Key words
MR imaging - MR spectroscopy - adipose tissue - brown fat - metabolic disorders
Introduction
Over the last decades, adipose tissue has become an increasingly important tissue
both in research and in clinical medicine. It is well known that adipocytes play a
crucial role in the storage and release of energy throughout the human body. Adipose
tissue as an active endocrine organ has only recently gained significant interest
in the scientific community. Studies during the last years have discovered adipose
tissue to express and secrete endocrine factors (e. g. leptin, adiponectin, complement
components, plasminogen activator inhibitor-1 and proteins of the renin-angiotensin
system) in response to specific stimuli. Through a system of afferent and efferent
signals, adipose tissue is involved in coordinating numerous biological processes
including energy metabolism, neuroendocrine function and immune function [1].
Diseases altering adipose tissue mass and function are linked to various metabolic
disturbances and cardiovascular risk factors. For example, obesity with its abundance
of adipose tissue is a chronic disease representing a leading cause for multi-morbidity
and mortality worldwide, while cancer cachexia as the opposite is the cause of death
in up to 30 % of cancer-related deaths [2]. There is therefore an urgent need for successful prevention and treatment options
for diseases related to metabolic dysfunction. Improved metabolic phenotyping and
risk stratification based on adipose tissue biomarkers can play an important role
in the clinical management of patients with metabolic dysfunction.
Indirect quantification methods such as dual energy X-ray absorptiometry (DEXA) do
not necessarily provide accurate information about the exact distribution of the adipose
tissue (e. g. visceral vs. subcutaneous depots) [3]. However, knowledge about the exact three-dimensional adipose tissue distribution
is critical for risk stratification with regard to disease development [4]. Magnetic resonance imaging (MRI) has been on the rise for direct imaging of fat
depots, after CT imaging, necessarily involving ionizing radiation, has been used
in earlier studies. Thanks to its non-invasive characteristics, MRI enables longitudinal
monitoring of larger human populations. After numerous studies applied MRI successfully
for quantitative direct evaluation of adipose tissue distribution, MRI is increasingly
considered as the gold standard for metabolic phenotyping [5].
Additionally, magnetic resonance spectroscopy (MRS) provides the opportunity to non-invasively
explore specific MR signatures based on the tissue´s endogenous biochemical and histological
characteristics. Furthermore, new developments in MRI techniques provide researchers
with further tools to characterize adipose tissue depots, exploiting the multi-contrast
capabilities of MRI.
The purpose of this review was the identification and presentation of the currently
available literature on MRI of adipose tissue in metabolic dysfunction.
Literature Search
PubMed (http://www.ncbi.nlm.nih.gov/pubmed) was used for keyword search up to August 2017 in order to identify relevant studies
for this review. No starting date limitation was used so that all the relevant literature
available in PubMed would be covered. Search terms used included “MRI”; “magnetic
resonance imaging” and “1H-based magnetic resonance spectroscopy” in combination with
“adipose tissue”; “fat”; “brown fat”, “brown adipose tissue”, “cachexia”; “anorexia”;
“diabetes”; “obesity”, “metabolic dysfunction” and “metabolic syndrome”; The search
was not restricted to studies in humans. Reference lists of relevant articles were
also searched.
Types of adipose tissue
There are several ways to classify adipose tissue. First, it should be noted that
“fat” and “adipose tissue” are terms used interchangeably in the daily routine. However,
“fat” and “adipose tissue” do not refer to the same body component [6]. Adipose tissue is a special type of connective tissue, which is composed mainly
of adipocytes and usually contains > 80 % lipids. Water, proteins and minerals are
other components of adipose tissue. Fat, on the other hand, is the lipid itself, usually
in the form of triglycerides. Fat can be found mainly in adipose tissue, but- especially
in pathologic states- also in other organs such as the liver and the muscle as the
so called ectopic fat [7].
Adipose tissue, for its part, can be classified based on its histological and functional
properties as white, brown or beige (brown-in-white) adipose tissue. The latter mentioned
beige adipose tissue will not be further addressed in this article, as methods to
characterize it with imaging are still in early stages of development [8]
[9]. MRI is a way to differentiate between white and brown adipose tissue (WAT and BAT)
based on their different characteristics from a histological (e. g. based on their
water content) and also functional (e. g. based on their blood perfusion) point of
view, as described in detail later on in the section “MRI characteristics of white
and brown adipose tissue”. White adipose tissue can be further classified based on
its location in the human body [6].
MRI methods
MRI has been used in various studies for evaluation of adipose tissue distribution
throughout the body and assessment of adipose tissue characteristics [10]
[11]
[12]
[13]
[14]. MRI techniques relying on the difference between the relaxation and chemical shift
properties of fat compared to water have been primarily used to segment adipose tissue
depots and measure fat concentration.
T1-weighted imaging is able to discriminate proton signals from water and fat based
on their different T1 relaxation times and the hyperintense signal of WAT in contrast to non-adipose tissues
[15]. T2-weighted imaging also relies on T2 relaxation times with adipose tissue appearing hyperintense on T2-weighted images. T1-weighted techniques have been predominantly used for adipose tissue segmentation
by applying signal intensity thresholding algorithms [16]
[17]. Besides the strong fat tissue contrast and their good spatial resolution, the widespread
availability of T1-weighted imaging is a big advantage of the method. Limitations of the method are
the sensitivity to B1 inhomogeneities and difficulties on setting the appropriate threshold in the presence
of partial volume effects.
MRS provides an approach to noninvasively characterize adipose tissue based on the
chemical shift properties of its constituents. 1H-MRS and 13C-MRS are two exemplary methods to assess the degree of unsaturation of adipose tissue
and have been primarily applied by using single-voxel MRS techniques [18]
[19]
[20]. MRS is the only available non-invasive technique to analyze the triglyceride composition
of a tissue (saturated, monounsaturated, polyunsaturated). An exemplary application
for fat composition assessment with MRS is described in a murine disease model of
an inherited defect in β-oxidation found in humans [21].
Chemical shift encoding-based water-fat MRI allows the acquisition of co-registered
water- and fat-separated images and fat, i. e. lipid quantification with high spatial
resolution. The simultaneous acquisition of water- and fat-separated images enables
the segmentation of adipose tissue depots using k-means clustering approaches [16]. In addition, the “proton density fat fraction” (PDFF) has been emerging as the
most reliable MR-based biomarker for fat quantification purposes. The MR-based PDFF
is defined as the ratio of density of mobile protons from fat (triglycerides) and
the total density of protons from mobile triglycerides and mobile water [22]. With the use of a prior known fat spectrum to model the spectral complexity of
fat, a low flip angle to alleviate bias from T1 relaxation, and acquisition of multiple echoes for T2* correction, PDFF reflects the proton density tissue content in fat. It can be acquired
by using for example a six-echo multi-echo gradient echo sequence. PDFF maps are then
generated using typically a complex-based water-fat separation algorithm. On these
maps, regions of interest can be defined and their PDFF is displayed in percent. PDFF
has been validated in the liver against reference histological methods [23]
[24]
[25]. In adipose tissues, PDFF shows a high variability, ranging from values as low as
50 – 65 % in BAT up to 90 – 100 % in WAT [13]
[26]
[27].
MRI characteristics of white and brown adipose tissue
MRI characteristics of white and brown adipose tissue
White adipose tissue
White adipose tissue (WAT) is a storage for fat in the form of triglycerides and has
several endocrine functions for inter-organ cross-talk [28]. Histologically, white adipose tissue is characterized by large adipocytes containing
a big intracellular lipid droplet, a small nucleus and only few cytoplasm. Blood supply
of WAT is sparse. WAT can be found in subcutaneous and visceral locations. It can
be further classified based on its body location [6]. Subcutaneous adipose tissue (SAT) is defined as the layer between the dermis and
the aponeuroses and fasciae of the muscles, including mammary adipose tissue. Abdominal
SAT can be further divided into deep and superficial subcutaneous adipose tissue.
The two subdivisions of SAT have been associated differently with cardiometabolic
parameters [29]. Visceral adipose tissue (VAT) is defined as fat depot within the abdomen, pelvis
and within the chest [6].
Various studies applied MRI in order to segment and quantify the amount of WAT [16]
[17]
[30]. MRI and MRS are widely used techniques to accurately quantify and characterize
WAT. T1-weighted and water-fat imaging methods enable easier segmentation of WAT and subsequently
volume quantification. Such quantitative measurements allow longitudinal monitoring
of the adipose tissue depots in observational or lifestyle intervention studies. T1
relaxation times might be also of use for the non-invasive assessment of WAT, as a
pilot study suggested by showing a difference in T1 relaxation times between severely
obese and lean subjects [31].
On fat quantification, WAT depots expose a high fat fraction or PDFF, which is reflective
of the high amount of lipids within the tissue. In WAT, the PDFF usually ranges around
90 % [13]
[26]
[32]
[33]
[34] ([Fig. 1]). In weight loss, however, the fatty component of adipose tissue can reduce, as
it was shown with the use of single-voxel 1H-MRS by Sadananthan et al. [35].
Fig. 1 Comparison of supraclavicular (first row) and gluteal (second row) fat depots in
a low BMI female subject (first column A, BMI: 17.5 kg/m²) and a high BMI female subject (second column B, BMI: 38 kg/m²). Regions of interest are marked with red ellipses. The high BMI subject
has higher supraclavicular PDFF (91 %) and higher gluteal PDFF (93 %) than the low
BMI subject (77 % supraclavicular and 88 % gluteal). The low PDFF in supraclavicular
adipose tissue in the low BMI subject A points to the presence of brown adipose tissue, which has a lower PDFF than white
adipose tissue. PDFF: proton density fat fraction, BMI: body mass index.
Abb. 1 Vergleich der supraklavikulären (erste Reihe) und glutealen (zweite Reihe) Fettkompartimente
bei einer weiblichen Probandin mit niedrigem BMI (erste Spalte A, BMI: 17,5 kg/m²) und einer Probandin mit hohem BMI (zweite Spalte B, BMI: 38 kg/m²). Die zu untersuchenden Regionen sind mit roten Ellipsen markiert.
Die Probandin mit hohem BMI weist eine höhere supraklavikuläre PDFF (91 %) und eine
höhere gluteale PDFF (93 %) auf als die Probandin mit niedrigem BMI (77 % supraklavikulär
und 88 % gluteal). Die niedrige PDFF im supraklavikulären Fettgewebe der Probandin
mit niedrigem BMI A weist auf das Vorhandensein von braunem Fettgewebe hin, welches eine niedrigere PDFF
aufweist als weißes Fettgewebe. PDFF: Proton Density Fat Fraction, BMI: Body Mass
Index.
MRS is able to give information about the fatty acid composition in WAT. Hamilton
showed that the triglyceride composition could be measured reliably using 1H-MRS at 3 T, and that different triglyceride compositions could be detected in different
WAT depots (visceral, superficial subcutaneous and deep subcutaneous), suggesting
that the particular WAT depots have distinct metabolic activities [36] Another study reporting on intra‐ and interindividual differences of fatty acid
composition in human adipose tissue is a recent work by Machann et al., showing distinct
patterns of fatty acid composition in six different locations in the body [37].
Brown adipose tissue
Brown adipose tissue (BAT) is a specialized adipose tissue oxidizing glucose and fatty
acids via non-shivering thermogenesis for maintenance of body temperature [38]
[39]
[40]. In humans, BAT can be identified in supraclavicular and cervical as well as paravertebral
depots. More anatomic sites where BAT can be found (e. g. perirenal, mediastinal)
have been described in postmortem studies [41]. On a cellular base, BAT is an extensively vascularized adipose tissue with large
amounts of iron-containing mitochondria, expressing uncoupling protein 1 (UCP-1),
a protein uncoupling oxidative phosphorylation from ATP production, thereby releasing
energy as heat [38]
[39]
. It contains a high quantity of intracellular water while lipid is present in the form
of large amounts of small lipid droplets. BAT is metabolically active mainly throughout
early childhood, but active BAT can also be detected in adults [41]
[42]. BAT recruitment was used as a therapeutic approach for obesity, elevated triglyceride
concentrations, and type 2 diabetes in rodents in several studies [13]
[43]. Furthermore, it has been shown that the amount of BAT is inversely correlated with
body-mass index (BMI) in humans, suggesting a potential role of BAT in adult human
metabolism [38]. Therefore, the identification of BAT and its further characterization has become
of great interest in the field of metabolic research. Until recently, the gold standard
of the detection of metabolically active BAT was 18F-fluorodeoxyglucose positron emission
tomography (PET) with computed tomography (CT). However, one major limitation of PET/CT
is the clinically significant doses of ionizing radiation. Furthermore, PET/CT depends
on the metabolic activity of BAT, thus BAT needs to be in an active state during the
examination, limiting the validity of this modality in measuring BAT volume at the
resting state [13].
With the use of MRI, BAT can be distinguished from WAT based on its higher water content.
This results in a lower fat fraction in BAT compared to WAT. Thus, the most commonly
used MRI methods to study BAT in humans are chemical shift-encoding based fat quantification
techniques [44]
[45]. After consideration of the aforementioned confounding factors, chemical shift encoding-based
fat quantification techniques can display the PDFF [22]. Several studies have shown BAT to have a significantly lower PDFF compared to WAT,
reflecting the higher water content and lower fat content of BAT compared to WAT [13] ([Fig. 1]).
With MRS being capable of depicting differences in fat fraction, T1 relaxation rate of the water component, and the degree of lipid saturation in BAT,
it provides the opportunity to depict the biochemical profile of the adipose tissue
([Fig. 2]). Hamilton et al. used MRS on a whole-body 3T-scanner on excised tissue samples
of murine BAT and WAT [46], showing a lower fat fraction, lower proportion of unsaturated triglycerides, and
a markedly lower T1 relaxation rate of the water component in BAT compared to WAT. However, with BAT
being located in the supraclavicular region in humans, the use of MRS is limited as
the region is affected by motion, large vessels and magnetic field inhomogeneities
due to the neighboring lungs and strongly alternating diameters (thorax/ neck).
Fig. 2 Multi-TE single-voxel STEAM MR spectroscopy at 3 T (TR: 3000 ms, TE: 12–20–28–36 ms)
in two female subjects. Supraclavicular spectra of subject with low supraclavicular
PDFF A and supraclavicular spectra of subject with high supraclavicular PDFF B. Note the large water peak in the subject with low PDFF compared to the subject with
high PDFF (suggesting the presence of brown adipose tissue in subject A) and the faster T2 decay of the water peak compared to the fat peaks. TE: echo time,
TR: repetition time.
Abb. 2 Multi-TE single-voxel STEAM MR Spektroskopie bei 3 T (TR: 3000 ms, TE: 12–20–28–36 ms)
bei zwei weiblichen Probandinnen. Spektren des supraklavikulären Fettgewebes bei einer
Probandin mit niedriger PDFF A und einer Probandin mit hoher PDFF B. Man beachte den höheren Wasser-Peak im supraklavikulären Fettgewebe bei der Probandin
mit niedriger PDFF im Vergleich zur Probandin mit hoher PDFF (als Hinweis auf das
Vorliegen von braunem Fett bei Probandin A) sowie den schnelleren T2-Zerfall des Wasser-Peaks verglichen mit den Fett-Peaks.
TE: Echozeit, TR: Repetitionszeit.
Newer MRI methods are capable of illustrating not only the chemical structure of BAT,
but also its functional activation status, which is of great interest in BAT research.
Until recently, BAT activation could only be detected with the use of 18F-FDG PET,
which implicates radiation exposure. MRI offers techniques to depict BAT activation
via its metabolites. Lau et al. used hyperpolarized 13C imaging to noninvasively identify activated depots of BAT in an in vivo rat model.
Regions of pharmacologically activated BAT could be detected by an increased conversion
of pre-polarized (1 – 13C) pyruvate into its downstream products 13C bicarbonate and
(1 – 13C) lactate [47]. Grimpo et al. used 1H MRS to monitor the metabolic dynamics in murine BAT and detected
a significant loss of free fatty acids from BAT after pharmacological stimulation
[48]. Quantification of blood flow and perfusion of BAT is another method to collect
information about BAT activation. Blood flow and perfusion can be illustrated with
the use of dynamic contrast enhanced MRI [49]. Arterial spin labeling MRI is a second technique to quantify blood perfusion, but
without the use of contrast material. Dai et al. showed increased perfusion activity
in BAT after cold stimulation compared with thermoneutral conditions [50]. A third way to depict the blood perfusion of a tissue is a T2*-weighted blood-oxygen-level-dependent MRI (BOLD-MRI). Khanna and Branca used BOLD-MRI
for detection of BAT metabolic activity in mice [51], while van Roojen et al. used BOLD-MRI in humans to depict BAT activation during
cold stimulation, leading to modulations in the T2*-weighted signal [52]. Branca et al. has finally published multiple studies about the use of hyperpolarized
xenon MRI for detection of BAT and thermogenic activity in mice [53].
MRI characteristics of adipose tissue in disease
MRI characteristics of adipose tissue in disease
Adipose tissue is a key player in various states of metabolic dysfunction. MRI can
assess adipose tissue distribution and characterize the tissue without the use of
ionizing radiation and without the need of contrast material. This can be of use in
risk assessment and monitoring of metabolic dysfunction.
Obesity, Diabetes and Metabolic Syndrome
The number of patients who suffer from obesity continues to rise [54]. An increased mass of adipose tissue is the primary phenotypic trait in obesity.
However, not only the mass, but also a specific distribution of the adipose tissue
is associated with the related diseases such as type 2 diabetes and the metabolic
syndrome [55]
[56].
Several studies were able to show that although both the amount of SAT and VAT are
correlated with metabolic risk factors, VAT remains more strongly associated with
an adverse metabolic risk profile [57]
[58]
[59]. This is supposed to be linked to VAT as an endocrine gland. In this context, VAT
is an important site for adipokine secretion, for example Interleukin-6, contributing
to systemic inflammation [60]. Additionally, the visceral fat mass presumably contributes significantly to the
levels of free fatty acids in the systemic circulation [61], which interferes with the insulin balance eventually leading to insulin resistance
[62].
MRI adipose tissue segmentation allows the precise differentiation between VAT and
SAT ([Fig. 3], [4]) and thus can contribute to a risk assessment for the development of obesity-related
complications [10]
[63]
[64]. In that regard, Machann et al. found that the fraction of unsaturated fatty acids
in VAT was lower in subjects with high total VAT volume [18]. Thomas et al. found the “thin-on-the-outside fat-on-the-inside” subphenotype, with
a higher ratio of intraabdominal over subcutaneous abdominal adipose tissue, to have
an increased metabolic risk. VAT furthermore seems to be a predictive factor for an
improvement of insulin sensitivity in lifestyle interventions [65]. A systematic review on the associations of different adipose tissue depots with
insulin resistance, as measured by homeostatic model assessment of insulin resistance
(HOMA-IR), found a strong correlation between HOMA-IR and visceral fat mass as well
as total fat mass [66].
Fig. 3 Comparison of different fat depots in three male subjects. First row shows water-separated
Dixon images, second row shows fat-separated Dixon images and third row shows SAT/VAT
segmentation maps (color-coded masking using red for SAT, yellow for VAT, blue for
non-adipose tissue and cyan for air). First column A shows a metabolically healthy subject, BMI: 20 kg/m². Second column B shows a subject with high BMI but normal insulin sensitivity, BMI: 31 kg/m². Third
column C shows a pre-diabetic subject, BMI 34 kg/m². Note the marked increase of VAT with
higher BMI compared to the increase of SAT. BMI: body mass index, SAT: subcutaneous
adipose tissue, VAT: visceral adipose tissue.
Abb. 3 Vergleich verschiedener Fettkompartimente bei drei männlichen Probanden. Die erste
Reihe zeigt die Wasser-separierten Dixon-Bilder, die zweite Reihe die Fett-separierten
und die dritte Reihe zeigt SAT/VAT Segmentierungs-Karten (farbkodiert, wobei rot SAT
entspricht, gelb VAT, blau nicht-fettigem Gewebe und türkis Luft). Die erste Spalte
A zeigt einen stoffwechselgesunden Probanden, BMI: 20 kg/m². Die zweite Spalte B zeigt einen Probanden mit erhöhtem BMI bei noch normaler Insulinsensitivität, BMI
31 kg/m². Die dritte Spalte C zeigt einen prädiabetischen Probanden, BMI 34 kg/m². Man beachte die deutliche Zunahme
des VAT mit steigendem BMI im Vergleich zum SAT. BMI: Body Mass Index, SAT: subkutanes
Fettgewebe, VAT: viszerales Fettgewebe.
Fig. 4 Comparison of different fat depots in three female subjects. First row shows water-separated
Dixon images, second row shows fat-separated Dixon images and third rows shows SAT/VAT
segmentation maps (color-coded masking using red for SAT, yellow for VAT, blue for
non-adipose tissue and cyan for air). First column A shows a metabolically healthy subject, BMI: 18 kg/m². Second column B shows a subject with high BMI but normal insulin sensitivity, BMI: 31 kg/m². Third
column C shows a pre-diabetic subject, BMI 35 kg/m². Note the marked increase of SAT with
higher BMI compared to the increase of VAT. Also note the difference in fat distribution
in females in [Fig. 4] compared to males in [Fig. 3] with a considerably higher SAT/VAT ratio in females compared to males. BMI: body
mass index, SAT: subcutaneous adipose tissue, VAT: visceral adipose tissue.
Abb. 4 Vergleich verschiedener Fettkompartimente bei drei weiblichen Probandinnen. Die erste
Reihe zeigt die Wasser- separierten Dixon-Bilder, die zweite Reihe die Fett-separierten
und die dritte Reihe zeigt SAT/VAT Segmentierungs-Karten (farbkodiert, wobei rot SAT
entspricht, gelb VAT, blau nicht-fettigem Gewebe und türkis Luft). Die erste Spalte
A zeigt eine stoffwechselgesunde Probandin, BMI: 18 kg/m². Die zweite Spalte B zeigt eine Probandin mit erhöhtem BMI bei noch normaler Insulinsensitivität, BMI
31 kg/m². Die dritte Spalte C zeigt eine prädiabetische Probandin, BMI 35 kg/m². Man beachte die deutliche Zunahme
des SAT mit steigendem BMI im Vergleich zur geringeren Zunahme des VAT. Zudem zeigt
sich ein gut sichtbarer Unterschied der Fettverteilungsmuster bei Frauen in [Fig. 4] verglichen mit Männern in [Fig. 3], wobei die Frauen einen deutlich höheren SAT/VAT Quotienten im Vergleich zu Männern
aufweisen. BMI: Body Mass Index, SAT: subkutanes Fettgewebe, VAT: viszerales Fettgewebe.
Anorexia and Cachexia
Both anorexia (nervosa) and cachexia are characterized by a loss of adipose tissue
in addition to skeletal muscle. In the eating disorder anorexia nervosa, MRI can be
helpful to determine the amount of adipose tissue in order to identify factors associated
with relapse, as relapse rates range from 30 % to 50 % in patients with anorexia nervosa
[67]. Bodell et al. showed that lower percent adipose tissue after short-term weight
normalization in anorexia nervosa is associated with poor clinical outcome in the
year following inpatient treatment [68]. Furthermore, in adult women with anorexia nervosa, Mayer et al. noted that weight
normalization in the short term is associated with a distribution of adipose tissue
compatible with the central adiposity phenotype, normalizing within a one-year period
of weight maintenance [69].
With regard to the presence of BAT, young women with anorexia nervosa were shown to
have low cold-activated BAT, which may be due to impaired BAT thermogenesis [70].
Cachexia, on the other hand, is defined as unintentional body weight loss, mainly
due to progressive wasting of skeletal muscle with or without loss of adipose tissue.
It occurs in various illnesses, e. g. in cancer, AIDS, inflammatory bowel disease
or multiple sclerosis.
In cancer patients, cachexia accounts for up to 30 % of cancer related deaths, attaching
importance to a more detailed knowledge about the development and the natural course
of cachexia. This renders impact to fat segmentation and quantification tools based
on MRI datasets, as the conventional BMI/weight monitoring does not account for example
for fluid retention or anasarca. Fouladiun et al. monitored the development of cachexia
in a cohort of 311 cancer patients and found that decrease in body weight was explained
by loss of body fat, preferentially from the trunk, followed by leg tissue and arm
tissue [71]. With more precise tools, Agustsson et al. found that WAT was decreased in cachectic
compared with normal weight cancer patients, with the cachectic subjects exhibiting
a selective decrease in visceral WAT [72]. However, reported data on the use of MRI techniques for monitoring adipose tissue
changes in cancer cachexia patients remains up to date limited.
When patients lose muscle mass and muscle function is impaired, this state is called
sarcopenia. Sarcopenia can occur in cachectic patients, but also in patients with
abnormally increased BMI (> 30 kg/m²). The latter state defines sarcopenic obesity.
Studies found that cancer patients with coexisting sarcopenic obesity showed poorer
survival rates compared to other patients [75].
Regarding BAT in cachexia patients, Petruzzelli et al. found in the mouse model that
WAT browning (i. e., a phenotypic switch from WAT to BAT) mediated by systemic inflammation
and interleukin-6 contributed to high energy expenditure in cancer-associated cachexia.
Inhibition of this process alleviated cachexia in mice [73]. However, data from retrospective studies in cancer patients did not show a clinically
relevant association between BAT and cancer cachexia [74].
Conclusion
The different types of adipose tissues play a crucial role in various types of metabolic
dysfunction. Without the use of ionizing radiation and without the need of contrast
material, MRI enables characterization of adipose tissue and permits the evaluation
of adipose tissue distribution. But not only the current gold standard methods for
metabolic phenotyping, namely T1- and T2-weighted imaging techniques and MRS, but also new developments in MRI techniques
provide researchers with further tools to characterize adipose tissue depots, offering
new possibilities for both monitoring and risk stratification purposes with regard
to the development and course of metabolic dysfunction.
