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      A population-based resource for intergenerational metabolomics analyses in pregnant women and their children: the Generation R Study

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          Abstract

          Introduction

          Adverse exposures in early life may predispose children to cardio-metabolic disease in later life. Metabolomics may serve as a valuable tool to disentangle the metabolic adaptations and mechanisms that potentially underlie these associations.

          Objectives

          To describe the acquisition, processing and structure of the metabolomics data available in a population-based prospective cohort from early pregnancy onwards and to examine the relationships between metabolite profiles of pregnant women and their children at birth and in childhood.

          Methods

          In a subset of 994 mothers-child pairs from a prospective population-based cohort study among pregnant women and their children from Rotterdam, the Netherlands, we used LC–MS/MS to determine concentrations of amino acids, non-esterified fatty acids, phospholipids and carnitines in blood serum collected in early pregnancy, at birth (cord blood), and at child’s age 10 years.

          Results

          Concentrations of diacyl-phosphatidylcholines, acyl-alkyl-phosphatidylcholines, alkyl-lysophosphatidylcholines and sphingomyelines were the highest in early pregnancy, concentrations of amino acids and non-esterified fatty acids were the highest at birth and concentrations of alkyl-lysophosphatidylcholines, free carnitine and acyl-carnitines were the highest at age 10 years. Correlations of individual metabolites between pregnant women and their children at birth and at the age of 10 years were low (range between r = − 0.10 and r = 0.35).

          Conclusion

          Our results suggest that unique metabolic profiles are present among pregnant women, newborns and school aged children, with limited intergenerational correlations between metabolite profiles. These data will form a valuable resource to address the early metabolic origins of cardio-metabolic disease.

          Electronic supplementary material

          The online version of this article (10.1007/s11306-020-01667-1) contains supplementary material, which is available to authorized users.

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          Most cited references 40

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          The Generation R Study: Biobank update 2015.

          The Generation R Study is a population-based prospective cohort study from fetal life until adulthood. The study is designed to identify early environmental and genetic causes and causal pathways leading to normal and abnormal growth, development and health from fetal life, childhood and young adulthood. In total, 9,778 mothers were enrolled in the study. Data collection in children and their parents include questionnaires, interviews, detailed physical and ultrasound examinations, behavioural observations, Magnetic Resonance Imaging and biological samples. Efforts have been conducted for collecting biological samples including blood, hair, faeces, nasal swabs, saliva and urine samples and generating genomics data on DNA, RNA and microbiome. In this paper, we give an update of the collection, processing and storage of these biological samples and available measures. Together with detailed phenotype measurements, these biological samples provide a unique resource for epidemiological studies focused on environmental exposures, genetic and genomic determinants and their interactions in relation to growth, health and development from fetal life onwards.
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            Design and analysis of metabolomics studies in epidemiologic research: a primer on -omic technologies.

            Metabolomics is the field of "-omics" research concerned with the comprehensive characterization of the small low-molecular-weight metabolites in biological samples. In epidemiology, it represents an emerging technology and an unprecedented opportunity to measure environmental and other exposures with improved precision and far less measurement error than with standard epidemiologic methods. Advances in the application of metabolomics in large-scale epidemiologic research are now being realized through a combination of improved sample preparation and handling, automated laboratory and processing methods, and reduction in costs. The number of epidemiologic studies that use metabolic profiling is still limited, but it is fast gaining popularity in this area. In the present article, we present a roadmap for metabolomic analyses in epidemiologic studies and discuss the various challenges these data pose to large-scale studies. We discuss the steps of data preprocessing, univariate and multivariate data analysis, correction for multiplicity of comparisons with correlated data, and finally the steps of cross-validation and external validation. As data from metabolomic studies accumulate in epidemiology, there is a need for large-scale replication and synthesis of findings, increased availability of raw data, and a focus on good study design, all of which will highlight the potential clinical impact of metabolomics in this field. © The Author 2014. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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              Opening up the "Black Box": metabolic phenotyping and metabolome-wide association studies in epidemiology.

              Metabolic phenotyping of humans allows information to be captured on the interactions between dietary, xenobiotic, other lifestyle and environmental exposures, and genetic variation, which together influence the balance between health and disease risks at both individual and population levels. We describe here the main procedures in large-scale metabolic phenotyping and their application to metabolome-wide association (MWA) studies. By use of high-throughput technologies and advanced spectroscopic methods, application of metabolic profiling to large-scale epidemiologic sample collections, including metabolome-wide association (MWA) studies for biomarker discovery and identification. Metabolic profiling at epidemiologic scale requires optimization of experimental protocol to maximize reproducibility, sensitivity, and quantitative reliability, and to reduce analytical drift. Customized multivariate statistical modeling approaches are needed for effective data visualization and biomarker discovery with control for false-positive associations since 100s or 1,000s of complex metabolic spectra are being processed. Metabolic profiling is an exciting addition to the armamentarium of the epidemiologist for the discovery of new disease-risk biomarkers and diagnostics, and to provide novel insights into etiology, biological mechanisms, and pathways.
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                Author and article information

                Contributors
                r.gaillard@erasmusmc.nl
                Journal
                Metabolomics
                Metabolomics
                Metabolomics
                Springer US (New York )
                1573-3882
                1573-3890
                23 March 2020
                23 March 2020
                2020
                : 16
                : 4
                Affiliations
                [1 ]GRID grid.5645.2, ISNI 000000040459992X, The Generation R Study Group, , Erasmus MC, University Medical Center, ; Rotterdam, The Netherlands
                [2 ]GRID grid.5645.2, ISNI 000000040459992X, Department of Pediatrics, , Erasmus MC, University Medical Center, ; Rotterdam, The Netherlands
                [3 ]GRID grid.5252.0, ISNI 0000 0004 1936 973X, Division of Metabolic and Nutritional Medicine, Dr. Von Hauner Children’s Hospital, , LMU - Ludwig-Maximilians Universität München, ; Munich, Germany
                [4 ]GRID grid.5645.2, ISNI 000000040459992X, The Generation R Study Group, , Erasmus MC, University Medical Center, ; Room Na-2908, PO Box 2040, 3000 CA Rotterdam, The Netherlands
                Article
                1667
                10.1007/s11306-020-01667-1
                7089886
                32206914
                © The Author(s) 2020

                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/.

                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100003061, Erasmus Medisch Centrum;
                Funded by: FundRef http://dx.doi.org/10.13039/501100001828, Erasmus Universiteit Rotterdam;
                Funded by: FundRef http://dx.doi.org/10.13039/501100001826, ZonMw;
                Award ID: ZonMw-VIDI 016.136.361
                Award ID: 543003109
                Award ID: 529051022
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000781, European Research Council;
                Award ID: ERC-2014-CoG-648916
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100002996, Hartstichting;
                Award ID: 2017T013
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003092, Diabetes Fonds;
                Award ID: 2017.81.002
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100010663, H2020 European Research Council;
                Award ID: 633595
                Award ID: GROWTH ERC-2012-AdG–no.322605
                Funded by: FundRef http://dx.doi.org/10.13039/501100002347, Bundesministerium für Bildung und Forschung;
                Award ID: 01 GI 0825
                Funded by: FundRef http://dx.doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft;
                Award ID: INST 409/224-1 FUGG
                Categories
                Original Article
                Custom metadata
                © Springer Science+Business Media, LLC, part of Springer Nature 2020

                Molecular biology

                amino acids, metabolomics, fatty acids, phospholipids, carnitines, birth cohort

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