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Su X, Cheung CYY, Zhong J, Ru Y, Fong CHY, Lee CH, Liu Y, Cheung CKY, Lam KSL, Xu A, Cai Z. Ten metabolites-based algorithm predicts the future development of type 2 diabetes in Chinese. J Adv Res 2023:S2090-1232(23)00365-X. [PMID: 38030128 DOI: 10.1016/j.jare.2023.11.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 11/10/2023] [Accepted: 11/21/2023] [Indexed: 12/01/2023] Open
Abstract
INTRODUCTION Type 2 diabetes (T2D) is a heterogeneous metabolic disease with large variations in the relative contributions of insulin resistance and β-cell dysfunction across different glucose tolerance subgroups and ethnicities. A more precise yet feasible approach to categorize risk preceding T2D onset is urgently needed. This study aimed to identify potential metabolic biomarkers that could contribute to the development of T2D and investigate whether their impact on T2D is mediated through insulin resistance and β-cell dysfunction. METHODS A non-targeted metabolomic analysis was performed in plasma samples of 196 incident T2D cases and 196 age- and sex-matched non-T2D controls recruited from a long-term prospective Chinese community-based cohort with a follow-up period of ∼ 16 years. RESULTS Metabolic profiles revealed profound perturbation of metabolomes before T2D onset. Overall metabolic shifts were strongly associated with insulin resistance rather than β-cell dysfunction. In addition, 188 out of the 578 annotated metabolites were associated with insulin resistance. Bi-directional mediation analysis revealed putative causal relationships among the metabolites, insulin resistance and T2D risk. We built a machine-learning based prediction model, integrating the conventional clinical risk factors (age, BMI, TyG index and 2hG) and 10 metabolites (acetyl-tryptophan, kynurenine, γ-glutamyl-phenylalanine, DG(18:2/22:6), DG(38:7), LPI(18:2), LPC(P-16:0), LPC(P-18:1), LPC(P-20:0) and LPE(P-20:0)) (AUROC = 0.894, 5.6% improvement comparing to the conventional clinical risk model), that successfully predicts the development of T2D. CONCLUSIONS Our findings support the notion that the metabolic changes resulting from insulin resistance, rather than β-cell dysfunction, are the primary drivers of T2D in Chinese adults. Metabolomes as a valuable phenotype hold potential clinical utility in the prediction of T2D.
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Affiliation(s)
- Xiuli Su
- State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong, China
| | - Chloe Y Y Cheung
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Junda Zhong
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Yi Ru
- State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong, China
| | - Carol H Y Fong
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Chi-Ho Lee
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Yan Liu
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Cynthia K Y Cheung
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Karen S L Lam
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China.
| | - Aimin Xu
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China; Department of Pharmacology & Pharmacy, The University of Hong Kong, Hong Kong, China.
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong, China.
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Slieker RC, Donnelly LA, Akalestou E, Lopez-Noriega L, Melhem R, Güneş A, Abou Azar F, Efanov A, Georgiadou E, Muniangi-Muhitu H, Sheikh M, Giordano GN, Åkerlund M, Ahlqvist E, Ali A, Banasik K, Brunak S, Barovic M, Bouland GA, Burdet F, Canouil M, Dragan I, Elders PJM, Fernandez C, Festa A, Fitipaldi H, Froguel P, Gudmundsdottir V, Gudnason V, Gerl MJ, van der Heijden AA, Jennings LL, Hansen MK, Kim M, Leclerc I, Klose C, Kuznetsov D, Mansour Aly D, Mehl F, Marek D, Melander O, Niknejad A, Ottosson F, Pavo I, Duffin K, Syed SK, Shaw JL, Cabrera O, Pullen TJ, Simons K, Solimena M, Suvitaival T, Wretlind A, Rossing P, Lyssenko V, Legido Quigley C, Groop L, Thorens B, Franks PW, Lim GE, Estall J, Ibberson M, Beulens JWJ, 't Hart LM, Pearson ER, Rutter GA. Identification of biomarkers for glycaemic deterioration in type 2 diabetes. Nat Commun 2023; 14:2533. [PMID: 37137910 PMCID: PMC10156700 DOI: 10.1038/s41467-023-38148-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 04/18/2023] [Indexed: 05/05/2023] Open
Abstract
We identify biomarkers for disease progression in three type 2 diabetes cohorts encompassing 2,973 individuals across three molecular classes, metabolites, lipids and proteins. Homocitrulline, isoleucine and 2-aminoadipic acid, eight triacylglycerol species, and lowered sphingomyelin 42:2;2 levels are predictive of faster progression towards insulin requirement. Of ~1,300 proteins examined in two cohorts, levels of GDF15/MIC-1, IL-18Ra, CRELD1, NogoR, FAS, and ENPP7 are associated with faster progression, whilst SMAC/DIABLO, SPOCK1 and HEMK2 predict lower progression rates. In an external replication, proteins and lipids are associated with diabetes incidence and prevalence. NogoR/RTN4R injection improved glucose tolerance in high fat-fed male mice but impaired it in male db/db mice. High NogoR levels led to islet cell apoptosis, and IL-18R antagonised inflammatory IL-18 signalling towards nuclear factor kappa-B in vitro. This comprehensive, multi-disciplinary approach thus identifies biomarkers with potential prognostic utility, provides evidence for possible disease mechanisms, and identifies potential therapeutic avenues to slow diabetes progression.
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Affiliation(s)
- Roderick C Slieker
- Department of Epidemiology and Data Science, Amsterdam Public Health Institute, Amsterdam Cardiovascular Sciences, Amsterdam UMC, location VUMC, Amsterdam, the Netherlands
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Louise A Donnelly
- Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Elina Akalestou
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Livia Lopez-Noriega
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Rana Melhem
- CHUM Research Centre and University of Montreal, Montreal, QC, Canada
| | - Ayşim Güneş
- IRCM and University of Montreal, Montreal, QC, Canada
| | | | - Alexander Efanov
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, US
| | - Eleni Georgiadou
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Hermine Muniangi-Muhitu
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Mahsa Sheikh
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | | | - Mikael Åkerlund
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Emma Ahlqvist
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Ashfaq Ali
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
| | - Karina Banasik
- Novo Nordisk Foundation Center for Protein Research, Copenhagen, Denmark
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Copenhagen, Denmark
| | - Marko Barovic
- Paul Langerhans Institute Dresden (PLID) of the Helmholtz Center Munich at the University Hospital Carl Gustav Carus and Medical Faculty, Dresden, Germany
| | - Gerard A Bouland
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Frédéric Burdet
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Mickaël Canouil
- INSERM U1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille University Hospital, Lille, F-59000, France
| | - Iulian Dragan
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Petra J M Elders
- Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC-location VUmc, Amsterdam, the Netherlands
| | | | - Andreas Festa
- Eli Lilly Regional Operations GmbH, Vienna, Austria
- 1st Medical Department, LK Stockerau, Niederösterreich, Austria
| | - Hugo Fitipaldi
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Phillippe Froguel
- INSERM U1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille University Hospital, Lille, F-59000, France
- Division of Systems Biology, Department of Diabetes, Endocrinology and Metabolism, Imperial College London, London, UK
| | - Valborg Gudmundsdottir
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Kopavogur, Iceland
| | - Vilmundur Gudnason
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Kopavogur, Iceland
| | | | - Amber A van der Heijden
- Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC-location VUmc, Amsterdam, the Netherlands
| | - Lori L Jennings
- Novartis Institutes for Biomedical Research, Cambridge, MA, 02139, USA
| | - Michael K Hansen
- Cardiovascular and Metabolic Disease Research, Janssen Research & Development, Spring House, PA, USA
| | - Min Kim
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
- Institute of Pharmaceutical Science, Faculty of Life Sciences and Medicines, King's College London, London, UK
| | - Isabelle Leclerc
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- CHUM Research Centre and University of Montreal, Montreal, QC, Canada
| | | | - Dmitry Kuznetsov
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | - Florence Mehl
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Diana Marek
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Olle Melander
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Anne Niknejad
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Filip Ottosson
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Imre Pavo
- Eli Lilly Regional Operations GmbH, Vienna, Austria
| | - Kevin Duffin
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, US
| | - Samreen K Syed
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, US
| | - Janice L Shaw
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, US
| | - Over Cabrera
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, US
| | - Timothy J Pullen
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Diabetes, Guy's Campus King's College London, London, UK
| | | | - Michele Solimena
- Paul Langerhans Institute Dresden (PLID) of the Helmholtz Center Munich at the University Hospital Carl Gustav Carus and Medical Faculty, Dresden, Germany
- Molecular Diabetology, University Hospital and Medical Faculty Carl Gustav Carus, TU Dresden, Dresden, Germany
| | | | | | - Peter Rossing
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Valeriya Lyssenko
- Department of Clinical Science, Center for Diabetes Research, University of Bergen, Bergen, Norway
- Genomics, Diabetes and Endocrinology Unit, Department of Clinical Sciences Malmö, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden
| | - Cristina Legido Quigley
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
- Institute of Pharmaceutical Science, Faculty of Life Sciences and Medicines, King's College London, London, UK
| | - Leif Groop
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- Finnish Institute of Molecular Medicine, Helsinki University, Helsinki, Finland
| | - Bernard Thorens
- Center for Integrative Genomics, University of Lausanne, CH-1015, Lausanne, Switzerland
| | - Paul W Franks
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
| | - Gareth E Lim
- CHUM Research Centre and University of Montreal, Montreal, QC, Canada
| | | | - Mark Ibberson
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Joline W J Beulens
- Department of Epidemiology and Data Science, Amsterdam Public Health Institute, Amsterdam Cardiovascular Sciences, Amsterdam UMC, location VUMC, Amsterdam, the Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Leen M 't Hart
- Department of Epidemiology and Data Science, Amsterdam Public Health Institute, Amsterdam Cardiovascular Sciences, Amsterdam UMC, location VUMC, Amsterdam, the Netherlands.
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands.
- Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.
| | - Ewan R Pearson
- Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK.
| | - Guy A Rutter
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.
- CHUM Research Centre and University of Montreal, Montreal, QC, Canada.
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
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Gonzalez-Covarrubias V, Martínez-Martínez E, del Bosque-Plata L. The Potential of Metabolomics in Biomedical Applications. Metabolites 2022; 12:metabo12020194. [PMID: 35208267 PMCID: PMC8880031 DOI: 10.3390/metabo12020194] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 01/28/2022] [Accepted: 01/30/2022] [Indexed: 12/12/2022] Open
Abstract
The metabolome offers a dynamic, comprehensive, and precise picture of the phenotype. Current high-throughput technologies have allowed the discovery of relevant metabolites that characterize a wide variety of human phenotypes with respect to health, disease, drug monitoring, and even aging. Metabolomics, parallel to genomics, has led to the discovery of biomarkers and has aided in the understanding of a diversity of molecular mechanisms, highlighting its application in precision medicine. This review focuses on the metabolomics that can be applied to improve human health, as well as its trends and impacts in metabolic and neurodegenerative diseases, cancer, longevity, the exposome, liquid biopsy development, and pharmacometabolomics. The identification of distinct metabolomic profiles will help in the discovery and improvement of clinical strategies to treat human disease. In the years to come, metabolomics will become a tool routinely applied to diagnose and monitor health and disease, aging, or drug development. Biomedical applications of metabolomics can already be foreseen to monitor the progression of metabolic diseases, such as obesity and diabetes, using branched-chain amino acids, acylcarnitines, certain phospholipids, and genomics; these can assess disease severity and predict a potential treatment. Future endeavors should focus on determining the applicability and clinical utility of metabolomic-derived markers and their appropriate implementation in large-scale clinical settings.
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Affiliation(s)
| | - Eduardo Martínez-Martínez
- Laboratory of Cell Communication and Extracellular Vesicles, Instituto Nacional de Medicina Genómica (INMEGEN), Mexico City 14610, Mexico;
| | - Laura del Bosque-Plata
- Laboratory of Nutrigenetics and Nutrigenomics, Instituto Nacional de Medicina Genómica (INMEGEN), Mexico City 14610, Mexico
- Correspondence: ; Tel.: +52-55-53-50-1974
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4
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Jing Z, Liu L, Shi Y, Du Q, Zhang D, Zuo L, Du S, Sun Z, Zhang X. Association of Coronary Artery Disease and Metabolic Syndrome: Usefulness of Serum Metabolomics Approach. Front Endocrinol (Lausanne) 2021; 12:692893. [PMID: 34630321 PMCID: PMC8498335 DOI: 10.3389/fendo.2021.692893] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 08/25/2021] [Indexed: 01/04/2023] Open
Abstract
Introduction Individuals with metabolic syndrome (MetS) are at increasing risk of coronary artery disease (CAD). We investigated the common metabolic perturbations of CAD and MetS via serum metabolomics to provide insight into potential associations. Methods Non-targeted serum metabolomics analyses were performed using ultra high-performance liquid chromatography coupled with Q Exactive hybrid quadrupole-orbitrap high-resolution accurate mass spectrometry (UHPLC-Q-Orbitrap HRMS) in samples from 492 participants (272 CAD vs. 121 healthy controls (HCs) as cohort 1, 55 MetS vs. 44 HCs as cohort 2). Cross-sectional data were obtained when the participants were recruited from the First Affiliated Hospital of Zhengzhou University. Multivariate statistics and Student's t test were applied to obtain the significant metabolites [with variable importance in the projection (VIP) values >1.0 and p values <0.05] for CAD and MetS. Logistic regression was performed to investigate the association of identified metabolites with clinical cardiac risk factors, and the association of significant metabolic perturbations between CAD and MetS was visualized by Cytoscape software 3.6.1. Finally, the receiver operating characteristic (ROC) analysis was evaluated for the risk prediction values of common changed metabolites. Results Thirty metabolites were identified for CAD, mainly including amino acids, lipid, fatty acids, pseudouridine, niacinamide; 26 metabolites were identified for MetS, mainly including amino acids, lipid, fatty acids, steroid hormone, and paraxanthine. The logistic regression results showed that all of the 30 metabolites for CAD, and 15 metabolites for MetS remained significant after adjustments of clinical risk factors. In the common metabolic signature association analysis between CAD and MetS, 11 serum metabolites were significant and common to CAD and MetS outcomes. Out of this, nine followed similar trends while two had differing directionalities. The nine common metabolites exhibiting same change trend improved risk prediction for CAD (86.4%) and MetS (90.9%) using the ROC analysis. Conclusion Serum metabolomics analysis might provide a new insight into the potential mechanisms underlying the common metabolic perturbations of CAD and MetS.
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Affiliation(s)
- Ziwei Jing
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Liwei Liu
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yingying Shi
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Qiuzheng Du
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Dingding Zhang
- Department of Vasculocardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lihua Zuo
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shuzhang Du
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhi Sun
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaojian Zhang
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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5
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Wedekind R, Keski-Rahkonen P, Robinot N, Viallon V, Rothwell JA, Boutron-Ruault MC, Aleksandrova K, Wittenbecher C, Schulze MB, Halkjaer J, Rostgaard-Hansen AL, Kaaks R, Katzke V, Masala G, Tumino R, Santucci de Magistris M, Krogh V, Sacerdote C, Jakszyn P, Weiderpass E, Gunter MJ, Huybrechts I, Scalbert A. Pepper Alkaloids and Processed Meat Intake: Results from a Randomized Trial and the European Prospective Investigation into Cancer and Nutrition (EPIC) Cohort. Mol Nutr Food Res 2021; 65:e2001141. [PMID: 33592132 DOI: 10.1002/mnfr.202001141] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 01/15/2021] [Indexed: 12/27/2022]
Abstract
SCOPE Processed meat intake has been associated with adverse health outcomes. However, little is known about the type of processed meat more particularly responsible for these effects. This study aims to identify novel biomarkers for processed meat intake. METHODS AND RESULTS In a controlled randomized cross-over dietary intervention study, 12 healthy volunteers consume different processed and non-processed meats for 3 consecutive days each. Metabolomics analyses are applied on post-intervention fasting blood and urine samples to identify discriminating molecular features of processed meat intake. Nine and five pepper alkaloid metabolites, including piperine, are identified as major discriminants of salami intake in urine and plasma, respectively. The associations with processed meat intake are tested for replication in a cross-sectional study (n = 418) embedded within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Three of the serum metabolites including piperine are associated with habitual intake of sausages and to a lesser extent of total processed meat. CONCLUSION Pepper alkaloids are major discriminants of intake for sausages that contain high levels of pepper used as ingredient. Further work is needed to assess if pepper alkaloids in combination with other metabolites may serve as biomarkers of processed meat intake.
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Affiliation(s)
- Roland Wedekind
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, 150 cours Albert Thomas, Lyon, France
| | - Pekka Keski-Rahkonen
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, 150 cours Albert Thomas, Lyon, France
| | - Nivonirina Robinot
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, 150 cours Albert Thomas, Lyon, France
| | - Vivian Viallon
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, 150 cours Albert Thomas, Lyon, France
| | - Joseph A Rothwell
- CESP, Faculté de Medicine, Université Paris-Saclay, Inserm, Villejuif, France
- Institut Gustave Roussy, Villejuif, France
| | | | - Krasimira Aleksandrova
- Department of Nutrition and Gerontology, Nutrition, Immunity and Metabolism Senior Scientist Group, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, Germany
- Institute of Nutritional Science, University of Potsdam, Potsdam, Germany
| | - Clemens Wittenbecher
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- German Center of Diabetes Research (DZD), Neuherberg, Germany
| | - Matthias B Schulze
- Institute of Nutritional Science, University of Potsdam, Potsdam, Germany
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - Jytte Halkjaer
- Danish Cancer Society Research Centre, Diet, Genes and Environment, Copenhagen, Denmark
| | | | - Rudolf Kaaks
- Department of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Verena Katzke
- Department of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Giovanna Masala
- Cancer Risk Factors and Life-Style Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network - ISPRO, Florence, Italy
| | - Rosario Tumino
- Cancer Registry and Histopathology Department, Provincial Health Authority (ASP 7), Ragusa, Italy
| | | | - Vittorio Krogh
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milano, Italy
| | - Carlotta Sacerdote
- Unit of Cancer Epidemiology, Città della Salute e della Scienza University-Hospital, Turin, Italy
| | - Paula Jakszyn
- Unit of Nutrition and Cancer, Cancer Epidemiology Research Programme, Catalan Institute of Oncology, Barcelona, Spain
- Blanquerna School of Health Sciences, Ramon Llull University, Barcelona, Spain
| | - Elisabete Weiderpass
- International Agency for Research on Cancer, 150 cours Albert Thomas, Lyon, France
| | - Marc J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, 150 cours Albert Thomas, Lyon, France
| | - Inge Huybrechts
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, 150 cours Albert Thomas, Lyon, France
| | - Augustin Scalbert
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, 150 cours Albert Thomas, Lyon, France
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Porcu E, Gilardi F, Darrous L, Yengo L, Bararpour N, Gasser M, Marques-Vidal P, Froguel P, Waeber G, Thomas A, Kutalik Z. Triangulating evidence from longitudinal and Mendelian randomization studies of metabolomic biomarkers for type 2 diabetes. Sci Rep 2021; 11:6197. [PMID: 33737653 PMCID: PMC7973501 DOI: 10.1038/s41598-021-85684-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 03/03/2021] [Indexed: 02/08/2023] Open
Abstract
The number of people affected by Type 2 diabetes mellitus (T2DM) is close to half a billion and is on a sharp rise, representing a major and growing public health burden. Given its mild initial symptoms, T2DM is often diagnosed several years after its onset, leaving half of diabetic individuals undiagnosed. While several classical clinical and genetic biomarkers have been identified, improving early diagnosis by exploring other kinds of omics data remains crucial. In this study, we have combined longitudinal data from two population-based cohorts CoLaus and DESIR (comprising in total 493 incident cases vs. 1360 controls) to identify new or confirm previously implicated metabolomic biomarkers predicting T2DM incidence more than 5 years ahead of clinical diagnosis. Our longitudinal data have shown robust evidence for valine, leucine, carnitine and glutamic acid being predictive of future conversion to T2DM. We confirmed the causality of such association for leucine by 2-sample Mendelian randomisation (MR) based on independent data. Our MR approach further identified new metabolites potentially playing a causal role on T2D, including betaine, lysine and mannose. Interestingly, for valine and leucine a strong reverse causal effect was detected, indicating that the genetic predisposition to T2DM may trigger early changes of these metabolites, which appear well-before any clinical symptoms. In addition, our study revealed a reverse causal effect of metabolites such as glutamic acid and alanine. Collectively, these findings indicate that molecular traits linked to the genetic basis of T2DM may be particularly promising early biomarkers.
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Affiliation(s)
- Eleonora Porcu
- grid.9851.50000 0001 2165 4204Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland ,grid.419765.80000 0001 2223 3006Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Federica Gilardi
- grid.150338.c0000 0001 0721 9812Unit of Forensic Toxicology and Chemistry, CURML, Lausanne University Hospital and Geneva University Hospitals, Geneva, Switzerland ,grid.9851.50000 0001 2165 4204Faculty Unit of Toxicology, CURML, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Liza Darrous
- grid.419765.80000 0001 2223 3006Swiss Institute of Bioinformatics, Lausanne, Switzerland ,grid.9851.50000 0001 2165 4204Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland
| | - Loic Yengo
- grid.1003.20000 0000 9320 7537Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Nasim Bararpour
- grid.150338.c0000 0001 0721 9812Unit of Forensic Toxicology and Chemistry, CURML, Lausanne University Hospital and Geneva University Hospitals, Geneva, Switzerland ,grid.9851.50000 0001 2165 4204Faculty Unit of Toxicology, CURML, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Marie Gasser
- grid.150338.c0000 0001 0721 9812Unit of Forensic Toxicology and Chemistry, CURML, Lausanne University Hospital and Geneva University Hospitals, Geneva, Switzerland ,grid.9851.50000 0001 2165 4204Faculty Unit of Toxicology, CURML, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Pedro Marques-Vidal
- grid.8515.90000 0001 0423 4662Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Philippe Froguel
- grid.410463.40000 0004 0471 8845Inserm UMR1283, CNRS UMR8199, European Genomic Institute for Diabetes (EGID), Université de Lille, Institut Pasteur de Lille, Lille University Hospital, Lille, France ,grid.7445.20000 0001 2113 8111Department of Metabolism, Imperial College London, London, UK
| | - Gerard Waeber
- grid.8515.90000 0001 0423 4662Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Aurelien Thomas
- grid.150338.c0000 0001 0721 9812Unit of Forensic Toxicology and Chemistry, CURML, Lausanne University Hospital and Geneva University Hospitals, Geneva, Switzerland ,grid.9851.50000 0001 2165 4204Faculty Unit of Toxicology, CURML, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Zoltán Kutalik
- grid.419765.80000 0001 2223 3006Swiss Institute of Bioinformatics, Lausanne, Switzerland ,grid.9851.50000 0001 2165 4204Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland
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Wedekind R, Keski-Rahkonen P, Robinot N, Mercier F, Engel E, Huybrechts I, Scalbert A. Metabolic Signatures of 10 Processed and Non-processed Meat Products after In Vitro Digestion. Metabolites 2020; 10:E272. [PMID: 32635215 PMCID: PMC7408382 DOI: 10.3390/metabo10070272] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 06/29/2020] [Accepted: 07/01/2020] [Indexed: 02/02/2023] Open
Abstract
The intake of processed meat has been associated with several adverse health outcomes such as type II diabetes and cancer; however, the mechanisms are not fully understood. A better knowledge of the metabolite profiles of different processed and non-processed meat products from this heterogeneous food group could help in elucidating the mechanisms associated with these health effects. Thirty-three different commercial samples of ten processed and non-processed meat products were digested in triplicate with a standardized static in vitro digestion method in order to mimic profiles of small molecules formed in the gut upon digestion. A metabolomics approach based on high-resolution mass spectrometry was used to identify metabolite profiles specific to the various meat products. Processed meat products showed metabolite profiles clearly distinct from those of non-processed meat. Several discriminant features related to either specific ingredients or processing methods were identified. Those were, in particular, syringol compounds deposited in meat during smoking, biogenic amines formed during meat fermentation and piperine and related compounds characteristic of pepper used as an ingredient. These metabolites, characteristic of specific processed meat products, might be used as potential biomarkers of intake for these foods. They may also help in understanding the mechanisms linking processed meat intake and adverse health outcomes such as cancer.
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Affiliation(s)
- Roland Wedekind
- Nutrition and Metabolism Section, International Agency for Research on Cancer, 69372 Lyon, France; (R.W.); (P.K.-R.); (N.R.); (I.H.)
| | - Pekka Keski-Rahkonen
- Nutrition and Metabolism Section, International Agency for Research on Cancer, 69372 Lyon, France; (R.W.); (P.K.-R.); (N.R.); (I.H.)
| | - Nivonirina Robinot
- Nutrition and Metabolism Section, International Agency for Research on Cancer, 69372 Lyon, France; (R.W.); (P.K.-R.); (N.R.); (I.H.)
| | - Frederic Mercier
- Micro-Contaminants, Aroma and Separation Sciences (MASS) Group, National Research Institute for Agriculture, Food and Environment (INRAE) UR370 QuaPA, 63122 Saint-Genès-Champanelle, France; (F.M.); (E.E.)
| | - Erwan Engel
- Micro-Contaminants, Aroma and Separation Sciences (MASS) Group, National Research Institute for Agriculture, Food and Environment (INRAE) UR370 QuaPA, 63122 Saint-Genès-Champanelle, France; (F.M.); (E.E.)
| | - Inge Huybrechts
- Nutrition and Metabolism Section, International Agency for Research on Cancer, 69372 Lyon, France; (R.W.); (P.K.-R.); (N.R.); (I.H.)
| | - Augustin Scalbert
- Nutrition and Metabolism Section, International Agency for Research on Cancer, 69372 Lyon, France; (R.W.); (P.K.-R.); (N.R.); (I.H.)
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8
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Monnerie S, Comte B, Ziegler D, Morais JA, Pujos-Guillot E, Gaudreau P. Metabolomic and Lipidomic Signatures of Metabolic Syndrome and its Physiological Components in Adults: A Systematic Review. Sci Rep 2020; 10:669. [PMID: 31959772 DOI: 10.1038/s41598-019-56909-7] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 12/19/2019] [Indexed: 12/20/2022] Open
Abstract
The aim of this work was to conduct a systematic review of human studies on metabolite/lipid biomarkers of metabolic syndrome (MetS) and its components, and provide recommendations for future studies. The search was performed in MEDLINE, EMBASE, EMB Review, CINHAL Complete, PubMed, and on grey literature, for population studies identifying MetS biomarkers from metabolomics/lipidomics. Extracted data included population, design, number of subjects, sex/gender, clinical characteristics and main outcome. Data were collected regarding biological samples, analytical methods, and statistics. Metabolites were compiled by biochemical families including listings of their significant modulations. Finally, results from the different studies were compared. The search yielded 31 eligible studies (2005–2019). A first category of articles identified prevalent and incident MetS biomarkers using mainly targeted metabolomics. Even though the population characteristics were quite homogeneous, results were difficult to compare in terms of modulated metabolites because of the lack of methodological standardization. A second category, focusing on MetS components, allowed comparing more than 300 metabolites, mainly associated with the glycemic component. Finally, this review included also publications studying type 2 diabetes as a whole set of metabolic risks, raising the interest of reporting metabolomics/lipidomics signatures to reflect the metabolic phenotypic spectrum in systems approaches.
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9
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Devi S, Nongkhlaw B, Limesh M, Pasanna RM, Thomas T, Kuriyan R, Kurpad AV, Mukhopadhyay A. Acyl ethanolamides in Diabetes and Diabetic Nephropathy: Novel targets from untargeted plasma metabolomic profiles of South Asian Indian men. Sci Rep 2019; 9:18117. [PMID: 31792390 PMCID: PMC6889195 DOI: 10.1038/s41598-019-54584-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 11/14/2019] [Indexed: 01/01/2023] Open
Abstract
The pathophysiology of diabetic nephropathy (DN) in type 2 diabetes (T2D) patients is minimally understood. We compared untargeted high-resolution accurate mass (HRAM) orbitrap-based plasma metabolomic profiles of 31 T2D-DN (with estimated glomerular filtration rate ≤80 mL/min/1.73 m2), 29 T2D and 30 normal glucose tolerance (NGT) Indian men. Of the 939 plasma metabolites that were differentially abundant amongst the NGT, T2D and T2D-DN (ANOVA, False Discovery Rate – FDR adjusted p-value < 0.05), 48 were associated with T2D irrespective of the renal function of the subjects. Acyl ethanolamides and acetylcholine were decreased while monoacylglycerols (MAGs) and cortisol were elevated in both T2D and T2D-DN. Sixteen metabolites, including amino acid metabolites Imidazolelactate and N-Acetylornithine, changed significantly between NGT, T2D and T2D-DN. 192 metabolites were specifically dysregulated in T2D-DN (ratio ≥2 or ≤0.5 between T2D-DN and T2D, similar abundance in NGT and T2D). These included increased levels of multiple acylcarnitine and amino acid metabolites. We observed a significant dysregulation of amino acid and fatty acid metabolism in South Asian Indian male T2D-DN subjects. Unique to this study, we report a reduction in acyl ethanolamide levels in both T2D and T2D-DN males. Those with dysregulation in acyl ethanolamides, which are endogenous agonists of GPR119, are likely to exhibit improved glycemic control with GPR119 agonists.
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Affiliation(s)
- Sarita Devi
- Division of Nutrition, St. John's Research Institute, St. John's National Academy of Health Sciences, Bangalore, India
| | - Bajanai Nongkhlaw
- Division of Nutrition, St. John's Research Institute, St. John's National Academy of Health Sciences, Bangalore, India
| | - M Limesh
- Department of Nephrology, St. John's Medical College and Hospital, St. John's National Academy of Health Sciences, Bangalore, India
| | - Roshni M Pasanna
- Division of Nutrition, St. John's Research Institute, St. John's National Academy of Health Sciences, Bangalore, India
| | - Tinku Thomas
- Department of Biostatistics, St. John's Medical College and Hospital, St. John's Research Institute, St. John's National Academy of Health Sciences, Bangalore, India
| | - Rebecca Kuriyan
- Division of Nutrition, St. John's Research Institute, St. John's National Academy of Health Sciences, Bangalore, India
| | - Anura V Kurpad
- Division of Nutrition, St. John's Research Institute, St. John's National Academy of Health Sciences, Bangalore, India
| | - Arpita Mukhopadhyay
- Division of Nutrition, St. John's Research Institute, St. John's National Academy of Health Sciences, Bangalore, India.
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Shi L, Brunius C, Lehtonen M, Auriola S, Bergdahl IA, Rolandsson O, Hanhineva K, Landberg R. Plasma metabolites associated with type 2 diabetes in a Swedish population: a case-control study nested in a prospective cohort. Diabetologia 2018; 61:849-861. [PMID: 29349498 PMCID: PMC6448991 DOI: 10.1007/s00125-017-4521-y] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2017] [Accepted: 11/13/2017] [Indexed: 01/09/2023]
Abstract
AIMS/HYPOTHESIS The aims of the present work were to identify plasma metabolites that predict future type 2 diabetes, to investigate the changes in identified metabolites among individuals who later did or did not develop type 2 diabetes over time, and to assess the extent to which inclusion of predictive metabolites could improve risk prediction. METHODS We established a nested case-control study within the Swedish prospective population-based Västerbotten Intervention Programme cohort. Using untargeted liquid chromatography-MS metabolomics, we analysed plasma samples from 503 case-control pairs at baseline (a median time of 7 years prior to diagnosis) and samples from a subset of 187 case-control pairs at 10 years of follow-up. Discriminative metabolites between cases and controls at baseline were optimally selected using a multivariate data analysis pipeline adapted for large-scale metabolomics. Conditional logistic regression was used to assess associations between discriminative metabolites and future type 2 diabetes, adjusting for several known risk factors. Reproducibility of identified metabolites was estimated by intra-class correlation over the 10 year period among the subset of healthy participants; their systematic changes over time in relation to diagnosis among those who developed type 2 diabetes were investigated using mixed models. Risk prediction performance of models made from different predictors was evaluated using area under the receiver operating characteristic curve, discrimination improvement index and net reclassification index. RESULTS We identified 46 predictive plasma metabolites of type 2 diabetes. Among novel findings, phosphatidylcholines (PCs) containing odd-chain fatty acids (C19:1 and C17:0) and 2-hydroxyethanesulfonate were associated with the likelihood of developing type 2 diabetes; we also confirmed previously identified predictive biomarkers. Identified metabolites strongly correlated with insulin resistance and/or beta cell dysfunction. Of 46 identified metabolites, 26 showed intermediate to high reproducibility among healthy individuals. Moreover, PCs with odd-chain fatty acids, branched-chain amino acids, 3-methyl-2-oxovaleric acid and glutamate changed over time along with disease progression among diabetes cases. Importantly, we found that a combination of five of the most robustly predictive metabolites significantly improved risk prediction if added to models with an a priori defined set of traditional risk factors, but only a marginal improvement was achieved when using models based on optimally selected traditional risk factors. CONCLUSIONS/INTERPRETATION Predictive metabolites may improve understanding of the pathophysiology of type 2 diabetes and reflect disease progression, but they provide limited incremental value in risk prediction beyond optimal use of traditional risk factors.
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Affiliation(s)
- Lin Shi
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.
- Department of Biology and Biological Engeneering, Food and Nutrition Science, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden.
| | - Carl Brunius
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Marko Lehtonen
- School of Pharmacy, University of Eastern Finland, Kuopio, Finland
- LC-MS Metabolomics Center, Biocenter Kuopio, Kuopio, Finland
| | - Seppo Auriola
- School of Pharmacy, University of Eastern Finland, Kuopio, Finland
- LC-MS Metabolomics Center, Biocenter Kuopio, Kuopio, Finland
| | | | - Olov Rolandsson
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Kati Hanhineva
- LC-MS Metabolomics Center, Biocenter Kuopio, Kuopio, Finland
- Institute of Public Health and Clinical Nutrition, Department of Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Rikard Landberg
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Unit of Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
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11
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Zhao G, Hou X, Li X, Qu M, Tong C, Li W. Metabolomics analysis of alloxan-induced diabetes in mice using UPLC–Q-TOF-MS after Crassostrea gigas polysaccharide treatment. Int J Biol Macromol 2018; 108:550-557. [DOI: 10.1016/j.ijbiomac.2017.12.057] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2017] [Revised: 12/07/2017] [Accepted: 12/07/2017] [Indexed: 01/12/2023]
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12
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Wang H, Zhang H, Yao L, Cui L, Zhang L, Gao B, Liu W, Wu D, Chen M, Li X, Ji A, Li Y. Serum metabolic profiling of type 2 diabetes mellitus in Chinese adults using an untargeted GC/TOFMS. Clin Chim Acta 2018; 477:39-47. [PMID: 29197503 DOI: 10.1016/j.cca.2017.11.036] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Revised: 11/27/2017] [Accepted: 11/28/2017] [Indexed: 12/12/2022]
Abstract
BACKGROUND Type 2 diabetes mellitus (T2DM) is a huge burden in China. The Chinese patients with T2DM have several special clinical characteristics. Metabolomics studies predominantly have identified several distinguishing metabolites associated with T2DM in Western ancestry population. However, few previous metabolomics studies were conducted in Chinese populations. METHODS We performed untargeted serum metabolic profiling between 30 T2DM patients and 30 healthy controls based on GC/TOFMS. Multivariate data analyses were applied to identify the distinguishing metabolites. RESULTS Excellent separation was obtained between the two cases. And overall 54 distinguishing metabolites were identified with VIP>1 and P<0.05, which were involved in metabolic pathways of amino acid, carbohydrate, lipids, membrane transport and nucleotides. To further analyze the correlation between the identified metabolites and T2DM, 17 metabolites were selected with FC>2.0, including gentisic acid, citraconic acid, succinic acid, 2-hydroxybutanoic acid and 3-hydroxy-l-proline, the corresponding FC were respectively 5.44, 2.21, 2.10, 2.21 and -2.04. CONCLUSION Our results demonstrated that untargeted GC/TOFMS-based metabolic approach processed well performance to identify serum distinguishing metabolites of T2DM in Chinese adults, which may be as potential biomarkers in diagnose and treatment of diabetes. And the results also provided new insight into understand the underlying molecular mechanism.
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13
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Peddinti G, Cobb J, Yengo L, Froguel P, Kravić J, Balkau B, Tuomi T, Aittokallio T, Groop L. Early metabolic markers identify potential targets for the prevention of type 2 diabetes. Diabetologia 2017; 60:1740-1750. [PMID: 28597074 PMCID: PMC5552834 DOI: 10.1007/s00125-017-4325-0] [Citation(s) in RCA: 80] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Accepted: 05/11/2017] [Indexed: 12/01/2022]
Abstract
AIMS/HYPOTHESIS The aims of this study were to evaluate systematically the predictive power of comprehensive metabolomics profiles in predicting the future risk of type 2 diabetes, and to identify a panel of the most predictive metabolic markers. METHODS We applied an unbiased systems medicine approach to mine metabolite combinations that provide added value in predicting the future incidence of type 2 diabetes beyond known risk factors. We performed mass spectrometry-based targeted, as well as global untargeted, metabolomics, measuring a total of 568 metabolites, in a Finnish cohort of 543 non-diabetic individuals from the Botnia Prospective Study, which included 146 individuals who progressed to type 2 diabetes by the end of a 10 year follow-up period. Multivariate logistic regression was used to assess statistical associations, and regularised least-squares modelling was used to perform machine learning-based risk classification and marker selection. The predictive performance of the machine learning models and marker panels was evaluated using repeated nested cross-validation, and replicated in an independent French cohort of 1044 individuals including 231 participants who progressed to type 2 diabetes during a 9 year follow-up period in the DESIR (Data from an Epidemiological Study on the Insulin Resistance Syndrome) study. RESULTS Nine metabolites were negatively associated (potentially protective) and 25 were positively associated with progression to type 2 diabetes. Machine learning models based on the entire metabolome predicted progression to type 2 diabetes (area under the receiver operating characteristic curve, AUC = 0.77) significantly better than the reference model based on clinical risk factors alone (AUC = 0.68; DeLong's p = 0.0009). The panel of metabolic markers selected by the machine learning-based feature selection also significantly improved the predictive performance over the reference model (AUC = 0.78; p = 0.00019; integrated discrimination improvement, IDI = 66.7%). This approach identified novel predictive biomarkers, such as α-tocopherol, bradykinin hydroxyproline, X-12063 and X-13435, which showed added value in predicting progression to type 2 diabetes when combined with known biomarkers such as glucose, mannose and α-hydroxybutyrate and routinely used clinical risk factors. CONCLUSIONS/INTERPRETATION This study provides a panel of novel metabolic markers for future efforts aimed at the prevention of type 2 diabetes.
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Affiliation(s)
- Gopal Peddinti
- Institute for Molecular Medicine Finland (FIMM), Nordic EMBL Partnership for Molecular Medicine, University of Helsinki, Helsinki, Finland.
- , Tietotie 2, P. O. Box 1000, FIN-02044 VTT, Espoo, Finland.
| | | | - Loic Yengo
- CNRS UMR8199, Pasteur Institute of Lille, Lille, France
- European Genomic Institute for Diabetes (EGID), FR-3508, Lille, France
- Lille University, Lille, France
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Philippe Froguel
- CNRS UMR8199, Pasteur Institute of Lille, Lille, France
- European Genomic Institute for Diabetes (EGID), FR-3508, Lille, France
- Lille University, Lille, France
- Department of Genomics of Common Disease, School of Public Health, Imperial College London, Hammersmith Hospital, London, UK
| | | | - Beverley Balkau
- CESP, Faculty of Medicine - University Paris-South; Faculty of Medicine - University Versailles-St Quentin; Inserm U1018, University Paris-Saclay, Villejuif, France
| | - Tiinamaija Tuomi
- Institute for Molecular Medicine Finland (FIMM), Nordic EMBL Partnership for Molecular Medicine, University of Helsinki, Helsinki, Finland
- Department of Endocrinology, Abdominal Centre, Helsinki University Central Hospital, Helsinki, Finland
- Folkhalsan Research Center and Research Programs Unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland (FIMM), Nordic EMBL Partnership for Molecular Medicine, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
| | - Leif Groop
- Institute for Molecular Medicine Finland (FIMM), Nordic EMBL Partnership for Molecular Medicine, University of Helsinki, Helsinki, Finland
- Lund University Diabetes Center, Lund, Sweden
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14
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Dudzik D, Zorawski M, Skotnicki M, Zarzycki W, García A, Angulo S, Lorenzo MP, Barbas C, Ramos MP. GC-MS based Gestational Diabetes Mellitus longitudinal study: Identification of 2-and 3-hydroxybutyrate as potential prognostic biomarkers. J Pharm Biomed Anal 2017; 144:90-98. [PMID: 28314466 DOI: 10.1016/j.jpba.2017.02.056] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Revised: 02/04/2017] [Accepted: 02/28/2017] [Indexed: 01/03/2023]
Abstract
Gestational Diabetes Mellitus (GDM) causes severe short- and long-term complications for the mother, fetus and neonate, including type 2-diabetes (T2DM) later in life. In this pilot study, GC-Q/MS analysis was applied for plasma metabolomics fingerprinting of 24 healthy and 24 women with GDM at different stages of gestation (second and third trimester) and postpartum (one and three months). Multivariate (unsupervised and supervised) statistical analysis was performed to investigate variance in the data, identify outliers and for unbiased assessment of data quality. Plasma fingerprints allowed for the discrimination of GDM pregnant women from controls both in the 2nd and 3rd trimesters of gestation. However, metabolic profiles tended to be similar after delivery. Follow up of these women revealed that 4 of them developed T2DM within 2 years postpartum. Multivariate PLS-DA models limited to women with GDM showed clear separation 3 months postpartum. In the 2nd trimester of gestation there was also a clear separation between GDM women that were normoglycemic after pregnancy and those with recognized postpartum T2DM. Metabolites that had the strongest discriminative power between these groups in the 2nd trimester of gestation were 2-hydroxybutyrate, 3-hydroxybutyrate, and stearic acid. We have described, that early GDM comprises metabotypes that are associated with the risk of future complications, including postpartum T2DM. In this pilot study, we provide evidence that 2-hydroxybutyrate and 3-hydroxybutyrate may be considered as future prognostic biomarkers to predict the onset of diabetic complications in women with gestational diabetes after delivery.
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Affiliation(s)
- Danuta Dudzik
- Center for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia, Universidad San Pablo CEU, Campus Monteprincipe, Boadilla del Monte, 28668, Madrid, Spain.
| | - Marcin Zorawski
- Department of Clinical Medicine, Faculty of Health Science, Medical University of Bialystok, 37 Szpitalna Street, 15-254, Bialystok, Poland.
| | - Mariusz Skotnicki
- Clinical Department of Perinatology, Public Clinic Hospital, Medical University of Bialystok, 24a Sklodowskiej-Curie Street, 15-276, Bialystok, Poland.
| | - Wieslaw Zarzycki
- Clinical Department of Endocrinology, Diabetology and Internal Diseases, Public Clinic Hospital, Medical University of Bialystok, 24a Sklodowskiej-Curie Street, 15-276, Bialystok, Poland.
| | - Antonia García
- Center for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia, Universidad San Pablo CEU, Campus Monteprincipe, Boadilla del Monte, 28668, Madrid, Spain.
| | - Santiago Angulo
- Center for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia, Universidad San Pablo CEU, Campus Monteprincipe, Boadilla del Monte, 28668, Madrid, Spain.
| | - M Paz Lorenzo
- Center for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia, Universidad San Pablo CEU, Campus Monteprincipe, Boadilla del Monte, 28668, Madrid, Spain.
| | - Coral Barbas
- Center for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia, Universidad San Pablo CEU, Campus Monteprincipe, Boadilla del Monte, 28668, Madrid, Spain.
| | - M Pilar Ramos
- Biochemistry and Molecular Biology, Facultad de Farmacia, Universidad San Pablo CEU, Boadilla del Monte, 28668, Madrid, Spain.
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