Keren Papier1, Maria G Kakkoura2, Huaidong Du3,4, Timothy J Key1, Yu Guo5,6, Anika Knuppel7, Pei Pei6, Tammy Y N Tong1, Canqing Yu8,9, Aurora Perez-Cornago1, Wing Ching Chang3, Junshi Chen10, Jun Lv8,9, Liming Li8,9, Zhengming Chen3,4. 1. Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK. 2. Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK. Maria.Kakkoura@ndph.ox.ac.uk. 3. Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK. 4. Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK. 5. Chinese Academy of Medical Sciences, Beijing, China. 6. Fuwai Hospital Chinese Academy of Medical Sciences, Beijing, China. 7. MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK. 8. Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China. 9. Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing, 100191, China. 10. China National Center for Food Safety Risk Assessment, Beijing, China.
Abstract
PURPOSE: Different populations may exhibit differences in dietary intakes, which may result in heterogeneities in diet-disease associations. We compared intakes of major food groups overall, by sex, and by socio-economic status (SES) (defined as both education and income), between participants in the China Kadoorie Biobank (CKB) and the UK Biobank (UKB). METHODS: Data were from ~ 25,000 CKB participants who completed a validated interviewer-administered computer-based questionnaire (2013-2014) and ~ 74,000 UKB participants who completed ≥ 3 web-based 24-h dietary assessments (2009-2012). Intakes of 12 major food groups and five beverages were harmonized and compared between the cohorts overall, by sex and by SES. Multivariable-adjusted linear regression examined the associations between dietary intakes and body mass index (BMI) in each cohort. RESULTS: CKB participants reported consuming more rice, eggs, vegetables, soya products, and less wheat, other staple foods (other than rice and wheat), fish, poultry, all dairy products, fruit, and beverages compared to UKB participants. Red meat intake was similar in both cohorts. Having a higher SES was generally associated with a higher consumption of foods and beverages in CKB, whereas in UKB dietary intakes differed more by education and income, with a positive association observed for meat and income in both UKB and CKB but an inverse association observed for education in UKB. Associations of dietary intakes with BMI varied between the two cohorts. CONCLUSION: The large differences in dietary intakes and their associations with SES and BMI could provide insight into the interpretation of potentially different diet-disease associations between CKB and UKB.
PURPOSE: Different populations may exhibit differences in dietary intakes, which may result in heterogeneities in diet-disease associations. We compared intakes of major food groups overall, by sex, and by socio-economic status (SES) (defined as both education and income), between participants in the China Kadoorie Biobank (CKB) and the UK Biobank (UKB). METHODS: Data were from ~ 25,000 CKB participants who completed a validated interviewer-administered computer-based questionnaire (2013-2014) and ~ 74,000 UKB participants who completed ≥ 3 web-based 24-h dietary assessments (2009-2012). Intakes of 12 major food groups and five beverages were harmonized and compared between the cohorts overall, by sex and by SES. Multivariable-adjusted linear regression examined the associations between dietary intakes and body mass index (BMI) in each cohort. RESULTS: CKB participants reported consuming more rice, eggs, vegetables, soya products, and less wheat, other staple foods (other than rice and wheat), fish, poultry, all dairy products, fruit, and beverages compared to UKB participants. Red meat intake was similar in both cohorts. Having a higher SES was generally associated with a higher consumption of foods and beverages in CKB, whereas in UKB dietary intakes differed more by education and income, with a positive association observed for meat and income in both UKB and CKB but an inverse association observed for education in UKB. Associations of dietary intakes with BMI varied between the two cohorts. CONCLUSION: The large differences in dietary intakes and their associations with SES and BMI could provide insight into the interpretation of potentially different diet-disease associations between CKB and UKB.
Authors: Anna Fry; Thomas J Littlejohns; Cathie Sudlow; Nicola Doherty; Ligia Adamska; Tim Sprosen; Rory Collins; Naomi E Allen Journal: Am J Epidemiol Date: 2017-11-01 Impact factor: 4.897
Authors: Darren C Greenwood; Laura J Hardie; Gary S Frost; Nisreen A Alwan; Kathryn E Bradbury; Michelle Carter; Paul Elliott; Charlotte E L Evans; Heather E Ford; Neil Hancock; Timothy J Key; Bette Liu; Michelle A Morris; Umme Z Mulla; Katerina Petropoulou; Gregory D M Potter; Elio Riboli; Heather Young; Petra A Wark; Janet E Cade Journal: Am J Epidemiol Date: 2019-10-01 Impact factor: 4.897