Plasma metabolite predictors of metabolic syndrome incidence and reversion
Journal article, 2024
Background: Metabolic Syndrome (MetS) is a progressive pathophysiological state defined by a cluster of cardiometabolic traits. However, little is known about metabolites that may be predictors of MetS incidence or reversion. Our objective was to identify plasma metabolites associated with MetS incidence or MetS reversion. Methods: The study included 1468 participants without cardiovascular disease (CVD) but at high CVD risk at enrollment from two case-cohort studies nested within the PREvención con DIeta MEDiterránea (PREDIMED) study with baseline metabolomics data. MetS was defined in accordance with the harmonized International Diabetes Federation and the American Heart Association/National Heart, Lung, and Blood Institute criteria, which include meeting 3 or more thresholds for waist circumference, triglyceride, HDL cholesterol, blood pressure, and fasting blood glucose. MetS incidence was defined by not having MetS at baseline but meeting the MetS criteria at a follow-up visit. MetS reversion was defined by MetS at baseline but not meeting MetS criteria at a follow-up visit. Plasma metabolome was profiled by LC-MS. Multivariable-adjusted Cox regression models and elastic net regularized regressions were used to assess the association of 385 annotated metabolites with MetS incidence and MetS reversion after adjusting for potential risk factors. Results: Of the 603 participants without baseline MetS, 298 developed MetS over the median 4.8-year follow-up. Of the 865 participants with baseline MetS, 285 experienced MetS reversion. A total of 103 and 88 individual metabolites were associated with MetS incidence and MetS reversion, respectively, after adjusting for confounders and false discovery rate correction. A metabolomic signature comprised of 77 metabolites was robustly associated with MetS incidence (HR: 1.56 (95 % CI: 1.33–1.83)), and a metabolomic signature of 83 metabolites associated with MetS reversion (HR: 1.44 (95 % CI: 1.25–1.67)), both p < 0.001. The MetS incidence and reversion signatures included several lipids (mainly glycerolipids and glycerophospholipids) and branched-chain amino acids. Conclusion: We identified unique metabolomic signatures, primarily comprised of lipids (including glycolipids and glycerophospholipids) and branched-chain amino acids robustly associated with MetS incidence; and several amino acids and glycerophospholipids associated with MetS reversion. These signatures provide novel insights on potential distinct mechanisms underlying the conditions leading to the incidence or reversion of MetS.
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Metabolic syndrome
PREDIMED
Metabolomics
Metabolic syndrome reversion
Metabolic syndrome incidence
Author
Zhila Semnani-Azad
Harvard School of Public Health
Estefania Toledo
University of Navarra
Instituto de Investigación Sanitaria de Navarra (IdiSNA)
Centro de Investigación Biomédica en Red-Fisiopatología de la Obesidad y Nutrición
Nancy Babio
Sant Joan de Reus University Hospital
Centro de Investigación Biomédica en Red-Fisiopatología de la Obesidad y Nutrición
Rovira i Virgili University
Miguel Ruiz-Canela
Instituto de Investigación Sanitaria de Navarra (IdiSNA)
Centro de Investigación Biomédica en Red-Fisiopatología de la Obesidad y Nutrición
University of Navarra
Clemens Wittenbecher
Chalmers, Life Sciences, Food and Nutrition Science
Cristina Razquin
University of Navarra
Instituto de Investigación Sanitaria de Navarra (IdiSNA)
Centro de Investigación Biomédica en Red-Fisiopatología de la Obesidad y Nutrición
Fenglei Wang
Harvard School of Public Health
Courtney Dennis
Broad Institute
Amy Deik
Broad Institute
Clary B. Clish
Broad Institute
Dolores Corella
Centro de Investigación Biomédica en Red-Fisiopatología de la Obesidad y Nutrición
Universitat de Valencia
Montserrat Fitó
Hospital del Mar Medical Research Institute
Centro de Investigación Biomédica en Red-Fisiopatología de la Obesidad y Nutrición
Ramon Estruch
University of Barcelona
Centro de Investigación Biomédica en Red-Fisiopatología de la Obesidad y Nutrición
Fernando Aros
Centro de Investigación Biomédica en Red-Fisiopatología de la Obesidad y Nutrición
University of the Basque Country (UPV/EHU)
Hospital Universitario Araba
Emilio Ros
University of Barcelona
Jesús F. García-Gavilán
Centro de Investigación Biomédica en Red-Fisiopatología de la Obesidad y Nutrición
Rovira i Virgili University
Liming Liang
Harvard School of Public Health
Jordi Salas-Salvadó
Centro de Investigación Biomédica en Red-Fisiopatología de la Obesidad y Nutrición
Sant Joan de Reus University Hospital
Rovira i Virgili University
Miguel A. Martínez-González
University of Navarra
Harvard School of Public Health
Instituto de Investigación Sanitaria de Navarra (IdiSNA)
Centro de Investigación Biomédica en Red-Fisiopatología de la Obesidad y Nutrición
Frank B. Hu
Harvard School of Public Health
Brigham and Women's Hospital
Marta Guasch-Ferré
Novo Nordisk Foundation
Harvard School of Public Health
Published in
Metabolism: Clinical and Experimental
0026-0495 (ISSN) 15328600 (eISSN)
Vol. 151 art. no 155742Categorizing
Subject Categories (SSIF 2011)
Endocrinology and Diabetes
Cardiac and Cardiovascular Systems
Nutrition and Dietetics
Identifiers
DOI
10.1016/j.metabol.2023.155742
PubMed
38007148