Quantitative Estimation of Insulin Sensitivity in Type 1 Diabetic Subjects Wearing a Sensor-augmented Insulin Pump

Overview

Journal Diabetes Care

Specialty Endocrinology

Date 2013 Dec 10

PMID 24319120

Citations 21

Authors

Michele Schiavon

Chiara Dalla Man

Yogish C Kudva

Ananda Basu

Claudio Cobelli

Affiliations

Soon will be listed here.

Abstract

Objective: The goal was to develop a new index of insulin sensitivity in patients with type 1 diabetes estimated from continuous glucose monitoring (CGM) and subcutaneous insulin delivery data under carefully controlled conditions.

Research Design And Methods: The database consists of 12 subjects with type 1 diabetes, studied during breakfast, lunch, and dinner, in a clinical research unit, wearing both subcutaneous insulin pump and CGM device. Frequent blood samples were drawn for measurements of plasma glucose and insulin concentrations in order to estimate insulin sensitivity with the oral minimal model (SI(MM)). The new index of insulin sensitivity (SI(SP)) was calculated with a simple algebraic formula for each meal, using only CGM and insulin pump data and compared with SI(MM).

Results: SI(SP) was well correlated with SI(MM) (r = 0.825; P < 10(-8)), and diurnal pattern was also similar to SI(MM).

Conclusions: A novel method for estimating insulin sensitivity in subjects with type 1 diabetes on sensor-augmented insulin pump therapy has been presented. This new index correlates well with the reference oral minimal model estimate of insulin sensitivity. The knowledge of patient-specific insulin sensitivity and its diurnal variation can help in optimizing insulin therapy in type 1 diabetes and could also inform next-generation closed-loop control systems.

Citing Articles

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The Minimally-Invasive Oral Glucose Minimal Model: Estimation of Gastric Retention, Glucose Rate of Appearance, and Insulin Sensitivity From Type 1 Diabetes Data Collected in Real-Life Conditions.

Faggionato E, Schiavon M, Ekhlaspour L, Buckingham B, Dalla Man C IEEE Trans Biomed Eng. 2023; 71(3):977-986.

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The importance of interpreting machine learning models for blood glucose prediction in diabetes: an analysis using SHAP.

Prendin F, Pavan J, Cappon G, Del Favero S, Sparacino G, Facchinetti A Sci Rep. 2023; 13(1):16865.

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Taylor G, Shaw A, Smith K, Wason J, McDonald T, Oram R Diabet Med. 2022; 39(5):e14814.

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Automated closed-loop control of diabetes: the artificial pancreas.

Kovatchev B Bioelectron Med. 2020; 4:14.

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