Body Composition Research Research Articles

A comparative study on the reliability and validity of body composition results by impedance method measurement device

The purpose of this study was to analyze the reliability and validity of the body composition results for each impedance device and use them as primary data for body composition research. Total of 58 participants, including 24 men and 34 women, were recruited. The correlation was analyzed by measuring two repetitive measurements for each device. Dual energy x-ray absorptiometry (DEXA) equipment was used as ref-erence equipment for body composition research. All data were ana-lyzed as IBM SPSS Statistics ver. 25.0, and the validity and reliability were estimated by calculating the timely correlation coefficient. As a result of the study, repeated measurements of the measuring instru-ment showed high reliability by gender and age, especially in the child age group, with relatively higher reliability than those in their 50s or old-er. In the validity analysis between DEXA, in the case of the male group, the validity of the fat mass amount was relatively higher than the body fat amount and body fat rate. In the case of the women’s group, the va-lidity of the body fat amount and body fat rate was relatively higher than that of the fat mass amount. The company B had the highest validity for body fat rate and body fat amount in both men and women. However, the validity for men’s fat mass amount was the lowest. In subsequent studies, it is believed that studies that have expanded the population further and studies targeting special groups such as athletes should be conducted.

Dual energy X-ray absorptiometry produces larger measurement error in non-Hispanic Caucasians than Hispanics

Dual energy X-ray absorptiometry (DXA) is often used as a criterion measure in body composition research and in clinical settings for the estimate of body fat percent (%Fat). The accuracy of DXA for predicting %Fat has primarily been conducted in non-Hispanic populations. The purpose of this study was to determine the agreement of DXA-derived %Fat in Hispanic and non-Hispanic Caucasian adults. The sample consisted of Hispanic males (n=96) and females (n=102) and non-Hispanic Caucasian males (n=145) and females (n=161). The %Fat of a whole-body DXA scan was compared against a criterion 4-compartment (4C) model via constant error (CE=DXA - 4C model) and 95% limits of agreement. Also, a 2×2 factorial ANOVA, using CE as a dependent variable, was conducted to examine the main and interaction effects of sex and ethnicity. When compared to the 4C model, DXA overestimated %Fat by 4.0% in Hispanics and 5.5% in non-Hispanic Caucasians (all p<0.05). The 95% limits of agreement ranged from ±5.5% to ±5.9% for all group comparisons. The 2×2 factorial ANOVA indicated the CE was greater in non-Hispanic Caucasians than Hispanics (CE difference=1.5%; p<0.05). Our findings revealed that DXA significantly overestimates %Fat in both populations (Hispanics and non-Hispanic Caucasians), when compared to a 4C model, regardless of sex (male or female). However, the error is more profound in non-Hispanic Caucasian adults. It is worth nothing that DXA may be useful for tracking changes in body composition that occur throughout a lifestyle intervention. Nonetheless, practitioners should be aware that the estimate of %Fat from DXA may be larger than the actual values obtained from a 4C model.

Automated versus manual analysis of body composition measures on computed tomography in patients with bladder cancer

PurposeManual measurement of body composition on computed tomography (CT) is time-consuming, limiting its clinical use. We validate a software program, Automatic Body composition Analyzer using Computed tomography image Segmentation (ABACS), for the automated measurement of body composition by comparing its performance to manual segmentation in a cohort of patients with bladder cancer. MethodWe performed a retrospective analysis of 285 patients treated for bladder cancer at the Duke University Health System from 1996 to 2017. Abdominal CT images were manually segmented at L3 using Slice-O-Matic. Automated segmentation was performed with ABACS on the same L3-level images. Measures of interest were skeletal muscle (SM) area, subcutaneous adipose tissue (SAT) area, and visceral adipose tissue (VAT) area. SM index, SAT index, and VAT index were calculated by dividing component areas by patient height2 (m2). Patients were dichotomized as sarcopenic, having excessive subcutaneous fat, or having excessive visceral fat using published cut-off values. Agreement between manual and automated segmentation was assessed using the Pearson product-moment correlation coefficient (PPMCC), the interclass correlation coefficient (ICC3), and the kappa statistic (κ). ResultsThere was strong agreement between manual and automatic segmentation, with PPMCCs > 0.90 and ICC3s > 0.90 for SM, SAT, and VAT areas. Categorization of patients as sarcopenic (κ = 0.73), having excessive subcutaneous fat (κ = 0.88), or having excessive visceral fat (κ = 0.90) displayed high agreement between methods. ConclusionsAutomated segmentation of body composition measures on CT using ABACS performs similarly to manual analysis and may expedite data collection in body composition research.

Concordance of Computed Tomography Regional Body Composition Analysis Using a Fully Automated Open-Source Neural Network versus a Reference Semi-Automated Program with Manual Correction.

Quick, efficient, fully automated open-source programs to segment muscle and adipose tissues from computed tomography (CT) images would be a great contribution to body composition research. This study examined the concordance of cross-sectional areas (CSA) and densities for muscle, visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and intramuscular adipose tissue (IMAT) from CT images at the third lumbar (L3) between an automated neural network (test method) and a semi-automatic human-based program (reference method). Concordance was further evaluated by disease status, sex, race/ethnicity, BMI categories. Agreement statistics applied included Lin’s Concordance (CCC), Spearman correlation coefficient (SCC), Sorensen dice-similarity coefficient (DSC), and Bland–Altman plots with limits of agreement (LOA) within 1.96 standard deviation. A total of 420 images from a diverse cohort of patients (60.35 ± 10.92 years; body mass index (BMI) of 28.77 ± 7.04 kg/m2; 55% female; 53% Black) were included in this study. About 30% of patients were healthy (i.e., received a CT scan for acute illness or pre-surgical donor work-up), while another 30% had a diagnosis of colorectal cancer. The CCC, SCC, and DSC estimates for muscle, VAT, SAT were all greater than 0.80 (>0.80 indicates good performance). Agreement analysis by diagnosis showed good performance for the test method except for critical illness (DSC 0.65–0.87). Bland–Altman plots revealed narrow LOA suggestive of good agreement despite minimal proportional bias around the zero-bias line for muscle, SAT, and IMAT CSA. The test method shows good performance and almost perfect concordance for L3 muscle, VAT, SAT, and IMAT per DSC estimates, and Bland–Altman plots even after stratification by sex, race/ethnicity, and BMI categories. Care must be taken to assess the density of the CT images from critically ill patients before applying the automated neural network (test method).

Quantification of muscle, bones, and fat on single slice thigh CT.

Muscle, bone, and fat segmentation of CT thigh slice is essential for body composition research. Voxel-wise image segmentation enables quantification of tissue properties including area, intensity and texture. Deep learning approaches have had substantial success in medical image segmentation, but they typically require substantial data. Due to high cost of manual annotation, training deep learning models with limited human labelled data is desirable but also a challenging problem. Inspired by transfer learning, we proposed a two-stage deep learning pipeline to address this issue in thigh segmentation. We study 2836 slices from Baltimore Longitudinal Study of Aging (BLSA) and 121 slices from Genetic and Epigenetic Signatures of Translational Aging Laboratory Testing (GESTALT). First, we generated pseudo-labels based on approximate hand-crafted approaches using CT intensity and anatomical morphology. Then, those pseudo labels are fed into deep neural networks to train models from scratch. Finally, the first stage model is loaded as initialization and fine-tuned with a more limited set of expert human labels. We evaluate the performance of this framework on 56 thigh CT scans and obtained average Dice of 0.979,0.969,0.953,0.980 and 0.800 for five tissues: muscle, cortical bone, internal bone, subcutaneous fat and intermuscular fat respectively. We evaluated generalizability by manually reviewing external 3504 BLSA single thighs from 1752 thigh slices. The result is consistent and passed human review with 150 failed thigh images, which demonstrates that the proposed method has strong generalizability.

Computed tomography reference values for visceral obesity and increased metabolic risk in a Caucasian cohort.

Visceral obesity is associated with the metabolic syndrome. The metabolic risk differs per ethnicity, but reference values for visceral obesity for body composition analyses using Computed Tomography (CT) scans in the Caucasian population are lacking. Therefore, the aim of this study was to define gender specific reference values for visceral obesity in a Caucasian cohort based upon the association between the amount of visceral adipose tissue (VAT) and markers of increased metabolic risk. Visceral Adipose Tissue Area Index (VATI cm2/m2) at the level of vertebra L3 was analyzed using CT scans of 416 healthy living kidney donor candidates. The use of antihypertensive drugs and/or statins was used as an indicator for increased metabolic risk. Gender specific cut-off values for VATI with a sensitivity ≥80% were calculated using receiver operating characteristic (ROC) curves. In both men and women who used antihypertensive drugs, statins or both, VATI was higher than in those who did not use these drugs (p≤0.013). In males and females respectively, a value of VATI of ≥38.7cm2/m2 and ≥24.9cm2/m2 was associated with increased metabolic risk with a sensitivity of 80%. ROC analysis showed that VATI was a better predictor of increased metabolic risk than BMI (area under ROC curve (AUC)=0.702 vs AUC=0.556 in males and AUC=0.757 vs AUC=0.630 in females). Gender and ethnicity specific cut-off values for visceral obesity are important in body composition research, although further validation is needed. This study also showed that quantification of VATI is a better predictor for metabolic risk than BMI.

Quantification of body composition in renal cell carcinoma patients: Comparing computed tomography and magnetic resonance imaging measurements

PurposeBody composition measures provide valuable information for prognostication and treatment election in cancer patients. We investigated the novel use of magnetic resonance imaging (MRI) for skeletal muscle and adipose tissue cross-sectional area measurements in preoperative renal cell carcinoma (RCC) patients. Materials and methodsRCC patients with pre-operative CT and MRI abdominal imaging were identified. Semi-automatic segmentation measurement of skeletal muscle area (SMA), intramuscular fat area (IMFA), visceral fat area (VFA), subcutaneous fat area (SFA), linear measurements of psoas, paraspinal muscles were performed. Pearson correlation coefficients, Bland-Altman plot analyses were done. Multivariable regression analysis examined the relationship between patient characteristics and skeletal muscle. ResultsImage analysis was performed on 58 RCC patients with preoperative CT and MRI imaging. For segmentation measures, r = 0.99, 0.99, 0.99, and 0.98 for SMA, IMFA, VFA, SFA, respectively, and 0.96 for linear measures of skeletal muscle. Bland-Altman analysis revealed a bias toward larger CT value for SMA (1.35 %), linear muscle measures (2.79 %), and SFA (10.34 %), and toward larger MRI values for IMFA (0.75 %) and VFA (5.81 %). ECOG ≥ 1 was associated with lower skeletal muscle than ECOG 0 for all measurements. ConclusionsStrong correlation of CT and MRI cross sectional measurements of skeletal muscle and adipose tissues supports the use of axial MRI images for comprehensive measurement of body composition. This has widespread implications for body composition research and cancer patient care.

Evaluation of automated computed tomography segmentation to assess body composition and mortality associations in cancer patients

BackgroundBody composition from computed tomography (CT) scans is associated with cancer outcomes including surgical complications, chemotoxicity, and survival. Most studies manually segment CT scans, but Automatic Body composition Analyser using Computed tomography image Segmentation (ABACS) software automatically segments muscle and adipose tissues to speed analysis. Here, we externally evaluate ABACS in an independent dataset.MethodsAmong patients with non‐metastatic colorectal (n = 3102) and breast (n = 2888) cancer diagnosed from 2005 to 2013 at Kaiser Permanente, expert raters annotated tissue areas at the third lumbar vertebra (L3). To compare ABACS segmentation results to manual analysis, we quantified the proportion of pixel‐level image overlap using Jaccard scores and agreement between methods using intra‐class correlation coefficients for continuous tissue areas. We examined performance overall and among subgroups defined by patient and imaging characteristics. To compare the strength of the mortality associations obtained from ABACS's segmentations to manual analysis, we computed Cox proportional hazards ratios (HRs) and 95% confidence intervals (95% CI) by tertile of tissue area.ResultsMean ± SD age was 63 ± 11 years for colorectal cancer patients and 56 ± 12 for breast cancer patients. There was strong agreement between manual and automatic segmentations overall and within subgroups of age, sex, body mass index, and cancer stage: average Jaccard scores and intra‐class correlation coefficients exceeded 90% for all tissues. ABACS underestimated muscle and visceral and subcutaneous adipose tissue areas by 1–2% versus manual analysis: mean differences were small at −2.35, −1.97 and −2.38 cm2, respectively. ABACS's performance was lowest for the <2% of patients who were underweight or had anatomic abnormalities. ABACS and manual analysis produced similar associations with mortality; comparing the lowest to highest tertile of skeletal muscle from ABACS versus manual analysis, the HRs were 1.23 (95% CI: 1.00–1.52) versus 1.38 (95% CI: 1.11–1.70) for colorectal cancer patients and 1.30 (95% CI: 1.01–1.66) versus 1.29 (95% CI: 1.00–1.65) for breast cancer patients.ConclusionsIn the first study to externally evaluate a commercially available software to assess body composition, automated segmentation of muscle and adipose tissues using ABACS was similar to manual analysis and associated with mortality after non‐metastatic cancer. Automated methods will accelerate body composition research and, eventually, facilitate integration of body composition measures into clinical care.

Perspective: Creating the Evidence Base for Nutritional Support in Childhood Cancer in Low- and Middle-Income Countries: Priorities for Body Composition Research

ABSTRACTThere is a striking disparity in survival rates for children in low- and middle-income countries (LMICs) compared with high-income countries (HICs). Many of the contributing factors are preventable, including the comorbidity of malnutrition. There are emerging data that malnutrition, as reflected in body composition changes, impacts survival of cancer. However, not enough priority is given to nutrition management of children with cancer, particularly in LMICs. The primary purpose of this article is to review the current knowledge on childhood cancer and body composition in LMICs and identify priorities for future research into the interlinking associations between cancer, body composition, and clinical outcomes for childhood cancer patients. Evidence will ensure feasible and effective nutrition management is prioritized in childhood cancer centers in LMICs and contribute to improving outcomes for children with cancer.

Steven B. Heymsfield, MD: Incoming President, The Obesity Society, 2018‐2019

Steven B. Heymsfield, MD: Incoming President, The Obesity Society, 2018‐2019

A window beneath the skin: how computed tomography assessment of body composition can assist in the identification of hidden wasting conditions in oncology that profoundly impact outcomes.

Advancements in image-based technologies and body composition research over the past decade has led to increased understanding of the importance of muscle abnormalities, such as low muscle mass (sarcopenia), and more recently low muscle attenuation (MA), as important prognostic indicators of unfavourable outcomes in patients with cancer. Muscle abnormalities can be highly prevalent in patients with cancer (ranging between 10 and 90 %), depending on the cohort under investigation and diagnostic criteria used. Importantly, both low muscle mass and low MA have been associated with poorer tolerance to chemotherapy, increased risk of post-operative infectious and non-infectious complications, increased length of hospital stay and poorer survival in patients with cancer. Studies have shown that systemic antineoplastic treatment can exacerbate losses in muscle mass and MA, with reported loss of skeletal muscle between 3 and 5 % per 100 d, which are increased exponentially with progressive disease and proximity to death. At present, no effective medical intervention to improve muscle mass and MA exists. Most research to date has focused on treating muscle depletion as part of the cachexia syndrome using nutritional, exercise and pharmacological interventions; however, these single-agent therapies have not provided promising results. Rehabilitation care to modify body composition, either increasing muscle mass and/or MA should be conducted, and its respective impact on oncology outcomes explored. Although the optimal timing and treatment strategy for preventing or delaying the development of muscle abnormalities are yet to be determined, multimodal interventions initiated early in the disease trajectory appear to hold the most promise.

Magnetic Resonance Imaging in the measurement of whole body muscle mass: A comparison of interval gap methods

PurposeMagnetic Resonance Imaging (MRI) is commonly used in body composition research to measure whole body skeletal muscle mass (SM). MRI calculation methods of SM can vary by analysing the images at different slice intervals (or interval gaps) along the length of the body. This study compared SM measurements made from MRI images of apparently healthy individuals using different interval gap methods to determine the error associated with each technique. It was anticipated that the results would inform researchers of optimum interval gap measurements to detect a predetermined minimum change in SM. MethodsA method comparison study was used to compare eight interval gap methods (interval gaps of 40, 50, 60, 70, 80, 100, 120 and 140 mm) against a reference 10 mm interval gap method for measuring SM from twenty MRI image sets acquired from apparently healthy participants. Pearson product-moment correlation analysis was used to determine the association between methods. Total error was calculated as the sum of the bias (systematic error) and the random error (limits of agreement) of the mean differences. Percentage error was used to demonstrate proportional error. ResultsPearson product-moment correlation analysis between the reference method and all interval gap methods demonstrated strong and significant associations (r > 0.99, p < 0.0001). The 40 mm interval gap method was comparable with the 10 mm interval reference method and had a low error (total error 0.95 kg, −3.4%). Analysis methods using wider interval gap techniques demonstrated larger errors than reported for dual-energy x-ray absorptiometry (DXA), a technique which is more available, less expensive, and less time consuming than MRI analysis of SM. ConclusionsResearchers using MRI to measure SM can be confident in using a 40 mm interval gap technique when analysing the images to detect minimum changes less than 1 kg. The use of wider intervals will introduce error that is no better than the error reported for DXA measurements of SM.

Body Composition: Assessment, Regulation, and Emerging Techniques

Body Composition: Assessment, Regulation, and Emerging Techniques

Body composition of Japanese children

There is limited information about total and regional body composition in children at the organ tissue level. The purpose of this review is to take note of the current body of knowledge regarding body composition research in Japanese children on the basis of recent data centering on fat mass and skeletal muscle (SM) mass. According to the author’s recent findings of fat mass, 1) the value of % fat in overweight boys and girls was over 35% using DXA measurement; 2) about 50% of the difference in whole body fat mass, between overweight and normal weight children, may be attributable to incremental increases in trunk fat mass in both boys and girls; and 3) equations using skinfold calipers and ultrasound seem to be more useful field methods in estimating fat mass specifically for children at the present time. Moreover, the author’s new studies of SM mass indicate that 1) whole body SM mass dramatically increases from prepubertal (9.2 kg for boys, 10.8 kg for girls) to adolescent stages (20.0 kg for boys, 14.6 kg for girls), which is similar to adults (22.3 kg for males, 13.5 kg for females); 2) the increase in SM mass, during each growth stage, seems to be fairly constant on the extremities and trunk area in both boys and girls; and 3) the adult ultrasound-derived prediction equations for SM mass are applicable for adolescents near 14 years of age, but are not valid for prepubertal children. Future research will involve the development of equations for estimating fat mass and SM mass for prepubertal children and adolescents.

Prediction of Skeletal Muscle and Fat Mass in Patients with Advanced Cancer Using a Metabolomic Approach1–3

Urine and plasma metabolites originate from endogenous metabolic pathways in different organs and exogenous sources (diet). Urine and plasma were obtained from advanced cancer patients and investigated to determine if variations in lean and fat mass, dietary intake, and energy metabolism relate to variation in metabolite profiles. Patients (n = 55) recorded their diets for 3 d and after an overnight fast they were evaluated by DXA and indirect calorimetry. Metabolites were measured by NMR and direct injection MS. Three algorithms were used [partial least squares discriminant-analysis, support vector machines (SVM), and least absolute shrinkage and selection operator] to relate patients' plasma/urine metabolic profile with their dietary/physiological assessments. Leave-one-out cross-validation and permutation testing were conducted to determine statistical validity. None of the algorithms, using 63 urine metabolites, could learn to predict variations in individual's resting energy expenditure, respiratory quotient, or their intake of total energy, fat, sugar, or carbohydrate. Urine metabolites predicted appendicular lean tissue (skeletal muscle) with excellent cross-validation accuracy (98% using SVM). Total lean tissue correlated highly with appendicular muscle (Pearson r = 0.98; P < 0.0001) and gave similar cross-validation accuracies. Fat mass was effectively predicted using the 63 urine metabolites or the 143 plasma metabolites, exclusively. In conclusion, in this population, lean and fat mass variation could be effectively predicted using urinary metabolites, suggesting a potential role for metabolomics in body composition research. Furthermore, variation in lean and fat mass potentially confounds metabolomic studies attempting to characterize diet or disease conditions. Future studies should account or correct for such variation.

Whole-Body Magnetic Resonance Imaging: Emerging Applications

Beyond established roles in the assessment of skeletal metastatic disease, in muscle diseases and in myeloma, WBMRI has the potential to offer many further valuable clinical applications. This article presents an overview of emerging clinical applications of WBMRI emphasizing its role in the assessment of musculoskeletal ailments, ranging from the assessment of arthropathy through to body composition research.

Role Of Body Mass Index And Race On Two And Three Component Percent Fat Of Young Adults: Tiger And Heritage Family Studies

Much of the classic body composition research used a two-component (2C) model to estimate percent body fat (BF%) from body density determined by underwater weighting (U-BD). Dual-energy X-ray absorptiometry (DXA), a three-component model, is becoming the laboratory method of choice to measure BF%. PURPOSE: To compare the effects of race and body mass index (BMI) on BF% measured by U-BD, 2-C and DXA methods. METHODS: The subjects included white (W) and African-American (AA) men and women from the HERITAGE family (n=385) and TIGER studies (n=826). The young adults ranged in age from 17 to 35 y. The subjects underwent in an exercise intervention and had their BF% measured 1 to 3 times over 9 months. The BF% of the TIGER subjects was measured by DXA. Sex and race specific 2-C equations were used to calculate BF% from U-BD of the HERITAGE subjects. Linear mixed models (LMM) regression was used to analyze the all observations, which were 1,266 for women and 820 for men. The independent variables were BMI, BF% method (2-C vs. DXA) and race (W vs. AA) and the dependent variable was BF%. The men and women‘s data were analyzed separately. RESULTS: The LMM results for women and men were similar. A significant BMI by method showed that below a BMI of about 25 kg/m2, the Siri BF% for a given BMI was lower than DXA BF%, but above 25 kg/m2 the 2-C BF% was higher than DXA BF%. Adding race to the LMM improved model fit (p <0.001) for both men and women. Graphic examination of the trends showed that for a given BMI, the DXA BF% of AA men was about 4.6% lower than W. The DXA BF% of AA women was nearly 2% lower than W women. CONCLUSIONS: These results showed there is both a race and method effect for young men and women when using the DXA and U-BD, 2-C laboratory methods to measure BF%. The method effect is a function of BMI level. Supported by NIH Grant DK062148 and HL45670

Mapping institutions and their weak ties in a specialty: A case study of cystic fibrosis body composition research

The paper demonstrates visualization technique that show the collaboration structure of institutions in the specialty and the researchers that function as weak ties among them. Institution names were extracted from the collection of papers and disambiguated using the Derwent Analytics (v1.2) software product. Institutions were clustered into collaboration groups based on their co-occurrence in papers. A crossmap of clustered institutions against research fronts, which were derived using bibliographic coupling analysis, shows the research fronts that specific institutions participate in, their collaborator institutions and the research fronts in which those collaborations occurred. A crossmap of institutions to author teams, derived from co-authorship analysis, reveals research teams in the specialty and their general institutional affiliation, and further identifies the researchers that function as weak ties and the institutions that they link. The case study reveals that the techniques introduced in this paper can be used to extract a large amount of useful information about institutions participating in a research specialty.

VALIDITY OF SKINFOLDS TO MEASURE CHANGE

VALIDITY OF SKINFOLDS TO MEASURE CHANGE

The end of body composition methodology research?

Over one century of research has led to methods for measuring all major body components at the atomic, molecular, cellular, and tissue-system levels. These remarkable developments have fueled a rapid and sustained increase in 'body composition' biological findings and related publications. Other than small, incremental improvements in available methods, is there no longer a need for developing new body composition methods? This review examines the question: are we approaching the 'end' of body composition methodology research? Emerging and rapidly growing areas outside of 'traditional' body composition research are highlighting the need for new and innovative method development. Recently introduced technologies such as positron emission tomography and functional magnetic resonance imaging extend 'mass' estimates to corresponding 'function' and physiology in humans. Although all major components are now measurable in humans, large gaps remain when considering factors such as radiation exposure, invasiveness, static versus dynamic measurements, and laboratory versus clinic and field assessments. The end of the first phase of body composition method development has now arrived: all major components are measurable in vivo. The accessibility of these methods is stimulating rapid advances in biological knowledge surrounding human body composition from in utero to old age. Sustaining advances in new body composition method development will require extending the boundaries of the field as it now exists.