Effect of Hand Positioning on DXA Total and Regional Bone and Body Composition Parameters, Precision Error, and Least Significant Change

Effect of exercise-induced body fluid redistribution on body composition in males using dual energy X-ray absorptiometry

We studied the effect of exercise-induced body fluid redistribution on dual-energy x-ray absorptiometry (DXA) body composition scores. Thirty males completed 30-min of upper-body exercise (UBE), lower-body exercise, and seated non-exercise control (NEC). ANOVA determined interactions between experimental conditions and measurements on body composition variables. For UBE, mean pre to post differences were found on tissue fat (M = 0.35 ± 0.12%; CI95%diff = 0.10 to 0.59%; p = 0.007), region fat (M = 0.32 ± 0.11%; CI95%diff = 0.09 to 0.55%; p = 0.008), lean mass (M = 0.27 ± 0.01 kg; CI95%diff = 0.18 to 0.37 kg; p ≤ 0.0001), and total mass (M = 0.27 ± 0.05 kg; CI95%diff = 0.17 to 0.36 kg; p ≤ 0.0001). Mean tissue pre to post differences were found for the total body in the NEC (M = 0.10 ± 0.04 kg; CI95%diff = 0.03 to 0.18 kg; p = 0.008), UBE (M = 0.19 ± 0.03 kg; CI95%diff = 0.14 to 0.24 kg; p ≤ 0.0001), and LBE (M = 0.31 ± 0.04 kg; CI95%diff = 0.24 to 0.39 kg; p ≤ 0.0001) conditions. High absolute reliability was found within experimental conditions. These findings have practical implications for technicians, since acute exercise elicited small changes in body composition scores using DXA.

The Effect of a Compression Bandage on Dual Energy X-ray Absorptiometry Body Composition Scores

BACKGROUND

Dual-energy X-ray absorptiometry (DXA) measures are affected by the noise produced by external factors such as textile compression found in loose clothing. The study aimed to determine the effect of a compression bandage (CB) on body composition (BC) assessed by DXA.

METHODS

Sixty volunteers (age=21.4±4.7yr.) underwent full-body DXA scans on a control (CTRL) condition and after wearing a 30-mmHg CB on the trunk, legs, and arms. ANOVA (2 genders by 2 experimental conditions) determined mean interactions in BC variables tissue body fat% (BF%), region body fat% (RBF%), body tissue (BT), fat mass (FM), lean mass (LM), bone mineral content (BMC), and total mass (TM). Absolute reliability in BC scores was studied by the typical error of the measurement (TEM), the coefficient of variability (CV), and Bland-Altman plots.

RESULTS

ANOVA interactions were found on tissue total BF% (p=0.049), RBF% (p=0.048), android lean mass (p=0.004), and android total mass (p=0.019). The CV was small for tissue BF% (2.61±0.93%, CI95%=0.79, 4.43%), RBF% (2.66±1.78%, CI95%=-0.83, 6.15%), BT (4.82±2.19%, CI95%=0.54, 9.10kg), FM (4.17±2.25%, CI95%=-0.24, 8.58kg), LM (3.25±2.44%, CI95%=-1.53, 8.04kg), BMC (4.81±2.96%, CI95%=-0.99, 10.62kg), and TM (2.84±2.80%, CI95%=-2.65, 8.33kg). Bland-Altman plots showed random error for BF%, LM, and BMC. A bias of 0.5% was observed on BF% in males.

CONCLUSION

A CB worn during a full-body DXA scan elicited similar BC scores than not wearing it. The variation in scores was <10% for most BC variables, and a trivial bias of 0.5% in BF% was detected in male scores.

A machine learning approach to identify important variables for distinguishing between fallers and non-fallers in older women

Falls are a significant ongoing public health concern for older adults. At present, few studies have concurrently explored the influence of multiple measures when seeking to determine which variables are most predictive of fall risks. As such, this cross-sectional study aimed to identify those functional variables (i.e. balance, gait and clinical measures) and physical characteristics (i.e. strength and body composition) that could best distinguish between older female fallers and non-fallers, using a machine learning approach. Overall, 60 community-dwelling older women (≥65 years), retrospectively classified as fallers (n = 21) or non-fallers (n = 39), attended three data collection sessions. Data (281 variables) collected from tests in five separate domains (balance, gait, clinical measures, strength and body composition) were analysed using random forest (RF) and leave-one-variable-out partial least squares correlation analysis (LOVO PLSCA) to assess variable importance. The strongest discriminators from each domain were then aggregated into a multi-domain dataset, and RF, LOVO PLSCA, and logistic regression models were constructed to identify the important variables in distinguishing between fallers and non-fallers. These models were used to classify participants as either fallers or non-fallers, with their performance evaluated using receiver operating characteristic (ROC) analysis. The study found that it is possible to classify fallers and non-fallers with a high degree of accuracy (e.g. logistic regression: sensitivity = 90%; specificity = 87%; AUC = 0.92; leave-one-out cross-validation accuracy = 63%) using a combination of 18 variables from four domains, with the gait and strength domains being particularly informative for screening programmes aimed at assessing falls risk.

Fatness, fitness and the aging brain: A cross sectional study of the associations between a physiological estimate of brain age and physical fitness, activity, sleep, and body composition

Introduction

Changes in brain structure and function occur with aging. However, there is substantial heterogeneity both in terms of when these changes begin, and the rate at which they progress. Understanding the mechanisms and/or behaviors underlying this heterogeneity may allow us to act to target and slow negative changes associated with aging.

Methods

Using T1 weighted MRI images, we applied a novel algorithm to determine the physiological age of the brain (brain-predicted age) and the predicted age difference between this physiologically based estimate and chronological age (BrainPAD) to 551 sedentary adults aged 65 to 84 with self-reported cognitive complaint measured at baseline as part of a larger study. We also assessed maximal aerobic capacity with a graded exercise test, physical activity and sleep with accelerometers, and body composition with dual energy x-ray absorptiometry. Associations were explored both linearly and logistically using categorical groupings.

Results

Visceral Adipose Tissue (VAT), Total Sleep Time (TST) and maximal aerobic capacity all showed significant associations with BrainPAD. Greater VAT was associated with higher (i.e,. older than chronological) BrainPAD (r = 0.149 p = 0.001)Greater TST was associated with higher BrainPAD (r = 0.087 p = 0.042) and greater aerobic capacity was associated with lower BrainPAD (r = - 0.088 p = 0.040). With linear regression, both VAT and TST remained significant (p = 0.036 and 0.008 respectively). Each kg of VAT predicted a 0.741 year increase in BrainPAD, and each hour of increased TST predicted a 0.735 year increase in BrainPAD. Maximal aerobic capacity did not retain statistical significance in fully adjusted linear models.

Discussion

Accumulation of visceral adipose tissue and greater total sleep time, but not aerobic capacity, total daily physical activity, or sleep quantity and/or quality are associated with brains that are physiologically older than would be expected based upon chronological age alone (BrainPAD).

Precision of the GE Lunar Total Body-Less Head Scan for the Measurement of Three-Compartment Body Composition in Athletes

INTRODUCTION

Dual energy X-ray absorptiometry (DXA) is widely used for the assessment of lean mass (LM), fat mass (FM) and bone mineral content (BMC). When observing standardised protocols, DXA has a high level of precision for the assessment of total body composition, including the head region. However, including the head region may have limited relevance in athletes and can be problematic when positioning taller athletes who exceed scan boundaries. This study investigated the precision of a new total-body-less-head (TBLH) DXA scan for three-compartment body composition measurement in athletes, with outcomes compared to the standard total-body DXA scan.

METHODS

Precision errors were calculated from two consecutive scans with re-positioning (Lunar iDXA, GE Healthcare, Madison, WI), in male and female athletes from a range of sports. TBLH precision was determined from repeat scans in 95 athletes (male n = 55; female n = 40; age: 26.0 ± 8.5 y; body mass: 81.2 ± 20.5 kg; stature: 1.77 ± 0.11 m), and standard total-body scan precision was derived from a sub-sample of 58 athletes (male n = 19; female n = 39; age: 27.6 ± 9.9 y; body mass: 69.6 ± 14.8 kg; stature: 1.72 ± 0.94 m). Data from the sub-sample were also used to compare precision error and 3-compartment body composition outcomes between the standard total-body scan and the TBLH scan.

RESULTS

TBLH precision errors [root mean squared-standard deviation, RMS-SD (coefficient of variation, %CV)] were bone mineral content (BMC): 15.6 g (0.5%), lean mass (LM): 254.3 g (0.4%) and fat mass (FM): 199.4 g (1.3%). These outcomes compared favourably to the precision errors derived from the standard total-body scan [BMC: 12.4 g (0.4%), LM: 202.2 g (0.4%), and FM: 160.8 g (1.1%)]. The TBLH scan resulted in lower BMC (-19.5%), LM (-6.6%), and FM (-4.5%) compared to the total-body scan (BMC: 2,308 vs. 2,865 g; LM: 46,954 vs. 50,276 g; FM: 15,183 vs. 15,888 g, all p&lt;0.005). ConclusionThe TBLH scan demonstrates high in-vivo precision comparable to that of the standard total-body scan in a heterogeneous cohort of athletes. Given the impact of head exclusion on total body composition outcomes, TBLH scans should not be used interchangeably with the standard total-body scan.

Official position of the Brazilian Association of Bone Assessment and Metabolism (ABRASSO) on the evaluation of body composition by densitometry: part I (technical aspects)—general concepts, indications, acquisition, and analysis

Objective

To review the technical aspects of body composition assessment by dual-energy X-ray absorptiometry (DXA) and other methods based on the most recent scientific evidence.

Materials and methods

This Official Position is a result of efforts by the Scientific Committee of the Brazilian Association of Bone Assessment and Metabolism (Associação Brasileira de Avaliação Óssea e Osteometabolismo, ABRASSO) and health care professionals with expertise in body composition assessment who were invited to contribute to the preparation of this document. The authors searched current databases for relevant publications. In this first part of the Official Position, the authors discuss the different methods and parameters used for body composition assessment, general principles of DXA, and aspects of the acquisition and analysis of DXA scans.

Conclusion

Considering aspects of accuracy, precision, cost, duration, and ability to evaluate all three compartments, DXA is considered the gold-standard method for body composition assessment, particularly for the evaluation of fat mass. In order to ensure reliable, adequate, and reproducible DXA reports, great attention is required regarding quality control procedures, preparation, removal of external artifacts, imaging acquisition, and data analysis and interpretation.

Body composition associations with muscle strength in older adults living in Auckland, New Zealand

BACKGROUND

Aging is associated with decreases in muscle strength and simultaneous changes in body composition, including decreases in muscle mass, muscle quality and increases in adiposity.

METHODS

Adults (n = 369; 236 females) aged 65-74 years living independently were recruited from the cross-sectional Researching Eating Activity and Cognitive Health (REACH) study. Body fat percentage and appendicular skeletal muscle mass (ASM) (sum of lean mass in the arms and legs) were assessed using Dual-energy X-ray Absorptiometry (Hologic, QDR Discovery A). The ASM index was calculated by ASM (kilograms) divided by height (meters) squared. Isometric grip strength was measured using a hand grip strength dynamometer (JAMAR HAND).

RESULTS

Linear regression analyses revealed that muscle strength was positively associated with the ASM index (R2 = 0.431, p < 0.001). When exploring associations between muscle strength and muscle mass according to obesity classifications (obesity ≥30% males; ≥40% females), muscle mass was a significant predictor of muscle strength in non-obese participants. However, in participants with obesity, muscle mass was no longer a significant predictor of muscle strength.

CONCLUSIONS

Body fat percentage should be considered when measuring associations between muscle mass and muscle strength in older adults.

UEFA expert group statement on nutrition in elite football. Current evidence to inform practical recommendations and guide future research

Football is a global game which is constantly evolving, showing substantial increases in physical and technical demands. Nutrition plays a valuable integrated role in optimising performance of elite players during training and match-play, and maintaining their overall health throughout the season. An evidence-based approach to nutrition emphasising, a 'food first' philosophy (ie, food over supplements), is fundamental to ensure effective player support. This requires relevant scientific evidence to be applied according to the constraints of what is practical and feasible in the football setting. The science underpinning sports nutrition is evolving fast, and practitioners must be alert to new developments. In response to these developments, the Union of European Football Associations (UEFA) has gathered experts in applied sports nutrition research as well as practitioners working with elite football clubs and national associations/federations to issue an expert statement on a range of topics relevant to elite football nutrition: (1) match day nutrition, (2) training day nutrition, (3) body composition, (4) stressful environments and travel, (5) cultural diversity and dietary considerations, (6) dietary supplements, (7) rehabilitation, (8) referees and (9) junior high-level players. The expert group provide a narrative synthesis of the scientific background relating to these topics based on their knowledge and experience of the scientific research literature, as well as practical experience of applying knowledge within an elite sports setting. Our intention is to provide readers with content to help drive their own practical recommendations. In addition, to provide guidance to applied researchers where to focus future efforts.

Development of an Anthropometric Prediction Model for Fat-Free Mass and Muscle Mass in Elite Athletes

The monitoring of body composition is common in sports given the association with performance. Surface anthropometry is often preferred when monitoring changes for its convenience, practicality, and portability. However, anthropometry does not provide valid estimates of absolute lean tissue in elite athletes. The aim of this investigation was to develop anthropometric models for estimating fat-free mass (FFM) and skeletal muscle mass (SMM) using an accepted reference physique assessment technique. Sixty-four athletes across 18 sports underwent surface anthropometry and dual-energy X-ray absorptiometry (DXA) assessment. Anthropometric models for estimating FFM and SMM were developed using forward selection multiple linear regression analysis and contrasted against previously developed equations. Most anthropometric models under review performed poorly compared with DXA. However, models derived from athletic populations such as the Withers equation demonstrated a stronger correlation with DXA estimates of FFM (r = .98). Equations that incorporated skinfolds with limb girths were more effective at explaining the variance in DXA estimates of lean tissue (Sesbreno FFM [R2 = .94] and Lee SMM [R2 = .94] models). The Sesbreno equation could be useful for estimating absolute indices of lean tissue across a range of physiques if an accepted option like DXA is inaccessible. Future work should explore the validity of the Sesbreno model across a broader range of physiques common to athletic populations.

Computer-aided osteoporosis detection from DXA imaging

BACKGROUND AND OBJECTIVE

Osteoporosis is a skeletal disease caused by a high rate of bone tissue loss, and it is a major cause of bone fracture. In contemporary society, osteoporosis is more common than cancer and stroke and results in a higher rate of morbidity and mortality in the human population. Osteoporosis can conclusively be diagnosed with dual energy X-ray absorptiometry (DXA). In this study, we propose a computer-aided osteoporosis detection (CAOD) technique that automatically measures bone mineral density (BMD) and generates an osteoporosis report from a DXA scan.

METHODS

The CAOD model denoise and segments DXA images using a non-local mean filter, Machine learning pixel label random forest respectively, and locates regions of interest with higher accuracy. Pixel label random forest classifies a pixel either bone or soft tissue; then contours are extracted from binary image to locate regions of interest and calculate BMD from bone and soft tissues pixels. Mean standard deviation and correlation coefficients statistical analysis were used to evaluate the consistency and accuracy of BMD measurements.

RESULTS

During a consistency test of BMD measurements using three consecutive scans from Computerized Imaging Reference Systems' Bona Fide Phantom (CIRS-BFP) for the spine, the CAOD model showed an averaged standard deviation of 0.0029 while the standard deviation from manual measurements on the same data set by three different individuals was recorded as 0.1199. During another correlation study of BMD measurements evaluating real human scan images by the CAOD model versus manual measurement, the model scored a correlation coefficient of R² = 0.9901 while the CIRS-BFP study scored a correlation coefficient of R² = 0.9709.

CONCLUSIONS

The CAOD model increases the preciseness and accuracy of BMD measurements. This CAOD method will help clinicians, untrained DXA operators, and researchers (medical scientists, doctors, and bone researchers) use the DXA system with reliable accuracy and overcome workload challenges. It will also improve osteoporosis diagnosis from DXA systems and increase system performance and value.

Muscle quality as a complementary prognostic tool in conjunction with sarcopenia assessment in younger and older individuals

Purpose

This pilot study investigated differences in lean tissue mass, muscle strength, muscle quality (strength per unit of muscle mass; MQ), and functional performance in healthy younger and older individuals. The most robust predictors of appendicular lean mass (ALM) were then determined in each group.

Methods

Fifty younger (18–45 years) and 50 older (60–80 years) participants completed tests of upper and lower body strength alongside body composition by dual-energy X-ray absorptiometry from which upper- and lower-body MQ were estimated. Available cut-points for older people were used to determine low upper-body MQ in both groups. Low lower-body MQ was determined as at least two standard deviations below the mean of the younger group. Functional performance was assessed by gait speed. Sarcopenia was identified using two established definitions.

Results

Upper and lower body strength, ALM, lower-body MQ and gait speed were significantly higher in the younger group (all p < 0.002). Sarcopenia was identified in 2–4% of the older group. Low upper-body MQ was evident in 32% and 42% of the younger and older group, respectively. Low lower-body MQ was observed in 4% of younger participants, and 50% of older participants. In both groups, the most robust predictors of ALM were upper and lower body strength (young R² = 0.74, 0.82; older R² = 0.68, 0.72).

Conclusions

Low MQ despite low prevalence rates of sarcopenia in both groups suggests a need for age-specific MQ cut-points. Muscle quality assessments might be useful complementary prognostic tools alongside existing sarcopenia definitions.

Interpretation of Dual-Energy X-Ray Absorptiometry-Derived Body Composition Change in Athletes: A Review and Recommendations for Best Practice

Dual-energy X-ray absorptiometry (DXA) is a medical imaging device which has become the method of choice for the measurement of body composition in athletes. The objectives of this review were to evaluate published longitudinal DXA body composition studies in athletic populations for interpretation of "meaningful" change, and to propose a best practice measurement protocol. An online search of PubMed and CINAHL via EBSCO Host and Web of Science enabled the identification of studies published until November 2016. Those that met the inclusion criteria were reviewed independently by 2 authors according to their methodological quality and interpretation of body composition change. Twenty-five studies published between 1996 and November 2016 were reviewed (male athletes: 13, female athletes: 3, mixed: 9) and sample sizes ranged from n = 1 to 212. The same number of eligible studies was published between 2013 and 2016, as over the 16 yr prior (between 1996 and 2012). Seven did not include precision error, and fewer than half provided athlete-specific precision error. There were shortfalls in the sample sizes on which precision estimates were based and inconsistencies in the level of pre-scan standardization, with some reporting full standardization protocols and others reporting only single (e.g., overnight fast) or no control measures. There is a need for standardized practice and reporting in athletic populations for the longitudinal measurement of body composition using DXA. Based on this review and those of others, plus the official position of the International Society for Clinical Densitometry, our recommendations and protocol are proposed as a guide to support best practice.

Estimates of the precision of regional and whole body composition by dual-energy x-ray absorptiometry in persons with chronic spinal cord injury

Study Design

Longitudinal design

Objectives

To determine the reproducibility of total- and regional-body composition assessments from a total-body scan using dual-energy x-ray absorptiometry (DXA) in persons with spinal cord injury (SCI).

Methods

Twenty-four individuals with SCI completed within-day short-term precision testing by repositioning study participants between scans. An additional and separate cohort of 22 individuals with SCI were scanned twice on a GE-Lunar DXA scanner separated by a 4-week interval to assess the long-term precision assessment. The root mean square coefficient of variation percent (RMS-CV%) values for the regional and total body composition was calculated.

Results

For the same day, short-term precision assessment, the RMS-CV% for each region did not exceed 5.6%, 2.7%, 3.8%, 6.5%, 5.8% and 2.3% for arms, legs, trunk, android and gynoid regions and total body mass, respectively. In the long-term precision assessment, the RMS-CV% for each region did not exceed 6.0%, 3.0%, 4.4%, 8.2%, 3.4% and 2.0% for arms, legs, trunk, android, gynoid and total body mass. Moreover, the interclass-correlation coefficient in the long-term precision group demonstrated excellent linear agreement between repeat scans for all regions (r> 0.97).

Conclusion

The precision error of the total body composition variables in our SCI cohort was similar to those reported in the literature for nondisabled individuals, and the precision errors of the regional body composition compartments were notably higher, but similar to the regional precision errors reported in the general population.