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Dawson LP, Carrington MJ, Haregu T, Nanayakkara S, Jennings G, Dart A, Stub D, Inouye M, Kaye D. Ten-Year Risk Equations for Incident Heart Failure in Established Atherosclerotic Cardiovascular Disease Populations. J Am Heart Assoc 2024; 13:e034254. [PMID: 38780153 PMCID: PMC11255645 DOI: 10.1161/jaha.124.034254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 04/29/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND Ten-year risk equations for incident heart failure (HF) are available for the general population, but not for patients with established atherosclerotic cardiovascular disease (ASCVD), which is highly prevalent in HF cohorts. This study aimed to develop and validate 10-year risk equations for incident HF in patients with known ASCVD. METHODS AND RESULTS Ten-year risk equations for incident HF were developed using the United Kingdom Biobank cohort (recruitment 2006-2010) including participants with established ASCVD but free from HF at baseline. Model performance was validated using the Australian Baker Heart and Diabetes Institute Biobank cohort (recruitment 2000-2011) and compared with the performance of general population risk models. Incident HF occurred in 13.7% of the development cohort (n=31 446, median 63 years, 35% women, follow-up 10.7±2.7 years) and in 21.3% of the validation cohort (n=1659, median age 65 years, 25% women, follow-up 9.4±3.7 years). Predictors of HF included in the sex-specific models were age, body mass index, systolic blood pressure (treated or untreated), glucose (treated or untreated), cholesterol, smoking status, QRS duration, kidney disease, myocardial infarction, and atrial fibrillation. ASCVD-HF equations had good discrimination and calibration in development and validation cohorts, with superior performance to general population risk equations. CONCLUSIONS ASCVD-specific 10-year risk equations for HF outperform general population risk models in individuals with established ASCVD. The ASCVD-HF equations can be calculated from readily available clinical data and could facilitate screening and preventative treatment decisions in this high-risk group.
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Affiliation(s)
- Luke P. Dawson
- Department of CardiologyThe Alfred HospitalMelbourneVictoriaAustralia
- Faculty of MedicineMonash UniversityMelbourneVictoriaAustralia
- Baker Heart and Diabetes InstituteMelbourneVictoriaAustralia
| | | | - Tilahun Haregu
- Department of CardiologyThe Alfred HospitalMelbourneVictoriaAustralia
- Baker Heart and Diabetes InstituteMelbourneVictoriaAustralia
| | - Shane Nanayakkara
- Department of CardiologyThe Alfred HospitalMelbourneVictoriaAustralia
- Baker Heart and Diabetes InstituteMelbourneVictoriaAustralia
| | - Garry Jennings
- Department of CardiologyThe Alfred HospitalMelbourneVictoriaAustralia
- Faculty of MedicineMonash UniversityMelbourneVictoriaAustralia
- Baker Heart and Diabetes InstituteMelbourneVictoriaAustralia
| | - Anthony Dart
- Department of CardiologyThe Alfred HospitalMelbourneVictoriaAustralia
- Baker Heart and Diabetes InstituteMelbourneVictoriaAustralia
| | - Dion Stub
- Department of CardiologyThe Alfred HospitalMelbourneVictoriaAustralia
- Faculty of MedicineMonash UniversityMelbourneVictoriaAustralia
- Baker Heart and Diabetes InstituteMelbourneVictoriaAustralia
| | - Michael Inouye
- Baker Heart and Diabetes InstituteMelbourneVictoriaAustralia
- Department of Public Health & Primary CareUniversity of CambridgeCambridgeUK
| | - David Kaye
- Department of CardiologyThe Alfred HospitalMelbourneVictoriaAustralia
- Faculty of MedicineMonash UniversityMelbourneVictoriaAustralia
- Baker Heart and Diabetes InstituteMelbourneVictoriaAustralia
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2
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Dawson LP, Rashid M, Dinh DT, Brennan A, Bloom JE, Biswas S, Lefkovits J, Shaw JA, Chan W, Clark DJ, Oqueli E, Hiew C, Freeman M, Taylor AJ, Reid CM, Ajani AE, Kaye DM, Mamas MA, Stub D. No-Reflow Prediction in Acute Coronary Syndrome During Percutaneous Coronary Intervention: The NORPACS Risk Score. Circ Cardiovasc Interv 2024; 17:e013738. [PMID: 38487882 DOI: 10.1161/circinterventions.123.013738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 01/31/2024] [Indexed: 04/18/2024]
Abstract
BACKGROUND Suboptimal coronary reperfusion (no reflow) is common in acute coronary syndrome percutaneous coronary intervention (PCI) and is associated with poor outcomes. We aimed to develop and externally validate a clinical risk score for angiographic no reflow for use following angiography and before PCI. METHODS We developed and externally validated a logistic regression model for prediction of no reflow among adult patients undergoing PCI for acute coronary syndrome using data from the Melbourne Interventional Group PCI registry (2005-2020; development cohort) and the British Cardiovascular Interventional Society PCI registry (2006-2020; external validation cohort). RESULTS A total of 30 561 patients (mean age, 64.1 years; 24% women) were included in the Melbourne Interventional Group development cohort and 440 256 patients (mean age, 64.9 years; 27% women) in the British Cardiovascular Interventional Society external validation cohort. The primary outcome (no reflow) occurred in 4.1% (1249 patients) and 9.4% (41 222 patients) of the development and validation cohorts, respectively. From 33 candidate predictor variables, 6 final variables were selected by an adaptive least absolute shrinkage and selection operator regression model for inclusion (cardiogenic shock, ST-segment-elevation myocardial infarction with symptom onset >195 minutes pre-PCI, estimated stent length ≥20 mm, vessel diameter <2.5 mm, pre-PCI Thrombolysis in Myocardial Infarction flow <3, and lesion location). Model discrimination was very good (development C statistic, 0.808; validation C statistic, 0.741) with excellent calibration. Patients with a score of ≥8 points had a 22% and 27% risk of no reflow in the development and validation cohorts, respectively. CONCLUSIONS The no-reflow prediction in acute coronary syndrome risk score is a simple count-based scoring system based on 6 parameters available before PCI to predict the risk of no reflow. This score could be useful in guiding preventative treatment and future trials.
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Affiliation(s)
- Luke P Dawson
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia (L.P.D., D.T.D., A.B., S.B., J.L., W.C., C.M.R., A.E.A., D.S.)
- Department of Cardiology, The Alfred Hospital, Melbourne, Victoria, Australia (L.P.D., J.E.B., J.A.S., A.J.T., D.M.K., D.S.)
- The Baker Institute, Melbourne, Victoria, Australia (L.P.D., J.E.B., J.A.S., D.M.K., D.S.)
| | - Muhammad Rashid
- Keele Cardiovascular Research Group, Centre for Prognosis Research, Stroke on Trent, United Kingdom (M.R., A.E.A., M.A.M.)
- Department of Cardiovascular Sciences, National Institute for Health and Care Research (NIHR) Leicester Biomedical Research Centre, Glenfield Hospital, University of Leicester, United Kingdom (M.R., A.E.A.)
- University Hospitals of Leicester National Health Service (NHS) Trust, United Kingdom (M.R., A.E.A.)
| | - Diem T Dinh
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia (L.P.D., D.T.D., A.B., S.B., J.L., W.C., C.M.R., A.E.A., D.S.)
| | - Angela Brennan
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia (L.P.D., D.T.D., A.B., S.B., J.L., W.C., C.M.R., A.E.A., D.S.)
| | - Jason E Bloom
- Department of Cardiology, The Alfred Hospital, Melbourne, Victoria, Australia (L.P.D., J.E.B., J.A.S., A.J.T., D.M.K., D.S.)
- The Baker Institute, Melbourne, Victoria, Australia (L.P.D., J.E.B., J.A.S., D.M.K., D.S.)
| | - Sinjini Biswas
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia (L.P.D., D.T.D., A.B., S.B., J.L., W.C., C.M.R., A.E.A., D.S.)
| | - Jeffrey Lefkovits
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia (L.P.D., D.T.D., A.B., S.B., J.L., W.C., C.M.R., A.E.A., D.S.)
- Department of Cardiology, Royal Melbourne Hospital, Victoria, Australia (J.L.)
| | - James A Shaw
- Department of Cardiology, The Alfred Hospital, Melbourne, Victoria, Australia (L.P.D., J.E.B., J.A.S., A.J.T., D.M.K., D.S.)
- The Baker Institute, Melbourne, Victoria, Australia (L.P.D., J.E.B., J.A.S., D.M.K., D.S.)
| | - William Chan
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia (L.P.D., D.T.D., A.B., S.B., J.L., W.C., C.M.R., A.E.A., D.S.)
- Department of Medicine, Melbourne University, Victoria, Australia (W.C.)
| | - David J Clark
- Department of Cardiology, Austin Health, Melbourne, Victoria, Australia (D.J.C.)
| | - Ernesto Oqueli
- Department of Cardiology, Grampians Health Ballarat, Victoria, Australia (E.O.)
- School of Medicine, Faculty of Health, Deakin University, Geelong, Victoria, Australia (E.O.)
| | - Chin Hiew
- Department of Cardiology, University Hospital Geelong, Victoria, Australia (C.H.)
| | - Melanie Freeman
- Department of Cardiology, Box Hill Hospital, Melbourne, Victoria, Australia (M.F.)
| | - Andrew J Taylor
- Department of Cardiology, The Alfred Hospital, Melbourne, Victoria, Australia (L.P.D., J.E.B., J.A.S., A.J.T., D.M.K., D.S.)
| | - Christopher M Reid
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia (L.P.D., D.T.D., A.B., S.B., J.L., W.C., C.M.R., A.E.A., D.S.)
- Centre of Clinical Research and Education, School of Public Health, Curtin University, Perth, Western Australia, Australia (C.M.R.)
| | - Andrew E Ajani
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia (L.P.D., D.T.D., A.B., S.B., J.L., W.C., C.M.R., A.E.A., D.S.)
- Keele Cardiovascular Research Group, Centre for Prognosis Research, Stroke on Trent, United Kingdom (M.R., A.E.A., M.A.M.)
- Department of Cardiovascular Sciences, National Institute for Health and Care Research (NIHR) Leicester Biomedical Research Centre, Glenfield Hospital, University of Leicester, United Kingdom (M.R., A.E.A.)
- University Hospitals of Leicester National Health Service (NHS) Trust, United Kingdom (M.R., A.E.A.)
| | - David M Kaye
- Department of Cardiology, The Alfred Hospital, Melbourne, Victoria, Australia (L.P.D., J.E.B., J.A.S., A.J.T., D.M.K., D.S.)
- The Baker Institute, Melbourne, Victoria, Australia (L.P.D., J.E.B., J.A.S., D.M.K., D.S.)
| | - Mamas A Mamas
- Keele Cardiovascular Research Group, Centre for Prognosis Research, Stroke on Trent, United Kingdom (M.R., A.E.A., M.A.M.)
| | - Dion Stub
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia (L.P.D., D.T.D., A.B., S.B., J.L., W.C., C.M.R., A.E.A., D.S.)
- Department of Cardiology, The Alfred Hospital, Melbourne, Victoria, Australia (L.P.D., J.E.B., J.A.S., A.J.T., D.M.K., D.S.)
- The Baker Institute, Melbourne, Victoria, Australia (L.P.D., J.E.B., J.A.S., D.M.K., D.S.)
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Grady SK, Dojcsak L, Harville EW, Wallace ME, Vilda D, Donneyong MM, Hood DB, Valdez RB, Ramesh A, Im W, Matthews-Juarez P, Juarez PD, Langston MA. Seminar: Scalable Preprocessing Tools for Exposomic Data Analysis. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:124201. [PMID: 38109119 PMCID: PMC10727037 DOI: 10.1289/ehp12901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 11/22/2023] [Accepted: 11/28/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND The exposome serves as a popular framework in which to study exposures from chemical and nonchemical stressors across the life course and the differing roles that these exposures can play in human health. As a result, data relevant to the exposome have been used as a resource in the quest to untangle complicated health trajectories and help connect the dots from exposures to adverse outcome pathways. OBJECTIVES The primary aim of this methods seminar is to clarify and review preprocessing techniques critical for accurate and effective external exposomic data analysis. Scalability is emphasized through an application of highly innovative combinatorial techniques coupled with more traditional statistical strategies. The Public Health Exposome is used as an archetypical model. The novelty and innovation of this seminar's focus stem from its methodical, comprehensive treatment of preprocessing and its demonstration of the positive effects preprocessing can have on downstream analytics. DISCUSSION State-of-the-art technologies are described for data harmonization and to mitigate noise, which can stymie downstream interpretation, and to select key exposomic features, without which analytics may lose focus. A main task is the reduction of multicollinearity, a particularly formidable problem that frequently arises from repeated measurements of similar events taken at various times and from multiple sources. Empirical results highlight the effectiveness of a carefully planned preprocessing workflow as demonstrated in the context of more highly concentrated variable lists, improved correlational distributions, and enhanced downstream analytics for latent relationship discovery. The nascent field of exposome science can be characterized by the need to analyze and interpret a complex confluence of highly inhomogeneous spatial and temporal data, which may present formidable challenges to even the most powerful analytical tools. A systematic approach to preprocessing can therefore provide an essential first step in the application of modern computer and data science methods. https://doi.org/10.1289/EHP12901.
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Affiliation(s)
- Stephen K. Grady
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, Tennessee, USA
| | - Levente Dojcsak
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, Tennessee, USA
| | - Emily W. Harville
- Department Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
| | - Maeve E. Wallace
- Department of Social, Behavioral, and Population Sciences, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
| | - Dovile Vilda
- Department of Social, Behavioral, and Population Sciences, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
| | | | - Darryl B. Hood
- Division of Environmental Health Sciences, College of Public Health, Ohio State University, Columbus, Ohio, USA
| | - R. Burciaga Valdez
- Department of Economics, University of New Mexico, Albuquerque, New Mexico, USA
| | - Aramandla Ramesh
- Department of Biochemistry, Cancer Biology, Neuroscience & Pharmacology, Meharry Medical College, Nashville, Tennessee, USA
| | - Wansoo Im
- Department of Family and Community Medicine, Meharry Medical College, Nashville, Tennessee, USA
| | | | - Paul D. Juarez
- Department of Family and Community Medicine, Meharry Medical College, Nashville, Tennessee, USA
- Institute on Health Disparities, Equity, and the Exposome, Meharry Medical College, Nashville, Tennessee, USA
| | - Michael A. Langston
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, Tennessee, USA
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4
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Renzetti S, Gennings C, Calza S. A weighted quantile sum regression with penalized weights and two indices. Front Public Health 2023; 11:1151821. [PMID: 37533534 PMCID: PMC10392701 DOI: 10.3389/fpubh.2023.1151821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 06/02/2023] [Indexed: 08/04/2023] Open
Abstract
Background New statistical methodologies were developed in the last decade to face the challenges of estimating the effects of exposure to multiple chemicals. Weighted Quantile Sum (WQS) regression is a recent statistical method that allows estimating a mixture effect associated with a specific health effect and identifying the components that characterize the mixture effect. Objectives In this study, we propose an extension of WQS regression that estimates two mixture effects of chemicals on a health outcome in the same model through the inclusion of two indices, one in the positive direction and one in the negative direction, with the introduction of a penalization term. Methods To evaluate the performance of this new model we performed both a simulation study and a real case study where we assessed the effects of nutrients on obesity among adults using the National Health and Nutrition Examination Survey (NHANES) data. Results The method showed good performance in estimating both the regression parameter and the weights associated with the single elements when the penalized term was set equal to the magnitude of the Akaike information criterion of the unpenalized WQS regression. The two indices further helped to give a better estimate of the parameters [Positive direction Median Error (PME): 0.022; Negative direction Median Error (NME): -0.044] compared to the standard WQS without the penalization term (PME: -0.227; NME: 0.215). In the case study, WQS with two indices was able to find a significant effect of nutrients on obesity in both directions identifying sodium and magnesium as the main actors in the positive and negative association, respectively. Discussion Through this work, we introduced an extension of WQS regression that improved the accuracy of the parameter estimates when considering a mixture of elements that can have both a protective and a harmful effect on the outcome; and the advantage of adding a penalization term when estimating the weights.
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Affiliation(s)
- Stefano Renzetti
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, Università degli Studi di Brescia, Brescia, Italy
| | - Chris Gennings
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Stefano Calza
- Department of Molecular and Translational Medicine, Università degli Studi di Brescia, Brescia, Italy
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5
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Maitre L, Bustamante M, Hernández-Ferrer C, Thiel D, Lau CHE, Siskos AP, Vives-Usano M, Ruiz-Arenas C, Pelegrí-Sisó D, Robinson O, Mason D, Wright J, Cadiou S, Slama R, Heude B, Casas M, Sunyer J, Papadopoulou EZ, Gutzkow KB, Andrusaityte S, Grazuleviciene R, Vafeiadi M, Chatzi L, Sakhi AK, Thomsen C, Tamayo I, Nieuwenhuijsen M, Urquiza J, Borràs E, Sabidó E, Quintela I, Carracedo Á, Estivill X, Coen M, González JR, Keun HC, Vrijheid M. Multi-omics signatures of the human early life exposome. Nat Commun 2022; 13:7024. [PMID: 36411288 PMCID: PMC9678903 DOI: 10.1038/s41467-022-34422-2] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 10/25/2022] [Indexed: 11/23/2022] Open
Abstract
Environmental exposures during early life play a critical role in life-course health, yet the molecular phenotypes underlying environmental effects on health are poorly understood. In the Human Early Life Exposome (HELIX) project, a multi-centre cohort of 1301 mother-child pairs, we associate individual exposomes consisting of >100 chemical, outdoor, social and lifestyle exposures assessed in pregnancy and childhood, with multi-omics profiles (methylome, transcriptome, proteins and metabolites) in childhood. We identify 1170 associations, 249 in pregnancy and 921 in childhood, which reveal potential biological responses and sources of exposure. Pregnancy exposures, including maternal smoking, cadmium and molybdenum, are predominantly associated with child DNA methylation changes. In contrast, childhood exposures are associated with features across all omics layers, most frequently the serum metabolome, revealing signatures for diet, toxic chemical compounds, essential trace elements, and weather conditions, among others. Our comprehensive and unique resource of all associations ( https://helixomics.isglobal.org/ ) will serve to guide future investigation into the biological imprints of the early life exposome.
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Affiliation(s)
- Léa Maitre
- Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Mariona Bustamante
- Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Center for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Carles Hernández-Ferrer
- Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Denise Thiel
- Department of Mathematics, Imperial College London, South Kensington, London, UK
| | - Chung-Ho E Lau
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Alexandros P Siskos
- Cancer Metabolism & Systems Toxicology Group, Division of Cancer, Department of Surgery & Cancer, Imperial College London, London, UK
| | - Marta Vives-Usano
- Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Center for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Carlos Ruiz-Arenas
- Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Dolors Pelegrí-Sisó
- Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Oliver Robinson
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Dan Mason
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Solène Cadiou
- Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Institute for Advanced Biosciences (IAB), Inserm, CNRS, Université Grenoble Alpes, Grenoble, France
| | - Rémy Slama
- Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Institute for Advanced Biosciences (IAB), Inserm, CNRS, Université Grenoble Alpes, Grenoble, France
| | - Barbara Heude
- Centre for Research in Epidemiology and Statistics (CRESS), Inserm, Université de Paris, Paris, France
| | - Maribel Casas
- Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Jordi Sunyer
- Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Eleni Z Papadopoulou
- Division of Climate and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Kristine B Gutzkow
- Division of Climate and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Sandra Andrusaityte
- Department of Environmental Sciences, Vytautas Magnus University, Kaunas, Lithuania
| | | | - Marina Vafeiadi
- Department of Social Medicine, Faculty of Medicine, University of Crete, Heraklion, Greece
| | - Leda Chatzi
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - Amrit K Sakhi
- Division of Climate and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Cathrine Thomsen
- Division of Climate and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Ibon Tamayo
- Computational Biology program, CIMA-University of Navarra, Pamplona, Spain
| | - Mark Nieuwenhuijsen
- Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Jose Urquiza
- Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Eva Borràs
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Center for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Eduard Sabidó
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Center for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Inés Quintela
- Medicine Genomics Group, Centro de Investigación Biomédica en Red Enfermedades Raras (CIBERER), University of Santiago de Compostela, CIMUS, Santiago de Compostela, Spain
| | - Ángel Carracedo
- Medicine Genomics Group, Centro de Investigación Biomédica en Red Enfermedades Raras (CIBERER), University of Santiago de Compostela, CIMUS, Santiago de Compostela, Spain
- Galician Foundation of Genomic Medicine, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Servicio Gallego de Salud (SERGAS), Santiago de Compostela, Galicia, Spain
| | - Xavier Estivill
- Center for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Muireann Coen
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Oncology Safety, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Juan R González
- Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Hector C Keun
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Cancer Metabolism & Systems Toxicology Group, Division of Cancer, Department of Surgery & Cancer, Imperial College London, London, UK
| | - Martine Vrijheid
- Institute for Global Health (ISGlobal), Barcelona, Spain.
- Universitat Pompeu Fabra (UPF), Barcelona, Spain.
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
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Ding E, Wang Y, Liu J, Tang S, Shi X. A review on the application of the exposome paradigm to unveil the environmental determinants of age-related diseases. Hum Genomics 2022; 16:54. [DOI: 10.1186/s40246-022-00428-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 10/29/2022] [Indexed: 11/11/2022] Open
Abstract
AbstractAge-related diseases account for almost half of all diseases among adults worldwide, and their incidence is substantially affected by the exposome, which is the sum of all exogenous and endogenous environmental exposures and the human body’s response to these exposures throughout the entire lifespan. Herein, we perform a comprehensive review of the epidemiological literature to determine the key elements of the exposome that affect the development of age-related diseases and the roles of aging hallmarks in this process. We find that most exposure assessments in previous aging studies have used a reductionist approach, whereby the effect of only a single environmental factor or a specific class of environmental factors on the development of age-related diseases has been examined. As such, there is a lack of a holistic and unbiased understanding of the effect of multiple environmental factors on the development of age-related diseases. To address this, we propose several research strategies based on an exposomic framework that could advance our understanding—in particular, from a mechanistic perspective—of how environmental factors affect the development of age-related diseases. We discuss the statistical methods and other methods that have been used in exposome-wide association studies, with a particular focus on multiomics technologies. We also address future challenges and opportunities in the realm of multidisciplinary approaches and genome–exposome epidemiology. Furthermore, we provide perspectives on precise public health services for vulnerable populations, public communications, the integration of risk exposure information, and the bench-to-bedside translation of research on age-related diseases.
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7
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Pavić D, Grbin D, Hudina S, Prosenc Zmrzljak U, Miljanović A, Košir R, Varga F, Ćurko J, Marčić Z, Bielen A. Tracing the oomycete pathogen Saprolegnia parasitica in aquaculture and the environment. Sci Rep 2022; 12:16646. [PMID: 36198674 PMCID: PMC9534867 DOI: 10.1038/s41598-022-16553-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 07/12/2022] [Indexed: 11/25/2022] Open
Abstract
Saprolegnia parasitica causes saprolegniosis, a disease responsible for significant economic losses in aquaculture and declines of fish populations in the wild, but the knowledge of its distribution and prevalence in the environment is limited. We developed a fast, sensitive and specific S. parasitica droplet digital PCR (ddPCR) assay and demonstrated its applicability for the detection and quantification of the pathogen in environmental samples: swab DNA collected from the host (trout skin, surface of eggs) and environmental DNA extracted from water. The developed assay was used to assess how abiotic (i.e. physico-chemical parameters of the water) and biotic (health status of the host) factors influence the S. parasitica load in the environment. The pathogen load in water samples was positively correlated with some site-specific abiotic parameters such as electrical conductivity (EC) and calcium, while fluorides were negatively correlated, suggesting that physico-chemical parameters are important for determining S. parasitica load in natural waters. Furthermore, skin swabs of injured trout had significantly higher pathogen load than swabs collected from healthy fish, confirming that S. parasitica is a widespread opportunistic pathogen. Our results provide new insights into various environmental factors that influence the distribution and abundance of S. parasitica.
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Affiliation(s)
- Dora Pavić
- Department of Biochemical Engineering, Faculty of Food Technology and Biotechnology, University of Zagreb, 10000, Zagreb, Croatia
| | - Dorotea Grbin
- Department of Biochemical Engineering, Faculty of Food Technology and Biotechnology, University of Zagreb, 10000, Zagreb, Croatia.,Department of Biology, Faculty of Science, University of Zagreb, 10000, Zagreb, Croatia
| | - Sandra Hudina
- Department of Biology, Faculty of Science, University of Zagreb, 10000, Zagreb, Croatia
| | | | - Anđela Miljanović
- Department of Biochemical Engineering, Faculty of Food Technology and Biotechnology, University of Zagreb, 10000, Zagreb, Croatia
| | - Rok Košir
- Labena Ltd, BIA Separations CRO - Molecular Biology Laboratory, 1000, Ljubljana, Slovenia
| | - Filip Varga
- Department of Seed Science and Technology, Faculty of Agriculture, University of Zagreb, 10000, Zagreb, Croatia.,Centre of Excellence for Biodiversity and Molecular Plant Breeding, CoE CroP-BioDiv), 10000, Zagreb, Croatia
| | - Josip Ćurko
- Department of Food Engineering, Faculty of Food Technology and Biotechnology, University of Zagreb, 10000, Zagreb, Croatia
| | - Zoran Marčić
- Department of Biology, Faculty of Science, University of Zagreb, 10000, Zagreb, Croatia
| | - Ana Bielen
- Department of Biochemical Engineering, Faculty of Food Technology and Biotechnology, University of Zagreb, 10000, Zagreb, Croatia.
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8
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Maitre L, Guimbaud JB, Warembourg C, Güil-Oumrait N, Petrone PM, Chadeau-Hyam M, Vrijheid M, Basagaña X, Gonzalez JR. State-of-the-art methods for exposure-health studies: Results from the exposome data challenge event. ENVIRONMENT INTERNATIONAL 2022; 168:107422. [PMID: 36058017 DOI: 10.1016/j.envint.2022.107422] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 06/22/2022] [Accepted: 07/15/2022] [Indexed: 06/15/2023]
Abstract
The exposome recognizes that individuals are exposed simultaneously to a multitude of different environmental factors and takes a holistic approach to the discovery of etiological factors for disease. However, challenges arise when trying to quantify the health effects of complex exposure mixtures. Analytical challenges include dealing with high dimensionality, studying the combined effects of these exposures and their interactions, integrating causal pathways, and integrating high-throughput omics layers. To tackle these challenges, the Barcelona Institute for Global Health (ISGlobal) held a data challenge event open to researchers from all over the world and from all expertises. Analysts had a chance to compete and apply state-of-the-art methods on a common partially simulated exposome dataset (based on real case data from the HELIX project) with multiple correlated exposure variables (P > 100 exposure variables) arising from general and personal environments at different time points, biological molecular data (multi-omics: DNA methylation, gene expression, proteins, metabolomics) and multiple clinical phenotypes in 1301 mother-child pairs. Most of the methods presented included feature selection or feature reduction to deal with the high dimensionality of the exposome dataset. Several approaches explicitly searched for combined effects of exposures and/or their interactions using linear index models or response surface methods, including Bayesian methods. Other methods dealt with the multi-omics dataset in mediation analyses using multiple-step approaches. Here we discuss features of the statistical models used and provide the data and codes used, so that analysts have examples of implementation and can learn how to use these methods. Overall, the exposome data challenge presented a unique opportunity for researchers from different disciplines to create and share state-of-the-art analytical methods, setting a new standard for open science in the exposome and environmental health field.
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Affiliation(s)
- Léa Maitre
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
| | - Jean-Baptiste Guimbaud
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Lyon, France.
| | - Charline Warembourg
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000 Rennes, France.
| | - Nuria Güil-Oumrait
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
| | | | - Marc Chadeau-Hyam
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Hospital, Norfolk Place, London W21PG, UK; MRC Centre for Environment and Health, Imperial College, London, UK.
| | - Martine Vrijheid
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
| | - Xavier Basagaña
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
| | - Juan R Gonzalez
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
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9
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Pavić D, Grbin D, Gregov M, Ćurko J, Vladušić T, Šver L, Miljanović A, Bielen A. Variations in the Sporulation Efficiency of Pathogenic Freshwater Oomycetes in Relation to the Physico-Chemical Properties of Natural Waters. Microorganisms 2022; 10:microorganisms10030520. [PMID: 35336096 PMCID: PMC8955207 DOI: 10.3390/microorganisms10030520] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 02/16/2022] [Accepted: 02/25/2022] [Indexed: 01/27/2023] Open
Abstract
Oomycete pathogens in freshwaters, such as Saprolegnia parasitica and Aphanomyces astaci, are responsible for fish/crayfish population declines in the wild and disease outbreaks in aquaculture. Although the formation of infectious zoospores in the laboratory can be triggered by washing their mycelium with natural water samples, the physico-chemical properties of the water that might promote sporulation are still unexplored. We washed the mycelia of A. astaci and S. parasitica with a range of natural water samples and observed differences in sporulation efficiency. The results of Partial Least Squares Regression (PLS-R) multivariate analysis showed that SAC (spectral absorption coefficient measured at 254 nm), DOC (dissolved organic carbon), ammonium-N and fluoride had the strongest positive effect on sporulation of S. parasitica, while sporulation of A. astaci was not significantly correlated with any of the analyzed parameters. In agreement with this, the addition of environmentally relevant concentrations of humic acid, an important contributor to SAC and DOC, to the water induced sporulation of S. parasitica but not of A. astaci. Overall, our results point to the differences in ecological requirements of these pathogens, but also present a starting point for optimizing laboratory protocols for the induction of sporulation.
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Matta K, Lefebvre T, Vigneau E, Cariou V, Marchand P, Guitton Y, Royer AL, Ploteau S, Le Bizec B, Antignac JP, Cano-Sancho G. Associations between persistent organic pollutants and endometriosis: A multiblock approach integrating metabolic and cytokine profiling. ENVIRONMENT INTERNATIONAL 2022; 158:106926. [PMID: 34649050 DOI: 10.1016/j.envint.2021.106926] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 09/09/2021] [Accepted: 10/06/2021] [Indexed: 05/09/2023]
Abstract
Humans are exposed daily to complex mixtures of chemical pollutants through their environment and diet, some of which have the potential to disrupt the bodies' natural endocrine functions and contribute to reproductive diseases like endometriosis. Increasing epidemiological and experimental evidence supports the association between endometriosis and certain persistent organic pollutants (POPs) like dioxins; however, little is known about the underlying linking mechanisms. The main objective of this study is to proof the methodological applicability and discovery potential of integrating ultra-trace mass spectrometry (MS) profiling of POP biomarkers and endogenous biomarker profiling (MS metabolomics and cytokines) in a case-control study for the etiological research of endometriosis. The approach is applied in a pilot clinical-based study conducted in France where women with and without surgically confirmed endometriosis were recruited. Serum samples were analysed with high-resolution MS for about 30 polychlorinated biphenyls (PCBs), organochlorinated pesticides and perfluoroalkyl substances (PFAS). About 600 serum metabolites and lipids were identified with targeted metabolomics using tandem MS with the Biocrates MxP® Quant 500 Kit. A panel of 4 pro-inflammatory cytokines were analysed using ELISA-based 4-PLEX analyser. Statistical analysis included a battery of variable selection approaches, multivariate logistic regression for single-chemical associations, Bayesian kernel machine regressions (BKMR) to identify mixture effects of POPs and a multiblock approach to identify shared biomarker signatures among high risk clusters. The results showed the positive associations between some POPs and endometriosis risk, including the pesticide trans-nonachlor Odds Ratio (95% Confidence Interval) 3.38 (2.06-5.98), p < 0.0001 and PCB 114 OR (95% CI) 1.83 (1.17-2.93), p = 0.009. The BKMR approach showed a tendency of a positive cumulative effect of the mixture, however trans-nonachlor exhibited significant associations within the mixture and interacted with other PCBs, strengthening the effects at highest concentrations. Finally, the multiblock analysis, relating the various blocks of data, revealed a latent cluster of women with higher risk of endometrioma presenting higher concentrations of trans-nonachlor, PCB 114 and dioxin-like toxic equivalents from PCBs, together with an increased inflammatory profile (i.e. elevated interleukin-8 and monocyte chemoattractant protein-1). It was also highlighted a specific metabolic pattern characterized by dysregulation of bile acid homeostasis and lipase activity. Further research will be required with larger sample size to confirm these findings and gain insight on the underlying mechanisms between POPs and endometriosis.
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Affiliation(s)
| | - Tiphaine Lefebvre
- LABERCA, Oniris, INRAE, 44307 Nantes, France; Faculty of Medicine, University of Nantes, Nantes, France; Department of Biology and Reproductive Medicine, University Hospital of Nantes, Nantes, France
| | | | | | | | | | | | - Stéphane Ploteau
- Faculty of Medicine, University of Nantes, Nantes, France; Service de Gynecologie-obstétrique, CIC FEA, Hôpital Mère Enfant, CHU Hôtel Dieu, Nantes, France
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11
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Dragičević P, Grbin D, Maguire I, Blažević SA, Abramović L, Tarandek A, Hudina S. Immune Response in Crayfish Is Species-Specific and Exhibits Changes along Invasion Range of a Successful Invader. BIOLOGY 2021; 10:1102. [PMID: 34827095 PMCID: PMC8615248 DOI: 10.3390/biology10111102] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 10/23/2021] [Accepted: 10/24/2021] [Indexed: 12/21/2022]
Abstract
Immunity is an important component of invasion success since it enables invaders' adaptation to conditions of the novel environment as they expand their range. Immune response of invaders may vary along the invasion range due to encountered parasites/microbial communities, conditions of the local environment, and ecological processes that arise during the range expansion. Here, we analyzed changes in the immune response along the invasion range of one of the most successful aquatic invaders, the signal crayfish, in the recently invaded Korana River, Croatia. We used several standard immune parameters (encapsulation response, hemocyte count, phenoloxidaze activity, and total prophenoloxidaze) to: i) compare immune response of the signal crayfish along its invasion range, and between species (comparison with co-occurring native narrow-clawed crayfish), and ii) analyze effects of specific predictors (water temperature, crayfish abundance, and body condition) on crayfish immune response changes. Immune response displayed species-specificity, differed significantly along the signal crayfish invasion range, and was mostly affected by water temperature and population abundance. Specific immune parameters showed density-dependent variation corresponding to increased investment in them during range expansion. Obtained results offer baseline insights for elucidating the role of immunocompetence in the invasion success of an invertebrate freshwater invader.
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Affiliation(s)
- Paula Dragičević
- Department of Biology, Faculty of Science, University of Zagreb, Rooseveltov Trg 6, 10000 Zagreb, Croatia; (P.D.); (I.M.); (S.A.B.); (L.A.); (A.T.)
| | - Dorotea Grbin
- Department of Biochemical Engineering, Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva Ulica 6, 10000 Zagreb, Croatia;
| | - Ivana Maguire
- Department of Biology, Faculty of Science, University of Zagreb, Rooseveltov Trg 6, 10000 Zagreb, Croatia; (P.D.); (I.M.); (S.A.B.); (L.A.); (A.T.)
| | - Sofia Ana Blažević
- Department of Biology, Faculty of Science, University of Zagreb, Rooseveltov Trg 6, 10000 Zagreb, Croatia; (P.D.); (I.M.); (S.A.B.); (L.A.); (A.T.)
| | - Lucija Abramović
- Department of Biology, Faculty of Science, University of Zagreb, Rooseveltov Trg 6, 10000 Zagreb, Croatia; (P.D.); (I.M.); (S.A.B.); (L.A.); (A.T.)
| | - Anita Tarandek
- Department of Biology, Faculty of Science, University of Zagreb, Rooseveltov Trg 6, 10000 Zagreb, Croatia; (P.D.); (I.M.); (S.A.B.); (L.A.); (A.T.)
| | - Sandra Hudina
- Department of Biology, Faculty of Science, University of Zagreb, Rooseveltov Trg 6, 10000 Zagreb, Croatia; (P.D.); (I.M.); (S.A.B.); (L.A.); (A.T.)
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12
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Laine JE, Bodinier B, Robinson O, Plusquin M, Scalbert A, Keski-Rahkonen P, Robinot N, Vermeulen R, Pizzi C, Asta F, Nawrot T, Gulliver J, Chatzi L, Kogevinas M, Nieuwenhuijsen M, Sunyer J, Vrijheid M, Chadeau-Hyam M, Vineis P. Prenatal Exposure to Multiple Air Pollutants, Mediating Molecular Mechanisms, and Shifts in Birthweight. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:14502-14513. [PMID: 33124810 DOI: 10.1021/acs.est.0c02657] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Mechanisms underlying adverse birth and later in life health effects from exposure to air pollution during the prenatal period have not been not fully elucidated, especially in the context of mixtures. We assessed the effects of prenatal exposure to mixtures of air pollutants of particulate matter (PM), PM2.5, PM10, nitrogen oxides, NO2, NOx, ultrafine particles (UFP), and oxidative potential (OP) of PM2.5 on infant birthweight in four European birth cohorts and the mechanistic underpinnings through cross-omics of metabolites and inflammatory proteins. The association between mixtures of air pollutants and birthweight z-scores (standardized for gestational age) was assessed for three different mixture models, using Bayesian machine kernel regression (BKMR). We determined the direct effect for PM2.5, PM10, NO2, and mediation by cross-omic signatures (identified using sparse partial least-squares regression) using causal mediation BKMR models. There was a negative association with birthweight z-scores and exposure to mixtures of air pollutants, where up to -0.21 or approximately a 96 g decrease in birthweight, comparing the 75th percentile to the median level of exposure to the air pollutant mixture could occur. Shifts in birthweight z-scores from prenatal exposure to PM2.5, PM10, and NO2 were mediated by molecular mechanisms, represented by cross-omics scores. Interleukin-17 and epidermal growth factor were identified as important inflammatory responses underlyingair pollution-associated shifts in birthweight. Our results signify that by identifying mechanisms through which mixtures of air pollutants operate, the causality of air pollution-associated shifts in birthweight is better supported, substantiating the need for reducing exposure in vulnerable populations.
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Affiliation(s)
- Jessica E Laine
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, Imperial College London, London SW7 2BU, United Kingdom
| | - Barbara Bodinier
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, Imperial College London, London SW7 2BU, United Kingdom
| | - Oliver Robinson
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, Imperial College London, London SW7 2BU, United Kingdom
| | - Michelle Plusquin
- Center for Environmental Sciences, Hasselt University, Hasselt 3500, Belgium
| | - Augustin Scalbert
- Nutrition and Metabolism Section, Biomarkers Group, International Agency for Research on Cancer (IARC), Lyon 69372, France
| | - Pekka Keski-Rahkonen
- Nutrition and Metabolism Section, Biomarkers Group, International Agency for Research on Cancer (IARC), Lyon 69372, France
| | - Nivonirina Robinot
- Nutrition and Metabolism Section, Biomarkers Group, International Agency for Research on Cancer (IARC), Lyon 69372, France
| | - Roel Vermeulen
- Institute for Risk Assessment Sciences, Environmental Epidemiology Division, Utrecht University, Utrecht 3584 CS, Netherlands
| | - Costanza Pizzi
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin and CPO-Piemonte, Turin 10126, Italy
| | - Federica Asta
- Department of Epidemiology, Lazio Regional Health Service, ASL Roma 1, Rome 00147, Italy
| | - Tim Nawrot
- Center for Environmental Sciences, Hasselt University, Hasselt 3500, Belgium
- Department of Public Health, Environment and Health Unit, Leuven University (KU Leuven), Leuven 3000, Belgium
| | - John Gulliver
- Centre for Environmental Health and Sustainability & School of Geography, Geology and the Environment, University of Leicester, Leicester LE1 7RH, United Kingdom
| | - Leda Chatzi
- Department of Social Medicine, Faculty of Medicine, University of Crete, Heraklion 700 13, Crete, Greece
| | - Manolis Kogevinas
- ISGlobal, Barcelona Institute for Global Health, Barcelona 08003, Spain
- CIBER Epidemiologia y Salud Pública (CIBERESP), Madrid 28029, Spain
- Universitat Pompeu Fabra (UPF), Barcelona 08002, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona 08003, Spain
| | | | - Jordi Sunyer
- ISGlobal, Barcelona Institute for Global Health, Barcelona 08003, Spain
- CIBER Epidemiologia y Salud Pública (CIBERESP), Madrid 28029, Spain
- Universitat Pompeu Fabra (UPF), Barcelona 08002, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona 08003, Spain
| | - Martine Vrijheid
- ISGlobal, Barcelona Institute for Global Health, Barcelona 08003, Spain
- CIBER Epidemiologia y Salud Pública (CIBERESP), Madrid 28029, Spain
- Universitat Pompeu Fabra (UPF), Barcelona 08002, Spain
| | - Marc Chadeau-Hyam
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, Imperial College London, London SW7 2BU, United Kingdom
| | - Paolo Vineis
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, Imperial College London, London SW7 2BU, United Kingdom
- Italian Institute of Technology, Genova 16163, Italy
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13
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Belloni M, Laurent O, Guihenneuc C, Ancelet S. Bayesian Profile Regression to Deal With Multiple Highly Correlated Exposures and a Censored Survival Outcome. First Application in Ionizing Radiation Epidemiology. Front Public Health 2020; 8:557006. [PMID: 33194957 PMCID: PMC7652768 DOI: 10.3389/fpubh.2020.557006] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 09/21/2020] [Indexed: 12/31/2022] Open
Abstract
As multifactorial and chronic diseases, cancers are among these pathologies for which the exposome concept is essential to gain more insight into the associated etiology and, ultimately, lead to better primary prevention strategies for public health. Indeed, cancers result from the combined influence of many genetic, environmental and behavioral stressors that may occur simultaneously and interact. It is thus important to properly account for multifactorial exposure patterns when estimating specific cancer risks at individual or population level. Nevertheless, the risk factors, especially environmental, are still too often considered in isolation in epidemiological studies. Moreover, major statistical difficulties occur when exposures to several factors are highly correlated due, for instance, to common sources shared by several pollutants. Suitable statistical methods must then be used to deal with these multicollinearity issues. In this work, we focused on the specific problem of estimating a disease risk from highly correlated environmental exposure covariates and a censored survival outcome. We extended Bayesian profile regression mixture (PRM) models to this context by assuming an instantaneous excess hazard ratio disease sub-model. The proposed hierarchical model incorporates an underlying truncated Dirichlet process mixture as an attribution sub-model. A specific adaptive Metropolis-Within-Gibbs algorithm-including label switching moves-was implemented to infer the model. This allows simultaneously clustering individuals with similar risks and similar exposure characteristics and estimating the associated risk for each group. Our Bayesian PRM model was applied to the estimation of the risk of death by lung cancer in a cohort of French uranium miners who were chronically and occupationally exposed to multiple and correlated sources of ionizing radiation. Several groups of uranium miners with high risk and low risk of death by lung cancer were identified and characterized by specific exposure profiles. Interestingly, our case study illustrates a limit of MCMC algorithms to fit full Bayesian PRM models even if the updating schemes for the cluster labels incorporate label-switching moves. Then, although this paper shows that Bayesian PRM models are promising tools for exposome research, it also opens new avenues for methodological research in this class of probabilistic models.
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Affiliation(s)
- Marion Belloni
- PSE-SANTE/SESANE/LEPID, Institut de Radioprotection et de Sûreté Nucléaire, Paris, France
| | - Olivier Laurent
- PSE-SANTE/SESANE/LEPID, Institut de Radioprotection et de Sûreté Nucléaire, Paris, France
| | - Chantal Guihenneuc
- Université de Paris, Unité de Recherche “Biostatistique, Traitement et Modélisation des données biologiques” BioSTM - UR 7537, Paris, France
| | - Sophie Ancelet
- PSE-SANTE/SESANE/LEPID, Institut de Radioprotection et de Sûreté Nucléaire, Paris, France
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14
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Matta K, Vigneau E, Cariou V, Mouret D, Ploteau S, Le Bizec B, Antignac JP, Cano-Sancho G. Associations between persistent organic pollutants and endometriosis: A multipollutant assessment using machine learning algorithms. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2020; 260:114066. [PMID: 32041029 DOI: 10.1016/j.envpol.2020.114066] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 12/17/2019] [Accepted: 01/23/2020] [Indexed: 06/10/2023]
Abstract
Endometriosis is a gynaecological disease characterised by the presence of endometriotic tissue outside of the uterus impacting a significant fraction of women of childbearing age. Evidence from epidemiological studies suggests a relationship between risk of endometriosis and exposure to some organochlorine persistent organic pollutants (POPs). However, these chemicals are numerous and occur in complex and highly correlated mixtures, and to date, most studies have not accounted for this simultaneous exposure. Linear and logistic regression models are constrained to adjusting for multiple exposures when variables are highly intercorrelated, resulting in unstable coefficients and arbitrary findings. Advanced machine learning models, of emerging use in epidemiology, today appear as a promising option to address these limitations. In this study, different machine learning techniques were compared on a dataset from a case-control study conducted in France to explore associations between mixtures of POPs and deep endometriosis. The battery of models encompassed regularised logistic regression, artificial neural network, support vector machine, adaptive boosting, and partial least-squares discriminant analysis with some additional sparsity constraints. These techniques were applied to identify the biomarkers of internal exposure in adipose tissue most associated with endometriosis and to compare model classification performance. The five tested models revealed a consistent selection of most associated POPs with deep endometriosis, including octachlorodibenzofuran, cis-heptachlor epoxide, polychlorinated biphenyl 77 or trans-nonachlor, among others. The high classification performance of all five models confirmed that machine learning may be a promising complementary approach in modelling highly correlated exposure biomarkers and their associations with health outcomes. Regularised logistic regression provided a good compromise between the interpretability of traditional statistical approaches and the classification capacity of machine learning approaches. Applying a battery of complementary algorithms may be a strategic approach to decipher complex exposome-health associations when the underlying structure is unknown.
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Affiliation(s)
| | | | | | | | - Stéphane Ploteau
- Service de Gynécologie-obstétrique, CIC FEA, Hôpital Mère Enfant, CHU Hôtel Dieu, Nantes, France
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15
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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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16
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Mustieles V, Arrebola JP. How polluted is your fat? What the study of adipose tissue can contribute to environmental epidemiology. J Epidemiol Community Health 2020; 74:401-407. [DOI: 10.1136/jech-2019-213181] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 12/19/2019] [Accepted: 01/10/2020] [Indexed: 12/12/2022]
Abstract
The study of the potential contribution of low-dose exposure to environmental chemicals on the development of chronic conditions in human populations is often hampered by methodological issues, including exposure misclassification and the inability to assess biological effects in target organs. White adipose tissue (WAT) presents the unique feature of being both an advantageous matrix for assessing long-term exposure to mixtures of persistent organic pollutants and an interesting tissue to investigate early preclinical effects. Moreover, other lipophilic non-persistent chemicals and heavy metals have been recently quantified in fat, suggesting that human WAT contains chemical mixtures more complex than initially thought. However, WAT has been scarcely used in environmental epidemiology due to collection difficulties. In this essay we discuss the potential of using human WAT as a source of both exposure and effect biomarkers, with the aim of advancing the epidemiological research of obesity-related diseases, including metabolic syndrome and cancer. Overall, we discuss the implications of investigating WAT in a multidisciplinary framework combining toxicological and epidemiological knowledge in order to improve the inference of causal relationships in observational settings. We finalise by suggesting feasible designs and scenarios in which WAT samples may be reasonably collected.
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Cai Y, Rosen Vollmar AK, Johnson CH. Analyzing Metabolomics Data for Environmental Health and Exposome Research. Methods Mol Biol 2020; 2104:447-467. [PMID: 31953830 DOI: 10.1007/978-1-0716-0239-3_22] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The exposome is the cumulative measure of environmental influences and associated biological responses across the life span, with critical relevance for understanding how exposures can impact human health. Metabolomics analysis of biological samples offers unique advantages for examining the exposome. Simultaneous analysis of external exposures, biological responses, and host susceptibility at a systems level can help establish links between external exposures and health outcomes. As metabolomics technologies continue to evolve for the study of the exposome, metabolomics ultimately will help provide valuable insights for exposure risk assessment, and disease prevention and management. Here, we discuss recent advances in metabolomics, and describe data processing protocols that can enable analysis of the exposome. This chapter focuses on using liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics for analysis of the exposome, including (1) preprocessing of untargeted metabolomics data, (2) identification of exposure chemicals and their metabolites, and (3) methods to establish associations between exposures and diseases.
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Affiliation(s)
- Yuping Cai
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA
| | - Ana K Rosen Vollmar
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA
| | - Caroline Helen Johnson
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA.
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Maitre L, Robinson O, Martinez D, Toledano MB, Ibarluzea J, Marina LS, Sunyer J, Villanueva CM, Keun HC, Vrijheid M, Coen M. Urine Metabolic Signatures of Multiple Environmental Pollutants in Pregnant Women: An Exposome Approach. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2018; 52:13469-13480. [PMID: 30285427 DOI: 10.1021/acs.est.8b02215] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Exposure to environmental pollutants, particularly during pregnancy, can have adverse consequences on child development but little is known about the effects of pollutant mixtures on endogenous metabolism in pregnant women. We aimed to identify urinary metabolic signatures associated with low level exposure to multiple environmental pollutants in pregnant women from the INMA (INfancia y Medio Ambiente) birth cohort (Spain, N = 750). 35 chemical exposures were quantified in first trimester blood samples (organochlorine pesticides, PCBs, PFAS), in cord blood (mercury), and twice in urine at 12 and 32 weeks of pregnancy (metals, phthalates, bisphenol A). 1H nuclear magnetic resonance (NMR) metabolic profiles of urine were acquired in the same samples as pollutants. We explored associations between exposures and metabolism through an exposome-metabolome wide association scan and multivariate O2PLS modeling. Novel and reproducible associations were found across two periods of pregnancy for three nonpersistent pollutants and across two subcohorts for four of the persistent pollutants. We found novel metabolic signatures associated with arsenic exposure: TMAO and dimethylamine possibly related to gut microbial methylamine metabolism and homarine related to fish intake. Tobacco smoke exposure was related to coffee metabolism and PCBs with 3-hydroxyvaleric acid, usually released under ketoacidosis. These findings will have implications for further understanding of maternal-fetal health, and health across the life-course.
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Affiliation(s)
- Léa Maitre
- ISGlobal, Institute for Global Health , Barcelona , Spain
- Universitat Pompeu Fabra (UPF) , Barcelona , Spain
- CIBER Epidemiología y Salud Pública (CIBERESP) , Madrid , Spain
| | - Oliver Robinson
- ISGlobal, Institute for Global Health , Barcelona , Spain
- Universitat Pompeu Fabra (UPF) , Barcelona , Spain
- CIBER Epidemiología y Salud Pública (CIBERESP) , Madrid , Spain
- Medical Research Council-Public Health England (MRC-PHE) Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health , Imperial College London , London , U.K
| | - David Martinez
- ISGlobal, Institute for Global Health , Barcelona , Spain
- Universitat Pompeu Fabra (UPF) , Barcelona , Spain
- CIBER Epidemiología y Salud Pública (CIBERESP) , Madrid , Spain
| | - Mireille B Toledano
- Medical Research Council-Public Health England (MRC-PHE) Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health , Imperial College London , London , U.K
| | - Jesús Ibarluzea
- CIBER Epidemiología y Salud Pública (CIBERESP) , Madrid , Spain
- School of Psychology , University of the Basque Country UPV/EHU , San Sebastian , Basque Country Spain
- Biodonostia Health Research Institute , San Sebastian , Basque Country Spain
- Sub-Directorate for Public Health of Gipuzkoa, Department of Health , Government of the Basque Country , San Sebastian , Basque Country Spain
| | - Loreto Santa Marina
- CIBER Epidemiología y Salud Pública (CIBERESP) , Madrid , Spain
- Biodonostia Health Research Institute , San Sebastian , Basque Country Spain
- Sub-Directorate for Public Health of Gipuzkoa, Department of Health , Government of the Basque Country , San Sebastian , Basque Country Spain
| | - Jordi Sunyer
- ISGlobal, Institute for Global Health , Barcelona , Spain
- Universitat Pompeu Fabra (UPF) , Barcelona , Spain
- CIBER Epidemiología y Salud Pública (CIBERESP) , Madrid , Spain
- Municipal Institute of Medical Research (IMIM-Hospital del Mar) , Barcelona , Spain
| | - Cristina M Villanueva
- ISGlobal, Institute for Global Health , Barcelona , Spain
- Universitat Pompeu Fabra (UPF) , Barcelona , Spain
- CIBER Epidemiología y Salud Pública (CIBERESP) , Madrid , Spain
- Municipal Institute of Medical Research (IMIM-Hospital del Mar) , Barcelona , Spain
| | - Hector C Keun
- Division of Cancer, Department of Surgery & Cancer, Faculty of Medicine , Imperial College London , U.K
| | - Martine Vrijheid
- ISGlobal, Institute for Global Health , Barcelona , Spain
- Universitat Pompeu Fabra (UPF) , Barcelona , Spain
- CIBER Epidemiología y Salud Pública (CIBERESP) , Madrid , Spain
| | - Muireann Coen
- Integrative Systems Medicine & Digestive Disease, Department of Surgery & Cancer, Faculty of Medicine , Imperial College London , U.K
- Oncology Safety, Drug Safety and Metabolism , IMED Biotech Unit, AstraZeneca, 1 Francis Crick Avenue , Cambridge CB2 0RE , U.K
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