Quantification of liver perfusion with [15O]H2O-PET and its relationship with glucose metabolism and substrate levels

Slimani, Lotfi; Kudomi, Nobuyuki; Oikonen, Vesa; Järvisalo, Mikko J.; Kišš, Ján; Naum, Alexandru; Borra, Ronald; Sipilä, Hannu; Ferrannini, Ele; Savunen, Timo; Nuutila, Pirjo; Iozzo, Patricia

doi:10.1016/j.jhep.2008.01.029

Cited by 19 publications

(14 citation statements)

References 35 publications

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“…From a numerical viewpoint, this task was accomplished applying the regularized Newton algorithm to the inverse problems associated to the multi-dimensional Cauchy problems describing the two-compartment gut and liver systems, respectively. In this setting, the computation of this algorithm, which is rather similar to other optimization algorithms applied in compartmental analysis [22-25], is rather effective, since the matrix differentiation step required at some stage can be realized analytically, thus, avoiding time consuming numerical differentiation. The advantages of this technique are the ability to correctly identify the response of rate constants to experimental interventions able to modify hepatic glucose metabolism and a notable robustness with respect to data optimization.…”

Section: Discussionmentioning

confidence: 99%

A new compartmental method for the analysis of liver FDG kinetics in small animal models

et al. 2015

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BackgroundCompartmental analysis is a standard method to quantify metabolic processes using fluorodeoxyglucose-positron emission tomography (FDG-PET). For liver studies, this analysis is complex due to the hepatocyte capability to dephosphorylate and release glucose and FDG into the blood. Moreover, a tracer is supplied to the liver by both the hepatic artery and the portal vein, which is not visible in PET images. This study developed an innovative computational approach accounting for the reversible nature of FDG in the liver and directly computing the portal vein tracer concentration by means of gut radioactivity measurements.MethodsTwenty-one mice were subdivided into three groups: the control group ‘CTR’ (n = 7) received no treatment, the short-term starvation group ‘STS’ (n = 7) was submitted to food deprivation with free access to water within 48 h before imaging, and the metformin group ‘MTF’ (n = 7) was treated with metformin (750 mg/Kg per day) for 1 month. All mice underwent a dynamic micro-PET study for 50 min after an 18F-FDG injection. The compartmental analysis considered two FDG pools (phosphorylated and free) in both the gut and liver. A tracer was carried into the liver by the hepatic artery and the portal vein, and tracer delivery from the gut was considered as the sole input for portal vein tracer concentration. Accordingly, both the liver and gut were characterized by two compartments and two exchange coefficients. Each one of the two two-compartment models was mathematically described by a system of differential equations, and data optimization was performed by applying a Newton algorithm to the inverse problems associated to these differential systems.ResultsAll rate constants were stable in each group. The tracer coefficient from the free to the metabolized compartment in the liver was increased by STS, while it was unaltered by MTF. By contrast, the tracer coefficient from the metabolized to the free compartment was reduced by MTF and increased by STS.ConclusionsData demonstrated that our method was able to analyze FDG kinetics under pharmacological or pathophysiological stimulation, quantifying the fraction of the tracer trapped in the liver or dephosphorylated and released into the bloodstream.

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Section: Discussionmentioning

confidence: 99%

A new compartmental method for the analysis of liver FDG kinetics in small animal models

et al. 2015

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“…Additionally, circular ROIs (15 mm diameter) were drawn on 10 consecutive planes on the respective dynamic images in the aorta ascendens and grouped to obtain one image-derived input function for each tracer. To quantify hepatic parenchymal perfusion, it was assumed that [ 15 O]H 2 O in liver can be described by a single-tissue compartment model as proposed and validated by Kudomi and coworkers (27,28). Here, C T ( t ), C A ( t ), and C P ( t ) represent liver, arterial blood, and portal venous blood time–activity curves, respectively, F A and F P are arterial and portal venous perfusion, respectively, and V T is the partition coefficient of water in liver.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, animal data revealed that graded steatosis decreased parenchymal microcirculation (25). In addition to these highly invasive methods, noninvasive in vivo studies of hepatic perfusion have also been performed using PET (26–28). However, little is known about the relationship between liver triglyceride content with hepatic perfusion or substrate metabolism in human type 2 diabetes.…”

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confidence: 99%

Liver Fat Content in Type 2 Diabetes: Relationship With Hepatic Perfusion and Substrate Metabolism

et al. 2010

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OBJECTIVEHepatic steatosis is common in type 2 diabetes. It is causally linked to the features of the metabolic syndrome, liver cirrhosis, and cardiovascular disease. Experimental data have indicated that increased liver fat may impair hepatic perfusion and metabolism. The aim of the current study was to assess hepatic parenchymal perfusion, together with glucose and fatty acid metabolism, in relation to hepatic triglyceride content.RESEARCH DESIGN AND METHODSFifty-nine men with well controlled type 2 diabetes and 18 age-matched healthy normoglycemic men were studied using positron emission tomography to assess hepatic tissue perfusion, insulin-stimulated glucose, and fasting fatty acid metabolism, respectively, in relation to hepatic triglyceride content, quantified by proton magnetic resonance spectroscopy. Patients were divided into two groups with hepatic triglyceride content below (type 2 diabetes-low) or above (type 2 diabetes-high) the median of 8.6%.RESULTSType 2 diabetes-high patients had the highest BMI and A1C and lowest whole-body insulin sensitivity (ANOVA, all P < 0.001). Compared with control subjects and type 2 diabetes-low patients, type 2 diabetes-high patients had the lowest hepatic parenchymal perfusion (P = 0.004) and insulin-stimulated hepatic glucose uptake (P = 0.013). The observed decrease in hepatic fatty acid influx rate constant, however, only reached borderline significance (P = 0.088). In type 2 diabetic patients, hepatic parenchymal perfusion (r = −0.360, P = 0.007) and hepatic fatty acid influx rate constant (r = −0.407, P = 0.007) correlated inversely with hepatic triglyceride content. In a pooled analysis, hepatic fat correlated with hepatic glucose uptake (r = −0.329, P = 0.004).CONCLUSIONSIn conclusion, type 2 diabetic patients with increased hepatic triglyceride content showed decreased hepatic parenchymal perfusion and hepatic insulin mediated glucose uptake, suggesting a potential modulating effect of hepatic fat on hepatic physiology.

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“…[1][2][3][4][5][6][7][8] Several noninvasive methods have been proposed to evaluate hepatic perfusion in clinical practice using nuclear medicine, ultrasonography, magnetic resonance imaging, and computed tomography (CT) techniques, [9][10][11][12][13][14][15][16] but quantitative evaluation is diffi cult with these methods.…”

Section: Introductionmentioning

confidence: 99%

Quantification of hepatic arterial and portal perfusion with dynamic computed tomography: comparison of maximum-slope and dual-input one-compartment model methods

et al. 2009

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Quantification of liver perfusion with [15O]H2O-PET and its relationship with glucose metabolism and substrate levels

Cited by 19 publications

References 35 publications

A new compartmental method for the analysis of liver FDG kinetics in small animal models

A new compartmental method for the analysis of liver FDG kinetics in small animal models

Liver Fat Content in Type 2 Diabetes: Relationship With Hepatic Perfusion and Substrate Metabolism

Quantification of hepatic arterial and portal perfusion with dynamic computed tomography: comparison of maximum-slope and dual-input one-compartment model methods

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