Correlation between low skeletal muscle index and 3D anthropometric data measured by 3D body scanner: screening sarcopenia

Front Med (Lausanne). 2024 Feb 22:11:1296418. doi: 10.3389/fmed.2024.1296418. eCollection 2024.

Abstract

Background: The screening tools for sarcopenia are measuring calf circumference, SARC-F or SPPB. However, not all of these tools have high sensitivity, specificity, and low margins of error. This research investigates potential of 3D anthropometry of the lower extremities on screening of sarcopenia.

Methods: From October 2022 to February 2023, we retrospectively analyzed results of 3D body scanner and bio-impedance analysis for patients aged 45 to 85 at risk of sarcopenia. The 3D scanner measured the surface and volume values of both thighs and calves. When skeletal muscle index (SMI) is less than 5.7, patients were classified to Low SMI group, indicative of sarcopenia.

Results: A total six out of 62 patients were classified to Low SMI group, showing significantly lower values of right, left, mean calf volumes and mean calf surface than the other patients (right calf volume 2.62 L vs. 3.34 L, p = 0.033; left calf volume 2.62 L vs. 3.25 L, p = 0.044; mean calf volume 2.62 L vs. 3.29 L, p = 0.029; mean calf surface 0.12 m2 vs. 0.13 m2, p = 0.049). There was no statistical difference in thigh volume and surface. Through AUC-ROC analysis, mean calf volume was the most significant cut-off value (right calf volume 2.80 L, AUC = 0.768; left calf volume 2.75 L, AUC = 0.753; mean calf volume 3.06 L, AUC = 0.774; mean calf surface 0.12 m2, AUC = 0.747).

Conclusion: The calf volume and surface values have significant relationship with low SMI, and the mean calf volume was the most significant cut-off screening value for Low SMI. The 3D scanner demonstrated its value as a new means for screening sarcopenia.

Keywords: 3D anthropometry; 3D scanner; bio-impedance analysis; sarcopenia; screening; skeletal muscle index.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was supported by Research Project through the Korea Evaluation Institute of Industrial Technology (KEIT) funded by the Ministry of Trade, Industry and Energy (No. 20016833, Development of 3D Human Body data Analyzer for symptom of disease and healthcare management).