High prevalence of low bone mineral density in middle-aged adults in Shanghai: a cross-sectional study

Abstract

Purpose

To assess bone mineral density (BMD) in middle-aged individuals in Shanghai, in order to improve awareness of osteopenia and osteoporosis screening.

Methods

The clinical data of 1107 permanent residents of Shanghai aged 40–60 years were collected using a random cluster sampling method. Osteoporosis questionnaire survey and BMD test were conducted. Mann-Whitney U and Chi-square test were used to compare sex, age and body mass index at different stages of bone mass, and Pearson test was used to conduct correlation analysis. Logistic regression was used to analyze the influencing factors.

Results

The detection rates of osteopenia and osteoporosis were 59% and 12.5% respectively, and bone mineral density was correlated with sex, age, and body mass index (P < 0.05).

Conclusion

The incidence of low bone mass is high in the assessed population, screening for low bone mass should be actively carried out to improve public awareness. It is also good for public health management.

Registered clinical trial

The trial was approved by Chinese Clinical Trial Registry on February 11, 2021(ChiCTR2100043369).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-024-08239-7.

Introduction

Osteoporosis (OP), the most common bone disease in humans, is characterized by reduced bone mass, degraded bone structure, decreased bone strength, and increased susceptibility to microfractures or complete fractures, which is often considered as a main complication following decreased BMD [1]. The main symptoms of osteopenia are joint pain, muscle spasms, decreased height, hunchback [2]. Without timely intervention, osteopenia may progress to OP with age. A 2022 meta-analysis found that the global prevalence of osteoporosis and osteopenia was 19.7% and 40.4% [3]. Recent research shows that 50% of women and 20% of men in developed countries suffer a fragility fracture after the age of 50 [4]. According to the results of the first epidemiological survey of osteoporosis in China released by the National Health Commission on October 19, 2018: the prevalence rate of OP in people aged 40–49 years in China was 3.2%, including 2.2% for men and 4.3% for women. The prevalence rate of OP in people over 50 years old was 19.2%, of which 6% for men and 32.1% for women [5]. A Chinese cross-sectional study found that the prevalence of clinical fragility fractures over the past 5 years was 4.1% in men and 4.2% in women [6]. Therefore, early intervention and prevention should be considered in patients with low bone mass. A cross-sectional study in Binzhou City, Shandong Province in 2021 found that the prevalence of osteopenia was high in the 55–65 age group, and family history was an independent risk factor for osteopenia [7]. A 2022 study of 1,359 adult women in Yanji, Heilongjiang Province, found that age, number of births, body mass index (BMI), time spent outdoors, milk consumption, and regular exercise were factors that affected bone mineral density levels [8]. Compared with the extensive literature on OP, including epidemiological and clinical studies, there are few studies on osteopenia. Similar cross-sectional studies in China also investigated BMD, however, most of them referred to T-value rather than Z-value. In this study, Z-score of BMD was used for men under 50 years old and premenopausal women, and T-score for men over 50 years old and postmenopausal women, and stratified statistical analysis was carried out. This study analyzed the clinical data of 1107 patients aged 40–60 years, with the aim of providing evidence of bone mass loss in this population and determining the need for early screening and intervention of osteopenia and osteoporosis. High prevalence of low bone mineral density in middle-aged people in Shanghai community is the expected possible outcome of the study. Firstly, osteopenia and OP is generally regarded as a disease of aging, unlike hypertension, diabetes and other diseases that are paid attention by middle-aged people. Secondly, at present, BMD has not been included in routine examination items in many community health surveys and physical examinations of employees, which may lead to low public attention to low bone mineral density.

Methods

Study population

This study was approved by the Ethics Committee of Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine (approval number 2020LCSY031). From June 2020 to January 2022, a random cluster sampling method was adopted to select Xuhui, Pudong New Area, Minhang, Changning, Jinshan and Baoshan District of Shanghai as the survey area based on the principle of convenience sampling. Direct selection method was adopted to select 1 urban street or rural township in each survey area, and 6 neighborhood committees or villages were selected from each street or township. Fifty households were selected from each neighborhood committee or village, and permanent residents aged 40 to 60 for more than 5 years were selected from each household to carry out the survey. Migrant workers and employees dispatched by foreign companies are not included in the selection. A total of 1863 cases were investigated and 1107 cases were included for statistical analysis.

Inclusion and exclusion criteria

The inclusion criteria for the study were as follows: aged 40 ~ 60 years of Chinese.

origin, male or female, provided informed consent.

The exclusion criteria for the study were as follows: patients with osteoporosis who are or have been treated; bone mass loss secondary to conditions such as hyperparathyroidism, rheumatoid arthritis, gout, and various endocrine diseases affecting bone metabolism, such as diabetes, diabetic nephropathy, etc.; concomitant fractures requiring surgical treatment; and diagnosis of psychological disorders.

Bone density test

Bone density of the lumbar vertebrae (L1–L4) and left hip was measured using the Hologic Discovery-CI dual-energy X-ray absorptiometry (DXA) System (Hologic, Marlborough, MA, USA). All subjects were tested with the same BMD instrument, and a morphometric vertebral phantom was used for scanning to monitor stability daily. We found that the machine has good long-term stability and accuracy. The coefficients of variation at the lumbar vertebrae and left hip was 1.1% and 1.3%.

Diagnostic criteria

DXA was performed to diagnose OP according to the World Health Organization (WHO) definition published in 1994 and the bone density standard of Asian populations. The BMD of men under 50 years of age and premenopausal women was as assessed using Z-score, with Z > -2.0 indicating normal BMD and Z ≤ -2.0 indicating OP. The BMD of men over 50 years old and postmenopausal women was assessed using T-score, with T ≥ -1 considered to indicate normal bone mass, -1 > T > -2.5 decreased bone mass, and T ≤ -2.5 OP [9]. The China Reference database was selected for a 2019 Chinese multi-center BMD survey for assessment of T-score in men [10]. The China Reference database was selected for a 2007 Chinese multi-center BMD survey for assessment of T- score in women [11].

Questionnaire

The OP screening assessment was developed by referring to OP and bone loss guidelines, clinical studies, and reviews, and the International Osteoporosis Foundation (IOF) one-minute OP risk assessment (https://riskcheck.osteoporosis.

foundation/). IOF was used as the questionnaire of this cross-sectional study to collect the information of the target population.

Statistical analysis

Statistical analyses were performed using SPSS software version 25.0 (IBM Corporation, Armonk, NY, USA). Non-normally distributed measurement data are expressed as the median (first quartile, third quartile), Differences between groups were compared by the Mann–Whitney U test. Count data were expressed as frequencies and percentages, with the chi-squared test used for comparison between the groups. Pearson test was used to conduct correlation analysis. Logistic regression was used to analyze the influencing factors. Bilateral tests were used, and P < 0.05 was considered statistically significant.

Flow diagram

At the beginning of this study, 1863 subjects were recruited, 748 were excluded, including 583 who did not meet the inclusion criteria: there were 176 subjects of untreated subjects because of previously determined low BMD; 298 subjects of bone mass loss secondary to conditions such as hyperparathyroidism, rheumatoid arthritis, gout, and various endocrine diseases affecting bone metabolism, such as diabetes, diabetic nephropathy, etc., among them, 102 subjects had diabetes mellitus; 66 subjects had concomitant fractures requiring surgical treatment; 43 subjects had diagnosis of psychological disorders. 124 who refused to participate and 41 for other reasons. 1115 subjects filled in the questionnaire and accepted BMD test, among them, 8 subjects withdrew because of discomfort. 1107 cases were included in the statistical analysis. As shown in Fig. 1.

Fig. 1.

Flow diagram

Results

Patient characteristics

Among the 1107 adults aged 40 ~ 60 years, 257 (23.22%) were male and 850 (76.28%) were female. The detection rates of osteopenia and osteoporosis were 58.99% and 12.47% respectively. No significant differences in sex or age or time since menopausal were observed between the groups; however, there were statistically significant differences in body mass index (BMI), menopausal status, BMI, smoking, vitamin D supplements, prevalent fractures and BMD values (P < 0.05), as shown in Table 1. There was a significant difference in age between men and women in the stage of normal bone mass, at the same, there was a significant difference in BMI between men and women in the stage of osteopenia and osteoporosis. As shown in Table 2.

Table 1.

Patient characteristics

Characteristic	Normal bone mass	Osteopenia	Osteoporosis	P value
Male, n	67 (26.07%)	148 (57.59%)	42 (16.34%)	0.372
Female, n	249 (29.29%)	505 (59.41%)	96 (11.29%)
Postmenopausal status, n	182 (21.41%)	426 (50.12%)	80 (9.41%)	< 0.001
Time since menopausal, years	7.8 (6.3, 9.4)	9.5 (7.6, 12.0)	11.7 (9.9, 13.8)	0.852
Age, years	55 (51, 58)	56 (52, 58)	55 (52, 58)	0.069
BMI, kg/m2	24.50 (22.48, 26.70)	23.30 (21.23, 25.39)	22.60 (20.20, 24.26)	< 0.001
Smoking status, n	27 (8.54%)	74 (11.33%)	27 (19.57%)	0.001
Vitamin D supplements, n	92 (29.11%)	470 (71.98%)	112 (81.16%)	< 0.001
Prevalent vertebral fractures in men and women, n	3 / 3 (0.95% / 0.95%)	12 / 16 (1.84% / 2.45%)	7 / 12 (5.07% / 8.70%)	0.003
LS BMD, g/cm2	1.032 (1.052, 1.213)	0.881(0.802, 0.953)	0.631 (0.551, 0.724)	< 0.001
Left hip BMD, g/cm2	0.983 (0.912, 1.055)	0.864(0.672, 1.047)	0.598(0.527, 0.688)	< 0.001

Age, BMI and BMD data are expressed as the median (first quartile, third quartile). BMI, body mass index. LS, lumbar spine; BMD, bone mineral density

Table 2.

Comparison of age and BMI between men and women at different stages

Stage	Sex	Age, years	P value	BMI, kg/m2	P value
Normal bone mass	Male	56 (52, 59)	<0.001	25.54 (23.71, 28.29)	0.345
	Female	52 (48, 54)		24.99 (22.00, 27.87)
Osteopenia	Male	55 (51, 58)	0.161	24.14 (22.04, 26.56)	<0.001
	Female	54 (51,57)		23.11 (20.00, 24.77)
Osteoporosis	Male	54 (51, 59)	0.891	24.44 (23.83, 27.88)	0.004
	Female	55 (51, 57)		21.36 (19.48, 23.53)

BMI was positively correlated with BMD, whereas age was negatively correlated with BMD; however, the correlation coefficients were low. The correlation between age and BMD, BMI and BMD gradually increases with the progression of osteopenia to OP. The results are presented in Table 3.

Table 3.

Correlation between baseline data and bone mineral density

Stage	Statistical index	Age	BMI
Osteopenia	r	-0.162	0.193
	P	< 0.001	< 0.001
Osteoporosis	r	-0.305	0.299
	P	< 0.001	< 0.001

r > 0, positive correlation; r = 0, no correlation; r < 0, negative correlation

BMD at different stages of bone mass loss

Assessment using Z-score revealed that 4.28% of males under 50 years of age and 0.59% of premenopausal women had OP. Assessment using T-scores revealed that 12.84% of males over 50 years of age and 9.41% of postmenopausal women had OP, These data are presented in Tables 4 and 5.

Table 4.

Population distribution of bone mass in males under 50 years old and premenopausal women

Sex	Normal bone mass	Osteoporosis
Male, n	40 (15.56%)	11 (4.28%)
LS BMD, g/cm2	1.125 (1.044, 1.227)	0.684 (0.602, 0.743)
Left hip BMD, g/cm2	1.108 (1.023, 1.184)	0.627(0.548, 0.705)
Female, n	157 (18.47%)	5 (0.59%)
LS BMD, g/cm2	1.128 (1.052, 1.235)	0.675 (0.585, 0.750)
Left hip BMD, g/cm2	1.110 (1.027, 1.190)	0.614 (0.533, 0.698)

Table 5.

Population distribution of bone mass in males over 50 years old and postmenopausal women

Sex	Normal bone mass	Osteopenia	Osteoporosis
Male, n	58 (22.57%)	115 (44.75%)	33 (12.84%)
LS BMD, g/cm2	1.058 (1.023, 1.129)	0.881 (0.802, 0.953)	0.648(0.577, 0.730)
Left hip BMD, g/cm2	1.040(1.019, 1.133)	0.864 (0.672, 1.047)	0.602(0.534, 0.682)
Female, n	182 (21.41%)	426 (50.12%)	80 (9.41%)
LS BMD, g/cm2	1.065(1.022, 1.143)	0.881 (0.802, 0.953)	0.637 (0.571, 0.725)
Left hip BMD, g/cm2	1.049 (1.021, 1.138)	0.864 (0.672, 1.047)	0.592 (0.527, 0.678)

IOF one-minute OP risk assessment

As shown in Table 6, lack of exercise and alcohol consumption were not significantly associated with bone mass loss, while smoking and lack of dairy products and the absence of calcium and vitamin D were significantly associated with bone mass loss.

Table 6.

Association of lifestyle factors with osteoporosis

Lifestyle factor	Normal bone mass 316	Osteopenia 653	Osteoporosis 138	P value
Alcohol consumption	55 (17.41%)	129 (19.75%)	36 (26.09%)	0.102
Smoking	27 (8.54%)	74 (11.33%)	27 (19.57%)	0.001
No dairy products	11 (3.48%)	62 (9.49%)	9 (6.52%)	0.001
Lack of exercise	83 (26.27%)	158 (24.20%)	41 (29.71%)	0.412
No calcium or vitamin D tablets	224 (70.89%)	183 (28.02%)	26 (18.84%)	< 0.001

Logistic regression analysis of influencing factors of osteopenia and OP

With osteopenia or OP as the dependent variable, and the factors with statistical significance in the comparison of distribution characteristics in Tables 1 and 6 as independent variables, univariate and multivariate logistic regression analysis was performed. Categorical variable assignments are shown in Table 7.Through univariate analysis, the results showed that the risk of osteopenia or OP was higher in the BMI < 24 kg/m² group than in the BMI ≥ 24 kg/m² group; the risk of osteopenia or OP in smokers is 3.02 times higher that of non-smokers; the risk of osteopenia or OP in those who do not consume dairy products is 1.31 times higher that of those who eat, and the risk of osteopenia or OP in those who do not consume calcium and vitamin D is 3.18 times higher that of those who eat. After adjusting for multiple factors, the risk was higher in the BMI < 24 kg/m² group than in the BMI ≥ 24 kg/m² group; the risk of smokers is 2.73 times higher that of non-smokers; the risk was 1.54 times higher in those who did not consume dairy products, the risk was 2.66 times higher in those who did not consume calcium and vitamin D. These data are presented in Table 8.

Table 7.

Variable assignment for logistic regression analysis of influencing factors of osteopenia and OP

Variable	Assignment specification
Osteopenia or osteoporosis	0 = No; 1 = Yes
BMI	1 =<24; 2 = ≥ 24
Smoking	0 = No; 1 = Yes
No dairy products	0 = No; 1 = Yes
No calcium or vitamin D tablets	0 = No; 1 = Yes

Table 8.

Logistic regression analysis of influencing factors of osteopenia and OP

Variable	Univariate analysis			Multiple factor analysis
	OR	95%CI	P	OR	95%CI	P
BMI, kg/m2
< 24	0.45	0.28–0.98	0.035	0.53	0.27–1.31	0.068
≥ 24	0.27	0.15–0.89	0.044	0.31	0.26–1.51	0.047
Smoking	3.02	2.04–5.11	<0.001	2.73	1.91–4.05	<0.001
No dairy products	1.31	1.05–2.18	0.042	1.54	1.08–2.32	0.034
No calcium or vitamin D tablets	3.18	2.15–5.37	<0.001	2.66	1.82–3.91	<0.001

Discussion

In this study, the detection rates of osteopenia and OP in this study were 59% and 12.5%, respectively. A survey [12] of 1560 cases aged 20∽80 years in Jinfeng District, Yinchuan City, Ningxia Province, found that bone loss accounted for 11% and osteoporosis accounted for 44.9%. Serum tests were performed on the subjects, and it was found that the worse the hyperglycemia and hyperuric acid, the more likely it was to damage bone mass. In a study [13] of 1540 middle-aged and elderly people (45∽80 years old) in 10 communities in Beijing, the detection rate of OP was 27.9%. It was found that there were great differences in the detection rate of OP between different sites. It was suggested that BMD and bone metabolism should be combined to diagnose OP. Compared to the previous two studies, which had the maximum age span of 80 years old, while our study was 60 years old, which seems to explain the lower rate of OP detection in our study. In addition, blood glucose, blood lipids, uric acid, bone metabolism and other serum indicators were not detected in the study, so it lacked objective indicators other than BMD, and the perspective of research result was relatively narrow.

In this study, the correlation between age, BMI and bone mineral density was statistically analyzed. We found that BMI was positively correlated with BMD, whereas age was negatively correlated with BMD; age, and BMI are therefore influencing factors for bone mass loss. In patients with osteopenia, the correlation coefficients between age, BMI, and BMD were small; these significantly increased in patients with OP. The correlation between age and BMD indicates that as life expectancy increases, OP will become an increasing issue. The prevalence of osteoporosis in postmenopausal women increased sharply, while the prevalence of osteoporosis in males over 50 years old increased relatively slowly, which is consistent with previous findings on postmenopausal OP [14]. Studies have shown that one in two postmenopausal women experience fragility fractures [15]. The role of estrogen in protecting and maintaining skeletal muscle has been established, and the rapid decline of estrogen levels in perimenopausal women accelerates bone loss and eventually leads to postmenopausal OP [16]. BMD analysis in this study showed that the Z-score in premenopausal women was higher than that in men under 50 years of age but there was no difference in the T-score between postmenopausal women and men over 50 years of age. A previous study showed in postmenopausal women, lumbar bone density decreases by 0.006 g/cm² every year, and the correlation between postmenopausal age and bone mineral density was stronger than that between premenopausal age [17]. In menopausal women, estrogen can be used to prevent and treat postmenopausal osteoporosis [15]. A 2-year randomized controlled trial of hormone therapy in postmenopausal Chinese women with OP revealed that estrogen administration had a greater effect on BMD in the spine than in the hip [18].

The correlation between BMI and BMD shown in the present study is consistent with a previous study which suggested that fat has a protective effect on skeleton [19]. A study on obese older adults showed that after intentional weight loss hip BMD decreased, and this decrease persisted for one year, regardless of weight regain [20]. However, it has also been shown that although fat has a certain protective effect on bones, beyond the healthy range fat is detrimental to bone health. Excess weight may harm bones through adverse effects on osteoblasts [21, 22]. Another study showed that in people with central obesity, visceral fat can lead to elevated levels of pro-inflammatory cytokines, this increases bone resorption and promotes OP [23]. This suggests that a body fat percentage within the normal range is recommended to prevent bone loss.

Additionally, a healthy lifestyle can prevent low bone mass [24]. In this study, smoking, lack of dairy products and the absence of calcium and vitamin D were associated with the development of low bone mass, but given the cross-sectional design permitted to find associations but not to demonstrate a link of causality, further intervention studies based on various influencing factors are needed to reveal the link of causality. In the study, the subjects with osteopenia or OP who have already taken calcium or vitamin D tablets are their own lifestyle habits, which may be related to the good maintenance awareness of Shanghai people, rather than knowing that they have been diagnosed with osteopenia or OP. A previous study demonstrated that the intake of milk, yogurt, vegetables, and calcium, potassium, and protein was positively correlated with tibial bone density [25]. A study showed that compared with non-smokers, smokers were associated with the prevalence of OP or osteopenia [26]. In a clinical trial involving patients undergoing orthodontic procedures, smokers experienced a 19.8% reduction in cortical bone thickness and a 23.5% decrease in cortical bone density, which damaged the alveolar bone [27]. The question about the alcohol consumption is allways tricky as a great deal of patients avoids honest answer on the topic, this may partly explain the lack of association between BMD and alcohol intake in this study’s findings. A risk-prediction model for OP showed that alcohol consumption was a risk factor [28]. Resistance exercise is a safe, non-pharmacological treatment for osteoporosis patients that improves physical function and exercises self-efficacy [29]. Active health education and guidance are the basis of the prevention and treatment of bone loss and form an important part of health management.

DXA test is expensive and radioactive, but it is considered very small- smaller than 1/10 for a chest X-ray. Some district hospitals and community hospitals are not equipped with DXA, leading to difficulties in community BMD screening. Radio-frequency ultrasonic multispectroscopy (REMS), an emerging technique for managing bone health, may facilitate a wider range of axial BMD measurement compared to DXA [30]. It has also been shown that quantitative computed tomography (QCT) images can complement existing clinical diagnostic systems and bone mass diagnosis models have successfully distinguished patients with OP and osteopenia [31]. However, QCT is generally considered to be expensive compared to DXA.

Osteoporotic fractures are a major global health issue and the economic burden is significant [32]. The internationally recognized DXA T-score ≤ -2.5 is the intervention threshold for OP. The disadvantage of this threshold is its sensitivity, and a large proportion of patients with a T-score > -2.5 experience fragility fractures; therefore, BMD assessment alone cannot identify individuals at risk of fractures [33]. The fracture risk assessment tool (FRAX) can be used to estimate an individual’s 10-year risk of severe fragility fractures, to provide better clinical risk estimates for patients with T-score in the osteopenia range (-1.0 to -2.5) [34]. Clinical risk factors for OP in the FRAX model include fragility fractures, parental history of hip fractures, smoking, excessive alcohol consumption, and other secondary OP, similar to the risk factors in the IOF one-minute OP risk assessment [35]. In addition to calculating the probability of fracture, the risk of death can also be evaluated, in contrast to other risk estimates. The currently available analysis may be uprgaded with trabecular bone score(TBS) analysis, which in combination with FRAX significantly improves fracture prediction [36]. This study has positive significance for public health strategy in Shanghai, the importance of screening in the community is emphasized in screening guidelines, IOF one-minute OP risk assessment screening may be important for the health management of middle-aged people in the community, those at high risk for OP, including those over 40, who have a low BMI, smoke, do not eat dairy products, vitamin D and calcium, and do not exercise, should have a bone density test. Family doctors can publicize the popular science of osteopenia and osteoporosis and conduct the questionnaire survey, followed by health education. There are some limitations in this study. First, the subjects were only included in some communities in Shanghai, which did not cover the whole population. The sample size was not large enough to represent the BMD of the entire population aged 40–60 in Shanghai. “Untreated subjects diagnosed with OP” is a group that we ignored when developing exclusion criteria. The inclusion of diabetes and diabetic nephropathy as exclusion criteria led to the exclusion of an important proportion of the Chinese population. In addition, due to the high proportion of female subjects, the research data on the relationship between gender and bone mineral density is biased. The study result showed that gender factor was not associated with osteopenia or OP. However, in another cross-sectional study in China [31], multiple logistic regression analysis found that women were a risk factor for osteopenia or OP. If the gender ratio in the sample is balanced, the result may be reversed, that is, the gender factor is associated with osteopenia or OP. Last but not least, cross-sectional design cannot establish causality, which can only provide us with ideas for further research. Possible solutions include further expanding the sample size to cover every neighborhood in Shanghai, with a view to becoming more universal. In addition, in the recruitment of subjects to increase publicity, pay attention to gender proportion, make the data more convincing. In follow-up studies, individual prospective studies can be designed for each of these factors to see if there is a causality between these factors and BMD.

Conclusion

The result showed that the detection rates of osteopenia and osteoporosis were 59% and 12.5% respectively, and both age and BMI were related to BMD. Bad habits such as smoking, avoiding dairy products, calcium tablets and vitamin D were associated with the development of low bone mass, which, in combination with previous studies, may exacerbate the process of bone loss. This is enough to arouse our attention to apply various risk assessment methods comprehensively and flexibly to improve the detection rate of bone loss and provide timely interventions to delay the progression of osteopenia to OP.

Electronic supplementary material

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Abbreviations

OP

Osteoporosis

DXA

Dual-energy X-ray Absorptiometry

WHO

World Health Organization

BMD

Bone Mineral Density

IOF

International Osteoporosis Foundation

BMI

Body Mass Index

FRAX

Fracture Risk Assessment Tool

REMS

Radio-frequency Ultrasonic Multispectroscopy

QCT

Quantitative Computed Tomography

TBS

Trabecular Bone Score

Author contributions

ZH designed the study; CZ, WM conducted the study; QC, FL, SG and FL collected data; QC, DL, SW and PC analysised data; QC, XL, WY and PL interpretated data; QC wrote the manuscript. All authors reviewed and approved the final manuscrip.

Funding

This work was supported by Ministry of Science and Technology of the People’s Republic of China[Demonstration study on Traditional Chinese Medicine Health Identification and Intervention Technology Promotion of Osteoporosis (NO:2019YFC1709905)]; Shanghai Municipal Health and Family Planning Commission[Three Year Action Plan of Shanghai to Further Accelerate the Inheritance, Innovation and Development of Traditional Chinese Medicine (ZY(2021–2023)-0201-01)]; Shanghai Pudong New Area Health and Health Commission[Pudong New Area Health System Pudong Famous PhysicianTraining Plan(PWRzm2020-15)]; Shanghai Municipal Health and Health Commission[Chinese Medicine Rehabilitation Service Capacity Improvement Pproject (Medical Matters032)]; Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine[Innovative Project of Longhua Hospital: Clinical Study of Acupotomy Combined with “Knee Balance Exercise” in The Treatment of Knee Osteoarthritis (CX202045)]. The object of the above funding project is Zhijun Hu.

Data availability

The datasets used during the current study are available from the corresponding author upon reasonable request.

Declarations

Ethical approval and consent to participate

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee (the Ethics Committee of Longhua Hospital, Shanghai University of Traditional Chinese Medicine [approval number 2020LCSY031]) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.