Nerea Becerra-Tomás1, Nancy Babio2, Miguel Ángel Martínez-González3, Dolores Corella4, Ramon Estruch5, Emilio Ros6, Montserrat Fitó7, Lluís Serra-Majem8, Itziar Salaverria9, Rosa M Lamuela-Raventós10, José Lapetra11, Enrique Gómez-Gracia12, Miguel Fiol13, Estefanía Toledo3, José V Sorlí4, Maria Roser Pedret-Llaberia14, Jordi Salas-Salvadó15. 1. Human Nutrition Unit, Faculty of Medicine and Health Sciences, Biochemistry & Biotechnology Department, Universitat Rovira i Virgili, and Hospital Universitari de Sant Joan de Reus, IISPV, Reus, Spain; Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain. 2. Human Nutrition Unit, Faculty of Medicine and Health Sciences, Biochemistry & Biotechnology Department, Universitat Rovira i Virgili, and Hospital Universitari de Sant Joan de Reus, IISPV, Reus, Spain; Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain. Electronic address: nancy.babio@urv.cat. 3. Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain. 4. Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Department of Preventive Medicine, University of Valencia, València, Spain. 5. Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Department of Internal Medicine, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clínic, University of Barcelona, Barcelona, Spain. 6. Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Lipid Clinic, Endocrinology and Nutrition Service, IDIBAPS, Hospital Clinic, University of Barcelona, Barcelona, Spain. 7. Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Cardiovascular Risk and Nutrition Research Group, Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Barcelona Biomedical Research Park, Barcelona, Spain. 8. Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Research Institute of Biomedical and Health Sciences, University of Las Palmas de Gran Canaria, Las Palmas, Spain. 9. Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Department of Cardiology, University Hospital Araba, Vitoria, Spain. 10. Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Nutrition and Food Science Department, School of Pharmacy, INSA, University of Barcelona, XARTA Barcelona, Spain. 11. Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Department of Family Medicine, Research Unit, Distrito Sanitario Atención Primaria Sevilla, Sevilla, Spain. 12. Department of Preventive Medicine, University of Malaga, Malaga, Spain. 13. Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Institute of Health Sciences, University of Balearic Islands and Son Espases Hospital, Palma de Mallorca, Spain. 14. Primary Care Division, Catalan Institute of Health, Institut d'Investigació en Atenció Primària Jordi Gol, Tarragona-Reus, Spain. 15. Human Nutrition Unit, Faculty of Medicine and Health Sciences, Biochemistry & Biotechnology Department, Universitat Rovira i Virgili, and Hospital Universitari de Sant Joan de Reus, IISPV, Reus, Spain; Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain. Electronic address: jordi.salas@urv.cat.
Abstract
BACKGROUND & AIMS: Few studies have assessed the association between consumption of red meat (RM) and processed red meats (PRM) and the incidence of metabolic syndrome (MetS) and results have been inconsistent. We investigated associations between total consumption of meat and its subtypes and incident MetS and estimated the effect of substituting RM or PRM for alternative protein-rich foods. METHODS: We analyzed 1868 participants (55-80 years-old) recruited into the PREDIMED study who had no MetS at baseline and were followed for a median of 3.2 years. MetS was defined using updated harmonized criteria. Anthropometric variables, dietary habits, and blood biochemistry were determined at baseline and yearly thereafter. Multivariable-adjusted hazard ratios (HRs) of MetS were estimated for the two upper tertiles (versus the lowest one) of mean consumption of meat and its subtypes during the follow-up as exposure. RESULTS: Comparing the highest vs the lowest tertile of consumption, we observed an increased risk of MetS incidence, with HRs of 1.23 (95% confidence interval [CI]: 1.03-1.45) and 1.46 (CI: 1.22-1.74) for total meat and pooled RM and PRM, respectively. Compared with participants in the lowest tertile, those in the highest tertile of poultry and rabbit consumption had a lower risk of MetS incidence. The risk of MetS was lower when one-serving/day of RM or PRM was replaced by legumes, poultry and rabbit, fish or eggs. CONCLUSION: RM and PRM consumption was associated with higher risk of MetS. Replacing RM or PRM with other protein-rich foods related to a lower risk of MetS and should, therefore, be encouraged. This trial was registered at controlled-trials.com as ISRCTN35739639.
BACKGROUND & AIMS: Few studies have assessed the association between consumption of red meat (RM) and processed red meats (PRM) and the incidence of metabolic syndrome (MetS) and results have been inconsistent. We investigated associations between total consumption of meat and its subtypes and incident MetS and estimated the effect of substituting RM or PRM for alternative protein-rich foods. METHODS: We analyzed 1868 participants (55-80 years-old) recruited into the PREDIMED study who had no MetS at baseline and were followed for a median of 3.2 years. MetS was defined using updated harmonized criteria. Anthropometric variables, dietary habits, and blood biochemistry were determined at baseline and yearly thereafter. Multivariable-adjusted hazard ratios (HRs) of MetS were estimated for the two upper tertiles (versus the lowest one) of mean consumption of meat and its subtypes during the follow-up as exposure. RESULTS: Comparing the highest vs the lowest tertile of consumption, we observed an increased risk of MetS incidence, with HRs of 1.23 (95% confidence interval [CI]: 1.03-1.45) and 1.46 (CI: 1.22-1.74) for total meat and pooled RM and PRM, respectively. Compared with participants in the lowest tertile, those in the highest tertile of poultry and rabbit consumption had a lower risk of MetS incidence. The risk of MetS was lower when one-serving/day of RM or PRM was replaced by legumes, poultry and rabbit, fish or eggs. CONCLUSION: RM and PRM consumption was associated with higher risk of MetS. Replacing RM or PRM with other protein-rich foods related to a lower risk of MetS and should, therefore, be encouraged. This trial was registered at controlled-trials.com as ISRCTN35739639.
Authors: Anne Mette L Würtz; Marianne U Jakobsen; Monica L Bertoia; Tao Hou; Erik B Schmidt; Walter C Willett; Kim Overvad; Qi Sun; JoAnn E Manson; Frank B Hu; Eric B Rimm Journal: Am J Clin Nutr Date: 2021-03-11 Impact factor: 7.045