Osteoporotic fractures known as low-trauma or non-traumatic fractures or fragility fractures, predominantly affect women and the elderly, particularly postmenopausal women who are at high risk for low bone mineral density (BMD) and osteoporosis (OP)
[1]
. OP is a chronic bone disease characterized by low bone density and deterioration of bone microstructure, which increases the risk of bone fragility and susceptibility to osteoporotic fractures
[2]
[3]
. Epidemiology estimates that about 22% of men and 50% of women over the age of 50 are susceptible to osteoporotic fractures
[4]
. The number of osteoporotic fractures in China is projected to reach 4.83 million in 2035 and 5.99 million in 2050
[5]
. Osteoporotic fractures can result in limited mobility, impaired physical function, decreased quality of life, and increased mortality in elderly patients
[6]
. Osteoporosis has obvious clinical and public health implications, affecting an estimated 750,000 people worldwide and the number of fragility fractures that occur each year is approximately 90,000
[7]
. Some studies have shown that the low quality of muscle tissue in elderly patients induced by sarcopenia is characterized by low CK levels
[8]
. In addition, sarcopenia was positively correlated with osteoporotic fractures
[9]
.
Currently, the clinical diagnosis of osteoporosis is mainly founded on bone mineral density (BMD) examination
[10]
. BMD was considered a predictor of osteoporotic fractures
[11]
. Moreover, bone transition markers (BTM), the World Health Organization (WHO) fracture risk assessment instrument (FRAX), quantitative computed tomography (CT), and quantitative ultrasound (QUS) 2 are recommended as predictors of osteoporotic fractures
[11]
. In economically underdeveloped regions, however, these detection methods are costly for hospitals and patients and require specialized equipment. Previous studies reported that clinical bone biochemical markers show high sensitivity and significance for predicting fracture healing, diagnosing, and determining the efficacy of treatment
[12]
. For instance, indicators of bone formation, bone resorption, calcium and phosphorus metabolism regulation, etc. However, the above methods also require specific biochemical detection instruments. Therefore, the development of a convenient and economical biochemical marker associated with osteoporotic fractures could help identify at-risk populations and design personalized fracture prevention strategies
[13]
.
In human anatomy, skeletal muscles are attached to bones by tendons, the damage to bone tissue may also cause damage to skeletal muscle
[14]
[15]
. Studies indicate that osteocalcin, sclerostin, and fibroblast growth factor-23, which are factors secreted by osteoblasts or osteocytes, may have a regulatory effect on skeletal muscle
[16]
. Nevertheless, the interaction between bone and skeletal muscle following fracture is still unknown. Both creatine kinase (CK) and creatine kinase myocardial band (CK-MB), are involved in the muscle contraction process and are crucial in assessing myocardial injury in patients
[17]
. Studies have found that skeletal muscle damage is detrimental to bone formation
[18]
. CK is a cytoplasmic and mitochondrial enzyme capable of catalyzing the reversible reaction of creatine or phosphocreatine with adenosine diphosphate
[19]
. CK-MB is an isoenzyme of CK that catalyzes the same chemical reaction as CK
[20]
. Both CK and CK-MB are considered to be important indicators of myocardial injury, but their role in skeletal muscle injury is unclear. Therefore, we hypothesized that the serum CK and CK-MB were associated with osteoporotic fractures. In pathological conditions, osteoporotic fractures often lead to a localized inflammation response and cause the release of inflammatory factors into the circulation. The serum level of C-reactive protein (CRP) accurately reflects the systemic inflammatory response, regulation of bone metabolism, and degree of fracture repair
[21]
. In addition, age and body mass index (BMI) are also considered influential factors in osteoporotic fractures.
Consequently, the purpose of this study is to investigate the relationship between serum biochemical indicators, especially serum CK, CK-MB, and osteoporotic fractures, to discover affordable and readily available clinical biomarkers for osteoporotic fractures.
<xref ref-type="bibr" rid="scirp.142861-"></xref>2. Materials and Methods2.1. Clinical Data
From January 2018 to January 2022, the clinical data of sixty patients with osteoporotic fractures and fifty-two normal subjects admitted to the Affiliated Hospital of Youjiang Medical University for Nationalities were retrospectively analyzed. To evaluate the sample size of this study, the GPower 3.1 program was used. The formula for calculating the effect size was: Effect size = [(Mean 1 − Mean 2)]/SD 1. The effect size was computed using peripheral blood osteocalcin data (15.1 ± 5.6 ng/mL vs 18.9 ± 7.7 ng/mL) from a previous study, and the difference between the non-osteoporosis group and osteoporosis group was statistically significant
[22]
. As a result, the effect size is equal to [(15.1 − 18.9)]/5.6 = 0.7. When the acceptable α error in a two-tailed test was less than 0.05 and the study’s power (1-β) was adjusted at 0.95, the final calculated sample size via GPower 3.1 software was 45 for each group. Finally, 52 healthy individuals and 60 patients with osteoporotic fractures were included in this study. Clinical data were collected on all patients with primary osteoporotic fractures (including patients with senile fractures, postmenopausal fractures, and idiopathic fractures). The control group consists of individuals who have undergone normal physical examinations. No other invasive treatment was performed other than blood collection. In this retrospective study, approximately equal numbers of male and female participants are represented. All participants consented to the investigation and signed an informed consent form. The protocol of the study was reviewed by the Ethics Committee of Youjiang Medical University for Nationalities, the ethical approval number is 20230601001.
The inclusion criteria of the participants were: (1) the patient was diagnosed with an osteoporotic fracture, according to the Guidelines for Primary Osteoporosis Diagnosis and Treatment (2022) of the Chinese Society of Osteoporosis and Bone Mineral Research, an osteoporotic fracture can be diagnosed if one of the following conditions is met: ① a fragility fracture of the hip or vertebral body; ② the bone mineral density of the axial bone or the bone mineral density of 1/3 of the distal radius was measured by DXA with T-value ≤ −2.5; ③ bone mineral density measurements were consistent with bone loss (−2.5 < T-value < −1.0) and proximal humerus, pelvis or a fragility fracture of the distal forearm
[23]
; (2) patients with no history of related fractures in the physical examination centre; (3) patients who voluntarily participate in this study and sign informed consent.
The exclusion criteria of the participants were: (1) patients who had previously taken drugs that could affect biochemical markers of bone metabolism; (2) patients with chronic diseases such as diabetes, chronic liver and kidney disease, and long-term treatment with hormone drugs; (3) patients with severe cardiovascular and cerebrovascular diseases; (4) patients with malignant tumours; (5) patients with incomplete basic information; (6) Patients with traumatic fractures or other soft tissue injuries were excluded.
In addition, the inclusion criteria of the control group were patients who did not meet the standard of osteoporosis, patients without fractures, and patients who voluntarily participated in this study and signed informed consent. Exclusion criteria are consistent with the exclusion criteria of the osteoporosis group. In the experimental group, the first pain assessment of the patient was carried out by the doctor in charge and the responsible nurse within 8 hours after admission, and the comprehensive assessment was carried out within 24 hours after admission to ensure that the pain management was consistent.
2.2. Method
The baseline characteristics (age, sex, height, weight, and BMI) of the participants were obtained from the clinical record. Fracture patients are asked to take blood samples within twenty-four hours of admission, which have undergone evaluation by clinical doctors and standardized procedures by nurses. Each patient was required to draw venous blood after fasting for 12 hours. Subsequently, the blood was centrifuged at 3000 r/min for 10 minutes to separate the serum. Biochemical indexes including CRP, CK, and CK-MB were detected by using a Roche 702 automatic biochemical analyzer for data analysis.
2.3. Statistical Analysis
Statistical analysis is conducted by using professional statistical software. The measurement data were expressed as mean ± standard deviation, and a two-independent sample t-test was used to compare the difference in clinical data between the two groups. The chi-square test was used to compare the differences in categorized data between the two groups. The correlations between clinical parameters were evaluated by using Pearson correlation analysis and multiple logistic regression analysis. The receiver operating characteristic (ROC) curve was constructed to determine the diagnostic accuracy of the biochemical indexes in the osteoporotic fracture. The area under the curve (AUCs) range 0.6 to 0.7 was categorized as poor, the range 0.7 to 0.8 was categorized as fair, the range 0.8 to 0.9 was categorized as good, and the range 0.9 to 1.0 was categorized as excellent
[24]
. P < 0.05 was considered statistically significant.
3. Results
Comparison of clinical data and biochemical indexes between osteoporotic fracture patients and normal subjects.
When compared to the control group, patients with osteoporotic fractures were observed to be older (P < 0.001). Lower levels of serum CK and CK-MB were found in the osteoporotic fracture group than those in the control group (P < 0.001). However, the serum CRP level and BMI were not different between the two groups (
Table 1
).
<xref ref-type="bibr" rid="scirp.142861-"></xref>Table1. Comparison of general data and biochemical indexes between the two groups.
Index (mean ± SD)
Control group (n = 52)
Fracture group (n = 60)
F value
P value
Age (years)
33.538 ± 4.570
70.733 ± 10.462
12.925
<0.001*
BMI (kg/m2)
22.429 ± 4.925
21.653 ± 3.078
0.871
0.353
CRP (mg/L)
21.769 ± 31.738
28.657 ± 66.858
1.341
0.249
CK (U/L)
531.000 ± 837.593
78.105 ± 148.356
35.320
<0.001*
CK-MB (U/L)
24.931 ± 23.290
15.551 ± 4.811
18.019
<0.001*
Data are reported as mean ± standard deviation. Two-tailed Student’s t-test or chi-square test was used for the comparisons between two groups, and a one-way analysis of variance (ANOVA) was used for the comparisons between multiple groups. * p < 0.05. BMI: body mass index; CRP: C-reactive protein; CK: creatine kinase; CK-MB: creatine kinase myocardial band.
3.1. Correlation Analysis among Different Indicators of the Participants
Data are analyzed by using Spearman’s rank correlation analysis. Red indicates a positive correlation and blue indicates a negative correlation. *p < 0.05. CRP: C-reactive protein; CK: creatine kinase; CK-MB: creatine kinase myocardial band; BMI: body mass index.Figure 1. Correlation analysis among different indicators.
Age was significantly negatively correlated with the serum CK and CK-MB levels, respectively (P < 0.05). The fracture condition was found to be negatively correlated with the serum CK and CK-MB levels, respectively (P < 0.05). However, there was no correlation between CRP, BMI, and serum CK, CK-MB levels (
Figure 1
).
According to the results of univariate analysis, adjusted for age, variables with statistical differences were included in the logistic regression analysis (backward method). Primarily, the occurrence of osteoporotic fractures was set as the dependent variable. In model 1, the serum CK level and the age were set as the independent variables. The result showed that a lower level of serum CK was a risk factor for osteoporotic fractures (OR: 0.991; 95% CI: 0.985 - 0.996). In model 2, the serum CK-MB level and age were set as the independent variables. The result indicated that the lower level of serum CK-MB was a risk factor for osteoporotic fractures (OR: 0.731; 95% CI: 0.637 - 0.839). In model 3, the serum levels of CK, CK-MB, and age were set as the independent variables, the lower levels of serum CK and CK-MB were both risk factors for osteoporotic fractures, and the effect of the serum CK-MB level (OR: 0.744; 95% CI: 0.633 - 0.874) was more significant than that of the serum CK level (OR: 0.999; 95% CI: 0.994 - 1.004). Since the CRP and BMI did not differ significantly between the two groups (P > 0.05), they were omitted from the model analysis (
Table 2
).
Data are reported as mean ± standard deviation. Two-tailed Student’s t-test or chi-square test was used for the comparisons between two groups, and a one-way analysis of variance (ANOVA) was used for the comparisons between multiple groups. *P < 0.05. CK: creatine kinase; CK-MB: creatine kinase myocardial band.
3.3. ROC Curves Analysis
A sensitivity of 81.67%, specificity of 82.69%, and likelihood ratio of 4.719 was obtained for serum CK level at the cut-off point of 73 U/L (AUC = 0.861, P < 0.001). A sensitivity of 71.67%, specificity of 51.92%, and likelihood ratio of 1.491 was obtained for serum CK-MB level at the cut-off point of 16.46 U/L (AUC = 0.610, P < 0.001) (
Figure 2
).
Receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) of serum CK and CK-MB to detect osteoporotic fracture. *p < 0.05. CK: creatine kinase; CK-MB: creatine kinase myocardial band.Figure 2. ROC curves analysis.
As the global population ages, senile fractures, particularly osteoporotic fractures, are becoming increasingly prevalent in clinical settings
[25]
. During the aging process, an osteoporotic fracture is more common and often accompanied by sarcopenia
[26]
. Osteoporosis combined with sarcopenia is prone to weakness, which increases the risk of fractures
[27]
. Muscle damage often occurs after a fracture
[28]
. This study aims to explore the correlation between bone fracture metabolic injury and muscle metabolic injury, providing new evidence for clinical indicators of osteoporotic fractures. Our results demonstrated that the levels of CK and CK-MB are lower in patients with osteoporotic fractures. This confirms that the severity of trauma in patients with fractures is closely related to their serological indicators.
In a study of different surgical approaches to treat intertrochanteric fractures in elderly patients, the serum CK-MB was found to decrease in the proximal femoral nail anti-rotation treatment groups
[29]
. However, the author focuses on the possibility of myocardial infarction in elderly patients with intertrochanteric fractures. In an overview study of musculoskeletal laboratory parameters in competitive athletes, serum CK was considered an indicator of muscle load and potential injury in competitive athletes
[30]
. In our study, the serum CK and CK-MB were found to be reduced in patients with osteoporotic fractures. The first reason may be that the degree of osteoporotic fractures varies significantly between studies
[31]
, and some patients may not have significant myocardial cell damage. The second reason may be that the detection time of CK or CK-MB may influence the statistical difference between groups
[32]
. The blood samples from patients with osteoporotic fractures were collected within 24 hours of admission in our study, and clinicians analyzed this blood biochemical index together with clinical symptoms and other examinations before providing patients with personalized treatment, which included surgery or conservative treatment. Since blood samples were collected before treatment, we believe that treatment will not affect patients’ CK and CK-MB levels. However, the interval between feeling ill and being admitted to the hospital may influence the CK and CK-MB levels in the blood.
Contrary to our results, Xinye Li et al. reported that serum CK levels increased and were related to osteoclasts
[33]
. The study showed that the number of osteoclasts decreased when CK release was increased to 2.6 times the basal value
[34]
. Consequently, we conjecture that serum CK and CK-MB will decrease to varying degrees when osteoporotic fractures occur, particularly in the elderly population. This is consistent with our findings. The activation of osteoblasts is typically normal, whereas the transformation of osteoclasts is abnormal, thus increasing the number of osteoclasts and bone resorption
[34]
. A decrease in CK may indicate an increase in osteoclast activity, thus increasing the incidence of osteoporotic fractures. In addition, an animal study from Hong Kong, China, found that impaired fracture healing in sarcopenic SAMP8 mice was attributed to increased expression of myostatin in callus and muscle, which was negatively correlated with callus formation
[7]
. In patients with osteoporotic fractures, our investigation also revealed a decrease in serum CK levels. According to multivariable logistic regression analysis, lower levels of CK and CK-MB were risk factors for osteoporotic fractures.
The serum levels of CK and CK-MB in patients with osteoporotic fractures were lower and negatively correlated with age. Decreased serum CK and CK-MB were risk factors for osteoporotic fractures. This may provide a novel concept for the clinical indicator of osteoporotic fractures and certain clinical significance.
<xref ref-type="bibr" rid="scirp.142861-"></xref>Limitation and Further Study
Indeed, this cross-sectional observation study is regional and only focuses on patients from southwest China. If the dynamic changes of CK and CK-MB can be monitored, it will be more conducive to the connection between bone and muscle, and their role in the prognosis and prediction of osteoporotic fractures will be more conducive to research. In addition, the muscle strength of the patients was not measured and there was no body composition data in this study, so we cannot assess whether sarcopenia is a risk factor for osteoporotic fractures, and these data should be included in subsequent studies.
Although AUC = 0.61, it is greater than 0.5 can still indicate that CK-MB has predictive value for osteoporotic fractures. In addition, due to its regional limitations, the AUC may increase further if the sample size is expanded.
Acknowledgements
This work was supported by the 2023 Innovation Project of Youjiang Medical University for Nationalities Graduate Education (YXCXJH2023023). This work was also supported by the Guangxi Zhuang Autonomous Region University student innovation and entrepreneurship training program under Grant numbers S202210599048 and S202210599057.
Data Availability
The data used to support the findings of this study are available from the corresponding author upon request.
Author Contributions
Y.L.: Design of the work, Acquisition, Analysis, Interpretation of data, Draft the manuscript; Q.Z.: Design of the work, Acquisition, Analysis; X.Z.: Acquisition, Experimental technical guidance, Analysis; Y.L.: Acquisition, Experimental technical guidance, Analysis; T.F.: Acquisition, Analysis; J.W.: Acquisition, Analysis; S.M.: Acquisition, Analysis; Z.D.: Conception, Funding Acquisition; L.H.: Conception, Design of the work, Interpretation of data, Revise the manuscript, Final approval of the manuscript to be published; J.W.: Conception, Design of the work, Interpretation of data, Revise the manuscript, Final approval of the manuscript to be published, Funding Acquisition.
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