Heterogeneity in glucose response curves during an oral glucose tolerance test and associated cardiometabolic risk

The characteristics of postprandial glycemic response patterns to white rice and glucose in healthy adults: Identifying subgroups by clustering analysis

OBJECTIVES

Large interpersonal variability in postprandial glycemic response (PGR) to white rice has been reported, and differences in the PGR patterns during the oral glucose tolerance test (OGTT) have been documented. However, there is scant study on the PGR patterns of white rice. We examined the typical PGR patterns of white rice and glucose and the association between them.

MATERIALS AND METHODS

We analyzed the data of 3-h PGRs to white rice (WR) and glucose (G) of 114 normoglycemic female subjects of similar age, weight status, and same ethnic group. Diverse glycemic parameters, based on the discrete blood glucose values, were calculated over 120 and 180 min. K-means clustering based on glycemic parameters calculated over 180 min was applied to identify subgroups and representative PGR patterns. Principal factor analysis based on the parameters used in the cluster analysis was applied to characterize PGR patterns. Simple correspondence analysis was performed on the clustering categories of WR and G.

RESULTS

More distinct differences were found in glycemic parameters calculated over 180 min compared with that calculated over 120 min, especially in the negative area under the curve and Nadir. We identified four distinct PGR patterns to WR (WR1, WR2, WR3, and WR4) and G (G1, G2, G3, and G4), respectively. There were significant differences among the patterns regard to postprandial hyperglycemia, hypoglycemic, and glycemic variability. The WR1 clusters had significantly lower glycemic index (59 ± 19), while no difference was found among the glycemic index based on the other three clusters. Each given G subgroup presented multiple patterns of PGR to WR, especially in the largest G subgroup (G1), and in subgroup with the greatest glycemic variability (G3).

CONCLUSION

Multiple subgroups could be classified based on the PGR patterns to white rice and glucose even in seemingly homogeneous subjects. Extending the monitoring time to 180 min was conducive to more effective discrimination of PGR patterns. It may not be reliable to extrapolate the patterns of PGR to rice from that to glucose, suggesting a need of combining OGTT and meal tolerance test for individualized glycemic management.

Mechanistic Insights Into the Heterogeneity of Glucose Response Classes in Youths With Obesity: A Latent Class Trajectory Approach

OBJECTIVE

In a large, multiethnic cohort of youths with obesity, we analyzed pathophysiological and genetic mechanisms underlying variations in plasma glucose responses to a 180 min oral glucose tolerance test (OGTT).

RESEARCH DESIGN AND METHODS

Latent class trajectory analysis was used to identify various glucose response profiles to a nine-point OGTT in 2,378 participants in the Yale Pathogenesis of Youth-Onset T2D study, of whom 1,190 had available TCF7L2 genotyping and 358 had multiple OGTTs over a 5 year follow-up. Insulin sensitivity, clearance, and β-cell function were estimated by glucose, insulin, and C-peptide modeling.

RESULTS

Four latent classes (1 to 4) were identified based on increasing areas under the curve for glucose. Participants in class 3 and 4 had the worst metabolic and genetic risk profiles, featuring impaired insulin sensitivity, clearance, and β-cell function. Model-predicted probability to be classified as class 1 and 4 increased across ages, while insulin sensitivity and clearance showed transient reductions and β-cell function progressively declined. Insulin sensitivity was the strongest determinant of class assignment at enrollment and of the longitudinal change from class 1 and 2 to higher classes. Transitions between classes 3 and 4 were explained only by changes in β-cell glucose sensitivity.

CONCLUSIONS

We identified four glucose response classes in youths with obesity with different genetic risk profiles and progressive impairment in insulin kinetics and action. Insulin sensitivity was the main determinant in the transition between lower and higher glucose classes across ages. In contrast, transitions between the two worst glucose classes were driven only by β-cell glucose sensitivity.

Oral glucose tolerance test in diabetes, the old method revisited

The oral glucose tolerance test (OGTT) has been widely used both in clinics and in basic research for a long time. It is applied to diagnose impaired glucose tolerance and/or type 2 diabetes mellitus in individuals. Additionally, it has been employed in research to investigate glucose utilization and insulin sensitivity in animals. The main aim of each was quite different, and the details are also somewhat varied. However, the time or duration of the OGTT was the same, using the 2-h post-glucose load glycemia in both, following the suggestions of the American Diabetes Association. Recently, the use of 30-min or 1-h post-glucose load glycemia in clinical practice has been recommended by several studies. In this review article, we describe this new view and suggest perspectives for the OGTT. Additionally, quantification of the glucose curve in basic research is also discussed. Unlike in clinical practice, the incremental area under the curve is not suitable for use in the studies involving animals receiving repeated treatments or chronic treatment. We discuss the potential mechanisms in detail. Moreover, variations between bench and bedside in the application of the OGTT are introduced. Finally, the newly identified method for the OGTT must achieve a recommendation from the American Diabetes Association or another official unit soon. In conclusion, we summarize the recent reports regarding the OGTT and add some of our own perspectives, including machine learning and others.

Clinical profiles of post-load glucose subgroups and their association with glycaemic traits over time: An IMI-DIRECT study

AIM

To examine the hypothesis that, based on their glucose curves during a seven-point oral glucose tolerance test, people at elevated type 2 diabetes risk can be divided into subgroups with different clinical profiles at baseline and different degrees of subsequent glycaemic deterioration.

METHODS

We included 2126 participants at elevated type 2 diabetes risk from the Diabetes Research on Patient Stratification (IMI-DIRECT) study. Latent class trajectory analysis was used to identify subgroups from a seven-point oral glucose tolerance test at baseline and follow-up. Linear models quantified the associations between the subgroups with glycaemic traits at baseline and 18 months.

RESULTS

At baseline, we identified four glucose curve subgroups, labelled in order of increasing peak levels as 1-4. Participants in Subgroups 2-4, were more likely to have higher insulin resistance (homeostatic model assessment) and a lower Matsuda index, than those in Subgroup 1. Overall, participants in Subgroups 3 and 4, had higher glycaemic trait values, with the exception of the Matsuda and insulinogenic indices. At 18 months, change in homeostatic model assessment of insulin resistance was higher in Subgroup 4 (β = 0.36, 95% CI 0.13-0.58), Subgroup 3 (β = 0.30; 95% CI 0.10-0.50) and Subgroup 2 (β = 0.18; 95% CI 0.04-0.32), compared to Subgroup 1. The same was observed for C-peptide and insulin. Five subgroups were identified at follow-up, and the majority of participants remained in the same subgroup or progressed to higher peak subgroups after 18 months.

CONCLUSIONS

Using data from a frequently sampled oral glucose tolerance test, glucose curve patterns associated with different clinical characteristics and different rates of subsequent glycaemic deterioration can be identified.

Post-load glucose subgroups and associated metabolic traits in individuals with type 2 diabetes: An IMI-DIRECT study

Aim

Subclasses of different glycaemic disturbances could explain the variation in characteristics of individuals with type 2 diabetes (T2D). We aimed to examine the association between subgroups based on their glucose curves during a five-point mixed-meal tolerance test (MMT) and metabolic traits at baseline and glycaemic deterioration in individuals with T2D.

Methods

The study included 787 individuals with newly diagnosed T2D from the Diabetes Research on Patient Stratification (IMI-DIRECT) Study. Latent class trajectory analysis (LCTA) was used to identify distinct glucose curve subgroups during a five-point MMT. Using general linear models, these subgroups were associated with metabolic traits at baseline and after 18 months of follow up, adjusted for potential confounders.

Results

At baseline, we identified three glucose curve subgroups, labelled in order of increasing glucose peak levels as subgroup 1–3. Individuals in subgroup 2 and 3 were more likely to have higher levels of HbA1c, triglycerides and BMI at baseline, compared to those in subgroup 1. At 18 months (n = 651), the beta coefficients (95% CI) for change in HbA1c (mmol/mol) increased across subgroups with 0.37 (-0.18–1.92) for subgroup 2 and 1.88 (-0.08–3.85) for subgroup 3, relative to subgroup 1. The same trend was observed for change in levels of triglycerides and fasting glucose.

Conclusions

Different glycaemic profiles with different metabolic traits and different degrees of subsequent glycaemic deterioration can be identified using data from a frequently sampled mixed-meal tolerance test in individuals with T2D. Subgroups with the highest peaks had greater metabolic risk.

The Oral Glucose Tolerance Test: 100 Years Later

For over 100 years, the oral glucose tolerance test (OGTT) has been the cornerstone for detecting prediabetes and type 2 diabetes (T2DM). In recent decades, controversies have arisen identifying internationally acceptable cut points using fasting plasma glucose (FPG), 2-h post-load glucose (2-h PG), and/or HbA1c for defining intermediate hyperglycemia (prediabetes). Despite this, there has been a steadfast global consensus of the 2-h PG for defining dysglycemic states during the OGTT. This article reviews the history of the OGTT and recent advances in its application, including the glucose challenge test and mathematical modeling for determining the shape of the glucose curve. Pitfalls of the FPG, 2-h PG during the OGTT, and HbA1c are considered as well. Finally, the associations between the 30-minute and 1-hour plasma glucose (1-h PG) levels derived from the OGTT and incidence of diabetes and its complications will be reviewed. The considerable evidence base supports modifying current screening and diagnostic recommendations with the use of the 1-h PG. Measurement of the 1-h PG level could increase the likelihood of identifying high-risk individuals when the pancreatic ß-cell function is substantially more intact with the added practical advantage of potentially replacing the conventional 2-h OGTT making it more acceptable in the clinical setting.

Review of methods for detecting glycemic disorders

Prediabetes (intermediate hyperglycemia) consists of two abnormalities, impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) detected by a standardized 75-gram oral glucose tolerance test (OGTT). Individuals with isolated IGT or combined IFG and IGT have increased risk for developing type 2 diabetes (T2D) and cardiovascular disease (CVD). Diagnosing prediabetes early and accurately is critical in order to refer high-risk individuals for intensive lifestyle modification. However, there is currently no international consensus for diagnosing prediabetes with HbA1c or glucose measurements based upon American Diabetes Association (ADA) and the World Health Organization (WHO) criteria that identify different populations at risk for progressing to diabetes. Various caveats affecting the accuracy of interpreting the HbA1c including genetics complicate this further. This review describes established methods for detecting glucose disorders based upon glucose and HbA1c parameters as well as novel approaches including the 1-hour plasma glucose (1-h PG), glucose challenge test (GCT), shape of the glucose curve, genetics, continuous glucose monitoring (CGM), measures of insulin secretion and sensitivity, metabolomics, and ancillary tools such as fructosamine, glycated albumin (GA), 1,5- anhydroglucitol (1,5-AG). Of the approaches considered, the 1-h PG has considerable potential as a biomarker for detecting glucose disorders if confirmed by additional data including health economic analysis. Whether the 1-h OGTT is superior to genetics and omics in providing greater precision for individualized treatment requires further investigation. These methods will need to demonstrate substantially superiority to simpler tools for detecting glucose disorders to justify their cost and complexity.

Different physiological mechanisms underlie an adverse cardiovascular disease risk profile in men and women

CVD affect about one-third of the population and are the leading cause of mortality. The prevalence of CVD is closely linked to the prevalence of obesity because obesity is commonly associated with metabolic abnormalities that are important risk factors for CVD, including insulin resistance, pre-diabetes, and type-2 diabetes, atherosclerotic dyslipidaemia, endothelial dysfunction and hypertension. Women have a more beneficial traditional CVD risk profile (lower fasting plasma glucose, less atherogenic lipid profile) and a lower absolute risk for CVD than men. However, the relative risk for CVD associated with hyperglycaemia and dyslipidaemia is several-fold higher in women than in men. The reasons for the sex differences in CVD risk associated with metabolic abnormalities are unclear but could be related to differences in the mechanisms that cause hyperglycaemia and dyslipidaemia in men and women, which could influence the pathogenic processes involved in CVD. In the present paper, we review the influence of a person's sex on key aspects of metabolism involved in the cardiometabolic disease process, including insulin action on endogenous glucose production, tissue glucose disposal, and adipose tissue lipolysis, insulin secretion and insulin plasma clearance, postprandial glucose, fatty acid, and triglyceride kinetics, hepatic lipid metabolism and myocardial substrate use. We conclude that there are marked differences in many aspects of metabolism in men and women that are not all attributable to differences in the sex hormone milieu. The mechanisms responsible for these differences and the clinical implications of these observations are unclear and require further investigation.

Heritability of Curve Patterns in Oral Glucose Tolerance Test

Type 2 diabetes, which is caused by both genetic and environmental factors, may be diagnosed using the oral glucose tolerance test (OGTT). Recent studies demonstrated specific patterns in glucose curves during OGTT associated with cardiometabolic risk profiles. As the relative contribution of genetic and environmental influences on glucose curve patterns is unknown, we aimed to investigate the heritability of these patterns. We studied twins from the Danish GEMINAKAR cohort aged 18-67 years and free from diabetes at baseline during 1997-2000; glucose concentrations were measured three times during a 2-h OGTT. Heterogeneity of the glucose response during OGTT was examined with latent class mixed-effects models, evaluating goodness of fit by Bayes information criterion. The genetic influence on curve patterns was estimated using quantitative genetic modeling based on linear structural equations. Overall, 1455 twins (41% monozygotic) had valid glucose concentrations measured from the OGTT, and four latent classes with different glucose response patterns were identified. Statistical modeling demonstrated genetic influence for belonging to a specific class or not, with heritability estimated to be between 45% and 67%. During ∼12 years of follow-up, the four classes were each associated with different incidence of type 2 diabetes. Hence, glucose response curve patterns associated with type 2 diabetes risk appear to be moderately to highly heritable.

An Acute, Placebo-Controlled, Single-Blind, Crossover, Dose-Response, Exploratory Study to Assess the Effects of New Zealand Pine Bark Extract (Enzogenol®) on Glycaemic Responses in Healthy Participants

An acute, placebo-controlled, single-blind, crossover, dose-response, exploratory study was designed to investigate the hypoglycaemic effects of New Zealand pine bark extract (Enzogenol®). Twenty-five healthy participants categorised into having a monophasic or complex (biphasic or triphasic) glucose curve shape at the control visit consumed a placebo and Enzogenol® (50 and 400 mg) on three separate occasions before an oral glucose tolerance test (OGTT). In the monophasic group, 50 and 400 mg of Enzogenol® significantly reduced the mean glucose incremental area under the curve (iAUC) compared to control 241.3 ± 20.2 vs. 335.4 ± 34.0 mmol/L·min, p = 0.034 and 249.3 ± 25.4 vs. 353.6 ± 31.5 mmol/L·min, p = 0.012, respectively. The 400 mg dose further reduced the percentage increment of postprandial glucose (%PG) 31.4% ± 7.9% vs. 47.5% ± 8.6%, p = 0.010, glucose peak 7.9 ± 0.3 vs. 8.9 ± 0.3 mmol/L, p = 0.025 and 2h-OGTT postprandial glucose (2hPG) 6.1 ± 0.3 vs. 6.7 ± 0.3 mmol/L, p = 0.027. Glucose iAUC was not significantly different in the complex group, except for reductions in %PG 28.7% ± 8.2% vs. 43.4% ± 5.9%, p = 0.012 after 50 mg dose and 27.7% ± 5.4% vs. 47.3% ± 7.2%, p = 0.025 after 400 mg dose. The results suggest that Enzogenol® may have hypoglycaemic effects in healthy participants, especially those exhibiting monophasic shapes.

The Effect of Prior Gestational Diabetes on the Shape of the Glucose Response Curve during an Oral Glucose Tolerance Test 3 Years after Delivery

OBJECTIVE

Monophasic glucose response (MGR) during an oral glucose tolerance test (OGTT) and gestational diabetes mellitus (GDM) are predictors of type 2 diabetes mellitus (T2DM). We investigated the association between current MGR and (1) glucose tolerance during a pregnancy 3 years before and (2) current glucose tolerance status. We also sought (3) other determinants of MGR. Research Design and Methods. We conducted a nested case-control study of GDM (n = 47 early GDM, diagnosed between 16 and 20 weeks of gestation; n = 40 late GDM, diagnosed between 24 and 28 weeks of gestation) and matched healthy controls (n = 37, normal glucose tolerance during pregnancy) all free from diabetes at follow-up 3.4 ± 0.6 years after delivery. Glucose tolerance was determined by 2-hour 75 g OGTT. Monophasic and biphasic groups were defined based on serum glucose measurements during OGTT.

RESULTS

The biphasic group was younger, had lower triglyceride levels and area under the OGTT glucose curve, and was less frequently diagnosed with early GDM (25 vs. 45%, all p < 0.05). Women with a biphasic response also tended to have lower systolic blood pressure (p < 0.1). No differences were found in fasting and 2-hour glucose and insulin levels, or BMI. According to multiple logistic regression, MGR was associated with prior early GDM (OR 2.14, 95% CI 0.92-4.99) and elevated triglyceride levels (OR 2.28, 95% CI 1.03-5.03/log (mmol/l)).

CONCLUSIONS

We found that more severe, early-onset GDM was an independent predictor of monophasic glucose response suggesting that monophasic response may represent an intermediate state between GDM and manifest type 2 diabetes.

The oral glucose tolerance test-derived incremental glucose peak is associated with greater arterial stiffness and maladaptive arterial remodeling: The Maastricht Study

Background

Daily glucose variability may contribute to vascular complication development irrespective of mean glucose values. The incremental glucose peak (IGP) during an oral glucose tolerance test (OGTT) can be used as a proxy of glucose variability. We investigated the association of IGP with arterial stiffness, arterial remodeling, and microvascular function, independent of HbA_1c and other confounders.

Methods

IGP was calculated as the peak minus baseline plasma glucose value during a seven-point OGTT in 2758 participants (age: 60 ± 8 years; 48% women) of The Maastricht Study, an observational population-based cohort. We assessed the cross-sectional associations between IGP and arterial stiffness (carotid-femoral pulse wave velocity [cf-PWV], carotid distensibility coefficient [carDC]), arterial remodeling (carotid intima-media thickness [cIMT]; mean [CWS_mean] and pulsatile [CWS_puls] circumferential wall stress), and microvascular function (retinal arteriolar average dilatation; heat-induced skin hyperemia) via multiple linear regression with adjustment for age, sex, HbA_1c, cardiovascular risk factors, lifestyle factors, and medication use.

Results

Higher IGP was independently associated with higher cf-PWV (regression coefficient [B]: 0.054 m/s [0.020; 0.089]) and with higher CWS_mean (B: 0.227 kPa [0.008; 0.446]). IGP was not independently associated with carDC (B: − 0.026 10⁻³/kPa [− 0.112; 0.060]), cIMT (B: − 2.745 µm [− 5.736; 0.245]), CWS_puls (B: 0.108 kPa [− 0.054; 0.270]), retinal arteriolar average dilatation (B: − 0.022% [− 0.087; 0.043]), or heat-induced skin hyperemia (B: − 1.380% [− 22.273; 19.513]).

Conclusions

IGP was independently associated with aortic stiffness and maladaptive carotid remodeling, but not with carotid stiffness, cIMT, and microvascular function measures. Future studies should investigate whether glucose variability is associated with cardiovascular disease.

Clusters of Glycemic Response to Oral Glucose Tolerance Tests Explain Multivariate Metabolic and Anthropometric Outcomes of Bariatric Surgery in Obese Patients

Glycemic responses to bariatric surgery are highly heterogeneous among patients and defining response types remains challenging. Recently developed data-driven clustering methods have uncovered subtle pathophysiologically informative patterns among patients without diabetes. This study aimed to explain responses among patients with and without diabetes to bariatric surgery with clusters of glucose concentration during oral glucose tolerance tests (OGTTs). We assessed 30 parameters at baseline and at four subsequent follow-up visits over one year on 154 participants in the Bialystok Bariatric Surgery Study. We applied latent trajectory classification to OGTTs and multinomial regression and generalized linear mixed models to explain differential responses among clusters. OGTT trajectories created four clusters representing increasing dysglycemias that were discordant from standard diabetes diagnosis criteria. The baseline OGTT cluster increased the predictive power of regression models by over 31% and aided in correctly predicting more than 83% of diabetes remissions. Principal component analysis showed that the glucose homeostasis response primarily occurred as improved insulin sensitivity concomitant with improved the OGTT cluster. In sum, OGTT clustering explained multiple, correlated responses to metabolic surgery. The OGTT is an intuitive and easy-to-implement index of improvement that stratifies patients into response types, a vital first step in personalizing diabetic care in obese subjects.

Pathophysiological Characteristics Underlying Different Glucose Response Curves: A Latent Class Trajectory Analysis From the Prospective EGIR-RISC Study

OBJECTIVE

Glucose measurements during an oral glucose tolerance test (OGTT) are useful in predicting diabetes and its complications. However, knowledge of the pathophysiology underlying differences in glucose curve shapes is sparse. We examined the pathophysiological characteristics that create different glucose curve patterns and studied their stability and reproducibility over 3 years of follow-up.

RESEARCH DESIGN AND METHODS

We analyzed data from participants without diabetes from the observational cohort from the European Group for the Study of Insulin Resistance: Relationship between Insulin Sensitivity and Cardiovascular Disease study; participants had a five-time point OGTT at baseline (n = 1,443) and after 3 years (n = 1,045). Measures of insulin sensitivity and secretion were assessed at baseline with a euglycemic-hyperinsulinemic clamp and intravenous glucose tolerance test. Heterogeneous glucose response patterns during the OGTT were identified using latent class trajectory analysis at baseline and at follow-up. Transitions between classes were analyzed with multinomial logistic regression models.

RESULTS

We identified four different glucose response patterns, which differed with regard to insulin sensitivity and acute insulin response, obesity, and plasma levels of lipids and inflammatory markers. Some of these associations were confirmed prospectively. Time to glucose peak was driven mainly by insulin sensitivity, whereas glucose peak size was related to both insulin sensitivity and secretion. The glucose patterns identified at follow-up were similar to those at baseline, suggesting that the latent class method is robust. We integrated our classification model into an easy-to-use online application that facilitates the assessment of glucose curve patterns for other studies.

CONCLUSIONS

The latent class analysis approach is a pathophysiologically insightful way to classify individuals without diabetes based on their response to glucose during an OGTT.

Stratification of type 2 diabetes based on routine clinical markers

AIMS

We hypothesized that patients with dysregulated type 2 diabetes may be stratified based on routine clinical markers.

METHODS

In this retrospective cohort study, diabetes related clinical measures including age at onset, diabetes duration, HbA_1c, BMI, HOMA2-β, HOMA2-IR and GAD65 autoantibodies, were used for sub-grouping patients by K-means clustering and for adjusting. Probability of diabetes complications (95% confidence interval), were calculated using logistic regression.

RESULTS

Based on baseline data from patients with type 2 diabetes (n = 2290), the cluster analysis suggested up to five sub-groups. These were primarily characterized by autoimmune β-cell failure (3%), insulin resistance with short disease duration (21%), non-autoimmune β-cell failure (22%), insulin resistance with long disease duration (32%), and presence of metabolic syndrome (22%), respectively. Retinopathy was more common in the sub-group characterized by non-autoimmune β-cell failure (52% (47.7-56.8)) compared to other sub-groups (22% (20.1-24.1)), adj. p < 0.001. The prevalence of cardiovascular disease, nephropathy and neuropathy also differed between sub-groups, but significance was lost after adjustment.

CONCLUSIONS

Patients with type 2 diabetes cluster into clinically relevant sub-groups based on routine clinical markers. The prevalence of diabetes complications seems to be sub-group specific. Our data suggests the need for a tailored strategy for the treatment of type 2 diabetes.

One-hour glucose value as a long-term predictor of cardiovascular morbidity and mortality: the Malmö Preventive Project

OBJECTIVE

To examine the predictive capability of a 1-h vs 2-h postload glucose value for cardiovascular morbidity and mortality.

DESIGN

Prospective, population-based cohort study (Malmö Preventive Project) with subject inclusion 1974-1992.

METHODS

4934 men without known diabetes and cardiovascular disease, who had blood glucose (BG) measured at 0, 20, 40, 60, 90 and 120 min during an OGTT (30 g glucose per m² body surface area), were followed for 27 years. Data on cardiovascular events and death were obtained through national and local registries. Predictive capabilities of fasting BG (FBG) and glucose values obtained during OGTT alone and added to a clinical prediction model comprising traditional cardiovascular risk factors were assessed using Harrell's concordance index (C-index) and integrated discrimination improvement (IDI).

RESULTS

Median age was 48 (25th-75th percentile: 48-49) years and mean FBG 4.6 ± 0.6 mmol/L. FBG and 2-h postload BG did not independently predict cardiovascular events or death. Conversely, 1-h postload BG predicted cardiovascular morbidity and mortality and remained an independent predictor of cardiovascular death (HR: 1.09, 95% CI: 1.01-1.17, P = 0.02) and all-cause mortality (HR: 1.10, 95% CI: 1.05-1.16, P < 0.0001) after adjusting for various traditional risk factors. Clinical risk factors with added 1-h postload BG performed better than clinical risk factors alone, in predicting cardiovascular death (likelihood-ratio test, P = 0.02) and all-cause mortality (likelihood-ratio test, P = 0.0001; significant IDI, P = 0.0003).

CONCLUSION

Among men without known diabetes, addition of 1-h BG, but not FBG or 2-h BG, to clinical risk factors provided incremental prognostic yield for prediction of cardiovascular death and all-cause mortality.

Glucose patterns during an oral glucose tolerance test and associations with future diabetes, cardiovascular disease and all-cause mortality rate

AIMS/HYPOTHESIS

In addition to blood glucose concentrations measured in the fasting state and 2 h after an OGTT, intermediate measures during an OGTT may provide additional information regarding a person's risk of future diabetes and cardiovascular disease (CVD). First, we aimed to characterise heterogeneity of glycaemic patterns based on three time points during an OGTT. Second, we compared the incidences of diabetes and CVD and all-cause mortality rates among those with different patterns.

METHODS

Our cohort study included 5861 participants without diabetes at baseline from the Danish Inter99 study. At baseline, all participants underwent an OGTT with measurements of plasma glucose levels at 0, 30 and 120 min. Latent class mixed-effects models were fitted to identify distinct patterns of glycaemic response during the OGTT. Information regarding incident diabetes, CVD and all-cause mortality rates during a median follow-up time of 11, 12 and 13 years, respectively, was extracted from national registers. Cox proportional hazard models with adjustment for several cardiometabolic risk factors were used to compare the risk of diabetes, CVD and all-cause mortality among individuals in the different latent classes.

RESULTS

Four distinct glucose patterns during the OGTT were identified. One pattern was characterised by high 30 min but low 2 h glucose values. Participants with this pattern had an increased risk of developing diabetes compared with participants with lower 30 min and 2 h glucose levels (HR 4.1 [95% CI 2.2, 7.6]) and participants with higher 2 h but lower 30 min glucose levels (HR 1.5 [95% CI 1.0, 2.2]). Furthermore, the all-cause mortality rate differed between the groups with significantly higher rates in the two groups with elevated 30 min glucose. Only small non-significant differences in risk of future CVD were observed across latent classes after confounder adjustment.

CONCLUSIONS/INTERPRETATION

Elevated 30 min glucose is associated with increased risk of diabetes and all-cause mortality rate independent of fasting and 2 h glucose levels. Therefore, subgroups at high risk may not be revealed when considering only fasting and 2 h glucose levels during an OGTT.

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

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.

Glucose patterns during the OGTT and risk of future diabetes in an urban Indian population: The CARRS study

AIMS

Traditionally, fasting and 2-hour post challenge plasma glucose have been used to diagnose diabetes. However, evidence indicates that clinically relevant pathophysiological information can be obtained by adding intermediate time-points to a standard oral glucose tolerance test (OGTT).

METHODS

We studied a population-based sample of 3666 Asian Indians without diabetes from the CARRS-Chennai Study, India. Participants underwent a three-point (fasting, 30-min, and 2-h) OGTT at baseline. Patterns of glycemic response during OGTT were identified using latent class mixed-effects models. After a median follow-up of two years, participants had a second OGTT. Logistic regression adjusted for diabetes risk factors was used to compare risk of incident diabetes among participants in different latent classes.

RESULTS

We identified four latent classes with different glucose patterns (Classes 1-4). Glucose values for Classes 1, 2, and 4 ranked consistently at all three time-points, but at gradually higher levels. However, Class 3 represented a distinct pattern, characterized by high 30-min (30minPG), normal fasting (FPG) and 2-h (2hPG) plasma glucose, moderately high insulin sensitivity, and low acute insulin response. Approximately 22% of participants were categorized as Class 3, and had a 10-fold risk of diabetes compared to the group with the most favorable glucose response, despite 92.5% of Class 3 participants having normal glucose tolerance (NGT) at baseline.

CONCLUSIONS

Elevated 30minPG is associated with high risk of incident diabetes, even in individuals classified as NGT by a traditional OGTT. Assessing 30minPG may identify a subgroup of high-risk individuals who remained unidentified by traditional measures.