{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T10:33:46Z","timestamp":1793010826381,"version":"4.5.0"},"reference-count":35,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,9,6]],"date-time":"2026-09-06T00:00:00Z","timestamp":1788652800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,6]],"date-time":"2026-09-06T00:00:00Z","timestamp":1788652800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J cachexia sarcopenia muscle"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate estimation of appendicular skeletal muscle mass (ASM) is crucial for sarcopenia diagnosis, but device\u2010dependent approaches (dual\u2010energy X\u2010ray absorptiometry [DXA] or bioelectrical impedance analysis [BIA]) limit widespread application in community settings. Developing prediction equations using readily available anthropometric measurements and common laboratory data (such as serum creatinine) offers a practical alternative for large\u2010scale screening. However, no population\u2010based prediction equations using these accessible parameters have been developed specifically for Asian populations, where body composition differs significantly from Western cohorts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We conducted a three\u2010phase investigation: Phase 1 validated existing NHANES\u2010derived equations across three cohorts (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200913\u2009582) from Taiwan (ILAS,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20092780; NAHSIT,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20096853) and Japan (NILS\u2010LSA,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20093949); Phase 2 developed new population\u2010based equations using the NAHSIT cohort; and Phase 3 performed external validation of newly developed NAHSIT\u2010derived equations in an independent Japanese cohort (NILS\u2010LSA). Model performance was assessed using\n                      <jats:italic>R<\/jats:italic>\n                      \u2010squared, root mean square error (RMSE) and area under the receiver operating characteristic curve (AUC) for detecting low muscle mass based on Asian Working Group for Sarcopenia (AWGS) 2019 criteria.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In Phase 1 external validation, existing equations showed acceptable to strong predictive accuracy across populations (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.52\u20130.89). Equation\u00a04 (incorporating sex, age, height and weight) demonstrated robust performance across ILAS (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.80, RMSE\u2009=\u20091.88), NILS\u2010LSA (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.86, RMSE\u2009=\u20091.51) and NAHSIT (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.89, RMSE\u2009=\u20091.52) cohorts. For detecting low muscle mass using AWGS 2019 criteria, AUC values ranged from 0.79 to 0.91 in males and 0.76 to 0.89 in females across cohorts. In Phase 2, our newly developed population\u2010based Equation\u00a04 achieved superior performance (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.90, RMSE\u2009=\u20091.45) with minimal prediction bias (median error: 0.06, IQR: 1.81) and excellent diagnostic capability (AUC\u2009=\u20090.90 in both sexes; sensitivity\/specificity: 0.85\/0.80 in males and 0.87\/0.80 in females). Phase 3 external validation in the independent Japanese cohort (NILS\u2010LSA) demonstrated robust cross\u2010population generalizability, with Equation\u00a04 achieving the highest AUC values (0.90 in males and 0.85 in females).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our three\u2010phase investigation has established and validated the first population\u2010based prediction equations for ASM estimation in Asian populations. These clinically accessible equations overcome device\u2010dependency limitations and enable practical, large\u2010scale sarcopenia screening in community settings, representing a significant advance for early identification of sarcopenia and promotion of healthy muscle aging across Asian populations.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jcsm.70360","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T06:23:52Z","timestamp":1788762232000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Population\u2010Based Prediction Equations for Appendicular Skeletal Muscle Mass: A Three\u2010Phase Validation Study in 13\u2009582 Asian Adults"],"prefix":"10.1002","volume":"17","author":[{"given":"Hsi\u2010Yu","family":"Lai","sequence":"first","affiliation":[{"name":"Graduate Institute of Clinical Pharmacy, College of Medicine National Taiwan University  Taipei Taiwan"},{"name":"Center for Healthy Longevity and Aging Sciences National Yang Ming Chiao Tung University  Taipei Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shu","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Center for Geriatrics and Gerontology  Obu Aichi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"I\u2010Tzu","family":"Chen","sequence":"additional","affiliation":[{"name":"Graduate Institute of Clinical Pharmacy, College of Medicine National Taiwan University  Taipei Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rei","family":"Otsuka","sequence":"additional","affiliation":[{"name":"National Center for Geriatrics and Gerontology  Obu Aichi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei\u2010Ju","family":"Lee","sequence":"additional","affiliation":[{"name":"Center for Healthy Longevity and Aging Sciences National Yang Ming Chiao Tung University  Taipei Taiwan"},{"name":"Department of Family Medicine Taipei Veterans General Hospital Yuanshan Branch  Yilan Taiwan"},{"name":"Department of Geriatric Medicine National Yang Ming Chiao Tung University School of Medicine  Taipei Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chih\u2010Kuang","family":"Liang","sequence":"additional","affiliation":[{"name":"Center for Healthy Longevity and Aging Sciences National Yang Ming Chiao Tung University  Taipei Taiwan"},{"name":"Department of Geriatric Medicine National Yang Ming Chiao Tung University School of Medicine  Taipei Taiwan"},{"name":"Center for Geriatrics and Gerontology Kaohsiung Veterans General Hospital  Kaohsiung Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ko\u2010Han","family":"Yen","sequence":"additional","affiliation":[{"name":"Center for Healthy Longevity and Aging Sciences National Yang Ming Chiao Tung University  Taipei Taiwan"},{"name":"Center for Geriatrics and Gerontology Taipei Veterans General Hospital  Taipei Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6747-8680","authenticated-orcid":false,"given":"Hidenori","family":"Arai","sequence":"additional","affiliation":[{"name":"National Center for Geriatrics and Gerontology  Obu Aichi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1660-9120","authenticated-orcid":false,"given":"Fei\u2010Yuan","family":"Hsiao","sequence":"additional","affiliation":[{"name":"Graduate Institute of Clinical Pharmacy, College of Medicine National Taiwan University  Taipei Taiwan"},{"name":"School of Pharmacy, College of Medicine National Taiwan University  Taipei Taiwan"},{"name":"Department of Pharmacy National Taiwan University Hospital  Taipei Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang\u2010Kung","family":"Chen","sequence":"additional","affiliation":[{"name":"Center for Healthy Longevity and Aging Sciences National Yang Ming Chiao Tung University  Taipei Taiwan"},{"name":"Center for Geriatrics and Gerontology Taipei Veterans General Hospital  Taipei Taiwan"},{"name":"Taipei Municipal Gan\u2010Dau Hospital (Managed by Taipei Veterans General Hospital)  Taipei 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