Publicaciones en las que colabora con Miguel Delgado Rodríguez (50)

2024

  1. Effect of a nutritional intervention based on an energy-reduced Mediterranean diet on environmental impact

    Science of the Total Environment, Vol. 928

  2. Objectively Measured Sleep Duration and Health-Related Quality of Life in Older Adults with Metabolic Syndrome: A One-Year Longitudinal Analysis of the PREDIMED-Plus Cohort

    Nutrients , Vol. 16, Núm. 16

2023

  1. Association of adiposity and its changes over time with COVID-19 risk in older adults with overweight/obesity and metabolic syndrome: a longitudinal evaluation in the PREDIMED-Plus cohort

    BMC Medicine, Vol. 21, Núm. 1

  2. Comparative effects of glucagon-like peptide-1 receptors agonists, 4-dipeptidyl peptidase inhibitors, and metformin on metabolic syndrome

    Biomedicine and Pharmacotherapy, Vol. 161

  3. Corrigendum: Role of NAFLD on the health related QoL response to lifestyle in patients with metabolic syndrome: The PREDIMED plus cohort(Front. Endocrinol., (2022), 13, (868795), 10.3389/fendo.2022.868795)

    Frontiers in Endocrinology

  4. Dietary Iron, Anemia Markers, Cognition, and Quality of Life in Older Community-Dwelling Subjects at High Cardiovascular Risk

    Nutrients, Vol. 15, Núm. 20

  5. Higher versus lower nut consumption and changes in cognitive performance over two years in a population at risk of cognitive decline: a cohort study

    American Journal of Clinical Nutrition, Vol. 118, Núm. 2, pp. 360-368

  6. How Did the COVID-19 Lockdown Pandemic Affect the Depression Symptomatology in Mediterranean Older Adults with Metabolic Syndrome?

    Depression and Anxiety, Vol. 2023

  7. Increased ultra-processed food consumption is associated with worsening of cardiometabolic risk factors in adults with metabolic syndrome: Longitudinal analysis from a randomized trial

    Atherosclerosis, Vol. 377, pp. 12-23

  8. Intensive Weight-Loss Lifestyle Intervention Using Mediterranean Diet and COVID-19 Risk in Older Adults: Secondary Analysis of PREDIMED-Plus Trial

    Journal of Nutrition, Health and Aging, Vol. 27, Núm. 12, pp. 1162-1167

  9. Ultra-processed foods consumption as a promoting factor of greenhouse gas emissions, water, energy, and land use: A longitudinal assessment

    Science of the Total Environment, Vol. 891

2022

  1. Adopting a High-Polyphenolic Diet Is Associated with an Improved Glucose Profile: Prospective Analysis within the PREDIMED-Plus Trial

    Antioxidants, Vol. 11, Núm. 2

  2. Association Among Polyphenol Intake, Uric Acid, and Hyperuricemia: A CrossSectional Analysis in a Population at High Cardiovascular Risk

    Journal of the American Heart Association, Vol. 11, Núm. 20

  3. Association between the Prime Diet Quality Score and depressive symptoms in a Mediterranean population with metabolic syndrome. Cross-sectional and 2-year follow-up assessment from PREDIMED-PLUS study

    British Journal of Nutrition, Vol. 128, Núm. 6, pp. 1170-1179

  4. Associations Between the Modified Food Standard Agency Nutrient Profiling System Dietary Index and Cardiovascular Risk Factors in an Elderly Population

    Frontiers in Nutrition, Vol. 9

  5. Circulating carotenoids are associated with favorable lipid and fatty acid profiles in an older population at high cardiovascular risk

    Frontiers in Nutrition, Vol. 9

  6. Contribution of cardio-vascular risk factors to depressive status in the PREDIMED-PLUS Trial. A cross-sectional and a 2-year longitudinal study

    PLoS ONE, Vol. 17, Núm. 4 April

  7. Factors associated with successful dietary changes in an energy-reduced Mediterranean diet intervention: a longitudinal analysis in the PREDIMED-Plus trial

    European Journal of Nutrition, Vol. 61, Núm. 3, pp. 1457-1475

  8. Integrative development of a short screening questionnaire of highly processed food consumption (sQ-HPF)

    International Journal of Behavioral Nutrition and Physical Activity, Vol. 19, Núm. 1

  9. Morbid liver manifestations are intrinsically bound to metabolic syndrome and nutrient intake based on a machine-learning cluster analysis

    Frontiers in Endocrinology, Vol. 13