Crystal Man Ying Lee1, Mark Woodward2, Nirmala Pandeya3, Robert Adams4, Elizabeth Barrett-Connor5, Edward J Boyko6, Mats Eliasson7, Laercio J Franco8, Wilfred Y Fujimoto9, Clicerio Gonzalez10, Barbara V Howard11, David R Jacobs12, Sirkka Keinanen-Kiukaanniemi13, Dianna Magliano14, Pamela Schreiner13, Jonathan E Shaw14, June Stevens15, Anne Taylor16, Jaakko Tuomilehto17, Lynne Wagenknecht18, Rachel R Huxley19. 1. School of Public Health, Curtin University, Australia; The Boden Institute of Obesity, Nutrition, Exercise & Eating Disorders, University of Sydney, Australia. Electronic address: crystal.lee@curtin.edu.au. 2. The George Institute for Global Health, Australia. 3. School of Public Health, University of Queensland, Australia. 4. The Health Observatory, Discipline of Medicine, University of Adelaide, Australia. 5. Department of Family Medicine and Public Health, University of California San Diego, USA. 6. School of Public Health, University of Washington, USA. 7. Department of Public Health and Clinical Medicine, Umea University, Sweden. 8. Department of Social Medicine, University of Sao Paulo, Brazil. 9. Division of Metabolism, Endocrinology and Nutrition, University of Washington, USA. 10. Unidad de Investigación en Diabetes y Riesgo Cardiovascular, Instituto Nacional de Salud Publica, Mexico. 11. MedStar Health Research Institute, Georgetown University Hospital, USA. 12. School of Public Health, University of Minnesota, USA. 13. Center for Life Course Epidemiology and Systems Medicine, University of Oulu, Finland. 14. Baker Heart & Diabetes Institute, Australia. 15. Departments of Nutrition and Epidemiology, University of North Carolina at Chapel Hill, USA. 16. Population Research & Outcomes Studies, Discipline of Medicine, University of Adelaide, Australia. 17. Dasman Diabetes Institute, Kuwait; Department of Neurosciences and Preventive Medicine, Danube-University Krems, Austria; Chronic Disease Prevention Unit, National Institute for Health and Welfare, Finland; Saudi Diabetes Research Group, King Abdulaziz University, Saudi Arabia. 18. Wake Forest School of Medicine, USA. 19. School of Public Health, Curtin University, Australia.
Abstract
AIMS: First, to conduct a detailed exploration of the prospective relations between four commonly used anthropometric measures with incident diabetes and to examine their consistency across different population subgroups. Second, to compare the ability of each of the measures to predict five-year risk of diabetes. METHODS: We conducted a meta-analysis of individual participant data on body mass index (BMI), waist circumference (WC), waist-hip and waist-height ratio (WHtR) from the Obesity, Diabetes and Cardiovascular Disease Collaboration. Cox proportional hazard models were used to estimate the association between a one standard deviation increment in each anthropometric measure and incident diabetes. Harrell's concordance statistic was used to test the predictive accuracy of each measure for diabetes risk at five years. RESULTS: Twenty-one studies with 154,998 participants and 9342 cases of incident diabetes were available. Each of the measures had a positive association with incident diabetes. A one standard deviation increment in each of the measures was associated with 64-80% higher diabetes risk. WC and WHtR more strongly associated with risk than BMI (ratio of hazard ratios: 0.95 [0.92,0.99] - 0.97 [0.95,0.98]) but there was no appreciable difference between the four measures in the predictive accuracy for diabetes at five years. CONCLUSIONS: Despite suggestions that abdominal measures of obesity have stronger associations with incident diabetes and better predictive accuracy than BMI, we found no overall advantage in any one measure at discriminating the risk of developing diabetes. Any of these measures would suffice to assist in primary diabetes prevention efforts.
AIMS: First, to conduct a detailed exploration of the prospective relations between four commonly used anthropometric measures with incident diabetes and to examine their consistency across different population subgroups. Second, to compare the ability of each of the measures to predict five-year risk of diabetes. METHODS: We conducted a meta-analysis of individual participant data on body mass index (BMI), waist circumference (WC), waist-hip and waist-height ratio (WHtR) from the Obesity, Diabetes and Cardiovascular Disease Collaboration. Cox proportional hazard models were used to estimate the association between a one standard deviation increment in each anthropometric measure and incident diabetes. Harrell's concordance statistic was used to test the predictive accuracy of each measure for diabetes risk at five years. RESULTS: Twenty-one studies with 154,998 participants and 9342 cases of incident diabetes were available. Each of the measures had a positive association with incident diabetes. A one standard deviation increment in each of the measures was associated with 64-80% higher diabetes risk. WC and WHtR more strongly associated with risk than BMI (ratio of hazard ratios: 0.95 [0.92,0.99] - 0.97 [0.95,0.98]) but there was no appreciable difference between the four measures in the predictive accuracy for diabetes at five years. CONCLUSIONS: Despite suggestions that abdominal measures of obesity have stronger associations with incident diabetes and better predictive accuracy than BMI, we found no overall advantage in any one measure at discriminating the risk of developing diabetes. Any of these measures would suffice to assist in primary diabetes prevention efforts.
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