Predicting glucose intolerance with normal fasting plasma glucose by the components of the metabolic syndrome.
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.


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.


CONCLUSIONS
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.
Pei
Dee
D
Department of Internal Medicine, Cardinal Tien Hospital, College of Medicine, Fu Jen Catholic University, Xindian, Taiwan, ROC.
Lin
Jiunn-Diann
JD
Wu
Du-An
DA
Hsieh
Chang-Hsun
CH
Hung
Yi-Jen
YJ
Kuo
Shi-Wen
SW
Kuo
Ko-Lin
KL
Wu
Chung-Ze
CZ
Li
Jer-Chuan
JC
D000328:Adult; D001786:Blood Glucose / Q000032:analysis*; D015992:Body Mass Index; D002784:Cholesterol / Q000097:blood; D005215:Fasting / Q000502:physiology; D005260:Female; D018149:Glucose Intolerance / Q000097:blood / Q000175:diagnosis* / Q000453:epidemiology / Q000503:physiopathology; D005951:Glucose Tolerance Test; D006801:Humans; D008297:Male; D015233:Models, Statistical; D019518:Postprandial Period / Q000502:physiology*; D011237:Predictive Value of Tests; D012372:ROC Curve; D018570:Risk Assessment; D014280:Triglycerides / Q000097:blood
D001786:Blood Glucose; D014280:Triglycerides; D002784:Cholesterol