Quantification of metabolic rate of glucose uptake with [18F]FDG
[18F]2-fluoro-2-deoxy-D-glucose ([18F]FDG) is a glucose analogue, where fluorine-18 (halflife 109.8 min) substitutes the hydroxyl group at the second position in the glucose molecule. [18F]FDG is commonly used to measure tissue glucose consumption in vivo. [18F]FDG enters the tissue via glucose transporters, and can then be either metabolized to [18F]FDG-6-phosphate, or transported from tissue back to blood. [18F]FDG-6-phosphate cannot be transported out of the tissue, and in most tissues further metabolism and dephosphorylation of [18F]FDG-6-phosphate is slow; phosphorylated [18F]FDG is therefore often assumed to be trapped inside the cells. However, depending on the tissue and animal species, further metabolism of [18F]FDG-6-P may be significant (Herrero et al., 2004; Rokka et al., 2017). [18F]FDG does not distribute into the intracellular lipid droplets, which will decrease the apparent tracer uptake measured per tissue volume; this should be considered when studying organs with high and variable fat content.
[18F]FDG study may be combined with hyperinsulinemic euglycemic clamp (DeFronzo et al., 1979) to assess the insulin sensitivity (or insulin resistance) of specific organs (Nuutila et al., 1993; Johansson et al., 2017). The whole body insulin sensitivity can be measured simultaneously as the M value by dividing the mean glucose infusion rate by the lean body mass.
[18F]2-fluoro-2-deoxy-D-glucose has high affinity for GLUTs, and low affinity for ATP-dependent sodium-glucose transporters SGLTs. The relative role of GLUTs and SGLTs can be studied with related glucose tracers (Sala-Rabanal et al., 2016).
The autoradiographic method for measuring regional metabolic rate of glucose in the brain of rat using [14C]deoxyglucose (Sokoloff et al. 1977) has been modified for human studies using positron emission tomography (PET) and [18F]2-fluoro-2-deoxy-D-glucose (Phelps et al., 1979); Reivich et al., 1979; Huang et al, 1980 and 1981).
The three-compartment model with four rate constants K1*, k2*, k3* and k4* is often simplified by assuming that the dephosphorylation rate of FDG-6-phosphate in brain tissue is small enough that it can be ignored (k4*=0). At least a part of the observed k4* may be explained by tissue heterogeneity (Schmidt et al., 1992). In the brain, BBB is the first rate-limiting step of [18F]FDG, as implicated by even distribution of glucose in the brain intra- and extracellular spaces (Pfeuffer et al., 2000), and therefore rate constants K1* and k2* in the brain represent the transport across BBB, and k3* represents the rate of intracellular phosphorylation. This is the case in the brain, not in other tissues, where the endothelial wall is highly permeable to glucose: K1* may then represent either (plasma) perfusion, transport from extracellular into intracellular space, or combination of those.
Metabolic rate of glucose (MRglu) can be calculated from equation
, where Cglu is the concentration of glucose in plasma, and LC is the lumped constant.
Originally FDG studies were always analyzed using compartment model with the three or four rate constants. Only later it was found out that a graphical method, Patlak plot can be used to directly estimate the combination of the model rate constants. This combined term, net uptake rate for FDG (Ki*) is robust and very fast to calculate, and is therefore also suitable for computation of parametric images.
Patlak plot provides not only Ki*, but also an index of FDG distribution volume in the tissue as the plots intercept with y axis. In tissues with high fat content both the distribution volume and Ki* will be reduced. Metabolic rate of glucose can be corrected for this effect by dividing Ki* with the Patlak plot intercept value (Keramida et al., 2016).
If only static PET scan is available, then Patlak plot can not be used; calculate FUR as a substitute for Ki:
Manual blood samples are collected for the measurement of concentration of FDG in plasma, to enable absolute quantification of glucose uptake. Semiquantitative methods (for example SUV), which do not require blood sampling, are be affected by differences in plasma clearance, that is, uptake to other organs, or excretion of FDG: for example, medication for hypertension may slow down the renal excretion of FDG (Zhao et al., 2013). Akers et al (2016) did not find any correlation between FDG SUV and renal function (eGFR). Correction for plasma glucose concentration is straightforward for Ki* but not for SUV.
Arterialized venous blood sampling is often used instead of arterial sampling in FDG studies, although it increases the variability and larger sample size is needed. Blood samples are processed in the PET blood laboratory to time-activity curves, which can be used as such. Image-derived input function (van der Weerdt et al., 2001; Christensen et al., 2014), Model-based input function, or population-based input function may be used as an alternative to blood sampling in some cases. When necessary, blood TAC can be converted to plasma TAC, or vice versa, using conversion functions (Figure 2) that are available in programs b2plasma and p2blood. In humans, FDG is rapidly equilibrated between plasma and RBC (Gambhir et al., 1989; Nahmias et al., 2000), although not to exactly same level (Ohtake et al., 1991). The blood-to-plasma ratio is ∼0.9, except in the very beginning, and using arterial blood instead of plasma overestimates Patlak Ki only ∼5% (Larsen et al., 2001). Note that FDG transport across erythrocyte membranes is usually slow in nonprimates, and therefore plasma TAC should be preferred over blood TAC as input function (Alf et al., 2013; Buxton, 2014). Fetal and neonatal erythrocytes of all mammalian species are highly permeable to glucose and FDG. Metabolites of FDG (Herrero et al., 2004) may have some effect on the plasma-to-blood ratio.
Figure 2. Plasma-to-blood ratio functions for FDG in human subjects, rats, and mice. The different conversion functions for mice, based on separate publications, may indicate differences between mice populations.
For Patlak analysis, correction for time delay is not required, but the estimates of compartmental model parameters may be very sensitive to the effect of time delay.
With high-resolution PET scanner (HRRT) and careful optimization of image reconstruction it is possible to avoid arterial blood sampling by deriving blood TAC from carotid artery in the dynamic image (Huisman et al., 2012). Three manual blood samples are needed to calibrate the image-derived input curve.
Compartment model fit for the brain can be calculated using fitk3 or fitk4. Parametric images of individual rate constants can be computed using linearized model or basis function method (Hong & Fryer, 2010), and used in SPM and connectivity analyses (Bahri et al., 2018).
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Make sure that you are using appropriate values for τ and φ. Note that the brain FDG model must not be applied to skeletal muscle!
Please read MET5731. Recommended LC for Patlak analysis of heart FDG studies is 1. If myocardial FDG data is analyzed using irreversible compartmental model, individual LC can be calculated based on the model rate constants (Bøtker et al., 1997 and 2000).
For precise quantification the analysis methods should account for the spillover and partial volume effects caused by respiratory motion and beating of the heart, especially in small animal studies. Gated PET images help to reduce these artifacts, and may additionally enable analysis of cardiac motion in CAD (Sims et al., 2018).
In heart studies the input function is usually derived from a ROI placed on the LV cavity or aorta. Arterialized venous blood sampling can be used instead of arterial sampling, although it contributes to variability in results (van der Weerdt et al., 2002).
For analysis instructions in TPC, please read MET5736, and the publication by Bertoldo et al. (2001). Recommended LC for Patlak analysis of skeletal muscle FDG studies is 1.2 (Peltoniemi et al., 2000). If compartmental analysis is used, it should include three tissue compartments (Bertoldo et al., 2001; Huang et al., 2011; Bahri et al., 2018).
In small animal studies the data quality does not usually allow fitting complex compartmental models. 2-tissue compartmental model for irreversible uptake has been applied to gastrocnemius muscles of mice, using image-derived input function from inferior vena cava (Cochran et al, 2016).
Hepatic glucose uptake can be estimated using FDG, if the dual input (arterial and portal vein input) is taken into consideration in the model, as validated in pig studies (Brix et al., 2001; Munk et al., 2001; Iozzo et al., 2007; Kudomi et al., 2009; Winterdahl et al., 2011; Rani et al., 2013). Alternatively the gut could be included as a compartment in the model (Vivaldi et al., 2013; Garbarino et al., 2015). Constant-infusion administration of FDG may yield more robust estimates of hepatic glucose metabolism than bolus injection (Trägårdh et al., 2015).
Notice that liver and intestine are gluconeogenetic organs, expressing glucose-6-phosphatase, and therefore we cannot assume that FDG uptake is irreversible. Yet, desphophorylation in the liver is relatively slow compared with phosphorylation, even during fasting, but especially during euglycemic hyperinsulinemic clamp (Iozzo et al., 2004; Rijzewijk et al., 2010).
Arterial plasma curve alone can be used as model input especially for liver tumours which are mainly fed by the hepatic artery (Choi et al., 1994; Keiding et al., 2000; Brix et al., 2001; Fukuda et al., 2004). Image-derived input function from abdominal aorta can be used in the analysis. Calculation of liver-to-blood ratio is suitable method for clinical studies, and provides better surrogate for Ki than SUV.
Hepatic glucose uptake is inversely associated with liver fat content (Borra et al., 2008; Rijzewijk et al., 2010). Glucose uptake in liver may be higher in men than in women, even when corrected for the different fat contents (Keranida and Peters, 2017).
Perfusion in liver is high, and blood-to-hepatocyte transfer is practically unlimited for [18F]FDG; therefore radioactivity concentration in the liver follows relatively well the [18F]FDG concentration in the blood. Liver is often used as a reference tissue in diagnostic imaging when calculating SUV ratio (SUR, or tumour-liver ratio, TLR) (Laffon et al., 2011; Boktor et al., 2013; Watanabe et al., 2013; Keramida et al., 2015; Hofheinz et al., 2016) or metabolic volume. TLR (or Deauville score) has high prognostic value for instance in imaging of lymphoma (Barrington et al., 2017; Nanni et al., 2017). Liver has also been used as reference tissue in mice BAT studies after intraperitoneal FDG injection (Wu et al., 2014).
SUV in liver correlates positively with blood glucose levels, even when patients have been fasting and serum glucose concentrations are considered to be at acceptable levels (Webb et al., 2015), has high inter-individual variance and is dependent on BMI and sex (Rubello et al., 2015). In contrast to liver, tumour SUV correlates negatively with blood glucose, making TLR very sensitive to blood glucose levels (Peters et al., 2018). Although tumour-to-blood ratio is preferable to tumour-to-liver ratio, both are clearly better than SUV in oncological FDG PET (van den Hoff et al., 2013; Hofheinz et al., 2016).
Liver SUV is decreased in steatotic liver, which causes overestimation of tumour-to-liver ratio or Deauville score, unless SUV is corrected using liver CT-derived HU-values (Salomon, 2018). HU-corrected liver SUV is increased in hepatic steatosis (Keramida et al., 2014). Fat distribution in the liver can be very heterogeneous (Décarier et al., 2011; Keramida et al., 2016).
Metabolic rate of glucose in intestine (duodenum and jejunum) and colon in humans has been measured using FDG PET (Honka et al., 2013; Bahler et al., 2017; Kang et al., 2017; Koffert et al., 2017; Motiani et al., 2017). Honka et al. (2013) and Motiani et al. (2017) applied LC=1.15, and a recovery coefficient of 2.5 for the colon.
FDG PET has also been used to assess glucose metabolism in the gut of mice (Massollo et al., 2013). Sala-Rabanal et al (2018) have studied intestinal absorption of glucose in mice using FDG and two other tracers, [18F]Me-4FDG and [18F]4-FDG, with different specificities for SGLTs and GLUTs.
Glucose consumption in tumours is increased because of increased glycolysis (Warburg effect). GLUT1 and GLUT3 are commonly overexpressed by tumour cells, and the level of hexokinase 2 is usually high. The enhanced glucose uptake can be quantitated or semi-quantitated using [18F]FDG. Part of the increase may be due to inflammation. [18F]FDG is not well suitable for imaging all tumours: neuroendocrine and prostate tumours often have low [18F]FDG uptake. Increased glucose transport may be due to SGLTs which do not transport [18F]FDG. Certain tumour cells may have low hexokinase 2 expression or increased glucose-6-phosphatase expression.
FDG can be used to assess the glucose uptake in white adipose tissue (WAT). FDG PET studies have shown that adipose tissue becomes insulin resistant in obesity and type 2 diabetes (Virtanen et al., 2001; Virtanen et al., 2005). Lumped constant for white adipose tissue is 1.14 (Virtanen et al., 2001).
Activity of brown adipose tissue (BAT) can be measured using FDG PET (Virtanen et al., 2009). FDG is also useful in identifying and locating the BAT for defining regions-of-interest in PET image analysis when using other tracers with lesser target-to-background contrast. FDG uptake in BAT is higher when fat fraction in BAT is smaller (Koskensalo et al., 2017), which may be caused by the subsequently higher water fraction and distribution volume.
Glucose is completely reabsorbed in the proximal tubules of the kidneys, but FDG is only partially reabsorbed (Kosuda et al., 1997; Moran et al., 1999). Reabsorption of 2-deoxy-D-glucose and FDG happens in distal tubules, possibly via GLUT1 (Miller et al., 1992; Kosuda et al., 1997). Partial excretion of FDG leads to high activity in the urine, hampering PET imaging of the pelvic regions, especially the kidneys and urinary bladder, but also prostate and uterine. Urinary bladder receives the highest radiation dose from FDG scan. Renal failure and medications can alter FDG excretion, which affects the total clearance of FDG and may therefore cause variability in SUV measures of other organs (Bach-Gansmo et al., 2012; Garbarino et al., 2014). Yet, Akers et al (2016) did not find any correlation between FDG SUV and renal function (eGFR).
Normal kidneys do not store appreciable amounts of glycogen (Biava et al., 1966), but can produce and release significant amounts of glucose via gluconeogenesis from circulating lactate, glycerol, and glutamine (Stumvoll et al., 1997). Gluconeogenesis is restricted to proximal tubules in the renal cortex, where the activities of related enzymes, including glucose-6-phosphatase, are high, while the activities of glycolytic enzymes are low in the cortex and high in the renal medulla (Burch et al., 1978; Guder & Ross, 1984). Therefore the uptake and phosphorylation of [18F]FDG in the renal cortex is reversible, like in the liver.
[18F]FDG study during hyperinsulinemic euglycemic clamp can provide information necessary to measure endogenous glucose production (EGP) (Iozzo et al., 2006); FDG plasma clearance is adjusted with the FDG lost in urine to determine the rate of glucose disappearance, and EGP is calculated by subtracting glucose infusion rate from the rate of disappearance.
- Net influx rate
- Multiple-time graphical analysis (MTGA)
- Fitting compartmental models
- Tissue-to-plasma ratio
- Fractional uptake rate (FUR)
- Metabolic tumour volume (MTV)
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Updated at: 2019-03-21
Created at: 2009-01-09
Written by: Vesa Oikonen