{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T05:48:42Z","timestamp":1782280122782,"version":"3.54.5"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T00:00:00Z","timestamp":1714003200000},"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. Med."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Multiple studies have shown that skeletal muscle index (SMI) measured on abdominal computed tomography (CT) is strongly associated with bone mineral density (BMD) and fracture risk as estimated by the fracture risk assessment tool (FRAX). Although some studies have reported that SMI at the level of the 12th thoracic vertebra (T12) measured on chest CT images can be used to diagnose sarcopenia, it is regrettable that no studies have investigated the relationship between SMI at T12 level and BMD or fracture risk. Therefore, we further investigated the relationship between SMI at T12 level and FRAX-estimated BMD and fracture risk in this study.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A total of 349 subjects were included in this study. After 1\u22361 propensity score matching (PSM) on height, weight, hypertension, diabetes, hyperlipidemia, hyperuricemia, body mass index (BMI), age, and gender, 162 subjects were finally included. The SMI, BMD, and FRAX score of the 162 participants were obtained. The correlation between SMI and BMD, as well as SMI and FRAX, was assessed using Spearman rank correlation. Additionally, the effectiveness of each index in predicting osteoporosis was evaluated through the receiver operating characteristic (ROC) curve analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The BMD of the lumbar spine (L1-4) demonstrated a strong correlation with SMI (<jats:italic>r<\/jats:italic>\u2009=\u20090.416, <jats:italic>p<\/jats:italic>\u2009&amp;lt;\u20090.001), while the BMD of the femoral neck (FN) also exhibited a correlation with SMI (<jats:italic>r<\/jats:italic>\u2009=\u20090.307, <jats:italic>p<\/jats:italic>\u2009&amp;lt;\u20090.001). SMI was significantly correlated with FRAX, both without and with BMD at the FN, for major osteoporotic fractures (<jats:italic>r<\/jats:italic>\u2009=\u2009\u22120.416, <jats:italic>p<\/jats:italic>\u2009&amp;lt;\u20090.001, and <jats:italic>r<\/jats:italic>\u2009=\u2009\u22120.431, <jats:italic>p<\/jats:italic>\u2009&amp;lt;\u20090.001, respectively) and hip fractures (<jats:italic>r<\/jats:italic>\u2009=\u2009\u22120.357, <jats:italic>p<\/jats:italic>\u2009&amp;lt;\u20090.001, and <jats:italic>r<\/jats:italic>\u2009=\u2009\u22120.311, <jats:italic>p<\/jats:italic>\u2009&amp;lt;\u20090.001, respectively). Moreover, the SMI of the non-osteoporosis group was significantly higher than that of the osteoporosis group (<jats:italic>p<\/jats:italic>\u2009&amp;lt;\u20090.001). SMI effectively predicts osteoporosis, with an area under the curve of 0.834 (95% confidence interval 0.771\u20130.897, <jats:italic>p<\/jats:italic>\u2009&amp;lt;\u20090.001).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>SMI based on CT images of the 12th thoracic vertebrae can effectively diagnose osteoporosis and predict fracture risk. Therefore, SMI can make secondary use of chest CT to screen people who are prone to osteoporosis and fracture, and carry out timely medical intervention.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fmed.2024.1387807","type":"journal-article","created":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T04:30:47Z","timestamp":1714019447000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Skeletal muscle index based on CT at the 12th thoracic spine level can predict osteoporosis and fracture risk: a propensity score-matched cohort study"],"prefix":"10.3389","volume":"11","author":[{"given":"Jia-sen","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ya-ping","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji-kui","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian-guang","family":"Ni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,4,25]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.bjpt.2018.11.011","article-title":"Exercise for the prevention of osteoporosis in postmenopausal women: an evidence-based guide to the optimal prescription","volume":"23","author":"Daly","year":"2019","journal-title":"Braz J Phys 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