Evaluating Childhood Obesity: Magnetic Resonance-Based Quantification of Abdominal Adipose Tissue and Liver Fat in Children

Abstract

Purpose: The purpose of this study is to establish and validate a magnetic resonance (MR)-based fat quantification package that provides an accurate assessment of abdominal adipose tissue and liver fat in children.

Materials and Methods: Ex vivo trials with a torso model and water-oil mixtures are conducted. Abdominal adipose tissue (AAT) is covered by magnetic resonance imaging (MRI) using a fat-selective sequence and is analyzed by a plug-in based on the open source software ImageJ. Liver fat (LF) is measured with localized ¹H Magnetic Resonance Spectroscopy (¹H MRS) and the jMRUI (java-based Magnetic Resonance User Interface) software package. Evaluation of the clinical methodology involved a study of 10 children in this feasibility study (mean age and body mass index: 13.3 yr; 33.3 kg/m²). To evaluate the method’s validity, reference measurements were performed.

Results: Ex vivo trials with the torso model showed that adipose tissue was measured appropriately with a systematic underestimation by 9.3 ± 0.2 % (0.32 ± 0.064 kg). Coefficients of variation for both intra- and inter-observer measurements ranged between 0 – 2.7 % and repeated analyses showed significant equivalent results (p < 0.01). The lipid content obtained by ¹H MRS ex vivo revealed significant equivalence with the predefined fat content in water-oil mixtures (p < 0.01). In vivo, the homemade plug-in significantly overestimated the AAT, with the visceral adipose tissue being most affected (+ 15.7 ± 8.4 %).

Conclusion: Although an overestimation of the AAT by the presented plug-in should be taken into consideration, this children-friendly package enables the quantification of both LF and AAT within 30 min on a freeware-based platform.

Zusammenfassung

Ziel: Ziel war, ein Magnetresonanz(MR)-basiertes Fettquantifizierungspaket zu entwickeln und zu validieren, welches die Beurteilung von abdominellem Fettgewebe (AF) und Leberfett (LF) in Kindern erlaubt.

Material und Methoden: Es wurden Ex-vivo-Versuche anhand eines Bauchmodells sowie Wasser-Öl-Phantomen durchgeführt. AF wurde mittels Magnetresonanztomografie unter Anwendung einer fettselektiven Sequenz erfasst und mithilfe eines Plug-Ins auf Basis der open-source Software ImageJ analysiert. LF wurde mittels ¹H-Magnetresonanzspektroskopie (¹H MRS) erfasst und innerhalb der jMRUI-Software berechnet. Um die klinische Durchführbarkeit zu beurteilen, wurden 10 Kinder untersucht (mittleres Alter und Body-Mass-Index: 13,3 Jahre; 33,3 kg/m²). Zur Methodenevaluierung wurden Referenzmessungen durchgeführt.

Ergebnisse: Die Ex-vivo-Versuche mit dem Bauchmodell zeigten, dass AF mit einer systematischen Unterschätzung von 9,3 ± 0,2 % (0,32 ± 0,064 kg) adäquat erfasst wurde. Die Variationskoeffizienten für die Analysen eines und die Analysen verschiedener Untersucher bewegten sich zwischen null und 2,7 %. Wiederholte Analysen zeigten signifikant gleiche Ergebnisse (p < 0,01). Hinsichtlich eines minimal-relevanten Unterschieds von δ = 1 /10 belegten die ex vivo mittels ¹H MRS erfassten Fettkonzentrationen signifikante Gleichheit mit den vordefinierten Fettkonzentrationen in den Wasser-Öl-Phantomen (p < 0,01). In vivo überschätzte das Plug-In signifikant AF, wobei Viszeralfett am stärksten betroffen war ( + 15,7 ± 8,4 %).

Schlussfolgerung: Obwohl eine Überschätzung des AF bei der Interpretation der Daten berücksichtigt werden sollte, erlaubt dieses Paket die Quantifizierung von LF und AF in Kindern und Jugendlichen innerhalb von 30 min mittels frei verfügbarer Software.

Key words

abdomen - MR spectroscopy - MR imaging - technology assessment - segmentation

• References

• 1 Speiser PW, Rudolf MC, Anhalt H et al. Consensus Statement: Childhood Obesity. J Clin Endocrinol Metab 2005; 90: 1871-1887
• 2 Franks PW, Hanson RL, Knowler WC et al. Childhood obesity, other cardiovascular risk factors, and premature death. N Engl J Med 2010; 362: 485-493
• 3 Springer F, Ehehalt S, Sommer J et al. Predicting volumes of metabolically important whole-body adipose tissue compartments in overweight and obese adolescents by different MRI approaches and anthropometry. Eur J Radiol 2011; DOI: 10.1016/j.ejrad.2011.04.006.
• 4 Stefan N, Machann J, Schick F et al. New Imaging Techniques of Fat, Muscle and Liver within the Context of Determining Insulin Sensitivity. Horm Res 2005; 64: 38-44
• 5 Cassidy FH, Yokoo T, Aganovic L et al. Fatty liver disease: MR imaging techniques for the detection and quantification of liver steatosis. Radiographics 2009; 29: 231-260
• 6 Schwenzer NF, Springer F, Schraml C et al. Non-invasive assessment and quantification of liver steatosis by ultrasound, computed tomography and magnetic resonance. J Hepatol 2009; 51: 433-445
• 7 Thamer C, Machann J, Haap M et al. Intrahepatic lipids are predicted by visceral adipose tissue mass in healthy subjects. Diabetes Care 2004; 27: 2726-2729
• 8 Schick F, Machann J, Brechtel K et al. MRI of muscular fat. Magn Reson Med 2002; 47: 720-727
• 9 Machann J, Häring H, Schick F et al. Intramyocellular lipids and insulin resistance. Diabetes Obes Metab 2004; 6: 239-248
• 10 Szczepaniak LS, Nurenberg P, Leonard D et al. Magnetic resonance spectroscopy to measure hepatic triglyceride content: prevalence of hepatic steatosis in the general population. Am J Physiol Endocrinol Metab 2005; 288: E462-E468
• 11 Taksali SE, Caprio S, Dziura J et al. High visceral and low abdominal subcutaneous fat stores in the obese adolescent: a determinant of an adverse metabolic phenotype. Diabetes 2008; 57: 367-371
• 12 Caprio S, Hyman LD, McCarthy S et al. Fat distribution and cardiovascular risk factors in obese adolescent girls: importance of the intraabdominal fat depot. Am J Clin Nutr 1996; 64: 12-17
• 13 Loomba R, Sirlin CB, Schwimmer JB et al. Advances in pediatric nonalcoholic fatty liver disease. Hepatology 2009; 50: 1282-1293
• 14 Schaefer JF, Kramer U. Whole-Body MRI in Children and Juveniles. Fortschr Röntgenstr 2011; 183: 24-36
• 16 Vanhamme L, van den Boogaart A, Van Huffel S. Improved method for accurate and efficient quantification of MRS data with use of prior knowledge. J Magn Reson 1997; 129: 35-43
• 17 Naressi A, Couturier C, Devos JM et al. Java-based graphical user interface for the MRUI quantitation package. MAGMA 2001; 12: 141-152
• 18 Hamilton G, Middleton MS, Bydder M et al. Effect of PRESS and STEAM sequences on magnetic resonance spectroscopic liver fat quantification. J Magn Reson Imaging 2009; 30: 145-152
• 19 Hamilton G, Yokoo T, Bydder M et al. In vivo characterization of the liver fat ¹H MR spectrum. NMR Biomed 2010; DOI: 10.1002/nbm.1622.
• 20 Bonekamp S, Ghosh P, Crawford S. Quantitative comparison and evaluation of software packages for assessment of abdominal adipose tissue distribution by magnetic resonance imaging. Int J Obes 2008; 32: 100-111
• 21 Demerath EW, Ritter KJ, Couch WA. Validity of a new automated software program for visceral adipose tissue estimation. Int J Obes 2007; 31: 285-291
• 22 Positano V, Gastaldelli A, Sironi AM et al. An accurate and robust method for unsupervised assessment of abdominal fat by MRI. J Magn Reson Imaging 2004; 20: 684-689
• 23 Thomsen C, Becker U, Winkler K et al. Quantification of liver fat using magnetic resonance spectroscopy. Magn Reson Imaging 1994; 12: 487-495
• 24 Kim H, Taksali SE, Dufour S et al. A Comparative MR study of hepatic fat quantification using single-voxel proton spectroscopy, two-point Dixon and three-point IDEAL. Magn Reson Med 2008; 59: 521-527