Mònica Bulló1,2,3,4, Christopher Papandreou1,2,3,4, Miguel Ruiz-Canela3,5,6, Marta Guasch-Ferré7, Jun Li7,8, Pablo Hernández-Alonso1,2,3,4,9, Estefania Toledo3,5,6, Liming Liang8,10, Cristina Razquin3,5,6, Dolores Corella3,11, Ramon Estruch3,12,13, Emilio Ros3,13,14, Montserrat Fitó3,15, Fernando Arós3,16, Miquel Fiol3,17, Lluís Serra-Majem3,18, Clary B Clish19, Nerea Becerra-Tomás1,2,3,4, Miguel A Martínez-González3,5,6,7, Frank B Hu7,8,20, Jordi Salas-Salvadó1,2,3,4. 1. Universitat Rovira i Virgili, Biochemistry and Biotechnology Department, Human Nutrition Unit, Reus, Spain. 2. Pere i Virgili Health Research Institute (IISPV), Reus, Spain. 3. CIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain. 4. Nutrition Unit, University Hospital of Sant Joan de Reus, Reus, Spain. 5. Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain. 6. Navarra Institute for Health Research (IdiSNA), Pamplona, Navarra, Spain. 7. Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA. 8. Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA. 9. Department of Endocrinology and Nutrition, Virgen de la Victoria University Hospital, University of Malaga (IBIMA), Malaga, Spain. 10. Department of Statistics, Harvard TH Chan School of Public Health, Boston, MA, USA. 11. Department of Preventive Medicine, University of Valencia, Valencia, Spain. 12. Department of Internal Medicine, Hospital Clínic, University of Barcelona, Barcelona, Spain. 13. August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain. 14. Lipid Clinic, Department of Endocrinology and Nutrition, Hospital Clínic, University of Barcelona, Barcelona, Spain. 15. Cardiovascular and Nutrition Research Group, Hospital del Mar Medical Research Institute, Barcelona, Spain. 16. Department of Cardiology, University Hospital of Alava, Vitoria, Spain. 17. Institute of Health Sciences (IUNICS), University of Balearic Islands and Hospital Son Espases, Palma de Mallorca, Spain. 18. Research Institute of Biomedical and Health Sciences (IUIBS), University of Las Palmas de Gran Canaria, Las Palmas, Spain. 19. Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA. 20. Channing Division for Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Abstract
BACKGROUND: The quality of carbohydrate consumed, assessed by the glycemic index (GI), glycemic load (GL), or carbohydrate quality index (CQI), affects the postprandial glycemic and insulinemic responses, which have been implicated in the etiology of several chronic diseases. However, it is unclear whether plasma metabolites involved in different biological pathways could provide functional insights into the role of carbohydrate quality indices in health. OBJECTIVES: We aimed to identify plasma metabolomic profiles associated with dietary GI, GL, and CQI. METHODS: The present study is a cross-sectional analysis of 1833 participants with overweight/obesity (mean age = 67 y) from 2 case-cohort studies nested within the PREDIMED (Prevención con Dieta Mediterránea) trial. Data extracted from validated FFQs were used to estimate the GI, GL, and CQI. Plasma concentrations of 385 metabolites were profiled with LC coupled to MS and associations of these metabolites with those indices were assessed with elastic net regression analyses. RESULTS: A total of 58, 18, and 57 metabolites were selected for GI, GL, and CQI, respectively. Choline, cotinine, γ-butyrobetaine, and 36:3 phosphatidylserine plasmalogen were positively associated with GI and GL, whereas they were negatively associated with CQI. Fructose-glucose-galactose was negatively and positively associated with GI/GL and CQI, respectively. Consistent associations of 21 metabolites with both GI and CQI were found but in opposite directions. Negative associations of kynurenic acid, 22:1 sphingomyelin, and 38:6 phosphatidylethanolamine, as well as positive associations of 32:1 phosphatidylcholine with GI and GL were also observed. Pearson correlation coefficients between GI, GL, and CQI and the metabolomic profiles were 0.30, 0.22, and 0.27, respectively. CONCLUSIONS: The GI, GL, and CQI were associated with specific metabolomic profiles in a Mediterranean population at high cardiovascular disease risk. Our findings may help in understanding the role of dietary carbohydrate indices in the development of cardiometabolic disorders. This trial was registered at isrctn.com as ISRCTN35739639.
BACKGROUND: The quality of carbohydrate consumed, assessed by the glycemic index (GI), glycemic load (GL), or carbohydrate quality index (CQI), affects the postprandial glycemic and insulinemic responses, which have been implicated in the etiology of several chronic diseases. However, it is unclear whether plasma metabolites involved in different biological pathways could provide functional insights into the role of carbohydrate quality indices in health. OBJECTIVES: We aimed to identify plasma metabolomic profiles associated with dietary GI, GL, and CQI. METHODS: The present study is a cross-sectional analysis of 1833 participants with overweight/obesity (mean age = 67 y) from 2 case-cohort studies nested within the PREDIMED (Prevención con Dieta Mediterránea) trial. Data extracted from validated FFQs were used to estimate the GI, GL, and CQI. Plasma concentrations of 385 metabolites were profiled with LC coupled to MS and associations of these metabolites with those indices were assessed with elastic net regression analyses. RESULTS: A total of 58, 18, and 57 metabolites were selected for GI, GL, and CQI, respectively. Choline, cotinine, γ-butyrobetaine, and 36:3 phosphatidylserine plasmalogen were positively associated with GI and GL, whereas they were negatively associated with CQI. Fructose-glucose-galactose was negatively and positively associated with GI/GL and CQI, respectively. Consistent associations of 21 metabolites with both GI and CQI were found but in opposite directions. Negative associations of kynurenic acid, 22:1 sphingomyelin, and 38:6 phosphatidylethanolamine, as well as positive associations of 32:1 phosphatidylcholine with GI and GL were also observed. Pearson correlation coefficients between GI, GL, and CQI and the metabolomic profiles were 0.30, 0.22, and 0.27, respectively. CONCLUSIONS: The GI, GL, and CQI were associated with specific metabolomic profiles in a Mediterranean population at high cardiovascular disease risk. Our findings may help in understanding the role of dietary carbohydrate indices in the development of cardiometabolic disorders. This trial was registered at isrctn.com as ISRCTN35739639.
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