Background To investigate the association between the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) and Homeostasis Model Assessment of Beta-cell function (HOMA-B) with the incidence of diabetes and pre-diabetes subtypes. Methods A total of 3101 normoglycemic people aged 20–70 years were included in the 6-year follow-up study. Multinomial logistic regression was used to calculate the incidence possibility of isolated Impaired Fasting Glucose (iIFG), isolated Impaired Glucose Tolerance (iIGT), Combined impaired fasting glucose & impaired glucose tolerance (CGI), and Diabetes Mellitus (DM) per standard deviation (SD) increment in HOMA-IR and HOMA-B in the crude and multivariable model. Results In the multivariate model, an increase in one SD change in HOMA-IR was associated with a 43, 42, 75, and 92% increased risk of iIFG, iIGT, CGI, and DM, respectively. There was a positive correlation between the increase in HOMA-B and the incidence of iIGT; however, after adjusting the results for metabolic syndrome components, it was inversely correlated with the incidence of iIFG [Odds Ratio = 0.86(0.75–0.99)]. Conclusions HOMA-IR is positively correlated with diabetes and pre-diabetes subtypes’ incidence, and HOMA-B is inversely correlated with the incidence of iIFG but positively correlated with iIGT incidence. However, none of these alone is a good criterion for predicting diabetes and pre-diabetes.

Objectives: Occurrence insulin dependent diabetes mellitus (IDDM) or Type 1 DM is growing worldwide. Checking serum glucose is necessary for management of DM. Serum glucose assessment involves needle puncture or venipuncture. The aim of this study was to assess the salivary glucose level in monitoring glycaemia in children with IDDM. Methods: Serum as well as stimulated and unstimulated saliva were collected from 34 IDDM and 34 non-diabetic subjects. Serum and saliva glucose levels were measured by GOD-POP. For the statistical analysis of student's t-test, Pearson correlation test and Receiver operating characteristic (ROC) analysis was used. Results: Stimulated and unstimulated salivary levels of glucose were significantly higher in children with IDDM compared to control subjects. Serum glucose concentration correlates with stimulated (r = 0.407; P = 0.005), but not with unstimulated salivary concentration of glucose (r = 0.189; P = 0.145). Serum HbA1c correlates with unstimulated (r = 0.404; P = 0.001), but not with stimulated salivary concentration of HbA1c (r = 0.0.95; P = 0.526). The cut-off value of stimulated and unstimulated salivary glucose for the diagnosis of IDDM were 2.15 mg/dL and 1.05 mg/dL, respectively. Conclusions: It seems that saliva glucose is higher in Type 1 diabetic mellitus subjects and checking of glucose in saliva may be applied as an index of DM.

Background: To investigate the association between Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) and Homeostasis Model Assessment of Beta-cell function (HOMA-B) with the incidence of diabetes and pre-diabetes subtypes.Methods: A total of 3101 normoglycemic people aged 20-70 years were included in the 6-year follow-up study. Multinomial logistic regression was used to calculate the incidence possibility of isolated Impaired Fasting Glucose (iIFG), isolated Impaired Glucose Tolerance (iIGT), Combined impaired fasting glucose & impaired glucose tolerance (CGI), and Diabetes Mellitus (DM) per standard deviation (SD) increment in HOMA-IR and HOMA-B in the crude and multivariable model. Results: In the multivariate model, per unit SD increase in HOMA-IR increased the odds of iIFG, iIGT, CGI, and DM by 43%, 42%, 75%, and 92%, respectively. There was a positive correlation between the increase in HOMA-B and incidence of iIGT; however, after adjusting the results for metabolic syndrome components, it was inversely correlated with the incidence of iIFG [Odds Ratio = 0.86(0.75-0.99)]. Conclusions: HOMA-IR is positively correlated with diabetes and pre-diabetes subtypes’ incidence, and HOMA-B is inversely correlated with the incidence of iIFG but positively correlated with iIGT incidence. However, none of these alone is a good criterion for predicting diabetes and pre-diabetes.