AIMS/HYPOTHESIS: The aims of this study were to assess the clinical significance of introducing HbA(1c) into a risk score for diabetes and to develop a scoring system to predict the 5 year incidence of diabetes in Japanese individuals. METHODS: The study included 7,654 non-diabetic individuals aged 40-75 years. Incident diabetes was defined as fasting plasma glucose (FPG) ≥7.0 mmol/l, HbA(1c) ≥6.5% (48 mmol/mol) or self-reported clinician-diagnosed diabetes. We constructed a risk score using non-laboratory assessments (NLA) and evaluated improvements in risk prediction by adding elevated FPG, elevated HbA(1c) or both to NLA. RESULTS: The discriminative ability of the NLA score (age, sex, family history of diabetes, current smoking and BMI) was 0.708. The difference in discrimination between the NLA + FPG and NLA + HbA(1c) scores was non-significant (0.836 vs 0.837; p = 0.898). A risk score including family history of diabetes, smoking, obesity and both FPG and HbA(1c) had the highest discrimination (0.887, 95% CI 0.871, 0.903). At an optimal cut-off point, sensitivity and specificity were high at 83.7% and 79.0%, respectively. After initial screening using NLA scores, subsequent information on either FPG or HbA(1c) resulted in a net reclassification improvement of 42.7% or 52.3%, respectively (p < 0.0001). When both were available, net reclassification improvement and integrated discrimination improvement were further improved at 56.7% (95% CI 47.3%, 66.1%) and 10.9% (9.7%, 12.1%), respectively. CONCLUSIONS/ INTERPRETATION: Information on HbA(1c) or FPG levels after initial screening by NLA can precisely refine diabetes risk reclassification.
AIMS/HYPOTHESIS: The aims of this study were to assess the clinical significance of introducing HbA(1c) into a risk score for diabetes and to develop a scoring system to predict the 5 year incidence of diabetes in Japanese individuals. METHODS: The study included 7,654 non-diabetic individuals aged 40-75 years. Incident diabetes was defined as fasting plasma glucose (FPG) ≥7.0 mmol/l, HbA(1c) ≥6.5% (48 mmol/mol) or self-reported clinician-diagnosed diabetes. We constructed a risk score using non-laboratory assessments (NLA) and evaluated improvements in risk prediction by adding elevated FPG, elevated HbA(1c) or both to NLA. RESULTS: The discriminative ability of the NLA score (age, sex, family history of diabetes, current smoking and BMI) was 0.708. The difference in discrimination between the NLA + FPG and NLA + HbA(1c) scores was non-significant (0.836 vs 0.837; p = 0.898). A risk score including family history of diabetes, smoking, obesity and both FPG and HbA(1c) had the highest discrimination (0.887, 95% CI 0.871, 0.903). At an optimal cut-off point, sensitivity and specificity were high at 83.7% and 79.0%, respectively. After initial screening using NLA scores, subsequent information on either FPG or HbA(1c) resulted in a net reclassification improvement of 42.7% or 52.3%, respectively (p < 0.0001). When both were available, net reclassification improvement and integrated discrimination improvement were further improved at 56.7% (95% CI 47.3%, 66.1%) and 10.9% (9.7%, 12.1%), respectively. CONCLUSIONS/ INTERPRETATION: Information on HbA(1c) or FPG levels after initial screening by NLA can precisely refine diabetes risk reclassification.
Authors: Richard Kahn; Peter Alperin; David Eddy; Knut Borch-Johnsen; John Buse; Justin Feigelman; Edward Gregg; Rury R Holman; M Sue Kirkman; Michael Stern; Jaakko Tuomilehto; Nick J Wareham Journal: Lancet Date: 2010-03-29 Impact factor: 79.321
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Authors: Henry S Kahn; Yiling J Cheng; Theodore J Thompson; Giuseppina Imperatore; Edward W Gregg Journal: Ann Intern Med Date: 2009-06-02 Impact factor: 25.391
Authors: Goodarz Danaei; Mariel M Finucane; Yuan Lu; Gitanjali M Singh; Melanie J Cowan; Christopher J Paciorek; John K Lin; Farshad Farzadfar; Young-Ho Khang; Gretchen A Stevens; Mayuree Rao; Mohammed K Ali; Leanne M Riley; Carolyn A Robinson; Majid Ezzati Journal: Lancet Date: 2011-06-24 Impact factor: 79.321
Authors: Manjusha Kulkarni; Randi E Foraker; Ann M McNeill; Cynthia Girman; Sherita H Golden; Wayne D Rosamond; Bruce Duncan; Maria Ines Schmidt; Jaakko Tuomilehto Journal: Diabetes Obes Metab Date: 2017-05-22 Impact factor: 6.577