Multi-component molecular-level body composition reference methods: evolving concepts and future directions

Female obesity: clinical and psychological assessment toward the best treatment

Obesity is a heterogeneous condition which results from complex interactions among sex/gender, sociocultural, environmental, and biological factors. Obesity is more prevalent in women in most developed countries, and several clinical and psychological obesity complications show sex-specific patterns. Females differ regarding fat distribution, with males tending to store more visceral fat, which is highly correlated to increased cardiovascular risk. Although women are more likely to be diagnosed with obesity and appear more motivated to lose weight, as confirmed by their greater representation in clinical trials, males show better outcomes in terms of body weight and intra-abdominal fat loss and improvements in the metabolic risk profile. However, only a few relatively recent studies have investigated gender differences in obesity, and sex/gender is rarely considered in the assessment and management of the disease. This review summarizes the evidence of gender differences in obesity prevalence, contributing factors, clinical complications, and psychological challenges. In addition, we explored gender differences in response to obesity treatments in the specific context of new anti-obesity drugs.

Design and conduct of a randomized controlled feeding trial in a residential setting with mitigation for COVID-19

BACKGROUND

Evaluating effects of different macronutrient diets in randomized trials requires well defined infrastructure and rigorous methods to ensure intervention fidelity and adherence.

METHODS

This controlled feeding study comprised two phases. During a Run-in phase (14-15 weeks), study participants (18-50 years, BMI, ≥27 kg/m2) consumed a very-low-carbohydrate (VLC) diet, with home delivery of prepared meals, at an energy level to promote 15 ± 3% weight loss. During a Residential phase (13 weeks), participants resided at a conference center. They received a eucaloric VLC diet for three weeks and then were randomized to isocaloric test diets for 10 weeks: VLC (5% energy from carbohydrate, 77% from fat), high-carbohydrate (HC)-Starch (57%, 25%; including 20% energy from refined grains), or HC-Sugar (57%, 25%; including 20% sugar). Outcomes included measures of body composition and energy expenditure, chronic disease risk factors, and variables pertaining to physiological mechanisms. Six cores provided infrastructure for implementing standardized protocols: Recruitment, Diet and Meal Production, Participant Support, Assessments, Regulatory Affairs and Data Management, and Statistics. The first participants were enrolled in May 2018. Participants residing at the conference center at the start of the COVID-19 pandemic completed the study, with each core implementing mitigation plans.

RESULTS

Before early shutdown, 77 participants were randomized, and 70 completed the trial (65% of planned completion). Process measures indicated integrity to protocols for weighing menu items, within narrow tolerance limits, and participant adherence, assessed by direct observation and continuous glucose monitoring.

CONCLUSION

Available data will inform future research, albeit with less statistical power than originally planned.

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.

Advances in body composition: a 100-year journey

Knowledge of human body composition at the dawn of the twentieth century was based largely on cadaver studies and chemical analyses of isolated organs and tissues. Matters soon changed by the nineteen twenties when the Czech anthropologist Jindřich Matiegka introduced an influential new anthropometric method of fractionating body mass into subcutaneous adipose tissue and other major body components. Today, one century later, investigators can not only quantify every major body component in vivo at the atomic, molecular, cellular, tissue-organ, and whole-body organizational levels, but go far beyond to organ and tissue-specific composition and metabolite estimates. These advances are leading to an improved understanding of adiposity structure-function relations, discovery of new obesity phenotypes, and a mechanistic basis of some weight-related pathophysiological processes and adverse clinical outcomes. What factors over the past one hundred years combined to generate these profound new body composition measurement capabilities in living humans? This perspective tracks the origins of these scientific innovations with the aim of providing insights on current methodology gaps and future research needs.

Development and Validation of a Method of Body Volume and Fat Mass Estimation Using Three-Dimensional Image Processing with a Mexican Sample

Body composition assessment using instruments such as dual X-ray densitometry (DXA) can be complex and their use is often limited to research. This cross-sectional study aimed to develop and validate a densitometric method for fat mass (FM) estimation using 3D cameras. Using two such cameras, stereographic images, and a mesh reconstruction algorithm, 3D models were obtained. The FM estimations were compared using DXA as a reference. In total, 28 adults, with a mean BMI of 24.5 (±3.7) kg/m2 and mean FM (by DXA) of 19.6 (±5.8) kg, were enrolled. The intraclass correlation coefficient (ICC) for body volume (BV) was 0.98-0.99 (95% CI, 0.97-0.99) for intra-observer and 0.98 (95% CI, 0.96-0.99) for inter-observer reliability. The coefficient of variation for kinetic BV was 0.20 and the mean difference (bias) for BV (liter) between Bod Pod and Kinect was 0.16 (95% CI, -1.2 to 1.6), while the limits of agreement (LoA) were 7.1 to -7.5 L. The mean bias for FM (kg) between DXA and Kinect was -0.29 (95% CI, -2.7 to 2.1), and the LoA was 12.1 to -12.7 kg. The adjusted R2 obtained using an FM regression model was 0.86. The measurements of this 3D camera-based system aligned with the reference measurements, showing the system's feasibility as a simpler, more economical screening tool than current systems.

Phase angle and body composition: A scoping review

The aim of the study was to map evidence on the association between phase angle (PhA) and body composition in populations healthy and clinical populations). A systematic search for information regarding the topic was conducted in nine electronic databases (CINAHL, LILACS, PubMed, SciELO, Scopus, SPORTDiscus, Science Direct, MEDLINE and Web of Science) between October and November 2021. Studies with different designs, which allowed extracting information about the relationship between PhA and body composition (body cell mass [BCM], muscle tissue, bone mineral content, lean mass, total fat mass, visceral fat, and lean soft tissue mass [LSTM]) were included. Of the total of 11,913 initially identified studies, 59 were included after reading titles, abstracts, full texts and references. Most studies (40.67%; n = 24) presented data from Brazilian samples. With regard to the design of studies, 15 (25.42%) had longitudinal design. The age group of studies was wide, with studies involved 3-year-old children and 88-year-old adults. Body fat mass was evaluated by 31 studies (52.54%) in which 11 observed inverse relationships, nine studies showed direct relationships and 11 observed no relationship. Regarding lean mass, muscle mass, and fat-free mass components, most studies observed direct relationship with PhA (n = 37; 86.04%). It could be concluded that the phase angle was directly associated with lean mass and muscle mass in different age groups (children, adolescents, adults and older adults) and in people with different health diagnoses (HIV, cancer, hemodialysis, sarcopenia and without the diagnosis of diseases). Regarding body fat and the other investigated components, there is not enough evidence to establish the direction of associations.

A Bioelectrical Impedance Analysis in Adult Subjects: The Relationship between Phase Angle and Body Cell Mass

The correct assessment of body composition is essential for an accurate diagnostic evaluation of nutritional status. The body mass index (BMI) is the most widely adopted indicator for evaluating undernutrition, overweight, and obesity, but it is unsuitable for differentiating changes in body composition. In recent times, bioelectrical impedance analyses (BIA) have been proven as a more accurate procedure for the assessment of body composition. Furthermore, the efficiency of bioelectrical impedance vector analyses, as an indicator of nutritional status and hydration, has been demonstrated. By applying a bioimpedance analysis, it is possible to detect fat mass (FM), fat free mass (FFM), phase angle, and body cell mass (BCM). It is important to point out that phase angle and BCM are strongly associated with health status. The aim of this research was to examine body composition and the association between the phase angle and BCM in 87 subjects (14 males and 73 females), aged between 23 and 54 years, with BMIs ranging from 17.0 to 32.0 kg/m2, according to sex. The BMI results revealed that the majority of the assessed subjects were within the normal range and had a normal percentage of FM. Our data indicate that a direct relation exists between phase angle and cellular health and that these values increase almost linearly. Consequently, a high phase angle may be related to increased BCM values.

Beyond BMI

This review examined the origins of the concept of the BMI in the work of Quetelet in the 19th century and its subsequent adoption and use in tracking the course of the pandemic of obesity during the 20th century. In this respect, it has provided a valuable international epidemiological tool that should be retained. However, as noted in this review, the BMI is deficient in at least three ways. First, it does not measure body fat distribution, which is probably a more important guide to the risk of excess adiposity than the BMI itself. Second, it is not a very good measure of body fat, and thus its application to the diagnosis of obesity or excess adiposity in the individual patient is limited. Finally, the BMI does not provide any insights into the heterogeneity of obesity or its genetic, metabolic, physiological or psychological origins. Some of these mechanisms are traced in this review.

Skeletal muscle mass can be estimated by creatine (methyl-d3) dilution and is correlated with fat-free mass in active young males

BACKGROUND

Assessing whole-body skeletal muscle mass (SMM) and fat-free mass (FFM) is essential for the adequate nutritional management and training evaluation of athletes and trained individuals. This study aimed to determine the relationship between SMM assessed using the creatine (methyl-d₃) dilution (D₃-creatine) method and SMM estimated by whole-body magnetic resonance imaging (MRI) in healthy young men undergoing exercise training. Additionally, we examined the association between FFM measured using the four-component (4C) method (FFM_4C) and the total body protein value estimated using 4C (TBpro_4C).

METHODS AND RESULTS

We analyzed the data of 29 males (mean age, 19.9 ± 1.8 years) who exercised regularly. SMM measurements were obtained using the D₃-creatine method (SMM_D3-creatine) and MRI (SMM_MRI). The SMM_D3-creatine adjusted to 4.3 g/SMM kg was significantly higher than SMM_MRI (p < 0.01). The fit of the creatine pool size compared with SMM_MRI was 5.0 g/SMM_MRI kg. SMM_MRI was significantly correlated with both SMM_D3-creatine adjusted to 4.3 g/kg and 5.1 g/kg. TBpro_4C was significantly lower than SMM_MRI (p < 0.01). Contrastingly, FFM_4C was significantly higher than SMM_MRI (p < 0.01).

CONCLUSIONS

SMM_D3-creatine adjusted to 4.3 g/SMM kg-a previously reported value-may differ for athletes and active young males. We believe that a value of 5.0-5.1 g/SMM kg better estimates the total muscle mass in this population. Traditional FFM estimation highly correlates with SMM_MRI in well-trained young males, and the relationships appear strong enough for total body protein or SMM to be estimated through the FFM value.

Multicompartment body composition analysis in older adults: a cross-sectional study

BACKGROUND

During aging, changes occur in the proportions of muscle, fat, and bone. Body composition (BC) alterations have a great impact on health, quality of life, and functional capacity. Several equations to predict BC using anthropometric measurements have been developed from a bi-compartmental (2-C) approach that determines only fat mass (FM) and fat-free mass (FFM). However, these models have several limitations, when considering constant density, progressive bone demineralization, and changes in the hydration of the FFM, as typical changes during senescence. Thus, the main purpose of this study was to propose and validate a new multi-compartmental anthropometric model to predict fat, bone, and musculature components in older adults of both sexes.

METHODS

This cross-sectional study included 100 older adults of both sexes. To determine the dependent variables (fat mass [FM], bone mineral content [BMC], and appendicular lean soft tissue [ALST]) whole total and regional dual-energy X-ray absorptiometry (DXA) body scans were performed. Twenty-nine anthropometric measures and sex were appointed as independent variables. Models were developed through multivariate linear regression. Finally, the predicted residual error sum of squares (PRESS) statistic was used to measure the effectiveness of the predicted value for each dependent variable.

RESULTS

An equation was developed to simultaneously predict FM, BMC, and ALST from only four variables: weight, half-arm span (HAS), triceps skinfold (TriSK), and sex. This model showed high coefficients of determination and low estimation errors (FM: R²_adj: 0.83 and SEE: 3.16; BMC: R²_adj: 0.61 and SEE: 0.30; ALST: R²_adj: 0.85 and SEE: 1.65).

CONCLUSION

The equations provide a reliable, practical, and low-cost instrument to monitor changes in body components during the aging process. The internal cross-validation method PRESS presented sufficient reliability in the model as an inexpensive alternative for clinical field use.

Metabolically favorable adiposity and bone mineral density: a Mendelian randomization analysis

OBJECTIVE

This analysis assessed the putative causal association between genetically predicted percent body fat and areal bone mineral density (aBMD) and, more specifically, the association between genetically predicted metabolically "favorable adiposity" (MFA) and aBMD at clinically relevant bone sites.

METHODS

Mendelian randomization was used to assess the relationship of MFA and percent body fat with whole-body, lumbar spine, femoral neck, and forearm aBMD. Sex-stratified and age-stratified exploratory analyses were conducted.

RESULTS

In all MR analyses, genetically predicted MFA was inversely associated with aBMD for the whole body (β = -0.053, p = 0.0002), lumbar spine (β = -0.075; p = 0.0001), femoral neck (β = -0.045; p = 0.008), and forearm (β = -0.115; p = 0.001). This negative relationship was strongest in older individuals and did not differ by sex. The relationship between genetically predicted percent body fat and aBMD was nonsignificant across all Mendelian randomization analyses. Several loci that were associated at a genome-wide significance level (p < 5 × 10^-8 ) in opposite directions with body fat and aBMD measures were also identified.

CONCLUSIONS

This study did not support the hypothesis that MFA protects against low aBMD. Instead, it showed that MFA may result in lower aBMD. Further research is needed to understand how MFA affects aBMD and other components of bone health such as bone turnover, bone architecture, and osteoporotic fractures.

Equations based on anthropometric measurements for adipose tissue, body fat, or body density prediction in children and adolescents: a scoping review

PURPOSE

Assessing the body composition of children and adolescents is important to monitor their health status. Anthropometric measurements are feasible and less-expensive than other techniques for body composition assessment. This study aimed to systematically map anthropometric equations to predict adipose tissue, body fat, or density in children and adolescents, and to analyze methodological aspects of the development of anthropometric equations using skinfolds.

METHODS

A scoping review was carried out following the PRISMA-ScR criteria. The search was carried out in eight databases. The methodological structure protocol of this scoping review was retrospectively registered in the Open Science Framework ( https://osf.io/35uhc/ ).

RESULTS

We included 78 reports and 593 anthropometric equations. The samples consisted of healthy individuals, people with different diseases or disabilities, and athletes from different sports. Dual-energy X-ray absorptiometry (DXA) was the reference method most commonly used in developing equations. Triceps and subscapular skinfolds were the anthropometric measurements most frequently used as predictors in the equations. Age, stage of sexual maturation, and peak height velocity were used as complementary variables in the equations.

CONCLUSION

Our scoping review identified equations proposed for children and adolescents with a great diversity of characteristics. In many of the reports, important methodological aspects were not addressed, a factor that may be associated with equation bias.

LEVEL IV

Evidence obtained from multiple time series analysis such as case studies. (NB: dramatic results in uncontrolled trials might also be regarded as this type of evidence).

Association between classic and specific bioimpedance vector analysis and sarcopenia in older adults: a cross-sectional study

Background

To verify (1) the association between classic and specific bioelectrical impedance vector analysis (BIVA) with body composition, hydration, and physical performance in older adults with and without sarcopenia; (2) which BIVA most accurately distinguishes sarcopenia.

Methods

A sample of 94 older adults with and without sarcopenia (29 men and 65 women, 60–85 years) was evaluated. The classic and specific BIVA procedures, Dual energy X-ray absorptiometry (DXA), and deuterium dilution were performed. Sarcopenia was defined by muscle weakness and low skeletal muscle index, while severity was indicated by low physical performance.

Results

The BIVA's potential to monitor hydration and muscle mass loss in older adults seems feasible. Classic and specific BIVA were able to distinguish sarcopenia in women (p < 0.001), but not in men. When the sarcopenia criteria were individually analyzed, both classic and specific BIVA were able to distinguish low skeletal muscle index in women, while only classic BIVA did for men. For the criterion of slow physical performance, only the classic BIVA showed severity differences for women. The vectors of adults without sarcopenia of both sexes tended to be positioned in the left region of the ellipses, revealing a predominance of soft tissues.

Conclusions

Classic BIVA has a distinct sarcopenic association with body composition, hydration, and physical performance in older adults, while specific BIVA was similar between groups. Both BIVAs are sensible to detect female morphological changes (skeletal muscle index) but not for functional (handgrip, 6-min walk test) sarcopenia criteria. These procedures are promising tools for monitoring sarcopenia risks during aging.

Gains in body mass and body water in pregnancy and relationships to birth weight of offspring in rural and urban Pune, India

Maternal size, weight gain in pregnancy, fetal gender, environment and gestational age are known determinants of birth weight. It is not clear which component of maternal weight or gained weight during pregnancy influences birth weight. We evaluated the association of maternal total body water measured by the deuterium dilution technique (TBW-D₂O) at 17 and 34 weeks of gestation with birth weight. A secondary aim was to examine the utility of bioimpedance spectroscopy (BIS) to determine total body water (TBW-BIS) in pregnancy. At 17 and 34 weeks of pregnancy, ninety-nine women (fifty-one rural and forty-eight urban) from Pune, India had measurements of body weight, TBW-D₂O, TBW-BIS and offspring birth weight. At 17 weeks of gestation, average weights for rural and urban women were 45⋅5 ± 4⋅8 (sd) and 50⋅7 ± 7⋅8 kg (P < 0⋅0001), respectively. Maternal weight gains over the subsequent 17 weeks for rural and urban women were 6⋅0 ± 2⋅2 and 7⋅5 ± 2⋅8 kg (P = 0⋅003) and water gains were 4⋅0 ± 2⋅4 and 4⋅8 ± 2⋅8 kg (P = 0⋅092), respectively. In both rural and urban women, birth weight was positively, and independently, associated with gestation and parity. Only for rural women, between 17 and 34 weeks, was an increase in dry mass (weight minus TBW-D₂O) or a decrease in TBW-D₂O as a percentage of total weight associated with a higher birth weight. At both 17 and 34 weeks, TBW-BIS increasingly underestimated TBW-D₂O as the water space increased. Differences in body composition during pregnancy between rural and urban environments and possible impacts of nutrition transition on maternal body composition and fetal growth were demonstrated.

Are methods of estimating fat-free mass loss with energy-restricted diets accurate?

BACKGROUND/OBJECTIVES

Fat-free mass (FFM) often serves as a body composition outcome variable in weight loss studies. An important assumption is that the proportions of components that make up FFM remain stable following weight loss; some body composition models rely on these "constants". This exploratory study examined key FFM component proportions before and following weight loss in two studies of participants with overweight and obesity.

SUBJECTS/METHODS

201 men and women consumed calorie-restricted moderate- or very-low carbohydrate diets leading to 10-18% weight loss in 9-15 weeks. Measured total body fat, lean mass, bone mineral, total body water (TBW), and body weight at baseline and follow-up were used to derive FFM and its chemical proportions using a four-component model.

RESULTS

A consistent finding in both studies was a non-significant reduction in bone mineral and a corresponding increase (p < 0.001) in bone mineral/FFM; FFM density increased significantly in one group of women and in all four participant groups combined (both, p < 0.05). FFM hydration (TBW/FFM) increased in all groups of men and women, one significantly (p < 0.01), and in the combined sample (borderline, p < 0.10). The proportion of FFM as protein decreased across all groups, two significantly (p < 0.05-0.01) and in the combined sample (p < 0.05).

CONCLUSION

FFM relative proportions of chemical components may not be identical before and after short-term weight loss, an observation impacting some widely used body composition models and methods. Caution is thus needed when applying FFM as a safety signal or to index metabolic evaluations in clinical trials when these body composition approaches are used.

Agreement and Precision of Deuterium Dilution for Total Body Water and Multicompartment Body Composition Assessment in Collegiate Athletes

BACKGROUND

Deuterium oxide (D2O) dilution is the criterion method for total body water (TBW) measurement, but results may vary depending on the specimen type, analysis method, and analyzing laboratory. Bioelectrical impedance (BIA) estimates TBW, but results may vary by device make and model.

OBJECTIVES

We investigated the accuracy and precision of TBW estimates and how measurement conditions affected the accuracy of body composition using multicompartment body composition models.

METHODS

Eighty collegiate athletes received duplicate TBW measures acquired from 3 BIA devices (S10, SFB7, and SOZO) and from unique D2O combinations of specimen type (saliva, urine), analysis methodology [Fourier transform infrared spectrophotometry (FTIR), isotope-ratio mass spectrometry (IRMS)], and 3 different laboratories. TBW measures were substituted into 2-compartment (2C) and 5-compartment (5C) body composition models. Criterion measures were compared using Lin's concordance correlation coefficient cutoff of poor (<0.90), moderate (0.90-0.95), substantial (0.95-0.99), and almost perfect (>0.99).

RESULTS

Fifty-one participants (26 female) completed the protocol. Using IRMS saliva as the criterion TBW, all other measures produced a substantial or almost perfect agreement, except for SFB7 (poor) and SOZO (moderate). The 2C body composition measures using D2O and BIA produced poor agreement except for moderate agreement for lab 3 FTIR saliva. The 5C body composition measures using D2O produced a substantial agreement, whereas the BIA device S10 and SOZO had a moderate agreement, while the SFB7 had a poor agreement to the criterion. Test-retest precision varied between techniques from 0.3% to 1.2% for TBW.

CONCLUSIONS

Small differences in TBW measurement led to significant differences in 2C models. The 5C models partially mitigate differences seen in 2C models when different TBW measures are used. Interchanging TBW measures in multicompartment models can be problematic and should be performed with these considerations.

The Body Adiposity Index is not applicable to the Brazilian adult population

Background

Obesity is a serious disease that burdens public health systems around the world. It is a risk factor for the development of several non-communicable chronic diseases that are related to the amount and distribution of body fat. Body composition assessment using simple and low-cost techniques can help in the early detection of excess fat, allowing for the prevention and treatment of both obesity and associated diseases. Thus, identifying and proposing valid anthropometric indices for this purpose can be a great ally of health programs.

Objective

To verify the validity of the Body Adiposity Index (BAI) in relation to Dual Energy X-Ray Absorptiometry (DXA) for estimating body fat percentage in Brazilian adults, as well as to propose a new mathematical model to estimate the fat-free mass of this population.

Methods

In a cross-sectional study, 424 subjects (of which 220 were women), aged between 20 and 59 years, were evaluated by BAI and DXA, then randomly divided into two groups stratified by sex: the development group (n = 283) and the cross-validation group (n = 141). Statistical analyses to test the validity of BAI as a predictor of fat mass, in addition to proposing a new mathematical model for estimating fat-free mass, using DXA as a reference method. The analysis included paired t-test, stepwise multiple regression, coefficient of concordance correlation, and Bland-Altman plots.

Results

The BAI validity analysis showed a low correlation coefficient of agreement [CCC = 0.626; ρ (precision) = 0.795; C_b(accuracy) = 0.787]; in addition, the mean difference in the Bland-Altman plot was different from zero in the cross-validation group (p < 0.01) and limits of agreement (LOA) ranged between−8.0 and 14.4 kg, indicating a poor agreement between the BAI and the reference method. The new mathematical model for estimating FFM showed a high correlation coefficient of agreement (CCC = 0.952; ρ = 0.953; C_b = 0.999), in addition to acceptable LOA in the Bland-Altman plot (-6.7 and 6.7).

Conclusion

In the studied sample, the BAI showed low validity for estimating body fat, while the new proposed model was found to be a good option to assess the body composition of Brazilian adults.

Tracking changes in body composition: comparison of methods and influence of pre-assessment standardisation

The present study reports the validity of multiple assessment methods for tracking changes in body composition over time and quantifies the influence of unstandardised pre-assessment procedures. Resistance-trained males underwent 6 weeks of structured resistance training alongside a hyperenergetic diet, with four total body composition evaluations. Pre-intervention, body composition was estimated in standardised (i.e. overnight fasted and rested) and unstandardised (i.e. no control over pre-assessment activities) conditions within a single day. The same assessments were repeated post-intervention, and body composition changes were estimated from all possible combinations of pre-intervention and post-intervention data. Assessment methods included dual-energy X-ray absorptiometry (DXA), air displacement plethysmography, three-dimensional optical imaging, single- and multi-frequency bioelectrical impedance analysis, bioimpedance spectroscopy and multi-component models. Data were analysed using equivalence testing, Bland-Altman analysis, Friedman tests and validity metrics. Most methods demonstrated meaningful errors when unstandardised conditions were present pre- and/or post-intervention, resulting in blunted or exaggerated changes relative to true body composition changes. However, some methods - particularly DXA and select digital anthropometry techniques - were more robust to a lack of standardisation. In standardised conditions, methods exhibiting the highest overall agreement with the four-component model were other multi-component models, select bioimpedance technologies, DXA and select digital anthropometry techniques. Although specific methods varied, the present study broadly demonstrates the importance of controlling and documenting standardisation procedures prior to body composition assessments across distinct assessment technologies, particularly for longitudinal investigations. Additionally, there are meaningful differences in the ability of common methods to track longitudinal body composition changes.

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.

Development and Cross-Validation of a Predictive Equation for Fat-Free Mass in Brazilian Adolescents by Bioelectrical Impedance

The bioelectrical impedance analysis (BIA) is one of the most commonly used techniques for assessing body composition in a clinical setting and in field approaches, as it has the advantages of easy application, fast, and non-invasive, in addition to its relatively low cost. However, the available predictive equations need to be valid for the evaluated subjects. The aim of this study was to verify the validity of several published BIA equations in estimating fat-free mass (FFM) among Brazilian adolescents, in addition to developing and cross-validating a BIA equation to estimate FFM appropriate for Brazilian adolescents. This is a cross-sectional study with 257 adolescents (128 girls) aged 10-19 years, randomly divided into two groups, namely, development (n = 172) and cross-validation (n = 85). The standard technique for assessing FFM was dual X-ray absorptiometry (DXA). The paired t-test, multiple regression, and the Bland-Altman plots were used to test the validity of the proposed models and to perform cross-validation of the model. The equation derived in this study was as follows: FFM = -17.189 + 0.498 (Height²/Resistance) + 0.226 Weight + 0.071 Reactance - 2.378 Sex + 0.097 Height + 0.222 Age; r ² = 0.92; standard error of the estimate = 2.49 kg; the new equation for FFM showed better agreement when compared with that of the equations developed in other countries. In conclusion, the newly developed equations provide a valid FFM estimation and are recommended for Brazilian adolescents with similar characteristics.

Obesity history, physical exam, laboratory, body composition, and energy expenditure: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2022

Background

This Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) on History, Physical Exam, Body Composition and Energy Expenditure is intended to provide clinicians an overview of the clinical and diagnostic evaluation of patients with pre-obesity/obesity.

Methods

The scientific information for this CPS is based upon published scientific citations, clinical perspectives of OMA authors, and peer review by the Obesity Medicine Association leadership.

Results

This CPS outlines important components of medical, dietary, and physical activity history as well as physical exams, with a focus on specific aspects unique to managing patients with pre-obesity or obesity. Patients with pre-obesity/obesity benefit from the same preventive care and general laboratory testing as those without an increase in body fat. In addition, patients with pre-obesity/obesity may benefit from adiposity-specific diagnostic testing - both generally and individually - according to patient presentation and clinical judgment. Body composition testing, such as dual energy x-ray absorptiometry, bioelectrical impedance, and other measures, each have their own advantages and disadvantages. Some patients in clinical research, and perhaps even clinical practice, may benefit from an assessment of energy expenditure. This can be achieved by several methods including direct calorimetry, indirect calorimetry, doubly labeled water, or estimated by equations. Finally, a unifying theme regarding the etiology of pre-obesity/obesity and effectiveness of treatments of obesity centers on the role of biologic and behavior efficiencies and inefficiencies, with efficiencies more often associated with increases in fat mass and inefficiencies more often associated with decreases in fat mass.

Conclusion

The Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) on History, Physical Exam, Body Composition and Energy Expenditure is one of a series of OMA CPSs designed to assist clinicians in the care of patients with the disease of pre-obesity/obesity.

Effect of Physical Training on Body Composition in Brazilian Military

The military are selected on the basis of physical standards and are regularly involved in strong physical activities, also related to particular sports training. The aims of the study were to analyze the effect of a 7-month military training program on body composition variables and the suitability of specific ‘bioelectrical impedance vector analysis’ (spBIVA), compared to DXA, to detect the changes in body composition. A sample of 270 male Brazilian cadets (19.1 ± 1.1 years), composed of a group practicing military physical training routine only (MT = 155) and a group involved in a specific sport training (SMT = 115), were measured by body composition assessments (evaluated by means of DXA and spBIVA) at the beginning and the end of the military routine year. The effect of training on body composition was similar in SMT and MT groups, with an increase in LST. DXA and spBIVA were correlated, with specific resistance (Rsp) and reactance (Xcsp) positively related to fat mass (FM), FM%, LST, and lean soft tissue index (LSTI), and phase angle positively related to LST and LSTI. Body composition variations due to physical training were recognized by spBIVA: the increase in muscle mass was indicated by the phase angle and Xcsp increase, and the stability of FM% was consistent with the unchanged values of Rsp. Military training produced an increase in muscle mass, but no change in FM%, independently of the sample characteristics at baseline and the practice of additional sports. SpBIVA is a suitable technique for the assessment of body composition in military people.

Validity of body fat percentage through different methods of body composition assessment in elite soccer referees

abstract The arbitration exercise in a soccer game requires high physical fitness and all federations apply physical tests to referees, including anthropometric tests, classifying them as fit or not for the role. The aim of this study was to assess the validity of the total body fat percentage (%BF) through different evaluation methods of body composition referenced in a four-compartment (4C) model. Cross-sectional study performed in 2018 with 21 elite male referees. %BF was estimated by 4 methods: anthropometry; bioelectrical impedance analysis (BIA); Dual X-ray absorptiometry (DXA) and air displacement plethysmography (ADP). Moreover, three and four-compartment (3 and 4C) models were calculated. Bland–Altman and intraclass correlations (ICC) analysis were performed to determine validity of all methods compared to a 4C reference. The results of one-way ANOVA revealed that there was no significant difference (F=1.541; p=0.182) between %BF analyzed by 4C model (15.98 ± 6.20), anthropometry (mean ± SD, 18.46 ± 7.03), ADP (16.19 ± 6.24), BIA (16.67 ± 5.30), DXA (20.33 ± 6.56) and 3C (16.92 ± 5.53). The Bland–Altman analysis showed that all methods analyzed overestimate %BF compared to the 4C model. The best agreement was obtained from the ADP evaluation (bias=-0.2), followed by BIA (bias=-0.6), 3C (bias=-0.9), anthropometry (bias=-2.4) and DXA (bias=-4.3). Validation assessed by ICC was excellent (ICC≥0.90) in most methods, except for anthropometry (ICC=0.80) and DXA (ICC=0.71). Overall, the results suggest that ADP, BIA and 3C were the best method to %BF evaluation. Nevertheless, anthropometry remains as a feasible method to monitor %BF of elite soccer referees.

Multicomponent density models for body composition: Review of the dual energy X-ray absorptiometry volume approach

Accurate and precise body composition estimates, notably of total body adiposity, are a vital component of in vivo physiology and metabolic studies. The reference against which other body composition approaches are usually validated or calibrated is the family of methods referred to as multicomponent "body density" models. These models quantify three to six components by combining measurements of body mass, body volume, total body water, and osseous mineral mass. Body mass is measured with calibrated scales, volume with underwater weighing or air-displacement plethysmography, total body water with isotope dilution, and osseous mineral mass by dual-energy X-ray absorptiometry. Body density is then calculated for use in model as body mass/volume. Studies over the past decade introduced a new approach to quantifying body volume that relies on dual-energy X-ray absorptiometry measurements, an advance that simplifies multicomponent density model development by eliminating the need for underwater weighing or air-displacement plethysmography systems when these technologies are unavailable and makes these methods more accessible to research and clinical programs. This review critically examines these new dual-energy X-ray approaches for quantifying body volume and density, explores their shortcomings, suggests alternative derivation approaches, and introduces ideas for potential future research studies.

Development and cross-validation of predictive equations for fat-free mass and lean soft tissue mass by bioelectrical impedance in Brazilian women

BACKGROUND/OBJECTIVES

Bioelectrical impedance is one of the most used clinical techniques to assess body composition; however, it is necessary that the available predictive equations are valid for the evaluated subjects. This study aimed to develop and cross-validate equations for fat-free mass (FFM) and lean soft tissue mass (LSTM) by bioelectrical impedance for Brazilian women, in addition to test the validity of other available equations.

SUBJECTS/METHODS

Cross-sectional study with 222 women aged 20-59 years, randomly divided into two groups: development and cross-validation. The standard technique for assessing fat mass, FFM and LSTM was dual energy X-ray absorptiometry. Paired t test, multiple regression, and Bland-Altman plots were used to test the validity of the proposed models, as well as to perform cross-validation of the models.

RESULTS

The equations derived in this study were: FFM = 16.284 + 0.442 × (Height²/Resistance) - 0.13 × age + 0.302 × Weight - 0.121 × Waist Circumference; r² = 0.86; SEE = 2.32 kg; and LSTM = 14.732 + 0.427 × (Height²/Resistance) - 0.125 × age + 0.291 × Weight - 0.115 × Waist Circumference; r² = 0.92; SEE = 2.29 kg. In addition, the new equation for FFM showed better agreement when compared to another equation developed for a Brazilian population.

CONCLUSIONS

The newly developed equations provide a valid FFM and LSTM estimation and are recommended for Brazilian women with similar characteristics.

Metabolic Dysfunction in Spinal Muscular Atrophy

Spinal muscular atrophy (SMA) is an autosomal recessive genetic disorder leading to paralysis, muscle atrophy, and death. Significant advances in antisense oligonucleotide treatment and gene therapy have made it possible for SMA patients to benefit from improvements in many aspects of the once devastating natural history of the disease. How the depletion of survival motor neuron (SMN) protein, the product of the gene implicated in the disease, leads to the consequent pathogenic changes remains unresolved. Over the past few years, evidence toward a potential contribution of gastrointestinal, metabolic, and endocrine defects to disease phenotype has surfaced. These findings ranged from disrupted body composition, gastrointestinal tract, fatty acid, glucose, amino acid, and hormonal regulation. Together, these changes could have a meaningful clinical impact on disease traits. However, it is currently unclear whether these findings are secondary to widespread denervation or unique to the SMA phenotype. This review provides an in-depth account of metabolism-related research available to date, with a discussion of unique features compared to other motor neuron and related disorders.

Making the choice between bioelectrical impedance measures for body hydration status assessment

Situational or persistent body fluid deficit (i.e., de- or hypo-hydration) is considered a significant health risk factor. Bioimpedance analysis (BIA) has been suggested as an alternative to less reliable subjective and biochemical indicators of hydration status. The present study aimed to compare various BIA models in the prediction of direct measures of body compartments associated with hydration/osmolality. Fish (n = 20) was selected as a biological model for physicochemically measuring proximate body compartments associated with hydration such as water, dissolved proteins, and non-osseous minerals as the references or criterion points. Whole-body and segmental/local impedance measures were used to investigate a pool of BIA models, which were compared by Akaike Information Criterion in their ability to accurately predict the body components. Statistical models showed that ‘volumetric-based’ BIA measures obtained in parallel, such as distance²/R_p, could be the best approach in predicting percent of body moisture, proteins, and minerals in the whole-body schema. However, serially-obtained BIA measures, such as the ratio of the reactance to resistance and the resistance adjusted for distance between electrodes, were the best fitting in predicting the compartments in the segmental schema. Validity of these results should be confirmed on humans before implementation in practice.

Segmental body composition estimated by specific BIVA and dual-energy X-ray absorptiometry

AIMS

The aim of this study was to analyse the association between specific bioelectric impedance vector analysis (BIVA) and dual-energy X-ray absorptiometry (DXA) to assess segmental body composition using DXA as the reference technique.

METHODS

The sample comprised 50 young active students who practised or played different sports (25 men, age: 24.37 ± 4.79 y; 25 women, age: 24.32 ± 4.43 y) from the National Institute of Physical Education of Catalonia (INEFC). Anthropometric data (height, weight, arm, waist, and calf circumferences) and bioelectrical measurements (R, ohm; Xc, ohm) were recorded. Body composition was analysed with specific BIVA. DXA was used as the reference method to assess body composition of the whole-body, the trunk, and the limbs. The percentage of fat mass (%FM_DXA) and fat-free mass index (FFMI_DXA = FFM/length²) were calculated. The agreement between specific BIVA and DXA was evaluated by a depth-depth analysis, two-way ANOVA, and Pearson's correlations.

RESULTS

The depth-depth analysis showed a good agreement between DXA and BIVA (F = 14.89, p < 0.001) in both sexes and all body segments. Specific vector length (Zsp; i.e. indicative of %FM) was correlated with %FM_DXA in the whole body and all body segments, and the phase angle was correlated with FFMI_DXA, with he trunk in women as the only exception. Specific BIVA demonstrated to balance the effect of body size on bioelectrical measurements in both whole and segmental approaches.

CONCLUSIONS

Segmental specific BIVA and DXA provided a consistent evaluation of body composition in both sexes, of the whole body and each body segment. The indices %FM and FFMI obtained with DXA were correlated to vector length and phase angle in each segment, respectively. Specific BIVA represents a promising technique for monitoring segmental body composition changes in sport science and clinical applications.

Comparison of body composition assessment across body mass index categories by two multifrequency bioelectrical impedance analysis devices and dual-energy X-ray absorptiometry in clinical settings

BACKGROUND

InBody-770 and SECA mBCA 515 are multifrequency bioelectrical impedance analysis (BIA) devices, which are commonly used in the clinic to assess fat-free mass (FFM) and body fat (BF). However, the accuracy between devices in clinical settings, across different body mass index (BMI) groups remains unclear.

METHODS

Body composition for 226 participants (51% men, aged 18-80 years, BMI 18-56 kg/m²) was assessed by two commercial multifrequency BIA devices requiring standing position and using eight-contact electrodes, InBody 770 and SECA mBCA 515, and compared to results from dual-energy X-ray absorptiometry (DXA). Measurements were performed in a random order, after a 3 h fast and no prior exercise. Lin's-concordance correlation and Bland-Altman analyses were used to compare between devices, and linear regression to assess accuracy in BF% across BMI groups.

RESULTS

We found strong correlation between DXA results for study population BF% and those obtained by InBody (ρ_c = 0.922, 95% confidence interval (CI) 0.902, 0.938) and DXA and SECA (ρ_c = 0.940, CI 0.923, 0.935), with 95% limits of agreements between 2.6 and -8.9, and 7.1 and -7.6, respectively. BF% assessment by SECA was similar to DXA (-0.3%, p = 0.267), and underestimated by InBody (-3.1%, p < 0.0001). InBody deviations were largest among normal weight people and decreased with increasing BMI group, while SECA measurements remained unaffected.

CONCLUSIONS

Both BIA devices agreed well with BF% assessment obtained by DXA. Unlike SECA, InBody underestimated BF% in both genders and was influenced by BMI categories. Therefore, in clinical settings, individual assessment of BF% should be taken with caution.

Five-component model validation of reference, laboratory and field methods of body composition assessment

This study reports the validity of body fat percentage (BF%) estimates from several commonly employed techniques as compared with a five-component (5C) model criterion. Healthy adults (n 170) were assessed by dual-energy X-ray absorptiometry (DXA), air displacement plethysmography (ADP), multiple bioimpedance techniques and optical scanning. Output was also used to produce a criterion 5C model, multiple variants of three- and four-component models (3C; 4C) and anthropometry-based BF% estimates. Linear regression, Bland-Altman analysis and equivalence testing were performed alongside evaluation of the constant error (CE), total error (TE), se of the estimate (SEE) and coefficient of determination (R2). The major findings were (1) differences between 5C, 4C and 3C models utilising the same body volume (BV) and total body water (TBW) estimates are negligible (CE ≤ 0·2 %; SEE < 0·5 %; TE ≤ 0·5 %; R2 1·00; 95 % limits of agreement (LOA) ≤ 0·9 %); (2) moderate errors from alternate TBW or BV estimates in multi-component models were observed (CE ≤ 1·3 %; SEE ≤ 2·1 %; TE ≤ 2·2 %; R2 ≥ 0·95; 95 % LOA ≤ 4·2 %); (3) small differences between alternate DXA (i.e. tissue v. region) and ADP (i.e. Siri v. Brozek equations) estimates were observed, and both techniques generally performed well (CE < 3·0 %; SEE ≤ 2·3 %; TE ≤ 3·6 %; R2 ≥ 0·88; 95 % LOA ≤ 4·8 %); (4) bioimpedance technologies performed well but exhibited larger individual-level errors (CE < 1·0 %; SEE ≤ 3·1 %; TE ≤ 3·3 %; R2 ≥ 0·94; 95 % LOA ≤ 6·2 %) and (5) anthropometric equations generally performed poorly (CE 0·6- 5·7 %; SEE ≤ 5·1 %; TE ≤ 7·4 %; R2 ≥ 0·67; 95 % LOA ≤ 10·6 %). Collectively, the data presented in this manuscript can aid researchers and clinicians in selecting an appropriate body composition assessment method and understanding the associated errors when compared with a reference multi-component model.

Impact of change in bedtime variability on body composition and inflammation: secondary findings from the Go Red for Women Strategically Focused Research Network

Variability in daily sleep patterns is an emerging factor linked to metabolic syndrome. However, whether reducing bedtime variability improves markers of disease risk has not been tested. Here, we assessed whether body composition and inflammation were impacted by changes in bedtime variability over a 6-week period, during which, women were instructed to maintain healthy, habitual sleep (HS) patterns (one arm of a randomized trial). Data were available for 37 women (age 34.9 ± 12.4 years, BMI 24.7 ± 2.9 kg/m2, sleep duration 7.58 ± 0.49 h/night). Body composition and leukocyte platelet aggregates (LPA) were measured at baseline and endpoint using magnetic resonance imaging and flow cytometry, respectively. Sleep data were collected daily using wrist actigraphy. Change in bedtime variability was calculated as the difference in the standard deviation (SD) of bedtimes measured during the 2-week screening period and the 6-week intervention period. Results showed that women who reduced their bedtime variability (n = 29) during the intervention had reductions in total (P < 0.001) and subcutaneous adipose tissue (P < 0.001) relative to women who increased/maintained (n = 8) bedtime variability. Similar effects were observed for LPA levels between women who reduced vs increased/maintained bedtime variability (P = 0.011). Thus, reducing bedtime variability, without changing sleep duration, could improve cardiometabolic health by reducing adiposity and inflammation.

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.

Performance of Bioelectrical Impedance Analysis in the Estimation of Bone Mineral Content in Healthy Children Aged 6-12 Years

BACKGROUND

Bioelectrical impedance analysis (BIA) is a widely available tool which provides mineral estimate. However, BIA is not currently recognized as a bone mineral measuring method. This study aimed to explore the ability of BIA to predict bone mineral content (BMC) in children, using dual-energy X-ray absorptiometry as a gold standard.

METHODS

Healthy children aged 6-12 years (n = 176) were recruited for BIA and dual-energy X-ray absorptiometry measurements. Predictive models were generated using basic indices (age, height, weight, waist circumference, hip circumference, etc.) and BIA parameters (minerals, fat mass, and fat free mass).

RESULTS

The root-mean-square deviation and R² for the total BMC predictive model were 0.089 kg and 0.926, respectively using height and weight as predictors whereas 0.113 kg and 0.886, respectively using minerals by BIA. The root-mean-square deviation and R² for the subtotal BMC predictive model were 0.080 kg and 0.935, respectively using height and weight as predictors whereas 0.098 kg and 0.906, respectively using minerals by BIA. The best predictive models included basic indices and BIA parameters as predictors, but they had only slightly better performance over simple models.

CONCLUSIONS

Mineral content by BIA was good predictor of total and subtotal BMC in healthy children but with similar overall model performance compared to basic indices. More complex models combined all the predictive variables gave better prediction power, but of little improvement to these simple models. The BIA instrument does not appear to be useful in estimating BMC in healthy children as basic indices are more widely available measures but provide comparable performance. Future studies are needed to determine the clinical usefulness of the more complex prediction model in children with disease or children in other subgroups.

Relative Accuracy of Bioelectrical Impedance Analysis for Assessing Body Composition in Children With Severe Obesity

OBJECTIVES

The accuracy of different bioelectrical impedance analysis (BIA) devices for assessing body composition in children with obesity is unclear. We determined the relative accuracy of 2 BIA devices compared to dual x-ray absorptiometry (DXA) in obese and severely obese children.

METHODS

We measured body composition in a cross-sectional study of 78 obese children by a handheld single frequency tetrapolar BIA device (Omron), a stationary multifrequency octopolar BIA device (InBody 370) and DXA. Intermethod agreement was assessed by intraclass correlations, paired t tests, and Bland-Altman analyses.

RESULTS

Participants (37% female, age 14.8 ± 2.7 years) had mean (±standard deviation) body mass index of 36.7 ± 7.5 kg/m, body fat percentage of 46.4% ± 5.2%, and appendicular lean mass of 22.5 ± 6.0 kg by DXA. Intraclass correlations with DXA for body fat percentage were 0.39 and 0.87 for single frequency tetrapolar and multifrequency octopolar BIA devices, respectively. The single frequency tetrapolar BIA underestimated body fat percentage by 5.5% ± 2.9% (P < 0.0001). Differences between the multifrequency octopolar BIA and DXA for body fat percentage (-1.1% ± 2.8%) and appendicular lean mass (-0.3 ± 1.4 kg) were small, and 95% limits of agreement were approximately ±5%.

CONCLUSIONS

BIA machines vary in relative accuracy in measuring body composition in children who are obese and severely obese. The multifrequency octopolar BIA device accurately estimated body fat percentage and appendicular lean mass relative to DXA and has the advantage of point of care performance.

Fat-free mass characteristics of Hispanic adults: Comparisons with non-Hispanic Caucasians and cadaver reference values

BACKGROUND

A four-compartment (4C) model quantifies fat, water, mineral and residual. As such, 4C models are more accurate than two-compartment (2C) models based off cadaver reference values (RV), which necessitate assumptions regarding fat-free mass (FFM) characteristics. Nonetheless, research has yet to determine whether the FFM characteristics of Hispanics are similar to non-Hispanic Caucasians and RV.

AIM

The aim of this analysis was to compare the FFM characteristics of Hispanics to non-Hispanic Caucasians and cadaver RV.

METHODS

Data from 2 separate research centers were pooled to create a sample of 100 and 119 Hispanic males and females (age: 18-54 yrs; BMI: 16.46-42.27 kg/m²), respectively, and 47 and 55 non-Hispanic Caucasian males and females (age: 18-54 yrs; BMI: 16.00-36.67 kg/m²), respectively (n = 331). A 4C model was determined using bioimpedance analysis for hydration, dual energy X-ray absorptiometry for mineral, and air displacement plethysmography for body density (4C-ADP). FFM was calculated via the 4C-ADP and FFM characteristics (i.e., density [D_FFM], water [TBW:FFM], bone mineral [Mo:FFM], and residual [R:FFM]) were compared between sexes and ethnicities using a one-way ANOVA and against RV via a one sample t-test.

RESULTS

In Hispanics, all FFM characteristics significantly differed from cadaver RV (all p < 0.05). In contrast, D_FFM and TBW:FFM of non-Hispanic Caucasians were similar to cadaver RV for both sexes (all p > 0.05). Moreover, the R:FFM of non-Hispanic Caucasian females did not differ from cadaver RV (p = 0.403) whereas all other comparisons were significantly different (all p < 0.05). Sex comparisons within Hispanic participants revealed FFM characteristics were similar between males and females other than Mo:FFM (p < 0.001) whereas all FFM characteristics were similar between non-Hispanic Caucasian males and females (all p > 0.05). All of the ethnicity comparisons within males were statistically significant (all p < 0.05). Moreover, ethnicity comparisons within females were statistically significant for all comparisons other than Mo:FFM (p = 0.258).

CONCLUSION

The observed differences in FFM characteristics of Hispanics as compared to non-Hispanics Caucasians and reference values indicate that allied health professionals should employ appropriate caution when estimating body composition via 2C models in Hispanic populations.

Effect of Over- and Underfeeding on Body Composition and Related Metabolic Functions in Humans

PURPOSE OF REVIEW

Methodological limitations of body composition methods limit the validity of changes in body composition that are used to interpret metabolic outcome parameters of weight loss and weight gain.

RECENT FINDINGS

Direct assessment of energy balance is necessary for the assessment of early weight changes (i.e., within the 1st week of weight change), whereas body composition analysis with a high accuracy and a low minimal detectable change is recommended to assess ongoing changes. The sequence of underfeeding and overfeeding impacts the method inherent assumptions, and the considerable day-to-day and inter-individual variance in body composition changes is a challenge to the precision of methods. Weight loss-associated changes in body composition do not resemble their changes with subsequent hypercaloric re-feeding. Individual body components are related to specific metabolic functions where the structure-function relationships change with changes in energy balance. Analysis of structure-function relationships in response to weight changes needs to address (a) the validity, precision, and different outcome parameters of body composition methods and (b) the variance of results taking into account study protocols and the dynamics of weight changes. As for future studies, repeated measurements of body weight, body composition, and metabolic functions are needed before, during, and after weight changes focusing on the intra- and interindividual variances of weight change rather than on mean data only.

Novel method for estimating the total blood volume: the importance of adjustment using the ideal body weight and age for the accurate prediction of haemodilution during cardiopulmonary bypass

OBJECTIVES

Although total blood volume (TBV) is central to the estimation of the haemodilution rate during cardiopulmonary bypass (CPB), conventional formulas lack sufficient accuracy. The aim of this study was to establish a new formula using ideal body weight (BW) with adjustment for gender or age to estimate TBV for a more accurate prediction of the haemodilution rate during CPB.

METHODS

A total of 214 consecutive patients who underwent cardiac surgery with CPB were included in this study. TBV was retrospectively estimated using the following formulae: (1) Conventional TBV = actual BW × fixed 70 ml/kg, (2) gender-based modified TBV = ideal BW × 75 ml/kg (male) or 65 ml/kg (female) and (3) age-based modified TBV = ideal BW × 70 ml/kg (<65 years old) or 60 ml/kg (≥65 years old). The relationship between actual and predicted haemodilution rates calculated by these formulas was examined.

RESULTS

The actual haemodilution rate based on the haematocrit value was 24.4 ± 4.4%. There was no significant correlation between the actual and predicted haemodilution rates obtained by the conventional formula, whereas both modified formulae with the ideal BW showed a significant correlation. Furthermore, the age-based modified formula showed the highest correlation level (r = 0.45, P < 0.001) as well as a strong correlation between the actual and predicted postdilution haematocrit values (y = 0.903x + 3.385, R2 = 0.892).

CONCLUSIONS

The conventional formula is unable to predict the actual haemodilution rate accurately. Our new formula with a combination of the ideal BW and adjustment for age was shown to be useful for the accurate prediction of the haemodilution rate during CPB.

High-Fat Breakfast Meal Replacement in Overweight and Obesity: Implications on Body Composition, Metabolic Markers, and Satiety

The purpose of this paper was to determine the effect of replacing breakfast with a high-fat drink on fat mass (FM), lean mass (LM), percent body fat (%BF), visceral fat (VAT), resting metabolic rate (RMR), fuel utilization (RER), blood lipids and satiety in overweight and obese adults. Healthy adults (n = 42; 21 Females; body mass index (BMI): 32.8 ± 4.6 kg·m⁻²) were randomized to control (CON; n = 21) or meal replacement (MRP; n = 22) groups. Body composition was measured using a four-compartment model; RMR and RER were assessed from indirect calorimetry. The MRP (70% fat) was consumed once daily for eight weeks. For males, there was no change (p > 0.05) in FM (mean difference (MD) = 0.41 ± 1.19 kg], %BF MD = 0.50 ± 1.09%, LM MD = −0.64 ± 1.79 kg, or VAT MD = −0.31 ± 1.36 cm for MRP versus CON. Similarly, no differences for females for FM MD = −0.73 ± 1.37 kg, %BF MD = −0.57 ± 1.26%, LM MD = 0.31 ± 1.37 kg, or VAT MD: −0.83 ± 1.2 cm. HDL was significantly reduced in the MRP group for females (adjusted mean change: −6.41 ± 4.44 units, p = 0.018). There was no effect on RMR or RER. Satiety increased in the afternoon for MRP (p = 0.021). Despite high fat, no negative impact on lipids resulted; increased satiety may be beneficial for controlling afternoon cravings, but does not affect body composition.

An anthropometry-based equation of fat mass percentage as a valid discriminator of obesity

OBJECTIVE

To develop a new predictive equation for fat mass percentage (%FM) based on anthropometric measurements and to assess its ability to discriminate between obese and non-obese individuals.

DESIGN

Cross-sectional study.

SETTING

Mexican adults.ParticipantsAdults (n 275; 181 women) aged 20-63 years with BMI between 17·4 and 42·4 kg/m2.

RESULTS

Thirty-seven per cent of our sample was obese using %FM measured by air-displacement plethysmography (BOD POD®; Life Measurement Instruments). The fat mass was computed from the difference between weight and fat-free mass (FFM). FFM was estimated using an equation obtained previously in the study from weight, height and sex of the individuals. The %FM estimated from the obtained FFM showed a sensitivity of 90·3 (95 % CI 86·8, 93·8) % and a specificity of 58·0 (95 % CI 52·1, 63·8) % in the diagnosis of obesity. Ninety-three per cent of participants with obesity and 65 % of participants without obesity were correctly classified.

CONCLUSIONS

The anthropometry-based equation obtained in the present study could be used as a screening tool in clinical and epidemiological studies not only to estimate the %FM, but also to discriminate the obese condition in populations with similar characteristics to the participant sample.

Perspective: Are We Ready to Measure Child Nutritional Status with Lasers?

The continued use of basic, manual anthropometric tools (e.g., boards and tapes) leaves anthropometry susceptible to human error. A potential solution, 3-dimensional (3D) imaging systems for anthropometry, has been around since the 1950s. In the 1980s, 3D imaging technology advanced from photographs to the use of lasers for body digitization; and by the 2000s, the falling price of 3D scanners made commercial application feasible. The garment sector quickly adopted imaging technology for surveys because of the need for numerous measurements and large sample sizes. In the health sector, 3D imaging for anthropometry was not widely adopted; its use was limited to research and specialized purposes. The different cost and logistical requirements for measurement in the garment and health sectors help to explain why the technology was adopted in one sector and not the other. Despite reductions, the price of 3D imaging systems remained a barrier to the use of 3D imaging for regular nutritional assessment in the health sector. Additional barriers in the health sector were that imaging systems required dedicated space and were not designed for capturing measurements in young children. In recent years, the development of light-coding technology may have removed these barriers, and a handheld imaging system was developed specifically for young children. There are not yet recommendations to replace manual equipment with 3D imaging for nutritional assessment, and there is a need for more research on low-cost, handheld imaging systems-particularly research that evaluates the ability of 3D imaging to improve the quality of anthropometric data and indicators.

Validation of rapid 4-component body composition assessment with the use of dual-energy X-ray absorptiometry and bioelectrical impedance analysis

Background

The 4-component (4C) model is a criterion method for human body composition that separates the body into fat, water, mineral, and protein, but requires 4 measurements with significant cost and time requirements that preclude wide clinical use. A simplified model integrating only 2 measurements-dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA)-and 10 min of patient time has been proposed.

Objective

We aimed to validate a rapid, simplified 4C DXA + BIA body composition model in a clinical population.

Design

This was a cross-sectional observational study of 31 healthy adults. Participants underwent whole-body DXA, segmental BIA, air displacement plethysmography (ADP), and total body water (TBW) measurement by deuterium (D2O) dilution. 4C composition was calculated through the use of the Lohman model [DXA mineral mass, D2O TBW, ADP body volume (BV), scale weight] and the simplified model (DXA mineral mass and BV, BIA TBW, scale weight). Accuracy of percentage of fat (%Fat) and protein measurements was assessed via linear regression. Test-retest precision was calculated with the use of duplicate DXA and BIA measurements.

Results

Of 31 participants, 23 were included in the analysis. TBWBIA showed good test-retest precision (%CV = 5.2 raw; 1.1 after outlier removal) and high accuracy to TBWD2O [TBWD2O = 0.956*TBWBIA, R2= 0.92, root mean squared error (RMSE) = 2.2 kg]. %Fat estimates from DXA, ADP, D2O, and BIA all showed high correlation with the Lohman model. However, only the 4C simplified model provides high accuracy for both %Fat (R2 = 0.96, RMSE = 2.33) and protein mass (R2= 0.76, RMSE = 1.8 kg). %Fat precision from 4C DXA + BIA was comparable with DXA (root mean square-SD = 0.8 and 0.6 percentage units, respectively).

Conclusions

This work validates a simplified 4C method that measures fat, water, mineral, and protein in a 10-min clinic visit. This model has broad clinical application to monitor many conditions including over/dehydration, malnutrition, obesity, sarcopenia, and cachexia.

Energy Balance over One Athletic Season

INTRODUCTION

Magnitude and variation in energy balance (EB) components over an athletic season are largely unknown.

PURPOSE

We investigated the longitudinal changes in EB over one season and explored the association between EB variation and change in the main fat-free mass (FFM) components in highly trained athletes.

METHODS

Eighty athletes (54 males; handball, volleyball, basketball, triathlete, and swimming) were evaluated from the beginning of the season to the main competition stage. Resting and total energy expenditure (REE and TEE, respectively) were assessed by indirect calorimetry and doubly labeled water, respectively. Physical activity energy expenditure was calculated as TEE - 0.1 TEE - REE. Fat mass (FM), FFM, and bone mineral were evaluated with dual-energy x-ray absorptiometry; changed body energy stores were calculated as 1.0(ΔFFM/Δtime) + 9.5(ΔFM/Δtime). Total-body water (TBW) and its compartments were assessed through dilution techniques, and total-body protein was calculated from a four-compartment model, with body volume assessed by air displacement plethysmography.

RESULTS

Although a negative EB of -17.4 ± 72.7 kcal·d was observed (P < 0.05), EB varied widely among sports and across sex groups resulting in a net weight increase (0.7 ± 2.3 kg) that is attributable to significant changes in FFM (1.2 ± 1.6 kg) and FM (-0.7 ± 1.5 kg) (P < 0.05). EB was related with TBW and intracellular water (r = 0.354, r = 0.257, P < 0.05, respectively), regardless of sex, sports, and age.

CONCLUSIONS

The mean negative EB observed over the season resulted from the rate of FM use and FFM accretion, but with a large variation by sex and sports. TBW, but not total-body protein or mineral balance, explained the magnitude of EB, which means that athletes under a positive or a negative EB showed a TBW expansion or shrinkage, respectively, specifically within the cells, over one athletic season.

A randomized study of dietary composition during weight-loss maintenance: Rationale, study design, intervention, and assessment

BACKGROUND

While many people with overweight or obesity can lose weight temporarily, most have difficulty maintaining weight loss over the long term. Studies of dietary composition typically focus on weight loss, rather than weight-loss maintenance, and rely on nutrition education and dietary counseling, rather than controlled feeding protocols. Variation in initial weight loss and insufficient differentiation among treatments confound interpretation of results and compromise conclusions regarding the weight-independent effects of dietary composition. The aim of the present study was to evaluate three test diets differing in carbohydrate-to-fat ratio during weight-loss maintenance.

DESIGN AND DIETARY INTERVENTIONS

Following weight loss corresponding to 12±2% of baseline body weight on a standard run-in diet, 164 participants aged 18 to 65years were randomly assigned to one of three test diets for weight-loss maintenance through 20weeks (test phase). We fed them high-carbohydrate (60% of energy from carbohydrate, 20% fat), moderate-carbohydrate (40% carbohydrate, 40% fat), and low-carbohydrate (20% carbohydrate, 60% fat) diets, controlled for protein content (20% of energy). During a 2-week ad libitum feeding phase following the test phase, we assessed the effect of the test diets on body weight.

OUTCOMES

The primary outcome was total energy expenditure, assessed by doubly-labeled water methodology. Secondary outcomes included resting energy expenditure and physical activity, chronic disease risk factors, and variables to inform an understanding of physiological mechanisms by which dietary carbohydrate-to-fat ratio might influence metabolism. Weight change during the ad libitum feeding phase was conceptualized as a proxy measure of hunger.

Fluid balance concepts in medicine: Principles and practice

The regulation of body fluid balance is a key concern in health and disease and comprises three concepts. The first concept pertains to the relationship between total body water (TBW) and total effective solute and is expressed in terms of the tonicity of the body fluids. Disturbances in tonicity are the main factor responsible for changes in cell volume, which can critically affect brain cell function and survival. Solutes distributed almost exclusively in the extracellular compartment (mainly sodium salts) and in the intracellular compartment (mainly potassium salts) contribute to tonicity, while solutes distributed in TBW have no effect on tonicity. The second body fluid balance concept relates to the regulation and measurement of abnormalities of sodium salt balance and extracellular volume. Estimation of extracellular volume is more complex and error prone than measurement of TBW. A key function of extracellular volume, which is defined as the effective arterial blood volume (EABV), is to ensure adequate perfusion of cells and organs. Other factors, including cardiac output, total and regional capacity of both arteries and veins, Starling forces in the capillaries, and gravity also affect the EABV. Collectively, these factors interact closely with extracellular volume and some of them undergo substantial changes in certain acute and chronic severe illnesses. Their changes result not only in extracellular volume expansion, but in the need for a larger extracellular volume compared with that of healthy individuals. Assessing extracellular volume in severe illness is challenging because the estimates of this volume by commonly used methods are prone to large errors in many illnesses. In addition, the optimal extracellular volume may vary from illness to illness, is only partially based on volume measurements by traditional methods, and has not been determined for each illness. Further research is needed to determine optimal extracellular volume levels in several illnesses. For these reasons, extracellular volume in severe illness merits a separate third concept of body fluid balance.