Nutrition, Metabolism & Cardiovascular Diseases (2013) 23, 23e30

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Type 2 diabetes mellitus: A disease of thegovernance of the glucose-insulin systemAn experimental metabolic control analysis study

M. Trombetta a, L. Boselli a, A. Cretti a, A. Calı̀ a, M. Vettore d, B. Caruso c,R. Dorizzi c, A. Avogaro d, M. Muggeo a,b, E. Bonora a,b, R.C. Bonadonna a,b,*

aDepartment of Medicine, Section of Endocrinology, University of Verona School of Medicine, Verona, ItalybDivision of Endocrinology and Metabolic Diseases, Azienda Ospedaliera Universitaria Integrata, Verona, ItalycDivision of Laboratory Medicine, Azienda Ospedaliera Universitaria Integrata, Verona, ItalydDepartment of Clinical and Experimental Medicine, Section of Metabolic Diseases, University of Padova Schoolof Medicine, Padova, Italy

Received 18 August 2010; received in revised form 28 April 2011; accepted 6 May 2011Available online 19 September 2011

KEYWORDSMetabolic controlanalysis;Type 2 diabetesmellitus;Insulin secretion;Insulin action

* Corresponding author. Division o0458123115; fax: þ39 045917374.

E-mail address: riccardo.bonadonn

0939-4753/$ - see front matter ª 201doi:10.1016/j.numecd.2011.05.006

Abstract Background and aims: The relatives role of each component of the glucose-insulinsystem in determining hyperglycemia in type 2 diabetes is still under debate. Metabolic ControlAnalysis (MCA) quantifies the control exerted by each component of a system on a variable ofinterest, by computing the relevant coefficients of control (CCs), which are systemic proper-ties. We applied MCA to the intravenous glucose tolerance test (IVGTT) to quantify the CCsof the main components of the glucose-insulin system on intravenous glucose tolerance.Methods and results: We combined in vivo phenotyping (IVGTT/euglycaemic insulin clamp)and in silico modeling (GLUKINSLOOP.1) to compute the CCs of intravenous glucose tolerancein healthy insulin-sensitive (n Z 9, NGR-IS), healthy insulin-resistant (n Z 7, NGR-IR) and sub-diabetic hyperglycemic (n Z 8, PreT2DM) individuals and in patients with newly diagnosedtype 2 diabetes (n Z 7, T2DM).

Altered insulin secretion and action were documented in NGR-IR and PreT2DM groups, butonly 1st phase insulin secretion was significantly lower in T2DM than in PreT2DM (p < 0.05).The CCs changed little in the nondiabetic groups. However, several CCs were significantlyaltered in the patients (e.g. CCs of beta cell: �0.75 � 0.10, �0.64 � 0.15, �0.56 � 0.09and �0.19 � 0.04 in NGR-IS, NGR-IR, PreT2DM and T2DM, respectively; p < 0.01 by MANOVA),and they could not be corrected by matching in silico nondiabetic and T2DM groups for 1stphase secretion.

f Endocrinology, Ospedale Civile Maggiore, Piazzale Stefani 1, 37126 Verona, Italy. Tel.: þ39

a@tiscali.it (R.C. Bonadonna).

1 Elsevier B.V. All rights reserved.

24 M. Trombetta et al.

1 The insulin subsystem and the glucthe glucose-insulin system are not conkinetic effects, i.e. glucose stimulatesenhances insulin-dependent glucoseendogenous glucose production. Thanalysis falls in the realm of the hierde la Fuente A, Snoep JL, Westerhocontrol in integrated biochemical sy269(18):4399e4408.) an extension osimplicity in this paper we will usepopularity.

Conclusions: Type 2 diabetes is characterized not only by loss of function of the elements ofthe glucose-insulin system, but also by changes in systemic properties (CCs). As such, it couldbe considered a disease of the governance of the glucose-insulin system.ª 2011 Elsevier B.V. All rights reserved.

Plasma glucose concentration normally is kept withinnarrow limits by a sophisticated physiologic system, inwhich insulin, as the only hormone with a net hypoglycemicaction, plays a prominent role [1].

Patients with type 2 diabetes [2], and even subjects withsubdiabetic hyperglycemia [3e6], exhibit remarkablechanges both in insulin secretion by the beta cell and ininsulin action in insulin-sensitive organs [7]. Although manystudies show alterations in single elements of the glucose-insulin system [7], the magnitude of the control exertedby its components in determining glucose concentration hasnot been quantified.

Metabolic control analysis (MCA) [8,9], is defined as “aframework to quantify the control of metabolic variables,such as steady state flux or a metabolite concentration, byparameters of the system”1 [10]. Recent analyses allow itsapplication also to nonsteady state conditions [11].

MCA quantifies control with the scaled control coeffi-cients (CCs), which assess the distribution of the control, orof the “governance” of a system. They typically rangebetween �1 and þ1, and the higher their absolute valuethe greater is the control exerted by the system parameteron the metabolic variable of interest [10,12]. Importantly,the CCs are system properties that depend on all thecomponents of the system, and cannot be inferred fromisolated measures of single elements of the system [12].

As to type 2 diabetes, the interest is focused on the CCsof plasma glucose concentration, because the latter is theparameter used to diagnose the disease [13], a primary riskfactor for specific complications and a primary target fortherapy [14].

In a previous work, we reported the CCs (thereindenominated “control strength coefficients”) of trans-membrane transport and post-transport steps in deter-mining muscle glucose uptake in vivo in humans [15],noticing that insulin caused a redistribution of control fromphosphorylation to transport. A subsequent study, per-formed with an FDG/PET technique, confirmed andextended our observations, highlighting also the role playedby transcapillary exchange [16]. However, in both cases thereported CCs referred to glucose fluxes, not concentra-tions, and only to a part of the glucose/insulin system, i.e.glucose utilization in skeletal muscle.

ose subsystem compoundingnected by mass flow, but byinsulin secretion and insulinutilization and suppresses

us, strictly speaking, theirarchical control analysis (10.ff HV, Mendes P: Metabolicstems. Eur J Biochem 2002,f MCA, but for the sake ofthe term MCA owing to its

We wondered whether insulin resistance and/or glucosetolerance affect the CCs of glucose concentration. To do so,we have applied MCA in humans, studying individualsbelonging to different classes of glucose regulation to covera wide range of glucose tolerance. Our results documentseveral profound changes in the CCs of type 2 diabeticpatients, but not of nondiabetic subjects, to indicate thattype 2 diabetes is characterized by widespread alterationsin the governance of the glucose-insulin system.

Methods

Subjects

Thirty-one subjects, 9 with normal glucose regulation andnormal insulin sensitivity (NGR-IS), 7 with normal glucoseregulation and insuin resistance (NGR-IR), 8 with sub-diabetic hyperglycemia (PreT2DM), i.e. with impairedfasting glucose and/or impaired glucose tolerance, and 7with type 2 diabetes mellitus (T2DM), were studied [13].Except for subdiabetic hyperglycemia and/or overweight/obesity, all nondiabetic subjects were healthy, as judged bytheir past medical history, physical examination and stan-dard laboratory exams. One patient with type 2 diabeteswas affected also by hypercholesterolemia. All subjectswere studied with a labeled IVGTT plus a euglycemic insulinclamp on one day and with an OGTT on a separate day. Eachsubject gave informed written consent before participatingin the study, which was approved by the Human Investiga-tion Committee of the Verona City Hospital. Details onstudy design and methods [17e19] are given in the ESMAPPENDIX.

Standard calculations

The amount of glucose metabolized during the last 60 minof the clamp (M value, not corrected for residual endoge-nous glucose production, reference insulin sensitivity) wascomputed with standard formulas [17,20] (Table 1).

Beta cell function, assessed with a mathematical model[4,21,22] applied to the glucose and C-peptide curvesduring the IVGTT, was splitted in several components: basalsecretion rate (BSR), glucose sensitivities of first phase(s1st) and second phase (s2nd) insulin secretion, whichquantify derivative/dynamic control and proportional/static control [23,24], respectively, exerted by glucose oninsulin secretion (Table 1).

Mathematical modeling of the glucose-insulinsystem

The modeling strategy herein used is based on the minimalmodels of insulin secretion and action first introduced in

Table 1 Demographic, anthropometric and metabolic characteristics of study subjects. NGR-IS: insulin-sensitive subjects withnormal glucose regulation; NGR-IR: insulin-resistant subjects with normal glucose regulation; PreT2DM: subjects with sub-diabetic hyperglycemia; T2DM: patients with newly diagnosed type 2 diabetes mellitus. BMI: body mass index; FPG: fastingplasma glucose; 2H-PG OGTT: plasma glucose concentration at 120 min of the OGTT; 1H-PG IVGTT: plasma glucose concen-tration at 60 min of the IVGTT; FSI: fasting serum insulin; Insulin Sensitivity: rate of glucose utilization (M Value) during the last60 min of the euglycemic insulin clamp; BSR: basal (pre-hepatic) insulin secretion rate; s1st: glucose sensitivity of 1st phaseinsulin secretion during the IVGTT, which measures the amount of insulin secreted by the beta cell in response to a rate ofincrease in glucose concentration of 1 mM per min lasting for 1 min; s2nd: glucose sensitivity of 2nd phase insulin secretionduring the IVGTT, which measures the steady state insulin secretion rate of the beta cell in response to a square wave increasein glucose concentration of 1 mM over the plasma glucose threshold.

NGR-IS NGR-IR PreT2DM T2DM P valueby MANOVA

N (M/F) 9 (4/5) 7 (2/5) 8 (4/4) 7 (4/3)Age (yrs) 24.6 � 0.7 28.4 � 4.4b 42.3 � 5.8a 49.0 � 4.7a 0.001BMI (kg min�2) 22.2 � 0.8 27.7 � 4.3 29.8 � 2.8a 30.4 � 0.8a 0.08FPG (mM) 4.8 � 0.1 4.6 � 0.1b 5.2 � 0.2a,b 6.7 � 0.3a 0.00012H-PG OGTT (mM) 6.6 � 0.2 6.3 � 0.3 9.2 � 0.4a,b 13.2 � 0.7a 0.00011H-PG IVGTT (mM) 5.7 � 0.2 5.7 � 0.6b 7.2 � 0.6a,b 9.4 � 0.4a 0.0001FSI (pmol/l) 34 � 3.3 62 � 18 54 � 11 137 � 68Insulin Sensitivity(mmol min�1 m�2)

1635 � 164 678 � 77a 1011 � 86a 869 � 81a 0.0001

BSR (pmol min�1 m�2) 51.1 � 3.0 88.2 � 33 90.6 � 18 128 � 17a 0.06s1st (pmol m�2)/(mM min�1) 232 � 36 271 � 71b 161 � 38b 14.6 � 8.20a 0.002s2nd (pmol min�1 m�2)/(mM) 36.6 � 5.3 44.6 � 5.7 41.5 � 8.7 35.2 � 4.6 0.72a P < 0.05 or less vs NGR-IS.b p < 0.05 or less vs T2DM.

Metabolic control analysis of glucose tolerance 25

the early ‘80s [25] and subsequently evolved into more andmore complex designs [19,23,26e29].

The comprehensive model used in this study can bedivided in four submodels: the beta cell submodel, theinsulin catabolism submodel, the glucose utilization (usingthe glucose tracer data during the IVGTT) submodel and theendogenous glucose output (using the glucose tracer dataduring the IVGTT) submodel. A formal detailed descriptionof the submodels can be found in the ESM APPENDIX. Ineach subject, the unknown parameters of these four sub-models were computed starting with the beta cell sub-model and identifying one submodel sequentially at eachstep.

Building in silico phenocopies of the glucose-insulin system

After identifying all four submodels in each subject, weassembled them and built an in silico phenocopy of theglucose-insulin system of each individual, which mimics insilico the autonomous behavior of the glucose-insulinsystem in vivo of each subject. We have named itGLUKINSLOOP.1.

In each in silico phenocopy we set initial conditionsidentical to those observed in vivo and we performed insilico an injection of glucose identical to the one donein vivo. Each in silico phenocopy, then, generated C-peptide, insulin, and glucose curves in response to theglucose challenge. Thus, at the end we had in silico 31reference IVGTT, one for each phenocopy created withGLUKINSLOOP.1. The similarity of the virtual IVGTT to thereal IVGTTwas remarkable, and is shown for plasma glucose

concentration at 60’ during the IVGTT (Figure ESM 2). Thisglucose value was chosen because it was the one mostclosely related to fasting glucose and 2-h glucose after theOGTT (Table ESM 4).

Metabolic control analysis (MCA)

MCA quantifies control in terms of control coefficients(CCs), which are defined as the ratio between the relativechange in the variable of interest and the relative change inthe parameter, imposed externally [8,9,11,12]. To computethe CCs of 1-h plasma glucose during the IVGTT, werepeated in silico the IVGTT in each GLUKINSLOOP.1 phe-nocopy, imposing an isolated change of þ1% to eachparameter we deemed of interest to understand thedistribution of control [30]. The 1-h plasma glucose ob-tained after each change and the 1-h plasma glucose foundin the respective in silico reference IVGTT, was used tocompute the CCs of the single elements of the glucose-insulin system according to the following formula [8e12]:

CCP of 1 hour Plasma Glucose duringVirtual IVGTTZðv lnj1H� PGjÞ=ðv ln PÞ ð1Þ

where 1H-PG is plasma glucose concentration at 600

during the in silico IVGTTs and P is the parameter which hasbeen varied in silico. The advantage of defining the controlcoefficient as a relative or logarithmic sensitivity is that it isdimensionless [12].

We also computed the CC of global beta cell function,which lumps the control of basal, first and second phaseinsulin secretion, the CC of total insulin action, whichaggregates insulin sensitivities of both glucose utilization

26 M. Trombetta et al.

and endogenous glucose output, and the CC of noninsulin-mediated glucose disposal (NIMGD), which is formed byconstant glucose utilization (brain and red blood cells),glucose-mediated glucose utilization and glycosuria (TableESM 1). Additional CCs were computed for the size of theglucose load (i.e. the external perturbation) and for fastingglucose (i.e. a clinically relevant initial condition).

Statistical analysis

The weighted residuals of C-peptide, insulin, GENDO (i.e.glucose concentrations derived from endogenous glucoseoutput) and GEXO (i.e. glucose concentration derived fromthe intravenous glucose load) are presented as mean � SD(Table ESM 2). All other results in tables and figures arepresented as mean � SEM. Overall comparisons werecarried out by MANOVA. Between-groups contrasts wereperformed keeping either the NGR-IS subjects or the T2DMpatients as reference group. Simple correlation andregression analyses were performed with standardformulas. All statistical analyses were carried out with SPSS11.0 software for MacOS X. Statistical significance wasdeclared at two-tailed P value of 0.05.

Results

Demographic, anthropometric and metabolicfeatures of the study subjects

The main features of the study subjects are summarized inTable 1. PreT2DM subjects (n Z 8) and T2DM patients(n Z 7) were older and fatter than the NGR-IS group(p < 0.05 or less, Table 1).

Glucose tolerance was superimposable in NGR-IS andNGR-IR groups. By study selection criteria, glucose toler-ance was progressively worse in the PreT2DM group(p < 0.01 vs NGR-IS) and in the T2DM group (p < 0.01 vsPreT2DM, NGR-IR and NGR-IS groups). Glucose

Figure 1 Control coefficients of fasting plasma glucose (Fasting Gfunction (Beta Cell), liver insulin extraction, insulin action (sum osensitivity of glucose utilization) and noninsulin-mediated glucosglycosuria and glucose-mediated glucose utilization) on 1-h plasmawith normal glucose regulation (NGR-IS, empty bars), in insulin-regrey bars), in subjects with subdiabetic hyperglycemia (PreT2DM,diabetes mellitus (T2DM, black bars). {P < 0.05 or less vs NGR-IS;

concentration at 60 min during the IVGTT closely mirroredthe pattern of oral glucose tolerance (Table 1).

Insulin sensitivity, as assessed by the euglycemic insulinclamp [17,20], was lower (p < 0.01) in the NGR-IR than inthe NGR-IS group by study selection criteria (Table 1).Insulin sensitivity was also lower in PreT2DM and T2DMthan in NGR-IS (p < 0.05 or less), but no significant differ-ences were detected among NGR-IR, PreT2DM and T2DM(Table 1).

Basal insulin secretion rate was higher in T2DM than inNGR-IS (p < 0.05). Glucose sensitivity of first phase secre-tion was lower in T2DM than in each of the nondiabeticgroups (p < 0.05 or less) (Table 1). No between-groupdifferences were detected in glucose sensitivity of secondphase secretion (Table 1).

Metabolic control analysis of the IVGTT

Metabolic modeling allowed us to close the glucose-insulinloop in silico (GLUKINSLOOP.1), thereby creating an in silicophenocopy of each individual (see ESM APPENDIX fordetails).

Each in silico phenocopy reproduced closely the glucose,C-peptide and insulin curves measured during the in vivoexperiments. In particular, plasma glucose concentrationsat 60 min of the in vivo IVGTT and of the in silico IVGTTwere very similar to each other (Figure ESM 2). Glucose atthis time point was the one most closely related to fastingand 2-h after OGTT glucose concentrations (Table ESM 4),i.e. the two glucose values used to diagnose diabetes [13].

Table ESM 3 reports the values of the CCs of the singleelements of the glucose-insulin systemon 1-h plasma glucoseduring the in silico IVGTT. The CCs of selected elements ofthe glucose-insulin system on 1-h plasma glucose valuesduring the IVGTT are depicted in Figs. 1 and 2.

In the NGR-IS, NGR-IR and PreT2DM groups, only smalldifferences were detected in the distribution of control. Incontrast, distribution of control was completely distorted inthe T2DM group (Fig. 1 and Table ESM 3), which showed

lucose), intravenous glucose load size (Glucose Load), beta cellf insulin sensitivity of endogenous glucose output plus insuline disposal (NIMGD; sum of brain/red blood cells utilization,glucose during the in silico IVGTT in insulin-sensitive subjectssistant subjects with normal glucose regulation (NGR-IR, lightdark grey bars) and in patients with newly diagnosed type 2

xP < 0.05 or less vs T2DM.

Figure 2 Control coefficients of first phase insulin secretion (First Phase), basal insulin secretion rate (Basal Secretion Rate),glycemic threshold of 2nd phase insulin secretion (Glucose Threshold), and second phase insulin secretion (Second Phase) (Panel A,left) and of insulin sensitivities of endogenous glucose output and glucose utilization (Panel B, right) on 1-h plasma glucose duringthe in silico IVGTT in insulin-sensitive subjects with normal glucose regulation (NGR-IS, empty bars), in insulin-resistant subjectswith normal glucose regulation (NGR-IR, light grey bars), in subjects with subdiabetic hyperglycemia (PreT2DM, dark grey bars) andin patients with newly diagnosed type 2 diabetes mellitus (T2DM, black bars). {P < 0.05 or less vs NGR-IS; xP < 0.05 or less vs T2DM.

Metabolic control analysis of glucose tolerance 27

significant differences in all CCs, but the one of NIMGD,whencompared to the nondiabetic groups (p < 0.05 or less).

In particular, the CCs of the glucose load (i.e. theexternal perturbation) and of fasting glucose (i.e. theinitial condition) were higher in T2DM than in NGR-IS(þ0.32 � 0.04 vs �0.09 � 0.09 and þ0.36 � 0.02 vsþ0.17 � 0.04, T2DM vs NGR-IS, p < 0.01 for both) (Fig. 1).Moreover, there were impressive falls in the CCs of beta cellfunction and insulin action (Fig. 1). They were due tosignificant reductions in the CCs of all elements of insulinsecretion and sensitivity (Fig. 2), except insulin sensitivityof endogenous glucose production during the IVGTT, whoseCC was super imposable in NGR-IS and T2DM (Fig. 2B).

Since the CCs were altered in diabetic patients even whencompared to the PreT2DM subjects (Table ESM 3 and Figs. 1and 2) and the only metabolic trait clearly differentbetween these two groups was glucose sensitivity of firstphase insulin secretion (Table 1), we also ran in silico IVGTTswithGLUKINSLOOP.1, inwhicheitherweabrogatedfirstphaseinsulin secretion in nondiabetic individuals or we restoreda normal first phase insulin secretion in diabetic patients.In both cases, in spite of “matched” first phase insulinsecretion, the CC of beta cell function of T2DM remainedsignificantly lower than in any nondiabetic group (Table 2).

Discussion

In this study, we appliedMCA for the first time to the glucose-insulin system in humans with the aim of better assessing therelative roles played by the various elements in determininghyperglycemia in type 2 diabetes mellitus. To do so, westudied NGR-IS individuals, patients with newly diagnosedtype 2 diabetes, as the earliest detectable stage of thedisease, and also two groups of nondiabetic individuals withintermediate phenotypes between normalcy and diabetes,i.e. an NGR-IR group and a group with subdiabetic hyper-glycemia (PreT2DM). This spectrum ofmetabolic phenotypes

should allow us to better define the role played by singlemetabolic alterations in determining the distribution ofcontrol in the glucose-insulin system.

The distribution of control of glucose tolerance was superimposable in NGR-IS and in NGR-IR groups (Figs. 1 and 2 andTable ESM 3), showing that insulin resistance per se, even ofthe same severity as in diabetic patients (Table 1), does notaffect the system properties of the glucose-insulin system. Asimilar pattern was present in the PreT2DM group, which, inspite of insulin resistance, of altered beta cell function andof a degree of hyperglycemia half-way between the healthyand the diabetic (Table 1), showed only a minor decline inthe CC of insulin action (Figs. 1 and 2B). Thus, in spite of anaccumulation of metabolic alterations, subdiabetic hyper-glycemia is not a condition severely affecting the glucose-insulin system properties. Notably, in all groups the CCs ofthe single elements of the system were small (Table ESM 3),which is the expected and more frequent case of a “demo-cratic” distribution of control [10,30].

In type 2 diabetic patients, several changes in the CCswere evident (Fig. 1).

First of all, the rises in the CCs of the glucose load (i.e.the external perturbation) and of fasting glucose (i.e. theinitial condition) (Fig. 1) marked the shift of control towardthe external environment (the load) and the immediatepast metabolic history (starting glycemia). These are thesystem counterparts of the documented influence of boththe carbohydrate load and fasting glycemia [31] on post-prandial glycemia in diabetic patients. Interestingly, thisinfluence is evident also in long term therapeutic inter-ventions [32,33]. Furthermore, the high CC of basal glucoseis an additional explanation for the strong relationshiplinking fasting glucose to glycosylated hemoglobin [31],which summarizes glucose control under both fasting andfed conditions. Moreover, the CCs of the diabetic patientspredict therapies targeting carbohydrate intake and fastingglycemia would result in improvements in overall glucosecontrol greater than in nondiabetic individuals.

Table 2 Results of the in silico simulation run in nondiabetic groups with ablation of first phase insulin secretion with GLU-KINSLOOP.1 model (upper part of the table) and of the in silico simulation run in the group of diabetic patients with restorationof first phase insulin secretion with GLUKINSLOOP.1 model (lower part of the table). The signs e before the CCs indicate that thedirections of the changes of beta cell secretion and of 1-h plasma glucose concentration during the IVGTTwere opposite to eachother, i.e. a 1% increase in beta cell insulin secretion caused a fall in 1-h plasma glucose concentration in the in silico simu-lation. NGR-IS: insulin-sensitive subjects with normal glucose regulation; NGR-IR: insulin-resistant subjects with normal glucoseregulation; PreT2DM: subjects with subdiabetic hyperglycemia; T2DM: patients with newly diagnosed type 2 diabetes mellitus.1H-PG IVGTT (simulation 1): plasma glucose level at 60 min during the IVGTT performed in silico in the NGR-IS, NGR-IR and inPreT2DM groups with deletion of 1st phase insulin secretion and during the IVGTT in vivo in the T2DM group. CC Beta Cell(simulation 1): CC of beta cell function on plasma glucose level at 60 min during the IVGTT performed in silico in the NGR-IS,NGR-IR and in PreT2DM groups with deletion of 1st phase insulin secretion and in the T2DM group with unchanged 1st phaseinsulin secretion. 1H-PG IVGTT (simulation 2): plasma glucose level at 60 min during the IVGTT in vivo in the NGR-IS, NGR-IR andin PreT2DM groups and during the IVGTT performed in silico in the T2DM group with restoration of 1st phase insulin secretion. CCBeta Cell (simulation 2): CC of beta cell function on plasma glucose level at 60 min during the IVGTT performed in silico in theT2DM group with restoration of 1st phase insulin secretion and in the NGR-IS, NGR-IR and in PreT2DM groups with unchanged 1stphase insulin secretion.

Simulation 1: abrogation of 1st phase insulin secretion T2DM P value byMANOVANGR-IS NGR-IR PreT2DM

1H-PG IVGTT(simulation 1)(mM)

7.07 � 0.18 7.20 � 0.52b 7.99 � 0.42b 9.70 � 0.36a 0.0001

CC Beta-Cell(simulation 1)

�0.61 � 0.09 �0.39 � 0.08a �0.47 � 0.08b �0.19 � 0.03a 0.005

Simulation 2: restoration of a“normal” 1st phaseinsulin secretion

NGR-IS NGR-IR PreT2DM T2DM P value byMANOVA

1H-PG IVGTT(simulation 2)(mM)

5.78 � 0.35 6.0 � 0.64b 7.25 � 0.59a,b 9.24 � 0.31a 0.0001

CC Beta-Cell(simulation 2)

�0.75 � 0.10 �0.64 � 0.15b �0.56 � 0.09b �0.23 � 0.05a 0.01

a P < 0.05 or less vs NGR-IS.b p < 0.05 or less vs T2DM.

28 M. Trombetta et al.

Secondly, in T2DM there are impressive declines in theCCs of beta cell function and insulin action (Figs. 1 and 2),due to significant decreases in the CCs of some, but not all,elements of insulin secretion and sensitivity (Fig. 2). Inparticular, insulin sensitivity of endogenous glucoseproduction retains a “normal” degree of control in type 2diabetes (�0.16 � 0.02 vs �0.17 � 0.02, T2DM vs NGR-IS,p Z NS) (Fig. 2B), predicting that pharmacological, butalso nonpharmacological [34], interventions targeting thiselement would provide prompt measurable improvementsof glucose tolerance in these patients. Comparable imme-diate effects are expected from drugs acting on global betacell function (CC in T2DM: �0.19 � 0.04), and lowerimmediate effects from drugs acting on peripheral insulinsensitivity (CC in T2DM: �0.07 � 0.02). However, the dia-betes associated declines in the CCs of glucose-stimulatedinsulin secretion and of peripheral insulin sensitivity(Fig. 2A and Fig. 2B) point out that comparable interven-tions on these targets would provide less impressive resultsin overall glucose control of diabetic patients than ofnondiabetic individuals. These data add a novel facet to theconcept that beta cell failure perhaps is the key componentof type 2 diabetes pathogenesis [2,4,5,7], in that this

failure involves not only beta cell secretory function, butalso its role in diabetic glucose homeostasis.

These data mirror what is known from pharmacologicprevention of type 2 diabetes. Rosiglitazone [35], which actsmainly on peripheral insulin sensitivity [36], and perhapsalso tolbutamide [37], an old sulphonylurea, which acts onglobal beta cell function [38], seemingly prevent theconversion of preT2DM individuals to type 2 diabetes moreeffectively than metformin [39], which targets endogenousglucose production [40]. The latter is the only componentwith a CC significantly lower in the PreT2DM group than in thediabetic patients (�0.10 � 0.02 vs �0.16 � 0.02, p < 0.05).Conversely, in type 2 diabetes both sulphonylureas andmetformin, in the first year, are as or more effective thanthiazolidinediones in improving glucose control [41].

The apparent threshold effect in the transition from thenondiabetic to the diabetic distribution of control (Fig. 1)may be the metabolic counterpart of the threshold effect inthe risk of diabetic complications [42e44].

The control distribution of the glucose-insulin system isa novel phenotype (Fig. 1 and Table ESM 3), of which thepossibile genetic bases, if any, may deserve to be investi-gated. Furthermore, extension of the present methodology

Metabolic control analysis of glucose tolerance 29

to different stages of the disease and/or to different tests,such as the OGTT and the meal tolerance test, may providenovel bases for therapeutic strategies. To this regard, ourdata (Figs. 1 and 2) indicate that treatment of T2DM, froma systemic viewpoint, may follow at least two differentstrategies.

One strategy would target the sites which still display CCsof reasonable size; the other aims at restoring the correctdistribution of control. Examples of the former approach canbe considered metformin and classic sulphonylureas, whichact primarily on endogenous glucose output (Fig. 2B) [40]and on global beta cell function (Fig. 1) [38], respectively.

As to the latter approach, it should focus on the wholesystem including the small T2DM CCs of glucose-stimulatedinsulin secretion (�0.11 � 0.02) and peripheral insulinsensitivity (�0.07 � 0.02). In this scenario major changes infirst phase insulin secretion might play a critical role, thelatter being themajor phenotypic trait significantly differentin type 2 diabetic patients vs PreT2DM subjects (Table 1).

However, when we abrogated 1st phase insulin secretionin the in silico phenocopies of all nondiabetic subjects, bothglucose levels and the CCs of the beta cell, albeit worsened,were still significantly higher (by 105e220%) than in T2DMpatients (Table 2). Conversely, whenwe restored a “normal”1st phase insulin secretion in the in silico phenocopies ofT2DMpatients,weobserved only a trivial improvement in theCC of the beta cell (from �0.19 � 0.03 to �0.23 � 0.05),which remained significantly lower (by 69e59%) than in eachof the nondiabetic groups (Table 2). Thus, from a systemviewpoint, the transition from PreT2DM to T2DM is not duesimply to the abrogation of 1st phase insulin secretion.

One important limitation of our work should be recog-nized. Since the CCs are computed in the in silico pheno-copies, their reliability parallels the one of the phenocopies.Although (see the ESM APPENDIX) a close resemblance wasobserved between real subjects and their in silico counter-parts, improved modeling tools may lead in future to betterestimates of the CCs of the glucose-insulin system.

Within these limits, our observations lead to a generalconsideration. When studied, for the first time, with theeyes of MCA, type 2 diabetes is a peculiar new organizationof the glucose-insulin system, which is entirely differentfrom nondiabetic, albeit with subdiabetic hyperglycemia,systems (Fig. 1). Hence, we propose that type 2 diabetes isthe first demonstrable example of a system control disease,thereby deserving the definition of disease of the gover-nance of the glucose-insulin system.

Acknowledgments

Monica Zardini and Federica Moschetta provided superbtechnical help. This work was supported in part byUniversity of Verona 60% grants to R.C.B., E.B. and M.M andby an EFSD/Novartis grant to R.C.B.

AppendixSupplementary data

Supplementary data associated with this article can befound, in the online version, at doi:10.1016/j.numecd.2011.05.006.

References

[1] Ferrannini E, DeFronzo RA. Insulin action in vivo: glucosemetabolism. 3rd ed., vol. 1. Chichester, England: John Wiley& Sons, Ltd; 2004.

[2] Basu A, Dalla Man C, Basu R, Toffolo G, Cobelli C, Rizza RA.Effects of type 2 diabetes on insulin secretion, insulin action,glucose effectiveness, and postprandial glucose metabolism.Diabetes Care 2009;32(5):866e72.

[3] Bonadonna RC, Stumvoll M, Fritsche A, Muggeo M, Haring H,Bonora E, et al. Altered homeostatic adaptation of first- andsecond-phase beta-cell secretion in the offspring of patientswith type 2 diabetes: studies with a minimal model to assessbeta-cell function. Diabetes 2003;52(2):470e80.

[4] Weiss R, Caprio S, Trombetta M, Taksali SE, Tamborlane WV,Bonadonna R. Beta-cell function across the spectrum ofglucose tolerance in obese youth. Diabetes 2005;54(6):1735e43.

[5] Ferrannini E, Gastaldelli A, Miyazaki Y, Matsuda M, Mari A,DeFronzo RA. Beta-cell function in subjects spanning therange from normal glucose tolerance to overt diabetes: a newanalysis. J Clin Endocrinol Metab 2005;90(1):493e500.

[6] Bock G, Dalla Man C, Campioni M, Chittilapilly E, Basu R,Toffolo G, et al. Pathogenesis of pre-diabetes: mechanisms offasting and postprandial hyperglycemia in people withimpaired fasting glucose and/or impaired glucose tolerance.Diabetes 2006;55(12):3536e49.

[7] DeFronzo RA, Mandarino L, Ferrannini E. Metabolic andmolecular pathogenesis of type 2 diabetes mellitus, vol. 1.Chichester, England: John Wiley & Sons, Ltd; 2004.

[8] Heinrich R, Rapoport TA. A linear steady-state treatment ofenzymatic chains. General properties, control and effectorstrength. Eur J Biochem 1974;42(1):89e95.

[9] Kacser H, Burns JA. The control of flux. Symp Soc Exp Biol1973;27:65e104.

[10] de la Fuente A, Snoep JL, Westerhoff HV, Mendes P. Metaboliccontrol in integrated biochemical systems. Eur J Biochem2002;269(18):4399e408.

[11] Ingalls BP, Sauro HM. Sensitivity analysis of stoichiometricnetworks: an extension of metabolic control analysis to non-steady state trajectories. J Theor Biol 2003;222(1):23e36.

[12] FellDA.Metabolic control analysis: a surveyof its theoreticalandexperimental development. Biochem J 1992;286(Pt 2):313e30.

[13] Diagnosis and classification of diabetes mellitus. DiabetesCare 2010;33(Suppl. 1):S62e9.

[14] Standards of medical care in diabetese2006. Diabetes Care2006;29(Suppl. 1):S4e42.

[15] Mandarino LJ, Bonadonna RC, McGuinness OP, Halseth A,Wasserman DH. Regulation of muscle glucose uptake in vivo.In: Jefferson LS, Cherrington AD, editors. Handbook of phys-iologyesection 7: the endocrine system. The endocrinepancreas and regulation of metabolism, vol. II. New York, NY,USA: Oxford University Press; 2001. p. 803e45.

[16] Bertoldo A, Pencek RR, Azuma K, Price JC, Kelley C, Cobelli C,et al. Interactions between delivery, transport, and phos-phorylation of glucose in governing uptake into human skel-etal muscle. Diabetes 2006;55(11):3028e37.

[17] Monauni T, Zenti MG, Cretti A, Daniels MC, Targher G, Caruso B,et al. Effects of glucosamine infusion on insulin secretion andinsulin action in humans. Diabetes 2000;49(6):926e35.

[18] Vicini P, Avogaro A, Spilker ME, Gallo A, Cobelli C. Epinephrineeffects on insulin-glucose dynamics: the labeled IVGTT two-compartment minimal model approach. Am J Physiol Endo-crinol Metab 2002;283(1):E78e84.

[19] Vicini P, Caumo A, Cobelli C. The hot IVGTT two-compartmentminimal model: indexes of glucose effectiveness and insulinsensitivity. Am J Phys 1997;273(5 Pt 1):E1024e32.

30 M. Trombetta et al.

[20] DeFronzo RA, Tobin JD, Andres R. Glucose clamp technique:a method for quantifying insulin secretion and resistance. AmJ Phys 1979;237(3):E214e23.

[21] Bonadonna RC, Heise T, Arbet-Engels C, Kapitza C, Avogaro A,Grimsby J, et al. Piragliatin (RO4389620), a novel glucokinaseactivator, lowersplasmaglucoseboth in thepostabsorptivestateand after a glucose challenge in patients with type 2 diabetesmellitus: a mechanistic study. J Clin Endocrinol Metab, in press.

[22] Barrett PH, Bell BM, Cobelli C, Golde H, Schumitzky A,Vicini P, et al. SAAM II: simulation, analysis, and modelingsoftware for tracer and pharmacokinetic studies. Metabolism1998;47(4):484e92.

[23] Cobelli C, Toffolo GM, Man CD, Campioni M, Denti P, Caumo A,et al. Assessment of beta-cell function in humans, simulta-neously with insulin sensitivity and hepatic extraction, fromintravenous and oral glucose tests. Am J Physiol EndocrinolMetab 2007;293(1):E1e15.

[24] Ferrannini E, Mari A. Beta cell function and its relation toinsulin action in humans: a critical appraisal. Diabetologia2004;47(5):943e56.

[25] Bergman RN, Phillips LS, Cobelli C. Physiologic evaluation offactors controlling glucose tolerance in man: measurement ofinsulin sensitivity and beta-cell glucose sensitivity from theresponse to intravenous glucose. J Clin Invest 1981;68(6):1456e67.

[26] Hovorka R, Shojaee-Moradie F, Carroll PV, Chassin LJ,Gowrie IJ, Jackson NC, et al. Partitioning glucose dis-tribution/transport, disposal, and endogenous productionduring IVGTT. Am J Physiol Endocrinol Metab 2002;282(5):E992e1007.

[27] Toffolo G, Campioni M, Basu R, Rizza RA, Cobelli C. A minimalmodel of insulin secretion and kinetics to assess hepaticinsulin extraction. Am J Physiol Endocrinol Metab 2006;290(1):E169e76.

[28] Toschi E, Camastra S, Sironi AM, Masoni A, Gastaldelli A,Mari A, et al. Effect of acute hyperglycemia on insulinsecretion in humans. Diabetes 2002;51(Suppl. 1):S130e3.

[29] Krudys KM, Dodds MG, Nissen SM, Vicini P. Integrated model ofhepatic and peripheral glucose regulation for estimation ofendogenous glucose production during the hot IVGTT. Am JPhysiol Endocrinol Metab 2005;288(5):E1038e46.

[30] Kacser H, Burns JA. Molecular democracy: who shares thecontrols? Biochem Soc Trans 1979;7(5):1149e60.

[31] Bonora E, Calcaterra F, Lombardi S, Bonfante N, Formentini G,Bonadonna RC, et al. Plasma glucose levels throughout theday and HbA(1c) interrelationships in type 2 diabetes: impli-cations for treatment and monitoring of metabolic control.Diabetes Care 2001;24(12):2023e9.

[32] Culling KS, Neil HA, Gilbert M, Frayn KN. Effects of short-termlow- and high-carbohydrate diets on postprandial metabolism

in non-diabetic and diabetic subjects. Nutr Metab CardiovascDis 2009;19(5):345e51.

[33] Klemsdal TO, Holme I, Nerland H, Pedersen TR, Tonstad S.Effects of a low glycemic load diet versus a low-fat diet insubjects with and without the metabolic syndrome. NutrMetab Cardiovasc Dis 2010;20(3):195e201.

[34] Pietraszek A, Gregersen S, Hermansen K. Alcohol and type 2diabetes. A review. Nutr Metab Cardiovasc Dis 2010;20(5):366e75.

[35] Gerstein HC, Yusuf S, Bosch J, Pogue J, Sheridan P, Dinccag N,et al. Effect of rosiglitazone on the frequency of diabetes inpatients with impaired glucose tolerance or impaired fastingglucose: a randomised controlled trial. Lancet 2006;368(9541):1096e105.

[36] Mayerson AB, Hundal RS, Dufour S, Lebon V, Befroy D, ClineGW,et al. The effects of rosiglitazone on insulin sensitivity, lipol-ysis, and hepatic and skeletal muscle triglyceride content inpatients with type 2 diabetes. Diabetes 2002;51(3):797e802.

[37] Sartor G, Schersten B, Carlstrom S, Melander A, Norden A,Persson G. Ten-year follow-up of subjects with impairedglucose tolerance: prevention of diabetes by tolbutamide anddiet regulation. Diabetes 1980;29(1):41e9.

[38] Groop L, Luzi L, Melander A, Groop PH, Ratheiser K,Simonson DC, et al. Different effects of glyburide and glipizideon insulin secretion and hepatic glucose production in normaland NIDDM subjects. Diabetes 1987;36(11):1320e8.

[39] Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF,Lachin JM, Walker EA, et al. Reduction in the incidence oftype 2 diabetes with lifestyle intervention or metformin. NEngl J Med 2002;346(6):393e403.

[40] Perriello G, Misericordia P, Volpi E, Santucci A, Santucci C,Ferrannini E, et al. Acute antihyperglycemic mechanisms ofmetformin in NIDDM. Evidence for suppression of lipid oxida-tion and hepatic glucose production. Diabetes 1994;43(7):920e8.

[41] Kahn SE, Haffner SM, Heise MA, Herman WH, Holman RR,Jones NP, et al. Glycemic durability of rosiglitazone, metfor-min, or glyburide monotherapy. N Engl J Med 2006;355(23):2427e43.

[42] Report of the expert committee on the diagnosis and classi-fication of diabetes mellitus. Diabetes Care 1997;20(7):1183e97.

[43] International expert committee report on the role of the A1Cassay in the diagnosis of diabetes. Diabetes Care 2009;32(7):1327e34.

[44] Sabanayagam C, Liew G, Tai ES, Shankar A, Lim SC,Subramaniam T, et al. Relationship between glycated hae-moglobin and microvascular complications: is there a naturalcut-off point for the diagnosis of diabetes? Diabetologia 2009;52(7):1279e89.