Lipidomic profiling identifies signatures of metabolic risk

Xiaoyan Yin; Christine M Willinger; Joshua Keefe; Jun Liu; Antonio Fernández-Ortiz; Borja Ibáñez; Jose Luis Peñalvo; Aram Adourian; George Chen; Dolores Corella; Reinald Pamplona; Manuel Portero-Otin; Mariona Jove; Paul Courchesne; Cornelia M van Duijn; Valentín Fuster; José M Ordovás; Ayşe Demirkan; Martin G Larson; Daniel Levy

doi:10.1016/j.ebiom.2019.10.046

Lipidomic profiling identifies signatures of metabolic risk

Xiaoyan Yin, Christine M Willinger, Joshua Keefe, Jun Liu, Antonio Fernández-Ortiz, Borja Ibáñez, Jose Luis Peñalvo, Aram Adourian, George Chen, Dolores Corella, Reinald Pamplona, Manuel Portero-Otin, Mariona Jove, Paul Courchesne, Cornelia M van Duijn, Valentín Fuster, José M Ordovás, Ayşe Demirkan, Martin G Larson, Daniel Levy

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Abstract

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.

Original language	English
Article number	102520
Journal	EBioMedicine
Volume	51
Number of pages	9
ISSN	2352-3964
DOIs	https://doi.org/10.1016/j.ebiom.2019.10.046
Publication status	Published - 2019

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Yin, X., Willinger, C. M., Keefe, J., Liu, J., Fernández-Ortiz, A., Ibáñez, B., Peñalvo, J. L., Adourian, A., Chen, G., Corella, D., Pamplona, R., Portero-Otin, M., Jove, M., Courchesne, P., van Duijn, C. M., Fuster, V., Ordovás, J. M., Demirkan, A., Larson, M. G., & Levy, D. (2019). Lipidomic profiling identifies signatures of metabolic risk. EBioMedicine, 51, Article 102520. https://doi.org/10.1016/j.ebiom.2019.10.046

@article{16415bb51706434b88be5184d725b9b3,

title = "Lipidomic profiling identifies signatures of metabolic risk",

abstract = "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.",

author = "Xiaoyan Yin and Willinger, {Christine M} and Joshua Keefe and Jun Liu and Antonio Fern{\'a}ndez-Ortiz and Borja Ib{\'a}{\~n}ez and Pe{\~n}alvo, {Jose Luis} and Aram Adourian and George Chen and Dolores Corella and Reinald Pamplona and Manuel Portero-Otin and Mariona Jove and Paul Courchesne and {van Duijn}, {Cornelia M} and Valent{\'i}n Fuster and Ordov{\'a}s, {Jos{\'e} M} and Ay{\c s}e Demirkan and Larson, {Martin G} and Daniel Levy",

note = "FTX; DOAJ; (CC BY-NC-ND 4.0); Copyright {\textcopyright} 2019. Published by Elsevier B.V.",

year = "2019",

doi = "10.1016/j.ebiom.2019.10.046",

language = "English",

volume = "51",

journal = "EBioMedicine",

issn = "2352-3964",

publisher = "Elsevier",

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Yin, X, Willinger, CM, Keefe, J, Liu, J, Fernández-Ortiz, A, Ibáñez, B, Peñalvo, JL, Adourian, A, Chen, G, Corella, D, Pamplona, R, Portero-Otin, M, Jove, M, Courchesne, P, van Duijn, CM, Fuster, V, Ordovás, JM, Demirkan, A, Larson, MG & Levy, D 2019, 'Lipidomic profiling identifies signatures of metabolic risk', EBioMedicine, vol. 51, 102520. https://doi.org/10.1016/j.ebiom.2019.10.046

TY - JOUR

T1 - Lipidomic profiling identifies signatures of metabolic risk

AU - Yin, Xiaoyan

AU - Willinger, Christine M

AU - Keefe, Joshua

AU - Liu, Jun

AU - Fernández-Ortiz, Antonio

AU - Ibáñez, Borja

AU - Peñalvo, Jose Luis

AU - Adourian, Aram

AU - Chen, George

AU - Corella, Dolores

AU - Pamplona, Reinald

AU - Portero-Otin, Manuel

AU - Jove, Mariona

AU - Courchesne, Paul

AU - van Duijn, Cornelia M

AU - Fuster, Valentín

AU - Ordovás, José M

AU - Demirkan, Ayşe

AU - Larson, Martin G

AU - Levy, Daniel

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

U2 - 10.1016/j.ebiom.2019.10.046

DO - 10.1016/j.ebiom.2019.10.046

M3 - A1: Web of Science-article

C2 - 31877415

SN - 2352-3964

VL - 51

JO - EBioMedicine

JF - EBioMedicine

M1 - 102520

ER -

Lipidomic profiling identifies signatures of metabolic risk

Abstract

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