{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T05:03:24Z","timestamp":1790053404116,"version":"4.0.1"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Nutr."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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\u2019s concordance correlation coefficient (CCC), calibration, Bland\u2013Altman 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      ASM_pred was strongly correlated with DXA-measured ASM (Pearson\n                      <jats:italic>r<\/jats:italic>\n                      = 0.892,\n                      <jats:italic>95%CI<\/jats:italic>\n                      = 0.873\u20130.909; 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 (\n                      <jats:italic>95%CrI<\/jats:italic>\n                      = 0.815\u20130.891). SMI_pred showed lower correlation and concordance with DXA-measured SMI (Pearson\n                      <jats:italic>r<\/jats:italic>\n                      = 0.756,\n                      <jats:italic>95%CI<\/jats:italic>\n                      = 0.716\u20130.791; CCC = 0.705), a mean bias of 0.457 kg\/m\n                      <jats:sup>2<\/jats:sup>\n                      , 95% limits of agreement from -1.19 to 2.10 kg\/m\n                      <jats:sup>2<\/jats:sup>\n                      , and a calibration slope of 0.738. The difference between the ASM and SMI correlations was 0.136 (bootstrap\n                      <jats:italic>95%CI<\/jats:italic>\n                      = 0.111\u20130.164;\n                      <jats:italic>P<\/jats:italic>\n                      = 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnut.2026.1887277","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T04:24:03Z","timestamp":1790051043000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Equation-derived SMI not generalizable for DXA-defined low muscle mass in Chinese adults: external validation study"],"prefix":"10.3389","volume":"13","author":[{"given":"Zhenchao","family":"Liu","sequence":"first","affiliation":[{"name":"School of Pharmacy, Qingdao University","place":["Qingdao, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gudi","family":"Shi","sequence":"additional","affiliation":[{"name":"Health Management Department, Shandong Medicine Technician College","place":["Taian, 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