Gastrointestinal health and serum proteins are associated with BMD in postmenopausal women: A cross-sectional study

Gastrointestinal health and serum proteins are associated with BMD in postmenopausal women: A cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Gastrointestinal health and serum proteins are associated with BMD in postmenopausal women: A cross-sectional study Han Wang, Qiuxia Jiang, Jiai Yan, Yang Ju, Jing Sun, Yingyu Wang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4250878/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background With increasing age, the social and economic burdens of postmenopausal osteoporosis are steadily increasing. This study aimed to investigate the factors that influence the development of postmenopausal osteoporosis. Methods Postmenopausal women at the Affiliated Hospital of Jiangnan University from January 2023 to December 2023 were recruited for BMD examination. The patients were divided into a normal group, an osteopenia group and an osteoporosis group according to their T value. Questionnaires, including the Gastrointestinal Symptom Rating Scale and Short Form 12, were administered through face-to-face interviews. Bone turnover markers and serum protein levels of Fasting venous blood were detected. Results A total of 222 postmenopausal women met the inclusion criteria were recruited. Univariate analysis revealed statistically significant differences in age, education, BMI, supplementation with soy products, supplementation with dairy products, supplementation with other nutritional supplements, exercise frequency, gastrointestinal symptom score, quality of life, 25(OH)D, total protein, albumin and prealbumin among the three groups (P < 0.05). Pearson correlation analysis revealed that gastrointestinal symptoms (r = -0.518, P<0.01) was negatively correlated with BMD in postmenopausal women, while PCS (r= 0.194, P= 0.004), MCS (r= 0.305, P < 0.01), 25(OH)D (r= 0.531, P < 0.01), total protein (r= 0.324, P < 0.01), albumin (r= 0.341, P < 0.01) and prealbumin (r= 0.259, P < 0.01) were positively correlated with BMD. Conclusion Increasing age, low BMI, gastrointestinal disorders, decreased serum protein and 25(OH)D levels may contribute to an increased risk of postmenopausal osteoporosis. Supplementation with soy and dairy products and consistent exercise may prevent postmenopausal osteoporosis. Postmenopausal osteoporosis Osteopenia Gastrointestinal symptoms Serum protein Bone turnover markers Bone mass density Figures Figure 1 Introduction Postmenopausal osteoporosis is the most common type of osteoporosis and is characterized by decreased bone mineral density, disintegration of the bone microstructure, increased bone fragility, and increased fracture susceptibility [ 1 ]. With increasing age, the social and economic burdens of osteoporosis are steadily increasing. The proportion of women with osteoporosis increases with age, and bone mineral density (BMD) is significantly lower in postmenopausal women due to a decrease in serum estrogen levels [ 2 ]. As an increasingly serious public health problem, osteoporosis can seriously affect the quality of life of patients. Fractures are common adverse outcomes and include increased pain, disability, caregiving tasks, overall healthcare costs and death [ 3 ]. Drugs such as bisphosphonates, the monoclonal antibody denosumab, and selective estrogen receptor modulators have been approved by the United States Federal Drug Administration (FDA) for the treatment of osteoporosis [ 4 ], however, there is currently no drug to cure osteoporosis [ 5 ], and osteoporosis remains an underdiagnosed and undertreated disease [ 6 ], with the number of patients receiving treatment being much smaller than the number of patients who are eligible for treatment [ 7 ]. Therefore, how to recognize the risk of osteoporosis in a timely manner and prevent it effectively are currently popular research topics in the field of osteoporosis care. At present, the clinical diagnosis of postmenopausal osteoporosis relies mainly on dual-energy X-rays for examining BMD, but this examination equipment is mostly deployed by large medical centers, and such examinations are expensive; therefore, there is an urgent need for simple, noninvasive predictors for early diagnosis of this disease. Bone turnover markers (BTMs) are biomarkers of fracture risk and are used to diagnose and evaluate the effect of treatment on PMO, which include the following biomarkers: bone formation markers, including serum alkaline phosphatase (ALP) and total procollagen type 1 N-terminal propeptide (TP1NP); bone resorption markers, including β-collagen degradation product (β-CTX/β‐CROSSL); bone mineral metabolism indicators, including calcium (Ca) and phosphorus (P); and bone regulatory hormone indicators, including 25 hydroxyvitamin D (25(OH)D). The gastrointestinal tract is a very complex system that has many important functions, such as digestion, absorption, detoxification, immunity and disease resistance [ 8 ], and is critical to human health, as gastrointestinal disorders affect nutritional status and bone health. Malnutrition is common in inflammatory bowel disease (IBD) patients, and a study by Yelencich et al. showed that approximately 16%~68% of IBD patients are malnourished [ 9 ]. A large cross-sectional study based on the National Health and Nutrition Examination Survey (NHANES) in the United States shows an inverse association between the Geriatric Nutritional Risk Index (GNRI) and the risk of osteoporosis in older adults [ 10 ]. GNRI values were also found to increase with BMD in the Chinese population [ 11 ]. BMD in the proximal femoral region (including the femoral neck and total hip) decreased by 12% in patients with nutritional limitations due to gastrectomy [ 12 ]. One cohort study revealed that the hip fracture rate in IBD patients was approximately 60% greater than that in matched controls [ 13 ]. Similarly, another study revealed that IBD patients had lower BMD outcomes and significant bone loss in both cortical and trabecular bone compared to healthy controls [ 14 ]. Given the above background, we conducted this study to explore the relationships of gastrointestinal health and nutritional status with bone health in postmenopausal women, providing new ideas for the prevention and treatment of postmenopausal osteoporosis. Methods Participants and study design A total of 222 postmenopausal female patients who underwent BMD examination at the Affiliated Hospital of Jiangnan University from January 2023 to September 2023 were recruited. Patients who had less than one year of menopause, secondary osteoporosis, or recent use of medications that may affect bone metabolism (such as thiazolidinediones, immunosuppressants, systemic glucocorticoids, or hormone replacement therapy) were excluded. According to the standards of the World Health Organization (WHO) [ 15 ], the subjects were divided into three groups according to their BMD status: the normal group (T≥-1), the osteopenia group (-2.5 < T < -1) and the osteoporosis group (T≤-2.5) (Fig. 1 ). Serum biochemical index measurements Peripheral venous blood was collected from all subjects and sent to the laboratory department for measurement. The serum ALP, Ca, P, albumin (ALB), total protein (TP), and prealbumin (Pa) concentrations were determined via an automatic biochemical analyzer. 25(OH)D, TP1NP, and β-CTX were detected by electrochemiluminescence. The normal reference values of these indicators are as follows: 25(OH)D (≥ 30 ng/mL); TP1NP (16–55 ng/mL); β-CTX (0-0.573 ng/mL); Ca (2.11–2.52 mmol/L); P (0.85–1.51 mmol/L); ALP (50-135U/L); ALB (40–55 g/L); TP (65–85 g/L); and Pa (180–350 mg/L). BMD measurements Bone densitometry was performed by the staff of the Department of Imaging at Jiangnan University using dual-energy X-ray absorptiometry (DXA). All tests were performed using the same instrument. The technicians were uniformly and formally trained, and the testing instrument was calibrated daily to rule out human error. The BMD machine was warmed by the staff, and a standardized periosteum was scanned point-to-point to obtain measurements at three fixed points (left femoral neck, total hip, and lumbar vertebrae L1-L4). Gastrointestinal symptoms and quality of life Gastrointestinal function assessment Gastrointestinal symptoms were assessed using the Gastrointestinal Symptom Rating Scale (GSRS) [ 16 ], which has 15 items covering five different gastrointestinal symptoms: reflux, abdominal pain, diarrhea, constipation, and dyspepsia; each item is divided into 4 options. The higher the score is, the more severe the gastrointestinal symptoms and the poorer the patient's intestinal health status. Quality of life assessment The subjects' quality of life was assessed using the Short Form 12 (SF-12) [ 17 ], 8 dimensions: physical functioning (PF), role physical (RP), bodily pain (BP), general health (GH), vitality (VT), social functioning (SF), role emotional (RE) and mental health (MH). Based on the domains, two summary measures may be estimated—the Physical Component Summary (PCS) and Mental Component Summary (MCS). The scale is scored on a percentage scale, and after the crude score is obtained, the scale is transformed to a standardized scale [ 18 ]. The higher the score, the higher the quality of life of the patient. Statistical analysis All the data were statistically analyzed using SPSS25.0. The Kolmogorov‒Smirnov test was used to test the normality of the distribution. Continuous variables are presented as the mean ± standard deviation (mean ± standard deviation) and were compared between groups using one-way analysis of variance (ANOVA). The categorical variables are expressed as percentages (%) and were compared using the chi-square test. Pearson’s correlation coefficient and multiple linear stepwise regression analysis were used to examine the relationships between the variables. Before the multiple linear regression analysis, the data were tested for multicollinearity, and the variance expansion coefficient (VIF) (≤ 10) indicated that independent variables could be included in the multivariate analysis. A P value < 0.05 was considered to indicate statistical significance. Results Baseline characteristics A total of 230 postmenopausal women who met the criteria were enrolled in this study, including 58 postmenopausal women with normal bone mass, 80 postmenopausal women with osteopenia and 84 postmenopausal women with osteoporosis. There were significant differences between the normal group, osteopenia group and osteoporosis group in age, education level, body mass index (BMI), dietary habits and exercise frequency (P < 0.05) (Table 1 ). The ages of the individuals in the normal, osteopenia and osteoporosis groups increased successively, and their BMI decreased successively. Greater bone loss occurred with age, and BMD was protected at higher BMIs within the normal range. The proportion of individuals in the normal group with a high school education and above was 36.2%, which was significantly greater than that in the osteopenia group (17.6%) and osteoporosis group (6%), and the proportions of individuals who supplemented with soy products, dairy products, and nutritional supplements (calcium, vitamin D, etc.) were greater than those in the osteopenia and osteoporosis groups. The frequency of exercise was significantly greater in the normal group than in the osteopenia and osteoporosis groups. Differences in diet and exercise may be related to differences in education. No significant differences were detected in the presence of other variables (such as alcohol consumption, smoking status, or common chronic diseases [hypertension, diabetes, and osteoarthritis]) among the three groups. Table 1 Baseline characteristics of the normal, osteopenia, and osteoporosis groups Characteristics Group F value/ χ 2 test P value Normal (n = 58) Osteopenia (n = 80) Osteoporosis (n = 84) Age 51.62 ± 9 56.73 ± 8.56 64.29 ± 9.26 36.210 <.001*** Educational level Primary and below 14(24.1%) 27(33.8%) 50(59.5%) 33.042 <.001*** Junior high school 23(39.7%) 39(48.8%) 29(34.5%) Senior high school 9(15.5%) 7(8.8%) 4(4.8%) University and above 12(20.7%) 7(8.8%) 1(1.2%) BMI 24.92 ± 3.32 23.56 ± 3.08 22.82 ± 3.08 7.630 0.001*** Chronic diseases Yes 33(56.9%) 33(41.3%) 38(45.2%) 3.446 0.179 No 25(43.1%) 47(58.8%) 46(54.8%) Soy products Yes 27(46.6%) 23(28.8%) 12(14.3%) 17.788 <.001*** No 31(53.4%) 57(71.3%) 72(85.7%) Dairy products Yes 38(65.5%) 28(35%) 18(21.4%) 28.783 <.001*** No 20(34.5%) 52(65%) 66(78.6%) supplements Yes 24(41.4%) 13(16.3%) 4(4.8%) 20.961 <.001*** No 34(58.6%) 67(83.8%) 80(95.2%) Smoking status Yes 0(0.00%) 0(0.00%) 0(0.00%) -- 1.000 No 58(100.00%) 80(100.00%) 84(100.00%) Drinking status Yes 0(0.00%) 1(1.3%) 0(0.00%) 1.783 0.410 No 58(100.00%) 79(98.8%) 84(100.00%) Exercise frequency 0–1 times 5(8.6%) 22(27.5%) 54(64.3%) 83.065 <.001*** 1–2 times 13(22.4%) 36(45%) 21(25%) 3–4 times 18(31%) 16(20%) 6(7.1%) ≥ 5 times 22(37.9%) 6(7.5%) 3(3.6%) P values were calculated using the chi-square test for categorical variables and one-way analysis of variance for continuous variables. “***” P ≤ 0.001. BMI, body mass index; BMD, bone mineral density. Gastrointestinal health of postmenopausal female subjects There were significant differences in gastrointestinal health among the normal, osteopenia and osteoporosis groups (P < 0.05) (Table 2 ). Gastrointestinal symptom scores were significantly lower in the normal group than in the osteopenia and osteoporosis groups. The patients in the osteoporosis group had the highest gastrointestinal symptom scores of the three groups, and their gastrointestinal health was the worst. Table 2 Differences in gastrointestinal function among the normal group, osteopenia group and osteoporosis group GSRS Group F value P value Normal (n = 58) Osteopenia (n = 80) Osteoporosis (n = 84) GSRS 16.91 ± 1.39 20.18 ± 2.62 20.43 ± 2.56 45.402 <0.001*** One-way analysis of variance. “***” P < 0.001. GSRS, Gastrointestinal Symptom Rating Scale. Quality of life in postmenopausal female subjects The normal, osteopenia and osteoporosis groups significantly differed (all P < 0.05) in the eight dimensions as well as in the combined physical and mental scores (Table 3 ). Among them, the osteoporosis group had the worst physical health, and the PCS score was significantly lower than that of the osteopenia group; the highest PCS score was in the normal group. The normal group also had the highest MCS score, which was significantly greater than that of the osteopenia group. Additionally, subjects in the osteoporosis group had the lowest MCS score among the three groups, with more severe negative emotions. Overall, the individuals in the osteoporosis group were strongly affected by the disease and had a poor quality of life. Table 3 Differences in quality of life among the normal, osteopenia, and osteoporosis groups SF-12 Group F value P value Normal (n = 58) Osteopenia (n = 80) Osteoporosis (n = 84) GH 42.47 ± 9.13 35.31 ± 7.27 29.9 ± 7.66 43.007 <0.001*** PF 53.5 ± 6.93 50.35 ± 8.88 43.79 ± 10.54 21.581 <0.001*** RP 44.47 ± 11.55 37.14 ± 12.16 32.17 ± 12.35 17.791 <0.001*** RE 67.26 ± 83.92 39.3 ± 14.46 35.04 ± 20.05 9.624 <0.001*** BP 44.09 ± 8.37 39.48 ± 7.3 32.94 ± 8.09 35.942 <0.001*** MH 51.84 ± 5.55 45.71 ± 6.93 41.61 ± 6.19 45.112 <0.001*** VT 55.24 ± 5.66 51.47 ± 5.65 45.64 ± 6.2 48.660 <0.001*** SF 44.38 ± 10.14 42.68 ± 7.25 36.13 ± 10.16 16.813 <0.001*** PCS 41.96 ± 18.96 41.64 ± 8.77 35.62 ± 9.26 6.429 0.002** MCS 58.68 ± 38.31 44.41 ± 9.79 40.28 ± 12.87 12.730 <0.001*** One-way analysis of variance. “**” P < 0.01, “***” P < 0.001. SF-12, Short Form 12; GH, general health; PF, physical functioning; RP, role-physical; BP, body pain; VT, vitality; SF, social functioning; RE, role-emotional; MH, mental health; MCS includes SF, RE, ME, and VT; PCS includes GH, PF, RP, and BP. Comparison of serum biochemical indices in postmenopausal female subjects Significant differences in 25(OH)D, TP, ALB, and Pa were found among the normal, osteopenia, and osteoporosis groups (all P < 0.05) (Table 4 ). Serum 25(OH)D and protein levels were significantly lower in subjects with reduced bone mass than in subjects with normal bone mass, and malnutrition affects bone health. Although the differences in TP1NP and β-CTX among the three groups were not significant, the TP1NP levels in the osteopenia group and the osteopenia group were lower than those in the normal group, and the β-CTX was greater than that in the normal group, which led to an increase in bone conversion. Table 4 Differences in the serum biochemical indices among the normal, osteopenia and osteoporosis groups Serum indices Group F value P value Normal (n = 58) Osteopenia (n = 80) Osteoporosis (n = 84) 25(OH)D 20.73 ± 6.9 14.81 ± 4.02 13.15 ± 5.27 36.120 <0.001*** TP1NP 60.58 ± 40.16 73.62 ± 79.87 72.09 ± 59.59 0.804 0.449 β-CTX 1 ± 2.69 0.74 ± 0.37 0.72 ± 0.47 0.781 0.459 TP 70.31 ± 5.97 67.98 ± 7.13 66.46 ± 6.66 5.723 0.004** ALB 41.92 ± 3.26 39.99 ± 4.42 38.95 ± 4.78 8.243 <0.001*** Pa 250.41 ± 46.75 237 ± 47.96 220.12 ± 46.59 7.321 0.001*** ALP 85.86 ± 36.63 81.68 ± 32.83 89.73 ± 60.92 0.619 0.539 Ca 2.37 ± 0.12 2.31 ± 0.13 4.65 ± 21.35 0.809 0.447 P 1.38 ± 0.23 1.33 ± 0.26 2.69 ± 13.4 0.695 0.500 One-way analysis of variance. “**” P < 0.01, “***” P ≤ 0.001. 25(OH)D, 25-hydroxyvitamin D; TP1NP, total procollagen type 1 N-terminal propeptide; β-CTX, β-collagen degradation product aps; TP, total protein; ALB, albumin; Pa, prealbumin; ALP, alkaline phosphatase; Ca, calcium; P, phosphorus. Associations of BMD with gastrointestinal health, quality of life, and serum marker levels There were correlations between gastrointestinal health, quality of life, bone turnover markers, protein nutritional levels and BMD in postmenopausal female subjects (Table 5 ). Gastrointestinal health and BMD in postmenopausal female subjects were negatively correlated, and the more gastrointestinal symptoms was associated with lower levels of BMD; There were significant positive correlations of PCS, MCS, 25(OH)D, TP, ALB, and Pa with BMD in postmenopausal female subjects, and the high quality of life of the subjects was suggestive of their comparative bone health, and the subjects with high levels of 25(OH)D and protein also had greater BMD. There were no significant correlations of TP1NP, β-CTX, ALP, Ca, or P with BMD in postmenopausal women (P > 0.05). Table 5 Correlation analysis of the quality of the GSRS score, SF-12 score, serum indices and BMD r value P value GSRS − .518 ** <0.01 PCS .194 ** 0.004 MCS .305 ** <0.01 25(OH)D .531 ** <0.01 TP1NP -0.116 0.086 β-CTX 0.044 0.517 TP .324 ** <0.01 ALB .341 ** <0.01 Pa .259 ** <0.01 ALP -0.053 0.433 Ca -0.07 0.297 P -0.041 0.544 Pearson correlation analysis 25(OH)D, 25-hydroxyvitamin D; TP1NP, total procollagen type 1 N-terminal propeptide; β-CTX, β-collagen degradation product aps; TP, total protein; ALB, albumin; Pa, prealbumin; ALP, alkaline phosphatase; Ca, calcium; P, phosphorus. ** The correlation was significant at the 0.01 level (two-tailed). * The correlation was significant at the 0.05 level (two-tailed). Logistic regression prediction of osteoporosis in postmenopausal women A collinearity diagnosis was performed prior to multivariate logistic regression analysis. All the VIFs calculated by SPSS25.0 were less than 5, indicating that the included variables do not have serious collinearity and can be entered into multiple linear regression analysis. With BMD as the dependent variable, age, BMI, soy supplementation, dairy supplementation, exercise frequency, gastrointestinal symptoms, and the 25(OH)D concentration could significantly predict BMD (Table 6 ). These variables explained 66.7% of the observed changes in BMD in postmenopausal women. Older age and a lower body mass index were associated with a greater risk of osteoporosis; postmenopausal women with more gastrointestinal symptoms were more prone to osteoporosis. Those with higher 25(OH)D levels had a lower risk of osteoporosis; postmenopausal women who supplemented with soy products, supplemented with dairy products and exercised regularly had a lower risk of osteoporosis. Table 6 Logistic regression analysis of risk factors for osteoporosis B SE Beta t P 95% CI Lower Upper Age -0.028 0.005 -0.261 -5.948 <0.001*** -0.038 -0.019 Educational level 0.098 0.051 0.081 1.911 0.057 -0.003 0.199 BMI 0.037 0.014 0.107 2.697 0.008** 0.010 0.064 Soy products -0.356 0.101 -0.143 -3.517 0.001*** -0.556 -0.157 Dairy products -0.242 0.100 -0.105 -2.410 0.017* -0.440 -0.044 Nutritional supplements -0.207 0.115 -0.072 -1.796 0.074 -0.434 0.020 Exercise frequency 0.204 0.049 0.192 4.144 <0.001*** 0.107 0.302 GSRS -0.107 0.017 -0.265 -6.363 <0.001*** -0.140 -0.074 25(OH)D 0.035 0.008 0.195 4.420 <0.001*** 0.020 0.051 TP 0.007 0.011 0.040 0.643 0.521 -0.015 0.029 ALB 0.035 0.019 0.131 1.877 0.062 -0.002 0.073 Pa 5.570E-05 0.001 0.002 0.051 0.959 -0.002 0.002 Dependent variable = BMD; B = unstandardized regression coefficient; SE = standard error; Beta = standardized regression coefficient; t = t-statistic; CI = confidence interval. “**” P < 0.05, “**” P < 0.01, “***” P ≤ 0.001. 25(OH)D, 25-hydroxyvitamin D; TP1NP, total type I collagen; TP, total protein; ALB, albumin; Pa, prealbumin. Discussion With the prolongation of the average human life expectancy and population aging, osteoporosis has become an important problem affecting the health of all humankind. There are more than 200 million people suffering from osteoporosis worldwide, and its incidence increases with age. Postmenopausal elderly women have the greatest prevalence of osteoporosis [ 19 ]. After menopause, bone density decreases by 2.5% per year, while premenopausal bone density decreases by approximately 0.13% per year [ 20 ] ; thus , the premenopausal period is key for preventing and delaying the development of osteoporosis. Postmenopausal osteoporosis is affected by a variety of risk factors, and early symptoms are not obvious. The early identification of relevant risk factors and screening of high-risk groups should be prioritized in the clinic so that efforts can be made to control the loss of bone mass by adjusting lifestyle habits, dietary structure, and other measures, thus reducing the incidence of osteoporosis. In addition to the effects of age gain and sex hormone reduction, factors such as gastrointestinal health, nutritional status, and bone conversion indices should not be ignored. In this study, postmenopausal women's age, BMI, literacy, dietary habits, exercise frequency, gastrointestinal symptoms, 25(OH)D, total protein, albumin, and prealbumin were included in regression analyses, with BMD as the dependent variable. The results showed that postmenopausal women's age, BMI, whether or not they supplemented with soybean products, exercise frequency, gastrointestinal health status, and serum 25(OH)D concentration could significantly predict BMD. Previous studies have shown that BMI can be used as an independent protective factor for BMD [ 21 ],but the relationship between BMI and osteoporosis has not been consistent among existing studies [ 22 – 26 ].In this study, the risk of osteoporosis increased with decreasing BMI. Although the BMI of the normal group was significantly greater than that of the osteopenia group and osteoporosis group, the BMI of the patients in each group was still within the normal range or was slightly overweight and did not reach the obesity level. The reason for the low risk of osteoporosis in the normal group under these conditions may be that adipose tissue can produce aromatase, which synthesizes estrogen outside the gonads and protects the bones [ 27 ]. Therefore, it is recommended that postmenopausal women try to gain weight within the normal BMI range. Dietary habits and exercise affect bone health. Studies have shown that supplementation with soy products, dairy products, nutritional supplements (calcium and vitamin D, etc.), and adherence to exercise have positive effects on BMD and bone metabolism[ 28 – 30 ]. Among the participants included in this study, compared with those in the low-BMD population, those in the high-BMD population had relatively higher education levels and may have been more familiar with health care, which may have led to better lifestyle habits, such as the habit of consuming soy products, dairy products, nutritional supplements (calcium and vitamin D, etc.), and exercising regularly, which would result in healthier bones. Therefore, it is necessary to strengthen health education for middle-aged and elderly women with low educational attainment, promote reasonable supplementation with soy and dairy products and other nutritional supplements such as calcium and vitamin D, and increase the amount of exercise appropriately to prevent the occurrence of osteoporosis. The presence of gastrointestinal symptoms indicated that a subject had gastrointestinal disease or was in the predisease stage. The high gastrointestinal symptom scores in the osteopenia and osteoporosis groups in this study may indicate that gastrointestinal disorders lead to impaired bone health. Intestinal inflammation can adversely affect the accumulation of bone minerals [ 31 – 33 ], and patients with celiac disease have many gastrointestinal symptoms and are prone to osteoporosis or osteopenia [ 34 ]. Decreased bone density is a common consequence of gastrointestinal disease and in turn leads to a decreased quality of life in postmenopausal osteoporosis patients. Osteoporosis in postmenopausal women can lead to pain, fracture, and spinal deformation and may also be accompanied by sleep disorders, hot flashes, and night sweats, which can trigger anxiety, fear, depression, and other adverse psychological states. In this study, the PCS score of the normal group was greater than that of the osteopenia group and osteoporosis group. These findings indicate that elderly postmenopausal women with osteopenia or osteoporosis were more likely to suffer pain and fracture than were those with normal bone mass, which could lead to a decrease in the self-care ability and mobility of postmenopausal women. In this study, the normal group had the highest MCS, the osteopenia group had the second highest score, and the osteoporosis group had the lowest score. Bone pain, sleep disorders, and night sweats caused by reduced bone density can increase the susceptibility of postmenopausal osteoporosis patients to depression and anxiety, which can seriously affect their mental health. TPIN is a specific marker of type I collagen deposition that is formed by shearing off the amino-terminal prepeptide of type I procollagen under the action of protease during the formation of bone organic type I collagen; moreover, TPIN is a specific marker of type I collagen deposition. It can be used as a metabolite to directly assess the activity of osteoblasts after entry into the bloodstream and can sensitively reflect the state of bone formation in the whole body [ 35 ]. Alkaline phosphatase, a widely distributed membrane-bound glycoprotein, is also a marker of bone formation [ 36 ]. β-CTX is a known marker of bone resorption and reflects the degree of bone matrix degradation. Mature type I collagen in the bone matrix degrades into β-CTX and is released into the blood during bone metabolism [ 37 ]. In this study, all three groups of subjects were postmenopausal women, which may be the reason why there was no statistical difference in the above indicators. However, it can be seen from the data that the TP1N and CROSSL of the three groups were higher than the normal reference range, the patients all exhibited high bone turnover; the bone absorption indices (β-CTX) of patients in the osteoporosis group and osteopenia group were lower than those of patients in the normal group; and the bone formation indices (TP1NP) were greater than those of patients in the normal group. These findings may be related to the low bone mass of postmenopausal women, which further activated the high bone turnover state and resulted in more active osteoblasts and osteoclasts. Although TP1NP and β-CTX do not predict osteoporosis risk in postmenopausal women, Vasikaran et al. collated evidence from prospective PubMed studies published between 2001 and 2010 and concluded that bone turnover markers can predict fracture risk independently of other risk factors in postmenopausal women [ 38 ]. Therefore, it is meaningful to use these two indicators to evaluate bone health in postmenopausal women. 25(OH)D is the main source of vitamin D in the body, it is involved in regulating the metabolism of calcium and phosphorus, and is one of the essential components for intestinal calcium and phosphorus absorption and bone mineralization [ 39 ]; in addition, it can promote the activity of calcium and phosphorus, osteoblast and osteoclast proliferation [ 40 ].In this study, since all three groups of subjects were postmenopausal women, the serum 25(OH)D level remained below normal in the normal group, although it was significantly greater than that in the osteopenia and osteoporosis groups. Lieben et al. reported that the levels of calcium and 25(OH)D in the body are related to the quality and content of bone [ 41 ]. A low serum 25(OH)D concentration is considered an important risk factor for low BMD, and the serum 25(OH)D concentration in the lumbar spine and femoral neck of premenopausal women is positively correlated with BMD [ 42 ]. Therefore, increasing the serum 25(OH)D concentration is relevant for the prevention of osteoporosis in postmenopausal women. Protein malnutrition reduces bone mass and changes muscle strength, leading to the development of osteoporotic fractures [ 43 ]. The results from the First National Health and Nutrition Examination Survey (NHANES I) also showed that hip fractures were more common in patients with low energy intake and low serum ALB levels [ 44 ]. In this study, the concentrations of total protein, albumin and prealbumin in the osteoporosis group were lower than those in the osteopenia group and much lower than those in the normal group, and the reduction in protein led to a reduction in BMD, which was confirmed by the correlation analysis results. Conclusion According to our cross-sectional analysis, there were negative correlations between intestinal health and osteoporosis risk in postmenopausal women, and there were significant positive correlations between PCS, MCS, 25(OH)D, TP, ALB and Pa and osteoporosis risk in postmenopausal women. TP1NP was higher in postmenopausal women with reduced BMD and β-CTX was lower than in postmenopausal women with normal BMD. These findings suggest that clinical health care providers should monitor the gastrointestinal health of postmenopausal women in their work. For this group of people, we can consider increasing the detection of biological indicators such as bone turnover markers and serum proteins during physical health examinations to help individuals identify and screen for osteoporosis at an early stage and provide individualized health education in a targeted manner, suggesting that they treat gastrointestinal diseases in a timely manner and improve gastrointestinal symptoms. Patients should also be instructed to consume appropriate amounts of vitamin D, calcium and protein, which are clinically useful for the prediction, diagnosis and treatment of osteoporosis. Abbreviations BMD，Bone mineral density BTM, Bone turnover marker ALP, Alkaline phosphatase TP1NP, Total procollagen type 1 N‐terminal propeptide β-CTX, β-collagen degradation product Ca, Calcium P, Phosphorus 25(OH)D, 25 hydroxyvitamin D IBD, Inflammatory bowel disease ALB, Albumin TP, Total protein Pa, Prealbumin GSRS, Gastrointestinal Symptom Rating Scale SF-12, Short Form 12 PCS, Physical Component Summary MCS, Mental Component Summary BMI, Body mass index Declarations Ethical approval The study protocol was approved by the Medical Ethics Committee of Jiangnan University (Ethical Review Number: JNU20220310IRB42). The study was conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants to participate in the research. Data availability The data that support the findings of this study are available from the corresponding author upon reasonable request. Conflict of interest The authors declare no conflict of interest. Funding This work was supported by grants of the National Key Research and Development Program of China (2023YFF1104305), the National Natural Science Foundation of China (32101033), the Natural Science Foundation of Jiangsu Province (BK20210060; BK20210468), the Key Research project of Health Commission of Jiangsu Province (K2023004; M2021055), Wuxi Science and Technology Bureau,“Taihu Light” Science and Technology Research program (Y2021001; K20221026), Key discipline construction program of Wuxi Commission of Health (CXTD2021003), “Shuangbai Talents” research program of Wuxi Commission of Health (HB2023061; HB2023062; HB2023063), Clinical Research and translational medicine research program of Affiliated Hospital of Jiangnan University (LCYJ202303; LCYJ202347; LCYJ202322; LCYJ202310), Medical research projects in research oriented hospitals of Affiliated Hospital of Jiangnan University (YJZ202305)． Authors' contributions WH and LD designed the research. WH, QJX completed data collection and analysis. LD, WH, YJA, YJ, WYY, SJ participated in the discussion. WH wrote a manuscript. CH, ZF and WXS revised the manuscript and confirmed the final draft with LD. All authors approved the submitted and final version. Acknowledgements The authors sincerely thank the patients from Affiliated Hospital of Jiangnan University who participated in this study. The authors also thank all the staff who participated in this study. References Li J, et al. The relationship between bone marrow adipose tissue and bone metabolism in postmenopausal osteoporosis. Cytokine Growth Factor Rev 2020, 52 : 88-98. Riggs BL, et al. Sex steroids and the construction and conservation of the adult skeleton. Endocr Rev 2002, 23 : 279-302. Imanpour A, et al. In silico engineering and simulation of RNA interferences nanoplatforms for osteoporosis treating and bone healing promoting. Sci Rep 2023, 13 : 18185. Russow G, et al. Anabolic Therapies in Osteoporosis and Bone Regeneration. Int J Mol Sci 2018, 20. Yoon H, et al. Association between body fat and bone mineral density in Korean adults: a cohort study. Sci Rep 2023, 13 : 17462. Oh S, et al. Evaluation of deep learning-based quantitative computed tomography for opportunistic osteoporosis screening. Sci Rep 2024, 14 : 363. Roux C, et al. Osteoporosis in 2017: Addressing the crisis in the treatment of osteoporosis. Nat Rev Rheumatol 2018, 14 : 67-68. Zhang M, et al. Peroxisome proliferator-activated receptors regulate the progression and treatment of gastrointestinal cancers. Front Pharmacol 2023, 14 : 1169566. Yelencich E, et al. Avoidant Restrictive Food Intake Disorder Prevalent Among Patients With Inflammatory Bowel Disease. Clin Gastroenterol Hepatol 2022, 20 : 1282-1289.e1281. Huang W, et al. Association of geriatric nutritional risk index with the risk of osteoporosis in the elderly population in the NHANES. Front Endocrinol (Lausanne) 2022, 13 : 965487. Qing B, et al. Association between geriatric nutrition risk index and bone mineral density in elderly Chinese people. Arch Osteoporos 2021, 16 : 55. Scibora LM. Skeletal effects of bariatric surgery: examining bone loss, potential mechanisms and clinical relevance. Diabetes Obes Metab 2014, 16 : 1204-1213. Card T, et al. Hip fractures in patients with inflammatory bowel disease and their relationship to corticosteroid use: a population based cohort study. Gut 2004, 53 : 251-255. Sigurdsson GV, et al. Young Adult Male Patients With Childhood-onset IBD Have Increased Risks of Compromised Cortical and Trabecular Bone Microstructures. Inflamm Bowel Dis 2023, 29 : 1065-1072. Zhang Z, et al. Association of hepatic/pancreatic iron overload evaluated by quantitative T2* MRI with bone mineral density and trabecular bone score. BMC Endocr Disord 2023, 23 : 2. Revicki DA, et al. Reliability and validity of the Gastrointestinal Symptom Rating Scale in patients with gastroesophageal reflux disease. Qual Life Res 1998, 7 : 75-83. Jenkinson C, et al. Development and testing of the UK SF-12 (short form health survey). J Health Serv Res Policy 1997, 2 : 14-18. Farivar SS, et al. Correlated physical and mental health summary scores for the SF-36 and SF-12 Health Survey, V.I. Health Qual Life Outcomes 2007, 5 : 54. Geiker NRW, et al. Impact of whole dairy matrix on musculoskeletal health and aging-current knowledge and research gaps. Osteoporos Int 2020, 31 : 601-615. Cooper C, et al. Hip fractures in the elderly: a world-wide projection. Osteoporos Int 1992, 2 : 285-289. Li Y. Association between obesity and bone mineral density in middle-aged adults. J Orthop Surg Res 2022, 17 : 268. De Laet C, et al. Body mass index as a predictor of fracture risk: a meta-analysis. Osteoporos Int 2005, 16 : 1330-1338. Johansson H, et al. A meta-analysis of the association of fracture risk and body mass index in women. J Bone Miner Res 2014, 29 : 223-233. Halade GV, et al. Obesity-mediated inflammatory microenvironment stimulates osteoclastogenesis and bone loss in mice. Exp Gerontol 2011, 46 : 43-52. Kim KK, et al. The Efficacy of Low Molecular Weight Heparin for the Prevention of Venous Thromboembolism after Hip Fracture Surgery in Korean Patients. Yonsei Med J 2016, 57 : 1209-1213. Reid IR. Fat and bone. Arch Biochem Biophys 2010, 503 : 20-27. Migliaccio S, et al. Is obesity in women protective against osteoporosis? Diabetes Metab Syndr Obes 2011, 4 : 273-282. Matthews VL, et al. Soy milk and dairy consumption is independently associated with ultrasound attenuation of the heel bone among postmenopausal women: the Adventist Health Study-2. Nutr Res 2011, 31 : 766-775. Suntornsaratoon P, et al. Running exercise with and without calcium supplementation from tuna bone reduced bone impairment caused by low calcium intake in young adult rats. Sci Rep 2023, 13 : 9568. Tang G, et al. Low BMI, blood calcium and vitamin D, kyphosis time, and outdoor activity time are independent risk factors for osteoporosis in postmenopausal women. Front Endocrinol (Lausanne) 2023, 14 : 1154927. Tilg H, et al. Gut, inflammation and osteoporosis: basic and clinical concepts. Gut 2008, 57 : 684-694. Taranta A, et al. Imbalance of osteoclastogenesis-regulating factors in patients with celiac disease. J Bone Miner Res 2004, 19 : 1112-1121. Wójciak-Kosior M, et al. The Stimulatory Effect of Strontium Ions on Phytoestrogens Content in Glycine max (L.) Merr. Molecules 2016, 21 : 90. Kurppa K, et al. Gastrointestinal symptoms, quality of life and bone mineral density in mild enteropathic coeliac disease: a prospective clinical trial. Scand J Gastroenterol 2010, 45 : 305-314. Krege JH, et al. PINP as a biological response marker during teriparatide treatment for osteoporosis. Osteoporos Int 2014, 25 : 2159-2171. Song M, et al. METTL3/YTHDC1-medicated m6A modification of circRNA3634 regulates the proliferation and differentiation of antler chondrocytes by miR-124486-5-MAPK1 axis. Cell Mol Biol Lett 2023, 28 : 101. Zhang H, et al. A Phase I, Randomized, Single-Dose Study to Evaluate the Biosimilarity of QL1206 to Denosumab Among Chinese Healthy Subjects. Front Pharmacol 2020, 11 : 01329. Vasikaran S, et al. Markers of bone turnover for the prediction of fracture risk and monitoring of osteoporosis treatment: a need for international reference standards. Osteoporos Int 2011, 22 : 391-420. Topan A, et al. 25 Hydroxyvitamin D Serum Concentration and COVID-19 Severity and Outcome-A Retrospective Survey in a Romanian Hospital. Nutrients 2023, 15. Si H, et al. Integrated Transcriptome and Microbiota Reveal the Regulatory Effect of 25-Hydroxyvitamin D Supplementation in Antler Growth of Sika Deer. Animals (Basel) 2022, 12. Lieben L, et al. Normocalcemia is maintained in mice under conditions of calcium malabsorption by vitamin D-induced inhibition of bone mineralization. J Clin Invest 2012, 122 : 1803-1815. Paul TV, et al. Prevalence of osteoporosis in ambulatory postmenopausal women from a semiurban region in Southern India: relationship to calcium nutrition and vitamin D status. Endocr Pract 2008, 14 : 665-671. Hamstra SI, et al. Beyond its Psychiatric Use: The Benefits of Low-dose Lithium Supplementation. Curr Neuropharmacol 2023, 21 : 891-910. Huang Z, et al. Nutrition, bone mass, and subsequent risk of hip fracture in white women. Am J Hum Biol 1998, 10 : 661-667. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board 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-4250878","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":290534834,"identity":"21db9b92-6d61-4b42-ba21-c782eebfaee1","order_by":0,"name":"Han Wang","email":"","orcid":"","institution":"Jiangnan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Wang","suffix":""},{"id":290534837,"identity":"58f907b4-1f26-4b68-bc1f-41215b599a57","order_by":1,"name":"Qiuxia Jiang","email":"","orcid":"","institution":"Affiliated Hospital of Jiangnan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qiuxia","middleName":"","lastName":"Jiang","suffix":""},{"id":290534840,"identity":"bb437f7b-ca20-483a-99b4-8893b8614a65","order_by":2,"name":"Jiai Yan","email":"","orcid":"","institution":"Affiliated Hospital of Jiangnan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiai","middleName":"","lastName":"Yan","suffix":""},{"id":290534843,"identity":"f9cd905a-3ed5-4673-9d6e-4daea8bb1490","order_by":3,"name":"Yang Ju","email":"","orcid":"","institution":"Affiliated Hospital of Jiangnan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Ju","suffix":""},{"id":290534844,"identity":"9c6cdc0c-5ad9-4870-96dc-30030d37ef6b","order_by":4,"name":"Jing Sun","email":"","orcid":"","institution":"Affiliated Hospital of Jiangnan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Sun","suffix":""},{"id":290534845,"identity":"715f9d29-67ea-46ff-a1d8-a8b21510dcb9","order_by":5,"name":"Yingyu Wang","email":"","orcid":"","institution":"Affiliated Hospital of Jiangnan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yingyu","middleName":"","lastName":"Wang","suffix":""},{"id":290534846,"identity":"f0650b29-3f52-4af0-abfc-14f352838d0f","order_by":6,"name":"Gege Huang","email":"","orcid":"","institution":"JITRI","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Gege","middleName":"","lastName":"Huang","suffix":""},{"id":290534847,"identity":"d644e115-7ffd-45cf-96aa-baf95e7c2996","order_by":7,"name":"Feng Zhang","email":"","orcid":"","institution":"Affiliated Hospital of Jiangnan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Zhang","suffix":""},{"id":290534848,"identity":"3b0bda9b-953b-4675-972e-abf2c0d42d75","order_by":8,"name":"Hong Cao","email":"","orcid":"","institution":"Affiliated Hospital of Jiangnan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Cao","suffix":""},{"id":290534849,"identity":"dea6c7d9-d18d-45ea-ad58-6023aab10398","order_by":9,"name":"Xuesong Wang","email":"","orcid":"","institution":"Affiliated Hospital of Jiangnan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xuesong","middleName":"","lastName":"Wang","suffix":""},{"id":290534850,"identity":"79cd7a6c-6108-411d-8690-c83618ee01a8","order_by":10,"name":"Dan Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIie3QsQrCMBCA4SuFuoTimFLoGwiRglOpr5Ig1KWIYzctQnwG38LJ+aRDl0rXjmZ36OhoXAVJ3Bzyz/dxyQG4XP8YgoecZcmyrWs1WhJAXhUpdM0hpdZEj4v9sJZTYiPC9oaoZOHVJyWBQp7M9gYSdRuOQma+Hwt538IqXaCBMCyZJkUQxOLIKKC4GEn/eJOGkOgqKbEig97Cu4ZS6lmSaNBb9JEZI0IfmVn8JezLuXqybHeetEqNVZ4Yyec7fxt3uVwu15dedFlLVpvTxxYAAAAASUVORK5CYII=","orcid":"","institution":"Affiliated Hospital of Jiangnan University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Dan","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-04-11 07:35:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4250878/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4250878/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55007755,"identity":"1435e6e2-f338-4414-8d94-b5da393acf85","added_by":"auto","created_at":"2024-04-19 19:00:52","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":22468,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the study. BMD, bone mineral density.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4250878/v1/154a489f195c00c404ef9d49.jpg"},{"id":56686416,"identity":"7cb25b45-59b7-4a65-8724-8ff4f29487c7","added_by":"auto","created_at":"2024-05-17 21:01:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1108761,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4250878/v1/c43d1d95-5b5a-4fbc-b5bc-3517a12d14b9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gastrointestinal health and serum proteins are associated with BMD in postmenopausal women: A cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePostmenopausal osteoporosis is the most common type of osteoporosis and is characterized by decreased bone mineral density, disintegration of the bone microstructure, increased bone fragility, and increased fracture susceptibility [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. With increasing age, the social and economic burdens of osteoporosis are steadily increasing. The proportion of women with osteoporosis increases with age, and bone mineral density (BMD) is significantly lower in postmenopausal women due to a decrease in serum estrogen levels [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As an increasingly serious public health problem, osteoporosis can seriously affect the quality of life of patients. Fractures are common adverse outcomes and include increased pain, disability, caregiving tasks, overall healthcare costs and death [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Drugs such as bisphosphonates, the monoclonal antibody denosumab, and selective estrogen receptor modulators have been approved by the United States Federal Drug Administration (FDA) for the treatment of osteoporosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], however, there is currently no drug to cure osteoporosis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and osteoporosis remains an underdiagnosed and undertreated disease [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], with the number of patients receiving treatment being much smaller than the number of patients who are eligible for treatment [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, how to recognize the risk of osteoporosis in a timely manner and prevent it effectively are currently popular research topics in the field of osteoporosis care. At present, the clinical diagnosis of postmenopausal osteoporosis relies mainly on dual-energy X-rays for examining BMD, but this examination equipment is mostly deployed by large medical centers, and such examinations are expensive; therefore, there is an urgent need for simple, noninvasive predictors for early diagnosis of this disease. Bone turnover markers (BTMs) are biomarkers of fracture risk and are used to diagnose and evaluate the effect of treatment on PMO, which include the following biomarkers: bone formation markers, including serum alkaline phosphatase (ALP) and total procollagen type 1 N-terminal propeptide (TP1NP); bone resorption markers, including β-collagen degradation product (β-CTX/β‐CROSSL); bone mineral metabolism indicators, including calcium (Ca) and phosphorus (P); and bone regulatory hormone indicators, including 25 hydroxyvitamin D (25(OH)D).\u003c/p\u003e \u003cp\u003eThe gastrointestinal tract is a very complex system that has many important functions, such as digestion, absorption, detoxification, immunity and disease resistance [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and is critical to human health, as gastrointestinal disorders affect nutritional status and bone health. Malnutrition is common in inflammatory bowel disease (IBD) patients, and a study by Yelencich et al. showed that approximately 16%~68% of IBD patients are malnourished [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A large cross-sectional study based on the National Health and Nutrition Examination Survey (NHANES) in the United States shows an inverse association between the Geriatric Nutritional Risk Index (GNRI) and the risk of osteoporosis in older adults [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. GNRI values were also found to increase with BMD in the Chinese population [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. BMD in the proximal femoral region (including the femoral neck and total hip) decreased by 12% in patients with nutritional limitations due to gastrectomy [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne cohort study revealed that the hip fracture rate in IBD patients was approximately 60% greater than that in matched controls [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Similarly, another study revealed that IBD patients had lower BMD outcomes and significant bone loss in both cortical and trabecular bone compared to healthy controls [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven the above background, we conducted this study to explore the relationships of gastrointestinal health and nutritional status with bone health in postmenopausal women, providing new ideas for the prevention and treatment of postmenopausal osteoporosis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and study design\u003c/h2\u003e \u003cp\u003eA total of 222 postmenopausal female patients who underwent BMD examination at the Affiliated Hospital of Jiangnan University from January 2023 to September 2023 were recruited. Patients who had less than one year of menopause, secondary osteoporosis, or recent use of medications that may affect bone metabolism (such as thiazolidinediones, immunosuppressants, systemic glucocorticoids, or hormone replacement therapy) were excluded. According to the standards of the World Health Organization (WHO) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], the subjects were divided into three groups according to their BMD status: the normal group (T\u0026ge;-1), the osteopenia group (-2.5\u0026thinsp;\u0026lt;\u0026thinsp;T \u0026lt; -1) and the osteoporosis group (T\u0026le;-2.5) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSerum biochemical index measurements\u003c/h2\u003e \u003cp\u003ePeripheral venous blood was collected from all subjects and sent to the laboratory department for measurement. The serum ALP, Ca, P, albumin (ALB), total protein (TP), and prealbumin (Pa) concentrations were determined via an automatic biochemical analyzer. 25(OH)D, TP1NP, and β-CTX were detected by electrochemiluminescence. The normal reference values of these indicators are as follows: 25(OH)D (\u0026ge;\u0026thinsp;30 ng/mL); TP1NP (16\u0026ndash;55 ng/mL); β-CTX (0-0.573 ng/mL); Ca (2.11\u0026ndash;2.52 mmol/L); P (0.85\u0026ndash;1.51 mmol/L); ALP (50-135U/L); ALB (40\u0026ndash;55 g/L); TP (65\u0026ndash;85 g/L); and Pa (180\u0026ndash;350 mg/L).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eBMD measurements\u003c/h2\u003e \u003cp\u003eBone densitometry was performed by the staff of the Department of Imaging at Jiangnan University using dual-energy X-ray absorptiometry (DXA). All tests were performed using the same instrument. The technicians were uniformly and formally trained, and the testing instrument was calibrated daily to rule out human error. The BMD machine was warmed by the staff, and a standardized periosteum was scanned point-to-point to obtain measurements at three fixed points (left femoral neck, total hip, and lumbar vertebrae L1-L4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eGastrointestinal symptoms and quality of life\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eGastrointestinal function assessment\u003c/h2\u003e \u003cp\u003eGastrointestinal symptoms were assessed using the Gastrointestinal Symptom Rating Scale (GSRS) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], which has 15 items covering five different gastrointestinal symptoms: reflux, abdominal pain, diarrhea, constipation, and dyspepsia; each item is divided into 4 options. The higher the score is, the more severe the gastrointestinal symptoms and the poorer the patient's intestinal health status.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eQuality of life assessment\u003c/h2\u003e \u003cp\u003eThe subjects' quality of life was assessed using the Short Form 12 (SF-12) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], 8 dimensions: physical functioning (PF), role physical (RP), bodily pain (BP), general health (GH), vitality (VT), social functioning (SF), role emotional (RE) and mental health (MH). Based on the domains, two summary measures may be estimated\u0026mdash;the Physical Component Summary (PCS) and Mental Component Summary (MCS). The scale is scored on a percentage scale, and after the crude score is obtained, the scale is transformed to a standardized scale [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The higher the score, the higher the quality of life of the patient.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll the data were statistically analyzed using SPSS25.0. The Kolmogorov‒Smirnov test was used to test the normality of the distribution. Continuous variables are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation) and were compared between groups using one-way analysis of variance (ANOVA). The categorical variables are expressed as percentages (%) and were compared using the chi-square test. Pearson\u0026rsquo;s correlation coefficient and multiple linear stepwise regression analysis were used to examine the relationships between the variables. Before the multiple linear regression analysis, the data were tested for multicollinearity, and the variance expansion coefficient (VIF) (\u0026le;\u0026thinsp;10) indicated that independent variables could be included in the multivariate analysis. A P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to indicate statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eA total of 230 postmenopausal women who met the criteria were enrolled in this study, including 58 postmenopausal women with normal bone mass, 80 postmenopausal women with osteopenia and 84 postmenopausal women with osteoporosis. There were significant differences between the normal group, osteopenia group and osteoporosis group in age, education level, body mass index (BMI), dietary habits and exercise frequency (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The ages of the individuals in the normal, osteopenia and osteoporosis groups increased successively, and their BMI decreased successively. Greater bone loss occurred with age, and BMD was protected at higher BMIs within the normal range. The proportion of individuals in the normal group with a high school education and above was 36.2%, which was significantly greater than that in the osteopenia group (17.6%) and osteoporosis group (6%), and the proportions of individuals who supplemented with soy products, dairy products, and nutritional supplements (calcium, vitamin D, etc.) were greater than those in the osteopenia and osteoporosis groups. The frequency of exercise was significantly greater in the normal group than in the osteopenia and osteoporosis groups. Differences in diet and exercise may be related to differences in education. No significant differences were detected in the presence of other variables (such as alcohol consumption, smoking status, or common chronic diseases [hypertension, diabetes, and osteoarthritis]) among the three groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the normal, osteopenia, and osteoporosis groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eF value/\u003c/p\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;58)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOsteopenia\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOsteoporosis\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;84)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.62\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.73\u0026thinsp;\u0026plusmn;\u0026thinsp;8.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.29\u0026thinsp;\u0026plusmn;\u0026thinsp;9.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary and below\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(24.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27(33.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50(59.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e33.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eJunior high school\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(39.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39(48.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29(34.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSenior high school\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(15.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(4.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUniversity and above\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(20.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(1.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.92\u0026thinsp;\u0026plusmn;\u0026thinsp;3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.56\u0026thinsp;\u0026plusmn;\u0026thinsp;3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.82\u0026thinsp;\u0026plusmn;\u0026thinsp;3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChronic diseases\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33(56.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33(41.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38(45.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(43.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47(58.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46(54.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSoy products\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27(46.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(28.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e17.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31(53.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57(71.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72(85.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDairy products\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38(65.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(21.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e28.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52(65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66(78.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003esupplements\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(41.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(16.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e20.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34(58.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67(83.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80(95.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58(100.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80(100.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84(100.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrinking status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.410\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58(100.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79(98.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84(100.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExercise frequency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0\u0026ndash;1 times\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(8.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(27.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54(64.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e83.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u0026ndash;2 times\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(22.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36(45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21(25%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u0026ndash;4 times\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(7.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;5 times\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22(37.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(7.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(3.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eP values were calculated using the chi-square test for categorical variables and one-way analysis of variance for continuous variables.\u003c/p\u003e \u003cp\u003e\u0026ldquo;***\u0026rdquo; P\u0026thinsp;\u0026le;\u0026thinsp;0.001.\u003c/p\u003e \u003cp\u003eBMI, body mass index; BMD, bone mineral density.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGastrointestinal health of postmenopausal female subjects\u003c/h2\u003e \u003cp\u003eThere were significant differences in gastrointestinal health among the normal, osteopenia and osteoporosis groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Gastrointestinal symptom scores were significantly lower in the normal group than in the osteopenia and osteoporosis groups. The patients in the osteoporosis group had the highest gastrointestinal symptom scores of the three groups, and their gastrointestinal health was the worst.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferences in gastrointestinal function among the normal group, osteopenia group and osteoporosis group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGSRS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eF value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNormal\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;58)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eOsteopenia\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;80)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eOsteoporosis\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;84)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGSRS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e16.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e20.18\u0026thinsp;\u0026plusmn;\u0026thinsp;2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e20.43\u0026thinsp;\u0026plusmn;\u0026thinsp;2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOne-way analysis of variance.\u003c/p\u003e \u003cp\u003e\u0026ldquo;***\u0026rdquo; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003cp\u003eGSRS, Gastrointestinal Symptom Rating Scale.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eQuality of life in postmenopausal female subjects\u003c/h2\u003e \u003cp\u003eThe normal, osteopenia and osteoporosis groups significantly differed (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the eight dimensions as well as in the combined physical and mental scores (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among them, the osteoporosis group had the worst physical health, and the PCS score was significantly lower than that of the osteopenia group; the highest PCS score was in the normal group. The normal group also had the highest MCS score, which was significantly greater than that of the osteopenia group. Additionally, subjects in the osteoporosis group had the lowest MCS score among the three groups, with more severe negative emotions. Overall, the individuals in the osteoporosis group were strongly affected by the disease and had a poor quality of life.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferences in quality of life among the normal, osteopenia, and osteoporosis groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSF-12\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eF value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;58)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOsteopenia\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOsteoporosis\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;84)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e42.47\u0026thinsp;\u0026plusmn;\u0026thinsp;9.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e35.31\u0026thinsp;\u0026plusmn;\u0026thinsp;7.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e29.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e53.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e50.35\u0026thinsp;\u0026plusmn;\u0026thinsp;8.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e43.79\u0026thinsp;\u0026plusmn;\u0026thinsp;10.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e44.47\u0026thinsp;\u0026plusmn;\u0026thinsp;11.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e37.14\u0026thinsp;\u0026plusmn;\u0026thinsp;12.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e32.17\u0026thinsp;\u0026plusmn;\u0026thinsp;12.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e67.26\u0026thinsp;\u0026plusmn;\u0026thinsp;83.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e39.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e35.04\u0026thinsp;\u0026plusmn;\u0026thinsp;20.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e44.09\u0026thinsp;\u0026plusmn;\u0026thinsp;8.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e39.48\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e32.94\u0026thinsp;\u0026plusmn;\u0026thinsp;8.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e51.84\u0026thinsp;\u0026plusmn;\u0026thinsp;5.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e45.71\u0026thinsp;\u0026plusmn;\u0026thinsp;6.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e41.61\u0026thinsp;\u0026plusmn;\u0026thinsp;6.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e55.24\u0026thinsp;\u0026plusmn;\u0026thinsp;5.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e51.47\u0026thinsp;\u0026plusmn;\u0026thinsp;5.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e45.64\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e44.38\u0026thinsp;\u0026plusmn;\u0026thinsp;10.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e42.68\u0026thinsp;\u0026plusmn;\u0026thinsp;7.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e36.13\u0026thinsp;\u0026plusmn;\u0026thinsp;10.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePCS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e41.96\u0026thinsp;\u0026plusmn;\u0026thinsp;18.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e41.64\u0026thinsp;\u0026plusmn;\u0026thinsp;8.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e35.62\u0026thinsp;\u0026plusmn;\u0026thinsp;9.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMCS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e58.68\u0026thinsp;\u0026plusmn;\u0026thinsp;38.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e44.41\u0026thinsp;\u0026plusmn;\u0026thinsp;9.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e40.28\u0026thinsp;\u0026plusmn;\u0026thinsp;12.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOne-way analysis of variance.\u003c/p\u003e \u003cp\u003e\u0026ldquo;**\u0026rdquo; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u0026ldquo;***\u0026rdquo; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003cp\u003eSF-12, Short Form 12; GH, general health; PF, physical functioning; RP, role-physical; BP, body pain; VT, vitality; SF, social functioning; RE, role-emotional; MH, mental health; MCS includes SF, RE, ME, and VT; PCS includes GH, PF, RP, and BP.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eComparison of serum biochemical indices in postmenopausal female subjects\u003c/h2\u003e \u003cp\u003eSignificant differences in 25(OH)D, TP, ALB, and Pa were found among the normal, osteopenia, and osteoporosis groups (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Serum 25(OH)D and protein levels were significantly lower in subjects with reduced bone mass than in subjects with normal bone mass, and malnutrition affects bone health. Although the differences in TP1NP and β-CTX among the three groups were not significant, the TP1NP levels in the osteopenia group and the osteopenia group were lower than those in the normal group, and the β-CTX was greater than that in the normal group, which led to an increase in bone conversion.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferences in the serum biochemical indices among the normal, osteopenia and osteoporosis groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSerum indices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eF value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;58)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eOsteopenia\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOsteoporosis\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;84)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e25(OH)D\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e20.73\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.81\u0026thinsp;\u0026plusmn;\u0026thinsp;4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.15\u0026thinsp;\u0026plusmn;\u0026thinsp;5.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTP1NP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e60.58\u0026thinsp;\u0026plusmn;\u0026thinsp;40.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.62\u0026thinsp;\u0026plusmn;\u0026thinsp;79.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72.09\u0026thinsp;\u0026plusmn;\u0026thinsp;59.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eβ-CTX\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e70.31\u0026thinsp;\u0026plusmn;\u0026thinsp;5.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.98\u0026thinsp;\u0026plusmn;\u0026thinsp;7.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.46\u0026thinsp;\u0026plusmn;\u0026thinsp;6.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e41.92\u0026thinsp;\u0026plusmn;\u0026thinsp;3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.99\u0026thinsp;\u0026plusmn;\u0026thinsp;4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.95\u0026thinsp;\u0026plusmn;\u0026thinsp;4.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e250.41\u0026thinsp;\u0026plusmn;\u0026thinsp;46.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e237\u0026thinsp;\u0026plusmn;\u0026thinsp;47.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e220.12\u0026thinsp;\u0026plusmn;\u0026thinsp;46.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e85.86\u0026thinsp;\u0026plusmn;\u0026thinsp;36.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.68\u0026thinsp;\u0026plusmn;\u0026thinsp;32.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89.73\u0026thinsp;\u0026plusmn;\u0026thinsp;60.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.65\u0026thinsp;\u0026plusmn;\u0026thinsp;21.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.69\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOne-way analysis of variance.\u003c/p\u003e \u003cp\u003e\u0026ldquo;**\u0026rdquo; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u0026ldquo;***\u0026rdquo; P\u0026thinsp;\u0026le;\u0026thinsp;0.001.\u003c/p\u003e \u003cp\u003e25(OH)D, 25-hydroxyvitamin D; TP1NP, total procollagen type 1 N-terminal propeptide; β-CTX, β-collagen degradation product aps; TP, total protein; ALB, albumin; Pa, prealbumin; ALP, alkaline phosphatase; Ca, calcium; P, phosphorus.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAssociations of BMD with gastrointestinal health, quality of life, and serum marker levels\u003c/h2\u003e \u003cp\u003eThere were correlations between gastrointestinal health, quality of life, bone turnover markers, protein nutritional levels and BMD in postmenopausal female subjects (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Gastrointestinal health and BMD in postmenopausal female subjects were negatively correlated, and the more gastrointestinal symptoms was associated with lower levels of BMD; There were significant positive correlations of PCS, MCS, 25(OH)D, TP, ALB, and Pa with BMD in postmenopausal female subjects, and the high quality of life of the subjects was suggestive of their comparative bone health, and the subjects with high levels of 25(OH)D and protein also had greater BMD. There were no significant correlations of TP1NP, β-CTX, ALP, Ca, or P with BMD in postmenopausal women (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation analysis of the quality of the GSRS score, SF-12 score, serum indices and BMD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003er value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGSRS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.518\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePCS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.194\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMCS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.305\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e25(OH)D\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.531\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTP1NP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eβ-CTX\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.324\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.341\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.259\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003ePearson correlation analysis\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e25(OH)D, 25-hydroxyvitamin D; TP1NP, total procollagen type 1 N-terminal propeptide; β-CTX, β-collagen degradation product aps; TP, total protein; ALB, albumin; Pa, prealbumin; ALP, alkaline phosphatase; Ca, calcium; P, phosphorus.\u003c/p\u003e \u003cp\u003e** The correlation was significant at the 0.01 level (two-tailed).\u003c/p\u003e \u003cp\u003e* The correlation was significant at the 0.05 level (two-tailed).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLogistic regression prediction of osteoporosis in postmenopausal women\u003c/h2\u003e \u003cp\u003eA collinearity diagnosis was performed prior to multivariate logistic regression analysis. All the VIFs calculated by SPSS25.0 were less than 5, indicating that the included variables do not have serious collinearity and can be entered into multiple linear regression analysis. With BMD as the dependent variable, age, BMI, soy supplementation, dairy supplementation, exercise frequency, gastrointestinal symptoms, and the 25(OH)D concentration could significantly predict BMD (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These variables explained 66.7% of the observed changes in BMD in postmenopausal women. Older age and a lower body mass index were associated with a greater risk of osteoporosis; postmenopausal women with more gastrointestinal symptoms were more prone to osteoporosis. Those with higher 25(OH)D levels had a lower risk of osteoporosis; postmenopausal women who supplemented with soy products, supplemented with dairy products and exercised regularly had a lower risk of osteoporosis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression analysis of risk factors for osteoporosis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.008**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSoy products\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDairy products\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.017*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNutritional supplements\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExercise frequency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGSRS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e25(OH)D\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.570E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDependent variable\u0026thinsp;=\u0026thinsp;BMD; B\u0026thinsp;=\u0026thinsp;unstandardized regression coefficient; SE\u0026thinsp;=\u0026thinsp;standard error; Beta\u0026thinsp;=\u0026thinsp;standardized regression coefficient; t\u0026thinsp;=\u0026thinsp;t-statistic; CI\u0026thinsp;=\u0026thinsp;confidence interval.\u003c/p\u003e \u003cp\u003e\u0026ldquo;**\u0026rdquo; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u0026ldquo;**\u0026rdquo; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u0026ldquo;***\u0026rdquo; P\u0026thinsp;\u0026le;\u0026thinsp;0.001.\u003c/p\u003e \u003cp\u003e25(OH)D, 25-hydroxyvitamin D; TP1NP, total type I collagen; TP, total protein; ALB, albumin; Pa, prealbumin.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWith the prolongation of the average human life expectancy and population aging, osteoporosis has become an important problem affecting the health of all humankind. There are more than 200\u0026nbsp;million people suffering from osteoporosis worldwide, and its incidence increases with age. Postmenopausal elderly women have the greatest prevalence of osteoporosis [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. After menopause, bone density decreases by 2.5% per year, while premenopausal bone density decreases by approximately 0.13% per year [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003csup\u003e; thus\u003c/sup\u003e, the premenopausal period is key for preventing and delaying the development of osteoporosis. Postmenopausal osteoporosis is affected by a variety of risk factors, and early symptoms are not obvious. The early identification of relevant risk factors and screening of high-risk groups should be prioritized in the clinic so that efforts can be made to control the loss of bone mass by adjusting lifestyle habits, dietary structure, and other measures, thus reducing the incidence of osteoporosis.\u003c/p\u003e \u003cp\u003eIn addition to the effects of age gain and sex hormone reduction, factors such as gastrointestinal health, nutritional status, and bone conversion indices should not be ignored. In this study, postmenopausal women's age, BMI, literacy, dietary habits, exercise frequency, gastrointestinal symptoms, 25(OH)D, total protein, albumin, and prealbumin were included in regression analyses, with BMD as the dependent variable. The results showed that postmenopausal women's age, BMI, whether or not they supplemented with soybean products, exercise frequency, gastrointestinal health status, and serum 25(OH)D concentration could significantly predict BMD.\u003c/p\u003e \u003cp\u003ePrevious studies have shown that BMI can be used as an independent protective factor for BMD [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e],but the relationship between BMI and osteoporosis has not been consistent among existing studies [\u003cspan additionalcitationids=\"CR23 CR24 CR25\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].In this study, the risk of osteoporosis increased with decreasing BMI. Although the BMI of the normal group was significantly greater than that of the osteopenia group and osteoporosis group, the BMI of the patients in each group was still within the normal range or was slightly overweight and did not reach the obesity level. The reason for the low risk of osteoporosis in the normal group under these conditions may be that adipose tissue can produce aromatase, which synthesizes estrogen outside the gonads and protects the bones [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Therefore, it is recommended that postmenopausal women try to gain weight within the normal BMI range.\u003c/p\u003e \u003cp\u003eDietary habits and exercise affect bone health. Studies have shown that supplementation with soy products, dairy products, nutritional supplements (calcium and vitamin D, etc.), and adherence to exercise have positive effects on BMD and bone metabolism[\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Among the participants included in this study, compared with those in the low-BMD population, those in the high-BMD population had relatively higher education levels and may have been more familiar with health care, which may have led to better lifestyle habits, such as the habit of consuming soy products, dairy products, nutritional supplements (calcium and vitamin D, etc.), and exercising regularly, which would result in healthier bones. Therefore, it is necessary to strengthen health education for middle-aged and elderly women with low educational attainment, promote reasonable supplementation with soy and dairy products and other nutritional supplements such as calcium and vitamin D, and increase the amount of exercise appropriately to prevent the occurrence of osteoporosis.\u003c/p\u003e \u003cp\u003eThe presence of gastrointestinal symptoms indicated that a subject had gastrointestinal disease or was in the predisease stage. The high gastrointestinal symptom scores in the osteopenia and osteoporosis groups in this study may indicate that gastrointestinal disorders lead to impaired bone health. Intestinal inflammation can adversely affect the accumulation of bone minerals [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], and patients with celiac disease have many gastrointestinal symptoms and are prone to osteoporosis or osteopenia [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Decreased bone density is a common consequence of gastrointestinal disease and in turn leads to a decreased quality of life in postmenopausal osteoporosis patients. Osteoporosis in postmenopausal women can lead to pain, fracture, and spinal deformation and may also be accompanied by sleep disorders, hot flashes, and night sweats, which can trigger anxiety, fear, depression, and other adverse psychological states. In this study, the PCS score of the normal group was greater than that of the osteopenia group and osteoporosis group. These findings indicate that elderly postmenopausal women with osteopenia or osteoporosis were more likely to suffer pain and fracture than were those with normal bone mass, which could lead to a decrease in the self-care ability and mobility of postmenopausal women. In this study, the normal group had the highest MCS, the osteopenia group had the second highest score, and the osteoporosis group had the lowest score. Bone pain, sleep disorders, and night sweats caused by reduced bone density can increase the susceptibility of postmenopausal osteoporosis patients to depression and anxiety, which can seriously affect their mental health.\u003c/p\u003e \u003cp\u003eTPIN is a specific marker of type I collagen deposition that is formed by shearing off the amino-terminal prepeptide of type I procollagen under the action of protease during the formation of bone organic type I collagen; moreover, TPIN is a specific marker of type I collagen deposition. It can be used as a metabolite to directly assess the activity of osteoblasts after entry into the bloodstream and can sensitively reflect the state of bone formation in the whole body [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Alkaline phosphatase, a widely distributed membrane-bound glycoprotein, is also a marker of bone formation [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. β-CTX is a known marker of bone resorption and reflects the degree of bone matrix degradation. Mature type I collagen in the bone matrix degrades into β-CTX and is released into the blood during bone metabolism [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, all three groups of subjects were postmenopausal women, which may be the reason why there was no statistical difference in the above indicators. However, it can be seen from the data that the TP1N and CROSSL of the three groups were higher than the normal reference range, the patients all exhibited high bone turnover; the bone absorption indices (β-CTX) of patients in the osteoporosis group and osteopenia group were lower than those of patients in the normal group; and the bone formation indices (TP1NP) were greater than those of patients in the normal group. These findings may be related to the low bone mass of postmenopausal women, which further activated the high bone turnover state and resulted in more active osteoblasts and osteoclasts. Although TP1NP and β-CTX do not predict osteoporosis risk in postmenopausal women, Vasikaran et al. collated evidence from prospective PubMed studies published between 2001 and 2010 and concluded that bone turnover markers can predict fracture risk independently of other risk factors in postmenopausal women [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Therefore, it is meaningful to use these two indicators to evaluate bone health in postmenopausal women.\u003c/p\u003e \u003cp\u003e25(OH)D is the main source of vitamin D in the body, it is involved in regulating the metabolism of calcium and phosphorus, and is one of the essential components for intestinal calcium and phosphorus absorption and bone mineralization [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]; in addition, it can promote the activity of calcium and phosphorus, osteoblast and osteoclast proliferation [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].In this study, since all three groups of subjects were postmenopausal women, the serum 25(OH)D level remained below normal in the normal group, although it was significantly greater than that in the osteopenia and osteoporosis groups. Lieben et al. reported that the levels of calcium and 25(OH)D in the body are related to the quality and content of bone [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. A low serum 25(OH)D concentration is considered an important risk factor for low BMD, and the serum 25(OH)D concentration in the lumbar spine and femoral neck of premenopausal women is positively correlated with BMD [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Therefore, increasing the serum 25(OH)D concentration is relevant for the prevention of osteoporosis in postmenopausal women.\u003c/p\u003e \u003cp\u003eProtein malnutrition reduces bone mass and changes muscle strength, leading to the development of osteoporotic fractures [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The results from the First National Health and Nutrition Examination Survey (NHANES I) also showed that hip fractures were more common in patients with low energy intake and low serum ALB levels [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In this study, the concentrations of total protein, albumin and prealbumin in the osteoporosis group were lower than those in the osteopenia group and much lower than those in the normal group, and the reduction in protein led to a reduction in BMD, which was confirmed by the correlation analysis results.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAccording to our cross-sectional analysis, there were negative correlations between intestinal health and osteoporosis risk in postmenopausal women, and there were significant positive correlations between PCS, MCS, 25(OH)D, TP, ALB and Pa and osteoporosis risk in postmenopausal women. TP1NP was higher in postmenopausal women with reduced BMD and β-CTX was lower than in postmenopausal women with normal BMD. These findings suggest that clinical health care providers should monitor the gastrointestinal health of postmenopausal women in their work. For this group of people, we can consider increasing the detection of biological indicators such as bone turnover markers and serum proteins during physical health examinations to help individuals identify and screen for osteoporosis at an early stage and provide individualized health education in a targeted manner, suggesting that they treat gastrointestinal diseases in a timely manner and improve gastrointestinal symptoms. Patients should also be instructed to consume appropriate amounts of vitamin D, calcium and protein, which are clinically useful for the prediction, diagnosis and treatment of osteoporosis.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMD，Bone mineral density\u003c/p\u003e\n\u003cp\u003eBTM, Bone turnover marker\u003c/p\u003e\n\u003cp\u003eALP, Alkaline phosphatase\u003c/p\u003e\n\u003cp\u003eTP1NP, Total procollagen type 1 N‐terminal propeptide\u003c/p\u003e\n\u003cp\u003e\u0026beta;-CTX,\u0026nbsp;\u0026beta;-collagen degradation product\u003c/p\u003e\n\u003cp\u003eCa, Calcium\u003c/p\u003e\n\u003cp\u003eP, Phosphorus\u003c/p\u003e\n\u003cp\u003e25(OH)D, 25 hydroxyvitamin D\u003c/p\u003e\n\u003cp\u003eIBD, Inflammatory bowel disease\u003c/p\u003e\n\u003cp\u003eALB, Albumin\u003c/p\u003e\n\u003cp\u003eTP, Total protein\u003c/p\u003e\n\u003cp\u003ePa, Prealbumin\u003c/p\u003e\n\u003cp\u003eGSRS,\u0026nbsp;Gastrointestinal Symptom Rating Scale\u003c/p\u003e\n\u003cp\u003eSF-12,\u0026nbsp;Short Form\u0026nbsp;12\u003c/p\u003e\n\u003cp\u003ePCS, Physical Component Summary\u003c/p\u003e\n\u003cp\u003eMCS, Mental Component Summary\u003c/p\u003e\n\u003cp\u003eBMI, Body mass index\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthical approval\u003c/h2\u003e\n\u003cp\u003eThe study protocol was approved by the Medical Ethics Committee of Jiangnan University (Ethical Review Number: JNU20220310IRB42). The study was conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants to participate in the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003ch3\u003eConflict of interest\u003c/h3\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by grants of the National Key Research and Development Program of China (2023YFF1104305), the National Natural Science Foundation of China (32101033), the Natural Science Foundation of Jiangsu Province (BK20210060; BK20210468), the Key Research project of Health Commission of Jiangsu Province (K2023004; M2021055), Wuxi Science and Technology Bureau,\u0026ldquo;Taihu Light\u0026rdquo; Science and Technology Research program (Y2021001; K20221026), Key discipline construction program of Wuxi Commission of Health (CXTD2021003), \u0026ldquo;Shuangbai Talents\u0026rdquo; research program of Wuxi Commission of Health (HB2023061; HB2023062; HB2023063), Clinical Research and translational medicine research program of Affiliated Hospital of Jiangnan University (LCYJ202303; LCYJ202347; LCYJ202322; LCYJ202310), Medical research projects in research oriented hospitals of Affiliated Hospital of Jiangnan University (YJZ202305)．\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eWH and LD designed the research. WH, QJX completed data collection and analysis. LD, WH, YJA, YJ, WYY, SJ participated in the discussion. WH wrote a manuscript. CH, ZF and WXS revised the manuscript and confirmed the final draft with LD. All authors approved the submitted and final version.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors sincerely thank the patients from Affiliated Hospital of Jiangnan University who participated in this study. The authors also thank all the staff who participated in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLi J, et al. The relationship between bone marrow adipose tissue and bone metabolism in postmenopausal osteoporosis. \u003cem\u003eCytokine Growth Factor Rev \u003c/em\u003e2020, 52\u003cstrong\u003e:\u003c/strong\u003e88-98.\u003c/li\u003e\n\u003cli\u003eRiggs BL, et al. Sex steroids and the construction and conservation of the adult skeleton. \u003cem\u003eEndocr Rev \u003c/em\u003e2002, 23\u003cstrong\u003e:\u003c/strong\u003e279-302.\u003c/li\u003e\n\u003cli\u003eImanpour A, et al. In silico engineering and simulation of RNA interferences nanoplatforms for osteoporosis treating and bone healing promoting. \u003cem\u003eSci Rep \u003c/em\u003e2023, 13\u003cstrong\u003e:\u003c/strong\u003e18185.\u003c/li\u003e\n\u003cli\u003eRussow G, et al. Anabolic Therapies in Osteoporosis and Bone Regeneration. \u003cem\u003eInt J Mol Sci \u003c/em\u003e2018, 20.\u003c/li\u003e\n\u003cli\u003eYoon H, et al. Association between body fat and bone mineral density in Korean adults: a cohort study. \u003cem\u003eSci Rep \u003c/em\u003e2023, 13\u003cstrong\u003e:\u003c/strong\u003e17462.\u003c/li\u003e\n\u003cli\u003eOh S, et al. Evaluation of deep learning-based quantitative computed tomography for opportunistic osteoporosis screening. \u003cem\u003eSci Rep \u003c/em\u003e2024, 14\u003cstrong\u003e:\u003c/strong\u003e363.\u003c/li\u003e\n\u003cli\u003eRoux C, et al. Osteoporosis in 2017: Addressing the crisis in the treatment of osteoporosis. \u003cem\u003eNat Rev Rheumatol \u003c/em\u003e2018, 14\u003cstrong\u003e:\u003c/strong\u003e67-68.\u003c/li\u003e\n\u003cli\u003eZhang M, et al. Peroxisome proliferator-activated receptors regulate the progression and treatment of gastrointestinal cancers. \u003cem\u003eFront Pharmacol \u003c/em\u003e2023, 14\u003cstrong\u003e:\u003c/strong\u003e1169566.\u003c/li\u003e\n\u003cli\u003eYelencich E, et al. Avoidant Restrictive Food Intake Disorder Prevalent Among Patients With Inflammatory Bowel Disease. \u003cem\u003eClin Gastroenterol Hepatol \u003c/em\u003e2022, 20\u003cstrong\u003e:\u003c/strong\u003e1282-1289.e1281.\u003c/li\u003e\n\u003cli\u003eHuang W, et al. Association of geriatric nutritional risk index with the risk of osteoporosis in the elderly population in the NHANES. \u003cem\u003eFront Endocrinol (Lausanne) \u003c/em\u003e2022, 13\u003cstrong\u003e:\u003c/strong\u003e965487.\u003c/li\u003e\n\u003cli\u003eQing B, et al. Association between geriatric nutrition risk index and bone mineral density in elderly Chinese people. \u003cem\u003eArch Osteoporos \u003c/em\u003e2021, 16\u003cstrong\u003e:\u003c/strong\u003e55.\u003c/li\u003e\n\u003cli\u003eScibora LM. Skeletal effects of bariatric surgery: examining bone loss, potential mechanisms and clinical relevance. \u003cem\u003eDiabetes Obes Metab \u003c/em\u003e2014, 16\u003cstrong\u003e:\u003c/strong\u003e1204-1213.\u003c/li\u003e\n\u003cli\u003eCard T, et al. Hip fractures in patients with inflammatory bowel disease and their relationship to corticosteroid use: a population based cohort study. \u003cem\u003eGut \u003c/em\u003e2004, 53\u003cstrong\u003e:\u003c/strong\u003e251-255.\u003c/li\u003e\n\u003cli\u003eSigurdsson GV, et al. Young Adult Male Patients With Childhood-onset IBD Have Increased Risks of Compromised Cortical and Trabecular Bone Microstructures. \u003cem\u003eInflamm Bowel Dis \u003c/em\u003e2023, 29\u003cstrong\u003e:\u003c/strong\u003e1065-1072.\u003c/li\u003e\n\u003cli\u003eZhang Z, et al. Association of hepatic/pancreatic iron overload evaluated by quantitative T2* MRI with bone mineral density and trabecular bone score. \u003cem\u003eBMC Endocr Disord \u003c/em\u003e2023, 23\u003cstrong\u003e:\u003c/strong\u003e2.\u003c/li\u003e\n\u003cli\u003eRevicki DA, et al. Reliability and validity of the Gastrointestinal Symptom Rating Scale in patients with gastroesophageal reflux disease. \u003cem\u003eQual Life Res \u003c/em\u003e1998, 7\u003cstrong\u003e:\u003c/strong\u003e75-83.\u003c/li\u003e\n\u003cli\u003eJenkinson C, et al. Development and testing of the UK SF-12 (short form health survey). \u003cem\u003eJ Health Serv Res Policy \u003c/em\u003e1997, 2\u003cstrong\u003e:\u003c/strong\u003e14-18.\u003c/li\u003e\n\u003cli\u003eFarivar SS, et al. Correlated physical and mental health summary scores for the SF-36 and SF-12 Health Survey, V.I. \u003cem\u003eHealth Qual Life Outcomes \u003c/em\u003e2007, 5\u003cstrong\u003e:\u003c/strong\u003e54.\u003c/li\u003e\n\u003cli\u003eGeiker NRW, et al. Impact of whole dairy matrix on musculoskeletal health and aging-current knowledge and research gaps. \u003cem\u003eOsteoporos Int \u003c/em\u003e2020, 31\u003cstrong\u003e:\u003c/strong\u003e601-615.\u003c/li\u003e\n\u003cli\u003eCooper C, et al. Hip fractures in the elderly: a world-wide projection. \u003cem\u003eOsteoporos Int \u003c/em\u003e1992, 2\u003cstrong\u003e:\u003c/strong\u003e285-289.\u003c/li\u003e\n\u003cli\u003eLi Y. Association between obesity and bone mineral density in middle-aged adults. \u003cem\u003eJ Orthop Surg Res \u003c/em\u003e2022, 17\u003cstrong\u003e:\u003c/strong\u003e268.\u003c/li\u003e\n\u003cli\u003eDe Laet C, et al. Body mass index as a predictor of fracture risk: a meta-analysis. \u003cem\u003eOsteoporos Int \u003c/em\u003e2005, 16\u003cstrong\u003e:\u003c/strong\u003e1330-1338.\u003c/li\u003e\n\u003cli\u003eJohansson H, et al. A meta-analysis of the association of fracture risk and body mass index in women. \u003cem\u003eJ Bone Miner Res \u003c/em\u003e2014, 29\u003cstrong\u003e:\u003c/strong\u003e223-233.\u003c/li\u003e\n\u003cli\u003eHalade GV, et al. Obesity-mediated inflammatory microenvironment stimulates osteoclastogenesis and bone loss in mice. \u003cem\u003eExp Gerontol \u003c/em\u003e2011, 46\u003cstrong\u003e:\u003c/strong\u003e43-52.\u003c/li\u003e\n\u003cli\u003eKim KK, et al. The Efficacy of Low Molecular Weight Heparin for the Prevention of Venous Thromboembolism after Hip Fracture Surgery in Korean Patients. \u003cem\u003eYonsei Med J \u003c/em\u003e2016, 57\u003cstrong\u003e:\u003c/strong\u003e1209-1213.\u003c/li\u003e\n\u003cli\u003eReid IR. Fat and bone. \u003cem\u003eArch Biochem Biophys \u003c/em\u003e2010, 503\u003cstrong\u003e:\u003c/strong\u003e20-27.\u003c/li\u003e\n\u003cli\u003eMigliaccio S, et al. Is obesity in women protective against osteoporosis? \u003cem\u003eDiabetes Metab Syndr Obes \u003c/em\u003e2011, 4\u003cstrong\u003e:\u003c/strong\u003e273-282.\u003c/li\u003e\n\u003cli\u003eMatthews VL, et al. Soy milk and dairy consumption is independently associated with ultrasound attenuation of the heel bone among postmenopausal women: the Adventist Health Study-2. \u003cem\u003eNutr Res \u003c/em\u003e2011, 31\u003cstrong\u003e:\u003c/strong\u003e766-775.\u003c/li\u003e\n\u003cli\u003eSuntornsaratoon P, et al. Running exercise with and without calcium supplementation from tuna bone reduced bone impairment caused by low calcium intake in young adult rats. \u003cem\u003eSci Rep \u003c/em\u003e2023, 13\u003cstrong\u003e:\u003c/strong\u003e9568.\u003c/li\u003e\n\u003cli\u003eTang G, et al. Low BMI, blood calcium and vitamin D, kyphosis time, and outdoor activity time are independent risk factors for osteoporosis in postmenopausal women. \u003cem\u003eFront Endocrinol (Lausanne) \u003c/em\u003e2023, 14\u003cstrong\u003e:\u003c/strong\u003e1154927.\u003c/li\u003e\n\u003cli\u003eTilg H, et al. Gut, inflammation and osteoporosis: basic and clinical concepts. \u003cem\u003eGut \u003c/em\u003e2008, 57\u003cstrong\u003e:\u003c/strong\u003e684-694.\u003c/li\u003e\n\u003cli\u003eTaranta A, et al. Imbalance of osteoclastogenesis-regulating factors in patients with celiac disease. \u003cem\u003eJ Bone Miner Res \u003c/em\u003e2004, 19\u003cstrong\u003e:\u003c/strong\u003e1112-1121.\u003c/li\u003e\n\u003cli\u003eW\u0026oacute;jciak-Kosior M, et al. The Stimulatory Effect of Strontium Ions on Phytoestrogens Content in Glycine max (L.) Merr. \u003cem\u003eMolecules \u003c/em\u003e2016, 21\u003cstrong\u003e:\u003c/strong\u003e90.\u003c/li\u003e\n\u003cli\u003eKurppa K, et al. Gastrointestinal symptoms, quality of life and bone mineral density in mild enteropathic coeliac disease: a prospective clinical trial. \u003cem\u003eScand J Gastroenterol \u003c/em\u003e2010, 45\u003cstrong\u003e:\u003c/strong\u003e305-314.\u003c/li\u003e\n\u003cli\u003eKrege JH, et al. PINP as a biological response marker during teriparatide treatment for osteoporosis. \u003cem\u003eOsteoporos Int \u003c/em\u003e2014, 25\u003cstrong\u003e:\u003c/strong\u003e2159-2171.\u003c/li\u003e\n\u003cli\u003eSong M, et al. METTL3/YTHDC1-medicated m6A modification of circRNA3634 regulates the proliferation and differentiation of antler chondrocytes by miR-124486-5-MAPK1 axis. \u003cem\u003eCell Mol Biol Lett \u003c/em\u003e2023, 28\u003cstrong\u003e:\u003c/strong\u003e101.\u003c/li\u003e\n\u003cli\u003eZhang H, et al. A Phase I, Randomized, Single-Dose Study to Evaluate the Biosimilarity of QL1206 to Denosumab Among Chinese Healthy Subjects. \u003cem\u003eFront Pharmacol \u003c/em\u003e2020, 11\u003cstrong\u003e:\u003c/strong\u003e01329.\u003c/li\u003e\n\u003cli\u003eVasikaran S, et al. Markers of bone turnover for the prediction of fracture risk and monitoring of osteoporosis treatment: a need for international reference standards. \u003cem\u003eOsteoporos Int \u003c/em\u003e2011, 22\u003cstrong\u003e:\u003c/strong\u003e391-420.\u003c/li\u003e\n\u003cli\u003eTopan A, et al. 25 Hydroxyvitamin D Serum Concentration and COVID-19 Severity and Outcome-A Retrospective Survey in a Romanian Hospital. \u003cem\u003eNutrients \u003c/em\u003e2023, 15.\u003c/li\u003e\n\u003cli\u003eSi H, et al. Integrated Transcriptome and Microbiota Reveal the Regulatory Effect of 25-Hydroxyvitamin D Supplementation in Antler Growth of Sika Deer. \u003cem\u003eAnimals (Basel) \u003c/em\u003e2022, 12.\u003c/li\u003e\n\u003cli\u003eLieben L, et al. Normocalcemia is maintained in mice under conditions of calcium malabsorption by vitamin D-induced inhibition of bone mineralization. \u003cem\u003eJ Clin Invest \u003c/em\u003e2012, 122\u003cstrong\u003e:\u003c/strong\u003e1803-1815.\u003c/li\u003e\n\u003cli\u003ePaul TV, et al. Prevalence of osteoporosis in ambulatory postmenopausal women from a semiurban region in Southern India: relationship to calcium nutrition and vitamin D status. \u003cem\u003eEndocr Pract \u003c/em\u003e2008, 14\u003cstrong\u003e:\u003c/strong\u003e665-671.\u003c/li\u003e\n\u003cli\u003eHamstra SI, et al. Beyond its Psychiatric Use: The Benefits of Low-dose Lithium Supplementation. \u003cem\u003eCurr Neuropharmacol \u003c/em\u003e2023, 21\u003cstrong\u003e:\u003c/strong\u003e891-910.\u003c/li\u003e\n\u003cli\u003eHuang Z, et al. Nutrition, bone mass, and subsequent risk of hip fracture in white women. \u003cem\u003eAm J Hum Biol \u003c/em\u003e1998, 10\u003cstrong\u003e:\u003c/strong\u003e661-667.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Postmenopausal osteoporosis, Osteopenia, Gastrointestinal symptoms, Serum protein, Bone turnover markers, Bone mass density","lastPublishedDoi":"10.21203/rs.3.rs-4250878/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4250878/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith increasing age, the social and economic burdens of postmenopausal osteoporosis are steadily increasing. This study aimed to investigate the factors that influence the development of postmenopausal osteoporosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePostmenopausal women at the Affiliated Hospital of Jiangnan University from January 2023 to December 2023 were recruited for BMD examination. The patients were divided into a normal group, an osteopenia group and an osteoporosis group according to their T value. Questionnaires, including the Gastrointestinal Symptom Rating Scale and Short Form 12, were administered through face-to-face interviews. Bone turnover markers and serum protein levels of Fasting venous blood were detected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 222 postmenopausal women met the inclusion criteria were recruited. Univariate analysis revealed statistically significant differences in age, education, BMI, supplementation with soy products, supplementation with dairy products, supplementation with other nutritional supplements, exercise frequency, gastrointestinal symptom score, quality of life, 25(OH)D, total protein, albumin and prealbumin among the three groups (P \u0026lt; 0.05). Pearson correlation analysis revealed that gastrointestinal symptoms (r = -0.518, P\u0026lt;0.01) was negatively correlated with BMD in postmenopausal women, while PCS (r= 0.194, P= 0.004), MCS (r= 0.305, P \u0026lt; 0.01), 25(OH)D (r= 0.531, P \u0026lt; 0.01), total protein (r= 0.324, P \u0026lt; 0.01), albumin (r= 0.341, P \u0026lt; 0.01) and prealbumin (r= 0.259, P \u0026lt; 0.01) were positively correlated with BMD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIncreasing age, low BMI, gastrointestinal disorders, decreased serum protein and 25(OH)D levels may contribute to an increased risk of postmenopausal osteoporosis. Supplementation with soy and dairy products and consistent exercise may prevent postmenopausal osteoporosis.\u003c/p\u003e","manuscriptTitle":"Gastrointestinal health and serum proteins are associated with BMD in postmenopausal women: A cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-19 19:00:47","doi":"10.21203/rs.3.rs-4250878/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"efd49e98-9d13-47c5-bea0-1c22dd975835","owner":[],"postedDate":"April 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-24T05:14:04+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-19 19:00:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4250878","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4250878","identity":"rs-4250878","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}