Dataset Information

Time to glucose peak during an oral glucose tolerance test identifies prediabetes risk.

ABSTRACT:

Context

Morphological characteristics of the glucose curve during an oral glucose tolerance test (OGTT) (time to peak and shape) may reflect different phenotypes of insulin secretion and action, but their ability to predict diabetes risk is uncertain.

Objective

To compare the ability of time to glucose peak and curve shape to detect prediabetes and β-cell function.

Design and participants

In a cross-sectional evaluation using an OGTT, 145 adults without diabetes (age 42±9 years (mean±SD), range 24-62 years, BMI 29.2±5.3 kg/m² , range 19.9-45.2 kg/m² ) were characterized by peak (30 minutes vs >30 minutes) and shape (biphasic vs monophasic).

Main outcome measures

Prediabetes and disposition index (DI)-a marker of β-cell function.

Results

Prediabetes was diagnosed in 36% (52/145) of participants. Peak>30 minutes, not monophasic curve, was associated with increased odds of prediabetes (OR: 4.0 vs 1.1; P<.001). Both monophasic curve and peak>30 minutes were associated with lower DI (P≤.01). Time to glucose peak and glucose area under the curves (AUC) were independent predictors of DI (adjR² =0.45, P<.001).

Conclusion

Glucose peak >30 minutes was a stronger independent indicator of prediabetes and β-cell function than the monophasic curve. Time to glucose peak may be an important tool that could enhance prediabetes risk stratification.

SUBMITTER: Chung ST

PROVIDER: S-EPMC5658251 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Time to glucose peak during an oral glucose tolerance test identifies prediabetes risk.

Chung Stephanie T ST Ha Joon J Onuzuruike Anthony U AU Kasturi Kannan K Galvan-De La Cruz Mirella M Bingham Brianna A BA Baker Rafeal L RL Utumatwishima Jean N JN Mabundo Lilian S LS Ricks Madia M Sherman Arthur S AS Sumner Anne E AE

Clinical endocrinology 20170806 5

<h4>Context</h4>Morphological characteristics of the glucose curve during an oral glucose tolerance test (OGTT) (time to peak and shape) may reflect different phenotypes of insulin secretion and action, but their ability to predict diabetes risk is uncertain.<h4>Objective</h4>To compare the ability of time to glucose peak and curve shape to detect prediabetes and β-cell function.<h4>Design and participants</h4>In a cross-sectional evaluation using an OGTT, 145 adults without diabetes (age 42±9 y ...[more]

PMID: 28681942

Similar Datasets

Project description:IntroductionImpaired glucose tolerance (IGT) is diagnosed by a standardized oral glucose tolerance test (OGTT). However, the OGTT is laborious, and when not performed, glucose tolerance cannot be determined from fasting samples retrospectively. We tested if glucose tolerance status is reasonably predictable from a combination of demographic, anthropometric, and laboratory data assessed at one time point in a fasting state.MethodsGiven a set of 22 variables selected upon clinical feasibility such as sex, age, height, weight, waist circumference, blood pressure, fasting glucose, HbA1c, hemoglobin, mean corpuscular volume, serum potassium, fasting levels of insulin, C-peptide, triglyceride, non-esterified fatty acids (NEFA), proinsulin, prolactin, cholesterol, low-density lipoprotein, HDL, uric acid, liver transaminases, and ferritin, we used supervised machine learning to estimate glucose tolerance status in 2,337 participants of the TUEF study who were recruited before 2012. We tested the performance of 10 different machine learning classifiers on data from 929 participants in the test set who were recruited after 2012. In addition, reproducibility of IGT was analyzed in 78 participants who had 2 repeated OGTTs within 1 year.ResultsThe most accurate prediction of IGT was reached with the recursive partitioning method (accuracy = 0.78). For all classifiers, mean accuracy was 0.73 ± 0.04. The most important model variable was fasting glucose in all models. Using mean variable importance across all models, fasting glucose was followed by NEFA, triglycerides, HbA1c, and C-peptide. The accuracy of predicting IGT from a previous OGTT was 0.77.ConclusionMachine learning methods yield moderate accuracy in predicting glucose tolerance from a wide set of clinical and laboratory variables. A substitution of OGTT does not currently seem to be feasible. An important constraint could be the limited reproducibility of glucose tolerance status during a subsequent OGTT.

Project description:Aims/hypothesisWe aimed to examine: (1) whether specific glucose-response curve shapes during OGTTs are predictive of type 1 diabetes development; and (2) the extent to which the glucose-response curve is influenced by insulin secretion.MethodsAutoantibody-positive relatives of people with type 1 diabetes whose baseline OGTT met the definition of a monophasic or biphasic glucose-response curve were followed for the development of type 1 diabetes (n = 2627). A monophasic curve was defined as an increase in OGTT glucose between 30 and 90 min followed by a decline of ≥ 0.25 mmol/l between 90 and 120 min. A biphasic response curve was defined as a decrease in glucose after an initial increase, followed by a second increase of ≥ 0.25 mmol/l. Associations of type 1 diabetes risk with glucose curve shapes were examined using cumulative incidence curve comparisons and proportional hazards regression. C-peptide responses were compared with and without adjustments for potential confounders.ResultsThe majority of participants had a monophasic curve at baseline (n = 1732 [66%] vs n = 895 [34%]). The biphasic group had a lower cumulative incidence of type 1 diabetes (p < 0.001), which persisted after adjustments for age, sex, BMI z score and number of autoantibodies (p < 0.001). Among the monophasic group, the risk of type 1 diabetes was greater for those with a glucose peak at 90 min than for those with a peak at 30 min; the difference persisted after adjustments (p < 0.001). Compared with the biphasic group, the monophasic group had a lower early C-peptide (30-0 min) response, a lower C-peptide index (30-0 min C-peptide/30-0 min glucose), as well as a greater 2 h C-peptide level (p < 0.001 for all).Conclusions/interpretationThose with biphasic glucose curves have a lower risk of progression to type 1 diabetes than those with monophasic curves, and the risk among the monophasic group is increased when the glucose peak occurs at 90 min than at 30 min. Differences in glucose curve shapes between the monophasic and biphasic groups appear to be related to C-peptide responses.

Dataset Information

Time to glucose peak during an oral glucose tolerance test identifies prediabetes risk.

Context

Objective

Design and participants

Main outcome measures

Results

Conclusion

Publications

Time to glucose peak during an oral glucose tolerance test identifies prediabetes risk.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets