胰岛素敏感性
灵敏度(控制系统)
内科学
胰岛素
内分泌学
糖耐量试验
BETA(编程语言)
β细胞
医学
胰岛素抵抗
计算机科学
工程类
小岛
电子工程
程序设计语言
作者
Joon Ha,Stephanie T. Chung,Max Springer,Joon Young Kim,Phil Chen,Aaryan Chhabra,Melanie Cree-Green,Cecilia Diniz Behn,Anne E. Sumner,Silva Arslanian,Arthur Sherman
出处
期刊:American Journal of Physiology-endocrinology and Metabolism
[American Physiological Society]
日期:2023-12-06
标识
DOI:10.1152/ajpendo.00189.2023
摘要
Efficient and accurate methods to estimate insulin sensitivity (SI) and beta-cell function (BCF) are of great importance for studying the pathogenesis and treatment effectiveness of type 2 diabetes. 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 SI 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 ISS (Insulin Secretion and Sensitivity) 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. The model was developed by fitting a pair of differential equations to glucose and insulin without need of deconvolution or C-peptide data. The model is derived from a published model for longitudinal simulation of T2D progression that represents glucose-insulin homeostasis, including post-challenge 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 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.
科研通智能强力驱动
Strongly Powered by AbleSci AI