The association between the uric acid-to-high-density lipoprotein cholesterol ratio and the risk of osteoporosis among U.S. adults: analysis of NHANES data (2011–2018)

The association between the uric acid-to-high-density lipoprotein cholesterol ratio and the risk of osteoporosis among U.S. adults: analysis of NHANES data (2011–2018) | 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 The association between the uric acid-to-high-density lipoprotein cholesterol ratio and the risk of osteoporosis among U.S. adults: analysis of NHANES data (2011–2018) Jinzhou Wang, Shanshan Li, Hongyu Pu, Ye Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4490969/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Middle-aged and older persons are frequently afflicted with osteoporosis and atherosclerosis; however, new evidence indicates a deeper relationship that goes beyond the normal aging process. One new inflammatory measure that has developed for evaluating the risk of cardiovascular disease is the uric acid to high density lipoprotein ratio (UHR).However, research on the relationship between UHR and the risk of developing osteoporosis is still awaiting. Methods Between 2011 and 2018, we gathered UHR and bone mineral density (BMD) data from 10,983 individuals in the National Health and Nutrition Examination Survey (NHANES). We employed multivariate linear regression to investigate the relationship between BMD and UHR. Smoothing curves were utilized to deal with nonlinearity. To investigate nonlinear relationships further, we employed a two-part linear regression model. Threshold effects were evaluated using both components of the linear regression model. We also conducted subgroup analyses to ensure the stability of the findings. Results In all three models, we found a negative correlation between UHR and lumbar BMD. there was an L-curve correlation between UHR and lumbar BMD, with a critical inflection point of 2.97. the fully adjusted model showed a decrease in lumbar BMD of 0.03 g/cm2 for those in the fourth quartile compared with the lowest quartile. the correlation was consistent across most subgroups, except in the subgroups with a body mass index less than 25 and age greater than 50 and those with diabetes. Conclusions According to this study, there appears to be a negative relationship between BMD and NHHR among US adults. More study is needed to determine the precise physiological pathways by which UHR contributes to the development of osteoporosis. UHR Bone mineral density Osteoporosis NHANES Uric acid Figures Figure 1 Figure 2 Figure 3 Introduction Osteoporosis is a widespread bone disease that affects the entire skeletal system, mainly manifested as a decrease in bone density and degradation of the bone tissue microstructure, causing an increase in bone fragility, which leads to an increased risk of fracture, especially in middle-aged and elderly people[ 1 , 2 ]. Even with long-term anti-osteoporosis pharmaceutical treatment, one or more fragility fractures occur in 25% of men and 44% of women over 60[ 3 ]. Such fractures can lead to serious discomfort, injury, and even fatalities[ 4 , 5 ]. The economic burden of accidental fractures and previous fragility fractures in Europe in 2010 was estimated at €37 billion[ 6 ]. As the world population ages, the global prevalence of osteoporosis has reached 19.7%. This is expected to have a major impact on the medical and economic structure of the world as a whole[ 7 ]. It is clear that osteoporosis is a global health crisis and a major public health challenge for people around the world. Therefore finding a convenient, accurate and cost-effective method to identify early osteoporosis progression and intervene early is important and urgent to control the incidence of osteoporosis-related diseases and reduce the economic health burden. Although there is presently debate among experts regarding the impact of uric acid and HDL on osteoporosis, it has been demonstrated that these two factors are highly significantly related with the disease. According to a study, the breakdown of UA might result in intracellular oxidative stress, which when combined with inflammatory factors caused by UA, increases the loss of bone[ 8 ]. According to a long-term study, Mexican women's poor bone mineral density (BMD) at several skeletal sites may be linked to higher serum UA levels[ 9 ]. However, according to several cross-sectional studies, sUA levels were reported to be positively associated with higher BMD within a certain range[ 10 – 12 ], due to its antioxidant properties in physiological state characterized by a protective effect on bone metabolism[ 8 ].According to one study, HDL influences bone metabolism by stimulating osteoblast development and causing osteoclasts to undergo apoptosis[ 13 ]. Additionally, a cross-sectional investigation revealed a favorable association between HDL-C levels and bone density[ 14 ]. On the other hand, a different cross-sectional study discovered that elevated HDL-C levels raised the risk of osteoporosis, with a greater effect in women[ 15 ]. Compared to conventional indicators, UHR, a novel biomarker of inflammation and metabolism, is a more reliable indicator of cardiovascular disease[ 16 , 17 ]. According to recent research, the pathophysiology of osteoporosis, atherosclerosis, and the aging-related degenerative processes may share similarities with immune and inflammatory responses[ 18 ].The relationship between UHR and osteoporosis has not yet been investigated, and as a newly discovered inflammatory and metabolic biomarker that takes into account the dual roles of HDL cholesterol and serum uric acid and avoids the limitations of previous separate studies, UHR may provide new insights into monitoring the severity of osteoporosis and is expected to fill a knowledge gap in the field of osteoporosis research. Furthermore, The UHR is a simple and cost-effective biomarker for illness evaluation. As a result, the current study used the NHANES 2011–2018 dataset to investigate the relationship between UHR and the incidence of osteoporosis in US adults. methods 2.1 Participants in the NHANES study From the 2011–2018 NHANES, we chose 39,156 individuals for our study. Following screening, 10,983 participants were ultimately included in the research (Fig. 1 ). To investigate the relationship between UHR and Lumbar bone density, we omitted people who lacked bone mineral data (9,798) and UHR data (14,485). Moreover, individuals with incomplete education-related data (897), as well as those under the age of eighteen (2993), were eliminated. The National Center for Health Research Ethics Committee authorized the study procedure, and all participants gave written informed consent. Population data were obtained from the National Center for Health Statistics website ( http://www.cdc.gov/nchs/nhanes/ ). granted informed consent in writing. 2.2 BMD measurement and osteopenia/osteoporosis Dual-energy x-ray bone densitometry was used to determine the bone density of the individuals. This method is widely respected and commonly utilized for evaluating bone density due to its favorable characteristics, including low radiation exposure, quick process, and simplicity[ 19 ]. All cases in this study were measured using dual-energy x-ray bone densitometry using a Hologic QDR 4500A device, followed by lumbar spine bone density assessment by an experienced radiology technician. Based on the standards formulated by the National Bone Health Alliance Task Force, bone mineral density (BMD) readings ranging from − 1 to -2.5 standard deviations (SDs) below the baseline suggest osteopenia. Furthermore, a bone mineral density exceeding 2.5 standard deviations below the reference mark warrants a diagnosis of osteoporosis[ 20 ]. The evaluation of bone loss and osteoporosis occurs at distinct anatomical locations in the lumbar region. Calculating the mean of measurements acquired from the initial to the fourth lumbar vertebrae is required for determination of lumbar spine bone mineral density[ 21 ]. 2.3 Measurement of UHR The serum uric acid-to-high-density lipoprotein cholesterol ratio (UHR) was determined by dividing sUA by serum HDL cholesterol.Serum specimens were processed, frozen at -30°C, and transferred to the Collaborative Laboratory Service for analysis. The concentration of SUA in serum was measured using the Beckman Coulter UniCel® DxC800 timed endpoint technique. This study utilized the Roche Cobas 6000 analyzer employing enzymatic assays to determine the concentrations of HDL-C (High-Density Lipoprotein Cholesterol) 2.4 Covariates Our analysis included other factors, known as confounders, that have the potential to affect the connection between UHR and osteoporosis. The covariates included gender, age, race, participation in moderate activities, marital status, poverty-to-income ratio (PIR), diabetes status, education level, Alcohol status and history of smoking. Moderate activity is defined as moderate or low-intensity activity with a slight increase in respiration or heart rate, such as walking at a brisk pace or carrying light objects continuously for at least 10 minutes. Smoking status is defined as "Have you smoked at least 100 cigarettes in your lifetime?" Diabetes was determined by "Has a doctor or health professional ever told you that you have diabetes?".Alcohol status is defined as a minimum intake of four alcoholic beverages per day.The Body Mass Index (BMI) is derived by dividing someone's weight in kilograms by their height in meters squared, serving as a kg/m^2 measurement[ 22 ]. Statistical analysis In order to address the intricacies of multistage clustered surveys, the statistical analyses incorporated NHANES sample weights, in accordance with recommendations from the Centres for Disease Control and Prevention (CDC). While percentages were used to represent the categorical data, averages and standard deviations (SD) were used to characterize the continuous variables. To guarantee an equitable distribution, the UHR data was normalized using a log2 transformation. The quartile rankings of the UHR were used to divide the participants into four groups,, with the lowest quartile (Q1) serving as the reference group. Weighted Student's t-tests were used for continuous variables, and weighted chi-square tests were used for categorical variables in order to evaluate the baseline features of UHR levels within these quartiles. Multiple linear regression analysis was used to investigate the relationship between UHR and BMD in the lumbar spine. This required determining the beta coefficients and the 95% confidence interval (CI).The research was conducted utilizing three distinct models. The study's regression coefficients are displayed, with the lower quartile serving as the study's reference point.The Model 1 remained unchanged and unaltered. Age, educational attainment, gender, and race were considered as covariates during the adjustment process of Model 2. The Model 3 exhibited the same attributes as Model 2, with the addition of supplementary modifications to account for PIR, BMI, moderate activity levels, diabetes, alcohol status and smoking. We used an estimating method employing a smoothing curve in conjunction with a generalized additive model to investigate the nonlinear association between UHR and BMD. Upon identifying nonlinearity, we adopted a recursive strategy to locate the inflection point within the connection between BMD and UHR. For a more detailed understanding of this nonlinear pattern, a biphasic linear regression model was applied around the identified inflection point. While NHANES employs advanced sampling procedures to increase the representativeness and application of its findings, weighted and unweighted analyses may provide skewed results in some circumstances. Finally, subgroup analyses were performed to assess the reliability and consistency of the data.The software packages PackageR and EmpowerStats, which may be found at http://www.r-project.org and http://www.empowerstats.com , respectively, were used to perform the statistical analyses. Less than 0.05 was the threshold for statistical significance. Results 4.1 Baseline characteristics A total of 10,983 participants met the inclusion and exclusion criteria for the study, with a mean age of 39.49 ± 11.69 years. There were 52.18 per cent males, 47.82 per cent females, 61.46 per cent non-Hispanic whites, 10.30 per cent Mexican Americans, 11.46 per cent non-Hispanic blacks, and 9.51 per cent of other races. Among all participants, the mean (scale) lumbar BMD ,uric acid concentration and HDL were 1.04 (0.15) g/cm2, 319.36 (81.63) umol/L, and 52.58 (15.73) mg/dL, respectively.Table 1 presents all clinical characteristics of the patients by UHR quartiles. Significant differences (P < 0.05) were found between race, gender, body mass index, marital status, education, smoking, alcohol use and PIR. Individuals in the highest quartile of UHR were predominantly male and non-Hispanic white compared to those in the lowest quartile. In addition, those with elevated UHR levels were characterised by lower educational attainment, as well as lower high-density lipoprotein cholesterol (HDL-C) levels and lumbar spine BMD, but had higher income levels and smoking prevalence, in addition to higher body mass index (BMI), uric acid levels. Table 1 Baseline characteristics of the study population according to the UA/HDL-C ratio Variables Total Q1 Q2 Q3 Q4 P -value N = 2739 N = 2742 N = 2752 N = 2750 Age(years) 39.49 ± 11.69 39.75 ± 11.67 39.48 ± 11.99 39.16 ± 11.61 39.58 ± 11.49 0.2790 Gender, n(%) < 0.0001 Male 52.18 15.80 42.15 66.27 85.00 Female 47.82 84.20 57.85 33.73 15.00 Race, n(%) < 0.0001 Mexican American 10.30 8.26 10.56 10.88 11.56 Other Hispanic 7.27 7.06 6.95 7.66 7.37 Non-Hispanic White 61.46 63.16 60.03 61.23 61.37 Non-Hispanic Black 11.46 12.98 12.62 10.69 9.52 Other Race 9.51 8.54 9.85 9.52 10.18 Education Level, n(%) < 0.0001 Less than high school 13.08 9.96 13.57 13.14 15.76 High school 21.72 20.54 21.95 20.82 23.44 More than high school 65.20 68.10 65.28 65.64 61.91 Diabetes, n(%) < 0.0001 Yes 7.67 7.77 7.17 7.29 8.56 No 92.33 92.23 92.83 92.71 91.44 Alcohol, n(%) 0.0108 Yes 81.21 80.68 79.03 82.85 82.13 No 18.79 19.32 20.97 17.15 17.87 Smoking, n(%) < 0.0001 Yes 41.68 28.08 30.32 32.61 37.57 No 58.32 71.92 69.68 67.39 62.43 Moderate activity, n(%) < 0.0001 Yes 44.37 38.26 44.16 46.35 48.83 No 55.63 95.17 93.23 92.05 88.81 Body mass index(kg/m 2 ) 29.05 ± 6.85 25.44 ± 5.27 28.35 ± 6.35 30.10 ± 7.02 32.37 ± 6.70 < 0.0001 PIR 2.96 ± 1.67 3.13 ± 1.68 2.92 ± 1.66 2.91 ± 1.67 2.85 ± 1.65 < 0.0001 Uric acid (umol/L, mean ± SD) 319.36 ± 81.60 239.70 ± 48.99 297.81 ± 52.08 338.00 ± 52.07 403.47 ± 67.70 < 0.0001 HDL-Cholesterol (mg/dL, mean ± SD) 52.58 ± 15.73 69.54 ± 15.56 55.66 ± 9.75 47.09 ± 7.32 37.74 ± 6.76 < 0.0001 Lumbar spine BMD (g/cm 2 , mean ± SD) 1.04 ± 0.15 1.05 ± 0.15 1.04 ± 0.15 1.03 ± 0.14 1.03 ± 0.15 0.0003 Weighted characteristics of the study population based on Log2-UHR quartiles Mean ± SD for continuous variables: the P value was calculated by the weighted linear regression model (%) for categorical variables: the P value was calculated by the weighted chi-square test BMD: bone mineral density UHR: serum uric acid-to-high-density lipoprotein cholesterol ratio 4.2 Association between UHR and BMD Table 2 presents a multivariate regression analysis that investigates the link between log2-transformed UHR and lumbar spine BMD. Initially, the unadjusted model indicated a negative correlation between UHR and lumbar spine BMD (β= -0.01, 95% CI: -0.01 to -0.01, P<0.0001). This association persisted as statistically significant even after adjusting for other factors in model 2 (β = -0.01, 95% CI: -0.01 to -0.00, P=0.0008).Upon fully adjusting for all covariates in model 3, a consistent decrease in lumbar spine BMD of 0.03 g/cm² was observed for each unit increase in UHR, with the association still significant (β = -0.02, 95% CI: -0.02, -0.01, P< 0.0001 ). Within the context of the fully adjusted third model (model3), it was observed that individuals in the highest UHR quartile exhibited a BMD that was 0.03 g/cm² lower than those in the lowest quartile (β = -0.03, 95% CI: -0.04 to -0.02, P< 0.0001). 4.3 A nonlinear association between UHR and BMD This study examined the non-linear connection between UHR levels and lumbar spine BMD using smooth curve fitting and a two-stage linear regression model. The study's findings highlighted the non-linear relationship between lumbar BMD and UHR levels by demonstrating a negative connection between the two, as seen in Fig. 2 . An L-shaped relationship between UHR and lumbar spine BMD was found using a two-stage linear regression model, as seen in Table 3 , with a significant inflection point of 2.97.For every incremental unit of UHR, BMD fell by 1% when NHHR was less than 2.97 (OR: -0.01, 95% CI: -0.02–0.00). In contrast, when the UHR was higher than 2.97, there was no discernible change in BMD. 4.4 Subgroup analysis Following participant division into subgroups according to gender, age, BMI, PIR, diabetes, drinking and smoking habits, and educational level, each subgroup's connection between UHR and osteoporosis was further examined (Fig. 3 ). The association between UHR and osteoporosis would be impacted in the interaction test by BMI 50, and the diabetic group. The remaining categories, including gender, PIR, drinking status, smoking status, and education, did not substantially change the link between UHR and osteoporosis risk (p for interaction > 0.05 for all comparisons). Discussion This cross-sectional study with 10,983 adult Americans showed for the first time that increased UHR levels are highly correlated with the risk of osteoporosis. The statistical study showed that even after controlling for every variable in the classification model, there was still a negative connection between lumbar BMD and UHR.This correlation was somewhat unstable in the subgroups of body mass index less than 25, age greater than 50, and diabetes mellitus, but otherwise remained consistent in all other subgroups. Furthermore, an L-shaped association with an inflection point of 2.97 was observed between UHR and lumbar BMD using threshold effect analysis and smoothed curve fitting. For every unit rise in UHR, BMD fell by 1% up until this inflection point, after which the association ceased to be statistically significant. Two prevalent health issues among the elderly population that present a number of dangers and difficulties are osteoporosis and atherosclerosis. Not only may they have detrimental effects on one's health individually, but they may also compound one another's effects[ 23 ]. The new inflammatory marker known as UHR is used to measure the extent of atherosclerosis. The association between blood uric acid, HDL-C, and osteoporosis has been discussed in the news, despite the fact that prior research have not examined the relevance of the UHR in osteoporosis.The new inflammatory marker known as UHR is used to measure the extent of atherosclerosis. Although previous studies have not investigated the significance of UHR in osteoporosis, discussions of the relationship between blood uric acid and HDL-C and osteoporosis have been reported repeatedly. Higher SUA levels were shown to be independently linked to lower lumbar spine bone mineral density in healthy Chinese postmenopausal women in a longitudinal follow-up investigation.[ 24 ]. In a cross-sectional study of 275 obese patients, lumbar BMD was inversely associated with hyperuricemia in obese men[ 25 ]. Another cross-sectional study of 7,320 adolescents aged 12–19 found that in female adolescents, higher sUA levels may have adverse effects on bone health[ 26 ]. In addition, a cross-sectional study of 667 postmenopausal women showed a favourable association between HDL levels and BMD of the lumbar spine and femoral neck[ 27 ].These aforementioned findings indirectly validate our experimental results. However, some researchers have opposing opinions in the role of uric acid on osteoporosis. Serum uric acid was found to be positively associated with lumbar spine bone density in a cross-sectional study that included 6,254 American men[ 28 ]. Furthermore, after examining 5,074 patients in The Rotterdam Study, researchers came to the conclusion that elevated SUA levels guarded against bone loss[ 29 ]. Additionally, using animal tests, the researchers found that there were no variations in bone density or bulk density between normovolemic control animals and hyperuricemic rats[ 30 ]. There is also disagreement about the relationship between HDL and osteoporosis. For example, in a cross-sectional study, high HDL-C levels were found to affect bone loss in a Chinese population aged 20 to 80 years, with obese men having the most severe bone loss[ 31 ]. Interpretation of the aforementioned controversial studies may be attributed to differences in basic participant characteristics, race, age, and assessment of osteoporosis. Thus, with the UHR novel inflammatory index, the current study provides new evidence to deepen the understanding of the association between uric acid and lipid levels and osteoporosis risk. The current cross-sectional investigation showed that UHR and BMD had a nonlinear negative connection in the U.S. population by multivariate linear regression as well as smoothed curve fitting. Understanding the underlying mechanisms between lipid and uric acid status and osteoporosis may help to explain the present work. Reactive oxygen species (ROS) are produced by oxidative stress, which is a separate risk factor for bone metabolism. These ROS disrupt intraosseous homeostasis and accelerate the development of osteoporosis by reducing bone formation in osteoblasts and osteoclasts and promoting bone resorption in osteoclasts[ 32 ]. The last byproduct of purine breakdown in the liver is UA[ 33 ], At healthy concentrations, UA is thought to be a potent endogenous antioxidant that scavenges singlet oxygen radicals, hydroxyl alone (.OH), and peroxyl alone (RO2). It can scavenge up to 60% of the blood serum's free radical scavenging ability[ 34 ], In addition, in vitro experiments conducted by an investigator found that UA inhibits osteoclastogenesis and reduces intracellular ROS levels in osteoclast precursors in mice[ 35 ], and thus UA prevents oxidative stress-associated bone loss and osteoporosis. Another study observed a negative correlation between sUA levels and type I collagen preprotein N-terminal (PINP) and type I collagen β-cross-linked c-terminal peptide (β-CTX) in postmenopausal women[ 35 ].PINP and β-CTX are markers that reflect bone formation and represent osteoblast activity. Bone formation and resorption are closely related to the process of bone conversion, and sUA was negatively correlated with bone formation markers, suggesting that sUA reduces bone conversion and thus bone loss, a finding also corroborated by previous cross-sectional studies[ 37 , 38 ]. However, in hyperuricemia or gouty arthritis, the role of UA in bone metabolism switches and it becomes a source of oxidative stress instead. When the concentration of uric acid in the blood exceeds its solubility limit, uric acid precipitates out of the fluid and forms urate (mainly monosodium urate, MSU) crystals.The urate crystals are recognized and phagocytosed by induced phagocytosis of NLRP3 inflammatory vesicles, and after release of various inflammatory factors such as Interleukin-1β(IL-1β), Tumor necrosis factor-α༈TNF-α༉, and Interleukin-1༈IL-6༉[ 39 – 41 ], inflammatory cells are recruited, mainly neutrophils, into the joint space, which exacerbating the local inflammatory environment. These inflammatory factors not only lead to acute inflammatory responses such as gout, but also have long-term effects on bone tissue. Because receptor activator of nuclear factor κ b ligand (RANKL) signaling and IL-1, a significant bone resorption activator, work together to support the growth and development of osteoclast progenitor cells[ 42 ], By raising RANK expression in osteoblasts, which triggers RANKL downstream pathways in osteoclasts, including activation of nuclear factor kappa light chain enhancer (NF-κB), JNK, and p38 in B cells, and enhancement of bone resorption by osteoclasts, IL-6 plays a beneficial indirect role in osteoclast differentiation[ 43 ]. One of the main players in the pathophysiology of inflammation, tumor necrosis factor-α (TNF-α), stimulates the expression of M-CSF on stromal cells and RANKL in macrophages and bone marrow stromal cells.The mechanism by which TNF-α promotes osteoblast differentiation and activity is through the RANKL and M-CSF production[ 44 ]. Furthermore, some research has highlighted the potential harm that UA may cause to the metabolism of vitamin D. This can directly lower 1α-hydroxylase activity in the kidney's proximal tubules, which lowers the body's concentration of 1,25-dihydroxyvitamin D3 (also known as active vitamin D, or 1,25(OH)2D), aggravating bone loss and raising the risk of fractures[ 45 ]. By altering the metabolism of vitamin D, excessive uric acid can also indirectly cause raised PTH levels. Prolonged high PTH levels can also cause a preponderance of osteoclast activity, which upsets the balance of bone metabolism and results in bone loss[ 46 ].In addition, the PI3K/Akt and MAPK/ERK signaling pathways are essential for the control of bone metabolism. HDL activates the PI3K/Akt pathway via the SR-BI receptor, which promotes the proliferation of osteoblasts and prevents apoptosis, and promotes the activity of Runx2, which further enhances osteoblast differentiation and mineralization. Activation of the MAPK/ERK signaling pathway promotes the proliferation of osteoblasts and the synthesis of bone matrix components (e.g., collagen) by osteoblasts, thereby promoting bone formation and mineralization. Therefore, HDL plays an important role in the signaling events that affect osteoblast development, specialization, and osteogenic activity[ 47 ]. Furthermore, in bone marrow adipose tissue, PPARγ and CEBPa are important regulators of adipocyte development. The main protein component of HDL cholesterol, apolipoprotein A1, can be deficient in, which is equal to having lower HDL levels. This deficiency can raise the expression of CEBPa and PPARγ, which can alter the population of bone precursor cells, increase the development of adipocytes, and reduce the generation of osteoblasts[ 48 ]. Strengths and limitations There are certain benefits to this study. First of all, this study's huge sample size improves the trustworthiness of the conclusions and lessens the impact of random variables on the research findings.Second, this study employed the UA/HDL-C composite ratio for the first time, confirming the significant association between UHR and BMD and improving clinical prediction accuracy while avoiding the limitations of uric acid and lipids primarily focusing on a single index in osteoporosis in previous studies. Lastly, standard blood test results may be used to simply calculate UHR, and the process is both economical and straightforward. Even in places with little medical care, it can be done. There are certain restrictions, though. Initially, the cross-sectional study design precluded us from verifying a causal association between UHR and osteoporosis. Second, while the lumbar spine (L1-4) is a typical location for BMD testing, degenerative alterations in the spine can impair lumbar BMD. Lumbar DXA testing may overestimate lumbar BMD, producing false-negative findings[ 49 , 50 ]. Several studies have indicated that measuring forearm bone mineral density (BMD) might be a suitable alternative. In postmenopausal women and those with spinal degeneration, forearm DXA is more reliable than lumbar DXA[ 51 ]. As a result, this alternate approach might be utilized to assess bone mineral density in future investigations. Finally, because this study only included Americans and requires additional validation in a wider group, the conclusions could not apply to people of other races or nations. Conclusion In the U.S. population, there is a negative correlation between UHR and lumbar spine bone density. This may be a valid indicator for identifying prevention of osteoporosis disease progression in the U.S. population, and may also help to improve the management and clinical decision-making of healthcare professionals for patients with osteoporosis, and to improve the effectiveness of anti-osteoporosis treatment. Abbreviations BMI Body mass index BMD Bone mineral density NHANES National Health and Nutrition Examination Survey PIR poverty-to-income ratio UHR Uric acid-to-high-density lipoprotein cholesterol ratio HDL-C High-density lipoprotein cholesterol UA Uric acid Declarations Acknowledgements We appreciated Shanshan Li for touching up this article. Authors’ contributions The author designed, conceptualised and conducted the study, prepared and revised the manuscript and performed all statistical analyses. The author (s) read and approved the final manuscript. Funding This study received no direct funding from any third-party donor or funding institution in the public, commercial, or non-profit sectors. Availability of data and materials The survey results are accessible to academics and data users worldwide over the Internet; for more information, see the NHANES website at http://www.cdc.gov/nchs/nhanes. Ethics approval and consent to participate In compliance with the Declaration of Helsinki, every NHANES protocol was approved by Ethics Review Board of National Center for Health Statistics. All participants signed the informed consent. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Ensrud KE, Crandall CJ. Osteoporosis. Ann Intern Med 2017,167:C17-C32. Osteoporosis prevention, diagnosis, and therapy. JAMA 2001,285:785-795. Frost SA, Kelly A, Gaudin J, Evoy LM, Wilson C, Marov L, et al.. 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Positive association of high-density lipoprotein cholesterol with lumbar and femoral neck bone mineral density in postmenopausal women. Maturitas 2021,147:41-46. Kim S, Lee S, Kwon H. Association between serum uric acid level and bone mineral density in men more than 50 years of age. Front Endocrinol (Lausanne) 2023,14:1259077. Muka T, de Jonge EAL, Kiefte-de Jong JC, Uitterlinden AG, Hofman A, Dehghan A, et al.. The Influence of Serum Uric Acid on Bone Mineral Density, Hip Geometry, and Fracture Risk: The Rotterdam Study. The Journal of clinical endocrinology and metabolism 2016,101:1113-1122. Zhang D, Bobulescu IA, Maalouf NM, Adams-Huet B, Poindexter J, Park S, et al.. Relationship between serum uric Acid and bone mineral density in the general population and in rats with experimental hyperuricemia. Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research 2015,30:992-999. Sun Y, Qi X, Wang X, Lin X, Zhou Y, Du Y, et al.. Association between high-density lipoprotein cholesterol and lumbar bone mineral density in Chinese: a large cross-sectional study. Lipids Health Dis 2024,23:27. Zhu C, Shen S, Zhang S, Huang M, Zhang L, Chen X. Autophagy in Bone Remodeling: A Regulator of Oxidative Stress. Front Endocrinol (Lausanne) 2022,13:898634. Fathallah-Shaykh SA, Cramer MT. Uric acid and the kidney. Pediatric nephrology (Berlin, Germany) 2014,29:999-1008. Fabbrini E, Serafini M, Colic Baric I, Hazen SL, Klein S. Effect of plasma uric acid on antioxidant capacity, oxidative stress, and insulin sensitivity in obese subjects. Diabetes 2014,63:976-981. Ahn SH, Lee SH, Kim B, Lim K, Bae SJ, Kim EH, et al.. Higher serum uric acid is associated with higher bone mass, lower bone turnover, and lower prevalence of vertebral fracture in healthy postmenopausal women. Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA 2013,24:2961-2970. Yan D, Wang J, Hou X, Bao Y, Zhang Z, Hu C, Jia W. Association of serum uric acid levels with osteoporosis and bone turnover markers in a Chinese population. Acta Pharmacol Sin 2018,39:626-632. Han W, Bai X, Han L, Sun X, Chen X. Association between higher serum uric acid levels within the normal physiological range and changes of lumbar spine bone mineral density in healthy Chinese postmenopausal women: a longitudinal follow-up study. Menopause (New York, N.Y.) 2021,28:1157-1165. Han W, Bai X, Wang N, Han L, Sun X, Chen X. Association between lumbar bone mineral density and serum uric acid in postmenopausal women: a cross-sectional study of healthy Chinese population. Arch Osteoporos 2017,12:50. Yokose K, Sato S, Asano T, Yashiro M, Kobayashi H, Watanabe H, et al.. TNF-α potentiates uric acid-induced interleukin-1β (IL-1β) secretion in human neutrophils. Mod Rheumatol 2018,28:513-517. di Giovine FS, Malawista SE, Thornton E, Duff GW. Urate crystals stimulate production of tumor necrosis factor alpha from human blood monocytes and synovial cells. Cytokine mRNA and protein kinetics, and cellular distribution. The Journal of clinical investigation 1991,87:1375-1381. Chhana A, Pool B, Callon KE, Tay ML, Musson D, Naot D, et al.. Monosodium urate crystals reduce osteocyte viability and indirectly promote a shift in osteocyte function towards a proinflammatory and proresorptive state. Arthritis Res Ther 2018,20:208. Lee Y, Fujikado N, Manaka H, Yasuda H, Iwakura Y. IL-1 plays an important role in the bone metabolism under physiological conditions. Int Immunol 2010,22:805-816. Udagawa N, Takahashi N, Katagiri T, Tamura T, Wada S, Findlay DM, et al.. Interleukin (IL)-6 induction of osteoclast differentiation depends on IL-6 receptors expressed on osteoblastic cells but not on osteoclast progenitors. The Journal of experimental medicine 1995,182:1461-1468. Ritchlin CT, Haas-Smith SA, Li P, Hicks DG, Schwarz EM. Mechanisms of TNF-alpha- and RANKL-mediated osteoclastogenesis and bone resorption in psoriatic arthritis. The Journal of clinical investigation 2003,111:821-831. Isnuwardana R, Bijukchhe S, Thadanipon K, Ingsathit A, Thakkinstian A. Association Between Vitamin D and Uric Acid in Adults: A Systematic Review and Meta-Analysis. Hormone and metabolic research = Hormon- und Stoffwechselforschung = Hormones et metabolisme 2020,52:732-741. Ponvilawan B, Charoenngam N. Vitamin D and uric acid: Is parathyroid hormone the missing link? Journal of clinical & translational endocrinology 2021,25:100263. Xu J, Qian J, Xie X, Lin L, Ma J, Huang Z, et al.. High density lipoprotein cholesterol promotes the proliferation of bone-derived mesenchymal stem cells via binding scavenger receptor-B type I and activation of PI3K/Akt, MAPK/ERK1/2 pathways. Mol Cell Biochem 2012,371:55-64. Kastrenopoulou A, Kypreos KE, Papachristou NI, Georgopoulos S, Mastora I, Papadimitriou-Olivgeri I, et al.. ApoA1 Deficiency Reshapes the Phenotypic and Molecular Characteristics of Bone Marrow Adipocytes in Mice. International journal of molecular sciences 2022,23. Pye SR, Reid DM, Adams JE, Silman AJ, O'Neill TW. Radiographic features of lumbar disc degeneration and bone mineral density in men and women. Ann Rheum Dis 2006,65:234-238. Choi MK, Kim SM, Lim JK. Diagnostic efficacy of Hounsfield units in spine CT for the assessment of real bone mineral density of degenerative spine: correlation study between T-scores determined by DEXA scan and Hounsfield units from CT. Acta Neurochir (Wien) 2016,158:1421-1427. Eftekhar-Sadat B, Ghavami M, Toopchizadeh V, Ghahvechi Akbari M. Wrist bone mineral density utility in diagnosing hip osteoporosis in postmenopausal women. Ther Adv Endocrinol Metab 2016,7:207-211. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4490969","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":312403555,"identity":"7713faea-2ff3-4098-94e8-45a7cd212797","order_by":0,"name":"Jinzhou Wang","email":"","orcid":"","institution":"the Affiliated Hospital of North Sichuan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Jinzhou","middleName":"","lastName":"Wang","suffix":""},{"id":312403556,"identity":"f2098537-bf8d-4242-8b88-af0df59e8ade","order_by":1,"name":"Shanshan Li","email":"","orcid":"","institution":"First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shanshan","middleName":"","lastName":"Li","suffix":""},{"id":312403557,"identity":"4b3f3cb1-d752-4e85-bb1a-233eda520be6","order_by":2,"name":"Hongyu Pu","email":"","orcid":"","institution":"Fushun People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hongyu","middleName":"","lastName":"Pu","suffix":""},{"id":312403558,"identity":"ba6dd0b9-bc6b-443f-855a-ad73480ffad8","order_by":3,"name":"Ye Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYFCCAwwHPlTY8PAzMx98kFBRQ5QWxoMzzqTJSbazJRs8OHOMKGuYD/O2HTY2OM9jJvmwhZmweoODxx+AtCTObOYxq0hsYGPgb+9OwK/lwIGEg3POpSf2M7OV3UjcIcMgcebsBrxazA4AwZsya6AtzNtuJJ5hYzCQyCWk5WDDAR425sQNhxnMChLbmInRcpjhIE+bs7HBYRYzBqK02B84xgAJ5Ga2ZImEM8d4CPpFcsbxxx/AUcl/+ODHHxU1cvztvfi1MEgcQOXz4FcOAvwNhNWMglEwCkbBCAcA9aNWROkvB2EAAAAASUVORK5CYII=","orcid":"","institution":"the Affiliated Hospital of North Sichuan Medical College","correspondingAuthor":true,"prefix":"","firstName":"Ye","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-05-28 12:33:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4490969/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4490969/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58243644,"identity":"a74b3a9b-cb73-4e48-8965-e39eef451b10","added_by":"auto","created_at":"2024-06-13 02:05:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":189548,"visible":true,"origin":"","legend":"\u003cp\u003eProcess Map for Sample Collection from NHANES\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-4490969/v1/2b0ed23acfe14aaad0a7863c.png"},{"id":58243646,"identity":"fcd76dee-34c0-4758-82fa-f9b702616889","added_by":"auto","created_at":"2024-06-13 02:05:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":70807,"visible":true,"origin":"","legend":"\u003cp\u003eThe association between NHHR and BMD. Blue bands represent the 95% confidence interval from the fit.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-4490969/v1/4e186e3750a9d16b4f48ca5f.png"},{"id":58243645,"identity":"74166bc9-4180-418f-b332-b624b35eaf72","added_by":"auto","created_at":"2024-06-13 02:05:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":663615,"visible":true,"origin":"","legend":"\u003cp\u003eThe association between UHR and lumbar bone density was analyzed. Covariates that need to be adjusted include sex, age, education level, PIR, smoking, diabetes, alcohol consumption, BMI. Covariates associated with stratification factors were not adjusted\u003c/p\u003e","description":"","filename":"Fig.31.png","url":"https://assets-eu.researchsquare.com/files/rs-4490969/v1/92c9219a74a06b61fd2db3fe.png"},{"id":60925699,"identity":"ba950c72-c609-4c99-9588-b3ad5e706dce","added_by":"auto","created_at":"2024-07-23 15:50:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1500145,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4490969/v1/3b0bb94f-fda3-49e2-ae90-07f28598de60.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The association between the uric acid-to-high-density lipoprotein cholesterol ratio and the risk of osteoporosis among U.S. adults: analysis of NHANES data (2011–2018)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOsteoporosis is a widespread bone disease that affects the entire skeletal system, mainly manifested as a decrease in bone density and degradation of the bone tissue microstructure, causing an increase in bone fragility, which leads to an increased risk of fracture, especially in middle-aged and elderly people[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Even with long-term anti-osteoporosis pharmaceutical treatment, one or more fragility fractures occur in 25% of men and 44% of women over 60[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Such fractures can lead to serious discomfort, injury, and even fatalities[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The economic burden of accidental fractures and previous fragility fractures in Europe in 2010 was estimated at \u0026euro;37 billion[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. As the world population ages, the global prevalence of osteoporosis has reached 19.7%. This is expected to have a major impact on the medical and economic structure of the world as a whole[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. It is clear that osteoporosis is a global health crisis and a major public health challenge for people around the world. Therefore finding a convenient, accurate and cost-effective method to identify early osteoporosis progression and intervene early is important and urgent to control the incidence of osteoporosis-related diseases and reduce the economic health burden. Although there is presently debate among experts regarding the impact of uric acid and HDL on osteoporosis, it has been demonstrated that these two factors are highly significantly related with the disease. According to a study, the breakdown of UA might result in intracellular oxidative stress, which when combined with inflammatory factors caused by UA, increases the loss of bone[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. According to a long-term study, Mexican women's poor bone mineral density (BMD) at several skeletal sites may be linked to higher serum UA levels[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, according to several cross-sectional studies, sUA levels were reported to be positively associated with higher BMD within a certain range[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], due to its antioxidant properties in physiological state characterized by a protective effect on bone metabolism[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].According to one study, HDL influences bone metabolism by stimulating osteoblast development and causing osteoclasts to undergo apoptosis[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Additionally, a cross-sectional investigation revealed a favorable association between HDL-C levels and bone density[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. On the other hand, a different cross-sectional study discovered that elevated HDL-C levels raised the risk of osteoporosis, with a greater effect in women[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Compared to conventional indicators, UHR, a novel biomarker of inflammation and metabolism, is a more reliable indicator of cardiovascular disease[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. According to recent research, the pathophysiology of osteoporosis, atherosclerosis, and the aging-related degenerative processes may share similarities with immune and inflammatory responses[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].The relationship between UHR and osteoporosis has not yet been investigated, and as a newly discovered inflammatory and metabolic biomarker that takes into account the dual roles of HDL cholesterol and serum uric acid and avoids the limitations of previous separate studies, UHR may provide new insights into monitoring the severity of osteoporosis and is expected to fill a knowledge gap in the field of osteoporosis research. Furthermore, The UHR is a simple and cost-effective biomarker for illness evaluation. As a result, the current study used the NHANES 2011\u0026ndash;2018 dataset to investigate the relationship between UHR and the incidence of osteoporosis in US adults.\u003c/p\u003e"},{"header":"methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants in the NHANES study\u003c/h2\u003e \u003cp\u003eFrom the 2011\u0026ndash;2018 NHANES, we chose 39,156 individuals for our study. Following screening, 10,983 participants were ultimately included in the research (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). To investigate the relationship between UHR and Lumbar bone density, we omitted people who lacked bone mineral data (9,798) and UHR data (14,485). Moreover, individuals with incomplete education-related data (897), as well as those under the age of eighteen (2993), were eliminated. The National Center for Health Research Ethics Committee authorized the study procedure, and all participants gave written informed consent. Population data were obtained from the National Center for Health Statistics website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003cspan address=\"http://www.cdc.gov/nchs/nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). granted informed consent in writing.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 BMD measurement and osteopenia/osteoporosis\u003c/h2\u003e \u003cp\u003eDual-energy x-ray bone densitometry was used to determine the bone density of the individuals. This method is widely respected and commonly utilized for evaluating bone density due to its favorable characteristics, including low radiation exposure, quick process, and simplicity[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. All cases in this study were measured using dual-energy x-ray bone densitometry using a Hologic QDR 4500A device, followed by lumbar spine bone density assessment by an experienced radiology technician. Based on the standards formulated by the National Bone Health Alliance Task Force, bone mineral density (BMD) readings ranging from \u0026minus;\u0026thinsp;1 to -2.5 standard deviations (SDs) below the baseline suggest osteopenia. Furthermore, a bone mineral density exceeding 2.5 standard deviations below the reference mark warrants a diagnosis of osteoporosis[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The evaluation of bone loss and osteoporosis occurs at distinct anatomical locations in the lumbar region. Calculating the mean of measurements acquired from the initial to the fourth lumbar vertebrae is required for determination of lumbar spine bone mineral density[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Measurement of UHR\u003c/h2\u003e \u003cp\u003eThe serum uric acid-to-high-density lipoprotein cholesterol ratio (UHR) was determined by dividing sUA by serum HDL cholesterol.Serum specimens were processed, frozen at -30\u0026deg;C, and transferred to the Collaborative Laboratory Service for analysis. The concentration of SUA in serum was measured using the Beckman Coulter UniCel\u0026reg; DxC800 timed endpoint technique. This study utilized the Roche Cobas 6000 analyzer employing enzymatic assays to determine the concentrations of HDL-C (High-Density Lipoprotein Cholesterol)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Covariates\u003c/h2\u003e \u003cp\u003eOur analysis included other factors, known as confounders, that have the potential to affect the connection between UHR and osteoporosis. The covariates included gender, age, race, participation in moderate activities, marital status, poverty-to-income ratio (PIR), diabetes status, education level, Alcohol status and history of smoking. Moderate activity is defined as moderate or low-intensity activity with a slight increase in respiration or heart rate, such as walking at a brisk pace or carrying light objects continuously for at least 10 minutes. Smoking status is defined as \"Have you smoked at least 100 cigarettes in your lifetime?\" Diabetes was determined by \"Has a doctor or health professional ever told you that you have diabetes?\".Alcohol status is defined as a minimum intake of four alcoholic beverages per day.The Body Mass Index (BMI) is derived by dividing someone's weight in kilograms by their height in meters squared, serving as a kg/m^2 measurement[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistical analysis\u003c/h3\u003e\n\u003cp\u003e In order to address the intricacies of multistage clustered surveys, the statistical analyses incorporated NHANES sample weights, in accordance with recommendations from the Centres for Disease Control and Prevention (CDC). While percentages were used to represent the categorical data, averages and standard deviations (SD) were used to characterize the continuous variables. To guarantee an equitable distribution, the UHR data was normalized using a log2 transformation. The quartile rankings of the UHR were used to divide the participants into four groups,, with the lowest quartile (Q1) serving as the reference group. Weighted Student's t-tests were used for continuous variables, and weighted chi-square tests were used for categorical variables in order to evaluate the baseline features of UHR levels within these quartiles. Multiple linear regression analysis was used to investigate the relationship between UHR and BMD in the lumbar spine. This required determining the beta coefficients and the 95% confidence interval (CI).The research was conducted utilizing three distinct models. The study's regression coefficients are displayed, with the lower quartile serving as the study's reference point.The Model 1 remained unchanged and unaltered. Age, educational attainment, gender, and race were considered as covariates during the adjustment process of Model 2. The Model 3 exhibited the same attributes as Model 2, with the addition of supplementary modifications to account for PIR, BMI, moderate activity levels, diabetes, alcohol status and smoking. We used an estimating method employing a smoothing curve in conjunction with a generalized additive model to investigate the nonlinear association between UHR and BMD. Upon identifying nonlinearity, we adopted a recursive strategy to locate the inflection point within the connection between BMD and UHR. For a more detailed understanding of this nonlinear pattern, a biphasic linear regression model was applied around the identified inflection point. While NHANES employs advanced sampling procedures to increase the representativeness and application of its findings, weighted and unweighted analyses may provide skewed results in some circumstances. Finally, subgroup analyses were performed to assess the reliability and consistency of the data.The software packages PackageR and EmpowerStats, which may be found at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.r-project.org\u003c/span\u003e\u003cspan address=\"http://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.empowerstats.com\u003c/span\u003e\u003cspan address=\"http://www.empowerstats.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, respectively, were used to perform the statistical analyses. Less than 0.05 was the threshold for statistical significance.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e4.1 Baseline characteristics\u003c/h2\u003e\n \u003cp\u003eA total of 10,983 participants met the inclusion and exclusion criteria for the study, with a mean age of 39.49\u0026thinsp;\u0026plusmn;\u0026thinsp;11.69 years. There were 52.18 per cent males, 47.82 per cent females, 61.46 per cent non-Hispanic whites, 10.30 per cent Mexican Americans, 11.46 per cent non-Hispanic blacks, and 9.51 per cent of other races. Among all participants, the mean (scale) lumbar BMD ,uric acid concentration and HDL were 1.04 (0.15) g/cm2, 319.36 (81.63) umol/L, and 52.58 (15.73) mg/dL, respectively.Table \u003cspan\u003e1\u003c/span\u003e presents all clinical characteristics of the patients by UHR quartiles. Significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were found between race, gender, body mass index, marital status, education, smoking, alcohol use and PIR. Individuals in the highest quartile of UHR were predominantly male and non-Hispanic white compared to those in the lowest quartile. In addition, those with elevated UHR levels were characterised by lower educational attainment, as well as lower high-density lipoprotein cholesterol (HDL-C) levels and lumbar spine BMD, but had higher income levels and smoking prevalence, in addition to higher body mass index (BMI), uric acid levels.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBaseline characteristics of the study population according to the UA/HDL-C ratio\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.49\u0026thinsp;\u0026plusmn;\u0026thinsp;11.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.75\u0026thinsp;\u0026plusmn;\u0026thinsp;11.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.48\u0026thinsp;\u0026plusmn;\u0026thinsp;11.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.16\u0026thinsp;\u0026plusmn;\u0026thinsp;11.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.58\u0026thinsp;\u0026plusmn;\u0026thinsp;11.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2790\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation Level, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMore than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlcohol, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate activity, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody mass index(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.05\u0026thinsp;\u0026plusmn;\u0026thinsp;6.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.44\u0026thinsp;\u0026plusmn;\u0026thinsp;5.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.35\u0026thinsp;\u0026plusmn;\u0026thinsp;6.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.10\u0026thinsp;\u0026plusmn;\u0026thinsp;7.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.37\u0026thinsp;\u0026plusmn;\u0026thinsp;6.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUric acid (umol/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e319.36\u0026thinsp;\u0026plusmn;\u0026thinsp;81.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e239.70\u0026thinsp;\u0026plusmn;\u0026thinsp;48.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e297.81\u0026thinsp;\u0026plusmn;\u0026thinsp;52.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e338.00\u0026thinsp;\u0026plusmn;\u0026thinsp;52.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e403.47\u0026thinsp;\u0026plusmn;\u0026thinsp;67.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL-Cholesterol (mg/dL, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.58\u0026thinsp;\u0026plusmn;\u0026thinsp;15.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.54\u0026thinsp;\u0026plusmn;\u0026thinsp;15.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.66\u0026thinsp;\u0026plusmn;\u0026thinsp;9.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.09\u0026thinsp;\u0026plusmn;\u0026thinsp;7.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.74\u0026thinsp;\u0026plusmn;\u0026thinsp;6.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLumbar spine BMD (g/cm\u003csup\u003e2\u003c/sup\u003e, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eWeighted characteristics of the study population based on Log2-UHR quartiles\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for continuous variables: the P value was calculated by the weighted linear regression model\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e(%) for categorical variables: the P value was calculated by the weighted chi-square test\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eBMD: bone mineral density\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eUHR: serum uric acid-to-high-density lipoprotein cholesterol ratio\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e4.2 Association between UHR and BMD\u003c/h2\u003e\n \u003cp\u003eTable 2 presents a multivariate regression analysis that investigates the link between log2-transformed UHR and lumbar spine BMD. Initially, the unadjusted model indicated a negative correlation between UHR and lumbar spine BMD (\u0026beta;= -0.01, 95% CI: -0.01 to -0.01, P\u0026lt;0.0001). This association persisted as statistically significant even after adjusting for other factors in model 2 (\u0026beta; = -0.01, 95% CI: -0.01 to -0.00, P=0.0008).Upon fully adjusting for all covariates in model 3, a consistent decrease in lumbar spine BMD of 0.03 g/cm\u0026sup2; was observed for each unit increase in UHR, with the association still significant (\u0026beta; = -0.02, 95% CI: -0.02, -0.01, P\u0026lt; 0.0001 ). Within the context of the fully adjusted third model (model3), it was observed that individuals in the highest UHR quartile exhibited a BMD that was 0.03 g/cm\u0026sup2; lower than those in the lowest quartile (\u0026beta; = -0.03, 95% CI: -0.04 to -0.02, P\u0026lt; 0.0001).\u0026nbsp;\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e4.3 A nonlinear association between UHR and BMD\u003c/h2\u003e\n \u003cp\u003eThis study examined the non-linear connection between UHR levels and lumbar spine BMD using smooth curve fitting and a two-stage linear regression model. The study\u0026apos;s findings highlighted the non-linear relationship between lumbar BMD and UHR levels by demonstrating a negative connection between the two, as seen in Fig. \u003cspan\u003e2\u003c/span\u003e. An L-shaped relationship between UHR and lumbar spine BMD was found using a two-stage linear regression model, as seen in Table \u003cspan\u003e3\u003c/span\u003e, with a significant inflection point of 2.97.For every incremental unit of UHR, BMD fell by 1% when NHHR was less than 2.97 (OR: -0.01, 95% CI: -0.02\u0026ndash;0.00). In contrast, when the UHR was higher than 2.97, there was no discernible change in BMD.\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1718117162.png\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv\u003e\n \u003cdiv align=\"left\"\u003e\u003cbr\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1718117161.png\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e4.4 Subgroup analysis\u003c/h2\u003e\n \u003cp\u003eFollowing participant division into subgroups according to gender, age, BMI, PIR, diabetes, drinking and smoking habits, and educational level, each subgroup\u0026apos;s connection between UHR and osteoporosis was further examined (Fig. \u003cspan\u003e3\u003c/span\u003e). The association between UHR and osteoporosis would be impacted in the interaction test by BMI\u0026thinsp;\u0026lt;\u0026thinsp;25, age\u0026thinsp;\u0026gt;\u0026thinsp;50, and the diabetic group. The remaining categories, including gender, PIR, drinking status, smoking status, and education, did not substantially change the link between UHR and osteoporosis risk (p for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all comparisons).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis cross-sectional study with 10,983 adult Americans showed for the first time that increased UHR levels are highly correlated with the risk of osteoporosis. The statistical study showed that even after controlling for every variable in the classification model, there was still a negative connection between lumbar BMD and UHR.This correlation was somewhat unstable in the subgroups of body mass index less than 25, age greater than 50, and diabetes mellitus, but otherwise remained consistent in all other subgroups. Furthermore, an L-shaped association with an inflection point of 2.97 was observed between UHR and lumbar BMD using threshold effect analysis and smoothed curve fitting. For every unit rise in UHR, BMD fell by 1% up until this inflection point, after which the association ceased to be statistically significant.\u003c/p\u003e \u003cp\u003eTwo prevalent health issues among the elderly population that present a number of dangers and difficulties are osteoporosis and atherosclerosis. Not only may they have detrimental effects on one's health individually, but they may also compound one another's effects[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The new inflammatory marker known as UHR is used to measure the extent of atherosclerosis. The association between blood uric acid, HDL-C, and osteoporosis has been discussed in the news, despite the fact that prior research have not examined the relevance of the UHR in osteoporosis.The new inflammatory marker known as UHR is used to measure the extent of atherosclerosis. Although previous studies have not investigated the significance of UHR in osteoporosis, discussions of the relationship between blood uric acid and HDL-C and osteoporosis have been reported repeatedly. Higher SUA levels were shown to be independently linked to lower lumbar spine bone mineral density in healthy Chinese postmenopausal women in a longitudinal follow-up investigation.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In a cross-sectional study of 275 obese patients, lumbar BMD was inversely associated with hyperuricemia in obese men[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Another cross-sectional study of 7,320 adolescents aged 12\u0026ndash;19 found that in female adolescents, higher sUA levels may have adverse effects on bone health[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In addition, a cross-sectional study of 667 postmenopausal women showed a favourable association between HDL levels and BMD of the lumbar spine and femoral neck[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].These aforementioned findings indirectly validate our experimental results. However, some researchers have opposing opinions in the role of uric acid on osteoporosis. Serum uric acid was found to be positively associated with lumbar spine bone density in a cross-sectional study that included 6,254 American men[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Furthermore, after examining 5,074 patients in The Rotterdam Study, researchers came to the conclusion that elevated SUA levels guarded against bone loss[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, using animal tests, the researchers found that there were no variations in bone density or bulk density between normovolemic control animals and hyperuricemic rats[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. There is also disagreement about the relationship between HDL and osteoporosis. For example, in a cross-sectional study, high HDL-C levels were found to affect bone loss in a Chinese population aged 20 to 80 years, with obese men having the most severe bone loss[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Interpretation of the aforementioned controversial studies may be attributed to differences in basic participant characteristics, race, age, and assessment of osteoporosis. Thus, with the UHR novel inflammatory index, the current study provides new evidence to deepen the understanding of the association between uric acid and lipid levels and osteoporosis risk.\u003c/p\u003e \u003cp\u003eThe current cross-sectional investigation showed that UHR and BMD had a nonlinear negative connection in the U.S. population by multivariate linear regression as well as smoothed curve fitting. Understanding the underlying mechanisms between lipid and uric acid status and osteoporosis may help to explain the present work. Reactive oxygen species (ROS) are produced by oxidative stress, which is a separate risk factor for bone metabolism. These ROS disrupt intraosseous homeostasis and accelerate the development of osteoporosis by reducing bone formation in osteoblasts and osteoclasts and promoting bone resorption in osteoclasts[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The last byproduct of purine breakdown in the liver is UA[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], At healthy concentrations, UA is thought to be a potent endogenous antioxidant that scavenges singlet oxygen radicals, hydroxyl alone (.OH), and peroxyl alone (RO2). It can scavenge up to 60% of the blood serum's free radical scavenging ability[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], In addition, in vitro experiments conducted by an investigator found that UA inhibits osteoclastogenesis and reduces intracellular ROS levels in osteoclast precursors in mice[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], and thus UA prevents oxidative stress-associated bone loss and osteoporosis. Another study observed a negative correlation between sUA levels and type I collagen preprotein N-terminal (PINP) and type I collagen β-cross-linked c-terminal peptide (β-CTX) in postmenopausal women[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].PINP and β-CTX are markers that reflect bone formation and represent osteoblast activity. Bone formation and resorption are closely related to the process of bone conversion, and sUA was negatively correlated with bone formation markers, suggesting that sUA reduces bone conversion and thus bone loss, a finding also corroborated by previous cross-sectional studies[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, in hyperuricemia or gouty arthritis, the role of UA in bone metabolism switches and it becomes a source of oxidative stress instead. When the concentration of uric acid in the blood exceeds its solubility limit, uric acid precipitates out of the fluid and forms urate (mainly monosodium urate, MSU) crystals.The urate crystals are recognized and phagocytosed by induced phagocytosis of NLRP3 inflammatory vesicles, and after release of various inflammatory factors such as Interleukin-1β(IL-1β), Tumor necrosis factor-α༈TNF-α༉, and Interleukin-1༈IL-6༉[\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], inflammatory cells are recruited, mainly neutrophils, into the joint space, which exacerbating the local inflammatory environment. These inflammatory factors not only lead to acute inflammatory responses such as gout, but also have long-term effects on bone tissue. Because receptor activator of nuclear factor κ b ligand (RANKL) signaling and IL-1, a significant bone resorption activator, work together to support the growth and development of osteoclast progenitor cells[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], By raising RANK expression in osteoblasts, which triggers RANKL downstream pathways in osteoclasts, including activation of nuclear factor kappa light chain enhancer (NF-κB), JNK, and p38 in B cells, and enhancement of bone resorption by osteoclasts, IL-6 plays a beneficial indirect role in osteoclast differentiation[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. One of the main players in the pathophysiology of inflammation, tumor necrosis factor-α (TNF-α), stimulates the expression of M-CSF on stromal cells and RANKL in macrophages and bone marrow stromal cells.The mechanism by which TNF-α promotes osteoblast differentiation and activity is through the RANKL and M-CSF production[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Furthermore, some research has highlighted the potential harm that UA may cause to the metabolism of vitamin D. This can directly lower 1α-hydroxylase activity in the kidney's proximal tubules, which lowers the body's concentration of 1,25-dihydroxyvitamin D3 (also known as active vitamin D, or 1,25(OH)2D), aggravating bone loss and raising the risk of fractures[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. By altering the metabolism of vitamin D, excessive uric acid can also indirectly cause raised PTH levels. Prolonged high PTH levels can also cause a preponderance of osteoclast activity, which upsets the balance of bone metabolism and results in bone loss[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].In addition, the PI3K/Akt and MAPK/ERK signaling pathways are essential for the control of bone metabolism. HDL activates the PI3K/Akt pathway via the SR-BI receptor, which promotes the proliferation of osteoblasts and prevents apoptosis, and promotes the activity of Runx2, which further enhances osteoblast differentiation and mineralization. Activation of the MAPK/ERK signaling pathway promotes the proliferation of osteoblasts and the synthesis of bone matrix components (e.g., collagen) by osteoblasts, thereby promoting bone formation and mineralization. Therefore, HDL plays an important role in the signaling events that affect osteoblast development, specialization, and osteogenic activity[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Furthermore, in bone marrow adipose tissue, PPARγ and CEBPa are important regulators of adipocyte development. The main protein component of HDL cholesterol, apolipoprotein A1, can be deficient in, which is equal to having lower HDL levels. This deficiency can raise the expression of CEBPa and PPARγ, which can alter the population of bone precursor cells, increase the development of adipocytes, and reduce the generation of osteoblasts[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e"},{"header":"Strengths and limitations","content":"\u003cp\u003eThere are certain benefits to this study. First of all, this study's huge sample size improves the trustworthiness of the conclusions and lessens the impact of random variables on the research findings.Second, this study employed the UA/HDL-C composite ratio for the first time, confirming the significant association between UHR and BMD and improving clinical prediction accuracy while avoiding the limitations of uric acid and lipids primarily focusing on a single index in osteoporosis in previous studies. Lastly, standard blood test results may be used to simply calculate UHR, and the process is both economical and straightforward. Even in places with little medical care, it can be done. There are certain restrictions, though. Initially, the cross-sectional study design precluded us from verifying a causal association between UHR and osteoporosis. Second, while the lumbar spine (L1-4) is a typical location for BMD testing, degenerative alterations in the spine can impair lumbar BMD. Lumbar DXA testing may overestimate lumbar BMD, producing false-negative findings[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Several studies have indicated that measuring forearm bone mineral density (BMD) might be a suitable alternative. In postmenopausal women and those with spinal degeneration, forearm DXA is more reliable than lumbar DXA[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. As a result, this alternate approach might be utilized to assess bone mineral density in future investigations. Finally, because this study only included Americans and requires additional validation in a wider group, the conclusions could not apply to people of other races or nations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn the U.S. population, there is a negative correlation between UHR and lumbar spine bone density. This may be a valid indicator for identifying prevention of osteoporosis disease progression in the U.S. population, and may also help to improve the management and clinical decision-making of healthcare professionals for patients with osteoporosis, and to improve the effectiveness of anti-osteoporosis treatment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Body mass index\u003c/p\u003e\n\u003cp\u003eBMD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Bone mineral density\u003c/p\u003e\n\u003cp\u003eNHANES \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; National Health and Nutrition Examination Survey\u003c/p\u003e\n\u003cp\u003ePIR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;poverty-to-income ratio\u003c/p\u003e\n\u003cp\u003eUHR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Uric acid-to-high-density lipoprotein cholesterol ratio\u003c/p\u003e\n\u003cp\u003eHDL-C \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; High-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eUA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Uric acid\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe appreciated Shanshan Li for touching up this article.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eThe author designed, conceptualised and conducted the study, prepared\u003c/p\u003e\n\u003cp\u003eand revised the manuscript and performed all statistical analyses. The author\u003c/p\u003e\n\u003cp\u003e(s) read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study received no direct funding from any third-party donor or funding\u003c/p\u003e\n\u003cp\u003einstitution in the public, commercial, or non-profit sectors.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe survey results are accessible to academics and data users worldwide over the Internet; for more information, see the NHANES website at http://www.cdc.gov/nchs/nhanes.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn compliance with the Declaration of Helsinki, every NHANES protocol was approved by Ethics Review Board of National Center for Health Statistics. All participants signed the informed consent.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEnsrud KE, Crandall CJ. Osteoporosis. Ann Intern Med 2017,167:C17-C32.\u003c/li\u003e\n\u003cli\u003eOsteoporosis prevention, diagnosis, and therapy. JAMA 2001,285:785-795.\u003c/li\u003e\n\u003cli\u003eFrost SA, Kelly A, Gaudin J, Evoy LM, Wilson C, Marov L, et al.. Establishing baseline absolute risk of subsequent fracture among adults presenting to hospital with a minimal-trauma-fracture. BMC Musculoskelet Disord 2020,21:133.\u003c/li\u003e\n\u003cli\u003eOsnes EK, Lofthus CM, Meyer HE, Falch JA, Nordsletten L, Cappelen I, Kristiansen IS. Consequences of hip fracture on activities of daily life and residential needs. 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Hormone and metabolic research = Hormon- und Stoffwechselforschung = Hormones et metabolisme 2020,52:732-741.\u003c/li\u003e\n\u003cli\u003ePonvilawan B, Charoenngam N. Vitamin D and uric acid: Is parathyroid hormone the missing link? Journal of clinical \u0026amp; translational endocrinology 2021,25:100263.\u003c/li\u003e\n\u003cli\u003eXu J, Qian J, Xie X, Lin L, Ma J, Huang Z, et al.. High density lipoprotein cholesterol promotes the proliferation of bone-derived mesenchymal stem cells via binding scavenger receptor-B type I and activation of PI3K/Akt, MAPK/ERK1/2 pathways. Mol Cell Biochem 2012,371:55-64.\u003c/li\u003e\n\u003cli\u003eKastrenopoulou A, Kypreos KE, Papachristou NI, Georgopoulos S, Mastora I, Papadimitriou-Olivgeri I, et al.. ApoA1 Deficiency Reshapes the Phenotypic and Molecular Characteristics of Bone Marrow Adipocytes in Mice. International journal of molecular sciences 2022,23.\u003c/li\u003e\n\u003cli\u003ePye SR, Reid DM, Adams JE, Silman AJ, O\u0026apos;Neill TW. Radiographic features of lumbar disc degeneration and bone mineral density in men and women. Ann Rheum Dis 2006,65:234-238.\u003c/li\u003e\n\u003cli\u003eChoi MK, Kim SM, Lim JK. Diagnostic efficacy of Hounsfield units in spine CT for the assessment of real bone mineral density of degenerative spine: correlation study between T-scores determined by DEXA scan and Hounsfield units from CT. Acta Neurochir (Wien) 2016,158:1421-1427.\u003c/li\u003e\n\u003cli\u003eEftekhar-Sadat B, Ghavami M, Toopchizadeh V, Ghahvechi Akbari M. Wrist bone mineral density utility in diagnosing hip osteoporosis in postmenopausal women. Ther Adv Endocrinol Metab 2016,7:207-211.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"UHR, Bone mineral density, Osteoporosis, NHANES, Uric acid","lastPublishedDoi":"10.21203/rs.3.rs-4490969/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4490969/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMiddle-aged and older persons are frequently afflicted with osteoporosis and atherosclerosis; however, new evidence indicates a deeper relationship that goes beyond the normal aging process. One new inflammatory measure that has developed for evaluating the risk of cardiovascular disease is the uric acid to high density lipoprotein ratio (UHR).However, research on the relationship between UHR and the risk of developing osteoporosis is still awaiting.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eBetween 2011 and 2018, we gathered UHR and bone mineral density (BMD) data from 10,983 individuals in the National Health and Nutrition Examination Survey (NHANES). We employed multivariate linear regression to investigate the relationship between BMD and UHR. Smoothing curves were utilized to deal with nonlinearity. To investigate nonlinear relationships further, we employed a two-part linear regression model. Threshold effects were evaluated using both components of the linear regression model. We also conducted subgroup analyses to ensure the stability of the findings.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn all three models, we found a negative correlation between UHR and lumbar BMD. there was an L-curve correlation between UHR and lumbar BMD, with a critical inflection point of 2.97. the fully adjusted model showed a decrease in lumbar BMD of 0.03 g/cm2 for those in the fourth quartile compared with the lowest quartile. the correlation was consistent across most subgroups, except in the subgroups with a body mass index less than 25 and age greater than 50 and those with diabetes.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAccording to this study, there appears to be a negative relationship between BMD and NHHR among US adults. More study is needed to determine the precise physiological pathways by which UHR contributes to the development of osteoporosis.\u003c/p\u003e","manuscriptTitle":"The association between the uric acid-to-high-density lipoprotein cholesterol ratio and the risk of osteoporosis among U.S. adults: analysis of NHANES data (2011–2018)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-13 02:05:29","doi":"10.21203/rs.3.rs-4490969/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a9e46678-c2d1-4a8e-b3ed-17def3b32753","owner":[],"postedDate":"June 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-23T15:42:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-13 02:05:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4490969","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4490969","identity":"rs-4490969","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}