BACKGROUND: We examined the degree of postprandial triglyceride (TG) response over the day, representing a highly dynamic state, with continuous metabolic adaptations, among normal-weight, overweight and obese patients, according to their metabolically healthy or abnormal status. MATERIALS AND METHODS:A total of 1002 patients from the CORDIOPREV clinical trial (NCT00924937) were submitted to anoral fat load test meal with 0·7 g fat/kg body weight (12% saturated fatty acids (SFA), 10% polyunsaturated fatty acids (PUFA), 43% monounsaturated fatty acids (MUFA), 10% protein and 25% carbohydrates). Serial blood test analysing lipid fractions and inflammation markers (high-sensitivity C-reactive protein (hs-CRP)) were drawn at 0, 1, 2, 3 and 4 h during postprandial state. We explored the dynamic response according to six body size phenotypes: (i) normal weight, metabolically healthy; (ii) normal weight, metabolically abnormal; (iii) overweight, metabolically healthy; (iv) overweight, metabolically abnormal; (v) obese, metabolically healthy; and (vi) obese, metabolically abnormal. RESULTS:Metabolically healthy patients displayed lower postprandial response of plasma TG and large triacylglycerol-rich lipoproteins (TRLs)-TG, compared with those metabolically abnormal, independently whether or not they were obese (P < 0·001 and P < 0·001, respectively). Moreover, the area under the curve (AUC) of TG and AUC of large TRLs-TG were greater in the group of metabolically abnormal compared with the group of metabolically healthy (P < 0·001 and P < 0·001, respectively). Interestingly, metabolically abnormal subjects displayed higher postprandial response of plasma hs-CRP than did the subgroup of normal, overweight and obese, metabolically healthy patients (P < 0·001). CONCLUSIONS: Our findings showed that certain types of the metabolic phenotypes of obesity are more favourable modulating phenotypic flexibility after a dynamic fat load test, through TG metabolism and inflammation homoeostasis. To identify, these phenotypes may be the best strategy for personalized treatment of obesity.
RCT Entities:
BACKGROUND: We examined the degree of postprandial triglyceride (TG) response over the day, representing a highly dynamic state, with continuous metabolic adaptations, among normal-weight, overweight and obesepatients, according to their metabolically healthy or abnormal status. MATERIALS AND METHODS: A total of 1002 patients from the CORDIOPREV clinical trial (NCT00924937) were submitted to an oral fat load test meal with 0·7 g fat/kg body weight (12% saturated fatty acids (SFA), 10% polyunsaturated fatty acids (PUFA), 43% monounsaturated fatty acids (MUFA), 10% protein and 25% carbohydrates). Serial blood test analysing lipid fractions and inflammation markers (high-sensitivity C-reactive protein (hs-CRP)) were drawn at 0, 1, 2, 3 and 4 h during postprandial state. We explored the dynamic response according to six body size phenotypes: (i) normal weight, metabolically healthy; (ii) normal weight, metabolically abnormal; (iii) overweight, metabolically healthy; (iv) overweight, metabolically abnormal; (v) obese, metabolically healthy; and (vi) obese, metabolically abnormal. RESULTS: Metabolically healthy patients displayed lower postprandial response of plasma TG and large triacylglycerol-rich lipoproteins (TRLs)-TG, compared with those metabolically abnormal, independently whether or not they were obese (P < 0·001 and P < 0·001, respectively). Moreover, the area under the curve (AUC) of TG and AUC of large TRLs-TG were greater in the group of metabolically abnormal compared with the group of metabolically healthy (P < 0·001 and P < 0·001, respectively). Interestingly, metabolically abnormal subjects displayed higher postprandial response of plasma hs-CRP than did the subgroup of normal, overweight and obese, metabolically healthy patients (P < 0·001). CONCLUSIONS: Our findings showed that certain types of the metabolic phenotypes of obesity are more favourable modulating phenotypic flexibility after a dynamic fat load test, through TG metabolism and inflammation homoeostasis. To identify, these phenotypes may be the best strategy for personalized treatment of obesity.
Authors: Ruth Blanco-Rojo; Juan F Alcala-Diaz; Suzan Wopereis; Pablo Perez-Martinez; Gracia M Quintana-Navarro; Carmen Marin; Jose M Ordovas; Ben van Ommen; Francisco Perez-Jimenez; Javier Delgado-Lista; Jose Lopez-Miranda Journal: Diabetologia Date: 2015-10-16 Impact factor: 10.122
Authors: Ruth Blanco-Rojo; Javier Delgado-Lista; Yu-Chi Lee; Chao-Qiang Lai; Pablo Perez-Martinez; Oriol Rangel-Zuñiga; Caren E Smith; Bertha Hidalgo; Juan F Alcala-Diaz; Francisco Gomez-Delgado; Laurence D Parnell; Donna K Arnett; Katherine L Tucker; Jose Lopez-Miranda; Jose M Ordovas Journal: Am J Clin Nutr Date: 2016-07-20 Impact factor: 7.045
Authors: Pablo Perez-Martinez; Juan F Alcala-Diaz; Edmon K Kabagambe; Antonio Garcia-Rios; Michael Y Tsai; Javier Delgado-Lista; Genovefa Kolovou; Robert J Straka; Francisco Gomez-Delgado; Paul N Hopkins; Carmen Marin; Ingrid Borecki; Elena M Yubero-Serrano; James E Hixson; Antonio Camargo; Michael A Province; Javier Lopez-Moreno; Fernando Rodriguez-Cantalejo; Francisco J Tinahones; Dimitri P Mikhailidis; Francisco Perez-Jimenez; Donna K Arnett; Jose M Ordovas; Jose Lopez-Miranda Journal: J Clin Lipidol Date: 2016-06-01 Impact factor: 4.766
Authors: Javier Delgado-Lista; Pablo Perez-Martinez; Antonio Garcia-Rios; Juan F Alcala-Diaz; Ana I Perez-Caballero; Francisco Gomez-Delgado; Francisco Fuentes; Gracia Quintana-Navarro; Fernando Lopez-Segura; Ana M Ortiz-Morales; Nieves Delgado-Casado; Elena M Yubero-Serrano; Antonio Camargo; Carmen Marin; Fernando Rodriguez-Cantalejo; Purificacion Gomez-Luna; Jose M Ordovas; Jose Lopez-Miranda; Francisco Perez-Jimenez Journal: Am Heart J Date: 2016-04-27 Impact factor: 4.749
Authors: Rainer J Klement; Petra S Koebrunner; Kelley Krage; Michael M Weigel; Reinhart A Sweeney Journal: Med Oncol Date: 2020-11-28 Impact factor: 3.064