J L Vassy1,2, N H Durant3, E K Kabagambe4, M R Carnethon5, L J Rasmussen-Torvik5, M Fornage6, C E Lewis7, D S Siscovick8, J B Meigs9,10. 1. General Medicine Division, Massachusetts General Hospital, 50 Staniford Street, 9th floor, Boston, MA, 02114, USA. jvassy@partners.org. 2. Department of Medicine, Harvard Medical School, Boston, MA, USA. jvassy@partners.org. 3. Division of Pediatrics and Adolescent Medicine, Department of Pediatrics, University of Alabama Birmingham School of Medicine, Birmingham, AL, USA. 4. Department of Epidemiology, University of Alabama Birmingham School of Public Health, Birmingham, AL, USA. 5. Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. 6. Institute of Molecular Medicine Research Center for Human Genetics, The University of Texas Health Science Center, Houston, TX, USA. 7. Division of Preventive Medicine, Department of Medicine, University of Alabama Birmingham, Birmingham, AL, USA. 8. Cardiovascular Health Research Unit, Departments of Medicine and Epidemiology, University of Washington, Seattle, WA, USA. 9. General Medicine Division, Massachusetts General Hospital, 50 Staniford Street, 9th floor, Boston, MA, 02114, USA. 10. Department of Medicine, Harvard Medical School, Boston, MA, USA.
Abstract
AIMS/HYPOTHESIS: Genotype does not change over the life course and may thus facilitate earlier identification of individuals at high risk for type 2 diabetes. We hypothesised that a genotype score predicts incident type 2 diabetes from young adulthood and improves diabetes prediction models based on clinical risk factors alone. METHODS: The Coronary Artery Risk Development in Young Adults (CARDIA) study followed young adults (aged 18-30 years, mean age 25) serially into middle adulthood. We used Cox regression to build nested prediction models for incident type 2 diabetes based on clinical risk factors assessed in young adulthood (age, sex, race, parental history of diabetes, BMI, mean arterial pressure, fasting glucose, HDL-cholesterol and triacylglyercol), without and with a 38-variant genotype score. Models were compared with C statistics and continuous net reclassification improvement indices (NRI). RESULTS: Of 2,439 participants, 830 (34%) were black and 249 (10%) had a BMI ≥ 30 kg/m(2) at baseline. Over a mean 23.9 years of follow-up, 215 (8.8%) participants developed type 2 diabetes. The genotype score significantly predicted incident diabetes in all models, with an HR of 1.08 per risk allele (95% CI 1.04, 1.13) in the full model. The addition of the score to the full model modestly improved reclassification (continuous NRI 0.285; 95% CI 0.126, 0.433) but not discrimination (C statistics 0.824 and 0.829 in full models with and without score). Race-stratified analyses were similar. CONCLUSIONS/ INTERPRETATION: Knowledge of genotype predicts type 2 diabetes over 25 years in white and black young adults but may not improve prediction over routine clinical measurements.
AIMS/HYPOTHESIS: Genotype does not change over the life course and may thus facilitate earlier identification of individuals at high risk for type 2 diabetes. We hypothesised that a genotype score predicts incident type 2 diabetes from young adulthood and improves diabetes prediction models based on clinical risk factors alone. METHODS: The Coronary Artery Risk Development in Young Adults (CARDIA) study followed young adults (aged 18-30 years, mean age 25) serially into middle adulthood. We used Cox regression to build nested prediction models for incident type 2 diabetes based on clinical risk factors assessed in young adulthood (age, sex, race, parental history of diabetes, BMI, mean arterial pressure, fasting glucose, HDL-cholesterol and triacylglyercol), without and with a 38-variant genotype score. Models were compared with C statistics and continuous net reclassification improvement indices (NRI). RESULTS: Of 2,439 participants, 830 (34%) were black and 249 (10%) had a BMI ≥ 30 kg/m(2) at baseline. Over a mean 23.9 years of follow-up, 215 (8.8%) participants developed type 2 diabetes. The genotype score significantly predicted incident diabetes in all models, with an HR of 1.08 per risk allele (95% CI 1.04, 1.13) in the full model. The addition of the score to the full model modestly improved reclassification (continuous NRI 0.285; 95% CI 0.126, 0.433) but not discrimination (C statistics 0.824 and 0.829 in full models with and without score). Race-stratified analyses were similar. CONCLUSIONS/ INTERPRETATION: Knowledge of genotype predicts type 2 diabetes over 25 years in white and black young adults but may not improve prediction over routine clinical measurements.
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