Papandreou, C. (Christopher)

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    Isotemporal substitution of inactive time with physical activity and time in bed: cross-sectional associations with cardiometabolic health in the PREDIMEDPlus study
    (Springer Science and Business Media LLC, 2019) Fernandez-Garcia, J.C. (José C.); Castañer, O. (Olga); Portoles, O. (Olga); Martinez, J.A. (José Alfredo); Martinez-Gonzalez, M.A. (Miguel Ángel); Micó-Pérez, R.M. (Rafael Manuel); Fiol, M. (Miquel); Riquelme-Gallego, B. (Blanca); Garcia-Rios, A. (Antonio); Fito, M. (Montserrat); Fiol, F. (Francisca); Konieczna, J. (Jadwiga); Daimiel, L. (Lidia); Tinahones, F.J. (Francisco J.); Compañ-Gabucio, L. (Laura); Vaquero-Luna, J. (Jessica); Vioque, J. (Jesús); Barón-López, F.J. (F. Javier); Becerra-Tomas, N. (Nerea); Tur, J.A. (Josep A.); Varela-Mato, V. (Veronica); Benavente-Marín, J.C. (Juan Carlos); Romaguera, D. (Dora); Vázquez, C. (Clotilde); Lapetra, J. (José); Matía-Martín, P. (Pilar); Papandreou, C. (Christopher); Schröder, H. (Helmut); Galmes-Panades, A.M. (Aina M.); Razquin, C. (Cristina); Ros, E. (Emilio); Ruiz-Canela, M. (Miguel); Mira-Castejón, L.A. (Luis Alfredo); Perez-Vega, K.A. (Karla Alejandra); Tomaino, L. (Laura); Casas, R. (Rosa); Alonso-Gomez, A. (Ángel); Wärnberg, J. (Julia); Estruch, R. (Ramón); Diaz-Lopez, A. (Andres); Asensio, E.M. (Eva M.); Gaforio, J.J. (José Juan); Santos-Lozano, J.M. (José M.); Serra-Majem, L. (Luis); Corella, D. (Dolores); Abete, I. (Itziar); Mascaró, C.M. (Catalina M.); Vidal, J. (Josep); Pinto, X. (Xavier); Salas-Salvado, J. (Jordi); Galera, A. (Ana); Garcia-Arellano, A. (Ana); Moreno-Rodríguez, A. (Anai)
    Background: This study explored the association between inactive time and measures of adiposity, clinical parameters, obesity, type 2 diabetes and metabolic syndrome components. It further examined the impact of reallocating inactive time to time in bed, light physical activity (LPA) or moderate-to-vigorous physical activity (MVPA) on cardio-metabolic risk factors, including measures of adiposity and body composition, biochemical parameters and blood pressure in older adults. Methods: This is a cross-sectional analysis of baseline data from 2189 Caucasian men and women (age 55-75 years, BMI 27-40 Kg/m2) from the PREDIMED-Plus study (http://www.predimedplus.com/). All participants had ≥3 components of the metabolic syndrome. Inactive time, physical activity and time in bed were objectively determined using triaxial accelerometers GENEActiv during 7 days (ActivInsights Ltd., Kimbolton, United Kingdom). Multiple adjusted linear and logistic regression models were used. Isotemporal substitution regression modelling was performed to assess the relationship of replacing the amount of time spent in one activity for another, on each outcome, including measures of adiposity and body composition, biochemical parameters and blood pressure in older adults. Results: Inactive time was associated with indicators of obesity and the metabolic syndrome. Reallocating 30 min per day of inactive time to 30 min per day of time in bed was associated with lower BMI, waist circumference and glycated hemoglobin (HbA1c) (all p-values < 0.05). Reallocating 30 min per day of inactive time with 30 min per day of LPA or MVPA was associated with lower BMI, waist circumference, total fat, visceral adipose tissue, HbA1c, glucose, triglycerides, and higher body muscle mass and HDL cholesterol (all p-values < 0.05). Conclusions: Inactive time was associated with a poor cardio-metabolic profile. Isotemporal substitution of inactive time with MVPA and LPA or time in bed could have beneficial impact on cardio-metabolic health.
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    Metabolites related to purine catabolism and risk of type 2 diabetes incidence; modifying efects of the TCF7L2-rs7903146 polymorphism
    (Springer Science and Business Media LLC, 2019) Martinez-Gonzalez, M.A. (Miguel Ángel); Clish, C.B. (Clary B.); Fito, M. (Montserrat); Rosique-Esteban, N. (Nuria); Hu, F.B. (Frank B.); Li, J. (Jun); Yu, E. (Edward); Zheng, Y. (Yan); Guasch-Ferre, M. (Marta); Papandreou, C. (Christopher); Razquin, C. (Cristina); Liang, L. (Liming); Ros, E. (Emilio); Ruiz-Canela, M. (Miguel); Bullo, M. (Monica); Estruch, R. (Ramón); Serra-Majem, L. (Luis); Corella, D. (Dolores); Salas-Salvado, J. (Jordi); Aros, F. (Fernando)
    Studies examining associations between purine metabolites and type 2 diabetes (T2D) are limited. We prospectively examined associations between plasma levels of purine metabolites with T2D risk and the modifying effects of transcription factor-7-like-2 (TCF7L2) rs7903146 polymorphism on these associations. This is a case-cohort design study within the PREDIMED study, with 251 incident T2D cases and a random sample of 694 participants (641 non-cases and 53 overlapping cases) without T2D at baseline (median follow-up: 3.8 years). Metabolites were semi-quantitatively profiled with LC-MS/MS. Cox regression analysis revealed that high plasma allantoin levels, including allantoin-to-uric acid ratio and high xanthine-to-hypoxanthine ratio were inversely and positively associated with T2D risk, respectively, independently of classical risk factors. Elevated plasma xanthine and inosine levels were associated with a higher T2D risk in homozygous carriers of the TCF7L2-rs7903146 T-allele. The potential mechanisms linking the aforementioned purine metabolites and T2D risk must be also further investigated.
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    Sleep Duration is Inversely Associated with Serum Uric Acid Concentrations and Uric Acid to Creatinine Ratio in an Elderly Mediterranean Population at High Cardiovascular Risk
    (MDPI AG, 2019) Babio, N. (Nancy); Fernandez-Garcia, J.C. (José C.); Castañer, O. (Olga); Oncina-Canovas, A. (Alejandro); Corbella, E. (Emili); Martinez-Gonzalez, M.A. (Miguel Ángel); Muñoz-Garach, A. (Araceli); Salaverria-Lete, I. (Itziar); Garcia-Rios, A. (Antonio); Tojal-Sierra, L. (Lucas); Martín-Sánchez, V. (Vicente); Pérez-Farinós, N. (Napoleón); Daimiel, L. (Lidia); Quifer, M. (Mireia); Compañ-Gabucio, L. (Laura); Vioque, J. (Jesús); Barón-López, F.J. (F. Javier); Becerra-Tomas, N. (Nerea); Tur, J.A. (Josep A.); Colom, A. (Antoni); Diez-Espino, J. (Javier); Romaguera, D. (Dora); Vázquez, C. (Clotilde); Lapetra, J. (José); Matía-Martín, P. (Pilar); Bueno-Cavanillas, A. (Aurora); Papandreou, C. (Christopher); Schröder, H. (Helmut); Delgado-Rodriguez, M. (Miguel); Ros, E. (Emilio); Ruiz-Canela, M. (Miguel); Bullo, M. (Monica); Bibiloni, M.M. (Maria del Mar); Casas, R. (Rosa); Alonso-Gomez, A. (Ángel); Wärnberg, J. (Julia); Estruch, R. (Ramón); Diaz-Lopez, A. (Andres); Cenoz-Osinaga, J.C. (Juan C.); Asensio, E.M. (Eva M.); Martinez, A. (Alfredo); Santos-Lozano, J.M. (José M.); Torras, L. (Laura); Sanchez-Villegas, A. (Almudena); Serra-Majem, L. (Luis); Corella, D. (Dolores); Abete, I. (Itziar); Vidal, J. (Josep); Pinto, X. (Xavier); Salas-Salvado, J. (Jordi); Sorli, J.V. (Jose V.); Morey, M. (Marga)
    The aim of the study was to evaluate sleep duration and sleep variability in relation to serum uric acid (SUA) concentrations and SUA to creatinine ratio. This is a cross-sectional analysis of baseline data from 1842 elderly participants with overweight/obesity and metabolic syndromein the (Prevención con Dieta Mediterránea) PREDIMED-Plus trial. Accelerometry-derived sleep duration and sleep variability were measured. Linear regression models were fitted to examine the aforementioned associations. A 1 hour/night increment in sleep duration was inversely associated with SUA concentrations (β = 0.07, p = 0.047). Further adjustment for leukocytes attenuated this association (p = 0.050). Each 1-hour increment in sleep duration was inversely associated with SUA to creatinine ratio (β = 0.15, p = 0.001). The findings of this study suggest that longer sleep duration is associated with lower SUA concentrations and lower SUA to creatinine ratio.
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    Fatty Acids Composition of Blood Cell Membranes and Peripheral Inflammation in the PREDIMED Study: A Cross-Sectional Analysis
    (MDPI AG, 2019) Castañer, O. (Olga); Martinez-Gonzalez, M.A. (Miguel Ángel); Fito, M. (Montserrat); Rosique-Esteban, N. (Nuria); Sala-Vila, A. (Aleix); Muralidharan, J. (Jananee); Papandreou, C. (Christopher); Razquin, C. (Cristina); Ros, E. (Emilio); Bullo, M. (Monica); Estruch, R. (Ramón); Corella, D. (Dolores); Salas-Salvado, J. (Jordi)
    There is limited evidence from epidemiological studies for the inflammatory or anti-inflammatory properties of fatty acids in blood cell membranes. Therefore, this study examined associations between baseline (n = 282) and 1-year (n = 143) changes in the levels of fatty acids in blood cell membranes with circulating inflammatory markers in older adults at high cardiovascular risk. The data for this cross-sectional analysis was obtained from a case-control study within the PREDIMED study. Linear regression with elastic net penalty was applied to test associations between measured fatty acids and inflammatory markers. Several fatty acids were associated with interferon-γ (IFNγ) and interleukins (ILs) IL-6, IL-8, and IL-10 at baseline and additionally also with IL-1b at 1 year. Omega-6 fatty acids were consistently positively associated with pro-inflammatory IL-6 and IL-8 at baseline. Omega-3 fatty acids including C20:5n3 and C18:3n3 were negatively associated with IFN-γ at 1 year. It is interesting to note that the cis and trans forms of C16:1n7 at 1 year were oppositely associated with the inflammatory markers. C16:1n7trans was negatively associated with IFN-γ, IL-6, IL-8, IL-10, and IL-1b, whereas C16:1n7cis was positively associated with IL-1b. This study adds to the growing body of evidence suggesting potential differences in inflammatory or anti-inflammatory properties of fatty acids in blood cell membranes.
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    Long daytime napping is associated with increased adiposity and type 2 diabetes in an elderly population with metabolic syndrome
    (MDPI AG, 2019) Babio, N. (Nancy); Buil, P. (Pilar); Fernandez-Garcia, J.C. (José C.); Castañer, O. (Olga); Martinez, J.A. (José Alfredo); Martinez-Gonzalez, M.A. (Miguel Ángel); Trias, F. (Ferran); Muñoz-Garach, A. (Araceli); Garcia-Rios, A. (Antonio); Gallardo-Alfaro, L. (Laura); Fito, M. (Montserrat); Martín-Sánchez, V. (Vicente); Pérez-Farinós, N. (Napoleón); Konieczna, J. (Jadwiga); González-Botella, A. (Andrés); Daimiel, L. (Lidia); Vaquero-Luna, J. (Jessica); Vioque, J. (Jesús); Barón-López, F.J. (F. Javier); Becerra-Tomas, N. (Nerea); García Hera, M (Manoli) de la; Tur, J.A. (Josep A.); Martinez-Lacruz, R. (Raul); Toledo, E. (Estefanía); Romaguera, D. (Dora); Vázquez, C. (Clotilde); Lapetra, J. (José); Matía-Martín, P. (Pilar); Bueno-Cavanillas, A. (Aurora); Papandreou, C. (Christopher); Schröder, H. (Helmut); Delgado-Rodriguez, M. (Miguel); Galmes-Panades, A.M. (Aina M.); Ros, E. (Emilio); Ruiz-Canela, M. (Miguel); Bullo, M. (Monica); Casas, R. (Rosa); Goday, A. (Albert); Alonso-Gomez, A. (Ángel); Wärnberg, J. (Julia); Estruch, R. (Ramón); Diaz-Lopez, A. (Andres); Santos-Lozano, J.M. (José M.); Serra-Majem, L. (Luis); Corella, D. (Dolores); Abete, I. (Itziar); Vidal, J. (Josep); Pinto, X. (Xavier); Salas-Salvado, J. (Jordi); Barragán-Arnal, R. (Rocío); Bautista-Castaño, I. (Inmaculada); Moreno-Rodríguez, A. (Anai)
    Research examining associations between objectively-measured napping time and type 2 diabetes (T2D) is lacking. This study aimed to evaluate daytime napping in relation to T2D and adiposity measures in elderly individuals from the Mediterranean region. A cross-sectional analysis of baseline data from 2190 elderly participants with overweight/obesity and metabolic syndrome, in the PREDIMED-Plus trial, was carried out. Accelerometer-derived napping was measured. Prevalence ratios (PR) and 95% confidence intervals (CI) for T2D were obtained using multivariable-adjusted Cox regression with constant time. Linear regression models were fitted to examine associations of napping with body mass index (BMI) and waist circumference (WC). Participants napping ≥90 min had a higher prevalence of T2D (PR 1.37 (1.06, 1.78)) compared with those napping 5 to <30 min per day. Significant positive associations with BMI and WC were found in those participants napping ≥30 min as compared to those napping 5 to <30 min per day. The findings of this study suggest that longer daytime napping is associated with higher T2D prevalence and greater adiposity measures in an elderly Spanish population at high cardiovascular risk.
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    Plasma Metabolites Associated with Coffee Consumption: A Metabolomic Approach within the PREDIMED Study
    (MDPI, 2019) Martinez-Gonzalez, M.A. (Miguel Ángel); Fiol, M. (Miquel); Clish, C.B. (Clary B.); Fito, M. (Montserrat); Hu, F.B. (Frank B.); Toledo, E. (Estefanía); Hernandez-Alonso, P. (Pablo); Yu, E. (Edward); Lapetra, J. (José); Guasch-Ferre, M. (Marta); Papandreou, C. (Christopher); Deik, A. (Amy); Razquin, C. (Cristina); Liang, L. (Liming); Ros, E. (Emilio); Ruiz-Canela, M. (Miguel); Bullo, M. (Monica); Dennis, C. (Courtney); Estruch, R. (Ramón); Corella, D. (Dolores); Salas-Salvado, J. (Jordi); Aros, F. (Fernando); Ruano, C. (Cristina)
    Few studies have examined the association of a wide range of metabolites with total and subtypes of coffee consumption. The aim of this study was to investigate associations of plasma metabolites with total, caffeinated, and decaffeinated coffee consumption. We also assessed the ability of metabolites to discriminate between coffee consumption categories. This is a cross-sectional analysis of 1664 participants from the PREDIMED study. Metabolites were semiquantitatively profiled using a multiplatform approach. Consumption of total coffee, caffeinated coffee and decaffeinated coffee was assessed by using a validated food frequency questionnaire. We assessed associations between 387 metabolite levels with total, caffeinated, or decaffeinated coffee consumption (≥50 mL coffee/day) using elastic net regression analysis. Ten-fold cross-validation analyses were used to estimate the discriminative accuracy of metabolites for total and subtypes of coffee. We identified different sets of metabolites associated with total coffee, caffeinated and decaffeinated coffee consumption. These metabolites consisted of lipid species (e.g., sphingomyelin, phosphatidylethanolamine, and phosphatidylcholine) or were derived from glycolysis (alpha-glycerophosphate) and polyphenol metabolism (hippurate). Other metabolites included caffeine, 5-acetylamino-6-amino-3-methyluracil, cotinine, kynurenic acid, glycocholate, lactate, and allantoin. The area under the curve (AUC) was 0.60 (95% CI 0.56–0.64), 0.78 (95% CI 0.75–0.81) and 0.52 (95% CI 0.49–0.55), in the multimetabolite model, for total, caffeinated, and decaffeinated coffee consumption, respectively. Our comprehensive metabolic analysis did not result in a new, reliable potential set of metabolites for coffee consumption.