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Floegel A, Kühn T, Sookthai D, Johnson T, Prehn C, Rolle-Kampczyk U, Otto W, Weikert C, Illig T, von Bergen M, Adamski J, Boeing H, Kaaks R, Pischon T. Serum metabolites and risk of myocardial infarction and ischemic stroke: a targeted metabolomic approach in two German prospective cohorts. Eur J Epidemiol 2017; 33:55-66. [PMID: 29181692 PMCID: PMC5803284 DOI: 10.1007/s10654-017-0333-0] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2017] [Accepted: 11/20/2017] [Indexed: 11/24/2022]
Abstract
Metabolomic approaches in prospective cohorts may offer a unique snapshot into early metabolic perturbations that are associated with a higher risk of cardiovascular diseases (CVD) in healthy people. We investigated the association of 105 serum metabolites, including acylcarnitines, amino acids, phospholipids and hexose, with risk of myocardial infarction (MI) and ischemic stroke in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam (27,548 adults) and Heidelberg (25,540 adults) cohorts. Using case-cohort designs, we measured metabolites among individuals who were free of CVD and diabetes at blood draw but developed MI (n = 204 and n = 228) or stroke (n = 147 and n = 121) during follow-up (mean, 7.8 and 7.3 years) and among randomly drawn subcohorts (n = 2214 and n = 770). We used Cox regression analysis and combined results using meta-analysis. Independent of classical CVD risk factors, ten metabolites were associated with risk of MI in both cohorts, including sphingomyelins, diacyl-phosphatidylcholines and acyl-alkyl-phosphatidylcholines with pooled relative risks in the range of 1.21–1.40 per one standard deviation increase in metabolite concentrations. The metabolites showed positive correlations with total- and LDL-cholesterol (r ranged from 0.13 to 0.57). When additionally adjusting for total-, LDL- and HDL-cholesterol, triglycerides and C-reactive protein, acyl-alkyl-phosphatidylcholine C36:3 and diacyl-phosphatidylcholines C38:3 and C40:4 remained associated with risk of MI. When added to classical CVD risk models these metabolites further improved CVD prediction (c-statistics increased from 0.8365 to 0.8384 in EPIC-Potsdam and from 0.8344 to 0.8378 in EPIC-Heidelberg). None of the metabolites was consistently associated with stroke risk. Alterations in sphingomyelin and phosphatidylcholine metabolism, and particularly metabolites of the arachidonic acid pathway are independently associated with risk of MI in healthy adults.
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Affiliation(s)
- Anna Floegel
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany. .,Leibniz Institute for Prevention Research and Epidemiology - BIPS, Achterstraße 30, 28359, Bremen, Germany.
| | - Tilman Kühn
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Disorn Sookthai
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Theron Johnson
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Cornelia Prehn
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Ulrike Rolle-Kampczyk
- Department of Molecular Systems Biology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany
| | - Wolfgang Otto
- Department of Molecular Systems Biology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany
| | - Cornelia Weikert
- Department of Food Safety, Federal Institute for Risk Assessment, Berlin, Germany.,Institute for Social Medicine, Epidemiology and Health Economics, Charité University Medical Center, Berlin, Germany
| | - Thomas Illig
- Hannover Unified Biobank, Hannover Medical School, Hannover, Germany.,Institute of Human Genetics, Hannover Medical School, Hannover, Germany
| | - Martin von Bergen
- Department of Molecular Systems Biology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany.,University of Aalborg, Fredrik Bajers Vej 7H, 9220, Aalborg East, Denmark
| | - Jerzy Adamski
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Heiner Boeing
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tobias Pischon
- Molecular Epidemiology Group, Max Delbrück Center for Molecular Medicine (MDC), Berlin, Germany.,Charité - Universitätsmedizin Berlin, Berlin, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany
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52
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Tokarz J, Haid M, Cecil A, Prehn C, Artati A, Möller G, Adamski J. Endocrinology Meets Metabolomics: Achievements, Pitfalls, and Challenges. Trends Endocrinol Metab 2017; 28:705-721. [PMID: 28780001 DOI: 10.1016/j.tem.2017.07.001] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2017] [Revised: 06/30/2017] [Accepted: 07/05/2017] [Indexed: 02/07/2023]
Abstract
The metabolome, although very dynamic, is sufficiently stable to provide specific quantitative traits related to health and disease. Metabolomics requires balanced use of state-of-the-art study design, chemical analytics, biostatistics, and bioinformatics to deliver meaningful answers to contemporary questions in human disease research. The technology is now frequently employed for biomarker discovery and for elucidating the mechanisms underlying endocrine-related diseases. Metabolomics has also enriched genome-wide association studies (GWAS) in this area by providing functional data. The contributions of rare genetic variants to metabolome variance and to the human phenotype have been underestimated until now.
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Affiliation(s)
- Janina Tokarz
- Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Mark Haid
- Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Alexander Cecil
- Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Cornelia Prehn
- Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Anna Artati
- Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Gabriele Möller
- Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Jerzy Adamski
- Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany; Lehrstuhl für Experimentelle Genetik, Technische Universität München, 85350 Freising-Weihenstephan, Germany; German Center for Diabetes Research (DZD), 85764 München-Neuherberg, Germany.
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53
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Rauschert S, Mori TA, Beilin LJ, Jacoby P, Uhl O, Koletzko B, Oddy WH, Hellmuth C. Early Life Factors, Obesity Risk, and the Metabolome of Young Adults. Obesity (Silver Spring) 2017; 25:1549-1555. [PMID: 28758369 DOI: 10.1002/oby.21915] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Revised: 05/30/2017] [Accepted: 06/01/2017] [Indexed: 12/16/2022]
Abstract
OBJECTIVE Noncommunicable diseases such as obesity have become a serious global public health epidemic. This study aimed to examine whether there was an association between early life factors (with a special focus on breastfeeding) BMI, waist circumference, and the metabolome in offspring at 20 years. METHODS Data from the Western Australian Pregnancy Cohort (Raine) Study were analyzed using 1,024 plasma samples from the 20-year follow-up. A liquid chromatography, tandem mass spectrometry metabolomics approach was used to measure metabolites. Multiple linear regression models were performed and adjusted for relevant confounders. Inverse probability weighting was used to adjust the 20-year data for differences in socioeconomic variables between participants and nonparticipants since the commencement of the study. RESULTS An inverse association between breastfeeding and BMI or waist circumference at 20 years was lost after adjusting for parental prepregnancy BMI and maternal smoking during pregnancy. There was no significant effect of breastfeeding on metabolite concentrations at 20 years. CONCLUSIONS Although other studies have shown associations between breastfeeding, obesity, and metabolite concentrations at younger ages, this was not evident in our study in young adults. We found no association of metabolites previously associated with waist circumference at 20 years and breastfeeding in early life.
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Affiliation(s)
- Sebastian Rauschert
- Ludwig Maximilians Universität München, Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children's Hospital, University of Munich Medical Center, Munich, Germany
- Telethon Kids Institute, University of Western Australia, Perth, Western Australia, Australia
| | - Trevor A Mori
- School of Medicine and Pharmacology, Royal Perth Hospital Unit, University of Western Australia, Perth, Western Australia, Australia
| | - Lawrence J Beilin
- School of Medicine and Pharmacology, Royal Perth Hospital Unit, University of Western Australia, Perth, Western Australia, Australia
| | - Peter Jacoby
- Telethon Kids Institute, University of Western Australia, Perth, Western Australia, Australia
| | - Olaf Uhl
- Ludwig Maximilians Universität München, Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children's Hospital, University of Munich Medical Center, Munich, Germany
| | - Berthold Koletzko
- Ludwig Maximilians Universität München, Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children's Hospital, University of Munich Medical Center, Munich, Germany
| | - Wendy H Oddy
- Telethon Kids Institute, University of Western Australia, Perth, Western Australia, Australia
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
| | - Christian Hellmuth
- Ludwig Maximilians Universität München, Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children's Hospital, University of Munich Medical Center, Munich, Germany
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54
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Carayol M, Leitzmann MF, Ferrari P, Zamora-Ros R, Achaintre D, Stepien M, Schmidt JA, Travis RC, Overvad K, Tjønneland A, Hansen L, Kaaks R, Kühn T, Boeing H, Bachlechner U, Trichopoulou A, Bamia C, Palli D, Agnoli C, Tumino R, Vineis P, Panico S, Quirós JR, Sánchez-Cantalejo E, Huerta JM, Ardanaz E, Arriola L, Agudo A, Nilsson J, Melander O, Bueno-de-Mesquita B, Peeters PH, Wareham N, Khaw KT, Jenab M, Key TJ, Scalbert A, Rinaldi S. Blood Metabolic Signatures of Body Mass Index: A Targeted Metabolomics Study in the EPIC Cohort. J Proteome Res 2017; 16:3137-3146. [PMID: 28758405 PMCID: PMC6198936 DOI: 10.1021/acs.jproteome.6b01062] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Metabolomics is now widely used to characterize metabolic phenotypes associated with lifestyle risk factors such as obesity. The objective of the present study was to explore the associations of body mass index (BMI) with 145 metabolites measured in blood samples in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Metabolites were measured in blood from 392 men from the Oxford (UK) cohort (EPIC-Oxford) and in 327 control subjects who were part of a nested case-control study on hepatobiliary carcinomas (EPIC-Hepatobiliary). Measured metabolites included amino acids, acylcarnitines, hexoses, biogenic amines, phosphatidylcholines, and sphingomyelins. Linear regression models controlled for potential confounders and multiple testing were run to evaluate the associations of metabolite concentrations with BMI. 40 and 45 individual metabolites showed significant differences according to BMI variations, in the EPIC-Oxford and EPIC-Hepatobiliary subcohorts, respectively. Twenty two individual metabolites (kynurenine, one sphingomyelin, glutamate and 19 phosphatidylcholines) were associated with BMI in both subcohorts. The present findings provide additional knowledge on blood metabolic signatures of BMI in European adults, which may help identify mechanisms mediating the relationship of BMI with obesity-related diseases.
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Affiliation(s)
- Marion Carayol
- International Agency for Research on Cancer, Section of Nutrition and Metabolism, 150 cours Albert Thomas, 69372 Lyon Cedex 08, France
| | - Michael F. Leitzmann
- International Agency for Research on Cancer, Section of Nutrition and Metabolism, 150 cours Albert Thomas, 69372 Lyon Cedex 08, France
| | - Pietro Ferrari
- International Agency for Research on Cancer, Section of Nutrition and Metabolism, 150 cours Albert Thomas, 69372 Lyon Cedex 08, France
| | - Raul Zamora-Ros
- International Agency for Research on Cancer, Section of Nutrition and Metabolism, 150 cours Albert Thomas, 69372 Lyon Cedex 08, France
| | - David Achaintre
- International Agency for Research on Cancer, Section of Nutrition and Metabolism, 150 cours Albert Thomas, 69372 Lyon Cedex 08, France
| | - Magdalena Stepien
- International Agency for Research on Cancer, Section of Nutrition and Metabolism, 150 cours Albert Thomas, 69372 Lyon Cedex 08, France
| | - Julie A. Schmidt
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Oxford, OX3 7LF, United Kingdom
| | - Ruth C. Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Oxford, OX3 7LF, United Kingdom
| | - Kim Overvad
- Aarhus University, Department of Public Health, Section for Epidemiology, Bartholins Alle 2, DK-8000 Aarhus C, Denmark
| | - Anne Tjønneland
- Danish Cancer Society Research Center, Strandboulevarden 49, DK-2100 Copenhagen, Denmark
| | - Louise Hansen
- Danish Cancer Society Research Center, Strandboulevarden 49, DK-2100 Copenhagen, Denmark
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, D-69120 Heidelberg, Germany
| | - Tilman Kühn
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, D-69120 Heidelberg, Germany
| | - Heiner Boeing
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany
| | - Ursula Bachlechner
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany
| | - Antonia Trichopoulou
- Hellenic Health Foundation, Alexandroupoleos 23, Athens 11527, Greece
- WHO Collaborating Center for Nutrition and Health, Unit of Nutritional Epidemiology and Nutrition in Public Health, Dept. of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, Mikras Asias 75, Goudi GR-11527, Athens, Greece
- Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue. Boston, Massachusetts 02115, USA
| | - Christina Bamia
- Hellenic Health Foundation, Alexandroupoleos 23, Athens 11527, Greece
- WHO Collaborating Center for Nutrition and Health, Unit of Nutritional Epidemiology and Nutrition in Public Health, Dept. of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, Mikras Asias 75, Goudi GR-11527, Athens, Greece
| | - Domenico Palli
- Molecular and Nutritional Epidemiology Unit, Cancer Research and Prevention Institute (ISPO), Ponte Nuovo, Via delle Oblate n.4, Padiglione 28-A Mario Fiori, 50141 Florence, Italy
| | - Claudia Agnoli
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Venezian, 1, 20133 Milan, Italy
| | - Rosario Tumino
- Cancer Registry and Histopathology Unit, "Civic - M.P. Arezzo" Hospital, Via Dante 109, 97100, ASP Ragusa, Italy
| | - Paolo Vineis
- Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, St Mary's Campus, Norfolk Place W2 1PG London, UK
- HuGeF Foundation, Via Nizza 52, 10126, Turin, Italy
| | - Salvatore Panico
- Dipartimento di Medicina Clinica e Chirurgia, Medical School of Naples, Federico II University, Via Sergio Pansini, 5, 80131, Naples, Italy
| | - J. Ramón Quirós
- EPIC Asturias, Public Health Directorate, Asturias, Ciriaco Miguel Vigil St, 9 33006 Oviedo, Spain
| | - Emilio Sánchez-Cantalejo
- Escuela Andaluza de Salud Pública. Instituto de Investigación Biosanitaria ibs. Granada. Hospitales Universitarios de Granada/Universidad de Granada, Cuesta del Observatorio, 4, 18011 Granada, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP). Av. Monforte de Lemos, 3-5, 28029, Madrid, Spain
| | - José María Huerta
- CIBER Epidemiología y Salud Pública (CIBERESP). Av. Monforte de Lemos, 3-5, 28029, Madrid, Spain
- Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca. Ronda de Levante, 11. 30008, Murcia, Spain
| | - Eva Ardanaz
- CIBER Epidemiología y Salud Pública (CIBERESP). Av. Monforte de Lemos, 3-5, 28029, Madrid, Spain
- Navarra Public Health Institute, C/ Leyre, 15, 31003, Pamplona Spain
- IdiSNA, Navarra Institute for Health Research, C/ Irunlarrea, 3, 31008, Pamplona Spain
| | - Larraitz Arriola
- CIBER Epidemiología y Salud Pública (CIBERESP). Av. Monforte de Lemos, 3-5, 28029, Madrid, Spain
- Public Health Division of Gipuzkoa, Instituto BIO-Donostia, Basque Government, Av. Navarra 4, 20013 San Sebastian, Spain
| | - Antonio Agudo
- Unit of Nutrition and Cancer. Cancer Epidemiology Research Program. Catalan Institute of Oncology-IDIBELL. Av. Gran Via de l'Hospitalet 199-203, 08908 L'Hospitalet de Llobregat, Spain
| | - Jan Nilsson
- Department of Clinical Sciences Malmö, Lund University, Jan Waldenströms gata 35, 20502 Malmö, Sweden
| | - Olle Melander
- Department of Clinical Sciences Malmö, Lund University, Jan Waldenströms gata 35, 20502 Malmö, Sweden
| | - Bas Bueno-de-Mesquita
- Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, St Mary's Campus, Norfolk Place W2 1PG London, UK
- Department for Determinants of Chronic Diseases (DCD), National Institute for Public Health and the Environment (RIVM), PO Box1, 3720 BA, Bilthoven, The Netherlands
- Department of Gastroenterology and Hepatology, University Medical Center Utrecht, Room number F02.649, Internal mail no F02.618, P.O. Box 85500, 3508 GA UTRECHT, The Netherlands
- Department of Social & Preventive Medicine, Faculty of Medicine, University of Malaya, Pantai Valley, 50603, Kuala Lumpur, Malaysia
| | - Petra H. Peeters
- Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, St Mary's Campus, Norfolk Place W2 1PG London, UK
- Dept of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, STR 6.131, PO Box 85500, 3508GA Utrecht, the Netherlands
| | - Nick Wareham
- Medical Research Council Epidemiology Unit, MRC Epidemiology Unit, University of Cambridge, School of Clinical Medicine, Box 285 Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
| | - Kay-Tee Khaw
- Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge CB1 8RN, UK
| | - Mazda Jenab
- International Agency for Research on Cancer, Section of Nutrition and Metabolism, 150 cours Albert Thomas, 69372 Lyon Cedex 08, France
| | - Timothy J. Key
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Oxford, OX3 7LF, United Kingdom
| | - Augustin Scalbert
- International Agency for Research on Cancer, Section of Nutrition and Metabolism, 150 cours Albert Thomas, 69372 Lyon Cedex 08, France
| | - Sabina Rinaldi
- International Agency for Research on Cancer, Section of Nutrition and Metabolism, 150 cours Albert Thomas, 69372 Lyon Cedex 08, France
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55
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Genetic variants including markers from the exome chip and metabolite traits of type 2 diabetes. Sci Rep 2017; 7:6037. [PMID: 28729637 PMCID: PMC5519666 DOI: 10.1038/s41598-017-06158-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2016] [Accepted: 06/08/2017] [Indexed: 01/19/2023] Open
Abstract
Diabetes-associated metabolites may aid the identification of new risk variants for type 2 diabetes. Using targeted metabolomics within a subsample of the German EPIC-Potsdam study (n = 2500), we tested previously published SNPs for their association with diabetes-associated metabolites and conducted an additional exploratory analysis using data from the exome chip including replication within 2,692 individuals from the German KORA F4 study. We identified a total of 16 loci associated with diabetes-related metabolite traits, including one novel association between rs499974 (MOGAT2) and a diacyl-phosphatidylcholine ratio (PC aa C40:5/PC aa C38:5). Gene-based tests on all exome chip variants revealed associations between GFRAL and PC aa C42:1/PC aa C42:0, BIN1 and SM (OH) C22:2/SM C18:0 and TFRC and SM (OH) C22:2/SM C16:1). Selecting variants for gene-based tests based on functional annotation identified one additional association between OR51Q1 and hexoses. Among single genetic variants consistently associated with diabetes-related metabolites, two (rs174550 (FADS1), rs3204953 (REV3L)) were significantly associated with type 2 diabetes in large-scale meta-analysis for type 2 diabetes. In conclusion, we identified a novel metabolite locus in single variant analyses and four genes within gene-based tests and confirmed two previously known mGWAS loci which might be relevant for the risk of type 2 diabetes.
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56
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Bressler J, Yu B, Mosley TH, Knopman DS, Gottesman RF, Alonso A, Sharrett AR, Wruck LM, Boerwinkle E. Metabolomics and cognition in African American adults in midlife: the atherosclerosis risk in communities study. Transl Psychiatry 2017; 7:e1173. [PMID: 28934192 PMCID: PMC5538110 DOI: 10.1038/tp.2017.118] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Revised: 04/05/2017] [Accepted: 04/20/2017] [Indexed: 12/21/2022] Open
Abstract
Clinical studies have shown alterations in metabolic profiles when patients with mild cognitive impairment and Alzheimer's disease dementia were compared to cognitively normal subjects. Associations between 204 serum metabolites measured at baseline (1987-1989) and cognitive change were investigated in 1035 middle-aged community-dwelling African American participants in the biracial Atherosclerosis Risk in Communities (ARIC) Study. Cognition was evaluated using the Delayed Word Recall Test (DWRT; verbal memory), the Digit Symbol Substitution Test (DSST; processing speed) and the Word Fluency Test (WFT; verbal fluency) at visits 2 (1990-1992) and 4 (1996-1998). In addition, Cox regression was used to analyze the metabolites as predictors of incident hospitalized dementia between baseline and 2011. There were 141 cases among 1534 participants over a median 17.1-year follow-up period. After adjustment for established risk factors, one standard deviation increase in N-acetyl-1-methylhistidine was significantly associated with greater 6-year change in DWRT scores (β=-0.66 words; P=3.65 × 10-4). Two metabolites (one unnamed and a long-chain omega-6 polyunsaturated fatty acid found in vegetable oils (docosapentaenoate (DPA, 22:5 n-6)) were significantly associated with less decline on the DSST (DPA: β=1.25 digit-symbol pairs, P=9.47 × 10-5). Two unnamed compounds and three sex steroid hormones were associated with an increased risk of dementia (all P<3.9 × 10-4). The association of 4-androstene-3beta, 17beta-diol disulfate 1 with dementia was replicated in European Americans. These results demonstrate that screening the metabolome in midlife can detect biologically plausible biomarkers that may improve risk stratification for cognitive impairment at older ages.
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Affiliation(s)
- J Bressler
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - B Yu
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - T H Mosley
- Department of Medicine, Division of Geriatrics, University of Mississippi Medical Center, Jackson, MS, USA
| | - D S Knopman
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - R F Gottesman
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - A Alonso
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - A R Sharrett
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - L M Wruck
- Department of Biostatistics, Gillings School of Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - E Boerwinkle
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
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57
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Schmidt JA, Fensom GK, Rinaldi S, Scalbert A, Appleby PN, Achaintre D, Gicquiau A, Gunter MJ, Ferrari P, Kaaks R, Kühn T, Floegel A, Boeing H, Trichopoulou A, Lagiou P, Anifantis E, Agnoli C, Palli D, Trevisan M, Tumino R, Bueno-de-Mesquita HB, Agudo A, Larrañaga N, Redondo-Sánchez D, Barricarte A, Huerta JM, Quirós JR, Wareham N, Khaw KT, Perez-Cornago A, Johansson M, Cross AJ, Tsilidis KK, Riboli E, Key TJ, Travis RC. Pre-diagnostic metabolite concentrations and prostate cancer risk in 1077 cases and 1077 matched controls in the European Prospective Investigation into Cancer and Nutrition. BMC Med 2017; 15:122. [PMID: 28676103 PMCID: PMC5497352 DOI: 10.1186/s12916-017-0885-6] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Accepted: 05/26/2017] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Little is known about how pre-diagnostic metabolites in blood relate to risk of prostate cancer. We aimed to investigate the prospective association between plasma metabolite concentrations and risk of prostate cancer overall, and by time to diagnosis and tumour characteristics, and risk of death from prostate cancer. METHODS In a case-control study nested in the European Prospective Investigation into Cancer and Nutrition, pre-diagnostic plasma concentrations of 122 metabolites (including acylcarnitines, amino acids, biogenic amines, glycerophospholipids, hexose and sphingolipids) were measured using targeted mass spectrometry (AbsoluteIDQ p180 Kit) and compared between 1077 prostate cancer cases and 1077 matched controls. Risk of prostate cancer associated with metabolite concentrations was estimated by multi-variable conditional logistic regression, and multiple testing was accounted for by using a false discovery rate controlling procedure. RESULTS Seven metabolite concentrations, i.e. acylcarnitine C18:1, amino acids citrulline and trans-4-hydroxyproline, glycerophospholipids PC aa C28:1, PC ae C30:0 and PC ae C30:2, and sphingolipid SM (OH) C14:1, were associated with prostate cancer (p < 0.05), but none of the associations were statistically significant after controlling for multiple testing. Citrulline was associated with a decreased risk of prostate cancer (odds ratio (OR1SD) = 0.73; 95% confidence interval (CI) 0.62-0.86; p trend = 0.0002) in the first 5 years of follow-up after taking multiple testing into account, but not after longer follow-up; results for other metabolites did not vary by time to diagnosis. After controlling for multiple testing, 12 glycerophospholipids were inversely associated with advanced stage disease, with risk reduction up to 46% per standard deviation increase in concentration (OR1SD = 0.54; 95% CI 0.40-0.72; p trend = 0.00004 for PC aa C40:3). Death from prostate cancer was associated with higher concentrations of acylcarnitine C3, amino acids methionine and trans-4-hydroxyproline, biogenic amine ADMA, hexose and sphingolipid SM (OH) C14:1 and lower concentration of glycerophospholipid PC aa C42:4. CONCLUSIONS Several metabolites, i.e. C18:1, citrulline, trans-4-hydroxyproline, three glycerophospholipids and SM (OH) C14:1, might be related to prostate cancer. Analyses by time to diagnosis indicated that citrulline may be a marker of subclinical prostate cancer, while other metabolites might be related to aetiology. Several glycerophospholipids were inversely related to advanced stage disease. More prospective data are needed to confirm these associations.
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Affiliation(s)
- Julie A. Schmidt
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF UK
| | - Georgina K. Fensom
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF UK
| | - Sabina Rinaldi
- International Agency for Research on Cancer, 69372 Lyon, CEDEX 08 France
| | - Augustin Scalbert
- International Agency for Research on Cancer, 69372 Lyon, CEDEX 08 France
| | - Paul N. Appleby
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF UK
| | - David Achaintre
- International Agency for Research on Cancer, 69372 Lyon, CEDEX 08 France
| | - Audrey Gicquiau
- International Agency for Research on Cancer, 69372 Lyon, CEDEX 08 France
| | - Marc J. Gunter
- International Agency for Research on Cancer, 69372 Lyon, CEDEX 08 France
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG UK
| | - Pietro Ferrari
- International Agency for Research on Cancer, 69372 Lyon, CEDEX 08 France
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Foundation under Public Law, DE-69120 Heidelberg, Germany
| | - Tilman Kühn
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Foundation under Public Law, DE-69120 Heidelberg, Germany
| | - Anna Floegel
- Department of Epidemiology, German Institute of Human Nutrition (DIfE) Potsdam-Rehbrücke, DE-14558 Nuthetal, Germany
| | - Heiner Boeing
- Department of Epidemiology, German Institute of Human Nutrition (DIfE) Potsdam-Rehbrücke, DE-14558 Nuthetal, Germany
| | - Antonia Trichopoulou
- Hellenic Health Foundation, GR-11527 Athens, Greece
- WHO Collaborating Center for Nutrition and Health, Unit of Nutritional Epidemiology and Nutrition in Public Health, Department of Hygiene, Epidemiology and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, GR-11527 Athens, Greece
| | - Pagona Lagiou
- Hellenic Health Foundation, GR-11527 Athens, Greece
- WHO Collaborating Center for Nutrition and Health, Unit of Nutritional Epidemiology and Nutrition in Public Health, Department of Hygiene, Epidemiology and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, GR-11527 Athens, Greece
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, 02115 Boston, Massachusetts USA
| | | | - Claudia Agnoli
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Venezian, 1, 20133 Milano, Italy
| | - Domenico Palli
- Cancer Risk Factors and Life-Style Epidemiology Unit, Cancer Research and Prevention Institute – ISPO, 50134 Florence, Italy
| | - Morena Trevisan
- Cancer Epidemiology Unit-CERMS, Department of Medical Sciences, University of Turin, 10126 Turin, Italy
- CPO-Piemonte, 10126 Turin, Italy
| | - Rosario Tumino
- Cancer Registry and Histopathology Unit, “Civic-M.P.Arezzo” Hospital, ASP 97100 Ragusa, Italy
| | - H. Bas Bueno-de-Mesquita
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG UK
- Department for Determinants of Chronic Diseases (DCD), National Institute for Public Health and the Environment (RIVM), 3720 BA Bilthoven, The Netherlands
| | - Antonio Agudo
- Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology-IDIBELL, 08908 L’Hospitalet de Llobregat Barcelona, Spain
| | - Nerea Larrañaga
- Public Health Division of Gipuzkoa, Regional Government of the Basque Country, 20014 Donostia-San Sebastián, Spain
- CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Daniel Redondo-Sánchez
- CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Escuela Andaluza de Salud Pública, Instituto de Investigación Biosanitaria ibs.GRANADA, Hospitales Universitarios de Granada/Universidad de Granada, 18012 Granada, Spain
| | - Aurelio Barricarte
- CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Navarra Public Health Institute, 31003 Pamplona, Spain
- Navarra Institute for Health Research (IdiSNA) Pamplona, Pamplona, Spain
| | - José Maria Huerta
- CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, 30003 Murcia, Spain
| | | | - Nick Wareham
- MRC Epidemiology Unit, University of Cambridge, CB2 0SR Cambridge, UK
| | - Kay-Tee Khaw
- School of Clinical Medicine, University of Cambridge, CB2 2QQ Cambridge, UK
| | - Aurora Perez-Cornago
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF UK
| | - Mattias Johansson
- International Agency for Research on Cancer, 69372 Lyon, CEDEX 08 France
| | - Amanda J. Cross
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG UK
| | - Konstantinos K. Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG UK
- Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, 45110 Ioannina, Greece
| | - Elio Riboli
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG UK
| | - Timothy J. Key
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF UK
| | - Ruth C. Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF UK
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Vlaanderen JJ, Janssen NA, Hoek G, Keski-Rahkonen P, Barupal DK, Cassee FR, Gosens I, Strak M, Steenhof M, Lan Q, Brunekreef B, Scalbert A, Vermeulen RCH. The impact of ambient air pollution on the human blood metabolome. ENVIRONMENTAL RESEARCH 2017; 156:341-348. [PMID: 28391173 DOI: 10.1016/j.envres.2017.03.042] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Revised: 03/01/2017] [Accepted: 03/27/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND Biological perturbations caused by air pollution might be reflected in the compounds present in blood originating from air pollutants and endogenous metabolites influenced by air pollution (defined here as part of the blood metabolome). We aimed to assess the perturbation of the blood metabolome in response to short term exposure to air pollution. METHODS We exposed 31 healthy volunteers to ambient air pollution for 5h. We measured exposure to particulate matter, particle number concentrations, absorbance, elemental/organic carbon, trace metals, secondary inorganic components, endotoxin content, gaseous pollutants, and particulate matter oxidative potential. We collected blood from the participants 2h before and 2 and 18h after exposure. We employed untargeted metabolite profiling to monitor 3873 metabolic features in 493 blood samples from these volunteers. We assessed lung function using spirometry and six acute phase proteins in peripheral blood. We assessed the association of the metabolic features with the measured air pollutants and with health markers that we previously observed to be associated with air pollution in this study. RESULTS We observed 89 robust associations between air pollutants and metabolic features two hours after exposure and 118 robust associations 18h after exposure. Some of the metabolic features that were associated with air pollutants were also associated with acute health effects, especially changes in forced expiratory volume in 1s. We successfully identified tyrosine, guanosine, and hypoxanthine among the associated features. Bioinformatics approach Mummichog predicted enriched pathway activity in eight pathways, among which tyrosine metabolism. CONCLUSIONS This study demonstrates for the first time the application of untargeted metabolite profiling to assess the impact of air pollution on the blood metabolome.
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Affiliation(s)
- J J Vlaanderen
- Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, The Netherlands.
| | - N A Janssen
- National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - G Hoek
- Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, The Netherlands
| | | | - D K Barupal
- International Agency for Research on Cancer, Lyon, France
| | - F R Cassee
- Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, The Netherlands; National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - I Gosens
- National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - M Strak
- Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, The Netherlands
| | - M Steenhof
- Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, The Netherlands
| | - Q Lan
- US National Cancer Institute, Bethesda, MD, USA
| | - B Brunekreef
- Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, The Netherlands
| | - A Scalbert
- International Agency for Research on Cancer, Lyon, France
| | - R C H Vermeulen
- Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, The Netherlands
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59
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Maitre L, Lau CHE, Vizcaino E, Robinson O, Casas M, Siskos AP, Want EJ, Athersuch T, Slama R, Vrijheid M, Keun HC, Coen M. Assessment of metabolic phenotypic variability in children's urine using 1H NMR spectroscopy. Sci Rep 2017; 7:46082. [PMID: 28422130 PMCID: PMC5395814 DOI: 10.1038/srep46082] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Accepted: 03/08/2017] [Indexed: 12/02/2022] Open
Abstract
The application of metabolic phenotyping in clinical and epidemiological studies is limited by a poor understanding of inter-individual, intra-individual and temporal variability in metabolic phenotypes. Using 1H NMR spectroscopy we characterised short-term variability in urinary metabolites measured from 20 children aged 8-9 years old. Daily spot morning, night-time and pooled (50:50 morning and night-time) urine samples across six days (18 samples per child) were analysed, and 44 metabolites quantified. Intraclass correlation coefficients (ICC) and mixed effect models were applied to assess the reproducibility and biological variance of metabolic phenotypes. Excellent analytical reproducibility and precision was demonstrated for the 1H NMR spectroscopic platform (median CV 7.2%). Pooled samples captured the best inter-individual variability with an ICC of 0.40 (median). Trimethylamine, N-acetyl neuraminic acid, 3-hydroxyisobutyrate, 3-hydroxybutyrate/3-aminoisobutyrate, tyrosine, valine and 3-hydroxyisovalerate exhibited the highest stability with over 50% of variance specific to the child. The pooled sample was shown to capture the most inter-individual variance in the metabolic phenotype, which is of importance for molecular epidemiology study design. A substantial proportion of the variation in the urinary metabolome of children is specific to the individual, underlining the potential of such data to inform clinical and exposome studies conducted early in life.
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Affiliation(s)
- Léa Maitre
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL) Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
| | - Chung-Ho E. Lau
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
- Division of Cancer, Department of Surgery and Cancer, Imperial College London, Institute of Reproductive and Developmental Biology (IRDB), Hammersmith Hospital, London W12 0NN, UK
| | - Esther Vizcaino
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
| | - Oliver Robinson
- MRC-PHE Centre for Environment and Health, School of Public Health, Faculty of Medicine, Imperial College London, London, W2 1PG, UK
| | - Maribel Casas
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL) Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Alexandros P. Siskos
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
- Division of Cancer, Department of Surgery and Cancer, Imperial College London, Institute of Reproductive and Developmental Biology (IRDB), Hammersmith Hospital, London W12 0NN, UK
| | - Elizabeth J. Want
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
| | - Toby Athersuch
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
- MRC-PHE Centre for Environment and Health, School of Public Health, Faculty of Medicine, Imperial College London, London, W2 1PG, UK
| | - Remy Slama
- Inserm, Univ. Grenoble Alpes, CNRS, IAB (Institute of Advanced Biosciences), Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, F-38000 Grenoble, France
| | - Martine Vrijheid
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL) Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Hector C. Keun
- Division of Cancer, Department of Surgery and Cancer, Imperial College London, Institute of Reproductive and Developmental Biology (IRDB), Hammersmith Hospital, London W12 0NN, UK
| | - Muireann Coen
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
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60
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Visceral adipose tissue but not subcutaneous adipose tissue is associated with urine and serum metabolites. PLoS One 2017; 12:e0175133. [PMID: 28403191 PMCID: PMC5389790 DOI: 10.1371/journal.pone.0175133] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Accepted: 03/21/2017] [Indexed: 12/27/2022] Open
Abstract
Obesity is a complex multifactorial phenotype that influences several metabolic pathways. Yet, few studies have examined the relations of different body fat compartments to urinary and serum metabolites. Anthropometric phenotypes (visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), the ratio between VAT and SAT (VSR), body mass index (BMI), waist circumference (WC)) and urinary and serum metabolite concentrations measured by nuclear magnetic resonance spectroscopy were measured in a population-based sample of 228 healthy adults. Multivariable linear and logistic regression models, corrected for multiple testing using the false discovery rate, were used to associate anthropometric phenotypes with metabolites. We adjusted for potential confounding variables: age, sex, smoking, physical activity, menopausal status, estimated glomerular filtration rate (eGFR), urinary glucose, and fasting status. In a fully adjusted logistic regression model dichotomized for the absence or presence of quantifiable metabolite amounts, VAT, BMI and WC were inversely related to urinary choline (ß = -0.18, p = 2.73*10−3), glycolic acid (ß = -0.20, 0.02), and guanidinoacetic acid (ß = -0.12, p = 0.04), and positively related to ethanolamine (ß = 0.18, p = 0.02) and dimethylamine (ß = 0.32, p = 0.02). BMI and WC were additionally inversely related to urinary glutamine and lactic acid. Moreover, WC was inversely associated with the detection of serine. VAT, but none of the other anthropometric parameters, was related to serum essential amino acids, such as valine, isoleucine, and phenylalanine among men. Compared to other adiposity measures, VAT demonstrated the strongest and most significant relations to urinary and serum metabolites. The distinct relations of VAT, SAT, VSR, BMI, and WC to metabolites emphasize the importance of accurately differentiating between body fat compartments when evaluating the potential role of metabolic regulation in the development of obesity-related diseases, such as insulin resistance, type 2 diabetes, and cardiovascular disease.
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Koch M, Freitag-Wolf S, Schlesinger S, Borggrefe J, Hov JR, Jensen MK, Pick J, Markus MRP, Höpfner T, Jacobs G, Siegert S, Artati A, Kastenmüller G, Römisch-Margl W, Adamski J, Illig T, Nothnagel M, Karlsen TH, Schreiber S, Franke A, Krawczak M, Nöthlings U, Lieb W. Serum metabolomic profiling highlights pathways associated with liver fat content in a general population sample. Eur J Clin Nutr 2017; 71:995-1001. [DOI: 10.1038/ejcn.2017.43] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Revised: 02/22/2017] [Accepted: 03/01/2017] [Indexed: 01/02/2023]
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Trabado S, Al-Salameh A, Croixmarie V, Masson P, Corruble E, Fève B, Colle R, Ripoll L, Walther B, Boursier-Neyret C, Werner E, Becquemont L, Chanson P. The human plasma-metabolome: Reference values in 800 French healthy volunteers; impact of cholesterol, gender and age. PLoS One 2017; 12:e0173615. [PMID: 28278231 PMCID: PMC5344496 DOI: 10.1371/journal.pone.0173615] [Citation(s) in RCA: 109] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2016] [Accepted: 02/23/2017] [Indexed: 12/19/2022] Open
Abstract
Metabolomic approaches are increasingly used to identify new disease biomarkers, yet normal values of many plasma metabolites remain poorly defined. The aim of this study was to define the "normal" metabolome in healthy volunteers. We included 800 French volunteers aged between 18 and 86, equally distributed according to sex, free of any medication and considered healthy on the basis of their medical history, clinical examination and standard laboratory tests. We quantified 185 plasma metabolites, including amino acids, biogenic amines, acylcarnitines, phosphatidylcholines, sphingomyelins and hexose, using tandem mass spectrometry with the Biocrates AbsoluteIDQ p180 kit. Principal components analysis was applied to identify the main factors responsible for metabolome variability and orthogonal projection to latent structures analysis was employed to confirm the observed patterns and identify pattern-related metabolites. We established a plasma metabolite reference dataset for 144/185 metabolites. Total blood cholesterol, gender and age were identified as the principal factors explaining metabolome variability. High total blood cholesterol levels were associated with higher plasma sphingomyelins and phosphatidylcholines concentrations. Compared to women, men had higher concentrations of creatinine, branched-chain amino acids and lysophosphatidylcholines, and lower concentrations of sphingomyelins and phosphatidylcholines. Elderly healthy subjects had higher sphingomyelins and phosphatidylcholines plasma levels than young subjects. We established reference human metabolome values in a large and well-defined population of French healthy volunteers. This study provides an essential baseline for defining the "normal" metabolome and its main sources of variation.
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Affiliation(s)
- Séverine Trabado
- Assistance Publique-Hôpitaux de Paris, Hôpitaux Universitaires Paris-Sud, Hôpital de Bicêtre, Service de Génétique moléculaire, Pharmacogénétique et Hormonologie, Le Kremlin Bicêtre, France
- Inserm U1185, Fac Med Paris Sud, Université Paris-Saclay, Le Kremlin-Bicêtre, France
| | - Abdallah Al-Salameh
- Assistance Publique-Hôpitaux de Paris, Hôpitaux Universitaires Paris-Sud, Hôpital de Bicêtre, Service d’Endocrinologie et des Maladies de la Reproduction, Le Kremlin Bicêtre, France
| | | | | | - Emmanuelle Corruble
- Univ Paris Sud, INSERM UMR 1178, Service de Psychiatrie, équipe "Dépression et Antidépresseurs", Hôpital Bicêtre, Assistance Publique Hôpitaux de Paris, Le Kremlin Bicêtre, France
| | - Bruno Fève
- UPMC Univ Paris 06, INSERM UMR S938, Centre de Recherche Saint-Antoine, Hôpital Saint-Antoine, Assistance Publique Hôpitaux de Paris, Paris, France
| | - Romain Colle
- Univ Paris Sud, INSERM UMR 1178, Service de Psychiatrie, équipe "Dépression et Antidépresseurs", Hôpital Bicêtre, Assistance Publique Hôpitaux de Paris, Le Kremlin Bicêtre, France
| | - Laurent Ripoll
- Institut de Recherches Internationales Servier, Suresnes, France
| | | | | | | | - Laurent Becquemont
- Département de Pharmacologie, Faculté de médecine Paris-Sud, Université Paris-Sud, UMR 1184, CEA, DSV/iMETI, Division d’Immuno-Virologie, IDMIT, INSERM Centre d’Immunologie des Infections virales et des Maladies Autoimmunes, Assistance Publique–Hôpitaux de Paris, Hôpital Bicêtre, Le Kremlin Bicêtre, France
| | - Philippe Chanson
- Inserm U1185, Fac Med Paris Sud, Université Paris-Saclay, Le Kremlin-Bicêtre, France
- Assistance Publique-Hôpitaux de Paris, Hôpitaux Universitaires Paris-Sud, Hôpital de Bicêtre, Service d’Endocrinologie et des Maladies de la Reproduction, Le Kremlin Bicêtre, France
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Hocher B, Adamski J. Metabolomics for clinical use and research in chronic kidney disease. Nat Rev Nephrol 2017; 13:269-284. [PMID: 28262773 DOI: 10.1038/nrneph.2017.30] [Citation(s) in RCA: 214] [Impact Index Per Article: 30.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Chronic kidney disease (CKD) has a high prevalence in the general population and is associated with high mortality; a need therefore exists for better biomarkers for diagnosis, monitoring of disease progression and therapy stratification. Moreover, very sensitive biomarkers are needed in drug development and clinical research to increase understanding of the efficacy and safety of potential and existing therapies. Metabolomics analyses can identify and quantify all metabolites present in a given sample, covering hundreds to thousands of metabolites. Sample preparation for metabolomics requires a very fast arrest of biochemical processes. Present key technologies for metabolomics are mass spectrometry and proton nuclear magnetic resonance spectroscopy, which require sophisticated biostatistic and bioinformatic data analyses. The use of metabolomics has been instrumental in identifying new biomarkers of CKD such as acylcarnitines, glycerolipids, dimethylarginines and metabolites of tryptophan, the citric acid cycle and the urea cycle. Biomarkers such as c-mannosyl tryptophan and pseudouridine have better performance in CKD stratification than does creatinine. Future challenges in metabolomics analyses are prospective studies and deconvolution of CKD biomarkers from those of other diseases such as metabolic syndrome, diabetes mellitus, inflammatory conditions, stress and cancer.
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Affiliation(s)
- Berthold Hocher
- Department of Basic Medicine, Medical College of Hunan University, 410006 Changsha, China
| | - Jerzy Adamski
- Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstaedter Landstrasse 1, 85764 Neuherberg, Germany
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64
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Siskos AP, Jain P, Römisch-Margl W, Bennett M, Achaintre D, Asad Y, Marney L, Richardson L, Koulman A, Griffin JL, Raynaud F, Scalbert A, Adamski J, Prehn C, Keun HC. Interlaboratory Reproducibility of a Targeted Metabolomics Platform for Analysis of Human Serum and Plasma. Anal Chem 2017; 89:656-665. [PMID: 27959516 PMCID: PMC6317696 DOI: 10.1021/acs.analchem.6b02930] [Citation(s) in RCA: 168] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
A critical question facing the field of metabolomics is whether data obtained from different centers can be effectively compared and combined. An important aspect of this is the interlaboratory precision (reproducibility) of the analytical protocols used. We analyzed human samples in six laboratories using different instrumentation but a common protocol (the AbsoluteIDQ p180 kit) for the measurement of 189 metabolites via liquid chromatography (LC) or flow injection analysis (FIA) coupled to tandem mass spectrometry (MS/MS). In spiked quality control (QC) samples 82% of metabolite measurements had an interlaboratory precision of <20%, while 83% of averaged individual laboratory measurements were accurate to within 20%. For 20 typical biological samples (serum and plasma from healthy individuals) the median interlaboratory coefficient of variation (CV) was 7.6%, with 85% of metabolites exhibiting a median interlaboratory CV of <20%. Precision was largely independent of the type of sample (serum or plasma) or the anticoagulant used but was reduced in a sample from a patient with dyslipidaemia. The median interlaboratory accuracy and precision of the assay for standard reference plasma (NIST SRM 1950) were 107% and 6.7%, respectively. Likely sources of irreproducibility were the near limit of detection (LOD) typical abundance of some metabolites and the degree of manual review and optimization of peak integration in the LC-MS/MS data after acquisition. Normalization to a reference material was crucial for the semi-quantitative FIA measurements. This is the first interlaboratory assessment of a widely used, targeted metabolomics assay illustrating the reproducibility of the protocol and how data generated on different instruments could be directly integrated in large-scale epidemiological studies.
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Affiliation(s)
| | - Pooja Jain
- Department of Surgery and Cancer, Imperial College London, W12 0NN, UK
| | - Werner Römisch-Margl
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Mark Bennett
- Department of Life Sciences, Imperial College London, SW7 2AZ, UK
| | - David Achaintre
- International Agency for Research on Cancer (IARC), Biomarkers Group, F-69372 Lyon, France
| | - Yasmin Asad
- The Institute of Cancer Research, ICR, Sutton, SM2 5NG, UK
| | - Luke Marney
- MRC Human Nutrition Research, Cambridge, CB1 9NL, UK
| | | | | | | | | | - Augustin Scalbert
- International Agency for Research on Cancer (IARC), Biomarkers Group, F-69372 Lyon, France
| | - Jerzy Adamski
- Genome Analysis Center, Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany
- Chair of Experimental Genetics, Center of Life and Food Sciences Weihenstephan, Technische Universität München, 85354 Freising-Weihenstephan, Germany
- German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Cornelia Prehn
- Genome Analysis Center, Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Hector C. Keun
- Department of Surgery and Cancer, Imperial College London, W12 0NN, UK
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Adamski J. Key elements of metabolomics in the study of biomarkers of diabetes. Diabetologia 2016; 59:2497-2502. [PMID: 27714446 DOI: 10.1007/s00125-016-4044-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2015] [Accepted: 04/27/2016] [Indexed: 12/21/2022]
Abstract
Metabolomics is instrumental in the analysis of disease mechanisms and biomarkers of disease. The human metabolome is influenced by genetics and environmental interactions and reveals characteristic signatures of disease. Population studies with metabolomics require special study designs and care needs to be taken with pre-analytics. Gas chromatography coupled to mass spectrometry, liquid chromatography coupled to mass spectrometry or NMR are popular techniques used for metabolomic analyses in human cohorts. Metabolomics has been successfully used in the biomarker search for disease prediction and progression, for analyses of drug action and for the development of companion diagnostics. Several metabolites or metabolite classes identified by metabolomics have gained much attention in the field of diabetes research in the search for early disease detection, differentiation of progressor types and compliance with medication. This review summarises a presentation given at the 'New approaches beyond genetics' symposium at the 2015 annual meeting of the EASD. It is accompanied by another review from this symposium by Bernd Mayer (DOI: 10.1007/s00125-016-4032-2 ) and an overview by the Session Chair, Leif Groop (DOI: 10.1007/s00125-016-4014-4 ).
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Affiliation(s)
- Jerzy Adamski
- Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Experimental Genetics, Genome Analysis Center, Ingolstaedter Landstrasse 1, 85764, Neuherberg, Germany.
- German Center for Diabetes Research (DZD), Neuherberg, Germany.
- Lehrstuhl für Experimentelle Genetik, Technische Universität München, Freising-Weihenstephan, Germany.
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66
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Serum metabolite profile associates with the development of metabolic co-morbidities in first-episode psychosis. Transl Psychiatry 2016; 6:e951. [PMID: 27845774 PMCID: PMC5314133 DOI: 10.1038/tp.2016.222] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2016] [Revised: 09/21/2016] [Accepted: 09/28/2016] [Indexed: 12/26/2022] Open
Abstract
Psychotic patients are at high risk for developing obesity, metabolic syndrome and type 2 diabetes. These metabolic co-morbidities are hypothesized to be related to both treatment side effects as well as to metabolic changes occurring during the psychosis. Earlier metabolomics studies have shown that blood metabolite levels are predictive of insulin resistance and type 2 diabetes in the general population as well as sensitive to the effects of antipsychotics. In this study, we aimed to identify the metabolite profiles predicting future weight gain and other metabolic abnormalities in psychotic patients. We applied comprehensive metabolomics to investigate serum metabolite profiles in a prospective study setting in 36 first-episode psychosis patients during the first year of the antipsychotic treatment and 19 controls. While corroborating several earlier findings when comparing cases and controls and the effects of the antipsychotic medication, we also found that prospective weight gain in psychotic patients was associated with increased levels of triacylglycerols with low carbon number and double-bond count at baseline, that is, lipids known to be associated with increased liver fat. Our study suggests that metabolite profiles may be used to identify the psychotic patients most vulnerable to develop metabolic co-morbidities, and may point to a pharmacological approach to counteract the antipsychotic-induced weight gain.
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67
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Loftfield E, Vogtmann E, Sampson JN, Moore SC, Nelson H, Knight R, Chia N, Sinha R. Comparison of Collection Methods for Fecal Samples for Discovery Metabolomics in Epidemiologic Studies. Cancer Epidemiol Biomarkers Prev 2016; 25:1483-1490. [PMID: 27543620 PMCID: PMC5093035 DOI: 10.1158/1055-9965.epi-16-0409] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2016] [Accepted: 07/05/2016] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND The gut metabolome may be associated with the incidence and progression of numerous diseases. The composition of the gut metabolome can be captured by measuring metabolite levels in the feces. However, there are little data describing the effect of fecal sample collection methods on metabolomic measures. METHODS We collected fecal samples from 18 volunteers using four methods: no solution, 95% ethanol, fecal occult blood test (FOBT) cards, and fecal immunochemical test (FIT). One set of samples was frozen after collection (day 0), and for 95% ethanol, FOBT, and FIT, a second set was frozen after 96 hours at room temperature. We evaluated (i) technical reproducibility within sample replicates, (ii) stability after 96 hours at room temperature for 95% ethanol, FOBT, and FIT, and (iii) concordance of metabolite measures with the putative "gold standard," day 0 samples without solution. RESULTS Intraclass correlation coefficients (ICC) estimating technical reproducibility were high for replicate samples for each collection method. ICCs estimating stability at room temperature were high for 95% ethanol and FOBT (median ICC > 0.87) but not FIT (median ICC = 0.52). Similarly, Spearman correlation coefficients (rs) estimating metabolite concordance with the "gold standard" were higher for 95% ethanol (median rs = 0.82) and FOBT (median rs = 0.70) than for FIT (median rs = 0.40). CONCLUSIONS Metabolomic measurements appear reproducible and stable in fecal samples collected with 95% ethanol or FOBT. Concordance with the "gold standard" is highest with 95% ethanol and acceptable with FOBT. IMPACT Future epidemiologic studies should collect feces using 95% ethanol or FOBT if interested in studying fecal metabolomics. Cancer Epidemiol Biomarkers Prev; 25(11); 1483-90. ©2016 AACR.
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Affiliation(s)
- Erikka Loftfield
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland.
| | - Emily Vogtmann
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Joshua N Sampson
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Steven C Moore
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Heidi Nelson
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota
- Department of Surgery, Mayo Clinic, Rochester, Minnesota
| | - Rob Knight
- Health Sciences Research, Mayo Clinic, Rochester, Minnesota
- Department of Pediatrics, University of California San Diego, San Diego, California
| | - Nicholas Chia
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota
- Department of Surgery, Mayo Clinic, Rochester, Minnesota
- Department of Computer Science and Engineering, University of California San Diego, San Diego, California
| | - Rashmi Sinha
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
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68
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Rankin NJ, Preiss D, Welsh P, Sattar N. Applying metabolomics to cardiometabolic intervention studies and trials: past experiences and a roadmap for the future. Int J Epidemiol 2016; 45:1351-1371. [PMID: 27789671 PMCID: PMC5100629 DOI: 10.1093/ije/dyw271] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/01/2016] [Indexed: 12/22/2022] Open
Abstract
Metabolomics and lipidomics are emerging methods for detailed phenotyping of small molecules in samples. It is hoped that such data will: (i) enhance baseline prediction of patient response to pharmacotherapies (beneficial or adverse); (ii) reveal changes in metabolites shortly after initiation of therapy that may predict patient response, including adverse effects, before routine biomarkers are altered; and( iii) give new insights into mechanisms of drug action, particularly where the results of a trial of a new agent were unexpected, and thus help future drug development. In these ways, metabolomics could enhance research findings from intervention studies. This narrative review provides an overview of metabolomics and lipidomics in early clinical intervention studies for investigation of mechanisms of drug action and prediction of drug response (both desired and undesired). We highlight early examples from drug intervention studies associated with cardiometabolic disease. Despite the strengths of such studies, particularly the use of state-of-the-art technologies and advanced statistical methods, currently published studies in the metabolomics arena are largely underpowered and should be considered as hypothesis-generating. In order for metabolomics to meaningfully improve stratified medicine approaches to patient treatment, there is a need for higher quality studies, with better exploitation of biobanks from randomized clinical trials i.e. with large sample size, adjudicated outcomes, standardized procedures, validation cohorts, comparison witth routine biochemistry and both active and control/placebo arms. On the basis of this review, and based on our research experience using clinically established biomarkers, we propose steps to more speedily advance this area of research towards potential clinical impact.
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Affiliation(s)
- Naomi J Rankin
- BHF Glasgow Cardiovascular Research Centre
- Glasgow Polyomics, University of Glasgow, Glasgow, UK
| | - David Preiss
- Clinical Trials Service Unit and Epidemiological Studies Unit, University of Oxford, Oxford, UK
| | - Paul Welsh
- BHF Glasgow Cardiovascular Research Centre
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Identification of Altered Metabolomic Profiles Following a Panchakarma-based Ayurvedic Intervention in Healthy Subjects: The Self-Directed Biological Transformation Initiative (SBTI). Sci Rep 2016; 6:32609. [PMID: 27611967 PMCID: PMC5017211 DOI: 10.1038/srep32609] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2016] [Accepted: 08/11/2016] [Indexed: 12/14/2022] Open
Abstract
The effects of integrative medicine practices such as meditation and Ayurveda on human physiology are not fully understood. The aim of this study was to identify altered metabolomic profiles following an Ayurveda-based intervention. In the experimental group, 65 healthy male and female subjects participated in a 6-day Panchakarma-based Ayurvedic intervention which included herbs, vegetarian diet, meditation, yoga, and massage. A set of 12 plasma phosphatidylcholines decreased (adjusted p < 0.01) post-intervention in the experimental (n = 65) compared to control group (n = 54) after Bonferroni correction for multiple testing; within these compounds, the phosphatidylcholine with the greatest decrease in abundance was PC ae C36:4 (delta = −0.34). Application of a 10% FDR revealed an additional 57 metabolites that were differentially abundant between groups. Pathway analysis suggests that the intervention results in changes in metabolites across many pathways such as phospholipid biosynthesis, choline metabolism, and lipoprotein metabolism. The observed plasma metabolomic alterations may reflect a Panchakarma-induced modulation of metabotypes. Panchakarma promoted statistically significant changes in plasma levels of phosphatidylcholines, sphingomyelins and others in just 6 days. Forthcoming studies that integrate metabolomics with genomic, microbiome and physiological parameters may facilitate a broader systems-level understanding and mechanistic insights into these integrative practices that are employed to promote health and well-being.
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70
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Dietrich S, Floegel A, Troll M, Kühn T, Rathmann W, Peters A, Sookthai D, von Bergen M, Kaaks R, Adamski J, Prehn C, Boeing H, Schulze MB, Illig T, Pischon T, Knüppel S, Wang-Sattler R, Drogan D. Random Survival Forest in practice: a method for modelling complex metabolomics data in time to event analysis. Int J Epidemiol 2016; 45:1406-1420. [PMID: 27591264 DOI: 10.1093/ije/dyw145] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/20/2016] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND The application of metabolomics in prospective cohort studies is statistically challenging. Given the importance of appropriate statistical methods for selection of disease-associated metabolites in highly correlated complex data, we combined random survival forest (RSF) with an automated backward elimination procedure that addresses such issues. METHODS Our RSF approach was illustrated with data from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study, with concentrations of 127 serum metabolites as exposure variables and time to development of type 2 diabetes mellitus (T2D) as outcome variable. Out of this data set, Cox regression with a stepwise selection method was recently published. Replication of methodical comparison (RSF and Cox regression) was conducted in two independent cohorts. Finally, the R-code for implementing the metabolite selection procedure into the RSF-syntax is provided. RESULTS The application of the RSF approach in EPIC-Potsdam resulted in the identification of 16 incident T2D-associated metabolites which slightly improved prediction of T2D when used in addition to traditional T2D risk factors and also when used together with classical biomarkers. The identified metabolites partly agreed with previous findings using Cox regression, though RSF selected a higher number of highly correlated metabolites. CONCLUSIONS The RSF method appeared to be a promising approach for identification of disease-associated variables in complex data with time to event as outcome. The demonstrated RSF approach provides comparable findings as the generally used Cox regression, but also addresses the problem of multicollinearity and is suitable for high-dimensional data.
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Affiliation(s)
- Stefan Dietrich
- Department of Epidemiology, German Institute of Human Nutrition, Nuthetal, Germany
| | - Anna Floegel
- Department of Epidemiology, German Institute of Human Nutrition, Nuthetal, Germany
| | - Martina Troll
- Research Unit of Molecular Epidemiology.,Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Tilman Kühn
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Wolfgang Rathmann
- Institute for Biometrics and Epidemiology, Leibniz Center for Diabetes Research at Heinrich Heine University, Germany.,German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Anette Peters
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,German Center for Diabetes Research (DZD), München-Neuherberg, Germany.,Department of Environmental Health, Harvard School of Public Health, Boston, MA, USA and
| | - Disorn Sookthai
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Martin von Bergen
- Department of Molecular Systems Biology, Helmholtz Centre for Environmental Research (UFZ), Institute of Biochemistry, Faculty of Biosciences, Pharmacy and Psychology, University of Leipzig, Leipzig, Germany and Department of Chemistry and Bioscience, University of Aalborg, Aalborg East, Denmark
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jerzy Adamski
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany.,Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, München-Neuherberg, Germany.,Lehrstuhl für Experimentelle Genetik, Technische Universität München, Freising-Weihenstephan, Germany
| | - Cornelia Prehn
- Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, München-Neuherberg, Germany
| | - Heiner Boeing
- Department of Epidemiology, German Institute of Human Nutrition, Nuthetal, Germany
| | - Matthias B Schulze
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany.,Department of Molecular Epidemiology, German Institute of Human Nutrition, Nuthetal, Germany
| | - Thomas Illig
- Research Unit of Molecular Epidemiology.,Hannover Unified Biobank, and Institute for Human Genetics, Hannover, Germany
| | - Tobias Pischon
- Department of Epidemiology, German Institute of Human Nutrition, Nuthetal, Germany.,Molecular Epidemiology Group, Max Delbruck Center for Molecular Medicine (MDC) Berlin-Buch, Berlin, Germany
| | - Sven Knüppel
- Department of Epidemiology, German Institute of Human Nutrition, Nuthetal, Germany
| | - Rui Wang-Sattler
- Research Unit of Molecular Epidemiology.,Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Dagmar Drogan
- Department of Epidemiology, German Institute of Human Nutrition, Nuthetal, Germany
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Bachlechner U, Floegel A, Steffen A, Prehn C, Adamski J, Pischon T, Boeing H. Associations of anthropometric markers with serum metabolites using a targeted metabolomics approach: results of the EPIC-potsdam study. Nutr Diabetes 2016; 6:e215. [PMID: 27348203 PMCID: PMC4931315 DOI: 10.1038/nutd.2016.23] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Revised: 05/07/2016] [Accepted: 05/29/2016] [Indexed: 12/13/2022] Open
Abstract
Background/Objectives: The metabolic consequences of type of body shape need further exploration. Whereas accumulation of body mass in the abdominal area is a well-established metabolic risk factor, accumulation in the gluteofemoral area is controversially debated. We evaluated the associations of anthropometric markers of overall body mass and body shape with 127 serum metabolites within a sub-sample of the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort. Subjects/Methods: The cross-sectional analysis was conducted in 2270 participants, randomly drawn from the EPIC-Potsdam cohort. Metabolites were measured by targeted metabolomics. To select metabolites related with both waist circumference (WC) (abdominal subcutaneous and visceral fat) and hip circumference (HC) (gluteofemoral fat, muscles and bone structure) correlations (r) with body mass index (BMI) as aggregating marker of body mass (lean and fat mass) were calculated. Relations with body shape were assessed by median metabolite concentrations across tertiles of WC and HC, mutually adjusted to each other. Results: Correlations revealed 23 metabolites related to BMI (r⩾I0.20 I). Metabolites showing relations with BMI were showing similar relations with HC adjusted WC (WCHC). In contrast, relations with WC adjusted HC (HCWC) were less concordant with relations of BMI and WCHC. In both sexes, metabolites with concordant relations regarding WCHC and HCWC included tyrosine, diacyl-phosphatidylcholine C38:3, C38:4, lyso-phosphatidylcholine C18:1, C18:2 and sphingomyelin C18:1; metabolites with opposite relations included isoleucine, diacyl-phosphatidylcholine C42:0, acyl–alkyl-phosphatidylcholine C34:3, C42:4, C42:5, C44:4 and C44:6. Metabolites specifically related to HCWC included acyl–alkyl-phosphatidylcholine C34:2, C36:2, C38:2 and C40:4, and were solely observed in men. Other metabolites were related to WCHC only. Conclusions: The study revealed specific metabolic profiles for HCWC as marker of gluteofemoral body mass differing from those for BMI and WCHC as markers of overall body mass and abdominal fat, respectively. Thus, the study suggests that gluteofemoral mass may have less-adverse metabolic implications than abdominal fat.
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Affiliation(s)
- U Bachlechner
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - A Floegel
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - A Steffen
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - C Prehn
- Institute of Experimental Genetics, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
| | - J Adamski
- Institute of Experimental Genetics, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany.,German Center for Diabetes Research, Neuherberg, Germany.,Institute of Experimental Genetics, Technical University of Munich, Freising-Weihenstephan, Germany
| | - T Pischon
- Molecular Epidemiology Group, Max Delbrück Center for Molecular Medicine (MDC), Berlin-Buch, Germany
| | - H Boeing
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
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Merz B, Nöthlings U, Wahl S, Haftenberger M, Schienkiewitz A, Adamski J, Suhre K, Wang-Sattler R, Grallert H, Thorand B, Pischon T, Bachlechner U, Floegel A, Peters A, Boeing H. Specific Metabolic Markers Are Associated with Future Waist-Gaining Phenotype in Women. PLoS One 2016; 11:e0157733. [PMID: 27322650 PMCID: PMC4920591 DOI: 10.1371/journal.pone.0157733] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2015] [Accepted: 06/04/2016] [Indexed: 02/08/2023] Open
Abstract
OBJECTIVE Our study aims to identify metabolic markers associated with either a gain in abdominal (measured by waist circumference) or peripheral (measured by hip circumference) body fat mass. METHODS Data of 4 126 weight-gaining adults (18-75 years) from three population-based, prospective German cohort studies (EPIC, KORA, DEGS) were analysed regarding a waist-gaining (WG) or hip-gaining phenotype (HG). The phenotypes were obtained by calculating the differences of annual changes in waist minus hip circumference. The difference was displayed for all cohorts. The highest 10% of this difference were defined as WG whereas the lowest 10% were defined as HG. A total of 121 concordant metabolite measurements were conducted using Biocrates AbsoluteIDQ® kits in EPIC and KORA. Sex-specific associations with metabolite concentration as independent and phenotype as the dependent variable adjusted for confounders were calculated. The Benjamini-Hochberg method was used to correct for multiple testing. RESULTS Across studies both sexes gained on average more waist than hip circumference. We could identify 12 metabolites as being associated with the WG (n = 8) or HG (n = 4) in men, but none were significant after correction for multiple testing; 45 metabolites were associated with the WG (n = 41) or HG (n = 4) in women. For WG, n = 21 metabolites remained significant after correction for multiple testing. Respective odds ratios (OR) ranged from 0.66 to 0.73 for tryptophan, the diacyl-phosphatidylcholines (PC) C32:3, C36:0, C38:0, C38:1, C42:2, C42:5, the acyl-alkyl-PCs C32:2, C34:0, C36:0, C36:1, C36:2, C38:0, C38:2, C40:1, C40:2, C40:5, C40:6, 42:2, C42:3 and lyso-PC C17:0. CONCLUSION Both weight-gaining men and women showed a clear tendency to gain more abdominal than peripheral fat. Gain of abdominal fat seems to be related to an initial metabolic state reflected by low concentrations of specific metabolites, at least in women. Thus, higher levels of specific PCs may play a protective role in gaining waist circumference.
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Affiliation(s)
- Benedikt Merz
- Department of Nutrition and Food Sciences, University of Bonn, Bonn, Germany
- Department of Epidemiology, German Institute of Human Nutrition, Potsdam-Rehbruecke, Germany
| | - Ute Nöthlings
- Department of Nutrition and Food Sciences, University of Bonn, Bonn, Germany
| | - Simone Wahl
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | | | - Anja Schienkiewitz
- Robert Koch-Institut, Department of Epidemiology and Health Monitoring, Berlin, Germany
| | - Jerzy Adamski
- Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Karsten Suhre
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Doha, Qatar
| | - Rui Wang-Sattler
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
| | - Harald Grallert
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Barbara Thorand
- Institute of Epidemiology II, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Tobias Pischon
- Molecular Epidemiology Research Group, Max Delbrück Center for Molecular Medicine (MDC), Berlin, Germany
| | - Ursula Bachlechner
- Department of Epidemiology, German Institute of Human Nutrition, Potsdam-Rehbruecke, Germany
| | - Anna Floegel
- Department of Epidemiology, German Institute of Human Nutrition, Potsdam-Rehbruecke, Germany
| | - Annette Peters
- Institute of Epidemiology II, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Heiner Boeing
- Department of Epidemiology, German Institute of Human Nutrition, Potsdam-Rehbruecke, Germany
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Dietrich S, Floegel A, Weikert C, Prehn C, Adamski J, Pischon T, Boeing H, Drogan D. Identification of Serum Metabolites Associated With Incident Hypertension in the European Prospective Investigation into Cancer and Nutrition-Potsdam Study. Hypertension 2016; 68:471-7. [PMID: 27245178 DOI: 10.1161/hypertensionaha.116.07292] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2016] [Accepted: 05/04/2016] [Indexed: 01/11/2023]
Abstract
Metabolomics is a promising tool to gain new insights into early metabolic alterations preceding the development of hypertension in humans. We therefore aimed to identify metabolites associated with incident hypertension using measured data of serum metabolites of the European Prospective Investigation Into Cancer and Nutrition (EPIC)-Potsdam study. Targeted metabolic profiling was conducted on serum blood samples of a randomly drawn EPIC-Potsdam subcohort consisting of 135 cases and 981 noncases of incident hypertension, all of them being free of hypertension and not on antihypertensive therapy at the time of blood sampling. Mean follow-up was 9.9 years. A validated set of 127 metabolites was statistically analyzed with a random survival forest backward selection algorithm to identify predictive metabolites of incident hypertension taking into account important epidemiological hypertension risk markers. Six metabolites were identified to be most predictive for the development of hypertension. Higher concentrations of serine, glycine, and acyl-alkyl-phosphatidylcholines C42:4 and C44:3 tended to be associated with higher and diacyl-phosphatidylcholines C38:4 and C38:3 with lower predicted 10-year hypertension-free survival, although visualization by partial plots revealed some nonlinearity in the above associations. The identified metabolites improved prediction of incident hypertension when used together with known risk markers of hypertension. In conclusion, these findings indicate that metabolic alterations occur early in the development of hypertension. However, these alterations are confined to a few members of the amino acid or phosphatidylcholine metabolism, respectively.
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Affiliation(s)
- Stefan Dietrich
- From the Department of Epidemiology, German Institute of Human Nutrition, Potsdam-Rehbruecke (DIfE), Nuthetal, Germany (S.D., A.F., H.B., D.D.); Department of Food Safety, Federal Institute for Risk Assessment, Berlin, Germany (C.W.); Institute for Social Medicine, Epidemiology, and Health Economics, Charité University Medical Center, Berlin, Germany (C.W.); DZHK (German Center for Cardiovascular Research), Partner Site Berlin, Germany (C.W., T.P.); Molecular Epidemiology Group, Max Delbrück Center for Molecular Medicine (MDC) Berlin-Buch, Berlin, Germany (T.P.); Charité-Universiätsmedizin Berlin, Berlin, Germany (T.P.); and AOK Research Institute (WIdO), AOK Bundesverband, Berlin, Germany (D.D.).
| | - Anna Floegel
- From the Department of Epidemiology, German Institute of Human Nutrition, Potsdam-Rehbruecke (DIfE), Nuthetal, Germany (S.D., A.F., H.B., D.D.); Department of Food Safety, Federal Institute for Risk Assessment, Berlin, Germany (C.W.); Institute for Social Medicine, Epidemiology, and Health Economics, Charité University Medical Center, Berlin, Germany (C.W.); DZHK (German Center for Cardiovascular Research), Partner Site Berlin, Germany (C.W., T.P.); Molecular Epidemiology Group, Max Delbrück Center for Molecular Medicine (MDC) Berlin-Buch, Berlin, Germany (T.P.); Charité-Universiätsmedizin Berlin, Berlin, Germany (T.P.); and AOK Research Institute (WIdO), AOK Bundesverband, Berlin, Germany (D.D.)
| | - Cornelia Weikert
- From the Department of Epidemiology, German Institute of Human Nutrition, Potsdam-Rehbruecke (DIfE), Nuthetal, Germany (S.D., A.F., H.B., D.D.); Department of Food Safety, Federal Institute for Risk Assessment, Berlin, Germany (C.W.); Institute for Social Medicine, Epidemiology, and Health Economics, Charité University Medical Center, Berlin, Germany (C.W.); DZHK (German Center for Cardiovascular Research), Partner Site Berlin, Germany (C.W., T.P.); Molecular Epidemiology Group, Max Delbrück Center for Molecular Medicine (MDC) Berlin-Buch, Berlin, Germany (T.P.); Charité-Universiätsmedizin Berlin, Berlin, Germany (T.P.); and AOK Research Institute (WIdO), AOK Bundesverband, Berlin, Germany (D.D.)
| | | | | | - Tobias Pischon
- From the Department of Epidemiology, German Institute of Human Nutrition, Potsdam-Rehbruecke (DIfE), Nuthetal, Germany (S.D., A.F., H.B., D.D.); Department of Food Safety, Federal Institute for Risk Assessment, Berlin, Germany (C.W.); Institute for Social Medicine, Epidemiology, and Health Economics, Charité University Medical Center, Berlin, Germany (C.W.); DZHK (German Center for Cardiovascular Research), Partner Site Berlin, Germany (C.W., T.P.); Molecular Epidemiology Group, Max Delbrück Center for Molecular Medicine (MDC) Berlin-Buch, Berlin, Germany (T.P.); Charité-Universiätsmedizin Berlin, Berlin, Germany (T.P.); and AOK Research Institute (WIdO), AOK Bundesverband, Berlin, Germany (D.D.)
| | - Heiner Boeing
- From the Department of Epidemiology, German Institute of Human Nutrition, Potsdam-Rehbruecke (DIfE), Nuthetal, Germany (S.D., A.F., H.B., D.D.); Department of Food Safety, Federal Institute for Risk Assessment, Berlin, Germany (C.W.); Institute for Social Medicine, Epidemiology, and Health Economics, Charité University Medical Center, Berlin, Germany (C.W.); DZHK (German Center for Cardiovascular Research), Partner Site Berlin, Germany (C.W., T.P.); Molecular Epidemiology Group, Max Delbrück Center for Molecular Medicine (MDC) Berlin-Buch, Berlin, Germany (T.P.); Charité-Universiätsmedizin Berlin, Berlin, Germany (T.P.); and AOK Research Institute (WIdO), AOK Bundesverband, Berlin, Germany (D.D.)
| | - Dagmar Drogan
- From the Department of Epidemiology, German Institute of Human Nutrition, Potsdam-Rehbruecke (DIfE), Nuthetal, Germany (S.D., A.F., H.B., D.D.); Department of Food Safety, Federal Institute for Risk Assessment, Berlin, Germany (C.W.); Institute for Social Medicine, Epidemiology, and Health Economics, Charité University Medical Center, Berlin, Germany (C.W.); DZHK (German Center for Cardiovascular Research), Partner Site Berlin, Germany (C.W., T.P.); Molecular Epidemiology Group, Max Delbrück Center for Molecular Medicine (MDC) Berlin-Buch, Berlin, Germany (T.P.); Charité-Universiätsmedizin Berlin, Berlin, Germany (T.P.); and AOK Research Institute (WIdO), AOK Bundesverband, Berlin, Germany (D.D.)
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Knebel B, Strassburger K, Szendroedi J, Kotzka J, Scheer M, Nowotny B, Müssig K, Lehr S, Pacini G, Finner H, Klüppelholz B, Giani G, Al-Hasani H, Roden M. Specific Metabolic Profiles and Their Relationship to Insulin Resistance in Recent-Onset Type 1 and Type 2 Diabetes. J Clin Endocrinol Metab 2016; 101:2130-40. [PMID: 26829444 DOI: 10.1210/jc.2015-4133] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
CONTEXT Insulin resistance reflects the inadequate insulin-mediated use of metabolites and predicts type 2 diabetes (T2D) but is also frequently seen in long-standing type 1 diabetes (T1D) and represents a major cardiovascular risk factor. OBJECTIVE We hypothesized that plasma metabolome profiles allow the identification of unique and common early biomarkers of insulin resistance in both diabetes types. DESIGN, SETTING, AND PATIENTS Two hundred ninety-five plasma metabolites were analyzed by mass spectrometry from patients of the prospective observational German Diabetes Study with T2D (n = 244) or T1D (n = 127) and known diabetes duration of less than 1 year and glucose-tolerant persons (CON; n = 129). Abundance of metabolites was tested for association with insulin sensitivity as assessed by hyperinsulinemic-euglycemic clamps and related metabolic phenotypes. MAIN OUTCOMES MEASURES Sixty-two metabolites with phenotype-specific patterns were identified using age, sex, and body mass index as covariates. RESULTS Compared with CON, the metabolome of T2D and T1D showed similar alterations in various phosphatidylcholine species and amino acids. Only T2D exhibited differences in free fatty acids compared with CON. Pairwise comparison of metabolites revealed alterations of 28 and 49 metabolites in T1D and T2D, respectively, when compared with CON. Eleven metabolites allowed differentiation between both diabetes types and alanine, α-amino-adipic acid, isoleucin, and stearic acid showed an inverse association with insulin sensitivity in both T2D and T1D combined. CONCLUSION Metabolome analyses from recent-onset T2D and T1D patients enables identification of defined diabetes type-specific differences and detection of biomarkers of insulin sensitivity. These analyses may help to identify novel clinical subphenotypes diabetes.
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Affiliation(s)
- Birgit Knebel
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
| | - Klaus Strassburger
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
| | - Julia Szendroedi
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
| | - Jorg Kotzka
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
| | - Marsel Scheer
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
| | - Bettina Nowotny
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
| | - Karsten Müssig
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
| | - Stefan Lehr
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
| | - Giovanni Pacini
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
| | - Helmut Finner
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
| | - Birgit Klüppelholz
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
| | - Guido Giani
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
| | - Hadi Al-Hasani
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
| | - Michael Roden
- Institute for Clinical Biochemistry and Pathobiochemistry (B.Kn., J.K., S.L., H.-A.H.), Institute for Biometrics and Epidemiology (K.S., M.S., H.F., B.Kl., G.G.), Institute for Clinical Diabetology (J.S., B.N., K.M., M.R.), German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, and Department of Endocrinology and Diabetology (J.S., B.N., K.M., M.R.), Medical Faculty, Heinrich Heine University 40225 Duesseldorf, Germany; German Center for Diabetes Research (B.Kn., K.S., J.S., J.K., M.S., B.N., K.M., S.L., H.F., B.Kl., G.G., H.-A.H., M.R.), 85764 Muenchen-Neuherberg, Germany; and Metabolic Unit (G.P.), Institute of Neuroscience, Research Program on Aging of the Italian Research Council, 35127 Padua, Italy
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Salihovic S, Ganna A, Fall T, Broeckling CD, Prenni JE, van Bavel B, Lind PM, Ingelsson E, Lind L. The metabolic fingerprint of p,p'-DDE and HCB exposure in humans. ENVIRONMENT INTERNATIONAL 2016; 88:60-66. [PMID: 26720637 DOI: 10.1016/j.envint.2015.12.015] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2015] [Revised: 12/04/2015] [Accepted: 12/14/2015] [Indexed: 06/05/2023]
Abstract
BACKGROUND Dichlorodiphenyldichloroethylene (p,p'-DDE) and hexachlorobenzene (HCB) are organochlorine pesticides with well-known endocrine disrupting properties. Exposure to p,p'-DDE and HCB concerns human populations worldwide and has been linked to metabolic disorders such as obesity and type 2 diabetes, but details about these associations in humans from the general population are largely unknown. OBJECTIVES We investigated the associations between p,p'-DDE and HCB exposure and global metabolomic profiles in serum samples from 1016 participants from the Swedish population-based Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) study. METHODS HCB and p,p'-DDE levels were determined using gas chromatography coupled to high-resolution mass spectrometry (GC-HRMS). Metabolite levels were determined by using a non-targeted metabolomics approach with ultra-performance liquid chromatography coupled to time-of- flight mass spectrometry (UPLC-TOFMS). Association analyses were performed using multivariate linear regression. RESULTS We found circulating levels of p,p-DDE and HCB to be significantly associated with circulating levels of 16 metabolites following adjustment for age, sex, education level, exercise habits, smoking, energy intake, and alcohol intake. The majority of the 16 metabolites belong to lipid metabolism pathways and include fatty acids, glycerophospholipids, sphingolipids, and glycerolipids. Overall, p,p'-DDE and HCB levels were found to be correlated to different metabolites, which suggests that different metabolic fingerprints may be related to circulating levels of these two pesticides. CONCLUSIONS Our findings establish a link between human exposure to organochlorine pesticides and metabolites of key metabolic processes mainly related to human lipid metabolism.
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Affiliation(s)
- Samira Salihovic
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden; MTM Research Centre, School of Science and Technology, Örebro University, Örebro, Sweden.
| | - Andrea Ganna
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, 7 Cambridge Center, Cambridge, MA 02142, USA
| | - Tove Fall
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Corey D Broeckling
- Proteomics and Metabolomics Facility, Colorado State University, Fort Collins, CO, USA
| | - Jessica E Prenni
- Proteomics and Metabolomics Facility, Colorado State University, Fort Collins, CO, USA
| | - Bert van Bavel
- MTM Research Centre, School of Science and Technology, Örebro University, Örebro, Sweden
| | - P Monica Lind
- Department of Medical Sciences, Occupational and Environmental Medicine, Uppsala University Hospital, Uppsala, Sweden
| | - Erik Ingelsson
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden; Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Lars Lind
- Department of Medical Sciences, Cardiovascular Epidemiology, Uppsala University, Uppsala, Sweden
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76
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Kühn T, Floegel A, Sookthai D, Johnson T, Rolle-Kampczyk U, Otto W, von Bergen M, Boeing H, Kaaks R. Higher plasma levels of lysophosphatidylcholine 18:0 are related to a lower risk of common cancers in a prospective metabolomics study. BMC Med 2016; 14:13. [PMID: 26817443 PMCID: PMC4730724 DOI: 10.1186/s12916-016-0552-3] [Citation(s) in RCA: 141] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2015] [Accepted: 01/05/2016] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND First metabolomics studies have indicated that metabolic fingerprints from accessible tissues might be useful to better understand the etiological links between metabolism and cancer. However, there is still a lack of prospective metabolomics studies on pre-diagnostic metabolic alterations and cancer risk. METHODS Associations between pre-diagnostic levels of 120 circulating metabolites (acylcarnitines, amino acids, biogenic amines, phosphatidylcholines, sphingolipids, and hexoses) and the risks of breast, prostate, and colorectal cancer were evaluated by Cox regression analyses using data of a prospective case-cohort study including 835 incident cancer cases. RESULTS The median follow-up duration was 8.3 years among non-cases and 6.5 years among incident cases of cancer. Higher levels of lysophosphatidylcholines (lysoPCs), and especially lysoPC a C18:0, were consistently related to lower risks of breast, prostate, and colorectal cancer, independent of background factors. In contrast, higher levels of phosphatidylcholine PC ae C30:0 were associated with increased cancer risk. There was no heterogeneity in the observed associations by lag time between blood draw and cancer diagnosis. CONCLUSION Changes in blood lipid composition precede the diagnosis of common malignancies by several years. Considering the consistency of the present results across three cancer types the observed alterations point to a global metabolic shift in phosphatidylcholine metabolism that may drive tumorigenesis.
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Affiliation(s)
- Tilman Kühn
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, D-69120, Heidelberg, Germany.
| | - Anna Floegel
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Arthur-Scheunert-Allee 114, D-14558, Nuthetal, Germany.
| | - Disorn Sookthai
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, D-69120, Heidelberg, Germany.
| | - Theron Johnson
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, D-69120, Heidelberg, Germany.
| | - Ulrike Rolle-Kampczyk
- Department of Metabolomics, Helmholtz Centre for Environmental Research (UFZ), Permoserstraße 15, D-04318, Leipzig, Germany.
| | - Wolfgang Otto
- Department of Proteomics, Helmholtz Centre for Environmental Research (UFZ), Permoserstraße 15, D-04318, Leipzig, Germany.
| | - Martin von Bergen
- Department of Metabolomics, Helmholtz Centre for Environmental Research (UFZ), Permoserstraße 15, D-04318, Leipzig, Germany. .,Department of Proteomics, Helmholtz Centre for Environmental Research (UFZ), Permoserstraße 15, D-04318, Leipzig, Germany. .,University of Aalborg, Fredrik Bajers Vej 7H, 9220, Aalborg East, Denmark.
| | - Heiner Boeing
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Arthur-Scheunert-Allee 114, D-14558, Nuthetal, Germany.
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, D-69120, Heidelberg, Germany.
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Begum H, Li B, Shui G, Cazenave-Gassiot A, Soong R, Ong RTH, Little P, Teo YY, Wenk MR. Discovering and validating between-subject variations in plasma lipids in healthy subjects. Sci Rep 2016; 6:19139. [PMID: 26743939 PMCID: PMC4705481 DOI: 10.1038/srep19139] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2015] [Accepted: 12/07/2015] [Indexed: 12/28/2022] Open
Abstract
Lipid levels are commonly used in clinical settings as disease biomarkers, and the advent of mass spectrometry-based (MS) lipidomics heralds the possibility of identifying additional lipids that can inform disease predispositions. However, the degree of natural variation for many lipids remains poorly understood, thus confounding downstream investigations on whether a specific intervention is driving observed lipid fluctuations. Here, we performed targeted mass spectrometry with multiple reaction monitoring across a comprehensive spectrum of 192 plasma lipids on eight subjects across three time-points separated by six hours and two standardized meals. A validation study to confirm the initial discoveries was performed in a further set of nine subjects, subject to the identical study design. Technical variation of the MS was assessed using duplicate measurements in the validation study, while biological variation was measured for lipid species with coefficients of variation <20%. We observed that eight lipid species from the phosphatidylethanolamine and phosphatidylcholine lipid classes were discovered and validated to vary consistently across the three time-points, where the within-subject variance can be up to 1.3-fold higher than between-subject variance. These findings highlight the importance of understanding the range of biological variation in plasma lipids as a precursor to their use in clinical biochemistry.
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Affiliation(s)
- Husna Begum
- NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore.,Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Life Sciences Institute, National University of Singapore, Singapore
| | - Bowen Li
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Guanghou Shui
- Life Sciences Institute, National University of Singapore, Singapore.,State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | | | - Richie Soong
- Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Rick Twee-Hee Ong
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Peter Little
- Life Sciences Institute, National University of Singapore, Singapore
| | - Yik-Ying Teo
- NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore.,Life Sciences Institute, National University of Singapore, Singapore.,Saw Swee Hock School of Public Health, National University of Singapore, Singapore.,Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore.,Department of Statistics and Applied Probability, National University of Singapore, Singapore
| | - Markus R Wenk
- NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore.,Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Life Sciences Institute, National University of Singapore, Singapore.,Department of Biological Sciences, National University of Singapore, Singapore
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Cocco E, Murgia F, Lorefice L, Barberini L, Poddighe S, Frau J, Fenu G, Coghe G, Murru MR, Murru R, Del Carratore F, Atzori L, Marrosu MG. (1)H-NMR analysis provides a metabolomic profile of patients with multiple sclerosis. NEUROLOGY-NEUROIMMUNOLOGY & NEUROINFLAMMATION 2015; 3:e185. [PMID: 26740964 PMCID: PMC4694073 DOI: 10.1212/nxi.0000000000000185] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2015] [Accepted: 10/01/2015] [Indexed: 11/15/2022]
Abstract
Objective: To investigate the metabolomic profiles of patients with multiple sclerosis (MS) and to define the metabolic pathways potentially related to MS pathogenesis. Methods: Plasma samples from 73 patients with MS (therapy-free for at least 90 days) and 88 healthy controls (HC) were analyzed by 1H-NMR spectroscopy. Data analysis was conducted with principal components analysis followed by a supervised analysis (orthogonal partial least squares discriminant analysis [OPLS-DA]). The metabolites were identified and quantified using Chenomx software, and the receiver operating characteristic (ROC) curves were calculated. Results: The model obtained with the OPLS-DA identified predictive metabolic differences between the patients with MS and HC (R2X = 0.615, R2Y = 0.619, Q2 = 0.476; p < 0.001). The differential metabolites included glucose, 5-OH-tryptophan, and tryptophan, which were lower in the MS group, and 3-OH-butyrate, acetoacetate, acetone, alanine, and choline, which were higher in the MS group. The suitability of the model was evaluated using an external set of samples. The values returned by the model were used to build the corresponding ROC curve (area under the curve of 0.98). Conclusion: NMR metabolomic analysis was able to discriminate different metabolic profiles in patients with MS compared with HC. With the exception of choline, the main metabolic changes could be connected to 2 different metabolic pathways: tryptophan metabolism and energy metabolism. Metabolomics appears to represent a promising noninvasive approach for the study of MS.
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Affiliation(s)
- Eleonora Cocco
- Department of Public Health (E.C., L.L., L.B., S.P., J.F., G.F., G.C., M.R.M., R.M.), Clinical and Molecular Medicine, Department of Biomedical Sciences (F.M., F.D.C., L.A.), and Department of Medical Science (M.G.M.), University of Cagliari, Cagliari, Italy
| | - Federica Murgia
- Department of Public Health (E.C., L.L., L.B., S.P., J.F., G.F., G.C., M.R.M., R.M.), Clinical and Molecular Medicine, Department of Biomedical Sciences (F.M., F.D.C., L.A.), and Department of Medical Science (M.G.M.), University of Cagliari, Cagliari, Italy
| | - Lorena Lorefice
- Department of Public Health (E.C., L.L., L.B., S.P., J.F., G.F., G.C., M.R.M., R.M.), Clinical and Molecular Medicine, Department of Biomedical Sciences (F.M., F.D.C., L.A.), and Department of Medical Science (M.G.M.), University of Cagliari, Cagliari, Italy
| | - Luigi Barberini
- Department of Public Health (E.C., L.L., L.B., S.P., J.F., G.F., G.C., M.R.M., R.M.), Clinical and Molecular Medicine, Department of Biomedical Sciences (F.M., F.D.C., L.A.), and Department of Medical Science (M.G.M.), University of Cagliari, Cagliari, Italy
| | - Simone Poddighe
- Department of Public Health (E.C., L.L., L.B., S.P., J.F., G.F., G.C., M.R.M., R.M.), Clinical and Molecular Medicine, Department of Biomedical Sciences (F.M., F.D.C., L.A.), and Department of Medical Science (M.G.M.), University of Cagliari, Cagliari, Italy
| | - Jessica Frau
- Department of Public Health (E.C., L.L., L.B., S.P., J.F., G.F., G.C., M.R.M., R.M.), Clinical and Molecular Medicine, Department of Biomedical Sciences (F.M., F.D.C., L.A.), and Department of Medical Science (M.G.M.), University of Cagliari, Cagliari, Italy
| | - Giuseppe Fenu
- Department of Public Health (E.C., L.L., L.B., S.P., J.F., G.F., G.C., M.R.M., R.M.), Clinical and Molecular Medicine, Department of Biomedical Sciences (F.M., F.D.C., L.A.), and Department of Medical Science (M.G.M.), University of Cagliari, Cagliari, Italy
| | - Giancarlo Coghe
- Department of Public Health (E.C., L.L., L.B., S.P., J.F., G.F., G.C., M.R.M., R.M.), Clinical and Molecular Medicine, Department of Biomedical Sciences (F.M., F.D.C., L.A.), and Department of Medical Science (M.G.M.), University of Cagliari, Cagliari, Italy
| | - Maria Rita Murru
- Department of Public Health (E.C., L.L., L.B., S.P., J.F., G.F., G.C., M.R.M., R.M.), Clinical and Molecular Medicine, Department of Biomedical Sciences (F.M., F.D.C., L.A.), and Department of Medical Science (M.G.M.), University of Cagliari, Cagliari, Italy
| | - Raffaele Murru
- Department of Public Health (E.C., L.L., L.B., S.P., J.F., G.F., G.C., M.R.M., R.M.), Clinical and Molecular Medicine, Department of Biomedical Sciences (F.M., F.D.C., L.A.), and Department of Medical Science (M.G.M.), University of Cagliari, Cagliari, Italy
| | - Francesco Del Carratore
- Department of Public Health (E.C., L.L., L.B., S.P., J.F., G.F., G.C., M.R.M., R.M.), Clinical and Molecular Medicine, Department of Biomedical Sciences (F.M., F.D.C., L.A.), and Department of Medical Science (M.G.M.), University of Cagliari, Cagliari, Italy
| | - Luigi Atzori
- Department of Public Health (E.C., L.L., L.B., S.P., J.F., G.F., G.C., M.R.M., R.M.), Clinical and Molecular Medicine, Department of Biomedical Sciences (F.M., F.D.C., L.A.), and Department of Medical Science (M.G.M.), University of Cagliari, Cagliari, Italy
| | - Maria Giovanna Marrosu
- Department of Public Health (E.C., L.L., L.B., S.P., J.F., G.F., G.C., M.R.M., R.M.), Clinical and Molecular Medicine, Department of Biomedical Sciences (F.M., F.D.C., L.A.), and Department of Medical Science (M.G.M.), University of Cagliari, Cagliari, Italy
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Schmidt JA, Rinaldi S, Ferrari P, Carayol M, Achaintre D, Scalbert A, Cross AJ, Gunter MJ, Fensom GK, Appleby PN, Key TJ, Travis RC. Metabolic profiles of male meat eaters, fish eaters, vegetarians, and vegans from the EPIC-Oxford cohort. Am J Clin Nutr 2015; 102:1518-26. [PMID: 26511225 PMCID: PMC4658459 DOI: 10.3945/ajcn.115.111989] [Citation(s) in RCA: 99] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2015] [Accepted: 09/15/2015] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Human metabolism is influenced by dietary factors and lifestyle, environmental, and genetic factors; thus, men who exclude some or all animal products from their diet might have different metabolic profiles than meat eaters. OBJECTIVE We aimed to investigate differences in concentrations of 118 circulating metabolites, including acylcarnitines, amino acids, biogenic amines, glycerophospholipids, hexose, and sphingolipids related to lipid, protein, and carbohydrate metabolism between male meat eaters, fish eaters, vegetarians, and vegans from the Oxford arm of the European Prospective Investigation into Cancer and Nutrition. DESIGN In this cross-sectional study, concentrations of metabolites were measured by mass spectrometry in plasma from 379 men categorized according to their diet group. Differences in mean metabolite concentrations across diet groups were tested by using ANOVA, and a false discovery rate-controlling procedure was used to account for multiple testing. Principal component analysis was used to investigate patterns in metabolic profiles. RESULTS Concentrations of 79% of metabolites differed significantly by diet group. In the vast majority of these cases, vegans had the lowest concentration, whereas meat eaters most often had the highest concentrations of the acylcarnitines, glycerophospholipids, and sphingolipids, and fish eaters or vegetarians most often had the highest concentrations of the amino acids and a biogenic amine. A clear separation between patterns in the metabolic profiles of the 4 diet groups was seen, with vegans being noticeably different from the other groups because of lower concentrations of some glycerophospholipids and sphingolipids. CONCLUSIONS Metabolic profiles in plasma could effectively differentiate between men from different habitual diet groups, especially vegan men compared with men who consume animal products. The difference in metabolic profiles was mainly explained by the lower concentrations of glycerophospholipids and sphingolipids in vegans.
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Affiliation(s)
- Julie A Schmidt
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Sabina Rinaldi
- International Agency for Research on Cancer, Lyon, France; and
| | - Pietro Ferrari
- International Agency for Research on Cancer, Lyon, France; and
| | - Marion Carayol
- International Agency for Research on Cancer, Lyon, France; and
| | - David Achaintre
- International Agency for Research on Cancer, Lyon, France; and
| | | | - Amanda J Cross
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Marc J Gunter
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Georgina K Fensom
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Paul N Appleby
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Timothy J Key
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Ruth C Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom;
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Carayol M, Licaj I, Achaintre D, Sacerdote C, Vineis P, Key TJ, Onland Moret NC, Scalbert A, Rinaldi S, Ferrari P. Reliability of Serum Metabolites over a Two-Year Period: A Targeted Metabolomic Approach in Fasting and Non-Fasting Samples from EPIC. PLoS One 2015; 10:e0135437. [PMID: 26274920 PMCID: PMC4537119 DOI: 10.1371/journal.pone.0135437] [Citation(s) in RCA: 101] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2015] [Accepted: 07/23/2015] [Indexed: 01/12/2023] Open
Abstract
OBJECTIVE Although metabolic profiles have been associated with chronic disease risk, lack of temporal stability of metabolite levels could limit their use in epidemiological investigations. The present study aims to evaluate the reliability over a two-year period of 158 metabolites and compare reliability over time in fasting and non-fasting serum samples. METHODS Metabolites were measured with the AbsolueIDQp180 kit (Biocrates, Innsbruck, Austria) by mass spectrometry and included acylcarnitines, amino acids, biogenic amines, hexoses, phosphatidylcholines and sphingomyelins. Measurements were performed on repeat serum samples collected two years apart in 27 fasting men from Turin, Italy, and 39 non-fasting women from Utrecht, The Netherlands, all participating in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Reproducibility was assessed by estimating intraclass correlation coefficients (ICCs) in multivariable mixed models. RESULTS In fasting samples, a median ICC of 0.70 was observed. ICC values were <0.50 for 48% of amino acids, 27% of acylcarnitines, 18% of lysophosphatidylcholines and 4% of phosphatidylcholines. In non-fasting samples, the median ICC was 0.54. ICC values were <0.50 for 71% of acylcarnitines, 48% of amino acids, 44% of biogenic amines, 36% of sphingomyelins, 34% of phosphatidylcholines and 33% of lysophosphatidylcholines. Overall, reproducibility was lower in non-fasting as compared to fasting samples, with a statistically significant difference for 19-36% of acylcarnitines, phosphatidylcholines and sphingomyelins. CONCLUSION A single measurement per individual may be sufficient for the study of 73% and 52% of the metabolites showing ICCs >0.50 in fasting and non-fasting samples, respectively. ICCs were higher in fasting samples that are preferable to non-fasting.
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Affiliation(s)
- Marion Carayol
- International Agency for Research on Cancer, Lyon, France
| | - Idlir Licaj
- International Agency for Research on Cancer, Lyon, France; Institute of Community Medicine, Faculty of Health Sciences, University of Tromsø, Tromsø, Norway
| | | | - Carlotta Sacerdote
- Unit of Cancer Epidemiology, AO Citta' della Salute e della Scienza-University of Turin and Center for Cancer Prevention (CPO-Piemonte), Turin, Italy
| | - Paolo Vineis
- Human Genetics Foundation (HuGeF), Turin, Italy; School of Public Health, Imperial College London, London, United Kingdom
| | - Timothy J Key
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - N Charlotte Onland Moret
- Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands
| | | | - Sabina Rinaldi
- International Agency for Research on Cancer, Lyon, France
| | - Pietro Ferrari
- International Agency for Research on Cancer, Lyon, France
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81
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Wittenbecher C, Mühlenbruch K, Kröger J, Jacobs S, Kuxhaus O, Floegel A, Fritsche A, Pischon T, Prehn C, Adamski J, Joost HG, Boeing H, Schulze MB. Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes. Am J Clin Nutr 2015; 101:1241-50. [PMID: 25948672 DOI: 10.3945/ajcn.114.099150] [Citation(s) in RCA: 87] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2014] [Accepted: 03/26/2015] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Habitual red meat consumption was consistently related to a higher risk of type 2 diabetes in observational studies. Potentially underlying mechanisms are unclear. OBJECTIVE This study aimed to identify blood metabolites that possibly relate red meat consumption to the occurrence of type 2 diabetes. DESIGN Analyses were conducted in the prospective European Prospective Investigation into Cancer and Nutrition-Potsdam cohort (n = 27,548), applying a nested case-cohort design (n = 2681, including 688 incident diabetes cases). Habitual diet was assessed with validated semiquantitative food-frequency questionnaires. Total red meat consumption was defined as energy-standardized summed intake of unprocessed and processed red meats. Concentrations of 14 amino acids, 17 acylcarnitines, 81 glycerophospholipids, 14 sphingomyelins, and ferritin were determined in serum samples from baseline. These biomarkers were considered potential mediators of the relation between total red meat consumption and diabetes risk in Cox models. The proportion of diabetes risk explainable by biomarker adjustment was estimated in a bootstrapping procedure with 1000 replicates. RESULTS After adjustment for age, sex, lifestyle, diet, and body mass index, total red meat consumption was directly related to diabetes risk [HR for 2 SD (11 g/MJ): 1.26; 95% CI: 1.01, 1.57]. Six biomarkers (ferritin, glycine, diacyl phosphatidylcholines 36:4 and 38:4, lysophosphatidylcholine 17:0, and hydroxy-sphingomyelin 14:1) were associated with red meat consumption and diabetes risk. The red meat-associated diabetes risk was significantly (P < 0.001) attenuated after simultaneous adjustment for these biomarkers [biomarker-adjusted HR for 2 SD (11 g/MJ): 1.09; 95% CI: 0.86, 1.38]. The proportion of diabetes risk explainable by respective biomarkers was 69% (IQR: 49%, 106%). CONCLUSION In our study, high ferritin, low glycine, and altered hepatic-derived lipid concentrations in the circulation were associated with total red meat consumption and, independent of red meat, with diabetes risk. The red meat-associated diabetes risk was largely attenuated after adjustment for selected biomarkers, which is consistent with the presumed mediation hypothesis.
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Affiliation(s)
- Clemens Wittenbecher
- From the Department of Molecular Epidemiology (CW, KM, JK, SJ, OK, and MBS), Department of Pharmacology (H-GJ), and the Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany (A Floegel and HB); the German Center for Diabetes Research, Neuherberg, Germany (CW, KM, JK, OK, A Fritsche, JA, H-GJ, and MBS); the Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease and Clinical Chemistry, University of Tübingen, Tübingen, Germany (A Fritsche); the Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine Berlin-Buch, Berlin, Germany (TP); the Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (CP and JA); and the Chair of Experimental Genetics, Technical University München, Freising-Weihenstephan, Germany (JA)
| | - Kristin Mühlenbruch
- From the Department of Molecular Epidemiology (CW, KM, JK, SJ, OK, and MBS), Department of Pharmacology (H-GJ), and the Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany (A Floegel and HB); the German Center for Diabetes Research, Neuherberg, Germany (CW, KM, JK, OK, A Fritsche, JA, H-GJ, and MBS); the Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease and Clinical Chemistry, University of Tübingen, Tübingen, Germany (A Fritsche); the Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine Berlin-Buch, Berlin, Germany (TP); the Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (CP and JA); and the Chair of Experimental Genetics, Technical University München, Freising-Weihenstephan, Germany (JA)
| | - Janine Kröger
- From the Department of Molecular Epidemiology (CW, KM, JK, SJ, OK, and MBS), Department of Pharmacology (H-GJ), and the Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany (A Floegel and HB); the German Center for Diabetes Research, Neuherberg, Germany (CW, KM, JK, OK, A Fritsche, JA, H-GJ, and MBS); the Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease and Clinical Chemistry, University of Tübingen, Tübingen, Germany (A Fritsche); the Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine Berlin-Buch, Berlin, Germany (TP); the Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (CP and JA); and the Chair of Experimental Genetics, Technical University München, Freising-Weihenstephan, Germany (JA)
| | - Simone Jacobs
- From the Department of Molecular Epidemiology (CW, KM, JK, SJ, OK, and MBS), Department of Pharmacology (H-GJ), and the Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany (A Floegel and HB); the German Center for Diabetes Research, Neuherberg, Germany (CW, KM, JK, OK, A Fritsche, JA, H-GJ, and MBS); the Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease and Clinical Chemistry, University of Tübingen, Tübingen, Germany (A Fritsche); the Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine Berlin-Buch, Berlin, Germany (TP); the Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (CP and JA); and the Chair of Experimental Genetics, Technical University München, Freising-Weihenstephan, Germany (JA)
| | - Olga Kuxhaus
- From the Department of Molecular Epidemiology (CW, KM, JK, SJ, OK, and MBS), Department of Pharmacology (H-GJ), and the Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany (A Floegel and HB); the German Center for Diabetes Research, Neuherberg, Germany (CW, KM, JK, OK, A Fritsche, JA, H-GJ, and MBS); the Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease and Clinical Chemistry, University of Tübingen, Tübingen, Germany (A Fritsche); the Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine Berlin-Buch, Berlin, Germany (TP); the Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (CP and JA); and the Chair of Experimental Genetics, Technical University München, Freising-Weihenstephan, Germany (JA)
| | - Anna Floegel
- From the Department of Molecular Epidemiology (CW, KM, JK, SJ, OK, and MBS), Department of Pharmacology (H-GJ), and the Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany (A Floegel and HB); the German Center for Diabetes Research, Neuherberg, Germany (CW, KM, JK, OK, A Fritsche, JA, H-GJ, and MBS); the Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease and Clinical Chemistry, University of Tübingen, Tübingen, Germany (A Fritsche); the Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine Berlin-Buch, Berlin, Germany (TP); the Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (CP and JA); and the Chair of Experimental Genetics, Technical University München, Freising-Weihenstephan, Germany (JA)
| | - Andreas Fritsche
- From the Department of Molecular Epidemiology (CW, KM, JK, SJ, OK, and MBS), Department of Pharmacology (H-GJ), and the Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany (A Floegel and HB); the German Center for Diabetes Research, Neuherberg, Germany (CW, KM, JK, OK, A Fritsche, JA, H-GJ, and MBS); the Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease and Clinical Chemistry, University of Tübingen, Tübingen, Germany (A Fritsche); the Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine Berlin-Buch, Berlin, Germany (TP); the Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (CP and JA); and the Chair of Experimental Genetics, Technical University München, Freising-Weihenstephan, Germany (JA)
| | - Tobias Pischon
- From the Department of Molecular Epidemiology (CW, KM, JK, SJ, OK, and MBS), Department of Pharmacology (H-GJ), and the Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany (A Floegel and HB); the German Center for Diabetes Research, Neuherberg, Germany (CW, KM, JK, OK, A Fritsche, JA, H-GJ, and MBS); the Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease and Clinical Chemistry, University of Tübingen, Tübingen, Germany (A Fritsche); the Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine Berlin-Buch, Berlin, Germany (TP); the Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (CP and JA); and the Chair of Experimental Genetics, Technical University München, Freising-Weihenstephan, Germany (JA)
| | - Cornelia Prehn
- From the Department of Molecular Epidemiology (CW, KM, JK, SJ, OK, and MBS), Department of Pharmacology (H-GJ), and the Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany (A Floegel and HB); the German Center for Diabetes Research, Neuherberg, Germany (CW, KM, JK, OK, A Fritsche, JA, H-GJ, and MBS); the Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease and Clinical Chemistry, University of Tübingen, Tübingen, Germany (A Fritsche); the Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine Berlin-Buch, Berlin, Germany (TP); the Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (CP and JA); and the Chair of Experimental Genetics, Technical University München, Freising-Weihenstephan, Germany (JA)
| | - Jerzy Adamski
- From the Department of Molecular Epidemiology (CW, KM, JK, SJ, OK, and MBS), Department of Pharmacology (H-GJ), and the Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany (A Floegel and HB); the German Center for Diabetes Research, Neuherberg, Germany (CW, KM, JK, OK, A Fritsche, JA, H-GJ, and MBS); the Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease and Clinical Chemistry, University of Tübingen, Tübingen, Germany (A Fritsche); the Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine Berlin-Buch, Berlin, Germany (TP); the Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (CP and JA); and the Chair of Experimental Genetics, Technical University München, Freising-Weihenstephan, Germany (JA)
| | - Hans-Georg Joost
- From the Department of Molecular Epidemiology (CW, KM, JK, SJ, OK, and MBS), Department of Pharmacology (H-GJ), and the Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany (A Floegel and HB); the German Center for Diabetes Research, Neuherberg, Germany (CW, KM, JK, OK, A Fritsche, JA, H-GJ, and MBS); the Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease and Clinical Chemistry, University of Tübingen, Tübingen, Germany (A Fritsche); the Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine Berlin-Buch, Berlin, Germany (TP); the Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (CP and JA); and the Chair of Experimental Genetics, Technical University München, Freising-Weihenstephan, Germany (JA)
| | - Heiner Boeing
- From the Department of Molecular Epidemiology (CW, KM, JK, SJ, OK, and MBS), Department of Pharmacology (H-GJ), and the Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany (A Floegel and HB); the German Center for Diabetes Research, Neuherberg, Germany (CW, KM, JK, OK, A Fritsche, JA, H-GJ, and MBS); the Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease and Clinical Chemistry, University of Tübingen, Tübingen, Germany (A Fritsche); the Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine Berlin-Buch, Berlin, Germany (TP); the Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (CP and JA); and the Chair of Experimental Genetics, Technical University München, Freising-Weihenstephan, Germany (JA)
| | - Matthias B Schulze
- From the Department of Molecular Epidemiology (CW, KM, JK, SJ, OK, and MBS), Department of Pharmacology (H-GJ), and the Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany (A Floegel and HB); the German Center for Diabetes Research, Neuherberg, Germany (CW, KM, JK, OK, A Fritsche, JA, H-GJ, and MBS); the Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease and Clinical Chemistry, University of Tübingen, Tübingen, Germany (A Fritsche); the Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine Berlin-Buch, Berlin, Germany (TP); the Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (CP and JA); and the Chair of Experimental Genetics, Technical University München, Freising-Weihenstephan, Germany (JA).
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Elliott P, Posma JM, Chan Q, Garcia-Perez I, Wijeyesekera A, Bictash M, Ebbels TMD, Ueshima H, Zhao L, van Horn L, Daviglus M, Stamler J, Holmes E, Nicholson JK. Urinary metabolic signatures of human adiposity. Sci Transl Med 2015; 7:285ra62. [PMID: 25925681 DOI: 10.1126/scitranslmed.aaa5680] [Citation(s) in RCA: 149] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2014] [Accepted: 04/10/2015] [Indexed: 12/22/2022]
Abstract
Obesity is a major public health problem worldwide. We used 24-hour urinary metabolic profiling by proton ((1)H) nuclear magnetic resonance (NMR) spectroscopy and ion exchange chromatography to characterize the metabolic signatures of adiposity in the U.S. (n = 1880) and UK (n = 444) cohorts of the INTERMAP (International Study of Macro- and Micronutrients and Blood Pressure) epidemiologic study. Metabolic profiling of urine samples collected over two 24-hour time periods 3 weeks apart showed reproducible patterns of metabolite excretion associated with adiposity. Exploratory analysis of the urinary metabolome using (1)H NMR spectroscopy of the U.S. samples identified 29 molecular species, clustered in interconnecting metabolic pathways, that were significantly associated (P = 1.5 × 10(-5) to 2.0 × 10(-36)) with body mass index (BMI); 25 of these species were also found in the UK validation cohort. We found multiple associations between urinary metabolites and BMI including urinary glycoproteins and N-acetyl neuraminate (related to renal function), trimethylamine, dimethylamine, 4-cresyl sulfate, phenylacetylglutamine and 2-hydroxyisobutyrate (gut microbial co-metabolites), succinate and citrate (tricarboxylic acid cycle intermediates), ketoleucine and the ketoleucine/leucine ratio (linked to skeletal muscle mitochondria and branched-chain amino acid metabolism), ethanolamine (skeletal muscle turnover), and 3-methylhistidine (skeletal muscle turnover and meat intake). We mapped the multiple BMI-metabolite relationships as part of an integrated systems network that describes the connectivities between the complex pathway and compartmental signatures of human adiposity.
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Affiliation(s)
- Paul Elliott
- Department of Epidemiology and Biostatistics, Medical Research Council-Public Health England (MRC-PHE) Centre for Environment and Health, School of Public Health, Imperial College London, London W2 1PG, UK.
| | - Joram M Posma
- Department of Epidemiology and Biostatistics, Medical Research Council-Public Health England (MRC-PHE) Centre for Environment and Health, School of Public Health, Imperial College London, London W2 1PG, UK. Biomolecular Medicine, Division of Computational and Systems Medicine, MRC-National Institute for Health Research (MRC-NIHR) National Phenome Centre, MRC-PHE Centre for Environment and Health, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
| | - Queenie Chan
- Department of Epidemiology and Biostatistics, Medical Research Council-Public Health England (MRC-PHE) Centre for Environment and Health, School of Public Health, Imperial College London, London W2 1PG, UK
| | - Isabel Garcia-Perez
- Biomolecular Medicine, Division of Computational and Systems Medicine, MRC-National Institute for Health Research (MRC-NIHR) National Phenome Centre, MRC-PHE Centre for Environment and Health, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
| | - Anisha Wijeyesekera
- Biomolecular Medicine, Division of Computational and Systems Medicine, MRC-National Institute for Health Research (MRC-NIHR) National Phenome Centre, MRC-PHE Centre for Environment and Health, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
| | - Magda Bictash
- Biomolecular Medicine, Division of Computational and Systems Medicine, MRC-National Institute for Health Research (MRC-NIHR) National Phenome Centre, MRC-PHE Centre for Environment and Health, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
| | - Timothy M D Ebbels
- Biomolecular Medicine, Division of Computational and Systems Medicine, MRC-National Institute for Health Research (MRC-NIHR) National Phenome Centre, MRC-PHE Centre for Environment and Health, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
| | - Hirotsugu Ueshima
- Department of Health Science, Shiga University of Medical Science, Otsu 520-2192, Japan
| | - Liancheng Zhao
- Department of Epidemiology, Fu Wai Hospital and Cardiovascular Institute, Chinese Academy of Medical Sciences, Beijing, Beijing 100037, People's Republic of China
| | - Linda van Horn
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Martha Daviglus
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA. College of Medicine at Chicago, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Jeremiah Stamler
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Elaine Holmes
- Biomolecular Medicine, Division of Computational and Systems Medicine, MRC-National Institute for Health Research (MRC-NIHR) National Phenome Centre, MRC-PHE Centre for Environment and Health, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
| | - Jeremy K Nicholson
- Biomolecular Medicine, Division of Computational and Systems Medicine, MRC-National Institute for Health Research (MRC-NIHR) National Phenome Centre, MRC-PHE Centre for Environment and Health, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK.
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83
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Zheng Y, Yu B, Alexander D, Couper DJ, Boerwinkle E. Medium-term variability of the human serum metabolome in the Atherosclerosis Risk in Communities (ARIC) study. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2014; 18:364-73. [PMID: 24910946 DOI: 10.1089/omi.2014.0019] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Metabolomics is a systems biology tool providing small molecule signatures of disease etiology. In order to estimate the biologic variability of the human serum metabolome, this study calculated intraclass correlation coefficients (ICCs) for 178 stably-detected metabolites measured by untargeted chromatography/mass spectrometry. We studied a subsample of 60 participants (57% males, 70% Caucasians, aged 73.77±5.3 years) in the Atherosclerosis Risk in Communities (ARIC) Study who provided two fasting serum samples 4-6 weeks apart. The median ICC across all metabolites was 0.60, and 82% of metabolites had at least fair variability (i.e., ICC>= 0.40). There was variation in the medium-term variability among metabolites, with those in the pathways of amino acid and lipid metabolism showing relatively high ICCs, and those in the carbohydrate pathway showing relatively low ICCs. The results of this study provide a valuable resource for future study design and outcome interpretation of mass spectrometry-based metabolomic studies in epidemiology.
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Affiliation(s)
- Yan Zheng
- 1 Division of Epidemiology, Human Genetics and Environmental Sciences, University of Texas Health Science Center at Houston , Houston, Texas
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84
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Kalkhof S, Dautel F, Loguercio S, Baumann S, Trump S, Jungnickel H, Otto W, Rudzok S, Potratz S, Luch A, Lehmann I, Beyer A, von Bergen M. Pathway and time-resolved benzo[a]pyrene toxicity on Hepa1c1c7 cells at toxic and subtoxic exposure. J Proteome Res 2014; 14:164-82. [PMID: 25362887 DOI: 10.1021/pr500957t] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Benzo[a]pyrene (B[a]P) is an environmental contaminant mainly studied for its toxic/carcinogenic effects. For a comprehensive and pathway orientated mechanistic understanding of the effects directly triggered by a toxic (5 μM) or a subtoxic (50 nM) concentration of B[a]P or indirectly by its metabolites, we conducted time series experiments for up to 24 h to study the effects in murine hepatocytes. These cells rapidly take up and actively metabolize B[a]P, which was followed by quantitative analysis of the concentration of intracellular B[a]P and seven representative degradation products. Exposure with 5 μM B[a]P led to a maximal intracellular concentration of 1604 pmol/5 × 10(4) cells, leveling at 55 pmol/5 × 10(4) cells by the end of the time course. Changes in the global proteome (>1000 protein profiles) and metabolome (163 metabolites) were assessed in combination with B[a]P degradation. Abundance profiles of 236 (both concentrations), 190 (only 5 μM), and 150 (only 50 nM) proteins were found to be regulated in response to B[a]P in a time-dependent manner. At the endogenous metabolite level amino acids, acylcarnitines and glycerophospholipids were particularly affected by B[a]P. The comprehensive chemical, proteome and metabolomic data enabled the identification of effects on the pathway level in a time-resolved manner. So in addition to known alterations, also protein synthesis, lipid metabolism, and membrane dysfunction were identified as B[a]P specific effects.
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Affiliation(s)
- Stefan Kalkhof
- Department of Proteomics, UFZ, Helmholtz-Centre for Environmental Research , Permoserstr. 15, 04318 Leipzig, Germany
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85
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Floegel A, Wientzek A, Bachlechner U, Jacobs S, Drogan D, Prehn C, Adamski J, Krumsiek J, Schulze MB, Pischon T, Boeing H. Linking diet, physical activity, cardiorespiratory fitness and obesity to serum metabolite networks: findings from a population-based study. Int J Obes (Lond) 2014; 38:1388-96. [PMID: 24608922 PMCID: PMC4229626 DOI: 10.1038/ijo.2014.39] [Citation(s) in RCA: 77] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2014] [Revised: 02/17/2014] [Accepted: 02/20/2014] [Indexed: 12/29/2022]
Abstract
OBJECTIVE It is not yet resolved how lifestyle factors and intermediate phenotypes interrelate with metabolic pathways. We aimed to investigate the associations between diet, physical activity, cardiorespiratory fitness and obesity with serum metabolite networks in a population-based study. METHODS The present study included 2380 participants of a randomly drawn subcohort of the European Prospective Investigation into Cancer and Nutrition-Potsdam. Targeted metabolomics was used to measure 127 serum metabolites. Additional data were available including anthropometric measurements, dietary assessment including intake of whole-grain bread, coffee and cake and cookies by food frequency questionnaire, and objectively measured physical activity energy expenditure and cardiorespiratory fitness in a subsample of 100 participants. In a data-driven approach, Gaussian graphical modeling was used to draw metabolite networks and depict relevant associations between exposures and serum metabolites. In addition, the relationship of different exposure metabolite networks was estimated. RESULTS In the serum metabolite network, the different metabolite classes could be separated. There was a big group of phospholipids and acylcarnitines, a group of amino acids and C6-sugar. Amino acids were particularly positively associated with cardiorespiratory fitness and physical activity. C6-sugar and acylcarnitines were positively associated with obesity and inversely with intake of whole-grain bread. Phospholipids showed opposite associations with obesity and coffee intake. Metabolite networks of coffee intake and obesity were strongly inversely correlated (body mass index (BMI): r = -0.57 and waist circumference: r = -0.59). A strong positive correlation was observed between metabolite networks of BMI and waist circumference (r = 0.99), as well as the metabolite networks of cake and cookie intake with cardiorespiratory fitness and intake of whole-grain bread (r = 0.52 and r = 0.50; respectively). CONCLUSIONS Lifestyle factors and phenotypes seem to interrelate in various metabolic pathways. A possible protective effect of coffee could be mediated via counterbalance of pathways of obesity involving hepatic phospholipids. Experimental studies should validate the biological mechanisms.
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Affiliation(s)
- A Floegel
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - A Wientzek
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - U Bachlechner
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - S Jacobs
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - D Drogan
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - C Prehn
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - J Adamski
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - J Krumsiek
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - M B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - T Pischon
- Molecular Epidemiology Group, Max Delbrück Center for Molecular Medicine (MDC) Berlin-Buch, Berlin-Buch, Germany
| | - H Boeing
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
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86
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Rankin NJ, Preiss D, Welsh P, Burgess KEV, Nelson SM, Lawlor DA, Sattar N. The emergence of proton nuclear magnetic resonance metabolomics in the cardiovascular arena as viewed from a clinical perspective. Atherosclerosis 2014; 237:287-300. [PMID: 25299963 PMCID: PMC4232363 DOI: 10.1016/j.atherosclerosis.2014.09.024] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2014] [Revised: 09/01/2014] [Accepted: 09/03/2014] [Indexed: 11/20/2022]
Abstract
The ability to phenotype metabolic profiles in serum has increased substantially in recent years with the advent of metabolomics. Metabolomics is the study of the metabolome, defined as those molecules with an atomic mass less than 1.5 kDa. There are two main metabolomics methods: mass spectrometry (MS) and proton nuclear magnetic resonance (1H NMR) spectroscopy, each with its respective benefits and limitations. MS has greater sensitivity and so can detect many more metabolites. However, its cost (especially when heavy labelled internal standards are required for absolute quantitation) and quality control is sub-optimal for large cohorts. 1H NMR is less sensitive but sample preparation is generally faster and analysis times shorter, resulting in markedly lower analysis costs. 1H NMR is robust, reproducible and can provide absolute quantitation of many metabolites. Of particular relevance to cardio-metabolic disease is the ability of 1H NMR to provide detailed quantitative data on amino acids, fatty acids and other metabolites as well as lipoprotein subparticle concentrations and size. Early epidemiological studies suggest promise, however, this is an emerging field and more data is required before we can determine the clinical utility of these measures to improve disease prediction and treatment. This review describes the theoretical basis of 1H NMR; compares MS and 1H NMR and provides a tabular overview of recent 1H NMR-based research findings in the atherosclerosis field, describing the design and scope of studies conducted to date. 1H NMR metabolomics-CVD related research is emerging, however further large, robustly conducted prospective, genetic and intervention studies are needed to advance research on CVD risk prediction and to identify causal pathways amenable to intervention. 1H NMR metabolomics is being increasingly applied to large cohort studies. Studies have identified potentially novel lipoprotein and metabolite predictors for CVD. Potential exists for the use of metabolomics in cardiovascular clinical practice. Current findings are too preliminary to translate into clinical recommendations. Further large scale studies are now needed to advance the field in a robust manner.
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Affiliation(s)
- Naomi J Rankin
- BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, G12 8TA, UK; Glasgow Polyomics, Joseph Black Building, University of Glasgow, Glasgow, G12 8QQ, UK.
| | - David Preiss
- BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, G12 8TA, UK
| | - Paul Welsh
- BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, G12 8TA, UK
| | - Karl E V Burgess
- Glasgow Polyomics, Joseph Black Building, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Scott M Nelson
- School of Medicine, University of Glasgow, Glasgow, G12 8TA, UK
| | - Debbie A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, BS8 2BN, UK; School of Social and Community Medicine, University of Bristol, Bristol, BS8 2PS, UK
| | - Naveed Sattar
- BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, G12 8TA, UK.
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87
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Jacobs S, Kröger J, Floegel A, Boeing H, Drogan D, Pischon T, Fritsche A, Prehn C, Adamski J, Isermann B, Weikert C, Schulze MB. Evaluation of various biomarkers as potential mediators of the association between coffee consumption and incident type 2 diabetes in the EPIC-Potsdam Study. Am J Clin Nutr 2014; 100:891-900. [PMID: 25057154 DOI: 10.3945/ajcn.113.080317] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND The inverse association between coffee consumption and the risk of type 2 diabetes (T2D) is well established; however, little is known about potential mediators of this association. OBJECTIVE We aimed to investigate the association between coffee consumption and diabetes-related biomarkers and their potential role as mediators of the association between coffee consumption and T2D. DESIGN We analyzed a case-cohort study (subcohort: n = 1610; verified incident T2D cases: n = 417) nested within the European Prospective Investigation into Cancer and Nutrition-Potsdam study involving 27,548 middle-aged participants. Habitual coffee consumption was assessed with a validated, semiquantitative food-frequency questionnaire. We evaluated the association between coffee consumption and several T2D-related biomarkers, such as liver markers (reflected by γ-glutamyltransferase, fetuin-A, and sex hormone-binding globulin), markers of dyslipidemia (high-density lipoprotein cholesterol and triglycerides), inflammation [C-reactive protein (CRP)], an adipokine (adiponectin), and metabolites, stratified by sex. RESULTS Coffee consumption was inversely associated with diacyl-phosphatidylcholine C32:1 in both sexes and with phenylalanine in men, as well as positively associated with acyl-alkyl-phosphatidylcholines C34:3, C40:6, and C42:5 in women. Furthermore, coffee consumption was inversely associated with fetuin-A (P-trend = 0.06) and CRP in women and γ-glutamyltransferase and triglycerides in men. Coffee consumption tended to be inversely associated with T2D risk in both sexes, reaching significance only in men [HR (95% CI): women: ≥4 compared with >0 to <2 cups coffee/d: 0.78 (0.46, 1.33); men: ≥5 compared with >0 to <2 cups coffee/d: 0.40 (0.19, 0.81)]. The association between coffee consumption and T2D risk in men was slightly reduced after adjustment for phenylalanine or lipid markers. CONCLUSIONS Coffee consumption was inversely associated with a diacyl-phosphatidylcholine and liver markers in both sexes and positively associated with certain acyl-alkyl-phosphatidylcholines in women. Furthermore, coffee consumption showed an inverse trend with CRP in women and with triglycerides and phenylalanine in men. However, these markers explained only to a small extent the inverse association between long-term coffee consumption and T2D risk.
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Affiliation(s)
- Simone Jacobs
- From the Departments of Molecular Epidemiology (SJ, JK, and MBS) and Epidemiology (A Floegel, HB, CW, and DD), German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (CP and JA); Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine Berlin-Buch, Berlin, Germany (TP); the Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease, and Clinical Chemistry, University Hospital of the Eberhard Karls University, Tübingen, Germany (A Fritsche); Institute for Diabetes Research and Metabolic Diseases of the Helmholz Centre Munich at the University of Tübingen (IDM), Tübingen, Germany (A Fritsche); the Department for Clinical Chemistry and Pathobiochemistry, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany (BI); and the German Center for Diabetes Research (DZD), Neuherberg, Germany (SJ, A Fritsche, JK, and MBS)
| | - Janine Kröger
- From the Departments of Molecular Epidemiology (SJ, JK, and MBS) and Epidemiology (A Floegel, HB, CW, and DD), German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (CP and JA); Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine Berlin-Buch, Berlin, Germany (TP); the Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease, and Clinical Chemistry, University Hospital of the Eberhard Karls University, Tübingen, Germany (A Fritsche); Institute for Diabetes Research and Metabolic Diseases of the Helmholz Centre Munich at the University of Tübingen (IDM), Tübingen, Germany (A Fritsche); the Department for Clinical Chemistry and Pathobiochemistry, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany (BI); and the German Center for Diabetes Research (DZD), Neuherberg, Germany (SJ, A Fritsche, JK, and MBS)
| | - Anna Floegel
- From the Departments of Molecular Epidemiology (SJ, JK, and MBS) and Epidemiology (A Floegel, HB, CW, and DD), German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (CP and JA); Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine Berlin-Buch, Berlin, Germany (TP); the Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease, and Clinical Chemistry, University Hospital of the Eberhard Karls University, Tübingen, Germany (A Fritsche); Institute for Diabetes Research and Metabolic Diseases of the Helmholz Centre Munich at the University of Tübingen (IDM), Tübingen, Germany (A Fritsche); the Department for Clinical Chemistry and Pathobiochemistry, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany (BI); and the German Center for Diabetes Research (DZD), Neuherberg, Germany (SJ, A Fritsche, JK, and MBS)
| | - Heiner Boeing
- From the Departments of Molecular Epidemiology (SJ, JK, and MBS) and Epidemiology (A Floegel, HB, CW, and DD), German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (CP and JA); Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine Berlin-Buch, Berlin, Germany (TP); the Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease, and Clinical Chemistry, University Hospital of the Eberhard Karls University, Tübingen, Germany (A Fritsche); Institute for Diabetes Research and Metabolic Diseases of the Helmholz Centre Munich at the University of Tübingen (IDM), Tübingen, Germany (A Fritsche); the Department for Clinical Chemistry and Pathobiochemistry, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany (BI); and the German Center for Diabetes Research (DZD), Neuherberg, Germany (SJ, A Fritsche, JK, and MBS)
| | - Dagmar Drogan
- From the Departments of Molecular Epidemiology (SJ, JK, and MBS) and Epidemiology (A Floegel, HB, CW, and DD), German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (CP and JA); Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine Berlin-Buch, Berlin, Germany (TP); the Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease, and Clinical Chemistry, University Hospital of the Eberhard Karls University, Tübingen, Germany (A Fritsche); Institute for Diabetes Research and Metabolic Diseases of the Helmholz Centre Munich at the University of Tübingen (IDM), Tübingen, Germany (A Fritsche); the Department for Clinical Chemistry and Pathobiochemistry, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany (BI); and the German Center for Diabetes Research (DZD), Neuherberg, Germany (SJ, A Fritsche, JK, and MBS)
| | - Tobias Pischon
- From the Departments of Molecular Epidemiology (SJ, JK, and MBS) and Epidemiology (A Floegel, HB, CW, and DD), German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (CP and JA); Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine Berlin-Buch, Berlin, Germany (TP); the Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease, and Clinical Chemistry, University Hospital of the Eberhard Karls University, Tübingen, Germany (A Fritsche); Institute for Diabetes Research and Metabolic Diseases of the Helmholz Centre Munich at the University of Tübingen (IDM), Tübingen, Germany (A Fritsche); the Department for Clinical Chemistry and Pathobiochemistry, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany (BI); and the German Center for Diabetes Research (DZD), Neuherberg, Germany (SJ, A Fritsche, JK, and MBS)
| | - Andreas Fritsche
- From the Departments of Molecular Epidemiology (SJ, JK, and MBS) and Epidemiology (A Floegel, HB, CW, and DD), German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (CP and JA); Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine Berlin-Buch, Berlin, Germany (TP); the Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease, and Clinical Chemistry, University Hospital of the Eberhard Karls University, Tübingen, Germany (A Fritsche); Institute for Diabetes Research and Metabolic Diseases of the Helmholz Centre Munich at the University of Tübingen (IDM), Tübingen, Germany (A Fritsche); the Department for Clinical Chemistry and Pathobiochemistry, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany (BI); and the German Center for Diabetes Research (DZD), Neuherberg, Germany (SJ, A Fritsche, JK, and MBS)
| | - Cornelia Prehn
- From the Departments of Molecular Epidemiology (SJ, JK, and MBS) and Epidemiology (A Floegel, HB, CW, and DD), German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (CP and JA); Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine Berlin-Buch, Berlin, Germany (TP); the Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease, and Clinical Chemistry, University Hospital of the Eberhard Karls University, Tübingen, Germany (A Fritsche); Institute for Diabetes Research and Metabolic Diseases of the Helmholz Centre Munich at the University of Tübingen (IDM), Tübingen, Germany (A Fritsche); the Department for Clinical Chemistry and Pathobiochemistry, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany (BI); and the German Center for Diabetes Research (DZD), Neuherberg, Germany (SJ, A Fritsche, JK, and MBS)
| | - Jerzy Adamski
- From the Departments of Molecular Epidemiology (SJ, JK, and MBS) and Epidemiology (A Floegel, HB, CW, and DD), German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (CP and JA); Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine Berlin-Buch, Berlin, Germany (TP); the Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease, and Clinical Chemistry, University Hospital of the Eberhard Karls University, Tübingen, Germany (A Fritsche); Institute for Diabetes Research and Metabolic Diseases of the Helmholz Centre Munich at the University of Tübingen (IDM), Tübingen, Germany (A Fritsche); the Department for Clinical Chemistry and Pathobiochemistry, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany (BI); and the German Center for Diabetes Research (DZD), Neuherberg, Germany (SJ, A Fritsche, JK, and MBS)
| | - Berend Isermann
- From the Departments of Molecular Epidemiology (SJ, JK, and MBS) and Epidemiology (A Floegel, HB, CW, and DD), German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (CP and JA); Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine Berlin-Buch, Berlin, Germany (TP); the Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease, and Clinical Chemistry, University Hospital of the Eberhard Karls University, Tübingen, Germany (A Fritsche); Institute for Diabetes Research and Metabolic Diseases of the Helmholz Centre Munich at the University of Tübingen (IDM), Tübingen, Germany (A Fritsche); the Department for Clinical Chemistry and Pathobiochemistry, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany (BI); and the German Center for Diabetes Research (DZD), Neuherberg, Germany (SJ, A Fritsche, JK, and MBS)
| | - Cornelia Weikert
- From the Departments of Molecular Epidemiology (SJ, JK, and MBS) and Epidemiology (A Floegel, HB, CW, and DD), German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (CP and JA); Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine Berlin-Buch, Berlin, Germany (TP); the Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease, and Clinical Chemistry, University Hospital of the Eberhard Karls University, Tübingen, Germany (A Fritsche); Institute for Diabetes Research and Metabolic Diseases of the Helmholz Centre Munich at the University of Tübingen (IDM), Tübingen, Germany (A Fritsche); the Department for Clinical Chemistry and Pathobiochemistry, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany (BI); and the German Center for Diabetes Research (DZD), Neuherberg, Germany (SJ, A Fritsche, JK, and MBS)
| | - Matthias B Schulze
- From the Departments of Molecular Epidemiology (SJ, JK, and MBS) and Epidemiology (A Floegel, HB, CW, and DD), German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany (CP and JA); Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine Berlin-Buch, Berlin, Germany (TP); the Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology, Vascular Disease, and Clinical Chemistry, University Hospital of the Eberhard Karls University, Tübingen, Germany (A Fritsche); Institute for Diabetes Research and Metabolic Diseases of the Helmholz Centre Munich at the University of Tübingen (IDM), Tübingen, Germany (A Fritsche); the Department for Clinical Chemistry and Pathobiochemistry, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany (BI); and the German Center for Diabetes Research (DZD), Neuherberg, Germany (SJ, A Fritsche, JK, and MBS)
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Midttun Ø, Townsend MK, Nygård O, Tworoger SS, Brennan P, Johansson M, Ueland PM. Most blood biomarkers related to vitamin status, one-carbon metabolism, and the kynurenine pathway show adequate preanalytical stability and within-person reproducibility to allow assessment of exposure or nutritional status in healthy women and cardiovascular patients. J Nutr 2014; 144:784-90. [PMID: 24647388 PMCID: PMC3985833 DOI: 10.3945/jn.113.189738] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2013] [Revised: 01/08/2014] [Accepted: 02/24/2014] [Indexed: 01/20/2023] Open
Abstract
Knowledge of stability during sample transportation and changes in biomarker concentrations within person over time are paramount for proper design and interpretation of epidemiologic studies based on a single measurement of biomarker status. Therefore, we investigated stability and intraindividual vs. interindividual variation in blood concentrations of biomarkers related to vitamin status, one-carbon metabolism, and the kynurenine pathway. Whole blood (EDTA and heparin, n = 12) was stored with an icepack for 24 or 48 h, and plasma concentrations of 38 biomarkers were determined. Stability was calculated as change per hour, intraclass correlation coefficient (ICC), and simple Spearman correlation. Within-person reproducibility of biomarkers was expressed as ICC in samples collected 1-2 y apart from 40 postmenopausal women and in samples collected up to 3 y apart from 551 patients with stable angina pectoris. Biomarker stability was similar in EDTA and heparin blood. Most biomarkers were essentially stable, except for choline and total homocysteine (tHcy), which increased markedly. Within-person reproducibility in postmenopausal women was excellent (ICC > 0.75) for cotinine, all-trans retinol, cobalamin, riboflavin, α-tocopherol, Gly, pyridoxal, methylmalonic acid, creatinine, pyridoxal 5'-phosphate, and Ser; was good to fair (ICC of 0.74-0.40) for pyridoxic acid, kynurenine, tHcy, cholecalciferol, flavin mononucleotide, kynurenic acid, xanthurenic acid, 3-hydroxykynurenine, sarcosine, anthranilic acid, cystathionine, homoarginine, 3-hydroxyanthranilic acid, betaine, Arg, folate, total cysteine, dimethylglycine, asymmetric dimethylarginine, neopterin, symmetric dimethylarginine, and Trp; and poor (ICC of 0.39-0.15) for methionine sulfoxide, Met, choline, and trimethyllysine. Similar reproducibilities were observed in patients with coronary heart disease. Thus, most biomarkers investigated were essentially stable in cooled whole blood for up to 48 h and had a sufficient within-person reproducibility to allow one-exposure assessment of biomarker status in epidemiologic studies. The Western Norway B Vitamin Intervention Trial was registered at clinicaltrials.gov as NTC00354081.
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Affiliation(s)
| | - Mary K. Townsend
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Ottar Nygård
- Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Heart Disease, Haukeland University Hospital, Bergen, Norway
| | - Shelley S. Tworoger
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
- Department of Epidemiology, Harvard School of Public Health, Boston, MA
| | - Paul Brennan
- International Agency for Research on Cancer, Lyon, France; and
| | | | - Per Magne Ueland
- Department of Clinical Science, University of Bergen, Bergen, Norway
- Laboratory of Clinical Biochemistry, Haukeland University Hospital, Bergen, Norway
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89
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Xiao Q, Moore SC, Boca SM, Matthews CE, Rothman N, Stolzenberg-Solomon RZ, Sinha R, Cross AJ, Sampson JN. Sources of variability in metabolite measurements from urinary samples. PLoS One 2014; 9:e95749. [PMID: 24788433 PMCID: PMC4006796 DOI: 10.1371/journal.pone.0095749] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2014] [Accepted: 03/30/2014] [Indexed: 01/02/2023] Open
Abstract
Background The application of metabolomics in epidemiological studies would potentially allow researchers to identify biomarkers associated with exposures and diseases. However, within-individual variability of metabolite levels caused by temporal variation of metabolites, together with technical variability introduced by laboratory procedures, may reduce the study power to detect such associations. We assessed the sources of variability of metabolites from urine samples and the implications for designing epidemiologic studies. Methods We measured 539 metabolites in urine samples from the Navy Colon Adenoma Study using liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectroscopy (GC-MS). The study collected 2–3 samples per person from 17 male subjects (age 38–70) over 2–10 days. We estimated between-individual, within-individual, and technical variability and calculated expected study power with a specific focus on large case-control and nested case-control studies. Results Overall technical reliability was high (median intraclass correlation = 0.92), and for 72% of the metabolites, the majority of total variance can be attributed to between-individual variability. Age, gender and body mass index explained only a small proportion of the total metabolite variability. For a relative risk (comparing upper and lower quartiles of “usual” levels) of 1.5, we estimated that a study with 500, 1,000, and 5,000 individuals could detect 1.0%, 4.5% and 75% of the metabolite associations. Conclusions The use of metabolomics in urine samples from epidemiological studies would require large sample sizes to detect associations with moderate effect sizes.
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Affiliation(s)
- Qian Xiao
- Nutritional Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States of America
- * E-mail:
| | - Steven C. Moore
- Nutritional Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States of America
| | - Simina M. Boca
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States of America
| | - Charles E. Matthews
- Nutritional Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States of America
| | - Nathaniel Rothman
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States of America
| | - Rachael Z. Stolzenberg-Solomon
- Nutritional Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States of America
| | - Rashmi Sinha
- Nutritional Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States of America
| | | | - Joshua N. Sampson
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States of America
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90
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Changes in the serum metabolite profile in obese children with weight loss. Eur J Nutr 2014; 54:173-81. [PMID: 24740590 DOI: 10.1007/s00394-014-0698-8] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2014] [Accepted: 04/02/2014] [Indexed: 01/01/2023]
Abstract
PURPOSE Childhood obesity is an increasing problem and is accompanied by metabolic disturbances. Recently, we have identified 14 serum metabolites by a metabolomics approach (FIA-MS/MS), which showed altered concentrations in obese children as compared to normal-weight children. Obese children demonstrated higher concentrations of two acylcarnitines and lower levels of three amino acids, six acyl-alkyl phosphatidylcholines, and three lysophosphatidylcholines. The aim of this study was to analyze whether these alterations normalize in weight loss. METHODS We analyzed the changes of these 14 metabolites by the same metabolic kit as in our previous study in serum samples of 80 obese children with substantial weight loss (BMI-SDS reduction >0.5) and in 80 obese children with stable weight status all participating in a 1-year lifestyle intervention. RESULTS In the children without weight change, no significant changes of metabolite concentrations could be observed. In children with substantial weight loss, glutamine, methionine, the lysophosphatidylcholines LPCaC18:1, LPCaC18:2, and LPCa20:4, as well as the acyl-alkyl phosphatidylcholine PCaeC36:2 increased significantly, while the acylcarnitines C12:1 and C16:1, proline, PCaeC34:1, PCaeC34:2, PCaeC34:3, PCaeC36:3, and PCaeC38:2 did not change significantly. CONCLUSIONS The changes of glutamine, methionine, LPCaC18:1, LPCaC18:2, LPCa20:4, and PCaeC36:2 seem to be related to the changes of dieting or exercise habits in lifestyle intervention or to be a consequence of overweight since they normalized in weight loss. Further studies should substantiate our findings.
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91
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Mullen W, Saigusa D, Abe T, Adamski J, Mischak H. Proteomics and Metabolomics as Tools to Unravel Novel Culprits and Mechanisms of Uremic Toxicity: Instrument or Hype? Semin Nephrol 2014; 34:180-90. [DOI: 10.1016/j.semnephrol.2014.02.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Targeted metabolomics identifies reliable and stable metabolites in human serum and plasma samples. PLoS One 2014; 9:e89728. [PMID: 24586991 PMCID: PMC3933650 DOI: 10.1371/journal.pone.0089728] [Citation(s) in RCA: 173] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2013] [Accepted: 01/23/2014] [Indexed: 12/31/2022] Open
Abstract
Background Information regarding the variability of metabolite levels over time in an individual is required to estimate the reproducibility of metabolite measurements. In intervention studies, it is critical to appropriately judge changes that are elicited by any kind of intervention. The pre-analytic phase (collection, transport and sample processing) is a particularly important component of data quality in multi-center studies. Methods Reliability of metabolites (within-and between-person variance, intraclass correlation coefficient) and stability (shipment simulation at different temperatures, use of gel-barrier collection tubes, freeze-thaw cycles) were analyzed in fasting serum and plasma samples of 22 healthy human subjects using a targeted LC-MS approach. Results Reliability of metabolite measurements was higher in serum compared to plasma samples and was good in most saturated short-and medium-chain acylcarnitines, amino acids, biogenic amines, glycerophospholipids, sphingolipids and hexose. The majority of metabolites were stable for 24 h on cool packs and at room temperature in non-centrifuged tubes. Plasma and serum metabolite stability showed good coherence. Serum metabolite concentrations were mostly unaffected by tube type and one or two freeze-thaw cycles. Conclusion A single time point measurement is assumed to be sufficient for a targeted metabolomics analysis of most metabolites. For shipment, samples should ideally be separated and frozen immediately after collection, as some amino acids and biogenic amines become unstable within 3 h on cool packs. Serum gel-barrier tubes can be used safely for this process as they have no effect on concentration in most metabolites. Shipment of non-centrifuged samples on cool packs is a cost-efficient alternative for most metabolites.
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93
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Uremic solutes and risk of end-stage renal disease in type 2 diabetes: metabolomic study. Kidney Int 2014; 85:1214-24. [PMID: 24429397 PMCID: PMC4072128 DOI: 10.1038/ki.2013.497] [Citation(s) in RCA: 163] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2013] [Revised: 09/18/2013] [Accepted: 10/10/2013] [Indexed: 12/17/2022]
Abstract
Here we studied plasma metabolomic profiles as determinants of progression to ESRD in patients with Type 2 diabetes (T2D). This nested case-control study evaluated 40 cases who progressed to ESRD during 8-12 years of follow-up and 40 controls who remained alive without ESRD from the Joslin Kidney Study cohort. Controls were matched with cases for baseline clinical characteristics; although controls had slightly higher eGFR and lower levels of urinary albumin excretion than T2D cases. Plasma metabolites at baseline were measured by mass spectrometry-based global metabolomic profiling. Of the named metabolites in the library, 262 were detected in at least 80% of the study patients. The metabolomic platform recognized 78 metabolites previously reported to be elevated in ESRD (uremic solutes). Sixteen were already elevated in the baseline plasma of our cases years before ESRD developed. Other uremic solutes were either not different or not commonly detectable. Essential amino acids and their derivatives were significantly depleted in the cases, whereas certain amino acid-derived acylcarnitines were increased. All findings remained statistically significant after adjustment for differences between study groups in albumin excretion rate, eGFR or HbA1c. Uremic solute differences were confirmed by quantitative measurements. Thus, abnormal plasma concentrations of putative uremic solutes and essential amino acids either contribute to progression to ESRD or are a manifestation of an early stage(s) of the disease process that leads to ESRD in T2D.
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94
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Halama A, Riesen N, Möller G, Hrabě de Angelis M, Adamski J. Identification of biomarkers for apoptosis in cancer cell lines using metabolomics: tools for individualized medicine. J Intern Med 2013; 274:425-39. [PMID: 24127940 DOI: 10.1111/joim.12117] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND Metabolomics is a versatile unbiased method to search for biomarkers of human disease. In particular, one approach in cancer therapy is to promote apoptosis in tumour cells; this could be improved with specific biomarkers of apoptosis for monitoring treatment. We recently observed specific metabolic patterns in apoptotic cell lines; however, in that study, apoptosis was only induced with one pro-apoptotic agent, staurosporine. OBJECTIVE The aim of this study was to find novel biomarkers of apoptosis by verifying our previous findings using two further pro-apoptotic agents, 5-fluorouracil and etoposide, that are commonly used in anticancer treatment. METHODS Metabolic parameters were assessed in HepG2 and HEK293 cells using the newborn screening assay adapted for cell culture approaches, quantifying the levels of amino acids and acylcarnitines with mass spectrometry. RESULTS We were able to identify apoptosis-specific changes in the metabolite profile. Moreover, the amino acids alanine and glutamate were both significantly up-regulated in apoptotic HepG2 and HEK293 cells irrespective of the apoptosis inducer. CONCLUSION Our observations clearly indicate the potential of metabolomics in detecting metabolic biomarkers applicable in theranostics and for monitoring drug efficacy.
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Affiliation(s)
- A Halama
- Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Experimental Genetics, Genome Analysis Center, Neuherberg, Germany
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95
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Zheng Y, Yu B, Alexander D, Manolio TA, Aguilar D, Coresh J, Heiss G, Boerwinkle E, Nettleton JA. Associations between metabolomic compounds and incident heart failure among African Americans: the ARIC Study. Am J Epidemiol 2013; 178:534-42. [PMID: 23788672 DOI: 10.1093/aje/kwt004] [Citation(s) in RCA: 64] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Heart failure is more prevalent among African Americans than in the general population. Metabolomic studies among African Americans may efficiently identify novel biomarkers of heart failure. We used untargeted methods to measure 204 stable serum metabolites and evaluated their associations with incident heart failure hospitalization (n = 276) after a median follow-up of 20 years (1987-2008) by using Cox regression in data from 1,744 African Americans aged 45-64 years without heart failure at baseline from the Jackson, Mississippi, field center of the Atherosclerosis Risk in Communities (ARIC) Study. After adjustment for established risk factors, we found that 16 metabolites (6 named with known structural identities and 10 unnamed with unknown structural identities, the latter denoted by using the format X-12345) were associated with incident heart failure (P < 0.0004 based on a modified Bonferroni procedure). Of the 6 named metabolites, 4 are involved in amino acid metabolism, 1 (prolylhydroxyproline) is a dipeptide, and 1 (erythritol) is a sugar alcohol. After additional adjustment for kidney function, 2 metabolites remained associated with incident heart failure (for metabolite X-11308, hazard ratio = 0.75, 95% confidence interval: 0.65, 0.86; for metabolite X-11787, hazard ratio = 1.23, 95% confidence interval: 1.10, 1.37). Further structural analysis revealed X-11308 to be a dihydroxy docosatrienoic acid and X-11787 to be an isoform of either hydroxyleucine or hydroxyisoleucine. Our metabolomic analysis revealed novel biomarkers associated with incident heart failure independent of traditional risk factors.
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Affiliation(s)
- Yan Zheng
- Department of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas Health Science Center at Houston, Houston, TX, USA
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96
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Floegel A, von Ruesten A, Drogan D, Schulze MB, Prehn C, Adamski J, Pischon T, Boeing H. Variation of serum metabolites related to habitual diet: a targeted metabolomic approach in EPIC-Potsdam. Eur J Clin Nutr 2013; 67:1100-8. [DOI: 10.1038/ejcn.2013.147] [Citation(s) in RCA: 99] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2013] [Revised: 07/11/2013] [Accepted: 07/12/2013] [Indexed: 12/19/2022]
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97
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Steinbrecher A, Pischon T. The potential use of biomarkers in the prevention of Type 2 diabetes. Expert Rev Endocrinol Metab 2013; 8:217-219. [PMID: 30780821 DOI: 10.1586/eem.13.11] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Astrid Steinbrecher
- a Molecular Epidemiology Research Group, Max-Delbrueck-Center for Molecular Medicine Berlin-Buch, Robert-Roessle-Strasse 10, 13125 Berlin, Germany
| | - Tobias Pischon
- b Molecular Epidemiology Research Group, Max-Delbrueck-Center for Molecular Medicine Berlin-Buch, Robert-Roessle-Strasse 10, 13125 Berlin, Germany.
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98
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Menni C, Zhai G, MacGregor A, Prehn C, Römisch-Margl W, Suhre K, Adamski J, Cassidy A, Illig T, Spector TD, Valdes AM. Targeted metabolomics profiles are strongly correlated with nutritional patterns in women. Metabolomics 2013; 9:506-514. [PMID: 23543136 PMCID: PMC3608890 DOI: 10.1007/s11306-012-0469-6] [Citation(s) in RCA: 88] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2012] [Accepted: 09/21/2012] [Indexed: 01/06/2023]
Abstract
Nutrition plays an important role in human metabolism and health. Metabolomics is a promising tool for clinical, genetic and nutritional studies. A key question is to what extent metabolomic profiles reflect nutritional patterns in an epidemiological setting. We assessed the relationship between metabolomic profiles and nutritional intake in women from a large cross-sectional community study. Food frequency questionnaires (FFQs) were applied to 1,003 women from the TwinsUK cohort with targeted metabolomic analyses of serum samples using the Biocrates Absolute-IDQ™ Kit p150 (163 metabolites). We analyzed seven nutritional parameters: coffee intake, garlic intake and nutritional scores derived from the FFQs summarizing fruit and vegetable intake, alcohol intake, meat intake, hypo-caloric dieting and a "traditional English" diet. We studied the correlation between metabolite levels and dietary intake patterns in the larger population and identified for each trait between 14 and 20 independent monozygotic twins pairs discordant for nutritional intake and replicated results in this set. Results from both analyses were then meta-analyzed. For the metabolites associated with nutritional patterns, we calculated heritability using structural equation modelling. 42 metabolite nutrient intake associations were statistically significant in the discovery samples (Bonferroni P < 4 × 10-5) and 11 metabolite nutrient intake associations remained significant after validation. We found the strongest associations for fruit and vegetables intake and a glycerophospholipid (Phosphatidylcholine diacyl C38:6, P = 1.39 × 10-9) and a sphingolipid (Sphingomyeline C26:1, P = 6.95 × 10-13). We also found significant associations for coffee (confirming a previous association with C10 reported in an independent study), garlic intake and hypo-caloric dieting. Using the twin study design we find that two thirds the metabolites associated with nutritional patterns have a significant genetic contribution, and the remaining third are solely environmentally determined. Our data confirm the value of metabolomic studies for nutritional epidemiologic research.
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Affiliation(s)
- Cristina Menni
- Department of Twin Research & Genetic Epidemiology, King’s College London, St Thomas Hospital, London, SE17EH UK
| | - Guangju Zhai
- Department of Twin Research & Genetic Epidemiology, King’s College London, St Thomas Hospital, London, SE17EH UK
- Faculty of Medicine, Memorial University of Newfoundland, St John’s, NL Canada
| | - Alexander MacGregor
- Department of Twin Research & Genetic Epidemiology, King’s College London, St Thomas Hospital, London, SE17EH UK
- Norwich Medical School, University of East Anglia, Norwich, UK
| | - Cornelia Prehn
- Helmholtz Zentrum München, Institute of Experimental Genetics, Genome Analysis Center, Neuherberg, Germany
| | - Werner Römisch-Margl
- Helmholtz Zentrum München, Institute of Bioinformatics and Systems Biology, Neuherberg, Germany
| | - Karsten Suhre
- Helmholtz Zentrum München, Institute of Bioinformatics and Systems Biology, Neuherberg, Germany
- Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Education City, Qatar Foundation, Doha, State of Qatar
- Faculty of Biology, Ludwig-Maximilians-Universität, Großhaderner Str. 2, Planegg-Martinsried, Germany
| | - Jerzy Adamski
- Helmholtz Zentrum München, Institute of Experimental Genetics, Genome Analysis Center, Neuherberg, Germany
- Lehrstuhl für Experimentelle Genetik, Technische Universität München, Freising-Weihenstephan, Germany
| | - Aedin Cassidy
- Norwich Medical School, University of East Anglia, Norwich, UK
| | - Thomas Illig
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
- Hannover Unified Biobank, Hannover Medical School, Hannover, Germany
| | - Tim D. Spector
- Department of Twin Research & Genetic Epidemiology, King’s College London, St Thomas Hospital, London, SE17EH UK
| | - Ana M. Valdes
- Department of Twin Research & Genetic Epidemiology, King’s College London, St Thomas Hospital, London, SE17EH UK
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Sampson JN, Boca SM, Shu XO, Stolzenberg-Solomon RZ, Matthews CE, Hsing AW, Tan YT, Ji BT, Chow WH, Cai Q, Liu DK, Yang G, Xiang YB, Zheng W, Sinha R, Cross AJ, Moore SC. Metabolomics in epidemiology: sources of variability in metabolite measurements and implications. Cancer Epidemiol Biomarkers Prev 2013; 22:631-40. [PMID: 23396963 DOI: 10.1158/1055-9965.epi-12-1109] [Citation(s) in RCA: 127] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Metabolite levels within an individual vary over time. This within-individual variability, coupled with technical variability, reduces the power for epidemiologic studies to detect associations with disease. Here, the authors assess the variability of a large subset of metabolites and evaluate the implications for epidemiologic studies. METHODS Using liquid chromatography/mass spectrometry (LC/MS) and gas chromatography-mass spectroscopy (GC/MS) platforms, 385 metabolites were measured in 60 women at baseline and year-one of the Shanghai Physical Activity Study, and observed patterns were confirmed in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening study. RESULTS Although the authors found high technical reliability (median intraclass correlation = 0.8), reliability over time within an individual was low. Taken together, variability in the assay and variability within the individual accounted for the majority of variability for 64% of metabolites. Given this, a metabolite would need, on average, a relative risk of 3 (comparing upper and lower quartiles of "usual" levels) or 2 (comparing quartiles of observed levels) to be detected in 38%, 74%, and 97% of studies including 500, 1,000, and 5,000 individuals. Age, gender, and fasting status factors, which are often of less interest in epidemiologic studies, were associated with 30%, 67%, and 34% of metabolites, respectively, but the associations were weak and explained only a small proportion of the total metabolite variability. CONCLUSION Metabolomics will require large, but feasible, sample sizes to detect the moderate effect sizes typical for epidemiologic studies. IMPACT We offer guidelines for determining the sample sizes needed to conduct metabolomic studies in epidemiology.
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Affiliation(s)
- Joshua N Sampson
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, 6120 Executive Blvd, Rockville, MD 20852, USA.
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Floegel A, Stefan N, Yu Z, Mühlenbruch K, Drogan D, Joost HG, Fritsche A, Häring HU, Hrabě de Angelis M, Peters A, Roden M, Prehn C, Wang-Sattler R, Illig T, Schulze MB, Adamski J, Boeing H, Pischon T. Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes 2013; 62:639-48. [PMID: 23043162 PMCID: PMC3554384 DOI: 10.2337/db12-0495] [Citation(s) in RCA: 720] [Impact Index Per Article: 65.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Metabolomic discovery of biomarkers of type 2 diabetes (T2D) risk may reveal etiological pathways and help to identify individuals at risk for disease. We prospectively investigated the association between serum metabolites measured by targeted metabolomics and risk of T2D in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam (27,548 adults) among all incident cases of T2D (n = 800, mean follow-up 7 years) and a randomly drawn subcohort (n = 2,282). Flow injection analysis tandem mass spectrometry was used to quantify 163 metabolites, including acylcarnitines, amino acids, hexose, and phospholipids, in baseline serum samples. Serum hexose; phenylalanine; and diacyl-phosphatidylcholines C32:1, C36:1, C38:3, and C40:5 were independently associated with increased risk of T2D and serum glycine; sphingomyelin C16:1; acyl-alkyl-phosphatidylcholines C34:3, C40:6, C42:5, C44:4, and C44:5; and lysophosphatidylcholine C18:2 with decreased risk. Variance of the metabolites was largely explained by two metabolite factors with opposing risk associations (factor 1 relative risk in extreme quintiles 0.31 [95% CI 0.21-0.44], factor 2 3.82 [2.64-5.52]). The metabolites significantly improved T2D prediction compared with established risk factors. They were further linked to insulin sensitivity and secretion in the Tübingen Family study and were partly replicated in the independent KORA (Cooperative Health Research in the Region of Augsburg) cohort. The data indicate that metabolic alterations, including sugar metabolites, amino acids, and choline-containing phospholipids, are associated early on with a higher risk of T2D.
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Affiliation(s)
- Anna Floegel
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.
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