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The Journal of Clinical Endocrinology & Metabolism Vol. 88, No. 5 2055-2060
Copyright © 2003 by The Endocrine Society

Insulin-Mediated Hepatic Glucose Uptake Is Impaired in Type 2 Diabetes: Evidence for a Relationship with Glycemic Control

Patricia Iozzo, Kirsti Hallsten, Vesa Oikonen, Kirsi A. Virtanen, Jukka Kemppainen, Olof Solin, Ele Ferrannini, Juhani Knuuti and Pirjo Nuutila

Turku PET Centre (P.I., K.H., V.O., K.A.V., J.Ke., O.S., J.Kn., P.N.), Department of Nuclear Medicine and Medicine, University of Turku, FIN-20520 Turku, Finland; Institute of Clinical Physiology (P.I., E.F.), PET Centre, National Research Council, 56124 Pisa, Italy; and Department of Internal Medicine (E.F.), University of Pisa School of Medicine, 56100 Pisa, Italy

Address all correspondence and requests for reprints to: Patricia Iozzo, M.D., Institute of Clinical Physiology, National Research Council, Via Moruzzi 1, 56100 Pisa, Italy. E-mail: patricia.iozzo{at}ifc.cnr.it.


   Abstract
Top
 Abstract
Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
Impaired hepatic glucose uptake (HGU) has been implicated in the development of hyperglycemia in type 2 diabetes; the relative impact of plasma glucose and insulin levels on this process remains controversial. We compared the effects of euglycemic hyperinsulinemia on HGU, skeletal muscle glucose uptake, and hepatic influx rate-constant (H-Ki) in 38 diet-treated diabetic patients and 22 nondiabetic controls, using positron emission tomography with 18F-fluorodeoxyglucose and the insulin clamp technique. Control subjects were divided into two subgroups: one including older, heavier, insulin-resistant controls (whole-body glucose uptake, M = 21.4 ± 5.4 µmol·min-1·kg-1) to match characteristics of diabetic patients (M = 20.4 ± 9.9); the other including younger, leaner, insulin-sensitive controls (M = 48.2 ± 9.9, P < 0.01). Skeletal muscle glucose uptake showed a similar group distribution as the M value. Insulin clearance rates were lower, whereas glycosylated hemoglobin and clamp plasma insulin levels were higher in diabetic patients than in controls. HGU and H-Ki were similar in the two nondiabetic subgroups and lower in diabetic patients than in controls (1.9 ± 0.5 vs. 2.3 ± 0.7 µmol·min-1·100 ml-1, and 0.37 ± 0.09 vs. 0.44 ± 0.14 ml·min-1·100 ml-1, P <= 0.01). In the whole dataset, H-Ki was inversely related to fasting plasma glucose (correlation coefficient = -0.40, P = 0.0018). In diabetic subjects, H-Ki was reciprocally related to glycosylated hemoglobin (correlation coefficient = -0.36, P = 0.029).

We conclude that insulin-mediated HGU is impaired, in type 2 diabetes, in some proportion to the degree of glycemic control.


   Introduction
Top
 Abstract
 Introduction
Subjects and Methods
 Results
 Discussion
 References
 
IMPAIRED INSULIN-MEDIATED GLUCOSE uptake plays an important role the pathogenesis of type 2 diabetes (1, 2). Sites of impairment and their relative contribution to the overall defect remain a field of active investigation. The liver plays an important role in the maintenance of glucose homeostasis, and insulin resistance at the level of hepatic glucose output is a mechanism of fasting hyperglycemia in type 2 diabetes (1, 2, 3, 4, 5). Vice versa, at plasma insulin levels similar to those encountered in the postprandial phase, hepatic glucose output is normally suppressed in diabetic subjects (6, 7). Growing evidence supports the concept that reduced hepatic glucose uptake (HGU) might significantly contribute to fasting (2) and postprandial hyperglycemia in patients with type 2 diabetes (8, 9). Insulin and glucose are both involved in the regulation of HGU. In particular, the former promotes short-term hepatic glucokinase transcription (10, 11, 12, 13) and HGU (10, 14, 15) and plays a permissive and synergistic role in glucose-mediated HGU (16). In a recent dose-response study, Basu et al. (2) confirmed that insulin promotes splanchnic glucose uptake in humans, and provided indirect evidence of a defect in the early steps of HGU in type 2 diabetic patients under conditions of mild hyperglycemia and hyperinsulinemia simulating the typical postabsorptive situation associated with the disease. This finding was subsequently extended to a condition of higher (but still modest) hyperinsulinemia (~50 µU·ml-1) (8). Studies conducted at postprandial plasma insulin levels in humans have been fewer and less conclusive. DeFronzo et al. (6) were unable to show a difference in splanchnic glucose uptake, between diabetic and healthy subjects, using the euglycemic clamp and iv glucose administration, whereas Ludvik et al. (9) reported a significant impairment in diabetic patients after combined iv and oral glucose administration. Differences in study design, experimental techniques, and data extrapolation (from the splanchnic vascular bed to the liver itself) contribute to the controversy. Furthermore, given that HGU is influenced both by plasma insulin and glucose levels, experiments combining hyperglycemia and hyperinsulinemia cannot discriminate which response is lost and to what extent in patients with type 2 diabetes.

The present study was undertaken to selectively and noninvasively evaluate whether insulin-stimulated HGU is impaired in type 2 diabetic subjects. To this purpose, liver glucose influx rates were estimated in 38 patients with newly diagnosed, diet-treated type 2 diabetes and 22 nondiabetic controls, using positron emission tomography (PET) in combination with 18F-fluorodeoxyglucose (18F-FDG) under conditions of euglycemic physiological hyperinsulinemia. Skeletal muscle glucose uptake (SGU) was also quantified by PET.


   Subjects and Methods
Top
 Abstract
 Introduction
 Subjects and Methods
Results
 Discussion
 References
 
Subjects

A total of 38 patients with type 2 diabetes, as defined by new World Health Organization criteria (17), and no diabetic complications and 22 healthy controls were recruited by advertisement and among employees of the occupational health service in Turku. Patients with cardiovascular disease, arterial blood pressure levels more than 160/100 mm Hg, previous or present abnormalities of hepatic or renal function, proliferative retinopathy, antidiabetic medication, anemia, or oral corticosteroid treatment were excluded. Written informed consent was obtained after the nature, purpose, and potential risks of the study were explained to the subjects. The study protocol was approved by The Ethical Committee of the Hospital District of Varsinais-Suomi.

Study design

Whole-body glucose uptake (M), SGU, HGU, and hepatic glucose influx rate-constants (H-Ki) were estimated by combining the euglycemic hyperinsulinemic clamp technique with 18F-FDG and PET. Alcohol was prohibited for 1 d before the study, and subjects were instructed to avoid strenuous physical activity on the day before the study. All PET studies were performed after an overnight fast.

PET study protocol

An eight-ring ECAT 931/08-tomograph (Siemens/CTI Corp., Knoxville, TN) was used for image acquisition. The scanner is employed with an axial resolution of 6.7 mm and an in-plane resolution of 6.5 mm. A 5-min transmission scan was performed using a removable ring source containing 68Ge to correct subsequent emission scans for photon attenuation. Two catheters were inserted: one in an antecubital vein for infusion of glucose and insulin and for injection of 18F-FDG, and one in the opposite radial artery for blood sampling. At 0 min, a primed-continuous iv infusion of insulin (40 mU·min-1·m-2) was started. The study for each subject consisted of a 150-min normoglycemic hyperinsulinemic period. During hyperinsulinemia, normoglycemia was maintained using a variable-rate, 20% glucose infusion (18). At 75 ± 15 min, 18F-FDG was injected, and dynamic scans of the regions of the thigh (20-min; 2 x 30, 4 x 60, 3 x 300 sec-frame) and of the liver (18-min; 6 x 180 sec-frame) were subsequently obtained. Arterial blood samples for the measurement of plasma radioactivity were drawn once during each time frame.

Image processing

All data were corrected for dead time, decay, and measured photon attenuation. For image processing, we used a recently developed Bayesian iterative reconstruction algorithm using median root prior with iterations, and a Bayesian coefficient of 0.3 was applied (19).

Data analysis

Tissue time-activity curves were derived from large circular regions of interest placed on two to four consecutive planes in the right lobe of the liver (HGU, H-Ki) and from regions of interest drawn on four consecutive planes in the anteromedial muscular compartments of the quadriceps femoris muscle (SGU). Radioactivity, measured in arterial blood samples over time, was used as input function. Plasma and tissue time-activity curves were analyzed graphically (20) to quantitate the fractional rate of tracer transport and phosphorylation, reflecting glucose influx rate constant (Ki) into the hepatic (H-Ki, in ml·min-1·ml-1) or skeletal muscle tissue component. Graphical analysis has been previously applied for in vivo liver glucose influx determination both in animal and human studies (21, 22, 23). In this model, a graph is generated by plotting:

where Ct is tissue radioactivity at each sampling time point (t) and Cp is plasma radioactivity. When net tracer influx occurs, the two variables describe a linear relationship after a few minutes of equilibration. The Ki is then given by the slope of the linear fit of the data, after excluding the first few values. An example from one of our patients is shown in Fig. 1Go. When tracer outflow from tissue is relevant, as in the liver in the fasting state, Ki reflects the balance between influx and outflux (or reflux). This would still be the case under hyperinsulinemic conditions, except that plasma insulin concentrations similar to those achieved in the present study are recognized to almost completely suppress hepatic glucose output in healthy subjects and in diabetic patients, thus minimizing the impact of reflux on Ki. Rate constant values were multiplied by the steady-state plasma glucose concentrations achieved during the clamp to derive HGU (µmol·min-1·100 ml-1) and SGU (µmol·min-1·kg-1) (24, 25). The relative affinities of glucose and 2-fluoro-2-deoxyglucose for liver glucose transporters and glucokinase are very close to unity, as shown by in vitro and in vivo experiments (21, 26); a ratio of 1.2 was used to derive SGU (27).



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  		Figure 1. Linear least-squares fit for graphical analysis, and corresponding measured data, during the clamp in one type 2 diabetic patient. Data are shown after the few-minute equilibration time that is, by rule, discarded from linear fitting. (t), Sampling time points.

 
M value was calculated during the period of PET scanning and expressed per kilogram of lean body mass (MLBM). Body fat content and lean mass were obtained by the electrical bioimpedance method (Body Impedance Analys, Akern, RJL Systems, Florence, Italy) or calculated according to Hume’s formula (28). Posthepatic insulin clearance rate (ICR) was estimated by dividing the exogenous insulin infusion rate by the steady-state plasma insulin concentrations during the clamp (29, 30). Under these conditions, the above ratio equals the metabolic clearance rate of systemically administered insulin (ICR), minus a small (though variable) component represented by residual insulin secretion (31). The product of ICR and fasting plasma insulin levels yields the posthepatic delivery rate of insulin.

Production of PET tracers

18F-FDG was synthesized with a computer-controlled apparatus, according to a modified method of Hamacher et al. (32). Specific activity at the end of synthesis was more than 70 GBq·µmol-1, and radiochemical purity exceeded 98%.

Biochemical analyses

Arterial and plasma glucose concentration was determined in duplicate by the glucose oxidase method (Analox GM9 Analyzer; Analox Instruments, London, UK). Glycosylated hemoglobin (HbA1c) was measured by fast protein liquid chromatography (MonoS; Pharmacia, Uppsala, Sweden). Serum free insulin and C-peptide concentrations were measured by double-antibody fluoroimmunoassays (Autodelfia; Wallac, Inc., Turku, Finland). Plasma lactate and serum total cholesterol, triglycerides, and high-density lipoprotein cholesterol were measured using standard enzymatic methods (Roche Molecular Biochemicals, Mannheim, Germany) with a fully automated analyzer (704; Hitachi Scientific Instruments, Inc., Tokyo, Japan). Serum low-density lipoprotein cholesterol was calculated according to the Friedewald equation (33). Serum free fatty acids (FFA) were determined by an enzymatic method (ACS-ACOD Method; Wako Pure Chemical Industries Ltd., Neuss, Germany). Plasma radioactivity was measured with an automatic {gamma}-counter (Wizard 1480 3'', Wallac, Inc.).

Statistical methods

All data are presented as mean ± SD. One-way ANOVA was used for group comparisons. Differences in paired data were evaluated using the Student’s paired t test for single repeated measurements. Regression analyses were carried out according to standard techniques. Significance was set at P < 0.05.


   Results
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
Discussion
 References
 
The anthropometric and metabolic characteristics of the study groups are shown in Table 1Go. Control subjects were divided into 2 subgroups: 1 consisting of 13 insulin-resistant controls (IR controls) matched to the diabetic patients by body mass index (BMI), body surface area, waist to hip ratio (WHR, 0.96 ± 0.05 vs. 0.95 ± 0.06, P = n.s.), percentage body fat, MLBM value, and (approximately) age; and the other including 9 younger, leaner, and more insulin-sensitive controls (IS controls). Mean MLBM value for all 22 control subjects was 32 ± 15 µmol.min-1·kg-1 (P = 0.0005 vs. diabetic patients). Similarly, SGU was significantly higher in controls than in diabetic patients (Fig. 2Go) and was lower in IR than in IS control subjects (P < 0.0001). HbA1c and fasting C-peptide levels were significantly higher in diabetic patients than in IR controls. Metabolic changes during the clamp are given in Table 2Go. Plasma glucose concentrations were higher in diabetic patients at baseline but similar in all groups during the clamp, whereas plasma insulin levels tended to be higher in diabetic patients at baseline and were significantly higher during the clamp; conversely, ICR during the clamp was significantly reduced in diabetic subjects. Plasma FFA concentrations were similar in all groups at baseline, and FFA release was suppressed during the clamp to a similar extent in all groups. As expected, MLBM was inversely correlated with indices of body fat mass and distribution [BMI, correlation coefficient (r) = -0.63, P < 0.0001; percentage body fat, r = -0.39, P = 0.01; and WHR, r = -0.29, P = 0.04], as well as with fasting plasma glucose and lactate levels [with r values of -0.52 (P < 0.0001) and -0.39 (P = 0.004), respectively]. In diabetic patients, MLBM was inversely associated with fasting insulin secretion, as reflected by plasma C-peptide concentrations (r = -0.36, P = 0.025).


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  		Table 1. Metabolic characteristics of the study populations

 


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  		Figure 2. Whole-body (MLBM, left) and SGU (right) glucose uptake in type 2 diabetic patients (pts; {square}) and control subjects ({blacksquare}). *, P <= 0.05 vs. diabetic patients.

 

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  		Table 2. Metabolic changes during the clamp

 
In the graphical analysis, the r values for the linear fit used for the determination of H-Ki were high and were comparable in diabetic patients (0.96 ± 0.05) and control subjects (0.97 ± 0.03, P = n.s.). Between the two control groups, HGU (2.3 ± 0.6 vs. 2.3 ± 0.9 µmol·min-1·100 ml-1 for IR and IS controls, respectively, P = n.s.) and H-Ki (0.44 ± 0.1 vs. 0.45 ± 0.2 ml·min-1·100 ml-1, P = n.s.) were similar despite their different MLBM values. In diabetic patients, HGU (1.9 ± 0.5 µmol·min-1·100 ml-1) and H-Ki (0.37 ± 0.09 ml·min-1·100 ml-1) were significantly lower than in controls (P <= 0.01) (Fig. 3Go). Because of the small (but significant) age difference between diabetic patients and IR controls, the group comparison was repeated after excluding subjects younger than 50 yr from the IR control group (n = 6; age, 57 ± 5; P = n.s. vs. diabetic patients). HGU and H-Ki in this subgroup were 2.5 ± 0.5 µmol·min-1·100 ml-1 and 0.47 ± 0.10 ml·min-1·100 ml-1, respectively (P = 0.012 vs. diabetic patients).



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  		Figure 3. Liver glucose uptake (HGU, left) and Ki (H-Ki, right) in diabetic subjects (D) and nondiabetic controls (C). *, P <= 0.01.

 
In regression analyses, H-Ki, and not HGU, was chosen as the dependent variable, to avoid the confounding effect of plasma glucose concentrations that are used to derive HGU. In the whole dataset, H-Ki was inversely related to fasting plasma glucose (Fig. 4Go) and lactate levels (r = -0.40, P = 0.0018; and r = -0.29, P = 0.038) and positively related to MLBM (r = 0.26, P = 0.049). In a multiple regression model, only fasting plasma glucose remained significantly associated with H-Ki (P = 0.006). A positive association of borderline significance was detected between H-Ki and the degree of FFA suppression achieved during the clamp (r = 0.22, P = 0.09).



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  		Figure 4. Relationship between liver glucose Ki (H-Ki) and fasting plasma glucose (FPG) concentrations in type 2 diabetic patients ({blacktriangleup}) and in control subjects (+, IS; {Delta}, IR).

 
In the insulin-resistant groups (IR controls and diabetic patients, n = 51, Fig. 5Go), H-Ki was inversely related to HbA1c levels (r = -0.41, P = 0.0032) and positively related to ICR (r = 0.28, P = 0.046). In diabetic patients alone, HbA1c was still associated with H-Ki (r = -0.36, P = 0.029).



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  		Figure 5. Relationship between liver glucose Ki (H-Ki) and HbA1c (top) and plasma ICRs (bottom) in insulin-resistant subjects, including IR controls and type 2 diabetic patients.

 

   Discussion
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
References
 
In the present study, HGU and H-Ki were reduced in patients with type 2 diabetes at plasma insulin levels in the physiological, postprandial range, as compared with accurately matched nondiabetic control subjects. Impaired chronic glycemic control and posthepatic plasma insulin clearance emerged as related factors. Our data extend previous observations (2, 6, 8, 9), in that we isolated the effects of hyperinsulinemia and hepatic insulin sensitivity from the confounding influence of hyperglycemia and glucose sensitivity; all measurements were done noninvasively, and the need to extrapolate splanchnic to hepatic data was avoided by the use of PET.

Hepatic glucose influx rates were decreased, on average, by approximately 20% in type 2 diabetic patients, which compares reasonably with the 35–60% reduction reported by Basu et al. (2, 8), who also accounted for glucose insensitivity. This indicates that an impairment of insulin-mediated HGU is an additional feature of type 2 diabetes, contributing to the development of hyperglycemia. Because our diabetic patients were newly diagnosed and diet treated, these results are a likely underestimate of the defect that would be encountered in more advanced stages of the disease. Notwithstanding the above, our data confirm the concept pioneered by DeFronzo et al. (6) that, in absolute terms, total insulin-mediated HGU represents a small fraction of M, under euglycemic conditions. The simultaneous presence of hyperglycemia potentiates insulin-stimulated HGU several-fold (4, 34), underscoring its importance (2, 8). The present method allowed us to estimate H-Ki, which describes glucose transport and phosphorylation, per unit of liver tissue volume, at equivalent levels of substrate availability and hormonal stimulation, thereby revealing an intrinsic HGU defect in type 2 diabetes. Thus, our results support the hypothesis of Basu et al. (2, 8), that a proximal step regulating HGU is impaired in type 2 diabetes, and are compatible with the demonstration by Brazilai and Rossetti (10), that glucokinase mRNA levels are decreased in the liver of diabetic rats, in which euglycemic hyperinsulinemia fails to reduce glucokinase Km as it does in control animals. In the present study, we avoided the separate evaluation of each step involved in glucose uptake by compartmental modeling (21, 25), because the use of an arterial input function, which ignores the portal vein contribution, may lead to underestimating some of the derived parameters (21). Though HGU does not seem to be one of the affected variables, we opted for graphical analysis, which: 1) simply evaluates net tracer accumulation in tissue over time; 2) is intrinsically independent of compartment number and configuration; and 3) does not seem to be affected by the choice of single- vs. dual (arterial and venous)-input function (20, 21). Therefore, although the ability of hyperinsulinemia (of the degree we applied) to suppress hepatic glucose output is preserved in most patients with type 2 diabetes, especially in patients with mild, newly diagnosed disease, we cannot completely exclude that some impairment in this insulin action might have contributed to the observed decrease in net HGU, thus representing an additional mechanism of impaired hepatic glucose retention.

Our further finding of an inverse relationship between H-Ki and HbA1c is in line with the recognized role of chronic hyperglycemia in countering insulin action on both SGU (35, 36) and hepatic glucose output (36, 37). A similar correlation was found between H-Ki and fasting plasma glucose levels in the whole dataset, suggesting that fasting glycemia might represent a common denominator in the regulation of HGU. These results suggest that the concept of glucose toxicity might be extended to the modulation of glucose uptake in the liver. Such interaction was surmised by Basu et al. (2), who could not explore it further because of study design limitations, i.e. overnight insulin infusion. The evidence that strict glycemic control enhances HGU in diabetic patients speaks in favor of this hypothesis (38). Clearly, defective HGU might precede (and be conducive to) the chronic increase of plasma glucose levels associated with type 2 diabetes. In other words, long-term hyperglycemia might interfere with HGU or derive, in part, from its impairment. More likely, the interaction occurs both ways, generating a vicious cycle, as in the case of SGU and hepatic glucose production (35, 36, 37).

Posthepatic plasma insulin clearance was reduced in diabetic subjects. Because the liver accounts for up to 70% of total-body insulin degradation (30, 39, 40, 41), the finding of higher steady-state plasma insulin levels under clamp conditions implicates the liver as a site of inefficient removal. However, impaired insulin-mediated inhibition of endogenous insulin secretion in diabetic patients might contribute to this finding to some extent. Also, fasting posthepatic insulin delivery was higher in our diabetic patients than in nondiabetic controls, indicating an increased rate of fasting insulin secretion (also reflected by the higher fasting plasma C-peptide concentrations). Reduced hepatic extraction of insulin is an expected finding in insulin-resistant individuals (42, 43, 44, 45), and it partly compensates for skeletal muscle insulin resistance by maintaining higher systemic plasma insulin levels. It may be speculated that a reduced interaction between insulin and hepatocytes may be linked with the observed impairment of insulin action on HGU. Stated otherwise, reduced hepatic insulin clearance and glucose uptake may be parallel consequences of hepatic insulin resistance. Although clear evidence for a physiological connection between insulin processing and intracellular insulin signaling in human liver is not available, and given the limitation that insulin secretion during the clamp was not measured in the present study and that the relationship between HGU and ICR was not very strong, it is intriguing that strict glycemic control in diabetic patients enhances both HGU and insulin clearance (46). The present study was not designed to address the relationship between insulin secretion/removal and HGU; specific studies would be required to establish to what extent the increased plasma insulin concentrations observed in our diabetic patients resulted from impaired clearance or enhanced secretion.

One unexpected finding in the present study was that HGU was only weakly (r = -0.36, P = 0.029) related to MLBM and unrelated to SGU, and was not significantly different between IR and IS controls, whereas it was reduced once insulin resistance was accompanied by hyperglycemia, as was the case in the diabetic group. One implication is that insulin resistance of glucose uptake in liver and such resistance in peripheral tissues are largely independent of one another and are regulated by different factors. However, it should be emphasized that, in the present study, HGU was not measured in the fasting state, and, therefore, insulin-induced changes in HGU could not be compared across groups, nor was it feasible to construct a full dose-response of HGU to plasma insulin. It is nevertheless interesting that none of the variables (BMI, WHR, and percentage body fat) commonly associated with insulin-mediated M value showed any relation to H-Ki. Such an observation was previously made by Basu et al. (2), who stressed the need for caution when extrapolating the concept of substrate competition (47, 48, 49) from one tissue to another.

In summary, our study demonstrates that: 1) in nondiabetic subjects, insulin-mediated HGU during euglycemic physiological hyperinsulinemia is related to fasting plasma glucose concentrations; and 2) in diabetic patients, insulin-mediated HGU is decreased in some proportion to the impairment of chronic glycemic control.

The present study supports the idea that reduced HGU may contribute to the fasting hyperglycemia of type 2 diabetes both by a direct mechanism and by altering glucose sensing within the hepatocyte, thereby disrupting the regulation of glucose release.


   Footnotes
 
Abbreviations: BMI, Body mass index; 18F-FDG, 18F-fluorodeoxyglucose; FFA, free fatty acids; H-Ki, hepatic glucose influx rate constant; HbA1c, glycosylated hemoglobin; HGU, hepatic glucose uptake; ICR, insulin clearance rate; IR controls, insulin-resistant controls; IS controls, insulin-sensitive controls; Ki, influx rate constant; M, whole-body glucose uptake; MLBM, M expressed per kg of lean body mass; n.s., not significant; PET, positron emission tomography; r, correlation coefficient; SGU, skeletal muscle glucose uptake; WHR, waist to hip ratio.

Received September 16, 2002.

Accepted January 27, 2003.


   References
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 

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