Equation-derived SMI not generalizable for DXA-defined low muscle mass in Chinese adults: external validation study.

BACKGROUND

Anthropometric equations are commonly used to estimate appendicular skeletal muscle mass (ASM) when direct body-composition measurements are unavailable. However, it remains unclear whether skeletal muscle index derived from predicted ASM (SMI_pred) can validly identify low muscle mass using thresholds developed for dual-energy X-ray absorptiometry (DXA). We externally validated the Wen et al. anthropometric equation against DXA in Chinese adults undergoing routine health examinations.

METHODS

This single-center retrospective external validation study included 501 Chinese adults who underwent whole-body DXA between 2016 and 2022. The published equation was applied without coefficient updating to calculate predicted ASM (ASM_pred), and SMI_pred was calculated as ASM_pred divided by height squared. Validation included Pearson correlation, Lin's concordance correlation coefficient (CCC), calibration, Bland-Altman agreement, and classification of DXA-defined low muscle mass by direct application of the Asian Working Group for Sarcopenia 2019 sex-specific DXA cut-offs. Simple linear recalibration was evaluated using repeated 10-fold cross-validation as a secondary analysis.

RESULTS

ASM_pred was strongly correlated with DXA-measured ASM (Pearson r = 0.892, 95%CI = 0.873-0.909; CCC = 0.862), but systematically overestimated ASM, with a mean bias of 1.25 kg and 95% limits of agreement from -3.14 to 5.64 kg. Its calibration slope was 0.853 (95%CrI = 0.815-0.891). SMI_pred showed lower correlation and concordance with DXA-measured SMI (Pearson r = 0.756, 95%CI = 0.716-0.791; CCC = 0.705), a mean bias of 0.457 kg/m2, 95% limits of agreement from -1.19 to 2.10 kg/m2, and a calibration slope of 0.738. The difference between the ASM and SMI correlations was 0.136 (bootstrap 95%CI = 0.111-0.164; P = 0.0002). Direct application of AWGS 2019 DXA cut-offs to SMI_pred yielded a sensitivity of 0.211 and specificity of 0.963, missing 97 of 123 DXA-defined low-muscle-mass cases. Cross-validated recalibration reduced prediction error but did not eliminate individual-level disagreement.

CONCLUSION

The Wen equation preserved strong association with DXA-measured absolute ASM but showed systematic bias and limited individual-level agreement. Performance deteriorated after derivation of SMI, and direct transfer of DXA-based AWGS thresholds to SMI_pred resulted in substantial under-detection of low muscle mass. Neither the original nor the locally recalibrated equation should be used as a stand-alone substitute for DXA when classification depends on fixed muscle-mass thresholds. Any use within a multivariable screening pathway requires separate validation of the complete screening rule.