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Plasma metabolite predictors of metabolic syndrome incidence and reversion.
Semnani-Azad, Zhila; Toledo, Estefanía; Babio, Nancy; Ruiz-Canela, Miguel; Wittenbecher, Clemens; Razquin, Cristina; Wang, Fenglei; Dennis, Courtney; Deik, Amy; Clish, Clary B; Corella, Dolores; Fitó, Montserrat; Estruch, Ramon; Arós, Fernando; Ros, Emilio; García-Gavilan, Jesús; Liang, Liming; Salas-Salvadó, Jordi; Martínez-González, Miguel A; Hu, Frank B; Guasch-Ferré, Marta.
Afiliação
  • Semnani-Azad Z; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA. Electronic address: zsemnaniazad@hsph.harvard.edu.
  • Toledo E; Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain; Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.
  • Babio N; Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, Reus, Spain; Institut d'Investigació Sanitària Pere i Vi
  • Ruiz-Canela M; Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain; Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.
  • Wittenbecher C; Division of Food and Nutrition Sciences, Department of Biology, Chalmers University of Technology, Gothenburg, Sweden. Electronic address: clemens.wittenbecher@chalmers.se.
  • Razquin C; Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain; Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.
  • Wang F; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA. Electronic address: fengleiwang@g.harvard.edu.
  • Dennis C; Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA. Electronic address: cdennis@broadinstitute.org.
  • Deik A; Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA. Electronic address: adeik@broadinstitute.org.
  • Clish CB; Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA. Electronic address: clary@broadinstitute.org.
  • Corella D; Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Preventive Medicine and Public Health, University of Valencia, Valencia, Spain. Electronic address: dolores.corella@uv.es.
  • Fitó M; Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; IMIM Hospital del Mar Medical Research Institute, Grup de Risc Cardiovascular i Nutrició, Barcelona, Spain. Electronic address: MFito@imim.es.
  • Estruch R; Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Internal Medicine, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spai
  • Arós F; Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Bioaraba Health Research Institute, Osakidetza Basque Health Service, Araba University Hospital, Vitoria-Gasteiz, Spain; University of the Basque Country (U
  • Ros E; Lipid Clinic, Department of Endocrinology and Nutrition, August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain. Electronic address: EROS@clinic.cat.
  • García-Gavilan J; Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, Reus, Spain. Electronic address: jesusfrancisco.garcia@i
  • Liang L; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. Electronic address: lliang@hsph.harvard.edu.
  • Salas-Salvadó J; Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, Reus, Spain; Institut d'Investigació Sanitària Pere i Vi
  • Martínez-González MA; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain; Centro de Investigación Biomédica en Red de Fisiopatología
  • Hu FB; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston,
  • Guasch-Ferré M; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Public Health and Novo Nordisk Foundation Center for Basic Metabolic Research (CBMR), University of Copenhagen, Copenhagen, Denmark. Electronic address: mguasch@hsph.harvard.edu.
Metabolism ; 151: 155742, 2024 Feb.
Article em En | MEDLINE | ID: mdl-38007148
ABSTRACT

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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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Doenças Cardiovasculares / Síndrome Metabólica Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Doenças Cardiovasculares / Síndrome Metabólica Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article