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Lipidomic profiling identifies signatures of metabolic risk

BACKGROUND: Metabolic syndrome (MetS), the clustering of metabolic risk factors, is associated with cardiovascular disease risk. We sought to determine if dysregulation of the lipidome may contribute to metabolic risk factors. METHODS: We measured 154 circulating lipid species in 658 participants fr...

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Autores principales: Yin, Xiaoyan, Willinger, Christine M., Keefe, Joshua, Liu, Jun, Fernández-Ortiz, Antonio, Ibáñez, Borja, Peñalvo, José, Adourian, Aram, Chen, George, Corella, Dolores, Pamplona, Reinald, Portero-Otin, Manuel, Jove, Mariona, Courchesne, Paul, van Duijn, Cornelia M., Fuster, Valentín, Ordovás, José M., Demirkan, Ayşe, Larson, Martin G., Levy, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6938899/
https://www.ncbi.nlm.nih.gov/pubmed/31877415
http://dx.doi.org/10.1016/j.ebiom.2019.10.046
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author Yin, Xiaoyan
Willinger, Christine M.
Keefe, Joshua
Liu, Jun
Fernández-Ortiz, Antonio
Ibáñez, Borja
Peñalvo, José
Adourian, Aram
Chen, George
Corella, Dolores
Pamplona, Reinald
Portero-Otin, Manuel
Jove, Mariona
Courchesne, Paul
van Duijn, Cornelia M.
Fuster, Valentín
Ordovás, José M.
Demirkan, Ayşe
Larson, Martin G.
Levy, Daniel
author_facet Yin, Xiaoyan
Willinger, Christine M.
Keefe, Joshua
Liu, Jun
Fernández-Ortiz, Antonio
Ibáñez, Borja
Peñalvo, José
Adourian, Aram
Chen, George
Corella, Dolores
Pamplona, Reinald
Portero-Otin, Manuel
Jove, Mariona
Courchesne, Paul
van Duijn, Cornelia M.
Fuster, Valentín
Ordovás, José M.
Demirkan, Ayşe
Larson, Martin G.
Levy, Daniel
author_sort Yin, Xiaoyan
collection PubMed
description BACKGROUND: Metabolic syndrome (MetS), the clustering of metabolic risk factors, is associated with cardiovascular disease risk. We sought to determine if dysregulation of the lipidome may contribute to metabolic risk factors. METHODS: We measured 154 circulating lipid species in 658 participants from the Framingham Heart Study (FHS) using liquid chromatography-tandem mass spectrometry and tested for associations with obesity, dysglycemia, and dyslipidemia. Independent external validation was sought in three independent cohorts. Follow-up data from the FHS were used to test for lipid metabolites associated with longitudinal changes in metabolic risk factors. RESULTS: Thirty-nine lipids were associated with obesity and eight with dysglycemia in the FHS. Of 32 lipids that were available for replication for obesity and six for dyslipidemia, 28 (88%) replicated for obesity and five (83%) for dysglycemia. Four lipids were associated with longitudinal changes in body mass index and four were associated with changes in fasting blood glucose in the FHS. CONCLUSIONS: We identified and replicated several novel lipid biomarkers of key metabolic traits. The lipid moieties identified in this study are involved in biological pathways of metabolic risk and can be explored for prognostic and therapeutic utility.
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spelling pubmed-69388992020-01-06 Lipidomic profiling identifies signatures of metabolic risk Yin, Xiaoyan Willinger, Christine M. Keefe, Joshua Liu, Jun Fernández-Ortiz, Antonio Ibáñez, Borja Peñalvo, José Adourian, Aram Chen, George Corella, Dolores Pamplona, Reinald Portero-Otin, Manuel Jove, Mariona Courchesne, Paul van Duijn, Cornelia M. Fuster, Valentín Ordovás, José M. Demirkan, Ayşe Larson, Martin G. Levy, Daniel EBioMedicine Research paper BACKGROUND: Metabolic syndrome (MetS), the clustering of metabolic risk factors, is associated with cardiovascular disease risk. We sought to determine if dysregulation of the lipidome may contribute to metabolic risk factors. METHODS: We measured 154 circulating lipid species in 658 participants from the Framingham Heart Study (FHS) using liquid chromatography-tandem mass spectrometry and tested for associations with obesity, dysglycemia, and dyslipidemia. Independent external validation was sought in three independent cohorts. Follow-up data from the FHS were used to test for lipid metabolites associated with longitudinal changes in metabolic risk factors. RESULTS: Thirty-nine lipids were associated with obesity and eight with dysglycemia in the FHS. Of 32 lipids that were available for replication for obesity and six for dyslipidemia, 28 (88%) replicated for obesity and five (83%) for dysglycemia. Four lipids were associated with longitudinal changes in body mass index and four were associated with changes in fasting blood glucose in the FHS. CONCLUSIONS: We identified and replicated several novel lipid biomarkers of key metabolic traits. The lipid moieties identified in this study are involved in biological pathways of metabolic risk and can be explored for prognostic and therapeutic utility. Elsevier 2019-12-24 /pmc/articles/PMC6938899/ /pubmed/31877415 http://dx.doi.org/10.1016/j.ebiom.2019.10.046 Text en Published by Elsevier B.V. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research paper
Yin, Xiaoyan
Willinger, Christine M.
Keefe, Joshua
Liu, Jun
Fernández-Ortiz, Antonio
Ibáñez, Borja
Peñalvo, José
Adourian, Aram
Chen, George
Corella, Dolores
Pamplona, Reinald
Portero-Otin, Manuel
Jove, Mariona
Courchesne, Paul
van Duijn, Cornelia M.
Fuster, Valentín
Ordovás, José M.
Demirkan, Ayşe
Larson, Martin G.
Levy, Daniel
Lipidomic profiling identifies signatures of metabolic risk
title Lipidomic profiling identifies signatures of metabolic risk
title_full Lipidomic profiling identifies signatures of metabolic risk
title_fullStr Lipidomic profiling identifies signatures of metabolic risk
title_full_unstemmed Lipidomic profiling identifies signatures of metabolic risk
title_short Lipidomic profiling identifies signatures of metabolic risk
title_sort lipidomic profiling identifies signatures of metabolic risk
topic Research paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6938899/
https://www.ncbi.nlm.nih.gov/pubmed/31877415
http://dx.doi.org/10.1016/j.ebiom.2019.10.046
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