Nerea Becerra-Tomás1,2,3,4, Guillermo Mena-Sánchez1,2,3,4, Andrés Díaz-López1,2,3,4, Miguel Ángel Martínez-González3,5,6, Nancy Babio1,2,3,4, Dolores Corella3,7, Gala Freixer8, Dora Romaguera3,9, Jesús Vioque10,11, Ángel M Alonso-Gómez3,12, Julia Wärnberg3,13, J Alfredo Martínez3,14,15, Lluís Serra-Majem3,16, Ramon Estruch3,17, José Carlos Fernández-García3,18, José Lapetra3,19, Xavier Pintó3,20, Josep A Tur3,9,21, José López-Miranda3,22, Aurora Bueno-Cavanillas10,23, José Juan Gaforio10,24, Pilar Matía-Martín25, Lidia Daimiel15, Vicente Martín-Sánchez10,26, Josep Vidal27,28, Clotilde Vázquez3,29, Emili Ros3,30, Cristina Razquin3,5, Iván Abellán Cano1,31, Jose V Sorli3,7, Laura Torres8, Marga Morey3,9, Eva Mª Navarrete-Muñoz10,11, Lucas Tojal Sierra3,12, Edelys Crespo-Oliva3,13, M Ángeles Zulet3,14, Almudena Sanchez-Villegas3,16, Rosa Casas3,17, M Rosa Bernal-Lopez3,18, José Manuel Santos-Lozano3,19, Emili Corbella3,20, Maria Del Mar Bibiloni3,9,21, Miguel Ruiz-Canela3,5, Rebeca Fernández-Carrión3,7, Mireia Quifer8, Rafel M Prieto3,9,32, Noelia Fernandez-Brufal10,11, Itziar Salaverria Lete12, Juan Carlos Cenoz5,33, Regina Llimona8, Jordi Salas-Salvadó34,35,36,37. 1. Unitat de Nutrició, Departament de Bioquímica i Biotecnologia, Faculty of Medicine and Health Sciences, Universitat Rovira i Virgili, C/Sant Llorenç 21, 43201, Reus, Spain. 2. Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain. 3. Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Institute of Health Carlos III, Madrid, Spain. 4. Nutrition Unit, University Hospital of Sant Joan de Reus, Reus, Spain. 5. Department of Preventive Medicine and Public Health, IDISNA, University of Navarra, Pamplona, Spain. 6. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 7. Department of Preventive Medicine, University of Valencia, Valencia, Spain. 8. Cardiovascular Risk and Nutrition research group (CARIN), Hospital del Mar Research Institute (IMIM), Barcelona, Spain. 9. Health Research Institute of the Balearic Islands (IdISBa), Palma, Spain. 10. CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain. 11. Miguel Hernandez University, ISABIAL-FISABIO, Alicante, Spain. 12. Department of Cardiology, Organización Sanitaria Integrada (OSI) ARABA, University Hospital Araba, Vitoria-Gasteiz, Spain. 13. Department of Nursing, School of Health Sciences, University of Málaga-Institute of Biomedical Research in Malaga (IBIMA), Málaga, Spain. 14. Department of Nutrition, Food Science and Physiology, IDISNA, University of Navarra, Pamplona, Spain. 15. Precision Nutrition Program, IMDEA Food, CEI UAM + CSIC, Madrid, Spain. 16. Research Institute of Biomedical and Health Sciences (IUIBS), Preventive Medicine Service, Centro Hospitalario Universitario Insular Materno Infantil (CHUIMI), Canarian Health Service, University of Las Palmas de Gran Canaria, Las Palmas, Spain. 17. Department of Internal Medicine, IDIBAPS, Hospital Clinic, University of Barcelona, Barcelona, Spain. 18. Internal Medicine Department. Regional University Hospital of Málaga. Instituto de Investigación Biomédica de Málaga (IBIMA), University of Málaga, Málaga, Spain. 19. Department of Family Medicine, Research Unit, Distrito Sanitario Atención Primaria Sevilla, Seville, Spain. 20. Lipids and Vascular Risk Unit, Internal Medicine, Hospital Universitario de Bellvitge, Hospitalet de Llobregat, Barcelona, Spain. 21. Research Group on Community Nutrition and Oxidative Stress, University of Balearic Islands, Palma de Mallorca, Spain. 22. Department of Internal Medicine, Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba, Córdoba, Spain. 23. Instituto de Investigación Biosanitaria ibs.GRANADA and Department of Preventive Medicine, University of Granada, Granada, Spain. 24. Department of Health Sciences and Center for Advanced Studies in Olive Grove and Olive Oils, University of Jaén, Jaén, Spain. 25. Department of Endocrinology and Nutrition, Instituto de Investigación Sanitaria Hospital Clínico San Carlos (IdISSC), Madrid, Spain. 26. Institute of Biomedicine (IBIOMED), University of León, León, Spain. 27. CIBER Diabetes y Enfermedades Metabólicas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain. 28. Departament of Endocrinology, IDIBAPS, Hospital Clínic, University of Barcelona, Barcelona, Spain. 29. Department of Endocrinology, Fundación Jiménez-Díaz, Madrid, Spain. 30. Lipid Clinic, Department of Endocrinology and Nutrition, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clínic, Barcelona, Spain. 31. Hospital Universitario Joan XXIII Tarragona-CAP Horts de Miró Reus, Tarragona, Spain. 32. Laboratory of Renal Lithiasis Research, University Institute of Health Sciences Research (IUNICS), University of Balearic Islands, Palma de Mallorca, Spain. 33. Servicio Navarro de Salud-Osasunbidea, Pamplona, Spain. 34. Unitat de Nutrició, Departament de Bioquímica i Biotecnologia, Faculty of Medicine and Health Sciences, Universitat Rovira i Virgili, C/Sant Llorenç 21, 43201, Reus, Spain. Jordi.salas@urv.cat. 35. Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain. Jordi.salas@urv.cat. 36. Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Institute of Health Carlos III, Madrid, Spain. Jordi.salas@urv.cat. 37. Nutrition Unit, University Hospital of Sant Joan de Reus, Reus, Spain. Jordi.salas@urv.cat.
Abstract
PURPOSE: To assess the association between the consumption of non-soy legumes and different subtypes of non-soy legumes and serum uric acid (SUA) or hyperuricemia in elderly individuals with overweight or obesity and metabolic syndrome. METHODS: A cross-sectional analysis was conducted in the framework of the PREDIMED-Plus study. We included 6329 participants with information on non-soy legume consumption and SUA levels. Non-soy legume consumption was estimated using a semi-quantitative food frequency questionnaire. Linear regression models and Cox regression models were used to assess the associations between tertiles of non-soy legume consumption, different subtypes of non-soy legume consumption and SUA levels or hyperuricemia prevalence, respectively. RESULTS: Individuals in the highest tertile (T3) of total non-soy legume, lentil and pea consumption, had 0.14 mg/dL, 0.19 mg/dL and 0.12 mg/dL lower SUA levels, respectively, compared to those in the lowest tertile (T1), which was considered the reference one. Chickpea and dry bean consumption showed no association. In multivariable models, participants located in the top tertile of total non-soy legumes [prevalence ratio (PR): 0.89; 95% CI 0.82-0.97; p trend = 0.01, lentils (PR: 0.89; 95% CI 0.82-0.97; p trend = 0.01), dry beans (PR: 0.91; 95% C: 0.84-0.99; p trend = 0.03) and peas (PR: 0.89; 95% CI 0.82-0.97; p trend = 0.01)] presented a lower prevalence of hyperuricemia (vs. the bottom tertile). Chickpea consumption was not associated with hyperuricemia prevalence. CONCLUSIONS: In this study of elderly subjects with metabolic syndrome, we observed that despite being a purine-rich food, non-soy legumes were inversely associated with SUA levels and hyperuricemia prevalence. TRIAL REGISTRATION: ISRCTN89898870. Registration date: 24 July 2014.
PURPOSE: To assess the association between the consumption of non-soy legumes and different subtypes of non-soy legumes and serum uric acid (SUA) or hyperuricemia in elderly individuals with overweight or obesity and metabolic syndrome. METHODS: A cross-sectional analysis was conducted in the framework of the PREDIMED-Plus study. We included 6329 participants with information on non-soy legume consumption and SUA levels. Non-soy legume consumption was estimated using a semi-quantitative food frequency questionnaire. Linear regression models and Cox regression models were used to assess the associations between tertiles of non-soy legume consumption, different subtypes of non-soy legume consumption and SUA levels or hyperuricemia prevalence, respectively. RESULTS: Individuals in the highest tertile (T3) of total non-soy legume, lentil and pea consumption, had 0.14 mg/dL, 0.19 mg/dL and 0.12 mg/dL lower SUA levels, respectively, compared to those in the lowest tertile (T1), which was considered the reference one. Chickpea and dry bean consumption showed no association. In multivariable models, participants located in the top tertile of total non-soy legumes [prevalence ratio (PR): 0.89; 95% CI 0.82-0.97; p trend = 0.01, lentils (PR: 0.89; 95% CI 0.82-0.97; p trend = 0.01), dry beans (PR: 0.91; 95% C: 0.84-0.99; p trend = 0.03) and peas (PR: 0.89; 95% CI 0.82-0.97; p trend = 0.01)] presented a lower prevalence of hyperuricemia (vs. the bottom tertile). Chickpea consumption was not associated with hyperuricemia prevalence. CONCLUSIONS: In this study of elderly subjects with metabolic syndrome, we observed that despite being a purine-rich food, non-soy legumes were inversely associated with SUA levels and hyperuricemia prevalence. TRIAL REGISTRATION: ISRCTN89898870. Registration date: 24 July 2014.
Authors: R Villegas; Y-B Xiang; T Elasy; W H Xu; H Cai; Q Cai; M F Linton; S Fazio; W Zheng; X-O Shu Journal: Nutr Metab Cardiovasc Dis Date: 2011-01-28 Impact factor: 4.222
Authors: N Babio; M A Martínez-González; R Estruch; J Wärnberg; J Recondo; M Ortega-Calvo; L Serra-Majem; D Corella; M Fitó; E Ros; N Becerra-Tomás; J Basora; J Salas-Salvadó Journal: Nutr Metab Cardiovasc Dis Date: 2014-10-31 Impact factor: 4.222
Authors: Lina Zgaga; Evropi Theodoratou; Janet Kyle; Susan M Farrington; Felix Agakov; Albert Tenesa; Marion Walker; Geraldine McNeill; Alan F Wright; Igor Rudan; Malcolm G Dunlop; Harry Campbell Journal: PLoS One Date: 2012-06-06 Impact factor: 3.240