{"paper_id":"06e1e402-0607-4306-a422-e43124ea3e51","body_text":"Association between Renal Injury and Prognosis of Elderly Overactive Bladder Patients: A Study Based on NHANES Database | 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 Association between Renal Injury and Prognosis of Elderly Overactive Bladder Patients: A Study Based on NHANES Database Yi Rong, Dingyang Lv, Jintang Hu, Jiacheng Gao, Huiyu zhou, Yinbo Kang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7615139/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background The prevalence of overactive bladder(OAB) rises with age and substantially impacts patients’ quality of life, sleep quality and mental health. However, many older adults perceive OAB as non-fatal, treatment-seeking behavior among seniors is low, particularly in economically disadvantaged regions. The aim of this study was to explore the influence of OAB on the survival time of the elderly and the relationship between renal injury index and survival time of OAB patients. Methods A total of 6065 participants from the National health and Nutrition examination survey(Nhanes) were included in the study. Tendency score matching is used to control data bias. Predictors of survival were identified through a three-stage process: initial univariate Cox regression, feature selection via Lasso regression, and confirmation with multivariate Cox regression. Mediating analysis was used to confirm whether OAB can independently affect survival time. Six machine learning algorithms, logistic Regression, Gradient Boosting, AdaBoost, Support Vector Machine, K-Nearest Neighbors, Gaussian Naive Bayes, and generalized additive model(GAM) were used to evaluate the correlation between renal injury index and prognosis of OAB patients. Results Under the control of other variables, the survival time of OAB patients was still shorter than normal people. OAB was an independent risk factor affecting the survival time of the elderly population, and the influence of OAB on the survival time did not depend on other covariates. Machine learning results showed that urinary albumin, serum creatinine, glomerular filtration rate(eGFR), urinary albumin-to-creatinine ratio(UACR) could predicted the survival time of OAB patients. The GAM results showed that UACR was negatively correlated with the survival time of OAB patients. Conclusion OAB could effect the long-term survival time of the elderly by damaging renal function, and UACR may be the potential index to predict the survival time of OAB patients. Overactive bladder Renal injury UACR eGFR Nhanes Machine learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction The International Continence Society defines Overactive Bladder (OAB) syndrome as [ 1 ] “the presence of urinary urgency, usually accompanied by frequency and nocturia, with or without urgency urinary incontinence”. Globally, approximately 363 million individuals across eight major countries—the United States, five European nations (France, Germany, Italy, Spain, and the United Kingdom), Japan, and China—lived with OAB in 2020, a figure projected to increase to 401.6 million by 2030 [ 2 ] . This number is expected to rise to 401.6 million by 2030 [ 2 ] .The prevalence of OAB rises with age and substantially impacts patients’ quality of life, sleep quality and mental health [ 3 – 4 ] . While elderly OAB patients are conventionally managed with anticholinergic drugs or the β₃ receptor agonist mirabegron, sustained treatment often results in significant adverse effects, including cognitive impairment in this vulnerable population [ 5 , 6 ] . A large number of studies have proved that OAB might be closely related to cardiovascular diseases, diabetes, hyperlipidemia and systemic inflammation [ 7 – 9 ] . It suggests that the survival time of elderly OAB patients is likely to be different from their non-affected peers. However, there are few studies on the relationship between OAB and survival time in the elderly population. The contents of serum creatinine(SCr), serum urea nitrogen, serum uric acid, urine albumin and urine creatinine are traditional indicators reflecting renal function injury. Although there is no relevant data of glomerular filtration rate in National Health and Nutrition Examination Survey(NHANES) database, the estimated glomerular filtration rate(eGFR) based on serum creatinine content and corrected by sex, race and age has been proved to reflect renal function injury well [ 10 – 12 ] . In addition, A large number of studies have proved that urinary albumin-to-creatinine ratio(UACR) can accurately predict the prognosis of many diseases [ 13 , 14 ] . Given that advanced OAB frequently complicates with urinary tract infections and subsequent renal impairment, monitoring renal function—through urinary protein, creatinine, and serum levels of creatinine and urea nitrogen—could provide valuable prognostic information for these patients. In this study, we collected publicly available data from NHANES database, a nationally representative cross-sectional survey, to examine the impact of OAB on all-cause mortality in elderly population. In addition, the study also discussed the ability of a series of indicators of renal injury to predict the survival time of OAB patients. 2. Methods 2.1 Study Population We utilized publicly available data from the 2005–2018 NHANES database. Eligible participants met stringent criteria for completeness in two domains: (1) OAB Scoring Data: Full responses were required to validated symptom queries, including self-reported urgency incontinence episodes, their frequency, and nocturia severity; (2) All-cause mortality data: Mortality status through December 31, 2019, was confirmed via linkage to the National Death Index (NDI). The primary endpoint remained all-cause mortality. 2.2 Covariates We considered several potential covariates for adjustment, including: Age Stratification: Three categories were created (60–70, 70–80, and ≥ 80 years); Demographics: Self-identified race/ethnicity (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, Other Race) and highest educational attainment (≤ high school vs. >high school); Income Status: Annual household income dichotomized at $ 20,000; Clinical Comorbidities: Hypertension (self-reported diagnosis or examination findings of mean systolic BP ≥ 140 mmHg/diastolic BP ≥ 90 mmHg); diabetes (self-reported diagnosis or HbA1c ≥ 6.5%/FPG ≥ 7.0 mmol/L); hyperlipidemia (self-reported diagnosis); Lifestyle Exposures: Lifetime tobacco use (≥ 100 cigarettes ever smoked); lifetime alcohol consumption (≥ 12 drinks consumed); BMI and mental health: Obesity defined as BMI ≥ 28 kg/m²; depression diagnosed via NHANES Mental Health Screener with score ≥ 10. 2.3 Renal injury index Serum creatinine, serum urea nitrogen, serum uric acid data was obtained from Laboratory Data—Standard Biochemistry Profile; Urine albumin and urine creatinine data was obtained from Laboratory Data—Albumin & Creatinine - Urine. UACR(mg/g) = Urine albumin(mg/dL)/Urine creatinine(g/d). UACR ≤ 30 is defined as normal renal function;30<UACR<300 is defined as mild renal injury༛UACR ≥ 300 is defined as heavy renal injury. eGFR(mL/min/1.73m 2 ) = 141×min(SCr/k,1) a ×max(SCr/k,1) −1.209 ×0.993 age ×(1.018 if women) ×(1.159 if black). Female: k = 0.7, a=-0.329; Male༚k = 0.9, a=-0.411. Categorized as eGFR<15, 15<eGFR ≤ 30, 30<eGFR ≤ 60, 60<eGFR ≤ 90, eGFR>90. 2.4 Statistical Analysis Data analysis was performed using IBM SPSS Statistics 25 and R 4.2.2. Baseline characteristics are summarized with means (± standard deviations) or medians (interquartile ranges) for continuous variables, and percentages for categorical variables, depending on their distribution. Propensity score matching was used to control the population baseline difference between OAB group and control group. Survival outcomes were analyzed using the Kaplan-Meier(KM) method. Predictors of survival were identified through a three-stage process: initial univariate Cox regression, feature selection via Lasso regression, and confirmation with multivariate Cox regression. Subgroup analysis and mediation analysis were used to further determine the relationship between OAB and survival time. Six machine learning algorithms, logistic Regression, Gradient Boosting, AdaBoost, Support Vector Machine, K-Nearest Neighbors and Gaussian Naive Bayes, were used to predict the predictive ability of various indexes of renal injury on the survival time of OAB patients. ROC curve, F1 score and Cross-Validation accuracy were used to determine the best model. Generalized Additive Model (GAM) was used to show the correlation between various indexes of renal injury and survival time of OAB patients. 3. Results 3.1 Baseline Characteristics of Study Participants A total of 6063 participants were included in the final analysis (Fig. 1 ). The overall prevalence of OAB among all participants was 41.25%. Compared with the normal population, OAB patients tended to be older, have lower education level and annual family income, and have higher prevalence rates of hypertension, diabetes, depression and obesity. In addition, there were obvious differences in race composition between OAB patients and the normal population(Table 1 ). In view of these substantial baseline differences between the two groups, this study conducted a 1:1 propensity score matching for OAB to reduce the population baseline differences. After matching the propensity score, there was no statistical difference between OAB group and control group(Table 1 ). Subsequent analyses were then conducted using the matched cohort . Table 1 Baseline characteristics of all participants Data before Tendency score matching Data after Tendency score matching Control groups (n = 3562) OAB groups (n = 2501) P Value Control groups (n = 2212) OAB groups (n = 2212) P Value Gender Male Female 1614(45.3%) 1948(54.7%) 1078(43.1%) 1423(56.9%) 0.088 983(44.4%) 1229(56.6%) 972(43.9%) 1304(56.1%) 0.739 Race Mexican American Other Hispanic Non-Hispanic White Non-Hispanic Black Other Race 338(9.5%) 328(9.2%) 2038(57.2%) 607(17.0%) 251(7.0%) 297(11.9%) 242(9.7%) 1238(49.5%) 583(23.3%) 141(5.6%) <0.001* 228(10.3%) 210(9.5%) 1184(53.5%) 465(21.0%) 120(5.7%) 240(10.8%) 214(9.7%) 1157(52.3%) 469(21.2%) 132(6.0%) 0.930 Age rank 60–70 70–80 ≥ 80 1963(55.1%) 1043(29.3%) 556(15.6%) 1101(44.0%) 844(33.7%) 556(22.2%) <0.001* 988(44.7%) 730(33.0%) 494(22.3%) 1020(46.1%) 738(33.4%) 454(20%) 0.326 Age 69.37 ± 6.92 71.02 ± 7.05 0.049 70.84 ± 7.20 70.72 ± 7.03 0.119 Annual Family Incomes ≤ 20000 >20000 896(25.2%) 2666 (74.8%) 828 (33.1%) 1673(66.9%) <0.001* 685(31.0%) 1527 (69.0%) 697 (31.2%) 1547(68.8%) 0.845 Education Education below senior high school High school education or above 825(23.2%) 2737 (76.8%) 847 (33.9%) 1654 (66.1%) <0.001* 641(29.0%) 1571(71.0%) 691(31.2%) 1521 (68.8%) 0.101 Hypertension NO YES 1395 (39.2%) 2167(60.8%) 710(28.4%) 1791 (71.6%) <0.001* 672(30.4%) 1540(69.6%) 672(30.4%) 1540(69.6%) 0.999 Hyperlipemia NO YES 1517 (42.6%) 2045 (57.4%) 1016(40.6%) 1485(59.4%) 0.127 952(43.0%) 1260 (57.0%) 916(41.4%) 1286(58.6%) 0.273 Diabetes NO YES 2731 (76.7%) 831 (23.3%) 1645(65.8%) 856 (34.2%) <0.001* 1543 (69.8%) 669 (30.2%) 1544 (69.8%) 668 (30.2%) 0.974 BMI ≥ 28 <28 1969(55.3%) 1593(44.7%) 1152(46.1%) 1349(53.9%) <0.001* 1076(48.6%) 1136(51.4%) 1080(48.8%) 1132(51.2%) 0.904 Alcohol use NO YES 1053(29.6%) 2509(70.4%) 824(32.9%) 1677(67.1%) 0.005 697(31.5%) 1515(68.5%) 733(33.1%) 1479(66.9%) 0.247 Smoking NO YES 1630(45.8%) 1932(54.2%) 1203(48.1%) 1298(51.9%) 0.072 1048(47.4%) 1164(52.6%) 1055(47.7%) 1157(52.3%) 0.833 Depression NO YES 3234(90.8%) 328(9.2%) 2008(80.3%) 493(19.7%) <0.001* 1907(86.2%) 302(13.81%) 1890(85.4%) 322(14.6%) 0.464 3.2 Effect of OAB on survival time KM survival analysis demonstrated that patients with OAB had a significantly shorter median survival time (128.18 ± 1.56 months) compared to non-OAB patients (139.57 ± 1.26 months; P < 0.01) (Fig. 2 ). Subgroup analysis was used to determine the influence of OAB on survival time in different populations. The results showed that the survival time of OAB patients in male, female, 60–80 years old, Other Hispanic, Non-Hispanic White, people with high school education or above, high-income, low-income, hypertension, non-hypertension, diabetes, non-diabetes, hyperlipidemia, non-hyperlipidemia, drinking, non-drinking, obesity, non-smoking, depression and non-depression participants was significantly lower than that in the control group(Fig. 3 ). Univariate Cox regression identified several potential predictors of survival, including OAB, gender, age, Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, income, hypertension, diabetes, BMI, smoking(Fig. 4 ). The above variables were included in Lasso regression for variable screening. The results show that the model reached the best when lambdm = 0.01890989, and all the above variables were preserved(Fig. 5 ). Multivariate Cox regression results showed that OAB was an independent risk factor for predicting survival time(p<0.0001, HR = 1.214, CI95%=1.089ཞ1.353). To further mitigate potential biases and isolate the direct effect of OAB on survival, the study employed mediation analysis. This technique investigated whether OAB's influence on survival time operates through intermediate variables. The results showed that there were not significant mediation effect in the influence of OAB on survival time(Table 2 ). Table 2 Mediation analysis of OAB's influence on survival time. Mediator Total_effect Direct_effect Indirect_effect Mediation_proportion Gender 0.028481 0.028976 -0.000495 -0.017384 Age 0.028481 0.034706 -0.006225 -0.218555 Race 0.028481 0.028537 -0.000056 -0.001969 Education 0.028481 0.027501 0.000980 0.034420 Income 0.028481 0.028284 0.000197 0.006912 Hypertension 0.028481 0.028481 -0.000001 -0.000001 Hyperlipemia 0.028481 0.028949 -0.000468 -0.016440 Diabetes 0.028481 0.028507 -0.000026 -0.000912 Drinking 0.028481 0.027788 0.000693 0.024333 BMI 0.028481 0.028330 0.000151 0.005297 Smoking 0.028481 0.028725 -0.000244 -0.008556 Depression 0.028481 0.028439 0.000042 0.001489 3.3 Predictors of survival time of OAB patients Six machine learning algorithms, logistic Regression, Gradient Boosting, AdaBoost, Support Vector Machine, K-Nearest Neighbors and Gaussian Naive Bayes, were used to construct models to predict the influence of renal injury index on the survival time of OAB patients. Model accuracy, ROC curve, F1 score and Cross-Validation accuracy were used to determine the best model. Model accuracy results showed that Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Gaussian Naive Bayes, Gradient Boosting, AdaBoost’s accuracy were 0.6876, 0.6961, 0.7104, 0.7461, 0.7632, 0.7361; AUC were 0.7555, 0.7430, 0.6672, 0.7385, 0.7571, 0.7551; F1 score were 0.5646, 0.5680, 0.3596, 0.3986, 0.4610, 0.4201; Cross-Validation accuracy were 0.6040 ± 0.1287, 0.6083 ± 0.1000, 0.6708 ± 0.0500, 0.6387 ± 0.1888, 0.6207 ± 0.1877, 0.6339 ± 0.1755(Fig. 6 , 7 ). To sum up, Gradient Boosting was the best model to evaluate the influence of renal injury index on the survival time of OAB patients. Therefore, histogram was used to show the variables in model Gradient Boosting that affect the survival time of OAB patients(Fig. 8 ). The results show that urinary albumin, age, serum creatinine, eGFR, UACR were the variable that has the most significant influence on survival time. GAM was used to show the relationship between various indexes of renal injury and survival time. The results showed that there was a significant negative correlation between UACR and OAB patients' survival time, while other variables had more complicated nonlinear relationship with the survival time of OAB patients(Fig. 9 ). 4. Discussion Overactive bladder is a highly prevalent condition affecting millions worldwide, impacting both men and women [15] . This disorder severely compromises patients' quality of life. However, many older adults mistakenly perceive OAB as non-fatal, viewing its symptoms—such as urinary leakage—as deeply embarrassing. Consequently, treatment-seeking behavior among seniors is low, particularly in economically disadvantaged regions [16-17] . Emerging evidence highlights the potential significance of lifestyle factors in the development of OAB [8] . Consistent with previous research [18-20] , our study identified that OAB patients exhibit a higher prevalence of comorbidities like diabetes and hypertension, tend to be older, and are more likely to engage in unhealthy behaviors such as smoking. These baseline disparities typically contribute to a shorter survival time compared to control groups. To mitigate the influence of these confounders, we employed propensity score matching. Even after this rigorous adjustment, our results indicate that elderly OAB patients have a significantly shorter average survival time than their non-OAB counterparts, establishing OAB as an independent risk factor for mortality. Mediation analysis further supports a direct adverse effect of OAB on mortality, beyond its association with other diseases. While our findings indicate that OAB does not pose an immediate fatal risk, they do suggest a potential long-term impact on patient survival. The pathological hallmarks of OAB are well-established and include heightened spontaneous myogenic activity, fused tetanic contractions, altered stimulus responsiveness, and distinctive ultrastructural smooth muscle modifications [21] . A large number of experiments have proved that OAB bladder collagen/smooth muscle ratio increases, and with the development of time, bladder tissue will appear significant fibrosis. Urodynamic results also showed that OAB could lead to a significant increase in bladder participation in urine volume over time [22-24] . In addition，study has shown that OAB could increase the number of Cajal and telocytes cells, which can significantly increase the tension of bladder detrusor [25,26] . These alterations result in sustained elevation of detrusor pressure. Prolonged exposure to this high pressure initiates a cascade of events, including detrusor wall remodeling and eventual fibrosis [27] . Clinically, this progressive increase in bladder pressure becomes critical when it surpasses 40cmH₂O, at which point it significantly impairs renal function. Consequently, chronic renal injury may serve as a key determinant of survival in OAB patients. Furthermore, the association of OAB with malignancies such as prostate and bladder cancer, along with its propensity for complicated urinary tract infections, represents additional factors that likely contribute to reduced survival times [28, 29] . Considering that patients with OAB have a shorter survival time, predicting the survival time of patients with OAB through indicators can help clinicians to treat patients with high risk of death more actively.Renal function injury is an important factor affecting the survival time of patients. Serum urea nitrogen, creatinine, urinary protein, urinary creatinine and other indicators reflecting renal function may be able to predict the survival time of OAB patients well. Machine learning results showed that urinary albumin, serum creatinine, eGFR, UACR could predicted the survival time of OAB patients. The GAM results showed that, among them, only UACR was negatively correlated with the survival time of OAB patients. UACR is a sensitive index reflecting kidney injury and has been used in numerous studies to evaluate renal injury [30–32] . Therefore, UACR may be the best index to reflect the degree of renal injury in patients with OAB, and clinicians can detect patients with high-risk renal injury in time by detecting UACR. So as to carry out active treatment for them. Despite employing a robust analytical framework, this study has several limitations that deserve acknowledgment. Primarily, the inherent cross-sectional design of the NHANES dataset precludes definitive causal inferences, whereby longitudinal assessments and serial measurements would offer enhanced relationships. Secondly, the exclusive utilization of NHANES data inherently confines the study population to U.S. residents, thereby potentially limiting the generalizability of findings to non-U.S. populations. In addition, although the database contains mortality records attributed to renal disease, the paucity of such cases, coupled with extensive missing in associated covariates precluded meaningful statistical analyses within this subgroup. 5. Conclusions OAB could effect the long-term survival time of the elderly by damaging renal function, and UACR may be the potential index to predict the survival time of OAB patients. Declarations Acknowledgements We thank all the efforts made by the healthcare workers in NCHS and CDC for the NHANES database. Funding This research was funded by the the Science and Technology Department of Shanxi Province grants 202104041101035. Authors contributions Y.R. and D.L. have equal contribution to this work. Y.R. and D.L. wrote the main manuscript text. W. S., Y.R. and D.L. conceptualize the study. Y.R., D.L, H.J. and J.G. collected data together. All authors conducted statistical analysis and prepared pictures together. All authors have reviewed and approved the final version of the manuscript. Data Availability All data were uploaded with the manuscript. Competing interests The authors declare no competing interest. Ethics approval and consent to participate The NHANES protocol was approved by the National Center for Health Statistics and the Institutional Review Board. All participants provided written informed consent. Clinical trial number ： Not applicable. Consent for publication ： Not applicable. References Haylen BT, de Ridder D, Freeman RM, et al. 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Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 30 Nov, 2025 Reviewers agreed at journal 22 Nov, 2025 Reviewers invited by journal 10 Nov, 2025 Editor invited by journal 16 Oct, 2025 Editor assigned by journal 03 Oct, 2025 Submission checks completed at journal 29 Sep, 2025 First submitted to journal 29 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-7615139\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":546507727,\"identity\":\"a178d246-424a-4568-a7ee-ef56629e474d\",\"order_by\":0,\"name\":\"Yi Rong\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"First Hospital of Shanxi Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yi\",\"middleName\":\"\",\"lastName\":\"Rong\",\"suffix\":\"\"},{\"id\":546507728,\"identity\":\"a78b5d38-7c60-4b2a-a3c7-442f22f038c1\",\"order_by\":1,\"name\":\"Dingyang 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A:Model accuracy comparison; B:Model AUC comparison; C:Model F1 score comparison; D:Cross-Validation accuracy comparison.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7615139/v1/ecf1fc85ece47e489679c70b.png\"},{\"id\":96285873,\"identity\":\"785edb9f-d39c-4508-a56d-07597c4fe1f2\",\"added_by\":\"auto\",\"created_at\":\"2025-11-19 11:59:57\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":115315,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eROC curves of six kinds of machine learning algorithm models.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7615139/v1/3febabaa9a4244847d1b0a74.png\"},{\"id\":96365726,\"identity\":\"aac00d2e-7bee-40b8-9b80-ea526c8aa9df\",\"added_by\":\"auto\",\"created_at\":\"2025-11-20 10:10:44\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":109547,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePredictive ability of various indexes on survival time of OAB patients in Gradient boosting model.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7615139/v1/cc64fb0258d87272ba149305.png\"},{\"id\":96363660,\"identity\":\"808aed7a-cf1d-474f-a75f-3d3e31593e57\",\"added_by\":\"auto\",\"created_at\":\"2025-11-20 10:07:39\",\"extension\":\"png\",\"order_by\":9,\"title\":\"Figure 9\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":169836,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eRelationship between renal injury index and survival time of OAB patients.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"9.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7615139/v1/031da3d8489f09b61d47648b.png\"},{\"id\":96453245,\"identity\":\"d2b97440-9ff7-49d6-8b45-1e7baa7ccfaa\",\"added_by\":\"auto\",\"created_at\":\"2025-11-21 09:58:56\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1654602,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7615139/v1/e28ee6df-6f21-479b-b4d7-ea89c5d7e4f7.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Association between Renal Injury and Prognosis of Elderly Overactive Bladder Patients: A Study Based on NHANES Database\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eThe International Continence Society defines Overactive Bladder (OAB) syndrome as\\u003csup\\u003e[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]\\u003c/sup\\u003e \\u0026ldquo;the presence of urinary urgency, usually accompanied by frequency and nocturia, with or without urgency urinary incontinence\\u0026rdquo;. Globally, approximately 363\\u0026nbsp;million individuals across eight major countries\\u0026mdash;the United States, five European nations (France, Germany, Italy, Spain, and the United Kingdom), Japan, and China\\u0026mdash;lived with OAB in 2020, a figure projected to increase to 401.6\\u0026nbsp;million by 2030\\u003csup\\u003e[\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]\\u003c/sup\\u003e. This number is expected to rise to 401.6\\u0026nbsp;million by 2030\\u003csup\\u003e[\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]\\u003c/sup\\u003e.The prevalence of OAB rises with age and substantially impacts patients\\u0026rsquo; quality of life, sleep quality and mental health\\u003csup\\u003e[\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]\\u003c/sup\\u003e. While elderly OAB patients are conventionally managed with anticholinergic drugs or the β₃ receptor agonist mirabegron, sustained treatment often results in significant adverse effects, including cognitive impairment in this vulnerable population \\u003csup\\u003e[\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]\\u003c/sup\\u003e. A large number of studies have proved that OAB might be closely related to cardiovascular diseases, diabetes, hyperlipidemia and systemic inflammation\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR8\\\" citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]\\u003c/sup\\u003e. It suggests that the survival time of elderly OAB patients is likely to be different from their non-affected peers. However, there are few studies on the relationship between OAB and survival time in the elderly population.\\u003c/p\\u003e\\u003cp\\u003eThe contents of serum creatinine(SCr), serum urea nitrogen, serum uric acid, urine albumin and urine creatinine are traditional indicators reflecting renal function injury. Although there is no relevant data of glomerular filtration rate in National Health and Nutrition Examination Survey(NHANES) database, the estimated glomerular filtration rate(eGFR) based on serum creatinine content and corrected by sex, race and age has been proved to reflect renal function injury well\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR11\\\" citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]\\u003c/sup\\u003e. In addition, A large number of studies have proved that urinary albumin-to-creatinine ratio(UACR) can accurately predict the prognosis of many diseases\\u003csup\\u003e[\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]\\u003c/sup\\u003e. Given that advanced OAB frequently complicates with urinary tract infections and subsequent renal impairment, monitoring renal function\\u0026mdash;through urinary protein, creatinine, and serum levels of creatinine and urea nitrogen\\u0026mdash;could provide valuable prognostic information for these patients.\\u003c/p\\u003e\\u003cp\\u003eIn this study, we collected publicly available data from NHANES database, a nationally representative cross-sectional survey, to examine the impact of OAB on all-cause mortality in elderly population. In addition, the study also discussed the ability of a series of indicators of renal injury to predict the survival time of OAB patients.\\u003c/p\\u003e\"},{\"header\":\"2. Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.1 Study Population\\u003c/h2\\u003e\\u003cp\\u003eWe utilized publicly available data from the 2005\\u0026ndash;2018 NHANES database. Eligible participants met stringent criteria for completeness in two domains: (1) OAB Scoring Data: Full responses were required to validated symptom queries, including self-reported urgency incontinence episodes, their frequency, and nocturia severity; (2) All-cause mortality data: Mortality status through December 31, 2019, was confirmed via linkage to the National Death Index (NDI). The primary endpoint remained all-cause mortality.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.2 \\u003cb\\u003eCovariates\\u003c/b\\u003e\\u003c/h2\\u003e\\u003cp\\u003eWe considered several potential covariates for adjustment, including: Age Stratification: Three categories were created (60\\u0026ndash;70, 70\\u0026ndash;80, and \\u0026ge;\\u0026thinsp;80 years); Demographics: Self-identified race/ethnicity (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, Other Race) and highest educational attainment (\\u0026le;\\u0026thinsp;high school vs. \\u0026gt;high school); Income Status: Annual household income dichotomized at \\u003cspan\\u003e$\\u003c/span\\u003e20,000; Clinical Comorbidities: Hypertension (self-reported diagnosis or examination findings of mean systolic BP\\u0026thinsp;\\u0026ge;\\u0026thinsp;140 mmHg/diastolic BP\\u0026thinsp;\\u0026ge;\\u0026thinsp;90 mmHg); diabetes (self-reported diagnosis or HbA1c\\u0026thinsp;\\u0026ge;\\u0026thinsp;6.5%/FPG\\u0026thinsp;\\u0026ge;\\u0026thinsp;7.0 mmol/L); hyperlipidemia (self-reported diagnosis); Lifestyle Exposures: Lifetime tobacco use (\\u0026ge;\\u0026thinsp;100 cigarettes ever smoked); lifetime alcohol consumption (\\u0026ge;\\u0026thinsp;12 drinks consumed);\\u003c/p\\u003e\\u003cp\\u003eBMI and mental health: Obesity defined as BMI\\u0026thinsp;\\u0026ge;\\u0026thinsp;28 kg/m\\u0026sup2;; depression diagnosed via NHANES Mental Health Screener with score\\u0026thinsp;\\u0026ge;\\u0026thinsp;10.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.3 Renal injury index\\u003c/h2\\u003e\\u003cp\\u003eSerum creatinine, serum urea nitrogen, serum uric acid data was obtained from Laboratory Data\\u0026mdash;Standard Biochemistry Profile; Urine albumin and urine creatinine data was obtained from Laboratory Data\\u0026mdash;Albumin \\u0026amp; Creatinine - Urine.\\u003c/p\\u003e\\u003cp\\u003eUACR(mg/g)\\u0026thinsp;=\\u0026thinsp;Urine albumin(mg/dL)/Urine creatinine(g/d). UACR\\u0026thinsp;\\u0026le;\\u0026thinsp;30 is defined as normal renal function;30\\u0026lt;UACR\\u0026lt;300 is defined as mild renal injury༛UACR\\u0026thinsp;\\u0026ge;\\u0026thinsp;300 is defined as heavy renal injury.\\u003c/p\\u003e\\u003cp\\u003eeGFR(mL/min/1.73m\\u003csup\\u003e2\\u003c/sup\\u003e)\\u0026thinsp;=\\u0026thinsp;141\\u0026times;min(SCr/k,1)\\u003csup\\u003ea\\u003c/sup\\u003e\\u0026times;max(SCr/k,1)\\u003csup\\u003e\\u0026minus;1.209\\u003c/sup\\u003e\\u0026times;0.993\\u003csup\\u003eage\\u003c/sup\\u003e\\u0026times;(1.018 if women) \\u0026times;(1.159 if black). Female: k\\u0026thinsp;=\\u0026thinsp;0.7, a=-0.329; Male༚k\\u0026thinsp;=\\u0026thinsp;0.9, a=-0.411. Categorized as eGFR\\u0026lt;15, 15\\u0026lt;eGFR\\u0026thinsp;\\u0026le;\\u0026thinsp;30, 30\\u0026lt;eGFR\\u0026thinsp;\\u0026le;\\u0026thinsp;60, 60\\u0026lt;eGFR\\u0026thinsp;\\u0026le;\\u0026thinsp;90, eGFR\\u0026gt;90.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.4 Statistical Analysis\\u003c/h2\\u003e\\u003cp\\u003eData analysis was performed using IBM SPSS Statistics 25 and R 4.2.2. Baseline characteristics are summarized with means (\\u0026plusmn;\\u0026thinsp;standard deviations) or medians (interquartile ranges) for continuous variables, and percentages for categorical variables, depending on their distribution. Propensity score matching was used to control the population baseline difference between OAB group and control group. Survival outcomes were analyzed using the Kaplan-Meier(KM) method. Predictors of survival were identified through a three-stage process: initial univariate Cox regression, feature selection via Lasso regression, and confirmation with multivariate Cox regression. Subgroup analysis and mediation analysis were used to further determine the relationship between OAB and survival time.\\u003c/p\\u003e\\u003cp\\u003eSix machine learning algorithms, logistic Regression, Gradient Boosting, AdaBoost, Support Vector Machine, K-Nearest Neighbors and Gaussian Naive Bayes, were used to predict the predictive ability of various indexes of renal injury on the survival time of OAB patients. ROC curve, F1 score and Cross-Validation accuracy were used to determine the best model. Generalized Additive Model (GAM) was used to show the correlation between various indexes of renal injury and survival time of OAB patients.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.1 Baseline Characteristics of Study Participants\\u003c/h2\\u003e\\u003cp\\u003eA total of 6063 participants were included in the final analysis (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). The overall prevalence of OAB among all participants was 41.25%. Compared with the normal population, OAB patients tended to be older, have lower education level and annual family income, and have higher prevalence rates of hypertension, diabetes, depression and obesity. In addition, there were obvious differences in race composition between OAB patients and the normal population(Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). In view of these substantial baseline differences between the two groups, this study conducted a 1:1 propensity score matching for OAB to reduce the population baseline differences. After matching the propensity score, there was no statistical difference between OAB group and control group(Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Subsequent analyses were then conducted using the matched cohort .\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eBaseline characteristics of all participants\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"8\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003eData before Tendency score matching\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c8\\\" namest=\\\"c6\\\"\\u003e\\u003cp\\u003eData after Tendency score matching\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eControl groups\\u003c/p\\u003e\\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;3562)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eOAB groups\\u003c/p\\u003e\\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;2501)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eP Value\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eControl groups\\u003c/p\\u003e\\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;2212)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eOAB groups\\u003c/p\\u003e\\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;2212)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003eP Value\\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\\u003cp\\u003eMale\\u003c/p\\u003e\\u003cp\\u003eFemale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1614(45.3%)\\u003c/p\\u003e\\u003cp\\u003e1948(54.7%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1078(43.1%)\\u003c/p\\u003e\\u003cp\\u003e1423(56.9%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.088\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e983(44.4%)\\u003c/p\\u003e\\u003cp\\u003e1229(56.6%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e972(43.9%)\\u003c/p\\u003e\\u003cp\\u003e1304(56.1%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.739\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRace\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMexican American\\u003c/p\\u003e\\u003cp\\u003eOther Hispanic\\u003c/p\\u003e\\u003cp\\u003eNon-Hispanic White\\u003c/p\\u003e\\u003cp\\u003eNon-Hispanic Black\\u003c/p\\u003e\\u003cp\\u003eOther Race\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e338(9.5%)\\u003c/p\\u003e\\u003cp\\u003e328(9.2%)\\u003c/p\\u003e\\u003cp\\u003e2038(57.2%)\\u003c/p\\u003e\\u003cp\\u003e607(17.0%)\\u003c/p\\u003e\\u003cp\\u003e251(7.0%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e297(11.9%)\\u003c/p\\u003e\\u003cp\\u003e242(9.7%)\\u003c/p\\u003e\\u003cp\\u003e1238(49.5%)\\u003c/p\\u003e\\u003cp\\u003e583(23.3%)\\u003c/p\\u003e\\u003cp\\u003e141(5.6%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;0.001*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e228(10.3%)\\u003c/p\\u003e\\u003cp\\u003e210(9.5%)\\u003c/p\\u003e\\u003cp\\u003e1184(53.5%)\\u003c/p\\u003e\\u003cp\\u003e465(21.0%)\\u003c/p\\u003e\\u003cp\\u003e120(5.7%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e240(10.8%)\\u003c/p\\u003e\\u003cp\\u003e214(9.7%)\\u003c/p\\u003e\\u003cp\\u003e1157(52.3%)\\u003c/p\\u003e\\u003cp\\u003e469(21.2%)\\u003c/p\\u003e\\u003cp\\u003e132(6.0%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.930\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAge rank\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e60\\u0026ndash;70\\u003c/p\\u003e\\u003cp\\u003e70\\u0026ndash;80\\u003c/p\\u003e\\u003cp\\u003e\\u0026ge;\\u0026thinsp;80\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1963(55.1%)\\u003c/p\\u003e\\u003cp\\u003e1043(29.3%)\\u003c/p\\u003e\\u003cp\\u003e556(15.6%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1101(44.0%)\\u003c/p\\u003e\\u003cp\\u003e844(33.7%)\\u003c/p\\u003e\\u003cp\\u003e556(22.2%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;0.001*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e988(44.7%)\\u003c/p\\u003e\\u003cp\\u003e730(33.0%)\\u003c/p\\u003e\\u003cp\\u003e494(22.3%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1020(46.1%)\\u003c/p\\u003e\\u003cp\\u003e738(33.4%)\\u003c/p\\u003e\\u003cp\\u003e454(20%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.326\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eAge\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e69.37\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.92\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e71.02\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7.05\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.049\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e70.84\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7.20\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e70.72\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7.03\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.119\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAnnual Family Incomes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u0026le;\\u0026thinsp;20000\\u003c/p\\u003e\\u003cp\\u003e\\u0026gt;20000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e896(25.2%)\\u003c/p\\u003e\\u003cp\\u003e2666 (74.8%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e828 (33.1%)\\u003c/p\\u003e\\u003cp\\u003e1673(66.9%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;0.001*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e685(31.0%)\\u003c/p\\u003e\\u003cp\\u003e1527 (69.0%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e697 (31.2%)\\u003c/p\\u003e\\u003cp\\u003e1547(68.8%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.845\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEducation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eEducation below senior high school\\u003c/p\\u003e\\u003cp\\u003eHigh school education or above\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e825(23.2%)\\u003c/p\\u003e\\u003cp\\u003e2737 (76.8%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e847 (33.9%)\\u003c/p\\u003e\\u003cp\\u003e1654 (66.1%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;0.001*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" 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align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;0.001*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e672(30.4%)\\u003c/p\\u003e\\u003cp\\u003e1540(69.6%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e672(30.4%)\\u003c/p\\u003e\\u003cp\\u003e1540(69.6%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.999\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHyperlipemia\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNO\\u003c/p\\u003e\\u003cp\\u003eYES\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1517 (42.6%)\\u003c/p\\u003e\\u003cp\\u003e2045 (57.4%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1016(40.6%)\\u003c/p\\u003e\\u003cp\\u003e1485(59.4%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.127\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e952(43.0%)\\u003c/p\\u003e\\u003cp\\u003e1260 (57.0%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e916(41.4%)\\u003c/p\\u003e\\u003cp\\u003e1286(58.6%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.273\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDiabetes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNO\\u003c/p\\u003e\\u003cp\\u003eYES\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2731 (76.7%)\\u003c/p\\u003e\\u003cp\\u003e831 (23.3%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1645(65.8%)\\u003c/p\\u003e\\u003cp\\u003e856 (34.2%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;0.001*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1543 (69.8%)\\u003c/p\\u003e\\u003cp\\u003e669 (30.2%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1544 (69.8%)\\u003c/p\\u003e\\u003cp\\u003e668 (30.2%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.974\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eBMI\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u0026ge;\\u0026thinsp;28\\u003c/p\\u003e\\u003cp\\u003e\\u0026lt;28\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1969(55.3%)\\u003c/p\\u003e\\u003cp\\u003e1593(44.7%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1152(46.1%)\\u003c/p\\u003e\\u003cp\\u003e1349(53.9%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;0.001*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1076(48.6%)\\u003c/p\\u003e\\u003cp\\u003e1136(51.4%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1080(48.8%)\\u003c/p\\u003e\\u003cp\\u003e1132(51.2%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.904\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAlcohol use\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNO\\u003c/p\\u003e\\u003cp\\u003eYES\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1053(29.6%)\\u003c/p\\u003e\\u003cp\\u003e2509(70.4%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e824(32.9%)\\u003c/p\\u003e\\u003cp\\u003e1677(67.1%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.005\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e697(31.5%)\\u003c/p\\u003e\\u003cp\\u003e1515(68.5%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e733(33.1%)\\u003c/p\\u003e\\u003cp\\u003e1479(66.9%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.247\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSmoking\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNO\\u003c/p\\u003e\\u003cp\\u003eYES\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1630(45.8%)\\u003c/p\\u003e\\u003cp\\u003e1932(54.2%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1203(48.1%)\\u003c/p\\u003e\\u003cp\\u003e1298(51.9%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.072\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1048(47.4%)\\u003c/p\\u003e\\u003cp\\u003e1164(52.6%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1055(47.7%)\\u003c/p\\u003e\\u003cp\\u003e1157(52.3%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.833\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDepression\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNO\\u003c/p\\u003e\\u003cp\\u003eYES\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3234(90.8%)\\u003c/p\\u003e\\u003cp\\u003e328(9.2%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2008(80.3%)\\u003c/p\\u003e\\u003cp\\u003e493(19.7%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;0.001*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1907(86.2%)\\u003c/p\\u003e\\u003cp\\u003e302(13.81%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1890(85.4%)\\u003c/p\\u003e\\u003cp\\u003e322(14.6%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.464\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.2 Effect of OAB on survival time\\u003c/h2\\u003e\\u003cp\\u003eKM survival analysis demonstrated that patients with OAB had a significantly shorter median survival time (128.18\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.56 months) compared to non-OAB patients (139.57\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.26 months; P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Subgroup analysis was used to determine the influence of OAB on survival time in different populations. The results showed that the survival time of OAB patients in male, female, 60\\u0026ndash;80 years old, Other Hispanic, Non-Hispanic White, people with high school education or above, high-income, low-income, hypertension, non-hypertension, diabetes, non-diabetes, hyperlipidemia, non-hyperlipidemia, drinking, non-drinking, obesity, non-smoking, depression and non-depression participants was significantly lower than that in the control group(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eUnivariate Cox regression identified several potential predictors of survival, including OAB, gender, age, Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, income, hypertension, diabetes, BMI, smoking(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). The above variables were included in Lasso regression for variable screening. The results show that the model reached the best when lambdm\\u0026thinsp;=\\u0026thinsp;0.01890989, and all the above variables were preserved(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). Multivariate Cox regression results showed that OAB was an independent risk factor for predicting survival time(p\\u0026lt;0.0001, HR\\u0026thinsp;=\\u0026thinsp;1.214, CI95%=1.089ཞ1.353). To further mitigate potential biases and isolate the direct effect of OAB on survival, the study employed mediation analysis. This technique investigated whether OAB's influence on survival time operates through intermediate variables. The results showed that there were not significant mediation effect in the influence of OAB on survival time(Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\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\\u003eMediation analysis of OAB's influence on survival time.\\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\\u003eMediator\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eTotal_effect\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eDirect_effect\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eIndirect_effect\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eMediation_proportion\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGender\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.028481\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.028976\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-0.000495\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e-0.017384\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAge\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.028481\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.034706\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-0.006225\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e-0.218555\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRace\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.028481\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.028537\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-0.000056\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e-0.001969\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEducation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.028481\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.027501\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.000980\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.034420\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eIncome\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.028481\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.028284\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.000197\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.006912\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHypertension\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.028481\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.028481\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-0.000001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e-0.000001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHyperlipemia\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.028481\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.028949\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-0.000468\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e-0.016440\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDiabetes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.028481\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.028507\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-0.000026\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e-0.000912\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDrinking\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.028481\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.027788\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.000693\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.024333\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eBMI\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.028481\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.028330\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.000151\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.005297\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSmoking\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.028481\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.028725\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-0.000244\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e-0.008556\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDepression\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.028481\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.028439\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.000042\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.001489\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.3 Predictors of survival time of OAB patients\\u003c/h2\\u003e\\u003cp\\u003eSix machine learning algorithms, logistic Regression, Gradient Boosting, AdaBoost, Support Vector Machine, K-Nearest Neighbors and Gaussian Naive Bayes, were used to construct models to predict the influence of renal injury index on the survival time of OAB patients. Model accuracy, ROC curve, F1 score and Cross-Validation accuracy were used to determine the best model. Model accuracy results showed that Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Gaussian Naive Bayes, Gradient Boosting, AdaBoost\\u0026rsquo;s accuracy were 0.6876, 0.6961, 0.7104, 0.7461, 0.7632, 0.7361; AUC were 0.7555, 0.7430, 0.6672, 0.7385, 0.7571, 0.7551; F1 score were 0.5646, 0.5680, 0.3596, 0.3986, 0.4610, 0.4201; Cross-Validation accuracy were 0.6040\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.1287, 0.6083\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.1000, 0.6708\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0500, 0.6387\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.1888, 0.6207\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.1877, 0.6339\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.1755(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eTo sum up, Gradient Boosting was the best model to evaluate the influence of renal injury index on the survival time of OAB patients. Therefore, histogram was used to show the variables in model Gradient Boosting that affect the survival time of OAB patients(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e). The results show that urinary albumin, age, serum creatinine, eGFR, UACR were the variable that has the most significant influence on survival time. GAM was used to show the relationship between various indexes of renal injury and survival time. The results showed that there was a significant negative correlation between UACR and OAB patients' survival time, while other variables had more complicated nonlinear relationship with the survival time of OAB patients(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eOveractive bladder is a highly prevalent condition affecting millions worldwide, impacting both men and women\\u003csup\\u003e[15]\\u003c/sup\\u003e. This disorder severely compromises patients' quality of life. However, many older adults mistakenly perceive OAB as non-fatal, viewing its symptoms—such as urinary leakage—as deeply embarrassing. Consequently, treatment-seeking behavior among seniors is low, particularly in economically disadvantaged regions \\u003csup\\u003e[16-17]\\u003c/sup\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003eEmerging evidence highlights the potential significance of lifestyle factors in the development of OAB\\u003csup\\u003e[8]\\u003c/sup\\u003e. Consistent with previous research\\u003csup\\u003e[18-20]\\u003c/sup\\u003e, our study identified that OAB patients exhibit a higher prevalence of comorbidities like diabetes and hypertension, tend to be older, and are more likely to engage in unhealthy behaviors such as smoking. These baseline disparities typically contribute to a shorter survival time compared to control groups. To mitigate the influence of these confounders, we employed propensity score matching. Even after this rigorous adjustment, our results indicate that elderly OAB patients have a significantly shorter average survival time than their non-OAB counterparts, establishing OAB as an independent risk factor for mortality. Mediation analysis further supports a direct adverse effect of OAB on mortality, beyond its association with other diseases.\\u003c/p\\u003e\\n\\u003cp\\u003eWhile our findings indicate that OAB does not pose an immediate fatal risk, they do suggest a potential long-term impact on patient survival. The pathological hallmarks of OAB are well-established and include heightened spontaneous myogenic activity, fused tetanic contractions, altered stimulus responsiveness, and distinctive ultrastructural smooth muscle modifications\\u003csup\\u003e[21]\\u003c/sup\\u003e. A large number of experiments have proved that OAB bladder collagen/smooth muscle ratio increases, and with the development of time, bladder tissue will appear significant fibrosis. Urodynamic results also showed that OAB could lead to a significant increase in bladder participation in urine volume over time\\u003csup\\u003e[22-24]\\u003c/sup\\u003e. In addition，study has shown that OAB could increase the number of Cajal and telocytes cells, which can significantly increase the tension of bladder detrusor\\u003csup\\u003e[25,26]\\u003c/sup\\u003e. These alterations result in sustained elevation of detrusor pressure. Prolonged exposure to this high pressure initiates a cascade of events, including detrusor wall remodeling and eventual fibrosis\\u003csup\\u003e[27]\\u003c/sup\\u003e. Clinically, this progressive increase in bladder pressure becomes critical when it surpasses 40cmH₂O, at which point it significantly impairs renal function. Consequently, chronic renal injury may serve as a key determinant of survival in OAB patients. Furthermore, the association of OAB with malignancies such as prostate and bladder cancer, along with its propensity for complicated urinary tract infections, represents additional factors that likely contribute to reduced survival times \\u003csup\\u003e[28, 29]\\u003c/sup\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003eConsidering that patients with OAB have a shorter survival time, predicting the survival time of patients with OAB through indicators can help clinicians to treat patients with high risk of death more actively.Renal function injury is an important factor affecting the survival time of patients. Serum urea nitrogen, creatinine, urinary protein, urinary creatinine and other indicators reflecting renal function may be able to predict the survival time of OAB patients well. Machine learning results showed that urinary albumin, serum creatinine, eGFR, UACR could predicted the survival time of OAB patients. The GAM results showed that, among them, only UACR was negatively correlated with the survival time of OAB patients. UACR is a sensitive index reflecting kidney injury and has been used in numerous studies to evaluate renal injury\\u003csup\\u003e[30–32]\\u003c/sup\\u003e. Therefore, UACR may be the best index to reflect the degree of renal injury in patients with OAB, and clinicians can detect patients with high-risk renal injury in time by detecting UACR. So as to carry out active treatment for them.\\u003c/p\\u003e\\n\\u003cp\\u003eDespite employing a robust analytical framework, this study has several limitations that deserve acknowledgment. Primarily, the inherent cross-sectional design of the NHANES dataset precludes definitive causal inferences, whereby longitudinal assessments and serial measurements would offer enhanced relationships. Secondly, the exclusive utilization of NHANES data inherently confines the study population to U.S. residents, thereby potentially limiting the generalizability of findings to non-U.S. populations. In addition, although the database contains mortality records attributed to renal disease, the paucity of such cases, coupled with extensive missing in associated covariates precluded meaningful statistical analyses within this subgroup.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"5. Conclusions\",\"content\":\"\\u003cp\\u003eOAB could effect the long-term survival time of the elderly by damaging renal function, and UACR may be the potential index to predict the survival time of OAB patients.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe thank all the efforts made by the healthcare workers in NCHS and CDC for\\u003c/p\\u003e\\n\\u003cp\\u003ethe NHANES database.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis research was funded by the the Science and Technology Department of Shanxi Province grants 202104041101035.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eY.R. and \\u0026nbsp; D.L. have equal contribution to this work. Y.R. and D.L. wrote the main manuscript text. W. S., Y.R. and D.L. conceptualize the study. Y.R., D.L, H.J. and J.G. collected data together. All authors conducted statistical analysis and prepared pictures together. All authors have reviewed and approved the final version of the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll data were uploaded with the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing interest.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe NHANES protocol was approved by the National Center for Health Statistics and the Institutional Review Board. All participants provided written informed consent.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eClinical trial number\\u003c/strong\\u003e\\u003cstrong\\u003e：\\u003c/strong\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003cstrong\\u003e：\\u003c/strong\\u003eNot applicable.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n \\u003cli\\u003eHaylen BT, de Ridder D, Freeman RM, et al. An International Urogynecological Association (IUGA)/International Continence Society (ICS) joint report on the terminology for female pelvic floor dysfunction[J]. Int Urogynecol J, 2010, 21(1): 5-26.\\u003c/li\\u003e\\n \\u003cli\\u003eZhang L, Cai N, Mo L, et al. Global Prevalence of Overactive Bladder: A Systematic Review and Meta-analysis[J]. Int Urogynecol J, 2025, Online ahead of print.\\u003c/li\\u003e\\n \\u003cli\\u003eStewart WF, Van Rooyen JB, Cundiff GW, et al. Prevalence and burden of overactive bladder in the United States[J]. World J. Urol, 2003, 20(3):327\\u0026ndash;336.\\u003c/li\\u003e\\n \\u003cli\\u003eLai HH, Walker D, Elsouda D, et al. Sleep disturbance among adults with overactive bladder: A cross-sectional survey[J]. Urology, 2023, 179: 23\\u0026ndash;31.\\u003c/li\\u003e\\n \\u003cli\\u003eTan CM, Juurlink D. Overactive bladder drugs and the risk of dementia. BMJ Med, 2025, 4(1):e001520.\\u003c/li\\u003e\\n \\u003cli\\u003ePark J, Chang Y, Choi HR, et al. Overactive bladder and cognitive impairment in middle-aged women: A cross-sectional study[J]. Maturitas, 2024, 187:108042.\\u003c/li\\u003e\\n \\u003cli\\u003eWei B, Zhao Y, Lin P, et al. The association between overactive bladder and systemic immunity-inflammation index: a cross-sectional study of NHANES 2005 to 2018[J]. Sci Rep, 2024, 14(1):12579.\\u003c/li\\u003e\\n \\u003cli\\u003eLi Z, Liu X, Li Y, et al. Association between cardiovascular health and overactive bladder[J]. Sci Rep, 2025, 15(1):5760.\\u003c/li\\u003e\\n \\u003cli\\u003eHe Q, Wu L, Deng C, et al. Diabetes mellitus, systemic inflammation and overactive bladder[J]. Front Endocrinol (Lausanne), 2024, 15:1386639.\\u003c/li\\u003e\\n \\u003cli\\u003eMazidi M, Mikhailidis DP, Dehghan A, et al. The association between coffee and caffeine consumption and renal function: insight from individual-level data, Mendelian randomization, and meta-analysis[J]. Arch Med Sci, 2021,18(4):900-911.\\u003c/li\\u003e\\n \\u003cli\\u003eDuggal V, Thomas IC, Montez-Rath ME, et al. National Estimates of CKD Prevalence and Potential Impact of Estimating Glomerular Filtration Rate Without Race[J]. J Am Soc Nephrol, 2021, 32(6):1454-1463.\\u003c/li\\u003e\\n \\u003cli\\u003eAdair KE, Bowden RG, Funderburk LK, et al. Metabolic Health, Obesity, and Renal Function: 2013-2018 National Health and Nutrition Examination Surveys[J]. Life (Basel), 2021,11(9):888.\\u003c/li\\u003e\\n \\u003cli\\u003ePeng J, Zhang Y, Zhu Y, et al. Estimated glucose disposal rate for predicting cardiovascular events and mortality in patients with non-diabetic chronic kidney disease: a prospective cohort study[J]. BMC Med, 2024, 22(1):411.\\u003c/li\\u003e\\n \\u003cli\\u003eWang Z, Chen Z, Zhuang H. Association between urinary albumin-to-creatinine ratio and all-cause and cardiovascular-cause mortality among MASLD: NHANES 2001-2018[J]. Front Nutr, 2025, 12:1528732.\\u003c/li\\u003e\\n \\u003cli\\u003eIrwin DE, Milsom I, Hunskaar S, et al. Population-based survey of urinary incontinence, overactive bladder, and other lower urinary tract symptoms in five countries: Results of the EPIC study[J]. Eur. Urol, 2006, 50(6):1306\\u0026ndash;1314\\u003c/li\\u003e\\n \\u003cli\\u003eReynolds WS, Fowke J, Dmochowski R. The Burden of Overactive Bladder on US Public Health[J]. Curr Bladder Dysfunct Rep, 2016, 11(1):8-13.\\u003c/li\\u003e\\n \\u003cli\\u003eYoo ES, Kim BS, Kim DY, Oh SJ, Kim JC. The impact of overactive bladder on health-related quality of life, sexual life and psychological health in Korea[J]. Int Neurourol J, 2011, 15(3):143-151.\\u003c/li\\u003e\\n \\u003cli\\u003eHui Z, Zewu Z, Yang L, Yu C. Association between weight-adjusted waist index and overactive bladder: a cross-sectional study based on 2009-2018 NHANES[J]. Front Nutr, 2024, 11:1423148.\\u003c/li\\u003e\\n \\u003cli\\u003ePark J, Chang Y, Kim JH, et al. Menopausal stages and overactive bladder symptoms in middle-aged women: A cross-sectional study[J]. BJOG, 2024, 131(13):1805-1814.\\u003c/li\\u003e\\n \\u003cli\\u003eZhang Y, Wu X, Liu G, et al. Association between overactive bladder and depression in American adults: A cross-sectional study from NHANES 2005-2018[J]. J Affect Disord, 2024, 356:545-553.\\u003c/li\\u003e\\n \\u003cli\\u003eSteers WD. Pathophysiology of overactive bladder and urge urinary incontinence[J]. Rev Urol, 2002,Suppl 4:S7-S18.\\u003c/li\\u003e\\n \\u003cli\\u003eAkan S, Tavuk\\u0026ccedil;u HH, Sogut I et al. Urethral monopolar cauterization: alternative infravesical obstruction model in male rats[J]. Rev Assoc Med Bras, 2022, 68(8):1084-1089.\\u003c/li\\u003e\\n \\u003cli\\u003eKim WH, Bae WJ, Park JW, et al. Development of an Improved Animal Model of overactive bladder: Transperineal Ligation versus Transperitoneal Ligation in male rats[J]. World J Mens Health, 2016, 34(2):137-144.\\u003c/li\\u003e\\n \\u003cli\\u003ePatra PB, Patra S. Research findings on overactive bladder[J]. Curr Urol, 2015, 8(1):1\\u0026ndash;21.\\u003c/li\\u003e\\n \\u003cli\\u003eKubota Y, Hashitani H, Shirasawa N, et al. Altered distribution of interstitial cells in the guinea pig bladder following bladder outlet obstruction[J]. Neurourol Urodyn, 2008, 27(4):330\\u0026ndash;340.\\u003c/li\\u003e\\n \\u003cli\\u003eWishahi M, Hassan S, Kamal N, et al. Is bladder outlet obstruction rat model to induce overactive bladder (OAB) has similarity to human OAB? Research on the events in smooth muscle, collagen, interstitial cell and telocyte distribution[J]. BMC Res Notes, 2024, 17(1):22.\\u003c/li\\u003e\\n \\u003cli\\u003eMorlacco A, Mancini M, Soligo M, Zattoni F, et al. Relevance of the Endoscopic Evaluation in the Diagnosis of Bladder Pain Syndrome/Interstitial Cystitis[J]. Urology, 2020, 144: 106-110.\\u003c/li\\u003e\\n \\u003cli\\u003eKhan A, Crump RT, Carlson KV, Baverstock RJ. The relationship between overactive bladder and prostate cancer: A scoping review[J]. Can Urol Assoc J, 2021, 15(9):E501-E509.\\u003c/li\\u003e\\n \\u003cli\\u003eNik-Ahd F, Lenore Ackerman A, Anger J. Recurrent Urinary Tract Infections in Females and the Overlap with Overactive Bladder. Curr Urol Rep, 2018, 19(11):94.\\u003c/li\\u003e\\n \\u003cli\\u003eHuang J, Li H, Yang X, et al. The relationship between dietary inflammatory index (DII) and early renal injury in population with/without hypertension: analysis of the National health and nutrition examination survey 2001-2002[J]. Ren Fail, 2024, 46(1):2294155.\\u003c/li\\u003e\\n \\u003cli\\u003eYin G, Zhao S, Zhao M, et al. Complex interplay of heavy metals and renal injury: New perspectives from longitudinal epidemiological evidence[J]. Ecotoxicol Environ Saf, 2024, 278:116424.\\u003c/li\\u003e\\n \\u003cli\\u003eChiang CH, Lan TY, Hsieh JH, et al. Diosgenin Reduces Acute Kidney Injury and Ameliorates the Progression to Chronic Kidney Disease by Modifying the NOX4/p65 Signaling Pathways[J]. J Agric Food Chem, 2024, 72(31):17444-17454.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-geriatrics\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"bgtc\",\"sideBox\":\"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/bgtc/default.aspx\",\"title\":\"BMC Geriatrics\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Overactive bladder, Renal injury, UACR, eGFR, Nhanes, Machine learning\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7615139/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7615139/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cb\\u003eBackground\\u003c/b\\u003e The prevalence of overactive bladder(OAB) rises with age and substantially impacts patients\\u0026rsquo; quality of life, sleep quality and mental health. However, many older adults perceive OAB as non-fatal, treatment-seeking behavior among seniors is low, particularly in economically disadvantaged regions. The aim of this study was to explore the influence of OAB on the survival time of the elderly and the relationship between renal injury index and survival time of OAB patients.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eMethods\\u003c/b\\u003e A total of 6065 participants from the National health and Nutrition examination survey(Nhanes) were included in the study. Tendency score matching is used to control data bias. Predictors of survival were identified through a three-stage process: initial univariate Cox regression, feature selection via Lasso regression, and confirmation with multivariate Cox regression. Mediating analysis was used to confirm whether OAB can independently affect survival time. Six machine learning algorithms, logistic Regression, Gradient Boosting, AdaBoost, Support Vector Machine, K-Nearest Neighbors, Gaussian Naive Bayes, and generalized additive model(GAM) were used to evaluate the correlation between renal injury index and prognosis of OAB patients.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eResults\\u003c/b\\u003e Under the control of other variables, the survival time of OAB patients was still shorter than normal people. OAB was an independent risk factor affecting the survival time of the elderly population, and the influence of OAB on the survival time did not depend on other covariates. Machine learning results showed that urinary albumin, serum creatinine, glomerular filtration rate(eGFR), urinary albumin-to-creatinine ratio(UACR) could predicted the survival time of OAB patients. The GAM results showed that UACR was negatively correlated with the survival time of OAB patients.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eConclusion\\u003c/b\\u003e OAB could effect the long-term survival time of the elderly by damaging renal function, and UACR may be the potential index to predict the survival time of OAB patients.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Association between Renal Injury and Prognosis of Elderly Overactive Bladder Patients: A Study Based on NHANES Database\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-11-19 11:59:52\",\"doi\":\"10.21203/rs.3.rs-7615139/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-12-01T00:43:02+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"111925774783551229907658042718677259714\",\"date\":\"2025-11-22T15:43:39+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-11-10T08:08:05+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2025-10-16T06:13:12+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-10-03T17:43:21+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-09-30T02:19:33+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Geriatrics\",\"date\":\"2025-09-30T02:14:21+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-geriatrics\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"bgtc\",\"sideBox\":\"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/bgtc/default.aspx\",\"title\":\"BMC Geriatrics\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"66420131-bfb8-4534-9b9d-12a35c1b3c77\",\"owner\":[],\"postedDate\":\"November 19th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-11-19T11:59:52+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-11-19 11:59:52\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7615139\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7615139\",\"identity\":\"rs-7615139\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}