Literature DB >> 29704560

Identification of an optimal threshold for detecting human brown adipose tissue using receiver operating characteristic analysis of IDEAL MRI fat fraction maps.

Terence A Jones1, Sarah C Wayte2, Narendra L Reddy3, Oludolapo Adesanya4, George K Dimitriadis3, Thomas M Barber3, Charles E Hutchinson5.   

Abstract

PURPOSE: Lower fat fraction (FF) in brown adipose tissue (BAT) than white adipose tissue (WAT) has been exploited using Dixon-based Magnetic Resonance Imaging (MRI) to differentiate these tissues in rodents, human infants and adults. We aimed to determine whether an optimal FF threshold could be determined to differentiate between BAT and WAT in adult humans in vivo.
METHODS: Sixteen volunteers were recruited (9 females, 7 males; 44.2 ± 19.2 years) based on BAT uptake on 18F-FDG PET/CT. Axial 3-echo TSE IDEAL sequences were acquired (TR(ms)/TE(ms)/matrix/NEX/FoV(cm) = 440/10.7-11.1/512 × 512/3/30-40), of the neck/upper thorax on a 3T HDxt MRI scanner (GE Medical Systems, Milwaukee, USA), and FF maps generated from the resulting water- and fat-only images. BAT depots were delineated on PET/CT based on standardized uptake values (SUV) >2.5 g/ml, and transposed onto FF maps. WAT depots were defined manually within subcutaneous fat. Receiver operating characteristic (ROC) analyses were performed, and optimal thresholds for differentiating BAT and WAT determined for each subject using Youden's J statistic.
RESULTS: There was large variation in optimal FF thresholds to differentiate BAT and WAT between subjects (0.68-0.85), with great variation in sensitivity (0.26-0.84) and specificity (0.62-0.99). FF was excellent or good at separating BAT and WAT in four cases (area under the curve [AUC] 0.84-0.92), but poor in 10 (AUC 0.25-0.68).
CONCLUSION: Although this technique was effective at differentiating BAT and WAT in some cases, no universal cut-off could be identified to reliably differentiate BAT and WAT in vivo in adult humans on the basis of FF.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Brown adipose tissue; Human; Magnetic resonance imaging; Positron-emission tomography

Mesh:

Substances:

Year:  2018        PMID: 29704560     DOI: 10.1016/j.mri.2018.04.013

Source DB:  PubMed          Journal:  Magn Reson Imaging        ISSN: 0730-725X            Impact factor:   2.546


  5 in total

Review 1.  How to best assess abdominal obesity.

Authors:  Hongjuan Fang; Elizabeth Berg; Xiaoguang Cheng; Wei Shen
Journal:  Curr Opin Clin Nutr Metab Care       Date:  2018-09       Impact factor: 4.294

2.  Cold exposure induces dynamic, heterogeneous alterations in human brown adipose tissue lipid content.

Authors:  Crystal L Coolbaugh; Bruce M Damon; Emily C Bush; E Brian Welch; Theodore F Towse
Journal:  Sci Rep       Date:  2019-09-19       Impact factor: 4.379

Review 3.  Imaging Metabolically Active Fat: A Literature Review and Mechanistic Insights.

Authors:  Joseph Frankl; Amber Sherwood; Deborah J Clegg; Philipp E Scherer; Orhan K Öz
Journal:  Int J Mol Sci       Date:  2019-11-05       Impact factor: 5.923

4.  Differentiating supraclavicular from gluteal adipose tissue based on simultaneous PDFF and T2 * mapping using a 20-echo gradient-echo acquisition.

Authors:  Daniela Franz; Maximilian N Diefenbach; Franziska Treibel; Dominik Weidlich; Jan Syväri; Stefan Ruschke; Mingming Wu; Christina Holzapfel; Theresa Drabsch; Thomas Baum; Holger Eggers; Ernst J Rummeny; Hans Hauner; Dimitrios C Karampinos
Journal:  J Magn Reson Imaging       Date:  2019-01-25       Impact factor: 4.813

Review 5.  Magnetic Resonance Imaging Techniques for Brown Adipose Tissue Detection.

Authors:  Mingming Wu; Daniela Junker; Rosa Tamara Branca; Dimitrios C Karampinos
Journal:  Front Endocrinol (Lausanne)       Date:  2020-08-07       Impact factor: 5.555

  5 in total

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