Annalise E Zemlin1, Tandi E Matsha2, Andre P Kengne3, Rajiv T Erasmus4. 1. Division of Chemical Pathology, National Health Laboratory Service, Tygerberg Hospital, Cape Town, South Africa; University of Stellenbosch, Tygerberg Hospital, Cape Town, South Africa. Electronic address: azemlin@sun.ac.za. 2. Department of Biomedical Sciences, Cape Peninsula University of Technology, Cape Town, South Africa. Electronic address: matshat@cput.ac.za. 3. Non-Communicable Diseases Research Unit, Medical Research Council, Cape Town, South Africa; University of Cape Town, Cape Town, South Africa. Electronic address: Andre.Kengne@mrc.ac.za. 4. Division of Chemical Pathology, National Health Laboratory Service, Tygerberg Hospital, Cape Town, South Africa; University of Stellenbosch, Tygerberg Hospital, Cape Town, South Africa. Electronic address: rte@sun.ac.za.
Abstract
INTRODUCTION: Prediabetes compromises impaired fasting glucose and impaired glucose tolerance and is a high risk for future diabetes mellitus and cardiovascular disease. Traditional diagnostic methods involve a fasting sample or oral glucose tolerance test, which is cumbersome, time-consuming and inconvenient. An HbA1c-based approach has been incorporated into new guidelines, but cut-offs may vary and have not been defined for all population groups. We derived and validated HbA1c cut-offs to diagnose prediabetes in mixed ancestry South Africans. METHODS: Participants were 667 (derivation sample), 234 (validation sample 1) and 674 (validation sample 2) diabetes-free individuals. They underwent standard 2-hour OGTT with HbA1c test. Receiver-operator characteristic curves were used to determine optimal HbA1c cut-off to predict prediabetes. RESULTS: A total of 27.7% participants in the derivation sample had prediabetes versus 17.5% (validation sample 1) and 15.4% (validation sample 2). The optimal cut-off was 5.75% in all three cohorts with sensitivity and specificity of 64.8% and 60.4% in combined derivation and validation sample 1, and 59.6% and 69.8% in validation sample 2. CONCLUSION: The discriminatory capacity of HbA1c for predicting prediabetes in this population is modest at the derived cut-off. The use of HbA1c alone in this setting may result in an inaccurate diagnosis.
INTRODUCTION:Prediabetes compromises impaired fasting glucose and impaired glucose tolerance and is a high risk for future diabetes mellitus and cardiovascular disease. Traditional diagnostic methods involve a fasting sample or oral glucose tolerance test, which is cumbersome, time-consuming and inconvenient. An HbA1c-based approach has been incorporated into new guidelines, but cut-offs may vary and have not been defined for all population groups. We derived and validated HbA1c cut-offs to diagnose prediabetes in mixed ancestry South Africans. METHODS:Participants were 667 (derivation sample), 234 (validation sample 1) and 674 (validation sample 2) diabetes-free individuals. They underwent standard 2-hour OGTT with HbA1c test. Receiver-operator characteristic curves were used to determine optimal HbA1c cut-off to predict prediabetes. RESULTS: A total of 27.7% participants in the derivation sample had prediabetes versus 17.5% (validation sample 1) and 15.4% (validation sample 2). The optimal cut-off was 5.75% in all three cohorts with sensitivity and specificity of 64.8% and 60.4% in combined derivation and validation sample 1, and 59.6% and 69.8% in validation sample 2. CONCLUSION: The discriminatory capacity of HbA1c for predicting prediabetes in this population is modest at the derived cut-off. The use of HbA1c alone in this setting may result in an inaccurate diagnosis.
Authors: Philippe B Katchunga; Patrick N Mirindi; Antoine S Kishabongo; Justin C Cikomola; Socrate Bwanamdogo; Jan Philippé; Marijn M Speeckaert; Joris R Delanghe Journal: Biochem Med (Zagreb) Date: 2015-10-15 Impact factor: 2.313
Authors: Kim A Nguyen; Nasheeta Peer; Anniza de Villiers; Barbara Mukasa; Tandi E Matsha; Edward J Mills; Andre P Kengne Journal: PLoS One Date: 2019-01-31 Impact factor: 3.240