Relationship Between SII Levels and OAB Incidence- A Cross-Sectional Study Based on NHANES 2005-2020

Relationship Between SII Levels and OAB Incidence- A Cross-Sectional Study Based on NHANES 2005-2020 | 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 Article Relationship Between SII Levels and OAB Incidence- A Cross-Sectional Study Based on NHANES 2005-2020 Changfeng Zhao, Xinyu Zhang, Yunkai Yang, Mengqi Wu, Dahong Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3978651/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 Objective This study aimed to investigate the relationship between the Systemic Inflammatory Index (SII) among US adults from 2005 to 2020 and the incidence of Overactive Bladder (OAB). Additionally, a predictive model for OAB risk was constructed based on SII. Methods This cross-sectional study involved 11,427 participants from the National Health and Nutrition Examination Survey (NHANES) spanning from 2005 to 2020. SII was calculated based on peripheral blood results, while data on urinary conditions were assessed using the Overactive Bladder Symptom Score (OABSS) to determine the presence of OAB. Participants were stratified into two groups based on the presence or absence of OAB, and baseline characteristics were compared. Then, the correlation between SII and OAB was explored using univariate and multivariate logistic regression analyses coupled with smoothed curve fitting. A multifactorial Logistic Regression model was established by selecting variables associated with OAB incidence, incorporating clinical significance, and constructing a nomogram. Lastly, the predictive ability of the nomogram for identifying OAB was evaluated using ROC curves, calibration curves, and Decision Curve Analysis (DCA). Results Among the examined 11,427 samples, individuals with OAB exhibited higher SII levels (475.59 (326.49–683.89) vs. 435.00 (315.00–615.57), p < 0.01). Both univariate and multivariate Logistic Regression analyses revealed a positive correlation between SII/1000 and the incidence of OAB. Moreover, smooth curve fitting demonstrated a non-linear positive correlation between OAB and SII/1000 (P-nonlinear < 0.05). Finally, our established nomogram could predict the risk of OAB (AUC = 0.754), holding clinical decision-making significance. Conclusion A positive correlation was identified between SII and the risk of OAB among American adults, highlighting the predictive value of SII for OAB incidence. However, larger-scale prospective studies are warranted to validate our findings. Biological sciences/Immunology/Inflammation Biological sciences/Immunology Health sciences/Urology Health sciences/Urology/Bladder Overactive Bladder (OAB) Systemic Inflammatory Index (SII)༛NHANES Cross-sectional study Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Overactive Bladder (OAB) refers to a symptom complex primarily characterized by the urgency of urination without the presence of urinary tract infections and other pathological alterations. As is well documented, its chief clinical manifestations include urinary frequency and nocturia, with or without urgency urinary incontinence [1] . These symptoms significantly affect patients' daily lives, causing considerable discomfort and distress. To date, its etiology and risk factors remain elusive, and current research encompasses various factors and theories, including neurotransmitters, hormonal imbalances, and others [2–5] . In the context of urological diseases, Wanjian Gu et al. reported that Systemic Inflammatory Index (SII) levels could be utilized for predicting the risk of urinary tract infections and determining the causative pathogen [10] . Additionally, in urological malignancies such as bladder cancer [11] and renal cell carcinoma [12] , among others, SII has been demonstrated to be associated with patient prognosis. However, there is a current paucity of research exploring the connection between SII and Overactive Bladder (OAB). Therefore, considering the significant role of SII in other urological conditions, we hypothesized a potential association between SII levels and OAB. To investigate this hypothesis, this cross-sectional study was conducted using samples from the NHANES database to explore the relationship between SII and OAB and the predictive value of this marker for OAB. Our purpose was to provide evidence supporting the pathogenesis of OAB. 2. Materials and Methods 2.1 Study Population This study collected data from the National Health and Nutrition Examination Survey (NHANES) database, which was created by the National Center for Health Statistics (NCHS) to collect health- and nutrition-related data on household populations across the United States and contains data from multiple measures and questionnaires for participants of different races, ages, genders, and regions. In the current study, consecutive cycles from 2005 to 2020 available in the NHANES database were selected as the study sample. Our study relied on the secondary analysis of publicly available data. The NHANES database has undergone ethical review and privacy protection measures. This study adhered to the guidelines and ethical principles outlined by the NHANES database to ensure the protection of participant privacy throughout the research process. All survey participants gave their written, informed consent, or in the case of individuals under 16 years old, their parents or legal guardians did. Detailed information regarding the NHANES study design can be accessed at https://www.cdc.gov/nchs/NHANES/. A total sample of 85,750 individuals was screened from the 15-year dataset. Samples with missing or incomplete data on Systemic Inflammatory Index (SII) and urinary status, as well as individuals below 18 years of age, were excluded from this study, eventually yielding a sample size of 40,601 individuals (n=40,601). Subsequently, individuals with missing data for various relevant covariates, namely family income to poverty ratio, educational level, marital status, BMI, smoking status, as well as medical history of diabetes, hyperlipidemia, hypertension, HDL levels, triglycerides levels, LDL levels, and alcohol consumption levels, were further excluded from the present study. This refined the sample size to a final count of 11,427 individuals (n=11,427) (Figure 1) 2.2 Assessment of OAB The data used to assess the presence of an Overactive Bladder (OAB) were derived from two questions in the "Kidney Conditions - Urology" questionnaire survey: "Urinated before reaching the toilet?" and "How many times do you urinate in the night?". Leveraging the questionnaire developed by Blaivas et al., namely the Overactive Bladder Symptom Score (OABSS) [13] , the frequency of nocturia and urgency urinary incontinence obtained from these were converted into an OAB scoring system (Table 1). Next, the scores from these two aspects were summed to obtain the total OABSS for each participant in the NHANES dataset. Individuals scoring ≥3 points were classified as experiencing OAB, the primary outcome variable in our study. TABLE 1 | Converting symptom questionnaires from the NHANES database into OABSS scores. Urinated before reaching the toilet (from NHANES) Urge urinary incontinence score (according to OABSS) Never 0 Less than once a month 1 A few times a month 1 A few times a week 2 Everyday and/or every night 3 Nocturia frequency (from NHANES) Nocturia score (according to OABSS) 0 0 1 1 2 2 3 3 4 3 5 or more 3 2.3 SII and Covariates The Systemic Inflammatory Index (SII) is determined by multiplying the amount of platelets by the number of neutrophils and then dividing the result by the number of lymphocytes. This study evaluated the exposure variable of SII, which refers to the combination of lymphocyte, neutrophil, and platelet counts. These counts were measured using automated hematology analyzing devices (Coulter® DxH 800 analyzer) and were reported as ×10 3 cells/mL. Other covariates included age, gender, race, education level, marital status, family income to poverty ratio, Body Mass Index (BMI), total cholesterol levels, High-Density Lipoprotein (HDL) levels, Low-Density Lipoprotein (LDL) levels, triglycerides levels, smoking status, and alcohol consumption status. Additionally, a medical history of hypertension, hyperlipidemia, and diabetes were considered. Alcohol consumption status was transformed from quantitative data to categorical data using specific criteria: Drinking at least 2 alcoholic beverages per day for girls or at least 3 alcoholic beverages per day for males on a daily basis, or consuming alcohol on ≥2 days per week for all genders was classified as moderate; Drinking three or more alcoholic beverages per day for girls or four or more alcoholic beverages per day for males on a daily basis, or consuming alcohol on ≥5 days per week for all genders was classified as heavy, and not fulfilling the above criteria was classified as mild. 2.4 Data Analysis Statistical packages R (The R Foundation; http://r-project.org; version 4.3.1) and EmpowerStats (www.empowerstats.com; X&Y solution inc) were utilized for statistical analyses. Data filtering was performed, which involved handling missing values, outliers, and duplicate data. Variables relevant to the research question were selected. Briefly, the sample was stratified into two groups based on the presence of an Overactive Bladder (OAB), and baseline tables were generated for each group. Afterward, descriptive statistical analyses were carried out for relevant variables. Normally distributed continuous variables were expressed as means ± standard errors. Skewed distributed data were presented using a combination of median and quartiles to depict the central tendency and distribution of the data. Categorical variables were presented as percentages. Thereafter, univariate and multivariate logistic regression analyses combined with smoothed curve fitting were conducted to examine the relationship between SII/1000 and OAB. Due to the insignificant effect size in Logistic Regression, the effect size of SII/1000 was magnified by a factor of 1000. The logistic regression analyses included three models: Model 1, without any variable adjustment; Model 2, adjusted for covariates comprising gender, age, and race; and Model 3, a stepwise regression excluding irrelevant variables and incorporating family income to poverty ratio, education level, BMI, smoking status, and total cholesterol level. Next, the multivariable Logistic Regression model was utilized to identify variables associated with the risk of Overactive Bladder (OAB), excluding variables that lacked clinical significance. This process was further narrowed down to five variables, namely age, gender, body mass index (BMI), Systemic Inflammatory Index (SII), and total cholesterol levels. Subsequently, a nomogram was generated using the "replot" package in R. To evaluate the predictive capability of the nomogram in identifying the risk of OAB, Receiver Operating Characteristic (ROC) curves, calibration curves, and Decision Curve Analysis (DCA) were plotted and analyzed. P < 0.05 was considered statistically significant. 3. Results 3.1 Baseline Characteristics of the Study Population The baseline characteristics of the population (Table 2) revealed that among the 11,427 samples, individuals with Overactive Bladder (OAB) were more likely to be female (61.66%), of Mexican-American ethnicity (44.84%), and those who were married or cohabiting (56.26%). Additionally, a significant proportion of individuals with OAB were also diagnosed with diabetes (70.02%). Nonetheless, the incidence of hypertension, as well as the alcohol consumption status and smoking status, were comparable between the two groups. Compared to those without OAB, participants with OAB were generally older (56.83 years old, p < 0.01) and had a higher mean BMI (31.38, p < 0.01). On the other hand, the average total cholesterol levels were similar between the two groups. Besides, individuals with OAB typically exhibited a higher median and quartile range for SII levels (475.59 (326.49 - 683.89) vs. 435.00 (315.00 - 615.57), p < 0.01), lower family income to poverty ratio, and higher triglyceride levels. TABLE 2 | Baseline characteristics of study population according to OAB status. Variable Overall (n = 11427) OAB status P-value a No (n = 9025) Yes (n = 2402) Age, Mean ± SD 47.34 ± 16.96 44.81 ± 16.36 56.83 ± 15.77 <.001 BMI, Mean ± SD 29.12 ± 7.15 28.52 ± 6.73 31.38 ± 8.15 <.001 HDL, Mean ± SD 1.42 ± 0.43 1.41 ± 0.42 1.44 ± 0.45 0.021 LDL, Mean ± SD 2.91 ± 0.91 2.93 ± 0.90 2.87 ± 0.93 0.018 Total Cholesterol Level, Mean ± SD 4.92 ± 1.04 4.92 ± 1.03 4.92 ± 1.07 0.917 Family income to poverty ratio, M (Q₁, Q₃) 2.46 (1.26 - 4.59) 2.62 (1.32 - 4.75) 2.07 (1.09 - 3.88) <.001 Triglyceride Level, M (Q₁, Q₃) 1.11 (0.76 - 1.60) 1.08 (0.74 - 1.58) 1.15 (0.80 - 1.65) <.001 SII, M (Q₁, Q₃) 443.14 (317.34 - 626.84) 435.00 (315.00 - 615.57) 475.59 (326.49 - 683.89) <.001 Gender, n (%) <.001 Male 6111（53.47） 5190 (57.51) 921 (38.34) Female 5316（46.52） 3835 (42.49) 1481 (61.66) Race/ethnicity, n (%) <.001 Non-Hispanic White 1630 (14.26) 1319 (14.61) 311 (12.95) Non-Hispanic Black 2343 (20.5) 1712 (18.97) 631 (26.27) Mexican American 5171 (45.25) 4094 (45.36) 1077 (44.84) Other Race 1038 (9.08) 822 (9.11) 216 (8.99) Other Hispanic 1245 (10.9) 1078 (11.94) 167 (6.95) Education attainment, n (%) <.001 Below High School 2041 (17.86) 1489 (16.50) 552 (22.98) High School Grad/GED or Equivalent 2575 (22.53) 1965 (21.77) 610 (25.40) College Graduate or above 6811 (59.6) 5571 (61.73) 1240 (51.62) Marital status, n (%) <.001 Never married 2242 (19.62) 1919 (21.26) 323 (13.45) Married/Living with Partner 6916 (60.52) 5564 (61.65) 1352 (56.29) Widowed/Divorced/Separated 2269 (19.86) 1542 (17.09) 727 (30.27) Smoker, n (%) <.001 Yes 5851 (51.2) 4800 (53.19) 1051 (43.76) No 5576 (48.8) 4225 (46.81) 1351 (56.24) Hyperlipidemia, n (%) <.001 Yes 3544 (31.01) 3018 (33.44) 526 (21.90) No 7883 (68.99) 6007 (66.56) 1876 (78.10) DM, n (%) <.001 Yes 9463 (82.81) 7781 (86.22) 1682 (70.02) No 1964 (17.19) 1244 (13.78) 720 (29.98) Hypertension, n (%) <.001 Yes 7122 (62.33) 6085 (67.42) 1037 (43.17) No 4305 (37.67) 2940 (32.58) 1365 (56.83) Alcohol Consumption, n (%) 0.006 mild 5720 (50.06) 4460 (49.42) 1260 (52.46) moderate 2574 (22.53) 2031 (22.50) 543 (22.61) heavy 3133 (27.42) 2534 (28.08) 599 (24.94) BMI，body mass index; DM，Diabetes Mellitus；HDL，High-Density Lipoprotein；LDL，Low-Density Lipoprotein a For continuous variables following a normal distribution, the P-value was determined using the t-test. For continuous variables with a skewed distribution, the P-value was determined using the Z-test. For categorical variables, the P-value was determined using the chi-square test. 3.2 Relationship between SII and OAB The Logistic Regression analysis revealed a correlation between SII and the occurrence of an Overactive Bladder (OAB). Specifically, the results indicated that the SII level was positively correlated with the likelihood of developing OAB. In the unadjusted model, there was a 39% increase in the risk of OAB for each unit increase in SII/1000. In Model 2, this risk was increased by 68%. Furthermore, in our fully adjusted Model 3, the risk of OAB was increased by 19% (Table 3). Following this, a smoothed curve fitting was employed to illustrate the relationship between OAB and SII/1000. The results displayed a non-linear relationship between OAB and SII/1000 (P-nonlinear < 0.05) (Figure 2). Additionally, the risk of OAB increased with an increase in SII/100 TABLE 3 | Association Between Systemic Immune-Inflammation Index and OAB. OR 1 (95%CI 2 ), p-value Model 1 3 （Non-adjusted） Model 2 4 （Minimally adjusted） Model 3 5 （Fully adjusted） SII/1000 1.39 (1.21,1.60),<0.01 1.68 (1.47,1.92),<0.01 1.19 (1.03,1.37),0.02 1 OR: odds ratio. 2 95% CI: 95% confidence interval. 3 Model 1: not adjusted for covariates. 4 Model 2: adjusted for gender, age, and race. 5 Model 3: adjusted for gender, age, race，education level，family income to poverty ratio , total cholesterol levels, body mass index（BMI）and smoking status. TABLE 4 | Multivariate logistic regression models of OAB Variables OR 1 (95% CI 2 ) p-value SII/1000 1.25 (1.08, 1.44) 0.003 Age 1.05 (1.05, 1.06) <0.001 Female (versus male) 2.66 (2.40, 2.96) <0.001 Race (versus Mexican American) Non-Hispanic Black 1.45 (1.22, 1.72) <0.001 Non-Hispanic White 0.95 (0.81, 1.12) 0.518 Other Hispanic 1.01 (0.82, 1.25) 0.929 Other 0.95 (0.76, 1.20) 0.683 Family Income to Poverty ratio 0.86 (0.83, 0.89) <0.001 Education (versus below high school) High school or GED 0.85 (0.73, 0.99) 0.035 Above high school 0.73 (0.64, 0.85) <0.001 BMI 1.05 (1.04, 1.06) <0.001 History of smoking (versus no) 1.30 (1.17, 1.44) <0.001 Total cholesterol level 0.91 (0.87, 0.95) <0.001 1 OR = Odds Ratio, 2 CI = Confidence Interval 3.3 Establishment and Validation of the Nomogram In our constructed Multivariate Logistic Regression models, significant indicators with p < 0.05 (SII/1000, age, gender, Family Income to Poverty ratio, body mass index (BMI), hypertension, and smoking status) were initially selected (Table 4). Next, based on practical clinical significance, five statistically significant variables (SII/1000, gender, age, body mass index (BMI), and total cholesterol level) were incorporated into the model. These variables were used to establish a predictive model that was visualized through a nomogram (Figure 3). The total sample of 11,427 individuals was randomly split into training and validation sets at a ratio of 7:3. As anticipated, the Receiver Operating Characteristic (ROC) curve illustrated the excellent discriminatory ability of the established nomogram. The Area Under the Curve (AUC) for the training and validation sets was 75.7% and 75.4%, respectively (Figure 4 BE). To further evaluate the calibration and clinical utility of the model, calibration curves and Decision Curve Analysis (DCA) were plotted to assess both the training and validation sets. The results showcased the favorable calibration of our model, with the green calibration curves closely aligned with the red original curves for both the training and validation sets (see Figure 4 AD). Furthermore, decision analysis curves were plotted to assess clinical utility (Figure 4 CF). Importantly, the results indicated a satisfactory clinical utility in both the training and validation sets. In the latter, the model demonstrated higher clinical benefits within the threshold range of 0.1 to 0.7. 4. Discussion Our study yielded several crucial findings. To the best of our knowledge, this is the first study to investigate the association between Systemic Inflammatory Index (SII) levels and the risk of Overactive Bladder (OAB). Herein, a non-linear positive correlation was observed between elevated SII levels and the risk of developing OAB. This conclusion remained valid even after conducting univariate and multivariate logistic regression analyses, signifying that SII levels can act as an independent risk factor for OAB. Furthermore, the nomogram derived from relevant factors selected based on Multivariate Logistic Regression models and clinical significance demonstrated outstanding predictive capabilities for OAB. Of note, the Systemic Inflammatory Index (SII) has been validated as a predictive indicator for Acute Pyelonephritis (APN) and Urinary Tract Infections (UTI) in earlier studies focusing on urinary system disorders [14–15] . At the same time, Deniz Karakaya et al. concluded that SII could assist in the diagnosis of APN in pediatric patients, demonstrating high sensitivity and specificity. Additionally, a retrospective study by Ali Güngör et al. exposed the predictive value of high SII for UTI in infants experiencing fever of unknown origin. Moreover, in the field of bladder tumors, Li J et al. discovered a significant correlation between elevated preoperative SII levels in bladder cancer patients and postoperative survival periods and adverse pathological characteristics [11] . The theory proposing chronic inflammation as one of the pathogenic mechanisms of Overactive Bladder (OAB) has garnered limited attention. In previous studies, Apostolos Apostolidis et al. detected inflammatory cells in the bladder epithelium of the majority of OAB patients who underwent biopsies [16] . Another study employed immunofluorescence staining and protein quantification on bladder specimens of OAB patients, revealing significantly elevated levels of E-cadherin, mast cell counts, apoptotic cell counts, and ZO-1 expression compared to those in healthy individuals. This collectively indicated a potential association between the subepithelial inflammatory status in the urinary tract and OAB pathogenesis [17] . Moreover, a prospective study analyzed the levels of chemokines, cytokines, growth factors, and soluble receptors in the urine of OAB patients and observed significantly increased levels of inflammatory and tissue repair biomarkers compared to the control group [18] . The association between inflammation and OAB could possibly be linked to stimuli or stress responses triggering the release of inflammatory cytokines [19] . Another biopsy-based study inferred the presence of physical signs of inflammation in OAB, excluding Urinary Tract Infection (UTI), further corroborating the aforementioned research findings [20] . Regarding the relationship between peripheral blood and OAB, a previous cross-sectional study recruiting Korean women established a positive correlation between increased Neutrophil-to-Lymphocyte Ratio (NLR) in serum and the risk and severity of OAB. Likewise, this study highlighted the potential of NLR as a biomarker for OAB [21] . Historically, the diagnosis of an Overactive Bladder (OAB) relied on invasive examinations such as voiding symptoms and urodynamic assessments of patients. However, a safe, reliable, and non-invasive alternative for examinations through the use of biomarkers has not been pioneered so far. Prior research has predominantly focused on identifying reliable diagnostic biomarkers from the urine of OAB patients. Numerous studies found elevated levels of Nerve Growth Factor (NGF) in the urine of male patients with bladder outlet obstruction and female patients with OAB, correlating the increased NGF levels to the severity of OAB symptoms [22–23] . Subsequent studies demonstrated that after treatment involving anticholinergics and intravesical injection of type A botulinum toxin in OAB patients, the urinary NGF level was lower than pre-treatment levels [24–26] . However, increased NGF levels have also been noted in other inflammatory-related urinary tract conditions, raising uncertainties regarding its specific clinical significance [27] . In contrast, the Systemic Inflammatory Index (SII) stands out as an objective blood-based marker. Its advantage lies in the ease of calculation based on peripheral blood test results without incurring additional costs or necessitating further blood draws. Compared to other diagnostic methods for OAB, SII is a cost-effective and readily available alternative. Based on our research findings, SII holds clinical significance in assisting with the prediction and timely screening of OAB. Our study has several strengths that merit acknowledgment. Firstly, it represents the first investigation into the association between Systemic Inflammatory Index (SII) and Overactive Bladder (OAB), marking the initial use of SII for predicting OAB, thereby presenting potential clinical value. It also provides compelling evidence to support the chronic inflammation theory in OAB. Secondly, the study included a sufficiently large and nationally representative sample size derived from the NHANES database, ensuring the accuracy and objectivity of the data source. Thirdly, transforming two questionnaire responses into an OAB scoring system enhanced disease diagnosis. Additionally, univariate and multivariate logistic regression models were established to ascertain the accuracy of our conclusion regarding the correlation between SII levels and OAB. Finally, a nomogram with positive discriminatory ability was constructed. However, our study also has limitations that cannot be overlooked. 1) The cross-sectional nature of the current study lacks the ability to establish a temporal sequence, thereby limiting the ability to establish causality between elevated SII and OAB. Further prospective studies are warranted to validate these findings. 2) While our nomogram model exhibited good overall predictive performance, it might not accurately represent the predictive effect of SII on OAB. 3) Due to inherent limitations in the NHANES database, information that could affect SII values, including the use of anti-inflammatory drugs, was unavailable. Additionally, variability in subjective perceptions within urinary status questionnaires and the inability to entirely exclude urinary tract infection (UTI) or benign prostatic hyperplasia (in male patients) might have compromised the uniformity and completeness of the results, potentially not comprehensively reflecting the actual situation. 5. Conclusion Our study revealed a positive correlation between elevated SII levels and the risk of OAB. Moreover, the nomogram established in this study exhibited strong diagnostic capabilities in identifying the risk of OAB. However, further larger-scale prospective research is required to corroborate our findings. Abbreviations SII: Systemic Inflammatory Index OAB: Overactive Bladder NHANES: National Health and Nutrition Examination Survey OABSS: Overactive Bladder Symptom Score DCA: Decision Curve Analysis NCHS: National Center for Health Statistics BMI: Body Mass Index HDL: High-Density Lipoprotein LDL: Low-Density Lipoprotein ROC: Receiver Operating Characteristic DM: Diabetes Mellitus AUC: Area Under the Curve APN: Acute Pyelonephritis UTI: Urinary Tract Infections NLR: Neutrophil-to-Lymphocyte Ratio NGF: Nerve Growth Facto Declarations ETHICS STATEMENT Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of the National Centre for Health Statistics. Informed Consent Statement: All participants provided informed consent before enrollment. DATA AVAILABILITY STATEMENT All NHANES data for this study are publicly available and can be found here: https://wwwn.cdc.gov/nchs/nhanes. FUNDING None AUTHOR CONTRIBUTIONS Conception, design and manuscript reviewing: CFZ, DHZ, MQW. Data analysis and manuscript drafting: CFZ, XYZ, YKY. Data collection and sorting: XYZ, DHZ, MQW. All authors contributed to the article and approved the submitted version. ACKOWNLEDGMENTS We thank the staff at the National Center for Health Statistics of the Centers for Disease Control for designing, collecting, and collating the NHANES data and creating the public database. COMPETING INTERESTS The authors declare no competing interests. References .Haylen BT, de Ridder D, Freeman RM, Swift SE, Berghmans B, Lee J, Monga A, Petri E, Rizk DE, Sand PK, Schaer GN. An International Urogynecological Association (IUGA)/International Continence Society (ICS) joint report on the terminology for female pelvic foor dysfunction. Int Urogynecol J 21:5–26 Huang EJ, Reichardt LF. Neurotrophins: roles in neuronal development and function. Annu Rev Neurosci 24:677–736 Tseng IL, Yang YF, Yu CW, Li WH, Liao VH. Phthalates induce neurotoxicity afecting locomotor and thermotactic behaviors and AFD neurons through oxidative stress in Caenorhabditis elegans.PLoS One 8:e82657 Xu X, Yang Y, Wang R, Wang Y, Ruan Q, Lu Y. Perinatal exposure to di-(2-ethylhexyl) phthalate afects anxiety- and depressionlike behaviors in mice. Chemosphere 124:22–31 David RM. Proposed mode of action for in utero efects of some phthalate esters on the developing male reproductive tract. Toxicol Pathol 34:209–219 Hu B, Yang XR, Xu Y, Sun YF, Sun C, Guo W, et al Systemic ImmuneInflammation Index Predicts Prognosis of Patients After Curative Resection for Hepatocellular Carcinoma. Clin Cancer Res (2014) 20:6212–22.doi: 10.1158/1078-0432.CCR-14-0442 Nøst TH, Alcala K, Urbarova I, et al. Systemic inflammation markers and cancer incidence in the UK Biobank. Eur J Epidemiol. 2021;36(8):841–848. doi: 10.1007/s10654-021-00752-6 Yang YL, Wu CH, Hsu PF, et al. Systemic immune-inflammation index (SII) predicted clinical outcome in patients with coronary artery disease. Eur J Clin Invest. 2020;50(5):e13230. doi: 10.1111/eci.13230 Liu B, Wang J, Li YY, Li KP, Zhang Q. The association between systemic immune-inflammation index and rheumatoid arthritis: evidence from NHANES 1999–2018. Arthritis Res Ther. 2023;25(1):34. Published 2023 Mar 4. doi: 10.1186/s13075-023-03018-6 Gu W, Huang W, Zhang J, Qian S, Cao H, Ge L. Evaluation of urinary inflammatory index in rapid screening of urinary tract infection. Sci Rep. 2020;10(1):19306. Published 2020 Nov 9. doi: 10.1038/s41598-020-76352-3 Li J, Cao D, Huang Y, et al. The Prognostic and Clinicopathological Significance of Systemic Immune-Inflammation Index in Bladder Cancer. Front Immunol. 2022;13:865643. Published 2022 Apr 28. doi: 10.3389/fimmu.2022.865643 De Giorgi U, Procopio G, Giannarelli D, et al. Association of Systemic Inflammation Index and Body Mass Index with Survival in Patients with Renal Cell Cancer Treated with Nivolumab. Clin Cancer Res. 2019;25(13):3839–3846. doi: 10.1158/1078-0432.CCR-18-3661 Blaivas JG, Panagopoulos G, Weiss JP, Somaroo C. Validation of the overactive bladder symptom score. J Urol. 2007;178(2):543–547; discussion 547. Karakaya D, Güngör T, Cakıcı EK, Yazılıtaş F, Celikkaya E, Bulbul M. Determining the effectiveness of the immature granulocyte percentage and systemic immune-inflammation index in predicting acute pyelonephritis. Postgrad Med. 2023;135(2):155–160. doi: 10.1080/00325481.2022.2152970 Güngör A, Göktuğ A, Yaradılmış RM, et al. Utility of the systemic immune-inflammation index to predict serious bacterial infections in infants with fever without a source. Postgrad Med. 2022;134(7):698–702. doi: 10.1080/00325481.2022.2091373 Apostolidis A, Jacques TS, Freeman A, et al. Histological changes in the urothelium and suburothelium of human overactive bladder following intradetrusor injections of botulinum neurotoxin type A for the treatment of neurogenic or idiopathic detrusor overactivity. Eur Urol. 2008;53(6):1245–1253. doi: 10.1016/j.eururo.2008.02.037 Wang CC, Kuo HC. Urothelial Dysfunction and Chronic Inflammation in Diabetic Patients with Overactive Bladder. Low Urin Tract Symptoms. 2017;9(3):151–156. doi: 10.1111/luts.12126 Tyagi P, Barclay D, Zamora R, et al. Urine cytokines suggest an inflammatory response in the overactive bladder: a pilot study. Int Urol Nephrol. 2010;42(3):629–635. doi: 10.1007/s11255-009-9647-5 Bettelli E, Oukka M, Kuchroo VK. T(H)-17 cells in the circle of immunity and autoimmunity. Nat Immunol. 2007;8(4):345–350. doi: 10.1038/ni0407-345 Loran OB, Pisarev SA, Kleĭmenova NV, Sukhorukov VS. Urologiia. 2007;(2):37–41. Kim S, Park JH, Oh YH, Kim HJ, Kong MH, Moon J. Correlation between neutrophil to lymphocyte ratio and overactive bladder in South Korean women: a community-based, cross-sectional study. BMJ Open. 2021;11(10):e048309. Published 2021 Oct 28. doi: 10.1136/bmjopen-2020-048309 Liu, H. T. & Kuo, H. C. Urinary nerve growth factor levels are increased in patients with bladder outlet obstruction with overactive bladder symptoms and reduced after successful medical treatment. Urology 72, 104–108 (2008). Yokoyama, T., Kumon, H. & Nagai, A. Correlation of urinary nerve growth factor level with pathogenesis of overactive bladder. Neurourol. Urodyn. 27, 417–420 (2008). Liu, H. T., Chancellor, M. B. & Kuo, H. C. Urinary nerve growth factor level could be a biomarker in the differential diagnosis of mixed urinary incontinence in women. BJU Int. 102, 1440–1444 (2008). Liu, H. T., Chen, C. Y. & Kuo, H. C. Urinary nerve growth factor in women with overactive bladder syndrome. BJU Int. doi: 10.1111/j.1464-410X.2010.09585.x . Liu, H. T., Chancellor, M. B. & Kuo, H. C. Decrease of urinary nerve growth factor levels after antimuscarinic therapy in patients with overactive bladder. BJU Int. 103, 1668–1672 (2009). Jacobs, B. L. et al. Increased nerve growth factor in neurogenic overactive bladder and interstitial cystitis patients. Can. J. Urol. 17, 4989–4994 (2010). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-3978651","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":278285861,"identity":"d329a75b-1d46-4129-9258-0e1d51f1a9af","order_by":0,"name":"Changfeng Zhao","email":"","orcid":"","institution":"Zhejiang Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Changfeng","middleName":"","lastName":"Zhao","suffix":""},{"id":278285862,"identity":"6ba5ff6b-873d-4f12-85c4-d760f5961874","order_by":1,"name":"Xinyu Zhang","email":"","orcid":"","institution":"Zhejiang Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Zhang","suffix":""},{"id":278285863,"identity":"c37c0cad-b260-4b42-ba75-f7754950319e","order_by":2,"name":"Yunkai Yang","email":"","orcid":"","institution":"Zhejiang Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yunkai","middleName":"","lastName":"Yang","suffix":""},{"id":278285864,"identity":"489751c6-6166-4d81-a806-ce35ffa66d17","order_by":3,"name":"Mengqi Wu","email":"","orcid":"","institution":"Zhejiang Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mengqi","middleName":"","lastName":"Wu","suffix":""},{"id":278285865,"identity":"bc60791f-c8ef-4cef-888f-43374f5eab44","order_by":4,"name":"Dahong Zhang","email":"data:image/png;base64,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","orcid":"","institution":"Zhejiang Provincial People’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Dahong","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-02-22 13:06:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3978651/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3978651/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52624146,"identity":"18557cca-a1af-4cee-b9d2-26e8a3960ac1","added_by":"auto","created_at":"2024-03-13 17:27:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":39455,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participant selection. NHANES, National Health and Nutrition Examination Survey\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3978651/v1/7a237839e65b5a40ddacaf1f.png"},{"id":52624147,"identity":"0ee8ec1f-1f0b-4e38-aeea-6b4723a45b27","added_by":"auto","created_at":"2024-03-13 17:27:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":132883,"visible":true,"origin":"","legend":"\u003cp\u003eThe association between SII and OAB. The solid red line represents the smooth curve fit between variables. Blue bands represent the 95% confidence interval from the fit.\u003c/p\u003e\n\u003cp\u003eSII /1000, systemic immune-inflammation index/1000; OAB, Overactive Bladder.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3978651/v1/bdcaf8de5191f9193c9c0ff8.png"},{"id":52624149,"identity":"7dad9f66-219f-426c-86f3-5b4aa0f21854","added_by":"auto","created_at":"2024-03-13 17:27:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":147022,"visible":true,"origin":"","legend":"\u003cp\u003eA nomogram for the risk of OAB\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3978651/v1/7b3ab4bd10b053553087dab7.png"},{"id":52624150,"identity":"61d12058-9e8d-45ea-a977-86906ca6f8be","added_by":"auto","created_at":"2024-03-13 17:27:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":575400,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of the model. A. Calibration curve of the model constructed from the training set; B. ROC curve of the model constructed from the training set; C. DCA curve of the model constructed from the training set; D. Calibration curve of the model constructed from the test set; E. ROC curve of the model constructed from the test set; F. DCA curve of the model constructed from the test set\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3978651/v1/381996c458d6352bc5afdd50.png"},{"id":59980184,"identity":"0aeb14d3-922c-4249-96be-00166fee6fc1","added_by":"auto","created_at":"2024-07-10 05:59:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1602301,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3978651/v1/335561d2-6de0-4429-8ab4-8f3d4349310e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relationship Between SII Levels and OAB Incidence- A Cross-Sectional Study Based on NHANES 2005-2020","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOveractive Bladder (OAB) refers to a symptom complex primarily characterized by the urgency of urination without the presence of urinary tract infections and other pathological alterations. As is well documented, its chief clinical manifestations include urinary frequency and nocturia, with or without urgency urinary incontinence \u003csup\u003e[1]\u003c/sup\u003e. These symptoms significantly affect patients' daily lives, causing considerable discomfort and distress. To date, its etiology and risk factors remain elusive, and current research encompasses various factors and theories, including neurotransmitters, hormonal imbalances, and others \u003csup\u003e[2\u0026ndash;5]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the context of urological diseases, Wanjian Gu et al. reported that Systemic Inflammatory Index (SII) levels could be utilized for predicting the risk of urinary tract infections and determining the causative pathogen \u003csup\u003e[10]\u003c/sup\u003e. Additionally, in urological malignancies such as bladder cancer \u003csup\u003e[11]\u003c/sup\u003e and renal cell carcinoma \u003csup\u003e[12]\u003c/sup\u003e, among others, SII has been demonstrated to be associated with patient prognosis. However, there is a current paucity of research exploring the connection between SII and Overactive Bladder (OAB).\u003c/p\u003e \u003cp\u003eTherefore, considering the significant role of SII in other urological conditions, we hypothesized a potential association between SII levels and OAB. To investigate this hypothesis, this cross-sectional study was conducted using samples from the NHANES database to explore the relationship between SII and OAB and the predictive value of this marker for OAB. Our purpose was to provide evidence supporting the pathogenesis of OAB.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study collected data from the National Health and Nutrition Examination Survey (NHANES) database, which was created by the National Center for Health Statistics (NCHS) to collect health- and nutrition-related data on household populations across the United States and contains data from multiple measures and questionnaires for participants of different races, ages, genders, and regions.\u003c/p\u003e\n\u003cp\u003eIn the current study, consecutive cycles from 2005 to 2020 available in the NHANES database were selected as the study sample. Our study relied on the secondary analysis of publicly available data. The NHANES database has undergone ethical review and privacy protection measures. This study adhered to the guidelines and ethical principles outlined by the NHANES database to ensure the protection of participant privacy throughout the research process. All survey participants gave their written, informed consent, or in the case of individuals under 16 years old, their parents or legal guardians did. Detailed information regarding the NHANES study design can be accessed at https://www.cdc.gov/nchs/NHANES/.\u003c/p\u003e\n\u003cp\u003eA total sample of 85,750 individuals was screened from the 15-year dataset. Samples with missing or incomplete data on Systemic Inflammatory Index (SII) and urinary status, as well as individuals below 18 years of age, were excluded from this study, eventually yielding a sample size of 40,601 individuals (n=40,601). Subsequently, individuals with missing data for various relevant covariates, namely family income to poverty ratio, educational level, marital status, BMI, smoking status, as well as medical history of diabetes, hyperlipidemia, hypertension, HDL levels, triglycerides levels, LDL levels, and alcohol consumption levels, were further excluded from the present study. This refined the sample size to a final count of 11,427 individuals (n=11,427) (Figure 1)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Assessment of OAB\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used to assess the presence of an Overactive Bladder (OAB) were derived from two questions in the \u0026quot;Kidney Conditions - Urology\u0026quot; questionnaire survey: \u0026quot;Urinated before reaching the toilet?\u0026quot; and \u0026quot;How many times do you urinate in the night?\u0026quot;. Leveraging the questionnaire developed by Blaivas et al., namely the Overactive Bladder Symptom Score (OABSS) \u003csup\u003e[13]\u003c/sup\u003e, the frequency of nocturia and urgency urinary incontinence obtained from these were converted into an OAB scoring system (Table 1). Next, the scores from these two aspects were summed to obtain the total OABSS for each participant in the NHANES dataset. Individuals scoring \u0026ge;3 points were classified as experiencing OAB, the primary outcome variable in our study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 1\u003c/strong\u003e | Converting symptom questionnaires from the NHANES database into OABSS scores.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrinated before reaching the toilet (from NHANES)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrge urinary incontinence score (according to OABSS)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eLess than once a month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eA few times a month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eA few times a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eEveryday and/or every night\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNocturia frequency (from NHANES)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNocturia score (according to OABSS)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e5 or more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 SII and Covariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Systemic Inflammatory Index (SII) is determined by multiplying the amount of platelets by the number of neutrophils and then dividing the result by the number of lymphocytes. This study evaluated the exposure variable of SII, which refers to the combination of lymphocyte, neutrophil, and platelet counts. These counts were measured using automated hematology analyzing devices (Coulter\u0026reg; DxH 800 analyzer) and were reported as \u0026times;10\u003csup\u003e3\u003c/sup\u003e cells/mL.\u003c/p\u003e\n\u003cp\u003eOther covariates included age, gender, race, education level, marital status, family income to poverty ratio, Body Mass Index (BMI), total cholesterol levels, High-Density Lipoprotein (HDL) levels, Low-Density Lipoprotein (LDL) levels, triglycerides levels, smoking status, and alcohol consumption status. Additionally, a medical history of hypertension, hyperlipidemia, and diabetes were considered. Alcohol consumption status was transformed from quantitative data to categorical data using specific criteria: Drinking at least 2 alcoholic beverages per day for girls or at least 3 alcoholic beverages per day for males on a daily basis, or consuming alcohol on \u0026ge;2 days per week for all genders was classified as moderate; Drinking three or more alcoholic beverages per day for girls or four or more alcoholic beverages per day for males on a daily basis, or consuming alcohol on \u0026ge;5 days per week for all genders was classified as heavy, and not fulfilling the above criteria was classified as mild.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Data Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical packages R (The R Foundation; http://r-project.org; version 4.3.1) and EmpowerStats (www.empowerstats.com; X\u0026amp;Y solution inc) were utilized for statistical analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData filtering was performed, which involved handling missing values, outliers, and duplicate data. Variables relevant to the research question were selected. Briefly, the sample was stratified into two groups based on the presence of an Overactive Bladder (OAB), and baseline tables were generated for each group. Afterward, descriptive statistical analyses were carried out for relevant variables. Normally distributed continuous variables were expressed as means \u0026plusmn; standard errors. Skewed distributed data were presented using a combination of median and quartiles to depict the central tendency and distribution of the data. Categorical variables were presented as percentages. Thereafter, univariate and multivariate logistic regression analyses combined with smoothed curve fitting were conducted to examine the relationship between SII/1000 and OAB. Due to the insignificant effect size in Logistic Regression, the effect size of SII/1000 was magnified by a factor of 1000. The logistic regression analyses included three models: Model 1, without any variable adjustment; Model 2, adjusted for covariates comprising gender, age, and race; and Model 3, a stepwise regression excluding irrelevant variables and incorporating family income to poverty ratio, education level, BMI, smoking status, and total cholesterol level.\u003c/p\u003e\n\u003cp\u003eNext, the multivariable Logistic Regression model was utilized to identify variables associated with the risk of Overactive Bladder (OAB), excluding variables that lacked clinical significance. This process was further narrowed down to five variables, namely age, gender, body mass index (BMI), Systemic Inflammatory Index (SII), and total cholesterol levels. Subsequently, a nomogram was generated using the \u0026quot;replot\u0026quot; package in R.\u003c/p\u003e\n\u003cp\u003eTo evaluate the predictive capability of the nomogram in identifying the risk of OAB, Receiver Operating Characteristic (ROC) curves, calibration curves, and Decision Curve Analysis (DCA) were plotted and analyzed. P \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Baseline Characteristics of the Study Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe baseline characteristics of the population (Table 2) revealed that among the 11,427 samples, individuals with Overactive Bladder (OAB) were more likely to be female (61.66%), of Mexican-American ethnicity (44.84%), and those who were married or cohabiting (56.26%). Additionally, a significant proportion of individuals with OAB were also diagnosed with diabetes (70.02%). Nonetheless, the incidence of hypertension, as well as the alcohol consumption status and smoking status, were comparable between the two groups. Compared to those without OAB, participants with OAB were generally older (56.83 years old, p \u0026lt; 0.01) and had a higher mean BMI (31.38, p \u0026lt; 0.01). On the other hand, the average total cholesterol levels were similar between the two groups. Besides, individuals with OAB typically exhibited a higher median and quartile range for SII levels (475.59 (326.49 - 683.89) vs. 435.00 (315.00 - 615.57), p \u0026lt; 0.01), lower family income to poverty ratio, and higher triglyceride levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e | Baseline characteristics of study population according to OAB status.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"116%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.673469387755105%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 11427)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.734693877551024%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eOAB status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.42857142857143%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 9025)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.57142857142857%\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 2402)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\"\u003e\n \u003cp\u003eAge, Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e47.34 \u0026plusmn; 16.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e44.81 \u0026plusmn; 16.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e56.83 \u0026plusmn; 15.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\"\u003e\n \u003cp\u003eBMI, Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e29.12 \u0026plusmn; 7.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e28.52 \u0026plusmn; 6.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e31.38 \u0026plusmn; 8.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\"\u003e\n \u003cp\u003eHDL, Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e1.42 \u0026plusmn; 0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e1.41 \u0026plusmn; 0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e1.44 \u0026plusmn; 0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\"\u003e\n \u003cp\u003eLDL, Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e2.91 \u0026plusmn; 0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e2.93 \u0026plusmn; 0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e2.87 \u0026plusmn; 0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\"\u003e\n \u003cp\u003eTotal Cholesterol Level, Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e4.92 \u0026plusmn; 1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e4.92 \u0026plusmn; 1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e4.92 \u0026plusmn; 1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.917\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\"\u003e\n \u003cp\u003eFamily income to poverty ratio, M (Q₁, Q₃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e2.46 (1.26 - 4.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e2.62 (1.32 - 4.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e2.07 (1.09 - 3.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\"\u003e\n \u003cp\u003eTriglyceride Level, M (Q₁, Q₃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e1.11 (0.76 - 1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e1.08 (0.74 - 1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e1.15 (0.80 - 1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\"\u003e\n \u003cp\u003eSII, M (Q₁, Q₃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e443.14\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(317.34 - 626.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e435.00\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(315.00 - 615.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e475.59\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(326.49 - 683.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\"\u003e\n \u003cp\u003eGender, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e6111（53.47）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"bottom\"\u003e\n \u003cp\u003e5190 (57.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\" valign=\"bottom\"\u003e\n \u003cp\u003e921 (38.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e5316（46.52）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\" valign=\"bottom\"\u003e\n \u003cp\u003e3835 (42.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\" valign=\"bottom\"\u003e\n \u003cp\u003e1481 (61.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\"\u003e\n \u003cp\u003eRace/ethnicity, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\"\u003e\n \u003cp\u003e\u0026nbsp; Non-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e1630 (14.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e1319 (14.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e311 (12.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\"\u003e\n \u003cp\u003e\u0026nbsp; Non-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e2343 (20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e1712 (18.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e631 (26.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\"\u003e\n \u003cp\u003e\u0026nbsp; Mexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e5171 (45.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e4094 (45.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e1077 (44.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\"\u003e\n \u003cp\u003e\u0026nbsp; Other Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e1038 (9.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e822 (9.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e216 (8.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\"\u003e\n \u003cp\u003e\u0026nbsp; Other Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e1245 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e1078 (11.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e167 (6.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\"\u003e\n \u003cp\u003eEducation attainment, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\"\u003e\n \u003cp\u003e\u0026nbsp; Below High School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e2041 (17.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e1489 (16.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e552 (22.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\"\u003e\n \u003cp\u003e\u0026nbsp; High School Grad/GED or Equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e2575 (22.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e1965 (21.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e610 (25.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\"\u003e\n \u003cp\u003e\u0026nbsp; College Graduate or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e6811 (59.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e5571 (61.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e1240 (51.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\"\u003e\n \u003cp\u003eMarital status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"bottom\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e2242 (19.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e1919 (21.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e323 (13.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Married/Living with Partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e6916 (60.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e5564 (61.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e1352 (56.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Widowed/Divorced/Separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e2269 (19.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e1542 (17.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e727 (30.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\"\u003e\n \u003cp\u003eSmoker, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e5851 (51.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e4800 (53.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e1051 (43.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e5576 (48.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e4225 (46.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e1351 (56.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\"\u003e\n \u003cp\u003eHyperlipidemia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e3544 (31.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e3018 (33.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e526 (21.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e7883 (68.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e6007 (66.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e1876 (78.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\"\u003e\n \u003cp\u003eDM, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e9463 (82.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e7781 (86.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e1682 (70.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e1964 (17.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e1244 (13.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e720 (29.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e7122 (62.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e6085 (67.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e1037 (43.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e4305 (37.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e2940 (32.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e1365 (56.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\"\u003e\n \u003cp\u003eAlcohol Consumption, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003emild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e5720 (50.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e4460 (49.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e1260 (52.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e2574 (22.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e2031 (22.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e543 (22.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.02061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eheavy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e3133 (27.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e2534 (28.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e599 (24.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eBMI，body mass index; DM，Diabetes Mellitus；HDL，High-Density Lipoprotein；LDL，Low-Density Lipoprotein\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eFor continuous variables following a normal distribution, the P-value was determined using the t-test. For continuous variables with a skewed distribution, the P-value was determined using the Z-test. For categorical variables, the P-value was determined using the chi-square test.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Relationship between SII and OAB\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Logistic Regression analysis revealed a correlation between SII and the occurrence of an Overactive Bladder (OAB). Specifically, the results indicated that the SII level was positively correlated with the likelihood of developing OAB. In the unadjusted model, there was a 39% increase in the risk of OAB for each unit increase in SII/1000. In Model 2, this risk was increased by 68%. Furthermore, in our fully adjusted Model 3, the risk of OAB was increased by 19% (Table 3).\u003c/p\u003e\n\u003cp\u003eFollowing this, a smoothed curve fitting was employed to illustrate the relationship between OAB and SII/1000. The results displayed a non-linear relationship between OAB and SII/1000 (P-nonlinear \u0026lt; 0.05) (Figure 2). Additionally, the risk of OAB increased with an increase in SII/100\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 3\u0026nbsp;\u003c/strong\u003e| Association Between Systemic Immune-Inflammation Index and OAB.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.169381107491857%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"85.83061889250814%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR \u003csup\u003e1\u003c/sup\u003e(95%CI \u003csup\u003e2\u003c/sup\u003e), p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.146341463414634%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.317073170731707%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003csup\u003e3\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e（Non-adjusted）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003csup\u003e4\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e（Minimally adjusted）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003csup\u003e5\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e（Fully adjusted）\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.146341463414634%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.317073170731707%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.146341463414634%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSII/1000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.317073170731707%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.39 (1.21,1.60),\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.26829268292683%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.68 (1.47,1.92),\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.26829268292683%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.19 (1.03,1.37),0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.146341463414634%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.317073170731707%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.26829268292683%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.26829268292683%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e1\u0026nbsp;\u003c/sup\u003eOR: odds ratio.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e95% CI: 95% confidence interval.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e3\u003c/sup\u003e Model 1: not adjusted for covariates.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e4\u003c/sup\u003e Model 2: adjusted for gender, age, and race.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e5\u0026nbsp;\u003c/sup\u003eModel 3: adjusted for gender, age, race，education level，family income to poverty ratio , total cholesterol levels, body mass index（BMI）and smoking status.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 4\u0026nbsp;\u003c/strong\u003e| Multivariate logistic regression models of OAB\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.65384615384615%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.07692307692308%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003csup\u003e1\u003c/sup\u003e\u003cstrong\u003e(95% CI\u003c/strong\u003e\u003csup\u003e2\u003c/sup\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eSII/1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.07692307692308%\" valign=\"top\"\u003e\n \u003cp\u003e1.25 (1.08, 1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.07692307692308%\" valign=\"top\"\u003e\n \u003cp\u003e1.05 (1.05, 1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eFemale (versus male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.07692307692308%\" valign=\"top\"\u003e\n \u003cp\u003e2.66 (2.40, 2.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eRace (versus Mexican American)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.07692307692308%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.07692307692308%\" valign=\"top\"\u003e\n \u003cp\u003e1.45 (1.22, 1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.07692307692308%\" valign=\"top\"\u003e\n \u003cp\u003e0.95 (0.81, 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.07692307692308%\" valign=\"top\"\u003e\n \u003cp\u003e1.01 (0.82, 1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.07692307692308%\" valign=\"top\"\u003e\n \u003cp\u003e0.95 (0.76, 1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eFamily Income to Poverty ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.07692307692308%\" valign=\"top\"\u003e\n \u003cp\u003e0.86 (0.83, 0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eEducation (versus below high school)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.07692307692308%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eHigh school or GED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.07692307692308%\" valign=\"top\"\u003e\n \u003cp\u003e0.85 (0.73, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eAbove high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.07692307692308%\" valign=\"top\"\u003e\n \u003cp\u003e0.73 (0.64, 0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.07692307692308%\" valign=\"top\"\u003e\n \u003cp\u003e1.05 (1.04, 1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eHistory of smoking (versus no)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.07692307692308%\" valign=\"top\"\u003e\n \u003cp\u003e1.30 (1.17, 1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.65384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eTotal cholesterol level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.07692307692308%\" valign=\"top\"\u003e\n \u003cp\u003e0.91 (0.87, 0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eOR = Odds Ratio, \u003csup\u003e2\u003c/sup\u003eCI = Confidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Establishment and Validation of the Nomogram\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our constructed Multivariate Logistic Regression models, significant indicators with p \u0026lt; 0.05 (SII/1000, age, gender, Family Income to Poverty ratio, body mass index (BMI), hypertension, and smoking status) were initially selected (Table 4). Next, based on practical clinical significance, five statistically significant variables (SII/1000, gender, age, body mass index (BMI), and total cholesterol level) were incorporated into the model. These variables were used to establish a predictive model that was visualized through a nomogram (Figure 3).\u003c/p\u003e\n\u003cp\u003eThe total sample of 11,427 individuals was randomly split into training and validation sets at a ratio of 7:3. As anticipated, the Receiver Operating Characteristic (ROC) curve illustrated the excellent discriminatory ability of the established nomogram. The Area Under the Curve (AUC) for the training and validation sets was 75.7% and 75.4%, respectively (Figure 4 BE). To further evaluate the calibration and clinical utility of the model, calibration curves and Decision Curve Analysis (DCA) were plotted to assess both the training and validation sets. The results showcased the favorable calibration of our model, with the green calibration curves closely aligned with the red original curves for both the training and validation sets (see Figure 4 AD). Furthermore, decision analysis curves were plotted to assess clinical utility (Figure 4 CF). Importantly, the results indicated a satisfactory clinical utility in both the training and validation sets. In the latter, the model demonstrated higher clinical benefits within the threshold range of 0.1 to 0.7.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur study yielded several crucial findings. To the best of our knowledge, this is the first study to investigate the association between Systemic Inflammatory Index (SII) levels and the risk of Overactive Bladder (OAB). Herein, a non-linear positive correlation was observed between elevated SII levels and the risk of developing OAB. This conclusion remained valid even after conducting univariate and multivariate logistic regression analyses, signifying that SII levels can act as an independent risk factor for OAB. Furthermore, the nomogram derived from relevant factors selected based on Multivariate Logistic Regression models and clinical significance demonstrated outstanding predictive capabilities for OAB.\u003c/p\u003e \u003cp\u003eOf note, the Systemic Inflammatory Index (SII) has been validated as a predictive indicator for Acute Pyelonephritis (APN) and Urinary Tract Infections (UTI) in earlier studies focusing on urinary system disorders \u003csup\u003e[14\u0026ndash;15]\u003c/sup\u003e. At the same time, Deniz Karakaya et al. concluded that SII could assist in the diagnosis of APN in pediatric patients, demonstrating high sensitivity and specificity. Additionally, a retrospective study by Ali G\u0026uuml;ng\u0026ouml;r et al. exposed the predictive value of high SII for UTI in infants experiencing fever of unknown origin. Moreover, in the field of bladder tumors, Li J et al. discovered a significant correlation between elevated preoperative SII levels in bladder cancer patients and postoperative survival periods and adverse pathological characteristics \u003csup\u003e[11]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe theory proposing chronic inflammation as one of the pathogenic mechanisms of Overactive Bladder (OAB) has garnered limited attention. In previous studies, Apostolos Apostolidis et al. detected inflammatory cells in the bladder epithelium of the majority of OAB patients who underwent biopsies \u003csup\u003e[16]\u003c/sup\u003e. Another study employed immunofluorescence staining and protein quantification on bladder specimens of OAB patients, revealing significantly elevated levels of E-cadherin, mast cell counts, apoptotic cell counts, and ZO-1 expression compared to those in healthy individuals. This collectively indicated a potential association between the subepithelial inflammatory status in the urinary tract and OAB pathogenesis \u003csup\u003e[17]\u003c/sup\u003e. Moreover, a prospective study analyzed the levels of chemokines, cytokines, growth factors, and soluble receptors in the urine of OAB patients and observed significantly increased levels of inflammatory and tissue repair biomarkers compared to the control group \u003csup\u003e[18]\u003c/sup\u003e. The association between inflammation and OAB could possibly be linked to stimuli or stress responses triggering the release of inflammatory cytokines \u003csup\u003e[19]\u003c/sup\u003e. Another biopsy-based study inferred the presence of physical signs of inflammation in OAB, excluding Urinary Tract Infection (UTI), further corroborating the aforementioned research findings \u003csup\u003e[20]\u003c/sup\u003e. Regarding the relationship between peripheral blood and OAB, a previous cross-sectional study recruiting Korean women established a positive correlation between increased Neutrophil-to-Lymphocyte Ratio (NLR) in serum and the risk and severity of OAB. Likewise, this study highlighted the potential of NLR as a biomarker for OAB \u003csup\u003e[21]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHistorically, the diagnosis of an Overactive Bladder (OAB) relied on invasive examinations such as voiding symptoms and urodynamic assessments of patients. However, a safe, reliable, and non-invasive alternative for examinations through the use of biomarkers has not been pioneered so far. Prior research has predominantly focused on identifying reliable diagnostic biomarkers from the urine of OAB patients. Numerous studies found elevated levels of Nerve Growth Factor (NGF) in the urine of male patients with bladder outlet obstruction and female patients with OAB, correlating the increased NGF levels to the severity of OAB symptoms \u003csup\u003e[22\u0026ndash;23]\u003c/sup\u003e. Subsequent studies demonstrated that after treatment involving anticholinergics and intravesical injection of type A botulinum toxin in OAB patients, the urinary NGF level was lower than pre-treatment levels \u003csup\u003e[24\u0026ndash;26]\u003c/sup\u003e. However, increased NGF levels have also been noted in other inflammatory-related urinary tract conditions, raising uncertainties regarding its specific clinical significance \u003csup\u003e[27]\u003c/sup\u003e. In contrast, the Systemic Inflammatory Index (SII) stands out as an objective blood-based marker. Its advantage lies in the ease of calculation based on peripheral blood test results without incurring additional costs or necessitating further blood draws. Compared to other diagnostic methods for OAB, SII is a cost-effective and readily available alternative. Based on our research findings, SII holds clinical significance in assisting with the prediction and timely screening of OAB.\u003c/p\u003e \u003cp\u003eOur study has several strengths that merit acknowledgment. Firstly, it represents the first investigation into the association between Systemic Inflammatory Index (SII) and Overactive Bladder (OAB), marking the initial use of SII for predicting OAB, thereby presenting potential clinical value. It also provides compelling evidence to support the chronic inflammation theory in OAB. Secondly, the study included a sufficiently large and nationally representative sample size derived from the NHANES database, ensuring the accuracy and objectivity of the data source. Thirdly, transforming two questionnaire responses into an OAB scoring system enhanced disease diagnosis. Additionally, univariate and multivariate logistic regression models were established to ascertain the accuracy of our conclusion regarding the correlation between SII levels and OAB. Finally, a nomogram with positive discriminatory ability was constructed. However, our study also has limitations that cannot be overlooked. 1) The cross-sectional nature of the current study lacks the ability to establish a temporal sequence, thereby limiting the ability to establish causality between elevated SII and OAB. Further prospective studies are warranted to validate these findings. 2) While our nomogram model exhibited good overall predictive performance, it might not accurately represent the predictive effect of SII on OAB. 3) Due to inherent limitations in the NHANES database, information that could affect SII values, including the use of anti-inflammatory drugs, was unavailable. Additionally, variability in subjective perceptions within urinary status questionnaires and the inability to entirely exclude urinary tract infection (UTI) or benign prostatic hyperplasia (in male patients) might have compromised the uniformity and completeness of the results, potentially not comprehensively reflecting the actual situation.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur study revealed a positive correlation between elevated SII levels and the risk of OAB. Moreover, the nomogram established in this study exhibited strong diagnostic capabilities in identifying the risk of OAB. However, further larger-scale prospective research is required to corroborate our findings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eSII: Systemic Inflammatory Index\u003c/p\u003e\n\u003cp\u003eOAB: Overactive Bladder\u003c/p\u003e\n\u003cp\u003eNHANES: National Health and Nutrition Examination Survey\u003c/p\u003e\n\u003cp\u003eOABSS: Overactive Bladder Symptom Score\u003c/p\u003e\n\u003cp\u003eDCA: Decision Curve Analysis\u003c/p\u003e\n\u003cp\u003eNCHS: National Center for Health Statistics\u003c/p\u003e\n\u003cp\u003eBMI: Body Mass Index\u003c/p\u003e\n\u003cp\u003eHDL: High-Density Lipoprotein\u003c/p\u003e\n\u003cp\u003eLDL: Low-Density Lipoprotein\u003c/p\u003e\n\u003cp\u003eROC: Receiver Operating Characteristic\u003c/p\u003e\n\u003cp\u003eDM: Diabetes Mellitus\u003c/p\u003e\n\u003cp\u003eAUC: Area Under the Curve\u003c/p\u003e\n\u003cp\u003eAPN: Acute Pyelonephritis\u003c/p\u003e\n\u003cp\u003eUTI: Urinary Tract Infections\u003c/p\u003e\n\u003cp\u003eNLR: Neutrophil-to-Lymphocyte Ratio\u003c/p\u003e\n\u003cp\u003eNGF: Nerve Growth Facto\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eETHICS STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u0026nbsp;\u003c/strong\u003eThe study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of the National Centre for Health Statistics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u0026nbsp;\u003c/strong\u003eAll participants provided informed consent before enrollment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll NHANES data for this study are publicly available and can be found here: https://wwwn.cdc.gov/nchs/nhanes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception, design and manuscript reviewing: CFZ, DHZ, MQW. Data analysis and manuscript drafting: CFZ, XYZ, YKY. Data collection and sorting: XYZ, DHZ, MQW. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKOWNLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the staff at the National Center for Health Statistics of the Centers for Disease Control for designing, collecting, and collating the NHANES data and creating the public database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e.Haylen BT, de Ridder D, Freeman RM, Swift SE, Berghmans B, Lee J, Monga A, Petri E, Rizk DE, Sand PK, Schaer GN. An International Urogynecological Association (IUGA)/International Continence Society (ICS) joint report on the terminology for female pelvic foor dysfunction. Int Urogynecol J 21:5\u0026ndash;26\u003c/li\u003e\n \u003cli\u003eHuang EJ, Reichardt LF. Neurotrophins: roles in neuronal development and function. Annu Rev Neurosci 24:677\u0026ndash;736\u003c/li\u003e\n \u003cli\u003eTseng IL, Yang YF, Yu CW, Li WH, Liao VH. Phthalates induce neurotoxicity afecting locomotor and thermotactic behaviors and AFD neurons through oxidative stress in Caenorhabditis elegans.PLoS One 8:e82657\u003c/li\u003e\n \u003cli\u003eXu X, Yang Y, Wang R, Wang Y, Ruan Q, Lu Y. Perinatal exposure to di-(2-ethylhexyl) phthalate afects anxiety- and depressionlike behaviors in mice. Chemosphere 124:22\u0026ndash;31\u003c/li\u003e\n \u003cli\u003eDavid RM. Proposed mode of action for in utero efects of some phthalate esters on the developing male reproductive tract. Toxicol Pathol 34:209\u0026ndash;219\u003c/li\u003e\n \u003cli\u003eHu B, Yang XR, Xu Y, Sun YF, Sun C, Guo W, et al Systemic ImmuneInflammation Index Predicts Prognosis of Patients After Curative Resection for Hepatocellular Carcinoma. Clin Cancer Res (2014) 20:6212\u0026ndash;22.doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1078-0432.CCR-14-0442\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eN\u0026oslash;st TH, Alcala K, Urbarova I, et al. Systemic inflammation markers and cancer incidence in the UK Biobank. Eur J Epidemiol. 2021;36(8):841\u0026ndash;848. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10654-021-00752-6\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eYang YL, Wu CH, Hsu PF, et al. Systemic immune-inflammation index (SII) predicted clinical outcome in patients with coronary artery disease. Eur J Clin Invest. 2020;50(5):e13230. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/eci.13230\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eLiu B, Wang J, Li YY, Li KP, Zhang Q. The association between systemic immune-inflammation index and rheumatoid arthritis: evidence from NHANES 1999\u0026ndash;2018. Arthritis Res Ther. 2023;25(1):34. Published 2023 Mar 4. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13075-023-03018-6\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eGu W, Huang W, Zhang J, Qian S, Cao H, Ge L. Evaluation of urinary inflammatory index in rapid screening of urinary tract infection. Sci Rep. 2020;10(1):19306. Published 2020 Nov 9. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-020-76352-3\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eLi J, Cao D, Huang Y, et al. The Prognostic and Clinicopathological Significance of Systemic Immune-Inflammation Index in Bladder Cancer. Front Immunol. 2022;13:865643. Published 2022 Apr 28. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2022.865643\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eDe Giorgi U, Procopio G, Giannarelli D, et al. Association of Systemic Inflammation Index and Body Mass Index with Survival in Patients with Renal Cell Cancer Treated with Nivolumab. Clin Cancer Res. 2019;25(13):3839\u0026ndash;3846. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1078-0432.CCR-18-3661\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eBlaivas JG, Panagopoulos G, Weiss JP, Somaroo C. Validation of the overactive bladder symptom score. J Urol. 2007;178(2):543\u0026ndash;547; discussion 547.\u003c/li\u003e\n \u003cli\u003eKarakaya D, G\u0026uuml;ng\u0026ouml;r T, Cakıcı EK, Yazılıtaş F, Celikkaya E, Bulbul M. Determining the effectiveness of the immature granulocyte percentage and systemic immune-inflammation index in predicting acute pyelonephritis. Postgrad Med. 2023;135(2):155\u0026ndash;160. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/00325481.2022.2152970\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eG\u0026uuml;ng\u0026ouml;r A, G\u0026ouml;ktuğ A, Yaradılmış RM, et al. Utility of the systemic immune-inflammation index to predict serious bacterial infections in infants with fever without a source. Postgrad Med. 2022;134(7):698\u0026ndash;702. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/00325481.2022.2091373\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eApostolidis A, Jacques TS, Freeman A, et al. Histological changes in the urothelium and suburothelium of human overactive bladder following intradetrusor injections of botulinum neurotoxin type A for the treatment of neurogenic or idiopathic detrusor overactivity. Eur Urol. 2008;53(6):1245\u0026ndash;1253. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.eururo.2008.02.037\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eWang CC, Kuo HC. Urothelial Dysfunction and Chronic Inflammation in Diabetic Patients with Overactive Bladder. Low Urin Tract Symptoms. 2017;9(3):151\u0026ndash;156. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/luts.12126\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eTyagi P, Barclay D, Zamora R, et al. Urine cytokines suggest an inflammatory response in the overactive bladder: a pilot study. Int Urol Nephrol. 2010;42(3):629\u0026ndash;635. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11255-009-9647-5\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eBettelli E, Oukka M, Kuchroo VK. T(H)-17 cells in the circle of immunity and autoimmunity. Nat Immunol. 2007;8(4):345\u0026ndash;350. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ni0407-345\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eLoran OB, Pisarev SA, Kleĭmenova NV, Sukhorukov VS. Urologiia. 2007;(2):37\u0026ndash;41.\u003c/li\u003e\n \u003cli\u003eKim S, Park JH, Oh YH, Kim HJ, Kong MH, Moon J. Correlation between neutrophil to lymphocyte ratio and overactive bladder in South Korean women: a community-based, cross-sectional study. BMJ Open. 2021;11(10):e048309. Published 2021 Oct 28. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjopen-2020-048309\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eLiu, H. T. \u0026amp; Kuo, H. C. Urinary nerve growth factor levels are increased in patients with bladder outlet obstruction with overactive bladder symptoms and reduced after successful medical treatment. Urology 72, 104\u0026ndash;108 (2008).\u003c/li\u003e\n \u003cli\u003eYokoyama, T., Kumon, H. \u0026amp; Nagai, A. Correlation of urinary nerve growth factor level with pathogenesis of overactive bladder. Neurourol. Urodyn. 27, 417\u0026ndash;420 (2008).\u003c/li\u003e\n \u003cli\u003eLiu, H. T., Chancellor, M. B. \u0026amp; Kuo, H. C. Urinary nerve growth factor level could be a biomarker in the differential diagnosis of mixed urinary incontinence in women. BJU Int. 102, 1440\u0026ndash;1444 (2008).\u003c/li\u003e\n \u003cli\u003eLiu, H. T., Chen, C. Y. \u0026amp; Kuo, H. C. Urinary nerve growth factor in women with overactive bladder syndrome. BJU Int. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1464-410X.2010.09585.x\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n \u003cli\u003eLiu, H. T., Chancellor, M. B. \u0026amp; Kuo, H. C. Decrease of urinary nerve growth factor levels after antimuscarinic therapy in patients with overactive bladder. BJU Int. 103, 1668\u0026ndash;1672 (2009).\u003c/li\u003e\n \u003cli\u003eJacobs, B. L. et al. Increased nerve growth factor in neurogenic overactive bladder and interstitial cystitis patients. Can. J. Urol. 17, 4989\u0026ndash;4994 (2010).\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":"Overactive Bladder (OAB), Systemic Inflammatory Index (SII)༛NHANES, Cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-3978651/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3978651/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to investigate the relationship between the Systemic Inflammatory Index (SII) among US adults from 2005 to 2020 and the incidence of Overactive Bladder (OAB). Additionally, a predictive model for OAB risk was constructed based on SII.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional study involved 11,427 participants from the National Health and Nutrition Examination Survey (NHANES) spanning from 2005 to 2020. SII was calculated based on peripheral blood results, while data on urinary conditions were assessed using the Overactive Bladder Symptom Score (OABSS) to determine the presence of OAB. Participants were stratified into two groups based on the presence or absence of OAB, and baseline characteristics were compared. Then, the correlation between SII and OAB was explored using univariate and multivariate logistic regression analyses coupled with smoothed curve fitting. A multifactorial Logistic Regression model was established by selecting variables associated with OAB incidence, incorporating clinical significance, and constructing a nomogram. Lastly, the predictive ability of the nomogram for identifying OAB was evaluated using ROC curves, calibration curves, and Decision Curve Analysis (DCA).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the examined 11,427 samples, individuals with OAB exhibited higher SII levels (475.59 (326.49\u0026ndash;683.89) vs. 435.00 (315.00\u0026ndash;615.57), p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Both univariate and multivariate Logistic Regression analyses revealed a positive correlation between SII/1000 and the incidence of OAB. Moreover, smooth curve fitting demonstrated a non-linear positive correlation between OAB and SII/1000 (P-nonlinear\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Finally, our established nomogram could predict the risk of OAB (AUC\u0026thinsp;=\u0026thinsp;0.754), holding clinical decision-making significance.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eA positive correlation was identified between SII and the risk of OAB among American adults, highlighting the predictive value of SII for OAB incidence. However, larger-scale prospective studies are warranted to validate our findings.\u003c/p\u003e","manuscriptTitle":"Relationship Between SII Levels and OAB Incidence- A Cross-Sectional Study Based on NHANES 2005-2020","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-13 17:27:09","doi":"10.21203/rs.3.rs-3978651/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":"ea8bd68e-e47e-45ab-89ba-64b908f25f5e","owner":[],"postedDate":"March 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29311516,"name":"Biological sciences/Immunology/Inflammation"},{"id":29311517,"name":"Biological sciences/Immunology"},{"id":29311518,"name":"Health sciences/Urology"},{"id":29311519,"name":"Health sciences/Urology/Bladder"}],"tags":[],"updatedAt":"2024-07-10T05:51:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-13 17:27:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3978651","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3978651","identity":"rs-3978651","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}