Insulin regulation of glucose transport and phosphorylation in skeletal muscle assessed by PET

David E. Kelley1,3, Katherine V. Williams1,3, and Julie C. Price2

Departments of 1 Medicine and 2 Radiology, University of Pittsburgh, and 3 Medical Research Service, Pittsburgh Veterans Affairs Medical Center, Pittsburgh, Pennsylvania 15261


    ABSTRACT
TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES

The current study examined in vivo insulin regulation of glucose transport and phosphorylation in skeletal muscle of healthy, lean volunteers. Positron emission tomography (PET) imaging and compartmental modeling of the time course of skeletal muscle uptake and utilization after a bolus injection of 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) was performed during metabolic steady-state conditions at four rates of euglycemic insulin infusion. Leg glucose uptake (LGU) was determined by arteriovenous limb balance assessments. The metabolism of [18F]FDG strongly correlated with skeletal muscle LGU (r = 0.72, P < 0.01). On the basis of compartmental modeling, the fraction of glucose undergoing phosphorylation (PF) increased in a dose-responsive manner from 11% during basal conditions to 74% at the highest insulin infusion rate (P < 0.001). The PF and LGU were highly correlated (r = 0.73, P < 0.001). Insulin also increased the volume of distribution of nonphosphorylated [18F]FDG (P < 0.05). In step-wise regression analysis, the volume of distribution of nonphosphorylated [18F]FDG and the rate constant for glucose phosphorylation accounted for most of the variance in LGU (r = 0.91, P < 0.001). These findings indicate an important interaction between transport and phosphorylation in the control of insulin-stimulated glucose metabolism in skeletal muscle.

insulin sensitivity; positron emission tomography; deoxy-D-glucose


    INTRODUCTION
TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES

INSULIN STIMULATES glucose transport into skeletal muscle. This is achieved both by promoting translocation of glucose transporters to the sarcolemma and transverse tubules (18) and by increasing glucose delivery through effects on muscle hemodynamics (3). Yet, muscle biopsy data would suggest that even with increased glucose transport, intracellular concentration of free glucose does not increase within skeletal muscle (19). This suggests that glucose transport is the principal site of control for insulin-stimulated glucose metabolism in skeletal muscle. However, there are additional data suggesting that steps distal to glucose transport may also contribute to the control of insulin-stimulated glucose metabolism. With the use of a hindlimb perfusion model in animals, Kubo and Foley (23) found that changes in glucose clearance during maximal insulin stimulation indicated that a process beyond glucose transport helped to regulate rates of glucose metabolism. A similar conclusion was reached in subsequent human forearm balance studies, which measured glucose utilization across a range of insulin and glucose concentrations (39). Ferrannini et al. (11) calculated that the volume of distribution of free glucose increased in response to insulin, on the basis of the kinetics of labeled glucose ([3-3H]glucose) disappearance, suggesting a step beyond glucose transport is involved in the control of insulin-stimulated glucose metabolism. More recently, a tracer method developed to examine the kinetics of glucose transport and phosphorylation across the human forearm indicates insulin regulation at both steps in healthy volunteers (31) and defects of both glucose transport and phosphorylation in patients with type 2 diabetes mellitus (DM) (4). Moreover, studies with magnetic resonance spectroscopy of human skeletal muscle have found reduced glucose 6-phosphate in type 2 DM during insulin-stimulated conditions (30), suggesting an important impairment at either transport or phosphorylation or, potentially, a combined impairment.

One of the classic approaches to in vitro studies of glucose transport and phosphorylation in skeletal muscle has been to use a deoxy-D-glucose analog, the metabolism of which is largely blocked distal to phosphorylation (14). Tracer amounts of radiolabeled deoxy-D-glucose have also been used for in vivo animal studies, but this approach does not facilitate measuring the kinetics of transport and phosphorylation due to a need for measuring the time course of tissue activity (14). The technology of emission imaging of the positron-emitting glucose analog 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) does enable acquisition of dynamic patterns (i.e., time course of tissue activity) in a relatively noninvasive manner. Positron emission tomography (PET) has been used for animal (27) and human investigations (17, 20, 29) of skeletal muscle glucose metabolism. Dynamic PET imaging denotes uninterrupted imaging of the target organ or tissue after a bolus radiotracer injection, so that a tissue-time activity curve can be determined. From these data, physiological modeling, with a three-compartmental model of [18F]FDG metabolism (15, 16, 34), can be employed to derive rate constants for glucose transport and phosphorylation. In our initial application of this methodology, the rate constants for glucose transport and phosphorylation were ascertained for skeletal muscle in response to a single dose of insulin infusion in lean, obese, and type 2 diabetic volunteers (20). In lean subjects, insulin stimulated an increase in the rate constants for both glucose transport and phosphorylation. Compared with the lean subjects, obese nondiabetic subjects had a reduction in the insulin response of the transport rate constant, whereas obese subjects with type 2 DM had reduced insulin activation of the rate constants for both transport and phosphorylation.

The dose-response effects of insulin on the regulation of glucose transport and phosphorylation in humans to our knowledge have not been studied. The current study was undertaken with a relatively novel application of PET imaging to more fully examine the effects of insulin on the regulation of glucose transport and phosphorylation in skeletal muscle of healthy lean volunteers. During steady-state metabolic conditions at four rates of euglycemic insulin infusion, dynamic PET imaging of skeletal muscle was performed and compartmental modeling of [18F]FDG metabolism was used to derive values for rate constants for glucose transport and phosphorylation. The three-compartmental model entails a plasma compartment, a tissue compartment for free [18F]FDG, and a tissue compartment for phosphorylated [18F]FDG and first-order rate constants for forward and reverse transport and phosphorylation. The individual rate constants can be used to estimate steady-state parameters for the volume of distribution between nonphosphorylated [18F]FDG in tissue and plasma and also to calculate the phosphorylation fraction, which is a parameter reflecting the disposition of [18F]FDG for phosphorylation or efflux from tissue to plasma (12). From the individual rate constants, an overall parameter for tissue utilization of [18F]FDG can also be calculated. To place the [18F]FDG data and the rate constants within context, we simultaneously measured steady-state rates of glucose metabolism by leg tissues with the arteriovenous limb balance method (40).


    MATERIALS AND METHODS
TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES

Subjects. Eighteen nonobese, glucose-tolerant subjects were recruited by advertisement and randomly assigned to euglycemic insulin infusion studies at rates of 0 (n = 4), 20 (n = 4), 40 (n = 6), and 120 (n = 4) mU · m-2 · min-1. Subjects were 35 ± 2 (means ± SE) yr old, had a mean body mass index of 24.6 ± 0.6 kg/m2, and had mean fasting plasma insulin of 5 ± 1 µU/ml. There were no significant differences across groups for these characteristics. Fourteen of the volunteers were male. Before participating in this study, each volunteer had a medical examination, and those found to be in good general health with stable weight and normal hematologic, renal, thyroid, and hepatic function and not taking chronic medication were invited to participate after giving informed consent. The University of Pittsburgh Institutional Review Board approved this investigation.

Insulin infusion and leg balance studies. Subjects were admitted to the University of Pittsburgh General Clinical Research Center on the evening before studies, having been previously instructed to ingest a diet containing at least 200 g carbohydrate for at least 3 days before studies and to refrain from exercise on the preceding day. Subjects received a dinner of a standard composition (10 kcal/kg; 50% carbohydrate, 30% fat, and 20% protein) on the evening of admission and then fasted overnight. PET-imaging studies of [18F]FDG uptake into mid-thigh skeletal muscle were performed at the University of Pittsburgh Positron Emission Tomography Center. On the morning of a study, at ~7 AM, an intravenous catheter was placed in an antecubital vein for infusion of insulin and glucose and for injection of [18F]FDG. To obtain the arterial samples for determination of [18F]FDG in plasma (to be used as an input function for the compartmental modeling of tissue uptake), a catheter was placed in a radial artery. For limb balance determinations across the leg, a catheter was placed in a femoral vein. After basal measurements of arterial insulin and arterial and femoral venous glucose, insulin infusions were begun. During insulin infusions, arterial glucose was measured at 5-min intervals and an infusion of 20% dextrose was adjusted to maintain euglycemia. Femoral venous glucose was measured every 30 min, increasing to every 10 min during PET imaging. Plasma glucose was measured with a YSI glucose analyzer, (Yellow Springs, OH). Arterial samples for later determination of plasma insulin concentration with a RIA were obtained every 30 min. Euglycemic insulin infusion was maintained for 3 h before the start of dynamic PET imaging, so that steady-state metabolic conditions would prevail and be maintained during PET imaging. Blood flow to the leg was measured with venous occlusion strain-gauge plethysmography, as previously described (9), and was obtained before PET imaging was initiated.

PET image acquisition. Subjects were positioned in the PET scanner so that the mid-thigh corresponded to the midpoint axial field-of-view. Before each emission scan, a 20-min transmission scan was performed with rotating rods of 68Ge/68Ga to correct the emission data for photon attenuation. An intravenous injection of 4 mCi of [18F]FDG, synthesized with a modification of the Hamacher method (13), was injected and a 90-min dynamic PET scan was simultaneously initiated (19 frames: 4 × 30 s, 4 × 2 min, 6 × 5 min, 5 × 10 min). The PET scans were acquired in two-dimensional and three-dimensional imaging modes with a Siemens CTI 951 R/31 (n = 10) scanner and an ECAT ART scanner (n = 8), respectively. The imaging characteristics of the two scanners were comparable. The Siemens 951R/31 scanner acquired 31 imaging planes simultaneously [two-dimensional, in-plane resolution 6.0 mm FWHM (ramp filter), axial slice width: 3.4 mm], whereas 47 imaging planes were acquired with the ECAT ART scanner [three-dimensional, in-plane resolution 6.0 mm FWHM (ramp filter), axial slice width: 3.4 mm]. The scatter fraction was low for the two-dimensional Siemens CTI 951 (13%; Ref. 2), and no scatter correction was performed after conventional methods. The three-dimensional ART had a scatter fraction that was ~37% (1), and these emission data were corrected for scattered photons with a model-based correction method (37). After correction of the PET data for radioactive decay, the tissue time-activity data were converted to units of radioactivity concentration (µCi/ml) with an empiric phantom-based calibration factor (µCi/ml of PET counts/pixel).

Plasma input function. Sampling of arterial blood for plasma [18F]FDG radioactivity began simultaneously with PET scanning. Arterial samples were obtained at 6-s intervals for 2 min, 20-s intervals for 1 min, 30-s intervals for 1 min, at 5, 7, 10, 15, 20 and 30 min, and then every 15 min until 90 min postinjection of [18F]FDG. Exact timing of each sample was recorded. Blood was centrifuged, and 200 µl of plasma were removed for assay of plasma radioactivity concentration with a Packard Canbarra well counter. The counts per minute value for each sample was corrected for radioactive decay and converted to units of µCi/ml based on the well-counter sensitivity.

Defining regions of interest in skeletal muscle. To more clearly define skeletal muscle on PET images, three cross-sectional computed tomography (CT) scans of 1-cm thickness were obtained at upper, mid, and lower boundaries of the region of mid-thigh to be scanned during PET imaging. These CT images were coregistered with the matching PET transmission images as previously described (20). Regions of interest (ROIs) were drawn in medial and lateral thigh muscle with Sunview (CTI PET Systems) software and saved as template files for application to PET images of [18F]FDG. The ROIs were applied to the dynamic PET scans and integrated and expressed as mean counts per pixel.

Modeling of PET data. Data of the time-activity curves of skeletal muscle [18F]FDG imaging were analyzed with a three-compartmental model (15, 16, 34). The three-compartmental model is an analytic method applied to the entire dynamic pattern of tissue activity, beginning from the point of [18F]FDG injection. Specific model equations are implemented and nonlinear least squares curve-fitting methods are used to determine individual kinetic parameters that correspond to the transport and metabolism of [18F]FDG.

Compartmental modeling, with the dynamically acquired PET data with the arterial plasma time course of [18F]FDG activity (CP) as a model input function, employed a nonlinear least squares method to iteratively derive values for the individual rate constants: k1 (ml · min-1 · ml-1), k2 (min-1), and k3 (min-1). The rate constants represent inward transport (k1), outward transport (k2), and phosphorylation (k3) of [18F]FDG as shown in the equation
<AR><R><C>FDG</C></R><R><C>IN PLASMA</C></R><R><C>(C<SUB>P</SUB>)</C></R></AR> <AR><R><C><IT>k</IT><SUB>1</SUB></C></R><R><C>&cjs0416;</C></R><R><C><IT>k</IT><SUB>2</SUB></C></R></AR> <AR><R><C>FDG</C></R><R><C>IN TISSUE</C></R><R><C>(C<SUB>E</SUB>)</C></R></AR> <AR><R><C><IT>k</IT><SUB>3</SUB></C></R><R><C>&cjs0416;</C></R><R><C><IT>k</IT><SUB>4</SUB></C></R></AR> <AR><R><C>FDG-6-<IT>P</IT></C></R><R><C>IN TISSUE</C></R><R><C>(C<SUB>M</SUB>)</C></R></AR>
Dephosphorylation of 2-deoxy-2-[18F]fluoro-D-glucose-6-phosphate ([18F]FDG-6-P), represented by the parameter k4 (min-1), is often assumed to be negligible during the relatively brief duration of emission studies (10), although in the current study a fixed or zero value for k4 was not employed.

After administration of [18F]FDG, the total [18F]FDG tissue concentration (Ci) is the sum of the free [18F]FDG (CE) and [18F]FDG-6-P (CM) skeletal muscle concentrations, and is expressed for each PET scan at time (t) as:
C<SUB>i</SUB>(<IT>t</IT>) = C<SUB>E</SUB>(<IT>t</IT>) + C<SUB>M</SUB>(<IT>t</IT>)
This model assumes that the arterial blood volume is negligible. In the present work, a vascular volume term (VV) was incorporated into this model as Ci (t) = CE (t) + CM (t) + (VV)(CP), and the values obtained were likewise exceedingly small and did not differ by insulin dose. According to the compartmental model shown above, the time derivatives of the skeletal muscle concentrations can be expressed in terms of the compartmental transport parameters, k1 through k4, and [18F]FDG in plasma, CP (16, 33)
<FR><NU>dC<SUB>E</SUB>(<IT>t</IT>)</NU><DE>d<IT>t</IT></DE></FR> = <IT>k</IT><SUB>1</SUB>C<SUB>P</SUB>(<IT>t</IT>) − (<IT>k</IT><SUB>2</SUB> + <IT>k</IT><SUB>3</SUB>)C<SUB>E</SUB>(<IT>t</IT>) + <IT>k</IT><SUB>4</SUB>C<SUB>M</SUB>(<IT>t</IT>) (1)
and
<FR><NU>dC<SUB>M</SUB>(<IT>t</IT>)</NU><DE>d<IT>t</IT></DE></FR> = <IT>k</IT><SUB>3</SUB>C<SUB>E</SUB>(<IT>t</IT>) − <IT>k</IT><SUB>4</SUB>C<SUB>M</SUB>(<IT>t</IT>) (2)
The solutions to the model equations are based on the Laplace transform method (6) and are given below (16)
C<SUB>E</SUB>(<IT>t</IT>) = <FR><NU><IT>k</IT><SUB>1</SUB></NU><DE>&agr;<SUB>2</SUB> − &agr;<SUB>1</SUB></DE></FR> (<IT>k</IT><SUB>4</SUB> − &agr;<SUB>1</SUB>)e<SUP>−&agr;<SUB>1</SUB><IT>t</IT></SUP> + (&agr;<SUB>2</SUB> − <IT>k</IT><SUB>4</SUB>)e<SUP>−&agr;<SUB>2</SUB><IT>t</IT></SUP>XC<SUB>p</SUB>(<IT>t</IT>) (3)
C<SUB>M</SUB>(<IT>t</IT>) = <FR><NU><IT>k</IT><SUB>1</SUB><IT>k</IT><SUB>3</SUB></NU><DE>&agr;<SUB>2</SUB> − &agr;<SUB>1</SUB></DE></FR> (e<SUP>−&agr;<SUB>1</SUB><IT>t</IT></SUP> − e<SUP>−&agr;<SUB>2</SUB><IT>t</IT></SUP>)XC<SUB>p</SUB>(<IT>t</IT>) (4)
where X denotes the operation of convolution and
&agr;<SUB>1</SUB> = <FENCE><IT>k</IT><SUB>2</SUB> + <IT>k</IT><SUB>3</SUB> + <IT>k</IT><SUB>4</SUB> − <RAD><RCD>(<IT>k</IT><SUB>2</SUB> + <IT>k</IT><SUB>3</SUB> + <IT>k</IT><SUB>4</SUB>)<SUP>2</SUP> − 4<IT>k</IT><SUB>2</SUB><IT>k</IT><SUB>4</SUB></RCD></RAD></FENCE>/2
&agr;<SUB>2</SUB> = <FENCE><IT>k</IT><SUB>2</SUB> + <IT>k</IT><SUB>3</SUB> + <IT>k</IT><SUB>4</SUB> + <RAD><RCD>(<IT>k</IT><SUB>2</SUB> + <IT>k</IT><SUB>3</SUB> + <IT>k</IT><SUB>4</SUB>)<SUP>2</SUP> − 4<IT>k</IT><SUB>2</SUB><IT>k</IT><SUB>4</SUB></RCD></RAD></FENCE>/2
The kinetic parameters (ki) were determined with iterative curve fitting and the minimization method of Marquardt (26). The validity of discrimination of the individual kinetic parameters was supported by the standard error values that were determined with the estimated covariance matrix of the fitted parameters. Occasionally the errors for the individual kinetic parameters exceeded 50%, but the level of error was reduced when the individual rate constants were combined to calculate the additional combined parameters described in the next paragraph. In the present work, computer-simulation experiments were performed to examine the impact of insulin on the kinetics of free (CE), phosphorylated (CM), and total (Ci) [18F]FDG concentrations in skeletal muscle with Eq. 3 and 4, with a group mean of the kinetic parameters and the arterial input function data for each insulin dose.

With the use of the individual kinetic parameters, three additional combined parameters were calculated that correspond to the distribution volume (DV) of free (nonphosphorylated) [18F]FDG (DVCE = k1/k2, ml/ml), the phosphorylation fraction (PF) of [18F]FDG [PF = k3/(k2 + k3)], and the overall uptake rate of [18F]FDG [K = (k1 × k3)/(k2 + k3), ml · min-1 · ml-1]. The DVCE parameter reflects the distribution of free [18F]FDG in skeletal muscle relative to plasma and indicates to what extent free [18F]FDG is available in the tissue precursor pool (15). An increase in DVCE would indicate an increase in the availability of free [18F]FDG within skeletal muscle. The PF is a fraction with a potential range of 0-1, reflecting the disposition for nonphosphorylated [18F]FDG within the tissue compartment to be phosphorylated (as k3 k2) or to egress from the tissue compartment back to the plasma compartment. It has been further interpreted as an index of the extent to which glucose phosphorylation vis a vis glucose transport serves as the rate-limiting step of glucose metabolism (12). For example, if k3 >> k2, then the value for PF approaches 1, indicating that phosphorylation occurs much more readily than efflux of nonphosphorylated glucose, and thus rates of glucose metabolism would be limited by glucose availability within tissue (i.e., transport) rather than by glucose phosphorylation. Conversely, if k3 << k2, then PF would approach 0, indicative that the hexokinase (HK) reaction poses a limitation on rates of glucose metabolism.

Application of this three-compartmental model is based on several assumptions, which are 1) that [18F]FDG is administered in trace amounts, 2) that glucose metabolism is in steady state, 3) that transport of [18F]FDG and [18F]FDG-6-P between compartments have first-order kinetics, and 4) that arterial plasma glucose concentration is constant (16). In addition, the model assumes that compartments are of homogenous composition and that arterial concentrations approximate mean capillary concentrations (16).

Statistics. Data are expressed as means ± SE. ANOVA, and when appropriate Kruskal-Wallis tests were used to examine for the effects of insulin on the various metabolic parameters [e.g., leg glucose uptake (LGU) and K] with P < 0.05 considered significant. Linear regression and step-wise regression were used to examine potential correlation among variables.


    RESULTS
TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES

Glucose uptake across the leg and systemic insulin sensitivity. Basal and insulin-stimulated values for the arteriovenous fractional extraction of glucose across the leg, rates of glucose uptake across the leg (LGU), and rates of exogenous glucose infusion needed to maintain euglycemia are shown in Table 1. Insulin stimulated a significant increase in the fractional extraction of glucose, increasing this value by ~20-fold across the dose range of insulin rates. Similar changes were observed for rates of LGU, and there was a significant correlation between LGU and respective rates of exogenous glucose infusion (r = 0.68, P < 0.01).

                              
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Table 1.   Insulin-stimulated glucose metabolism obtained by leg balance methods

Rate constants from modeling tissue activity of [18F]FDG in skeletal muscle. The rate constant K, reflecting net utilization of [18F]FDG from the arterial compartment into skeletal muscle of the mid-thigh, was determined by the three-compartmental modeling method. Compared with values for K in the absence of insulin infusion, values for K during insulin infusions increased ~10-fold (P < 0.001) and were significantly correlated with rates of LGU (r = 0.72, P < 0.01).

Rate constants for glucose transport and phosphorylation. Representative examples of time-activity curves and model fits are shown in Fig. 1. The mean values for individual rate constants and for the parameters of the distribution volume of nonphosphorylated [18F]FDG and the phosphorylation fraction are shown in Table 2. Values for k1 and k2 did not change significantly in response to insulin, although the DVCE did increase significantly compared with basal. The relatively large SE for the DVCE shown in Table 2 reflects the wide interindividual variation in this parameter. However, the ability of the model to predict DVCE was robust, with a mean within-subject error of 28% (excluding 2 outliers). Likewise, insulin significantly increased values for k3 (the rate constant for phosphorylation) and for PF compared with basal. The effect of insulin on the PF revealed a clear dose-response pattern of increase. The fraction of [18F]FDG within the tissue compartment that was phosphorylated increased from 0.11 ± 0.02 during basal conditions to 0.74 ± 0.12 during the infusion of insulin at 120 mU/m2. Values obtained for k4 were negligible (0.004-0.013 min-1) across insulin doses.


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Fig. 1.   Representative examples of time-activity curves and model fits at basal (0 insulin) and under insulin-stimulated (120 U) conditions. Observed () and estimated (solid line) values are shown. Units for skeletal muscle 18F are in µCi/ml. Fluoro-D-glucose, FDG.


                              
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Table 2.   Rate constants from three-compartmental modeling of dynamic positron emission tomography of [18F]FDG metabolism in skeletal muscle

On the basis of the mean values of the rate constants at each insulin infusion rate and the time course of tissue activity within skeletal muscle, the concentrations of nonphosphorylated [18F]FDG (CE) and phosphorylated [18F]FDG (CM) were calculated from Eqs. 3 and 4 and are depicted graphically in Fig. 2. On the basis of the observed increase in the phosphorylation fraction in response to insulin, a progressive increase in phosphorylated [18F]FDG was observed across insulin doses. In contrast, the relative proportion of nonphosphorylated [18F]FDG appeared to rise and then fall across insulin doses, reflecting the pattern observed for k1 and the DVCE.


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Fig. 2.   Computer-generated simulations of tissue concentrations of nonphosphorylated [18F]FDG (CE; ), phosphorylated [18F]FDG (CM; ), and total tissue [18F]FDG (CE + CM; black-triangle) at each insulin dose, calculated with mean values of rate constants (Table 2) and plasma data.

Relation of rate constants to LGU. To examine the extent to which the rate constants derived from compartmental modeling corresponded to the patterns of insulin-stimulated glucose metabolism in skeletal muscle, as determined by the collateral leg balance studies of true glucose metabolism, regression analysis was performed with these two sets of independently measured parameters. The correlations between rate constants for [18F]FDG and the independent measures of fractional extraction of glucose across the leg, blood flow, or rates of LGU are shown in Table 3. The fractional extraction of glucose across the leg bore a significant correlation with k2 and k3, as well as with the DVCE, but the strongest correlation was with PF, as shown in Fig. 3. For leg blood flow, only the inward transport rate constant, k1, had a significant correlation as opposed to no relationship between leg blood flow and rate constants reflecting later time points. For LGU, both the k1-to-k2 ratio and PF had strong positive correlations of similar magnitude, whereas the k2 rate constant had a modest negative correlation.

                              
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Table 3.   Correlations between rate constants for [18F]FDG transport and phosphorylation determined by dynamic positron emission tomography and leg balance across all insulin levels



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Fig. 3.   Scatterplot of relationship between fractional extraction of glucose across leg and phosphorylation fraction of [18F]FDG. k2, Outward transport; k3, phosphorylation.

With the use of step-wise multivariate regression analysis with fractional extraction of glucose as the dependent variable, after inclusion of PF (r = 0.86, P < 0.001), the k1-to-k2 ratio added a slight amount of additional significance (r = 0.89, P < 0.001). With the use of LGU as the dependent variable, the k1-to-k2 ratio was the strongest simple correlate (r = 0.74, P < 0.001), and after adjusting for this, the rate constant k3 added substantial additional significance (F = 20.7, P < 0.001), and this model (k1-to-k2 ratio and k3, together) accounted for 84% of the variance in LGU. This model suggests that partitioning of free nonphosphorylated [18F]FDG from plasma to muscle, and the efficiency with which it phosphorylated, account for most of the variance in insulin-stimulated glucose metabolism in skeletal muscle.


    DISCUSSION
TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES

There is considerable evidence that glucose transport is a key point of control for glucose metabolism within skeletal muscle (3, 18, 19), yet there are also data suggesting that control of insulin-stimulated glucose metabolism is distributed to additional steps (4, 11, 20, 23, 30, 39). The current study was undertaken to test the hypothesis that insulin modulates the relative importance of glucose transport and phosphorylation as points of control over glucose metabolism in skeletal muscle. To examine insulin regulation of the interaction between glucose transport and phosphorylation within skeletal muscle in healthy volunteers, dynamic PET was used to image muscle metabolism of the deoxyglucose analog [18F]FDG. The tissue time-activity patterns were analyzed with a compartmental physiological model to derive rate constants for transport and phosphorylation. The principal findings are that insulin increases both the distribution volume for glucose within muscle and the efficiency with which glucose is phosphorylated, that each effect is strongly related to insulin-stimulated glucose metabolism, and that interaction between these steps, although evident across the dose range of insulin stimulation, is modulated in a dynamic manner by insulin.

Recent studies (4, 30), including one from our laboratory with dynamic PET and [18F]FDG (20), implicate defects at both glucose transport and glucose phosphorylation within insulin-resistant skeletal muscle in individuals with type 2 DM. If impediments at each step do contribute to insulin resistance, then this raises anew the question of how insulin might affect the interaction between these two interdependent proximal steps of glucose metabolism. The step of glucose phosphorylation, catalyzed by HK, serves to trap glucose within muscle, and this serves to sustain a favorable gradient for the movement of glucose across the sarcolemma (38). Prior animal studies, with single muscle fiber analysis, indicate strong coregulation in expression and activity of GLUT-4 and HK II (22). Transgenic studies of overexpression of HK II in skeletal muscle indicate that increased HK II enhances insulin-stimulated glucose metabolism, although the effects are relatively modest and not discernible during fasting conditions (7). Overexpression of GLUT-4 within skeletal muscle in transgenic animals leads to enhanced rates of glucose utilization (24), whereas a combined overexpression of GLUT-4 and HK II improves insulin sensitivity but not absolute rates of glucose metabolism (25). Thus it would seem that the effects of enhanced glucose transport capacity are more clearly discernible than is an impact of increased HK II.

In the current study, the rate constant for glucose phosphorylation increased progressively in response to increasing insulin concentrations. This is consistent with, and extends further, our previous PET studies in which the effect of a single rate of insulin infusion was compared with basal conditions (20). Our present findings of insulin activation of the efficiency of glucose phosphorylation are also consistent with findings from a forearm tracer method developed to investigate insulin regulation of transport and phosphorylation (4, 31). In the current study, the phosphorylation fraction during basal conditions was ~10%, indicating a relatively inefficient net retention of free glucose. At the highest rate of insulin infusion examined in our study (120 mU · m-2 · min-1), which achieved circulating insulin levels substantially higher than are attained during usual conditions of daily living, the phosphorylation fraction increased to ~75%. This high efficiency of glucose phosphorylation suggests that during maximal insulin stimulation virtually all glucose entering muscle would undergo phosphorylation. Thus further increases in rates of intracellular glucose metabolism would require greater transport of additional free glucose. Yet, it is also important to emphasize that between the two extremes of fasting and maximal (or near-maximal) insulin stimulation, the phosphorylation fraction at physiological levels of insulin stimulation (as attained with insulin infusions of 20 and 40 mU · m-2 · min-1) had values of 40 and 60%, respectively. Our interpretation is that HK-mediated glucose phosphorylation does contribute to the control of insulin-stimulated glucose metabolism in skeletal muscle and that this contribution may be of particular importance within physiological levels of insulin stimulation.

In addition to these data on the effects of insulin on glucose phosphorylation, the current study reaffirms the crucial importance of insulin-stimulated glucose transport. Insulin had a robust effect to increase the volume of distribution of nonphosphorylated [18F]FDG within skeletal muscle. This finding is indicative of a strong effect of insulin to enhance glucose transport. Within the current study, in relating PET-imaging data to rates of muscle glucose metabolism measured by arteriovenous leg balance methods, the strongest predictor (r = 0.74, P < 0.001) of LGU was the volume of distribution of nonphosphorylated [18F]FDG. Thus, the availability of glucose as achieved by the process of glucose transport is clearly an important control point across the range of insulin doses. It is of interest, however, that a clear dose-response effect of insulin was not observed to increase the rate constant for glucose transport or the volume of distribution of free glucose in muscle tissue. One explanation might be that different individuals were studied at the various insulin doses, rather than the same subjects across the entire dose range. Our approach, although introducing potentially confounding effects of interindividual variation in insulin sensitivity, was largely dictated by the rigors of the experimental design that entailed not only PET imaging but also leg balance studies. Another explanation is that insulin stimulates transport but achieves a plateau effect at relatively modest insulin concentrations and that subsequent steps of metabolism, such as phosphorylation, modulate whether the increase in glucose transport is carried through to increased rates of glucose metabolism. This is essentially the hypothesis being tested in the current study, and the patterns of muscle free glucose concentrations estimated by the modeling of dynamic PET imaging are consistent with this concept. As the insulin doses increase from infusion rates of 20 to 40 and then to 120 mU · m-2 · min-1, the estimated muscle free glucose concentration declines, and the rate of increase in phosphorylated glucose increases. This suggests that the higher rates of metabolized glucose (as reflected by phosphorylated glucose) derive not only from availability of free glucose but also from the efficiency with which glucose is phosphorylated and that insulin modulates the interplay of these steps in a dynamic manner. Thus, at progressively higher levels of insulin stimulation, the share of each step in the control of metabolism is shifted and realigned. Additional evidence, albeit inferential, is that in multivariate regression analysis, with LGU as the dependent variable, the PET parameters of transport and phosphorylation contributed independently to account for ~80% of the variance in rates of glucose metabolism by skeletal muscle. This is a robust relationship, and it indicates both the crucial level of regulation over insulin-stimulated skeletal muscle glucose metabolism that is posited at these two proximal steps and the strong interaction between these two steps of glucose metabolism.

The three-compartmental model used in the current study was originally developed by Sokoloff et al. (34) to investigate cerebral glucose metabolism, and does not employ any tissue-specific constants or coefficients. A three-compartmental model has been widely used to study glucose metabolism in myocardium (8, 28, 29, 36), and data suggest that in myocardium, the control point for glucose metabolism may shift from transport toward phosphorylation under the influence of insulin (5). As this model has not been widely used to investigate skeletal muscle (20, 32), we also sought in the current study to carry out collateral methods to place the findings within a broader context. As reported previously (20, 21), and described above, there was substantial correlation between rates of [18F]FDG metabolism by skeletal muscle and rates of actual glucose metabolism. With respect to the individual rate constants, there was also strong relation to other parameters of glucose metabolism ascertained by leg balance methods and in a pattern that would be logical to expect. The phosphorylation fraction, a modeling parameter representing the efficiency with which skeletal muscle traps glucose, was strongly correlated with arteriovenous glucose differences, a parameter that also represents the net trapping of glucose within tissue. In addition, there was a significant correlation between rates of blood flow (measured by venous occlusion plethysmography) and values for the inward transport rate constant (k1), the latter a parameter signifying movement from the plasma compartment to tissue. This is a parameter that is conceptually dependent on rates of flow and is a finding similar to compartmental modeling studies in cerebral tissue and myocardium (15). A recent PET study of skeletal muscle flow and glucose metabolism has found that skeletal muscle uptake of [18F]FDG spatially colocalizes with insulin-stimulated blood flow (35). In a separate report, we recently presented data on the effect of insulin on the skeletal muscle "lumped constant," the parameter that represented the quantitative relation between metabolism of [18F]FDG and that of actual glucose (21). Briefly, these measurements indicate a mean value for the lumped constant of ~1.2, and an effect of insulin to modulate the lumped constant was not observed (21). It is of course axiomatic that all physiological models, regardless of their complexity or simplicity, and the three-compartmental model that is a relatively simple model, are imperfect. Furthermore, it is difficult to fully validate parameters that are otherwise difficult to attain, notably the parameters of in vivo glucose transport and phosphorylation in human skeletal muscle. The relation of the rate constants determined within the current study to values obtained by the classic and independently measured limb balance method represents progress in this direction and reflect positively on the potential of emission tomography to provide unique data on the spatial mapping of metabolism in humans. Future studies will examine the robustness of kinetic parameters under basal vs. insulin-stimulated conditions to characterize under which conditions the rate constants are optimally described.

In summary, the findings of the current study indicate that insulin stimulates both glucose transport and its phosphorylation within skeletal muscle. The effect of insulin to increase the availability of glucose is a key determinant of rates of insulin-stimulated glucose metabolism, yet our findings suggest that transport is not the sole locus of control over rates of insulin-stimulated glucose metabolism. The present findings indicate that insulin also modulates the efficiency of glucose phosphorylation. During basal conditions, the efficiency of glucose phosphorylation is quite low, as represented by a phosphorylation fraction of ~10%, and this appears to increase in a step-wise manner across the range of insulin stimulation. At moderate levels of insulin stimulation, the efficiency of the phosphorylation fraction is ~50%, whereas at near-maximal insulin stimulation, there is a higher degree of efficiency for glucose phosphorylation. This dynamic response of the efficiency of glucose phosphorylation across the dose range of insulin stimulation is consistent with an important role in modulating insulin-stimulated glucose metabolism within skeletal muscle. In conclusion, physiological modeling of dynamic emission tomography of insulin-stimulated [18F]FDG metabolism in skeletal muscle indicates distributed control between the steps of glucose transport and phosphorylation in the regulation of glucose metabolism. Further application of these methods could be important in better understanding dysregulated patterns of glucose metabolism within insulin- resistant skeletal muscle.


    ACKNOWLEDGEMENTS

We are grateful to our research volunteers and for the nursing, dietary, and technical staff support at the General Clinical Research Center and the Positron Emission Tomography Center of the University of Pittsburgh. We would also like to acknowledge the technical expertise of Sue Andreko and Janice Beattie.


    FOOTNOTES

These studies were supported by a Veterans Affairs Merit Award (D. E. Kelley), the University of Pittsburgh General Clinical Research Center (2MO1 RR00056-36), and a National Institutes of Health Research Training in Diabetes and Endocrinology Grant (2T32 DK-07052-22, K. V. Williams).

The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. §1734 solely to indicate this fact.

Address for reprint requests and other correspondence: D. E. Kelley, Associate Professor of Medicine, Univ. of Pittsburgh School of Medicine, Division of Endocrinology and Metabolism, E-1140 Biomedical Science Tower, Pittsburgh, PA 15261 (E-mail: kelley{at}med1.dept-med.pitt.edu).

Received 6 January 1999; accepted in final form 15 April 1999.


    REFERENCES
TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
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Am J Physiol Endocrinol Metab 277(2):E361-E369
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