TY - JOUR
T1 - Predicting glucose intolerance with normal fasting plasma glucose by the components of the metabolism syndrome
AU - Pei, Dee
AU - Lin, Jiunn Dann
AU - Wu, Du An
AU - Hsieh, Chang Hsun
AU - Hung, Yi Jen
AU - Kuo, Shi Wen
AU - Kuo, Ko Lin
AU - Wu, Chung Ze
AU - Li, Jer Chuan
PY - 2007
Y1 - 2007
N2 - Background: Surprisingly, it is estimated that about half of type 2 diabetics remain undetected. The possible causes may be partly attributable to people with normal fasting plasma glucose (FPG) but abnormal postprandial hyperglycemia. We attempted to develop an effective predictive model by using the metabolic syndrome (MeS) components as parameters to identify such persons. Subjects and methods: All participants received a standard 75-g oral glucose tolerance test, which showed that 106 had normal glucose tolerance, 61 had impaired glucose tolerance, and 6 had diabetes-on-isolated postchallenge hyperglycemia. We tested five models, which included various MeS components. Model 0: FPG; Model 1 (clinical history model): family history (FH), FPG, age and sex; Model 2 (MeS model): Model 1 plus triglycerides, high-density lipoprotein cholesterol, body mass index, systolic blood pressure and diastolic blood pressure; Model 3: Model 2 plus fasting plasma insulin (FPI); Model 4: Model 3 plus homeostasis model assessment of insulin resistance. A receiver-operating characteristic (ROC) curve was used to determine the predictive discrimination of these models. Results: The area under the ROC curve of the Model 0 was significantly larger than the area under the diagonal reference line. All the other 4 models had a larger area under the ROC curve than Model 0. Considering the simplicity and lower cost of Model 2, it would be the best model to use. Nevertheless, Model 3 had the largest area under the ROC curve. Conclusion: We demonstrated that Model 2 and 3 have a significantly better predictive discrimination to identify persons with normal FPG at high risk for glucose intolerance.
AB - Background: Surprisingly, it is estimated that about half of type 2 diabetics remain undetected. The possible causes may be partly attributable to people with normal fasting plasma glucose (FPG) but abnormal postprandial hyperglycemia. We attempted to develop an effective predictive model by using the metabolic syndrome (MeS) components as parameters to identify such persons. Subjects and methods: All participants received a standard 75-g oral glucose tolerance test, which showed that 106 had normal glucose tolerance, 61 had impaired glucose tolerance, and 6 had diabetes-on-isolated postchallenge hyperglycemia. We tested five models, which included various MeS components. Model 0: FPG; Model 1 (clinical history model): family history (FH), FPG, age and sex; Model 2 (MeS model): Model 1 plus triglycerides, high-density lipoprotein cholesterol, body mass index, systolic blood pressure and diastolic blood pressure; Model 3: Model 2 plus fasting plasma insulin (FPI); Model 4: Model 3 plus homeostasis model assessment of insulin resistance. A receiver-operating characteristic (ROC) curve was used to determine the predictive discrimination of these models. Results: The area under the ROC curve of the Model 0 was significantly larger than the area under the diagonal reference line. All the other 4 models had a larger area under the ROC curve than Model 0. Considering the simplicity and lower cost of Model 2, it would be the best model to use. Nevertheless, Model 3 had the largest area under the ROC curve. Conclusion: We demonstrated that Model 2 and 3 have a significantly better predictive discrimination to identify persons with normal FPG at high risk for glucose intolerance.
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U2 - 10.5144/0256-4947.2007.339
DO - 10.5144/0256-4947.2007.339
M3 - Article
C2 - 17921690
AN - SCOPUS:37149003310
SN - 0256-4947
VL - 27
SP - 339
EP - 346
JO - Annals of Saudi Medicine
JF - Annals of Saudi Medicine
IS - 5
ER -