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Oral Glucose Load (oral + glucose_load)

Distribution by Scientific Domains

Medical Sciences	75%

Selected Abstracts

A multivariate logistic regression equation to screen for dysglycaemia: development and validation

DIABETIC MEDICINE, Issue 5 2005
B. P. Tabaei
Abstract Aims To develop and validate an empirical equation to screen for dysglycaemia [impaired fasting glucose (IFG), impaired glucose tolerance (IGT) and undiagnosed diabetes]. Methods A predictive equation was developed using multiple logistic regression analysis and data collected from 1032 Egyptian subjects with no history of diabetes. The equation incorporated age, sex, body mass index (BMI), post-prandial time (self-reported number of hours since last food or drink other than water), systolic blood pressure, high-density lipoprotein (HDL) cholesterol and random capillary plasma glucose as independent covariates for prediction of dysglycaemia based on fasting plasma glucose (FPG) , 6.1 mmol/l and/or plasma glucose 2 h after a 75-g oral glucose load (2-h PG) , 7.8 mmol/l. The equation was validated using a cross-validation procedure. Its performance was also compared with static plasma glucose cut-points for dysglycaemia screening. Results The predictive equation was calculated with the following logistic regression parameters: P = 1 + 1/(1 + e,X) = where X = ,8.3390 + 0.0214 (age in years) + 0.6764 (if female) + 0.0335 (BMI in kg/m2) + 0.0934 (post-prandial time in hours) + 0.0141 (systolic blood pressure in mmHg) , 0.0110 (HDL in mmol/l) + 0.0243 (random capillary plasma glucose in mmol/l). The cut-point for the prediction of dysglycaemia was defined as a probability , 0.38. The equation's sensitivity was 55%, specificity 90% and positive predictive value (PPV) 65%. When applied to a new sample, the equation's sensitivity was 53%, specificity 89% and PPV 63%. Conclusions This multivariate logistic equation improves on currently recommended methods of screening for dysglycaemia and can be easily implemented in a clinical setting using readily available clinical and non-fasting laboratory data and an inexpensive hand-held programmable calculator. [source]

Serum insulin patterns and the relationship between insulin sensitivity and glycaemic profile in women with polycystic ovary syndrome

BJOG : AN INTERNATIONAL JOURNAL OF OBSTETRICS & GYNAECOLOGY, Issue 13 2009
HR Seneviratne
Objective, To evaluate serum insulin levels and insulin sensitivity in women with polycystic ovary syndrome (PCOS) in relation to their glycaemic status. Design, An observational study. Setting, A tertiary-level reproductive health centre in Sri Lanka. Sample, Infertile women diagnosed as having PCOS (n = 168) on the basis of the Rotterdam criteria were included in the study. Methods, Glycaemic status and serum insulin values were assessed at fasting and at 2 hours after a 75-g oral glucose load and stratified as diabetes mellitus (DM) (10.12%), impaired glucose tolerance (IGT) (23.21%) and normoglycaemia (66.67%). The normoglycaemic group was restratified as groups A (10.7%), B (79.5%) and C (9.8%) on the basis of serum insulin levels, with group A having the lowest and group C the highest values. The Quantitative Insulin Sensitivity Check Index (QUICKI) scores of women with DM and IGT and those in groups A, B and C in the normoglycaemic category were compared. Main outcome measures, Insulin sensitivity in these groups of women. Results, Body mass index (BMI) exceeded 23 kg/m2 in 77.38% of the women. In normoglycaemic women with PCOS, insulin sensitivity was highest in group A. In groups B and C, insulin sensitivities corresponded to those found for women with IGT and DM respectively. This pattern was also reflected in the BMI. Conclusions, Normoglycaemic women with PCOS are heterogeneous regarding insulin sensitivity. The treatment offered to those with DM and IGT could be extended to subgroups B and C of normoglycaemic subjects. Normoglycaemic women with PCOS with high insulin sensitivity (group A) would not qualify for this treatment. [source]

The predictive value of serum 1,5-anhydro-D-glucitol in pregnancies at increased risk of gestational diabetes mellitus and gestational impaired glucose tolerance

BJOG : AN INTERNATIONAL JOURNAL OF OBSTETRICS & GYNAECOLOGY, Issue 7 2001
Wing-Hung Tam
The objective of the study was to determine the efficacy of 1,5-anhydro-D-glucitol (1,5 AG) for the prediction of gestational diabetes and gestational impaired glucose tolerance (GIGT). One hundred and eighty-five pregnant women with epidemiological risk factors of gestational diabetes or GIGT underwent 75 g oral glucose tolerance test and plasma 1,5 AG assay at 26 to 28 weeks of gestation. There was no significant difference in plasma 1,5 AG either before or after an oral glucose load. The area under the receiver operator characteristic curve for 1,5 AG was only 0.485 which implies that 1,5 AG is a poor predictor of GIGT or gestational diabetes. [source]

Genetic variations in the leptin and leptin receptor genes are associated with type 2 diabetes mellitus and metabolic traits in the Korean female population

CLINICAL GENETICS, Issue 2 2008
HR Han
Type 2 diabetes mellitus (T2DM) is a metabolic disorder that is characterized by insulin resistance and hyperglycemia. Leptin inhibits the glucose-stimulated insulin secretion, and leptin receptors are present on , cells as well as on fat cells, thus enabling leptin to modulate both insulin secretion and action. Therefore, leptin (LEP) and leptin receptor (LEPR) genes could play a role in the regulation of glucose and insulin after an oral glucose load. For the association study of LEP and LEPR with T2DM and metabolic traits, 752 women from Seoul National University Hospital (SNUH data) and 532 women from the Korean Health and Genome Study (KHGS data) were selected. Using the SNUH data, we identified that LEP,632G>A and +4998A>C polymorphisms were marginally associated with T2DM, LEP+4950G>A was significantly associated with several metabolic traits, and LEPR+5193G>A, +7187A>C, +27265G>A, +35861T>C, and +52289A>G showed strongly significant association with body mass index (BMI). We observed reproducibility of these results using the KHGS data; LEP+4950G>A and +4998A>C were significantly associated with systolic blood pressure and low-density lipoprotein cholesterol level, respectively. In conclusion, we observed that several polymorphisms in LEPR that had previous reports of association with BMI were significantly replicated in our samples and newly found that some variations of LEP were associated with T2DM and metabolic traits. [source]