Relationship between bone metabolic markers and bone mineral density in natural postmenopausal women

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Methods: A total of 147 women aged 45-72 years who had undergone natural menopause for more than 2 years were selected. Dual-energy X-ray Absorptiometry (DEXA) was used to scan the standard BMD of the lumbar spine and hip. Fasting venous blood was collected in the morning to detect serum bone metabolism markers like amino-terminal pro-peptide of type Ⅰ pro-collagen (PINP) and β-collagen degradation products (β-CTX). According to BMD the patients were divided into normal bone mass group, osteopenia group and osteoporosis group. The differences of PINP and β-CTX among the three groups were compared. The correlation between PINP, β-CTX and general data was analyzed. The correlation between lumbar BMD, hip BMD and each variable; Correlation between β-CTX and P1NP, lumbar spine BMD and hip BMD. Results : P1NP had no significant difference among the three groups. PINP and β-CTX were not correlated with age, menopausal age, height, weight, BMI and glycosylated hemoglobin (P > 0.05). Lumbar spine BMD was correlated with age, menopausal age, height, weight and BMI (P < 0. 05), but not with Hba1c, PINP and β-CTX. Hip BMD was correlated with menopausal age, height, weight, BMI and β-CTX (P < 0. 05), but not with age, Hba1c and P1NP. 05), and lumbar BMD was correlated with hip BMD (P < 0. 05). Conclusion: Bone resorption and bone formation are in a state of high metabolism, and osteoporosis patients may lose bone mass faster. Bone mineral density is better than PINP and β-CTX in reflecting bone mass. PINP β-CTX Lumbar bone mineral density Hip bone mineral density Postmenopausal women and hip bone density Introduction Osteoporosis is an important and growing public health problem worldwide [1] . It is the loss of bone mass and structural changes in bone that lead to increased bone fragility and fracture risk [2] . Osteoporosis is mainly seen in elderly people, postmenopausal women, hypogonadism, premature ovarian failure, ethnic background, rheumatoid arthritis, low BMD, Vitamin D deficiency, low calcium intake, current smoking, alcoholism, fixed and long-term use of certain drugs, diet, genetics, lifestyle etc [3, 4] . Osteoporosis was defined as 2.5 standard deviations below the mean of young healthy women according to World Health Organization criteria, and the diagnosis of osteoporosis was determined by using DEXA to measure BMD at the hip and spine [5, 6] . Osteoporosis mainly occurs in postmenopausal women and elderly men, and osteoporotic fractures are currently the main concern of postmenopausal women and elderly men [7] . Due to bone mass loss caused by menopause, women are more likely to suffer from osteopenia and the morbidity and mortality of osteoporotic fractures in postmenopausal women are significantly increased [8] . Bone metabolic markers can help predict bone mineral density changes in postmenopausal women [9] . These markers help assess bone turnover and can indicate whether a woman is at risk for osteoporosis [10] . By monitoring these markers, patient can take steps to prevent bone loss and maintain good bone health [11] . Osteoporosis is the most common metabolic bone disease, leading to an increased risk of fracture [12] . Due to osteoporotic fractures and their complications, the costs and costs of admission and medical treatment increase [13] . Low bone mineral density is a risk factor for osteoporotic fractures [14] . Hip and lumbar bone mineral density measurement is the gold standard for evaluating osteoporosis, with good accuracy and high precision [15] . Blood is rarely used to detect bone mineral density in clinic, and clinical markers of bone metabolism can also reflect bone mineral density, such as relatively sensitive indicators PINP and β-CTX, which generally reflect bone resorption sensitively [16] . PINP and β-CTX are specific substances secreted by bone cells during metabolic activities [17] . Generally, P1NP is sensitive to bone formation, so bone mineral density at two sites and bone metabolism markers and general data in serum of volunteers can be measured to compare the relationship between bone metabolism markers and bone mineral density and the correlation between bone mineral density and bone metabolism markers and general data [18, 19] . The most important thing is to clearly diagnose the risk of fractures in osteoporosis patients so that appropriate treatment can be conducted to prevent these fractures, so that preventive and protective measures can be enhanced to reduce the risk of fracture occurrence [20, 21] . Materials and Methods 1.1 Patients The study was approved by the ethics board of the Sixth Affiliated Hospital of Xinjiang Medical University. All the patients signed informed consent to publish their results before receiving the tests. All the procedures followed the ethical guidelines of the declaration of Helsinki. A total of 176 women who had undergone natural menopause for more than 2 years and met the inclusion and exclusion criteria were selected, 27 women with no results of bone mineral density measurements and 2 women with the absence of bone metabolism markers were excluded, and 147 women with accurate measurements and complete acquisition of bone mineral density, body mass index, body weight, glycated hemoglobin, PINP and β-CTX were included. Of these, 55 were in the group with normal bone mass, 56 were in the group with reduced bone mass and 36 were in the group with osteoporosis. Through physical examination, careful inquiry and relevant questionnaire survey, the following groups were excluded: (1) receiving calcitonin, bisphosphonate, raloxifene, estrogen, estrogen and other drug therapy within 12 months; (2) have other bone metabolic diseases in addition to osteoporosis (such as diabetes, hyperthyroidism, hyperparathyroidism, renal insufficiency, Cushing's disease, gastrointestinal diseases, etc.); (3) severe chronic diseases including malignant tumors; (4) Taking drugs that affect bone metabolism; (5) Have received radiation therapy; (6) Abnormal liver and kidney function test; (7) Smokers or former smokers; (8) Do not agree to participate in this researcher. Following the principles of the Declaration of Helsinki, the study was approved by the Institutional Ethics Committee and each participant signed an informed consent form. 1.2 Methods 1.2.1 Measurement of body weight and body mass index: Under the light beam calibrated in the morning, the same medical height and weight measuring instrument was used to measure height and weight on an empty stomach. The height was accurate to 0.1cm and the weight was accurate to 0.01kg. We calculate body mass index (BMI) as the ratio of weight (kg) to height squared (m2). Participants were divided into two BMI groups: normal weight (BMI <24.0 kg/m2) and overweight and obesity (BMI≥24kg/m2). 1.2.2 Measurement of bone mineral density: All participants underwent a room-temperature resting DEXA scan with standard BMD measurements of the lumbar spine and hip. Bone mineral content (BMC), T-Score and Z-Score were also obtained. The diagnosis of osteoporosis/osteopenia was based on the T-score: if the T-score was ≥-1.0, it is considered normal; If -2.5 <T score <-1.0, bone mass decreased; If T score ≤-2.5, osteoporosis. In the morning, 5ml of venous blood was collected on an empty stomach and the serum was separated to detect serum PINP and β-CTX; 2ml of venous blood was collected at the same time to detect glycosylated hemoglobin and accurately record the data. 1.3 Statistical processing SPSS22.0 medical statistical software was used to analyze all data. Normal analysis was used for all measurement data. For variables subject to normal distribution, mean and standard deviation were used for statistical description, while for non-normal data, median and quad spacing were used for description. Analysis of normal distribution and homogeneity of variance was carried out on all the data for difference analysis, which followed normal distribution and homogeneity of variance. Analysis of variance of multiple samples could be used. The rank sum test could be used, and Pearson correlation was used for correlation analysis. P < 0.05 was considered statistically significant. Results The comparison of PINP and β-CTX among the normal bone mass group, the osteopenia group and the osteoporosis group showed that there were significant differences in β-CTX among the three groups (P 0.05). The results are shown in Table 1. Table 1 Comparison of PINP and β-CTX among normal bone mass group, decreased bone mass group and osteoporosis group (g/cm²) Table 1 Comparison of β-CTX and P1NP between normal bone mass, osteopenia group and osteoporosis group (g/cm 2 ) Group n β-CTX P1NP Normal bone mass 55 0.432（0.161） 65.991（23.149） Bone loss 56 0.398（0.145） 65.404（23.862） Osteoporosis 36 0.494（0.158） 80.958（57.657） F 4.252 2.585 P 0.016 0.079 Pearson correlation analysis showed that PINP and β-CTX were not correlated with age, menopause age, height, body weight, BMI and glycosylated hemoglobin (P > 0.05). The results are shown in Table 2. Table 2 Pearson correlation analysis of PINP, β-CTX and various variables (r)Table 2 Pearson correlation analysis of PINP, β-CTX and each variable (r) Project P1NP β-CTX r P r P Age -0.143 0.083 -0.116 0.161 Age of menopause -0.090 0.278 -0.019 0.823 Height 0.079 0.340 0.041 0.621 Weight -0.096 0.245 -0.080 0.336 BMI -0.133 0.108 -0.096 0.247 Hemoglobin A1C -0.057 0.491 -0.058 0.487 Pearson correlation analysis showed that lumbar vertebra bone density was correlated with age, menopause age, height, weight and BMI (P < 0.05), but not with glycosylated hemoglobin, PINP and β-CTX. Hip bone mineral density was correlated with menopause age, height, weight, BMI and β-CTX (P < 0.05), but not with age, hemoglobin a1c and P1NP. The results are shown in Table 3. Table 3 Pearson correlation analysis of lumbar spine bone mineral density, hip bone mineral density and various variables (r) Project Lumbar bone density Hip bone density r P r P Age -0.169 0.040 -0.066 0.424 Age of menopause -0.259 0.002 -0.180 0.029 Height 0.238 0.004 0.228 0.006 Weight 0.354 0.000 0.478 0.000 BMI 0.293 0.000 0.436 0.000 Hemoglobin A1C -0.018 0.829 0.031 0.708 P1NP -0.148 0.074 -0.148 0.074 β-CTX -0.134 0.105 -0.180 0.029 Pearson correlation analysis showed that β-CTX was correlated with P1NP (P < 0. 05), and lumbar spine BMD was correlated with hip BMD (P < 0. 05). The results are shown in Table 4. Table 4 Pearson correlation analysis of β-CTX and P1NP, lumbar spine bone mineral density and hip bone mineral density (r) P1NP Lumbar bone density r P r P β-CTX 0.651 0.000 Hip bone density 0.659 0.000 Discussion Osteoporosis is a multifactorial disease, widely occurring in all kinds of people, mostly in postmenopausal women, and the incidence has been significantly increasing in recent years [22, 23] . In the elderly population, the low level of bone conversion products in serum may indicate serious osteoporosis in elderly patients [24] . Bone mineral density is the final result of the continuous change and development of bone metabolism [25] , reflecting the overall situation of bone mass in a period of time, and cannot reflect the dynamic change of bone metabolism in vivo, while the change of bone metabolism in osteoporosis patients is often before the change of bone mineral density [26, 27] . There are differences in blood lipid levels among people with different bone mass [28] , but the correlation between blood lipid levels and bone density in specific parts of hip is controversial [29, 30] . The results of this study show that bone resorption and bone formation are in a state of high metabolism. There is no significant difference in bone formation among the three groups, but there is a significant difference in bone resorption. The osteoporosis group is significantly higher than the other two groups, indicating that the osteoporosis group may lose bone mass faster. There was no significant correlation between PINP and β-CTX and general data, while BMD was correlated with age, menopausal age, height, weight, BMI, etc., which was in line with the results of most studies, indicating that BMD can better reflect the level of bone mass. The results showed that the lumbar spine BMD was positively correlated with the hip BMD, indicating that the loss of bone mass was systemic. In clinical work, more attention was paid to hip, lumbar spine, and distal radius fractures, while ignoring other parts. Through a variety of ways to clearly diagnose patients with osteoporosis at risk of fracture, so as to carry out appropriate treatment and targeted therapy, so as to strengthen prevention and protective measures and reduce the risk of fracture. Although the results of this study suggest that bone resorption and bone formation are in a hyper-metabolic state, there is no significant difference in bone formation among the three groups, but there is a significant difference in bone resorption among the three groups, and the osteoporosis group is significantly higher than the other two groups. There was no significant correlation between PINP and β-CTX and general data, while BMD was correlated with age, menopausal age, height, weight, BMI, etc. There is a positive correlation between lumbar BMD and hip BMD, but there are some limitations. A potential source of bias in this study is residual confounding due to risk factors that we could not account for in our analyses (socioeconomic status, education level, physical activity level, smoking, alcohol consumption, vitamin D status, sex hormone levels, and nutritional status, among others) and that could not be adjusted for unknown confounders. To allow for more precise results, our study included participants who were randomly selected from the general population. A limitation of our findings is that we included women who had undergone natural menopause more than 2 years earlier between the ages of 45 and 72 years, and our results may not apply to broader age groups and to early and premenopausal women. Another limitation is that only women were included in the study. Whether there is an obvious correlation between lumbar spine and hip BMD, PINP and β-CTX in men needs to be further investigated in future studies. The results of our study have important clinical significance. First of all, it can provide a reference for subsequent scientific research. Secondly, it can provide reference for the further strengthening of the demand for osteoporosis prevention in natural postmenopausal women. In conclusion, our study showed that bone resorption and bone formation were in a hyper-metabolic state, and bone resorption was significantly higher in the osteoporosis group than in the other two groups, indicating that bone mass loss may be faster in the osteoporosis population. There was no significant correlation between PINP and β-CTX and general data, while BMD was correlated with age, menopausal age, height, weight, BMI, etc., which was in line with the results of most studies, indicating that BMD can better reflect the level of bone mass. The results show that the lumbar spine BMD is positively correlated with the hip BMD, indicating that the loss of bone mass is systemic. In clinical work, more attention should be paid to hip, lumbar spine, and distal radius fractures, while ignoring other fractures, so that prevention and protection measures can be strengthened to reduce the risk of fracture. In particular, natural postmenopausal women with faster bone resorption have a demand for osteoporosis prevention. Through a variety of ways to clearly diagnose patients with osteoporosis at risk of fracture, so as to carry out appropriate treatment and targeted therapy, so as to strengthen prevention and protective measures and reduce the risk of fracture. Declarations Ethics approval and consent to participate The study was approved by the ethics board of the Sixth Affiliated Hospital of Xinjiang Medical University and each participant signed an informed consent form. All the procedures followed the ethical guidelines of the declaration of Helsinki. Consent for publication All the patients signed informed consent to publish their results before receiving the tests. Availability of data and materials All the data can be acquired from the corresponding author upon request. Competing interests The authors declare no conflict of interest regarding this manuscript. Funding The study was funded by the Natural Scientific Foundation of Xinjiang Uyghur Autonomous Region (No：2018E02061; 2022D01C586). Authors' contributions Li Guohua designed the study, acquired and analyzed the data, wrote the manuscript. Muyashaer Abudushalamu analyzed the data and revised the manuscript. Aikeremujiang Muheremu designed the study, interpreted the data, wrote and revised the manuscript. Consent for Publication Not applicable References Johnston, C.B. and M. Dagar, Osteoporosis in Older Adults . Med Clin North Am, 2020. 104 (5): p. 873-884. Reid, I.R. and E.O. Billington, Drug therapy for osteoporosis in older adults. Lancet, 2022. 399 (10329): p. 1080-1092. Brown, J.P., Long-Term Treatment of Postmenopausal Osteoporosis. Endocrinol Metab (Seoul), 2021. 36 (3): p. 544-552. Arceo-Mendoza, R.M. and P.M. Camacho, Postmenopausal Osteoporosis: Latest Guidelines. Endocrinol Metab Clin North Am, 2021. 50 (2): p. 167-178. Matsumoto, T., et al., Abaloparatide Increases Lumbar Spine and Hip BMD in Japanese Patients With Osteoporosis: The Phase 3 ACTIVE-J Study. J Clin Endocrinol Metab, 2022. 107 (10): p. e4222-e4231. Alay, I., et al., The relation of body mass index, menopausal symptoms, and lipid profile with bone mineral density in postmenopausal women. Taiwanese Journal of Obstetrics and Gynecology, 2020. 59 (1): p. 61-66. Chandran, M., The why and how of sequential and combination therapy in osteoporosis. A review of the current evidence. Arch Endocrinol Metab, 2022. 66 (5): p. 724-738. Zhao, S., et al., Declining serum bone turnover markers are associated with the short-term positive change of lumbar spine bone mineral density in postmenopausal women. Menopause (New York, NY), 2022. 29 (3): p. 335. Azimi-Shomali, S., et al., The relationship between usual daily physical activity with serum markers related to bone metabolism and demographic characteristics in postmenopausal women aged 50–65 years. Journal of Physical Activity and Health, 2022. 19 (6): p. 417-424. Shoback, D., et al., Pharmacological Management of Osteoporosis in Postmenopausal Women: An Endocrine Society Guideline Update. J Clin Endocrinol Metab, 2020. 105 (3). Starup-Linde, J.K., et al., Associations of Circulating Osteoglycin With Bone Parameters and Metabolic Markers in Patients With Diabetes. Front Endocrinol (Lausanne), 2021. 12 : p. 649718. Harris, K., C.A. Zagar, and K.V. Lawrence, Osteoporosis: Common Questions and Answers. Am Fam Physician, 2023. 107 (3): p. 238-246. Reid, I.R., A broader strategy for osteoporosis interventions. Nat Rev Endocrinol, 2020. 16 (6): p. 333-339. Vujasinovic, M., et al., Low Bone Mineral Density and Risk for Osteoporotic Fractures in Patients with Chronic Pancreatitis. Nutrients, 2021. 13 (7). Kemmler, W., et al., Effects of Different Types of Exercise on Bone Mineral Density in Postmenopausal Women: A Systematic Review and Meta-analysis. Calcif Tissue Int, 2020. 107 (5): p. 409-439. Bhattoa, H.P., et al., Analytical considerations and plans to standardize or harmonize assays for the reference bone turnover markers PINP and β-CTX in blood. Clin Chim Acta, 2021. 515 : p. 16-20. Lin, X.B., et al., Analysis of changes in serum high t-PINP/β-CTX ratio and risk of re-fracture after vertebral osteoporotic fracture surgery. Eur Rev Med Pharmacol Sci, 2023. 27 (22): p. 10860-10867. Hu, Q., et al., Combination of Calcitriol and Zoledronic Acid on PINP and β-CTX in Postoperative Patients with Diabetic Osteoporosis: A Randomized Controlled Trial. Dis Markers, 2022. 2022 : p. 6053410. Bellissimo, M.P., et al., Metabolomic Associations with Serum Bone Turnover Markers. Nutrients, 2020. 12 (10). Vasikaran, S.D., et al., Practical Considerations for the Clinical Application of Bone Turnover Markers in Osteoporosis. Calcif Tissue Int, 2023. 112 (2): p. 148-157. Ayers, C., et al., Effectiveness and Safety of Treatments to Prevent Fractures in People With Low Bone Mass or Primary Osteoporosis: A Living Systematic Review and Network Meta-analysis for the American College of Physicians. Ann Intern Med, 2023. 176 (2): p. 182-195. Walker, M.D. and E. Shane, Postmenopausal Osteoporosis . N Engl J Med, 2023. 389 (21): p. 1979-1991. Walker, M.D. and E. Shane, Postmenopausal Osteoporosis. New England Journal Of Medicine, 2023. 389 (21): p. 1979-1991. Huidrom, S., M.A. Beg, and T. Masood, Post-menopausal Osteoporosis and Probiotics. Curr Drug Targets, 2021. 22 (7): p. 816-822. Zhao, Y.-X., et al., Association between bile acid metabolism and bone mineral density in postmenopausal women. Clinics, 2020. 75 . Chen, Y., et al., Systemic Inflammation Markers Associated with Bone Mineral Density in perimenopausal and Postmenopausal Women. Journal of Inflammation Research, 2023: p. 297-309. Shieh, A., et al., Associations of age at menopause with postmenopausal bone mineral density and fracture risk in women. The Journal of Clinical Endocrinology & Metabolism, 2022. 107 (2): p. e561-e569. Zhao, H., et al., Blood lipid levels in patients with osteopenia and osteoporosis:a systematic review and meta-analysis. J Bone Miner Metab, 2021. 39 (3): p. 510-520. Zhang, Q., et al., Association Between Bone Mineral Density and Lipid Profile in Chinese Women. Clin Interv Aging, 2020. 15 : p. 1649-1664. Kang, S., et al., Association between Serum Uric Acid Levels and Bone Mineral Density in Postmenopausal Women: A Cross-Sectional and Longitudinal Study. Healthcare (Basel), 2021. 9 (12). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 Apr, 2024 Editor assigned by journal 02 Apr, 2024 Submission checks completed at journal 02 Apr, 2024 First submitted to journal 07 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4023574","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":286678706,"identity":"39b48d9c-b63a-412b-915f-37ee27107642","order_by":0,"name":"Guohua Li","email":"","orcid":"","institution":"Xinjiang Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guohua","middleName":"","lastName":"Li","suffix":""},{"id":286678707,"identity":"0f529b25-d3a6-4d1e-becb-5992447cbb58","order_by":1,"name":"Muyashaer Abudushalamu","email":"","orcid":"","institution":"Xinjiang Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Muyashaer","middleName":"","lastName":"Abudushalamu","suffix":""},{"id":286678708,"identity":"c38bd487-8cc4-4dbc-a9d7-c9dd5f6a731d","order_by":2,"name":"Aikeremujiang Muheremu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYFACHijN3tj48ANpWngONxtLkKZFIr1NgAevSijQnZF78HFFxbbE/pkP2xgkGOzkdBsIaDG7kZdseObM7cQZtxPbHhQwJBubHSCoJcdMsrHtdmLD7cR2AwmGA4nbiNBi/rPx3+3E+TcPtknwEKnFjLERaMWGG4zEajnzxliy4dht441nEoGBbECMX47nGH5sqLktO+/48YcPP1TYyRHUggYMSFM+CkbBKBgFowAHAABu5El2ukeUZAAAAABJRU5ErkJggg==","orcid":"","institution":"Xinjiang Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Aikeremujiang","middleName":"","lastName":"Muheremu","suffix":""}],"badges":[],"createdAt":"2024-03-07 08:30:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4023574/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4023574/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54170623,"identity":"80e9b839-a1b6-40f5-80e4-8c3915ecc65f","added_by":"auto","created_at":"2024-04-05 14:31:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":262344,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4023574/v1/1093561a-08fb-4ca1-90a4-228d7eb12f6b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relationship between bone metabolic markers and bone mineral density in natural postmenopausal women","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOsteoporosis is an important and growing public health problem worldwide\u003csup\u003e[1]\u003c/sup\u003e. It is the loss of bone mass and structural changes in bone that lead to increased bone fragility and fracture risk\u003csup\u003e[2]\u003c/sup\u003e. Osteoporosis is mainly seen in elderly people, postmenopausal women, hypogonadism, premature ovarian failure, ethnic background, rheumatoid arthritis, low BMD, Vitamin D deficiency, low calcium intake, current smoking, alcoholism, fixed and long-term use of certain drugs, diet, genetics, lifestyle etc\u003csup\u003e[3, 4]\u003c/sup\u003e. Osteoporosis was defined as 2.5 standard deviations below the mean of young healthy women according to World Health Organization criteria, and the diagnosis of osteoporosis was determined by using DEXA to measure BMD at the hip and spine\u003csup\u003e[5, 6]\u003c/sup\u003e. Osteoporosis mainly occurs in postmenopausal women and elderly men, and osteoporotic fractures are currently the main concern of postmenopausal women and elderly men\u003csup\u003e[7]\u003c/sup\u003e. Due to bone mass loss caused by menopause, women are more likely to suffer from osteopenia and the morbidity and mortality of osteoporotic fractures in postmenopausal women are significantly increased\u003csup\u003e[8]\u003c/sup\u003e. Bone metabolic markers can help predict bone mineral density changes in postmenopausal women\u003csup\u003e[9]\u003c/sup\u003e. These markers help assess bone turnover and can indicate whether a woman is at risk for osteoporosis\u003csup\u003e[10]\u003c/sup\u003e. By monitoring these markers, patient can take steps to prevent bone loss and maintain good bone health\u003csup\u003e[11]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eOsteoporosis is the most common metabolic bone disease, leading to an increased risk of fracture\u003csup\u003e[12]\u003c/sup\u003e. Due to osteoporotic fractures and their complications, the costs and costs of admission and medical treatment increase\u003csup\u003e[13]\u003c/sup\u003e. Low bone mineral density is a risk factor for osteoporotic fractures\u003csup\u003e[14]\u003c/sup\u003e. Hip and lumbar bone mineral density measurement is the gold standard for evaluating osteoporosis, with good accuracy and high precision\u003csup\u003e[15]\u003c/sup\u003e. Blood is rarely used to detect bone mineral density in clinic, and clinical markers of bone metabolism can also reflect bone mineral density, such as relatively sensitive indicators PINP and \u0026beta;-CTX, which generally reflect bone resorption sensitively\u003csup\u003e[16]\u003c/sup\u003e. PINP and \u0026beta;-CTX are specific substances secreted by bone cells during metabolic activities\u003csup\u003e[17]\u003c/sup\u003e. Generally, P1NP is sensitive to bone formation, so bone mineral density at two sites and bone metabolism markers and general data in serum of volunteers can be measured to compare the relationship between bone metabolism markers and bone mineral density and the correlation between bone mineral density and bone metabolism markers and general data\u003csup\u003e[18, 19]\u003c/sup\u003e. The most important thing is to clearly diagnose the risk of fractures in osteoporosis patients so that appropriate treatment can be conducted to prevent these fractures, so that preventive and protective measures can be enhanced to reduce the risk of fracture occurrence\u003csup\u003e[20, 21]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e1.1 Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the ethics board of the Sixth Affiliated Hospital of Xinjiang Medical University. All the patients signed informed consent to publish their results before receiving the tests. All the procedures followed the ethical guidelines of the declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eA total of 176 women who had undergone natural menopause for more than 2 years and met the inclusion and exclusion criteria were selected, 27 women with no results of bone mineral density measurements and 2 women with the absence of bone metabolism markers were excluded, and 147 women with accurate measurements and complete acquisition of bone mineral density, body mass index, body weight, glycated hemoglobin, PINP and \u0026beta;-CTX were included. Of these, 55 were in the group with normal bone mass, 56 were in the group with reduced bone mass and 36 were in the group with osteoporosis. Through physical examination, careful inquiry and relevant questionnaire survey, the following groups were excluded: (1) receiving calcitonin, bisphosphonate, raloxifene, estrogen, estrogen and other drug therapy within 12 months; (2) have other bone metabolic diseases in addition to osteoporosis (such as diabetes, hyperthyroidism, hyperparathyroidism, renal insufficiency, Cushing\u0026apos;s disease, gastrointestinal diseases, etc.); (3) severe chronic diseases including malignant tumors; (4) Taking drugs that affect bone metabolism; (5) Have received radiation therapy; (6) Abnormal liver and kidney function test; (7) Smokers or former smokers; (8) Do not agree to participate in this researcher. Following the principles of the Declaration of Helsinki, the study was approved by the Institutional Ethics Committee and each participant signed an informed consent form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2.1 Measurement of body weight and body mass index:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnder the light beam calibrated in the morning, the same medical height and weight measuring instrument was used to measure height and weight on an empty stomach. The height was accurate to 0.1cm and the weight was accurate to 0.01kg. We calculate body mass index (BMI) as the ratio of weight (kg) to height squared (m2). Participants were divided into two BMI groups: normal weight (BMI \u0026lt;24.0 kg/m2) and overweight and obesity (BMI\u0026ge;24kg/m2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2.2 Measurement of bone mineral density:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll participants underwent a room-temperature resting DEXA scan with standard BMD measurements of the lumbar spine and hip. Bone mineral content (BMC), T-Score and Z-Score were also obtained. The diagnosis of osteoporosis/osteopenia was based on the T-score: if the T-score was \u0026ge;-1.0, it is considered normal; If -2.5 \u0026lt;T score \u0026lt;-1.0, bone mass decreased; If T score \u0026le;-2.5, osteoporosis.\u003c/p\u003e\n\u003cp\u003eIn the morning, 5ml of venous blood was collected on an empty stomach and the serum was separated to detect serum PINP and \u0026beta;-CTX; 2ml of venous blood was collected at the same time to detect glycosylated hemoglobin and accurately record the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 Statistical processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSPSS22.0 medical statistical software was used to analyze all data. Normal analysis was used for all measurement data. For variables subject to normal distribution, mean and standard deviation were used for statistical description, while for non-normal data, median and quad spacing were used for description. Analysis of normal distribution and homogeneity of variance was carried out on all the data for difference analysis, which followed normal distribution and homogeneity of variance. Analysis of variance of multiple samples could be used. The rank sum test could be used, and Pearson correlation was used for correlation analysis. P \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe comparison of PINP and \u0026beta;-CTX among the normal bone mass group, the osteopenia group and the osteoporosis group showed that there were significant differences in \u0026beta;-CTX among the three groups (P \u0026lt; 0.05), but no significant differences in P1NP among the three groups (P \u0026gt; 0.05). The results are shown in Table 1.\u003c/p\u003e\n\u003cp\u003eTable 1 Comparison of PINP and \u0026beta;-CTX among normal bone mass group, decreased bone mass group and osteoporosis group (g/cm\u0026sup2;)\u003c/p\u003e\n\u003cp\u003eTable 1 Comparison of \u0026beta;-CTX and P1NP between normal bone mass, osteopenia group and osteoporosis group (g/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026beta;-CTX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eP1NP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eNormal bone mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.432（0.161）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e65.991（23.149）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eBone loss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.398（0.145）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e65.404（23.862）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eOsteoporosis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.494（0.158）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e80.958（57.657）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e4.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e2.585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePearson correlation analysis showed that PINP and \u0026beta;-CTX were not correlated with age, menopause age, height, body weight, BMI and glycosylated hemoglobin (P \u0026gt; 0.05). The results are shown in Table 2.\u003c/p\u003e\n\u003cp\u003eTable 2 Pearson correlation analysis of PINP, \u0026beta;-CTX and various variables (r)Table 2 Pearson correlation analysis of PINP, \u0026beta;-CTX and each variable (r)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.3986013986014%\" rowspan=\"2\" valign=\"top\" style=\"width: 23.922%;\"\u003e\n \u003cp\u003eProject\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.83916083916084%\" colspan=\"2\" valign=\"top\" style=\"width: 45.4946%;\"\u003e\n \u003cp\u003eP1NP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.96503496503497%\" colspan=\"2\" valign=\"top\" style=\"width: 22.2133%;\"\u003e\n \u003cp\u003e\u0026beta;-CTX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.691358024691358%\" valign=\"top\" style=\"width: 22.2133%;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.925925925925927%\" valign=\"top\" style=\"width: 23.2813%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.185185185185187%\" valign=\"top\" style=\"width: 18.1551%;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.19753086419753%\" valign=\"top\" style=\"width: 3.9514%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.3986013986014%\" valign=\"top\" style=\"width: 23.922%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\" style=\"width: 22.2133%;\"\u003e\n \u003cp\u003e-0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.356643356643357%\" valign=\"top\" style=\"width: 23.2813%;\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.832167832167833%\" valign=\"top\" style=\"width: 18.1551%;\"\u003e\n \u003cp\u003e-0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.132867132867133%\" valign=\"top\" style=\"width: 3.9514%;\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.3986013986014%\" valign=\"top\" style=\"width: 23.922%;\"\u003e\n \u003cp\u003eAge of menopause\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\" style=\"width: 22.2133%;\"\u003e\n \u003cp\u003e-0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.356643356643357%\" valign=\"top\" style=\"width: 23.2813%;\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.832167832167833%\" valign=\"top\" style=\"width: 18.1551%;\"\u003e\n \u003cp\u003e-0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.132867132867133%\" valign=\"top\" style=\"width: 3.9514%;\"\u003e\n \u003cp\u003e0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.3986013986014%\" valign=\"top\" style=\"width: 23.922%;\"\u003e\n \u003cp\u003eHeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\" style=\"width: 22.2133%;\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.356643356643357%\" valign=\"top\" style=\"width: 23.2813%;\"\u003e\n \u003cp\u003e0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.832167832167833%\" valign=\"top\" style=\"width: 18.1551%;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.132867132867133%\" valign=\"top\" style=\"width: 3.9514%;\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.3986013986014%\" valign=\"top\" style=\"width: 23.922%;\"\u003e\n \u003cp\u003eWeight\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\" style=\"width: 22.2133%;\"\u003e\n \u003cp\u003e-0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.356643356643357%\" valign=\"top\" style=\"width: 23.2813%;\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.832167832167833%\" valign=\"top\" style=\"width: 18.1551%;\"\u003e\n \u003cp\u003e-0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.132867132867133%\" valign=\"top\" style=\"width: 3.9514%;\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.3986013986014%\" valign=\"top\" style=\"width: 23.922%;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\" style=\"width: 22.2133%;\"\u003e\n \u003cp\u003e-0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.356643356643357%\" valign=\"top\" style=\"width: 23.2813%;\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.832167832167833%\" valign=\"top\" style=\"width: 18.1551%;\"\u003e\n \u003cp\u003e-0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.132867132867133%\" valign=\"top\" style=\"width: 3.9514%;\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.3986013986014%\" valign=\"top\" style=\"width: 23.922%;\"\u003e\n \u003cp\u003eHemoglobin A1C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.482517482517483%\" valign=\"top\" style=\"width: 22.2133%;\"\u003e\n \u003cp\u003e-0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.356643356643357%\" valign=\"top\" style=\"width: 23.2813%;\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.832167832167833%\" valign=\"top\" style=\"width: 18.1551%;\"\u003e\n \u003cp\u003e-0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.132867132867133%\" valign=\"top\" style=\"width: 3.9514%;\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePearson correlation analysis showed that lumbar vertebra bone density was correlated with age, menopause age, height, weight and BMI (P \u0026lt; 0.05), but not with glycosylated hemoglobin, PINP and \u0026beta;-CTX. Hip bone mineral density was correlated with menopause age, height, weight, BMI and \u0026beta;-CTX (P \u0026lt; 0.05), but not with age, hemoglobin a1c and P1NP. The results are shown in Table 3.\u003c/p\u003e\n\u003cp\u003eTable 3 Pearson correlation analysis of lumbar spine bone mineral density, hip bone mineral density and various variables (r)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.223985890652557%\" rowspan=\"2\" valign=\"top\" style=\"width: 18.4076%;\"\u003e\n \u003cp\u003eProject\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.15343915343915%\" colspan=\"2\" valign=\"top\" style=\"width: 46.5091%;\"\u003e\n \u003cp\u003eLumbar bone density\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.80070546737213%\" colspan=\"2\" valign=\"top\" style=\"width: 27.6658%;\"\u003e\n \u003cp\u003eHip bone density\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.799126637554586%\" valign=\"top\" style=\"width: 22.8733%;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.67248908296943%\" valign=\"top\" style=\"width: 23.6358%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.362445414847162%\" valign=\"top\" style=\"width: 22.4376%;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.67248908296943%\" valign=\"top\" style=\"width: 5.2282%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.223985890652557%\" valign=\"top\" style=\"width: 18.4076%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.223985890652557%\" valign=\"top\" style=\"width: 22.8733%;\"\u003e\n \u003cp\u003e-0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.929453262786595%\" valign=\"top\" style=\"width: 23.6358%;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.871252204585538%\" valign=\"top\" style=\"width: 22.4376%;\"\u003e\n \u003cp\u003e-0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.929453262786595%\" valign=\"top\" style=\"width: 5.2282%;\"\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.223985890652557%\" valign=\"top\" style=\"width: 18.4076%;\"\u003e\n \u003cp\u003eAge of menopause\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.223985890652557%\" valign=\"top\" style=\"width: 22.8733%;\"\u003e\n \u003cp\u003e-0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.929453262786595%\" valign=\"top\" style=\"width: 23.6358%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.871252204585538%\" valign=\"top\" style=\"width: 22.4376%;\"\u003e\n \u003cp\u003e-0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.929453262786595%\" valign=\"top\" style=\"width: 5.2282%;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.223985890652557%\" valign=\"top\" style=\"width: 18.4076%;\"\u003e\n \u003cp\u003eHeight\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.223985890652557%\" valign=\"top\" style=\"width: 22.8733%;\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.929453262786595%\" valign=\"top\" style=\"width: 23.6358%;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.871252204585538%\" valign=\"top\" style=\"width: 22.4376%;\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.929453262786595%\" valign=\"top\" style=\"width: 5.2282%;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.223985890652557%\" valign=\"top\" style=\"width: 18.4076%;\"\u003e\n \u003cp\u003eWeight\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.223985890652557%\" valign=\"top\" style=\"width: 22.8733%;\"\u003e\n \u003cp\u003e0.354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.929453262786595%\" valign=\"top\" style=\"width: 23.6358%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.871252204585538%\" valign=\"top\" style=\"width: 22.4376%;\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.929453262786595%\" valign=\"top\" style=\"width: 5.2282%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.223985890652557%\" valign=\"top\" style=\"width: 18.4076%;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.223985890652557%\" valign=\"top\" style=\"width: 22.8733%;\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.929453262786595%\" valign=\"top\" style=\"width: 23.6358%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.871252204585538%\" valign=\"top\" style=\"width: 22.4376%;\"\u003e\n \u003cp\u003e0.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.929453262786595%\" valign=\"top\" style=\"width: 5.2282%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.223985890652557%\" valign=\"top\" style=\"width: 18.4076%;\"\u003e\n \u003cp\u003eHemoglobin A1C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.223985890652557%\" valign=\"top\" style=\"width: 22.8733%;\"\u003e\n \u003cp\u003e-0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.929453262786595%\" valign=\"top\" style=\"width: 23.6358%;\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.871252204585538%\" valign=\"top\" style=\"width: 22.4376%;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.929453262786595%\" valign=\"top\" style=\"width: 5.2282%;\"\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.223985890652557%\" valign=\"top\" style=\"width: 18.4076%;\"\u003e\n \u003cp\u003eP1NP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.223985890652557%\" valign=\"top\" style=\"width: 22.8733%;\"\u003e\n \u003cp\u003e-0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.929453262786595%\" valign=\"top\" style=\"width: 23.6358%;\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.871252204585538%\" valign=\"top\" style=\"width: 22.4376%;\"\u003e\n \u003cp\u003e-0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.929453262786595%\" valign=\"top\" style=\"width: 5.2282%;\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.223985890652557%\" valign=\"top\" style=\"width: 18.4076%;\"\u003e\n \u003cp\u003e\u0026beta;-CTX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.223985890652557%\" valign=\"top\" style=\"width: 22.8733%;\"\u003e\n \u003cp\u003e-0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.929453262786595%\" valign=\"top\" style=\"width: 23.6358%;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.871252204585538%\" valign=\"top\" style=\"width: 22.4376%;\"\u003e\n \u003cp\u003e-0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.929453262786595%\" valign=\"top\" style=\"width: 5.2282%;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePearson correlation analysis showed that \u0026beta;-CTX was correlated with P1NP (P \u0026lt; 0. 05), and lumbar spine BMD was correlated with hip BMD (P \u0026lt; 0. 05). The results are shown in Table 4.\u003c/p\u003e\n\u003cp\u003eTable 4 Pearson correlation analysis of \u0026beta;-CTX and P1NP, lumbar spine bone mineral density and hip bone mineral density (r)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.15641476274165%\" rowspan=\"2\" valign=\"top\" style=\"width: 16.5443%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.070298769771526%\" colspan=\"2\" valign=\"top\" style=\"width: 42.6823%;\"\u003e\n \u003cp\u003eP1NP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.96133567662566%\" colspan=\"2\" valign=\"top\" style=\"width: 24.0822%;\"\u003e\n \u003cp\u003eLumbar bone density\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.70806100217865%\" valign=\"top\" style=\"width: 22.2222%;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.74727668845316%\" valign=\"top\" style=\"width: 20.4601%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.657952069716774%\" valign=\"top\" style=\"width: 19.5791%;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.40087145969499%\" valign=\"top\" style=\"width: 4.5032%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.190140845070424%\" valign=\"top\" style=\"width: 16.5443%;\"\u003e\n \u003cp\u003e\u0026beta;-CTX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.774647887323944%\" valign=\"top\" style=\"width: 22.2222%;\"\u003e\n \u003cp\u003e0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.190140845070424%\" valign=\"top\" style=\"width: 20.4601%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.309859154929576%\" valign=\"top\" style=\"width: 19.5791%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.718309859154928%\" valign=\"top\" style=\"width: 4.5032%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.190140845070424%\" valign=\"top\" style=\"width: 16.5443%;\"\u003e\n \u003cp\u003eHip bone density\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.774647887323944%\" valign=\"top\" style=\"width: 22.2222%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.190140845070424%\" valign=\"top\" style=\"width: 20.4601%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.309859154929576%\" valign=\"top\" style=\"width: 19.5791%;\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.718309859154928%\" valign=\"top\" style=\"width: 4.5032%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion ","content":"\u003cp\u003eOsteoporosis is a multifactorial disease, widely occurring in all kinds of people, mostly in postmenopausal women, and the incidence has been significantly increasing in recent years\u003csup\u003e[22, 23]\u003c/sup\u003e. In the elderly population, the low level of bone conversion products in serum may indicate serious osteoporosis in elderly patients\u003csup\u003e[24]\u003c/sup\u003e. Bone mineral density is the final result of the continuous change and development of bone metabolism\u003csup\u003e[25]\u003c/sup\u003e, reflecting the overall situation of bone mass in a period of time, and cannot reflect the dynamic change of bone metabolism in vivo, while the change of bone metabolism in osteoporosis patients is often before the change of bone mineral density\u003csup\u003e[26, 27]\u003c/sup\u003e. There are differences in blood lipid levels among people with different bone mass\u003csup\u003e[28]\u003c/sup\u003e, but the correlation between blood lipid levels and bone density in specific parts of hip is controversial\u003csup\u003e[29, 30]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe results of this study show that bone resorption and bone formation are in a state of high metabolism. There is no significant difference in bone formation among the three groups, but there is a significant difference in bone resorption. The osteoporosis group is significantly higher than the other two groups, indicating that the osteoporosis group may lose bone mass faster. There was no significant correlation between PINP and \u0026beta;-CTX and general data, while BMD was correlated with age, menopausal age, height, weight, BMI, etc., which was in line with the results of most studies, indicating that BMD can better reflect the level of bone mass. The results showed that the lumbar spine BMD was positively correlated with the hip BMD, indicating that the loss of bone mass was systemic. In clinical work, more attention was paid to hip, lumbar spine, and distal radius fractures, while ignoring other parts. Through a variety of ways to clearly diagnose patients with osteoporosis at risk of fracture, so as to carry out appropriate treatment and targeted therapy, so as to strengthen prevention and protective measures and reduce the risk of fracture.\u003c/p\u003e\n\u003cp\u003eAlthough the results of this study suggest that bone resorption and bone formation are in a hyper-metabolic state, there is no significant difference in bone formation among the three groups, but there is a significant difference in bone resorption among the three groups, and the osteoporosis group is significantly higher than the other two groups. There was no significant correlation between PINP and \u0026beta;-CTX and general data, while BMD was correlated with age, menopausal age, height, weight, BMI, etc. There is a positive correlation between lumbar BMD and hip BMD, but there are some limitations. A potential source of bias in this study is residual confounding due to risk factors that we could not account for in our analyses (socioeconomic status, education level, physical activity level, smoking, alcohol consumption, vitamin D status, sex hormone levels, and nutritional status, among others) and that could not be adjusted for unknown confounders. To allow for more precise results, our study included participants who were randomly selected from the general population. A limitation of our findings is that we included women who had undergone natural menopause more than 2 years earlier between the ages of 45 and 72 years, and our results may not apply to broader age groups and to early and premenopausal women. Another limitation is that only women were included in the study. Whether there is an obvious correlation between lumbar spine and hip BMD, PINP and \u0026beta;-CTX in men needs to be further investigated in future studies.\u003c/p\u003e\n\u003cp\u003eThe results of our study have important clinical significance. First of all, it can provide a reference for subsequent scientific research. Secondly, it can provide reference for the further strengthening of the demand for osteoporosis prevention in natural postmenopausal women.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our study showed that bone resorption and bone formation were in a hyper-metabolic state, and bone resorption was significantly higher in the osteoporosis group than in the other two groups, indicating that bone mass loss may be faster in the osteoporosis population. There was no significant correlation between PINP and \u0026beta;-CTX and general data, while BMD was correlated with age, menopausal age, height, weight, BMI, etc., which was in line with the results of most studies, indicating that BMD can better reflect the level of bone mass. The results show that the lumbar spine BMD is positively correlated with the hip BMD, indicating that the loss of bone mass is systemic. In clinical work, more attention should be paid to hip, lumbar spine, and distal radius fractures, while ignoring other fractures, so that prevention and protection measures can be strengthened to reduce the risk of fracture. In particular, natural postmenopausal women with faster bone resorption have a demand for osteoporosis prevention. Through a variety of ways to clearly diagnose patients with osteoporosis at risk of fracture, so as to carry out appropriate treatment and targeted therapy, so as to strengthen prevention and protective measures and reduce the risk of fracture.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the ethics board of the Sixth Affiliated Hospital of Xinjiang Medical University and each participant signed an informed consent form. All the procedures followed the ethical guidelines of the declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the patients signed informed consent to publish their results before receiving the tests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data can be acquired from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest regarding this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was funded by the Natural Scientific Foundation of Xinjiang Uyghur Autonomous Region (No：2018E02061; 2022D01C586).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLi Guohua designed the study, acquired and analyzed the data, wrote the manuscript. Muyashaer Abudushalamu analyzed the data and revised the manuscript. Aikeremujiang Muheremu designed the study, interpreted the data, wrote and revised the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJohnston, C.B. and M. Dagar, Osteoporosis in Older Adults\u003cem\u003e.\u003c/em\u003e Med Clin North Am, 2020. \u003cstrong\u003e104\u003c/strong\u003e(5): p. 873-884.\u003c/li\u003e\n\u003cli\u003eReid, I.R. and E.O. Billington, Drug therapy for osteoporosis in older adults. Lancet, 2022. \u003cstrong\u003e399\u003c/strong\u003e(10329): p. 1080-1092.\u003c/li\u003e\n\u003cli\u003eBrown, J.P., Long-Term Treatment of Postmenopausal Osteoporosis. Endocrinol Metab (Seoul), 2021. \u003cstrong\u003e36\u003c/strong\u003e(3): p. 544-552.\u003c/li\u003e\n\u003cli\u003eArceo-Mendoza, R.M. and P.M. Camacho, Postmenopausal Osteoporosis: Latest Guidelines. Endocrinol Metab Clin North Am, 2021. \u003cstrong\u003e50\u003c/strong\u003e(2): p. 167-178.\u003c/li\u003e\n\u003cli\u003eMatsumoto, T., et al., Abaloparatide Increases Lumbar Spine and Hip BMD in Japanese Patients With Osteoporosis: The Phase 3 ACTIVE-J Study. J Clin Endocrinol Metab, 2022. \u003cstrong\u003e107\u003c/strong\u003e(10): p. e4222-e4231.\u003c/li\u003e\n\u003cli\u003eAlay, I., et al., The relation of body mass index, menopausal symptoms, and lipid profile with bone mineral density in postmenopausal women. Taiwanese Journal of Obstetrics and Gynecology, 2020. \u003cstrong\u003e59\u003c/strong\u003e(1): p. 61-66.\u003c/li\u003e\n\u003cli\u003eChandran, M., The why and how of sequential and combination therapy in osteoporosis. A review of the current evidence. Arch Endocrinol Metab, 2022. \u003cstrong\u003e66\u003c/strong\u003e(5): p. 724-738.\u003c/li\u003e\n\u003cli\u003eZhao, S., et al., Declining serum bone turnover markers are associated with the short-term positive change of lumbar spine bone mineral density in postmenopausal women. Menopause (New York, NY), 2022. \u003cstrong\u003e29\u003c/strong\u003e(3): p. 335.\u003c/li\u003e\n\u003cli\u003eAzimi-Shomali, S., et al., The relationship between usual daily physical activity with serum markers related to bone metabolism and demographic characteristics in postmenopausal women aged 50\u0026ndash;65 years. Journal of Physical Activity and Health, 2022. \u003cstrong\u003e19\u003c/strong\u003e(6): p. 417-424.\u003c/li\u003e\n\u003cli\u003eShoback, D., et al., Pharmacological Management of Osteoporosis in Postmenopausal Women: An Endocrine Society Guideline Update. J Clin Endocrinol Metab, 2020. \u003cstrong\u003e105\u003c/strong\u003e(3).\u003c/li\u003e\n\u003cli\u003eStarup-Linde, J.K., et al., Associations of Circulating Osteoglycin With Bone Parameters and Metabolic Markers in Patients With Diabetes. Front Endocrinol (Lausanne), 2021. \u003cstrong\u003e12\u003c/strong\u003e: p. 649718.\u003c/li\u003e\n\u003cli\u003eHarris, K., C.A. Zagar, and K.V. Lawrence, Osteoporosis: Common Questions and Answers. Am Fam Physician, 2023. \u003cstrong\u003e107\u003c/strong\u003e(3): p. 238-246.\u003c/li\u003e\n\u003cli\u003eReid, I.R., A broader strategy for osteoporosis interventions. Nat Rev Endocrinol, 2020. \u003cstrong\u003e16\u003c/strong\u003e(6): p. 333-339.\u003c/li\u003e\n\u003cli\u003eVujasinovic, M., et al., Low Bone Mineral Density and Risk for Osteoporotic Fractures in Patients with Chronic Pancreatitis. Nutrients, 2021. \u003cstrong\u003e13\u003c/strong\u003e(7).\u003c/li\u003e\n\u003cli\u003eKemmler, W., et al., Effects of Different Types of Exercise on Bone Mineral Density in Postmenopausal Women: A Systematic Review and Meta-analysis. Calcif Tissue Int, 2020. \u003cstrong\u003e107\u003c/strong\u003e(5): p. 409-439.\u003c/li\u003e\n\u003cli\u003eBhattoa, H.P., et al., Analytical considerations and plans to standardize or harmonize assays for the reference bone turnover markers PINP and \u0026beta;-CTX in blood. Clin Chim Acta, 2021. \u003cstrong\u003e515\u003c/strong\u003e: p. 16-20.\u003c/li\u003e\n\u003cli\u003eLin, X.B., et al., Analysis of changes in serum high t-PINP/\u0026beta;-CTX ratio and risk of re-fracture after vertebral osteoporotic fracture surgery. Eur Rev Med Pharmacol Sci, 2023. \u003cstrong\u003e27\u003c/strong\u003e(22): p. 10860-10867.\u003c/li\u003e\n\u003cli\u003eHu, Q., et al., Combination of Calcitriol and Zoledronic Acid on PINP and \u0026beta;-CTX in Postoperative Patients with Diabetic Osteoporosis: A Randomized Controlled Trial. Dis Markers, 2022. \u003cstrong\u003e2022\u003c/strong\u003e: p. 6053410.\u003c/li\u003e\n\u003cli\u003eBellissimo, M.P., et al., Metabolomic Associations with Serum Bone Turnover Markers. Nutrients, 2020. \u003cstrong\u003e12\u003c/strong\u003e(10).\u003c/li\u003e\n\u003cli\u003eVasikaran, S.D., et al., Practical Considerations for the Clinical Application of Bone Turnover Markers in Osteoporosis. Calcif Tissue Int, 2023. \u003cstrong\u003e112\u003c/strong\u003e(2): p. 148-157.\u003c/li\u003e\n\u003cli\u003eAyers, C., et al., Effectiveness and Safety of Treatments to Prevent Fractures in People With Low Bone Mass or Primary Osteoporosis: A Living Systematic Review and Network Meta-analysis for the American College of Physicians. Ann Intern Med, 2023. \u003cstrong\u003e176\u003c/strong\u003e(2): p. 182-195.\u003c/li\u003e\n\u003cli\u003eWalker, M.D. and E. Shane, Postmenopausal Osteoporosis\u003cem\u003e.\u003c/em\u003e N Engl J Med, 2023. \u003cstrong\u003e389\u003c/strong\u003e(21): p. 1979-1991.\u003c/li\u003e\n\u003cli\u003eWalker, M.D. and E. Shane, Postmenopausal Osteoporosis. New England Journal Of Medicine, 2023. \u003cstrong\u003e389\u003c/strong\u003e(21): p. 1979-1991.\u003c/li\u003e\n\u003cli\u003eHuidrom, S., M.A. Beg, and T. Masood, Post-menopausal Osteoporosis and Probiotics. Curr Drug Targets, 2021. \u003cstrong\u003e22\u003c/strong\u003e(7): p. 816-822.\u003c/li\u003e\n\u003cli\u003eZhao, Y.-X., et al., Association between bile acid metabolism and bone mineral density in postmenopausal women. Clinics, 2020. \u003cstrong\u003e75\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eChen, Y., et al., Systemic Inflammation Markers Associated with Bone Mineral Density in perimenopausal and Postmenopausal Women. Journal of Inflammation Research, 2023: p. 297-309.\u003c/li\u003e\n\u003cli\u003eShieh, A., et al., Associations of age at menopause with postmenopausal bone mineral density and fracture risk in women. The Journal of Clinical Endocrinology \u0026amp; Metabolism, 2022. \u003cstrong\u003e107\u003c/strong\u003e(2): p. e561-e569.\u003c/li\u003e\n\u003cli\u003eZhao, H., et al., Blood lipid levels in patients with osteopenia and osteoporosis:a systematic review and meta-analysis. J Bone Miner Metab, 2021. \u003cstrong\u003e39\u003c/strong\u003e(3): p. 510-520.\u003c/li\u003e\n\u003cli\u003eZhang, Q., et al., Association Between Bone Mineral Density and Lipid Profile in Chinese Women. Clin Interv Aging, 2020. \u003cstrong\u003e15\u003c/strong\u003e: p. 1649-1664.\u003c/li\u003e\n\u003cli\u003eKang, S., et al., Association between Serum Uric Acid Levels and Bone Mineral Density in Postmenopausal Women: A Cross-Sectional and Longitudinal Study. Healthcare (Basel), 2021. \u003cstrong\u003e9\u003c/strong\u003e(12).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"PINP, β-CTX, Lumbar bone mineral density, Hip bone mineral density, Postmenopausal women and hip bone density","lastPublishedDoi":"10.21203/rs.3.rs-4023574/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4023574/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo investigate the relationship between bone metabolic markers and bone mineral density (BMD) in natural postmenopausal women more than 2 years after menopause.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA total of 147 women aged 45-72 years who had undergone natural menopause for more than 2 years were selected. Dual-energy X-ray Absorptiometry (DEXA) was used to scan the standard BMD of the lumbar spine and hip. Fasting venous blood was collected in the morning to detect serum bone metabolism markers like amino-terminal pro-peptide of type Ⅰ pro-collagen (PINP) and β-collagen degradation products (β-CTX). According to BMD the patients were divided into normal bone mass group, osteopenia group and osteoporosis group. The differences of PINP and β-CTX among the three groups were compared. The correlation between PINP, β-CTX and general data was analyzed. The correlation between lumbar BMD, hip BMD and each variable; Correlation between β-CTX and P1NP, lumbar spine BMD and hip BMD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: P1NP had no significant difference among the three groups. PINP and β-CTX were not correlated with age, menopausal age, height, weight, BMI and glycosylated hemoglobin (P \u0026gt; 0.05). Lumbar spine BMD was correlated with age, menopausal age, height, weight and BMI (P \u0026lt; 0. 05), but not with Hba1c, PINP and β-CTX. Hip BMD was correlated with menopausal age, height, weight, BMI and β-CTX (P \u0026lt; 0. 05), but not with age, Hba1c and P1NP. 05), and lumbar BMD was correlated with hip BMD (P \u0026lt; 0. 05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eBone resorption and bone formation are in a state of high metabolism, and osteoporosis patients may lose bone mass faster. Bone mineral density is better than PINP and β-CTX in reflecting bone mass.\u003c/p\u003e","manuscriptTitle":"Relationship between bone metabolic markers and bone mineral density in natural postmenopausal women","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-05 14:23:48","doi":"10.21203/rs.3.rs-4023574/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-02T14:08:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-02T08:36:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-02T08:36:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2024-03-07T08:21:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0d91a6b9-8c43-4fd7-b9d4-ce497e3a0ca3","owner":[],"postedDate":"April 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-04-07T02:29:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-05 14:23:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4023574","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4023574","identity":"rs-4023574","version":["v1"]},"buildId":"zQwnuV7TCBrMSSSToR1PI","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}