Increased glucose uptake in visceral versus subcutaneous adipose tissue revealed by PET imaging

Abstract

Objective

The current study tested the hypothesis that glucose utilization differs between VAT and SAT and investigated potential mechanisms for such a finding.

Background

Visceral adipose tissue (VAT) burden correlates better with cardiovascular risk than does subcutaneous adipose tissue (SAT) burden. Beyond volumetric measurement, glucose uptake in adipose tissue (AT) might reflect metabolic activity and provide pathophysiologic insight and aid risk stratification.

Methods

We retrospectively studied tissue-specific glucose uptake in vivo in clinically obtained whole-body fluorodeoxyglucose positron emission tomography (FdG-PET) scans. We also assessed glucose uptake in vitro, using stromal vascular cells isolated from SAT and VAT of diet-induced obese C57BL/6 mice. Quantitative PCR evaluated the expression of multiple genes involved in cellular glucose metabolism, including glucose transporters (GLUT 1, 3 and 4) and hexokinases (HK-1 and 2) in SAT and VAT of obese C57BL/6 mice.

Results

We analyzed whole-body FdG-PET scans from 31 obese and 26 lean patients. VAT exhibited higher FdG uptake compared to SAT (p<0.0001) independent of age, gender, body-mass index, comorbidities, and medications. To investigate mechanisms underlying this observation, we studied glucose uptake in the stromal vascular cell fraction of AT, rich in inflammatory cells. Stromal vascular cells from VAT of diet-induced obese C57BL/6 mice exhibited higher glucose uptake than those from SAT (p=0.01). Evaluation of expression of glucose transporters (GLUT 1, 3 and 4) and hexokinases (HK-1 and 2), revealed increased expression of HK-1 in VAT- compared to SAT-derived stromal vascular cells, and also in visceral versus subcutaneous unfractionated AT.

Conclusions

In humans in vivo, VAT has increased glucose uptake compared with SAT, as determined non-invasively with FdG PET imaging. Differential stromal metabolic activity may be one mechanism underlying differences in metabolic activity of visceral and subcutaneous AT.

Introduction

The dramatic increase in obesity and related metabolic complications during the last decade threatens to reverse the progressive decrease in cardiovascular deaths realized over the last 50 years (1). Although excess overall adiposity associates with cardiovascular morbidity and mortality, the distribution of body fat across different adipose tissue (AT) compartments provides additional important information on risk (2,3). Indeed, while the amount of visceral adipose tissue (VAT) correlates highly with an adverse risk factor profile (4,5), the subcutaneous adipose tissue (SAT) has a less ominous importance in obese individuals (6).

The potential clinical impact of biologic diversity between fat depots has triggered intense research to better understand physiologic and molecular differences based on AT location. Several studies have already demonstrated differences between VAT and SAT regarding secretion of inflammatory mediators, gene expression, and cell morphology. Yet glucose utilization between these two adipose compartments remains incompletely understood (7–10). A recent study shows that AT glucose uptake determined by fluorodeoxyglucose positron emission tomography (FdG-PET) may complement volumetric measurements of fat for the purpose of risk estimation (11).

This study demonstrates higher FdG uptake in VAT compared to SAT in humans using FdG-PET imaging technique, and investigates potential molecular mechanisms mediating this effect.

Materials and methods

Human subjects and FdG-PET imaging

The institutional review board of Brigham and Women’s Hospital approved the human studies. We retrospectively analyzed 31 obese patients and 26 lean patients who underwent whole body PET-CT scanning for diagnosis or staging of primary lung cancer with respect to the intensity of FdG uptake in abdominal visceral and subcutaneous AT. Individuals exhibiting abnormal FdG uptake in the abdominal region suspicious for metastases, and two individuals with fat SUV values that were 8 and 18 standard deviations above the mean, were excluded from analysis. After fasting for 4 to 6 hours, the patients received FdG (777 ± 111 MBq, 21 ± 3 mCi) intravenously, and whole-body images were acquired on a Discovery ST PET/CT scanner (General Electric, Milwaukee, WI, USA), 83 ± 22 minutes after tracer administration as described previously (12). Briefly, non-contrast computed tomography (CT) scan was performed first for attenuation correction and localization. Immediately after the CT scan, the PET scan was acquired in 2-dimensional mode at 6 to 7 bed positions (4 minutes for each position). PET data were reconstructed using an ordered-subset expectation maximization iterative algorithm.

Hermes Gold 2.1 software (Hermes Medical Solutions, Stockholm, Sweden) was used for image analysis (Figure 1). We first created a “fat mask” of the original CT image set by removing pixels with densities below −110 or above −70 Hounsfield units, a range corresponding to AT densities in human subjects (13). We then divided the obtained CT fat mask by itself to acquire a binary CT fat mask in which pixels had the value of 1 in AT and 0 in all other tissues. We further resampled the original PET file to match the slice thickness and matrix size of the CT file, and multiplied the resampled PET image set by the binary CT fat mask. This operation left only those pixels in the PET image set which corresponded to the pixels with the value of 1 in the CT fat mask. We converted this PET image set into the original matrix size and proceeded with the analysis as follows. We drew regions of interest (ROIs) in visceral and subcutaneous AT in 15 to 25 consecutive slices to create a volume of interest (VOI). We recorded tracer activity (Bq/cc) in each pixel displaying a value above 0 and converted this number to the standard uptake value (SUV) using a standard formula accounting for pixel activity (Bq/cc), patient weight (kg), and injected dose (Bq), decay corrected to the time of injection. We then averaged SUVs from each pixel in a VOI to obtain a single SUV for a given VOI in both SAT and VAT regions.

Figure 1. Flow-chart of FdG-PET/CT image analysis.

1. We created a binary “fat mask” of the CT image by removing pixels with densities below −110 or above −70 Hounsfield units. We resampled the PET image to match slice thickness and the matrix size of the CT image. 2. We then multiplied the fat masked CT image and resampled PET image to create a fat mask of the PET image. 3. We drew regions of interest on several slices and combined them to obtain a volume of interest (VOI). 4. We recorded tracer activity (Bq/cc) in each pixel and converted it to the standard uptake value (SUV). 5. We discarded zero values and averaged all positive values to obtain an average SUV for the VOI.

Experimental animals and isolation of stromal vascular cells

C57BL/6 mice were obtained from The Jackson Laboratory (Bar Harbor, ME, USA) and kept at a barrier animal facility of the Harvard Medical School. All experiments conformed to animal care protocols approved by the institutional review board. Mice were fed a high-fat diet (PicoLab Rodent Chow D12108, containing 40% kcal from fat, 1.25% cholesterol, 0% cholate, Research Diets). At sacrifice, we perfused animals with normal saline to remove blood cells from the vascular system and dissected abdominal subcutaneous and peri-epididymal visceral fat pads.

To isolate stromal vascular cells (SVCs), we minced the fat pads in phosphate-buffered saline (PBS) containing 2% bovine serum albumin and 250 U/mL of collagenase II (Worthington Biochemical Corporation, Lakewood, NJ, USA), and incubated it at 37°C for 1 hour. We passed the digested tissue through 70-μm nylon cell strainer (BD Biosciences, San Jose, CA, USA) and centrifuged the flow-through. After removing the supernatant, we lysed red blood cells with ACK lysis buffer (Gibco, Carlsbad, CA, USA) and washed and counted the remaining cells, which we then used for cell culture or RNA isolation, as described below.

Cytokines and chemicals

Murine cytokines were obtained from Peprotech (Rocky Hill, NJ, USA) and tested by the manufacturer for the absence of lipopolysaccharide (LPS). We prepared all cytokine stock solutions in LPS-free sterile PBS and diluted the working solutions with culture medium before stimulation. Specific biological activities of cytokines were 1×10⁷ units/mg for IFN-γ, 1×10⁷ units/mg for TNF-α, and 5×10⁸ units/mg for IL-1β. We describe cytokine concentrations in sections detailing cell stimulations. We purchased 2-deoxy-D-glucose (2dG) and cytochalasin B from Sigma (St. Louis, MO, USA), and ³H-2dG from PerkinElmer (Waltham, MA, USA).

Glucose uptake assay

Cultured SVCs were incubated in KRH buffer (136 mM NaCl, 4.7 mM KCl, 1.25 mM MgSO₄, 1.25 mM CaCl₂, 50 mM HEPES) for 30 minutes at 37ºC to deplete endogenous glucose stores. Next, we added ³H-2dG and unlabeled 2dG to the cells (final concentration, 100 μM for each, and specific activity of 0.5 μCi/sample for ³H-2dG) with or without the inhibitor cytochalasin B (final concentration 10 μM) to determine carrier-nonspecific uptake (14). We incubated cells for 5 minutes at room temperature, after which we removed the incubation buffer and washed the cells twice with 25 mM ice-cold unlabeled d-glucose to stop uptake of radiolabeled glucose via saturation of transporters. We lysed cells with 1% Triton X-100 (Sigma). We used lysates for liquid scintillation counting (Beckman LS 6000IC counter) and protein measurements using BCA assay (Pierce). The net uptake of ³H-2dG by cells is normalized to protein content and expressed as nmol of glucose per mg of protein per minute. We analyzed each sample in duplicates and the values were averaged. The data presented on the graphs and in the tables represent mean ± SEM of data obtained on cells from several experiments (n for each experiment is indicated in respective figure legend). All graphs show GLUT-mediated glucose uptake (total uptake minus cytochalasin B-inhibited uptake).

Cell culture and stimulations

After isolation, we cultured Murine SVCs in RPMI-1640 medium supplemented with 10% fetal calf serum (FCS), 100 U/mL penicillin, 100 μg/mL streptomycin, and 2 mM L-glutamine. We plated cells on 24-well plates at a density of 250,000 cells/well and allowed them to adhere for 24 hrs, after which we stimulated them with a mixture of Murine TNF-α and IL-1β (10 ng/mL each). We then performed glucose uptake in cultured cells at baseline and at 24 and 48 hours after stimulation.

RNA isolation and analysis of gene expression

RNA isolation from the SVCs was performed using RNeasy mini spin columns (Qiagen, Germantown, MD, USA) according to the manufacturer’s instructions. We carried out RNA isolation from whole AT with RNeasy Lipid Tissue Midi Kit (Qiagen) according to the manufacturer’s instructions. We assessed RNA concentration and purity using Nanodrop 1000 UV spectrophotometer (Thermo Scientific, Wilmington, DE, USA). 1 μg of RNA was reverse transcribed into complementary DNA (cDNA) using Superscript II reverse transcriptase (Invitrogen, Carlsbad, CA, USA) and oligo-dT primers (Invitrogen).

We performed quantitative PCR on 2 μL of cDNA with the MyiQ Real-Time PCR Detection System (BioRad) using the primer sequences indicated in Table 1. We calculated relative expression ratios of genes in visceral fat compared to subcutaneous fat using the ΔΔCq method with GAPDH mRNA expression as reference. Further details on RNA isolation and qPCR can be found in the online Materials and Methods supplement to the article.

Table 1.

Sequences of primers used for RNA amplification

Gene name (GeneBank accession number)	Forward primer	Reverse primer
GLUT-1 (NM_011400.3)	5′-ATGGATCCCAGCAGCAAG-3′	5′-CCAGTGTTATAGCCGAACTGC-3′
GLUT-3 (NM_011401.3)	5′-GGTGGCTGGCTGTTGTAACT-3′	5′-GCAGCGAAGATGATAAAAACG-3′
GLUT-4 (NM_009204.2)	5′-GACGGACACTCCATCTGTTG-3′	5′-GCCACGATGGAGACATAGC-3′
HK-1 (NM_010438.2)	5′-GCGAGGACAGGCTGTAGATG-3′	5′-CCGCATGGCATACAGATACTT-3′
HK-2 (NM_013820.3)	5′-CAACTCCGGATGGGACAG-3′	5′-CACACGGAAGTTGGTTCCTC-3′
GAPDH (XM_001473623.1)	5′-TGGGTGTGAACCATGAGAAG-3′	5′-GCTAAGCAGTTGGTGGTGC-3′

Abbreviations: GLUT = glucose transporter, HK = hexokinase, GAPDH = glyceraldehyde-3-phosphate dehydrogenase.

Statistical analysis

We performed statistical analysis with the GraphPad Prism 4.0 (GraphPad Software Inc., La Jolla, CA, USA), StatView 5.0.1 and SAS version 9.1 (SAS Institute Inc., Cary, NC, USA) software packages. Descriptive summaries included means, standard deviations, and percentages. The obese and lean cohorts were compared in terms of patient characteristics and fat SUV using the chi-square or Wilcoxon rank sum test. The within-subject analysis of VAT compared with SAT was a mixed models analysis of variance, with patient as a random effect and fixed effects of fat depot (SAT or VAT) and cohort (obese or lean). Potential confounding variables were added to the model individually, with each model having three fixed effects. Reliability of VAT and SAT SUV values in a subset of eight obese and eight lean patients was assessed by the Pearson correlation coefficient between the first and second repeated measures, the 95% confidence interval around the mean difference, and the intraclass correlation coefficient with 95% confidence interval. Statistical analysis on graphs 2 and 4 was performed using Mann-Whitney U test, and Wilcoxon signed rank test was used on graph 3. Nominal p-values were reported, unadjusted for multiple testing.

The authors had full access to and take responsibility for the integrity of the data. All authors have read and agreed to the manuscript as written.

Results

We assessed FdG-PET scans from 31 obese (BMI>30) and 26 lean (BMI<25) subjects. Table 2 provides patient characteristics. There were significantly more obese patients with hypertension or on blood pressure medications, as compared with lean patients (65% vs. 35%, p=0.02). The cohorts did not differ significantly in other patient characteristics.

Table 2.

Baseline characteristics of patients

	Lean patients (n=26)	Obese patients (n=31)	p-value
Females, n (%)	14 (54)	15 (48)	0.68
Age (mean ± SD)	66.0 ± 14.8	61.4 ± 10.4	0.08
BMI (mean ± SD)	21.7 ± 0.6	36.4 ± 3.5	<0.001
Hypertension or anti-hypertensive drug, n (%)	9 (35)	20 (65)	0.02
Diabetes, n (%)	2 (8)	8 (26)	0.07
Smoking, n (%)	15 (58)	14 (45)	0.35
CAD, n (%)	2 (8)	7 (23)	0.12
Diuretic, n (%)	3 (12)	8 (26)	0.17
β-blocker, n (%)	5 (19)	9 (29)	0.39
Statin, n (%)	2 (8)	7 (23)	0.12
Ca2+ blocker, n (%)	1 (4)	5 (16)	0.13
ACE inhibitor, n (%)	3 (12)	6 (19)	0.42
NSAID, n (%)	6 (23)	7 (23)	0.96
Any drug, n (%)	9 (35)	17 (55)	0.13

Abbreviations: BMI = body mass index, CAD = coronary artery disease, SD = standard deviation, ACE = angiotensin-converting enzyme, NSAID = non-steroidal anti-inflammatory drug.

Visceral fat in humans exhibits higher FdG uptake than subcutaneous fat

Analysis of FdG-PET/CT scans from patients revealed that lean and obese cohorts did not differ significantly with respect to subcutaneous (0.30 ± 0.09 vs. 0.33 ± 0.08, p=0.18) or visceral (0.88 vs 0.18, 0.81 ± 0.23 p=0.15) FdG uptake. Visceral fat showed significantly higher FdG uptake than subcutaneous abdominal AT in both the lean (BMI<25) (p<0.0001) and obese (BMI>30) subjects (p<0.0001) (Figure 2). Potentially important covariates, such as age, gender, diabetes, a history of coronary artery disease (CAD), hypertension or use of anti-hypertensive therapy, and smoking history, did not affect this difference in FdG signal associated with fat depots. FDG uptake in SAT and VAT in men and women overall and within both lean and obese groups is shown in the online supplemental figure. Medications (statins, diuretics, β-adrenergic blockers, Ca²⁺ channel blockers, angiotensin-converting enzyme (ACE) inhibitors, and aspirin, or a combination of one or more drugs) also did not alter results.

Figure 2. FdG uptake in human SAT and VAT.

Average intensity of FdG uptake (in standard uptake value (SUV)) in subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) in lean patients (BMI between 21 and 22.7) and obese patients (BMI between 32.8 and 45.3). The horizontal line in the box plots represents the median value; the boxed area is the interquartile range and the whiskers denote the 10th and 90th percentiles. * denotes p<0.05.

To investigate the reliability of the measurement, the assessment of FdG uptake was repeated in eight obese and eight lean patients. Table 3 shows mean differences and 95% confidence intervals around the mean differences, along with correlations and intraclass correlation coefficients. The measurements were most reliable for subcutaneous fat, with a correlation of 0.88 (p<0.0001) and an intraclass correlation coefficient of 0.87 (95% CI 0.68, 0.95).

Table 3.

Evaluation of reliability in measurement of FdG uptake

	Group	Mean difference (95% CI)	Correlation	ICC* (95% CI)
Subcutaneous	Overall	0.001 (−0.018, 0.019)	0.88, p<0.0001	0.87 (0.68, 0.95)
	Lean	0.010 (−0.018, 0.039)	0.93, p=0.001	0.92 (0.72, 0.98)
	Obese	−0.009 (−0.038, 0.021)	0.67, p=0.07	0.67 (0.09, 0.91)
Visceral	Overall	−0.065 (−0.15, 0.021)	0.60, p= 0.01	0.56 (0.13, 0.81)
	Lean	−0.028 (−0.158, 0.102)	0.56, p=0.15	0.59 (−0.04, 0.88)
	Obese	−0.102 (−0.242, 0.039)	0.70, p=0.05	0.53 (−0.13, 0.86)

ICC = intraclass correlation coefficient

To probe the mechanisms contributing to higher FdG uptake in visceral than subcutaneous AT, we conducted a series of ex vivo and in vitro experiments on cells obtained from mouse AT.

2-deoxy-glucose uptake is higher in mouse stromal vascular cells from visceral fat compared to subcutaneous fat

We first isolated SVCs from subcutaneous and visceral fat depots from mice with diet-induced obesity and measured glucose uptake in these cells. Baseline glucose uptake, normalized for protein content, was higher in SVCs from VAT compared to those from SAT (Figure 3A). Interestingly, there were no apparent differences in the rate of glucose uptake in animals of different age (data not shown). These findings corroborated our PET data in humans. SVCs from visceral fat also tended to show higher glucose uptake at 24 and 48 hours after plating (Figure 3A). TNF-α and IL-1β, both important cytokines involved in AT inflammation, boosted glucose uptake in visceral and subcutaneous SVCs (Figure 3B).

Figure 3. Glucose uptake in SAT- and VAT-derived stromal vascular cells.

A. Rate of glucose uptake in stromal vascular cells (SVCs) isolated from mouse subcutaneous adiposed tissue (SAT; white bars) and visceral adipose tissue (VAT; black bars). Glucose uptake is shown at baseline (0 hours) and after 24 and 48 hours of culture. Data are shown as mean ± SEM of 3–7 measurements. B. Rate of glucose uptake in SVCs from SAT (white bars) and VAT (black bars) stimulated with inflammatory cytokines TNF-α and IL-1β for 24 and 48 hours. Data are shown as mean ± SEM of six independent experiments. Cells obtained from subcutaneous depot were pooled from 2–3 animals. * denotes p<0.05.

Expression of glucose metabolism-related genes in mouse AT

To examine further mechanisms that might increase glucose uptake in visceral fat, we studied the expression of glucose metabolism-related genes in mouse SVCs and unfractionated AT. We performed real-time PCR analysis for the expression of GLUT-1, 3, and 4 as well as HK-1 and 2 in SVCs isolated from SAT and VAT and in unfractionated AT from both depots. SVCs from VAT exhibited significantly higher expression of HK-1 compared to SVCs from SAT (Figure 4A). This difference was observed in SVCs when data was pooled from both 8-week-old and 16-week-old mice (Figure 4) or analyzed separately at each age (data not shown). Levels of mRNAs encoding GLUT-1, 3 and 4 were lower in SVCs from visceral fat compared to SVCs from subcutaneous fat, but these differences were of much lesser magnitude than that of HK-1 induction (Figures 4A). Real-time PCR on whole fat samples from mice also revealed increased HK-1 expression in visceral compared to subcutaneous AT (Figure 4B).

Figure 4. Expression of glucose metabolism-related genes in SAT and VAT.

Expression of glucose metabolism-related mRNAs in stromal vascular cells (SVCs) (A) and in unfractionated adipose tissue (B) isolated from murine subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT). Gene expression in VAT-derived SVCs or unfractionated VAT (black bars in A and B, respectively) is shown relative to SAT-derived SVCs or unfractionated SAT (white bars in A and B, respectively) and normalized to housekeeping gene expression (GAPDH). Data are shown as mean ± SEM. * denotes p<0.05.

Discussion

Physiologic differences between AT compartments in the body, particularly between subcutaneous and visceral depots, have long attracted attention. VAT burden portends higher cardiometabolic risk (4,5), while increased amount of SAT shows the opposite association in obese individuals (6). Biologically, the two adipose depots also differ in the spectrum of inflammatory mediators they secrete.

This study tested the hypothesis that non-invasive functional imaging could reveal metabolic differences between AT depots in intact humans. Most clinical studies to date have used volumetric quantification of AT depots by computed tomography (CT) or magnetic resonance imaging (MRI). A recent preliminary report, however, suggested that volumetric measurements alone may not be sufficient and could be improved with a functional readout of the metabolic activity in a fat compartment by means of PET (11). This report, however, did not investigate whether PET can detect differences between VAT and SAT compartments.

We show that VAT exhibits higher FdG uptake compared to SAT in humans in vivo. This difference remains significant after adjustment for potentially confounding covariates (age, gender, BMI, concomitant medications, or comorbidities including established diabetes, coronary artery disease, or hypertension) in the multivariate analysis. Thus, VAT displays higher metabolic activity than SAT, as assessed non-invasively in humans. Our results agree with earlier studies by Virtanen et al (9,10) who showed higher rates of glucose uptake in VAT compared to SAT in groups of healthy and diabetic men under euglycemic hyperinsulinemic conditions. Although well designed, these studies enrolled only males with or without diabetes and did not assess basal rates of glucose uptake (9), which might limit the applicability of results to the general population. Our study complements these results by assessing basal rates of glucose uptake in a heterogeneous group of subjects including both sexes, with or without comorbidities. Taken together, these results suggest that FdG-PET could enable non-invasive functional in vivo analysis of AT metabolic activity.

The clinical findings prompted a search for possible causes of different metabolic activity in SAT and VAT. In light of previous studies showing the presence of inflammatory cells in AT (15,16), we hypothesized that these cells may contribute to differential glucose uptake in VAT and SAT. SVCs derived from AT contain inflammatory cells such as macrophages and T cells. Indeed, VAT-derived SVCs isolated from diet-induced obese C57BL/6 mice exhibited significantly higher absolute rates of glucose uptake than those from SAT. We normalized glucose uptake values for protein concentration, which suggests increased rates of glucose uptake per cell of VAT rather than an increase due to greater cellularity in VAT. These findings indicate that not only increased cellularity, but also greater glucose uptake in each cell, contribute to the higher glucose uptake detected in vivo in human VAT compared to SAT.

Finally, to identify molecular mechanisms underlying differential glucose uptake in VAT and SAT, we assessed the expression of genes involved in the transport and phosphorylation of glucose. Glucose transporters (GLUTs) mediate cellular uptake of this sugar (17). Upon entering the cell, glucose undergoes phosphorylation by hexokinase (HK) which exists in two main isoforms (HK-1 and HK-2), exhibiting different tissue localization (18). VAT-derived SVCs show higher HK-1 expression than SAT-derived SVCs. At the same time, expression of GLUTs 1, 3, and 4 was lower in VAT-derived SVCs, but exhibited much smaller differences between SAT- and VAT-derived SVCs, which questions their biologic importance. Moreover, HK-1 expression was also higher in unfractionated VAT compared to SAT, while GLUT 1, 3, and 4 and HK-2 expression levels did not differ. This suggests that the reduced expression of GLUT 1, 3, and 4 and HK-2 in SVCs within the visceral adipose tissue may be compensated by their higher level of expression in non-SVCs in the same fat pad. Therefore, the increase in HK-1 expression may be sufficient to increase glucose uptake in VAT and likely results from accentuated differences in expression in the SVCs. Perrini et al. (8) likewise showed that GLUT 1 and 4 expression did not differ between VAT and SAT SVC-derived adipocytes. These observations suggest that increase in HK-1 expression may be sufficient to increase glucose uptake in VAT, as detected non-invasively by FdG-PET. Additional studies are nevertheless necessary to draw definitive conclusions.

Despite the association of obesity with inflammation, this study found no difference in FdG uptake in VAT between obese and lean individuals. We conjecture that even though SVCs in VAT from obese individuals contain more inflammatory cells which could augment glucose uptake in VAT from obese subjects, their contribution to overall glucose uptake in such individuals may be counterbalanced by the greater mass of presumably insulin-resistant adipocytes, the most abundant cell type within this tissue.

Study limitations: we quantified FdG uptake in adipose tissue using SUVs, a clinically accepted way of analysis of PET studies. Nonetheless, SUVs may have inter- and intra-subject variability as a result of multiple factors including plasma glucose and insulin concentrations and the time of image acquisition after FdG injection. Moreover, quantification of FdG uptake requires 4-compartment kinetic analysis (19,20). Such analysis was not performed in our study and therefore our measurements are semiquantitative in nature.

In conclusion, we demonstrated significant differences in metabolic activity between SAT and VAT in humans using a standard non-invasive FdG-PET technique. Investigating mechanisms underlying this observation, we found increased glucose uptake in SVCs from VAT compared to SVCs from SAT in diet-induced obese mice. Further, we showed that the increase in glucose uptake in VAT may be attributed, in part, to increased expression of HK-1 in VAT-derived SVCs. These results affirm that different fat depots associated with distinct clinical outcomes exhibit differential metabolic activity in situ in intact humans.

Visceral adiposity has become a therapeutic target for numerous novel pharmacologic and other strategies aimed at improvement of dysmetabolism. While plasma biomarkers provide one window on efficacy, availability of an imaging modality to directly probe the target tissue would help drug development programs evaluate efficacy and perform better dose selection in clinical trials. These examples illustrate potential practical applications for use of imaging adipose tissue metabolic activity.