2026-10-11T11:03:59Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33961570 2026-09-22T04:21:03Z category_374 portal_316 item_type_3 month_year_09_2026

Supplementary file 1_Equation-derived SMI not generalizable for DXA-defined low muscle mass in Chinese adults: external validation study.doc Zhenchao Liu (8346036) Gudi Shi (25087291) Yuqian Ren (1996141) Yun Wang (32846) Xinbo Zhao (6340964) Clinical and Sports Nutrition anthropometric prediction equation DXA external validation sarcopenia skeletal muscle index BackgroundAnthropometric 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.MethodsThis 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.ResultsASM_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.ConclusionThe 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. 2026-09-22T04:21:03Z Dataset Dataset 10.3389/fnut.2026.1887277.s001 https://figshare.com/articles/dataset/Supplementary_file_1_Equation-derived_SMI_not_generalizable_for_DXA-defined_low_muscle_mass_in_Chinese_adults_external_validation_study_doc/33961570 CC BY 4.0