Estimating insulin sensitivity and ��-cell function from the oral glucose tolerance test: validation of a new insulin sensitivity and secretion (ISS) model.
Joon Ha, Stephanie T Chung, Max Springer, Joon Young Kim, Phil Chen, Aaryan Chhabra, Melanie G Cree, Cecilia Diniz Behn, Anne E Sumner, Silva A Arslanian, Arthur S Sherman
Author Information
Joon Ha: Department of Mathematics, Howard University, Washington, District of Columbia, United States.
Stephanie T Chung: Section on Pediatric Diabetes, Obesity, and Metabolism, Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, United States.
Max Springer: Department of Mathematics, University of Maryland, College Park, Maryland, United States. ORCID
Joon Young Kim: Department of Exercise Science, David B. Falk College of Sport and Human Dynamics, Syracuse University, Syracuse, New York, United States.
Phil Chen: Irvine, California, United States.
Aaryan Chhabra: Department of Biology, Indian Institute of Science Education and Research, Pune, India.
Melanie G Cree: Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States.
Cecilia Diniz Behn: Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States. ORCID
Anne E Sumner: Intramural Research Program, National Institute on Minority Health and Health Disparities (NIMHD), National Institutes of Health, Bethesda, Maryland, United States.
Silva A Arslanian: Division of Pediatric Endocrinology, Metabolism and Diabetes Mellitus, Center for Pediatric Research in Obesity and Metabolism, UPMC Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania, United States.
Arthur S Sherman: Laboratory of Biological Modeling, National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, United States. ORCID
Efficient and accurate methods to estimate insulin sensitivity () and ��-cell function (BCF) are of great importance for studying the pathogenesis and treatment effectiveness of type 2 diabetes (T2D). Existing methods range in sensitivity, input data, and technical requirements. Oral glucose tolerance tests (OGTTs) are preferred because they are simpler and more physiological than intravenous methods. However, current analytical methods for OGTT-derived and BCF also range in complexity; the oral minimal models require mathematical expertise for deconvolution and fitting differential equations, and simple algebraic surrogate indices (e.g., Matsuda index, insulinogenic index) may produce unphysiological values. We developed a new insulin secretion and sensitivity (ISS) model for clinical research that provides precise and accurate estimates of SI and BCF from a standard OGTT, focusing on effectiveness, ease of implementation, and pragmatism. This model was developed by fitting a pair of differential equations to glucose and insulin without need of deconvolution or C-peptide data. This model is derived from a published model for longitudinal simulation of T2D progression that represents glucose-insulin homeostasis, including postchallenge suppression of hepatic glucose production and first- and second-phase insulin secretion. The ISS model was evaluated in three diverse cohorts across the lifespan. The new model had a strong correlation with gold-standard estimates from intravenous glucose tolerance tests and insulin clamps. The ISS model has broad applicability among diverse populations because it balances performance, fidelity, and complexity to provide a reliable phenotype of T2D risk. The pathogenesis of type 2 diabetes (T2D) is determined by a balance between insulin sensitivity () and ��-cell function (BCF), which can be determined by gold standard direct measurements or estimated by fitting differential equation models to oral glucose tolerance tests (OGTTs). We propose and validate a new differential equation model that is simpler to use than current models and requires less data while maintaining good correlation and agreement with gold standards. Matlab and Python code is freely available.
