A Field-based Three-Compartment Model Derived from Ultrasonography and Bioimpedance for Estimating Body Composition Changes

A Decade of Progress in Ultrasound Assessments of Subcutaneous and Total Body Fat: A Scoping Review

Body composition assessment by ultrasonography is a vivid research field. Ultrasound (US) can be used to quantify subcutaneous and visceral fat, to evaluate the quantity and quality of skeletal muscle, and to infer intracellular fat content. This scoping review aimed to summarize recent advancements in subcutaneous fat estimation using US and related applications. A systematic search was conducted on PubMed, MEDLINE, Scopus, Google Scholar, and Web of Science to identify original articles published in English between 1 January 2014 and 20 December 2024. A total of 1869 articles were screened based on their titles and abstracts, and 283 were retrieved for full-text evaluation. Our search and selection strategy resulted in 89 eligible documents. The literature discussed in this review suggests that US is a reliable and valid technique for measuring subcutaneous fat thickness at selected anatomic locations. Standardized measurement protocols enabled accurate subcutaneous adipose tissue (SAT) patterning in various populations (e.g., athletes, children, adults, and patients with anorexia nervosa). Further research is warranted to establish clinically relevant cutoff values. US-derived SAT thicknesses can also provide whole-body fat estimates of fat mass (FM), fat-free mass (FFM), and body fat percentage (%BF). To this end, prediction formulas were developed to ensure agreement with criterion measures given by laboratory techniques, or multicompartment models based on combinations thereof. The resulting assessments of global adiposity were reliable but inaccurate in certain populations (e.g., overweight and obese). Nevertheless, due to its high reliability, US might be used to track changes in body fat content during nutritional and/or lifestyle interventions. Future investigations will be needed to evaluate its accuracy in this respect and to improve the validity of whole-body fat estimation compared to multicompartment models.

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.

Agreement Between A 2-Dimensional Digital Image-Based 3-Compartment Body Composition Model and Dual Energy X-Ray Absorptiometry for The Estimation of Relative Adiposity

The purpose of this study was to compare relative adiposity (%Fat) derived from a 2-dimensional image-based 3-component (3C) model (%Fat_3C-IMAGE) and dual-energy X-ray absorptiometry (DXA) (%Fat_DXA) against a 5-component (5C) laboratory criterion (%Fat_5C). 57 participants were included (63.2% male, 84.2% White/Caucasian, 22.5±4.7 yrs., 23.9±2.8 kg/m²). For each participant, body mass and standing height were measured to the nearest 0.1 kg and 0.1 cm, respectively. A digital image of each participant was taken using a 9.7 inch, 16g iPad Air 2 and analyzed using a commercially available application (version 1.1.2, made Health and Fitness, USA) for the estimation of body volume (BV) and inclusion in %Fat_3C-IMAGE . %Fat_3C-IMAGE and %Fat_5C included measures of total body water derived from bioimpedance spectroscopy. The criterion %Fat_5C included BV estimates derived from underwater weighing and bone mineral content measures via DXA. %Fat_DXA estimates were calculated from a whole-body DXA scan. A standardized mean effect size (ES) assessed the magnitude of differences between models with values of 0.2, 0.5, and 0.8 for small, moderate, and large differences, respectively. Data are presented as mean ± standard deviation. A strong correlation (r = 0.94, p <.001) and small mean difference (ES = 0.24, p <.001) was observed between %Fat_3C-IMAGE (19.20±5.80) and %Fat_5C (17.69±6.20) whereas a strong correlation (r = 0.87, p <.001) and moderate-large mean difference (ES = 0.70, p <.001) was observed between %Fat_DXA (22.01±6.81) and %Fat_5C. Furthermore, %Fat_3C-IMAGE (SEE = 2.20 %Fat, TE= 2.6) exhibited smaller SEE and TE than %Fat_DXA (SEE = 3.14 %Fat, TE = 5.5). The 3C image-based model performed slightly better in our sample of young adults than the DXA 3C model. Thus, the 2D image analysis program provides an accurate and non-invasive estimate of %Fat within a 3C model in young adults. Compared to DXA, the 3C image-based model allows for a more cost-effective and portable method of body composition assessment, potentially increasing accessibility to multi-component methods.

Validity of a 3-compartment body composition model using body volume derived from a novel 2-dimensional image analysis program

BACKGROUND/OBJECTIVES

The purpose of this study was: (1) to compare body volume (BV) estimated from a 2-dimensional (2D) image analysis program (BV_IMAGE), and a dual-energy x-ray absorptiometry (DXA) equation (BV_{DXA-Smith-Ryan}) to an underwater weighing (UWW) criterion (BV_UWW); (2) to compare relative adiposity (%Fat) derived from a 3-compartment (3C) model using BV_IMAGE (%Fat_3C-IMAGE), and a 4-compartment (4C) model using BV_{DXA-Smith-Ryan} (%Fat_{4C-DXA-Smith-Ryan}) to a 4C criterion model using BV_UWW (%Fat_4C-UWW).

SUBJECT/METHODS

Forty-eight participants were included (60% male, 22.9 ± 5.0 years, 24.2 ± 2.6 kg/m²). BV_IMAGE was derived using a single digital image of each participant taken from the rear/posterior view. DXA-derived BV was calculated according to Smith-Ryan et al. Bioimpedance spectroscopy and DXA were used to measure total body water and bone mineral content, respectively, in the 3C and 4C models. A standardized mean effect size (ES) assessed the magnitude of differences between models with values of 0.2, 0.5, and 0.8 for small, moderate, and large differences, respectively. Data are presented as mean ± standard deviation.

RESULTS

Near-perfect correlation (r = 0.998, p < 0.001) and no mean differences (p = 0.267) were observed between BV_IMAGE (69.6 ± 11.5 L) and BV_UWW (69.5 ± 11.4 L). No mean differences were observed between %Fat_{4C-DXA-Smith-Ryan} and the %Fat_4C-UWW criterion (p = 0.988). Small mean differences were observed between %Fat_3C-IMAGE and %Fat_4C-UWW (ES = 0.2, p < 0.001). %Fat_3C-IMAGE exhibited smaller SEE and TE, and tighter limits of agreement than %Fat_{4C-DXA-Smith-Ryan}.

CONCLUSIONS

The 2D image analysis program provided an accurate and non-invasive estimate of BV, and subsequently %Fat within a 3C model in generally healthy, young adults.

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

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