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Routine OGTT: a robust model including incretin effect for precise identification of insulin sensitivity and secretion in a single individual.

De Gaetano A, Panunzi S, Matone A, Samson A, Vrbikova J, Bendlova B, Pacini G - PLoS ONE (2013)

Bottom Line: ANOVA on kxgi values across groups resulted significant overall (P<0.001), and post-hoc comparisons highlighted the presence of three different groups: NGT (8.62×10(-5)±9.36×10(-5) min(-1)pM(-1)), IFG (5.30×10(-5)±5.18×10(-5)) and combined IGT, IFG+IGT and T2DM (2.09×10(-5)±1.95×10(-5), 2.38×10(-5)±2.28×10(-5) and 2.38×10(-5)±2.09×10(-5) respectively).No significance was obtained when comparing ISCOMO or ISDMMO across groups.The kxgi index, reflecting insulin secretion dependency on glycemia, also significantly differentiates clinically diverse subject groups.

View Article: PubMed Central - PubMed

Affiliation: Institute of System Analysis and Informatics (IASI) A. Ruberti, National Research Council (CNR), Rome, Italy.

ABSTRACT
In order to provide a method for precise identification of insulin sensitivity from clinical Oral Glucose Tolerance Test (OGTT) observations, a relatively simple mathematical model (Simple Interdependent glucose/insulin MOdel SIMO) for the OGTT, which coherently incorporates commonly accepted physiological assumptions (incretin effect and saturating glucose-driven insulin secretion) has been developed. OGTT data from 78 patients in five different glucose tolerance groups were analyzed: normal glucose tolerance (NGT), impaired glucose tolerance (IGT), impaired fasting glucose (IFG), IFG+IGT, and Type 2 Diabetes Mellitus (T2DM). A comparison with the 2011 Salinari (COntinuos GI tract MOdel, COMO) and the 2002 Dalla Man (Dalla Man MOdel, DMMO) models was made with particular attention to insulin sensitivity indices ISCOMO, ISDMMO and kxgi (the insulin sensitivity index for SIMO). ANOVA on kxgi values across groups resulted significant overall (P<0.001), and post-hoc comparisons highlighted the presence of three different groups: NGT (8.62×10(-5)±9.36×10(-5) min(-1)pM(-1)), IFG (5.30×10(-5)±5.18×10(-5)) and combined IGT, IFG+IGT and T2DM (2.09×10(-5)±1.95×10(-5), 2.38×10(-5)±2.28×10(-5) and 2.38×10(-5)±2.09×10(-5) respectively). No significance was obtained when comparing ISCOMO or ISDMMO across groups. Moreover, kxgi presented the lowest sample average coefficient of variation over the five groups (25.43%), with average CVs for ISCOMO and ISDMMO of 70.32% and 57.75% respectively; kxgi also presented the strongest correlations with all considered empirical measures of insulin sensitivity. While COMO and DMMO appear over-parameterized for fitting single-subject clinical OGTT data, SIMO provides a robust, precise, physiologically plausible estimate of insulin sensitivity, with which habitual empirical insulin sensitivity indices correlate well. The kxgi index, reflecting insulin secretion dependency on glycemia, also significantly differentiates clinically diverse subject groups. The SIMO model may therefore be of value for the quantification of glucose homeostasis from clinical OGTT data.

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COMO, SIMO and DMMO model data fitting.Panels A reports glucose and insulin dynamics for one IFG patient. Panel A1 reports observed (circles) plasma glucose concentrations together with their prediction using the COMO model (continuous line). Panels A2 and A3 report respectively glycemia (A2) and insulinemia (A3) concentrations (circles), together with the corresponding predictions obtained with the SIMO model (continuous line). Panels B report glucose and insulin dynamics for one T2DM patient. Panel B1 reports observed (circles) plasma glucose concentrations together with their prediction using the DMMO model (continuous line). Panels B2 and B3 report respectively glycemia (B2) and insulinemia (B3) concentrations (circles), together with the corresponding predictions obtained with the SIMO model (continuous line).
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pone-0070875-g005: COMO, SIMO and DMMO model data fitting.Panels A reports glucose and insulin dynamics for one IFG patient. Panel A1 reports observed (circles) plasma glucose concentrations together with their prediction using the COMO model (continuous line). Panels A2 and A3 report respectively glycemia (A2) and insulinemia (A3) concentrations (circles), together with the corresponding predictions obtained with the SIMO model (continuous line). Panels B report glucose and insulin dynamics for one T2DM patient. Panel B1 reports observed (circles) plasma glucose concentrations together with their prediction using the DMMO model (continuous line). Panels B2 and B3 report respectively glycemia (B2) and insulinemia (B3) concentrations (circles), together with the corresponding predictions obtained with the SIMO model (continuous line).

Mentions: In order to study model behavior in case of extreme parameter estimates, Figure 5 panel A1 reports the predicted and observed plasma glucose concentrations obtained with the COMO model for one IFG patient whose insulin sensitivity index ISCOMO was estimated at the low limit of optimization (10−10), while Figure 5 panel B1 reports the predicted and observed plasma glucose concentrations obtained with the DMMO model for one T2DM patient whose insulin sensitivity index ISDMMO was estimated at 3.26×10−3 (very high). The OGTT data from these same two patients are reported again in Figure 5 panels A2–A3 (IFG patient) and panels B2–B3 (T2DM patient), together with curves obtained by fitting these subjects with the SIMO model.


Routine OGTT: a robust model including incretin effect for precise identification of insulin sensitivity and secretion in a single individual.

De Gaetano A, Panunzi S, Matone A, Samson A, Vrbikova J, Bendlova B, Pacini G - PLoS ONE (2013)

COMO, SIMO and DMMO model data fitting.Panels A reports glucose and insulin dynamics for one IFG patient. Panel A1 reports observed (circles) plasma glucose concentrations together with their prediction using the COMO model (continuous line). Panels A2 and A3 report respectively glycemia (A2) and insulinemia (A3) concentrations (circles), together with the corresponding predictions obtained with the SIMO model (continuous line). Panels B report glucose and insulin dynamics for one T2DM patient. Panel B1 reports observed (circles) plasma glucose concentrations together with their prediction using the DMMO model (continuous line). Panels B2 and B3 report respectively glycemia (B2) and insulinemia (B3) concentrations (circles), together with the corresponding predictions obtained with the SIMO model (continuous line).
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Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3756988&req=5

pone-0070875-g005: COMO, SIMO and DMMO model data fitting.Panels A reports glucose and insulin dynamics for one IFG patient. Panel A1 reports observed (circles) plasma glucose concentrations together with their prediction using the COMO model (continuous line). Panels A2 and A3 report respectively glycemia (A2) and insulinemia (A3) concentrations (circles), together with the corresponding predictions obtained with the SIMO model (continuous line). Panels B report glucose and insulin dynamics for one T2DM patient. Panel B1 reports observed (circles) plasma glucose concentrations together with their prediction using the DMMO model (continuous line). Panels B2 and B3 report respectively glycemia (B2) and insulinemia (B3) concentrations (circles), together with the corresponding predictions obtained with the SIMO model (continuous line).
Mentions: In order to study model behavior in case of extreme parameter estimates, Figure 5 panel A1 reports the predicted and observed plasma glucose concentrations obtained with the COMO model for one IFG patient whose insulin sensitivity index ISCOMO was estimated at the low limit of optimization (10−10), while Figure 5 panel B1 reports the predicted and observed plasma glucose concentrations obtained with the DMMO model for one T2DM patient whose insulin sensitivity index ISDMMO was estimated at 3.26×10−3 (very high). The OGTT data from these same two patients are reported again in Figure 5 panels A2–A3 (IFG patient) and panels B2–B3 (T2DM patient), together with curves obtained by fitting these subjects with the SIMO model.

Bottom Line: ANOVA on kxgi values across groups resulted significant overall (P<0.001), and post-hoc comparisons highlighted the presence of three different groups: NGT (8.62×10(-5)±9.36×10(-5) min(-1)pM(-1)), IFG (5.30×10(-5)±5.18×10(-5)) and combined IGT, IFG+IGT and T2DM (2.09×10(-5)±1.95×10(-5), 2.38×10(-5)±2.28×10(-5) and 2.38×10(-5)±2.09×10(-5) respectively).No significance was obtained when comparing ISCOMO or ISDMMO across groups.The kxgi index, reflecting insulin secretion dependency on glycemia, also significantly differentiates clinically diverse subject groups.

View Article: PubMed Central - PubMed

Affiliation: Institute of System Analysis and Informatics (IASI) A. Ruberti, National Research Council (CNR), Rome, Italy.

ABSTRACT
In order to provide a method for precise identification of insulin sensitivity from clinical Oral Glucose Tolerance Test (OGTT) observations, a relatively simple mathematical model (Simple Interdependent glucose/insulin MOdel SIMO) for the OGTT, which coherently incorporates commonly accepted physiological assumptions (incretin effect and saturating glucose-driven insulin secretion) has been developed. OGTT data from 78 patients in five different glucose tolerance groups were analyzed: normal glucose tolerance (NGT), impaired glucose tolerance (IGT), impaired fasting glucose (IFG), IFG+IGT, and Type 2 Diabetes Mellitus (T2DM). A comparison with the 2011 Salinari (COntinuos GI tract MOdel, COMO) and the 2002 Dalla Man (Dalla Man MOdel, DMMO) models was made with particular attention to insulin sensitivity indices ISCOMO, ISDMMO and kxgi (the insulin sensitivity index for SIMO). ANOVA on kxgi values across groups resulted significant overall (P<0.001), and post-hoc comparisons highlighted the presence of three different groups: NGT (8.62×10(-5)±9.36×10(-5) min(-1)pM(-1)), IFG (5.30×10(-5)±5.18×10(-5)) and combined IGT, IFG+IGT and T2DM (2.09×10(-5)±1.95×10(-5), 2.38×10(-5)±2.28×10(-5) and 2.38×10(-5)±2.09×10(-5) respectively). No significance was obtained when comparing ISCOMO or ISDMMO across groups. Moreover, kxgi presented the lowest sample average coefficient of variation over the five groups (25.43%), with average CVs for ISCOMO and ISDMMO of 70.32% and 57.75% respectively; kxgi also presented the strongest correlations with all considered empirical measures of insulin sensitivity. While COMO and DMMO appear over-parameterized for fitting single-subject clinical OGTT data, SIMO provides a robust, precise, physiologically plausible estimate of insulin sensitivity, with which habitual empirical insulin sensitivity indices correlate well. The kxgi index, reflecting insulin secretion dependency on glycemia, also significantly differentiates clinically diverse subject groups. The SIMO model may therefore be of value for the quantification of glucose homeostasis from clinical OGTT data.

Show MeSH
Related in: MedlinePlus