S Soulimane1, D Simon, J E Shaw, P Z Zimmet, S Vol, D Vistisen, D J Magliano, K Borch-Johnsen, B Balkau. 1. Inserm, CESP Centre for Research in Epidemiology and Population Health, U1018, Epidemiology of Diabetes, Obesity and Chronic Kidney Diseases over the Lifecourse, Villejuif Université Paris Sud, UMRS 1018, Villejuif, France. soraya.soulimane@inserm.fr
Abstract
AIM: We examined the ability of fasting plasma glucose and HbA(1c) to predict 5-year incident diabetes for an Australian cohort and a Danish cohort and 6-year incident diabetes for a French cohort, as defined by the corresponding criteria. METHODS: We studied 6025 men and women from AusDiab (Australian), 4703 from Inter99 (Danish) and 3784 from DESIR (French), not treated for diabetes and with fasting plasma glucose < 7.0 mmol/l and HbA(1c) < 48 mmol/mol (6.5%) at inclusion. Diabetes was defined as fasting plasma glucose ≥ 7.0 mmol/l and/or treatment for diabetes or as HbA(1c) ≥ 48 mmol/mol (6.5%) and/or treatment for diabetes. RESULTS: For AusDiab, incident fasting plasma glucose-defined diabetes was more frequent than HbA(1c) -defined diabetes (P(McNemar)<0.0001), the reverse applied to Inter99 (P(McNemar) < 0.007) and for DESIR there was no difference (P(McNema)=0.17). Less than one third of the incident cases were detected by both criteria. Logistic regression models showed that baseline fasting plasma glucose and baseline HbA(1c) predicted incident diabetes defined by the corresponding criteria. The standardized odds ratios (95% confidence interval) for HbA(1c) were a little higher than for fasting plasma glucose, but not significantly so. They were respectively, 5.0 (4.1-6.1) and 4.1 (3.5-4.9) for AusDiab, 5.0 (3.6-6.8) and 4.8 (3.6-6.3) for Inter99, 4.8 (3.6-6.5) and 4.6 (3.6-5.9) for DESIR. CONCLUSIONS: Fasting plasma glucose and HbA(1c) are good predictors of incident diabetes defined by the corresponding criteria. Despite Diabetes Control and Complications Trial-alignment of the three HbA(1c) assays, there was a large difference in the HbA(1c) distributions between these studies, conducted some 10 years ago. Thus, it is difficult to compare absolute values of diabetes prevalence and incidence based on HbA(1c) measurements from that time.
AIM: We examined the ability of fasting plasma glucose and HbA(1c) to predict 5-year incident diabetes for an Australian cohort and a Danish cohort and 6-year incident diabetes for a French cohort, as defined by the corresponding criteria. METHODS: We studied 6025 men and women from AusDiab (Australian), 4703 from Inter99 (Danish) and 3784 from DESIR (French), not treated for diabetes and with fasting plasma glucose < 7.0 mmol/l and HbA(1c) < 48 mmol/mol (6.5%) at inclusion. Diabetes was defined as fasting plasma glucose ≥ 7.0 mmol/l and/or treatment for diabetes or as HbA(1c) ≥ 48 mmol/mol (6.5%) and/or treatment for diabetes. RESULTS: For AusDiab, incident fasting plasma glucose-defined diabetes was more frequent than HbA(1c) -defined diabetes (P(McNemar)<0.0001), the reverse applied to Inter99 (P(McNemar) < 0.007) and for DESIR there was no difference (P(McNema)=0.17). Less than one third of the incident cases were detected by both criteria. Logistic regression models showed that baseline fasting plasma glucose and baseline HbA(1c) predicted incident diabetes defined by the corresponding criteria. The standardized odds ratios (95% confidence interval) for HbA(1c) were a little higher than for fasting plasma glucose, but not significantly so. They were respectively, 5.0 (4.1-6.1) and 4.1 (3.5-4.9) for AusDiab, 5.0 (3.6-6.8) and 4.8 (3.6-6.3) for Inter99, 4.8 (3.6-6.5) and 4.6 (3.6-5.9) for DESIR. CONCLUSIONS: Fasting plasma glucose and HbA(1c) are good predictors of incident diabetes defined by the corresponding criteria. Despite Diabetes Control and Complications Trial-alignment of the three HbA(1c) assays, there was a large difference in the HbA(1c) distributions between these studies, conducted some 10 years ago. Thus, it is difficult to compare absolute values of diabetes prevalence and incidence based on HbA(1c) measurements from that time.
Authors: Soraya Soulimane; Dominique Simon; William H Herman; Celine Lange; Crystal M Y Lee; Stephen Colagiuri; Jonathan E Shaw; Paul Z Zimmet; Dianna Magliano; Sandra R G Ferreira; Yanghu Dong; Lei Zhang; Torben Jorgensen; Jaakko Tuomilehto; Viswanathan Mohan; Dirk L Christensen; Lydia Kaduka; Jacqueline M Dekker; Giel Nijpels; Coen D A Stehouwer; Olivier Lantieri; Wilfred Y Fujimoto; Donna L Leonetti; Marguerite J McNeely; Knut Borch-Johnsen; Edward J Boyko; Dorte Vistisen; Beverley Balkau Journal: Diabetologia Date: 2013-09-25 Impact factor: 10.122
Authors: Simone P Rauh; Martijn W Heymans; Anitra D M Koopman; Giel Nijpels; Coen D Stehouwer; Barbara Thorand; Wolfgang Rathmann; Christa Meisinger; Annette Peters; Tonia de Las Heras Gala; Charlotte Glümer; Oluf Pedersen; Henna Cederberg; Johanna Kuusisto; Markku Laakso; Ewan R Pearson; Paul W Franks; Femke Rutters; Jacqueline M Dekker Journal: PLoS One Date: 2017-02-10 Impact factor: 3.240
Authors: Anne J Wanders; Wendy A M Blom; Peter L Zock; Johanna M Geleijnse; Ingeborg A Brouwer; Marjan Alssema Journal: BMJ Open Diabetes Res Care Date: 2019-02-08
Authors: Carolien Ruijgrok; Jacqueline M Dekker; Joline W Beulens; Ingeborg A Brouwer; Veerle M H Coupé; Martijn W Heymans; Femke P C Sijtsma; David J Mela; Peter L Zock; Margreet R Olthof; Marjan Alssema Journal: Diabetologia Date: 2017-10-10 Impact factor: 10.122