Evidence map›Paper›PMID 38725469›Full record

ArticleFrontiers in medicine2024

Skeletal muscle index based on CT at the 12th thoracic spine level can predict osteoporosis and fracture risk: a propensity score-matched cohort study.

Jia-Sen Hu, Ya-Ping Jin, Ji-Kui Wu, Jian-Guang Ni

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In one paragraph

Article in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
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1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Jia-Sen HuDepartment of Orthopedics, Affiliated Yueqing Hospital of Wenzhou Medical University, Wenzhou, China.
Ya-Ping JinDepartment of Orthopedics, Affiliated Yueqing Hospital of Wenzhou Medical University, Wenzhou, China.
Ji-Kui WuDepartment of Orthopedics, Affiliated Yueqing Hospital of Wenzhou Medical University, Wenzhou, China.
Jian-Guang NiDepartment of Orthopedics, Affiliated Yueqing Hospital of Wenzhou Medical University, Wenzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: 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. Methods: A total of 349 subjects were included in this study. After 1∶1 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. Results: The BMD of the lumbar spine (L1-4) demonstrated a strong correlation with SMI ( Conclusion: 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.

Indexed as

bone mineral densitycomputed tomographyfracture risk assessment toolosteoporosispropensity score matchingskeletal muscle index

Identifiers

PMID38725469
PMCPMC11079204

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