Hepatic insulin sensitivity assessed by integrated model of hepatic and peripheral glucose regulation

Index of glucose effectiveness derived from oral glucose tolerance test

Aim of this study was to formulate an index for glucose effectiveness (Sg), SgIo, based on 3-point (0, 30 and 120 min) 75 g oral glucose tolerance test (OGTT). The equation for SgI(O) was developed in the Chikuma cohort (n = 502). Firstly, post-loading plasma glucose without insulin action and Sg (PPG-without insulin and Sg) was calculated as follows: fasting plasma glucose (mg/dl) + [0.75 × 75,000]/[0.19 × BW(kg) × 10]. Secondly, 'PPG-without insulin/with Sg' was obtained from inverse correlation between log(10)DI(O) and 2-h post-glucose plasma glucose at OGTT (2hPG) in each glucose tolerance category: DI(O) denotes oral disposition index, a product of the Matsuda Index and δIRI(0-30)/δPG(0-30). Thirdly, expected 2hPG (2hPG(E)) of a given subject was obtained from the regression, and the ratio of 2hPG to 2hPG(E) (2hPG/2hPG(E)) was determined as an adjustment factor. Lastly, SgI(O) ([mg/dl]/min) was calculated as [PPG-without insulin and Sg]-[PPG-without insulin / with Sg] x [(2hPG) / 2hPG(E)]. SgI(O) was validated against Sg obtained by frequently sampled intravenous glucose tolerance test in the Jichi cohort (n = 205). Also, the accuracy of prediction of Sg by SgIo was tested by the Bland-Altman plot. SgI(O) was 3.61 ± 0.73, 3.17 ± 0.74 and 2.15 ± 0.60 in subjects with normal glucose tolerance (NGT), non-diabetic hyperglycemia and diabetes, respectively, in the Chikuma cohort. In the Jichi cohort, SgI(O) was significantly correlated with Sg in the entire group (r = 0.322, P < 0.001) and in subjects with NGT (r = 0.286, P < 0.001), and SgIo accurately predicted Sg. In conclusion, SgI(O) could be a simple, quantitative index for Sg.

Ethnic differences in glucose disposal, hepatic insulin sensitivity, and endogenous glucose production among African American and European American women

Intravenous glucose tolerance tests have demonstrated lower whole-body insulin sensitivity (S(I)) among African Americans (AA) compared with European Americans (EA). Whole-body S(I) represents both insulin-stimulated glucose disposal, primarily by skeletal muscle, and insulin's suppression of endogenous glucose production (EGP) by liver. A mathematical model was recently introduced that allows for distinction between disposal and hepatic S(I). The purpose of this study was to examine specific indexes of S(I) among AA and EA women to determine whether lower whole-body S(I) in AA may be attributed to insulin action at muscle, liver, or both. Participants were 53 nondiabetic, premenopausal AA and EA women. Profiles of EGP and indexes of Disposal S(I) and Hepatic S(I) were calculated by mathematical modeling and incorporation of a stable isotope tracer ([6,6-(2)H(2)]glucose) into the intravenous glucose tolerance test. Body composition was assessed by dual-energy x-ray absorptiometry. After adjustment for percentage fat, both Disposal S(I) and Hepatic S(I) were lower among AA (P = .009 for both). Time profiles for serum insulin and EGP revealed higher peak insulin response and corresponding lower EGP among AA women compared with EA. Indexes from a recently introduced mathematical model suggest that lower whole-body S(I) among nondiabetic AA women is due to both hepatic and peripheral components. Despite lower Hepatic S(I), AA displayed lower EGP, resulting from higher postchallenge insulin levels. Future research is needed to determine the physiological basis of lower insulin sensitivity among AA and its implications for type 2 diabetes mellitus risk.

Diabetes: Models, Signals, and Control

The control of diabetes is an interdisciplinary endeavor, which includes a significant biomedical engineering component, with traditions of success beginning in the early 1960s. It began with modeling of the insulin-glucose system, and progressed to large-scale in silico experiments, and automated closed-loop control (artificial pancreas). Here, we follow these engineering efforts through the last, almost 50 years. We begin with the now classic minimal modeling approach and discuss a number of subsequent models, which have recently resulted in the first in silico simulation model accepted as substitute to animal trials in the quest for optimal diabetes control. We then review metabolic monitoring, with a particular emphasis on the new continuous glucose sensors, on the analyses of their time-series signals, and on the opportunities that they present for automation of diabetes control. Finally, we review control strategies that have been successfully employed in vivo or in silico, presenting a promise for the development of a future artificial pancreas and, in particular, discuss a modular architecture for building closed-loop control systems, including insulin delivery and patient safety supervision layers. We conclude with a brief discussion of the unique interactions between human physiology, behavioral events, engineering modeling and control relevant to diabetes.