Subcutaneous Fat Thickness of the Lower Limb is Associated with Trunk Muscle Mass in Patients with Hepatocellular Carcinoma: A Simple Assessment for Sarcopenia Using Conventional Ultrasonography.

OBJECTIVE: Trunk muscle mass can be evaluated by skeletal muscle index (SMI), which is a prognostic factor in patients with hepatocellular carcinoma (HCC); however, this requires the use of computed tomography, and a simpler assessment for trunk muscle mass is urgently needed. We aimed to examine whether an association between SMI and lower extremity compartments including muscle and subcutaneous fat thickness of lower limbs (SFT-LL) could be identified by means of ultrasonography in patients with HCC. METHODS: We retrospectively enrolled male patients with HCC (n=30). Trunk muscle mass was evaluated by SMI using computed tomography. Ultrasonography was used for assessment of muscle and SFT-LL. Factors associated with SMI were evaluated by decision-tree analysis. RESULTS: There was no significant correlation between SMI and muscle thickness of lower limbs. However, a significant correlation was seen between SMI and left SFT-LL (r=0.406, P=0.026). In decision-tree analysis for SMI, left SFT-LL was selected as the initial split variable with an optimal cut-off value of 5 mm. In patients with left SFT-LL ≥ 5 mm, SMI was 39.4±3.4 cm2/m2, whereas SMI was 31.6±6.3 cm2/m2 in patients with left SFT-LL <5 mm. CONCLUSION: Left SFT-LL evaluated by ultrasonography was associated with SMI. Thus, ultrasonography may be a useful tool to evaluate trunk muscle mass. Moreover, left SFT-LL may be a useful indicator of sarcopenia in patients with HCC.

Subcutaneous and visceral fat assessment by DXA and MRI in older adults and children.

OBJECTIVE: Given the importance of body fat distribution in chronic disease development, feasible methods to assess body fat are essential. This study compared dual-energy x-ray absorptiometry (DXA) in measuring visceral and subcutaneous adipose tissue (VAT and SAT) with magnetic resonance imaging (MRI). METHODS: VAT and SAT were assessed using similar DXA and MRI protocols among 1,795 elderly participants of the Adiposity Phenotype Study (APS) and 309 children/adolescents in Shape Up! Kids (SKids). Spearman correlations, Bland-Altman plots, and coefficients of determination (R2 ) assessed agreement between DXA and MRI measures. RESULTS: DXA overestimated SAT values in APS (315 vs. 229 cm2 ) and SKids (212 vs. 161 cm2 ), whereas DXA underestimated VAT measures (141 vs. 167 cm2 ) in adults only. The correlations between DXA and MRI values were stronger for SAT than VAT (APS: r = 0.92 vs. 0.88; SKids: 0.90 vs. 0.74). Bland-Altman plots confirmed better agreement for SAT than VAT despite differences by sex, ethnicity, and weight status with respective R2 values for SAT and VAT of 0.88 and 0.84 (APS) and 0.81 and 0.69 (SKids). CONCLUSION: These findings indicate that SAT by DXA reflects MRI measures in children and older adults, whereas agreement for VAT is weaker for individuals with low VAT levels.

Skeletal muscle radiodensity and visceral adipose tissue index are associated with survival in renal cell cancer - A multicenter population-based cohort study.

INTRODUCTION: Body composition has been associated with disease outcome in several cancer types. Results for localized and metastatic renal cell cancer (RCC) are limited and inconsistent. Our aim was to examine the association between body composition and survival in RCC. METHODS: We conducted a population-based historical cohort study including patients diagnosed with RCC from 2008 to 2012. Diagnostic Computed Tomography images at the third lumbar vertebra (L3) were assessed for skeletal muscle index (SMI), skeletal muscle density (SMD), visceral adipose tissue index (VATI) and subcutaneous adipose tissue index (SATI). Clinical data was retrieved from medical records. Multivariable Cox regressions with restricted cubic splines were used to determine hazard ratios (HRs) and 95% confidence intervals (95%CIs) for 10-unit increases in body composition features with overall survival (OS) and recurrence-free survival (RFS). RESULTS: We included 719 stage I-III (of whom 254 (35.3%) died and 148 (21.9%) experienced recurrence) and 320 stage IV RCC patients (of whom 298 (93.1%) died). Median follow-up was 6.35 years (interquartile range; 1.41-8.23). For stage I-III, higher SMD was associated with better OS (men: HR 0.86; 95% CI 0.68-1.08; women: HR 0.69; 95% CI 0.50-0.95). Lower compared to median VATI was associated with worse OS for both men (HR 1.38; 95%CI 1.05-1.83 for VATI = 25) and women (HR 1.67; 95%CI 1.01-2.78 for VATI = 20). For stage IV, higher SMD and higher VATI were associated with better OS among men (HR 0.74; 95% CI 0.59-0.94 and HR 0.93; 95% CI 0.88-0.99, respectively). Results for women were similar but non-significant. No statistically significant associations were found for SMI or SATI. CONCLUSION: Higher SMD and higher VATI were marginally associated with better survival in RCC patients and might be useful for better prognostication. However, the added value to current prognostic scores needs to be investigated.

Comparison between handheld ultrasound and regional and whole-body dual energy x-ray absorptiometry (DXA) for body fat assessment.

OBJECTIVES: To determine the extent of agreement between a handheld ultrasound (US) attached to an android tablet and the reference method dual energy x-ray absorptiometry (DXA) for the measurement of adiposity. METHODS: A whole-body DXA scan and abdominal adipose tissue thickness measurements using a handheld US were obtained from 104 adults (63 females, 41 males). Body fat percent (BF%), total fat mass (kg), and trunk fat mass (kg) were obtained from DXA. Subcutaneous adipose tissue (SAT), superficial subcutaneous adipose tissue (SSAT), and deep subcutaneous adipose tissue (DSAT) thickness were obtained from US. Sex-specific total fat mass, trunk fat mass, and BF% estimates by US were compared with DXA. Spearman's correlations and Bland-Altman plots were used to assess agreement between the methods. RESULTS: US SAT correlated strongly with total fat mass for both females (rs = 0.74) and males (rs = 0.87) as did trunk fat mass (females, rs = 0.81; males, rs = 0.83); as did SSAT and DSAT (females: rs = 0.65 and rs = 0.66; males: rs = 0.63 and rs = 0.85, respectively, all p-values < 0.0001). Bland-Altman plots demonstrated strong agreement for total and trunk fat mass for both males and females. For BF%, acceptable limits of agreement were observed for males but not for females, substantial proportional bias as indicated by a negative slope was noted for BF% using SAT (r = -0.298, p = 0.0177). CONCLUSION: The handheld US and technique to analyze abdominal adipose tissue thickness showed strong agreement with DXA results and generated highly comparable estimates for total and trunk fat mass for both sexes.

Deep neural network for automatic volumetric segmentation of whole-body CT images for body composition assessment.

BACKGROUND & AIMS: Body composition analysis on CT images is a valuable tool for sarcopenia assessment. We aimed to develop and validate a deep neural network applicable to whole-body CT images of PET-CT scan for the automatic volumetric segmentation of body composition. METHODS: For model development, one hundred whole-body or torso 18F-fluorodeoxyglucose PET-CT scans of 100 patients were retrospectively included. Two radiologists semi-automatically labeled the following seven body components in every CT image slice, providing a total of 46,967 image slices from the 100 scans for training the 3D U-Net (training, 39,268 slices; tuning, 3116 slices; internal validation, 4583 slices): skin, bone, muscle, abdominal visceral fat, subcutaneous fat, internal organs with vessels, and central nervous system. The segmentation accuracy was assessed using reference masks from three external datasets: two Korean centers (4668 and 4796 image slices from 20 CT scans, each) and a French public dataset (3763 image slices from 24 CT scans). The 3D U-Net-driven values were clinically validated using bioelectrical impedance analysis (BIA) and by assessing the model's diagnostic performance for sarcopenia in a community-based elderly cohort (n = 522). RESULTS: The 3D U-Net achieved accurate body composition segmentation with an average dice similarity coefficient of 96.5%-98.9% for all masks and 92.3%-99.3% for muscle, abdominal visceral fat, and subcutaneous fat in the validation datasets. The 3D U-Net-derived torso volume of skeletal muscle and fat tissue and the average area of those tissues in the waist were correlated with BIA-derived appendicular lean mass (correlation coefficients: 0.71 and 0.72, each) and fat mass (correlation coefficients: 0.95 and 0.93, each). The 3D U-Net-derived average areas of skeletal muscle and fat tissue in the waist were independently associated with sarcopenia (P < .001, each) with adjustment for age and sex, providing an area under the curve of 0.858 (95% CI, 0.815 to 0.901). CONCLUSIONS: This deep neural network model enabled the automatic volumetric segmentation of body composition on whole-body CT images, potentially expanding adjunctive sarcopenia assessment on PET-CT scan and volumetric assessment of metabolism in whole-body muscle and fat tissues.

Comparison of ultrasound speed-of-sound of the lower extremity and lumbar muscle assessed with computed tomography for muscle loss assessment.

ABSTRACT: To compare the speed of propagation of ultrasound (US) waves (SoS) of the lower leg with the clinical reference standard computed tomography (CT) at the level of lumbar vertebra 3 (L3) for muscle loss assessment. Both calf muscles of 50 patients scheduled for an abdominal CT were prospectively examined with ultrasound. A plexiglas-reflector located on the opposite side of the probe with the calf in between was used as a timing reference for SoS (m/s). CT measurements were performed at the level of L3 and included area (cm2) and attenuation (HU) of the psoas muscle, abdominal muscles, subcutaneous fat, visceral fat and abdominal area. Correlations between SoS, body mass index (BMI) and CT were determined using Pearson's correlation coefficient. Based on reported CT sarcopenia threshold values, receiver operating characteristic (ROC) analysis was performed for SoS. Inter-examiner agreement was assessed with the median difference, inter-quartile range (IQR) and intraclass correlation coefficients. SoS of the calf correlated moderately with abdominal muscle attenuation (r = 0.48; P < .001), psoas muscle attenuation (r = 0.40; P < .01), abdominal area (r = -0.44; P < .01) and weakly with subcutaneous fat area (r = -0.37; P < .01). BMI correlated weakly with psoas attenuation (r = -0.28; P < .05) and non-significantly with abdominal muscle attenuation. Normalization with abdominal area resulted in moderate correlations with abdominal muscle area for SoS (r = 0.43; P < .01) and BMI (r = -0.46; P < .001). Based on sarcopenia threshold values for skeletal muscle attenuation (SMRA), area under curve (AUC) for SoS was 0.724. Median difference between both examiners was -3.4 m/s with IQR = 15.1 m/s and intraclass correlation coefficient = 0.794. SoS measurements of the calf are moderately accurate based on CT sarcopenia threshold values, thus showing potential for muscle loss quantification.

CT-based assessment of body composition following neoadjuvant chemohormonal therapy in patients with castration-naïve oligometastatic prostate cancer.

BACKGROUND: The purpose of this study is to assess the body composition changes in men with recently diagnosed oligometastatic prostate cancer following neoadjuvant chemohormonal therapy. Further, we evaluated whether CT-based body composition parameters are associated with biochemical recurrence or imaging progression. MATERIAL AND METHODS: Recently diagnosed castration-naïve oligometastatic prostate cancer patients who received neoadjuvant docetaxel chemotherapy and androgen deprivation treatment (ADT) before prostatectomy and consolidation of local and oligometastatic disease (total eradication therapy), as part of a phase-II prospective clinical trial were included. Body composition parameters including cross-sectional areas of the psoas muscle, total, visceral, and subcutaneous adipose tissue were measured on serial CT scans obtained before and following completion of neoadjuvant treatment. RESULTS: A total of 22 prostate cancer patients were included (median age 58 years, median Gleason score 8). The median time intervals between commencement of neoadjuvant chemohormonal therapy and first and second follow-up CTs were 3 and 12 months, respectively. Compared to the baseline scan, there were significant declines in psoas muscle cross-sectional areas with estimated percentage declines of -13.9% (IQR: 7.6%-16.5%, p < .001) and -13.2% (IQR: 6%-11.2%, p < .001) on first and second follow-up CTs. There were significant increases in subcutaneous adipose tissue following neoadjuvant chemohormonal therapy with percentage increases of +8.9% (IQR: 5.1%-21.5%, p = .002) and +18.9% (IQR: 6.1%-33.8%, p < .001), respectively. The median follow-up was 34.5 months. The estimated 2-year prostate-specific antigen progression-free and radiologic progression-free survival were 95.5%. No significant association between baseline or percentage change in body composition parameters and disease progression were identified. CONCLUSIONS: Our findings showed a significant reduction in muscle mass and an increase in subcutaneous adiposity in men treated with neoadjuvant docetaxel and ADT, more pronounced on the first follow-up scan after completion of neoadjuvant treatment. Body composition parameters were not found to be significant predictors of disease progression in our cohort.

Reliability of body composition assessment using A-mode ultrasound in a heterogeneous sample.

BACKGROUND/OBJECTIVES: Several studies have addressed the validity of ultrasound (US) for body composition assessment, but few have evaluated its reliability. This study aimed to determine the reliability of percent body fat (%BF) estimates using A-mode US in a heterogeneous sample. SUBJECTS/METHODS: A group of 144 healthy adults (81 men and 63 women), 30.4 (10.1) years (mean (SD)), BMI 24.6 (4.7) kg/m2, completed 6 consecutive measurements of the subcutaneous fat layer thickness at 8 anatomical sites. The measurements were done, alternatively, by two testers, using a BodyMetrix™ instrument. To compute %BF, 4 formulas from the BodyView™ software were applied: 7-sites Jackson and Pollock, 3-sites Jackson and Pollock, 3-sites Pollock, and 1-point biceps. RESULTS: The formula with the most anatomic sites provided the best reliability quantified by the following measures: intraclass correlation coefficient (ICC) = 0.979 for Tester 1 (T1) and 0.985 for T2, technical error of measurement (TEM) = 1.07% BF for T1 and 0.89% BF for T2, and minimal detectable change (MDC) = 2.95% BF for T1, and 2.47% BF for T2. The intertester bias was -0.5% BF, whereas the intertester ICC was 0.972. The intertester MDC was 3.43% BF for the entire sample, 3.24% BF for men, and 3.65% BF for women. CONCLUSIONS: A-mode US is highly reliable for %BF assessments, but it is more precise for men than for women. Examiner performance is a source of variability that needs to be mitigated to further improve the precision of this technique.

Measurement of absorption and reduced scattering coefficients in Asian human epidermis, dermis, and subcutaneous fat tissues in the 400- to 1100-nm wavelength range for optical penetration depth and energy deposition analysis.

SIGNIFICANCE: In laser therapy and diagnosis of skin diseases, the irradiated light distribution, which is determined by the absorption coefficient µa and reduced scattering coefficient µs' of the epidermis, dermis, and subcutaneous fat, affects the treatment outcome and diagnosis accuracy. Although values for µa and µs' have been reported, detailed analysis for Asian skin tissues is still lacking. AIM: We present µa and µs' measurements of Asian skin tissues in the 400- to 1100-nm wavelength range for evaluating optical penetration depth and energy deposition. APPROACH: The measurements with Asian human skin samples are performed employing a double integrating sphere spectrometric system and an inverse Monte Carlo technique. Using the measured parameters, the optical penetration depth and energy deposition are quantitatively analyzed. RESULTS: The µa of the epidermis layer varies among different ethnic groups, while the µa of the other layers and the µs' of all of the layers exhibit almost no differences. The analysis reveals that the optical penetration depth and the energy deposition affect the photodynamic therapy treatment depth and the heat production in skin tissue, respectively. CONCLUSIONS: The experimentally measured values of µa and µs' for Asian skin tissues are presented, and the light behavior in Asian skin tissues is analyzed using a layered tissue model.

Novel approaches for the assessment of relative body weight and body fat in diagnosis and treatment of anorexia nervosa: A cross-sectional study.

BACKGROUND & AIMS: Anorexia nervosa (AN) is a severe psychosomatic disease that seriously affects nutritional status. Therapeutic approaches primarily aim for rapid weight restoration by high caloric diets and activity restriction. This often promotes abdominal body fat gain, which potentially negatively influences the patient's compliance and increases the risk of relapse. This study focused on the evaluation of body weight and subcutaneous adipose tissue (SAT) in AN patients by novel approaches. METHODS: The SAT of AN patients (n = 18, body mass index (BMI) 15.3 ± 1.3 kg/m2) was determined by a highly accurate and reliable ultrasound method. The sum of SAT thicknesses of eight sites (DINCL) was calculated. Individual metabolic profiles were analyzed. The mass index (MI), which considers body proportions, was used in addition to BMI. Additional to the standard laboratory diagnostics, dermal carotenoids measured by resonance Raman spectroscopy, leptin, and oxidative stress indicators were determined. RESULTS: The mean MI was 15.7 ± 1.4 kg/m2. The DINCL considerably differed between individuals with the same BMI. Half of the patients (Group 1) had low DINCL: 1.3-28.4 mm, and Group 2 showed values up to 58.2 mm (corresponding to approximately 6 kg SAT mass). The two group means differed by more than 300% (P < 0.001). Accordingly, leptin levels significantly differed (P < 0.001). Mean SAT thicknesses were significantly higher in Group 2 at all eight sites. The groups also significantly differed in two oxidative stress parameters: total antioxidative capacity, malondialdehyde-modified low density lipoprotein immunoglobulin M (MDA-LDL IgM), and in the carotenoid level. CONCLUSION: Half of the patients had sufficiently high fat mass, despite very low BMI. Consequently, their muscle (and other organ) masses must have been extremely low. Diagnostic criteria and treatment protocols for AN should consider each patient's body composition. In addition to dietary treatments, muscle training at low energy turnover rates may be essential for avoiding unnecessary body fat gain, better treatment results, and long-term recovery.

Body fat percentage assessment by ultrasound subcutaneous fat thickness measurements in middle-aged and older adults.

BACKGROUND & AIMS: B-mode ultrasound accurately measures both muscle mass, body density and percent body fat (% BF) in younger adults, but how well it can estimate % BF in middle-aged and older adults using DXA-derived %BF as the criterion is unclear. We sought to develop % BF prediction equations for middle-aged and older adults using ultrasound subcutaneous fat thickness (SFT). METHODS: A cross-sectional study of Japanese adults (n = 414, 50-79 years) where 276 subjects were randomly assigned to a model development group and the other 138 subjects were assigned to a cross-validation group. B-mode ultrasound measured SFT at nine sites. Dual energy X-ray absorptiometry (DXA) measured % BF, arm fat mass (FM) and leg FM. Stepwise multiple linear regression developed prediction equations from anthropometric data (body mass, height, waist and hip circumference) and ultrasound SFT sites. Bland-Altman plots assessed validity of the prediction equations to measure % BF in the cross-validation group. RESULTS: The best prediction equation for % BF was the following: [% BF = 15.709 + (1.753*anterior trunk SFT) + (5.626*Sex) + (3.635*posterior upper arm SFT) - (4.428*anterior lower leg SFT) - (0.170*height) + (0.264*waist) + (anterior thigh SFT*2.241); r2 = 0.809, standard error of the estimate (SEE) = 3.3 kg]. Arm FM and leg FM prediction equations had r2 values ranging from 0.690 to 0.812 and SEEs of 0.29 and 0.75 kg. A small mean bias was noted for estimating % BF (-0.14%), but large limits of agreement were found (-8.0-7.7%) and systematic error was noted in all of the equations (r = 0.275 to 0.515, p < 0.05). CONCLUSIONS: Despite high r2 values and a small mean bias found between predicted and DXA % BF, wide limits of agreement were found with some systematic error present. Therefore, these prediction equations for middle-aged and older adults may not be sufficiently accurate to use in a clinical setting.

Morphometric Assessment as a Predictor of Outcome in Older Vascular Surgery Patients.

BACKGROUND: Frailty is a recognized predictor of poor outcome in patients undergoing surgical intervention. Frailty is intricately linked with body morphology, which can be evaluated using morphometric assessment via computerized tomographic (CT) imaging. We aimed to assess the predictive power of such objective assessments in a broad cohort of vascular surgical patients. METHODS: A consecutive series of patients aged over 65 years admitted to a vascular unit, who had undergone CT imaging of the abdomen, were analyzed. Demographic and patient-specific data were collated alongside admission relevant information. Outcomes included mortality, length of stay, health care-related costs, and discharge destination. Images were analyzed for 4 morphometric measurements: (1) psoas muscle area, (2) mean psoas density, (3) subcutaneous fat depth, and (4) intra-abdominal fat depth, all taken at the level of the fourth lumbar vertebra. RESULTS: Two hundred and ten patients were initially analyzed. Forty-four patients had significant retroperitoneal and abdominal abnormalities that limited appropriate CT analysis. Decreased subcutaneous fat depth was significantly associated with mortality, readmission within 12 months, and increased cost of health care (P < 0.01, adjusted for confounders). Psoas muscle area was significantly associated with readmission-free survival. CONCLUSIONS: Morphometric analysis predicts poorer outcome in a broad cohort of vascular surgery patients. Such assessment is likely to enhance patient counseling regarding individual risk as well as enhancing the ability to undertake risk-modified surgical audit.

Objective assessment of leg edema using ultrasonography with a gel pad.

Ultrasonography (US) is useful for visual detection of edematous tissues to assess subcutaneous echogenicity. However, visualization of subcutaneous echogenicity is interpreted differently among operators because the evaluation is subjective and individual operators have unique knowledge. This study objectively assessed leg edema using US with a gel pad including fat for normalization of echogenicity in subcutaneous tissue. Five younger adults and four elderly people with leg edema were recruited. We compared assessments of US and limb circumference before and after the intervention of vibration to decrease edema in younger adults, and edema prior to going to sleep and reduced edema in the early morning in elderly people. These assessments were performed twice in elderly people by three operators and reliability, interrater differences, and bias were assessed. For US assessment, echogenicity in subcutaneous tissue was normalized to that of the gel pad by dividing the mean echogenicity of subcutaneous tissue by the mean echogenicity of the gel pad. In younger adults, the normalized subcutaneous echogenicity before the intervention was significantly higher than that after the intervention. In elderly people, echogenicity indicating edema was significantly higher than that after edema reduction. Edema was detected with accuracy rates of 76.9% in younger adults and 75.0% in elderly people. Meanwhile, limb circumference could be used to detect edema in 50.0% of healthy adults and 87.8% of elderly people. The intra-reliability was excellent (intraclass correlation coefficient > 0.9, p < 0.01), and the inter-reliability was good (intraclass correlation coefficient > 0.7, p < 0.01) for normalized subcutaneous echogenicity. Bland-Altman plots revealed that inter-rater differences and systematic bias were small. Normalized subcutaneous echogenicity with the pad can sensitively and objectively assess leg edema with high reliability. Therefore, this method has the potential to become a new gold standard for objective assessment of leg edema in clinical practice.

Validation of a free software for unsupervised assessment of abdominal fat in MRI.

PURPOSE: To demonstrate the accuracy of an unsupervised (fully automated) software for fat segmentation in magnetic resonance imaging. The proposed software is a freeware solution developed in ImageJ that enables the quantification of metabolically different adipose tissues in large cohort studies. METHODS: The lumbar part of the abdomen (19cm in craniocaudal direction, centered in L3) of eleven healthy volunteers (age range: 21-46years, BMI range: 21.7-31.6kg/m2) was examined in a breath hold on expiration with a GE T1 Dixon sequence. Single-slice and volumetric data were considered for each subject. The results of the visceral and subcutaneous adipose tissue assessments obtained by the unsupervised software were compared to supervised segmentations of reference. The associated statistical analysis included Pearson correlations, Bland-Altman plots and volumetric differences (VD%). RESULTS: Values calculated by the unsupervised software significantly correlated with corresponding supervised segmentations of reference for both subcutaneous adipose tissue - SAT (R=0.9996, p<0.001) and visceral adipose tissue - VAT (R=0.995, p<0.001). Bland-Altman plots showed the absence of systematic errors and a limited spread of the differences. In the single-slice analysis, VD% were (1.6±2.9)% for SAT and (4.9±6.9)% for VAT. In the volumetric analysis, VD% were (1.3±0.9)% for SAT and (2.9±2.7)% for VAT. CONCLUSIONS: The developed software is capable of segmenting the metabolically different adipose tissues with a high degree of accuracy. This free add-on software for ImageJ can easily have a widespread and enable large-scale population studies regarding the adipose tissue and its related diseases.

Quantitative Assessment of Visceral Obesity and Postoperative Colon Cancer Outcomes.

BACKGROUND: Quantitative computed tomography (CT) assessment of visceral adiposity may be superior to body mass index (BMI) as a predictor of surgical morbidity. We sought to examine the association of CT measures of obesity and BMI with short-term postoperative outcomes in colon cancer patients. METHODS: In this retrospective study, 110 patients treated with colectomy for stage I-III colon cancer were classified as obese or non-obese by preoperative CT-based measures of adiposity or BMI [obese: BMI ≥ 30 kg/m2, visceral fat area (VFA) to subcutaneous fat area ratio (V/S) ≥0.4, and VFA > 100 cm2]. Postoperative morbidity and mortality rates were compared. RESULTS: Obese patients, by V/S and VFA but not BMI, were more likely to be male and have preexisting hypertension and diabetes. The overall complication rate was 25.5%, and there were no mortalities. Obese patients by VFA (with a trend for V/S but not BMI) were more likely to develop postoperative complications as compared to patients classified as non-obese: VFA (30.5 vs.10.7%, p = 0.03), V/S (29.2 vs. 9.5%, p = 0.05), and BMI (32.4 vs. 21.9%, p = 0.23). CONCLUSIONS: Elevated visceral obesity quantified by CT is associated with the presence of key metabolic comorbidities and increased postoperative morbidity and may be superior to BMI for risk stratification.

Central Obesity and Liver Iron Content: A Noninvasive Assessment in General Population by Magnetic Resonance Imaging.

BACKGROUND/AIM: Existing evidence points to an altered status of iron metabolism in obesity. We aimed to investigate whether central obesity is independently associated with estimated liver iron content (liver T2* value) in general population that used the noninvasive assessment method by MRI. METHODS: The study was carried out on 200 subjects randomly selected from the Golestan Cohort Study who underwent abdominal MRI. Quantitative T2* maps of entire cross-sectional area of liver were calculated using a semi-automated software for estimating the levels of iron content. Segmentation and calculation of visceral (VFA) and subcutaneous fat area (SFA) were also performed. RESULTS: There was no significant difference in mean liver T2* values between obese (body mass index, BMI >30 kg/m2) and non-obese (BMI ≤30 kg/m2) subjects. After controlling for other covariates, no statistically significant association was detected between liver T2* values and VFA, SFA and VFA/SFA ratio. The drop in the relative signal intensity as an indicator of steatosis and serum ferritin predicted liver T2* values that almost had the same strength (standardized ß of -0.41 and -0.41, respectively). CONCLUSIONS: Central obesity indices are not correlated with estimated liver iron content by MRI. Hepatic steatosis and serum ferritin seem to be the best predictors of hepatic T2* value. Since central obesity indices were not direct predictors of hepatic T2* value after the adjustment for confounding factors, it is possible that lipid accumulation in the liver locally, but not systematically, influences hepatic iron metabolism.

In vivo assessment of subcutaneous fat in dogs by real-time ultrasonography and image analysis.

BACKGROUND: Systems for estimating body condition score (BCS) are currently used in canine practice to monitor fatness levels. These tools are cheap and easy to use but lack the necessary precision to monitor small changes in body fat, particularly during weight control treatments or in research. The present work aims to study the application of real-time ultrasonography (RTU) together with image analysis in the assessment of subcutaneous fat depots in dogs. Ultrasound images were collected from five anatomical locations (chest, flank, abdomen, thigh and lumbar) from 28 healthy dogs of different breeds and with a body weight (BW) ranging from 5.2 to 33.0 kg. BCS was collected by visual appraisal using a 5-point scale. Subcutaneous fat thickness (SFT) was estimated from RTU images, using the average of three measurements taken in fat deposits located above the muscles represented in each image. Correlations were established between SFT and BW or BCS as well as a classification of BCS-based fatness [overweight (BCS = 4), ideal (BCS = 3) and lean (BCS = 2)]. RESULTS: SFT was found to differ between the five regions considered (P < 0.001). Abdomen and thigh were the areas displaying the widest variation for the different dogs included in the study and also those correlating most with BW, in contrast to the chest, which showed the least variation. Overall, a strong correlation was found between BCS and SFT. The highest correlations were established for the flank, abdomen and lumbar areas. In every anatomical area, a decrease in SFT was observed across all three BCS classes, ranging from 48 to 65 % among overweight and ideal dogs, and from 46 to 83 % among ideal and lean dogs. CONCLUSIONS: Preliminary data showed that within this population there was a strong correlation between BCS and SFT estimated from RTU images. It was also observed that RTU measurements for fat thickness differed among the anatomical points surveyed suggesting differences in their sensitivity to a change in BCS. The images displaying the best prediction value for fatness variations were those collected at the lumbar and abdomen areas.

Diffuse optical spectroscopic imaging of subcutaneous adipose tissue metabolic changes during weight loss.

BACKGROUND: Changes in subcutaneous adipose tissue (AT) structure and metabolism have been shown to correlate with the development of obesity and related metabolic disorders. Measurements of AT physiology could provide new insight into metabolic disease progression and response to therapy. An emerging functional imaging technology, diffuse optical spectroscopic imaging (DOSI), was used to obtain quantitative measures of near infrared (NIR) AT optical and physiological properties. METHODS: Ten overweight or obese adults were assessed during 3 months on calorie-restricted diets. DOSI-derived tissue concentrations of hemoglobin, water and lipid and the wavelength-dependent scattering amplitude (A) and slope (b) obtained from 30 abdominal locations and three time points (T0, T6, T12) were calculated and analyzed using linear mixed-effects models and were also used to form 3D surface images. RESULTS: Subjects lost a mean of 11.7±3.4% of starting weight, while significant changes in A (+0.23±0.04 mm(-1), adj. P<0.001),b (-0.17±0.04, adj. P<0.001), tissue water fraction (+7.2±1.1%, adj. P<0.001) and deoxyhemoglobin (1.1±0.3 µM, adj. P<0.001) were observed using mixed-effect model analysis. DISCUSSION: Optical scattering signals reveal alterations in tissue structure that possibly correlate with reductions in adipose cell volume, while water and hemoglobin dynamics suggest improved AT perfusion and oxygen extraction. These results suggest that DOSI measurements of NIR optical and physiological properties could be used to enhance understanding of the role of AT in metabolic disorders and provide new strategies for diagnostic monitoring of obesity and weight loss.

Automated assessment of thigh composition using machine learning for Dixon magnetic resonance images.

OBJECTIVES: To develop and validate a machine learning based automated segmentation method that jointly analyzes the four contrasts provided by Dixon MRI technique for improved thigh composition segmentation accuracy. MATERIALS AND METHODS: The automatic detection of body composition is formulized as a three-class classification issue. Each image voxel in the training dataset is assigned with a correct label. A voxel classifier is trained and subsequently used to predict unseen data. Morphological operations are finally applied to generate volumetric segmented images for different structures. We applied this algorithm on datasets of (1) four contrast images, (2) water and fat images, and (3) unsuppressed images acquired from 190 subjects. RESULTS: The proposed method using four contrasts achieved most accurate and robust segmentation compared to the use of combined fat and water images and the use of unsuppressed image, average Dice coefficients of 0.94 ± 0.03, 0.96 ± 0.03, 0.80 ± 0.03, and 0.97 ± 0.01 has been achieved to bone region, subcutaneous adipose tissue (SAT), inter-muscular adipose tissue (IMAT), and muscle respectively. CONCLUSION: Our proposed method based on machine learning produces accurate tissue quantification and showed an effective use of large information provided by the four contrast images from Dixon MRI.