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MRI estimates of brown adipose tissue in children - Associations to adiposity, osteocalcin, and thigh muscle volume
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology.
Hallands Hosp Halmstad, Dept Pediat, Halmstad, Sweden;Univ Gothenburg, Sahlgrenska Acad, Inst Clin Sci, Dept Pediat, Gothenburg, Sweden.
Univ Gothenburg, Sahlgrenska Acad, Inst Clin Sci, Dept Pediat, Gothenburg, Sweden.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology. Antaros Med, Molndal, Sweden.
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2019 (English)In: Magnetic Resonance Imaging, ISSN 0730-725X, E-ISSN 1873-5894, Vol. 58, p. 135-142Article in journal (Refereed) Published
Abstract [en]

Context: Brown adipose tissue is of metabolic interest. The tissue is however poorly explored in children.

Methods: Sixty-three 7-year old subjects from the Swedish birth-cohort Halland Health and Growth Study were recruited. Care was taken to include both normal weight and overweight children, but the subjects were otherwise healthy. Only children born full term were included. Water-fat separated whole-body MRI scans, anthropometric measurements, and measurements of fasting glucose and levels of energy homeostasis related hormones, including the insulin-sensitizer osteocalcin, were performed. The fat fraction (FF) and effective transverse relaxation time (T-2(star)) of suspected brown adipose tissue in the cervical-supraclavicular-axillary fat depot (sBAT) and the FFs of abdominal visceral (VAT) and subcutaneous adipose tissue (SAT) were measured. Volumes of sBAT, abdominal VAT and SAT, and thigh muscle volumes were measured.

Results: The FF in the sBAT depot was lower than in VAT and SAT for all children. In linear correlations including sex and age as explanatory variables, sBAT FF correlated positively with all measures of adiposity (p < 0.01), except for VAT FF and weight, positively with sBAT T-2* (p = 0.036), and negatively with osteocalcin (p = 0.017). When adding measures of adiposity as explanatory variables, sBAT FF also correlated negatively with thigh muscle volume (p < 0.01).

Conclusions: Whole-body water-fat MRI of children allows for measurements of sBAT. The FF of sBAT was lower than that of VAT and SAT, indicating presence of BAT. Future studies could confirm whether the observed correlations corresponds to a hormonally active BAT.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE INC , 2019. Vol. 58, p. 135-142
Keywords [en]
Brown adipose tissue, Magnetic resonance imaging, Adiposity, Osteocalcin, Muscle volume, Quantitative MRI
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:uu:diva-380416DOI: 10.1016/j.mri.2019.02.001ISI: 000461412300018PubMedID: 30742901OAI: oai:DiVA.org:uu-380416DiVA, id: diva2:1301478
Funder
Swedish Research Council, 2013-3013Swedish Research Council, 2016-01040Region Västra GötalandAvailable from: 2019-04-02 Created: 2019-04-02 Last updated: 2019-08-14Bibliographically approved
In thesis
1. Water–fat separation in magnetic resonance imaging and its application in studies of brown adipose tissue
Open this publication in new window or tab >>Water–fat separation in magnetic resonance imaging and its application in studies of brown adipose tissue
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Virtually all the magnetic resonance imaging (MRI) signal of a human originates from water and fat molecules. By utilizing the property chemical shift the signal can be separated, creating water- and fat-only images. From these images it is possible to calculate quantitative fat fraction (FF) images, where the value of each voxel is equal to the percentage of its signal originating from fat. In papers I and II methods for water–fat signal separation are presented and evaluated.

The method in paper I utilizes a graph-cut to separate the signal and was designed to perform well even for a low signal-to-noise ratio (SNR). The method was shown to perform as well as previous methods at high SNRs, and better at low SNRs.

The method presented in paper II uses convolutional neural networks to perform the signal separation. The method was shown to perform similarly to a previous method using a graph-cut when provided non-undersampled input data. Furthermore, the method was shown to be able to separate the signal using undersampled data. This may allow for accelerated MRI scans in the future.

Brown adipose tissue (BAT) is a thermogenic organ with the main purpose of expending chemical energy to prevent the body temperature from falling too low. Its energy expending capability makes it a potential target for treating overweight/obesity and metabolic dysfunctions, such as type 2 diabetes. The most well-established way of estimating the metabolic potential of BAT is through measuring glucose uptake using 18F-fludeoxyglucose (18F-FDG) positron emission tomography (PET) during cooling. This technique exposes subjects to potentially harmful ionizing radiation, and alternative methods are desired. One alternative method is measuring the BAT FF using MRI.

In paper III the BAT FF in 7-year olds was shown to be negatively associated with blood serum levels of the bone-specific protein osteocalcin and, after correction for adiposity, thigh muscle volume. This may have implications for how BAT interacts with both bone and muscle tissue.

In paper IV the glucose uptake of BAT during cooling of adult humans was measured using 18F-FDG PET. Additionally, their BAT FF was measured using MRI, and their skin temperature during cooling near a major BAT depot was measured using infrared thermography (IRT). It was found that both the BAT FF and the temperature measured using IRT correlated with the BAT glucose uptake, meaning these measurements could be potential alternatives to 18F-FDG PET in future studies of BAT.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2019. p. 65
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 1589
Keywords
brown adipose tissue, magnetic resonance imaging, water–fat signal separation, graph-cut, positron emission tomography, 18F-fludeoxyglucose, infrared thermography, machine learning, artificial neural networks, deep learning, convolutional neural networks
National Category
Radiology, Nuclear Medicine and Medical Imaging Medical Image Processing
Research subject
Radiology
Identifiers
urn:nbn:se:uu:diva-390436 (URN)978-91-513-0718-3 (ISBN)
Public defence
2019-09-13, Enghoffsalen, Entrance 50, Akademiska sjukhuset, Uppsala, 13:15 (Swedish)
Opponent
Supervisors
Available from: 2019-08-23 Created: 2019-08-14 Last updated: 2019-09-17

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Andersson, JonathanAhlström, HåkanKullberg, Joel

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