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Zaghlool SB, Mook-Kanamori DO, Kader S, Stephan N, Halama A, Engelke R, Sarwath H, Al-Dous EK, Mohamoud YA, Roemisch-Margl W, Adamski J, Kastenmüller G, Friedrich N, Visconti A, Tsai PC, Spector T, Bell JT, Falchi M, Wahl A, Waldenberger M, Peters A, Gieger C, Pezer M, Lauc G, Graumann J, Malek JA, Suhre K. Deep molecular phenotypes link complex disorders and physiological insult to CpG methylation. Hum Mol Genet 2019; 27:1106-1121. [PMID: 29325019 PMCID: PMC5886112 DOI: 10.1093/hmg/ddy006] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Accepted: 01/02/2018] [Indexed: 01/12/2023] Open
Abstract
Epigenetic regulation of cellular function provides a mechanism for rapid organismal adaptation to changes in health, lifestyle and environment. Associations of cytosine-guanine di-nucleotide (CpG) methylation with clinical endpoints that overlap with metabolic phenotypes suggest a regulatory role for these CpG sites in the body's response to disease or environmental stress. We previously identified 20 CpG sites in an epigenome-wide association study (EWAS) with metabolomics that were also associated in recent EWASs with diabetes-, obesity-, and smoking-related endpoints. To elucidate the molecular pathways that connect these potentially regulatory CpG sites to the associated disease or lifestyle factors, we conducted a multi-omics association study including 2474 mass-spectrometry-based metabolites in plasma, urine and saliva, 225 NMR-based lipid and metabolite measures in blood, 1124 blood-circulating proteins using aptamer technology, 113 plasma protein N-glycans and 60 IgG-glyans, using 359 samples from the multi-ethnic Qatar Metabolomics Study on Diabetes (QMDiab). We report 138 multi-omics associations at these CpG sites, including diabetes biomarkers at the diabetes-associated TXNIP locus, and smoking-specific metabolites and proteins at multiple smoking-associated loci, including AHRR. Mendelian randomization suggests a causal effect of metabolite levels on methylation of obesity-associated CpG sites, i.e. of glycerophospholipid PC(O-36: 5), glycine and a very low-density lipoprotein (VLDL-A) on the methylation of the obesity-associated CpG loci DHCR24, MYO5C and CPT1A, respectively. Taken together, our study suggests that multi-omics-associated CpG methylation can provide functional read-outs for the underlying regulatory response mechanisms to disease or environmental insults.
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Affiliation(s)
- Shaza B Zaghlool
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Education City, PO Box 24144, Doha, Qatar.,Computer Engineering Department, Virginia Tech, Blacksburg, VA 24061, USA
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Centre, PO Box 9600, 2300 RC Leiden, The Netherlands
| | - Sara Kader
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Education City, PO Box 24144, Doha, Qatar
| | - Nisha Stephan
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Education City, PO Box 24144, Doha, Qatar
| | - Anna Halama
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Education City, PO Box 24144, Doha, Qatar
| | - Rudolf Engelke
- Proteomics Core, Weill Cornell Medicine-Qatar, Education City, PO Box 24144, Doha, Qatar
| | - Hina Sarwath
- Proteomics Core, Weill Cornell Medicine-Qatar, Education City, PO Box 24144, Doha, Qatar
| | - Eman K Al-Dous
- Genomics Core, Weill Cornell Medicine-Qatar, Education City, PO Box 24144, Doha, Qatar
| | - Yasmin A Mohamoud
- Genomics Core, Weill Cornell Medicine-Qatar, Education City, PO Box 24144, Doha, Qatar
| | - Werner Roemisch-Margl
- Institute of Bioinformatics and Systems Biology, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstrasse, 85764 Neuherberg, Germany
| | - Jerzy Adamski
- Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstrasse, 85764 Neuherberg, Germany.,German Center for Diabetes Research (DZD), Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Gabi Kastenmüller
- Institute of Bioinformatics and Systems Biology, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstrasse, 85764 Neuherberg, Germany.,German Center for Diabetes Research (DZD), Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Nele Friedrich
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Alessia Visconti
- Department of Twin Research & Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Pei-Chien Tsai
- Department of Twin Research & Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Tim Spector
- Department of Twin Research & Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Jordana T Bell
- Department of Twin Research & Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Mario Falchi
- Department of Twin Research & Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Annika Wahl
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, D-85764 Neuherberg, Bavaria, Germany.,Institute of Epidemiology II, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, D-85764 Neuherberg, Bavaria, Germany
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, D-85764 Neuherberg, Bavaria, Germany.,Institute of Epidemiology II, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, D-85764 Neuherberg, Bavaria, Germany
| | - Annette Peters
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, D-85764 Neuherberg, Bavaria, Germany.,Institute of Epidemiology II, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, D-85764 Neuherberg, Bavaria, Germany.,DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Bavaria, Germany
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, D-85764 Neuherberg, Bavaria, Germany.,Institute of Epidemiology II, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, D-85764 Neuherberg, Bavaria, Germany
| | - Marija Pezer
- Glycoscience Research Laboratory, Genos Ltd, HR-10000, Zagreb, Croatia
| | - Gordan Lauc
- Glycoscience Research Laboratory, Genos Ltd, HR-10000, Zagreb, Croatia
| | - Johannes Graumann
- Proteomics Core, Weill Cornell Medicine-Qatar, Education City, PO Box 24144, Doha, Qatar.,Scientific Service Group Biomolecular Mass Spectrometry, Max Planck Institute for Heart and Lung Research, W.G. Kerckhoff Institute, 61231 Bad Nauheim, Germany
| | - Joel A Malek
- Genomics Core, Weill Cornell Medicine-Qatar, Education City, PO Box 24144, Doha, Qatar
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Education City, PO Box 24144, Doha, Qatar
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2
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Xu T, Brandmaier S, Messias AC, Herder C, Draisma HHM, Demirkan A, Yu Z, Ried JS, Haller T, Heier M, Campillos M, Fobo G, Stark R, Holzapfel C, Adam J, Chi S, Rotter M, Panni T, Quante AS, He Y, Prehn C, Roemisch-Margl W, Kastenmüller G, Willemsen G, Pool R, Kasa K, van Dijk KW, Hankemeier T, Meisinger C, Thorand B, Ruepp A, Hrabé de Angelis M, Li Y, Wichmann HE, Stratmann B, Strauch K, Metspalu A, Gieger C, Suhre K, Adamski J, Illig T, Rathmann W, Roden M, Peters A, van Duijn CM, Boomsma DI, Meitinger T, Wang-Sattler R. Effects of metformin on metabolite profiles and LDL cholesterol in patients with type 2 diabetes. Diabetes Care 2015; 38:1858-67. [PMID: 26251408 DOI: 10.2337/dc15-0658] [Citation(s) in RCA: 78] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2015] [Accepted: 06/24/2015] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Metformin is used as a first-line oral treatment for type 2 diabetes (T2D). However, the underlying mechanism is not fully understood. Here, we aimed to comprehensively investigate the pleiotropic effects of metformin. RESEARCH DESIGN AND METHODS We analyzed both metabolomic and genomic data of the population-based KORA cohort. To evaluate the effect of metformin treatment on metabolite concentrations, we quantified 131 metabolites in fasting serum samples and used multivariable linear regression models in three independent cross-sectional studies (n = 151 patients with T2D treated with metformin [mt-T2D]). Additionally, we used linear mixed-effect models to study the longitudinal KORA samples (n = 912) and performed mediation analyses to investigate the effects of metformin intake on blood lipid profiles. We combined genotyping data with the identified metformin-associated metabolites in KORA individuals (n = 1,809) and explored the underlying pathways. RESULTS We found significantly lower (P < 5.0E-06) concentrations of three metabolites (acyl-alkyl phosphatidylcholines [PCs]) when comparing mt-T2D with four control groups who were not using glucose-lowering oral medication. These findings were controlled for conventional risk factors of T2D and replicated in two independent studies. Furthermore, we observed that the levels of these metabolites decreased significantly in patients after they started metformin treatment during 7 years' follow-up. The reduction of these metabolites was also associated with a lowered blood level of LDL cholesterol (LDL-C). Variations of these three metabolites were significantly associated with 17 genes (including FADS1 and FADS2) and controlled by AMPK, a metformin target. CONCLUSIONS Our results indicate that metformin intake activates AMPK and consequently suppresses FADS, which leads to reduced levels of the three acyl-alkyl PCs and LDL-C. Our findings suggest potential beneficial effects of metformin in the prevention of cardiovascular disease.
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Affiliation(s)
- Tao Xu
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany
| | - Stefan Brandmaier
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany
| | - Ana C Messias
- Institute of Structural Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Christian Herder
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany German Center for Diabetes Research, Düsseldorf, Germany
| | - Harmen H M Draisma
- Department of Biological Psychology, Faculty of Psychology and Education, VU University Amsterdam, Amsterdam, the Netherlands EMGO Institute for Health and Care Research, Amsterdam, the Netherlands Neuroscience Campus Amsterdam, Amsterdam, the Netherlands
| | - Ayse Demirkan
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Zhonghao Yu
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany
| | - Janina S Ried
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Toomas Haller
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Margit Heier
- Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany
| | - Monica Campillos
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Gisela Fobo
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Renee Stark
- Institute of Health Economics and Health Care Management, Helmholtz Zentrum München, Neuherberg, Germany
| | - Christina Holzapfel
- Else Kroener-Fresenius-Center for Nutritional Medicine, Faculty of Medicine, Technische Universität München, Munich, Germany
| | - Jonathan Adam
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany
| | - Shen Chi
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany
| | - Markus Rotter
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany
| | - Tommaso Panni
- Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany
| | - Anne S Quante
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
| | - Ying He
- Shanghai Center for Bioinformation Technology, Shanghai, China Bioinformatics Center, Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Cornelia Prehn
- Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, Neuherberg, Germany
| | - Werner Roemisch-Margl
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Gabi Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Gonneke Willemsen
- Department of Biological Psychology, Faculty of Psychology and Education, VU University Amsterdam, Amsterdam, the Netherlands EMGO Institute for Health and Care Research, Amsterdam, the Netherlands
| | - René Pool
- Department of Biological Psychology, Faculty of Psychology and Education, VU University Amsterdam, Amsterdam, the Netherlands EMGO Institute for Health and Care Research, Amsterdam, the Netherlands
| | - Katarina Kasa
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Ko Willems van Dijk
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Thomas Hankemeier
- Faculty of Science, Leiden Academic Centre for Drug Research, Analytical BioSciences, the Netherlands
| | - Christa Meisinger
- Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany
| | - Barbara Thorand
- Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany
| | - Andreas Ruepp
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Martin Hrabé de Angelis
- Institute of Experimental Genetics, Helmholtz Zentrum München, Neuherberg, Germany Chair of Experimental Genetics, Center of Life and Food Sciences Weihenstephan, Technische Universität München, Freising, Germany German Center for Diabetes Research, Neuherberg, Germany
| | - Yixue Li
- Shanghai Center for Bioinformation Technology, Shanghai, China Bioinformatics Center, Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - H-Erich Wichmann
- Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany Institute of Medical Statistics and Epidemiology, Technische Universität München, Munich, Germany
| | - Bernd Stratmann
- Heart and Diabetes Center NRW, Diabetes Center, Ruhr-University Bochum, Bad Oeynhausen, Germany
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
| | | | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany
| | - Karsten Suhre
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany Faculty of Biology, Ludwig-Maximilians-Universität, Planegg-Martinsried, Germany Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Education City, Qatar Foundation, Doha, Qatar
| | - Jerzy Adamski
- Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, Neuherberg, Germany Chair of Experimental Genetics, Center of Life and Food Sciences Weihenstephan, Technische Universität München, Freising, Germany German Center for Diabetes Research, Neuherberg, Germany
| | - Thomas Illig
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Education City, Qatar Foundation, Doha, Qatar Hannover Unified Biobank, Hannover Medical School, Hannover, Germany
| | - Wolfgang Rathmann
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
| | - Michael Roden
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany German Center for Diabetes Research, Düsseldorf, Germany Department of Endocrinology and Diabetology, Medical Faculty, Düsseldorf, Germany
| | - Annette Peters
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany German Center for Diabetes Research, Neuherberg, Germany Department of Environmental Health, Harvard School of Public Health, Boston, MA
| | - Cornelia M van Duijn
- Neuroscience Campus Amsterdam, Amsterdam, the Netherlands Center for Medical Systems Biology, Leiden, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Faculty of Psychology and Education, VU University Amsterdam, Amsterdam, the Netherlands EMGO Institute for Health and Care Research, Amsterdam, the Netherlands
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, Neuherberg, Germany Institute of Human Genetics, Technische Universität München, Munich, Germany
| | - Rui Wang-Sattler
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany German Center for Diabetes Research, Neuherberg, Germany
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3
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Xu T, Holzapfel C, Dong X, Bader E, Yu Z, Prehn C, Perstorfer K, Jaremek M, Roemisch-Margl W, Rathmann W, Li Y, Wichmann HE, Wallaschofski H, Ladwig KH, Theis F, Suhre K, Adamski J, Illig T, Peters A, Wang-Sattler R. Effects of smoking and smoking cessation on human serum metabolite profile: results from the KORA cohort study. BMC Med 2013; 11:60. [PMID: 23497222 PMCID: PMC3653729 DOI: 10.1186/1741-7015-11-60] [Citation(s) in RCA: 91] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2012] [Accepted: 03/04/2013] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Metabolomics helps to identify links between environmental exposures and intermediate biomarkers of disturbed pathways. We previously reported variations in phosphatidylcholines in male smokers compared with non-smokers in a cross-sectional pilot study with a small sample size, but knowledge of the reversibility of smoking effects on metabolite profiles is limited. Here, we extend our metabolomics study with a large prospective study including female smokers and quitters. METHODS Using targeted metabolomics approach, we quantified 140 metabolite concentrations for 1,241 fasting serum samples in the population-based Cooperative Health Research in the Region of Augsburg (KORA) human cohort at two time points: baseline survey conducted between 1999 and 2001 and follow-up after seven years. Metabolite profiles were compared among groups of current smokers, former smokers and never smokers, and were further assessed for their reversibility after smoking cessation. Changes in metabolite concentrations from baseline to the follow-up were investigated in a longitudinal analysis comparing current smokers, never smokers and smoking quitters, who were current smokers at baseline but former smokers by the time of follow-up. In addition, we constructed protein-metabolite networks with smoking-related genes and metabolites. RESULTS We identified 21 smoking-related metabolites in the baseline investigation (18 in men and six in women, with three overlaps) enriched in amino acid and lipid pathways, which were significantly different between current smokers and never smokers. Moreover, 19 out of the 21 metabolites were found to be reversible in former smokers. In the follow-up study, 13 reversible metabolites in men were measured, of which 10 were confirmed to be reversible in male quitters. Protein-metabolite networks are proposed to explain the consistent reversibility of smoking effects on metabolites. CONCLUSIONS We showed that smoking-related changes in human serum metabolites are reversible after smoking cessation, consistent with the known cardiovascular risk reduction. The metabolites identified may serve as potential biomarkers to evaluate the status of smoking cessation and characterize smoking-related diseases.
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Affiliation(s)
- Tao Xu
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, Neuherberg 85764, Germany
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4
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Mittelstrass K, Ried JS, Yu Z, Krumsiek J, Gieger C, Prehn C, Roemisch-Margl W, Polonikov A, Peters A, Theis FJ, Meitinger T, Kronenberg F, Weidinger S, Wichmann HE, Suhre K, Wang-Sattler R, Adamski J, Illig T. Discovery of sexual dimorphisms in metabolic and genetic biomarkers. PLoS Genet 2011; 7:e1002215. [PMID: 21852955 PMCID: PMC3154959 DOI: 10.1371/journal.pgen.1002215] [Citation(s) in RCA: 279] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2010] [Accepted: 06/17/2011] [Indexed: 02/06/2023] Open
Abstract
Metabolomic profiling and the integration of whole-genome genetic association data has proven to be a powerful tool to comprehensively explore gene regulatory networks and to investigate the effects of genetic variation at the molecular level. Serum metabolite concentrations allow a direct readout of biological processes, and association of specific metabolomic signatures with complex diseases such as Alzheimer's disease and cardiovascular and metabolic disorders has been shown. There are well-known correlations between sex and the incidence, prevalence, age of onset, symptoms, and severity of a disease, as well as the reaction to drugs. However, most of the studies published so far did not consider the role of sexual dimorphism and did not analyse their data stratified by gender. This study investigated sex-specific differences of serum metabolite concentrations and their underlying genetic determination. For discovery and replication we used more than 3,300 independent individuals from KORA F3 and F4 with metabolite measurements of 131 metabolites, including amino acids, phosphatidylcholines, sphingomyelins, acylcarnitines, and C6-sugars. A linear regression approach revealed significant concentration differences between males and females for 102 out of 131 metabolites (p-values<3.8×10(-4); Bonferroni-corrected threshold). Sex-specific genome-wide association studies (GWAS) showed genome-wide significant differences in beta-estimates for SNPs in the CPS1 locus (carbamoyl-phosphate synthase 1, significance level: p<3.8×10(-10); Bonferroni-corrected threshold) for glycine. We showed that the metabolite profiles of males and females are significantly different and, furthermore, that specific genetic variants in metabolism-related genes depict sexual dimorphism. Our study provides new important insights into sex-specific differences of cell regulatory processes and underscores that studies should consider sex-specific effects in design and interpretation.
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Affiliation(s)
- Kirstin Mittelstrass
- Unit of Molecular Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
| | - Janina S. Ried
- Institute of Genetic Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
| | - Zhonghao Yu
- Unit of Molecular Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jan Krumsiek
- Institute of Bioinformatics and Systems Biology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
| | - Christian Gieger
- Institute of Genetic Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
| | - Cornelia Prehn
- Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
| | - Werner Roemisch-Margl
- Institute of Bioinformatics and Systems Biology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
| | - Alexey Polonikov
- Department of Biology, Medical Genetics, and Ecology, Kursk State Medical University, Kursk, Russia
| | - Annette Peters
- Institute of Epidemiology II, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
| | - Fabian J. Theis
- Institute of Bioinformatics and Systems Biology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany
| | - Florian Kronenberg
- Division of Genetic Epidemiology, Department of Medical Genetics and Molecular and Clinical Pharmacology, Innsbruck Medical University, Innsbruck, Austria
| | - Stephan Weidinger
- Department of Dermatology, Venereology, and Allergy, University Hospital Schleswig-Holstein, Kiel, Germay
| | - Heinz Erich Wichmann
- Institute of Epidemiology I, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Medical Informatics, Biometry, and Epidemiology, Chair of Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
- Klinikum Grosshadern, Munich, Germany
| | - Karsten Suhre
- Institute of Bioinformatics and Systems Biology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
- Faculty of Biology, Ludwig-Maximilians-Universität, Planegg-Martinsried, Germany
- Weill Cornell Medical College in Qatar, Qatar Foundation, Education City, Doha, Qatar
| | - Rui Wang-Sattler
- Unit of Molecular Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jerzy Adamski
- Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
- Lehrstuhl für Experimentelle Genetik, Technische Universität München, Munich, Germany
- * E-mail: (TI); (JA)
| | - Thomas Illig
- Unit of Molecular Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
- * E-mail: (TI); (JA)
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5
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Yu Z, Kastenmüller G, He Y, Belcredi P, Möller G, Prehn C, Mendes J, Wahl S, Roemisch-Margl W, Ceglarek U, Polonikov A, Dahmen N, Prokisch H, Xie L, Li Y, Wichmann HE, Peters A, Kronenberg F, Suhre K, Adamski J, Illig T, Wang-Sattler R. Differences between human plasma and serum metabolite profiles. PLoS One 2011; 6:e21230. [PMID: 21760889 PMCID: PMC3132215 DOI: 10.1371/journal.pone.0021230] [Citation(s) in RCA: 312] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2010] [Accepted: 05/24/2011] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Human plasma and serum are widely used matrices in clinical and biological studies. However, different collecting procedures and the coagulation cascade influence concentrations of both proteins and metabolites in these matrices. The effects on metabolite concentration profiles have not been fully characterized. METHODOLOGY/PRINCIPAL FINDINGS We analyzed the concentrations of 163 metabolites in plasma and serum samples collected simultaneously from 377 fasting individuals. To ensure data quality, 41 metabolites with low measurement stability were excluded from further analysis. In addition, plasma and corresponding serum samples from 83 individuals were re-measured in the same plates and mean correlation coefficients (r) of all metabolites between the duplicates were 0.83 and 0.80 in plasma and serum, respectively, indicating significantly better stability of plasma compared to serum (p = 0.01). Metabolite profiles from plasma and serum were clearly distinct with 104 metabolites showing significantly higher concentrations in serum. In particular, 9 metabolites showed relative concentration differences larger than 20%. Despite differences in absolute concentration between the two matrices, for most metabolites the overall correlation was high (mean r = 0.81±0.10), which reflects a proportional change in concentration. Furthermore, when two groups of individuals with different phenotypes were compared with each other using both matrices, more metabolites with significantly different concentrations could be identified in serum than in plasma. For example, when 51 type 2 diabetes (T2D) patients were compared with 326 non-T2D individuals, 15 more significantly different metabolites were found in serum, in addition to the 25 common to both matrices. CONCLUSIONS/SIGNIFICANCE Our study shows that reproducibility was good in both plasma and serum, and better in plasma. Furthermore, as long as the same blood preparation procedure is used, either matrix should generate similar results in clinical and biological studies. The higher metabolite concentrations in serum, however, make it possible to provide more sensitive results in biomarker detection.
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Affiliation(s)
- Zhonghao Yu
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Gabi Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Ying He
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Shanghai Center for Bioinformation Technology, Shanghai, China
- Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Petra Belcredi
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Gabriele Möller
- Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Cornelia Prehn
- Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Joaquim Mendes
- Solid State NMR Spectroscopy and Center for Integrated Protein Science, Department Chemie, Technische Universität München, Garching, Germany
- Institute of Structural Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Simone Wahl
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Werner Roemisch-Margl
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Uta Ceglarek
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, Leipzig, Germany
| | - Alexey Polonikov
- Department of Biology, Medical Genetics and Ecology, Kursk State Medical University, Kursk, Russian Federation
| | - Norbert Dahmen
- Department for Psychiatry, University of Mainz, Mainz, Germany
| | - Holger Prokisch
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Lu Xie
- Shanghai Center for Bioinformation Technology, Shanghai, China
| | - Yixue Li
- Shanghai Center for Bioinformation Technology, Shanghai, China
- Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - H. -Erich Wichmann
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
| | - Annette Peters
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Florian Kronenberg
- Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Innsbruck, Austria
| | - Karsten Suhre
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Faculty of Biology, Ludwig-Maximilians-Universität, Planegg-Martinsried, Germany
- Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Education City - Qatar Foundation, Doha, Qatar
| | - Jerzy Adamski
- Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Lehrstuhl für Experimentelle Genetik, Technische Universität München, Munich, Germany
| | - Thomas Illig
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Rui Wang-Sattler
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- * E-mail:
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