J Konieczna1, A Yañez2, M Moñino1, N Babio3, E Toledo4, M A Martínez-González5, J V Sorlí6, J Salas-Salvadó3, R Estruch7, E Ros8, A Alonso-Gómez9, H Schröder10, J Lapetra11, Ll Serra-Majem12, X Pintó13, M Gutiérrez-Bedmar14, A Díaz-López3, J I González6, M Fitó15, L Forga16, M Fiol1, D Romaguera17. 1. Instituto de Investigación Sanitaria Illes Balears (IdISBa), University Hospital Son Espases, Palma de Mallorca, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain. 2. Instituto de Investigación Sanitaria Illes Balears (IdISBa), University Hospital Son Espases, Palma de Mallorca, Spain; Department of Nursing and Physiotheraphy, University of the Balearic Islands, Palma de Mallorca, Spain. 3. CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Rovira i Virgili University Department of Biochemistry and Biotechnology, Human Nutrition Unit, IISPV, Hospital Universitari Sant Joan de Reus, Reus, Spain. 4. CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; University of Navarra-Navarra Institute for Health Research, Department of Preventive Medicine and Public Health, Pamplona, Spain. 5. CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; University of Navarra-Navarra Institute for Health Research, Department of Preventive Medicine and Public Health, Pamplona, Spain; Department of Nutrition, Harvard TH Chan School of Public Health, Boston, USA. 6. CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Preventive Medicine, University of Valencia, Valencia, Spain. 7. CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Internal Medicine, August Pi i Sunyer Institute of Biomedical Research (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain. 8. Lipid Clinics. Department of Endocrinology, August Pi i Sunyer Institute of Biomedical Research (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain. 9. CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Cardiology, University Hospital Araba, Vitoria, Spain. 10. Cardiovascular Risk and Nutrition Research Group (CARIN), Institut Hospital del Mar d' Investigacions Mèdiques (IMIM), Barcelona Biomedical Research Park, Barcelona, Spain; CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain. 11. CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Familiy Medicine, Research Unit, Distrito Sanitario Atención Primaria Sevilla, Sevilla, Spain. 12. CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Research Institute of Biomedical and Health Sciences (IUIBS), University of Las Palmas de Gran Canaria and Service of Preventive Medicine, Complejo Hospitalario Universitario Insular Materno Infantil (CHUIMI), Canary Health Service, Las Palmas de Gran Canaria, Spain. 13. CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Internal Medicine, Bellvitge Hospital, Barcelona, Spain. 14. Department of Preventive Medicine and Public Health, University of Malaga, Malaga, Spain. 15. CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Cardiovascular Risk and Nutrition Research Group (CARIN), Institut Hospital del Mar d' Investigacions Mèdiques (IMIM), Barcelona Biomedical Research Park, Barcelona, Spain. 16. Servicio de Endocrinología y Nutrición, Complejo Hospitalario de Navarra, Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain. 17. Instituto de Investigación Sanitaria Illes Balears (IdISBa), University Hospital Son Espases, Palma de Mallorca, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain. Electronic address: mariaadoracion.romaguera@ssib.es.
Abstract
BACKGROUND & AIMS: Little is known about the impact of specific dietary patterns on the development of obesity phenotypes. We aimed to determine the association of longitudinal changes in adherence to the traditional Mediterranean diet (MedDiet) with the transition between different obesity phenotypes. METHODS: Data of 5801 older men and women at high cardiovascular risk from PREDIMED trial were used. Adherence to MedDiet was measured with the validated 14p-Mediterranean Diet Adherence Screener (MEDAS). Using the simultaneous combination of metabolic health- and body size-related parameters participants were categorized into one of four phenotypes: metabolically healthy and abnormal obese (MHO and MAO), metabolically healthy and abnormal non-obese (MHNO and MANO). Cox regression models with yearly repeated measures during 5-year of follow-up were built with use of Markov chain assumption. RESULTS: Each 2-point increase in MEDAS was associated with the following transitions: in MAO participants, with a 16% (95% CI 3-31%) greater likelihood of becoming MHO; in MHO participants with a 14% (3-23%) lower risk of becoming MAO; in MHNO participants with a 18% (5-30%) lower risk of becoming MHO. In MANO women, but not in men, MEDAS was associated with 20% (5-38%) greater likely of becoming MHNO (p for interaction by gender 0.014). No other significant associations were observed. CONCLUSIONS: Better adherence to the traditional MedDiet is associated with transitions to healthier phenotypes, promoting metabolic health improvement in MAO, MANO (only in women), and MHO, as well as protecting against obesity incidence in MHNO subjects.
BACKGROUND & AIMS: Little is known about the impact of specific dietary patterns on the development of obesity phenotypes. We aimed to determine the association of longitudinal changes in adherence to the traditional Mediterranean diet (MedDiet) with the transition between different obesity phenotypes. METHODS: Data of 5801 older men and women at high cardiovascular risk from PREDIMED trial were used. Adherence to MedDiet was measured with the validated 14p-Mediterranean Diet Adherence Screener (MEDAS). Using the simultaneous combination of metabolic health- and body size-related parameters participants were categorized into one of four phenotypes: metabolically healthy and abnormal obese (MHO and MAO), metabolically healthy and abnormal non-obese (MHNO and MANO). Cox regression models with yearly repeated measures during 5-year of follow-up were built with use of Markov chain assumption. RESULTS: Each 2-point increase in MEDAS was associated with the following transitions: in MAO participants, with a 16% (95% CI 3-31%) greater likelihood of becoming MHO; in MHO participants with a 14% (3-23%) lower risk of becoming MAO; in MHNOparticipants with a 18% (5-30%) lower risk of becoming MHO. In MANO women, but not in men, MEDAS was associated with 20% (5-38%) greater likely of becoming MHNO (p for interaction by gender 0.014). No other significant associations were observed. CONCLUSIONS: Better adherence to the traditional MedDiet is associated with transitions to healthier phenotypes, promoting metabolic health improvement in MAO, MANO (only in women), and MHO, as well as protecting against obesity incidence in MHNO subjects.
Authors: Laura Martin-Piedra; Juan F Alcala-Diaz; Francisco M Gutierrez-Mariscal; Antonio P Arenas de Larriva; Juan L Romero-Cabrera; Jose D Torres-Peña; Javier Caballero-Villarraso; Raul M Luque; Pablo Perez-Martinez; Jose Lopez-Miranda; Javier Delgado-Lista Journal: Nutrients Date: 2021-11-12 Impact factor: 5.717