Nicola M McKeown1, Hassan S Dashti2,3,4, Jiantao Ma5, Danielle E Haslam6, Jessica C Kiefte-de Jong7,8, Caren E Smith9, Toshiko Tanaka10, Mariaelisa Graff11, Rozenn N Lemaitre12, Denis Rybin13, Emily Sonestedt14, Alexis C Frazier-Wood15, Dennis O Mook-Kanamori16,17, Yanping Li18, Carol A Wang19, Elisabeth T M Leermakers7, Vera Mikkilä20,21, Kristin L Young11, Kenneth J Mukamal22, L Adrienne Cupples5,23, Christina-Alexandra Schulz14, Tzu-An Chen15, Ruifang Li-Gao16, Tao Huang18, Wendy H Oddy24,25, Olli Raitakari20,26, Kenneth Rice27, James B Meigs28,29,30, Ulrika Ericson14, Lyn M Steffen31, Frits R Rosendaal16, Albert Hofman7, Mika Kähönen32, Bruce M Psaty12,33,34,35, Louise Brunkwall14, Andre G Uitterlinden7, Jorma Viikari36,37, David S Siscovick38, Ilkka Seppälä39, Kari E North11, Dariush Mozaffarian40, Josée Dupuis5,23, Marju Orho-Melander14, Stephen S Rich41, Renée de Mutsert16, Lu Qi18, Craig E Pennell19, Oscar H Franco7, Terho Lehtimäki40, Mark A Herman42. 1. Nutritional Epidemiology Program, Jean Mayer US Department of Agriculture Human Nutrition Research Center on Aging, Tufts University, 711 Washington Street, Boston, MA, 02111, USA. nicola.mckeown@tufts.edu. 2. Nutrition & Genomics Laboratory, Jean Mayer US Department of Agriculture Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA. hassan.dashti@mgh.harvard.edu. 3. Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114, USA. hassan.dashti@mgh.harvard.edu. 4. Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA. hassan.dashti@mgh.harvard.edu. 5. National Heart, Lung, and Blood Institute's Framingham Heart Study and Population Sciences Branch, Framingham, MA, USA. 6. Nutritional Epidemiology Program, Jean Mayer US Department of Agriculture Human Nutrition Research Center on Aging, Tufts University, 711 Washington Street, Boston, MA, 02111, USA. 7. Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands. 8. Global Public Health, Leiden University College, The Hague, the Netherlands. 9. Nutrition & Genomics Laboratory, Jean Mayer US Department of Agriculture Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA. 10. Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA. 11. Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA. 12. Department of Medicine, University of Washington, Seattle, WA, USA. 13. Boston University Data Coordinating Center, Boston University, Boston, MA, USA. 14. Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden. 15. USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA. 16. Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands. 17. Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, the Netherlands. 18. Department of Nutrition, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA, USA. 19. School of Women's and Infants' Health, The University of Western Australia, Crawley, WA, Australia. 20. Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland. 21. Department of Food and Environmental Sciences, University of Helsinki, Helsinki, Finland. 22. Division of General Medicine and Primary Care, Harvard Medical School and Beth Israel Deaconess Medical Center, Boston, MA, USA. 23. Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA. 24. Telethon Kids Institute, Subiaco, WA, Australia. 25. Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia. 26. Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland. 27. Department of Biostatistics, University of Washington, Seattle, WA, USA. 28. Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA. 29. Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA. 30. Department of Medicine, Harvard Medical School, Boston, MA, USA. 31. Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA. 32. Department of Clinical Physiology, Tampere University Hospital, and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland. 33. Department of Epidemiology, University of Washington, Seattle, WA, USA. 34. Department of Health Services, University of Washington, Seattle, WA, USA. 35. Group Health Research Institute, Group Health Cooperative, Seattle, WA, USA. 36. Department of Medicine, University of Turku, Turku, Finland. 37. Division of Medicine, Turku University Hospital, Turku, Finland. 38. The New York Academy of Medicine, New York, NY, USA. 39. Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland. 40. Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA. 41. Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA. 42. Division Of Endocrinology, Metabolism, and Nutrition, Department of Medicine, Duke University School of Medicine, Durham, NC, USA.
Abstract
AIMS/HYPOTHESIS: Sugar-sweetened beverages (SSBs) are a major dietary contributor to fructose intake. A molecular pathway involving the carbohydrate responsive element-binding protein (ChREBP) and the metabolic hormone fibroblast growth factor 21 (FGF21) may influence sugar metabolism and, thereby, contribute to fructose-induced metabolic disease. We hypothesise that common variants in 11 genes involved in fructose metabolism and the ChREBP-FGF21 pathway may interact with SSB intake to exacerbate positive associations between higher SSB intake and glycaemic traits. METHODS: Data from 11 cohorts (six discovery and five replication) in the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium provided association and interaction results from 34,748 adults of European descent. SSB intake (soft drinks, fruit punches, lemonades or other fruit drinks) was derived from food-frequency questionnaires and food diaries. In fixed-effects meta-analyses, we quantified: (1) the associations between SSBs and glycaemic traits (fasting glucose and fasting insulin); and (2) the interactions between SSBs and 18 independent SNPs related to the ChREBP-FGF21 pathway. RESULTS: In our combined meta-analyses of discovery and replication cohorts, after adjustment for age, sex, energy intake, BMI and other dietary covariates, each additional serving of SSB intake was associated with higher fasting glucose (β ± SE 0.014 ± 0.004 [mmol/l], p = 1.5 × 10-3) and higher fasting insulin (0.030 ± 0.005 [log e pmol/l], p = 2.0 × 10-10). No significant interactions on glycaemic traits were observed between SSB intake and selected SNPs. While a suggestive interaction was observed in the discovery cohorts with a SNP (rs1542423) in the β-Klotho (KLB) locus on fasting insulin (0.030 ± 0.011 log e pmol/l, uncorrected p = 0.006), results in the replication cohorts and combined meta-analyses were non-significant. CONCLUSIONS/ INTERPRETATION: In this large meta-analysis, we observed that SSB intake was associated with higher fasting glucose and insulin. Although a suggestive interaction with a genetic variant in the ChREBP-FGF21 pathway was observed in the discovery cohorts, this observation was not confirmed in the replication analysis. TRIAL REGISTRATION: Trials related to this study were registered at clinicaltrials.gov as NCT00005131 (Atherosclerosis Risk in Communities), NCT00005133 (Cardiovascular Health Study), NCT00005121 (Framingham Offspring Study), NCT00005487 (Multi-Ethnic Study of Atherosclerosis) and NCT00005152 (Nurses' Health Study).
AIMS/HYPOTHESIS: Sugar-sweetened beverages (SSBs) are a major dietary contributor to fructose intake. A molecular pathway involving the carbohydrate responsive element-binding protein (ChREBP) and the metabolic hormone fibroblast growth factor 21 (FGF21) may influence sugar metabolism and, thereby, contribute to fructose-induced metabolic disease. We hypothesise that common variants in 11 genes involved in fructose metabolism and the ChREBP-FGF21 pathway may interact with SSB intake to exacerbate positive associations between higher SSB intake and glycaemic traits. METHODS: Data from 11 cohorts (six discovery and five replication) in the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium provided association and interaction results from 34,748 adults of European descent. SSB intake (soft drinks, fruit punches, lemonades or other fruit drinks) was derived from food-frequency questionnaires and food diaries. In fixed-effects meta-analyses, we quantified: (1) the associations between SSBs and glycaemic traits (fasting glucose and fasting insulin); and (2) the interactions between SSBs and 18 independent SNPs related to the ChREBP-FGF21 pathway. RESULTS: In our combined meta-analyses of discovery and replication cohorts, after adjustment for age, sex, energy intake, BMI and other dietary covariates, each additional serving of SSB intake was associated with higher fasting glucose (β ± SE 0.014 ± 0.004 [mmol/l], p = 1.5 × 10-3) and higher fasting insulin (0.030 ± 0.005 [log e pmol/l], p = 2.0 × 10-10). No significant interactions on glycaemic traits were observed between SSB intake and selected SNPs. While a suggestive interaction was observed in the discovery cohorts with a SNP (rs1542423) in the β-Klotho (KLB) locus on fasting insulin (0.030 ± 0.011 log e pmol/l, uncorrected p = 0.006), results in the replication cohorts and combined meta-analyses were non-significant. CONCLUSIONS/ INTERPRETATION: In this large meta-analysis, we observed that SSB intake was associated with higher fasting glucose and insulin. Although a suggestive interaction with a genetic variant in the ChREBP-FGF21 pathway was observed in the discovery cohorts, this observation was not confirmed in the replication analysis. TRIAL REGISTRATION: Trials related to this study were registered at clinicaltrials.gov as NCT00005131 (Atherosclerosis Risk in Communities), NCT00005133 (Cardiovascular Health Study), NCT00005121 (Framingham Offspring Study), NCT00005487 (Multi-Ethnic Study of Atherosclerosis) and NCT00005152 (Nurses' Health Study).
Entities:
Keywords:
Carbohydrate metabolism; Epidemiology; Genetics; Meta-analysis; Nutrition; Type 2 diabetes
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