italy
| | - Paula Gabriela Jakszyn
- Unit of Nutrition and Cancer, Cancer Epidemiology Research Programme, Catalan Institute of Oncology (ICO-IDIBELL), Barcelona, Spain; Blanquerna School of Health Sciences, Ramon Llull University, Barcelona, Spain
| | - Maria-Jose Sánchez
- Escuela Andaluza de Salud Pública (EASP), 18011, Granada, Spain; Instituto de Investigación Biosanitaria ibs.GRANADA, 18012, Granada, Spain; Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029, Madrid, Spain; Department of Preventive Medicine and Public Health, University of Granada, 18071, Granada, Spain
| | - Jesús Castilla
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029, Madrid, Spain; Instituto de Salud Pública de Navarra - IdiSNA, Pamplona, Spain
| | - María-Dolores Chirlaque
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029, Madrid, Spain; Department of Epidemiology, Regional Health Council, IMIB-Arrixaca, Murcia University, Murcia, Spain
| | - Amaia Aizpurua Atxega
- Ministry of Health of the Basque Government, Sub Directorate for Public Health and Addictions of Gipuzkoa, San Sebastian, Spain; Biodonostia Health Research Institute, Epidemiology of Chronic and Communicable Diseases Group, San Sebastián, Spain
| | - Bethany van Guelpen
- Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden; Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Alicia K Heath
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Keren Papier
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Tammy Y N Tong
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Scott A Summers
- Department of Nutrition and Integrative Physiology and the Diabetes and Metabolism Research Center, University of Utah, Salt Lake City, Utah, USA
| | - Mary Playdon
- Department of Nutrition and Integrative Physiology and the Diabetes and Metabolism Research Center, University of Utah, Salt Lake City, Utah, USA; Cancer Control and Population Sciences, Huntsman Cancer Institute, Salt Lake City, Utah, USA
| | - Amanda J Cross
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Pekka Keski-Rahkonen
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France
| | - Véronique Chajès
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France
| | - Neil Murphy
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France
| | - Marc J Gunter
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France; Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
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Lu TY, Wu CD, Huang YT, Chen YC, Chen CJ, Yang HI, Pan WC. Exposure to PM 2.5 Metal Constituents and Liver Cancer Risk in REVEAL-HBV. J Epidemiol 2024; 34:87-93. [PMID: 36908115 PMCID: PMC10751193 DOI: 10.2188/jea.je20220262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 02/05/2023] [Indexed: 03/13/2023] Open
Abstract
BACKGROUND Ambient particulate matter is classified as a human Class 1 carcinogen, and recent studies found a positive relationship between fine particulate matter (PM2.5) and liver cancer. Nevertheless, little is known about which specific metal constituent contributes to the development of liver cancer. OBJECTIVE To evaluate the association of long-term exposure to metal constituents in PM2.5 with the risk of liver cancer using a Taiwanese cohort study. METHODS A total of 13,511 Taiwanese participants were recruited from the REVEAL-HBV in 1991-1992. Participants' long-term exposure to eight metal constituents (Ba, Cu, Mn, Sb, Zn, Pb, Ni, and Cd) in PM2.5 was based on ambient measurement in 2002-2006 followed by a land-use regression model for spatial interpolation. We ascertained newly developed liver cancer (ie, hepatocellular carcinoma [HCC]) through data linkage with the Taiwan Cancer Registry and national health death certification in 1991-2014. A Cox proportional hazards model was utilized to assess the association between exposure to PM2.5 metal component and HCC. RESULTS We identified 322 newly developed HCC with a median follow-up of 23.1 years. Long-term exposure to PM2.5 Cu was positively associated with a risk of liver cancer. The adjusted hazard ratio (HR) was 1.13 (95% confidence interval [CI], 1.02-1.25; P = 0.023) with one unit increment on Cu normalized by PM2.5 mass concentration in the logarithmic scale. The PM2.5 Cu-HCC association remained statistically significant with adjustment for co-exposures to other metal constituents in PM2.5. CONCLUSION Our findings suggest PM2.5 containing Cu may attribute to the association of PM2.5 exposure with liver cancer.
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Affiliation(s)
- Tzu-Yi Lu
- Institute of Environmental and Occupational Health Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chih-Da Wu
- Department of Geomatics, National Cheng Kung University, Chiayi, Taiwan
- Department of Environmental Health, Harvard School of Public Health, Boston, MA, USA
| | - Yen-Tsung Huang
- Institue of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Yu-Cheng Chen
- National Institution of Environmental Health Sciences, National Health Research Institute, Mioli, Taiwan
| | - Chien-Jen Chen
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
- Graduate Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
| | - Hwai-I Yang
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Wen-Chi Pan
- Institute of Environmental and Occupational Health Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
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Bodinier B, Filippi S, Nøst TH, Chiquet J, Chadeau-Hyam M. Automated calibration for stability selection in penalised regression and graphical models. J R Stat Soc Ser C Appl Stat 2023; 72:1375-1393. [PMID: 38143734 PMCID: PMC10746547 DOI: 10.1093/jrsssc/qlad058] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 06/01/2023] [Accepted: 06/12/2023] [Indexed: 12/26/2023]
Abstract
Stability selection represents an attractive approach to identify sparse sets of features jointly associated with an outcome in high-dimensional contexts. We introduce an automated calibration procedure via maximisation of an in-house stability score and accommodating a priori-known block structure (e.g. multi-OMIC) data. It applies to [Least Absolute Shrinkage Selection Operator (LASSO)] penalised regression and graphical models. Simulations show our approach outperforms non-stability-based and stability selection approaches using the original calibration. Application to multi-block graphical LASSO on real (epigenetic and transcriptomic) data from the Norwegian Women and Cancer study reveals a central/credible and novel cross-OMIC role of LRRN3 in the biological response to smoking. Proposed approaches were implemented in the R package sharp.
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Affiliation(s)
- Barbara Bodinier
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Sarah Filippi
- Department of Mathematics, Imperial College London, London, UK
| | - Therese Haugdahl Nøst
- Department of Community Medicine, Faculty of Health Sciences, UiT, The Arctic University of Norway, NO-9037 Tromsø, Norway
| | - Julien Chiquet
- Université Paris-Saclay, AgroParisTech INRAE, UMR MIA, SolsTIS team, Paris, France
| | - Marc Chadeau-Hyam
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
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7
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Ng S, Masarone S, Watson D, Barnes MR. The benefits and pitfalls of machine learning for biomarker discovery. Cell Tissue Res 2023; 394:17-31. [PMID: 37498390 PMCID: PMC10558383 DOI: 10.1007/s00441-023-03816-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 07/12/2023] [Indexed: 07/28/2023]
Abstract
Prospects for the discovery of robust and reproducible biomarkers have improved considerably with the development of sensitive omics platforms that can enable measurement of biological molecules at an unprecedented scale. With technical barriers to success lowering, the challenge is now moving into the analytical domain. Genome-wide discovery presents a problem of scale and multiple testing as standard statistical methods struggle to distinguish signal from noise in increasingly complex biological systems. Machine learning and AI methods are good at finding answers in large datasets, but they have a tendency to overfit solutions. It may be possible to find a local answer or mechanism in a specific patient sample or small group of samples, but this may not generalise to wider patient populations due to the high likelihood of false discovery. The rise of explainable AI offers to improve the opportunity for true discovery by providing explanations for predictions that can be explored mechanistically before proceeding to costly and time-consuming validation studies. This review aims to introduce some of the basic concepts of machine learning and AI for biomarker discovery with a focus on post hoc explanation of predictions. To illustrate this, we consider how explainable AI has already been used successfully, and we explore a case study that applies AI to biomarker discovery in rheumatoid arthritis, demonstrating the accessibility of tools for AI and machine learning. We use this to illustrate and discuss some of the potential challenges and solutions that may enable AI to critically interrogate disease and response mechanisms.
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Affiliation(s)
- Sandra Ng
- Centre for Translational Bioinformatics, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Sara Masarone
- Centre for Translational Bioinformatics, William Harvey Research Institute, Queen Mary University of London, London, UK
- Alan Turing Institute, London, UK
| | - David Watson
- Department of Informatics, King's College London, London, UK
| | - Michael R Barnes
- Centre for Translational Bioinformatics, William Harvey Research Institute, Queen Mary University of London, London, UK.
- Alan Turing Institute, London, UK.
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8
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Chong D, Jones NC, Schittenhelm RB, Anderson A, Casillas-Espinosa PM. Multi-omics Integration and Epilepsy: Towards a Better Understanding of Biological Mechanisms. Prog Neurobiol 2023:102480. [PMID: 37286031 DOI: 10.1016/j.pneurobio.2023.102480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 05/09/2023] [Accepted: 06/03/2023] [Indexed: 06/09/2023]
Abstract
The epilepsies are a group of complex neurological disorders characterised by recurrent seizures. Approximately 30% of patients fail to respond to anti-seizure medications, despite the recent introduction of many new drugs. The molecular processes underlying epilepsy development are not well understood and this knowledge gap impedes efforts to identify effective targets and develop novel therapies against epilepsy. Omics studies allow a comprehensive characterisation of a class of molecules. Omics-based biomarkers have led to clinically validated diagnostic and prognostic tests for personalised oncology, and more recently for non-cancer diseases. We believe that, in epilepsy, the full potential of multi-omics research is yet to be realised and we envisage that this review will serve as a guide to researchers planning to undertake omics-based mechanistic studies.
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Affiliation(s)
- Debbie Chong
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia
| | - Nigel C Jones
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia; Department of Medicine (The Royal Melbourne Hospital), The University of Melbourne, 3000, Victoria, Australia; Department of Neurology, Alfred Health, Melbourne, 3004, Victoria, Australia
| | - Ralf B Schittenhelm
- Monash Proteomics & Metabolomics Facility and Monash Biomedicine Discovery Institute, Monash University, Clayton, Victoria, 3800, Australia
| | - Alison Anderson
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia; Department of Medicine (The Royal Melbourne Hospital), The University of Melbourne, 3000, Victoria, Australia; Department of Neurology, Alfred Health, Melbourne, 3004, Victoria, Australia
| | - Pablo M Casillas-Espinosa
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia; Department of Medicine (The Royal Melbourne Hospital), The University of Melbourne, 3000, Victoria, Australia; Department of Neurology, Alfred Health, Melbourne, 3004, Victoria, Australia
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9
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Muli S, Brachem C, Alexy U, Schmid M, Oluwagbemigun K, Nöthlings U. Exploring the association of physical activity with the plasma and urine metabolome in adolescents and young adults. Nutr Metab (Lond) 2023; 20:23. [PMID: 37020289 PMCID: PMC10074825 DOI: 10.1186/s12986-023-00742-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 03/29/2023] [Indexed: 04/07/2023] Open
Abstract
BACKGROUND Regular physical activity elicits many health benefits. However, the underlying molecular mechanisms through which physical activity influences overall health are less understood. Untargeted metabolomics enables system-wide mapping of molecular perturbations which may lend insights into physiological responses to regular physical activity. In this study, we investigated the associations of habitual physical activity with plasma and urine metabolome in adolescents and young adults. METHODS This cross-sectional study included participants from the DONALD (DOrtmund Nutritional and Anthropometric Longitudinally Designed) study with plasma samples n = 365 (median age: 18.4 (18.1, 25.0) years, 58% females) and 24 h urine samples n = 215 (median age: 18.1 (17.1, 18.2) years, 51% females). Habitual physical activity was assessed using a validated Adolescent Physical Activity Recall Questionnaire. Plasma and urine metabolite concentrations were determined using ultra-high-performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) methods. In a sex-stratified analysis, we conducted principal component analysis (PCA) to reduce the dimensionality of metabolite data and to create metabolite patterns. Multivariable linear regression models were then applied to assess the associations between self-reported physical activity (metabolic equivalent of task (MET)-hours per week) with single metabolites and metabolite patterns, adjusted for potential confounders and controlling the false discovery rate (FDR) at 5% for each set of regressions. RESULTS Habitual physical activity was positively associated with the "lipid, amino acids and xenometabolite" pattern in the plasma samples of male participants only (β = 1.02; 95% CI: 1.01, 1.04, p = 0.001, adjusted p = 0.042). In both sexes, no association of physical activity with single metabolites in plasma and urine and metabolite patterns in urine was found (all adjusted p > 0.05). CONCLUSIONS Our explorative study suggests that habitual physical activity is associated with alterations of a group of metabolites reflected in the plasma metabolite pattern in males. These perturbations may lend insights into some of underlying mechanisms that modulate effects of physical activity.
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Affiliation(s)
- Samuel Muli
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Friedrich-Hirzebruch- Allee 7, 53115, Bonn, Germany.
| | - Christian Brachem
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Friedrich-Hirzebruch- Allee 7, 53115, Bonn, Germany
| | - Ute Alexy
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Friedrich-Hirzebruch- Allee 7, 53115, Bonn, Germany
| | - Matthias Schmid
- Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), University Hospital Bonn, Venusberg Campus 1, 53127, Bonn, Germany
| | - Kolade Oluwagbemigun
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Friedrich-Hirzebruch- Allee 7, 53115, Bonn, Germany
| | - Ute Nöthlings
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Friedrich-Hirzebruch- Allee 7, 53115, Bonn, Germany
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10
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Ganeshalingam M, Enstad S, Sen S, Cheema S, Esposito F, Thomas R. Role of lipidomics in assessing the functional lipid composition in breast milk. Front Nutr 2022; 9:899401. [PMID: 36118752 PMCID: PMC9478754 DOI: 10.3389/fnut.2022.899401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 08/02/2022] [Indexed: 11/13/2022] Open
Abstract
Breast milk is the ideal source of nutrients for infants in early life. Lipids represent 2–5% of the total breast milk composition and are a major energy source providing 50% of an infant’s energy intake. Functional lipids are an emerging class of lipids in breast milk mediating several different biological functions, health, and developmental outcome. Lipidomics is an emerging field that studies the structure and function of lipidome. It provides the ability to identify new signaling molecules, mechanisms underlying physiological activities, and possible biomarkers for early diagnosis and prognosis of diseases, thus laying the foundation for individualized, targeted, and precise nutritional management strategies. This emerging technique can be useful to study the major role of functional lipids in breast milk in several dimensions. Functional lipids are consumed with daily food intake; however, they have physiological benefits reported to reduce the risk of disease. Functional lipids are a new area of interest in lipidomics, but very little is known of the functional lipidome in human breast milk. In this review, we focus on the role of lipidomics in assessing functional lipid composition in breast milk and how lipid bioinformatics, a newly emerging branch in this field, can help to determine the mechanisms by which breast milk affects newborn health.
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Affiliation(s)
- Moganatharsa Ganeshalingam
- School of Science and the Environment/Boreal Ecosystems Research Initiative, Memorial University of Newfoundland, Corner Brook, NL, Canada
- *Correspondence: Moganatharsa Ganeshalingam,
| | - Samantha Enstad
- Neonatal Intensive Care Unit, Orlando Health Winne Palmer Hospital for Women and Babies, Orlando, FL, United States
| | - Sarbattama Sen
- Department of Pediatric Newborn Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Sukhinder Cheema
- Department of Biochemistry, Memorial University of Newfoundland, St. John’s, NL, Canada
| | - Flavia Esposito
- Department of Mathematics, University of Bari Aldo Moro, Bari, Italy
| | - Raymond Thomas
- School of Science and the Environment/Boreal Ecosystems Research Initiative, Memorial University of Newfoundland, Corner Brook, NL, Canada
- Raymond Thomas,
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11
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Austin TR, McHugh CP, Brody JA, Bis JC, Sitlani CM, Bartz TM, Biggs ML, Bansal N, Buzkova P, Carr SA, deFilippi CR, Elkind MSV, Fink HA, Floyd JS, Fohner AE, Gerszten RE, Heckbert SR, Katz DH, Kizer JR, Lemaitre RN, Longstreth WT, McKnight B, Mei H, Mukamal KJ, Newman AB, Ngo D, Odden MC, Vasan RS, Shojaie A, Simon N, Smith GD, Davies NM, Siscovick DS, Sotoodehnia N, Tracy RP, Wiggins KL, Zheng J, Psaty BM. Proteomics and Population Biology in the Cardiovascular Health Study (CHS): design of a study with mentored access and active data sharing. Eur J Epidemiol 2022; 37:755-765. [PMID: 35790642 PMCID: PMC9255954 DOI: 10.1007/s10654-022-00888-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 06/03/2022] [Indexed: 11/08/2022]
Abstract
BACKGROUND In the last decade, genomic studies have identified and replicated thousands of genetic associations with measures of health and disease and contributed to the understanding of the etiology of a variety of health conditions. Proteins are key biomarkers in clinical medicine and often drug-therapy targets. Like genomics, proteomics can advance our understanding of biology. METHODS AND RESULTS In the setting of the Cardiovascular Health Study (CHS), a cohort study of older adults, an aptamer-based method that has high sensitivity for low-abundance proteins was used to assay 4979 proteins in frozen, stored plasma from 3188 participants (61% women, mean age 74 years). CHS provides active support, including central analysis, for seven phenotype-specific working groups (WGs). Each CHS WG is led by one or two senior investigators and includes 10 to 20 early or mid-career scientists. In this setting of mentored access, the proteomic data and analytic methods are widely shared with the WGs and investigators so that they may evaluate associations between baseline levels of circulating proteins and the incidence of a variety of health outcomes in prospective cohort analyses. We describe the design of CHS, the CHS Proteomics Study, characteristics of participants, quality control measures, and structural characteristics of the data provided to CHS WGs. We additionally highlight plans for validation and replication of novel proteomic associations. CONCLUSION The CHS Proteomics Study offers an opportunity for collaborative data sharing to improve our understanding of the etiology of a variety of health conditions in older adults.
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Affiliation(s)
- Thomas R Austin
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA. .,Department of Epidemiology, University of Washington, Seattle, WA, USA.
| | | | - Jennifer A Brody
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.,Department of Medicine, University of Washington, Seattle, WA, USA
| | - Joshua C Bis
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.,Department of Medicine, University of Washington, Seattle, WA, USA
| | - Colleen M Sitlani
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.,Department of Medicine, University of Washington, Seattle, WA, USA
| | - Traci M Bartz
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.,Department of Medicine, University of Washington, Seattle, WA, USA.,Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Mary L Biggs
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.,Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Nisha Bansal
- Division of Nephrology, University of Washington, Seattle, WA, USA
| | - Petra Buzkova
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | | | | | | | - Howard A Fink
- Geriatric Research Education & Clinical Center, Minneapolis VA Healthcare System, Minneapolis, MN, USA
| | - James S Floyd
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.,Department of Epidemiology, University of Washington, Seattle, WA, USA.,Department of Medicine, University of Washington, Seattle, WA, USA
| | - Alison E Fohner
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.,Department of Epidemiology, University of Washington, Seattle, WA, USA.,Institute of Public Health Genetics, University of Washington, Seattle, WA, USA
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Susan R Heckbert
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.,Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Daniel H Katz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Jorge R Kizer
- Cardiology Section, San Francisco VA Health Care System, San Francisco, CA, USA.,Department of Biostatistics, University of California San Francisco, San Francisco, CA, USA.,Department of Epidemology, University of California San Francisco, San Francisco, CA, USA.,Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Rozenn N Lemaitre
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.,Department of Medicine, University of Washington, Seattle, WA, USA
| | - W T Longstreth
- Department of Epidemiology, University of Washington, Seattle, WA, USA.,Department of Neurology, University of Washington, Seattle, WA, USA
| | - Barbara McKnight
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Hao Mei
- Department of Data Science, University of Mississippi Medical Center, Jackson, MS, USA
| | | | - Anne B Newman
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Debby Ngo
- Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Michelle C Odden
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA
| | - Ramachandran S Vasan
- Department of Epidemiology, School of Public Health, Boston University, Boston, MA, USA.,Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Ali Shojaie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Noah Simon
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Neil M Davies
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK.,K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Norwegian, Norway.,Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | | | - Nona Sotoodehnia
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.,Division of Cardiology, University of Washington, Seattle, WA, USA
| | - Russell P Tracy
- Departments of Pathology & Laboratory Medicine, and Biochemistry, Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - Kerri L Wiggins
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.,Department of Medicine, University of Washington, Seattle, WA, USA
| | - Jie Zheng
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.,Department of Epidemiology, University of Washington, Seattle, WA, USA.,Department of Medicine, University of Washington, Seattle, WA, USA.,Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA
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12
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Canali S, Leonelli S. Reframing the environment in data-intensive health sciences. STUDIES IN HISTORY AND PHILOSOPHY OF SCIENCE 2022; 93:203-214. [PMID: 35576883 DOI: 10.1016/j.shpsa.2022.04.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 02/25/2022] [Accepted: 04/20/2022] [Indexed: 06/15/2023]
Abstract
In this paper, we analyse the relation between the use of environmental data in contemporary health sciences and related conceptualisations and operationalisations of the notion of environment. We consider three case studies that exemplify a different selection of environmental data and mode of data integration in data-intensive epidemiology. We argue that the diversification of data sources, their increase in scale and scope, and the application of novel analytic tools have brought about three significant conceptual shifts. First, we discuss the EXPOsOMICS project, an attempt to integrate genomic and environmental data which suggests a reframing of the boundaries between external and internal environments. Second, we explore the MEDMI platform, whose efforts to combine health, environmental and climate data instantiate a reframing and expansion of environmental exposure. Third, we illustrate how extracting epidemiological insights from extensive social data collected by the CIDACS institute yields innovative attributions of causal power to environmental factors. Identifying these shifts highlights the benefits and opportunities of new environmental data, as well as the challenges that such tools bring to understanding and fostering health. It also emphasises the constraints that data selection and accessibility pose to scientific imagination, including how researchers frame key concepts in health-related research.
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Affiliation(s)
- Stefano Canali
- Department of Electronics, Information and Bioengineering and META - Social Sciences and Humanities for Science and Technology, Politecnico di Milano, Milan, Italy.
| | - Sabina Leonelli
- Department of Sociology, Philosophy and Anthropology and Exeter Centre for the Study of the Life Sciences (Egenis), University of Exeter, Exeter, UK.
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13
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Petrovic D, Bodinier B, Dagnino S, Whitaker M, Karimi M, Campanella G, Haugdahl Nøst T, Polidoro S, Palli D, Krogh V, Tumino R, Sacerdote C, Panico S, Lund E, Dugué PA, Giles GG, Severi G, Southey M, Vineis P, Stringhini S, Bochud M, Sandanger TM, Vermeulen RCH, Guida F, Chadeau-Hyam M. Epigenetic mechanisms of lung carcinogenesis involve differentially methylated CpG sites beyond those associated with smoking. Eur J Epidemiol 2022; 37:629-640. [PMID: 35595947 PMCID: PMC9288379 DOI: 10.1007/s10654-022-00877-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 04/25/2022] [Indexed: 12/24/2022]
Abstract
Smoking-related epigenetic changes have been linked to lung cancer, but the contribution of epigenetic alterations unrelated to smoking remains unclear. We sought for a sparse set of CpG sites predicting lung cancer and explored the role of smoking in these associations. We analysed CpGs in relation to lung cancer in participants from two nested case-control studies, using (LASSO)-penalised regression. We accounted for the effects of smoking using known smoking-related CpGs, and through conditional-independence network. We identified 29 CpGs (8 smoking-related, 21 smoking-unrelated) associated with lung cancer. Models additionally adjusted for Comprehensive Smoking Index-(CSI) selected 1 smoking-related and 49 smoking-unrelated CpGs. Selected CpGs yielded excellent discriminatory performances, outperforming information provided by CSI only. Of the 8 selected smoking-related CpGs, two captured lung cancer-relevant effects of smoking that were missed by CSI. Further, the 50 CpGs identified in the CSI-adjusted model complementarily explained lung cancer risk. These markers may provide further insight into lung cancer carcinogenesis and help improving early identification of high-risk patients.
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Affiliation(s)
- Dusan Petrovic
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
- Department of Epidemiology and Health Systems (DESS), University Centre for General Medicine and Public Health (UNISANTE), Lausanne, Switzerland
- Department and Division of Primary Care Medicine, University Hospital of Geneva, Geneva, Switzerland
| | - Barbara Bodinier
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
| | - Sonia Dagnino
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
| | - Matthew Whitaker
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
| | - Maryam Karimi
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
- Bureau de Biostatistique et d'Épidémiologie, Institut Gustave Roussy, Université Paris-Saclay, Villejuif, France
- Oncostat U1018, Inserm, Équipe Labellisée Ligue Contre Le Cancer, Université Paris-Saclay, Villejuif, France
| | - Gianluca Campanella
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
| | - Therese Haugdahl Nøst
- Department of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway
| | | | - 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
- Hyblean Association for Epidemiological Research, AIRE- ONLUS, Ragusa, Italy
| | - Carlotta Sacerdote
- Unit of Cancer Epidemiology Città Della Salute e della Scienza University-Hospital, Via Santena 7, 10126, Turin, Italy
| | - Salvatore Panico
- Department of Clinical Medicine and Surgery, Federico II University, Naples, Italy
| | - Eiliv Lund
- Department of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway
- The Norwegian Cancer Registry, Oslo, Norway
| | - Pierre-Antoine Dugué
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Australia
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Australia
| | - Gianluca Severi
- Centre for Research in Epidemiology and Population Health, Inserm (Institut National de La Sante Et de a Recherche Medicale), Villejuif, France
| | - Melissa Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Australia
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, Australia
| | - Paolo Vineis
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
| | - Silvia Stringhini
- Department of Epidemiology and Health Systems (DESS), University Centre for General Medicine and Public Health (UNISANTE), Lausanne, Switzerland
- Department and Division of Primary Care Medicine, University Hospital of Geneva, Geneva, Switzerland
| | - Murielle Bochud
- Department of Epidemiology and Health Systems (DESS), University Centre for General Medicine and Public Health (UNISANTE), Lausanne, Switzerland
| | - Torkjel M Sandanger
- Department of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway
| | - Roel C H Vermeulen
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
- Julius Centre for Health Sciences and Primary Care, University Medical Centre, Utrecht, Utrecht, The Netherlands
| | - Florence Guida
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
- Group of Genetic Epidemiology, International Agency for Research on Cancer (IARC) - World Health Organization (WHO), Lyon, France
| | - Marc Chadeau-Hyam
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK.
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands.
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14
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Applying the exposome concept to working life health. Environ Epidemiol 2022; 6:e185. [PMID: 35434456 PMCID: PMC9005258 DOI: 10.1097/ee9.0000000000000185] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 11/14/2021] [Indexed: 12/14/2022] Open
Abstract
Exposures at work have a major impact on noncommunicable diseases (NCDs). Current risk reduction policies and strategies are informed by existing scientific evidence, which is limited due to the challenges of studying the complex relationship between exposure at work and outside work and health. We define the working life exposome as all occupational and related nonoccupational exposures. The latter includes nonoccupational exposures that may be directly or indirectly influenced by or interact with the working life of the individual in their relation to health. The Exposome Project for Health and Occupational Research aims to advance knowledge on the complex working life exposures in relation to disease beyond the single high exposure–single health outcome paradigm, mapping and relating interrelated exposures to inherent biological pathways, key body functions, and health. This will be achieved by combining (1) large-scale harmonization and pooling of existing European cohorts systematically looking at multiple exposures and diseases, with (2) the collection of new high-resolution external and internal exposure data. Methods and tools to characterize the working life exposome will be developed and applied, including sensors, wearables, a harmonized job exposure matrix (EuroJEM), noninvasive biomonitoring, omics, data mining, and (bio)statistics. The toolbox of developed methods and knowledge will be made available to policy makers, occupational health practitioners, and scientists. Advanced knowledge on working life exposures in relation to NCDs will serve as a basis for evidence-based and cost-effective preventive policies and actions. The toolbox will also enable future scientists to further expand the working life exposome knowledge base.
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15
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Keil AP, Buckley JP, Kalkbrenner AE. Bayesian G-Computation for Estimating Impacts of Interventions on Exposure Mixtures: Demonstration With Metals From Coal-Fired Power Plants and Birth Weight. Am J Epidemiol 2021; 190:2647-2657. [PMID: 33751055 DOI: 10.1093/aje/kwab053] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Revised: 11/19/2020] [Accepted: 12/03/2020] [Indexed: 02/05/2023] Open
Abstract
The importance of studying the health impacts of exposure mixtures is increasingly being recognized, but such research presents many methodological and interpretation difficulties. We used Bayesian g-computation to estimate effects of a simulated public health action on exposure mixtures and birth weights in Milwaukee, Wisconsin, in 2011-2013. We linked data from birth records with census-tract-level air toxics data from the Environmental Protection Agency's National Air Toxics Assessment model. We estimated the difference between observed and expected birth weights that theoretically would have followed a hypothetical intervention to reduce exposure to 6 airborne metals by decommissioning 3 coal-fired power plants in Milwaukee County prior to 2010. Using Bayesian g-computation, we estimated a 68-g (95% credible interval: 25, 135) increase in birth weight following this hypothetical intervention. This example demonstrates the utility of our approach for using observational data to evaluate and contrast possible public health actions. Additionally, Bayesian g-computation offers a flexible strategy for estimating the effects of highly correlated exposures, addressing statistical issues such as variance inflation, and addressing conceptual issues such as the lack of interpretability of independent effects.
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16
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Escriba-Montagut X, Basagaña X, Vrijheid M, Gonzalez JR. Software Application Profile: exposomeShiny—a toolbox for exposome data analysis. Int J Epidemiol 2021. [PMCID: PMC8855999 DOI: 10.1093/ije/dyab220] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Motivation Studying the role of the exposome in human health and its impact on different omic layers requires advanced statistical methods. Many of these methods are implemented in different R and Bioconductor packages, but their use may require strong expertise in R, in writing pipelines and in using new R classes which may not be familiar to non-advanced users. ExposomeShiny provides a bridge between researchers and most of the state-of-the-art exposome analysis methodologies, without the need of advanced programming skills. Implementation ExposomeShiny is a standalone web application implemented in R. It is available as source files and can be installed in any server or computer avoiding problems with data confidentiality. It is executed in RStudio which opens a browser window with the web application. General features The presented implementation allows the conduct of: (i) data pre-processing: normalization and missing imputation (including limit of detection); (ii) descriptive analysis; (iii) exposome principal component analysis (PCA) and hierarchical clustering; (iv) exposome-wide association studies (ExWAS) and variable selection ExWAS; (v) omic data integration by single association and multi-omic analyses; and (vi) post-exposome data analyses to gain biological insight for the exposures, genes or using the Comparative Toxicogenomics Database (CTD) and pathway analysis. Availability The exposomeShiny source code is freely available on Github at [https://github.com/isglobal-brge/exposomeShiny], Git tag v1.4. The software is also available as a Docker image [https://hub.docker.com/r/brgelab/exposome-shiny], tag v1.4. A user guide with information about the analysis methodologies as well as information on how to use exposomeShiny is freely hosted at [https://isglobal-brge.github.io/exposome_bookdown/].
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Affiliation(s)
| | - Xavier Basagaña
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Martine Vrijheid
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
- Department of Mathematics, Universitat Autònoma de Barcelona (UAB), Bellaterra (Barcelona), Spain
| | - Juan R Gonzalez
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
- Department of Mathematics, Universitat Autònoma de Barcelona (UAB), Bellaterra (Barcelona), Spain
- Corresponding author. Barcelona Biomedical Research Park (PRBB), Doctor Aiguader, 88. 08003 Barcelona, Spain. E-mail:
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Cadiou S, Basagaña X, Gonzalez JR, Lepeule J, Vrijheid M, Siroux V, Slama R. Performance of approaches relying on multidimensional intermediary data to decipher causal relationships between the exposome and health: A simulation study under various causal structures. ENVIRONMENT INTERNATIONAL 2021; 153:106509. [PMID: 33774494 DOI: 10.1016/j.envint.2021.106509] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 01/10/2021] [Accepted: 03/06/2021] [Indexed: 06/12/2023]
Abstract
Challenges in the assessment of the health effects of the exposome, defined as encompassing all environmental exposures from the prenatal period onwards, include a possibly high rate of false positive signals. It might be overcome using data dimension reduction techniques. Data from the biological layers lying between the exposome and its possible health consequences, such as the methylome, may help reducing exposome dimension. We aimed to quantify the performances of approaches relying on the incorporation of an intermediary biological layer to relate the exposome and health, and compare them with agnostic approaches ignoring the intermediary layer. We performed a Monte-Carlo simulation, in which we generated realistic exposome and intermediary layer data by sampling with replacement real data from the Helix exposome project. We generated a Gaussian outcome assuming linear relationships between the three data layers, in 2381 scenarios under five different causal structures, including mediation and reverse causality. We tested 3 agnostic methods considering only the exposome and the health outcome: ExWAS (for Exposome-Wide Association study), DSA, LASSO; and 3 methods relying on an intermediary layer: two implementations of our new oriented Meet-in-the-Middle (oMITM) design, using ExWAS and DSA, and a mediation analysis using ExWAS. Methods' performances were assessed through their sensitivity and FDP (False-Discovery Proportion). The oMITM-based methods generally had lower FDP than the other approaches, possibly at a cost in terms of sensitivity; FDP was in particular lower under a structure of reverse causality and in some mediation scenarios. The oMITM-DSA implementation showed better performances than oMITM-ExWAS, especially in terms of FDP. Among the agnostic approaches, DSA showed the highest performance. Integrating information from intermediary biological layers can help lowering FDP in studies of the exposome health effects; in particular, oMITM seems less sensitive to reverse causality than agnostic exposome-health association studies.
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Affiliation(s)
- Solène Cadiou
- Team of Environmental Epidemiology, IAB, Institute for Advanced Biosciences, Inserm, CNRS, CHU-Grenoble-Alpes, University Grenoble-Alpes, Grenoble, France
| | - Xavier Basagaña
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Juan R Gonzalez
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Johanna Lepeule
- Team of Environmental Epidemiology, IAB, Institute for Advanced Biosciences, Inserm, CNRS, CHU-Grenoble-Alpes, University Grenoble-Alpes, Grenoble, France
| | - Martine Vrijheid
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Valérie Siroux
- Team of Environmental Epidemiology, IAB, Institute for Advanced Biosciences, Inserm, CNRS, CHU-Grenoble-Alpes, University Grenoble-Alpes, Grenoble, France
| | - Rémy Slama
- Team of Environmental Epidemiology, IAB, Institute for Advanced Biosciences, Inserm, CNRS, CHU-Grenoble-Alpes, University Grenoble-Alpes, Grenoble, France.
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Dagnino S, Bodinier B, Guida F, Smith-Byrne K, Petrovic D, Whitaker MD, Haugdahl Nøst T, Agnoli C, Palli D, Sacerdote C, Panico S, Tumino R, Schulze MB, Johansson M, Keski-Rahkonen P, Scalbert A, Vineis P, Johansson M, Sandanger TM, Vermeulen RCH, Chadeau-Hyam M. Prospective Identification of Elevated Circulating CDCP1 in Patients Years before Onset of Lung Cancer. Cancer Res 2021; 81:3738-3748. [PMID: 33574093 PMCID: PMC7611235 DOI: 10.1158/0008-5472.can-20-3454] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 12/15/2020] [Accepted: 02/08/2021] [Indexed: 01/10/2023]
Abstract
Increasing evidence points to a role for inflammation in lung carcinogenesis. A small number of circulating inflammatory proteins have been identified as showing elevated levels prior to lung cancer diagnosis, indicating the potential for prospective circulating protein concentration as a marker of early carcinogenesis. To identify novel markers of lung cancer risk, we measured a panel of 92 circulating inflammatory proteins in 648 prediagnostic blood samples from two prospective cohorts in Italy and Norway (women only). To preserve the comparability of results and protect against confounding factors, the main statistical analyses were conducted in women from both studies, with replication sought in men (Italian participants). Univariate and penalized regression models revealed for the first time higher blood levels of CDCP1 protein in cases that went on to develop lung cancer compared with controls, irrespective of time to diagnosis, smoking habits, and gender. This association was validated in an additional 450 samples. Associations were stronger for future cases of adenocarcinoma where CDCP1 showed better explanatory performance. Integrative analyses combining gene expression and protein levels of CDCP1 measured in the same individuals suggested a link between CDCP1 and the expression of transcripts of LRRN3 and SEM1. Enrichment analyses indicated a potential role for CDCP1 in pathways related to cell adhesion and mobility, such as the WNT/β-catenin pathway. Overall, this study identifies lung cancer-related dysregulation of CDCP1 expression years before diagnosis. SIGNIFICANCE: Prospective proteomics analyses reveal an association between increased levels of circulating CDCP1 and lung carcinogenesis irrespective of smoking and years before diagnosis, and integrating gene expression indicates potential underlying mechanisms.See related commentary by Itzstein et al., p. 3441.
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Affiliation(s)
- Sonia Dagnino
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Barbara Bodinier
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Florence Guida
- International Agency for Research on Cancer (IARC), Lyon, France
| | - Karl Smith-Byrne
- International Agency for Research on Cancer (IARC), Lyon, France
| | - Dusan Petrovic
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- Department of Epidemiology and Health Systems (DESS), University Center for General Medicine and Public Health (UNISANTE), Lausanne, Switzerland
- Department and Division of Primary Care Medicine, University Hospital of Geneva, Geneva, Switzerland
| | - Matthew D Whitaker
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Therese Haugdahl Nøst
- Department of Community Medicine, UiT- The Arctic University of Norway, Tromsø, Norway
| | - Claudia Agnoli
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Domenico Palli
- Cancer Risk Factors and Life-Style Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network - ISPRO, Florence, Italy
| | - Carlotta Sacerdote
- Unit of Cancer Epidemiology, Città della Salute e della Scienza University-Hospital, Turin, Italy
| | - Salvatore Panico
- Department of Clinical Medicine and Surgery, Federico II University, Naples, Italy
| | - Rosario Tumino
- Cancer Registry and Histopathology Department, Provincial Health Authority (ASP) Ragusa, Italy
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
- Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany
| | - Mikael Johansson
- Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden
| | | | | | - Paolo Vineis
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- Italian Institute of Technology, Genova, Italy
| | | | - Torkjel M Sandanger
- Department of Community Medicine, UiT- The Arctic University of Norway, Tromsø, Norway
| | - Roel C H Vermeulen
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
| | - Marc Chadeau-Hyam
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.
- Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
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Appenzeller BMR, Chadeau-Hyam M, Aguilar L. Skin exposome science in practice : current evidence on hair biomonitoring and future perspectives. J Eur Acad Dermatol Venereol 2021; 34 Suppl 4:26-30. [PMID: 32677066 DOI: 10.1111/jdv.16640] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 05/04/2020] [Accepted: 05/11/2020] [Indexed: 01/04/2023]
Abstract
The skin exposome, defined as the totality of environmental exposures from conception to death that can induce or modify various skin conditions, compiles environmental, lifestyle and psychosocial exposures, as well as the resulting internal biological and physiological responses to these exposures. Biomonitoring can be used to obtain information on the internal dose of pollutants. The concentration of biomarkers in body fluids is highly variable over time due to differential elimination kinetics of chemicals, whereas they accumulate in hair. Hair analysis thus provides information on cumulative exposure over a longer period of time, and so can be used for assessing chronic exposure to pollutants. Studies on hair samples collected from 204 women living in two cities in China with different levels of pollution demonstrated that hair damage and the skin microbiome are biomarkers of a polluted city and long-term exposure to pollution and UV can increase signs of facial ageing. Adopting an exposome approach to skin health requires assessing multiple exposures and biological consequences, possibly in relation to longitudinally followed-up health outcomes. Leveraging "omics" data (e.g. metabolomics, proteomics, genomics and microbiome) and big data analytics, in particular multivariate analysis, will help to further understand the impact of pollution on skin and the combined effects with other exposome factors, including solar radiation and other environmental exposures.
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Affiliation(s)
- B M R Appenzeller
- Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg
| | | | - L Aguilar
- L'Oréal, Advanced Research, Aulnay-sous-Bois, France
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20
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Saberi Hosnijeh F, Casabonne D, Nieters A, Solans M, Naudin S, Ferrari P, Mckay JD, Benavente Y, Weiderpass E, Freisling H, Severi G, Boutron Ruault M, Besson C, Agnoli C, Masala G, Sacerdote C, Tumino R, Huerta JM, Amiano P, Rodriguez‐Barranco M, Bonet C, Barricarte A, Christakoudi S, Knuppel A, Bueno‐de‐Mesquita B, Schulze MB, Kaaks R, Canzian F, Späth F, Jerkeman M, Rylander C, Tjønneland A, Olsen A, Borch KB, Vermeulen R. Association between anthropometry and lifestyle factors and risk of B-cell lymphoma: An exposome-wide analysis. Int J Cancer 2021; 148:2115-2128. [PMID: 33128820 PMCID: PMC8048490 DOI: 10.1002/ijc.33369] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 09/28/2020] [Accepted: 10/13/2020] [Indexed: 12/12/2022]
Abstract
To better understand the role of individual and lifestyle factors in human disease, an exposome-wide association study was performed to investigate within a single-study anthropometry measures and lifestyle factors previously associated with B-cell lymphoma (BCL). Within the European Prospective Investigation into Cancer and nutrition study, 2402 incident BCL cases were diagnosed from 475 426 participants that were followed-up on average 14 years. Standard and penalized Cox regression models as well as principal component analysis (PCA) were used to evaluate 84 exposures in relation to BCL risk. Standard and penalized Cox regression models showed a positive association between anthropometric measures and BCL and multiple myeloma/plasma cell neoplasm (MM). The penalized Cox models additionally showed the association between several exposures from categories of physical activity, smoking status, medical history, socioeconomic position, diet and BCL and/or the subtypes. PCAs confirmed the individual associations but also showed additional observations. The PC5 including anthropometry, was positively associated with BCL, diffuse large B-cell lymphoma (DLBCL) and MM. There was a significant positive association between consumption of sugar and confectionary (PC11) and follicular lymphoma risk, and an inverse association between fish and shellfish and Vitamin D (PC15) and DLBCL risk. The PC1 including features of the Mediterranean diet and diet with lower inflammatory score showed an inverse association with BCL risk, while the PC7, including dairy, was positively associated with BCL and DLBCL risk. Physical activity (PC10) was positively associated with DLBCL risk among women. This study provided informative insights on the etiology of BCL.
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Affiliation(s)
- Fatemeh Saberi Hosnijeh
- Division of Environmental Epidemiology, Institute for Risk Assessment SciencesUtrecht UniversityUtrechtThe Netherlands
- Department of Immunology, Laboratory Medical Immunology, Erasmus MCUniversity Medical CenterRotterdamThe Netherlands
| | - Delphine Casabonne
- Unit of Infections and Cancer, Cancer Epidemiology Research Programme, IDIBELLCatalan Institute of OncologyBadalonaSpain
- Centro de Investigación Biomédica en Red: Epidemiología y Salud Pública (CIBERESP)MadridSpain
| | - Alexandra Nieters
- Institute for Immunodeficiency, Faculty of Medicine and Medical CenterUniversity of FreiburgFreiburgGermany
| | - Marta Solans
- Centro de Investigación Biomédica en Red: Epidemiología y Salud Pública (CIBERESP)MadridSpain
- Research Group on Statistics, Econometrics and Health (GRECS)University of GironaGironaSpain
| | - Sabine Naudin
- Nutritional Methodology and Biostatistics Group, International Agency for Research on CancerWorld Health OrganizationLyonFrance
| | - Pietro Ferrari
- Nutritional Methodology and Biostatistics Group, International Agency for Research on CancerWorld Health OrganizationLyonFrance
| | - James D. Mckay
- Section of GeneticsInternational Agency for Research on CancerLyonFrance
| | - Yolanda Benavente
- Unit of Infections and Cancer, Cancer Epidemiology Research Programme, IDIBELLCatalan Institute of OncologyBadalonaSpain
- Centro de Investigación Biomédica en Red: Epidemiología y Salud Pública (CIBERESP)MadridSpain
| | | | - Heinz Freisling
- Nutritional Methodology and Biostatistics Group, International Agency for Research on CancerWorld Health OrganizationLyonFrance
| | - Gianluca Severi
- Université Paris‐Saclay, UVSQCESP U1018 INSERMVillejuifFrance
- Gustave RoussyVillejuifFrance
- Department of Statistics, Computer Science, Applications “G. Parenti”University of FlorenceFlorenceItaly
| | | | - Caroline Besson
- Université Paris‐Saclay, UVSQCESP U1018 INSERMVillejuifFrance
- UFR sciences de la santéUniversité Versailles Saint Quentin en Yvelines, Université Paris‐Saclay, Communaute Paris‐Saclay (Carol)Saint‐AubinFrance
- Versailles Hospital, Unit of Hematology–OncologyLe ChesnayFrance
| | - Claudia Agnoli
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei TumoriMilanItaly
| | - Giovanna Masala
- Cancer Risk Factors and Life‐Style Epidemiology UnitInstitute for Cancer Research, Prevention and Clinical Network—ISPROFlorenceItaly
| | - Carlotta Sacerdote
- Unit of Cancer Epidemiology, Città della Salute e della Scienza University‐Hospital and Center for Cancer Prevention (CPO)TurinItaly
| | - Rosario Tumino
- Cancer Registry and Histopathology DepartmentAzienda Sanitaria ProvincialeRagusaItaly
| | - José María Huerta
- Department of EpidemiologyMurcia Regional Health Council, IMIB‐ArrixacaMurciaSpain
- CIBER Epidemiología y Salud Pública (CIBERESP)MadridSpain
| | - Pilar Amiano
- Public Health Division of Gipuzkoa, BioDonostia Research Institute, San Sebastian; CIBER Epidemiología y Salud PúblicaMadridSpain
| | - Miguel Rodriguez‐Barranco
- Escuela Andaluza de Salud Pública (EASP)GranadaSpain
- Instituto de Investigación Biosanitaria ibs.GRANADAGranadaSpain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP)MadridSpain
| | - Catalina Bonet
- Unit of Nutrition and Cancer, Catalan Institute of Oncology—ICO, Nutrition and Cancer Group, Bellvitge Biomedical Research Institute—IDIBELL, L'Hospitalet de LlobregatBarcelonaSpain
| | - Aurelio Barricarte
- Centro de Investigación Biomédica en Red: Epidemiología y Salud Pública (CIBERESP)MadridSpain
- Navarra Public Health InstitutePamplonaSpain
- Navarra Institute for Health Research (IdiSNA)PamplonaSpain
| | - Sofia Christakoudi
- Department of Epidemiology and BiostatisticsImperial College LondonLondonUK
- MRC Centre for TransplantationKing's College LondonLondonUK
| | - Anika Knuppel
- Cancer Epidemiology Unit, Nuffield Department of Population HealthUniversity of OxfordOxfordUK
| | - Bas Bueno‐de‐Mesquita
- Department for Determinants of Chronic Diseases, National Institute for Public Health and the Environment (RIVM)The Netherlands
- Department of Gastroenterology and HepatologyUniversity Medical CentreUtrechtThe Netherlands
| | - Matthias B. Schulze
- Department of Molecular EpidemiologyGerman Institute of Human Nutrition Potsdam‐RehbrueckeNuthetalGermany
- Institute of Nutritional SciencesUniversity of PotsdamNuthetalGermany
| | - Rudolf Kaaks
- Division of Cancer EpidemiologyGerman Cancer Research Center (DKFZ)HeidelbergGermany
| | - Federico Canzian
- Research Group Genomic EpidemiologyGerman Cancer Research Center (DKFZ)HeidelbergGermany
| | - Florentin Späth
- Department of Radiation Sciences, Oncology and Cancer center, Department of HematologyUmeå UniversityUmeåSweden
| | - Mats Jerkeman
- Department of OncologyLund UniversityLundSweden
- Department of OncologySkane University HospitalLundSweden
| | | | - Anne Tjønneland
- Department of Public HealthUniversity of CopenhagenCopenhagenDenmark
- Danish Cancer Society Research CenterCopenhagenDenmark
| | - Anja Olsen
- Danish Cancer Society Research CenterCopenhagenDenmark
| | | | - Roel Vermeulen
- Division of Environmental Epidemiology, Institute for Risk Assessment SciencesUtrecht UniversityUtrechtThe Netherlands
- Julius Center for Health Sciences and Primary CareUniversity Medical Center UtrechtUtrechtThe Netherlands
- MRC‐PHE Centre for Environment and Health, Department of Epidemiology and BiostatisticsImperial College LondonLondonUK
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Zwep LB, Duisters KLW, Jansen M, Guo T, Meulman JJ, Upadhyay PJ, van Hasselt JGC. Identification of high-dimensional omics-derived predictors for tumor growth dynamics using machine learning and pharmacometric modeling. CPT Pharmacometrics Syst Pharmacol 2021; 10:350-361. [PMID: 33792207 PMCID: PMC8099445 DOI: 10.1002/psp4.12603] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 01/07/2021] [Accepted: 02/01/2021] [Indexed: 12/26/2022] Open
Abstract
Pharmacometric modeling can capture tumor growth inhibition (TGI) dynamics and variability. These approaches do not usually consider covariates in high-dimensional settings, whereas high-dimensional molecular profiling technologies ("omics") are being increasingly considered for prediction of anticancer drug treatment response. Machine learning (ML) approaches have been applied to identify high-dimensional omics predictors for treatment outcome. Here, we aimed to combine TGI modeling and ML approaches for two distinct aims: omics-based prediction of tumor growth profiles and identification of pathways associated with treatment response and resistance. We propose a two-step approach combining ML using least absolute shrinkage and selection operator (LASSO) regression with pharmacometric modeling. We demonstrate our workflow using a previously published dataset consisting of 4706 tumor growth profiles of patient-derived xenograft (PDX) models treated with a variety of mono- and combination regimens. Pharmacometric TGI models were fit to the tumor growth profiles. The obtained empirical Bayes estimates-derived TGI parameter values were regressed using the LASSO on high-dimensional genomic copy number variation data, which contained over 20,000 variables. The predictive model was able to decrease median prediction error by 4% as compared with a model without any genomic information. A total of 74 pathways were identified as related to treatment response or resistance development by LASSO, of which part was verified by literature. In conclusion, we demonstrate how the combined use of ML and pharmacometric modeling can be used to gain pharmacological understanding in genomic factors driving variation in treatment response.
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Affiliation(s)
- Laura B. Zwep
- Leiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
- Mathematical InstituteLeiden UniversityLeidenThe Netherlands
| | | | - Martijn Jansen
- Leiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
| | - Tingjie Guo
- Leiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
- Department of Intensive Care MedicineAmsterdam UMCVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | | | - Parth J. Upadhyay
- Leiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
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Use of Exposomic Methods Incorporating Sensors in Environmental Epidemiology. Curr Environ Health Rep 2021; 8:34-41. [PMID: 33569731 DOI: 10.1007/s40572-021-00306-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/29/2021] [Indexed: 10/22/2022]
Abstract
PURPOSE OF REVIEW The exposome is a recently coined concept that comprises the totality of nongenetic factors that affect human health. It is recognized as a major conceptual advancement in environmental epidemiology, and there is increased demand for technologies that capture the spatial, temporal, and chemical variability of exposures across individuals (i.e., "exposomic sensors"). We review a selection of these tools, highlighting their strengths and limitations with regard to epidemiological research. RECENT FINDINGS Wearable passive samplers are emerging as promising exposomic sensors for individuals. In conjunction with targeted and untargeted assays, these sensors enable the measurement of complex multipollutant mixtures, which can include both known and previously unknown environmental contaminants. Because of their minimally burdensome and noninvasive nature, they are deployable among sensitive populations, such as seniors, pregnant women, and children. The integration of exposomic data captured by these sensors with other omic data (e.g., transcriptomic and metabolomic) presents exciting opportunities for investigating disease risk factors. For example, the linkage of exposomic sensor data with other omic data may indicate perturbation by multipollutant mixtures at multiple physiological levels, which would strengthen evidence of their effects and potentially indicate targets for interventions. However, there remain considerable theoretical and methodological challenges that must be overcome to realize the potential promise of omic integration. Through continued investment and improvement in exposomic sensor technologies, it may be possible to refine their application and reduce their outstanding limitations to advance the fields of exposure science and epidemiology.
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23
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Govarts E, Portengen L, Lambrechts N, Bruckers L, Den Hond E, Covaci A, Nelen V, Nawrot TS, Loots I, Sioen I, Baeyens W, Morrens B, Schoeters G, Vermeulen R. Early-life exposure to multiple persistent organic pollutants and metals and birth weight: Pooled analysis in four Flemish birth cohorts. ENVIRONMENT INTERNATIONAL 2020; 145:106149. [PMID: 33002701 DOI: 10.1016/j.envint.2020.106149] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 09/14/2020] [Accepted: 09/16/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND AIMS Prenatal chemical exposure has frequently been associated with reduced fetal growth although results have been inconsistent. Most studies associate single pollutant exposure to this health outcome, even though this does not reflect real life situations as humans are exposed to many pollutants during their life time. The objective of this study is to investigate the association between prenatal exposure to a mixture of persistent environmental chemicals and birth weight using multipollutant models. METHODS We combined exposure biomarker data measured in cord blood samples of 1579 women from four Flemish birth cohorts collected over a 10 years' time period. The common set of available and detectable exposure measures in these cohorts are three polychlorinated biphenyls (PCB) congeners (138, 153 and 180), hexachlorobenzene (HCB), dichlorodiphenyldichloroethylene (p,p'-DDE) and the metals cadmium and lead. Multiple linear regression (MLR), Bayesian Information Criterion (BIC), penalized regression using minimax concave penalty (MCP) and Bayesian Adaptive Sampling (BAS) were applied to assess the influence of multiple pollutants in a single analysis on birth weight, adjusted for a priori selected covariates. RESULTS In the pooled dataset, a median (P25-P75) birth weight and gestational age of 3420 (3140-3700) grams and 39 (39-40) weeks was observed respectively. The median contaminant levels in cord blood were: 15.8, 26.5, 18.0, 16.9 and 91.5 ng/g lipid for PCB 138, PCB 153, PCB 180, HCB and p,p'-DDE, respectively, 0.075 µg/L for cadmium and 9.7 µg/L for lead. According to the applied statistical methods for multipollutant assessment, p,p'-DDE and PCB 180 were most consistently associated with birth weight. In addition, PCB 153 was selected when applying MCP and BAS. An inverse association with birth weight was found for the PCB congeners, while an increased birth weight was observed for elevated levels of p,p'-DDE. CONCLUSIONS Assessing the health risk of combinations of exposure biomarkers reflects better real-world situations and thereby allows more effective risk assessment. Our results add to the existing evidence based on detrimental effects of PCBs on birth weight and indicate a possible increase in birth weight due to p,p'-DDE (while correcting for PCBs).
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Affiliation(s)
- Eva Govarts
- VITO Health, Flemish Institute for Technological Research (VITO), Mol, Belgium.
| | - Lützen Portengen
- Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
| | - Nathalie Lambrechts
- VITO Health, Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Liesbeth Bruckers
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Diepenbeek, Belgium
| | | | - Adrian Covaci
- Toxicological Centre, University of Antwerp, Antwerp, Belgium
| | - Vera Nelen
- Provincial Institute of Hygiene, Antwerp, Belgium
| | - Tim S Nawrot
- Centre for Environmental Sciences, Hasselt University, Diepenbeek, Belgium; Leuven University, Department of Public Health & Primary Care, Leuven, Belgium
| | - Ilse Loots
- Faculty Social Sciences, University of Antwerp, Antwerp, Belgium
| | - Isabelle Sioen
- Department of Public Health, Ghent University, Ghent, Belgium
| | - Willy Baeyens
- Department of Analytical, Environmental and Geochemistry (AMGC), Free University Brussels (VUB), Brussels, Belgium
| | - Bert Morrens
- Faculty Social Sciences, University of Antwerp, Antwerp, Belgium
| | - Greet Schoeters
- VITO Health, Flemish Institute for Technological Research (VITO), Mol, Belgium; Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium; University of Southern Denmark, Institute of Public Health, Department of Environmental Medicine, Odense, Denmark
| | - Roel Vermeulen
- Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
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24
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Pan WC, Yeh SY, Wu CD, Huang YT, Chen YC, Chen CJ, Yang HI. Association Between Traffic Count and Cardiovascular Mortality: A Prospective Cohort Study in Taiwan. J Epidemiol 2020; 31:343-349. [PMID: 32565497 PMCID: PMC8021879 DOI: 10.2188/jea.je20200082] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Background Exposure to traffic-related pollution is positively associated with cardiovascular diseases (CVD), but little is known about how different sources of traffic pollution (eg, gasoline-powered cars, diesel-engine vehicles) contribute to CVD. Therefore, we evaluated the association between exposure to different types of engine exhaust and CVD mortality. Methods We recruited 12,098 participants from REVEAL-HBV cohort in Taiwan. The CVD mortality in 2000–2014 was ascertained by the Taiwan Death Certificates. Traffic pollution sources (2005–2013) were based on information provided by the Directorate General of Highway in 2005. Exposure to PM2.5 was based on a land-use regression model. We applied Cox proportional hazard models to assess the association of traffic vehicle exposure and CVD mortality. A causal mediation analysis was applied to evaluate the mediation effect of PM2.5 on the relationship between traffic and CVD mortality. Results A total of 382 CVD mortalities were identified from 2000 to 2014. We found participants exposed to higher volumes of small car and truck exhausts had an increased CVD mortality. The adjusted hazard ratio (HR) was 1.10 for small cars (95% confidence interval [CI], 0.94–1.27; P-value = 0.23) and 1.24 for truck (95% CI, 1.03–1.51; P-value = 0.03) per one unit increment of the logarithm scale. The findings were still robust with further adjustment for different types of vehicles. A causal mediation analysis revealed PM2.5 had an over 60% mediation effect on traffic-CVD association. Conclusions Exposure to exhaust from trucks or gasoline-powered cars is positively associated with CVD mortality, and air pollution may play a role in this association.
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Affiliation(s)
- Wen-Chi Pan
- Institute of Environmental and Occupational Health Sciences, National Yang-Ming University.,Center of Preventive Medicine, National Yang-Ming University
| | - Szu-Yu Yeh
- Institute of Environmental and Occupational Health Sciences, National Yang-Ming University.,Center of Preventive Medicine, National Yang-Ming University
| | - Chih-Da Wu
- Department of Geomatics, National Cheng Kung University.,National Health Research Institutes, National Institute of Environmental Health Sciences
| | | | - Yu-Cheng Chen
- National Institution of Environmental Health Sciences, National Health Research Institute
| | - Chien-Jen Chen
- Genomics Research Center, Academia Sinica.,Graduate Institute of Epidemiology and Preventive Medicine, National Taiwan University
| | - Hwai-I Yang
- Genomics Research Center, Academia Sinica.,Institute of Clinical Medicine, National Yang-Ming University
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25
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Luque de Castro M, Quiles-Zafra R. Lipidomics: An omics discipline with a key role in nutrition. Talanta 2020; 219:121197. [DOI: 10.1016/j.talanta.2020.121197] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 05/16/2020] [Accepted: 05/19/2020] [Indexed: 12/14/2022]
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26
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Canali S. Making evidential claims in epidemiology: Three strategies for the study of the exposome. STUDIES IN HISTORY AND PHILOSOPHY OF BIOLOGICAL AND BIOMEDICAL SCIENCES 2020; 82:101248. [PMID: 32307253 DOI: 10.1016/j.shpsc.2019.101248] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 10/15/2019] [Accepted: 12/27/2019] [Indexed: 06/11/2023]
Abstract
How is scientific data used to represent phenomena and as evidence for claims about phenomena? In this paper, I propose that a specific type of claims - evidential claims - is involved in data practices to define and restrict the representational and evidential content of a dataset. I present an account of data practices in the epidemiology of the exposome based on the notion of evidential claims, which helps unpack the approaches, assumptions and warrants that connect different stages of research. I identify three different strategies to generate different types of evidential claims in this case. The macro strategy, which individuates the dataset that serves as the initial evidential space for research. The micro strategy, which is used to generate evidential claims about the microscopic and individual component of target phenomena. The association strategy, that uses evidence from the other strategies to identify a dataset as representation of the different levels and relations of exposure and disease. Differentiating between these strategies sheds light on the multi-faceted landscape of biomedical research on environment and health; and the roles of data and evidence in the process of inquiry.
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Affiliation(s)
- Stefano Canali
- Institute for Philosophy, Leibniz Universität Hannover, Lange Laube 32, 30159, Hannover, Germany.
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27
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Turek C, Wróbel S, Piwowar M. OmicsON - Integration of omics data with molecular networks and statistical procedures. PLoS One 2020; 15:e0235398. [PMID: 32726348 PMCID: PMC7390260 DOI: 10.1371/journal.pone.0235398] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Accepted: 06/15/2020] [Indexed: 12/05/2022] Open
Abstract
A huge amount of atomized biological data collected in various databases and the need for a description of their relation by theoretical methods causes the development of data integration methods. The omics data analysis by integration of biological knowledge with mathematical procedures implemented in the OmicsON R library is presented in the paper. OmicsON is a tool for the integration of two sets of data: transcriptomics and metabolomics. In the workflow of the library, the functional grouping and statistical analysis are applied. Subgroups among the transcriptomic and metabolomics sets are created based on the biological knowledge stored in Reactome and String databases. It gives the possibility to analyze such sets of data by multivariate statistical procedures like Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). The integration of metabolomic and transcriptomic data based on the methodology contained in OmicsON helps to easily obtain information on the connection of data from two different sets. This information can significantly help in assessing the relationship between gene expression and metabolite concentrations, which in turn facilitates the biological interpretation of the analyzed process.
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Affiliation(s)
- Cezary Turek
- Department of Bioinformatics and Telemedicine, Jagiellonian University–Medical College, Krakow, Poland
| | - Sonia Wróbel
- Department of Medical Physics, Jagiellonian University, Marian Smoluchowski Institute of Physics, Krakow, Poland
| | - Monika Piwowar
- Department of Bioinformatics and Telemedicine, Jagiellonian University–Medical College, Krakow, Poland
- * E-mail:
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28
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McGee EE, Kiblawi R, Playdon MC, Eliassen AH. Nutritional Metabolomics in Cancer Epidemiology: Current Trends, Challenges, and Future Directions. Curr Nutr Rep 2020; 8:187-201. [PMID: 31129888 DOI: 10.1007/s13668-019-00279-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
PURPOSE OF REVIEW Metabolomics offers several opportunities for advancement in nutritional cancer epidemiology; however, numerous research gaps and challenges remain. This narrative review summarizes current research, challenges, and future directions for epidemiologic studies of nutritional metabolomics and cancer. RECENT FINDINGS Although many studies have used metabolomics to investigate either dietary exposures or cancer, few studies have explicitly investigated diet-cancer relationships using metabolomics. Most studies have been relatively small (≤ ~ 250 cases) or have assessed a limited number of nutritional metabolites (e.g., coffee or alcohol-related metabolites). Nutritional metabolomic investigations of cancer face several challenges in study design; biospecimen selection, handling, and processing; diet and metabolite measurement; statistical analyses; and data sharing and synthesis. More metabolomics studies linking dietary exposures to cancer risk, prognosis, and survival are needed, as are biomarker validation studies, longitudinal analyses, and methodological studies. Despite the remaining challenges, metabolomics offers a promising avenue for future dietary cancer research.
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Affiliation(s)
- Emma E McGee
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Rama Kiblawi
- Division of Cancer Population Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Mary C Playdon
- Division of Cancer Population Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
- Department of Nutrition and Integrative Physiology, University of Utah, Salt Lake City, UT, USA
| | - A Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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29
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Passarelli C, Selvatici R, Carrieri A, Di Raimo FR, Falzarano MS, Fortunato F, Rossi R, Straub V, Bushby K, Reza M, Zharaieva I, D'Amico A, Bertini E, Merlini L, Sabatelli P, Borgiani P, Novelli G, Messina S, Pane M, Mercuri E, Claustres M, Tuffery-Giraud S, Aartsma-Rus A, Spitali P, T'Hoen PAC, Lochmüller H, Strandberg K, Al-Khalili C, Kotelnikova E, Lebowitz M, Schwartz E, Muntoni F, Scapoli C, Ferlini A. Tumor Necrosis Factor Receptor SF10A (TNFRSF10A) SNPs Correlate With Corticosteroid Response in Duchenne Muscular Dystrophy. Front Genet 2020; 11:605. [PMID: 32719714 PMCID: PMC7350910 DOI: 10.3389/fgene.2020.00605] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Accepted: 05/18/2020] [Indexed: 12/23/2022] Open
Abstract
Background Duchenne muscular dystrophy (DMD) is a rare and severe X-linked muscular dystrophy in which the standard of care with variable outcome, also due to different drug response, is chronic off-label treatment with corticosteroids (CS). In order to search for SNP biomarkers for corticosteroid responsiveness, we genotyped variants across 205 DMD-related genes in patients with differential response to steroid treatment. Methods and Findings We enrolled a total of 228 DMD patients with identified dystrophin mutations, 78 of these patients have been under corticosteroid treatment for at least 5 years. DMD patients were defined as high responders (HR) if they had maintained the ability to walk after 15 years of age and low responders (LR) for those who had lost ambulation before the age of 10 despite corticosteroid therapy. Based on interactome mapping, we prioritized 205 genes and sequenced them in 21 DMD patients (discovery cohort or DiC = 21). We identified 43 SNPs that discriminate between HR and LR. Discriminant Analysis of Principal Components (DAPC) prioritized 2 response-associated SNPs in the TNFRSF10A gene. Validation of this genotype was done in two additional larger cohorts composed of 46 DMD patients on corticosteroid therapy (validation cohorts or VaC1), and 150 non ambulant DMD patients and never treated with corticosteroids (VaC2). SNP analysis in all validation cohorts (N = 207) showed that the CT haplotype is significantly associated with HR DMDs confirming the discovery results. Conclusion We have shown that TNFRSF10A CT haplotype correlates with corticosteroid response in DMD patients and propose it as an exploratory CS response biomarker.
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Affiliation(s)
- Chiara Passarelli
- Unit of Medical Genetics, Department of Medical Sciences, University of Ferrara, Ferrara, Italy.,U.O.C. Laboratory of Medical Genetics, Paediatric Hospital Bambino Gesù, IRCCS, Rome, Italy
| | - Rita Selvatici
- Unit of Medical Genetics, Department of Medical Sciences, University of Ferrara, Ferrara, Italy
| | - Alberto Carrieri
- Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy
| | | | - Maria Sofia Falzarano
- Unit of Medical Genetics, Department of Medical Sciences, University of Ferrara, Ferrara, Italy
| | - Fernanda Fortunato
- Unit of Medical Genetics, Department of Medical Sciences, University of Ferrara, Ferrara, Italy
| | - Rachele Rossi
- Unit of Medical Genetics, Department of Medical Sciences, University of Ferrara, Ferrara, Italy
| | - Volker Straub
- John Walton Muscular Dystrophy Research Centre, Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Katie Bushby
- John Walton Muscular Dystrophy Research Centre, Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Mojgan Reza
- John Walton Muscular Dystrophy Research Centre, Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Irina Zharaieva
- Dubowitz Neuromuscular Center, University College London Institute of Child Health & Great Ormond Street Hospital, London, United Kingdom
| | - Adele D'Amico
- Molecular Medicine and Unit of Neuromuscular and Neurodegenerative Diseases, Paediatric Hospital Bambino Gesù, IRCCS, Rome, Italy
| | - Enrico Bertini
- Molecular Medicine and Unit of Neuromuscular and Neurodegenerative Diseases, Paediatric Hospital Bambino Gesù, IRCCS, Rome, Italy
| | - Luciano Merlini
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Patrizia Sabatelli
- IRCCS Rizzoli & Institute of Molecular Genetics, National Research Council of Italy, Bologna, Italy
| | - Paola Borgiani
- Genetics Unit, Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
| | - Giuseppe Novelli
- Genetics Unit, Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy.,Istituto Neuromed, IRCCS, Pozzilli, Italy
| | - Sonia Messina
- Department of Clinical and Experimental Medicine, Nemo Sud Clinical Center, University of Messina, Messina, Italy
| | - Marika Pane
- Paediatric Neurology Unit, Centro Clinico Nemo, IRCCS Fondazione Policlinico A. Gemelli, Universita' Cattolica del Sacro Cuore, Rome, Italy
| | - Eugenio Mercuri
- Paediatric Neurology Unit, Centro Clinico Nemo, IRCCS Fondazione Policlinico A. Gemelli, Universita' Cattolica del Sacro Cuore, Rome, Italy
| | - Mireille Claustres
- Laboratory of Genetics of Rare Diseases, University of Montpellier, Montpellier, France
| | - Sylvie Tuffery-Giraud
- Laboratory of Genetics of Rare Diseases, University of Montpellier, Montpellier, France
| | - Annemieke Aartsma-Rus
- John Walton Muscular Dystrophy Research Centre, Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom.,Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
| | - Pietro Spitali
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
| | - Peter A C T'Hoen
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands.,Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Hanns Lochmüller
- Department of Neuropediatrics and Muscle Disorders, Faculty of Medicine, Medical Center - University of Freiburg, Freiburg, Germany.,Centro Nacional de Análisis Genómico (CNAG-CRG), Center for Genomic Regulation, Barcelona Institute of Science and Technology (BIST), Barcelona, Spain.,Children's Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada.,Division of Neurology, Department of Medicine, The Ottawa Hospital, Ottawa, ON, Canada.,Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada
| | - Kristin Strandberg
- Department of Systems Biology, School of Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Cristina Al-Khalili
- Department of Systems Biology, School of Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden
| | | | | | | | - Francesco Muntoni
- Dubowitz Neuromuscular Center, University College London Institute of Child Health & Great Ormond Street Hospital, London, United Kingdom.,NIH Great Ormond Street Hospital Biomedical Research Centre, Great Ormond Street Institute of Child Health, University College London, London, United Kingdom.,Great Ormond Street Hospital Trust, London, United Kingdom
| | - Chiara Scapoli
- Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy
| | - Alessandra Ferlini
- Unit of Medical Genetics, Department of Medical Sciences, University of Ferrara, Ferrara, Italy.,Dubowitz Neuromuscular Center, University College London Institute of Child Health & Great Ormond Street Hospital, London, United Kingdom
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30
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Vineis P, Avendano-Pabon M, Barros H, Bartley M, Carmeli C, Carra L, Chadeau-Hyam M, Costa G, Delpierre C, D'Errico A, Fraga S, Giles G, Goldberg M, Kelly-Irving M, Kivimaki M, Lepage B, Lang T, Layte R, MacGuire F, Mackenbach JP, Marmot M, McCrory C, Milne RL, Muennig P, Nusselder W, Petrovic D, Polidoro S, Ricceri F, Robinson O, Stringhini S, Zins M. Special Report: The Biology of Inequalities in Health: The Lifepath Consortium. Front Public Health 2020; 8:118. [PMID: 32478023 PMCID: PMC7235337 DOI: 10.3389/fpubh.2020.00118] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Accepted: 03/24/2020] [Indexed: 12/16/2022] Open
Abstract
Funded by the European Commission Horizon 2020 programme, the Lifepath research consortium aimed to investigate the effects of socioeconomic inequalities on the biology of healthy aging. The main research questions included the impact of inequalities on health, the role of behavioral and other risk factors, the underlying biological mechanisms, the efficacy of selected policies, and the general implications of our findings for theories and policies. The project adopted a life-course and comparative approach, considering lifetime effects from childhood and adulthood, and pooled data on up to 1.7 million participants of longitudinal cohort studies from Europe, USA, and Australia. These data showed that socioeconomic circumstances predicted mortality and functional decline as strongly as established risk factors currently targeted by global prevention programmes. Analyses also looked at socioeconomically patterned biological markers, allostatic load, and DNA methylation using richly phenotyped cohorts, unraveling their association with aging processes across the life-course. Lifepath studies suggest that socioeconomic circumstances are embedded in our biology from the outset—i.e., disadvantage influences biological systems from molecules to organs. Our findings have important implications for policy, suggesting that (a) intervening on unfavorable socioeconomic conditions is complementary and as important as targeting well-known risk factors, such as tobacco and alcohol consumption, low fruit and vegetable intake, obesity and a sedentary lifestyle, and that (b) effects of preventive interventions in early life integrate interventions in adulthood. The report has an executive summary that refers to the different sections of the main paper.
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Affiliation(s)
- Paolo Vineis
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
| | - Mauricio Avendano-Pabon
- Department of Social Sciences, Health and Medicine, King's College London, London, United Kingdom
| | - Henrique Barros
- EPIUnit - Institute of Public Health University of Porto, Porto, Portugal
| | - Mel Bartley
- Department of Epidemiology & Public Health, University College London, London, United Kingdom
| | - Cristian Carmeli
- Center for Primary Care and Public Health (UNISANTE), University of Lausanne, Lausanne, Switzerland
| | | | - Marc Chadeau-Hyam
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
| | - Giuseppe Costa
- Department of Clinical Science & Biology, Turin University Medical School, Turin, Italy
| | - Cyrille Delpierre
- UMR LEASP, Université de Toulouse III, UPS, Inserm, Toulouse, France
| | | | - Silvia Fraga
- EPIUnit - Institute of Public Health University of Porto, Porto, Portugal
| | - Graham Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, VIC, Australia
| | - Marcel Goldberg
- UMS 011 Inserm - UVSQ ≪ Cohortes épidémiologiques en population ≫, Villejuif, France
| | | | - Mika Kivimaki
- Department of Epidemiology and Public Health, University College London, London, United Kingdom
| | - Benoit Lepage
- UMR LEASP, Université de Toulouse III, UPS, Inserm, Toulouse, France
| | - Thierry Lang
- UMR LEASP, Université de Toulouse III, UPS, Inserm, Toulouse, France
| | - Richard Layte
- Department of Sociology, School of Social Sciences and Philosophy, Trinity College Dublin, Dublin, Ireland
| | - Frances MacGuire
- Department of Epidemiology and Public Health, University College London, London, United Kingdom
| | - Johan P Mackenbach
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Michael Marmot
- Department of Epidemiology and Public Health, University College London, London, United Kingdom
| | - Cathal McCrory
- Department of Medical Gerontology, Trinity College Dublin, Dublin, Ireland
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, VIC, Australia
| | - Peter Muennig
- Mailman School of Public Health, Columbia University, New York, NY, United States
| | - Wilma Nusselder
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Dusan Petrovic
- Center for Primary Care and Public Health (UNISANTE), University of Lausanne, Lausanne, Switzerland
| | - Silvia Polidoro
- Molecular Epidemiology and Exposomics Unit, Italian Institute for Genomic Medicine, Turin, Italy
| | - Fulvio Ricceri
- Department of Clinical Science & Biology, Turin University Medical School, Turin, Italy.,Department of Epidemiology, ASL TO3, Turin, Italy
| | - Oliver Robinson
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
| | - Silvia Stringhini
- Unit of Population Epidemiology, Division of Primary Care, Geneva University Hospitals, Geneva, Switzerland
| | - Marie Zins
- UMS 011 Inserm - UVSQ ≪ Cohortes épidémiologiques en population ≫, Villejuif, France
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Blum MGB, Valeri L, François O, Cadiou S, Siroux V, Lepeule J, Slama R. Challenges Raised by Mediation Analysis in a High-Dimension Setting. ENVIRONMENTAL HEALTH PERSPECTIVES 2020; 128:55001. [PMID: 32379489 PMCID: PMC7263455 DOI: 10.1289/ehp6240] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 04/14/2020] [Accepted: 04/15/2020] [Indexed: 05/19/2023]
Abstract
BACKGROUND Mediation analysis is used in epidemiology to identify pathways through which exposures influence health. The advent of high-throughput (omics) technologies gives opportunities to perform mediation analysis with a high-dimension pool of covariates. OBJECTIVE We aimed to highlight some biostatistical issues of this expanding field of high-dimension mediation. DISCUSSION The mediation techniques used for a single mediator cannot be generalized in a straightforward manner to high-dimension mediation. Causal knowledge on the relation between covariates is required for mediation analysis, and it is expected to be more limited as dimension and system complexity increase. The methods developed in high dimension can be distinguished according to whether mediators are considered separately or as a whole. Methods considering each potential mediator separately do not allow efficient identification of the indirect effects when mutual influences exist among the mediators, which is expected for many biological (e.g., epigenetic) parameters. In this context, methods considering all potential mediators simultaneously, based, for example, on data reduction techniques, are more adapted to the causal inference framework. Their cost is a possible lack of ability to single out the causal mediators. Moreover, the ability of the mediators to predict the outcome can be overestimated, in particular because many machine-learning algorithms are optimized to increase predictive ability rather than their aptitude to make causal inference. Given the lack of overarching validated framework and the generally complex causal structure of high-dimension data, analysis of high-dimension mediation currently requires great caution and effort to incorporate a priori biological knowledge. https://doi.org/10.1289/EHP6240.
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Affiliation(s)
- Michaël G B Blum
- Laboratoire Techniques de l'Imagerie Médicale et de la Complexité (TIMC-IMAG; UMR 5525), French National Centre for Scientific Research (CNRS), University Grenoble Alpes, La Tronche, France
- OWKIN, Paris, France
| | - Linda Valeri
- Department of Biostatistics, Columbia University Mailman School of Public Health, New York, New York, USA
| | - Olivier François
- Laboratoire Techniques de l'Imagerie Médicale et de la Complexité (TIMC-IMAG; UMR 5525), French National Centre for Scientific Research (CNRS), University Grenoble Alpes, La Tronche, France
| | - Solène Cadiou
- Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Institute for Advanced Biosciences (IAB) joint research center, Institut national de la santé et de la recherché médicale (Inserm), CNRS, University Grenoble-Alpes, Grenoble, France
| | - Valérie Siroux
- Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Institute for Advanced Biosciences (IAB) joint research center, Institut national de la santé et de la recherché médicale (Inserm), CNRS, University Grenoble-Alpes, Grenoble, France
| | - Johanna Lepeule
- Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Institute for Advanced Biosciences (IAB) joint research center, Institut national de la santé et de la recherché médicale (Inserm), CNRS, University Grenoble-Alpes, Grenoble, France
| | - Rémy Slama
- Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Institute for Advanced Biosciences (IAB) joint research center, Institut national de la santé et de la recherché médicale (Inserm), CNRS, University Grenoble-Alpes, Grenoble, France
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32
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Vermeulen R, Schymanski EL, Barabási AL, Miller GW. The exposome and health: Where chemistry meets biology. Science 2020; 367:392-396. [PMID: 31974245 DOI: 10.1126/science.aay3164] [Citation(s) in RCA: 418] [Impact Index Per Article: 104.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Despite extensive evidence showing that exposure to specific chemicals can lead to disease, current research approaches and regulatory policies fail to address the chemical complexity of our world. To safeguard current and future generations from the increasing number of chemicals polluting our environment, a systematic and agnostic approach is needed. The "exposome" concept strives to capture the diversity and range of exposures to synthetic chemicals, dietary constituents, psychosocial stressors, and physical factors, as well as their corresponding biological responses. Technological advances such as high-resolution mass spectrometry and network science have allowed us to take the first steps toward a comprehensive assessment of the exposome. Given the increased recognition of the dominant role that nongenetic factors play in disease, an effort to characterize the exposome at a scale comparable to that of the human genome is warranted.
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Affiliation(s)
- Roel Vermeulen
- Institute for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, Utrecht, the Netherlands. .,Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Emma L Schymanski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Albert-László Barabási
- Network Science Institute, Northeastern University, Boston, MA, USA.,Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Network and Data Science, Central European University, Budapest, Hungary
| | - Gary W Miller
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA.
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Applying the exposome concept in birth cohort research: a review of statistical approaches. Eur J Epidemiol 2020; 35:193-204. [PMID: 32221742 PMCID: PMC7154018 DOI: 10.1007/s10654-020-00625-4] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 03/17/2020] [Indexed: 12/30/2022]
Abstract
The exposome represents the totality of life course environmental exposures (including lifestyle and other non-genetic factors), from the prenatal period onwards. This holistic concept of exposure provides a new framework to advance the understanding of complex and multifactorial diseases. Prospective pregnancy and birth cohort studies provide a unique opportunity for exposome research as they are able to capture, from prenatal life onwards, both the external (including lifestyle, chemical, social and wider community-level exposures) and the internal (including inflammation, metabolism, epigenetics, and gut microbiota) domains of the exposome. In this paper, we describe the steps required for applying an exposome approach, describe the main strengths and limitations of different statistical approaches and discuss their challenges, with the aim to provide guidance for methodological choices in the analysis of exposome data in birth cohort studies. An exposome approach implies selecting, pre-processing, describing and analyzing a large set of exposures. Several statistical methods are currently available to assess exposome-health associations, which differ in terms of research question that can be answered, of balance between sensitivity and false discovery proportion, and between computational complexity and simplicity (parsimony). Assessing the association between many exposures and health still raises many exposure assessment issues and statistical challenges. The exposome favors a holistic approach of environmental influences on health, which is likely to allow a more complete understanding of disease etiology.
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Schmidt JA, Fensom GK, Rinaldi S, Scalbert A, Appleby PN, Achaintre D, Gicquiau A, Gunter MJ, Ferrari P, Kaaks R, Kühn T, Boeing H, Trichopoulou A, Karakatsani A, Peppa E, Palli D, Sieri S, Tumino R, Bueno-de-Mesquita B, Agudo A, Sánchez MJ, Chirlaque MD, Ardanaz E, Larrañaga N, Perez-Cornago A, Assi N, Riboli E, Tsilidis KK, Key TJ, Travis RC. Patterns in metabolite profile are associated with risk of more aggressive prostate cancer: A prospective study of 3,057 matched case-control sets from EPIC. Int J Cancer 2020; 146:720-730. [PMID: 30951192 PMCID: PMC6916595 DOI: 10.1002/ijc.32314] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2018] [Revised: 03/15/2019] [Accepted: 03/19/2019] [Indexed: 01/13/2023]
Abstract
Metabolomics may reveal novel insights into the etiology of prostate cancer, for which few risk factors are established. We investigated the association between patterns in baseline plasma metabolite profile and subsequent prostate cancer risk, using data from 3,057 matched case-control sets from the European Prospective Investigation into Cancer and Nutrition (EPIC). We measured 119 metabolite concentrations in plasma samples, collected on average 9.4 years before diagnosis, by mass spectrometry (AbsoluteIDQ p180 Kit, Biocrates Life Sciences AG). Metabolite patterns were identified using treelet transform, a statistical method for identification of groups of correlated metabolites. Associations of metabolite patterns with prostate cancer risk (OR1SD ) were estimated by conditional logistic regression. Supplementary analyses were conducted for metabolite patterns derived using principal component analysis and for individual metabolites. Men with metabolite profiles characterized by higher concentrations of either phosphatidylcholines or hydroxysphingomyelins (OR1SD = 0.77, 95% confidence interval 0.66-0.89), acylcarnitines C18:1 and C18:2, glutamate, ornithine and taurine (OR1SD = 0.72, 0.57-0.90), or lysophosphatidylcholines (OR1SD = 0.81, 0.69-0.95) had lower risk of advanced stage prostate cancer at diagnosis, with no evidence of heterogeneity by follow-up time. Similar associations were observed for the two former patterns with aggressive disease risk (the more aggressive subset of advanced stage), while the latter pattern was inversely related to risk of prostate cancer death (OR1SD = 0.77, 0.61-0.96). No associations were observed for prostate cancer overall or less aggressive tumor subtypes. In conclusion, metabolite patterns may be related to lower risk of more aggressive prostate tumors and prostate cancer death, and might be relevant to etiology of advanced stage prostate cancer.
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Affiliation(s)
- Julie A Schmidt
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Georgina K Fensom
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Sabina Rinaldi
- International Agency for Research on Cancer, Lyon, France
| | | | - Paul N Appleby
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | | | | | - Marc J Gunter
- International Agency for Research on Cancer, Lyon, France
| | - Pietro Ferrari
- International Agency for Research on Cancer, Lyon, France
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tilman Kühn
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Heiner Boeing
- Department of Epidemiology, German Institute of Human Nutrition (DIfE) Potsdam-Rehbrücke, Nuthetal, Germany
| | | | - Anna Karakatsani
- Hellenic Health Foundation, Athens, Greece
- 2nd Pulmonary Medicine Department, School of Medicine, National and Kapodistrian University of Athens, "ATTIKON" University Hospital, Haidari, Greece
| | | | - Domenico Palli
- Cancer Risk Factors and Life-Style Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), Florence, Italy
| | - Sabina Sieri
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy
| | - Rosario Tumino
- Cancer Registry and Histopathology Department, "Civic - M.P.Arezzo" Hospital, Azienda Sanitaria Provinciale Di Ragusa (ASP), Ragusa, Italy
| | - Bas Bueno-de-Mesquita
- Department for Determinants of Chronic Diseases (DCD), National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
- Department of Gastroenterology and Hepatology, University Medical Centre, Utrecht, The Netherlands
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- Department of Social & Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Antonio Agudo
- Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Maria-Jose Sánchez
- CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Escuela Andaluza de Salud Pública, Instituto de Investigación Biosanitaria ibs.GRANADA, Hospitales Universitarios de Granada/Universidad de Granada, Granada, Spain
| | - María-Dolores Chirlaque
- CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Department of Epidemiology, Regional Health Council, IMIB-Arrixaca, Murcia, Spain
- Department of Health and Social Sciences, Murcia University, Murcia, Spain
| | - Eva Ardanaz
- CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Navarra Public Health Institute, Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Nerea Larrañaga
- CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Basque Regional Health Department, Public Health Division of Gipuzkoa-BIODONOSTIA, San Sebastian, Spain
| | - Aurora Perez-Cornago
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Nada Assi
- International Agency for Research on Cancer, Lyon, France
| | - Elio Riboli
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Konstantinos K Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Timothy J Key
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Ruth C Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
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Vineis P, Avendano-Pabon M, Barros H, Bartley M, Carmeli C, Carra L, Chadeau-Hyam M, Costa G, Delpierre C, D'Errico A, Fraga S, Giles G, Goldberg M, Kelly-Irving M, Kivimaki M, Lepage B, Lang T, Layte R, MacGuire F, Mackenbach JP, Marmot M, McCrory C, Milne RL, Muennig P, Nusselder W, Petrovic D, Polidoro S, Ricceri F, Robinson O, Stringhini S, Zins M. Special Report: The Biology of Inequalities in Health: The Lifepath Consortium. Front Public Health 2020. [PMID: 32478023 DOI: 10.3389/fpubh.2020.00118/full] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/22/2023] Open
Abstract
Funded by the European Commission Horizon 2020 programme, the Lifepath research consortium aimed to investigate the effects of socioeconomic inequalities on the biology of healthy aging. The main research questions included the impact of inequalities on health, the role of behavioral and other risk factors, the underlying biological mechanisms, the efficacy of selected policies, and the general implications of our findings for theories and policies. The project adopted a life-course and comparative approach, considering lifetime effects from childhood and adulthood, and pooled data on up to 1.7 million participants of longitudinal cohort studies from Europe, USA, and Australia. These data showed that socioeconomic circumstances predicted mortality and functional decline as strongly as established risk factors currently targeted by global prevention programmes. Analyses also looked at socioeconomically patterned biological markers, allostatic load, and DNA methylation using richly phenotyped cohorts, unraveling their association with aging processes across the life-course. Lifepath studies suggest that socioeconomic circumstances are embedded in our biology from the outset-i.e., disadvantage influences biological systems from molecules to organs. Our findings have important implications for policy, suggesting that (a) intervening on unfavorable socioeconomic conditions is complementary and as important as targeting well-known risk factors, such as tobacco and alcohol consumption, low fruit and vegetable intake, obesity and a sedentary lifestyle, and that (b) effects of preventive interventions in early life integrate interventions in adulthood. The report has an executive summary that refers to the different sections of the main paper.
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Affiliation(s)
- Paolo Vineis
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
| | - Mauricio Avendano-Pabon
- Department of Social Sciences, Health and Medicine, King's College London, London, United Kingdom
| | - Henrique Barros
- EPIUnit - Institute of Public Health University of Porto, Porto, Portugal
| | - Mel Bartley
- Department of Epidemiology & Public Health, University College London, London, United Kingdom
| | - Cristian Carmeli
- Center for Primary Care and Public Health (UNISANTE), University of Lausanne, Lausanne, Switzerland
| | | | - Marc Chadeau-Hyam
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
| | - Giuseppe Costa
- Department of Clinical Science & Biology, Turin University Medical School, Turin, Italy
| | - Cyrille Delpierre
- UMR LEASP, Université de Toulouse III, UPS, Inserm, Toulouse, France
| | | | - Silvia Fraga
- EPIUnit - Institute of Public Health University of Porto, Porto, Portugal
| | - Graham Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, VIC, Australia
| | - Marcel Goldberg
- UMS 011 Inserm - UVSQ ≪ Cohortes épidémiologiques en population ≫, Villejuif, France
| | | | - Mika Kivimaki
- Department of Epidemiology and Public Health, University College London, London, United Kingdom
| | - Benoit Lepage
- UMR LEASP, Université de Toulouse III, UPS, Inserm, Toulouse, France
| | - Thierry Lang
- UMR LEASP, Université de Toulouse III, UPS, Inserm, Toulouse, France
| | - Richard Layte
- Department of Sociology, School of Social Sciences and Philosophy, Trinity College Dublin, Dublin, Ireland
| | - Frances MacGuire
- Department of Epidemiology and Public Health, University College London, London, United Kingdom
| | - Johan P Mackenbach
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Michael Marmot
- Department of Epidemiology and Public Health, University College London, London, United Kingdom
| | - Cathal McCrory
- Department of Medical Gerontology, Trinity College Dublin, Dublin, Ireland
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, VIC, Australia
| | - Peter Muennig
- Mailman School of Public Health, Columbia University, New York, NY, United States
| | - Wilma Nusselder
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Dusan Petrovic
- Center for Primary Care and Public Health (UNISANTE), University of Lausanne, Lausanne, Switzerland
| | - Silvia Polidoro
- Molecular Epidemiology and Exposomics Unit, Italian Institute for Genomic Medicine, Turin, Italy
| | - Fulvio Ricceri
- Department of Clinical Science & Biology, Turin University Medical School, Turin, Italy
- Department of Epidemiology, ASL TO3, Turin, Italy
| | - Oliver Robinson
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
| | - Silvia Stringhini
- Unit of Population Epidemiology, Division of Primary Care, Geneva University Hospitals, Geneva, Switzerland
| | - Marie Zins
- UMS 011 Inserm - UVSQ ≪ Cohortes épidémiologiques en population ≫, Villejuif, France
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Griffiths JI, Cohen AL, Jones V, Salgia R, Chang JT, Bild AH. Opportunities for improving cancer treatment using systems biology. ACTA ACUST UNITED AC 2019; 17:41-50. [PMID: 32518857 DOI: 10.1016/j.coisb.2019.10.018] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Current cancer therapies target a limited set of tumor features, rather than considering the tumor as a whole. Systems biology aims to reveal therapeutic targets associated with a variety of facets in an individual's tumor, such as genetic heterogeneity and its evolution, cancer cell-autonomous phenotypes, and microenvironmental signaling. These disparate characteristics can be reconciled using mathematical modeling that incorporates concepts from ecology and evolution. This provides an opportunity to predict tumor growth and response to therapy, to tailor patient-specific approaches in real time or even prospectively. Importantly, as data regarding patient tumors is often available from only limited time points during treatment, systems-based approaches can address this limitation by interpolating longitudinal events within a principled framework. This review outlines areas in medicine that could benefit from systems biology approaches to deconvolve the complexity of cancer.
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Affiliation(s)
- Jason I Griffiths
- Department of Mathematics, University of Utah, Salt Lake City, UT 84112, USA
| | - Adam L Cohen
- Huntsman Cancer Institute, Department of Internal Medicine, University of Utah, Salt Lake City, UT 84112, USA
| | - Veronica Jones
- Department of Surgery, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Ravi Salgia
- Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Jeffrey T Chang
- Department of Integrative Biology and Pharmacology, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Andrea H Bild
- Department of Medical Oncology, Division of Molecular Pharmacology, Beckman Research Institute of City of Hope, Duarte, CA 91010, USA
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Béranger R, Hardy EM, Binter AC, Charles MA, Zaros C, Appenzeller BMR, Chevrier C. Multiple pesticides in mothers' hair samples and children's measurements at birth: Results from the French national birth cohort (ELFE). Int J Hyg Environ Health 2019; 223:22-33. [PMID: 31708466 DOI: 10.1016/j.ijheh.2019.10.010] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 10/25/2019] [Accepted: 10/25/2019] [Indexed: 10/25/2022]
Abstract
BACKGROUND A growing body of studies now suggests that the general population is continuously and ubiquitously exposed to numerous pesticides. However, studies investigating the possible role of environmental exposure to pesticides on fetal growth have focused on a limited set of substances, despite the hundreds of modern pesticides currently available. AIM To explore the relation between maternal hair concentrations of 64 pesticides and metabolites and their newborns' measurements at birth, with data from the ELFE French nationwide birth cohort. METHODS We measured 64 compounds (10-100% detection) in bundles of hair 9 cm long collected at birth from 311 women who gave birth in France in 2011. We assessed their associations with birth weight, length, and head circumference, adjusted for potential confounders, and used elastic net regularization to simultaneously select the strongest predictors of measurements at birth. Selected variables were multiply imputed for missing values, and unpenalized estimators were assessed by standard linear regression. RESULTS We observed statistically significant associations between maternal hair concentrations of seven pesticides or pesticide metabolites and birth measurements (weight: fipronil sulfone; length: TCPy, bitertanol, DEP, and isoproturon; head circumference: tebuconazole and prochloraz). Analyses restricted to boys identified 12 additional compounds: 8 independently associated with birth weight (3Me4NP, DCPMU, DMST, fipronil, mecoprop, propoxur, fenhexamid, and thiabendazole), 2 with birth length (dieldrin and β-endosulfan), and 6 with head circumference (β-endosulfan, β-HCH, fenuron, DCPMU, propoxur, and thiabendazole). CONCLUSION Our results suggest that prenatal exposure to 19 pesticides or metabolites from various chemical families may influence measurements at birth. As with any exploratory research findings, results should be interpreted cautiously, until they are replicated or verified by further epidemiological or mechanistic studies.
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Affiliation(s)
- Rémi Béranger
- Univ Rennes, CHU Rennes, Inserm, EHESP, Irset (Institut de Recherche en santé, Environnement et Travail), UMR_S 1085, F-35000, Rennes, France.
| | - Emilie M Hardy
- Human Biomonitoring Research Unit, Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Anne-Claire Binter
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en santé, Environnement et Travail), UMR_S 1085, F-35000, Rennes, France
| | | | - Cécile Zaros
- Ined, Inserm, EFS, ELFE Joint Unit, Paris, France
| | - Brice M R Appenzeller
- Human Biomonitoring Research Unit, Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Cécile Chevrier
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en santé, Environnement et Travail), UMR_S 1085, F-35000, Rennes, France
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38
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Barber D, Villaseñor A, Escribese MM. Metabolomics strategies to discover new biomarkers associated to severe allergic phenotypes. Asia Pac Allergy 2019; 9:e37. [PMID: 31720248 PMCID: PMC6826109 DOI: 10.5415/apallergy.2019.9.e37] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2019] [Accepted: 10/28/2019] [Indexed: 01/11/2023] Open
Abstract
In the last decades have emerged new technological platforms that allow evaluation of genes, transcripts, proteins, or metabolites of a living being, so-called omics sciences. More importantly, new technics for their integration have provided access to a complete set of information of the current conditions and features of a specific biological sample in a precise moment. Thus, omic sciences are now considered an essential tool for patient stratification in base to their severity, to understand disease progression and to identify new biomarkers. Severe patients, that are out of control, provide an excellent model to understand disease evolution and to identify new intervention and biomarkers strategies. Here we discuss the use of metabolomics to understand severity in allergic diseases in a strategy that opens new insights as well as identify new biological systems relevant for allergy progression. Metabolomics strategies are based in parallel evaluation of different allergy severity models by mean of untargeted analysis that allows the identification of potential biomarkers. Overlapping of different biomarkers in multiple models, provides information of general as well as specific biological systems involved in each model. Later a selected panel of biomarkers will be used in a target method to explore the diagnosis potential to stratify allergic patients.
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Affiliation(s)
- Domingo Barber
- IMMA, Instituto de Medicina Molecular Aplicada, Facultad de Medicina, Universidad San Pablo CEU, Madrid, Spain
| | - Alma Villaseñor
- IMMA, Instituto de Medicina Molecular Aplicada, Facultad de Medicina, Universidad San Pablo CEU, Madrid, Spain
| | - Maria M Escribese
- IMMA, Instituto de Medicina Molecular Aplicada, Facultad de Medicina, Universidad San Pablo CEU, Madrid, Spain.,Departamento de Ciencias Médicas Básicas, Facultad de Medicina, Universidad San Pablo CEU, Madrid, Spain
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Braun JM, Kalloo G, Kingsley SL, Li N. Using phenome-wide association studies to examine the effect of environmental exposures on human health. ENVIRONMENT INTERNATIONAL 2019; 130:104877. [PMID: 31200158 PMCID: PMC6682449 DOI: 10.1016/j.envint.2019.05.071] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 05/10/2019] [Accepted: 05/27/2019] [Indexed: 05/04/2023]
Abstract
The field of environmental epidemiology has been using "-omics" technologies, including the exposome, metabolome, and methylome, to understand the potential effects and biological pathways of a number of environmental pollutants. However, the majority of studies have focused on a single disease or phenotype, and have not systematically considered patterns of multimorbidity and whether environmental pollutants have pleiotropic effects. These questions could be addressed by examining the relation between environmental exposures and the phenome - the patterns and profiles of human health that individuals experience from birth to death. By conducting Phenome Wide Association Studies (PheWAS), we can generate new hypotheses about new or poorly understood exposures, identify novel associations for established toxicants, and better understand biological pathways affected by environmental pollutants. In this article, we provide a conceptual framework for conducting PheWAS in environmental epidemiology and summarize some of the advantages and challenges to using the PheWAS to study environmental pollutant exposures. Ultimately, by adding the PheWAS to our "-omics" toolbox, we could substantially improve our understanding of the potential health effects of environmental pollutants.
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Affiliation(s)
- Joseph M Braun
- Department of Epidemiology, Brown University, Providence, RI, United States of America.
| | - Geetika Kalloo
- Department of Epidemiology, Brown University, Providence, RI, United States of America
| | - Samantha L Kingsley
- Department of Epidemiology, Brown University, Providence, RI, United States of America
| | - Nan Li
- Department of Epidemiology, Brown University, Providence, RI, United States of America
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Santaolalla A, Garmo H, Grigoriadis A, Ghuman S, Hammar N, Jungner I, Walldius G, Lambe M, Holmberg L, Van Hemelrijck M. Metabolic profiles to predict long-term cancer and mortality: the use of latent class analysis. BMC Mol Cell Biol 2019; 20:28. [PMID: 31337337 PMCID: PMC6651931 DOI: 10.1186/s12860-019-0210-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Accepted: 07/09/2019] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Metabolites are genetically and environmentally determined. Consequently, they can be used to characterize environmental exposures and reveal biochemical mechanisms that link exposure to disease. To explore disease susceptibility and improve population risk stratification, we aimed to identify metabolic profiles linked to carcinogenesis and mortality and their intrinsic associations by characterizing subgroups of individuals based on serum biomarker measurements. We included 13,615 participants from the Swedish Apolipoprotein MOrtality RISk Study who had measurements for 19 biomarkers representative of central metabolic pathways. Latent Class Analysis (LCA) was applied to characterise individuals based on their biomarker values (according to medical cut-offs), which were then examined as predictors of cancer and death using multivariable Cox proportional hazards models. RESULTS LCA identified four metabolic profiles within the population: (1) normal values for all markers (63% of population); (2) abnormal values for lipids (22%); (3) abnormal values for liver functioning (9%); (4) abnormal values for iron and inflammation metabolism (6%). All metabolic profiles (classes 2-4) increased risk of cancer and mortality, compared to class 1 (e.g. HR for overall death was 1.26 (95% CI: 1.16-1.37), 1.67 (95% CI: 1.47-1.90), and 1.21 (95% CI: 1.05-1.41) for class 2, 3, and 4, respectively). CONCLUSION We present an innovative approach to risk stratify a well-defined population based on LCA metabolic-defined subgroups for cancer and mortality. Our results indicate that standard of care baseline serum markers, when assembled into meaningful metabolic profiles, could help assess long term risk of disease and provide insight in disease susceptibility and etiology.
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Affiliation(s)
- Aida Santaolalla
- School of Cancer & Pharmaceutical Sciences, Translational Oncology and Urology Research, King’s College London, London, UK
| | - Hans Garmo
- School of Cancer & Pharmaceutical Sciences, Translational Oncology and Urology Research, King’s College London, London, UK
- Regional Oncologic Centre, Uppsala University, Uppsala, Sweden
| | - Anita Grigoriadis
- School of Cancer & Pharmaceutical Sciences, Cancer Bioinformatics, Breast Cancer Now, King’s College London, London, UK
| | - Sundeep Ghuman
- School of Cancer & Pharmaceutical Sciences, Translational Oncology and Urology Research, King’s College London, London, UK
- Guy’s and St Thomas, NHS Foundation Trust, London, UK
| | - Niklas Hammar
- Department of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Ingmar Jungner
- Department of Medicine, Clinical Epidemiological Unit, Karolinska Institutet and CALAB Research, Stockholm, Sweden
| | - Göran Walldius
- Unit of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Mats Lambe
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Lars Holmberg
- School of Cancer & Pharmaceutical Sciences, Translational Oncology and Urology Research, King’s College London, London, UK
| | - Mieke Van Hemelrijck
- School of Cancer & Pharmaceutical Sciences, Translational Oncology and Urology Research, King’s College London, London, UK
- Unit of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
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Abstract
Extensive research demonstrates unequivocally that nutrition plays a fundamental role in maintaining health and preventing disease. In parallel nutrition research provides evidence that the risks and benefits of diet and lifestyle choices do not affect people equally, as people are inherently variable in their responses to nutrition and associated interventions to maintain health and prevent disease. To simplify the inherent complexity of human subjects and their nutrition, with the aim of managing expectations for dietary guidance required to ensure healthy populations and individuals, nutrition researchers often seek to group individuals based on commonly used criteria. This strategy relies on demonstrating meaningful conclusions based on comparison of group mean responses of assigned groups. Such studies are often confounded by the heterogeneous nutrition response. Commonly used criteria applied in grouping study populations and individuals to identify mechanisms and determinants of responses to nutrition often contribute to the problem of interpreting the results of group comparisons. Challenges of interpreting the group mean using diverse populations will be discussed with respect to studies in human subjects, in vivo and in vitro model systems. Future advances in nutrition research to tackle inter-individual variation require a coordinated approach from funders, learned societies, nutrition scientists, publishers and reviewers of the scientific literature. This will be essential to develop and implement improved study design, data recording, analysis and reporting to facilitate more insightful interpretation of the group mean with respect to population diversity and the heterogeneous nutrition response.
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Vriens A, Nawrot TS, Janssen BG, Baeyens W, Bruckers L, Covaci A, De Craemer S, De Henauw S, Den Hond E, Loots I, Nelen V, Schettgen T, Schoeters G, Martens DS, Plusquin M. Exposure to Environmental Pollutants and Their Association with Biomarkers of Aging: A Multipollutant Approach. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2019; 53:5966-5976. [PMID: 31041867 DOI: 10.1021/acs.est.8b07141] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Mitochondrial DNA (mtDNA) content and telomere length are putative aging biomarkers and are sensitive to environmental stressors, including pollutants. Our objective was to identify, from a set of environmental exposures, which exposure is associated with leukocyte mtDNA content and telomere length in adults. This study includes 175 adults from 50 to 65 years old from the cross-sectional Flemish Environment and Health study, of whom leukocyte telomere length and mtDNA content were determined using qPCR. The levels of exposure of seven metals, 11 organohalogens, and four perfluorinated compounds (PFHxS, PFNA, PFOA, PFOS) were measured. We performed sparse partial least-squares regression analyses followed by ordinary least-squares regression to assess the multipollutant associations. While accounting for possible confounders and coexposures, we identified that urinary cadmium (6.52%, 95% confidence interval, 1.06, 12.28), serum hexachlorobenzene (2.89%, 018, 5.68), and perfluorooctanesulfonic acid (11.38%, 5.97, 17.08) exposure were positively associated ( p < 0.05) with mtDNA content, while urinary copper (-9.88%, -14.82, -4.66) and serum perfluorohexanesulfonic acid (-4.75%, -8.79, -0.54) exposure were inversely associated with mtDNA content. Urinary antimony (2.69%, 0.45, 4.99) and mercury (1.91%, 0.42, 3.43) exposure were positively associated with leukocyte telomere length, while urinary copper (-3.52%, -6.60, -0.34) and serum perfluorooctanesulfonic acid (-3.64%, -6.60, -0.60) showed an inverse association. Our findings support the hypothesis that environmental pollutants interact with molecular hallmarks of aging.
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Affiliation(s)
- Annette Vriens
- Centre for Environmental Sciences , Hasselt University , Hasselt 3500 , Belgium
| | - Tim S Nawrot
- Centre for Environmental Sciences , Hasselt University , Hasselt 3500 , Belgium
- Department of Public Health & Primary Care , Leuven University , Leuven 3000 , Belgium
| | - Bram G Janssen
- Centre for Environmental Sciences , Hasselt University , Hasselt 3500 , Belgium
| | - Willy Baeyens
- Department of Analytical and Environmental Chemistry , Vrije Universiteit Brussel , Brussels 1050 , Belgium
| | - Liesbeth Bruckers
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics , Hasselt University , Diepenbeek 3590 , Belgium
| | | | - Sam De Craemer
- Department of Analytical and Environmental Chemistry , Vrije Universiteit Brussel , Brussels 1050 , Belgium
| | - Stefaan De Henauw
- Department of Public Health , Ghent University , Ghent 9000 , Belgium
| | - Elly Den Hond
- Provincial Institute for Hygiene , Antwerp 2000 , Belgium
| | | | - Vera Nelen
- Provincial Institute for Hygiene , Antwerp 2000 , Belgium
| | - Thomas Schettgen
- Institute for Occupational, Social and Environmental Medicine, Medical Faculty , RWTH Aachen University , Aachen 52062 , Germany
| | - Greet Schoeters
- Environmental Risk and Health , Flemish Institute for Technological Research (VITO) , Mol 2400 , Belgium
| | - Dries S Martens
- Centre for Environmental Sciences , Hasselt University , Hasselt 3500 , Belgium
| | - Michelle Plusquin
- Centre for Environmental Sciences , Hasselt University , Hasselt 3500 , Belgium
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43
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Chen L, Luo K, Etzel R, Zhang X, Tian Y, Zhang J. Co-exposure to environmental endocrine disruptors in the US population. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2019; 26:7665-7676. [PMID: 30666576 DOI: 10.1007/s11356-018-04105-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Accepted: 12/27/2018] [Indexed: 06/09/2023]
Abstract
Exposure to environmental endocrine disruptors (EEDs) has been linked to adverse health outcomes. The vast majority of studies examined one class of EEDs at a time but humans often are exposed to multiple EEDs at the same time. It is, therefore, important to know the co-exposure status of multiple EEDs in an individual, to preclude and control for potential confounding effects posed by co-exposed EEDs. This study examined the concentrations of seven classes of EEDs in the US population utilizing the data from the National Health and Nutrition Examination Survey (NHANES), 2009-2014 survey cycles. We applied linear correlation and cluster analysis to characterize the correlation profile and cluster patterns of these EEDs. We found that EEDs with a similar structure are often highly correlated. Among between-class correlations, mercury and perfluoroalkyl substances (PFAS) and cadmium and polycyclic aromatic hydrocarbons (PAHs) were two significantly correlated EEDs. In epidemiologic studies, measurement and control for co-exposure to pollutants, especially those with similar biological effects, are critical when attempting to make causal inferences. Appropriate statistical methods to handle within- and between-class correlations are needed.
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Affiliation(s)
- Lin Chen
- Ministry of Education-Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Kai Luo
- School of Public Health, Shanghai Jiao Tong University, Shanghai, China
| | - Ruth Etzel
- Milkin Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Xiaoyu Zhang
- Ministry of Education-Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Ying Tian
- Ministry of Education-Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
- School of Public Health, Shanghai Jiao Tong University, Shanghai, China
| | - Jun Zhang
- Ministry of Education-Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China.
- School of Public Health, Shanghai Jiao Tong University, Shanghai, China.
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44
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Verboom DM, Koster-Brouwer ME, Varkila MRJ, Bonten MJM, Cremer OL. Profile of the SeptiCyte™ LAB gene expression assay to diagnose infection in critically ill patients. Expert Rev Mol Diagn 2019; 19:95-108. [PMID: 30623693 DOI: 10.1080/14737159.2019.1567333] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Sepsis is a severe and frequently occurring clinical syndrome, caused by the inflammatory response to infections. Recent studies on the human transcriptome during sepsis have yielded several gene-expression assays that might assist physicians during clinical assessment of patients suspected of sepsis. SeptiCyte™ LAB (Immunexpress, Seattle, WA) is the first gene expression assay that was cleared by the FDA in the United States to distinguish infectious from non-infectious causes of systemic inflammation in critically ill patients. The test consists of the simultaneous amplification of four RNA transcripts (CEACAM4, LAMP1, PLAC8, and PLA2G7) in whole blood using a quantitative real-time PCR reaction. This review provides an overview of the challenges in the diagnosis of sepsis, the development of gene expression signatures, and a detailed description of available clinical performance studies evaluating SeptiCyte™ LAB.
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Affiliation(s)
- D M Verboom
- a Julius Center for Health Sciences and Primary Care , University Medical Center Utrecht , Utrecht , The Netherlands.,b Department of Intensive Care , University Medical Center Utrecht , Utrecht , The Netherlands
| | - M E Koster-Brouwer
- a Julius Center for Health Sciences and Primary Care , University Medical Center Utrecht , Utrecht , The Netherlands.,b Department of Intensive Care , University Medical Center Utrecht , Utrecht , The Netherlands
| | - M R J Varkila
- a Julius Center for Health Sciences and Primary Care , University Medical Center Utrecht , Utrecht , The Netherlands.,b Department of Intensive Care , University Medical Center Utrecht , Utrecht , The Netherlands
| | - M J M Bonten
- a Julius Center for Health Sciences and Primary Care , University Medical Center Utrecht , Utrecht , The Netherlands.,c Department of Medical Microbiology , University Medical Center Utrecht , Utrecht , The Netherlands
| | - O L Cremer
- b Department of Intensive Care , University Medical Center Utrecht , Utrecht , The Netherlands
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45
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Lazarevic N, Barnett AG, Sly PD, Knibbs LD. Statistical Methodology in Studies of Prenatal Exposure to Mixtures of Endocrine-Disrupting Chemicals: A Review of Existing Approaches and New Alternatives. ENVIRONMENTAL HEALTH PERSPECTIVES 2019; 127:26001. [PMID: 30720337 PMCID: PMC6752940 DOI: 10.1289/ehp2207] [Citation(s) in RCA: 127] [Impact Index Per Article: 25.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2017] [Revised: 01/09/2019] [Accepted: 01/10/2019] [Indexed: 05/19/2023]
Abstract
BACKGROUND Prenatal exposures to endocrine-disrupting chemicals (EDCs) during critical developmental windows have been implicated in the etiologies of a wide array of adverse perinatal and pediatric outcomes. Epidemiological studies have concentrated on the health effects of individual chemicals, despite the understanding that EDCs act together via common mechanisms, that pregnant women are exposed to multiple EDCs simultaneously, and that substantial toxicological evidence of adverse developmental effects has been documented. There is a move toward multipollutant models in environmental epidemiology; however, there is no current consensus on appropriate statistical methods. OBJECTIVES We aimed to review the statistical methods used in these studies, to identify additional applicable methods, and to determine the strengths and weaknesses of each method for addressing the salient statistical and epidemiological challenges. METHODS We searched Embase, MEDLINE, and Web of Science for epidemiological studies of endocrine-sensitive outcomes in the children of mothers exposed to EDC mixtures during pregnancy and identified alternative statistical methods from the wider literature. DISCUSSION We identified 74 studies and analyzed the methods used to estimate mixture health effects, identify important mixture components, account for nonmonotonicity in exposure–response relationships, assess interactions, and identify windows of exposure susceptibility. We identified both frequentist and Bayesian methods that are robust to multicollinearity, performing shrinkage, variable selection, dimension reduction, statistical learning, or smoothing, including methods that were not used by the studies included in our review. CONCLUSIONS Compelling motivation exists for analyzing EDCs as mixtures, yet many studies make simplifying assumptions about EDC additivity, relative potency, and linearity, or overlook the potential for bias due to asymmetries in chemical persistence. We discuss the potential impacts of these choices and suggest alternative methods to improve analyses of prenatal exposure to EDC mixtures. https://doi.org/10.1289/EHP2207.
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Affiliation(s)
- Nina Lazarevic
- School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - Adrian G Barnett
- School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Peter D Sly
- Child Health Research Centre, The University of Queensland, Brisbane, Queensland, Australia
| | - Luke D Knibbs
- School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
- Centre for Air Quality & Health Research and Evaluation, Glebe, New South Wales, Australia
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46
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Bartell SM. Understanding and Mitigating the Replication Crisis, for Environmental Epidemiologists. Curr Environ Health Rep 2019; 6:8-15. [DOI: 10.1007/s40572-019-0225-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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47
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Niedzwiecki MM, Walker DI, Vermeulen R, Chadeau-Hyam M, Jones DP, Miller GW. The Exposome: Molecules to Populations. Annu Rev Pharmacol Toxicol 2019; 59:107-127. [PMID: 30095351 DOI: 10.1146/annurev-pharmtox-010818-021315] [Citation(s) in RCA: 117] [Impact Index Per Article: 23.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Derived from the term exposure, the exposome is an omic-scale characterization of the nongenetic drivers of health and disease. With the genome, it defines the phenome of an individual. The measurement of complex environmental factors that exert pressure on our health has not kept pace with genomics and historically has not provided a similar level of resolution. Emerging technologies make it possible to obtain detailed information on drugs, toxicants, pollutants, nutrients, and physical and psychological stressors on an omic scale. These forces can also be assessed at systems and network levels, providing a framework for advances in pharmacology and toxicology. The exposome paradigm can improve the analysis of drug interactions and detection of adverse effects of drugs and toxicants and provide data on biological responses to exposures. The comprehensive model can provide data at the individual level for precision medicine, group level for clinical trials, and population level for public health.
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Affiliation(s)
- Megan M Niedzwiecki
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; ,
| | - Douglas I Walker
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; ,
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, USA
- Clinical Biomarkers Laboratory, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, Emory University, Atlanta, Georgia 30322, USA;
| | - Roel Vermeulen
- Institute for Risk Assessment Sciences, Utrecht University, 3584 CM Utrecht, Netherlands;
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3584 Utrecht, Netherlands
- MRC/PHE Centre for Environmental Health, Department of Epidemiology and Public Health, Imperial College London, W2 1PG London, United Kingdom;
| | - Marc Chadeau-Hyam
- Institute for Risk Assessment Sciences, Utrecht University, 3584 CM Utrecht, Netherlands;
- MRC/PHE Centre for Environmental Health, Department of Epidemiology and Public Health, Imperial College London, W2 1PG London, United Kingdom;
| | - Dean P Jones
- Clinical Biomarkers Laboratory, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, Emory University, Atlanta, Georgia 30322, USA;
| | - Gary W Miller
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, USA
- Current affiliation: Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University Medical Center, New York, NY 10032, USA;
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48
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Sun Y, Jiang Y, Li Y, Ma S. Identification of cancer omics commonality and difference via community fusion. Stat Med 2018; 38:1200-1212. [PMID: 30421444 DOI: 10.1002/sim.8027] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Revised: 10/06/2018] [Accepted: 10/13/2018] [Indexed: 12/18/2022]
Abstract
The analysis of cancer omics data is a "classic" problem; however, it still remains challenging. Advancing from early studies that are mostly focused on a single type of cancer, some recent studies have analyzed data on multiple "related" cancer types/subtypes, examined their commonality and difference, and led to insightful findings. In this article, we consider the analysis of multiple omics datasets, with each dataset on one type/subtype of "related" cancers. A Community Fusion (CoFu) approach is developed, which conducts marker selection and model building using a novel penalization technique, informatively accommodates the network community structure of omics measurements, and automatically identifies the commonality and difference of cancer omics markers. Simulation demonstrates its superiority over direct competitors. The analysis of TCGA lung cancer and melanoma data leads to interesting findings.
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Affiliation(s)
- Yifan Sun
- Center for Applied Statistics, Renmin University of China, Beijing, China.,School of Statistics, Renmin University of China, Beijing, China
| | - Yu Jiang
- School of Public Health, The University of Memphis, Memphis, Tennessee
| | - Yang Li
- Center for Applied Statistics, Renmin University of China, Beijing, China.,School of Statistics, Renmin University of China, Beijing, China.,Statistical Consulting Center, Renmin University of China, Beijing, China
| | - Shuangge Ma
- School of Statistics, Renmin University of China, Beijing, China.,Department of Biostatistics, Yale University, New Haven, Connecticut
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49
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Hamra GB, Buckley JP. Environmental exposure mixtures: questions and methods to address them. CURR EPIDEMIOL REP 2018; 5:160-165. [PMID: 30643709 PMCID: PMC6329601 DOI: 10.1007/s40471-018-0145-0] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
PURPOSE OF THIS REVIEW This review provides a summary of statistical approaches that researchers can use to study environmental exposure mixtures. Two primary considerations are the form of the research question and the statistical tools best suited to address that question. Because the choice of statistical tools is not rigid, we make recommendations about when each tool may be most useful. RECENT FINDINGS When dimensionality is relatively low, some statistical tools yield easily interpretable estimates of effect (e.g., risk ratio, odds ratio) or intervention impacts. When dimensionality increases, it is often necessary to compromise this interpretablity in favor of identifying interesting statistical signals from noise; this requires applying statistical tools that are oriented more heavily towards dimension reduction via shrinkage and/or variable selection. SUMMARY The study of complex exposure mixtures has prompted development of novel statistical methods. We suggest that further validation work would aid practicing researchers in choosing among existing and emerging statistical tools for studying exposure mixtures.
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Affiliation(s)
- Ghassan B Hamra
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, MD, USA
| | - Jessie P Buckley
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, MD, USA
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, MD, USA
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50
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Piwowar M, Jurkowski W. Missing data in open-data era – a barrier to multiomics integration. BIO-ALGORITHMS AND MED-SYSTEMS 2018. [DOI: 10.1515/bams-2017-0026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
AbstractThe exploration of complex interactions in biological systems is one of the main aims in nature science nowadays. Progress in this area is possible because of high-throughput omics technologies and the computational surge. The development of analytical methods “is trying to keep pace” with the development of molecular biology methods that provide increasingly large amounts of data – omics data. Specialized databases consist of ever-larger collections of experiments that are usually conducted by one next-generation sequencing technique (e.g. RNA-seq). Other databases integrate data by defining qualitative relationships between individual objects in the form of ontologies, interactions, and pathways (e.g. GO, KEGG, and String). However, there are no open-source complementary quantitative data sets for the biological processes studied, including information from many levels of the organism organization, which would allow the development of multidimensional data analysis methods (multiscale and insightful overviews of biological processes). In the paper, the lack of omics complementary quantitative data set, which would help integrate the defined qualitative biological relationships of individual biomolecules with statistical, computational methods, is discussed.
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