Solène Cadiou1, Mariona Bustamante2, Lydiane Agier1, Sandra Andrusaityte3, Xavier Basagaña2, Angel Carracedo4, Leda Chatzi5, Regina Grazuleviciene3, Juan R Gonzalez2, Kristine B Gutzkow6, Léa Maitre2, Dan Mason7, Frédéric Millot8, Mark Nieuwenhuijsen2, Eleni Papadopoulou6, Gillian Santorelli7, Pierre-Jean Saulnier9, Marta Vives10, John Wright7, Martine Vrijheid2, Rémy Slama11. 1. Team of Environmental Epidemiology, IAB, Institute for Advanced Biosciences, Inserm, CNRS, CHU-Grenoble-Alpes, University Grenoble-Alpes, Grenoble, France. 2. ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain. 3. Department of Environmental Sciences, Vytautas Magnus University, Kaunas, Lithuania. 4. Fundación Pública Galega de Medicina Xenómica (SERGAS), IDIS, Santiago de Compostela, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), CIMUS, Universidade de Santiago de Compostela, Santiago de Compostela, Spain. 5. Department of Preventive Medicine, University of Southern California, Los Angeles, USA. 6. Norwegian Institute of Public Health, Oslo, Norway. 7. Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, United Kingdom. 8. CHU Poitiers, Clinical Investigation Centre, CIC 1402, Poitiers, France; Poitiers University, Clinical Investigation Centre CIC 1402, Poitiers, France. 9. CHU Poitiers, Clinical Investigation Centre, CIC 1402, Poitiers, France; Poitiers University, Clinical Investigation Centre CIC 1402, Poitiers, France; INSERM, CIC 1402, F-86000 Poitiers, France; CHU Poitiers, Endocrinology, Diabetology, Nutrition Service, Poitiers, France. 10. CIBER Epidemiología y Salud Pública (CIBERESP), Spain. 11. Team of Environmental Epidemiology, IAB, Institute for Advanced Biosciences, Inserm, CNRS, CHU-Grenoble-Alpes, University Grenoble-Alpes, Grenoble, France. Electronic address: Remy.slama@univ-grenoble-alpes.fr.
Abstract
BACKGROUND: The exposome is defined as encompassing all environmental exposures one undergoes from conception onwards. Challenges of the application of this concept to environmental-health association studies include a possibly high false-positive rate. OBJECTIVES: We aimed to reduce the dimension of the exposome using information from DNA methylation as a way to more efficiently characterize the relation between exposome and child body mass index (BMI). METHODS: Among 1,173 mother-child pairs from HELIX cohort, 216 exposures ("whole exposome") were characterized. BMI and DNA methylation from immune cells of peripheral blood were assessed in children at age 6-10 years. A priori reduction of the methylome to preselect BMI-relevant CpGs was performed using biological pathways. We then implemented a tailored Meet-in-the-Middle approach to identify from these CpGs candidate mediators in the exposome-BMI association, using univariate linear regression models corrected for multiple testing: this allowed to point out exposures most likely to be associated with BMI ("reduced exposome"). Associations of this reduced exposome with BMI were finally tested. The approach was compared to an agnostic exposome-wide association study (ExWAS) ignoring the methylome. RESULTS: Among the 2284 preselected CpGs (0.6% of the assessed CpGs), 62 were associated with BMI. Four factors (3 postnatal and 1 prenatal) of the exposome were associated with at least one of these CpGs, among which postnatal blood level of copper and PFOS were directly associated with BMI, with respectively positive and negative estimated effects. The agnostic ExWAS identified 18 additional postnatal exposures, including many persistent pollutants, generally unexpectedly associated with decreased BMI. DISCUSSION: Our approach incorporating a priori information identified fewer significant associations than an agnostic approach. We hypothesize that this smaller number corresponds to a higher specificity (and possibly lower sensitivity), compared to the agnostic approach. Indeed, the latter cannot distinguish causal relations from reverse causation, e.g. for persistent compounds stored in fat, whose circulating level is influenced by BMI.
BACKGROUND: The exposome is defined as encompassing all environmental exposures one undergoes from conception onwards. Challenges of the application of this concept to environmental-health association studies include a possibly high false-positive rate. OBJECTIVES: We aimed to reduce the dimension of the exposome using information from DNA methylation as a way to more efficiently characterize the relation between exposome and child body mass index (BMI). METHODS: Among 1,173 mother-child pairs from HELIX cohort, 216 exposures ("whole exposome") were characterized. BMI and DNA methylation from immune cells of peripheral blood were assessed in children at age 6-10 years. A priori reduction of the methylome to preselect BMI-relevant CpGs was performed using biological pathways. We then implemented a tailored Meet-in-the-Middle approach to identify from these CpGs candidate mediators in the exposome-BMI association, using univariate linear regression models corrected for multiple testing: this allowed to point out exposures most likely to be associated with BMI ("reduced exposome"). Associations of this reduced exposome with BMI were finally tested. The approach was compared to an agnostic exposome-wide association study (ExWAS) ignoring the methylome. RESULTS: Among the 2284 preselected CpGs (0.6% of the assessed CpGs), 62 were associated with BMI. Four factors (3 postnatal and 1 prenatal) of the exposome were associated with at least one of these CpGs, among which postnatal blood level of copper and PFOS were directly associated with BMI, with respectively positive and negative estimated effects. The agnostic ExWAS identified 18 additional postnatal exposures, including many persistent pollutants, generally unexpectedly associated with decreased BMI. DISCUSSION: Our approach incorporating a priori information identified fewer significant associations than an agnostic approach. We hypothesize that this smaller number corresponds to a higher specificity (and possibly lower sensitivity), compared to the agnostic approach. Indeed, the latter cannot distinguish causal relations from reverse causation, e.g. for persistent compounds stored in fat, whose circulating level is influenced by BMI.
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Authors: Martine Vrijheid; Xavier Basagaña; Juan R Gonzalez; Vincent W V Jaddoe; Genon Jensen; Hector C Keun; Rosemary R C McEachan; Joana Porcel; Valerie Siroux; Morris A Swertz; Cathrine Thomsen; Gunn Marit Aasvang; Sandra Andrušaitytė; Karine Angeli; Demetris Avraam; Ferran Ballester; Paul Burton; Mariona Bustamante; Maribel Casas; Leda Chatzi; Cécile Chevrier; Natacha Cingotti; David Conti; Amélie Crépet; Payam Dadvand; Liesbeth Duijts; Esther van Enckevort; Ana Esplugues; Serena Fossati; Ronan Garlantezec; María Dolores Gómez Roig; Regina Grazuleviciene; Kristine B Gützkow; Mònica Guxens; Sido Haakma; Ellen V S Hessel; Lesley Hoyles; Eleanor Hyde; Jana Klanova; Jacob D van Klaveren; Andreas Kortenkamp; Laurent Le Brusquet; Ivonne Leenen; Aitana Lertxundi; Nerea Lertxundi; Christos Lionis; Sabrina Llop; Maria-Jose Lopez-Espinosa; Sarah Lyon-Caen; Lea Maitre; Dan Mason; Sandrine Mathy; Edurne Mazarico; Tim Nawrot; Mark Nieuwenhuijsen; Rodney Ortiz; Marie Pedersen; Josep Perelló; Míriam Pérez-Cruz; Claire Philippat; Pavel Piler; Costanza Pizzi; Joane Quentin; Lorenzo Richiardi; Adrian Rodriguez; Theano Roumeliotaki; José Manuel Sabin Capote; Leonardo Santiago; Susana Santos; Alexandros P Siskos; Katrine Strandberg-Larsen; Nikos Stratakis; Jordi Sunyer; Arthur Tenenhaus; Marina Vafeiadi; Rebecca C Wilson; John Wright; Tiffany Yang; Remy Slama Journal: Environ Epidemiol Date: 2021-10-01