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
Timothy J. Renier,
Htun Ja Mai,
Zheshi Zheng,
Mary Ellen Vajravelu,
Emily Hirschfeld,
Diane Gilbert-Diamond,
Joyce M. Lee,
Jennifer L. Meijer
Affiliations
Timothy J. Renier
Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
Htun Ja Mai
Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
Zheshi Zheng
Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
Mary Ellen Vajravelu
Division of Pediatric Endocrinology, Diabetes and Metabolism, UPMC—Children’s Hospital of Pittsburgh, Pittsburgh, PA 15224, USA
Emily Hirschfeld
Department of Pediatrics, Division of Pediatric Endocrinology, Susan B. Meister Child Health Evaluation and Research Center, University of Michigan, Ann Arbor, MI 48109, USA
Diane Gilbert-Diamond
Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
Joyce M. Lee
Department of Pediatrics, Division of Pediatric Endocrinology, Susan B. Meister Child Health Evaluation and Research Center, University of Michigan, Ann Arbor, MI 48109, USA
Jennifer L. Meijer
Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
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.
