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      Prenatal environmental exposures associated with sex differences in childhood obesity and neurodevelopment

      research-article
      1 , 2 , 3 , , 1 , 4 , 1 , 2 , 5 , 6 , 7 , 8 , 9 , 10 , 4 , 11 , 12 , 1 , 2 , 5 , 10 , 1 , 2 , 5 , 12 , 10 , 1 , 11 , 1 , 2 , 5 , 10 , 13 , 13 , 1 , 2 , 5 , 1 , 2 , 5 , 1 , 2 , 14 ,
      BMC Medicine
      BioMed Central
      Prenatal environment, Sexual dimorphism, Childhood obesity, Neurodevelopment, DNA methylation, Causal inference, Multiexposure profile

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          Abstract

          Background

          Obesity and neurodevelopmental delay are complex traits that often co-occur and differ between boys and girls. Prenatal exposures are believed to influence children’s obesity, but it is unknown whether exposures of pregnant mothers can confer a different risk of obesity between sexes, and whether they can affect neurodevelopment.

          Methods

          We analyzed data from 1044 children from the HELIX project, comprising 93 exposures during pregnancy, and clinical, neuropsychological, and methylation data during childhood (5–11 years). Using exposome-wide interaction analyses, we identified prenatal exposures with the highest sexual dimorphism in obesity risk, which were used to create a multiexposure profile. We applied causal random forest to classify individuals into two environments: E1 and E0. E1 consists of a combination of exposure levels where girls have significantly less risk of obesity than boys, as compared to E0, which consists of the remaining combination of exposure levels. We investigated whether the association between sex and neurodevelopmental delay also differed between E0 and E1. We used methylation data to perform an epigenome-wide association study between the environments to see the effect of belonging to E1 or E0 at the molecular level.

          Results

          We observed that E1 was defined by the combination of low dairy consumption, non-smokers’ cotinine levels in blood, low facility richness, and the presence of green spaces during pregnancy (OR interaction = 0.070, P = 2.59 × 10 −5). E1 was also associated with a lower risk of neurodevelopmental delay in girls, based on neuropsychological tests of non-verbal intelligence (OR interaction = 0.42, P = 0.047) and working memory (OR interaction = 0.31, P = 0.02). In line with this, several neurodevelopmental functions were enriched in significant differentially methylated probes between E1 and E0.

          Conclusions

          The risk of obesity can be different for boys and girls in certain prenatal environments. We identified an environment combining four exposure levels that protect girls from obesity and neurodevelopment delay. The combination of single exposures into multiexposure profiles using causal inference can help determine populations at risk.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12916-023-02815-9.

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          Most cited references48

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          mice: Multivariate Imputation by Chained Equations inR

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            Multiple imputation using chained equations: Issues and guidance for practice

            Multiple imputation by chained equations is a flexible and practical approach to handling missing data. We describe the principles of the method and show how to impute categorical and quantitative variables, including skewed variables. We give guidance on how to specify the imputation model and how many imputations are needed. We describe the practical analysis of multiply imputed data, including model building and model checking. We stress the limitations of the method and discuss the possible pitfalls. We illustrate the ideas using a data set in mental health, giving Stata code fragments. 2010 John Wiley & Sons, Ltd.
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              Development of a WHO growth reference for school-aged children and adolescents

              OBJECTIVE: To construct growth curves for school-aged children and adolescents that accord with the WHO Child Growth Standards for preschool children and the body mass index (BMI) cut-offs for adults. METHODS: Data from the 1977 National Center for Health Statistics (NCHS)/WHO growth reference (1-24 years) were merged with data from the under-fives growth standards' cross-sectional sample (18-71 months) to smooth the transition between the two samples. State-of-the-art statistical methods used to construct the WHO Child Growth Standards (0-5 years), i.e. the Box-Cox power exponential (BCPE) method with appropriate diagnostic tools for the selection of best models, were applied to this combined sample. FINDINGS: The merged data sets resulted in a smooth transition at 5 years for height-for-age, weight-for-age and BMI-for-age. For BMI-for-age across all centiles the magnitude of the difference between the two curves at age 5 years is mostly 0.0 kg/m² to 0.1 kg/m². At 19 years, the new BMI values at +1 standard deviation (SD) are 25.4 kg/m² for boys and 25.0 kg/m² for girls. These values are equivalent to the overweight cut-off for adults (> 25.0 kg/m²). Similarly, the +2 SD value (29.7 kg/m² for both sexes) compares closely with the cut-off for obesity (> 30.0 kg/m²). CONCLUSION: The new curves are closely aligned with the WHO Child Growth Standards at 5 years, and the recommended adult cut-offs for overweight and obesity at 19 years. They fill the gap in growth curves and provide an appropriate reference for the 5 to 19 years age group.
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                Author and article information

                Contributors
                alejandro.caceres@isglobal.org
                juanr.gonzalez@isglobal.org
                Journal
                BMC Med
                BMC Med
                BMC Medicine
                BioMed Central (London )
                1741-7015
                12 April 2023
                12 April 2023
                2023
                : 21
                : 142
                Affiliations
                [1 ]GRID grid.434607.2, ISNI 0000 0004 1763 3517, Instituto de Salud Global de Barcelona (ISGlobal), ; 08003 Barcelona, Spain
                [2 ]GRID grid.466571.7, ISNI 0000 0004 1756 6246, Centro de Investigación Biomédica en Red en Epidemiología Y Salud Pública (CIBERESP), ; Madrid, Spain
                [3 ]GRID grid.6835.8, ISNI 0000 0004 1937 028X, Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), , Universitat Politècnica de Catalunya, ; 08019 Barcelona, Spain
                [4 ]GRID grid.19190.30, ISNI 0000 0001 2325 0545, Department of Environmental Science, , Vytautas Magnus University, ; 44248 Kaunas, Lithuania
                [5 ]GRID grid.5612.0, ISNI 0000 0001 2172 2676, Department of Health and Experimental Sciences, , Universitat Pompeu Fabra (UPF), ; Barcelona, Spain
                [6 ]GRID grid.11478.3b, ISNI 0000 0004 1766 3695, Center for Genomic Regulation (CRG), Barcelona, Institute of Science and Technology (BIST), ; Barcelona, Spain
                [7 ]GRID grid.11794.3a, ISNI 0000000109410645, Medicine Genomics Group, Centro de Investigación Biomédica en Red Enfermedades Raras (CIBERER), , CIMUS, University of Santiago de Compostela, ; Santiago de Compostela, Spain
                [8 ]GRID grid.488911.d, ISNI 0000 0004 0408 4897, Galician Foundation of Genomic Medicine, , Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Servicio Gallego de Salud (SERGAS), ; Galicia, Santiago de Compostela Spain
                [9 ]GRID grid.42505.36, ISNI 0000 0001 2156 6853, Department of Preventive Medicine, Keck School of Medicine, , University of Southern California, ; Los Angeles, USA
                [10 ]TruDiagnostic, Lexington, KY USA
                [11 ]GRID grid.418193.6, ISNI 0000 0001 1541 4204, Division of Climate and Environmental Health, , Norwegian Institute of Public Health, ; 0456 Oslo, Norway
                [12 ]GRID grid.418110.d, ISNI 0000 0004 0642 0153, Institut National de La Santé Et de La Recherche Médicale (Inserm) and Université Grenoble-Alpes, Institute for Advanced Biosciences (IAB), Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, ; Grenoble, France
                [13 ]GRID grid.418449.4, ISNI 0000 0004 0379 5398, Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, ; Bradford, UK
                [14 ]GRID grid.7080.f, ISNI 0000 0001 2296 0625, Department of Mathematics, , Universitat Autònoma de Barcelona, ; Bellaterra, 08193 Barcelona , Spain
                Author information
                http://orcid.org/0000-0001-8551-6695
                Article
                2815
                10.1186/s12916-023-02815-9
                10099694
                37046291
                e3523cdb-e7ae-494b-b3fb-7244db1de2c7
                © The Author(s) 2023

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 2 November 2022
                : 6 March 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004963, Seventh Framework Programme;
                Award ID: 308333 (HELIX project)
                Award Recipient :
                Funded by: Preventing Disease Programme
                Award ID: 874583 (ATHLETE project)
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100010269, Wellcome Trust;
                Award ID: WT101597MA
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100009187, Medical Research Foundation;
                Award ID: MR/N024397/1
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100004587, Instituto de Salud Carlos III;
                Funded by: FundRef http://dx.doi.org/10.13039/501100008458, Comissió Interdepartamental de Recerca i Innovació Tecnològica;
                Funded by: Lithuanian Agency for Science Innovation and Technology
                Award ID: 6-04-2014_31V-66
                Award Recipient :
                Funded by: Norwegian Ministry of Health and Care Services and the Ministry of Education and Research
                Funded by: EU FP6-2003-Food-3-NewGeneris, EU FP6. STREP Hiwate, EU FP7 ENV.2007.1.2.2.2. Project No 211250 Escape, EU FP7-2008-ENV-1.2.1.4 Envirogenomarkers, EU FP7-HEALTH-2009- single stage CHICOS, EU FP7 ENV.2008.1.2.1.6. Proposal No 226285 ENRIECO, EU- FP7- HEALTH
                Funded by: Greek Ministry of Health
                Funded by: FundRef http://dx.doi.org/10.13039/501100004837, Ministerio de Ciencia e Innovación;
                Award ID: CEX2018-000806-S
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100010552, Departament de Salut, Generalitat de Catalunya;
                Award ID: SLT017/20/00006
                Award ID: SLT017/20/000119
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2023

                Medicine
                prenatal environment,sexual dimorphism,childhood obesity,neurodevelopment,dna methylation,causal inference,multiexposure profile

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