Examining Race-Related Error in Two-Compartment Models of Body Composition Assessment: A Systematic Review and Meta-Analysis

Adult obesity diagnostic tool: A narrative review

Obesity is a complex chronic metabolic disorder characterized by abnormalities in lipid metabolism. Obesity is not only associated with various chronic diseases but also has negative effects on physiological functions such as the cardiovascular, endocrine and immune systems. As a global health problem, the incidence and prevalence of obesity have increased significantly in recent years. Therefore, understanding assessment methods and measurement indicators for obesity is critical for early screening and effective disease control. Current methods for measuring obesity in adult include density calculation, anthropometric measurements, bioelectrical impedance analysis, dual-energy X-ray absorptiometry, computerized imaging, etc. Measurement indicators mainly include weight, hip circumference, waist circumference, neck circumference, skinfold thickness, etc. This paper provides a comprehensive review of the literature to date, summarizes and analyzes various assessment methods and measurement indicators for adult obesity, and provides insights and guidance for the innovation of obesity assessment indicators.

Assessing reliability and validity of different stiffness measurement tools on a multi-layered phantom tissue model

Changes in the mechanical properties (i.e., stiffness) of soft tissues have been linked to musculoskeletal disorders, pain conditions, and cancer biology, leading to a rising demand for diagnostic methods. Despite the general availability of different stiffness measurement tools, it is unclear as to which are best suited for different tissue types and the related measurement depths. The study aimed to compare different stiffness measurement tools' (SMT) reliability on a multi-layered phantom tissue model (MPTM). A polyurethane MPTM simulated the four layers of the thoracolumbar region: cutis (CUT), subcutaneous connective tissue (SCT), fascia profunda (FPR), and erector spinae (ERS), with varying stiffness parameters. Evaluated stiffness measurement tools included Shore Durometer, Semi-Electronic Tissue Compliance Meter (STCM), IndentoPRO, MyotonPRO, and ultrasound imaging. Measurements were made by two independent, blinded examiners. Shore Durometer, STCM, IndentoPRO, and MyotonPRO reliably detected stiffness changes in three of the four MPTM layers, but not in the thin (1 mm thick) layer simulating FPR. With ultrasound imaging, only stiffness changes in layers thicker than 3 mm could be measured reliably. Significant correlations ranging from 0.70 to 0.98 (all p < 0.01) were found. The interrater reliability ranged from good to excellent (ICC(2,2) = 0.75-0.98). The results are encouraging for researchers and clinical practitioners as the investigated stiffness measurement tools are easy-to-use and comparatively affordable.

Body composition in individuals with cystic fibrosis

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BMI is used to characterize nutritional status but may not accurately depict body composition in CF.

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DXA and bioelectrical impedance are the most commonly used methods for assessing BC.

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Lower fat-free mass associates with worse pulmonary function and greater CF disease severity.

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Fat-free mass associates with greater bone mineral density in individuals with CF.

Because nutritional status is intimately linked with pulmonary function and survival, nutrition has been at the mainstay of cystic fibrosis (CF) care. Body Mass Index (BMI) is traditionally used to define nutritional status because of the ease with which it can be calculated, but it has a number of limitations including its inability to differentiate fat mass (FM) from lean body mass (LBM), the latter thought to confer health advantage. A number of tools are available to quantify body composition including dual-energy x-ray absorptiometry (DXA), bioelectrical impedance, MRI, CT, air displacement plethysmography, and stable isotopes, and these have been used to varying degrees in studies of CF. In CF, LBM tends to be lower for a given BMI, particularly at lower BMI. In adults, lower fat-free mass (FFM) correlates with greater CF disease severity, lower pulmonary function and higher inflammatory markers. FFM is also positively associated with greater bone mineral density, while greater FM is associated with greater loss of lumbar spine bone mineral density over 2 years. In youth, LBM is positively associated with pulmonary function. The predictive value of body composition for functional and clinical outcomes and the role of improving LBM on these outcomes remain undefined. With improvements in BMI accompanying highly-effective modulator therapy, closer evaluations of body composition may inform risk for more traditional, non-CF adult outcomes in CF.

Explaining Discrepancies Between Total and Segmental DXA and BIA Body Composition Estimates Using Bayesian Regression

INTRODUCTION/BACKGROUND

Few investigations have sought to explain discrepancies between dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA) body composition estimates. The purpose of this analysis was to explore physiological and anthropometric predictors of discrepancies between DXA and BIA total and segmental body composition estimates.

METHODOLOGY

Assessments via DXA (GE Lunar Prodigy) and single-frequency BIA (RJL Systems Quantum V) were performed in 179 adults (103 F, 76 M, age: 33.6 ± 15.3 yr; BMI: 24.9 ± 4.3 kg/m²). Potential predictor variables for differences between DXA and BIA total and segmental fat mass (FM) and lean soft tissue (LST) estimates were obtained from demographics and laboratory techniques, including DXA, BIA, bioimpedance spectroscopy, air displacement plethysmography, and 3-dimensional optical scanning. To determine meaningful predictors, Bayesian robust regression models were fit using a t-distribution and regularized hierarchical shrinkage "horseshoe" prior. Standardized model coefficients (β) were generated, and leave-one-out cross validation was used to assess model predictive performance.

RESULTS

LST hydration (i.e., total body water:LST) was a predictor of discrepancies in all FM and LST variables (|β|: 0.20-0.82). Additionally, extracellular fluid percentage was a predictor for nearly all outcomes (|β|: 0.19-0.40). Height influenced the agreement between whole-body estimates (|β|: 0.74-0.77), while the mass, length, and composition of body segments were predictors for segmental LST estimates (|β|: 0.23-3.04). Predictors of segmental FM errors were less consistent. Select sex-, race-, or age-based differences between methods were observed. The accuracy of whole-body models was superior to segmental models (leave-one-out cross-validation-adjusted R² of 0.83-0.85 for FM_TOTAL and LST_TOTAL vs. 0.20-0.76 for segmental estimates). For segmental models, predictive performance decreased in the order of: appendicular lean soft tissue, LST_LEGS, LST_TRUNK and FM_LEGS, FM_ARMS, FM_TRUNK, and LST_ARMS.

CONCLUSIONS

These findings indicate the importance of LST hydration, extracellular fluid content, and height for explaining discrepancies between DXA and BIA body composition estimates. These general findings and quantitative interpretation based on the presented data allow for a better understanding of sources of error between 2 popular segmental body composition techniques and facilitate interpretation of estimates from these technologies.

Fat-free mass characteristics vary based on sex, race, and weight status in US adults

Common body composition estimation techniques necessitate assumptions of uniform fat-free mass (FFM) characteristics, although variation due to sex, race, and body characteristics may occur. National Health and Nutrition Examination Survey data from 1999 to 2004, during which paired dual-energy x-ray absorptiometry (DXA) and bioimpedance spectroscopy assessments were performed, were used to estimate FFM characteristics in a sample of 4619 US adults. Calculated FFM characteristics included the density and water, bone mineral, and residual content of FFM. A rapid 4-component model was also produced using DXA and bioimpedance spectroscopy data. Study variables were compared across sex, race/ethnicity, body mass index (BMI), and age categories using multiple pairwise comparisons. A general linear model was used to estimate body composition after controlling for other variables. Statistical analyses accounted for 6-year sampling weights and complex sampling design of the National Health and Nutrition Examination Survey and were based on 5 multiply imputed datasets. Differences in FFM characteristics across sex, race, and BMI were observed, with notable dissimilarities between men and women for all outcome variables. In racial/ethnic comparisons, non-Hispanic blacks most commonly presented distinct FFM characteristics relative to other groups, including greater FFM density and proportion of bone mineral. Body composition errors between DXA and the 4-component model were significantly influenced by sex, age, race, and BMI. In conclusion, FFM characteristics, which are often assumed in body composition estimation methods, vary due to sex, race/ethnicity, and weight status. The variation of FFM characteristics in diverse populations should be considered when body composition is evaluated.