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Semnani-Azad Z, Toledo E, Babio N, Ruiz-Canela M, Wittenbecher C, Razquin C, Wang F, Dennis C, Deik A, Clish CB, Corella D, Fitó M, Estruch R, Arós F, Ros E, García-Gavilan J, Liang L, Salas-Salvadó J, Martínez-González MA, Hu FB, Guasch-Ferré M. Plasma metabolite predictors of metabolic syndrome incidence and reversion. Metabolism 2024; 151:155742. [PMID: 38007148 PMCID: PMC10872312 DOI: 10.1016/j.metabol.2023.155742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 11/19/2023] [Accepted: 11/19/2023] [Indexed: 11/27/2023]
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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Affiliation(s)
- Zhila Semnani-Azad
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Estefanía Toledo
- 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.
| | - Nancy Babio
- 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 Virgili, Hospital Universitari Sant Joan de Reus, Reus, Spain.
| | - Miguel Ruiz-Canela
- 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.
| | - Clemens Wittenbecher
- Division of Food and Nutrition Sciences, Department of Biology, Chalmers University of Technology, Gothenburg, Sweden.
| | - Cristina Razquin
- 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.
| | - Fenglei Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Courtney Dennis
- Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Amy Deik
- Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Clary B Clish
- Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Dolores Corella
- 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.
| | - Montserrat Fitó
- 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.
| | - Ramon Estruch
- 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, Spain.
| | - Fernando Arós
- 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 (UPV/EHU), Vitoria-Gasteiz, Spain.
| | - Emilio Ros
- Lipid Clinic, Department of Endocrinology and Nutrition, August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain.
| | - Jesús García-Gavilan
- 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.
| | - Liming Liang
- 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.
| | - Jordi Salas-Salvadó
- 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 Virgili, Hospital Universitari Sant Joan de Reus, Reus, Spain.
| | - Miguel A Martínez-González
- 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 de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.
| | - Frank B Hu
- 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, MA, USA.
| | - Marta Guasch-Ferré
- 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.
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Sinatora RV, Chagas EFB, Mattera FOP, Mellem LJ, dos Santos ARDO, Pereira LP, Aranão ALDC, Guiguer EL, Araújo AC, Haber JFDS, Guissoni LC, Barbalho SM. Relationship of Inflammatory Markers and Metabolic Syndrome in Postmenopausal Women. Metabolites 2022; 12:73. [PMID: 35050195 PMCID: PMC8779625 DOI: 10.3390/metabo12010073] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/07/2022] [Accepted: 01/10/2022] [Indexed: 02/04/2023] Open
Abstract
The increased deposition of visceral fat in the postmenopause period increases the production of inflammatory cytokines and the release of tumor necrosis factor- α (TNF-α), interleukin-6 (IL-6), and decrease in IL-10. This study investigated the relationship between inflammatory biomarkers and metabolic syndrome (MS) in postmenopausal women considering different diagnostic criteria. We conducted a cross-sectional observational study based on STROBE. Data were collected regarding the diagnostic criteria for MS (International Diabetes Federation; NCEP (International Diabetes Federation (IDF), National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP-III), and Harmonized criteria), body composition, comorbidities, time without menstruation, values of IL-6, IL-10, and TNF-α. ANOVA, Kruskal-Wallis, Levene tests, ROC, and odds ratio were performed to analyze the data. The results showed no significant difference between the methods and no interaction between the method and the presence of MS. However, for the values of WC, body fat percentage, TNF-α, and IL-10/TNF-α ratio, a significant effect of MS was observed. In subjects with MS, lower values of body fat percentage and TNF-α and higher values of the IL-10/TNF-α ratio were also observed. The higher IL-10/TNF-α ratio in the MS group is related to the greater anti-inflationary action of IL-10. The IL-10/TNF-α ratio showed significant accuracy to discriminate patients with MS according to the NCEP-ATP III criteria.
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Affiliation(s)
- Renata Vargas Sinatora
- Postgraduate Program in Structural and Functional Interactions in Rehabilitation, University of Marilia (UNIMAR), Marília 17525-902, SP, Brazil; (R.V.S.); (E.F.B.C.); (F.O.P.M.); (L.J.M.); (L.P.P.); (A.L.d.C.A.); (E.L.G.); (A.C.A.); (L.C.G.)
| | - Eduardo Federighi Baisi Chagas
- Postgraduate Program in Structural and Functional Interactions in Rehabilitation, University of Marilia (UNIMAR), Marília 17525-902, SP, Brazil; (R.V.S.); (E.F.B.C.); (F.O.P.M.); (L.J.M.); (L.P.P.); (A.L.d.C.A.); (E.L.G.); (A.C.A.); (L.C.G.)
- Interdisciplinary Center on Diabetes (CENID), University of Marilia (UNIMAR), Marília 17525-902, SP, Brazil;
- Postgraduate Program, Faculty of Medicine of Marília (FAMEMA), Marília 17519-030, SP, Brazil
| | - Fernando Otavio Pires Mattera
- Postgraduate Program in Structural and Functional Interactions in Rehabilitation, University of Marilia (UNIMAR), Marília 17525-902, SP, Brazil; (R.V.S.); (E.F.B.C.); (F.O.P.M.); (L.J.M.); (L.P.P.); (A.L.d.C.A.); (E.L.G.); (A.C.A.); (L.C.G.)
- Interdisciplinary Center on Diabetes (CENID), University of Marilia (UNIMAR), Marília 17525-902, SP, Brazil;
| | - Luciano Junqueira Mellem
- Postgraduate Program in Structural and Functional Interactions in Rehabilitation, University of Marilia (UNIMAR), Marília 17525-902, SP, Brazil; (R.V.S.); (E.F.B.C.); (F.O.P.M.); (L.J.M.); (L.P.P.); (A.L.d.C.A.); (E.L.G.); (A.C.A.); (L.C.G.)
- Interdisciplinary Center on Diabetes (CENID), University of Marilia (UNIMAR), Marília 17525-902, SP, Brazil;
| | - Ana Rita de Oliveira dos Santos
- Department of Biochemistry and Pharmacology, School of Medicine, University of Marília (UNIMAR), Avenida Higino Muzzi Filho, 1001, Marília 17525-902, SP, Brazil;
| | - Larissa Pires Pereira
- Postgraduate Program in Structural and Functional Interactions in Rehabilitation, University of Marilia (UNIMAR), Marília 17525-902, SP, Brazil; (R.V.S.); (E.F.B.C.); (F.O.P.M.); (L.J.M.); (L.P.P.); (A.L.d.C.A.); (E.L.G.); (A.C.A.); (L.C.G.)
| | - Ana Luíza de Carvalho Aranão
- Postgraduate Program in Structural and Functional Interactions in Rehabilitation, University of Marilia (UNIMAR), Marília 17525-902, SP, Brazil; (R.V.S.); (E.F.B.C.); (F.O.P.M.); (L.J.M.); (L.P.P.); (A.L.d.C.A.); (E.L.G.); (A.C.A.); (L.C.G.)
| | - Elen Landgraf Guiguer
- Postgraduate Program in Structural and Functional Interactions in Rehabilitation, University of Marilia (UNIMAR), Marília 17525-902, SP, Brazil; (R.V.S.); (E.F.B.C.); (F.O.P.M.); (L.J.M.); (L.P.P.); (A.L.d.C.A.); (E.L.G.); (A.C.A.); (L.C.G.)
- Department of Biochemistry and Pharmacology, School of Medicine, University of Marília (UNIMAR), Avenida Higino Muzzi Filho, 1001, Marília 17525-902, SP, Brazil;
- School of Food and Technology of Marilia (FATEC), Marília 17500-000, SP, Brazil
| | - Adriano Cressoni Araújo
- Postgraduate Program in Structural and Functional Interactions in Rehabilitation, University of Marilia (UNIMAR), Marília 17525-902, SP, Brazil; (R.V.S.); (E.F.B.C.); (F.O.P.M.); (L.J.M.); (L.P.P.); (A.L.d.C.A.); (E.L.G.); (A.C.A.); (L.C.G.)
- Department of Biochemistry and Pharmacology, School of Medicine, University of Marília (UNIMAR), Avenida Higino Muzzi Filho, 1001, Marília 17525-902, SP, Brazil;
| | - Jesselina F. dos Santos Haber
- Interdisciplinary Center on Diabetes (CENID), University of Marilia (UNIMAR), Marília 17525-902, SP, Brazil;
- Department of Biochemistry and Pharmacology, School of Medicine, University of Marília (UNIMAR), Avenida Higino Muzzi Filho, 1001, Marília 17525-902, SP, Brazil;
| | - Leila Campos Guissoni
- Postgraduate Program in Structural and Functional Interactions in Rehabilitation, University of Marilia (UNIMAR), Marília 17525-902, SP, Brazil; (R.V.S.); (E.F.B.C.); (F.O.P.M.); (L.J.M.); (L.P.P.); (A.L.d.C.A.); (E.L.G.); (A.C.A.); (L.C.G.)
- Department of Biochemistry and Pharmacology, School of Medicine, University of Marília (UNIMAR), Avenida Higino Muzzi Filho, 1001, Marília 17525-902, SP, Brazil;
| | - Sandra Maria Barbalho
- Postgraduate Program in Structural and Functional Interactions in Rehabilitation, University of Marilia (UNIMAR), Marília 17525-902, SP, Brazil; (R.V.S.); (E.F.B.C.); (F.O.P.M.); (L.J.M.); (L.P.P.); (A.L.d.C.A.); (E.L.G.); (A.C.A.); (L.C.G.)
- Interdisciplinary Center on Diabetes (CENID), University of Marilia (UNIMAR), Marília 17525-902, SP, Brazil;
- Department of Biochemistry and Pharmacology, School of Medicine, University of Marília (UNIMAR), Avenida Higino Muzzi Filho, 1001, Marília 17525-902, SP, Brazil;
- School of Food and Technology of Marilia (FATEC), Marília 17500-000, SP, Brazil
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