Time to the Peak, Shape of the Curve and Combination of These Glucose Response Characteristics During Oral Glucose Tolerance Test as Indicators of Early Beta-cell Dysfunction in Obese Adolescents

Utilizing the Glucose and Insulin Response Shape of an Oral Glucose Tolerance Test to Predict Dysglycemia in Children with Overweight and Obesity, Ages 8-18 Years

Common dysglycemia measurements including fasting plasma glucose (FPG), oral glucose tolerance test (OGTT)-derived 2 h plasma glucose, and hemoglobin A1c (HbA1c) have limitations for children. Dynamic OGTT glucose and insulin responses may better reflect underlying physiology. This analysis assessed glucose and insulin curve shapes utilizing classifications-biphasic, monophasic, or monotonically increasing-and functional principal components (FPCs) to predict future dysglycemia. The prospective cohort included 671 participants with no previous diabetes diagnosis (BMI percentile ≥ 85th, 8-18 years old); 193 returned for follow-up (median 14.5 months). Blood was collected every 30 min during the 2 h OGTT. Functional data analysis was performed on curves summarizing glucose and insulin responses. FPCs described variation in curve height (FPC1), time of peak (FPC2), and oscillation (FPC3). At baseline, both glucose and insulin FPC1 were significantly correlated with BMI percentile (Spearman correlation r = 0.22 and 0.48), triglycerides (r = 0.30 and 0.39), and HbA1c (r = 0.25 and 0.17). In longitudinal logistic regression analyses, glucose and insulin FPCs predicted future dysglycemia (AUC = 0.80) better than shape classifications (AUC = 0.69), HbA1c (AUC = 0.72), or FPG (AUC = 0.50). Further research should evaluate the utility of FPCs to predict metabolic diseases.

The Glycemic Curve during the Oral Glucose Tolerance Test: Is It Only Indicative of Glycoregulation?

The shape of the glycemic curve during the oral glucose tolerance test (OGTT), interpreted in the correct context, can predict impaired glucose homeostasis. Our aim was to reveal information inherent in the 3 h glycemic trajectory that is of physiological relevance concerning the disruption of glycoregulation and complications beyond, such as components of metabolic syndrome (MS).

METHODS

In 1262 subjects (1035 women, 227 men) with a wide range of glucose tolerance, glycemic curves were categorized into four groups: monophasic, biphasic, triphasic, and multiphasic. The groups were then monitored in terms of anthropometry, biochemistry, and timing of the glycemic peak.

RESULTS

Most curves were monophasic (50%), then triphasic (28%), biphasic (17.5%), and multiphasic (4.5%). Men had more biphasic curves than women (33 vs. 14%, respectively), while women had more triphasic curves than men (30 vs. 19%, respectively) (p < 0.01). Monophasic curves were more frequent in people with impaired glucose regulation and MS compared to bi-, tri-, and multiphasic ones. Peak delay was the most common in monophasic curves, in which it was also most strongly associated with the deterioration of glucose tolerance and other components of MS.

CONCLUSION

The shape of the glycemic curve is gender dependent. A monophasic curve is associated with an unfavorable metabolic profile, especially when combined with a delayed peak.

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.