Imaging Body Fat

Wang, H.; Chen, Y.E.; Eitzman, Daniel T.

doi:10.1161/atvbaha.114.303036

Cited by 57 publications

(34 citation statements)

References 57 publications

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“…Moreover CTCB has been used to derive an predictive cardio-metabolic risk equation, after a large triethnic sample, according to ethnicity-and sex-specific data [23]. This type of evidence endorsed other specific research, analyzing pericardial fat, intrathoracic fat and epicardial fat, showing the potential contribution in CMR stratification [24]. Also, CT images targeted on the III lumbar vertebra, could be theoretically performed solely, since they result in similar chest.…”

Section: Computed Tomography (Ct) and Ct Body Composition (Ctbc)mentioning

confidence: 82%

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Body composition in clinical practice

Andréoli

Garaci

Cafarelli

et al. 2016

European Journal of Radiology

174

121

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a b s t r a c tNutritional status is the results of nutrients intake, absorption and utilization, able to influence physiological and pathological conditions. Nutritional status can be measured for individuals with different techniques, such as CT Body Composition, quantitative Magnetic Resonance Imaging, Ultrasound, DualEnergy X-Ray Absorptiometry and Bioimpendance. Because obesity is becoming a worldwide epidemic, there is an increasing interest in the study of body composition to monitor conditions and delay in development of obesity-related diseases. The emergence of these evidence demonstrates the need of standard assessment of nutritional status based on body weight changes, playing an important role in several clinical setting, such as in quantitative measurement of tissues and their fluctuations in body composition, in survival rate, in pathologic condition and illnesses. Since body mass index has been shown to be an imprecise measurement of fat-free and fat mass, body cell mass and fluids, providing no information if weight changes, consequently there is the need to find a better way to evaluate body composition, in order to assess fat-free and fat mass with weight gain and loss, and during ageing. Monitoring body composition can be very useful for nutritional and medical interventional. This review is focused on the use of Body Composition in Clinical Practice.

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Section: Computed Tomography (Ct) and Ct Body Composition (Ctbc)mentioning

confidence: 82%

“…MRI, showing fat depots, can measure adipose tissue and, characterizing disease processes provide unique insight into associations between different patterns of adiposity [24]. The use of quantitative magnetic resonance (QMR) for measurement of body composition has long been established in animal studies.…”

Section: Magnetic Resonance Imaging (Mri)mentioning

confidence: 99%

Body composition in clinical practice

Andréoli

Garaci

Cafarelli

et al. 2016

European Journal of Radiology

174

121

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“…However, we did find a correlation between percent body fat and IGT score and that this association was almost completely driven by leptin levels. If body composition effects are important, the lack of association between BMI and IGT score can be explained by the fact that leptin is more closely associated with percent body fat rather than BMI, which is a less accurate method to estimate body fat than DXA scan [31]. …”

Section: Discussionmentioning

confidence: 99%

Higher insulin and higher body fat via leptin are associated with disadvantageous decisions in the Iowa gambling task

Chang

Piaggi

Burkholder

et al. 2016

Physiology & Behavior

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Elevated body mass index and post-prandial state are associated with disadvantageous choices on the Iowa Gambling Task (IGT). Whether physiological factors including percent body fat, and peripheral glucose, insulin, and leptin concentrations, are associated with IGT performance is unknown. In 196 healthy adults without diabetes, we measured body fat by DXA scan, glucose, insulin and leptin (n=138) concentrations during an oral glucose tolerance test and IGT performance after a standardized meal. Glucose was not associated with IGT performance. Disadvantageous IGT performance was associated with higher percent body fat (r = −0.16, p = 0.03), 30-min insulin concentrations (insulin30, r = −0.27, p < 0.001), and 30-min leptin concentrations (leptin30, r = −0.23, p=0.008). Mediation analysis demonstrated that leptin30 was almost completely responsible for the percent body fat effect on IGT performance. Even adjusted for age, sex, race, and education, insulin30 (b = −46.5, p = 0.03) and leptin30 (b = −50.9, p = 0.03) concentrations remained independently associated with IGT performance and interacted together such that higher leptin30 blunted effects of higher insulin30 (b = 23.8, p = 0.048). These findings may indicate an internal metabolic signature of energy availability (higher body fat, insulin, and leptin levels) associated with disadvantageous IGT performance.

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“…Imaging modalities such as magnetic resonance imaging (MRI) and X-ray computed tomography (CT) are both reliable methods for the assessment of abdominal adipose tissue volume [16] [17][18][19][20][21][22][23][24][25][26][27]. Overall, there is a growing interest in quantifying VAT as a CV risk marker and as a readout for therapeutic approaches [22,28,29].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of a Semi-automated Method for [18F]FDG Uptake in Abdominal Visceral Adipose Tissue

et al. 2018

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Purpose: Severity of abdominal obesity and possibly levels of metabolic activity of abdominal visceral adipose tissue (VAT) are associated with an increased risk for cardiovascular disease (CVD). In this context, the purpose of the current study was to evaluate the reproducibility and repeatability of a semi-automated method for assessment of the metabolic activity of VAT using 2-deoxy-2-[ PET/low-dose (LD) CT scans within 1 week were included. Abdominal VAT was automatically segmented using CT between levels L1-L5. The initial CT-based segmentation was further optimized using PET data with a standardized uptake value (SUV) threshold approach (range 1.0-2.5) and morphological erosion (range 0-5 pixels). The [ 18 F]FDG uptake in SUV that was measured by the automated method was compared with manual analysis. The reproducibility and repeatability were quantified using intraclass correlation coefficients (ICCs).Results: The metabolic assessment of VAT on [ 18 F]FDG PET/LDCT scans expressed as SUV mean , using an automated method showed high inter and intra observer (all ICCs 9 0.99) and overall repeatability (ICC = 0.98). The manual method showed reproducible inter observer (all ICCs 9 0.92), but less intra observer (ICC = 0.57) and less overall repeatability (ICC = 0.78) compared with the automated method.

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Imaging Body Fat

Cited by 57 publications

References 57 publications

Body composition in clinical practice

Body composition in clinical practice

Higher insulin and higher body fat via leptin are associated with disadvantageous decisions in the Iowa gambling task

Performance Evaluation of a Semi-automated Method for [18F]FDG Uptake in Abdominal Visceral Adipose Tissue

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