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Equations for Assessing Body Composition by Ultrasound in Older Adults: A Narrative Review
Lara Vilar Fernandes1, Gabriela Benatti de Oliveira2, Ana Carolina Junqueira Vasques1
1School of Applied Sciences, State University of Campinas, Limeira 13484-350, Brazil.
Healthcare (Basel, Switzerland)
|June 13, 2025
Summary
Ultrasound shows promise for assessing body composition in older adults, offering a portable and cost-effective alternative. However, standardized cut-off points and further validation are needed for reliable clinical use in aging populations.
Area of Science:
- Gerontology
- Biomedical Engineering
- Clinical Assessment
Background:
- Aging leads to physiological body composition changes, increasing health risks in the elderly.
- Gold-standard body composition methods are often complex, costly, and lack portability.
- Simpler, portable, and cost-effective methods are needed for clinical assessment in older adults.
Purpose of the Study:
- To review equations using ultrasound for body composition assessment in older adults.
- To evaluate ultrasound as a portable and cost-effective alternative to gold-standard methods.
- To identify research gaps in ultrasound-based body composition analysis for the aging population.
Main Methods:
- Narrative review of scientific literature.
- Electronic database searches (MEDLINE/PubMed, Web of Science, Scopus, Google Scholar).
- Keywords: ultrasound, body composition, muscle, fat, older adults, aging, equation.
Main Results:
- Ultrasound, particularly B-mode, is a potential portable and safe method for body composition assessment.
- No standardized cut-off points exist for defining low muscle mass or high body fat using ultrasound in older adults.
- Various predictive equations utilizing ultrasound have been developed.
Conclusions:
- Ultrasound offers a promising, accessible approach for body composition assessment in older adults.
- Further research is essential to validate ultrasound against gold standards and compare A-mode and B-mode devices.
- Careful consideration of equipment and population factors is crucial when applying predictive equations in clinical practice.

