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

BACKGROUND: Metabolic 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 t...

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Autores principales: Shi, Mengya, Han, Siyu, Klier, Kristin, Fobo, Gisela, Montrone, Corinna, Yu, Shixiang, Harada, Makoto, Henning, Ann-Kristin, Friedrich, Nele, Bahls, Martin, Dörr, Marcus, Nauck, Matthias, Völzke, Henry, Homuth, Georg, Grabe, Hans J., Prehn, Cornelia, Adamski, Jerzy, Suhre, Karsten, Rathmann, Wolfgang, Ruepp, Andreas, Hertel, Johannes, Peters, Annette, Wang-Sattler, Rui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10276453/
https://www.ncbi.nlm.nih.gov/pubmed/37328862
http://dx.doi.org/10.1186/s12933-023-01862-z
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author Shi, Mengya
Han, Siyu
Klier, Kristin
Fobo, Gisela
Montrone, Corinna
Yu, Shixiang
Harada, Makoto
Henning, Ann-Kristin
Friedrich, Nele
Bahls, Martin
Dörr, Marcus
Nauck, Matthias
Völzke, Henry
Homuth, Georg
Grabe, Hans J.
Prehn, Cornelia
Adamski, Jerzy
Suhre, Karsten
Rathmann, Wolfgang
Ruepp, Andreas
Hertel, Johannes
Peters, Annette
Wang-Sattler, Rui
author_facet Shi, Mengya
Han, Siyu
Klier, Kristin
Fobo, Gisela
Montrone, Corinna
Yu, Shixiang
Harada, Makoto
Henning, Ann-Kristin
Friedrich, Nele
Bahls, Martin
Dörr, Marcus
Nauck, Matthias
Völzke, Henry
Homuth, Georg
Grabe, Hans J.
Prehn, Cornelia
Adamski, Jerzy
Suhre, Karsten
Rathmann, Wolfgang
Ruepp, Andreas
Hertel, Johannes
Peters, Annette
Wang-Sattler, Rui
author_sort Shi, Mengya
collection PubMed
description BACKGROUND: Metabolic 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. METHODS: We 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. RESULTS: We 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. CONCLUSION: Our 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12933-023-01862-z.
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spelling pubmed-102764532023-06-18 Identification of candidate metabolite biomarkers for metabolic syndrome and its five components in population-based human cohorts Shi, Mengya Han, Siyu Klier, Kristin Fobo, Gisela Montrone, Corinna Yu, Shixiang Harada, Makoto Henning, Ann-Kristin Friedrich, Nele Bahls, Martin Dörr, Marcus Nauck, Matthias Völzke, Henry Homuth, Georg Grabe, Hans J. Prehn, Cornelia Adamski, Jerzy Suhre, Karsten Rathmann, Wolfgang Ruepp, Andreas Hertel, Johannes Peters, Annette Wang-Sattler, Rui Cardiovasc Diabetol Research BACKGROUND: Metabolic 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. METHODS: We 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. RESULTS: We 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. CONCLUSION: Our 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12933-023-01862-z. BioMed Central 2023-06-16 /pmc/articles/PMC10276453/ /pubmed/37328862 http://dx.doi.org/10.1186/s12933-023-01862-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Shi, Mengya
Han, Siyu
Klier, Kristin
Fobo, Gisela
Montrone, Corinna
Yu, Shixiang
Harada, Makoto
Henning, Ann-Kristin
Friedrich, Nele
Bahls, Martin
Dörr, Marcus
Nauck, Matthias
Völzke, Henry
Homuth, Georg
Grabe, Hans J.
Prehn, Cornelia
Adamski, Jerzy
Suhre, Karsten
Rathmann, Wolfgang
Ruepp, Andreas
Hertel, Johannes
Peters, Annette
Wang-Sattler, Rui
Identification of candidate metabolite biomarkers for metabolic syndrome and its five components in population-based human cohorts
title Identification of candidate metabolite biomarkers for metabolic syndrome and its five components in population-based human cohorts
title_full Identification of candidate metabolite biomarkers for metabolic syndrome and its five components in population-based human cohorts
title_fullStr Identification of candidate metabolite biomarkers for metabolic syndrome and its five components in population-based human cohorts
title_full_unstemmed Identification of candidate metabolite biomarkers for metabolic syndrome and its five components in population-based human cohorts
title_short Identification of candidate metabolite biomarkers for metabolic syndrome and its five components in population-based human cohorts
title_sort identification of candidate metabolite biomarkers for metabolic syndrome and its five components in population-based human cohorts
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10276453/
https://www.ncbi.nlm.nih.gov/pubmed/37328862
http://dx.doi.org/10.1186/s12933-023-01862-z
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