The Association between Uric Acid to High-Density Lipoprotein Cholesterol Ratio and Kidney Stones in White Americans: Evidence from NHANES 2011-2020

The Association between Uric Acid to High-Density Lipoprotein Cholesterol Ratio and Kidney Stones in White Americans: Evidence from NHANES 2011-2020 | 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 Uric Acid to High-Density Lipoprotein Cholesterol Ratio and Kidney Stones in White Americans: Evidence from NHANES 2011-2020 Zhen Zhang, Lei Zhou, Zongsan Cheng, Xiaoma Zhang, Zongyao Hao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6489721/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 Kidney stones represent a prevalent urinary tract disorder, exhibiting notable racial disparities in incidence, resulting in substantial medical and economic burdens. The uric acid to high-density lipoprotein cholesterol ratio (UHR) is a comprehensive index reflecting metabolic and inflammatory abnormalities, yet its association with kidney stones remains unclear. This study evaluated the relationship between UHR and kidney stones in non-Hispanic white adults using nationally representative data. Methods A cross-sectional analysis of 8,629 non-Hispanic White participants from the National Health and Nutrition Examination Survey (NHANES) 2011–2020 was conducted. Multivariate logistic regression models were conducted to assess the association between UHR and kidney stones, while dose-response relationships were explored using smooth curve fitting. Stratified analyses were performed to evaluate the stability of the outcomes. Results The kidney stones incidence was 12.7% (1,100/8,629). Participants in the highest UHR tertile (>12.3%) exhibited significantly higher kidney stone prevalence (15.38%) compared to the lowest tertile (<8.4%) at 9.55%. In fully adjusted models, higher UHR was associated with increased risk of kidney stones (OR=1.02, 95% CI: 1.00–1.04, p=0.0206). Participants in the highest tertile had a 31% increased risk relative to the lowest tertile (OR=1.31, 95% CI: 1.03–1.67, p=0.03), with a significant positive dose-response trend (p for trend=0.0338). Associations remained consistent across subgroups without significant interaction effects. Conclusions The present study has demonstrated that UHR is positively associated with kidney stones in White Americans. This finding offers new perspectives for the assessment and prevention of kidney stones. Uric Acid to High-density Lipoprotein Cholesterol Ratio (UHR) Kidney Stones NHANES Cross-sectional study Figures Figure 1 Figure 2 Background Kidney stones, a prevalent urinary system disorder, exhibit significant racial disparities in incidence[ 1 ], with White populations showing higher rates compared to Black individuals[ 2 – 4 ]. The disease exhibits a high recurrence rate (50% within 10 years),which imposes substantial medical and economic burdens[ 5 ]. The limited predictive capacity of traditional indicators necessitates the exploration of novel multi-omics approaches to elucidate molecular mechanisms and develop personalised prevention strategies[ 6 – 8 ]. The uric acid to high-density lipoprotein cholesterol ratio (UHR) is an emerging indicator reflecting metabolic abnormalities and inflammatory states, with recent studies demonstrating its significant associations with key metabolic disorders[ 9 , 10 ]. UHR shows a strong positive correlation with metabolic syndrome, exhibiting a predictive sensitivity of 89.07% and specificity of 77.03% when exceeding 9.5%, closely linked to insulin resistance and chronic inflammatory mechanisms[ 11 ]. In type 2 diabetes, UHR has been demonstrated to positively correlate with glycated hemoglobin and fasting blood glucose levels, with a notable elevation observed in prediabetes patients[ 12 , 13 ]. It is important to note that UHR plays a crucial role in cardiovascular diseases, which are significantly associated with acute coronary syndrome and arterial stiffness. In addition, chronic kidney disease risk increases substantially in high UHR quartiles[ 14 , 15 ]. Further investigations have revealed the potential of UHR to serve as an early indicator of metabolic dysfunction, with significance demonstrated across various population subgroups[ 16 – 19 ]. UHR is a pioneering metabolic inflammatory indicator with considerable potential for early disease screening and risk assessment. It is distinguished by its simplicity and cost-effectiveness. However, the association between UHR and kidney stones remains unproven. Therefore, the present study was undertaken to investigate this association. Utilising the National Health and Nutrition Examination Survey (NHANES), a comprehensive, population-based surveillance system, we investigated the association between UHR and kidney stones among non-Hispanic White adults. Addressing a critical gap in metabolic epidemiology, our findings contribute to the understanding of the underlying causes of kidney stones in this demographic. Materials and methods Study population The present study was conducted using a cross-sectional design of the NHANES dataset, with data from the NHANES database collected at two-year intervals. All protocols were approved by the institutional review board of the National Center for Health Statistics, informed consent was obtained from participants, and a total of 45,462 participants were enrolled from 2011 to 2020. The exclusion criteria comprised participants who were non-white, those with missing kidney stones data, missing UHR data, and outliers. The final analysis included 8629 participants ( Fig. 1). Assessment of kidney stones The history of kidney stones was determined by the Kidney Conditions Questionnaire. The survey participants were asked “Have you ever had kidney stones?” and were categorised as having the history of kidney stones if they responded “Yes”. Assessment of UHR UHR has emerged as a new indicator and has garnered significant attention in recent years for its role in metabolic diseases. It was calculated by dividing serum uric acid (UA) (mg/dL) by high-density lipoprotein cholesterol (HDL-C) (mg/dL) and multiplying the result by 100%. Blood samples were collected from fasting study participants in the morning and analysed to ascertain the levels of UA and HDL-C. The detailed measurement protocols can be accessed at the following link: https://www.cdc.gov/nchs/nhanes/index.htm. Covariates definition The research identified and adjusted for potential covariates based on previous studies. Therefore, the following variables were included to construct the fully adjusted model: age (years), gender (male, female), body mass index(BMI) (kg/m 2 ), education level (lower than high school, high school, more than high school), marital status, poverty to income ratio(PIR), smoking history, alcohol drinking history (drinks/week), diabetes mellitus, hypertension, and coronary heart disease. Participants were stratified into two groups based on their self-reported smoking history regarding whether they had smoked at least 100 cigarettes during their lifetime (yes or no). PIR was classified into low ( 3.5). Alcohol drinking history was evaluated with a questionnaire that inquired about frequency and quantity over the past 12 months, classified as follows: <1, 1–3, or ≥ 4 drinks per week. Hypertension was defined as a systolic blood pressure ≥ 140 mmHg, or diastolic blood pressure ≥ 90 mmHg, or self-reported hypertension, or use of antihypertensive medication. Diabetes mellitus was diagnosed based on self-report, or use of insulin or oral hypoglycemic agents, or meeting at least one of the following criteria: HbA1c ≥ 6.5%, or fasting blood glucose (FBG) ≥ 7.0 mmol/l, or two-hour OGTT blood glucose ≥ 11.1 mmol/l. Information regarding coronary heart disease was collected via questionnaire, where patients were asked whether they had been informed of their diagnosis (yes or no). Statistical analysis All statistical analyses were performed in accordance with CDC guidelines (https://wwwn.cdc.gov/nchs/nhanes/tutorials/default.aspx). Continuous variables were reported as means ± standard deviations, while categorical variables were expressed as percentages. Differences in baseline characteristics were assessed using chi-square tests, analysis of variance (ANOVA), and Kruskal-Wallis H tests to calculate for differences among different UHR groups (tertiles). To investigate the association between UHR and kidney stones risk, multiple logistic regression models were constructed. Crude Model was unadjusted for covariates; Model I adjusted for age, gender; and Model II further adjusted for BMI, education level, PIR, marital status, smoking history, alcohol drinking history, diabetes mellitus, hypertension, and coronary heart disease. To explore the linear relationship between UHR and the risk of kidney stones, smooth curve fitting and generalized additive models (GAM) were utilized. Subsequently, multiple regression analysis was performed on a stratified basis, with the strata defined by gender, age, diabetes mellitus, and hypertension. Interaction terms were included, and log-likelihood ratio tests were conducted to analyze heterogeneity, ensuring the robustness of the results. All statistical analyses were performed using R software (version 4.0.2) and EmpowerStats (www.empowerstats.com), with a significance level set at p < 0.05. Results Baseline characteristics of study participants Following the application of the exclusion criteria, the analysis dataset comprised data from 8,629 white participants (Fig. 1 ). The following Table 1 presents a description of the selected participants' sociodemographic characteristics and other covariates, according to the UHR tertiles. This study categorized participants into three tertile groups based on UHR levels: Tertile 1 (UHR 12.3, N = 3140). A pronounced trend emerged in demographic and clinical characteristics, with marked changes in key parameters. Male representation dramatically increased from 20.36–74.04%, while BMI rose from 26.14 to 32.19, indicating potential metabolic associations. Chronic disease prevalence progressively increased: hypertension from 36.42–54.04%, diabetes from 12.12–29.94%, and kidney stones from 9.55–15.38%. Concurrently, socioeconomic factors showed shifts, with lower income proportions increasing and higher education levels declining across tertiles. Table 1 Characteristics of Study Population Characteristics UHR(%) p -value Tertile 1 (12.3) N 2765 2724 3140 Age(years) 52.51 ± 18.81 52.80 ± 18.81 52.75 ± 18.60 0.829 BMI(kg/m 2 ) 26.14 ± 5.72 29.37 ± 6.78 32.19 ± 7.22 < 0.001 Gender < 0.001 Male 563 (20.36%) 1392 (51.10%) 2325 (74.04%) Female 2202 (79.64%) 1332 (48.90%) 815 (25.96%) Education Level < 0.001 Less than high school 285 (10.31%) 350 (12.85%) 473 (15.06%) High school 535 (19.36%) 646 (23.72%) 833 (26.53%) More than high school 1944 (70.33%) 1727 (63.42%) 1834 (58.41%) Marital Status 0.007 Cohabitation 1679 (60.75%) 1600 (58.74%) 1970 (62.74%) Solitude 1085 (39.25%) 1124 (41.26%) 1170 (37.26%) PIR < 0.001 <1.3 561 (21.88%) 656 (25.89%) 889 (30.12%) 1.3–3.5 919 (35.84%) 947 (37.37%) 1093 (37.03%) ≥ 3.5 1084 (42.28%) 931 (36.74%) 970 (32.86%) Smoking history < 0.001 Non-smoker 1476 (53.42%) 1341 (49.23%) 1377 (43.88%) Smoker 1287 (46.58%) 1383 (50.77%) 1761 (56.12%) Alcohol drinking history < 0.001 < 1 1059 (49.35%) 1180 (56.89%) 1370 (59.62%) 1–3 508 (23.67%) 434 (20.93%) 503 (21.89%) ≥ 4 579 (26.98%) 460 (22.18%) 425 (18.49%) Hypertension < 0.001 No 1758 (63.58%) 1503 (55.18%) 1443 (45.96%) Yes 1007 (36.42%) 1221 (44.82%) 1697 (54.04%) Diabetes mellitus < 0.001 No 2430 (87.88%) 2130 (78.19%) 2200 (70.06%) Yes 335 (12.12%) 594 (21.81%) 940 (29.94%) Coronary heart disease < 0.001 No 2654 (96.23%) 2555 (94.07%) 2829 (90.64%) Yes 104 (3.77%) 161 (5.93%) 292 (9.36%) Kidney stones < 0.001 No 2501 (90.45%) 2371 (87.04%) 2657 (84.62%) Yes 264 (9.55%) 353 (12.96%) 483 (15.38%) Abbreviations: UHR, uric acid to high-density lipoprotein cholesterol ratio. BMI, body mass index. PIR, poverty to income ratio. Associations of UHR with Kidney stones The associations between UHR and kidney stones risk were systematically analyzed using multivariate logistic regression models (Table 2 ). In the crude model, UHR as a continuous variable was significantly associated with kidney stones (OR = 1.04, 95% CI: 1.03–1.05, p < 0.0001). This association remained significant after adjusting for age and gender in Model I. In the fully adjusted model (Model II), which accounted for age, gender, BMI, PIR, alcohol consumption, education level, marital status, smoking history, diabetes mellitus, hypertension, and coronary heart disease, UHR remained significantly associated with kidney stone risk (OR = 1.02, 95% CI: 1.00–1.04, p = 0.0206). When categorized into tertiles, participants in the highest tertile (Tertile 3) had a significantly elevated risk compared to those in the lowest tertile (OR = 1.31, 95% CI: 1.03–1.67, p = 0.03) in Model II. The trend test also indicated a significant positive dose-response relationship between increasing UHR levels and kidney stone risk (p for trend = 0.0338). Additonally, smooth curve fittings were used to visualize the linear relationships between UHR and kidney stones formation and UHR showed a positive association with kidney stones formation (Fig. 2 ) Table 2 Associations between UHR and Kidney stones Outcome Crude model Model I Moedl II OR((95%CI) p -Value OR((95%CI) p -Value OR((95%CI) p -Value UHR 1.04 (1.03, 1.05) < 0.0001 1.04 (1.03, 1.05) < 0.0001 1.02 (1.00, 1.04) 0.0206 UHR Tertile 1 Reference Reference Reference Tertile 2 1.41 (1.19, 1.67) < 0.0001 1.37 (1.15, 1.63) 0.0004 1.20 (0.96, 1.50) 0.1099 Tertile 3 1.72 (1.47, 2.02) < 0.0001 1.64 (1.37, 1.95) < 0.0001 1.31 (1.03, 1.67) 0.0300 p for trend < 0.0001 < 0.0001 0.0338 Abbreviations: UHR, uric acid to high-density lipoprotein cholesterol ratio. CI, confidence interval. OR, odds ratio. Crude model adjust for: None Model I adjust for: age; Gender Model II adjust for: age; Gender; BMI PIR; Alcohol; Education Level; Marital Status; Smoking history; Diabetes mellitus; Hypertension; Coronary heart disease. Subgroup analysis Based on the subgroup analysis in Table 3 , the association between UHR and kidney stones risk remained consistent across various demographic and clinical subgroups. In the fully adjusted Model II, the association was persistent but slightly attenuated, with minimal heterogeneity across strata. Notably, the interaction p-values were all non-significant, suggesting that these factors did not substantially modify the UHR-kidney stones relationship. The young age group (20–41 years) maintained a statistically significant association (OR 1.05, 95% CI 1.02–1.09) even after comprehensive adjustment, while associations in older age groups were less pronounced but still showed a consistent trend. Table 3 Subgroup analysis between UHR and kidney stones . Characteristic Crude Model OR (95%CI) Model I OR (95%CI) Model II OR (95%CI) p for interaction* Stratified by gender 0.6965 Male 1.03 (1.02, 1.05) 1.04 (1.02, 1.05) 1.02 (1.00, 1.05) Femal 1.05 (1.03, 1.07) 1.05 (1.03, 1.08) 1.03 (1.00, 1.06) Stratified by age (years) 0.1341 20–41 1.03 (1.01, 1.05) 1.06 (1.04, 1.09) 1.05 (1.02, 1.09) 42–63 1.05 (1.03, 1.07) 1.05 (1.03, 1.07) 1.02 (0.99, 1.05) 64–80 1.05 (1.03, 1.07) 1.03 (1.01, 1.05) 1.01 (0.98, 1.04) Stratifiedby hypertension 0.4252 NO 1.04 (1.02, 1.06) 1.05 (1.02, 1.07) 1.03 (1.00, 1.06) YES 1.03 (1.02, 1.05) 1.03 (1.01, 1.04) 1.02 (0.99, 1.04) Stratified by diabetes 0.6813 NO 1.03 (1.02, 1.05) 1.04 (1.02, 1.05) 1.02 (1.00, 1.04) YES 1.04 (1.02, 1.06) 1.03 (1.01, 1.05) 1.03 (1.00, 1.06) Abbreviations: UHR, uric acid to high-density lipoprotein cholesterol ratio. CI, confidence interval. OR, odds ratio. Crude Model = no covariates were adjusted. Model I = age, gender, were adjusted.Model II = adjusted for all covariates except effect modifier.*Means only in model II. Disscussion This study analyzes NHANES data from 2011 to 2020, featuring 8,629 US White adults, of whom 1,100 developed kidney stones (12.7% incidence). The findings demonstrate a significant positive association between UHR and kidney stones formation, with each unit increase in UHR correlating with a 2% increase in risk (OR = 1.02, p = 0.0206) after adjusting for confounders. UHR's three categories show the same relationship. Subgroup analysis and interaction tests confirm these associations remain stable across different subgroups. Previous research has unveiled the intricate associations between the UHR and diverse metabolic disorders. Liu et al.[ 20 ] demonstrated that Chinese obese children with high UHR levels exhibited a 2.45-fold increased risk of metabolic dysfunction-associated steatotic liver disease (MASLD) (95% CI: 1.67–3.59, p < 0.001), while Aktas et al.[ 21 ] revealed a dose-dependent relationship between UHR and diabetic kidney injury (DKI), with each standard deviation increase in UHR associated with a 34% higher DKI risk (HR: 1.34, 95% CI: 1.12–1.59, p = 0.002). These findings, coupled with the current study's observation of a positive correlation between UHR and kidney stones, reinforce UHR's potential as a comprehensive metabolic risk marker, suggesting shared underlying mechanisms of chronic inflammation, oxidative stress, and metabolic dysregulation. The mechanistic insights into UHR's significance extend beyond simple metabolic indicators. Zhou et al.[ 22 ] demonstrated a significant correlation between UHR and insulin resistance in type 2 diabetes, revealing its role in reflecting cellular-level metabolic stress. Li et al. further elucidated UHR's association with metabolic syndrome and cardiovascular disease risk[ 23 ], while Huang et al. uncovered a negative correlation between UHR and circulating α-klotho levels, highlighting its potential as an inflammatory status indicator[ 24 ]. In the context of kidney stones, the positive correlation between the UHR and kidney stone formation can be attributed to a complex interplay of metabolic and pathophysiological mechanisms. Metabolic syndrome and insulin resistance create a conducive environment for stone formation by impairing uric acid excretion, lowering urinary pH, and promoting oxidative stress[ 25 ]. Insulin resistance inhibits renal uric acid clearance, while simultaneously reducing HDL-C levels, which compromises the kidney's antioxidant and anti-inflammatory capabilities[ 26 ]. Low HDL-C further exacerbates this process by enhancing local renal inflammation, damaging tubular epithelial cells, and facilitating crystal nucleation through increased oxidative stress. The metabolic dysregulation associated with elevated UHR leads to hyperuricemia, altered urinary biochemical environment, and reduced uric acid reabsorption, creating conditions favorable for stone formation[ 27 ]. Additionally, the inflammatory microenvironment induced by metabolic abnormalities promotes epithelial cell apoptosis and releases cellular debris that accelerates crystallization[ 25 , 28 ]. These interconnected mechanisms underscore the importance of comprehensive metabolic management to prevent kidney stone development, highlighting UHR as a potential integrated biomarker for metabolic risk assessment[ 29 ]. The clinical implications of these findings are substantial. The observed dose-response relationship between UHR and kidney stones suggests its potential as an early risk assessment biomarker. Monitoring UHR levels could help identify individuals at higher risk, enabling targeted interventions such as lifestyle modifications and more frequent clinical follow-ups. By integrating information about metabolic dysregulation, insulin resistance, and chronic inflammation, UHR transcends traditional single-parameter assessments. It provides clinicians with a more comprehensive metabolic risk evaluation tool, promising significant potential in early diagnosis, risk stratification, and personalized treatment of metabolic disorders. The large sample size and comprehensive confounder adjustments in these studies further strengthen the validity of UHR as a valuable clinical marker. This study, utilizing a nationally representative sample of 8,629 non-Hispanic White adults from the NHANES database, demonstrated significant strengths through comprehensive data analysis and rigorous statistical methods, revealing a dose-response relationship between UHR and kidney stones. However, the cross-sectional design limits causal inference, and self-reported kidney stone diagnoses may introduce recall bias, while the absence of specific UHR cutoff values constrains immediate clinical applicability. Despite these limitations, the research provides valuable evidence supporting UHR as a potential metabolic risk biomarker, with future prospective studies recommended to establish causality, explore generalizability across populations, and determine precise clinical thresholds. Conclusion This study identifies a significant correlation between UHR and the incidence of kidney stones among White Americans, based on data from the NHANES spanning 2011 to 2020. UHR emerges as a potentially valuable marker for evaluating kidney stone risk, as it may reflect underlying metabolic dysregulation and chronic inflammation. These findings underscore the importance of comprehensive metabolic management in the prevention of kidney stone formation. Future research should focus on establishing causal relationships through prospective studies and evaluating the applicability of UHR as an indicator across diverse populations. Statements and Declarations Competing Interests ： All the authors declare that they have no confict of interest. Funding: Not applicable. Acknowledgements Gratitude is extended to the NHANES databases for providing access to this valuable data. Authors’ contribution Z.Z. and Z.H. conceptualized the study. Z.Z. and L.Z. developed the methodology. 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Wu M, Wang R, Zeng Q, Shuai WL, Zhang HC, Dong Y.(2024) Association Between Uric Acid to High‐Density Lipoprotein Cholesterol Ratio and Kidney Function in Patients With Primary Aldosteronism: A Cross‐Sectional Study. J Clin Hypertens27(1). https://doi.org/10.1111/jch.14960. Moftakhar L, Jafari F, Johari MG, Rezaeianzadeh R, Hosseini SV, Rezaianzadeh A.(2022) Prevalence and Risk Factors of Kidney Stone Disease in Population Aged 40–70 Years Old in Kharameh Cohort Study: A Cross-Sectional Population-Based Study in Southern Iran. Bmc Urol22(1). https://doi.org/10.1186/s12894-022-01161-x. Wang H, Ba Y, Gao X, et al.(2023) Association Between Serum Uric Acid to High Density Lipoprotein-Cholesterol Ratio and Arterial Stiffness in a Japanese Population. Medicine102(31) : e34182. https://doi.org/10.1097/md.0000000000034182. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6489721","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":495645336,"identity":"95b66d3b-dc26-4f07-84b5-5ded4c948c58","order_by":0,"name":"Zhen Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Zhang","suffix":""},{"id":495645337,"identity":"09847154-a072-44f2-8dda-f80b190ea297","order_by":1,"name":"Lei Zhou","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Zhou","suffix":""},{"id":495645338,"identity":"496799f9-95bd-4d44-9f6f-03fc7b1c83aa","order_by":2,"name":"Zongsan Cheng","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zongsan","middleName":"","lastName":"Cheng","suffix":""},{"id":495645342,"identity":"b141c4f1-2780-47f2-bd62-2aea540ab215","order_by":3,"name":"Xiaoma Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoma","middleName":"","lastName":"Zhang","suffix":""},{"id":495645346,"identity":"9abbc3e3-fbe1-4693-bed5-9c551f70805b","order_by":4,"name":"Zongyao Hao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBACAwST+QAzhJFAtBa2BJK18BgQp8Wc/fDhDx931Cb2t/d8/FyYc5iBnz3HgOHnDtxaLHvSEgxnnjmeOOPM2c3SM7cdZpDseWPA2HsGj8MO5Bgk87YdS2y4kbtBmheoxeBGjgEzYxseLeffGBwGaZl/I+fxb5AWe4JabuQYNvO21SRuuJHDBrFFgqCWZ8mMM9sOGG88c8zMmndbOo/EmWcFB3vxOiwZGGJtdbLzjjc/vs27zVqOvz1544OfeLRAwWHHBiiLB0QcIKiBgaHOnghFo2AUjIJRMFIBAHH5WF1sasFQAAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of Anhui Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zongyao","middleName":"","lastName":"Hao","suffix":""}],"badges":[],"createdAt":"2025-04-20 14:08:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6489721/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6489721/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88500215,"identity":"211c6533-30bb-4524-8bd6-369e2197c9bb","added_by":"auto","created_at":"2025-08-07 06:50:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":23770,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the study population selection process.\u003c/p\u003e\n\u003cp\u003eNHANES: National Health and Nutrition Examination Survey; UHR: uric acid to high-density lipoprotein cholesterol ratio\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6489721/v1/e3c3dba3f2c53e78e6d26eac.png"},{"id":88502626,"identity":"5daffb54-7b6c-4590-82f9-dc6fe010de3c","added_by":"auto","created_at":"2025-08-07 06:58:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":37044,"visible":true,"origin":"","legend":"\u003cp\u003eGraphics of smooth curve fittings between UHR and risk of kidney stones among non-Hispanic White adultsin NHANES 2011–2020. OR were adjusted for age, Gender, BMI, PIR, Alcohol drinking history; Education Level, Marital Status, Smoking history, Diabetes mellitus, Hypertension, Coronary heart disease.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6489721/v1/47ba8b44362f64ea0e187c68.png"},{"id":89500511,"identity":"44070c64-fc17-4571-9e79-dee4e59bf578","added_by":"auto","created_at":"2025-08-20 15:54:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":847543,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6489721/v1/92b751fa-c860-4cee-9d27-67cdf8d4defa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Association between Uric Acid to High-Density Lipoprotein Cholesterol Ratio and Kidney Stones in White Americans: Evidence from NHANES 2011-2020","fulltext":[{"header":"Background","content":"\u003cp\u003eKidney stones, a prevalent urinary system disorder, exhibit significant racial disparities in incidence[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], with White populations showing higher rates compared to Black individuals[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The disease exhibits a high recurrence rate (50% within 10 years),which imposes substantial medical and economic burdens[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The limited predictive capacity of traditional indicators necessitates the exploration of novel multi-omics approaches to elucidate molecular mechanisms and develop personalised prevention strategies[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe uric acid to high-density lipoprotein cholesterol ratio (UHR) is an emerging indicator reflecting metabolic abnormalities and inflammatory states, with recent studies demonstrating its significant associations with key metabolic disorders[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. UHR shows a strong positive correlation with metabolic syndrome, exhibiting a predictive sensitivity of 89.07% and specificity of 77.03% when exceeding 9.5%, closely linked to insulin resistance and chronic inflammatory mechanisms[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In type 2 diabetes, UHR has been demonstrated to positively correlate with glycated hemoglobin and fasting blood glucose levels, with a notable elevation observed in prediabetes patients[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. It is important to note that UHR plays a crucial role in cardiovascular diseases, which are significantly associated with acute coronary syndrome and arterial stiffness. In addition, chronic kidney disease risk increases substantially in high UHR quartiles[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Further investigations have revealed the potential of UHR to serve as an early indicator of metabolic dysfunction, with significance demonstrated across various population subgroups[\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. UHR is a pioneering metabolic inflammatory indicator with considerable potential for early disease screening and risk assessment. It is distinguished by its simplicity and cost-effectiveness. However, the association between UHR and kidney stones remains unproven. Therefore, the present study was undertaken to investigate this association.\u003c/p\u003e\u003cp\u003eUtilising the National Health and Nutrition Examination Survey (NHANES), a comprehensive, population-based surveillance system, we investigated the association between UHR and kidney stones among non-Hispanic White adults. Addressing a critical gap in metabolic epidemiology, our findings contribute to the understanding of the underlying causes of kidney stones in this demographic.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eStudy population\u003c/h2\u003e\n \u003cp\u003eThe present study was conducted using a cross-sectional design of the NHANES dataset, with data from the NHANES database collected at two-year intervals. All protocols were approved by the institutional review board of the National Center for Health Statistics, informed consent was obtained from participants, and a total of 45,462 participants were enrolled from 2011 to 2020. The exclusion criteria comprised participants who were non-white, those with missing kidney stones data, missing UHR data, and outliers. The final analysis included 8629 participants ( Fig.\u0026nbsp;1).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eAssessment of kidney stones\u003c/h3\u003e\n\u003cp\u003eThe history of kidney stones was determined by the Kidney Conditions Questionnaire. The survey participants were asked \u0026ldquo;Have you ever had kidney stones?\u0026rdquo; and were categorised as having the history of kidney stones if they responded \u0026ldquo;Yes\u0026rdquo;.\u003c/p\u003e\n\u003ch3\u003eAssessment of UHR\u003c/h3\u003e\n\u003cp\u003eUHR has emerged as a new indicator and has garnered significant attention in recent years for its role in metabolic diseases. It was calculated by dividing serum uric acid (UA) (mg/dL) by high-density lipoprotein cholesterol (HDL-C) (mg/dL) and multiplying the result by 100%. Blood samples were collected from fasting study participants in the morning and analysed to ascertain the levels of UA and HDL-C. The detailed measurement protocols can be accessed at the following link: https://www.cdc.gov/nchs/nhanes/index.htm.\u003c/p\u003e\n\u003ch3\u003eCovariates definition\u003c/h3\u003e\n\u003cp\u003eThe research identified and adjusted for potential covariates based on previous studies. Therefore, the following variables were included to construct the fully adjusted model: age (years), gender (male, female), body mass index(BMI) (kg/m\u003csup\u003e2\u003c/sup\u003e), education level (lower than high school, high school, more than high school), marital status, poverty to income ratio(PIR), smoking history, alcohol drinking history (drinks/week), diabetes mellitus, hypertension, and coronary heart disease.\u003c/p\u003e\n\u003cp\u003eParticipants were stratified into two groups based on their self-reported smoking history regarding whether they had smoked at least 100 cigarettes during their lifetime (yes or no). PIR was classified into low (\u0026lt;\u0026thinsp;1.3), median (1.3\u0026ndash;3.5), and high (\u0026gt;\u0026thinsp;3.5). Alcohol drinking history was evaluated with a questionnaire that inquired about frequency and quantity over the past 12 months, classified as follows: \u0026lt;1, 1\u0026ndash;3, or \u0026ge;\u0026thinsp;4 drinks per week. Hypertension was defined as a systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg, or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, or self-reported hypertension, or use of antihypertensive medication. Diabetes mellitus was diagnosed based on self-report, or use of insulin or oral hypoglycemic agents, or meeting at least one of the following criteria: HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5%, or fasting blood glucose (FBG)\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/l, or two-hour OGTT blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/l. Information regarding coronary heart disease was collected via questionnaire, where patients were asked whether they had been informed of their diagnosis (yes or no).\u003c/p\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eAll statistical analyses were performed in accordance with CDC guidelines (https://wwwn.cdc.gov/nchs/nhanes/tutorials/default.aspx). Continuous variables were reported as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations, while categorical variables were expressed as percentages. Differences in baseline characteristics were assessed using chi-square tests, analysis of variance (ANOVA), and Kruskal-Wallis H tests to calculate for differences among different UHR groups (tertiles). To investigate the association between UHR and kidney stones risk, multiple logistic regression models were constructed. Crude Model was unadjusted for covariates; Model I adjusted for age, gender; and Model II further adjusted for BMI, education level, PIR, marital status, smoking history, alcohol drinking history, diabetes mellitus, hypertension, and coronary heart disease. To explore the linear relationship between UHR and the risk of kidney stones, smooth curve fitting and generalized additive models (GAM) were utilized. Subsequently, multiple regression analysis was performed on a stratified basis, with the strata defined by gender, age, diabetes mellitus, and hypertension. Interaction terms were included, and log-likelihood ratio tests were conducted to analyze heterogeneity, ensuring the robustness of the results. All statistical analyses were performed using R software (version 4.0.2) and EmpowerStats (www.empowerstats.com), with a significance level set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eBaseline characteristics of study participants\u003c/h2\u003e\u003cp\u003eFollowing the application of the exclusion criteria, the analysis dataset comprised data from 8,629 white participants (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The following Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a description of the selected participants' sociodemographic characteristics and other covariates, according to the UHR tertiles. This study categorized participants into three tertile groups based on UHR levels: Tertile 1 (UHR\u0026thinsp;\u0026lt;\u0026thinsp;8.4, N\u0026thinsp;=\u0026thinsp;2765), Tertile 2 (8.4\u0026thinsp;\u0026le;\u0026thinsp;UHR\u0026thinsp;\u0026le;\u0026thinsp;12.3, N\u0026thinsp;=\u0026thinsp;2724), and Tertile 3 (UHR\u0026thinsp;\u0026gt;\u0026thinsp;12.3, N\u0026thinsp;=\u0026thinsp;3140). A pronounced trend emerged in demographic and clinical characteristics, with marked changes in key parameters. Male representation dramatically increased from 20.36\u0026ndash;74.04%, while BMI rose from 26.14 to 32.19, indicating potential metabolic associations. Chronic disease prevalence progressively increased: hypertension from 36.42\u0026ndash;54.04%, diabetes from 12.12\u0026ndash;29.94%, and kidney stones from 9.55\u0026ndash;15.38%. Concurrently, socioeconomic factors showed shifts, with lower income proportions increasing and higher education levels declining across tertiles.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of Study Population\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUHR(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e -value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTertile 1 (\u0026lt;\u0026thinsp;8.4)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTertile 2 (8.4\u0026ndash;12.3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTertile 3 (\u0026gt;12.3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2724\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge(years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52.51\u0026thinsp;\u0026plusmn;\u0026thinsp;18.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52.80\u0026thinsp;\u0026plusmn;\u0026thinsp;18.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52.75\u0026thinsp;\u0026plusmn;\u0026thinsp;18.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.829\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.14\u0026thinsp;\u0026plusmn;\u0026thinsp;5.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.37\u0026thinsp;\u0026plusmn;\u0026thinsp;6.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.19\u0026thinsp;\u0026plusmn;\u0026thinsp;7.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e563 (20.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1392 (51.10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2325 (74.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2202 (79.64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1332 (48.90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e815 (25.96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e285 (10.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e350 (12.85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e473 (15.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e535 (19.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e646 (23.72%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e833 (26.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMore than high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1944 (70.33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1727 (63.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1834 (58.41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCohabitation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1679 (60.75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1600 (58.74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1970 (62.74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolitude\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1085 (39.25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1124 (41.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1170 (37.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePIR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e561 (21.88%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e656 (25.89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e889 (30.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.3\u0026ndash;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e919 (35.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e947 (37.37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1093 (37.03%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1084 (42.28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e931 (36.74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e970 (32.86%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1476 (53.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1341 (49.23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1377 (43.88%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1287 (46.58%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1383 (50.77%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1761 (56.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol drinking history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1059 (49.35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1180 (56.89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1370 (59.62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e508 (23.67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e434 (20.93%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e503 (21.89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e579 (26.98%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e460 (22.18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e425 (18.49%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1758 (63.58%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1503 (55.18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1443 (45.96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1007 (36.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1221 (44.82%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1697 (54.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes mellitus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2430 (87.88%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2130 (78.19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2200 (70.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e335 (12.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e594 (21.81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e940 (29.94%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoronary heart disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2654 (96.23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2555 (94.07%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2829 (90.64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e104 (3.77%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e161 (5.93%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e292 (9.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKidney stones\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2501 (90.45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2371 (87.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2657 (84.62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e264 (9.55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e353 (12.96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e483 (15.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: UHR, uric acid to high-density lipoprotein cholesterol ratio. BMI, body mass index. PIR, poverty to income ratio.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAssociations of UHR with Kidney stones\u003c/h3\u003e\n\u003cp\u003eThe associations between UHR and kidney stones risk were systematically analyzed using multivariate logistic regression models (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the crude model, UHR as a continuous variable was significantly associated with kidney stones (OR\u0026thinsp;=\u0026thinsp;1.04, 95% CI: 1.03\u0026ndash;1.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). This association remained significant after adjusting for age and gender in Model I. In the fully adjusted model (Model II), which accounted for age, gender, BMI, PIR, alcohol consumption, education level, marital status, smoking history, diabetes mellitus, hypertension, and coronary heart disease, UHR remained significantly associated with kidney stone risk (OR\u0026thinsp;=\u0026thinsp;1.02, 95% CI: 1.00\u0026ndash;1.04, p\u0026thinsp;=\u0026thinsp;0.0206). When categorized into tertiles, participants in the highest tertile (Tertile 3) had a significantly elevated risk compared to those in the lowest tertile (OR\u0026thinsp;=\u0026thinsp;1.31, 95% CI: 1.03\u0026ndash;1.67, p\u0026thinsp;=\u0026thinsp;0.03) in Model II. The trend test also indicated a significant positive dose-response relationship between increasing UHR levels and kidney stone risk (p for trend\u0026thinsp;=\u0026thinsp;0.0338). Additonally, smooth curve fittings were used to visualize the linear relationships between UHR and kidney stones formation and UHR showed a positive association with kidney stones formation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociations between UHR and Kidney stones\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCrude model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel I\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMoedl II\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR((95%CI) \u003cem\u003ep\u003c/em\u003e -Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR((95%CI) \u003cem\u003ep\u003c/em\u003e -Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR((95%CI) \u003cem\u003ep\u003c/em\u003e -Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.04 (1.03, 1.05)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.04 (1.03, 1.05)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.02 (1.00, 1.04) 0.0206\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.41 (1.19, 1.67)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.37 (1.15, 1.63) 0.0004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.20 (0.96, 1.50) 0.1099\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertile 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.72 (1.47, 2.02)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.64 (1.37, 1.95)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.31 (1.03, 1.67) 0.0300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0338\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: UHR, uric acid to high-density lipoprotein cholesterol ratio. CI, confidence interval. OR, odds ratio.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eCrude model adjust for: None\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel I adjust for: age; Gender\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel II adjust for: age; Gender; BMI PIR; Alcohol; Education Level; Marital Status; Smoking history; Diabetes mellitus; Hypertension; Coronary heart disease.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eSubgroup analysis\u003c/h2\u003e\u003cp\u003eBased on the subgroup analysis in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the association between UHR and kidney stones risk remained consistent across various demographic and clinical subgroups. In the fully adjusted Model II, the association was persistent but slightly attenuated, with minimal heterogeneity across strata. Notably, the interaction p-values were all non-significant, suggesting that these factors did not substantially modify the UHR-kidney stones relationship. The young age group (20\u0026ndash;41 years) maintained a statistically significant association (OR 1.05, 95% CI 1.02\u0026ndash;1.09) even after comprehensive adjustment, while associations in older age groups were less pronounced but still showed a consistent trend.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSubgroup analysis between UHR and kidney stones .\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCrude Model OR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel I OR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel II OR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e for interaction*\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStratified by gender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.6965\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.03 (1.02, 1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.04 (1.02, 1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.02 (1.00, 1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.05 (1.03, 1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.05 (1.03, 1.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.03 (1.00, 1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStratified by age (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1341\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.03 (1.01, 1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.06 (1.04, 1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.05 (1.02, 1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e42\u0026ndash;63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.05 (1.03, 1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.05 (1.03, 1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.02 (0.99, 1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e64\u0026ndash;80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.05 (1.03, 1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.03 (1.01, 1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.01 (0.98, 1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStratifiedby hypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4252\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.04 (1.02, 1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.05 (1.02, 1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.03 (1.00, 1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.03 (1.02, 1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.03 (1.01, 1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.02 (0.99, 1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStratified by diabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.6813\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.03 (1.02, 1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.04 (1.02, 1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.02 (1.00, 1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.04 (1.02, 1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.03 (1.01, 1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.03 (1.00, 1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: UHR, uric acid to high-density lipoprotein cholesterol ratio. CI, confidence interval. OR, odds ratio.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eCrude Model\u0026thinsp;=\u0026thinsp;no covariates were adjusted.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel I\u0026thinsp;=\u0026thinsp;age, gender, were adjusted.Model II\u0026thinsp;=\u0026thinsp;adjusted for all covariates except effect modifier.*Means only in model II.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Disscussion","content":"\u003cp\u003eThis study analyzes NHANES data from 2011 to 2020, featuring 8,629 US White adults, of whom 1,100 developed kidney stones (12.7% incidence). The findings demonstrate a significant positive association between UHR and kidney stones formation, with each unit increase in UHR correlating with a 2% increase in risk (OR\u0026thinsp;=\u0026thinsp;1.02, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0206) after adjusting for confounders. UHR's three categories show the same relationship. Subgroup analysis and interaction tests confirm these associations remain stable across different subgroups.\u003c/p\u003e\u003cp\u003ePrevious research has unveiled the intricate associations between the UHR and diverse metabolic disorders. Liu et al.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] demonstrated that Chinese obese children with high UHR levels exhibited a 2.45-fold increased risk of metabolic dysfunction-associated steatotic liver disease (MASLD) (95% CI: 1.67\u0026ndash;3.59, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while Aktas et al.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] revealed a dose-dependent relationship between UHR and diabetic kidney injury (DKI), with each standard deviation increase in UHR associated with a 34% higher DKI risk (HR: 1.34, 95% CI: 1.12\u0026ndash;1.59, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). These findings, coupled with the current study's observation of a positive correlation between UHR and kidney stones, reinforce UHR's potential as a comprehensive metabolic risk marker, suggesting shared underlying mechanisms of chronic inflammation, oxidative stress, and metabolic dysregulation.\u003c/p\u003e\u003cp\u003eThe mechanistic insights into UHR's significance extend beyond simple metabolic indicators. Zhou et al.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] demonstrated a significant correlation between UHR and insulin resistance in type 2 diabetes, revealing its role in reflecting cellular-level metabolic stress. Li et al. further elucidated UHR's association with metabolic syndrome and cardiovascular disease risk[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], while Huang et al. uncovered a negative correlation between UHR and circulating α-klotho levels, highlighting its potential as an inflammatory status indicator[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the context of kidney stones, the positive correlation between the UHR and kidney stone formation can be attributed to a complex interplay of metabolic and pathophysiological mechanisms. Metabolic syndrome and insulin resistance create a conducive environment for stone formation by impairing uric acid excretion, lowering urinary pH, and promoting oxidative stress[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Insulin resistance inhibits renal uric acid clearance, while simultaneously reducing HDL-C levels, which compromises the kidney's antioxidant and anti-inflammatory capabilities[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Low HDL-C further exacerbates this process by enhancing local renal inflammation, damaging tubular epithelial cells, and facilitating crystal nucleation through increased oxidative stress. The metabolic dysregulation associated with elevated UHR leads to hyperuricemia, altered urinary biochemical environment, and reduced uric acid reabsorption, creating conditions favorable for stone formation[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Additionally, the inflammatory microenvironment induced by metabolic abnormalities promotes epithelial cell apoptosis and releases cellular debris that accelerates crystallization[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These interconnected mechanisms underscore the importance of comprehensive metabolic management to prevent kidney stone development, highlighting UHR as a potential integrated biomarker for metabolic risk assessment[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe clinical implications of these findings are substantial. The observed dose-response relationship between UHR and kidney stones suggests its potential as an early risk assessment biomarker. Monitoring UHR levels could help identify individuals at higher risk, enabling targeted interventions such as lifestyle modifications and more frequent clinical follow-ups. By integrating information about metabolic dysregulation, insulin resistance, and chronic inflammation, UHR transcends traditional single-parameter assessments. It provides clinicians with a more comprehensive metabolic risk evaluation tool, promising significant potential in early diagnosis, risk stratification, and personalized treatment of metabolic disorders. The large sample size and comprehensive confounder adjustments in these studies further strengthen the validity of UHR as a valuable clinical marker.\u003c/p\u003e\u003cp\u003eThis study, utilizing a nationally representative sample of 8,629 non-Hispanic White adults from the NHANES database, demonstrated significant strengths through comprehensive data analysis and rigorous statistical methods, revealing a dose-response relationship between UHR and kidney stones. However, the cross-sectional design limits causal inference, and self-reported kidney stone diagnoses may introduce recall bias, while the absence of specific UHR cutoff values constrains immediate clinical applicability. Despite these limitations, the research provides valuable evidence supporting UHR as a potential metabolic risk biomarker, with future prospective studies recommended to establish causality, explore generalizability across populations, and determine precise clinical thresholds.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identifies a significant correlation between UHR and the incidence of kidney stones among White Americans, based on data from the NHANES spanning 2011 to 2020. UHR emerges as a potentially valuable marker for evaluating kidney stone risk, as it may reflect underlying metabolic dysregulation and chronic inflammation. These findings underscore the importance of comprehensive metabolic management in the prevention of kidney stone formation. Future research should focus on establishing causal relationships through prospective studies and evaluating the applicability of UHR as an indicator across diverse populations.\u003c/p\u003e"},{"header":"Statements and Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eAll the authors declare that they have no confict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGratitude is extended to the NHANES databases for providing access to this valuable data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZ.Z. and Z.H. conceptualized the study. Z.Z. and L.Z. developed the methodology. Z.Z., L.Z. and Z.C. conducted analysis and investigation. Z.Z. wrote the original draft. X.Z. and Z.H. contributed to review and editing. All authors reviewed and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eHsi RS, Kabagambe EK, Shu X, Han X, Miller NL, Lipworth L.(2018) Race- and Sex-related Differences in Nephrolithiasis Risk Among Blacks and Whites in the Southern Community Cohort Study. Urology118\u003cstrong\u003e:\u003c/strong\u003e36-42. https://doi.org/10.1016/j.urology.2018.04.036.\u003c/li\u003e\n \u003cli\u003eD\u0026apos;Amico M, Babayan RK, Wang DS, Wason S, Cozier YC.(2024) Clinical, Diagnostic, and Metabolic Characteristics Associated with Nephrolithiasis in the Black Women\u0026apos;s Health Study. J Clin Med13(19). https://doi.org/10.3390/jcm13195948.\u003c/li\u003e\n \u003cli\u003eMaalouf NM.(2011) Approach to the Adult Kidney Stone Former. 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Lipids Health Dis23(1)\u003cstrong\u003e:\u003c/strong\u003e244. https://doi.org/10.1186/s12944-024-02234-6.\u003c/li\u003e\n \u003cli\u003eXu Z, Yao X, Duan C, Liu H, Xu H.(2023) Metabolic Changes in Kidney Stone Disease. Front Immunol14. https://doi.org/10.3389/fimmu.2023.1142207.\u003c/li\u003e\n \u003cli\u003eHan R, Duan L, Zhang Y, Jiang X.(2023) Serum Uric Acid Is a Better Indicator of Kidney Impairment Than Serum Uric Acid-to-Creatinine Ratio and Serum Uric Acid-to-High-Density Lipoprotein Ratio: A Cross-Sectional Study of Type 2 Diabetes Mellitus Patients. Diabetes Metabolic Syndrome and Obesity Targets and TherapyVolume 16\u003cstrong\u003e:\u003c/strong\u003e2695-2703. https://doi.org/10.2147/dmso.s425511.\u003c/li\u003e\n \u003cli\u003eWu M, Wang R, Zeng Q, Shuai WL, Zhang HC, Dong Y.(2024) Association Between Uric Acid to High‐Density Lipoprotein Cholesterol Ratio and Kidney Function in Patients With Primary Aldosteronism: A Cross‐Sectional Study. J Clin Hypertens27(1). https://doi.org/10.1111/jch.14960.\u003c/li\u003e\n \u003cli\u003eMoftakhar L, Jafari F, Johari MG, Rezaeianzadeh R, Hosseini SV, Rezaianzadeh A.(2022) Prevalence and Risk Factors of Kidney Stone Disease in Population Aged 40\u0026ndash;70 Years Old in Kharameh Cohort Study: A Cross-Sectional Population-Based Study in Southern Iran. Bmc Urol22(1). https://doi.org/10.1186/s12894-022-01161-x.\u003c/li\u003e\n \u003cli\u003eWang H, Ba Y, Gao X, et al.(2023) Association Between Serum Uric Acid to High Density Lipoprotein-Cholesterol Ratio and Arterial Stiffness in a Japanese Population. Medicine102(31)\u003cstrong\u003e:\u003c/strong\u003ee34182. https://doi.org/10.1097/md.0000000000034182.\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":"Uric Acid to High-density Lipoprotein Cholesterol Ratio (UHR), Kidney Stones, NHANES, Cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-6489721/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6489721/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKidney stones represent a prevalent urinary tract disorder, exhibiting notable racial disparities in incidence, resulting in substantial medical and economic burdens. The uric acid to high-density lipoprotein cholesterol ratio (UHR) is a comprehensive index reflecting metabolic and inflammatory abnormalities, yet its association with kidney stones remains unclear. This study evaluated the relationship between UHR and kidney stones in non-Hispanic white adults using nationally representative data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA cross-sectional analysis of 8,629 non-Hispanic White participants from the National Health and Nutrition Examination Survey (NHANES) 2011–2020 was conducted. Multivariate logistic regression models were conducted to assess the association between UHR and kidney stones, while dose-response relationships were explored using smooth curve fitting. Stratified analyses were performed to evaluate the stability of the outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe kidney stones incidence was 12.7% (1,100/8,629). Participants in the highest UHR tertile (\u0026gt;12.3%) exhibited significantly higher kidney stone prevalence (15.38%) compared to the lowest tertile (\u0026lt;8.4%) at 9.55%. In fully adjusted models, higher UHR was associated with increased risk of kidney stones (OR=1.02, 95% CI: 1.00–1.04, p=0.0206). Participants in the highest tertile had a 31% increased risk relative to the lowest tertile (OR=1.31, 95% CI: 1.03–1.67, p=0.03), with a significant positive dose-response trend (p for trend=0.0338). Associations remained consistent across subgroups without significant interaction effects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study has demonstrated that UHR is positively associated with kidney stones in White Americans. This finding offers new perspectives for the assessment and prevention of kidney stones.\u003c/p\u003e","manuscriptTitle":"The Association between Uric Acid to High-Density Lipoprotein Cholesterol Ratio and Kidney Stones in White Americans: Evidence from NHANES 2011-2020","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-07 06:50:33","doi":"10.21203/rs.3.rs-6489721/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":"a1771bb1-b403-45cf-946a-6213f59a522f","owner":[],"postedDate":"August 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-20T15:53:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-07 06:50:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6489721","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6489721","identity":"rs-6489721","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}