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.