Predictive Risk Factors for Osteoporosis in Older Overweight Adults

Predictive Risk Factors for Osteoporosis in Older Overweight Adults | 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 Predictive Risk Factors for Osteoporosis in Older Overweight Adults Liang Li, Zhenggang Zhou, Jianlin Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4127118/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 Purpose To investigate significant risk factors for osteoporosis in older overweight adults, which primarily included clinical indicators and laboratory examinations. Patients and Methods: A total of 1173 participants (617 men and 556 postmenopausal women) with BMI ≥ 25 who were older than 50 and received bone density scans of the lumbar spine were enrolled in the present study. All participants had complete baseline data, including clinical indicators and biochemical indices. Participants were divided into three groups by the T-score of the lumbar spine. The Student’s t-test, Mann–Whitney U test, one-way analysis of variance, Kruskal-Wallis test and chi-square test were used to compare the continuous and categorical clinical variables among the different groups. Spearman correlation tests, Pearson correlation tests and linear regression analysis were performed to identify independent variables associated with bone mineral density (BMD) and their multicollinearity in older overweight adults. In addition, binary logistic regression analysis was performed to determine the independent risk factors associated with osteoporosis. A P-value < 0.05 was considered statistically significant. Result Compared to those in the normal group and the osteopenia group, man and postmenopausal women with osteoporosis were older and had decreased BMI (p < 0.05, respectively). Correlation analysis and multiple linear regression analysis revealed that the BMD values of the lumbar vertebrae were significantly positively correlated with BMI and serum uric acid (UA) and negatively correlated with age in men and postmenopausal women. Finally, binary logistic regression analyses revealed that after adjusting for many variables, osteoporosis was significantly and positively associated with age and inversely associated with BMI and serum UA in both men and postmenopausal women (p < 0.05, respectively). Conclusions This study demonstrates that osteoporosis might be associated with advanced age, increased BMI and higher levels of UA in older overweight adults. bone mineral density obesity serum uric acid BMI Figures Figure 1 Figure 2 Introduction Osteoporosis is one of the most common systemic diseases and is characterized by disordered bone metabolism and low bone mineral density; it affects more than 200 million people all worldwide. 1 , 2 Given the accelerated growth of the aging population, the problem of increasing numbers of older individuals with osteoporosis has become a focus of all societies in the last decades. Osteoporosis is regarded as the major cause of osteoporotic fractures and bone fragility. 3 Unfortunately, the incidence of osteoporosis is increasing, resulting in rising morbidity and mortality of osteoporotic fractures in older adults. 4 Meanwhile, osteoporotic fractures seriously impact human health and convey heavy financial burdens to families and society. The medical burden of osteoporotic fractures was reportedly higher than the projected annual costs of breast cancer, stroke, or diabetes in the United States. The increasing clinical consequences and economic burden of this disease require measures to assess individuals at high risk for osteoporosis. Assessment of existing bone mineral density (BMD), identifying individuals with high risk of osteoporosis, and making decisions regarding the appropriate intervention based on these risk factors are the ultimate goals when evaluating risk factors associated with osteoporosis. T-scores based on BMD measured by dual-energy X-ray absorptiometry are used to measure the prevalence of osteoporosis across populations. Currently, the International Society for Clinical Densitometry and the National Osteoporosis Foundation consider T-score of the spine as one of the preferred diagnosis measurements for osteoporosis in the elderly. 5 Clinically, no consensus has been reached regarding risk factors or protective factors of osteoporosis. Overweight and obesity are defined by the World Health Organization (WHO) as abnormal or excessive fat accumulation. 6 In addition, the overweight and obesity population accounts for the majority of older adults in some countries. 1 In today’s society, obesity and osteoporosis have become major health problems because their prevalence has dramatically increased over the last decades. A cross-sectional survey showed that the prevalence of overweight and obesity significantly increased among Chinese adults during the past two decades. 7 In addition, obesity can cause or exacerbate several chronic health problems, including cardiovascular disease, diabetes, arthritis, hypoventilation syndrome and hypertension. 8 Gkastaris et al. reported that overweight and obesity are correlated with the high incidence of falls in older people. 1 Gao et al. reported that initial beliefs about the key role of obesity in bone metabolism were primarily supported by the significant positive correlation between body mass index (BMI) and BMD in obese adults. 9 Furthermore, many studies have suggested that overweight and obesity are associated with reduced BMD and osteoporosis. 10 However, in the last several decades, the belief that BMI is positively associated with BMD has been challenged. Some researcher have suggested that BMD in obese individuals might be overestimated due to the overlying subcutaneous tissue. 11 The accretion in the morbidity of both conditions and the interaction between obese and osteoporosis prompts the necessity of better understanding the association between laboratory examinations and BMD in older obese adults. In previous studies, several clinical risk factors have been considered associated with osteoporosis, such as age, gender, ethnicity, obesity, hypertension, diabetes, uric acid and abnormal metabolism of trace elements. 12 – 15 Several behavioral risk factors also increase the prevalence of osteoporosis. Cigarette smoking was identified as a risk factor associated with increased bone loss and risk of vertebral fracture in older adults. Some studies have highlighted that smoking increases bone loss and decreases intestinal calcium absorption efficiency. 16 Hannan et al. reported that alcohol intake of more than 207 mL every week was also a risk factor for bone loss. 17 A large number of studies have indicated that oxidative stress and antioxidant levels are associated with BMD and osteoporosis. 18 – 20 Many studies have also shown that uric acid (UA) plays a protective role in various illnesses caused by oxidative stress. 21 , 22 Given these findings, it is generally considered that high levels of UA exert a protective effect on bone. Meanwhile, disorders of glucose, lipid and bone metabolism are also regarded as risk factors for osteoporosis in older adults. Although many studies have reported the relationship between diabetes, serum UA, age, BMI and BMD, the conclusions have been inconsistent and controversial. To the best of our knowledge, much research has focused on understanding the correlation between UA and BMD in postmenopausal women or in patients with diabetes, but there are a limited number of studies assessing the association between clinical risk factors and BMD in older obese adults. Therefore, the purposes of this study were to (i) explore the correlation between metabolism indices and osteoporosis, (ii) identify significant independent factors associated with BMD in men and postmenopausal women, and (iii) provide theoretical support and a basis for clinical intervention and prevention of osteoporosis in older overweight and obese adults. Material and methods Participants The study population consisted of 4762 participants older than 50 who received bone density scans of the lumbar spine in the Affiliated Hospital of Qingdao University from January 2016 to December 2019. The inclusion criteria were defined as follows: 1) Men and postmenopausal women aged ≥ 50; 2) participants who accepted bone density scans of lumbar spine and received T scores; 3) participants with BMI ≥ 25 kg/m 2 . The exclusion criteria were defined as follows: 1) nonphysiological postmenopausal women; 2) participants with long term use of osteoporosis drugs, hypouricemia drugs or drugs that might affect metabolism or the oxidant system for more than 6 months, such as vitamin D, calcium supplements, glucocorticoids, estrogens, allopurinol, etc.; 3) participants with diseases that may effect bone metabolism, such as cancer, infection, hyperparathyroidism, etc.; 4) participants with incomplete baseline data, information of clinical indicators or laboratory examinations. After applying these inclusion/exclusion criteria, a total of 1173 participants (617 men and 556 women) were enrolled in the study. Demographic Data All participants’ age, gender, diastolic blood pressure (DBP), systolic blood pressure (SBP), height, weight, BMI, date of birth, tobacco use, and history of drinking were recorded and collected. BMI BMI is a widely used measure of participants’ degree of obesity and body fat. BMI is calculated based on a person’s height (m) and weight (kg), as follows: BMI = weight/height 2 (kg/m 2 ). The WHO defines a person with BMI of 25–30 kg/m 2 as overweight, and obesity is defined as BMI ≥ 30 kg/m 2 . BMD Measurement All participants’ BMD for the lumbar spine were detected by dual-energy X-ray absorptiometry. T-score was calculated based on the BMD of the lumbar spine. Participants were divided into 3 groups according to The WHO classification criteria of T-score: the normal group (T-score ≥ -1.0), the osteopenia group (-1.0 > T-score > − 2.5), and the osteoporosis group (T-score ≤ − 2.5). 23 Laboratory Examinations All participants were asked to fast for more than 8 hours, and venous blood samples were collected in the morning. Laboratory measurements included 25-hydroxyvitamin D (25(OH)D), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), glycosylated hemoglobin (HbA1c), total cholesterol (TC), serum UA, C-reactive protein (CRP), triglyceride (TG), fasting blood glucose (FBG), serum calcium (Ca), serum phosphorus (P), osteocalcin (OC), and serum urea/creatinine (Ur/Cr) ratio. Statistical Analysis All analyses were performed using SPSS Statistics software, version 22.0. (SPSS Inc., Chicago, IL, USA). Continuous variables are shown as a mean and standard deviation (x ± s), and categorical variables are shown as a percentage (%). Student’s t-test or Mann–Whitney U test was applied to compare continuous clinical variables between the two cohorts. Categorical clinical variables were assessed with Chi-square test. One-way analysis of variance (ANOVA) or the Kruskal-Wallis test was used to compare the continuous clinical variables among three or more groups. Spearman correlation or Pearson correlation test was performed to assess correlations between BMD and parameters. Multiple stepwise linear regression analysis was used to identify independent variables associated with BMD and to assess whether multicollinearity exists in these variables. In addition, binary logistic regression analysis was performed to compute odds ratios (OR) and 95% confidence intervals (CI) to determine the association between osteoporosis and biochemical indices. A P-value < 0.05 was considered statistically significant, and all P-values were 2-sided. Results Demographic Characteristics of Men and Postmenopausal Women Table 1 compares male and postmenopausal female participants with respect to baseline data, clinical indicators and biochemical indices. LDL-C, HDL-C, SBP, OC, Ur/Cr ratio, Ca, P and prevalence of osteoporosis were significantly higher in postmenopausal women than in men (p < 0.05, respectively). Compared to man, postmenopausal women exhibited lower DBP, 25(OH)D, serum UA, T-score and rates of history of drinking and tobacco use (p < 0.05, respectively). There was no significant difference between male and postmenopausal female participants with respect to age, BMI, CRP, HbA1c, FBG, TG or TC. Table 1 Baseline characteristics of participants Man (n = 617) Postmenopausal Women ( n = 556) P-value Age (yr) 65.7 ± 8.45 65.9 ± 8.65 0.592 BMI(Kg/m^2) 29.0 ± 6.74 28.94 ± 3.46 0.850 Tobacco use (y/n) 165 (26.74%) 70 (12.59%) < 0.001 Drinking (y/n) 219 (35.49%) 61 (10.97%) < 0.001 DBP (mmHg) 79.89 ± 10.90 77.55 ± 10.89 < 0.001 SBP (mmHg) 137.76 ± 16.72 140.91 ± 18.89 0.003 LDL-C (mmol/l) 2.49 ± 0.94 2.85 ± 0.98 < 0.001 HDL-C (mmol/l) 1.11 ± 0.28 1.30 ± 0.34 < 0.001 25(OH)D (ng/ml) 17.05 ± 6.76 15.57 ± 6.16 < 0.001 CRP (mg/l) 2.84 ± 7.78 3.25 ± 9.68 0.425 HbA1c (%) 8.17 ± 1.83 8.19 ± 1.80 0.824 FBG (mmol/l) 7.77 ± 2.37 7.82 ± 2.23 0.716 UA (µmol/l) 336.92 ± 87.18 310.4 ± 77.45 < 0.001 TG (mmol/l) 1.58 ± 1.06 1.74 ± 1.09 0.110 TC (mmol/l) 4.55 ± 1.18 4.58 ± 1.25 0.705 OC (ng/ml) 9.12 ± 9.32 11.01 ± 8.68 < 0.001 Ur/Cr 26.32 ± 6.63 30.98 ± 9.32 < 0.001 Ca (mmol/l) 2.24 ± 0.10 2.27 ± 0.11 < 0.001 P (mmol/l) 1.17 ± 0.17 1.29 ± 0.32 < 0.001 T-score 0.3 ± 2.01 -1.4 ± 1.54 < 0.001 Osteoporosis (y/n) 44 (7.13%) 150 (26.98%) < 0.001 Comparison of Demographic Characteristics and Biochemical Parameters among the 3 Groups Compared to those in the normal group and the osteopenia group, male participants with osteoporosis were older, had reduced BMI, FBG, serum UA, OC, Ur/Cr and serum Ca (p < 0.05, respectively). There were no significant differences among the three groups with respect to levels of SBP, DBP, LDL-C, HDL-C, HbA1c, TC, serum P or rates of history of drinking and tobacco use in male participants. Postmenopausal women with osteoporosis were older and exhibited reduced BMI compared to participants with normal BMD and osteopenia (p < 0.05, respectively). There was no significant difference with respect to the level of SBP, CRP, TC, TG, OC, Ur/Cr, serum Ca or rates of history of drinking and tobacco use among the three groups in postmenopausal women. Additional demographic data and biochemical parameters among the three groups in male and postmenopausal female participants are shown in Table 2 . Table 2 Comparison of general characteristics, biochemical indices and BMD among the three groups. Man Postmenopausal Women Normal group ( n = 385) Osteopenia group ( n = 188) Osteoporosis group ( n = 44) P-value Normal group ( n = 156) Osteopenia group ( n = 250) Osteoporosis group ( n = 150) P-value Age (yr) 64.57 ± 8.38 66.46 ± 7.93 a 71.8 ± 8.4 a,b < 0.001 63.27 ± 8.17 65.72 ± 8.44 69.72 ± 8.21 a,b < 0.001 BMI(Kg/m^2) 29.78 ± 8.17 28.19 ± 2.35 a 25.60 ± 3.20 a,b < 0.001 29.90 ± 3.61 28.90 ± 3.33 a 28.00 ± 3.27 a,b < 0.001 Tobacco use (y/n) 109 (28.31%) 44 (23.40%) 12 (27.27%) 0.458 19 (12.18%) 28 (11.20%) 23 (15.33%) 0.475 Drinking (y/n) 142 (36.88%) 61 (32.45%) 16 (36.36%) 0.577 16 (10.26%) 27 (10.80%) 18 (12.00%) 0.882 DBP (mmHg) 79.90 ± 10.16 79.10 ± 11.90 83.2 ± 12.32 0.241 78.88 ± 11.09 76.43 ± 10.79 a 78.03 ± 10.72 b 0.006 SBP (mmHg) 137.98 ± 16.59 136.77 ± 17.59 140.00 ± 13.78 0.314 140.19 ± 18.19 140.18 ± 18.27 142.87 ± 20.55 0.553 LDL-C (mmol/l) 2.48 ± 0.94 2.50 ± 0.95 2.54 ± 0.90 0.799 2.93 ± 0.87 2.76 ± 1.08 a 2.93 ± 0.89 0.019 HDL-C (mmol/l) 1.11 ± 0.25 1.13 ± 0.32 1.11 ± 0.34 0.922 1.26 ± 0.33 1.29 ± 0.35 1.35 ± 0.33 b 0.028 25(OH)D (ng/ml) 17.59 ± 7.05 15.99 ± 5.77 a 16.92 ± 7.69 0.041 15.02 ± 5.77 16.47 ± 6.40 14.64 ± 5.98 b 0.008 CRP (mg/l) 3.09 ± 8.56 2.55 ± 6.97 1.97 ± 0.72 b 0.036 3.89 ± 13.47 3.02 ± 8.13 2.97 ± 6.99 0.748 HbA1c (%) 8.10 ± 1.73 8.26 ± 2.00 8.40 ± 1.83 0.564 8.70 ± 2.08 8.11 ± 1.66 a 7.80 ± 1.57 a < 0.001 FBG (mmol/l) 7.92 ± 2.26 7.74 ± 2.60 6.60 ± 1.92 a,b < 0.001 7.98 ± 2.41 8.08 ± 2.23 7.23 ± 1.90 b 0.003 UA (µmol/l) 348.57 ± 79.79 333.59 ± 87.50 249.23 ± 97.92 a,b < 0.001 325.15 ± 83.49 312.49 ± 83.70 291.57 ± 52.86 a 0.004 TG (mmol/l) 1.62 ± 1.00 1.57 ± 1.23 1.29 ± 0.65 a 0.019 1.95 ± 1.48 1.62 ± 0.90 1.73 ± 0.85 0.216 TC (mmol/l) 4.58 ± 1.20 4.49 ± 1.19 4.52 ± 0.96 0.639 4.69 ± 1.17 4.55 ± 1.36 4.52 ± 1.15 0.253 OC (ng/ml) 9.29 ± 8.18 9.28 ± 11.18 7.03 ± 9.85 a,b < 0.001 9.62 ± 6.86 11.98 ± 9.38 10.84 ± 8.99 0.144 Ur/Cr 26.58 ± 7.09 26.43 ± 5.77 23.47 ± 5.06 a,b 0.027 31.23 ± 8.72 31.61 ± 9.63 29.68 ± 9.34 0.118 Ca (mmol/l) 2.24 ± 0.11 2.24 ± 0.09 2.19 ± 0.09 a,b 0.024 2.26 ± 0.11 2.27 ± 0.10 2.27 ± 0.14 0.251 P (mmol/l) 1.18 ± 0.18 1.16 ± 0.14 1.13 ± 0.19 0.216 1.38 ± 0.41 1.20 ± 0.19 a 1.34 ± 0.36 < 0.001 Correlation Analysis between BMD and Biochemical Indices BMD values of the lumbar spine were significantly and positively correlated with BMI, FBG and serum UA and negatively correlated with age in both overweight men and postmenopausal women (p < 0.05, all). BMD was also positively correlated with levels of TG in men (p = 0.043). Meanwhile, in postmenopausal women, levels of HbA1c were positively correlated with BMD values (p < 0.001) (Table 3 ). Table 3 Correlation analysis between BMD and general conditions and biochemical indices. Man Postmenopausal Women r p r p Age (yr) -0.184 < 0.001 -0.288 < 0.001 BMI(Kg/m^2) 0.268 < 0.001 0.287 < 0.001 Tobacco use (y/n) 0.037 0.357 -0.036 0.402 Drinking (y/n) 0.036 0.377 -0.027 0.530 DBP (mmHg) 0.045 0.264 0.069 0.106 SBP (mmHg) 0.054 0.182 -0.035 0.411 LDL-C (mmol/l) -0.015 0.717 0.043 0.313 HDL-C (mmol/l) -0.059 0.142 -0.079 0.387 25(OH)D (ng/ml) 0.064 0.111 0.003 0.950 CRP (mg/l) 0.038 0.343 0.049 0.247 HbA1c (%) -0.026 0.511 0.156 < 0.001 FBG (mmol/l) 0.180 < 0.001 0.107 0.011 UA (µmol/l) 0.169 < 0.001 0.198 < 0.001 TG (mmol/l) 0.081 0.043 0.018 0.680 TC (mmol/l) 0.053 0.191 0.055 0.199 OC (ng/ml) 0.049 0.221 -0.37 0.387 Ur/Cr -0.031 0.444 0.015 0.723 Ca (mmol/l) 0.065 0.109 0.043 0.314 P (mmol/l) 0.006 0.874 0.077 0.070 Multiple Linear Regression Analysis of the Relationship between BMD and Biochemical Indices BMI (R 2 = 0.056, P < 0.001), serum UA (R 2 = 0.033, P < 0.001) and FBG (R 2 = 0.023, P = 0.001) were significantly positively correlated with T-score of the lumbar vertebrae in men. Meanwhile, in men, T-scores were significantly negatively correlated with age (R 2 = 0.032, P < 0.001) (Fig. 1 ). On the other hand, the T-score of the lumbar spine was also significantly positive correlated with BMI (R 2 = 0.076, P < 0.001), serum UA (R 2 = 0.027, P < 0.001) and HbA1c (R 2 = 0.018, P = 0.002) and negatively correlated with age (R 2 = 0.070, P < 0.001) in postmenopausal women (Fig. 2 ). B inary Logistic Regression Analysis of the Relationship between Osteoporosis and Biochemical Indices After adjustment was made, osteoporosis was significantly and positively associated with age (men: OR = 1.080, 95% CI = 1.033–1.129, P = 0.001; postmenopausal women: OR = 1.079, 95% CI = 1.053–1.106, P < 0.001) and inversely associated with BMI (men: OR = 0.726, 95% CI = 0.619–0.851, P < 0.001; postmenopausal women: OR = 0.897, 95% CI = 0.839–0.959, P = 0.001) and serum UA (men: OR = 0.87, 95% CI = 0.981–0.992, P < 0.001; postmenopausal women: OR = 0.995, 95% CI = 0.993–0.999, P = 0.003) in both men and postmenopausal women. In addition, FBG (OR = 0.793, 95% CI = 0.643–0.976, P = 0.029) was significantly inversely associated with osteoporosis in men. HbA1c (OR = 0.811, 95% CI = 0.713–0.922, P = 0.001) was also significantly inversely associated with osteoporosis in postmenopausal women (Table 4 ). Table 4 Binary logistic regression analysis of osteoporosis in older overweight adults Man Postmenopausal Women OR 95% CI P OR 95% CI P Age (yr) 1.080 1.033–1.129 0.001 1.079 1.053–1.106 < 0.001 BMI(Kg/m^2) 0.726 0.619–0.851 < 0.001 0.897 0.839–0.959 0.001 UA (µmol/l) 0.987 0.981–0.992 < 0.001 0.995 0.993–0.999 0.003 FBG (mmol/l) 0.793 0.643–0.976 0.029 - - - HbA1c (%) - - - 0.811 0.713–0.922 0.001 Discussion Osteoporosis and obesity have become a global epidemic and are major health issues around the world. Currently, BMD is considered the gold standard for evaluating bone mass and diagnosing osteoporosis in the clinic. In addition, it was thought that obesity has an obvious protective effect on BMD in daily clinical practice. Clinicians have attempted to identify the primary significant risk factors for osteoporosis in recent years. Clinically, several demographic characteristic and biochemical parameters are considered to affect BMD, including age, tobacco use, history of drinking, BMI, HbA1c, FBG, 25(OH)D and TG. To the best of our knowledge, the correlation of risk factors and osteoporosis are still controversial, and risk factors identified for osteoporosis in different studies and subgroups are dissimilar. Risk Factors Compared to male participants, postmenopausal women had significantly lower T-score of the lumbar spine and a higher prevalence of osteoporosis. Both genders lose bone mass continually with advancing age, but cessation of ovarian function accelerates bone loss in postmenopausal women. 17 , 24 Cooper et al. reported that aging itself was a risk factor for increased bone loss. 25 Given that, participants with osteoporosis were older than those in the normal group and the osteopenia group in the present study. In addition, age was significantly negatively correlated with the T-score of the lumbar spine in correlation and multiple linear regression analyses. According to the results of correlation analysis, linear regression analysis and logistic regression analysis, BMI was significantly and positively associated with BMD in older overweight adults. Overweight and obesity may primarily affect bone health through estrogens and leptin as obese adults have been shown to have higher concentrations of serum estrogens. Meanwhile, estrogens play a key role in promoting bone formation, maintaining skeletal homeostasis, and reducing bone resorption. 1 Leptin was identified as an adipocyte-derived cytokine-like hormone. As such, hyperleptinemia is a common feature in obese people, and leptin synthesis and secretion are significantly increased in obese people. 26 Bao et al. observed significantly reduced bone volume and BMD after leptin receptor gene were completely removed. 27 Thomas et al. reported that leptin acts on marrow stromal cells to stimulate differentiation to osteoblasts in vitro . 28 However, different studies have reported both positive and negative functions of leptin. It has become apparent that leptin is not the only risk factors affecting BMD. We observed that BMI was an independent risk factor for osteoporosis in older overweight adults, but the mechanisms of the effect merit further exploration. In addition, several studies have highlighted that overweight and obesity are associated with falls, especially in older adults. 1 , 29 Given this finding, reasonable control of weight is an important measure to prevent osteoporotic fracture from occurring. Yao et al. revealed a positive relationship between UA and BMD of the lumbar vertebrae among most older adults. 30 Wang et al. demonstrated that gout was significantly associated with osteoporotic fracture risk in older Chinese women. 4 The mechanism of the increase in BMD induced by high levels of UA has been discussed in many studies. As the final product of purine metabolism, UA effectively blocks formation of the oxidant peroxynitrite and plays a key role in the procedure of antioxidation. 14 Loss of bone mass is closely related to increased oxidative stress or decreased antioxidants. 31 In addition, UA not only stimulates the differentiation of osteoblasts but can also inhibit the generation of osteoclasts, exerting dual effects of increasing bone formation and reducing bone absorption. A large population study demonstrated that increased levels of UA are protective for bone strength and BMD. 32 Similarly, we found that UA was positively and linearly related to increased lumbar BMD in both men and postmenopausal women in the present study. Furthermore, this result may also support the hypothesis that UA play an important role in the osteogenic differentiation of mesenchymal stem cells. Many clinical studies have focused on understanding the correlation between risk factors for BMD in patients with diabetes. In fact, osteoporosis is one of the complications associated with diabetes mellitus. 33 Many studies have reported that both type 1 and 2 diabetes mellitus influence BMD and the risk of bone fractures. 34 Yan et al. reported that higher blood glucose levels increase BMD. 15 HbA1c and FBG are better indicators for diabetes and are considered the main clinical parameters when assessing this disorder. Consistent with previous research, the results of our study showed that FBG and HbA1c were significantly and positively associated with BMD in men and postmenopausal women, respectively. Although BMD is the gold standard for the diagnosis of osteoporosis, many studies have demonstrated that BMD measurement alone is not adequate for assessing the risk of osteoporotic fracture in older adults. Some studies have reported that diabetics have a greater risk of fracture but higher lumbar spine BMD compared to nondiabetics. 34 Si et al.reported that hyperglycemia can lead to bone fragility. 35 In addition, the increased risk of osteoporosis fracture, even with increased BMD, has been attributed to bone fragility in older adults with hyperglycemia. 15 , 36 Limitations There are some limitations in present study. First, we did not have detailed information on some urine bone indices and other metabolic indicators. Therefore, some potential risk factors may have been neglected in this study. Second, further large-sample and longitudinal studies are needed to verify the results of the present study. Finally, BMD measurement alone is not adequate; changes in bone microstructure, bone fragility and the incidence of falls also should be tabulated when assessing the risk of osteoporotic fracture. Conclusions This study demonstrates that osteoporosis might be associated with advanced age, increased BMI and higher levels of UA in older overweight adults. Declarations Statement Of Human Rights This study was approved by the ethics committee of the Affiliated Hospital of Qingdao University. Written informed consent was obtained from all participants. Meanwhile，all personal details were erased before analysis to cover patient data and comply with the Declaration of Helsinki. Acknowledgments This work was supported by grants from the National Natural Science Foundation of China (81672200, 81871804) and National Key Research and Development Project (CN) (2019YFC0121400). We are grateful to the Orthopedic Medical Center of the Affiliated Hospital of Qingdao University and the special procedures team. Disclosure The authors confirm that they have no conflict of interest with respect to the manuscript content or funding. Author Contribution Li and Zhou wrote the main manuscript text and Li prepared figures and tables. All authors reviewed the manuscript References Gkastaris K, Goulis DG, Potoupnis M, Anastasilakis AD, Kapetanos G. Obesity, osteoporosis and bone metabolism. J Musculoskelet Neuronal Interact. 2020;20(3):372–81. Lane JM, Russell L, Khan SN, Osteoporosis. Clin Orthop Relat Res. 2000;372139–50. 10.1097/00003086-200003000-00016 . Manolagas SC. From estrogen-centric to aging and oxidative stress: a revised perspective of the pathogenesis of osteoporosis. Endocr Rev. 2010;31(3):266–300. 10.1210/er.2009-0024 . Wang Y, Zhou R, Zhong W, Hu C, Lu S, Chai Y. Association of gout with osteoporotic fractures. 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Marked decrease in plasma antioxidants in aged osteoporotic women: results of a cross-sectional study. J Clin Endocrinol Metab. 2003;88(4):1523–7. 10.1210/jc.2002-021496 . Sugiura M, Nakamura M, Ogawa K, Ikoma Y, Ando F, Yano M. Bone mineral density in post-menopausal female subjects is associated with serum antioxidant carotenoids. Osteoporos Int. 2008;19(2):211–9. 10.1007/s00198-007-0457-2 . Sendur OF, Turan Y, Tastaban E, Serter M. Antioxidant status in patients with osteoporosis: a controlled study. Joint Bone Spine. 2009;76(5):514–8. 10.1016/j.jbspin.2009.02.005 . Keizman D, Ish-Shalom M, Berliner S, et al. Low uric acid levels in serum of patients with ALS: further evidence for oxidative stress? J Neurol Sci. 2009;285(1–2):95–9. 10.1016/j.jns.2009.06.002 . Hyassat D, Alyan T, Jaddou H, Ajlouni KM. Prevalence and Risk Factors of Osteoporosis Among Jordanian Postmenopausal Women Attending the National Center for Diabetes, Endocrinology and Genetics in Jordan. Biores Open Access. 2017;6(1):85–93. 10.1089/biores.2016.0045 . Kanis JA, McCloskey EV, Johansson H, Oden A, Melton LJ 3rd, Khaltaev N. A reference standard for the description of osteoporosis. Bone. 2008;42(3):467–75. 10.1016/j.bone.2007.11.001 . Garnero P, Sornay-Rendu E, Chapuy MC, Delmas PD. Increased bone turnover in late postmenopausal women is a major determinant of osteoporosis. J Bone Min Res. 1996;11(3):337–49. 10.1002/jbmr.5650110307 . Jordan KM, Cooper C. Epidemiology of osteoporosis. Best Pract Res Clin Rheumatol. 2002;16(5):795–806. 10.1053/berh.2002.0264 . Shirakura Y, Sugiyama T, Tanaka H, Taguchi T, Kawai S. Hyperleptinemia in female patients with ossification of spinal ligaments. Biochem Biophys Res Commun. 2000;267(3):752–5. 10.1006/bbrc.1999.2027 . Bao D, Ma Y, Zhang X, et al. Preliminary Characterization of a Leptin Receptor Knockout Rat Created by CRISPR/Cas9 System. Sci Rep. 2015;5:15942. 10.1038/srep15942 . Thomas T, Gori F, Khosla S, Jensen MD, Burguera B, Riggs BL. Leptin acts on human marrow stromal cells to enhance differentiation to osteoblasts and to inhibit differentiation to adipocytes. Endocrinology. 1999;140(4):1630–8. 10.1210/endo.140.4.6637 . Scott D, Sanders KM, Aitken D, Hayes A, Ebeling PR, Jones G. Sarcopenic obesity and dynapenic obesity: 5-year associations with falls risk in middle-aged and older adults. Obes (Silver Spring). 2014;22(6):1568–74. 10.1002/oby.20734 . Yao X, Chen L, Xu H, Zhu Z. The Association between Serum Uric Acid and Bone Mineral Density in Older Adults. Int J Endocrinol. 2020;2020:3082318. 10.1155/2020/3082318 . Zhao X, Yu X, Zhang X. Association between Uric Acid and Bone Mineral Density in Postmenopausal Women with Type 2 Diabetes Mellitus in China: A Cross-Sectional Inpatient Study. J Diabetes Res. 2020;2020:3982831. 10.1155/2020/3982831 . Ishii S, Miyao M, Mizuno Y, Tanaka-Ishikawa M, Akishita M, Ouchi Y. Association between serum uric acid and lumbar spine bone mineral density in peri- and postmenopausal Japanese women. Osteoporos Int. 2014;25(3):1099–105. 10.1007/s00198-013-2571-7 . Abdulameer SA, Sulaiman SA, Hassali MA, Subramaniam K, Sahib MN. Osteoporosis and type 2 diabetes mellitus: what do we know, and what we can do? Patient Prefer Adherence. 2012;6:435–48. 10.2147/ppa.S32745 . Vestergaard P. Discrepancies in bone mineral density and fracture risk in patients with type 1 and type 2 diabetes–a meta-analysis. Osteoporos Int. 2007;18(4):427–44. 10.1007/s00198-006-0253-4 . Si Y, Wang C, Guo Y, Xu G, Ma Y. Prevalence of Osteoporosis in Patients with Type 2 Diabetes Mellitus in the Chinese Mainland: A Systematic Review and Meta-Analysis. Iran J Public Health. 2019;48(7):1203–14. Oei L, Zillikens MC, Dehghan A, et al. High bone mineral density and fracture risk in type 2 diabetes as skeletal complications of inadequate glucose control: the Rotterdam Study. Diabetes Care. 2013;36(6):1619–28. 10.2337/dc12-1188 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4127118","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":281612480,"identity":"1e8fa088-6a24-48a4-adfe-6d416758e33d","order_by":0,"name":"Liang Li","email":"","orcid":"","institution":"Qindao Chengyang People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Li","suffix":""},{"id":281612482,"identity":"b4d1f078-4a24-4b07-8f17-d63b3e6be360","order_by":1,"name":"Zhenggang Zhou","email":"","orcid":"","institution":"Qindao Chengyang People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhenggang","middleName":"","lastName":"Zhou","suffix":""},{"id":281612484,"identity":"65e8c3aa-e36c-4750-8dec-beb1ec43e88d","order_by":2,"name":"Jianlin Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYFCCww0Qmr2x8eEH4rQchGrhOdxsLEGcFkaoFon0NgEeYjToNh5s/My7Z1vihpsP2xgkGOzkdBsIaDE7cLBZmufZ7cQNtxPbHhQwJBubHSCspY2Z5wBYS7uBBMOBxG3Ea7l5sE2ChzQtNxiJ19IsOefAbeOZZxKBgWxAjF9uHD744c2B27J9x48/fPihwk6OoBYGiQMMTMDocFwAVmlASDkI8DcwMP5gYLCXbyBG9SgYBaNgFIxIAAC9RFKqv4IAqwAAAABJRU5ErkJggg==","orcid":"","institution":"Qindao Chengyang People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jianlin","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2024-03-19 04:14:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4127118/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4127118/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53255324,"identity":"20675e8d-c348-4de6-a8ac-8bd001b6aa1c","added_by":"auto","created_at":"2024-03-22 13:28:09","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":406651,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the relation between age, BMI, UA, FBG and lumbar spine T score in man, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003eMultiple linear regression analysis revealed that age was negatively correlated with lumbar spine T score (r = -0.18, p \u0026lt; 0.001), but BMI, UA, FBG was positively correlated with lumbar spine T score (r = 0.24, p \u0026lt; 0.001; r = 0.18, p \u0026lt; 0.001;r = 0.15, p \u0026lt; 0.001, respectively).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4127118/v1/b83e204eedde6bb33035dbeb.jpeg"},{"id":53255323,"identity":"5523f286-562e-4766-bb0b-be4f2b78cab0","added_by":"auto","created_at":"2024-03-22 13:28:09","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":343030,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the relation between age, BMI, UA, Hb1Ac and lumbar spine T score in postmenopausal women, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003eMultiple linear regression analysis revealed that age was negatively correlated with lumbar spine T score (r = -0.27, p \u0026lt; 0.001), but BMI, UA, FBG was positively correlated with lumbar spine T score (r = 0.28, p \u0026lt; 0.001; r = 0.16, p \u0026lt; 0.001; r = 0.13, p \u0026lt; 0.001, respectively).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4127118/v1/ae3fcec21db27eb1481ab987.jpeg"},{"id":55264407,"identity":"71780b57-6183-40d9-a66b-63d4002a9a8a","added_by":"auto","created_at":"2024-04-25 01:42:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1136385,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4127118/v1/310a1e55-a07b-4ec2-95ca-3e1c33ff227e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive Risk Factors for Osteoporosis in Older Overweight Adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOsteoporosis is one of the most common systemic diseases and is characterized by disordered bone metabolism and low bone mineral density; it affects more than 200\u0026nbsp;million people all worldwide.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Given the accelerated growth of the aging population, the problem of increasing numbers of older individuals with osteoporosis has become a focus of all societies in the last decades. Osteoporosis is regarded as the major cause of osteoporotic fractures and bone fragility.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Unfortunately, the incidence of osteoporosis is increasing, resulting in rising morbidity and mortality of osteoporotic fractures in older adults.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Meanwhile, osteoporotic fractures seriously impact human health and convey heavy financial burdens to families and society. The medical burden of osteoporotic fractures was reportedly higher than the projected annual costs of breast cancer, stroke, or diabetes in the United States. The increasing clinical consequences and economic burden of this disease require measures to assess individuals at high risk for osteoporosis. Assessment of existing bone mineral density (BMD), identifying individuals with high risk of osteoporosis, and making decisions regarding the appropriate intervention based on these risk factors are the ultimate goals when evaluating risk factors associated with osteoporosis. T-scores based on BMD measured by dual-energy X-ray absorptiometry are used to measure the prevalence of osteoporosis across populations. Currently, the International Society for Clinical Densitometry and the National Osteoporosis Foundation consider T-score of the spine as one of the preferred diagnosis measurements for osteoporosis in the elderly.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Clinically, no consensus has been reached regarding risk factors or protective factors of osteoporosis.\u003c/p\u003e \u003cp\u003eOverweight and obesity are defined by the World Health Organization (WHO) as abnormal or excessive fat accumulation.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e In addition, the overweight and obesity population accounts for the majority of older adults in some countries.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e In today\u0026rsquo;s society, obesity and osteoporosis have become major health problems because their prevalence has dramatically increased over the last decades. A cross-sectional survey showed that the prevalence of overweight and obesity significantly increased among Chinese adults during the past two decades.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e In addition, obesity can cause or exacerbate several chronic health problems, including cardiovascular disease, diabetes, arthritis, hypoventilation syndrome and hypertension.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Gkastaris et al. reported that overweight and obesity are correlated with the high incidence of falls in older people. \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Gao et al. reported that initial beliefs about the key role of obesity in bone metabolism were primarily supported by the significant positive correlation between body mass index (BMI) and BMD in obese adults.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Furthermore, many studies have suggested that overweight and obesity are associated with reduced BMD and osteoporosis.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e However, in the last several decades, the belief that BMI is positively associated with BMD has been challenged. Some researcher have suggested that BMD in obese individuals might be overestimated due to the overlying subcutaneous tissue.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe accretion in the morbidity of both conditions and the interaction between obese and osteoporosis prompts the necessity of better understanding the association between laboratory examinations and BMD in older obese adults. In previous studies, several clinical risk factors have been considered associated with osteoporosis, such as age, gender, ethnicity, obesity, hypertension, diabetes, uric acid and abnormal metabolism of trace elements.\u003csup\u003e\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Several behavioral risk factors also increase the prevalence of osteoporosis. Cigarette smoking was identified as a risk factor associated with increased bone loss and risk of vertebral fracture in older adults. Some studies have highlighted that smoking increases bone loss and decreases intestinal calcium absorption efficiency.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Hannan et al. reported that alcohol intake of more than 207 mL every week was also a risk factor for bone loss.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e A large number of studies have indicated that oxidative stress and antioxidant levels are associated with BMD and osteoporosis.\u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Many studies have also shown that uric acid (UA) plays a protective role in various illnesses caused by oxidative stress.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Given these findings, it is generally considered that high levels of UA exert a protective effect on bone. Meanwhile, disorders of glucose, lipid and bone metabolism are also regarded as risk factors for osteoporosis in older adults.\u003c/p\u003e \u003cp\u003eAlthough many studies have reported the relationship between diabetes, serum UA, age, BMI and BMD, the conclusions have been inconsistent and controversial. To the best of our knowledge, much research has focused on understanding the correlation between UA and BMD in postmenopausal women or in patients with diabetes, but there are a limited number of studies assessing the association between clinical risk factors and BMD in older obese adults. Therefore, the purposes of this study were to (i) explore the correlation between metabolism indices and osteoporosis, (ii) identify significant independent factors associated with BMD in men and postmenopausal women, and (iii) provide theoretical support and a basis for clinical intervention and prevention of osteoporosis in older overweight and obese adults.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003e The study population consisted of 4762 participants older than 50 who received bone density scans of the lumbar spine in the Affiliated Hospital of Qingdao University from January 2016 to December 2019.\u003c/p\u003e \u003cp\u003eThe inclusion criteria were defined as follows: 1) Men and postmenopausal women aged\u0026thinsp;\u0026ge;\u0026thinsp;50; 2) participants who accepted bone density scans of lumbar spine and received T scores; 3) participants with BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe exclusion criteria were defined as follows: 1) nonphysiological postmenopausal women; 2) participants with long term use of osteoporosis drugs, hypouricemia drugs or drugs that might affect metabolism or the oxidant system for more than 6 months, such as vitamin D, calcium supplements, glucocorticoids, estrogens, allopurinol, etc.; 3) participants with diseases that may effect bone metabolism, such as cancer, infection, hyperparathyroidism, etc.; 4) participants with incomplete baseline data, information of clinical indicators or laboratory examinations. After applying these inclusion/exclusion criteria, a total of 1173 participants (617 men and 556 women) were enrolled in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDemographic Data\u003c/h2\u003e \u003cp\u003eAll participants\u0026rsquo; age, gender, diastolic blood pressure (DBP), systolic blood pressure (SBP), height, weight, BMI, date of birth, tobacco use, and history of drinking were recorded and collected.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eBMI\u003c/h2\u003e \u003cp\u003eBMI is a widely used measure of participants\u0026rsquo; degree of obesity and body fat. BMI is calculated based on a person\u0026rsquo;s height (m) and weight (kg), as follows: BMI\u0026thinsp;=\u0026thinsp;weight/height\u003csup\u003e2\u003c/sup\u003e (kg/m\u003csup\u003e2\u003c/sup\u003e). The WHO defines a person with BMI of 25\u0026ndash;30 kg/m\u003csup\u003e2\u003c/sup\u003e as overweight, and obesity is defined as BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eBMD Measurement\u003c/h2\u003e \u003cp\u003eAll participants\u0026rsquo; BMD for the lumbar spine were detected by dual-energy X-ray absorptiometry. T-score was calculated based on the BMD of the lumbar spine. Participants were divided into 3 groups according to The WHO classification criteria of T-score: the normal group (T-score \u0026ge; -1.0), the osteopenia group (-1.0\u0026thinsp;\u0026gt;\u0026thinsp;T-score\u0026thinsp;\u0026gt;\u0026thinsp;\u0026minus;\u0026thinsp;2.5), and the osteoporosis group (T-score\u0026thinsp;\u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;2.5).\u003csup\u003e23\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eLaboratory Examinations\u003c/h2\u003e \u003cp\u003eAll participants were asked to fast for more than 8 hours, and venous blood samples were collected in the morning. Laboratory measurements included 25-hydroxyvitamin D (25(OH)D), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), glycosylated hemoglobin (HbA1c), total cholesterol (TC), serum UA, C-reactive protein (CRP), triglyceride (TG), fasting blood glucose (FBG), serum calcium (Ca), serum phosphorus (P), osteocalcin (OC), and serum urea/creatinine (Ur/Cr) ratio.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll analyses were performed using SPSS Statistics software, version 22.0. (SPSS Inc., Chicago, IL, USA). Continuous variables are shown as a mean and standard deviation (x\u0026thinsp;\u0026plusmn;\u0026thinsp;s), and categorical variables are shown as a percentage (%). Student\u0026rsquo;s t-test or Mann\u0026ndash;Whitney U test was applied to compare continuous clinical variables between the two cohorts. Categorical clinical variables were assessed with Chi-square test. One-way analysis of variance (ANOVA) or the Kruskal-Wallis test was used to compare the continuous clinical variables among three or more groups. Spearman correlation or Pearson correlation test was performed to assess correlations between BMD and parameters. Multiple stepwise linear regression analysis was used to identify independent variables associated with BMD and to assess whether multicollinearity exists in these variables. In addition, binary logistic regression analysis was performed to compute odds ratios (OR) and 95% confidence intervals (CI) to determine the association between osteoporosis and biochemical indices. A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant, and all P-values were 2-sided.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDemographic Characteristics of Men and Postmenopausal Women\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e compares male and postmenopausal female participants with respect to baseline data, clinical indicators and biochemical indices. LDL-C, HDL-C, SBP, OC, Ur/Cr ratio, Ca, P and prevalence of osteoporosis were significantly higher in postmenopausal women than in men (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively). Compared to man, postmenopausal women exhibited lower DBP, 25(OH)D, serum UA, T-score and rates of history of drinking and tobacco use (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively). There was no significant difference between male and postmenopausal female participants with respect to age, BMI, CRP, HbA1c, FBG, TG or TC.\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 participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\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\u003eMan (n\u0026thinsp;=\u0026thinsp;617)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePostmenopausal Women ( n\u0026thinsp;=\u0026thinsp;556)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\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\u003eAge (yr)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.592\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI(Kg/m^2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.94\u0026thinsp;\u0026plusmn;\u0026thinsp;3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTobacco use (y/n)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165 (26.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (12.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrinking (y/n)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e219 (35.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (10.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDBP (mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.89\u0026thinsp;\u0026plusmn;\u0026thinsp;10.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.55\u0026thinsp;\u0026plusmn;\u0026thinsp;10.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSBP (mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137.76\u0026thinsp;\u0026plusmn;\u0026thinsp;16.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140.91\u0026thinsp;\u0026plusmn;\u0026thinsp;18.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL-C (mmol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL-C (mmol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e25(OH)D (ng/ml)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.05\u0026thinsp;\u0026plusmn;\u0026thinsp;6.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.57\u0026thinsp;\u0026plusmn;\u0026thinsp;6.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRP (mg/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.84\u0026thinsp;\u0026plusmn;\u0026thinsp;7.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.25\u0026thinsp;\u0026plusmn;\u0026thinsp;9.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHbA1c (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFBG (mmol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.77\u0026thinsp;\u0026plusmn;\u0026thinsp;2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.82\u0026thinsp;\u0026plusmn;\u0026thinsp;2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUA (\u0026micro;mol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e336.92\u0026thinsp;\u0026plusmn;\u0026thinsp;87.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e310.4\u0026thinsp;\u0026plusmn;\u0026thinsp;77.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTG (mmol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.74\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTC (mmol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOC (ng/ml)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.12\u0026thinsp;\u0026plusmn;\u0026thinsp;9.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.01\u0026thinsp;\u0026plusmn;\u0026thinsp;8.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUr/Cr\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.32\u0026thinsp;\u0026plusmn;\u0026thinsp;6.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.98\u0026thinsp;\u0026plusmn;\u0026thinsp;9.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCa (mmol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP (mmol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT-score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOsteoporosis (y/n)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (7.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (26.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;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 \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eComparison of Demographic Characteristics and Biochemical Parameters among the 3 Groups\u003c/h2\u003e \u003cp\u003eCompared to those in the normal group and the osteopenia group, male participants with osteoporosis were older, had reduced BMI, FBG, serum UA, OC, Ur/Cr and serum Ca (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively). There were no significant differences among the three groups with respect to levels of SBP, DBP, LDL-C, HDL-C, HbA1c, TC, serum P or rates of history of drinking and tobacco use in male participants. Postmenopausal women with osteoporosis were older and exhibited reduced BMI compared to participants with normal BMD and osteopenia (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively). There was no significant difference with respect to the level of SBP, CRP, TC, TG, OC, Ur/Cr, serum Ca or rates of history of drinking and tobacco use among the three groups in postmenopausal women. Additional demographic data and biochemical parameters among the three groups in male and postmenopausal female participants are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003eComparison of general characteristics, biochemical indices and BMD among the three groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\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\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eMan\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003ePostmenopausal Women\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal group ( n\u0026thinsp;=\u0026thinsp;385)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOsteopenia group\u003c/p\u003e \u003cp\u003e( n\u0026thinsp;=\u0026thinsp;188)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOsteoporosis group\u003c/p\u003e \u003cp\u003e( n\u0026thinsp;=\u0026thinsp;44)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNormal group ( n\u0026thinsp;=\u0026thinsp;156)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOsteopenia group ( n\u0026thinsp;=\u0026thinsp;250)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOsteoporosis group ( n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\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\u003eAge (yr)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.57\u0026thinsp;\u0026plusmn;\u0026thinsp;8.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.46\u0026thinsp;\u0026plusmn;\u0026thinsp;7.93\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.27\u0026thinsp;\u0026plusmn;\u0026thinsp;8.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65.72\u0026thinsp;\u0026plusmn;\u0026thinsp;8.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e69.72\u0026thinsp;\u0026plusmn;\u0026thinsp;8.21\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI(Kg/m^2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.78\u0026thinsp;\u0026plusmn;\u0026thinsp;8.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.19\u0026thinsp;\u0026plusmn;\u0026thinsp;2.35\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.60\u0026thinsp;\u0026plusmn;\u0026thinsp;3.20\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.90\u0026thinsp;\u0026plusmn;\u0026thinsp;3.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.90\u0026thinsp;\u0026plusmn;\u0026thinsp;3.33\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28.00\u0026thinsp;\u0026plusmn;\u0026thinsp;3.27\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTobacco use (y/n)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109 (28.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (23.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (27.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19 (12.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28 (11.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23 (15.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrinking (y/n)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142 (36.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (32.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (36.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16 (10.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27 (10.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18 (12.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDBP (mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.90\u0026thinsp;\u0026plusmn;\u0026thinsp;10.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.10\u0026thinsp;\u0026plusmn;\u0026thinsp;11.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e78.88\u0026thinsp;\u0026plusmn;\u0026thinsp;11.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e76.43\u0026thinsp;\u0026plusmn;\u0026thinsp;10.79\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e78.03\u0026thinsp;\u0026plusmn;\u0026thinsp;10.72\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSBP (mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137.98\u0026thinsp;\u0026plusmn;\u0026thinsp;16.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136.77\u0026thinsp;\u0026plusmn;\u0026thinsp;17.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140.00\u0026thinsp;\u0026plusmn;\u0026thinsp;13.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e140.19\u0026thinsp;\u0026plusmn;\u0026thinsp;18.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e140.18\u0026thinsp;\u0026plusmn;\u0026thinsp;18.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e142.87\u0026thinsp;\u0026plusmn;\u0026thinsp;20.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL-C (mmol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL-C (mmol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e25(OH)D (ng/ml)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.59\u0026thinsp;\u0026plusmn;\u0026thinsp;7.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.99\u0026thinsp;\u0026plusmn;\u0026thinsp;5.77\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.92\u0026thinsp;\u0026plusmn;\u0026thinsp;7.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.02\u0026thinsp;\u0026plusmn;\u0026thinsp;5.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.47\u0026thinsp;\u0026plusmn;\u0026thinsp;6.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.64\u0026thinsp;\u0026plusmn;\u0026thinsp;5.98\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRP (mg/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.09\u0026thinsp;\u0026plusmn;\u0026thinsp;8.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.55\u0026thinsp;\u0026plusmn;\u0026thinsp;6.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.89\u0026thinsp;\u0026plusmn;\u0026thinsp;13.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.02\u0026thinsp;\u0026plusmn;\u0026thinsp;8.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.97\u0026thinsp;\u0026plusmn;\u0026thinsp;6.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHbA1c (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.26\u0026thinsp;\u0026plusmn;\u0026thinsp;2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.70\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFBG (mmol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.92\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.74\u0026thinsp;\u0026plusmn;\u0026thinsp;2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.92\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.98\u0026thinsp;\u0026plusmn;\u0026thinsp;2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.08\u0026thinsp;\u0026plusmn;\u0026thinsp;2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUA (\u0026micro;mol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e348.57\u0026thinsp;\u0026plusmn;\u0026thinsp;79.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e333.59\u0026thinsp;\u0026plusmn;\u0026thinsp;87.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e249.23\u0026thinsp;\u0026plusmn;\u0026thinsp;97.92\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e325.15\u0026thinsp;\u0026plusmn;\u0026thinsp;83.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e312.49\u0026thinsp;\u0026plusmn;\u0026thinsp;83.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e291.57\u0026thinsp;\u0026plusmn;\u0026thinsp;52.86\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTG (mmol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTC (mmol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.52\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOC (ng/ml)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.29\u0026thinsp;\u0026plusmn;\u0026thinsp;8.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.28\u0026thinsp;\u0026plusmn;\u0026thinsp;11.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.03\u0026thinsp;\u0026plusmn;\u0026thinsp;9.85\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.62\u0026thinsp;\u0026plusmn;\u0026thinsp;6.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.98\u0026thinsp;\u0026plusmn;\u0026thinsp;9.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.84\u0026thinsp;\u0026plusmn;\u0026thinsp;8.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUr/Cr\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.58\u0026thinsp;\u0026plusmn;\u0026thinsp;7.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.43\u0026thinsp;\u0026plusmn;\u0026thinsp;5.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.47\u0026thinsp;\u0026plusmn;\u0026thinsp;5.06\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.23\u0026thinsp;\u0026plusmn;\u0026thinsp;8.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.61\u0026thinsp;\u0026plusmn;\u0026thinsp;9.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29.68\u0026thinsp;\u0026plusmn;\u0026thinsp;9.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCa (mmol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP (mmol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;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 \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation Analysis between BMD and Biochemical Indices\u003c/h2\u003e \u003cp\u003eBMD values of the lumbar spine were significantly and positively correlated with BMI, FBG and serum UA and negatively correlated with age in both overweight men and postmenopausal women (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, all). BMD was also positively correlated with levels of TG in men (p\u0026thinsp;=\u0026thinsp;0.043). Meanwhile, in postmenopausal women, levels of HbA1c were positively correlated with BMD values (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eCorrelation analysis between BMD and general conditions and biochemical indices.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMan\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePostmenopausal Women\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\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 (yr)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI(Kg/m^2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTobacco use (y/n)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrinking (y/n)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDBP (mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSBP (mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL-C (mmol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL-C (mmol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e25(OH)D (ng/ml)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRP (mg/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHbA1c (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFBG (mmol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUA (\u0026micro;mol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTG (mmol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTC (mmol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOC (ng/ml)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUr/Cr\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCa (mmol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP (mmol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMultiple Linear Regression Analysis of the Relationship between BMD and Biochemical Indices\u003c/h2\u003e \u003cp\u003eBMI (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.056, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), serum UA (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.033, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and FBG (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.023, P\u0026thinsp;=\u0026thinsp;0.001) were significantly positively correlated with T-score of the lumbar vertebrae in men. Meanwhile, in men, T-scores were significantly negatively correlated with age (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.032, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). On the other hand, the T-score of the lumbar spine was also significantly positive correlated with BMI (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.076, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), serum UA (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.027, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and HbA1c (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.018, P\u0026thinsp;=\u0026thinsp;0.002) and negatively correlated with age (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.070, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in postmenopausal women (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eB\u003c/b\u003e \u003cb\u003einary Logistic Regression Analysis of the Relationship between Osteoporosis and Biochemical Indices\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAfter adjustment was made, osteoporosis was significantly and positively associated with age (men: OR\u0026thinsp;=\u0026thinsp;1.080, 95% CI\u0026thinsp;=\u0026thinsp;1.033\u0026ndash;1.129, P\u0026thinsp;=\u0026thinsp;0.001; postmenopausal women: OR\u0026thinsp;=\u0026thinsp;1.079, 95% CI\u0026thinsp;=\u0026thinsp;1.053\u0026ndash;1.106, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and inversely associated with BMI (men: OR\u0026thinsp;=\u0026thinsp;0.726, 95% CI\u0026thinsp;=\u0026thinsp;0.619\u0026ndash;0.851, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; postmenopausal women: OR\u0026thinsp;=\u0026thinsp;0.897, 95% CI\u0026thinsp;=\u0026thinsp;0.839\u0026ndash;0.959, P\u0026thinsp;=\u0026thinsp;0.001) and serum UA (men: OR\u0026thinsp;=\u0026thinsp;0.87, 95% CI\u0026thinsp;=\u0026thinsp;0.981\u0026ndash;0.992, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; postmenopausal women: OR\u0026thinsp;=\u0026thinsp;0.995, 95% CI\u0026thinsp;=\u0026thinsp;0.993\u0026ndash;0.999, P\u0026thinsp;=\u0026thinsp;0.003) in both men and postmenopausal women. In addition, FBG (OR\u0026thinsp;=\u0026thinsp;0.793, 95% CI\u0026thinsp;=\u0026thinsp;0.643\u0026ndash;0.976, P\u0026thinsp;=\u0026thinsp;0.029) was significantly inversely associated with osteoporosis in men. HbA1c (OR\u0026thinsp;=\u0026thinsp;0.811, 95% CI\u0026thinsp;=\u0026thinsp;0.713\u0026ndash;0.922, P\u0026thinsp;=\u0026thinsp;0.001) was also significantly inversely associated with osteoporosis in postmenopausal women (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\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\u003eBinary logistic regression analysis of osteoporosis in older overweight adults\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMan\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ePostmenopausal Women\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\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 (yr)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.033\u0026ndash;1.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.053\u0026ndash;1.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI(Kg/m^2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.619\u0026ndash;0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.839\u0026ndash;0.959\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\u003eUA (\u0026micro;mol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.981\u0026ndash;0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.993\u0026ndash;0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFBG (mmol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.643\u0026ndash;0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHbA1c (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.713\u0026ndash;0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOsteoporosis and obesity have become a global epidemic and are major health issues around the world. Currently, BMD is considered the gold standard for evaluating bone mass and diagnosing osteoporosis in the clinic. In addition, it was thought that obesity has an obvious protective effect on BMD in daily clinical practice. Clinicians have attempted to identify the primary significant risk factors for osteoporosis in recent years. Clinically, several demographic characteristic and biochemical parameters are considered to affect BMD, including age, tobacco use, history of drinking, BMI, HbA1c, FBG, 25(OH)D and TG. To the best of our knowledge, the correlation of risk factors and osteoporosis are still controversial, and risk factors identified for osteoporosis in different studies and subgroups are dissimilar.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRisk Factors\u003c/h2\u003e \u003cp\u003eCompared to male participants, postmenopausal women had significantly lower T-score of the lumbar spine and a higher prevalence of osteoporosis. Both genders lose bone mass continually with advancing age, but cessation of ovarian function accelerates bone loss in postmenopausal women.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Cooper et al. reported that aging itself was a risk factor for increased bone loss.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Given that, participants with osteoporosis were older than those in the normal group and the osteopenia group in the present study. In addition, age was significantly negatively correlated with the T-score of the lumbar spine in correlation and multiple linear regression analyses.\u003c/p\u003e \u003cp\u003eAccording to the results of correlation analysis, linear regression analysis and logistic regression analysis, BMI was significantly and positively associated with BMD in older overweight adults. Overweight and obesity may primarily affect bone health through estrogens and leptin as obese adults have been shown to have higher concentrations of serum estrogens. Meanwhile, estrogens play a key role in promoting bone formation, maintaining skeletal homeostasis, and reducing bone resorption.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Leptin was identified as an adipocyte-derived cytokine-like hormone. As such, hyperleptinemia is a common feature in obese people, and leptin synthesis and secretion are significantly increased in obese people.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Bao et al. observed significantly reduced bone volume and BMD after leptin receptor gene were completely removed.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Thomas et al. reported that leptin acts on marrow stromal cells to stimulate differentiation to osteoblasts \u003cem\u003ein vitro\u003c/em\u003e.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e However, different studies have reported both positive and negative functions of leptin. It has become apparent that leptin is not the only risk factors affecting BMD. We observed that BMI was an independent risk factor for osteoporosis in older overweight adults, but the mechanisms of the effect merit further exploration. In addition, several studies have highlighted that overweight and obesity are associated with falls, especially in older adults.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Given this finding, reasonable control of weight is an important measure to prevent osteoporotic fracture from occurring.\u003c/p\u003e \u003cp\u003eYao et al. revealed a positive relationship between UA and BMD of the lumbar vertebrae among most older adults.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Wang et al. demonstrated that gout was significantly associated with osteoporotic fracture risk in older Chinese women.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e The mechanism of the increase in BMD induced by high levels of UA has been discussed in many studies. As the final product of purine metabolism, UA effectively blocks formation of the oxidant peroxynitrite and plays a key role in the procedure of antioxidation.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Loss of bone mass is closely related to increased oxidative stress or decreased antioxidants.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e In addition, UA not only stimulates the differentiation of osteoblasts but can also inhibit the generation of osteoclasts, exerting dual effects of increasing bone formation and reducing bone absorption. A large population study demonstrated that increased levels of UA are protective for bone strength and BMD.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Similarly, we found that UA was positively and linearly related to increased lumbar BMD in both men and postmenopausal women in the present study. Furthermore, this result may also support the hypothesis that UA play an important role in the osteogenic differentiation of mesenchymal stem cells.\u003c/p\u003e \u003cp\u003eMany clinical studies have focused on understanding the correlation between risk factors for BMD in patients with diabetes. In fact, osteoporosis is one of the complications associated with diabetes mellitus.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e Many studies have reported that both type 1 and 2 diabetes mellitus influence BMD and the risk of bone fractures.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e Yan et al. reported that higher blood glucose levels increase BMD.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e HbA1c and FBG are better indicators for diabetes and are considered the main clinical parameters when assessing this disorder. Consistent with previous research, the results of our study showed that FBG and HbA1c were significantly and positively associated with BMD in men and postmenopausal women, respectively. Although BMD is the gold standard for the diagnosis of osteoporosis, many studies have demonstrated that BMD measurement alone is not adequate for assessing the risk of osteoporotic fracture in older adults. Some studies have reported that diabetics have a greater risk of fracture but higher lumbar spine BMD compared to nondiabetics.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e Si et al.reported that hyperglycemia can lead to bone fragility.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e In addition, the increased risk of osteoporosis fracture, even with increased BMD, has been attributed to bone fragility in older adults with hyperglycemia.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThere are some limitations in present study. First, we did not have detailed information on some urine bone indices and other metabolic indicators. Therefore, some potential risk factors may have been neglected in this study. Second, further large-sample and longitudinal studies are needed to verify the results of the present study. Finally, BMD measurement alone is not adequate; changes in bone microstructure, bone fragility and the incidence of falls also should be tabulated when assessing the risk of osteoporotic fracture.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrates that osteoporosis might be associated with advanced age, increased BMI and higher levels of UA in older overweight adults.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eStatement Of Human Rights\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the ethics committee of the Affiliated Hospital of Qingdao University. Written informed consent was obtained from all participants. Meanwhile，all personal details were erased before analysis to cover patient data and comply with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the National Natural Science Foundation of China (81672200, 81871804) and National Key Research and Development Project (CN) (2019YFC0121400). We are grateful to the Orthopedic Medical Center of the Affiliated Hospital of Qingdao University and the special procedures team.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that they have no conflict of interest with respect to the manuscript content or funding.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLi and Zhou wrote the main manuscript text and Li prepared figures and tables. 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Diabetes Care. 2013;36(6):1619\u0026ndash;28. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/dc12-1188\u003c/span\u003e\u003cspan address=\"10.2337/dc12-1188\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":"bone mineral density, obesity, serum uric acid, BMI","lastPublishedDoi":"10.21203/rs.3.rs-4127118/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4127118/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTo investigate significant risk factors for osteoporosis in older overweight adults, which primarily included clinical indicators and laboratory examinations.\u003c/p\u003e\u003ch2\u003ePatients and Methods:\u003c/h2\u003e \u003cp\u003e A total of 1173 participants (617 men and 556 postmenopausal women) with BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 who were older than 50 and received bone density scans of the lumbar spine were enrolled in the present study. All participants had complete baseline data, including clinical indicators and biochemical indices. Participants were divided into three groups by the T-score of the lumbar spine. The Student\u0026rsquo;s t-test, Mann\u0026ndash;Whitney U test, one-way analysis of variance, Kruskal-Wallis test and chi-square test were used to compare the continuous and categorical clinical variables among the different groups. Spearman correlation tests, Pearson correlation tests and linear regression analysis were performed to identify independent variables associated with bone mineral density (BMD) and their multicollinearity in older overweight adults. In addition, binary logistic regression analysis was performed to determine the independent risk factors associated with osteoporosis. A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eCompared to those in the normal group and the osteopenia group, man and postmenopausal women with osteoporosis were older and had decreased BMI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively). Correlation analysis and multiple linear regression analysis revealed that the BMD values of the lumbar vertebrae were significantly positively correlated with BMI and serum uric acid (UA) and negatively correlated with age in men and postmenopausal women. Finally, binary logistic regression analyses revealed that after adjusting for many variables, osteoporosis was significantly and positively associated with age and inversely associated with BMI and serum UA in both men and postmenopausal women (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study demonstrates that osteoporosis might be associated with advanced age, increased BMI and higher levels of UA in older overweight adults.\u003c/p\u003e","manuscriptTitle":"Predictive Risk Factors for Osteoporosis in Older Overweight Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-22 13:28:04","doi":"10.21203/rs.3.rs-4127118/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":"609e4af1-dde9-4ce7-af8b-05aed36d132a","owner":[],"postedDate":"March 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-22T00:07:10+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-22 13:28:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4127118","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4127118","identity":"rs-4127118","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}