4.7 Article

Lipidomic profiling identifies signatures of metabolic risk

Journal

EBIOMEDICINE
Volume 51, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.ebiom.2019.10.046

Keywords

Metabolic risk; Metabolic syndrome; Cardiovascular disease; Dysglycemia; Dyslipidemia; Biomarker

Funding

  1. National Institutes of Health (NIH) [N01-HC-25195]
  2. Division of Intramural Research of the National Heart, Lung, and Blood Institute (NHLBI)
  3. Division of Intramural Research of NHLBI
  4. Center for Information Technology, NIH, Bethesda, MD
  5. National Center for Cardiovascular Research Carlos III (CNIC)
  6. Bank of Santander
  7. Spanish Ministry of Science, Innovation and Universities
  8. Instituto de Salud Carlos III
  9. proCNIC Foundation
  10. AstraZeneca (TANSNIP project)
  11. US Department of Agriculture [8050-51000-098-00D]
  12. Institute of Health Carlos III grant [PI 17-00134]
  13. Spanish Ministry of Economy and Competitiveness, Institute of Health Carlos III [PI14/00328]
  14. FEDER funds from the European Union ('A way to built Europe')
  15. Generalitat of Catalonia, Department of Health [SLT002/16/00250]
  16. Department of Business and Knowledge [2017SGR696]

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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. Published by Elsevier B.V.

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