Jadwiga Konieczna1, Itziar Abete2, Aina M Galmés1, Nancy Babio3, Antoni Colom4, Maria Angeles Zulet2, Ramón Estruch5, Josep Vidal6, Estefanía Toledo7, Andrés Díaz-López3, Miguel Fiol4, Rosa Casas5, Josep Vera8, Pilar Buil-Cosiales9, Vicente Martín10, Albert Goday11, Jordi Salas-Salvadó3, J Alfredo Martínez12, Dora Romaguera13. 1. Instituto de Investigación Sanitaria Illes Balears (IdISBa), University Hospital Son Espases, Palma de Mallorca, Spain. 2. CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Nutrition, Food Sciences and Physiology, Center for Nutrition Research, University of Navarra (UNAV), Pamplona, Spain. 3. CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Human Nutrition Unit, University Hospital of Sant Joan de Reus, Department of Biochemistry and Biotechnology, Pere Virgili Institute for Health Research, Rovira i Virgili University, Reus, Spain. 4. 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. 5. CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Internal Medicine, Hospital Clinic, IDIBAPS August Pi i Sunyer Biomedical Research Institute, University of Barcelona, Barcelona, Spain. 6. Department of Endocrinology, Hospital Clinic, University of Barcelona, Barcelona, Spain; CIBER Diabetes y enfermedades metabólicas (CIBERdem), Instituto de Salud Carlos III (ISCIII), Spain. 7. CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Preventive Medicine and Public Health, University of Navarra-Navarra Institute for Health Research, Pamplona, Spain. 8. IDIBAPS August Pi i Sunyer Biomedical Research Institute, University of Barcelona, Barcelona, Spain. 9. CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Preventive Medicine and Public Health, University of Navarra-Navarra Institute for Health Research, Pamplona, Spain; Atención Primaria, Servicio Navarro de Salud-Osasunbidea, Pamplona, Spain. 10. Instituto de Biomedicina (IBIOMED), University of León, León, Spain; CIBER Epidemiología y Salud Pública (CIBEResp), Instituto de Salud Carlos III (ISCIII), Spain. 11. CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Cardiovascular Risk and Nutrition Research Group, Hospital del Mar Medical Research Institute (IMIM), Department of Medicine, University of Barcelona, Barcelona, Spain. 12. CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Nutrition, Food Sciences and Physiology, Center for Nutrition Research, University of Navarra (UNAV), Pamplona, Spain; Madrid Institute for Advanced Studies (IMDEA) Food Institute, Madrid, Spain. 13. 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: Excess adiposity is associated with poor cardiometabolic (CM) health. To date, several techniques and indicators have been developed to determine adiposity. We aimed to compare the ability of traditional anthropometric, as well as standard and novel DXA-derived parameters related to overall and regional adiposity, to evaluate CM risk. METHODS: Using the cross-sectional design in the context of the PREDIMED-Plus trial, 1207 Caucasian senior men and women with overweight/obesity and metabolic syndrome (MetS) were assessed. At baseline, anthropometry- and DXA-measured parameters of central, visceral, peripheral and central-to-peripheral adiposity together with comprehensive set of CM risk factors were obtained. Partial correlations and areas under the ROC curve (AUC) were estimated to compare each adiposity measure with CM risk parameters, separately for men and women, and in the overall sample. RESULTS: DXA-derived indicators, other than percentage of total body fat, showed stronger correlations (rho -0.172 to 0.206, p < 0.001) with CM risk than anthropometric indicators, after controlling for age, diabetes and medication use. In both sexes, DXA-derived visceral adipose tissue measures (VAT, VAT/Total fat, visceral-to-subcutaneous fat) together with lipodystrophy indicators (Trunk/Legs fat and Android/Gynoid fat) were strongly and positively correlated (p < 0.001) with glycated hemoglobin (HbA1c), the triglyceride and glucose index (TyG), triglycerides (TG), the ratio TG/HDL-cholesterol (TG/HDL-C), and were inversely related to HDL-C levels (p < 0.001). Furthermore, in AUC analyses for both sexes, VAT/Total fat showed the highest predictive ability for abnormal HbA1c levels (AUC = 0.629), VAT for TyG (AUC = 0.626), both lipodystrophy indicators for TG (AUCs = 0.556), and Trunk/Legs fat for HDL-C (AUC = 0.556) and TG/HDL-C (AUC = 0.581). CONCLUSIONS: DXA regional adiposity measures offer advantages beyond traditional anthropometric and DXA overall adiposity indicators for CM risk assessment in senior overweight/obese subjects with MetS. In particular, in both sexes, visceral adiposity better stratifies individuals at risk for glucose abnormalities, and indicators of lipodystrophy better predict markers of dyslipidemia.
BACKGROUND & AIMS: Excess adiposity is associated with poor cardiometabolic (CM) health. To date, several techniques and indicators have been developed to determine adiposity. We aimed to compare the ability of traditional anthropometric, as well as standard and novel DXA-derived parameters related to overall and regional adiposity, to evaluate CM risk. METHODS: Using the cross-sectional design in the context of the PREDIMED-Plus trial, 1207 Caucasian senior men and women with overweight/obesity and metabolic syndrome (MetS) were assessed. At baseline, anthropometry- and DXA-measured parameters of central, visceral, peripheral and central-to-peripheral adiposity together with comprehensive set of CM risk factors were obtained. Partial correlations and areas under the ROC curve (AUC) were estimated to compare each adiposity measure with CM risk parameters, separately for men and women, and in the overall sample. RESULTS: DXA-derived indicators, other than percentage of total body fat, showed stronger correlations (rho -0.172 to 0.206, p < 0.001) with CM risk than anthropometric indicators, after controlling for age, diabetes and medication use. In both sexes, DXA-derived visceral adipose tissue measures (VAT, VAT/Total fat, visceral-to-subcutaneous fat) together with lipodystrophy indicators (Trunk/Legs fat and Android/Gynoid fat) were strongly and positively correlated (p < 0.001) with glycated hemoglobin (HbA1c), the triglyceride and glucose index (TyG), triglycerides (TG), the ratio TG/HDL-cholesterol (TG/HDL-C), and were inversely related to HDL-C levels (p < 0.001). Furthermore, in AUC analyses for both sexes, VAT/Total fat showed the highest predictive ability for abnormal HbA1c levels (AUC = 0.629), VAT for TyG (AUC = 0.626), both lipodystrophy indicators for TG (AUCs = 0.556), and Trunk/Legs fat for HDL-C (AUC = 0.556) and TG/HDL-C (AUC = 0.581). CONCLUSIONS: DXA regional adiposity measures offer advantages beyond traditional anthropometric and DXA overall adiposity indicators for CM risk assessment in senior overweight/obese subjects with MetS. In particular, in both sexes, visceral adiposity better stratifies individuals at risk for glucose abnormalities, and indicators of lipodystrophy better predict markers of dyslipidemia.
Authors: Rocío Zamanillo-Campos; Alice Chaplin; Dora Romaguera; Itziar Abete; Jordi Salas-Salvadó; Vicente Martín; Ramón Estruch; Josep Vidal; Miguel Ruiz-Canela; Nancy Babio; Francisca Fiol; José Antonio de Paz; Rosa Casas; Romina Olbeyra; Miguel A Martínez-González; Jesús F García-Gavilán; Albert Goday; Cesar I Fernandez-Lazaro; J Alfredo Martínez; Frank B Hu; Jadwiga Konieczna Journal: Clin Nutr Date: 2022-08-20 Impact factor: 7.643
Authors: Aina M Galmes-Panades; Jadwiga Konieczna; Veronica Varela-Mato; Itziar Abete; Nancy Babio; Miquel Fiol; José Antonio de Paz; Rosa Casas; Romina Olbeyra; Miguel Ruiz-Canela; Antoni Palau-Galindo; Olga Castañer; Arturo Martín-García; Ramón Estruch; Josep Vidal; Pilar Buil-Cosiales; Julia Wärnberg; Jordi Salas-Salvadó; J Alfredo Martínez; Dora Romaguera Journal: BMC Med Date: 2021-01-06 Impact factor: 8.775
Authors: Marta López-Bueno; Ángel Fernández-Aparicio; Emilio González-Jiménez; Miguel Ángel Montero-Alonso; Jacqueline Schmidt-RioValle Journal: Int J Environ Res Public Health Date: 2021-11-25 Impact factor: 3.390
Authors: Aina M Galmes-Panades; Jadwiga Konieczna; Itziar Abete; Antoni Colom; Núria Rosique-Esteban; Maria Angeles Zulet; Zenaida Vázquez; Ramón Estruch; Josep Vidal; Estefanía Toledo; Nancy Babio; Miguel Fiol; Rosa Casas; Josep Vera; Pilar Buil-Cosiales; José Antonio de Paz; Albert Goday; Jordi Salas-Salvadó; J Alfredo Martínez; Dora Romaguera Journal: PLoS One Date: 2019-01-25 Impact factor: 3.240
Authors: Andrea Costa; Bàrbara Reynés; Jadwiga Konieczna; Marian Martín; Miquel Fiol; Andreu Palou; Dora Romaguera; Paula Oliver Journal: Sci Rep Date: 2021-09-15 Impact factor: 4.379