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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 references40

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          The Emerging Role of Metabolomics in the Diagnosis and Prognosis of Cardiovascular Disease.

          Perturbations in cardiac energy metabolism are major contributors to a number of cardiovascular pathologies. In addition, comorbidities associated with cardiovascular disease (CVD) can alter systemic and myocardial metabolism, often contributing to the worsening of cardiac function and health outcomes. State-of-the-art metabolomic technologies give us the ability to measure thousands of metabolites in biological fluids or biopsies, providing us with a metabolic fingerprint of individual patients. These metabolic profiles may serve as diagnostic and/or prognostic tools that have the potential to significantly alter the management of CVD. Herein, the authors review how metabolomics can assist in the interpretation of perturbed metabolic processes, and how this has improved our ability to understand the pathology of ischemic heart disease, atherosclerosis, and heart failure. Taken together, the integration of metabolomics with other "omics" platforms will allow us to gain insight into pathophysiological interactions of metabolites, proteins, genes, and disease states, while advancing personalized medicine.
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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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              Are we close to defining a metabolomic signature of human obesity? A systematic review of metabolomics studies

              Introduction Obesity is a disorder characterized by a disproportionate increase in body weight in relation to height, mainly due to the accumulation of fat, and is considered a pandemic of the present century by many international health institutions. It is associated with several non-communicable chronic diseases, namely, metabolic syndrome, type 2 diabetes mellitus (T2DM), cardiovascular diseases (CVD), and cancer. Metabolomics is a useful tool to evaluate changes in metabolites due to being overweight and obesity at the body fluid and cellular levels and to ascertain metabolic changes in metabolically unhealthy overweight and obese individuals (MUHO) compared to metabolically healthy individuals (MHO). Objectives We aimed to conduct a systematic review (SR) of human studies focused on identifying metabolomic signatures in obese individuals and obesity-related metabolic alterations, such as inflammation or oxidative stress. Methods We reviewed the literature to identify studies investigating the metabolomics profile of human obesity and that were published up to May 7th, 2019 in SCOPUS and PubMed through an SR. The quality of reporting was evaluated using an adapted of QUADOMICS. Results Thirty-three articles were included and classified according to four types of approaches. (i) studying the metabolic signature of obesity, (ii) studying the differential responses of obese and non-obese subjects to dietary challenges (iii) studies that used metabolomics to predict weight loss and aimed to assess the effects of weight loss interventions on the metabolomics profiles of overweight or obese human subjects (iv) articles that studied the effects of specific dietary patterns or dietary compounds on obesity-related metabolic alterations in humans. Conclusion The present SR provides state-of-the-art information about the use of metabolomics as an approach to understanding the dynamics of metabolic processes involved in human obesity and emphasizes metabolic signatures related to obesity phenotypes. Electronic supplementary material The online version of this article (10.1007/s11306-019-1553-y) contains supplementary material, which is available to authorized users.
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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
                : 43
                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
                Author information
                http://orcid.org/0000-0002-7967-4600
                Article
                1667
                10.1007/s11306-020-01667-1
                7089886
                32206914
                43491c85-cbb3-4f7a-a21a-b984642adf81
                © 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/.

                History
                : 3 December 2019
                : 16 March 2020
                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
                metabolomics,amino acids,fatty acids,phospholipids,carnitines,birth cohort
                Molecular biology
                metabolomics, amino acids, fatty acids, phospholipids, carnitines, birth cohort

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