Mariona Pinart1, Stephanie Jeran1, Heiner Boeing2, Marta Stelmach-Mardas2,3, Marie Standl4, Holger Schulz4, Carla Harris4,5, Andrea von Berg6, Gunda Herberth7, Sybille Koletzko8,9, Jakob Linseisen10,11, Taylor A Breuninger10, Ute Nöthlings12, Janett Barbaresko12,13, Stefan Benda12, Carl Lachat14, Chen Yang14, Paolo Gasparini15,16, Antonietta Robino16, Gemma Rojo-Martínez17,18, Luís Castaño19, Michèle Guillaume20, Anne-Françoise Donneau20, Axelle Hoge20, Nicolas Gillain20, Demetris Avraam21, Paul R Burton21, Jildau Bouwman22, Tobias Pischon1,23,24,25, Katharina Nimptsch1. 1. Molecular Epidemiology Research Group, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany. 2. Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany. 3. Department of Treatment of Obesity, Metabolic Disorders and Clinical Dietetics, Poznan University of Medical Sciences, Poznan, Poland. 4. Helmholtz Centre Munich-German Research Center for Environmental Health, Institute of Epidemiology, Neuherberg/Munich, Germany. 5. Division of Metabolic and Nutritional Medicine, LMU - Ludwig Maximilian University Munich, Dr. von Hauner Children's Hospital, LMU University Hospitals, Munich, Germany. 6. Department of Pediatrics, Research Institute, Marien-Hospital Wesel, Wesel, Germany. 7. Department of Environmental Immunology, Helmholtz Centre for Environmental Research-Zentrum für Umweltforschung (UFZ), Leipzig, Germany. 8. Department of Pediatrics, Dr. von Hauner Children's Hospital, LMU - Ludwig Maximilian University Hospital, University of Munich, Munich, Germany. 9. Department of Pediatrics, Gastroenterology and Nutrition, School of Medicine Collegium Medicum University of Warmia and Mazury, Olsztyn, Poland. 10. Helmholtz Centre Munich, Clinical Epidemiology, Neuherberg/Munich, Germany. 11. Ludwig Maximilians University (LMU) Munich, Medical Faculty, Chair of Epidemiology at University Center for Health Sciences at the Klinikum Augsburg (UNIKA-T), Ausburg, Germany. 12. Department of Nutrition and Food Sciences, University of Bonn, Bonn, Germany. 13. Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany. 14. Department of Food Technology, Safety and Health, Ghent University, Ghent, Belgium. 15. Department of Medical Sciences, University of Trieste, Trieste, Italy. 16. Institute for Maternal and Child Health-Mother and Child Referral Hospital and Research Institute (IRCCS) "Burlo Garofolo," Trieste, Italy. 17. Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain. 18. Clinical Management Unit (CMU) Endocrinology and Nutrition, Regional University Hospital of Malaga, Institute of Biomedical Research in Malaga (IBIMA), Málaga, Spain. 19. Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Rare Diseases Networking Biomedical Research Centre (CIBERER), BioCruces-University Hospital Cruces-The University of the Basque Country (Basque: Euskal Herriko Unibertsitatea/Spanish: Universidad del País Vasco [UPV/EHU]), Barakaldo, Spain. 20. Department of Public Health, University of Liège, Liège, Belgium. 21. Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom. 22. Research group Microbiology and Systems Biology, Netherlands Organization for Applied Scientific Research, Zeist, The Netherlands. 23. Charité University Medicine Berlin, Berlin, Germany. 24. Max Delbrück Center for Molecular Medicine (MDC)/Berlin Institute of Health (BIH) Biobank, Berlin, Germany. 25. German Centre for Cardiovascular Research (DZHK), Berlin, Germany.
Abstract
BACKGROUND: Associations between increased dietary fat and decreased carbohydrate intake with circulating HDL and non-HDL cholesterol have not been conclusively determined. OBJECTIVE: We assessed these relations in 8 European observational human studies participating in the European Nutritional Phenotype Assessment and Data Sharing Initiative (ENPADASI) using harmonized data. METHODS: Dietary macronutrient intake was recorded using study-specific dietary assessment tools. Main outcome measures were lipoprotein cholesterol concentrations: HDL cholesterol (mg/dL) and non-HDL cholesterol (mg/dL). A cross-sectional analysis on 5919 participants (54% female) aged 13-80 y was undertaken using the statistical platform DataSHIELD that allows remote/federated nondisclosive analysis of individual-level data. Generalized linear models (GLM) were fitted to assess associations between replacing 5% of energy from carbohydrates with equivalent energy from total fats, SFAs, MUFAs, or PUFAs with circulating HDL cholesterol and non-HDL cholesterol. GLM were adjusted for study source, age, sex, smoking status, alcohol intake and BMI. RESULTS: The replacement of 5% of energy from carbohydrates with total fats or MUFAs was statistically significantly associated with 0.67 mg/dL (95% CI: 0.40, 0.94) or 0.99 mg/dL (95% CI: 0.37, 1.60) higher HDL cholesterol, respectively, but not with non-HDL cholesterol concentrations. The replacement of 5% of energy from carbohydrates with SFAs or PUFAs was not associated with HDL cholesterol, but SFAs were statistically significantly associated with 1.94 mg/dL (95% CI: 0.08, 3.79) higher non-HDL cholesterol, and PUFAs with -3.91 mg/dL (95% CI: -6.98, -0.84) lower non-HDL cholesterol concentrations. A statistically significant interaction by sex for the association of replacing carbohydrates with MUFAs and non-HDL cholesterol was observed, showing a statistically significant inverse association in males and no statistically significant association in females. We observed no statistically significant interaction by age. CONCLUSIONS: The replacement of dietary carbohydrates with fats had favorable effects on lipoprotein cholesterol concentrations in European adolescents and adults when fats were consumed as MUFAs or PUFAs but not as SFAs.
BACKGROUND: Associations between increased dietary fat and decreased carbohydrate intake with circulating HDL and non-HDL cholesterol have not been conclusively determined. OBJECTIVE: We assessed these relations in 8 European observational human studies participating in the European Nutritional Phenotype Assessment and Data Sharing Initiative (ENPADASI) using harmonized data. METHODS: Dietary macronutrient intake was recorded using study-specific dietary assessment tools. Main outcome measures were lipoprotein cholesterol concentrations: HDL cholesterol (mg/dL) and non-HDL cholesterol (mg/dL). A cross-sectional analysis on 5919 participants (54% female) aged 13-80 y was undertaken using the statistical platform DataSHIELD that allows remote/federated nondisclosive analysis of individual-level data. Generalized linear models (GLM) were fitted to assess associations between replacing 5% of energy from carbohydrates with equivalent energy from total fats, SFAs, MUFAs, or PUFAs with circulating HDL cholesterol and non-HDL cholesterol. GLM were adjusted for study source, age, sex, smoking status, alcohol intake and BMI. RESULTS: The replacement of 5% of energy from carbohydrates with total fats or MUFAs was statistically significantly associated with 0.67 mg/dL (95% CI: 0.40, 0.94) or 0.99 mg/dL (95% CI: 0.37, 1.60) higher HDL cholesterol, respectively, but not with non-HDL cholesterol concentrations. The replacement of 5% of energy from carbohydrates with SFAs or PUFAs was not associated with HDL cholesterol, but SFAs were statistically significantly associated with 1.94 mg/dL (95% CI: 0.08, 3.79) higher non-HDL cholesterol, and PUFAs with -3.91 mg/dL (95% CI: -6.98, -0.84) lower non-HDL cholesterol concentrations. A statistically significant interaction by sex for the association of replacing carbohydrates with MUFAs and non-HDL cholesterol was observed, showing a statistically significant inverse association in males and no statistically significant association in females. We observed no statistically significant interaction by age. CONCLUSIONS: The replacement of dietary carbohydrates with fats had favorable effects on lipoprotein cholesterol concentrations in European adolescents and adults when fats were consumed as MUFAs or PUFAs but not as SFAs.
Keywords:
adolescents; adults; blood lipids; carbohydrates; data integration; data sharing; dietary intake; energy density models; fatty acids; substitution