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A molecular signature for the metabolic syndrome by urine metabolomics

BACKGROUND: Metabolic syndrome (MetS) is a multimorbid long-term condition without consensual medical definition and a diagnostic based on compatible symptomatology. Here we have investigated the molecular signature of MetS in urine. METHODS: We used NMR-based metabolomics to investigate a European...

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Autores principales: Bruzzone, Chiara, Gil-Redondo, Rubén, Seco, Marisa, Barragán, Rocío, de la Cruz, Laura, Cannet, Claire, Schäfer, Hartmut, Fang, Fang, Diercks, Tammo, Bizkarguenaga, Maider, González-Valle, Beatriz, Laín, Ana, Sanz-Parra, Arantza, Coltell, Oscar, de Letona, Ander López, Spraul, Manfred, Lu, Shelly C., Buguianesi, Elisabetta, Embade, Nieves, Anstee, Quentin M., Corella, Dolores, Mato, José M., Millet, Oscar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8320177/
https://www.ncbi.nlm.nih.gov/pubmed/34320987
http://dx.doi.org/10.1186/s12933-021-01349-9
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author Bruzzone, Chiara
Gil-Redondo, Rubén
Seco, Marisa
Barragán, Rocío
de la Cruz, Laura
Cannet, Claire
Schäfer, Hartmut
Fang, Fang
Diercks, Tammo
Bizkarguenaga, Maider
González-Valle, Beatriz
Laín, Ana
Sanz-Parra, Arantza
Coltell, Oscar
de Letona, Ander López
Spraul, Manfred
Lu, Shelly C.
Buguianesi, Elisabetta
Embade, Nieves
Anstee, Quentin M.
Corella, Dolores
Mato, José M.
Millet, Oscar
author_facet Bruzzone, Chiara
Gil-Redondo, Rubén
Seco, Marisa
Barragán, Rocío
de la Cruz, Laura
Cannet, Claire
Schäfer, Hartmut
Fang, Fang
Diercks, Tammo
Bizkarguenaga, Maider
González-Valle, Beatriz
Laín, Ana
Sanz-Parra, Arantza
Coltell, Oscar
de Letona, Ander López
Spraul, Manfred
Lu, Shelly C.
Buguianesi, Elisabetta
Embade, Nieves
Anstee, Quentin M.
Corella, Dolores
Mato, José M.
Millet, Oscar
author_sort Bruzzone, Chiara
collection PubMed
description BACKGROUND: Metabolic syndrome (MetS) is a multimorbid long-term condition without consensual medical definition and a diagnostic based on compatible symptomatology. Here we have investigated the molecular signature of MetS in urine. METHODS: We used NMR-based metabolomics to investigate a European cohort including urine samples from 11,754 individuals (18–75 years old, 41% females), designed to populate all the intermediate conditions in MetS, from subjects without any risk factor up to individuals with developed MetS (4–5%, depending on the definition). A set of quantified metabolites were integrated from the urine spectra to obtain metabolic models (one for each definition), to discriminate between individuals with MetS. RESULTS: MetS progression produces a continuous and monotonic variation of the urine metabolome, characterized by up- or down-regulation of the pertinent metabolites (17 in total, including glucose, lipids, aromatic amino acids, salicyluric acid, maltitol, trimethylamine N-oxide, and p-cresol sulfate) with some of the metabolites associated to MetS for the first time. This metabolic signature, based solely on information extracted from the urine spectrum, adds a molecular dimension to MetS definition and it was used to generate models that can identify subjects with MetS (AUROC values between 0.83 and 0.87). This signature is particularly suitable to add meaning to the conditions that are in the interface between healthy subjects and MetS patients. Aging and non-alcoholic fatty liver disease are also risk factors that may enhance MetS probability, but they do not directly interfere with the metabolic discrimination of the syndrome. CONCLUSIONS: Urine metabolomics, studied by NMR spectroscopy, unravelled a set of metabolites that concomitantly evolve with MetS progression, that were used to derive and validate a molecular definition of MetS and to discriminate the conditions that are in the interface between healthy individuals and the metabolic syndrome. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12933-021-01349-9.
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spelling pubmed-83201772021-07-30 A molecular signature for the metabolic syndrome by urine metabolomics Bruzzone, Chiara Gil-Redondo, Rubén Seco, Marisa Barragán, Rocío de la Cruz, Laura Cannet, Claire Schäfer, Hartmut Fang, Fang Diercks, Tammo Bizkarguenaga, Maider González-Valle, Beatriz Laín, Ana Sanz-Parra, Arantza Coltell, Oscar de Letona, Ander López Spraul, Manfred Lu, Shelly C. Buguianesi, Elisabetta Embade, Nieves Anstee, Quentin M. Corella, Dolores Mato, José M. Millet, Oscar Cardiovasc Diabetol Original Investigation BACKGROUND: Metabolic syndrome (MetS) is a multimorbid long-term condition without consensual medical definition and a diagnostic based on compatible symptomatology. Here we have investigated the molecular signature of MetS in urine. METHODS: We used NMR-based metabolomics to investigate a European cohort including urine samples from 11,754 individuals (18–75 years old, 41% females), designed to populate all the intermediate conditions in MetS, from subjects without any risk factor up to individuals with developed MetS (4–5%, depending on the definition). A set of quantified metabolites were integrated from the urine spectra to obtain metabolic models (one for each definition), to discriminate between individuals with MetS. RESULTS: MetS progression produces a continuous and monotonic variation of the urine metabolome, characterized by up- or down-regulation of the pertinent metabolites (17 in total, including glucose, lipids, aromatic amino acids, salicyluric acid, maltitol, trimethylamine N-oxide, and p-cresol sulfate) with some of the metabolites associated to MetS for the first time. This metabolic signature, based solely on information extracted from the urine spectrum, adds a molecular dimension to MetS definition and it was used to generate models that can identify subjects with MetS (AUROC values between 0.83 and 0.87). This signature is particularly suitable to add meaning to the conditions that are in the interface between healthy subjects and MetS patients. Aging and non-alcoholic fatty liver disease are also risk factors that may enhance MetS probability, but they do not directly interfere with the metabolic discrimination of the syndrome. CONCLUSIONS: Urine metabolomics, studied by NMR spectroscopy, unravelled a set of metabolites that concomitantly evolve with MetS progression, that were used to derive and validate a molecular definition of MetS and to discriminate the conditions that are in the interface between healthy individuals and the metabolic syndrome. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12933-021-01349-9. BioMed Central 2021-07-28 /pmc/articles/PMC8320177/ /pubmed/34320987 http://dx.doi.org/10.1186/s12933-021-01349-9 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 Original Investigation
Bruzzone, Chiara
Gil-Redondo, Rubén
Seco, Marisa
Barragán, Rocío
de la Cruz, Laura
Cannet, Claire
Schäfer, Hartmut
Fang, Fang
Diercks, Tammo
Bizkarguenaga, Maider
González-Valle, Beatriz
Laín, Ana
Sanz-Parra, Arantza
Coltell, Oscar
de Letona, Ander López
Spraul, Manfred
Lu, Shelly C.
Buguianesi, Elisabetta
Embade, Nieves
Anstee, Quentin M.
Corella, Dolores
Mato, José M.
Millet, Oscar
A molecular signature for the metabolic syndrome by urine metabolomics
title A molecular signature for the metabolic syndrome by urine metabolomics
title_full A molecular signature for the metabolic syndrome by urine metabolomics
title_fullStr A molecular signature for the metabolic syndrome by urine metabolomics
title_full_unstemmed A molecular signature for the metabolic syndrome by urine metabolomics
title_short A molecular signature for the metabolic syndrome by urine metabolomics
title_sort molecular signature for the metabolic syndrome by urine metabolomics
topic Original Investigation
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8320177/
https://www.ncbi.nlm.nih.gov/pubmed/34320987
http://dx.doi.org/10.1186/s12933-021-01349-9
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