AIM: The objective of this study is to propose a non-parametric pharmacokinetic prediction model that addresses the individualized risk of developing type-2 diabetes in subjects with family history of type-2 diabetes. METHOD: All selected 191 healthy subjects had both parents as type-2 diabetic. Glucose was administered intravenously (0.5 g/kg body weight) and 13 blood samples taken at specified times were analyzed for plasma insulin and glucose concentrations. All subjects were followed for an average of 13-14 years for diabetic or normal (non-diabetic) outcome. RESULTS: The new logistic regression model predicts the development of diabetes based on body mass index and only one blood sample at 90 min analyzed for insulin concentration. Our model correctly identified 4.5 times more subjects (54% versus 11.6%) predicted to develop diabetes and more than twice the subjects (99% versus 46.4%) predicted not to develop diabetes compared to current non-pharmacokinetic probability estimates for development of type-2 diabetes. CONCLUSION: Our model can be useful for individualized prediction of development of type-2 diabetes in subjects with family history of type-2 diabetes. This improved prediction may be an important mediating factor for better perception of risk and may result in an improved intervention.
AIM: The objective of this study is to propose a non-parametric pharmacokinetic prediction model that addresses the individualized risk of developing type-2 diabetes in subjects with family history of type-2 diabetes. METHOD: All selected 191 healthy subjects had both parents as type-2 diabetic. Glucose was administered intravenously (0.5 g/kg body weight) and 13 blood samples taken at specified times were analyzed for plasma insulin and glucose concentrations. All subjects were followed for an average of 13-14 years for diabetic or normal (non-diabetic) outcome. RESULTS: The new logistic regression model predicts the development of diabetes based on body mass index and only one blood sample at 90 min analyzed for insulin concentration. Our model correctly identified 4.5 times more subjects (54% versus 11.6%) predicted to develop diabetes and more than twice the subjects (99% versus 46.4%) predicted not to develop diabetes compared to current non-pharmacokinetic probability estimates for development of type-2 diabetes. CONCLUSION: Our model can be useful for individualized prediction of development of type-2 diabetes in subjects with family history of type-2 diabetes. This improved prediction may be an important mediating factor for better perception of risk and may result in an improved intervention.
Authors: T S Harwell; N Dettori; B N Flook; L Priest; D F Williamson; S D Helgerson; D Gohdes Journal: Diabetes Care Date: 2001-11 Impact factor: 19.112
Authors: Tabitha A Harrison; Lucia A Hindorff; Helen Kim; Roberta C M Wines; Deborah J Bowen; Barbara B McGrath; Karen L Edwards Journal: Am J Prev Med Date: 2003-02 Impact factor: 5.043
Authors: J Tuomilehto; J Lindström; J G Eriksson; T T Valle; H Hämäläinen; P Ilanne-Parikka; S Keinänen-Kiukaanniemi; M Laakso; A Louheranta; M Rastas; V Salminen; M Uusitupa Journal: N Engl J Med Date: 2001-05-03 Impact factor: 91.245
Authors: William C Knowler; Elizabeth Barrett-Connor; Sarah E Fowler; Richard F Hamman; John M Lachin; Elizabeth A Walker; David M Nathan Journal: N Engl J Med Date: 2002-02-07 Impact factor: 91.245