Search tips
Search criteria 


Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
J Nucl Med. Author manuscript; available in PMC 2010 April 21.
Published in final edited form as:
PMCID: PMC2857655

Time-Course of Alterations in Myocardial Glucose Utilization in the Zucker Diabetic Fatty (ZDF) Rat with Correlation to Gene Expression of Glucose Transporters: A Small Animal PET Investigation



Diabetic cardiomyopathy is associated with abnormalities in glucose metabolism. We evaluated myocardial glucose metabolism in a rodent model of Type 2 Diabetes (T2D), namely the Zucker Diabetic Rat (ZDF), and validated PET measures of glucose uptake against gene and protein expression of glucose transporters.


Six lean and ZDF rat underwent small animal PET imaging at the age of 14 weeks and at the age of 19 weeks. The imaging protocol consisted of a 60-minute dynamic acquisition with 18FDG (0.5–0.8mCi). Dynamic images were reconstructed using filtered back projection (FBP) with a 2.5 zoom on the heart and 40 frames per imaging session. PET measures of myocardial glucose uptake rate (MGUp) and utilization were determined with an input function derived by the Hybrid Image- Blood Sampling (HIBS) algorithm on recovery-corrected anterolateral myocardial regions of interest. Following the PET imaging session at week 19, hearts were extracted for gene and protein expression analysis of GLUT1 and GLUT4. The dependence of MGUp on gene expression of GLUT1 and GLUT4 was characterized by multiple-regression analysis.


Compared to lean littermate control rats, MGUp was significantly depressed in ZDF rats at both week 14 and week 19 (P<0.006). Moreover, lean rats at week 19 displayed significantly higher MGUp than week 14 (P=0.007). Consistent with diminished MGUp, gene expression of GLUT4 was significantly (P=0.004) lower in ZDF rats. Finally, MGUp significantly (P=0.0003) correlated with gene expression of GLUT4.


Using small animal PET, we confirmed alterations in myocardial glucose utilization and validated PET measure of MGUp against gene and protein expression of glucose transporters in the diabetic heart of an animal model of T2D.

Keywords: Diabetes, gene expression, small animal PET, FDG, ZDF


Heart disease is the leading cause of death among diabetic patients (1, 2). There is increased evidence suggesting that diabetic patients have a predisposition to heart failure resulting from impairment in heart muscle contraction, particularly abnormalities in diastolic function (3). This impairment in muscle contraction, termed diabetic cardiomyopathy, is independent of vascular abnormalities as diastolic dysfunction is evident in both Type 1 and Type 2 diabetic patients. Several theories have put forth to explain diabetic cardiomyopathy, including stiffness of the LV due to accumulation of connective tissue and insoluble collagen (4) and abnormalities in calcium flux (5). One prominent hypothesis argues that diabetic cardiomyopathy is a consequence of alterations in myocardial fuel metabolism (6, 7).

The heart can utilize multiple substrates including fatty acids (FA), carbohydrates, amino acids, and ketones (8). Under normal conditions, the heart generates 50–70% of ATP needs through FA oxidation whereas glucose and lactate account for 30–50% of energy provided to the cardiac muscle(9, 10). In the diabetic heart, however, the contribution of glucose to total overall energy expenditure is diminished to the extent that the heart muscle relies exclusively on FA for energy needs (11). It has recently been demonstrated that normalizing cardiac metabolism in diabetic animals reverses the development of cardiomyopathy (12). As such, it is highly desirable to characterize substrate utilization in vivo non-invasively in both human and animal models of diabetes as it would provide an avenue for staging both disease and efficacy of therapy.

Positron Emission Tomography (PET) has been used to quantify myocardial substrate utilization in humans (1317) (reviewed in (18, 19)); however, small animal PET imaging of substrate utilization has been met with several challenges including partial volume averaging, extraction of the input function for quantification of PET images, and, more generally, challenges in validating PET outcome measures. In a series of publications we have characterized methods to extract the input function (2022) and developed multi-parameter small animal PET quantification techniques (23) to assess myocardial blood flow (24) and substrate metabolism (25), as a proof of concept. In this work, we characterize myocardial glucose utilization in an animal model of Type 2 Diabetes (T2D), namely the Zucker Diabetic Fatty (ZDF).

The ZDF rat is a well characterized model for T2D with obesity resulting from loss-of-function mutation in the leptin receptor and onset of diabetes at the age of 12 weeks (26). As diabetic cardiomyopathy is associated with diminished myocardial glucose utilization, our aim was to assess myocardial glucose uptake and utilization in the ZDF rat and its lean littermate, non-invasively, using small animal PET in conjunction with 18FDG. Furthermore, we aimed to validate PET measures of glucose utilization both by virtue of the animal model and by correlating PET measures of glucose uptake to gene and protein expression of glucose transporters, namely GLUT1 and GLUT4. In doing so, we demonstrate alterations in myocardial glucose utilization in the diabetic heart. Furthermore, consistent with metabolic findings, gene and protein expression of GLUT4 are markedly diminished in the heart of ZDF rats. Finally, PET measures of 18FDG uptake rate correlate with gene and protein expression of GLUT4.


All chemicals, unless otherwise stated, were purchased fromAldrich Chemical Co., Inc. Radioactivesamples were counted on a Beckman 8000 γ-counter. Small-animalPET was performed on either the microPET® Focus-120 (27)or Focus-220 (28) (Siemens Inc., Knoxville, TN).

Synthesis of Radiopharmaceuticals

FDG is produced routinely in our laboratorywith a commercially available module (CTI Molecular Imaging).

Animals Utilized in this Study

The study utilized six male ZDF rats (fa/fa) and six age-matched lean male littermates (fa/+) (Table 1). ZDF and lean littermates were fed Purina Constant Nutrition 5008, consisting of 26.8% protein, 16.7% fat, and 56.4% carbohydrates. With the abovementioned diet, ZDF rats are expected to develop diabetes by the age of 12 weeks (Charles River Laboratories). ZDF rats were initially scanned at the age of 14 weeks, thus allowing 2 additional weeks for full onset of T2D.

Table 1
Animal demographics, hemodynamics, and FFA levels for lean (fa/+) and ZDF (fa/fa) rats

Animal Preparation

Six hours before the imaging session, rats were fasted in metabolism cages while water was given ad libitum. On the day of the study rats were anesthetized with 2% 2.5% isoflurane by inhalation via an induction chamber. Anesthesia was maintained throughout the imaging session by delivering 1%–1.5% isoflurane via a custom-designed nose cone. The rat’s neck was shaved and scrubbed in preparation for a sterile cut-down procedure. A 1- to 1.5-cm incision was made over the right jugular vein. The vein was exposed and ligated, and a microrenathane catheter (0.025-mm outer diameter, 0.012-mm inner diameter) was sutured in place. Rats were globally heparinized (10 mg/kg) to prevent the catheter from clotting. Body temperature was maintained using a circulating water blanket as well as a heat lamp. Pediatric electrocardiogram leads (Red Dot Infant Electrodes; M.M.M. Co.) were placed on the rat’s hind limbs to measure and record heart rate. All animal experiments were conducted in compliance with the Guidelines for the Care and Use of Research Animals established by Washington University’s Animal Studies Committee. For more detailed description of animal handling methodology the reader is referred to Sharp et al. (23).

Echocardiography (ECHO) Measurements

Non-invasive ultrasound examination of the heart was performed using a Vevo770 Ultrasound System (VisualSonics Inc, Toronto, Ontario, Canada) at ages 13 and 18 weeks according to the following procedures. Rats were anesthetized with continuous inhalation of 1% gaseous Isoflurane administered through customized nose cone. The animals were secured on an imaging platform in supine position, physiologic parameters including heart rate, respiratory rate, and core body temperature were continuously monitored by built-in monitoring system. The anterior chest was shaved and ultrasonic coupling gel was applied. Ultrasound studies were performed using an RMV-716 transducer (16 MHz imaging frequency). Care was taken to maintain adequate contact while avoiding excessive pressure on the chest. Complete two-dimensional and M-mode examination was performed from multiple views. Image analysis included standard echocardiographic parameters of left ventricular structure and systolic/diastolic function. In addition, measures of left ventricular structure were used for partial volume corrections performed in conjunction with kinetic modeling (see below).

Small Animal PET Imaging Protocol

The animals were secured in a custom-designed acrylic restraining device and were placed inside the field of view (FOV) of the small animal imaging PET scanner. Five (5) seconds following a bolus injection of the radiopharmaceutical via the right jugular catheter, dynamic PET acquisition was started. Each rat was imaged at two time points: once at the age of 14 weeks and again at the age of 19 weeks. The imaging protocol consisted of a 60-minute dynamic acquisition with 18FDG (0.5–0.8mCi) to characterize glucose utilization. During each imaging session, 5–6 whole-blood arterial samples were collected from the femoral artery to measure whole blood glucose (5 μL), free fatty acid (FFA; 20 μL), and insulin (5 μL) levels. Finally, heart rates were recorded at baseline and throughout the study. Dynamic images were reconstructed using filtered back projection (FBP) with a 2.5 zoom on the heart and 40 frames per imaging session.

Substrate Analysis

All substrate measurements were performed using commercially available, well-documented methods that have been validated in small animals (23). Briefly, whole blood (20–25 μL) was drawn into a Wiretrol II precision disposable micropipette (Drummond Scientific Co.) for insulin and FFA analysis. The blood was spun in a microcentrifuge (13,460g for 2 min) to separate red blood cells and plasma. The plasma was immediately placed in a 280°C freezer until analyzed. FFA levels were measured using a standard Nefa-C kit (Wako Chemicals USA, Inc.) by the Diabetes and Metabolism Core Laboratory of the Washington University School of Medicine (WUSM), Department of Endocrinology. Insulin levels were measured using a Rat Insulin ELISA Test kit (Crystal Chem, Inc.) by the Developmental Biology Core Laboratory of the WUSM, Department of Pediatrics. Plasma glucose levels were measured by placing whole blood (1 μL) on a glucose test strip for immediate analysis (using an Accu-Chek Plasma blood glucose analyzer [Roche Diagnostics, Inc.]). The substrate and insulin values reported within correspond to values obtained at baseline, just prior to PET imaging. Finally, percent HbA1C levels were determined by a Bayer 2000+ Analyzer.

Kinetic Analysis of 18FDG

Extraction of input function

The input function was reconstructed by applying the hybrid image- and blood- sampling (HIBS) algorithm (21).

Partial volume correction

We previously correlated ECHO-derived measures of left-ventricular internal diameter (LVID) and posterior wall-thickness (LVPW) to microCT derived measures (unpublished data). To that end, we utilized ECHO measurements to construct a digital phantom of the left-ventricle, which was subsequently used to generate partial volume values for individual studies. For example, in calculating the myocardial partial volume in the systolic state, a digital LV was constructed with an internal diameter and myocardial wall thickness values derived from ECHO measurements. The digital LV was subsequently smoothed with a full width half maximum (FWHM) of 1.76mm corresponding to the spatial resolution of 18F radionuclide. Partial volume coefficients were characterized as described in (29). In a similar fashion, partial volume coefficients for the diastolic state were determined. Finally, average partial volume coefficient for each study was determined by weighting the diastolic values at 60% and the systolic values at 40%. We note that there were no significant differences in myocardial wall thickness (Table 2) between the groups. As such, there were no significant differences in partial volume between groups and across ages.

Table 2
Echocardiographic (ECHO) measurements for lean (fa/+) and ZDF (fa/fa) rats

Determination of MGUp and MGU

Glucose uptake rate and utilization were characterized by performing a Patlak graphical analysis (30) of FDG kinetics. After some time t>t* (typically, last 30-minutes of image acquisition), a linear regression model (of order 1) was optimized against the normalized plasma TAC vs. myocardial tissue TAC (an anterolateral VROI). The slope of the linear regression line provides the myocardial glucose uptake rate, MGUp, of FDG. The uptake rate was subsequently corrected for partial volume effects by the ratio MGUp/rM, where rM denotes the recovery coefficient of the myocardium derived from ECHO measurements. Myocardial glucose utilization is calculated by MGU=LC*MGUp*[GLU]P, where [GLU]P denotes the peripheral concentration of glucose with a lumped-constant (LC) LC=1 based on previous work on T2D (15, 17, 31).

RNA Isolation and Real-Time RT-PCR

Following PET image acquisition at week 19, animals were sacrificed and the heart extracted and frozen at −80°C until RNA was isolated for gene-expression analysis. Total RNA was isolated from heart by using RNAzol B (Tel-test) according the manufacturer’s instructions. RNA concentration and purity were determined by spectrophotometric absorbency at two dilutions. First-strand cDNA was generated by reverse transcription using 500 ng total RNA and the Applied Biosystems (Foster City, CA) reverse transcription (RT) kit. Real-time RT-PCR was performed using the ABI PRISM 7500 Fast sequence detection system and TAQMAN Fast Universal master mix (Applied Biosystems, Foster City, CA). Arbitrary units of target gene mRNA were corrected to 36B4 RNA content to control for loading.

Western Blot Analysis

Frozen heart tissues were homogenized in ice cold buffer containing: 50 mM sodium fluoride, 10 mM sodium phosphate, 1 mM EDTA, 1 mM EGTA, 1 mM DTT, 1% triton X, and protease inhibitors using a sonifier. Samples were incubated on ice for 30 minutes and then centrifuged for 15 min at 15,000 X G. The supernatants were collected and the protein concentrations determined. For GLUT1 and GLUT4 immunoblots, 5 μg of whole cell protein was prepared in non-denaturing sample buffer and subjected to SDS-PAGE.

Protein was then transferred from the gel to nitrocellulose membrane. Membranes were blocked for 1–1.5 hours in TBS containing 0.1% Tween 20 (TBS-T) and 5% non-fat milk. Blots were then probed using antibodies raised in rabbit hosts (anti-GLUT4 (gift of Mike Mueckler) or anti-GLUT1 (gift of Mike Mueckler) overnight at 4°C in TBS-T. Blots were then washed in TBS-T and then incubated with donkey anti-rabbit HRP-labeled IgG secondary antibody for 1 hour at room temperature in TBS-T. After again washing in TBS-T, bands were visualized by ECL and autoradiographic film.

Statistical Analysis

Differences between Lean and ZDF Measurements

A two-tailed student’s t-test was performed to test for significant differences between groups (e.g., lean vs. ZDF). When comparing within a group (i.e., between week 14 and week 19), a paired t-test was performed.

Dependence of MGUp on Gene Expression of GLUT Transporters

Regression analysis was performed to characterize the dependence of MGUp on gene expression of GLUT1 and GLUT4. Three regression models—M1, M2, and M3—were employed: M1: MGUp=β01·GLUT1+β2·GLUT4; M2: MGUp=β01·GLUT1; and M3: MGUp=β02·GLUT4. In each model βi(i=0…2) denotes the coefficient of regression and GLUT1 and GLUT4 denote gene expression levels of respective GLUT transporters. Statistical analysis on the significance of βi(i=0…2) and the goodness-of-fit of the models was performed using the statistical package SPSS (SPSS, Inc.). Table 3 summarizes results of the statistical analysis.

Table 3
Statistical Analysis of Regression Models Depicting Dependence of MGUp on Gene Expression of GLUT1 and GLUT4

In all cases, a P-value less than P<0.05 was considered significant.


Hemodynamics and blood substrate levels

On average, lean rats at week 14 and week 19 had significantly higher heart rates than age-matched ZDF rats (P=0.0038, P=0.0047, respectively). There was no significant difference in heart rates in the aging rat from week 14 to week 19 within either lean or ZDF groups. Compared to lean littermate controls, ZDF rats exhibited significantly increased glucose (P=0.007), HbA1C (P=1.84×10−8), free fatty acids (P=0.013), and insulin (P=0.001) levels at week 14 (Table 1, Figure 1). At week 19, only HBA1c and glucose remained significantly (P=8.47×10−8 and P=0.002, respectively) elevated.

Figure 1
Peripheral insulin (A) and glucose (B) levels at week 14 (W14) and week 19 (W19) in lean (fa/+) and ZDF (fa/fa) rats. Significance values are denoted above bar-plots. A P-value of P<0.05 was considered significant.

Echocardiographic (ECHO) measurements

ECHO measurements are summarized in Table 2. On average, the diastolic LV internal diameter (LVIDs) of lean rats at week 13 was significantly lower than LVIDs of lean rats at week 18 (P=0.05) and age-matched ZDF rats (P=0.01). Similarly, the LV mass (LVM) of lean rats at week 13 was significantly lower than week 18 (P=0.004) and when compared with age-matched ZDF rats at week 13 (P=0.02). We observed no significant differences in fractional shortening (FS) between and with groups.

PET Measurements

Myocardial glucose uptake (MGUp) and utilization (MGU) are depicted in Figure 2. ZDF rats exhibited significantly lower glucose uptake rate than age-matched lean rats at both weeks 14 and 19 (P=0.006 and 0.0001, respectively). Moreover, the aging lean rat exhibited increasing uptake rate of glucose from week 14 to week 19 (P=0.007), which was not apparent in the aging ZDF rats (Figure 2A and 2B). MGU in the lean rat is significantly (P=0.04) higher at week 19 relative to week 14. Finally, there is a significant (P=0.04) decrease in MGU when comparing ZDF and lean rats at week 19.

Figure 2
Myocardial glucose uptake (MGUp) (A) and myocardial glucose utilization (MGU) (B) at week 14 (W14) and week 19 (W19) in the lean (fa/+) and ZDF (fa/fa) rats. Significance values are denoted above bar-plots. A P-value of P<0.05 was considered significant. ...

Gene and Protein Expression of GLUT1 and GLUT4

Gene expression levels of mRNA encoding for glucose transporters GLUT1 and GLUT4 in heart of rats at 19 weeks of age is provided in Figure 3A. The expression of GLUT1 was not altered in ZDF rats compared to controls. However, gene expression of GLUT4 was markedly diminished in ZDF rats (P=0.004). The above findings were in agreement with protein expression levels of GLUT1 and GLUT4 (Figure 3B).

Figure 3
(A) GLUT1 and GLUT4 gene expression levels in lean (fa/+) and ZDF (fa/fa) rats at week 19. Gene expression was normalized to levels observed in lean rats. Significance values are denoted above bar-plots. A P-value of P<0.05 was considered significant. ...

Dependence of MGUp on Gene Expression of GLUT Transporters

The dependence of MGUp on gene expression of GLUT transporters is captured in models M1, M2, M3 (Table 3). When both GLUT1 and GLUT4 are considered as covariates (M1), only the inclusion of GLUT4 is significant (P=0.002) with a correlation coefficient of R=0.91 (R2=0.83). When GLUT1 is excluded from the regression analysis (M3), the inclusion of GLUT4 is significant at P=0.0003 with a correlation coefficient R=0.90 (R2=0.81) suggesting that GLUT4 is sufficient to describe the data (Figure 4).

Figure 4
Regression model M3 was used to characterize the dependence of MGUp on gene expression of GLUT4 with a R2=0.81, which is significant at P=0.0003. ZDF data points are denoted by a solid square while lean data points are denoted by a solid circle. The regression ...


Ever since Sokoloff’s pioneering work on 18FDG (32, 33) the literature has been ripe with controversy concerning the utility of FDG. At the heart of the matter are potential differences in affinities of FDG and glucose for GLUT transporters and the hexokinase enzyme. The lumped-constant (LC) has been devised to correct for abovementioned differences (33). Most references to LC note a constant value that is applied to FDG measures of MGUp. For example, Yokoyama el al. (17) and others (15, 31) utilized LC=1 in assessing myocardial glucose utilization in humans with T2D. Botker et al. (34) proposed that the LC is a function of the rates of transport and phophorylation of 18FDG and applied the proposed method to assess myocardial glucose utilization in humans with ischemic cardiomyopathy (35). Herrero and colleagues (36) argued that the latter method leads to significant underestimation of glucose utilization in large animals. We chose LC=1 in keeping with previous work on T2D (15, 17, 31). Nevertheless, further studies are needed to characterize the LC, in particular, in small animal PET imaging of the heart.

There are several advantages in using 18FDG over 11C-glucose, particularly in small animal imaging. As a radiolabelled analog of endogenous glucose, 11C-glucose is metabolized much like endogenous glucose. As such, it can be incorporated in the glycogen pool and undergoes anaerobic and oxidative metabolism following phosphorylation and pyruvate formation. Herrero and colleagues (14) have devised kinetic models to delineate abovementioned processes in the human heart. Glucose metabolism in the rodent heart, needless to say, is significantly faster than in humans, which may prohibit the application of complex kinetic models. In contrast, FDG is trapped following phophorylation by hexokinase thus providing an enhanced signal. In addition, glucose metabolism generates 11C-lactate which can further be metabolized, thus potentially confounding kinetic analysis. Recently, Herrero et al. have incorporated the contribution of lactate in a kinetic model of glucose metabolism in the human heart (13). However, since glucose metabolism is significantly faster in rodents, it is feasible that the contribution of lactate to overall PET signal is not trivial in small animals, which may explain the apparent contradiction between this work and previous work using 11C-glucose in ZDF rats (25). Therefore, while further work is needed to correlate 11C-glucose with FDG metabolism in small animals, at this stage FDG is a unique imaging marker for glucose metabolism in rodents.

Under normal conditions, glucose metabolism is regulated through multiple steps including uptake. Glucose uptake, in turn, is dependent on the transmembrane glucose gradient and density of glucose transporters GLUT1 and GLUT4. GLUT1 is more pronounced in the sarcolemmal and represents basal glucose uptake (37). GLUT4, in contrast, is the dominant transporter in the adult heart and under basal conditions, a majority of this transporter in located in the intracellular pool (38). In the presence of increased insulin levels, GLUT4 is translocated to the sarcolemmal membrane (37, 38) thus increasing density of GLUT4. In addition, insulin influences glucose transport through regulation of GLUT gene-expression (39, 40). In T2D, insulin resistance results in diminished translocation of GLUT4 to the sarcolemmal membrane thus affecting glucose transport.

Our data indicates that expression of glucose transporters is diminished in the diabetic heart (Figure 3). Accordingly, myocardial glucose uptake (MGUp) (Figure 2A) is lower in ZDF rats independent of age. Interestingly, MGUp was found to be significantly higher in the lean rat at W19 compared with W14. Although our data does not provide a full explanation for this observation, the data may suggest that the heart undergoes a maturation process with regards to glucose utilization or insulin sensitivity during this time. This is not observed in ZDF rats likely due to the progression of diabetes and associated metabolic changes in this model. Finally, PET measures of MGUp (Figure 2) agree with both protein and gene expression analysis (Figure 3). Regression analysis suggests that gene expression of GLUT4 correlates significantly with MGUp (Figure 4). Ideally, the uptake rate-constant, K1, should be correlated with gene expression levels; however, due to limitations in temporal sampling and noisy nature of initial frames, we were unable characterize K1. Nevertheless, K1 is a scaling factor in calculating MGUp. Taken together, our findings support the notion that PET measures of myocardial glucose uptake rate, MGUp, correlate with the density of GLUT4 and its regulation by insulin.

It is interesting to note that while myocardial glucose uptake rate, MGUp, is significantly higher in lean rats at week 14 compared with age-matched ZDF rats, there is no statistical difference in total myocardial glucose utilization, MGU (Figure 2B). The main difference between the two measures is that the former captures the intrinsic capacity of the heart to utilize glucose while the latter includes peripheral effects, namely peripheral concentration of glucose. With that in mind, hyperglycemia in the ZDF rat at week 14 countered reduced MGUp. At week 19, however, lean’s MGUp is significantly higher than at week 14. Alterations in total glucose utilization are apparent by week 19 and possibly earlier. We did not observe diminished fractional shortening (FS) (Table 2), a measure contractile dysfunction, in the time course of the study. Zhou et al. (12), however, report diminished contractile function in the 20 week-old ZDF rat. Thus, PET measures of MGUp and MGU may provide complimentary measures of disease with MGUp capturing early alterations in cardiac-intrinsic mechanisms preceding contractile dysfunction.


Imaging myocardial metabolism can provide invaluable information about the metabolic state of the heart, noninvasively. Small animal PET imaging, in particular, offers a unique platform to validate new imaging probes and targets as well as novel therapeutic interventions for translation to humans. In this work, we utilized small animal PET with 18FDG to quantify myocardial glucose metabolism in an animal model of T2D, namely the ZDF rat. We characterized, noninvasively, alterations in myocardial glucose metabolism, consistent with theories concerning the etiology of diabetic cardiomyopathy. In particular, we demonstrated reduced myocardial glucose uptake, independent of age, in the ZDF diabetic heart. Interestingly, differences in myocardial glucose utilization were only apparent at late stages of diabetes suggesting that alterations in myocardial glucose uptake (MGUp) may provide an early imaging marker for diabetic cardiomyopathy. Finally, we validated FDG-PET measures of myocardial glucose uptake rate against gene and protein expression of GLUT transporters. We believe that our findings underscore both the translational capability and the potential use of small animal PET in assessing the efficacy of therapies.


We thank Lori Strong, Margaret M. Morris, Amanda Roth, Paul Eisenbeis, Ann Stroncek, and Jerrel Rutlin for technical assistance and the Washington University School of Medicine cyclotron staff for synthesis of radiopharmaceuticals. This work was supported by NIH/NHLBI grant 2-PO1-HL-13851.


1. Wilson PW. Diabetes mellitus and coronary heart disease. Endocrinol Metab Clin North Am. 2001 Dec;30(4):857–881. [PubMed]
2. Stamler J, Vaccaro O, Neaton JD, Wentworth D. Diabetes, other risk factors, and 12-yr cardiovascular mortality for men screened in the Multiple Risk Factor Intervention Trial. Diabetes Care. 1993 Feb;16(2):434–444. [PubMed]
3. Fang ZY, Prins JB, Marwick TH. Diabetic cardiomyopathy: evidence, mechanisms, and therapeutic implications. Endocr Rev. 2004 Aug;25(4):543–567. [PubMed]
4. Saito F, Kawaguchi M, Izumida J, Asakura T, Maehara K, Maruyama Y. Alteration in haemodynamics and pathological changes in the cardiovascular system during the development of Type 2 diabetes mellitus in OLETF rats. Diabetologia. 2003 Aug;46(8):1161–1169. [PubMed]
5. Golfman LS, Takeda N, Dhalla NS. Cardiac membrane Ca(2+)-transport in alloxan-induced diabetes in rats. Diabetes Res Clin Pract. 1996 Jul;31 (Suppl):S73–77. [PubMed]
6. Lopaschuk GD, Belke DD, Gamble J, Itoi T, Schonekess BO. Regulation of fatty acid oxidation in the mammalian heart in health and disease. Biochim Biophys Acta. 1994 Aug 4;1213(3):263–276. [PubMed]
7. Lopaschuk GD. Metabolic abnormalities in the diabetic heart. Heart Fail Rev. 2002 Apr;7(2):149–159. [PubMed]
8. Avogaro A, Nosadini R, Doria A, et al. Myocardial metabolism in insulin-deficient diabetic humans without coronary artery disease. Am J Physiol. 1990 Apr;258(4 Pt 1):E606–618. [PubMed]
9. Neely JR, Rovetto MJ, Oram JF. Myocardial utilization of carbohydrate and lipids. Prog Cardiovasc Dis. 1972 Nov-Dec;15(3):289–329. [PubMed]
10. Saddik M, Lopaschuk GD. Myocardial triglyceride turnover and contribution to energy substrate utilization in isolated working rat hearts. J Biol Chem. 1991 May 5;266(13):8162–8170. [PubMed]
11. Saddik M, Lopaschuk GD. Triacylglycerol turnover in isolated working hearts of acutely diabetic rats. Can J Physiol Pharmacol. 1994 Oct;72(10):1110–1119. [PubMed]
12. Zhou YT, Grayburn P, Karim A, et al. Lipotoxic heart disease in obese rats: implications for human obesity. Proc Natl Acad Sci U S A. 2000 Feb 15;97(4):1784–1789. [PubMed]
13. Herrero P, Kisrieva-Ware Z, Dence CS, et al. PET measurements of myocardial glucose metabolism with 1–11C-glucose and kinetic modeling. J Nucl Med. 2007 Jun;48(6):955–964. [PubMed]
14. Herrero P, Weinheimer CJ, Dence C, Oellerich WF, Gropler RJ. Quantification of myocardial glucose utilization by PET and 1-carbon-11-glucose. J Nucl Cardiol. 2002 Jan–Feb;9(1):5–14. [PubMed]
15. Lautamaki R, Airaksinen KE, Seppanen M, et al. Rosiglitazone improves myocardial glucose uptake in patients with type 2 diabetes and coronary artery disease: a 16-week randomized, double-blind, placebo-controlled study. Diabetes. 2005 Sep;54(9):2787–2794. [PubMed]
16. Ohtake T, Yokoyama I, Watanabe T, et al. Myocardial glucose metabolism in noninsulin-dependent diabetes mellitus patients evaluated by FDG-PET. J Nucl Med. 1995 Mar;36(3):456–463. [PubMed]
17. Yokoyama I, Inoue Y, Moritan T, Ohtomo K, Nagai R. Myocardial glucose utilisation in type II diabetes mellitus patients treated with sulphonylurea drugs. Eur J Nucl Med Mol Imaging. 2006 Jun;33(6):703–708. [PubMed]
18. Herrero P, Gropler RJ. Imaging of myocardial metabolism. J Nucl Cardiol. 2005 May–Jun;12(3):345–358. [PubMed]
19. Kudo T. Metabolic imaging using PET. Eur J Nucl Med Mol Imaging. 2007 Jun;34 (Suppl 1):S49–61. [PubMed]
20. Su Y, Welch MJ, Shoghi KI. The application of maximum likelihood factor analysis (MLFA) with uniqueness constraints on dynamic cardiac microPET data. Phys Med Biol. 2007 Apr 21;52(8):2313–2334. [PubMed]
21. Shoghi KI, Welch MJ. Hybrid image and blood sampling input function for quantification of small animal dynamic PET data. Nucl Med Biol. 2007 Nov;34(8):989–994. [PMC free article] [PubMed]
22. Kim J, Herrero P, Sharp T, et al. Minimally invasive method of determining blood input function from PET images in rodents. J Nucl Med. 2006 Feb;47(2):330–336. [PubMed]
23. Sharp TL, Dence CS, Engelbach JA, Herrero P, Gropler RJ, Welch MJ. Techniques necessary for multiple tracer quantitative small-animal imaging studies. Nucl Med Biol. 2005 Nov;32(8):875–884. [PubMed]
24. Herrero P, Kim J, Sharp TL, et al. Assessment of myocardial blood flow using 15O-water and 1–11C-acetate in rats with small-animal PET. J Nucl Med. 2006 Mar;47(3):477–485. [PubMed]
25. Welch MJ, Lewis JS, Kim J, et al. Assessment of myocardial metabolism in diabetic rats using small-animal PET: a feasibility study. J Nucl Med. 2006 Apr;47(4):689–697. [PubMed]
26. Phillips MS, Liu Q, Hammond HA, et al. Leptin receptor missense mutation in the fatty Zucker rat. Nat Genet. 1996 May;13(1):18–19. [PubMed]
27. Laforest R, Longford D, Siegel S, Newport DF, Yap J. Performance evaluation of the microPET (R) - FOCUS-F120. Ieee Transactions on Nuclear Science. 2007 Feb;54(1):42–49.
28. Tai YC, Ruangma A, Rowland D, et al. Performance evaluation of the microPET focus: a third-generation microPET scanner dedicated to animal imaging. J Nucl Med. 2005 Mar;46(3):455–463. [PubMed]
29. Shoghi KI, Rowland DJ, Laforest R, Welch MJ. Characterization of Spillover and Recovery Coefficients in the Gated Mouse Heart for Non-Invasive Extraction of Input Function in microPET Studies: Feasibility and Sensitivity Analysis. IEEE Nuclear Science Symposium. 2006;4:2134–2136.
30. Patlak CS, Blasberg RG. Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. Generalizations. J Cereb Blood Flow Metab. 1985 Dec;5(4):584–590. [PubMed]
31. Hallsten K, Virtanen KA, Lonnqvist F, et al. Enhancement of insulin-stimulated myocardial glucose uptake in patients with Type 2 diabetes treated with rosiglitazone. Diabet Med. 2004 Dec;21(12):1280–1287. [PubMed]
32. Reivich M, Kuhl D, Wolf A, et al. Measurement of local cerebral glucose metabolism in man with 18F-2-fluoro-2-deoxy-d-glucose. Acta Neurol Scand Suppl. 1977;64:190–191. [PubMed]
33. Sokoloff L. [1–14C]-2-deoxy-d-glucose method for measuring local cerebral glucose utilization. Mathematical analysis and determination of the “lumped” constants. Neurosci Res Program Bull. 1976 Sep;14(4):466–468. [PubMed]
34. Botker HE, Goodwin GW, Holden JE, Doenst T, Gjedde A, Taegtmeyer H. Myocardial glucose uptake measured with fluorodeoxyglucose: a proposed method to account for variable lumped constants. J Nucl Med. 1999 Jul;40(7):1186–1196. [PubMed]
35. Wiggers H, Bottcher M, Nielsen TT, Gjedde A, Botker HE. Measurement of myocardial glucose uptake in patients with ischemic cardiomyopathy: application of a new quantitative method using regional tracer kinetic information. J Nucl Med. 1999 Aug;40(8):1292–1300. [PubMed]
36. Herrero P, Sharp TL, Dence C, Haraden BM, Gropler RJ. Comparison of 1-(11)C-glucose and (18)F-FDG for quantifying myocardial glucose use with PET. J Nucl Med. 2002 Nov;43(11):1530–1541. [PubMed]
37. Chang L, Chiang SH, Saltiel AR. Insulin signaling and the regulation of glucose transport. Mol Med. 2004 Jul–Dec;10(7–12):65–71. [PMC free article] [PubMed]
38. Shepherd PR, Kahn BB. Glucose transporters and insulin action--implications for insulin resistance and diabetes mellitus. N Engl J Med. 1999 Jul 22;341(4):248–257. [PubMed]
39. Olson AL, Liu ML, Moye-Rowley WS, Buse JB, Bell GI, Pessin JE. Hormonal/metabolic regulation of the human GLUT4/muscle-fat facilitative glucose transporter gene in transgenic mice. J Biol Chem. 1993 May 5;268(13):9839–9846. [PubMed]
40. Olson AL, Pessin JE. Transcriptional regulation of the human GLUT4 gene promoter in diabetic transgenic mice. J Biol Chem. 1995 Oct 6;270(40):23491–23495. [PubMed]