Dataset Information

Real-life glycaemic profiles in non-diabetic individuals with low fasting glucose and normal HbA1c: the A1C-Derived Average Glucose (ADAG) study.

ABSTRACT:

Aims/hypothesis

Real-life glycaemic profiles of healthy individuals are poorly studied. Our aim was to analyse to what extent individuals without diabetes exceed OGTT thresholds for impaired glucose tolerance (IGT) and diabetes.

Methods

In the A1C-Derived Average Glucose (ADAG) study, 80 participants without diabetes completed an intensive glucose monitoring period of 12 weeks. From these data, we calculated the average 24 h glucose exposure as time spent above different plasma glucose thresholds. We also derived indices of postprandial glucose levels, glucose variability and HbA(1c).

Results

We found that 93% of participants reached glucose concentrations above the IGT threshold of 7.8 mmol/l and spent a median of 26 min/day above this level during continuous glucose monitoring. Eight individuals (10%) spent more than 2 h in the IGT range. They had higher HbA(1c), fasting plasma glucose (FPG), age and BMI than those who did not. Seven participants (9%) reached glucose concentrations above 11.1 mmol/l during monitoring.

Conclusions/interpretation

Even though the non-diabetic individuals monitored in the ADAG study were selected on the basis of a very low level of baseline FPG, 10% of these spent a considerable amount of time at glucose levels considered to be 'prediabetic' or indicating IGT. This highlights the fact that exposure to moderately elevated glucose levels remains under-appreciated when individuals are classified on the basis of isolated glucose measurements.

SUBMITTER: Borg R

PROVIDER: S-EPMC2892065 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature

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Publications

Real-life glycaemic profiles in non-diabetic individuals with low fasting glucose and normal HbA1c: the A1C-Derived Average Glucose (ADAG) study.

Borg R R Kuenen J C JC Carstensen B B Zheng H H Nathan D M DM Heine R J RJ Nerup J J Borch-Johnsen K K Witte D R DR

Diabetologia 20100416 8

<h4>Aims/hypothesis</h4>Real-life glycaemic profiles of healthy individuals are poorly studied. Our aim was to analyse to what extent individuals without diabetes exceed OGTT thresholds for impaired glucose tolerance (IGT) and diabetes.<h4>Methods</h4>In the A1C-Derived Average Glucose (ADAG) study, 80 participants without diabetes completed an intensive glucose monitoring period of 12 weeks. From these data, we calculated the average 24 h glucose exposure as time spent above different plasma gl ...[more]

PMID: 20396998

Similar Datasets

Project description:ObjectiveVarious methods are used to quantify postprandial glycemia or glucose variability, but few have been compared and none are standardized. Our objective was to examine the relationship among common indexes of postprandial glycemia, overall hyperglycemia, glucose variability, and A1C using detailed glucose measures obtained during everyday life and to study which blood glucose values of the day provide the strongest prediction of A1C.Research design and methodsIn the A1C-Derived Average Glucose (ADAG) study, glucose levels were monitored in 507 participants (268 type 1 diabetic, 159 type 2 diabetic, and 80 nondiabetic subjects) with continuous glucose monitoring (CGM) and frequent self-monitoring of blood glucose (SMBG) during 16 weeks. We calculated several indexes of glycemia and analyzed their intercorrelations. The association between glucose measurements at different times of the day (pre- and postprandial) and A1C was examined using multiple linear regression.ResultsIndexes of glucose variability showed strong intercorrelation. Among postprandial indexes, the area under the glucose curve calculated from CGM 2 h after a meal correlated well with the 90-min SMBG postprandial measurements. Fasting blood glucose (FBG) levels were only moderately correlated with indexes of hyperglycemia and average or postprandial glucose levels. Indexes derived with SMBG strongly correlated with those from CGM. Some SMBG time points had a stronger association with A1C than others. Overall, preprandial glucose values had a stronger association with A1C than postprandial values for both diabetes types, particularly for type 2 diabetes.ConclusionsIndexes of glucose variability and average and postprandial glycemia intercorrelate strongly within each category. Variability indexes are weakly correlated with the other categories, indicating that these measures convey different information. FBG is not a clear indicator of general glycemia. Preprandial glucose values have a larger impact on A1C levels than postprandial values.

Project description:BackgroundDiabetes mellitus (DM) is a chronic metabolic disease that could produce severe complications threatening life. Its early detection is thus quite important for the timely prevention and treatment. Normally, fasting blood glucose (FBG) by physical examination is used for large-scale screening of DM; however, some people with normal fasting glucose (NFG) actually have suffered from diabetes but are missed by the examination. This study aimed to investigate whether common physical examination indexes for diabetes can be used to identify the diabetes individuals from the populations with NFG.MethodsThe physical examination data from over 60,000 individuals with NFG in three Chinese cohorts were used. The diabetes patients were defined by HbA1c ≥ 48 mmol/mol (6.5%). We constructed the models using multiple machine learning methods, including logistic regression, random forest, deep neural network, and support vector machine, and selected the optimal one on the validation set. A framework using permutation feature importance algorithm was devised to discover the personalized risk factors.ResultsThe prediction model constructed by logistic regression achieved the best performance with an AUC, sensitivity, and specificity of 0.899, 85.0%, and 81.1% on the validation set and 0.872, 77.9%, and 81.0% on the test set, respectively. Following feature selection, the final classifier only requiring 13 features, named as DRING (diabetes risk of individuals with normal fasting glucose), exhibited reliable performance on two newly recruited independent datasets, with the AUC of 0.964 and 0.899, the balanced accuracy of 84.2% and 81.1%, the sensitivity of 100% and 76.2%, and the specificity of 68.3% and 86.0%, respectively. The feature importance ranking analysis revealed that BMI, age, sex, absolute lymphocyte count, and mean corpuscular volume are important factors for the risk stratification of diabetes. With a case, the framework for identifying personalized risk factors revealed FBG, age, and BMI as significant hazard factors that contribute to an increased incidence of diabetes. DRING webserver is available for ease of application ( http://www.cuilab.cn/dring ).ConclusionsDRING was demonstrated to perform well on identifying the diabetes individuals among populations with NFG, which could aid in early diagnosis and interventions for those individuals who are most likely missed.

Dataset Information

Real-life glycaemic profiles in non-diabetic individuals with low fasting glucose and normal HbA1c: the A1C-Derived Average Glucose (ADAG) study.

Aims/hypothesis

Methods

Results

Conclusions/interpretation

Publications

Real-life glycaemic profiles in non-diabetic individuals with low fasting glucose and normal HbA1c: the A1C-Derived Average Glucose (ADAG) study.

Similar Datasets

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