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

Tracking Body Composition Over a Competitive Season in Elite Soccer Players Using Laboratory- and Field-Based Assessment Methods

ABSTRACT

Bongiovanni, T, Lacome, M, Rodriguez, C, and Tinsley, GM. Tracking body composition over a competitive season in elite soccer players using laboratory- and field-based assessment methods. J Strength Cond Res 38(3): e104-e115, 2024-The purpose of this study was to describe body composition changes in professional soccer players over the course of a competitive playing season and compare the ability of different assessment methods to detect changes. Twenty-one elite male soccer players (age: 23.7 ± 4.8 years; height: 185.0 ± 5.2 cm; body mass: 80.7 ± 5.5 kg; body fat: 12.8 ± 2.2%) playing for an Italian national second league (Serie B) championship team were assessed at 4 time points throughout a competitive season: T0 (mid-October), T1 (mid-December), T2 (mid-February), and T3 (end of April). Dual-energy x-ray absorptiometry (DXA), skinfolds (SKF), and bioelectrical impedance analysis were performed at each time point, and multiple SKF-based equations were applied. A modified 4-compartment (4C) model was also produced. Data were analyzed using repeated measures analysis of variance, relevant post hoc tests, and Pearson's correlations. Dual-energy x-ray absorptiometry, 4C, and the SKF-based equations of Reilly and Civar detected differences in fat-free mass (FFM) between time points, with the most differences observed for DXA. Fat-free mass increased from T0 values to a peak at T2, followed by a decrease by T3, although FFM values remained higher than T0. Fat-free mass gain was primarily driven by increases in the lower limbs. Fat-free mass changes between all methods were significantly correlated, with correlation coefficients of 0.70-0.97. No significant differences between time points were observed for absolute fat mass or body fat percentage, although significant correlations between several methods for change values were observed. Select laboratory and field methods can detect changes in FFM over the course of a season in elite, professional soccer athletes, with a more limited ability to detect changes in adiposity-related variables. For SKF in this population, the equation of Reilly is recommended.

Skeletal muscle estimation using magnetic-resonance-imaging-based equations for dual-energy X-ray absorptiometry and bioelectrical impedance analysis

BACKGROUND/OBJECTIVES

Skeletal muscle mass (SMM) estimation is important but challenging in clinical settings. Criterion methods, such as magnetic resonance imaging (MRI), are often inaccessible. However, surrogate methods, such as dual-energy X-ray absorptiometry (DXA) and multi-frequency bioelectrical impedance analysis (MFBIA), can use MRI-based equations to estimate SMM, although the agreement between these methods is unclear.

SUBJECTS/METHODS

Total and segmental SMM were estimated with DXA and MFBIA using MRI-based equations in 313 healthy adults (120 M, 193 F; age 30.2 ± 13.0 y; BMI 24.6 ± 4.0 kg/m2). DXA total SMM was estimated using the Kim and McCarthy equations, and segmental SMM was estimated using the McCarthy equations. Relationships between DXA and MFBIA SMM were examined using Deming regression, Lin's concordance correlation coefficient (CCC), equivalence testing, Bland-Altman analysis, and related tests.

RESULTS

Strong linear relationships were observed for total (R2 0.95, CCC 0.96-0.97), leg (R2 0.90, CCC 0.85) and arm (R2 0.93, CCC 0.93) SMM in the entire sample. Kim equation SMM demonstrated statistical equivalence with MFBIA for total SMM, but the Deming regression slope differed from 1 and proportional bias was present. McCarthy equation total SMM exhibited a regression slope that did not differ from 1, and no proportional bias was present in the entire sample. However, equivalence with MFBIA was not observed. Systematically higher leg and arm SMM values were observed with DXA as compared to MFBIA.

CONCLUSIONS

While DXA and MFBIA total SMM generally exhibited strong agreement, higher appendicular SMM by DXA highlights technical differences between methods.

Development and validation of bioelectrical impedance prediction equations estimating regional lean soft tissue mass in middle-aged adults

BACKGROUND/OBJECTIVES

Bioelectrical impedance (BIA) whole-body and regional raw parameters have been used to develop prediction models to estimate whole-body lean soft tissue (LSTM), with less attention being given to the development of models for regional LSTM. Therefore, we aimed to develop and validate BIA-derived equations predicting regional LSTM against dual x-ray absorptiometry (DXA) in healthy adults.

SUBJECTS/METHODS

149 adults were included in this cross-sectional investigation. Whole-body and regional LSTM were assessed by DXA, and raw bioelectrical parameters of distinct body regions were measured using a 50 kHz phase sensitive BIA analyzer. BIA-derived equations were developed using a stepwise multiple linear regression approach in 2/3 of the sample and cross-validated in the remaining sample.

RESULTS

Slopes and intercepts of predicted LSTM and DXA measured LSTM did not differ from 1 and 0, respectively, for each region (p ≥ 0.05), with the exception for the trunk (p < 0.05). The BIA-derived equations exhibited a strong relationship (p < 0.001) between the predicted and measured LSTM for each of the following body regions: right and left arms (R = 0.94; R = 0.96), right and left legs (R = 0.88; R = 0.88), upper body (R = 0.96), lower body (R = 0.89), right and left sides of the body (R = 0.94; R = 0.94), and trunk (R = 0.90). Agreement analyses revealed no associations between the differences and the means of the predicted and DXA-derived LSTM.

CONCLUSION

The developed BIA-derived equations provide a valid estimate of regional LSTM in middle-aged healthy adults, representing a cost-effective and time-efficient alternative to DXA for the assessment and identification of LSTM imbalances in both clinical and sport-specific contexts.

A comparative analysis of body composition assessment by BIA and DXA in children with type II and III spinal muscular atrophy

BACKGROUND

Body composition analysis is a valuable tool for assessing and monitoring the nutritional status of children with spinal muscular atrophy (SMA). This study was designed to compare the consistency of bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA), as the gold standard method for assessing body composition in clinical practice when treating children with type II and III SMA.

METHODS

From 2019 to 2021, we performed a retrospective analysis of body composition by DXA and BIA measurement methods in patients with type II and III SMA treated at a Chinese tertiary children's hospital. Fat mass (FM), muscle mass (MM), bone mineral content (BMC), and visceral fat area (VFA) were compared using paired sample t-tests. We calculated Lin's concordance correlation coefficient (CCC) and Spearman correlation coefficient to verify the correlation between DXA and BIA measurements. Bland-Altman analysis was used to assess the consistency of the two methods.

RESULTS

Fifty-seven children with type II and III SMA were recruited. Compared with body composition measured by DXA, the average FM measured by BIA is significantly lower (P <0.001), whereas the average MM, BMC, and VFA measured by BIA are significantly higher (P < 0.001) in children with SMA. Overall, the difference between MM (Delta [BIA-DAX] = 1.6 kg) and FM (Delta [BIA-DAX] = -1.6 kg) measured by DXA and BIA was minor, whereas the difference of VFA (Delta [BIA-DAX] = -43.5 cm) was significantly large. Correlation analysis indicated a substantial correlation of MM (CCC = 0.96 [95% confidence interval (CI) = 0.93-0.98], r = 0.967 [P < 0.0001]) and FM (CCC = 0.95 [95% CI = 0.92-0.97], r = 0.953 [P < 0.0001]), and poor correlation of BMC (CCC = 0.61 [95% CI = 0.42-0.75], r = 0.612 [P < 0.0001]) and VFA (CCC = 0.54 [95% CI = 0.33-0.70], r = 0.689 [P < 0.0001]) measurements between the two methods. The Bland-Altman analysis suggests that the majority of participants were within LOA. In addition, differences in MM and VFA measurements between BIA and DAX increased according to patients' increasing height, whereas differences in FM and BMC did not differ with height.

CONCLUSION

BIA overestimates MM and underestimates the FM, BMC, and VFA in children with SMA compared with DXA measurements. Overall, the non-invasive, easy-to-use, and repeatable BIA measurements were found to be in good agreement with DXA measurements, especially for FM and MM, which are essential parameters for the nutritional evaluation of children with SMA.

Reliability of Multifrequency Bioelectrical Impedance Analysis to Quantify Body Composition in Patients After Musculoskeletal Trauma

BACKGROUND

Changes in body composition, especially loss of lean mass, commonly occur in the orthopedic trauma population due to physical inactivity and inadequate nutrition. The purpose of this study was to assess inter-rater and intra-rater reliability of a portable bioelectrical impedance analysis (BIA) device to measure body composition in an orthopedic trauma population after operative fracture fixation. BIA uses a weak electric current to measure impedance (resistance) in the body and uses this to calculate the components of body composition using extensively studied formulas.

METHODS

Twenty subjects were enrolled, up to 72 hours after operative fixation of musculoskeletal injuries and underwent body composition measurements by two independent raters. One measurement was obtained by each rater at the time of enrollment and again between 1-4 hours after the initial measurement. Reliability was assessed using intraclass correlation coefficients (ICC) and minimum detectable change (MDC) values were calculated from these results.

RESULTS

Inter-rater reliability was excellent with ICC values for body fat mass (BFM), lean body mass (LBM), skeletal muscle mass (SMM), dry lean mass (DLM), and percent body fat (PBF) of 0.993, 0.984, 0.984, 0.979, and 0.986 respectively. Intra-rater reliability was also high for BFM, LBM, SMM, DLM, and PBF, at 0.994, 0.989, 0.990, 0.983, 0.987 (rater 1) and 0.994, 0.988, 0.989, 0.985, 0.989 (rater 2). MDC values were calculated to be 4.05 kg for BFM, 4.10 kg for LBM, 2.45 kg for SMM, 1.21 kg for DLM, and 4.83% for PBF.

CONCLUSION

Portable BIA devices are a versatile and attractive option that can reliably be used to assess body composition and changes in lean body mass in the orthopedic trauma population for both research and clinical endeavors. Level of Evidence: III.

Accuracy of body composition measurement techniques across the age span

This study investigated the acceptable accuracy of common body composition techniques compared with the reference 4-compartment (4C-R) model, which has not been investigated in a sample with diverse characteristics, including age and sex. Techniques included components of the 4C-R model [dual-energy X-ray absorptiometry, air displacement plethysmography, deuterium dilution (DD)] and surrogate compartment models, which utilised bioelectrical impedance spectroscopy (BIS) rather than DD. Men and women (sex = 1:1, 18-85 years, n = 90) completed body composition testing under best-practice guidance. For measurement of individuals, only the reference 3-compartment (3C-R) equation met acceptable error limits (<5% error among individuals) within the a priori cut-point (80%) for fat-free mass (FFM; CV = 0.52%) and fat mass (FM; CV = 1.61%). However, all investigated techniques reached equivalency to the 4C-R model for FFM on average (CV = 0.52-4.31%), but for FM only the 3C and 4C equations that included quantification of total body water (TBW) by DD or BIS reached equivalency overall (CV = 1.61-6.68%). Sex and age minimally influenced accuracy. Only the 3C-R or 4C-R equations are supported for acceptable individual accuracy for both FFM and FM. For group estimates any investigated technique could be used with acceptable accuracy for FFM; however, for FM, inclusion of TBW measurement within a compartment model is necessary. Novelty: Only the referent 3C and 4C models (including deuterium dilution) provide accurate body composition results that are acceptable for measurement of individuals in the general population. For group estimates of lean mass in the general population, compartments models that include TBW must be used for accurate measurement.

[Assessment and technical monitoring of nutritional status of patients in intensive and intermediate care units : Position paper of the Section Metabolism and Nutrition of the German Interdisciplinary Association for Intensive and Emergency Medicine (DIVI)]

Die Erhebung des Ernährungsstatus zum Zeitpunkt der Aufnahme im Intensiv- oder Intermediate Care Bereich hat sowohl prognostische als auch therapeutische Relevanz im Hinblick auf die Planung einer individualisierten medizinischen Ernährungstherapie (engl. „medical nutrition therapy“, MNT). Diese Planung wird im Rahmen der Erstversorgung eines vital bedrohlichen Krankheitsbilds nachvollziehbar nicht priorisiert, jedoch im weiteren Verlauf häufig auch oft nicht mehr angemessen durchgeführt. Vor allem bei längerer Verweildauer besteht das Risiko einer Mangelernährung mit Aufbau eines kumulativen, prognoserelevanten Makro- und/oder Mikronährstoffdefizits. Bisher gibt es für Patient*innen auf Intensiv- und Intermediate Care Einheiten keine strukturierten Empfehlungen zur Erhebung des Ernährungsstatus. Das vorliegende Positionspapier der Sektion Metabolismus und Ernährung der Deutschen Interdisziplinären Vereinigung für Intensiv- und Notfallmedizin (DIVI) beinhaltet konsensbasierte Empfehlungen zur Erfassung und zum apparativen Monitoring des Ernährungsstatus von Patient*innen auf Intensiv- und Intermediate Care Stationen. Diese Empfehlungen ergänzen die aktuelle S2k-Leitlinie „Klinische Ernährung in der Intensivmedizin“ der Deutschen Gesellschaft für Ernährungsmedizin (DGEM) und der DIVI.

Association of body-shape phenotypes with imaging measures of body composition in the UK Biobank cohort: relevance to colon cancer risk

Background

Body mass index (BMI), waist and hip circumference are strongly correlated and do not reflect body composition. A Body Shape Index (ABSI) and Hip Index (HI) define waist and hip size among individuals with the same weight and height and would thus reflect body density. We examined differences in body composition between body-shape phenotypes defined with ABSI and HI and used this information to propose explanations for associations between body-shape phenotypes and colon cancer risk.

Methods

We used data from the UK Biobank Resource for 15,520 men, 16,548 women with dual-emission X-ray absorptiometry (DXA) measurements; 3997 men, 4402 women with magnetic resonance imaging (MRI) measurements; 200,289 men, 230,326 women followed-up for colon cancer. We defined body-shape phenotypes as: large-ABSI-small-HI (“apple”), small-ABSI-large-HI (“pear”), small-ABSI-small-HI (“slim”), large-ABSI-large-HI (“wide”). We evaluated differences in body composition in linear models and associations with colon cancer risk in Cox proportional hazards models adjusted for confounders and explored heterogeneity by BMI.

Results

Among individuals with the same height and weight, visceral adipose tissue (VAT) was lowest for “pear” and highest for “apple”, while abdominal subcutaneous adipose tissue (ASAT) was lowest for “slim” and highest for “wide” phenotype. In the gynoid region, differences between “apple” and “pear” phenotypes were accounted for mainly by fat mass in women but by lean mass in men. In men, lean mass was inversely associated with waist size, while the pattern of gynoid fat resembled ASAT in women. Lean and fat mass were higher for higher BMI, but not hand grip strength. Compared to normal weight “pear”, the risk of colon cancer in men (1029 cases) was higher for “apple” phenotype for normal weight (hazard ratio HR = 1.77; 95% confidence interval: 1.16–2.69) and comparably for overweight and obese, higher for “wide” phenotype for overweight (HR = 1.60; 1.14–2.24) and comparably for obese, but higher for “slim” phenotype only for obese (HR = 1.98; 1.35–2.88). Associations with colon cancer risk in women (889 cases) were weaker.

Conclusions

ABSI-by-HI body-shape phenotypes provide information for body composition. Colon cancer risk in men appears related to ASAT quantity for “slim” and “wide” but to factors determining VAT accumulation for “apple” phenotype.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08820-6.

Cross-sectional and longitudinal associations between subcutaneous adipose tissue thickness and dual-energy X-ray absorptiometry fat mass

The present study examined cross-sectional and longitudinal relationships between total and segmental subcutaneous tissue thicknesses from ultrasonography (US) and total and segmental fat mass (FM) estimates from dual-energy X-ray absorptiometry (DXA). Traditional US FM estimates were also examined. Twenty resistance-trained males (mean ± SD; age: 22.0 ± 2.6 years; body mass: 74.8 ± 11.5 kg; DXA fat: 17.5 ± 4.5%) completed a 6-week supervised resistance training programme while consuming a hypercaloric diet. Pre- and post-intervention body composition was assessed by DXA and B-mode US. Data were analysed using Pearson's correlation (r), Lin's correlation coefficient (CCC), paired t-tests, Wilcoxon signed-rank tests and Bland-Altman analysis, as appropriate. Cross-sectionally, correlations were observed between total DXA FM and total subcutaneous tissue thickness (r = 0.88). Longitudinally, a correlation was observed between total DXA FM changes and total subcutaneous tissue changes (r = 0.49, CCC = 0.38). Correlations of similar magnitudes were observed for the upper body and trunk estimates, but DXA FM changes were unrelated to subcutaneous tissue changes for the lower body and arms. Cross-sectionally, US 2-compartment FM and DXA FM were correlated (r = 0.91, CCC = 0.83). Longitudinally, a weaker correlation was observed (r = 0.47, CCC = 0.33). In summary, longitudinal associations between US and DXA are weaker than cross-sectional relationships; additionally, correlations between US subcutaneous tissue and whole-body DXA FM appear to be driven by the trunk region rather than appendages. Reporting raw skinfold thicknesses rather than FM estimates alone may improve the utility of techniques based on subcutaneous tissue thickness, such as US and skinfolds.