The loss of bone mass and skeletal muscle is often one of the important triggers of falls and prolonged bed rest in the elderly population (1, 2). Studies have shown that the body loses muscle mass at a rate of 1% per year by the age of 40 and older (3). Sarcopenia is an age-related decline in overall muscle mass and strength or physical muscle function—a degenerative disease resulting in a marked decrease in balance and mobility (4). The primary diagnostic approach for sarcopenia in clinical guidelines involves assessing participants’ skeletal muscle index (SMI) using methods like dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA) (5). In addition, many studies have shown that SMI can be well reflected by calculating the area of a single chest or abdominal CT image (6–8). Shen et al. (6) demonstrated that SMI based on T12 levels can predict in-hospital mortality in elderly patients. In addition, Tan et al. (7) demonstrated that SMI of the T12 thoracic vertebrae contributes to the diagnosis of sarcopenia in the Chinese population. Based on the above studies, SMI at the T12 level may be a potential indicator to predict osteoporosis and fracture risk propensity.

Osteoporosis is a bone metabolic disorder in which the loss of bone mass causes changes in the microstructure of bone, resulting in decreased mechanical structural properties and increased fragility of bone (9, 10). Risk factors for osteoporosis include aging, sex (more common in women), family genetics, poor diet, lack of exercise, and certain chronic diseases (11). Similarly, these risk factors are strongly associated with changes in muscle mass (12). Bone mineral density (BMD) assessment via DXA serves as the gold standard for diagnosing osteoporosis (13). The Fracture risk assessment tool (FRAX) is often applied to predict the risk of osteoporotic fracture in individuals, such as a femoral neck (FN) fracture in the elderly, using risk factors associated with osteoporosis (14, 15). In addition, FRAX is widely used worldwide because of the reliability and ease of use of its predictive results.

There have been several studies showing that osteoporosis is strongly associated with sarcopenia (16, 17). Multiple studies have shown that SMI measured on abdominal computed tomography (CT) is strongly associated with BMD and fracture risk (18, 19). Although the authors demonstrated that SMI measured on CT images at the level of the 12th thoracic vertebra (T12) can be used to diagnose sarcopenia (7), no subsequent studies have delved into the correlation between SMI and BMD at the T12 level, or the connection between SMI and fracture risk. In addition, chest CT has been more and more widely used in physical examination and hospitalization, such as follow-up of lung nodules and screening of lung cancer.

In summary, in this study, our team explored the relationship between SMI and BMD at the T12 level and the relationship between SMI and fracture risk. When patients undergo a chest CT examination (for other clinical reasons), clinicians have the opportunity to use these imaging data to screen for osteoporosis and fracture risk.

Table 1 displays clinical characteristics data for the eligible patients. This study included 349 subjects, with 249 in the non-osteoporosis group and 100 in the osteoporosis group. Significant differences were observed between the two groups in terms of age, gender, height, weight, BMI, and diabetes (p-values were all <0.05). However, there were no significant differences in hypertension, hyperlipidemia, and hyperuricemia between the two groups. After PSM analysis, 162 subjects were enrolled in the study, including 81 in the non-osteoporosis group and 81 in the osteoporosis group. Furthermore, age was the only significant difference between the two groups (64 versus 68 p = 0.028), while height, weight, BMI, sex, hypertension, hyperlipidemia, diabetes, and hyperuricemia were not. Patients obtained from PSM analysis were evaluated for fracture risk, and their fracture risk factors are shown in Table 2. Among the 162 patients, 22 had a history of brittle fractures, 18 had a history of smoking, 3 had a history of parental hip fractures, 4 had a history of systemic glucocorticoid use, 4 had a history of rheumatoid arthritis, 27 had a history of risk factors for secondary osteoporosis, and 13 had a history of excess alcohol intake.

The measurement results after propensity score matching analysis are presented in Table 3. The SMI was 32.46 ± 6.64 cm²/m². L1-4 and FN BMD were 0.91 ± 0.13 g/cm² and 0.74 ± 0.11 g/cm², respectively. Moreover, the probability of major osteoporotic fractures was 6.59 ± 5.14% and 2.33 ± 3.55% for hip fractures when the femoral neck BMD was included. The probability of major osteoporotic fractures was 6.63 ± 4.11% and 2.48 ± 3.61% for hip fractures when the femoral neck BMD was not included.

The BMD of L1-4 exhibited a strong correlation with SMI (r = 0.416, p < 0.001, as shown in Figure 2A). Additionally, BMD of the FN showed a correlation with SMI (r = 0.307, p < 0.001, as depicted in Figure 2B). SMI also displayed correlations with FRAX, both with and without BMD at the FN. For major osteoporotic fractures, these correlations were as follows: r = −0.431, p < 0.0001, and r = −0.416, p < 0.001, respectively. For hip fractures, the correlations were: r = −0.311, p < 0.001, and r = −0.357, p < 0.001 (refer to Figure 3). Furthermore, Figure 4A illustrates that the SMI of the non-osteoporosis group was significantly higher than that of the osteoporosis group (p < 0.001). SMI proved to be an effective predictor of osteoporosis, with an area under the curve of 0.834 (95% confidence interval 0.771–0.897, p < 0.001). In order to visualize the prediction performance of SMI, the ROC curve was drawn, as shown in Figure 4B.

Our findings indicate that SMI, measured at the T12 level, demonstrates a robust correlation with both lumbar spine and femoral neck (FN) BMD. Moreover, SMI serves as a reliable predictor of osteoporosis. Additionally, our results show a strong association between SMI and fracture risk as calculated by FRAX.

Osteoporosis is closely related to skeletal muscle; the two interact to make older people prone to falls and brittle fractures. Reduced muscle mass, strength, and function increase the risk of osteoporosis, and similarly, reduced bone mass increases the risk of sarcopenia (23). The bone and skeletal muscle relationship is not just a simple mechanical one. Bone and muscle are endocrine target organs and secretory organs interacting with each other through paracrine and endocrine signals. The skeletal muscle is the largest endocrine organ in the human body, secreting factors that regulate bone metabolism. These muscle-secreting factors mainly include myostatin, β-aminoisobutyric acid and irisin, and so on. In addition, circadian rhythm s, nutritional deficiencies, aging, and nervous system networks also affect bone and muscle (24–26). Therefore, maintaining skeletal muscle mass can prevent sarcopenia and osteoporosis. In this study, SMI based on T12 was closely related to BMD of the FN and lumbar spine. Similar to previous studies, SMI measured by DXA or BIA was strongly associated with BMD (27, 28). In addition, we found that low-quality SMI was effective in predicting osteoporosis, with moderate predictive power after ROC analysis. These results further support the close relationship between SMI and BMD.

Due to the lack of awareness of osteoporosis in the general population, the first diagnosis of osteoporosis is usually found at the time of fracture using DXA examination. However, when patients develop osteoporotic fractures, they have a poorer prognosis and higher mortality than non-osteoporotic fractures (29, 30). Therefore, more and more research is devoted to using a more convenient method to detect osteoporosis, treat osteoporosis in time and prevent osteoporosis fractures. Among them, Kajiki et al. (18) proved that psoas muscle index, as measured by abdominal CT, is strongly associated with the BMD and is a valuable predictor of osteoporosis. However, most patients do not have a specific reason to undergo abdominal CT. Chest CT has been more and more widely used, especially for the follow-up of lung nodules and screening of lung cancer in middle-aged and elderly people. This study found that SMI based on chest CT measurements can predict osteoporosis, providing a new way to more easily screen for osteoporosis. Therefore, when patients undergo chest CT examination (for other clinical reasons), doctors have the opportunity to use these image data for osteoporosis screening, thereby reducing the waste of medical resources to a certain extent.

In this study, SMI exhibited a strong association with fracture risk, as evaluated using FRAX. This outcome aligns with previous research findings that demonstrated a higher fracture risk assessed by FRAX in individuals with low muscle mass (31–33). FRAX effectively improves patient lifestyles and prevents fractures, reducing fracture-related mortality (34). Our study suggests that SMI can be used as an indicator to assess a patient’s risk of fracture. SMI based on chest CT measurement is helpful for early detection of high-risk patients and timely intervention.

This study found that SMI based on chest CT measurements can be used to predict osteoporosis and fracture risk. With the development of medical technology, chest CT has been widely used, especially in routine physical examination. With clinicians’ secondary use of these chest CT images to detect osteoporosis early through SMI, patients can benefit from timely intervention and improved management, potentially reducing the risk of fractures and other related complications. Patients can both improve their quality of life and reduce subsequent medical costs.

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 images to screen people who are prone to osteoporosis and fracture, and carry out timely medical intervention to reduce the waste of medical resources to a certain extent.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving humans were approved by the Department of Orthopedics, Affiliated Yueqing Hospital of Wenzhou Medical University, Wenzhou, China. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin because this study is a retrospective study and does not involve any impact on patient health and safety.

J-sH: Data curation, Formal analysis, Writing – original draft. Y-pJ: Data curation, Writing – original draft. J-kW: Data curation, Writing – original draft. J-gN: Writing – review & editing.