Identification of candidate metabolite biomarkers for metabolic syndrome and its five components in population-based human cohorts

Cardiovascular Diabetology

Research

Karsten Suhre¹  Kristin Klier²  Hans J. Grabe³  Johannes Hertel⁴  Wolfgang Rathmann⁵  Annette Peters⁶  Martin Bahls⁷  Marcus Dörr⁷  Henry Völzke⁸  Ann-Kristin Henning⁹  Nele Friedrich^1,10  Matthias Nauck^1,10  Corinna Montrone^1,11  Gisela Fobo^1,11  Andreas Ruepp^1,11  Jerzy Adamski^1,12  Makoto Harada^1,13  Rui Wang-Sattler^1,14  Georg Homuth^1,15  Cornelia Prehn^1,16  Shixiang Yu^1,17  Mengya Shi^1,18  Siyu Han^1,18 

[1] Department of Physiology and Biophysics, Weill Cornell Medicine—Qatar, Education City—Qatar Foundation, Doha, Qatar;Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany;Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany;German Center for Neurodegenerative Diseases (DZNE), Greifswald, Germany;Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany;German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany;German Center for Diabetes Research (DZD), Partner Düsseldorf, Neuherberg, Germany;Institute of Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany;German Center for Diabetes Research (DZD), Partner Neuherberg, Munich-Neuherberg, Germany;Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany;Institute for Medical Information Processing, Biometry, and Epidemiology, Pettenkofer School of Public Health, Ludwig Maximilian University of Munich (LMU), Munich, Germany;Munich Heart Alliance, German Center for Cardiovascular Health (DZHK E.V., Partner-Site Munich), Munich, Germany;German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany;Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany;German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany;Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany;German Centre for Diabetes Research (DZD), Partner Greifswald, Neuherberg, Germany;Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany;Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany;German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany;Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany;Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany;Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore;Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia;Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany;German Center for Diabetes Research (DZD), Partner Neuherberg, Munich-Neuherberg, Germany;Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany;German Center for Diabetes Research (DZD), Partner Neuherberg, Munich-Neuherberg, Germany;Institute for Medical Information Processing, Biometry, and Epidemiology, Pettenkofer School of Public Health, Ludwig Maximilian University of Munich (LMU), Munich, Germany;Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany;Metabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany;TUM School of Medicine, Technical University of Munich (TUM), Munich, Germany;Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany;TUM School of Medicine, Technical University of Munich (TUM), Munich, Germany;Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany;German Center for Diabetes Research (DZD), Partner Neuherberg, Munich-Neuherberg, Germany;

关键词: Metabolic syndrome;    Obesity;    Cardiovascular disease;    Hypertension;    Hyperglycemia;    Metabolomics;    Amino acids;    BCAAs;    Phosphatidylcholines;    Lysophosphatidylcholines;   

DOI  :  10.1186/s12933-023-01862-z

 received in 2023-04-03, accepted in 2023-05-20,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundMetabolic Syndrome (MetS) is characterized by risk factors such as abdominal obesity, hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C), hypertension, and hyperglycemia, which contribute to the development of cardiovascular disease and type 2 diabetes. Here, we aim to identify candidate metabolite biomarkers of MetS and its associated risk factors to better understand the complex interplay of underlying signaling pathways.MethodsWe quantified serum samples of the KORA F4 study participants (N = 2815) and analyzed 121 metabolites. Multiple regression models adjusted for clinical and lifestyle covariates were used to identify metabolites that were Bonferroni significantly associated with MetS. These findings were replicated in the SHIP-TREND-0 study (N = 988) and further analyzed for the association of replicated metabolites with the five components of MetS. Database-driven networks of the identified metabolites and their interacting enzymes were also constructed.ResultsWe identified and replicated 56 MetS-specific metabolites: 13 were positively associated (e.g., Val, Leu/Ile, Phe, and Tyr), and 43 were negatively associated (e.g., Gly, Ser, and 40 lipids). Moreover, the majority (89%) and minority (23%) of MetS-specific metabolites were associated with low HDL-C and hypertension, respectively. One lipid, lysoPC a C18:2, was negatively associated with MetS and all of its five components, indicating that individuals with MetS and each of the risk factors had lower concentrations of lysoPC a C18:2 compared to corresponding controls. Our metabolic networks elucidated these observations by revealing impaired catabolism of branched-chain and aromatic amino acids, as well as accelerated Gly catabolism.ConclusionOur identified candidate metabolite biomarkers are associated with the pathophysiology of MetS and its risk factors. They could facilitate the development of therapeutic strategies to prevent type 2 diabetes and cardiovascular disease. For instance, elevated levels of lysoPC a C18:2 may protect MetS and its five risk components. More in-depth studies are necessary to determine the mechanism of key metabolites in the MetS pathophysiology.

【 授权许可】

CC BY   
© The Author(s) 2023

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