AIM: The aim of this study was to assess the predictive power of glycated hemoglobin (HbA1c) for future type 2 diabetes risk. RESEARCH DESIGN AND METHODS: Six hundred eighty-seven subjects who were free of type 2 diabetes mellitus (T2DM) participated in the study. Each subject received a 75-g oral glucose tolerance test at baseline and 624 received a repeat oral glucose tolerance test after 3.5 ± 0.1 yr of follow-up. Anthropometric measurements, lipid profile, and HbA1c were measured during the baseline visit. Logistic multivariate models were created with T2DM status at follow-up as the dependent variable and other parameters as the independent variables. The receiver-operating characteristic (ROC) was used to assess the predictive discrimination of the various models. RESULTS: HbA1c was a significant predictor of future T2DM risk (area under the ROC curve = 0.73, P < 0.0001). A HbA1c cut point of 5.65% had the maximal sum of sensitivity and specificity. Although the area under the ROC curve of HbA1c was smaller than the area under the ROC curve of both the 1-h plasma glucose concentration and a multivariate logistic model (including anthropometric parameters, lipid profile, and fasting plasma glucose), the addition of HbA1c to both the 1-h plasma glucose and the multivariate logistic model significantly increased their predictive power. CONCLUSION: Although HbA1c alone is a weaker predictor of future T2DM risk compared with the 1-h plasma glucose, it provides additive information about future T2DM risk when added to previously published prediction models.
AIM: The aim of this study was to assess the predictive power of glycated hemoglobin (HbA1c) for future type 2 diabetes risk. RESEARCH DESIGN AND METHODS: Six hundred eighty-seven subjects who were free of type 2 diabetes mellitus (T2DM) participated in the study. Each subject received a 75-g oral glucose tolerance test at baseline and 624 received a repeat oral glucose tolerance test after 3.5 ± 0.1 yr of follow-up. Anthropometric measurements, lipid profile, and HbA1c were measured during the baseline visit. Logistic multivariate models were created with T2DM status at follow-up as the dependent variable and other parameters as the independent variables. The receiver-operating characteristic (ROC) was used to assess the predictive discrimination of the various models. RESULTS: HbA1c was a significant predictor of future T2DM risk (area under the ROC curve = 0.73, P < 0.0001). A HbA1c cut point of 5.65% had the maximal sum of sensitivity and specificity. Although the area under the ROC curve of HbA1c was smaller than the area under the ROC curve of both the 1-h plasma glucose concentration and a multivariate logistic model (including anthropometric parameters, lipid profile, and fasting plasma glucose), the addition of HbA1c to both the 1-h plasma glucose and the multivariate logistic model significantly increased their predictive power. CONCLUSION: Although HbA1c alone is a weaker predictor of future T2DM risk compared with the 1-h plasma glucose, it provides additive information about future T2DM risk when added to previously published prediction models.
Authors: Y Heianza; Y Arase; S D Hsieh; K Saito; H Tsuji; S Kodama; S Tanaka; Y Ohashi; H Shimano; N Yamada; S Hara; H Sone Journal: Diabetologia Date: 2012-09-07 Impact factor: 10.122
Authors: Michael Bergman; Muhammad Abdul-Ghani; Ralph A DeFronzo; Melania Manco; Giorgio Sesti; Teresa Vanessa Fiorentino; Antonio Ceriello; Mary Rhee; Lawrence S Phillips; Stephanie Chung; Celeste Cravalho; Ram Jagannathan; Louis Monnier; Claude Colette; David Owens; Cristina Bianchi; Stefano Del Prato; Mariana P Monteiro; João Sérgio Neves; Jose Luiz Medina; Maria Paula Macedo; Rogério Tavares Ribeiro; João Filipe Raposo; Brenda Dorcely; Nouran Ibrahim; Martin Buysschaert Journal: Diabetes Res Clin Pract Date: 2020-06-01 Impact factor: 5.602
Authors: Vasudha Ahuja; Pasi Aronen; T A Pramodkumar; Helen Looker; Angela Chetrit; Aini H Bloigu; Auni Juutilainen; Cristina Bianchi; Lucia La Sala; Ranjit Mohan Anjana; Rajendra Pradeepa; Ulagamadesan Venkatesan; Sarvanan Jebarani; Viswanathan Baskar; Teresa Vanessa Fiorentino; Patrick Timpel; Ralph A DeFronzo; Antonio Ceriello; Stefano Del Prato; Muhammad Abdul-Ghani; Sirkka Keinänen-Kiukaanniemi; Rachel Dankner; Peter H Bennett; William C Knowler; Peter Schwarz; Giorgio Sesti; Rie Oka; Viswanathan Mohan; Leif Groop; Jaakko Tuomilehto; Samuli Ripatti; Michael Bergman; Tiinamaija Tuomi Journal: Diabetes Care Date: 2021-04 Impact factor: 19.112
Authors: Maria A Marini; Elena Succurro; Simona Frontoni; Simona Mastroianni; Franco Arturi; Angela Sciacqua; Renato Lauro; Marta L Hribal; Francesco Perticone; Giorgio Sesti Journal: Diabetes Care Date: 2012-02-22 Impact factor: 19.112
Authors: Deborah Taira Juarez; Kendra M Demaris; Roy Goo; Christina Louise Mnatzaganian; Helen Wong Smith Journal: Diabetes Metab Syndr Obes Date: 2014-10-20 Impact factor: 3.168