The triangular relationship of serum uric acid, osteoporosis or osteopenia, and body mass index for men and postmenopausal women

The triangular relationship of serum uric acid, osteoporosis or osteopenia, and body mass index for men and postmenopausal women | 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 Article The triangular relationship of serum uric acid, osteoporosis or osteopenia, and body mass index for men and postmenopausal women Ziran Xiu, Zhengnan Gao, Lan Luo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5748318/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 6 You are reading this latest preprint version Abstract Background Osteoporosis (OP) is a systemic bone disease characterized by reduced bone density and quality, leading to increased bone fragility and a higher risk of fractures. The relationship between serum uric acid (SUA) levels and OP or osteopenia remains controversial, as does the impact of weight change on these conditions. Moreover, few studies have investigated whether body mass index (BMI) serves as a mediator in the association between SUA and OP or osteopenia. Objective This study aimed to elucidate the complex interactions between SUA, OP or osteopenia, and BMI. Methods A cross-sectional study from the REACTION study was conducted to examine the association between SUA and OP or osteopenia. Various logistic regression and restricted cubic spline models were employed to analyze the pairwise correlations among these variables, and interaction analysis was performed to assess differences between subgroups. Mediation models were utilized to determine the mediating role of BMI. Results The findings indicated that both SUA and BMI were inversely associated with OP or osteopenia, while a positive correlation was observed between SUA and BMI. Analysis of the dose-response relationship between SUA and OP or osteopenia showed a linear negative correlation, and a significant nonlinear association between BMI and the risk of OP or osteopenia. No significant interactions were found within subgroups. Additionally, BMI was found to mediate 13.6% of the potential effects of SUA on OP or osteopenia. Conclusions SUA appears to have a protective effect against OP or osteopenia, with BMI potentially serving as a mediator. Thereby, maintaining SUA and BMI within an optimal range may. Health sciences/Diseases Health sciences/Endocrinology Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Osteoporosis or osteopenia Serum uric acid Body mass index Bone mineral density Mediation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Osteoporosis (OP) is a systemic bone disease characterized by low bone mass and deterioration of bone microarchitecture, leading to increased fragility and a higher risk of fractures (1–3). Op and osteopenia affect approximately 19.7% and 40.4% of the general population, respectively. In postmenopausal women, the prevalence is even higher, reaching 27.4% for OP and 42.1% for osteopenia(4). Due to their high prevalence, significant economic burden, and elevated disability rates, OP and osteopenia have become serious public health concerns. Serum uric acid (SUA) is the end product of purine nucleotide degradation in the organism and a well-established risk factor for gout (5). Elevated SUA levels, or hyperuricemia, can lead to various diseases, including chronic kidney disease (CKD), cardiovascular disease, and metabolic syndrome(6–8). Recent studies have also suggested that increased SUA levels may be associated with a reduced prevalence of osteoporosis(9). Research has shown that SUA acts as a natural endogenous antioxidant, capable of scavenging free radicals and exhibiting anti-oxidative stress effects(10–12). Consequently, SUA may exert a protective effect against OP through direct actions on bone metabolism-related cells(13). Body Mass Index (BMI) is a key indicator of the body's nutritional status. Most studies suggest that higher body weight increases the physical load on bones, thereby stimulating bone formation and enhancing bone density. Additionally, elevated body weight and BMI have long been shown to protect against the decline in bone mineral density (BMD)(14). Conversely, being underweight is an independent risk factor for osteoporosis(15). A meta-analysis found that low BMI is significantly associated with a higher risk of fractures(16). Furthermore, research indicates that BMI is positively correlated with SUA levels (17,18). Given the close associations among these factors and the significant physiological and environmental differences between men and women, it is essential to further assess their intrinsic relationships across different gender groups. This study investigated the complex associations between SUA, BMI, and OP or osteopenia in men over 40 years of age and postmenopausal women using cross-sectional methods. It also closely examined the mediating role of BMI in the relationship between SUA and OP or osteopenia. The findings aim to support the development of more effective and targeted preventive strategies. Materials and methods Study population In this study, 10,207 community residents aged over 40 from Dalian who participated in the REACTION study, a project investigating the risk of malignancies in T2DM patients in China from August to December 2011, were initially considered. Among them, male and postmenopausal female participants who completed bone mineral density examination were selected, a total of 1724 cases. We excluded 82 patients due to the presence of bone diseases, malignant tumors, severe cardiac, hepatic, or renal dysfunction, or chronic use of glucocorticoids that could impact bone metabolism. After removing an additional 86 individuals with incomplete data on BMI and SUA, 1,556 participants were ultimately included in the study. The detailed exclusion process is depicted in Figure 1. Ethical approval was granted by the REACTION Research Ethics Committee (Approval No. 2011 Linlun Review No. 14), and informed consent was obtained from all participants. All methods were performed in accordance with the relevant guidelines and regulations. Measures Definitions of OP and Osteopenia The diagnosis of osteoporosis primarily depends on bone mineral density (BMD) measurements, which are obtained using dual-energy X-ray absorptiometry (DXA). The T-score is employed to evaluate the deviation of an individual’s BMD from that of a standard reference population of young healthy adults. The diagnostic criteria, based on the World Health Organization (WHO) definitions(19), are as follows: osteopenia is defined by a T-score between -1.0 and -2.5, while osteoporosis is characterized by a T-score of less than -2.5. In our study, BMD was measured using the LUNAR PRODIGY dual-energy X-ray absorptiometry (DXA) system, manufactured by GE in the United States. Specifically, the T-score for the lumbar spine (L1-L4) BMD was assessed. Following the diagnostic criteria for osteoporosis, and patients are categorized into normal, OP or osteopenia groups based on their BMD measurements. Assessment of SUA The exposure variable of this study was SUA, which has been measured using an automatic biochemical detector (ADVIA 2400 automatic Biochemical detector). Meanwhile, for statistical analysis, quartiles of SUA levels were divided into four groups: tquartile1 (Q1): ≤25th percentile, quartile2 (Q2): >25–50 percentile, quartile3 (Q3): >50–75 percentile, quartile4 (Q4): >75th percentile. Assessment of BMI We measured height and weight, allowing for the computation of BMI as the weight in kilograms divided by the square of the height in meters. Meanwhile, quartiles of BMI levels were divided into four groups: tquartile1 (Q1): ≤25th percentile, quartile2 (Q2): >25–50 percentile, quartile3 (Q3): >50–75 percentile, quartile4 (Q4): >75th percentile. Covariates Age, sex, educational level, the history of diabetes (DM), stroke, coronary heart disease (CHD), hypertension, hyperlipidemia and fatty liver and smoking status, alcohol consumption were self-reported by participants. Upon admission, systolic blood pressure (SBP), and diastolic blood pressure (DBP) were measured. Laboratory measurements included high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), total cholesterol (TC), triglycerides (TG), alanine aminotransferase (ALT), glucose (Glu), hemoglobin A1c (HbA1c), homeostatic model assessment for insulin resistance (HOMA-IR) and creatinine (Cr). Statistical analysis Normality tests were conducted for all data. For data following a normal distribution, a t-test was performed, while the Mann–Whitney U -test was applied to data that did not follow a normal distribution. Data are presented as median values with interquartile ranges (Q25, Q75). Categorical variables are represented by N (%) and analyzed by chi-square test. Samples with missing covariate data were excluded. Various logistic regression approaches were used to explore the association between SUA, BMI and OP, osteopenia. In the absence of predefined cut-off points of SUA and BMI, the interquartile method was used for analysis. Three analytical models were developed: Model 1 (unadjusted), Model 2 (adjusted for age and sex), and Model 3 (adjusted for multiple variables including age, sex, DBP, HDL, LDL, ALT, HOMA-IR, Cr, educational level, DM, hyperlipidemia, fatty liver, smoking status and alcohol consumption). The nonlinear association between UA, BMI, OP and osteopenia was further investigated by using 3-knot restricted cubic splines (RCS). Subgroup analyses were conducted to explore potential modifications by covariates such as age, sex, BMI, hypertension, Stroke, CHD, DM, hyperlipidemia, fatty liver might modify the OP or osteopenia on the relationship between UA and OP or osteopenia, considering interactions significant at P<0.05. Finally, the mediating effect of BMI between UA and OP or osteopenia was investigated by mediating analysis. All statistical analyses were performed using EmpowerStats (version 4.2) and R software (version 4.3.2), with statistical significance set at P < 0.05. Results Baseline characteristics Table 1 presents the baseline characteristics of the 1,556 study participants, with a mean age of 60.4 years, comprising 21.5% men and 78.5% women. The prevalence of OP or osteopenia among these participants was 47.8% (744/1556). Both BMI and SUA levels were significantly lower in the OP or osteopenia group compared to those in normal subjects. Baseline data indicated significant associations of OP or osteopenia with sex, age, DBP, HDL, LDL, TC, ALT, HOMA-IR, Cr, educational level, DM, hyperlipidemia, fatty liver, smoking, and alcohol consumption. Table1. Baseline characteristics Variable Total Normal OP or osteopenia P-value (N=1556) (N=812) (N=744) Age, years 60.4 (55.9, 67.2) 59.64 (55.15, 65.68) 61.13 (56.87, 68.57) <0.001 SUA (umol/L) 315.0 (274.0, 368.0) 328.00 (283.00, 381.25) 304.00 (267.00, 348.50) <0.001 BMI (Kg/m2) 25.5 (23.2, 27.8) 26.14 (23.93, 28.23) 24.75 (22.51, 26.98) <0.001 SBP (mmHg) 139.7 (125.7, 155.0) 141.00 (126.00, 155.67) 138.00 (125.25, 153.08) 0.242 DBP (mmHg) 80.3 (73.0, 87.1 81.00 (73.33, 88.33) 79.00 (72.00, 86.33) 0.002 HDL (mmol/L) 1.3 (1.1, 1.5) 1.27 (1.11, 1.47) 1.37 (1.19, 1.57) <0.001 LDL (mmol/L) 3.3 (2.8, 3.8) 3.25 (2.69, 3.80) 3.37 (2.85, 3.89) 0.008 TC (mmol/L) 5.5 (4.9, 6.2) 5.44 (4.83, 6.14) 5.62 (5.03, 6.33) <0.001 TG (mmol/L) 1.4 (1.0, 1.9) 1.38 (1.02, 2.01) 1.34 (0.99, 1.89) 0.139 ALT (U/L) 16.0 (12.0, 22.0) 17.00 (13.00, 23.00) 16.00 (12.00, 21.00) 0.001 Glu (mmol/L) 5.7 (5.3, 6.5) 5.72 (5.31, 6.59) 5.67 (5.32, 6.33) 0.088 HbA1c(%) 5.8 (5.6, 6.2) 5.90 (5.60, 6.30) 5.80 (5.60, 6.20) 0.382 HOMA-IR 2.1 (1.4, 3.2) 2.29 (1.52, 3.33) 1.98 (1.33, 2.99) <0.001 Cr (umol/L) 65.1 (60.2, 71.8) 66.80 (60.80, 75.23) 63.90 (59.30, 69.32) <0.001 Sex (%) <0.001 Man 335 (21.5%) 264 (32.51%) 71 (9.54%) Woman 1221 (78.5%) 548 (67.49%) 673 (90.46%) Educational level (%) <0.001 Primary school and below 257 (16.5%) 99 (12.21%) 158 (21.29%) Middle school and high school 1112 (71.6%) 592 (73.00%) 520 (70.08%) University and above 184 (11.8%) 120 (14.80%) 64 (8.63%) DM (%) 0.002 Yes 225 (14.5%) 139 (17.12%) 86 (11.56%) No 1331 (85.5%) 673 (82.88%) 658 (88.44%) Stroke (%) 0.999 Yes 23 (1.5%) 12 (1.48%) 11 (1.48%) No 1533 (98.5%) 800 (98.52%) 733 (98.52%) CHD (%) 0.218 Yes 118 (7.6%) 68 (8.37%) 50 (6.72%) No 1438 (92.4%) 744 (91.63%) 694 (93.28%) Hypertension (%) 0.162 Yes 376 (24.2%) 208 (25.62%) 168 (22.58%) No 1180 (75.8%) 604 (74.38%) 576 (77.42%) Hyperlipidemia (%) 0.006 Yes 203 (13.0%) 124 (15.27%) 79 (10.62%) No 1353 (87.0%) 688 (84.73%) 665 (89.38%) Fatty liver（%） 0.028 Yes 166 (10.7%) 100 (12.32%) 66 (8.87%) No 1390 (89.3%) 712 (87.68%) 678 (91.13%) Smoking (%) <0.001 Yes 138 (8.9%) 92 (11.33%) 46 (6.18%) No 1418 (91.1%) 720 (88.67%) 698 (93.82%) alcohol consumption (%) <0.001 Yes 315 (20.2%) 213 (26.23%) 102 (13.71%) No 1241 (79.8%) 599 (73.77%) 642 (86.29%) SUA serum uric acid, BMI Body mass index, SBP Systolic blood pressure, DBP Diastolic blood pressure, HDL High-density lipoprotein, LDL Low-density lipoprotein, TC Total cholesterol, TG Triglycerides, Glu glucose, HbA1c Hemoglobin A1c, HOMA-IR Homeostatic model assessment for insulin resistance, ALT Alanine aminotransferase, UA Uric acid, Cr Creatinine, CHD Coronary heart disease, DM diabetes mellitus Association between SUA and OP or osteopenia As detailed in Table 2, after adjusting for all covariates, the correlation between SUA and OP or osteopenia in Model 3 remained significant (OR = 0.997, 95% CI 0.996–0.999, P = 0.002). SUA levels were categorized into quartiles to explore their associations with OP or osteopenia. Subsequent adjustments for potential confounders revealed that the odds ratios (ORs) from multiple logistic regression models were 0.921 (95% CI 0.676–1.255) for Q2, 0.744 (95% CI 0.543–1.019) for Q3, and 0.651 (95% CI 0.464–0.913) for Q4, compared to Q1. Using 3-knot restricted cubic spline (RCS) models, we analyzed the dose-response relationship between SUA and OP or osteopenia, identifying a linear negative correlation (P = 0.009) with no significant nonlinearity (P = 0.919) in Fig. 2. Table 2. Relationship between SUA and OP or osteopenia Model 1 Model 2 Model 3 OR (95% CI) P -value OR (95% CI) P -value OR (95% CI) P -value SUA 0.995 (0.993, 0.996) <0.001 0.996 (0.994, 0.998) <0.001 0.997 (0.996, 0.999) 0.002 Q1 1.000 (Ref.) 1.000 (Ref.) 1.000 (Ref.) Q2 0.829 (0.624, 1.100) 0.194 0.863 (0.641, 1.163) 0.332 0.921 (0.676, 1.255) 0.602 Q3 0.595 (0.448, 0.792) <0.001 0.670 (0.495, 0.907) 0.009 0.744 (0.543, 1.019) 0.066 Q4 0.402 (0.301, 0.537) <0.001 0.498 (0.364, 0.681) <0.001 0.651 (0.464, 0.913) 0.013 P for trend <0.001 <0.001 0.006 Model 1: non-adjusted Model 2: adjusted for age and sex Model 3: further adjusted for age, sex, DBP, HDL, LDL, ALT, HOMA-IR, Cr, educational level, DM, hyperlipidemia, fatty liver, smoking status and alcohol consumption Association between BMI and OP or osteopenia In Table 3, the relationship between BMI and OP or osteopenia is presented. After adjusting for all covariates, the correlation remained significant in Model 3 (OR = 0.881, 95% CI 0.849–0.914, P < 0.001). BMI was categorized into quartiles, and multiple logistic regression was used for analysis. After adjusting for confounding factors in Model 3, the ORs were as follows: for Q2, the OR was 0.547 (95% CI 0.396–0.754); for Q3, the OR was 0.425 (95% CI 0.305–0.594); and for Q4, the OR was 0.292 (95% CI 0.204–0.417), compared to Q1. Additionally, using 3-knot RCS models, we identified a significant nonlinear relationship between BMI and the risk of OP or osteopenia, after adjusting for confounding factors. Specifically, the risk decreased with increasing BMI, began to decline more slowly, and eventually plateaued at a BMI of approximately 23.979 in Fig. 3. Table 3 Relationship between BMI and OP or osteopenia Model 1 Model 2 Model 3 OR (95% CI) P -value OR (95% CI) P -value OR (95% CI) P -value BMI 0.899 (0.873, 0.937) <0.001 0.861 (0.833, 0.890) <0.001 0.881 (0.849, 0.914) <0.001 Q1 1.000 (Ref.) 1.000 (Ref.) 1.000 (Ref.) Q2 0.556 (0.417, 0.740) <0.001 0.49 4(0.362, 0.673) <0.001 0.547 (0.396, 0.754) <0.001 Q3 0.468 (0.351, 0.623) <0.001 0.365 (0.270, 0.499) <0.001 0.425 (0.305, 0.594) <0.001 Q4 0.353 (0.264, 0.472) <0.001 0.237 (0.172, 0.328) <0.001 0.292 (0.204, 0.417) <0.001 P for trend <0.001 <0.001 <0.001 Model 1: non-adjusted Model 2: adjusted for age and sex Model 3: further adjusted for age, sex, DBP, HDL, LDL, ALT, HOMA-IR, Cr, educational level, DM, hyperlipidemia, fatty liver, smoking status and alcohol consumption Association between SUA and BMI The correlation between SUA and BMI was assessed using multiple regression analysis, with the results presented in Table 4. In the unadjusted model, SUA was positively associated with BMI (β = 4.744 95% CI 3.795–5.693 P < 0.001). After adjusting for covariates, the correlation remained significant in Model 2 (β = 4.711, 95% CI 3.804–5.618, P < 0.001) and Model 3 (β = 3.451, 95% CI 2.494–4.408, P < 0.001). When BMI was categorized into quartiles, the association between BMI and SUA continued to be statistically significant at higher BMI levels. Specifically, subjects in the highest BMI quartile experienced an 34.558 µmol/L increase in SUA compared to those in the lowest quartile (Model 3 β = 34.558, 95% CI 24.581–44.534, P < 0.001). Table 4 displays the results of the multiple regression analysis between SUA and BMI. Fig. 4 illustrates the smooth curve fitting of the relationship between SUA and BMI. Table 4. Relationship between BMI and UA Model 1 Model 2 Model 3 β (95% CI) P -value β (95% CI) P -value β (95% CI) P -value BMI 4.744 (3.795, 5.693) <0.001 4.711 (3.804, 5.618) <0.001 3.451 (2.494, 4.408) <0.001 Q1 0 (Ref.) 0 (Ref.) 0 (Ref.) Q2 17.274 (7.368, 27.180) <0.001 14.670 (5.252, 24.089) 0.002 8.125 (-1.227, 17.477) 0.089 Q3 33.312 (23.456, 43.167) <0.001 31.610 (22.219, 41.001) <0.001 21.448 (11.853, 31.044) <0.001 Q4 48.144 (38.263, 58.025) <0.001 47.175 (37.716, 56.633) <0.001 34.558 (24.581, 44.534) <0.001 P for trend <0.001 <0.001 <0.001 Model 1: non-adjusted Model 2: adjusted for age and sex Model 3: further adjusted for age, sex, DBP, HDL, LDL, ALT, HOMA-IR, Cr, educational level, DM, hyperlipidemia, fatty liver, smoking status and alcohol consumption Subgroup analyses Subgroup analyses by sex, age, BMI, hypertension, stroke, CHD, DM, hyperlipidemia, and fatty liver (reported in Fig. 5) revealed no notable differences in the relationships between SUA and OP or osteopenia across the subgroups. Mediating role of BMI In the mediation analysis, we explored the role of BMI as a mediator in the association between SUA and OP or osteopenia (Fig 6). The results indicated that BMI weakly, yet significantly, mediated this association after adjusting for all covariates. Specifically, 13.6% of the total effect of SUA on OP or osteopenia was mediated by BMI, which was statistically significant (p = 0.032). Discussion The purpose of this study was to explore the relationship between SUA, OP or osteopenia, and BMI. Our findings indicate that in men over 40 years of age and postmenopausal women, the risk of OP or osteopenia decreases as SUA levels increase, demonstrating a negative correlation between the two. Additionally, we confirmed that BMI is inversely nonlinear associated with OP or osteopenia, while a positive correlation exists between SUA and BMI. Mediation analysis further revealed that BMI mediates 13.6% of the potential effects of SUA on OP or osteopenia. OP and osteopenia represent significant public health challenges. Previously viewed as an unavoidable consequence of aging, they are now recognized as serious, treatable diseases due to advances in medical treatments. Identifying additional risk factors and screening for high-risk individuals are crucial for the early prevention and management of these conditions, ultimately improving patient outcomes and reducing economic burdens. Numerous epidemiological studies have investigated the relationship between SUA and BMD; however, the findings have been inconsistent. A meta-analysis indicated that elevated SUA levels might play a protective role in bone metabolism disorders(20). Further, several studies have established a significant association between SUA and BMD, suggesting a protective effect on bone metabolism in diverse groups, including healthy adults(21), elderly populations(22), patients with type 2 diabetes(23), osteoporosis patients(24), and postmenopausal women(25). Additionally, one of the Rotterdam cohort studies found that higher SUA levels are correlated with increased BMD (at the expense of thicker cortices and narrower bone diameters) (26). Our results show that higher SUA is associated with the risk of OP and osteopenia, consistent with these results. However, other studies have found no association between SUA and BMD in American adult men(27) and postmenopausal women with type 2 diabetes(28). We consider these results to be different due to differences in study populations and statistical methods. The action mechanism of SUA on OP or osteopenia remains unclear; however, its potential protective mechanism could be delineated as follows: SUA acts as a significant endogenous antioxidant, particularly under oxidative stress(11,29), which facilitates the removal of free radicals from the blood. Reactive oxygen species (ROS) can suppress the differentiation of osteoblasts while promoting the differentiation and activation of osteoclasts, ultimately leading to osteopenia(30,31). By scavenging free radicals and ROS, SUA may reduce bone loss. Lower SUA concentrations are associated with reduced serum parathyroid hormone (PTH) level(32). higher SUA levels correlate with diminished kidney function, leading to decreased production of 1,25-dihydroxyvitamin D. This reduction in turn stimulates PTH production and increases serum calcium levels(33), thus increasing bone density. Additionally, another study demonstrated that uric acid enhances osteogenic differentiation and inhibits adipogenic differentiation of human bone mesenchymal stem cells(34). The results of the mediation analysis indicated that part of the effect of SUA on OP or osteopenia might be mediated through BMI. Previous studies have consistently confirmed a positive association between BMI and SUA (17,18). Simultaneously, research has shown that BMI is positively correlated with BMD(14,35). Notably, obese women exhibit a lower prevalence of osteopenia and osteoporosis compared to their normal-weight and overweight counterparts(36). High BMI, acting as a mechanical load, enhances the stimulation of bone and muscle, thereby promoting bone reconstruction and reducing the incidence of OP(37). On the other hand, by modulating the nuclear factor-kappa B (NF-κB) receptor activator of nuclear factor kappa-B ligand (RANKL)/osteoprotegerin (OPG) pathway, osteoclast differentiation is inhibited, and bone resorption is consequently reduced (38). It is essential that we identify how, compared to leaner individuals, obese persons typically exhibit higher serum levels of estrogen and PTH, lower levels of 25-hydroxyvitamin D (25OHD) and sex hormone-binding globulin (SHBG), and possibly reduced levels of 1,25-dihydroxyvitamin D3 (1,25(OH)2D3)(39–41), Each of these factors exerts specific actions on bone health. These findings constitute a supporting factor for the results of our mediation analysis. This study redefines our comprehension of the complex relationship among SUA, OP or osteopenia, and BMI, emphasizing BMI's potential role as a mediator. This analysis has several limitations. Firstly, other potentially relevant biochemical markers, including PTH and plasma phosphate levels, were not assessed in these patients, yet they may significantly influence bone metabolism. Secondly, the study did not account for the effects of medications, particularly the impact of uric acid-lowering drugs on serum uric acid levels. Thirdly, since the study population was drawn from a single center and utilized a cross-sectional design, it was not possible to establish a causal relationship between SUA and OP or osteopenia. Thus, further validation in multiple cohort studies is necessary. Conclusions In conclusion, this study sought to examine the complex relationships among BMI, SUA, and OP or osteopenia in men over 40 years of age and postmenopausal women. It identified a linear negative correlation between SUA and OP or osteopenia. Furthermore, BMI was found to influence and mediate the risk of OP or osteopenia associated with SUA. These findings offer new insights for the prevention and treatment of these conditions. Declarations Data availability The datasets analysed during the current study are available from the corresponding author on reasonable request. 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Osteoporos Int (1997) 7 Suppl 3:S108-116. doi: 10.1007/BF03194355 Veronese N, Carraro S, Bano G, Trevisan C, Solmi M, Luchini C, Manzato E, Caccialanza R, Sergi G, Nicetto D, et al. Hyperuricemia protects against low bone mineral density, osteoporosis and fractures: a systematic review and meta-analysis. Eur J Clin Invest (2016) 46:920–930. doi: 10.1111/eci.12677 Ibrahim WN, Younes N, Shi Z, Abu-Madi MA. Serum Uric Acid Level Is Positively Associated With Higher Bone Mineral Density at Multiple Skeletal Sites Among Healthy Qataris. Front Endocrinol (Lausanne) (2021) 12:653685. doi: 10.3389/fendo.2021.653685 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. doi: 10.1155/2020/3082318 Xu M, Su J, Hao J, Zhong N, Zhang Z, Cui R, Li F, Sheng C, Zhang G, Sheng H, et al. Positive association between serum uric acid and bone mineral density in Chinese type 2 diabetes mellitus stratified by gender and BMI. J Bone Miner Metab (2018) 36:609–619. doi: 10.1007/s00774-017-0877-9 Xu M-Z, Lu K, Yang X-F, Ye Y-W, Xu S-M, Shi Q, Gong Y-Q, Li C. Association between serum uric acid levels and bone mineral density in patients with osteoporosis: a cross-sectional study. BMC Musculoskelet Disord (2023) 24:306. doi: 10.1186/s12891-023-06414-w Bonaccorsi G, Trentini A, Greco P, Tisato V, Gemmati D, Bianchi N, Giganti M, Rossini M, Guglielmi G, Cervellati C. Changes in Adipose Tissue Distribution and Association between Uric Acid and Bone Health during Menopause Transition. Int J Mol Sci (2019) 20:6321. doi: 10.3390/ijms20246321 Muka T, de Jonge EAL, Kiefte-de Jong JC, Uitterlinden AG, Hofman A, Dehghan A, Zillikens MC, Franco OH, Rivadeneira F. The Influence of Serum Uric Acid on Bone Mineral Density, Hip Geometry, and Fracture Risk: The Rotterdam Study. J Clin Endocrinol Metab (2016) 101:1113–1122. doi: 10.1210/jc.2015-2446 No association between serum uric acid and lumbar spine bone mineral density in US adult males: a cross sectional study - PubMed. https://pubmed.ncbi.nlm.nih.gov/34341438/ [Accessed October 23, 2024] 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. doi: 10.1155/2020/3982831 Uric acid and oxidative stress - PubMed. https://pubmed.ncbi.nlm.nih.gov/16375736/ [Accessed October 23, 2024] Lee HS, Hwang JS. Impact of Type 2 Diabetes Mellitus and Antidiabetic Medications on Bone Metabolism. Curr Diab Rep (2020) 20:78. doi: 10.1007/s11892-020-01361-5 Sh A, Sh L, Bj K, Kh L, Sj B, Eh K, Hk K, Jw C, Jm K, Gs K. Higher serum uric acid is associated with higher bone mass, lower bone turnover, and lower prevalence of vertebral fracture in healthy postmenopausal women. Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA (2013) 24: doi: 10.1007/s00198-013-2377-7 Relationships between serum uric acid concentrations, uric acid lowering medications, and vertebral fracture in community-dwelling elderly Japanese men: Fujiwara-kyo Osteoporosis Risk in Men (FORMEN) Cohort Study - PubMed. https://pubmed.ncbi.nlm.nih.gov/32622874/ [Accessed October 23, 2024] Gómez-de-Tejada-Romero M-J, Murias-Henríquez C, Saavedra-Santana P, Sablón-González N, Abreu DR, Sosa-Henríquez M. Influence of serum uric acid on bone and fracture risk in postmenopausal women. Aging Clinical and Experimental Research (2024) 36:156. doi: 10.1007/s40520-024-02819-2 Li H-Z, Chen Z, Hou C-L, Tang Y-X, Wang F, Fu Q-G. Uric Acid Promotes Osteogenic Differentiation and Inhibits Adipogenic Differentiation of Human Bone Mesenchymal Stem Cells. J Biochem Mol Toxicol (2015) 29:382–387. doi: 10.1002/jbt.21707 Pirro M, Mannarino MR, Bianconi V, De Vuono S, Sahebkar A, Bagaglia F, Franceschini L, Scarponi AM, Mannarino E, Merriman T. Uric acid and bone mineral density in postmenopausal osteoporotic women: the link lies within the fat. Osteoporos Int (2017) 28:973–981. doi: 10.1007/s00198-016-3792-3 Mazocco L, Chagas P. Association between body mass index and osteoporosis in women from northwestern Rio Grande do Sul. Rev Bras Reumatol Engl Ed (2017) 57:299–305. doi: 10.1016/j.rbre.2016.10.002 Gkastaris K, Goulis DG, Potoupnis M, Anastasilakis AD, Kapetanos G. Obesity, osteoporosis and bone metabolism. Journal of Musculoskeletal & Neuronal Interactions (2020) 20:372. Balasubramanian A, Zhang J, Chen L, Wenkert D, Daigle SG, Grauer A, Curtis JR. Risk of subsequent fracture after prior fracture among older women. Osteoporos Int (2019) 30:79–92. doi: 10.1007/s00198-018-4732-1 Konradsen S, Ag H, Lindberg F, Hexeberg S, Jorde R. Serum 1,25-dihydroxy vitamin D is inversely associated with body mass index. Eur J Nutr (2008) 47:87–91. doi: 10.1007/s00394-008-0700-4 Prince RL, Smith M, Dick IM, Price RI, Webb PG, Henderson NK, Harris MM. Prevention of postmenopausal osteoporosis. A comparative study of exercise, calcium supplementation, and hormone-replacement therapy. N Engl J Med (1991) 325:1189–1195. doi: 10.1056/NEJM199110243251701 Zhao L-J, Jiang H, Papasian CJ, Maulik D, Drees B, Hamilton J, Deng H-W. Correlation of obesity and osteoporosis: effect of fat mass on the determination of osteoporosis. J Bone Miner Res (2008) 23:17–29. doi: 10.1359/jbmr.070813 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 Apr, 2025 Reviews received at journal 09 Apr, 2025 Reviewers agreed at journal 03 Apr, 2025 Reviewers invited by journal 01 Apr, 2025 Submission checks completed at journal 01 Apr, 2025 First submitted to journal 23 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies 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-5748318","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":437125151,"identity":"93b8ba5f-b33e-4b1a-9ca5-e37f5311bfbe","order_by":0,"name":"Ziran Xiu","email":"","orcid":"","institution":"Central Hospital of Dalian University of Technology (Dalian Municipal Central Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Ziran","middleName":"","lastName":"Xiu","suffix":""},{"id":437125152,"identity":"df4ac07f-295e-4ef9-ac7e-4e2de9613213","order_by":1,"name":"Zhengnan Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYBACNv7GhgMfKmp4+OUfH3yQUFFDWAufxOHGhzPOHJORbEhLNnhw5hhhLXIM6c3GvG3MNgYHctQkH7YwE+EwhoNt0jxn2HgkG86wVSQ2sDHwt3cn4NfC3NgmOadChoefsffYjcQdMgwSZ85uIGiLxBuQLc18aTcSz7AxGEjkEtKS2CYB9AuPwTEes4LENmaitDQbgrWc4TFjIE6LxEFwIPNIzmBLlkgAMgj6Rb6//QEoKu35JZgPfvxRUSPH396LXwsG4CFN+SgYBaNgFIwCrAAAretLoflivFEAAAAASUVORK5CYII=","orcid":"","institution":"Central Hospital of Dalian University of Technology (Dalian Municipal Central Hospital)","correspondingAuthor":true,"prefix":"","firstName":"Zhengnan","middleName":"","lastName":"Gao","suffix":""},{"id":437125153,"identity":"c90b48e7-cc5f-4249-b584-d657c9eda101","order_by":2,"name":"Lan Luo","email":"","orcid":"","institution":"Central Hospital of Dalian University of Technology (Dalian Municipal Central Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Lan","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2025-01-02 02:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5748318/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5748318/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-10191-y","type":"published","date":"2025-07-10T15:57:45+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79848671,"identity":"8c08b3da-c856-41c9-9818-b118c46c35a1","added_by":"auto","created_at":"2025-04-03 14:15:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":629263,"visible":true,"origin":"","legend":"\u003cp\u003eScreening flow chat\u003c/p\u003e","description":"","filename":"Fig.1.Screeningflowchat.png","url":"https://assets-eu.researchsquare.com/files/rs-5748318/v1/04f51e6a7e62dac08f1a0497.png"},{"id":79848672,"identity":"dfe46e9d-c50e-44ab-9153-07e55bfd360c","added_by":"auto","created_at":"2025-04-03 14:15:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":489157,"visible":true,"origin":"","legend":"\u003cp\u003eThe dose–response relationship between SUA and OP or osteopenia. Graphs show OR for OP or osteopenia adjusted for age, sex, DBP, HDL, LDL, ALT, HOMA-IR, Cr, educational level, DM, hyperlipidemia, fatty liver, smoking status and alcohol consumption. Data were fitted by multivariate logistic regression models. Solid lines indicate OR, and shadow shapes indicate 95% CIs.\u003c/p\u003e","description":"","filename":"Fig.2.AssociationbetweenSUAandosteoporosisorosteopenia.png","url":"https://assets-eu.researchsquare.com/files/rs-5748318/v1/94c93f081719f27eaf07471c.png"},{"id":79849678,"identity":"cfb8d433-654f-4508-9a6c-60b306b8e3d5","added_by":"auto","created_at":"2025-04-03 14:23:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":451469,"visible":true,"origin":"","legend":"\u003cp\u003eThe dose–response relationship between BMI and OP or osteopenia. Graphs show OR for OP or osteopenia adjusted for age, sex, DBP, HDL, LDL, ALT, HOMA-IR, Cr, educational level, DM, hyperlipidemia, fatty liver, smoking status and alcohol consumption. Data were fitted by multivariate logistic regression models. Solid lines indicate OR, and shadow shapes indicate 95% CIs.\u003c/p\u003e","description":"","filename":"Fig.3.AssociationbetweenBMIandosteoporosisorosteopenia.png","url":"https://assets-eu.researchsquare.com/files/rs-5748318/v1/b848911dd7b7ee3bb6de67a0.png"},{"id":79849679,"identity":"97bd7f5c-68aa-4b7a-8376-2e34a5d15f64","added_by":"auto","created_at":"2025-04-03 14:23:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":709788,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between SUA and BMI. (a) Each black dot represents a sample. (b) The solid line indicates a smooth curve fit between the variables. The blue band indicates the 95% confidence interval of the fit. Adjusted for age, sex, DBP, HDL, LDL, ALT, HOMA-IR, Cr, educational level, DM, hyperlipidemia, fatty liver, smoking status and alcohol consumption.\u003c/p\u003e","description":"","filename":"Fig.4.AssociationbetweenSUAandBMI.png","url":"https://assets-eu.researchsquare.com/files/rs-5748318/v1/1686fa32bca6058c259bbd1c.png"},{"id":79848678,"identity":"4e6dc6a7-7f95-4431-a00f-860c8949f1dc","added_by":"auto","created_at":"2025-04-03 14:15:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":649733,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis for the association of SUA and OP or osteopenia. Subgroup analysis of the association between SUA and OP or osteopenia, stratified by age, sex, BMI, hypertension, stroke, CHD, DM, hyperlipidemia, and fatty liver. Each square in the forest plot represents an estimated Odds Ratio (OR) for a specific subgroup. The size of the square is usually proportional to the statistical weight or sample size of that estimate. “P for interaction” tests whether there is any interaction between the subgroups.\u003c/p\u003e","description":"","filename":"Fig.5.SubgroupanalysisfortheassociationofSUAandOPorosteopenia.png","url":"https://assets-eu.researchsquare.com/files/rs-5748318/v1/c323741b1790e51f373f1e6e.png"},{"id":79848679,"identity":"64bbb594-9073-4a21-aecb-1e0b3e177668","added_by":"auto","created_at":"2025-04-03 14:15:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":64044,"visible":true,"origin":"","legend":"\u003cp\u003ePath diagram of the mediation analysis of BMI on the relationship between SUA and OP or osteopenia *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5748318/v1/c1153dda3605d023f1d1c5c8.png"},{"id":86699418,"identity":"f5305453-58e1-414c-a377-e187a2b755a3","added_by":"auto","created_at":"2025-07-14 16:09:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3809780,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5748318/v1/5b465c23-115f-4b9b-bf63-845e5522ee61.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The triangular relationship of serum uric acid, osteoporosis or osteopenia, and body mass index for men and postmenopausal women","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOsteoporosis (OP) is a systemic bone disease characterized by low bone mass and deterioration of bone microarchitecture, leading to increased fragility and a higher risk of fractures (1\u0026ndash;3). Op and osteopenia affect approximately 19.7% and 40.4% of the general population, respectively. In postmenopausal women, the prevalence is even higher, reaching 27.4% for OP and 42.1% for osteopenia(4).\u0026nbsp;Due to their high prevalence, significant economic burden, and elevated disability rates, OP and osteopenia have become serious public health concerns.\u003c/p\u003e\n\u003cp\u003eSerum uric acid (SUA) is the end product of purine nucleotide degradation in the organism and a well-established risk factor for gout (5). Elevated SUA levels, or hyperuricemia, can lead to various diseases, including chronic kidney disease (CKD), cardiovascular disease, and metabolic syndrome(6\u0026ndash;8). Recent studies have also suggested that increased SUA levels may be associated with a reduced prevalence of osteoporosis(9). Research has shown that SUA acts as a natural endogenous antioxidant, capable of scavenging free radicals and exhibiting anti-oxidative stress effects(10\u0026ndash;12). Consequently, SUA may exert a protective effect against OP through direct actions on bone metabolism-related cells(13).\u003c/p\u003e\n\u003cp\u003eBody Mass Index (BMI) is a key indicator of the body\u0026apos;s nutritional status. Most studies suggest that higher body weight increases the physical load on bones, thereby stimulating bone formation and enhancing bone density. Additionally, elevated body weight and BMI have long been shown to protect against the decline in bone mineral density (BMD)(14). Conversely, being underweight is an independent risk factor for osteoporosis(15). A meta-analysis found that low BMI is significantly associated with a higher risk of fractures(16). Furthermore, research indicates that BMI is positively correlated with SUA levels (17,18).\u003c/p\u003e\n\u003cp\u003eGiven the close associations among these factors and the significant physiological and environmental differences between men and women, it is essential to further assess their intrinsic relationships across different gender groups. This study investigated the complex associations between SUA, BMI, and OP or osteopenia in men over 40 years of age and postmenopausal women using cross-sectional methods. It also closely examined the mediating role of BMI in the relationship between SUA and OP or osteopenia. The findings aim to support the development of more effective and targeted preventive strategies.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eStudy population\u003c/p\u003e\n\u003cp\u003eIn this study, 10,207 community residents aged over 40 from Dalian who participated in the REACTION study, a project investigating the risk of malignancies in T2DM patients in China from August to December 2011, were initially considered. Among them, male and postmenopausal female participants who completed bone mineral density examination were selected, a total of 1724 cases. We excluded 82 patients due to the presence of bone diseases, malignant tumors, severe cardiac, hepatic, or renal dysfunction, or chronic use of glucocorticoids that could impact bone metabolism. After removing an additional 86 individuals with incomplete data on BMI and SUA, 1,556 participants were ultimately included in the study. The detailed exclusion process is depicted in Figure 1. Ethical approval was granted by the REACTION Research Ethics Committee (Approval No. 2011 Linlun Review No. 14), and informed consent was obtained from all participants. All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDefinitions of OP and Osteopenia\u003c/p\u003e\n\u003cp\u003eThe diagnosis of osteoporosis primarily depends on bone mineral density (BMD) measurements, which are obtained using dual-energy X-ray absorptiometry (DXA). The T-score is employed to evaluate the deviation of an individual\u0026rsquo;s BMD from that of a standard reference population of young healthy adults. The diagnostic criteria, based on the World Health Organization (WHO) definitions(19), are as follows: osteopenia is defined by a T-score between -1.0 and -2.5, while osteoporosis is characterized by a T-score of less than -2.5. In our study, BMD was measured using the LUNAR PRODIGY dual-energy X-ray absorptiometry (DXA) system, manufactured by GE in the United States. Specifically, the T-score for the lumbar spine (L1-L4) BMD was assessed. Following the diagnostic criteria for osteoporosis, and patients are categorized into normal, OP or osteopenia groups based on their BMD measurements.\u003c/p\u003e\n\u003cp\u003eAssessment of\u0026nbsp;SUA\u003c/p\u003e\n\u003cp\u003eThe exposure variable of this study was SUA, which has been measured using an automatic biochemical detector (ADVIA 2400 automatic Biochemical detector). Meanwhile, for statistical analysis, quartiles of SUA levels were divided into four groups: tquartile1 (Q1): \u0026le;25th percentile, quartile2 (Q2): \u0026gt;25\u0026ndash;50 percentile, quartile3 (Q3): \u0026gt;50\u0026ndash;75 percentile, quartile4 (Q4): \u0026gt;75th percentile.\u003c/p\u003e\n\u003cp\u003eAssessment of BMI\u003c/p\u003e\n\u003cp\u003eWe measured height and weight, allowing for the computation of BMI as the weight in kilograms divided by the square of the height in meters. Meanwhile, quartiles of BMI levels were divided into four groups: tquartile1 (Q1): \u0026le;25th percentile, quartile2 (Q2): \u0026gt;25\u0026ndash;50 percentile, quartile3 (Q3): \u0026gt;50\u0026ndash;75 percentile, quartile4 (Q4): \u0026gt;75th percentile.\u003c/p\u003e\n\u003cp\u003eCovariates\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAge, sex, educational level, the history of diabetes (DM), stroke,\u0026nbsp;coronary heart disease (CHD), hypertension, hyperlipidemia and fatty liver and smoking status, alcohol consumption were self-reported by participants. Upon admission, systolic blood pressure (SBP), and diastolic blood pressure (DBP) were measured. Laboratory measurements included high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), total cholesterol (TC), triglycerides (TG), alanine aminotransferase (ALT), glucose (Glu), hemoglobin A1c (HbA1c), homeostatic model assessment for insulin resistance (HOMA-IR) and creatinine (Cr).\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNormality tests were conducted for all data. For data following a normal distribution, a t-test was performed, while the\u0026nbsp;Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e-test was applied to data that did not follow a normal distribution. Data are presented as median values with interquartile ranges (Q25, Q75). Categorical variables are represented by N (%) and analyzed by chi-square test. Samples with missing covariate data were excluded. Various logistic regression approaches were used to explore the association between SUA, BMI and OP, osteopenia. In the absence of predefined cut-off points of SUA and BMI, the interquartile method was used for analysis. Three analytical models were developed: Model 1 (unadjusted), Model 2 (adjusted for age and sex), and Model 3 (adjusted for multiple variables including age, sex, DBP, HDL, LDL, ALT, HOMA-IR, Cr, educational level, DM, hyperlipidemia, fatty liver, smoking status and alcohol consumption). The nonlinear association between UA, BMI, OP and osteopenia was further investigated by using 3-knot restricted cubic splines (RCS). Subgroup analyses were conducted to explore potential modifications by covariates such as age, sex, BMI, hypertension, Stroke, CHD, DM, hyperlipidemia, fatty liver might modify the OP or osteopenia on the relationship between UA and OP or osteopenia, considering interactions significant at P\u0026lt;0.05. Finally, the mediating effect of BMI between UA and OP or osteopenia was investigated by mediating analysis. All statistical analyses were performed using EmpowerStats (version 4.2) and R software (version 4.3.2), with statistical significance set at P \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBaseline characteristics\u003c/p\u003e\n\u003cp\u003eTable 1 presents the baseline characteristics of the 1,556 study participants, with a mean age of 60.4 years, comprising 21.5% men and 78.5% women. The prevalence of OP or osteopenia among these participants was 47.8% (744/1556). Both BMI and SUA levels were significantly lower in the OP or osteopenia group compared to those in normal subjects. Baseline data indicated significant associations of OP or osteopenia with sex, age, DBP, HDL, LDL, TC, ALT, HOMA-IR, Cr, educational level, DM, hyperlipidemia, fatty liver, smoking, and alcohol consumption.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable1. Baseline characteristics\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"105%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003eOP or osteopenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e(N=1556)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e(N=812)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e(N=744)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e60.4 (55.9, 67.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e59.64 (55.15, 65.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e61.13 (56.87, 68.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eSUA (umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e315.0 (274.0, 368.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e328.00 (283.00, 381.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e304.00 (267.00, 348.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eBMI (Kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e25.5 (23.2, 27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e26.14 (23.93, 28.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e24.75 (22.51, 26.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e139.7 (125.7, 155.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e141.00 (126.00, 155.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e138.00 (125.25,\u0026nbsp;153.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e80.3 (73.0, 87.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e81.00 (73.33, 88.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e79.00 (72.00, 86.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eHDL (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e1.3 (1.1, 1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e1.27 (1.11, 1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e1.37 (1.19, 1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eLDL (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e3.3 (2.8, 3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e3.25 (2.69, 3.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e3.37 (2.85, 3.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e5.5 (4.9, 6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e5.44 (4.83, 6.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e5.62 (5.03, 6.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e1.4 (1.0, 1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e1.38 (1.02, 2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e1.34 (0.99, 1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eALT\u0026nbsp;(U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e16.0 (12.0, 22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e17.00 (13.00, 23.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e16.00 (12.00, 21.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eGlu (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e5.7 (5.3, 6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e5.72 (5.31, 6.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e5.67 (5.32, 6.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eHbA1c(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e5.8 (5.6, 6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e5.90 (5.60, 6.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e5.80 (5.60, 6.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.382\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eHOMA-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e2.1 (1.4, 3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e2.29 (1.52, 3.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e1.98 (1.33, 2.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eCr (umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e65.1 (60.2, 71.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e66.80 (60.80, 75.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e63.90 (59.30, 69.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eSex (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eMan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e335 (21.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e264 (32.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e71 (9.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eWoman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e1221 (78.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e548 (67.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e673 (90.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eEducational level (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003ePrimary school and below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e257 (16.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e99 (12.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e158 (21.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eMiddle school and high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e1112 (71.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e592 (73.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e520 (70.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eUniversity and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e184 (11.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e120 (14.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e64 (8.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eDM (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e225 (14.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e139 (17.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e86 (11.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e1331 (85.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e673 (82.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e658 (88.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eStroke (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e23 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e12 (1.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e11 (1.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e1533 (98.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e800 (98.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e733 (98.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eCHD (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e118 (7.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e68 (8.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e50 (6.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e1438 (92.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e744 (91.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e694 (93.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eHypertension (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e376 (24.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e208 (25.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e168 (22.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e1180 (75.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e604 (74.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e576 (77.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eHyperlipidemia (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e203 (13.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e124 (15.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e79 (10.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e1353 (87.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e688 (84.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e665 (89.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eFatty liver（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e166 (10.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e100 (12.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e66 (8.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e1390 (89.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e712 (87.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e678 (91.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eSmoking (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e138 (8.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e92 (11.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e46 (6.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e1418 (91.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e720 (88.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e698 (93.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003ealcohol consumption (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e315 (20.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e213 (26.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e102 (13.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.8041%;\"\u003e\n \u003cp\u003e1241 (79.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7732%;\"\u003e\n \u003cp\u003e599 (73.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e642 (86.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSUA serum uric acid, BMI Body mass index, SBP Systolic blood pressure, DBP Diastolic blood pressure, HDL High-density lipoprotein, LDL Low-density lipoprotein, TC Total cholesterol, TG Triglycerides, Glu glucose, HbA1c Hemoglobin A1c,\u0026nbsp;HOMA-IR Homeostatic model assessment for insulin resistance, ALT Alanine aminotransferase, UA Uric acid, Cr Creatinine, CHD Coronary heart disease, DM diabetes mellitus\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAssociation between SUA and OP or osteopenia\u003c/p\u003e\n\u003cp\u003eAs detailed in Table 2, after adjusting for all covariates, the correlation between SUA and OP or osteopenia in Model 3 remained significant (OR = 0.997, 95% CI 0.996\u0026ndash;0.999, P = 0.002). SUA levels were categorized into quartiles to explore their associations with OP or osteopenia. Subsequent adjustments for potential confounders revealed that the odds ratios (ORs) from multiple logistic regression models were 0.921 (95% CI 0.676\u0026ndash;1.255) for Q2, 0.744 (95% CI 0.543\u0026ndash;1.019) for Q3, and 0.651 (95% CI 0.464\u0026ndash;0.913) for Q4, compared to Q1. Using 3-knot restricted cubic spline (RCS) models, we analyzed the dose-response relationship between SUA and OP or osteopenia, identifying a linear negative correlation (P = 0.009) with no significant nonlinearity (P = 0.919) in Fig. 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Relationship between SUA and OP or osteopenia\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"617\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.859%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 30.47%;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 29.6596%;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 29.0113%;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.859%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.9076%;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5624%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7731%;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.88655%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4765%;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5348%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.859%;\"\u003e\n \u003cp\u003eSUA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.9076%;\"\u003e\n \u003cp\u003e0.995 (0.993, 0.996)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5624%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7731%;\"\u003e\n \u003cp\u003e0.996 (0.994, 0.998)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.88655%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4765%;\"\u003e\n \u003cp\u003e0.997 (0.996, 0.999)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5348%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.859%;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.9076%;\"\u003e\n \u003cp\u003e1.000 (Ref.)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5624%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7731%;\"\u003e\n \u003cp\u003e1.000 (Ref.)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.88655%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4765%;\"\u003e\n \u003cp\u003e1.000 (Ref.)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5348%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.859%;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.9076%;\"\u003e\n \u003cp\u003e0.829 (0.624, 1.100)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5624%;\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7731%;\"\u003e\n \u003cp\u003e0.863 (0.641, 1.163)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.88655%;\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4765%;\"\u003e\n \u003cp\u003e0.921 (0.676, 1.255)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5348%;\"\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.859%;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.9076%;\"\u003e\n \u003cp\u003e0.595 (0.448, 0.792)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5624%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7731%;\"\u003e\n \u003cp\u003e0.670 (0.495, 0.907)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.88655%;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4765%;\"\u003e\n \u003cp\u003e0.744 (0.543, 1.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5348%;\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.859%;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.9076%;\"\u003e\n \u003cp\u003e0.402 (0.301, 0.537) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5624%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7731%;\"\u003e\n \u003cp\u003e0.498 (0.364, 0.681)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.88655%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.4765%;\"\u003e\n \u003cp\u003e0.651 (0.464, 0.913) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5348%;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.859%;\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 30.47%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 29.6596%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 29.0113%;\"\u003e\n \u003cp\u003e\u0026nbsp;0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel 1: non-adjusted\u003c/p\u003e\n\u003cp\u003eModel 2: adjusted for age and sex\u003c/p\u003e\n\u003cp\u003eModel 3: further adjusted for age, sex, DBP, HDL, LDL, ALT, HOMA-IR, Cr, educational level, DM, hyperlipidemia, fatty liver, smoking status and alcohol consumption\u003c/p\u003e\n\u003cp\u003eAssociation between BMI and OP or osteopenia\u003c/p\u003e\n\u003cp\u003eIn Table 3, the relationship between BMI and OP or osteopenia is presented. After adjusting for all covariates, the correlation remained significant in Model 3 (OR = 0.881, 95% CI 0.849\u0026ndash;0.914, P \u0026lt; 0.001). BMI was categorized into quartiles, and multiple logistic regression was used for analysis. After adjusting for confounding factors in Model 3, the ORs were as follows: for Q2, the OR was 0.547 (95% CI 0.396\u0026ndash;0.754); for Q3, the OR was 0.425 (95% CI 0.305\u0026ndash;0.594); and for Q4, the OR was 0.292 (95% CI 0.204\u0026ndash;0.417), compared to Q1. Additionally, using 3-knot RCS models, we identified a significant nonlinear relationship between BMI and the risk of OP or osteopenia, after adjusting for confounding factors. Specifically, the risk decreased with increasing BMI, began to decline more slowly, and eventually plateaued at a BMI of approximately 23.979 in Fig. 3.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 Relationship between BMI and OP or osteopenia\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"592\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.6554%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 28.7162%;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 29.5608%;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 30.0676%;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.6554%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5946%;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.12162%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4392%;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.12162%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4392%;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.62838%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.6554%;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5946%;\"\u003e\n \u003cp\u003e0.899 (0.873, 0.937)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.12162%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4392%;\"\u003e\n \u003cp\u003e0.861 (0.833, 0.890)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.12162%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4392%;\"\u003e\n \u003cp\u003e0.881 (0.849, 0.914)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.62838%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.6554%;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5946%;\"\u003e\n \u003cp\u003e1.000 (Ref.)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.12162%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4392%;\"\u003e\n \u003cp\u003e1.000 (Ref.)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.12162%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4392%;\"\u003e\n \u003cp\u003e1.000 (Ref.)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.62838%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.6554%;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5946%;\"\u003e\n \u003cp\u003e0.556 (0.417, 0.740)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.12162%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4392%;\"\u003e\n \u003cp\u003e0.49 4(0.362, 0.673)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.12162%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4392%;\"\u003e\n \u003cp\u003e0.547 (0.396, 0.754)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.62838%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.6554%;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5946%;\"\u003e\n \u003cp\u003e0.468 (0.351, 0.623)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.12162%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4392%;\"\u003e\n \u003cp\u003e0.365 (0.270, 0.499)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.12162%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4392%;\"\u003e\n \u003cp\u003e0.425 (0.305, 0.594)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.62838%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.6554%;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5946%;\"\u003e\n \u003cp\u003e0.353 (0.264, 0.472)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.12162%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4392%;\"\u003e\n \u003cp\u003e0.237 (0.172, 0.328)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.12162%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.4392%;\"\u003e\n \u003cp\u003e0.292 (0.204, 0.417)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.62838%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.6554%;\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 28.7162%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 29.5608%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 30.0676%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eModel 1: non-adjusted\u003c/p\u003e\n\u003cp\u003eModel 2: adjusted for age and sex\u003c/p\u003e\n\u003cp\u003eModel 3: further adjusted for age, sex, DBP, HDL, LDL, ALT, HOMA-IR, Cr, educational level, DM, hyperlipidemia, fatty liver, smoking status and alcohol consumption\u003c/p\u003e\n\u003cp\u003eAssociation between SUA and BMI\u003c/p\u003e\n\u003cp\u003eThe correlation between SUA and BMI was assessed using multiple regression analysis, with the results presented in Table 4. In the unadjusted model, SUA was positively associated with BMI (\u0026beta; = 4.744 95% CI 3.795\u0026ndash;5.693 P \u0026lt; 0.001). After adjusting for covariates, the correlation remained significant in Model 2 (\u0026beta; = 4.711, 95% CI 3.804\u0026ndash;5.618, P \u0026lt; 0.001) and Model 3 (\u0026beta; = 3.451, 95% CI 2.494\u0026ndash;4.408, P \u0026lt; 0.001). When BMI was categorized into quartiles, the association between BMI and SUA continued to be statistically significant at higher BMI levels. Specifically, subjects in the highest BMI quartile experienced an 34.558 \u0026micro;mol/L increase in SUA compared to those in the lowest quartile (Model 3 \u0026beta; = 34.558, 95% CI 24.581\u0026ndash;44.534, P \u0026lt; 0.001). Table 4 displays the results of the multiple regression analysis between SUA and BMI. Fig. 4 illustrates the smooth curve fitting of the relationship between SUA and BMI.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4. Relationship between BMI and UA\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"613\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 191px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 191px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e4.744 (3.795,\u0026nbsp;5.693)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e4.711 (3.804, 5.618)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e3.451 (2.494,\u0026nbsp;4.408)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e0 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e0 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e17.274 (7.368, 27.180)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e14.670 (5.252, 24.089)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e8.125 (-1.227, 17.477)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e33.312 (23.456, 43.167)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e31.610 (22.219, 41.001)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e21.448 (11.853,\u0026nbsp;31.044)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e48.144 (38.263, 58.025)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e47.175 (37.716, 56.633)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e34.558 (24.581, 44.534)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 191px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 191px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel 1: non-adjusted\u003c/p\u003e\n\u003cp\u003eModel 2: adjusted for age and sex\u003c/p\u003e\n\u003cp\u003eModel 3: further adjusted for age, sex, DBP, HDL, LDL, ALT, HOMA-IR, Cr, educational level, DM, hyperlipidemia, fatty liver, smoking status and alcohol consumption\u003c/p\u003e\n\u003cp\u003eSubgroup analyses\u003c/p\u003e\n\u003cp\u003eSubgroup analyses by sex, age, BMI, hypertension, stroke, CHD, DM, hyperlipidemia, and fatty liver (reported in Fig. 5) revealed no notable differences in the relationships between SUA and OP or osteopenia across the subgroups.\u003c/p\u003e\n\u003cp\u003eMediating role of BMI\u003c/p\u003e\n\u003cp\u003eIn the mediation analysis, we explored the role of BMI as a mediator in the association between SUA and OP or osteopenia (Fig 6). The results indicated that BMI weakly, yet significantly, mediated this association after adjusting for all covariates. Specifically, 13.6% of the total effect of SUA on OP or osteopenia was mediated by BMI, which was statistically significant (p = 0.032).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe purpose of this study was to explore the relationship between SUA, OP or osteopenia, and BMI. Our findings indicate that in men over 40 years of age and postmenopausal women, the risk of OP or osteopenia decreases as SUA levels increase, demonstrating a negative correlation between the two. Additionally, we confirmed that BMI is inversely nonlinear associated with OP or osteopenia, while a positive correlation exists between SUA and BMI. Mediation analysis further revealed that BMI mediates 13.6% of the potential effects of SUA on OP or osteopenia.\u003c/p\u003e\n\u003cp\u003eOP and osteopenia represent significant public health challenges. Previously viewed as an unavoidable consequence of aging, they are now recognized as serious, treatable diseases due to advances in medical treatments. Identifying additional risk factors and screening for high-risk individuals are crucial for the early prevention and management of these conditions, ultimately improving patient outcomes and reducing economic burdens.\u003c/p\u003e\n\u003cp\u003eNumerous epidemiological studies have investigated the relationship between SUA and BMD; however, the findings have been inconsistent. A meta-analysis indicated that elevated SUA levels might play a protective role in bone metabolism disorders(20). Further, several studies have established a significant association between SUA and BMD, suggesting a protective effect on bone metabolism in diverse groups, including healthy adults(21), elderly populations(22), patients with type 2 diabetes(23), osteoporosis patients(24), and postmenopausal women(25). Additionally, one of the Rotterdam cohort studies found that higher SUA levels are correlated with increased BMD (at the expense of thicker cortices and narrower bone diameters) (26).\u0026nbsp;Our results show that higher SUA is associated with the risk of OP and osteopenia, consistent with these results. However, other studies have found no association between SUA and BMD in American adult men(27) and postmenopausal women with type 2 diabetes(28).\u0026nbsp;We consider these results to be different due to differences in study populations and statistical methods. The action mechanism of SUA on OP or osteopenia remains unclear; however, its potential protective mechanism could be delineated as follows: SUA acts as a significant endogenous antioxidant, particularly under oxidative stress(11,29), which facilitates the removal of free radicals from the blood. Reactive oxygen species (ROS) can suppress the differentiation of osteoblasts while promoting the differentiation and activation of osteoclasts, ultimately leading to osteopenia(30,31). By scavenging free radicals and ROS, SUA may reduce bone loss. Lower SUA concentrations are associated with reduced serum parathyroid hormone (PTH) level(32). higher SUA levels correlate with diminished kidney function, leading to decreased production of 1,25-dihydroxyvitamin D. This reduction in turn stimulates PTH production and increases serum calcium levels(33), thus increasing bone density. Additionally, another study demonstrated that uric acid enhances osteogenic differentiation and inhibits adipogenic differentiation of human bone mesenchymal stem cells(34).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results of the mediation analysis indicated that part of the effect of SUA on OP or osteopenia might be mediated through BMI. Previous studies have consistently confirmed a positive association between BMI and SUA (17,18). Simultaneously, research has shown that BMI is positively correlated with BMD(14,35). Notably, obese women exhibit a lower prevalence of osteopenia and osteoporosis compared to their normal-weight and overweight counterparts(36). High BMI, acting as a mechanical load, enhances the stimulation of bone and muscle, thereby promoting bone reconstruction and reducing the incidence of OP(37). On the other hand, by modulating the nuclear factor-kappa B (NF-\u0026kappa;B) receptor activator of nuclear factor kappa-B ligand (RANKL)/osteoprotegerin (OPG) pathway, osteoclast differentiation is inhibited, and bone resorption is consequently reduced\u0026nbsp;(38). It is essential that we identify how, compared to leaner individuals, obese persons typically exhibit higher serum levels of estrogen and PTH, lower levels of 25-hydroxyvitamin D (25OHD) and sex hormone-binding globulin (SHBG), and possibly reduced levels of 1,25-dihydroxyvitamin D3 (1,25(OH)2D3)(39\u0026ndash;41), Each of these factors exerts specific actions on bone health. These findings constitute a supporting factor for the results of our mediation analysis.\u003c/p\u003e\n\u003cp\u003eThis study redefines our comprehension of the complex relationship among SUA, OP or osteopenia, and BMI, emphasizing BMI\u0026apos;s potential role as a mediator. This analysis has several limitations. Firstly, other potentially relevant biochemical markers, including PTH and plasma phosphate levels, were not assessed in these patients, yet they may significantly influence bone metabolism. Secondly, the study did not account for the effects of medications, particularly the impact of uric acid-lowering drugs on serum uric acid levels. Thirdly, since the study population was drawn from a single center and utilized a cross-sectional design, it was not possible to establish a causal relationship between SUA and OP or osteopenia. Thus, further validation in multiple cohort studies is necessary.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this study sought to examine the complex relationships among BMI, SUA, and OP or osteopenia in men over 40 years of age and postmenopausal women. It identified a linear negative correlation between SUA and OP or osteopenia. Furthermore, BMI was found to influence and mediate the risk of OP or osteopenia associated with SUA. These findings offer new insights for the prevention and treatment of these conditions.\u0026nbsp;\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe datasets analysed during the current study are available from the corresponding author on reasonable request.\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDrug therapy for osteoporosis in older adults - PubMed. https://pubmed.ncbi.nlm.nih.gov/35279261/ [Accessed October 22, 2024]\u003c/li\u003e\n\u003cli\u003eKlara J, Lewandowska-Łańcucka J. How Efficient are Alendronate-Nano/Biomaterial Combinations for Anti-Osteoporosis Therapy? An Evidence-Based Review of the Literature. \u003cem\u003eInt J Nanomedicine\u003c/em\u003e (2022) 17:6065\u0026ndash;6094. doi: 10.2147/IJN.S388430\u003c/li\u003e\n\u003cli\u003eEnsrud KE, Crandall CJ. 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The relationship between serum uric acid (SUA) levels and OP or osteopenia remains controversial, as does the impact of weight change on these conditions. Moreover, few studies have investigated whether body mass index (BMI) serves as a mediator in the association between SUA and OP or osteopenia.\u003c/p\u003e\n\u003cp\u003eObjective This study aimed to elucidate the complex interactions between SUA, OP or osteopenia, and BMI.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods A cross-sectional study from the REACTION study was conducted to examine the association between SUA and OP or osteopenia. Various logistic regression and restricted cubic spline models were employed to analyze the pairwise correlations among these variables, and interaction analysis was performed to assess differences between subgroups. Mediation models were utilized to determine the mediating role of BMI.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults The findings indicated that both SUA and BMI were inversely associated with OP or osteopenia, while a positive correlation was observed between SUA and BMI. Analysis of the dose-response relationship between SUA and OP or osteopenia showed a linear negative correlation, and a significant nonlinear association between BMI and the risk of OP or osteopenia. No significant interactions were found within subgroups. Additionally, BMI was found to mediate 13.6% of the potential effects of SUA on OP or osteopenia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusions SUA appears to have a protective effect against OP or osteopenia, with BMI potentially serving as a mediator. Thereby, maintaining SUA and BMI within an optimal range may.\u003c/p\u003e","manuscriptTitle":"The triangular relationship of serum uric acid, osteoporosis or osteopenia, and body mass index for men and postmenopausal women","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-03 14:15:31","doi":"10.21203/rs.3.rs-5748318/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-18T05:15:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-09T15:24:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311809477659585672780279108041429226062","date":"2025-04-04T03:36:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-02T01:22:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-01T04:18:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-23T23:01:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cbb62b6a-8fbb-4572-b908-24a8bb4d2aff","owner":[],"postedDate":"April 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46545343,"name":"Health sciences/Diseases"},{"id":46545344,"name":"Health sciences/Endocrinology"},{"id":46545345,"name":"Health sciences/Health care"},{"id":46545346,"name":"Health sciences/Medical research"},{"id":46545347,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-07-14T16:03:23+00:00","versionOfRecord":{"articleIdentity":"rs-5748318","link":"https://doi.org/10.1038/s41598-025-10191-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-10 15:57:45","publishedOnDateReadable":"July 10th, 2025"},"versionCreatedAt":"2025-04-03 14:15:31","video":"","vorDoi":"10.1038/s41598-025-10191-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-10191-y","workflowStages":[]},"version":"v1","identity":"rs-5748318","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5748318","identity":"rs-5748318","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}