Magnetic Resonance Imaging and Bioelectrical Impedance Analysis to Assess Visceral and Abdominal Adipose Tissue.
Journal
Obesity (Silver Spring, Md.)
ISSN: 1930-739X
Titre abrégé: Obesity (Silver Spring)
Pays: United States
ID NLM: 101264860
Informations de publication
Date de publication:
02 2020
02 2020
Historique:
received:
15
07
2019
accepted:
18
10
2019
pubmed:
4
1
2020
medline:
21
8
2020
entrez:
4
1
2020
Statut:
ppublish
Résumé
This study aimed to compare a state-of-the-art bioelectrical impedance analysis (BIA) device with two-point Dixon magnetic resonance imaging (MRI) for the quantification of visceral adipose tissue (VAT) as a health-related risk factor. A total of 63 male participants were measured using a 3-T MRI scanner and a segmental, multifrequency BIA device. MRI generated fat fraction (FF) maps, in which VAT volume, total abdominal adipose tissue volume, and FF of visceral and total abdominal compartments were quantified. BIA estimated body fat mass and VAT area. Coefficients of determination between abdominal (r Visceral BIA measurements agreed better with MRI measurements of the total abdomen than of the visceral compartment, indicating that BIA visceral fat area assessment cannot differentiate adipose tissue between visceral and abdominal compartments in young and older participants.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
277-283Subventions
Organisme : Bayerische Forschungsstiftung
ID : 1044-12
Pays : International
Informations de copyright
© 2020 The Authors. Obesity published by Wiley Periodicals, Inc. on behalf of The Obesity Society (TOS).
Références
Hilton TN, Tuttle LJ, Bohnert KL, Mueller MJ, Sinacore DR. Excessive adipose tissue infiltration in skeletal muscle in individuals with obesity, diabetes mellitus, and peripheral neuropathy: association with performance and function. Phys Ther 2008;88:1336-1344.
Beasley LE, Koster A, Newman AB, et al. Inflammation and race and gender differences in computerized tomography-measured adipose depots. Obesity (Silver Spring) 2009;17:1062-1069.
Cruz-Jentoft AJ, Baeyens JP, Bauer JM, et al. Sarcopenia: European consensus on definition and diagnosis: report of the European Working Group on Sarcopenia in Older People. Age Ageing 2010;39:412-423.
Fischer-Posovszky P, Wabitsch M, Hochberg Z. Endocrinology of adipose tissue - an update. Horm Metab Res 2007;39:314-321.
Mazzali G, Di Francesco V, Zoico E, et al. Interrelations between fat distribution, muscle lipid content, adipocytokines, and insulin resistance: effect of moderate weight loss in older women. Am J Clin Nutr 2006;84:1193-1199.
Tuttle LJ, Sinacore DR, Mueller MJ. Intermuscular adipose tissue is muscle specific and associated with poor functional performance. J Aging Res 2012;2012:172957. doi:10.1155/2012/172957
Zoico E, Rossi A, Di Francesco V, et al. Adipose tissue infiltration in skeletal muscle of healthy elderly men: relationships with body composition, insulin resistance, and inflammation at the systemic and tissue level. J Gerontol A Biol Sci Med Sci 2010;65:295-299.
Shen W, Wang Z, Punyanita M, et al. Adipose tissue quantification by imaging methods: a proposed classification. Obes Res 2003;11:5-16.
Lakka HM, Laaksonen DE, Lakka TA, et al. The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. JAMA 2002;288:2709-2716.
Rodriguez A, Catalán V, Gómez-Ambrosi J, Frühbeck G. Visceral and subcutaneous adiposity: are both potential therapeutic targets for tackling the metabolic syndrome? Curr Pharm Des 2007;13:2169-2175.
Jialal I, Devaraj S. Subcutaneous adipose tissue biology in metabolic syndrome. Horm Mol Biol Clin Investig 2018;33. doi:10.1515/hmbci-2017-0074
Sam S. Differential effect of subcutaneous abdominal and visceral adipose tissue on cardiometabolic risk. Horm Mol Biol Clin Investig 2018;33. doi:10.1515/hmbci-2018-0014
Lu HK, Chen YY, Yeh C, et al. Discrepancies between leg-to-leg bioelectrical Impedance analysis and computerized tomography in abdominal visceral fat measurement. Sci Rep 2017;7:9102. doi:10.1038/s41598-017-08991-y
Kullberg J, Johansson L, Ahlström H, et al. Automated assessment of whole-body adipose tissue depots from continuously moving bed MRI: a feasibility study. J Magn Reson Imaging 2009;30:185-193.
Ludwig UA, Klausmann F, Baumann S, et al. Whole-body MRI-based fat quantification: a comparison to air displacement plethysmography. J Magn Reson Imaging 2014;40:1437-1444.
Grimm A, Meyer H, Nickel MD, et al. Evaluation of 2-point, 3-point, and 6-point Dixon magnetic resonance imaging with flexible echo timing for muscle fat quantification. Eur J Radiol 2018;103:57-64.
Zhong X, Nickel MD, Kannengiesser SA, Dale BM, Kiefer B, Bashir MR. Liver fat quantification using a multi-step adaptive fitting approach with multi-echo GRE imaging. Magn Reson Med 2014;72:1353-1365.
Nickel MD, Kannengiesser SAR, Kiefer B. Time-domain calibration of fat signal dephasing from multi-echo STEAM spectroscopy for multi-gradient-echo imaging based fat quantification. In: Proceedings of the 23rd Annual Meeting of the International Society for Magnetic Resonance in Medicine; May 30-31, 2015; Toronto, Canada. Abstract 3658.
Ling CH, de Craen AJ, Slagboom PE, et al. Accuracy of direct segmental multi-frequency bioimpedance analysis in the assessment of total body and segmental body composition in middle-aged adult population. Clin Nutr 2011;30:610-615.
Kemmler W, Weissenfels A, Teschler M, et al. Whole-body electromyostimulation and protein supplementation favorably affect sarcopenic obesity in community-dwelling older men at risk: the randomized controlled FranSO study. Clin Interv Aging 2017;12:1503-1513.
Langner T, Hedström A, Morwald K, et al. Fully convolutional networks for automated segmentation of abdominal adipose tissue depots in multicenter water-fat MRI. Magn Reson Med 2019;81:2736-2745.
Shen J, Baum T, Cordes C, et al. Automatic segmentation of abdominal organs and adipose tissue compartments in water-fat MRI: application to weight-loss in obesity. Eur J Radiol 2016;85:1613-1621.
Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 1979;9:62-66.
Enzi G, Gasparo M, Biondetti PR, Fiore D, Semisa M, Zurlo F. Subcutaneous and visceral fat distribution according to sex, age, and overweight, evaluated by computed tomography. Am J Clin Nutr 1986;44:739-746.
Browning LM, Mugridge O, Chatfield MD, et al. Validity of a new abdominal bioelectrical impedance device to measure abdominal and visceral fat: comparison with MRI. Obesity (Silver Spring) 2010;18:2385-2391.
Park KS, Lee DH, Lee J, et al. Comparison between two methods of bioelectrical impedance analyses for accuracy in measuring abdominal visceral fat area. J Diabetes Complications 2016;30:343-349.
Schaudinn A, Linder N, Garnov N, et al. Predictive accuracy of single- and multi-slice MRI for the estimation of total visceral adipose tissue in overweight to severely obese patients. NMR Biomed 2015;28:583-590.
Maislin G, Ahmed MM, Gooneratne N, et al. Single slice vs. volumetric MR assessment of visceral adipose tissue: reliability and validity among the overweight and obese. Obesity (Silver Spring) 2012;20:2124-2132.
Demerath EW, Shen W, Lee M, et al. Approximation of total visceral adipose tissue with a single magnetic resonance image. Am J Clin Nutr 2007;85:362-368.
Schweitzer L, Geisler C, Pourhassan M, et al. Estimation of skeletal muscle mass and visceral adipose tissue volume by a single magnetic resonance imaging slice in healthy elderly adults. J Nutr 2016;146:2143-2148.
Shen W, Chen J, Gantz M, Velasquez G, Punyanitya M, Heymsfield SB. A single MRI slice does not accurately predict visceral and subcutaneous adipose tissue changes during weight loss. Obesity (Silver Spring) 2012;20:2458-2463.
Sun G, French CR, Martin GR, et al. Comparison of multifrequency bioelectrical impedance analysis with dual-energy X-ray absorptiometry for assessment of percentage body fat in a large, healthy population. Am J Clin Nutr 2005;81:74-78.
Dehghan M, Merchant AT. Is bioelectrical impedance accurate for use in large epidemiological studies? Nutr J 2008;7:26. doi:10.1186/1475-2891-7-26
Buckinx F, Reginster JY, Dardenne N, et al. Concordance between muscle mass assessed by bioelectrical impedance analysis and by dual energy X-ray absorptiometry: a cross-sectional study. BMC Musculoskelet Disord 2015;16:60. doi:10.1186/s12891-015-0510-9
Androutsos O, Gerasimidis K, Karanikolou A, Reilly JJ, Edwards CA. Impact of eating and drinking on body composition measurements by bioelectrical impedance. J Hum Nutr Diet 2015;28:165-171.
Slinde F, Rossander-Hulthén L. Bioelectrical impedance: effect of 3 identical meals on diurnal impedance variation and calculation of body composition. Am J Clin Nutr 2001;74:474-478.
Thompson DL, Thompson WR, Prestridge TJ, et al. Effects of hydration and dehydration on body composition analysis: a comparative study of bioelectric impedance analysis and hydrodensitometry. J Sports Med Phys Fitness 1991;31:565-570.
Gualdi-Russo E, Toselli S. Influence of various factors on the measurement of multifrequency bioimpedance. Homo 2002;53:1-16.
Kushner RF, Gudivaka R, Schoeller DA. Clinical characteristics influencing bioelectrical impedance analysis measurements. Am J Clin Nutr 1996;64:423S-427S.
Kyle UG, Bosaeus I, De Lorenzo AD, et al. Bioelectrical impedance analysis, part II: utilization in clinical practice. Clin Nutr 2004;23:1430-1453.
Grimm A, Meyer H, Nickel MD, et al. A comparison between 6-point Dixon MRI and MR spectroscopy to quantify muscle fat in the thigh of subjects with sarcopenia. J Frailty Aging 2019;8:21-26.
Hines CD, Yu H, Shimakawa A, McKenzie CA, Brittain JH, Reeder SB. T1 independent, T2* corrected MRI with accurate spectral modeling for quantification of fat: validation in a fat-water-SPIO phantom. J Magn Reson Imaging 2009;30:1215-1222.
Reeder SB, Sirlin CB. Quantification of liver fat with magnetic resonance imaging. Magn Reson Imaging Clin N Am 2010;18:337-357, ix.
Fallah F, Machann J, Martirosian P, Bamberg F, Schick F, Yang B. Comparison of T1-weighted 2D TSE, 3D SPGR, and two-point 3D Dixon MRI for automated segmentation of visceral adipose tissue at 3 Tesla. MAGMA 2017;30:139-151.