Influence of serum uric acid on bone and fracture risk in postmenopausal women

Influence of serum uric acid on bone and fracture risk in 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 Research Article Influence of serum uric acid on bone and fracture risk in postmenopausal women María-Jesús Gómez-de-Tejada-Romero, Carmen Murias-Henríquez, Pedro Saavedra-Santana, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4735028/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Aug, 2024 Read the published version in Aging Clinical and Experimental Research → Version 1 posted 9 You are reading this latest preprint version Abstract Aims Uric acid has been associated with several metabolic conditions, including bone diseases. Our objective here was to consider the relationship between serum uric acid levels and various bone parameters (bone mineral density, ultrasonographic parameters, vitamin D, PTH and serum calcium), as well as the prevalence and risk of fragility fracture. Methods An observational and cross-sectional study carried out on 679 postmenopausal women, classified into 3 groups according to their serum uric acid levels, in whom bone densitometry, calcaneus ultrasounds, PTH, vitamin D and serum calcium analysis were done. Bone fractures were collected through the clinical history and lateral spinal X-ray. Results Higher uric acid levels were found in women with older age, high BMI, diabetes, and high blood pressure. Higher levels of PTH and serum calcium were also observed, but did not effect on vitamin D. Serum uric acid was positively related to densitometric and ultrasonic parameters and negatively associated with vertebral fractures. Conclusions In the population of postmenopausal women studied, sUA levels were correlated with BMD, BUA, and QUI-Stiffness, and this correlation was independent of age and BMI. In addition, sUA was associated with a decrease in vertebral fractures. These results imply a beneficial influence of sUA on bone metabolism, with both a quantitative and qualitative positive effect, reflected in the lower prevalence of vertebral fractures. uric acid bone mineral density calcaneus ultrasounds fragility fractures Figures Figure 1 1. INTRODUCTION Uric acid, a product of the degradation of purines, is part of the complex network of human metabolism and has been related to obesity, diabetes, and arterial hypertension [ 1 – 3 ]. Along with these metabolic diseases, a role of serum uric acid (sUA) in bone metabolism has also been pointed out in recent years, since its antioxidant action (in physiological ranges) could prevent bone loss and, consequently, osteoporosis (OP) [ 4 ]. However, not all studies confirm this. It is true that many of them have shown that there is a positive correlation between serum levels within the physiological range of sUA and bone mineral density (BMD), the main factor for the diagnosis of OP [ 5 – 9 ]. For some, this relationship is a consequence of a common element, fatty tissue [ 10 ], while others give relevance to this correlation directly, finding some causality independent of fatty tissue [ 7 ]. However, a recent study found no link between BMD and sUA [ 11 ]. Furthermore, various studies have been carried out to ascertain the effect that the different levels in the physiological range of sUA could have on the production of fragility fractures, obtaining varied results [ 12 – 15 ]. On the other hand, quantitative ultrasound (QUS) is a measurement technique of bone structure, so that its parameters (BUA, SOS and Stiffness) are considered indirect indicators of bone quality [ 16 , 17 ], one of the determining aspects of bone status. We have only found two studies that observed the effect of uric acid levels on these ultrasonographic parameters [ 18 , 19 ] and with different results. In this study, carried out in a large number of postmenopausal women (to eliminate bias by sex and hormonal status), our objectives were: first, to observe the relationship between sUA levels and BMD measured by DXA and the QUS parameters (as indicators of bone quantity and quality, respectively), as well as to see if there is an influence of sUA levels on relevant hormonal parameters in bone metabolism; considering in all this the effect of fatty tissue (as the main confounding variable and represented by the body mass index -BMI-). Secondly, we observed the effect of serum uric acid levels on the prevalence and risk of fragility fracture. 2. MATERIAL AND METHODS This is an observational, cross-sectional study carried out on 679 postmenopausal women treated at the Bone Metabolic Unit of the Hospital University Insular, Gran Canaria, Spain, from January 1st, 2018 to December 31st, 2019. For all subjects a questionnaire, previously validated and used in other similar clinical studies, was completed to gather clinical data. Body weight (in kg) and body height (cm) were measured to the nearest 0.1 kg and 0.1 cm, respectively, using a SECA-marked stadiometer. Weight was measured with light clothes. The BMI as weight/height 2 (kg/m 2 ) was calculated for each individual. Obesity was defined as a BMI ≥ 30. The diagnosis of type 2 diabetes mellitus (T2DM) was made following the criteria of The American Diabetes Association [ 20 ]. 2.1. Serum biochemical measurements: sample collection and laboratory techniques Serum biochemical parameters were measured from blood samples collected in the early morning, after a fasting night, in the appropriate specific tubes for each determination, with the least possible venous compression and centrifuged at 1,500 g for 10 minutes. The serum was separated into aliquots and stored within one hour of extraction at -20°C until the biochemical analyzes were carried out, although most were done on the same day as the extraction. Serum uric acid, creatinine and calcium were analyzed by a biochemical automatic analyzer Cobas® 8000 (Roche Diagnostics, Switzerland), in which normal levels are: uric acid: 3.5–7.2 mg/dL; creatinine: 0.67–1.17 mg/dL); and calcium: 8.5–10.5 mg/dL. Serum calcium was corrected by total proteins based on the formula: Corrected calcium (mg/dL): serum calcium (mg/dL) / [Total proteins (g/dL) /16 + 0.55] Serum levels of 25(OH) vitamin D were measured by immunochemical luminescence, according to the Nichols method (Nichols Institute Diagnostics, San Clemente, California, USA). Serum parathyroid hormone (PTH) concentrations for the intact molecule were determined by immunochemical luminiscence, according to the Nichols Advantage method (PTH normal values: 15–88 pg/mL). Creatinine clearance (CCr) was calculated using the Cockcroft-Gault formula: $$\:CCr=\frac{\left(140-Age\right)\times\:Weight\left(Kg\right)\times\:{0.85}^{}}{72\times\:\left[SerumCreatinine\left(mg/dL\right)\right]}$$ Estimated Glomerular Filtration Rate (GFR) was calculated using MDRD-4 IDMS formula: eGFR = 175 x (serum creatinine/88,4) −1,154 x (age) −0,203 x 0,742 2.2. Bone mineral density (BMD) BMD was measured by dual x-ray absorptiometry (DXA), both in lumbar spine (L2-L4) and proximal femur, with a Hologic Discovery® densitometer, (Hologic Inc. Waltham, USA). All the measurements were made by the same operator, so there was no inter-observer variation. BMD values were done as g/cm 2 . Diagnosis of osteoporosis was based on the WHO densitometric criteria (BMD T-score ≤ − 2.5 in at least one of the anatomic sites, including the lumbar spine, the femoral neck and the total hip). 2.3. Fragility fractures Vertebral fractures : A lateral thoracic-lumbar X-ray was carried out on the subjects. All the X-rays were collated and studied by two different observers: one was a radiologist and the second was an expert on bone metabolic diseases. According to the Genant criteria, the existence of vertebral deformity was recorded when there was a reduction in the vertebral height higher than 20% [ 21 ]. Non-vertebral fractures : The remaining fragility fractures were confirmed by hospital clinical reports, from the emergency services or by radiography study, excluding the patients’ self-diagnosis of fractures. 2.4. Quantitative Ultrasound (QUS) measurements All subjects underwent calcaneus measurement by QUS. This was carried out using the Sahara Clinical sonometer (Hologic Inc., Bedford, MA) which measures 3 parameters at a fixed region of interest in the mid-calcaneus: broadband ultrasound attenuation (BUA); speed of sound (SOS); and quantitative ultrasound index (QUI), a combination of BUA and SOS resulting in the formula: QUI = 0.41 X (BUA + SOS) − 571 2.5. Ethics The study was carried out in accordance with the Declaration of Helsinki [ 22 ] and was approved by the Ethics Committee of the Insular University Hospital. All patients were informed of the study objectives and their informed consent was requested. 2.6. Statistical analysis Univariate analysis. Categorical variables are expressed as frequencies, percentages and continuous as mean and standard deviation (SD) when data followed a normal distribution, or as median and interquartile range (IQR = 25th − 75th percentile) when distribution departed from normal. The percentages were compared, as appropriate, using the Chi-square ( \(\:{\chi\:}^{2}\) ) test or the exact Fisher test, the means by the t-test and the medians by the Wilcoxon test for independent data. Additive models for the bone markers. For each of the DXA and QUS markers, a multidimensional analysis was performed in which, in addition to uric acid, age, BMI, diabetes mellitus status and vitamin-D were entered as co-variates. First, a variable selection based on the best subset and then Akaike Information Criterion (AIC) were conducted. Once the variables were selected, the eventual nonlinear effect of the continuous variables was explored by the additive models using cubic splines. The final models were summarized, in addition to P -values, in coefficients and standard errors (SE) for linear effects or cubic splines together with 95% confidence bands (95% CI). Logistic models for vertebral and fragility fractures. For each of the binary factors, vertebral fractures versus non-fractures and fragility fractures versus non-fractures, two multivariate logistic analyses were carried out. In the first, the variables age, BMI, sUA (continuous scale), T2DM, CCr and Vitamin D were entered. In the second analysis, BMD markers were added. In both analyses, a selection of variables based on the best subset regression and Akaike Information Criterion (AIC) was then performed [ 3 ]. The models were summarized as p-values (likelihood ratio test) and odds-ratio, which were estimated by means 95% CI. Statistical significance was set at p < 0.05. Data were analyzed using the R package, version 3.6.1 (R Development Core Team, 2019). 3. RESULTS To consider the behavior of the different variables studied in relation to sUA levels, the study participants were grouped into 3 groups according to levels. The cut-off points were set taking into account that most of the women had normal sUA levels. Thus, so that a similar number and homogeneous of patients in the 3 groups could be obtained, they were grouped into: low levels (below 4 mg/dl; medium levels (from 4 to 5 mg/dl); and high levels (above 5 mg/dl) (Tables 1 and 2 ). Table 1 Characteristics of the women: overall and according to the level of serum uric acid Levels of serum uric acid Overall N = 913 5 mg/dL N = 295 P -value Age (years) 61.4 ± 13.4 59.8 ± 13.2 59.9 ± 13.5 64.7 ± 13.0 < .001 BMI (kg/m 2 ) 27.3 ± 5.4 25.6 ± 4.7 27.4 ± 5.2 29.2 ± 5.5 < .001 Obesity 270 (29.6) 64 (18.8) 86 (31.1) 120 (40.7) < .001 Fractures By fragility (all) 298 (32.6) 108 (31.7) 81 (29.2) 109 (37.0) 0.129 Vertebral 103 (11.3) 52 (15.2) 21 (7.6) 30 (10.2) 0.009 Non vertebral 226 (24.8) 70 (20.5) 69 (24.9) 87 (29.5) 0.033 Uric acid (mg/dL) 4.5 ± 1.3 3.3 ± 0.5 4.4 ± 0.3 5.9 ± 1.0 < 0.001 Diabetes mellitus 125 (13.7) 35 (10.3) 36 (13.0) 54 (18.3) 0.012 Arterial hypertension 393 (43.0) 113 (33.1) 118 (42.6) 162 (54.9) < .001 Urolithiasis 148 (16.3) 42 (12.4) 48 (17.4) 58 (19.7) 0.037 Osteoporosis 395 (44.0) 169 (50.1) 123 (44.9) 103 (35.9) 0.002 CCr (mL/min) 70.8 (56.5 ; 86.2) 71.9 (58.6 ; 87.1) 73.7 (62.4 ; 90.0) 65.3 (52.0 ; 82.3) < .001 GFR (mL/min/1,73 m 2 ) 73 (63 ; 84) 77 (69 ; 89) 74 (65 ; 85) 66 (53 ; 76) < .001 25(OH) vitamin D (ng/mL) 22.4 (16.0 ; 30.0) 23.0 (16.0 ; 31.0) 21.9 (15.9 ; 29.7) 22.2 (16.0 ; 29.9) 0.501 PTH (pg/mL) 49.9 (36.0 ; 80.0) 43.0 (32.7 ; 59.4) 48.9 (36.8 ; 80.2) 62.8 (41.8 ; 100.8) < .001 Corrected calcium (mg/dL) 9.9 (9.5 ; 10.3) 9.8 (9.4 ; 10.1) 9.9 (9.6 ; 10.3) 10.0 (9.6 ; 10.6) < .001 Data are means SD, medias (IQR) and frequencies (%) Table 2 DXA and US markers adjusted by age and BMI ( according to the level of serum uric acid) Levels of uric acid 5 mg/dL p -value Spine lumbar 0.002 g/cm 2 0.839 [0.822 ; 0.857] 0.851 [0.832 ; 0.870] 0.886 [0.867 ; 0.905] T-score -1.932 [-2.100 ; -1.764] -1.818 [-2.002 ; -1.635] -1.478 [-1.661 ; -1.296] Femoral Neck 0.588 g/cm 2 0.675 [0.663 ; 0.687] 0.672 [0.659 ; 0.685] 0.682 [0.669 ; 0.695] T-score -1.511 [-1.621 ; -1.402] -1.537 [-1.656 ; -1.418] -1.451 [-1.570 ; -1.332] Total Hip 0.153 g/cm 2 0.789 [0.775 ; 0.803] 0.798 [0.782 ; 0.813] 0.810 [0.794 ; 0.825] T-score -1.344 [-1.489 ; -1.199] -1.252 [-1.410 ; -1.094] -1.128 [-1.286 ; -0.970] BUA 0.004 dB/MHz 58.9 [56.6 ; 61.3] 60.2 [57.8 ; 62.5] 64.3 [62.0 ; 66.7] T-score -1.187 [-1.334 ; -1.040] -1.107 [-1.255 ; -0.959] -0.846 [-0.992 ; -0.701] SOS 0.207 m/s 1520.7 [1516.8 ; 1524.6] 1522.9 [1518.9 ; 1526.8] 1525.8 [1521.9 ; 1529.7] T-score -1.470 [-1.598 ; -1.342] -1.398 [-1.527 ; -1.270] -1.304 [-1.430 ; -1.177] Qui-Stiffness 0.097 Measure 77.0 [74.5 ; 79.6] 77.4 [74.9 ; 80.0] 80.7 [78.2 ; 83.2] T-score -1.411 [-1.550 ; -1.273] -1.390 [-1.529 ; -1.251] -1.213 [-1.349 ; -1.076] Adjusted means (95% CI) by age and body mass index (BMI) obtained by least squares regression 3.1. Characteristics of the women according to the level of serum uric acid. The results showed that age and BMI were significantly higher in the groups with the highest levels of sUA, as well the presence of obesity, T2DM and arterial hypertension; nevertheless, osteoporosis diagnosis was lower. GFR and CCr were higher in the groups with lower sUA levels. 3.2. Bone metabolism parameters. Regarding the variables related to bone metabolism, no significant differences were observed in 25(OH) vitamin D levels among the 3 groups, but PTH increased significantly in the groups with the highest levels of sUA (p < 0.001). This result was reflected in protein-corrected calcemia, which was higher in these same groups (p < 0.001). 3.3. DXA and QUS parameters. The lumbar BMD and BUA levels were notably higher in the groups with raised figures in sUA (p = 0.002 and 0.004, respectively) (Table 2 ). For each one of the DXA and QUS parameters, Table 3 and Fig. 1 summarizes the additive regression models. For lumbar spine, total hip, BUA and Qui-Stiffness, uric acid showed significant linear association, adjusting for those co-variates that were selected by the best subset method and AIC (Age, BMI, sUA, DM2, CCr and Vitamin-D). In the models corresponding to lumbar spine and BUA, the effect of age on each of these markers was nonlinear. The corresponding cubic splines are shown in Fig. 1 . Table 3 Additive models for the DXA and QUS parameters Bone marker Covariates Coefficients (SE) P -value Lumbar spine (g/cm 2 ) (Intercept) 0.5631 (0.0330) < 0.001 Age (years) Nonlinear effect < 0.001 BMI (Kg/m 2 ) 0.0079 (0.0011) < 0.001 Uric Acid (mg/dL) 0.0159 (0.0044) < 0.001 Vitamin-D ≥ 20 0.0230 (0.0113) 0.042 Femoral neck (g/cm 2 ) (Intercept) 0.6528 (0.0328) < 0.001 Age (years) -0.0043 (0.0004) < 0.001 BMI (Kg/m 2 ) 0.0077 (0.0010) < 0.001 Uric Acid (mg/dL) 0.0057 (0.0032) 0.071 Vitamin-D ≥ 20 0.0237 (0.0078) 0.003 CCr 0.0005 (0.0002) 0.022 Total hip (g/cm 2 ) (Intercept) 0.6531 (0.0318) < 0.001 Age (years) -0.0045 (0.0003) < 0.001 BMI (kg/m 2 ) 0.0135 (0.0009) < 0.001 Uric Acid (mg/dL) 0.0072 (0.0036) 0.046 Vitamin-D 0.0010 (0.0004) 0.007 BUA (dB/MHz) (Intercept) 35.0588 (3.8507) < 0.001 Age (years) Nonlinear effect < 0.001 BMI (Kg/m 2 ) 0.6401 (0.1363) < 0.001 Uric Acid (mg/dL) 1.9139 (0.5356) < 0.001 SOS ( m/s) (Intercept) 1585.9 (8.88) < 0.001 Age (years) -1.3444 (0.1161) < 0.001 BMI (Kg/m 2 ) 1.3253 (0.2778) < 0.001 CCr -0.2070 (0.0719) 0.004 Qui-Stiffness (Intercept) 99.9 (4.68) < 0.001 Age (years) -0.6840 (0.0546) < 0.001 BMI (Kg/m 2 ) 0.5529 (0.1493) < 0.001 Uric Acid (mg/dL) 1.3696 (0.5879) 0.02 Age , BMI , Uric Acid (continuous scale), Diabetes Mellitus , CCr and Vitamin-D were entered in all analysis. Selection of variables were carried out using the best subset regression method and the AIC. The effects of the selected covariates on the marker are shown for each of the bone markers. When the effects were nearly linear (effective degree of freedom ~ 1), the effect was considered linear. The nonlinear effects are shown in Fig. 1 . 3.4. Multivariate logistic analyses for fractures. Multivariate logistic analyses for vertebral fractures versus non-fractures and fragility fractures versus non-fractures are summarized in Table 4 . For the vertebral fractures, type one logistic analyses (not including BMD markers) showed that the factors with independent association with the outcome (AIC) were age (per year, OR = 1.06; 95% CI = 1.04–1.09) and uric acid level (per md/dL, OR = 0.78; 95% CI = 0.65–0.94). When BMD markers are added in the analysis (type 2), BMI and BMD at the total hip are added in the model according to the AIC. Uric acid levels were maintained in the model (per mg/dL, OR = 0.81: 95% CI = 0.66 ; 0. 98). For the fragility fractures, the variables selected for the first model are age (per year, OR = 1.05; 95% CI = 1.04 ; 1.06) and T2DM (OR = 1.50; 95% CI = 0.99–2.28). When BMD markers are added in the analysis, BMI and BMD at total hip are added in the model according to the AIC. Uric acid level showed no statistical association with this outcome in any of the analyses (AIC). Table 4 Multivariate logistic analysis for fragility fractures and vertebral fractures Analysis 1 Analysis 2 Outcome Covariates P -value Odd-Ratio (95% CI) P -value Odd-Ratio (95% CI) Vertebral fractures * Age , per year < 0.001 1.06 (1.04 ; 1.09) < .001 1.04 (1.02 ; 1.07) BMI , per kg/m 2 - - 0.009 1.08 (1.02 ; 1.14) Uric Acid , per mg/dL 0.006 0.78 (0.65 ; 0.94) 0.03 0.81 (0.66 ; 0.98) Total hip , per g/cm 2 - - < .001 0.00 (0.00 ; 0.02) Fragility fractures Age , per year < 0.001 1.05 (1.04 ; 1.06) < 0.001 1.03 (1.02 ; 1.05) BMI , per kg/m 2 - - < 0.001 1.07 (1.03 ; 1.11) Diabetes mellitus 0.055 1.50 (0.99 ; 2.28) 0.05 1.56 (1.00 ; 2.42) Total hip , per g/cm 2 - - < 0.001 0.01 (0.00 ; 0.05) Non vertebral fractures** Age , per year 20 0.099 0.74 (0.52 ; 1.06) - - Femoral neck , per g/cm 2 - - < 0.001 0.02 (0.00 ; 0.12) (*) Model for Vertebral Fractures versus Non-Fractures (**) Model for Only Non-vertebral fractures versus Non-Fractures The covariates entered in the first analysis were the Age , BMI , Uric Acid (continuous scale), Diabetes Mellitus, CCr and Vitamin-D and in the second analysis, BMD markers were added . Selection of variables was carried out using the AIC. P -value corresponding to the likelihood ratio test. 4. DISCUSSION Our initial results were as expected: sUA is related to the metabolic syndrome and, therefore, to obesity (and its determinant, BMI), T2DM, and arterial hypertension, as other studies have shown [ 23 – 26 ], which is explained by various metabolic mechanisms [ 1 ]. On the other hand, the decrease in GFR produces an increase in CCr and sUA, but both GFR and CCr generally decrease with age, so sUA levels should increase with age (and vice versa). 4.1. sUA and bone metabolism parameters. The women under study had higher mean PTH levels in the groups with the highest sUA. In parallel, calcium levels also increased with sUA. All were within normal limits, except for some cases of high PTH levels, in which hyperparathyroidism was secondary to very low levels of vitamin D. It should be noted that the vitamin D values found in the women studied were generally very low, considered insufficient (< 30 ng/mL) and even close to deficiency (values < 20 ng/mL). Other authors have reported similar results [ 6 , 8 , 9 ]. The higher levels of uric acid found in our study are related to a decrease in renal function (lower CCr and lower GFR), which leads to a lower production of 1,25 (OH) 2 vitamin D, which in turn stimulates the production of PTH, increasing calcemia. There were no significant differences in 25(OH) vitamin D levels between the groups analyzed. Furthermore, we did not find a correlation between this parameter and uric acid, but this metabolite of vitamin D is not related to renal function, so it does not tell us anything in this sense. However, we could consider PTH as an indirect marker of 1,25 (OH) 2 vitamin D status (and, therefore, of renal function). 4.2. sUA and DXA and QUS parameters. Focusing on the bone parameters, we have obtained a positive association between sUA and the BMD values measured in the different locations, but the association was greater with lumbar BMD. Many different authors have linked sUA and BMD in studies conducted in different adult populations [ 5 – 10 , 12 , 13 ]. However, a recent report, conducted in a large number of adult men (n = 6704) in the United States of America, found no such association [ 11 ]. In another study, Dalbeth et al. [ 27 ] point out that the correlation between these 2 variables is not causal but coincidental, with confounding variables such as BMI, adiposity or hormonal status. Pirro et al. [ 10 ] also conclude in their study that the relationship between sUA and BMD is mediated by adiposity. Navipour et al. [ 5 ], in their study also conducted in men, did find a relationship between sUA and BMD, even when adjusting for age and BMI. Ibrahim et al. [ 8 ], in a study conducted in a large population of 2,981 healthy Qatari adults, only found a relationship between both variables in non-obese, young, and smoking women. In our study, as in that of Yan et al. [ 7 ], the association between BMD and sUA was independent of BMI (as additive regression models show), and sex and hormonal status were the same (all were postmenopausal women), so there do not seem to be any confounding variables in our correlation. Given that the influence is greater in the lumbar spine, this could be due to the increased metabolic activity in the trabecular bone, the main component of the vertebrae. Of the 2 studies that linked sUA levels with the ultrasonographic parameters BUA, SOS and Qui-Stiffness, one of them was carried out only in men [ 18 ], finding a positive association. The other, carried out in both men and women, found an association in men, but not in women [ 19 ]. In our study, sUA levels were positively related to both BUA and Qui-Stiffness in postmenopausal women. These results do not coincide with the previous ones by Scitara et al. [ 19 ], who found no relationship between Qui-Stiffness (they do not assess BUA or SOS) and the levels of sUA in the women studied. Nor did they find a relationship between sUA and lumbar BMD and total hip in them, a relationship that we did find in our study. The authors justify this finding with the fact that their female population was predominantly premenopausal, and that at these ages the levels of sUA are lower and with a lower degree of variability than in postmenopausal women, therefore their influence on BMD and Qui-Stiffness is less noticeable. In our study, we note that the sUA levels were not associated with the ultrasound parameter SOS. The BUA parameter correlates with the BMD better than the SOS which could explain this fact [ 28 , 29 ]. 4.3. sUA and fractures. Few published studies consider the influence of sUA on fracture risk. In addition, they are carried out in different populations, the majority in men [ 14 , 15 , 30 ], or in populations of people > 50 years of both sexes [ 13 , 31 , 32 ]. We have only found two studies conducted in postmenopausal women [ 12 , 33 ], similar to ours. On the other hand, not everyone assesses the same type of fracture. The results of all of them differ: some find no relationship between levels of sUA and risk of fracture [ 13 , 31 ]; others only relate them to hip fracture [ 32 ], but most find an inverse relationship between sUA levels and vertebral fractures [ 12 , 14 , 15 , 33 ], as in our study. We found our results regarding fractures of great interest and observed that sUA is a variable that influences vertebral fractures, but not non-vertebral ones. However, this influence is enhanced by the BMD, as can be seen by including this parameter in the second analysis. Somehow these results are consistent with the greater direct association of sUA with lumbar BMD. Studies carried out with larger populations of age and sex obtain similar results [ 14 , 15 ]. In a meta-analysis conducted by Yin et al. [ 34 ] the authors found an association between higher sUA levels and low overall fracture risk. This disparity in results is probably due to the different population groups studied in the aforementioned works. Our study has the strength of being carried out in a very homogeneous population (postmenopausal women) and with a number that allows statistical robustness. The fact that vertebral fractures are the most frequent in postmenopausal women would also explain our results. In addition, the more active metabolism of trabecular bone may be an important factor for uric acid to have a greater influence on the risk of vertebral fracture, although studies in this regard would be required to confirm this hypothesis. 5. CONCLUSIONS In the population of postmenopausal women studied, sUA levels were correlated with BMD, BUA, and QUI-Stiffness, and this correlation was independent of age and BMI. Regarding fractures, sUA was associated with a decrease in vertebral fractures. These results lead us to consider that there is a beneficial influence of sUA on bone metabolism, with a positive effect both quantitative (BMD) and qualitative (QUS), which is reflected in the lower prevalence of vertebral fractures. Declarations Author Contribution Contributions of the authorsCMH , MJGDTR, PSS participate in: Conceptualization, Methodology, Formal analysis Writing and reviewing, Supervision, Investigation and Data adquisition.NSG participate in: Conceptualization, Methodology, Supervision and Data adquisition.DRA participate in: ConceptualizationMethodology, Supervision, Data adquisition and figure review.MSH participate in: Conceptualization, Methodology, Formal analysis Writing and reviewing, Supervision, Investigation, Data adquisition and was the general coordination and lead the study. References Johnson RJ, Nakagawa T, Sanchez-Lozada LG, Shafiu M, Sundaram S, Le M, Ishimoto T, Sautin YY, Lanaspa MA (2013) Sugar, uric acid, and the etiology of diabetes and obesity. Diabetes 62(10):3307–3315. 10.2337/db12-1814 PubMed PMID: 24065788; PMCID: PMC3781481 King C, Lanaspa MA, Jensen T, Tolan DR, Sánchez-Lozada LG, Johnson RJ (2018) Uric acid as a cause of the metabolic syndrome. 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PubMed PMID: 26684274 Lane NE, Parimi N, Lui LY, Wise BL, Yao W, Lay YAE, Cawthon PM, Orwoll E, Osteoporotic Fractures in Men Study Group (2014) Association of serum uric acid and incident nonspine fractures in elderly men: the Osteoporotic Fractures in Men (MrOS) study. J Bone Min Res 29(7):1701–1707. 10.1002/jbmr.2164 PubMed PMID: 24347506; PMCID: PMC4351860 Iki M, Yura A, Fujita Y, Kouda K, Tachiki T, Tamaki J, Sato Y, Moon JS, Hamada M, Kajita E, Okamoto N, Kurumatani N (2020) 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. Bone 139:115519. 10.1016/j.bone.2020.115519 Epub 2020 Jul 2. PubMed PMID: 32622874 Raum K, Grimal Q, Varga P, Barkmann R, Glüer CC, Laugier P (2014) Ultrasound to assess bone quality. Curr Osteoporos Rep 12(2):154–162. 10.1007/s11914-014-0205-4 PubMed PMID: 24652476 Hans D, Baim S (2017) Quantitative Ultrasound (QUS) in the management of osteoporosis and assessment of fracture risk. J Clin Densitom 20(3):322–333. 10.1016/j.jocd.2017.06.018 Epub 2017 Jul 21. PubMed PMID: 28739081 Hernández JL, Nan D, Martínez J, Pariente E, Sierra L, González-Macías J, Olmos JL (2015) Serum uric acid is associated with quantitative ultrasound parameters in men: data from Camargo cohort. Osteoporos Int 26:1989–1995. 10.1007/s00198-015-3083-4 Epub 2015 Mar 3. PubMed PMID: 25731808 Sritara C, Ongphiphadhanakul B, Chailurkit L, Yamwong S, Ratanachaiwong W, Sritara P (2013) Serum uric acid levels in relation to bone-related phenotypes in men and women. J Clin Densitom 16(3):336–340. 10.1016/j.jocd.2012.05.008 Epub 2012 Jun 21. PubMed PMID: 22727551 Chamberlain JJ, Rhinehart AS, Shaefer CF Jr, Neuman A (2016) Diagnosis and management of diabetes: synopsis of the 2016 American Diabetes Association Standards of Medical Care in Diabetes. Ann Intern Med 164(8):542–552. 10.7326/M15-3016 Epub 2016 Mar 1. PubMed PMID: 26928912 Genant HK, Wu CY, van Kuijk C, Nevitt MC (1993) Vertebral fracture assessment using a semiquantitative technique. J Bone Min Res 8(9):1137–1148. 10.1002/jbmr.5650080915 PubMed PMID: 8237484 World Medical Association (2013) World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA 310(20):2191–2194. 10.1001/jama.2013.281053 PubMed PMID: 24141714 Ren P, Gao M (2018) Association between metabolic syndrome and the serum uric acid: a cohort study. Clin Lab 64(5):719–726 doi: 10.7754/Clin.Lab.2017.171037. PubMed PMID: 29739051 Chen S, Wu N, Yu C, Xu Y, Xu C, Huang Y, Zhao J, Li N, Pan XF (2021) Association between baseline and changes in serum uric acid and incident metabolic syndrome: a nation-wide cohort study and updated meta-analysis. Nutr Metab (Lond) 18(1):59. 10.1186/s12986-021-00584-x PubMed PMID: 34108010; PMCID: PMC8191036 Wei F, Sun N, Cai C, Feng S, Tian J, Shi W, Xu W, Wang Y, Yang X, Li WD (2016) Associations between serum uric acid and the incidence of hypertension: a Chinese senior dynamic cohort study. J Transl Med 14(1):110. 10.1186/s12967-016-0866-0 PubMed PMID: 27129957; PMCID: PMC4851787 Ali N, Perveen R, Rahman S, Mahmood S, Rahman S, Islam S et al (2018) Prevalence of hyperuricemia and the relationship between serum uric acid and obesity: A study on Bangladeshi adults. PLoS ONE 13(11):e0206850. 10.1371/journal.pone.0206850 eCollection 2018. PubMed PMID: 30383816; PMCID: PMC6211757 Dalbeth N, Topless R, Flynn T, Cadzow M, Bolland MJ, Merriman TR (2015) Mendelian randomization analysis to examine for a causal effect of urate on bone mineral density. J Bone Min Res 30(6):985–991. 10.1002/jbmr.2434 PubMed PMID: 25502344 Wang Q, Nicholson PHF, Timonen J, Alen M, Moilanen P, Suominen H, Cheng S (2008) Monitoring bone growth using quantitative ultrasound in comparison with DXA and pQCT. J Clin Densitom 11(2):295–301. 10.1016/j.jocd.2007.10.003 Epub 2007 Dec 26. PubMed PMID: 18158265 Tromp AM, Smit JH, Deeg DJ, Lips P (1999) Quantitative ultrasound measurements of the tibia and calcaneus in comparison with DXA measurements at various skeletal sites. Osteoporos Int 9(3):230–235. 10.1007/s001980050142 PubMed PMID: 10450412 Kim BJ, Baek S, Ahn SH, Kim SH, Jo MW, Bae SJ, Kim HK, Choe J, Park GM, Kim YH, Lee SH, Kim GS, Koh JM (2014) Higher serum uric acid as a protective factor against incident osteoporotic fractures in Korean men: a longitudinal study using the National Claim Registry. Osteoporos Int 25(7):1837–1844. 10.1007/s00198-014-2697-2 Epub 2014 Mar 26. PubMed PMID: 24668006 Veronese N, Bolzetta F, De Rui M, Maggi S, Noale M, Zambon S, Corti MC, Toffanello ED, Baggio G, Perissinotto E, Crepaldi G, Manzato E, Sergi G (2015) Serum uric acid and incident osteoporotic fractures in old people: The PRO.V.A study. Bone 79:183–189 Epub 2015 Jun 12. PubMed PMID: 26079996 Preyer O, Concin H, Nagel G, Zitt E, Ulmer H, Brozek W (2021) Serum uric acid is associated with incident hip fractures in women and men. Results from a large Austrian population-based cohort study. Maturitas 148:46–53 Epub 2021 Mar 8. PubMed PMID: 33836935 Tanaka KI, Kanazawa I, Notsu M, Sugimoto T (2020) Higher serum uric acid is a risk factor of vertebral fractures in postmenopausal women with type 2 diabetes mellitus. Exp Clin Endocrinol Diabetes 128(1):66–71. 10.1055/a-0815-4954 Epub 2018 Dec 18. PubMed PMID: 30562825 Yin P, Lv H, Li Y, Meng Y, Zhang L, Tang P (2017) The association between serum uric acid level and the risk of fractures: a systematic review and meta-analysis. Osteoporos Int 28(8):2299–2307 doi: 10.1007/s00198-017-4059-3. Epub 2017 May 9. PubMed PMID: 28488134 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 Aug, 2024 Read the published version in Aging Clinical and Experimental Research → Version 1 posted Editorial decision: Revision requested 18 Jul, 2024 Reviews received at journal 18 Jul, 2024 Reviewers agreed at journal 15 Jul, 2024 Reviews received at journal 15 Jul, 2024 Reviewers agreed at journal 15 Jul, 2024 Reviewers invited by journal 15 Jul, 2024 Editor assigned by journal 15 Jul, 2024 Submission checks completed at journal 14 Jul, 2024 First submitted to journal 13 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4735028","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":328913549,"identity":"70812cc0-917e-4821-991b-2dfe520f4a9e","order_by":0,"name":"María-Jesús Gómez-de-Tejada-Romero","email":"","orcid":"","institution":"University of Seville","correspondingAuthor":false,"prefix":"","firstName":"María-Jesús","middleName":"","lastName":"Gómez-de-Tejada-Romero","suffix":""},{"id":328913551,"identity":"5d426ce3-7ec3-4b81-99bc-24cb47c1a409","order_by":1,"name":"Carmen Murias-Henríquez","email":"","orcid":"","institution":"University of Las Palmas de Gran Canaria. Osteoporosis and mineral metabolism research group. Las Palmas de Gran Canaria","correspondingAuthor":false,"prefix":"","firstName":"Carmen","middleName":"","lastName":"Murias-Henríquez","suffix":""},{"id":328913553,"identity":"14dd4d29-c51e-4fb7-b63b-844e27681894","order_by":2,"name":"Pedro Saavedra-Santana","email":"","orcid":"","institution":"University of Las Palmas de Gran Canaria. Osteoporosis and mineral metabolism research group. Las Palmas de Gran Canaria","correspondingAuthor":false,"prefix":"","firstName":"Pedro","middleName":"","lastName":"Saavedra-Santana","suffix":""},{"id":328913554,"identity":"8379c636-1a5f-4389-a98d-23c8887a1c71","order_by":3,"name":"Nery Sablón-González","email":"","orcid":"","institution":"University of Las Palmas de Gran Canaria. Osteoporosis and mineral metabolism research group. Las Palmas de Gran Canaria","correspondingAuthor":false,"prefix":"","firstName":"Nery","middleName":"","lastName":"Sablón-González","suffix":""},{"id":328913557,"identity":"32211e1d-edb3-4eef-95a5-05bae94e4b41","order_by":4,"name":"Delvys Rodríguez Abreu","email":"","orcid":"","institution":"University of Las Palmas de Gran Canaria. Osteoporosis and mineral metabolism research group. Las Palmas de Gran Canaria","correspondingAuthor":false,"prefix":"","firstName":"Delvys","middleName":"Rodríguez","lastName":"Abreu","suffix":""},{"id":328913559,"identity":"9577b2bf-cbb8-4a4d-b617-8cd54d8e0b1a","order_by":5,"name":"Manuel Sosa-Henríquez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYNCCAjDJ+ADKIAYYgElmAyiDeC1sEkRp0W0/e0zigwGDPT//4WfVPAZ37PobmA9/wKfF7ExemuQMA4bEmTPSzG7zGDxLnnGALU0Cr5YDOWbSPAYMCQY3GEBaDicbMPCY4XWY2fk3ZtJ/gA4zOH/8WzFEC/9n/A67AbQF6GvGDUDrmIFa7IC2MOB32I03xpY9BhJAv+QUS84xeJYgcZjNDL+W8zmGN35U2ABD7PjGD28q7tjztzc/xuswKIAbeyCxgZkI9cjggD2JGkbBKBgFo2AEAAB6uURjPghP8gAAAABJRU5ErkJggg==","orcid":"","institution":"University of Las Palmas de Gran Canaria. Osteoporosis and mineral metabolism research group. Las Palmas de Gran Canaria","correspondingAuthor":true,"prefix":"","firstName":"Manuel","middleName":"","lastName":"Sosa-Henríquez","suffix":""}],"badges":[],"createdAt":"2024-07-13 11:54:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4735028/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4735028/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s40520-024-02819-2","type":"published","date":"2024-08-01T15:56:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62541551,"identity":"bffaeb6c-5c65-46ef-b711-54b8dc279fcc","added_by":"auto","created_at":"2024-08-15 15:01:09","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":172055,"visible":true,"origin":"","legend":"\u003cp\u003eCubic splines (95% CI) corresponding to the nonlinear effects of the age on the BMD in lumbar spine and the \u003cem\u003eBUA\u003c/em\u003e. Note that the effects on the \u003cem\u003efemoral neck\u003c/em\u003e, \u003cem\u003eTotal Hip\u003c/em\u003e, \u003cem\u003eSOS\u003c/em\u003e and \u003cem\u003eQui-Stiffness\u003c/em\u003e were linear (see Table 2)\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4735028/v1/8945c64326f2cf91679a6d1c.jpeg"},{"id":62543032,"identity":"7fa82324-4e24-4563-b090-ea3b3218cb55","added_by":"auto","created_at":"2024-08-15 15:09:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1109799,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4735028/v1/cf754217-94f4-486a-953f-f4880ad21d99.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Influence of serum uric acid on bone and fracture risk in postmenopausal women","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eUric acid, a product of the degradation of purines, is part of the complex network of human metabolism and has been related to obesity, diabetes, and arterial hypertension [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Along with these metabolic diseases, a role of serum uric acid (sUA) in bone metabolism has also been pointed out in recent years, since its antioxidant action (in physiological ranges) could prevent bone loss and, consequently, osteoporosis (OP) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, not all studies confirm this. It is true that many of them have shown that there is a positive correlation between serum levels within the physiological range of sUA and bone mineral density (BMD), the main factor for the diagnosis of OP [\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. For some, this relationship is a consequence of a common element, fatty tissue [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], while others give relevance to this correlation directly, finding some causality independent of fatty tissue [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, a recent study found no link between BMD and sUA [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Furthermore, various studies have been carried out to ascertain the effect that the different levels in the physiological range of sUA could have on the production of fragility fractures, obtaining varied results [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the other hand, quantitative ultrasound (QUS) is a measurement technique of bone structure, so that its parameters (BUA, SOS and Stiffness) are considered indirect indicators of bone quality [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], one of the determining aspects of bone status. We have only found two studies that observed the effect of uric acid levels on these ultrasonographic parameters [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and with different results.\u003c/p\u003e \u003cp\u003eIn this study, carried out in a large number of postmenopausal women (to eliminate bias by sex and hormonal status), our objectives were: first, to observe the relationship between sUA levels and BMD measured by DXA and the QUS parameters (as indicators of bone quantity and quality, respectively), as well as to see if there is an influence of sUA levels on relevant hormonal parameters in bone metabolism; considering in all this the effect of fatty tissue (as the main confounding variable and represented by the body mass index -BMI-). Secondly, we observed the effect of serum uric acid levels on the prevalence and risk of fragility fracture.\u003c/p\u003e"},{"header":"2. MATERIAL AND METHODS","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis is an observational, cross-sectional study carried out on 679 postmenopausal women treated at the Bone Metabolic Unit of the Hospital University Insular, Gran Canaria, Spain, from January 1st, 2018 to December 31st, 2019. For all subjects a questionnaire, previously validated and used in other similar clinical studies, was completed to gather clinical data. Body weight (in kg) and body height (cm) were measured to the nearest 0.1 kg and 0.1 cm, respectively, using a SECA-marked stadiometer. Weight was measured with light clothes. The BMI as weight/height\u003csup\u003e2\u003c/sup\u003e (kg/m\u003csup\u003e2\u003c/sup\u003e) was calculated for each individual. Obesity was defined as a BMI\u0026thinsp;\u0026ge;\u0026thinsp;30. The diagnosis of type 2 diabetes mellitus (T2DM) was made following the criteria of The American Diabetes Association [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Serum biochemical measurements: sample collection and laboratory techniques\u003c/h2\u003e \u003cp\u003eSerum biochemical parameters were measured from blood samples collected in the early morning, after a fasting night, in the appropriate specific tubes for each determination, with the least possible venous compression and centrifuged at 1,500 g for 10 minutes. The serum was separated into aliquots and stored within one hour of extraction at -20\u0026deg;C until the biochemical analyzes were carried out, although most were done on the same day as the extraction.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eSerum uric acid, creatinine and calcium were analyzed by a biochemical automatic analyzer Cobas\u0026reg; 8000 (Roche Diagnostics, Switzerland), in which normal levels are: uric acid: 3.5\u0026ndash;7.2 mg/dL; creatinine: 0.67\u0026ndash;1.17 mg/dL); and calcium: 8.5\u0026ndash;10.5 mg/dL. Serum calcium was corrected by total proteins based on the formula:\u003c/p\u003e\u003cp\u003e \u003cem\u003eCorrected calcium (mg/dL): serum calcium (mg/dL) / [Total proteins (g/dL) /16\u0026thinsp;+\u0026thinsp;0.55]\u003c/em\u003e \u003c/p\u003e\u003cp\u003eSerum levels of 25(OH) vitamin D were measured by immunochemical luminescence, according to the Nichols method (Nichols Institute Diagnostics, San Clemente, California, USA). Serum parathyroid hormone (PTH) concentrations for the intact molecule were determined by immunochemical luminiscence, according to the Nichols Advantage method (PTH normal values: 15\u0026ndash;88 pg/mL).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eCreatinine clearance (CCr) was calculated using the Cockcroft-Gault formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:CCr=\\frac{\\left(140-Age\\right)\\times\\:Weight\\left(Kg\\right)\\times\\:{0.85}^{}}{72\\times\\:\\left[SerumCreatinine\\left(mg/dL\\right)\\right]}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eEstimated Glomerular Filtration Rate (GFR) was calculated using MDRD-4 IDMS formula:\u003c/p\u003e \u003cp\u003eeGFR\u0026thinsp;=\u0026thinsp;175 x (serum creatinine/88,4)\u003csup\u003e\u0026minus;1,154\u003c/sup\u003e x (age)\u003csup\u003e\u0026minus;0,203\u003c/sup\u003e x 0,742\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Bone mineral density (BMD)\u003c/h2\u003e \u003cp\u003eBMD was measured by dual x-ray absorptiometry (DXA), both in lumbar spine (L2-L4) and proximal femur, with a Hologic Discovery\u0026reg; densitometer, (Hologic Inc. Waltham, USA). All the measurements were made by the same operator, so there was no inter-observer variation. BMD values were done as g/cm\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDiagnosis of osteoporosis was based on the WHO densitometric criteria (BMD T-score\u0026thinsp;\u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;2.5 in at least one of the anatomic sites, including the lumbar spine, the femoral neck and the total hip).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Fragility fractures\u003c/h2\u003e \u003cp\u003e \u003cem\u003eVertebral fractures\u003c/em\u003e:\u003c/p\u003e \u003cp\u003eA lateral thoracic-lumbar X-ray was carried out on the subjects. All the X-rays were collated and studied by two different observers: one was a radiologist and the second was an expert on bone metabolic diseases. According to the Genant criteria, the existence of vertebral deformity was recorded when there was a reduction in the vertebral height higher than 20% [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eNon-vertebral fractures\u003c/em\u003e:\u003c/p\u003e \u003cp\u003eThe remaining fragility fractures were confirmed by hospital clinical reports, from the emergency services or by radiography study, excluding the patients\u0026rsquo; self-diagnosis of fractures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Quantitative Ultrasound (QUS) measurements\u003c/h2\u003e \u003cp\u003eAll subjects underwent calcaneus measurement by QUS. This was carried out using the Sahara Clinical sonometer (Hologic Inc., Bedford, MA) which measures 3 parameters at a fixed region of interest in the mid-calcaneus: broadband ultrasound attenuation (BUA); speed of sound (SOS); and quantitative ultrasound index (QUI), a combination of BUA and SOS resulting in the formula:\u003c/p\u003e \u003cp\u003e \u003cem\u003eQUI\u0026thinsp;=\u0026thinsp;0.41 X (BUA\u0026thinsp;+\u0026thinsp;SOS) \u0026minus;\u0026thinsp;571\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Ethics\u003c/h2\u003e \u003cp\u003eThe study was carried out in accordance with the Declaration of Helsinki [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and was approved by the Ethics Committee of the Insular University Hospital. All patients were informed of the study objectives and their informed consent was requested.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Statistical analysis\u003c/h2\u003e \u003cp\u003e \u003cem\u003eUnivariate analysis.\u003c/em\u003e Categorical variables are expressed as frequencies, percentages and continuous as mean and standard deviation (SD) when data followed a normal distribution, or as median and interquartile range (IQR\u0026thinsp;=\u0026thinsp;25th \u0026minus;\u0026thinsp;75th percentile) when distribution departed from normal. The percentages were compared, as appropriate, using the Chi-square (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003e) test or the exact Fisher test, the means by the t-test and the medians by the Wilcoxon test for independent data.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAdditive models for the bone markers.\u003c/em\u003e For each of the DXA and QUS markers, a multidimensional analysis was performed in which, in addition to uric acid, age, BMI, diabetes mellitus status and vitamin-D were entered as co-variates. First, a variable selection based on the best subset and then Akaike Information Criterion (AIC) were conducted. Once the variables were selected, the eventual nonlinear effect of the continuous variables was explored by the additive models using cubic splines. The final models were summarized, in addition to \u003cem\u003eP\u003c/em\u003e-values, in coefficients and standard errors (SE) for linear effects or cubic splines together with 95% confidence bands (95% CI).\u003c/p\u003e \u003cp\u003e \u003cem\u003eLogistic models for vertebral and fragility fractures.\u003c/em\u003e For each of the binary factors, vertebral fractures versus non-fractures and fragility fractures versus non-fractures, two multivariate logistic analyses were carried out. In the first, the variables age, BMI, sUA (continuous scale), T2DM, CCr and Vitamin D were entered. In the second analysis, BMD markers were added. In both analyses, a selection of variables based on the best subset regression and Akaike Information Criterion (AIC) was then performed [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The models were summarized as p-values (likelihood ratio test) and odds-ratio, which were estimated by means 95% CI.\u003c/p\u003e \u003cp\u003eStatistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Data were analyzed using the R package, version 3.6.1 (R Development Core Team, 2019).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003eTo consider the behavior of the different variables studied in relation to sUA levels, the study participants were grouped into 3 groups according to levels. The cut-off points were set taking into account that most of the women had normal sUA levels. Thus, so that a similar number and homogeneous of patients in the 3 groups could be obtained, they were grouped into: low levels (below 4 mg/dl; medium levels (from 4 to 5 mg/dl); and high levels (above 5 mg/dl) (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the women: overall and according to the level of serum uric acid\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLevels of serum uric acid\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOverall\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eN\u0026thinsp;=\u0026thinsp;913\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;4 mg/dL\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eN\u0026thinsp;=\u0026thinsp;341\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4\u0026ndash;5 mg/dL\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eN\u0026thinsp;=\u0026thinsp;277\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt;\u0026thinsp;5 mg/dL\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eN\u0026thinsp;=\u0026thinsp;295\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003cb\u003e-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.4\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.9\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.7\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e270 (29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86 (31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120 (40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFractures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBy fragility (all)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e298 (32.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108 (31.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e109 (37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVertebral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon vertebral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e226 (24.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69 (24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87 (29.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125 (13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArterial hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e393 (43.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113 (33.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e118 (42.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e162 (54.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrolithiasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148 (16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58 (19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOsteoporosis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e395 (44.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e169 (50.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123 (44.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e103 (35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCr (mL/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.8 (56.5 ; 86.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.9 (58.6 ; 87.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.7 (62.4 ; 90.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.3 (52.0 ; 82.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFR (mL/min/1,73 m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (63 ; 84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (69 ; 89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (65 ; 85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66 (53 ; 76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25(OH) vitamin D (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.4 (16.0 ; 30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.0 (16.0 ; 31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.9 (15.9 ; 29.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.2 (16.0 ; 29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTH (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.9 (36.0 ; 80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.0 (32.7 ; 59.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.9 (36.8 ; 80.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62.8 (41.8 ; 100.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorrected calcium (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.9 (9.5 ; 10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.8 (9.4 ; 10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.9 (9.6 ; 10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.0 (9.6 ; 10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eData are means SD, medias (IQR) and frequencies (%)\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eDXA and US markers adjusted by age and BMI\u003c/b\u003e \u003cb\u003e(\u003c/b\u003e\u003cb\u003eaccording to the level of serum uric acid)\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLevels of uric acid\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;4 mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u0026ndash;5 mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5 mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSpine lumbar\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eg/cm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003cp\u003e[0.822 ; 0.857]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003cp\u003e[0.832 ; 0.870]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003cp\u003e[0.867 ; 0.905]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT-score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.932\u003c/p\u003e \u003cp\u003e[-2.100 ; -1.764]\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.818\u003c/p\u003e \u003cp\u003e[-2.002 ; -1.635]\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.478\u003c/p\u003e \u003cp\u003e[-1.661 ; -1.296]\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemoral Neck\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eg/cm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003cp\u003e[0.663 ; 0.687]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003cp\u003e[0.659 ; 0.685]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003cp\u003e[0.669 ; 0.695]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT-score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.511\u003c/p\u003e \u003cp\u003e[-1.621 ; -1.402]\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.537\u003c/p\u003e \u003cp\u003e[-1.656 ; -1.418]\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.451\u003c/p\u003e \u003cp\u003e[-1.570 ; -1.332]\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal Hip\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eg/cm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003cp\u003e[0.775 ; 0.803]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003cp\u003e[0.782 ; 0.813]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003cp\u003e[0.794 ; 0.825]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT-score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.344\u003c/p\u003e \u003cp\u003e[-1.489 ; -1.199]\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.252\u003c/p\u003e \u003cp\u003e[-1.410 ; -1.094]\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.128\u003c/p\u003e \u003cp\u003e[-1.286 ; -0.970]\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBUA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edB/MHz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.9\u003c/p\u003e \u003cp\u003e[56.6 ; 61.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.2\u003c/p\u003e \u003cp\u003e[57.8 ; 62.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.3\u003c/p\u003e \u003cp\u003e[62.0 ; 66.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT-score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.187\u003c/p\u003e \u003cp\u003e[-1.334 ; -1.040]\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.107\u003c/p\u003e \u003cp\u003e[-1.255 ; -0.959]\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.846\u003c/p\u003e \u003cp\u003e[-0.992 ; -0.701]\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSOS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003em/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1520.7\u003c/p\u003e \u003cp\u003e[1516.8 ; 1524.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1522.9\u003c/p\u003e \u003cp\u003e[1518.9 ; 1526.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1525.8\u003c/p\u003e \u003cp\u003e[1521.9 ; 1529.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT-score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.470\u003c/p\u003e \u003cp\u003e[-1.598 ; -1.342]\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.398\u003c/p\u003e \u003cp\u003e[-1.527 ; -1.270]\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.304\u003c/p\u003e \u003cp\u003e[-1.430 ; -1.177]\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQui-Stiffness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.0\u003c/p\u003e \u003cp\u003e[74.5 ; 79.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.4\u003c/p\u003e \u003cp\u003e[74.9 ; 80.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.7\u003c/p\u003e \u003cp\u003e[78.2 ; 83.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT-score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.411\u003c/p\u003e \u003cp\u003e[-1.550 ; -1.273]\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.390\u003c/p\u003e \u003cp\u003e[-1.529 ; -1.251]\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.213\u003c/p\u003e \u003cp\u003e[-1.349 ; -1.076]\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAdjusted means (95% CI) by \u003cem\u003eage\u003c/em\u003e and \u003cem\u003ebody mass index\u003c/em\u003e (BMI) obtained by least squares regression\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Characteristics of the women according to the level of serum uric acid.\u003c/h2\u003e \u003cp\u003eThe results showed that age and BMI were significantly higher in the groups with the highest levels of sUA, as well the presence of obesity, T2DM and arterial hypertension; nevertheless, osteoporosis diagnosis was lower. GFR and CCr were higher in the groups with lower sUA levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Bone metabolism parameters.\u003c/h2\u003e \u003cp\u003eRegarding the variables related to bone metabolism, no significant differences were observed in 25(OH) vitamin D levels among the 3 groups, but PTH increased significantly in the groups with the highest levels of sUA (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This result was reflected in protein-corrected calcemia, which was higher in these same groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3. DXA and QUS parameters.\u003c/h2\u003e \u003cp\u003eThe lumbar BMD and BUA levels were notably higher in the groups with raised figures in sUA (p\u0026thinsp;=\u0026thinsp;0.002 and 0.004, respectively) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor each one of the DXA and QUS parameters, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the additive regression models. For lumbar spine, total hip, BUA and Qui-Stiffness, uric acid showed significant linear association, adjusting for those co-variates that were selected by the best subset method and AIC (Age, BMI, sUA, DM2, CCr and Vitamin-D). In the models corresponding to lumbar spine and BUA, the effect of age on each of these markers was nonlinear. The corresponding cubic splines are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdditive models for the \u003cem\u003eDXA\u003c/em\u003e and \u003cem\u003eQUS\u003c/em\u003e parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone marker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCovariates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficients (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLumbar spine\u003c/em\u003e (g/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5631 (0.0330)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNonlinear effect\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMI (Kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0079 (0.0011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUric Acid (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0159 (0.0044)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitamin-D \u0026ge; 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0230 (0.0113)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFemoral neck\u003c/em\u003e (g/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6528 (0.0328)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0043 (0.0004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMI (Kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0077 (0.0010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUric Acid (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0057 (0.0032)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.071\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitamin-D \u0026ge; 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0237 (0.0078)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0005 (0.0002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTotal hip\u003c/em\u003e (g/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6531 (0.0318)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0045 (0.0003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0135 (0.0009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUric Acid (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0072 (0.0036)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitamin-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0010 (0.0004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBUA\u003c/em\u003e (dB/MHz)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.0588 (3.8507)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNonlinear effect\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMI (Kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6401 (0.1363)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUric Acid (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.9139 (0.5356)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSOS (\u003c/em\u003em/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1585.9 (8.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.3444 (0.1161)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMI (Kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3253 (0.2778)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.2070 (0.0719)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eQui-Stiffness\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.9 (4.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.6840 (0.0546)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMI (Kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5529 (0.1493)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUric Acid (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.3696 (0.5879)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eAge\u003c/em\u003e, \u003cem\u003eBMI\u003c/em\u003e, \u003cem\u003eUric Acid\u003c/em\u003e (continuous scale), \u003cem\u003eDiabetes Mellitus\u003c/em\u003e, \u003cem\u003eCCr\u003c/em\u003e and \u003cem\u003eVitamin-D\u003c/em\u003e were entered in all analysis.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSelection of variables were carried out using the best subset regression method and the AIC.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eThe effects of the selected covariates on the marker are shown for each of the bone markers. When the effects were nearly linear (effective degree of freedom\u0026thinsp;~\u0026thinsp;1), the effect was considered linear. The nonlinear effects are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Multivariate logistic analyses for fractures.\u003c/h2\u003e \u003cp\u003eMultivariate logistic analyses for vertebral fractures versus non-fractures and fragility fractures versus non-fractures are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. For the vertebral fractures, type one logistic analyses (not including BMD markers) showed that the factors with independent association with the outcome (AIC) were age (per year, OR\u0026thinsp;=\u0026thinsp;1.06; 95% CI\u0026thinsp;=\u0026thinsp;1.04\u0026ndash;1.09) and uric acid level (per md/dL, OR\u0026thinsp;=\u0026thinsp;0.78; 95% CI\u0026thinsp;=\u0026thinsp;0.65\u0026ndash;0.94). When BMD markers are added in the analysis (type 2), BMI and BMD at the total hip are added in the model according to the AIC. Uric acid levels were maintained in the model (per mg/dL, OR\u0026thinsp;=\u0026thinsp;0.81: 95% CI\u0026thinsp;=\u0026thinsp;0.66 ; 0. 98). For the fragility fractures, the variables selected for the first model are age (per year, OR\u0026thinsp;=\u0026thinsp;1.05; 95% CI\u0026thinsp;=\u0026thinsp;1.04 ; 1.06) and T2DM (OR\u0026thinsp;=\u0026thinsp;1.50; 95% CI\u0026thinsp;=\u0026thinsp;0.99\u0026ndash;2.28). When BMD markers are added in the analysis, BMI and BMD at total hip are added in the model according to the AIC. Uric acid level showed no statistical association with this outcome in any of the analyses (AIC).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate logistic analysis for \u003cem\u003efragility fractures\u003c/em\u003e and \u003cem\u003evertebral fractures\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAnalysis 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eAnalysis 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCovariates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOdd-Ratio (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOdd-Ratio (95% CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003eVertebral fractures *\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAge\u003c/em\u003e, per year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 (1.04 ; 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.04 (1.02 ; 1.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBMI\u003c/em\u003e, per kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.08 (1.02 ; 1.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUric Acid\u003c/b\u003e, \u003cb\u003eper mg/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.78 (0.65 ; 0.94)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.81 (0.66 ; 0.98)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTotal hip\u003c/em\u003e, per g/cm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00 (0.00 ; 0.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003eFragility fractures\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAge\u003c/em\u003e, per year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05 (1.04 ; 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.03 (1.02 ; 1.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBMI\u003c/em\u003e, per kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.07 (1.03 ; 1.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDiabetes mellitus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.50 (0.99 ; 2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.56 (1.00 ; 2.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTotal hip\u003c/em\u003e, per g/cm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01 (0.00 ; 0.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cem\u003eNon vertebral fractures**\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAge\u003c/em\u003e, per year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05 (1.03 ; 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.02 (1.01 ; 1.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBMI\u003c/em\u003e, per kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.06 (1.02 ; 1.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDiabetes mellitus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.61 (1.01 ; 2.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.60 (0.98 ; 2.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eVitamin-D\u0026thinsp;\u0026gt;\u0026thinsp;20\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74 (0.52 ; 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFemoral neck\u003c/em\u003e, per g/cm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02 (0.00 ; 0.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e(*) Model for \u003cem\u003eVertebral Fractures\u003c/em\u003e versus \u003cem\u003eNon-Fractures\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e(**) Model for Only Non-vertebral fractures versus Non-Fractures\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eThe covariates entered in the first analysis were the \u003cem\u003eAge\u003c/em\u003e, \u003cem\u003eBMI\u003c/em\u003e, \u003cem\u003eUric Acid\u003c/em\u003e (continuous scale), \u003cem\u003eDiabetes Mellitus, CCr\u003c/em\u003e and \u003cem\u003eVitamin-D\u003c/em\u003e and in the second analysis, \u003cb\u003eBMD markers were added\u003c/b\u003e. Selection of variables was carried out using the AIC. \u003cem\u003eP\u003c/em\u003e-value corresponding to the likelihood ratio test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eOur initial results were as expected: sUA is related to the metabolic syndrome and, therefore, to obesity (and its determinant, BMI), T2DM, and arterial hypertension, as other studies have shown [\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], which is explained by various metabolic mechanisms [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the other hand, the decrease in GFR produces an increase in CCr and sUA, but both GFR and CCr generally decrease with age, so sUA levels should increase with age (and vice versa).\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1. sUA and bone metabolism parameters.\u003c/h2\u003e \u003cp\u003eThe women under study had higher mean PTH levels in the groups with the highest sUA. In parallel, calcium levels also increased with sUA. All were within normal limits, except for some cases of high PTH levels, in which hyperparathyroidism was secondary to very low levels of vitamin D. It should be noted that the vitamin D values found in the women studied were generally very low, considered insufficient (\u0026lt;\u0026thinsp;30 ng/mL) and even close to deficiency (values\u0026thinsp;\u0026lt;\u0026thinsp;20 ng/mL). Other authors have reported similar results [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The higher levels of uric acid found in our study are related to a decrease in renal function (lower CCr and lower GFR), which leads to a lower production of 1,25 (OH)\u003csub\u003e2\u003c/sub\u003e vitamin D, which in turn stimulates the production of PTH, increasing calcemia. There were no significant differences in 25(OH) vitamin D levels between the groups analyzed. Furthermore, we did not find a correlation between this parameter and uric acid, but this metabolite of vitamin D is not related to renal function, so it does not tell us anything in this sense. However, we could consider PTH as an indirect marker of 1,25 (OH)\u003csub\u003e2\u003c/sub\u003e vitamin D status (and, therefore, of renal function).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2. sUA and DXA and QUS parameters.\u003c/h2\u003e \u003cp\u003eFocusing on the bone parameters, we have obtained a positive association between sUA and the BMD values measured in the different locations, but the association was greater with lumbar BMD. Many different authors have linked sUA and BMD in studies conducted in different adult populations [\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, a recent report, conducted in a large number of adult men (n\u0026thinsp;=\u0026thinsp;6704) in the United States of America, found no such association [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In another study, Dalbeth et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] point out that the correlation between these 2 variables is not causal but coincidental, with confounding variables such as BMI, adiposity or hormonal status. Pirro et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] also conclude in their study that the relationship between sUA and BMD is mediated by adiposity. Navipour et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], in their study also conducted in men, did find a relationship between sUA and BMD, even when adjusting for age and BMI. Ibrahim et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], in a study conducted in a large population of 2,981 healthy Qatari adults, only found a relationship between both variables in non-obese, young, and smoking women. In our study, as in that of Yan et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], the association between BMD and sUA was independent of BMI (as additive regression models show), and sex and hormonal status were the same (all were postmenopausal women), so there do not seem to be any confounding variables in our correlation. Given that the influence is greater in the lumbar spine, this could be due to the increased metabolic activity in the trabecular bone, the main component of the vertebrae.\u003c/p\u003e \u003cp\u003eOf the 2 studies that linked sUA levels with the ultrasonographic parameters BUA, SOS and Qui-Stiffness, one of them was carried out only in men [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], finding a positive association. The other, carried out in both men and women, found an association in men, but not in women [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In our study, sUA levels were positively related to both BUA and Qui-Stiffness in postmenopausal women. These results do not coincide with the previous ones by Scitara et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], who found no relationship between Qui-Stiffness (they do not assess BUA or SOS) and the levels of sUA in the women studied. Nor did they find a relationship between sUA and lumbar BMD and total hip in them, a relationship that we did find in our study. The authors justify this finding with the fact that their female population was predominantly premenopausal, and that at these ages the levels of sUA are lower and with a lower degree of variability than in postmenopausal women, therefore their influence on BMD and Qui-Stiffness is less noticeable. In our study, we note that the sUA levels were not associated with the ultrasound parameter SOS. The BUA parameter correlates with the BMD better than the SOS which could explain this fact [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3. sUA and fractures.\u003c/h2\u003e \u003cp\u003eFew published studies consider the influence of sUA on fracture risk. In addition, they are carried out in different populations, the majority in men [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], or in populations of people\u0026thinsp;\u0026gt;\u0026thinsp;50 years of both sexes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. We have only found two studies conducted in postmenopausal women [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], similar to ours. On the other hand, not everyone assesses the same type of fracture. The results of all of them differ: some find no relationship between levels of sUA and risk of fracture [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]; others only relate them to hip fracture [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], but most find an inverse relationship between sUA levels and vertebral fractures [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], as in our study. We found our results regarding fractures of great interest and observed that sUA is a variable that influences vertebral fractures, but not non-vertebral ones. However, this influence is enhanced by the BMD, as can be seen by including this parameter in the second analysis. Somehow these results are consistent with the greater direct association of sUA with lumbar BMD. Studies carried out with larger populations of age and sex obtain similar results [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In a meta-analysis conducted by Yin et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] the authors found an association between higher sUA levels and low overall fracture risk. This disparity in results is probably due to the different population groups studied in the aforementioned works. Our study has the strength of being carried out in a very homogeneous population (postmenopausal women) and with a number that allows statistical robustness. The fact that vertebral fractures are the most frequent in postmenopausal women would also explain our results. In addition, the more active metabolism of trabecular bone may be an important factor for uric acid to have a greater influence on the risk of vertebral fracture, although studies in this regard would be required to confirm this hypothesis.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. CONCLUSIONS","content":"\u003cp\u003eIn the population of postmenopausal women studied, sUA levels were correlated with BMD, BUA, and QUI-Stiffness, and this correlation was independent of age and BMI. Regarding fractures, sUA was associated with a decrease in vertebral fractures. These results lead us to consider that there is a beneficial influence of sUA on bone metabolism, with a positive effect both quantitative (BMD) and qualitative (QUS), which is reflected in the lower prevalence of vertebral fractures.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eContributions of the authorsCMH , MJGDTR, PSS participate in: Conceptualization, Methodology, Formal analysis Writing and reviewing, Supervision, Investigation and Data adquisition.NSG participate in: Conceptualization, Methodology, Supervision and Data adquisition.DRA participate in: ConceptualizationMethodology, Supervision, Data adquisition and figure review.MSH participate in: Conceptualization, Methodology, Formal analysis Writing and reviewing, Supervision, Investigation, Data adquisition and was the general coordination and lead the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJohnson RJ, Nakagawa T, Sanchez-Lozada LG, Shafiu M, Sundaram S, Le M, Ishimoto T, Sautin YY, Lanaspa MA (2013) Sugar, uric acid, and the etiology of diabetes and obesity. 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PubMed PMID: 28488134\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"aging-clinical-and-experimental-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"acer","sideBox":"Learn more about [Aging Clinical and Experimental Research](http://link.springer.com/journal/40520)","snPcode":"40520","submissionUrl":"https://submission.nature.com/new-submission/40520/3","title":"Aging Clinical and Experimental Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"uric acid, bone mineral density, calcaneus ultrasounds, fragility fractures","lastPublishedDoi":"10.21203/rs.3.rs-4735028/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4735028/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eAims\u003c/b\u003e\u003c/p\u003e \u003cp\u003eUric acid has been associated with several metabolic conditions, including bone diseases. Our objective here was to consider the relationship between serum uric acid levels and various bone parameters (bone mineral density, ultrasonographic parameters, vitamin D, PTH and serum calcium), as well as the prevalence and risk of fragility fracture.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAn observational and cross-sectional study carried out on 679 postmenopausal women, classified into 3 groups according to their serum uric acid levels, in whom bone densitometry, calcaneus ultrasounds, PTH, vitamin D and serum calcium analysis were done. Bone fractures were collected through the clinical history and lateral spinal X-ray.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eHigher uric acid levels were found in women with older age, high BMI, diabetes, and high blood pressure. Higher levels of PTH and serum calcium were also observed, but did not effect on vitamin D. Serum uric acid was positively related to densitometric and ultrasonic parameters and negatively associated with vertebral fractures.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn the population of postmenopausal women studied, sUA levels were correlated with BMD, BUA, and QUI-Stiffness, and this correlation was independent of age and BMI. In addition, sUA was associated with a decrease in vertebral fractures. These results imply a beneficial influence of sUA on bone metabolism, with both a quantitative and qualitative positive effect, reflected in the lower prevalence of vertebral fractures.\u003c/p\u003e","manuscriptTitle":"Influence of serum uric acid on bone and fracture risk in postmenopausal women","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-15 15:01:04","doi":"10.21203/rs.3.rs-4735028/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-18T22:50:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-18T22:48:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"197504536935995347838123512431620614387","date":"2024-07-15T16:46:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-15T13:27:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"95448139670957886362357049829614155982","date":"2024-07-15T13:11:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-15T11:09:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-15T11:07:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-15T01:53:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Aging Clinical and Experimental Research","date":"2024-07-13T11:53:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"aging-clinical-and-experimental-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"acer","sideBox":"Learn more about [Aging Clinical and Experimental Research](http://link.springer.com/journal/40520)","snPcode":"40520","submissionUrl":"https://submission.nature.com/new-submission/40520/3","title":"Aging Clinical and Experimental Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f4d81ce6-f7c6-4ba1-b521-5d16c91ff95e","owner":[],"postedDate":"August 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-15T15:01:05+00:00","versionOfRecord":{"articleIdentity":"rs-4735028","link":"https://doi.org/10.1007/s40520-024-02819-2","journal":{"identity":"aging-clinical-and-experimental-research","isVorOnly":false,"title":"Aging Clinical and Experimental Research"},"publishedOn":"2024-08-01 15:56:51","publishedOnDateReadable":"August 1st, 2024"},"versionCreatedAt":"2024-08-15 15:01:04","video":"","vorDoi":"10.1007/s40520-024-02819-2","vorDoiUrl":"https://doi.org/10.1007/s40520-024-02819-2","workflowStages":[]},"version":"v1","identity":"rs-4735028","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4735028","identity":"rs-4735028","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}