Mònica Bulló1, Christopher Papandreou1, Jesus García-Gavilán1, Miguel Ruiz-Canela2, Jun Li3, Marta Guasch-Ferré4, Estefanía Toledo2, Clary Clish5, Dolores Corella6, Ramon Estruch7, Emilio Ros8, Montserrat Fitó9, Chih-Hao Lee10, Kerry Pierce5, Cristina Razquin2, Fernando Arós11, Lluís Serra-Majem12, Liming Liang13, Miguel A Martínez-González14, Frank B Hu15, Jordi Salas-Salvadó16. 1. Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, Reus, Spain; Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain; Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; University Hospital of Sant Joan de Reus, Nutrition Unit, Reus, Spain. 2. Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; University of Navarra, Department of Preventive Medicine and Public Health, Pamplona, Spain; Navarra Institute for Health Research (IdiSNA), Pamplona, Navarra, Spain. 3. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 4. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division for Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, MA, USA. 5. Broad Institute of MIT and Harvard University, Cambridge, MA, USA. 6. Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Preventive Medicine, University of Valencia, Valencia, Spain. 7. Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Internal Medicine, Department of Endocrinology and Nutrition Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain. 8. Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Lipid Clinic, Department of Endocrinology and Nutrition Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain. 9. Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Cardiovascular and Nutrition Research Group, Institut de Recerca Hospital del Mar, Barcelona, Spain. 10. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Molecular Metabolism (C.-H.L.), Harvard T.H. Chan School of Public Health, Boston, MA, USA. 11. Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Cardiology, University Hospital of Alava, Vitoria, Spain. 12. Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Institute of Health Sciences IUNICS, University of Balearic Islands and Hospital Son Espases, Palma de Mallorca, Spain. 13. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Statistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 14. Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; University of Navarra, Department of Preventive Medicine and Public Health, Pamplona, Spain; Navarra Institute for Health Research (IdiSNA), Pamplona, Navarra, Spain; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 15. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division for Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Statistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 16. Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, Reus, Spain; Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain; Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; University Hospital of Sant Joan de Reus, Nutrition Unit, Reus, Spain. Electronic address: jordi.salas@urv.cat.
Abstract
BACKGROUND: Tricarboxylic acid (TCA) cycle deregulation may predispose to cardiovascular diseases, but the role of TCA cycle-related metabolites in the development of atrial fibrillation (AF) and heart failure (HF) remains unexplored. This study sought to investigate the association of TCA cycle-related metabolites with risk of AF and HF. METHODS: We used two nested case-control studies within the PREDIMED study. During a mean follow-up for about 10 years, 512 AF and 334 HF incident cases matched by age (±5 years), sex and recruitment center to 616 controls and 433 controls, respectively, were included in this study. Baseline plasma levels of citrate, aconitate, isocitrate, succinate, malate and d/l-2-hydroxyglutarate were measured with liquid chromatography-tandem mass spectrometry. Multivariable conditional logistic regression models were fitted to estimate odds ratios (OR) and 95% confidence intervals (95% CI) for metabolites and the risk of AF or HF. Potential confounders included smoking, family history of premature coronary heart disease, physical activity, alcohol intake, body mass index, intervention groups, dyslipidemia, hypertension, type 2 diabetes and medication use. RESULTS: Comparing extreme quartiles of metabolites, elevated levels of succinate, malate, citrate and d/l-2-hydroxyglutarate were associated with a higher risk of AF [ORQ4 vs. Q1 (95% CI): 1.80 (1.21-2.67), 2.13 (1.45-3.13), 1.87 (1.25-2.81) and 1.95 (1.31-2.90), respectively]. One SD increase in aconitate was directly associated with AF risk [OR (95% CI): 1.16 (1.01-1.34)]. The corresponding ORs (95% CI) for HF comparing extreme quartiles of malate, aconitate, isocitrate and d/l-2-hydroxyglutarate were 2.15 (1.29-3.56), 2.16 (1.25-3.72), 2.63 (1.56-4.44) and 1.82 (1.10-3.04), respectively. These associations were confirmed in an internal validation, except for aconitate and AF. CONCLUSION: These findings underscore the potential role of the TCA cycle in the pathogenesis of cardiac outcomes.
BACKGROUND: Tricarboxylic acid (TCA) cycle deregulation may predispose to cardiovascular diseases, but the role of TCA cycle-related metabolites in the development of atrial fibrillation (AF) and heart failure (HF) remains unexplored. This study sought to investigate the association of TCA cycle-related metabolites with risk of AF and HF. METHODS: We used two nested case-control studies within the PREDIMED study. During a mean follow-up for about 10 years, 512 AF and 334 HF incident cases matched by age (±5 years), sex and recruitment center to 616 controls and 433 controls, respectively, were included in this study. Baseline plasma levels of citrate, aconitate, isocitrate, succinate, malate and d/l-2-hydroxyglutarate were measured with liquid chromatography-tandem mass spectrometry. Multivariable conditional logistic regression models were fitted to estimate odds ratios (OR) and 95% confidence intervals (95% CI) for metabolites and the risk of AF or HF. Potential confounders included smoking, family history of premature coronary heart disease, physical activity, alcohol intake, body mass index, intervention groups, dyslipidemia, hypertension, type 2 diabetes and medication use. RESULTS: Comparing extreme quartiles of metabolites, elevated levels of succinate, malate, citrate and d/l-2-hydroxyglutarate were associated with a higher risk of AF [ORQ4 vs. Q1 (95% CI): 1.80 (1.21-2.67), 2.13 (1.45-3.13), 1.87 (1.25-2.81) and 1.95 (1.31-2.90), respectively]. One SD increase in aconitate was directly associated with AF risk [OR (95% CI): 1.16 (1.01-1.34)]. The corresponding ORs (95% CI) for HF comparing extreme quartiles of malate, aconitate, isocitrate and d/l-2-hydroxyglutarate were 2.15 (1.29-3.56), 2.16 (1.25-3.72), 2.63 (1.56-4.44) and 1.82 (1.10-3.04), respectively. These associations were confirmed in an internal validation, except for aconitate and AF. CONCLUSION: These findings underscore the potential role of the TCA cycle in the pathogenesis of cardiac outcomes.
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Authors: M Vallejo; A García; J Tuñón; D García-Martínez; S Angulo; J L Martin-Ventura; L M Blanco-Colio; P Almeida; J Egido; C Barbas Journal: Anal Bioanal Chem Date: 2009-01-28 Impact factor: 4.142
Authors: Miguel Á Martínez-González; Estefanía Toledo; Fernando Arós; Miquel Fiol; Dolores Corella; Jordi Salas-Salvadó; Emilio Ros; Maria I Covas; Joaquín Fernández-Crehuet; José Lapetra; Miguel A Muñoz; Monserrat Fitó; Luis Serra-Majem; Xavier Pintó; Rosa M Lamuela-Raventós; Jose V Sorlí; Nancy Babio; Pilar Buil-Cosiales; Valentina Ruiz-Gutierrez; Ramón Estruch; Alvaro Alonso Journal: Circulation Date: 2014-04-30 Impact factor: 29.690
Authors: Luis F Ferreira-Divino; Tommi Suvitaival; Cristina Legido-Quigley; Peter Rossing; Viktor Rotbain Curovic; Nete Tofte; Kajetan Trošt; Ismo M Mattila; Simone Theilade; Signe A Winther; Tine W Hansen; Marie Frimodt-Møller Journal: Cardiovasc Diabetol Date: 2022-07-18 Impact factor: 8.949