Stress Hyperglycemia Ratio as a Predictor of All-Cause and Cardiovascular Mortality in Patients With Urinary Incontinence: Evidence From NHANES 2001–2018

Stress Hyperglycemia Ratio as a Predictor of All-Cause and Cardiovascular Mortality in Patients With Urinary Incontinence: Evidence From NHANES 2001–2018 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Stress Hyperglycemia Ratio as a Predictor of All-Cause and Cardiovascular Mortality in Patients With Urinary Incontinence: Evidence From NHANES 2001–2018 Yuqing Huang, Heqian Liu, Xia Fang, Chenhao Deng, Jia Feng, Yuling Yang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7685623/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The stress hyperglycemia ratio (SHR) is a potential marker of stress-related glucose dysregulation and adverse health outcomes. However, its association with mortality risk in individuals with urinary incontinence (UI) remains poorly understood. Objective: This study aimed to evaluate the relationship between SHR and mortality risk in adults with UI, specifically examining its role as a predictor of all-cause and cardiovascular mortality. Methods: We analyzed data from 5,933 adults with UI from the NHANES 2001–2018 cohort, with mortality follow-up through 2019. SHR was categorized into quartiles. Survival differences were assessed using Kaplan-Meier curves and log-rank tests. Cox proportional hazards models were applied with stepwise adjustments for potential confounders. Nonlinear trends were explored using restricted cubic spline regression, and subgroup analyses evaluated potential effect modifications. Results: A U-shaped association was observed between SHR and both all-cause and cardiovascular mortality. Optimal thresholds for SHR were 0.829 for all-cause mortality and 0.850 for cardiovascular mortality. Participants in the highest SHR quartile had a 27% increased risk of all-cause mortality (HR = 1.27, 95% CI: 1.05–1.53) after full adjustment. Mortality risk escalated sharply beyond these thresholds (7.20-fold for all-cause mortality, 95% CI: 4.36–11.90; 8.90-fold for cardiovascular mortality, 95% CI: 4.55–17.41). The association was stronger in males (P = 0.04) and other Hispanic subgroups (P = 0.03). Conclusions: SHR was an independent predictor of mortality risk in adults with UI. This finding suggests that SHR may serve as a useful biomarker for identifying high-risk individuals and guiding early interventions to reduce mortality in this population. SHR All-Cause Mortality Cardiovascular Mortality Urinary Incontinence NHANES Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Urinary incontinence (UI) is a common pathological condition characterized by the involuntary leakage of urine 1 . According to the International Continence Society, UI is mainly classified into three types 1 : mixed urinary incontinence (MUI), which involves involuntary leakage associated with both increased abdominal pressure and urgency; stress urinary incontinence (SUI), defined as unintentional urine leakage during activities such as coughing, lifting, or exercising; and urgency urinary incontinence (UUI), which is characterized by a sudden, intense urge to urinate that is difficult to control. UI is more prevalent among women 2 and significantly impairs quality of life. It has been reported that 61.8% of adult women experience UI, corresponding to approximately 78,297,094 individuals, with 32.4% reporting at least one episode of UI per month 3 . Meanwhile, the prevalence of UI in men has shown a marked upward trend. According to epidemiological data, the prevalence of UI in men aged 45 years and older was 4.5% in 1992 and increased to 10.5% by 2003 4 . Globally, studies from various countries have demonstrated considerable variation in the prevalence of UI, ranging from approximately 5% to 70%, with most studies reporting a prevalence between 25% and 45% 5 . The prevalence of UI increases with age, and this trend is particularly pronounced among women 6 . Although UI is not a life-threatening condition, it can profoundly disrupt social interactions and impose significant burdens on both patients and their families 7 . In recent years, an increasing number of studies have indicated a potential association between UI and mortality 8 , 9 . Therefore, early detection of UI is crucial for improving patients' quality of life and psychological well-being. Advancing age 7 , body mass index (BMI) ≥ 25 3 , glycated hemoglobin (HbA1c) 10 , and blood glucose levels 11 have been reported as risk factors associated with UI. However, the direct relationship between diabetes and UI remains controversial. While some studies have found no significant association between diabetes and the incidence of UI 3 , others have reported that diabetes may increase the risk of developing UI 12 . In hospitalized patients, hyperglycemia has been linked to higher morbidity and mortality. SHR, defined as a relative elevation in blood glucose caused by inflammation or neurohormonal dysregulation 13 , is considered to reflect the severity of illness. Traditional absolute measures of hyperglycemia may be insufficient for accurately predicting outcomes in critically ill patients. SHR, calculated using admission blood glucose and glycated hemoglobin (HbA1c), represents a patient's relative hyperglycemic state, thereby minimizing the influence of baseline glucose variability. Research suggests that SHR may better identify patients at risk from relative hyperglycemia and provide a more individualized risk assessment compared to absolute hyperglycemia, especially in populations with varying baseline glucose levels 14 . However, the potential association between SHR and all-cause mortality among patients with UI has not yet been reported. Accordingly, this study sought to examine the relationship between the stress hyperglycemia ratio (SHR) and all-cause mortality among individuals with urinary incontinence, utilizing data from the 2001 to 2018 cycles of the National Health and Nutrition Examination Survey (NHANES). 2. Methods 2.1 Study Population This study employed data from the National Health and Nutrition Examination Survey (NHANES) covering the years 2001 to 2018. The National Health and NHANES is a nationally representative health surveillance initiative collaboratively conducted by the Centers for Disease Control and Prevention (CDC) and the National Center for Health Statistics (NCHS), aimed at providing comprehensive information on the health and nutritional status of the civilian, non-institutionalized U.S. population. The program applies a complex, multistage, stratified probability sampling design to ensure that its findings are representative at the national level, thereby supporting public health monitoring and policymaking. Data collection in NHANES integrates standardized interviews, physical examinations, and laboratory assessments, allowing for the examination of a wide range of health indicators, including metabolic, cardiovascular, and behavioral risk factors. All NHANES procedures are conducted in alignment with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines 15 , ensuring methodological rigor and transparency. Ethical oversight is provided by the NCHS Research Ethics Review Board, and all participants provide written informed consent prior to enrollment. The breadth and quality of NHANES data make it an invaluable resource for epidemiological research, particularly for examining health disparities and long-term outcomes across diverse subpopulations in the United States. The NHANES database contains comprehensive information, including sociodemographic characteristics (e.g., age, sex, race), socioeconomic status (e.g., education level and poverty income ratio [PIR]), lifestyle behaviors (e.g., smoking, alcohol use), as well as clinical examinations, laboratory tests, and physiological measurements. For this analysis, we accessed publicly available data covering nine continuous NHANES cycles (2001–2018), which were downloaded from the official NHANES website. Initially, 91,351 individuals were enrolled in the study. Exclusions were made for participants with missing SHR values (n = 63,102), incomplete or unavailable UI data (n = 7,981 and n = 12,658, respectively), and those lacking data on key covariates including BMI, PIR, alcohol consumption, and serum cotinine levels (n = 1,677). After applying all exclusion criteria, the final analytic sample consisted of 5,933 participants, as illustrated in Fig. 1. These individuals had complete and valid data on SHR, UI status, and relevant covariates, making them eligible for the subsequent analyses examining the relationship between SHR and mortality risk. 2.2 Covariates of Interest This study assessed several essential covariates, including chronological age, biological sex, race, and educational level (classified as high school education or less versus college education or higher). The PIR, representing the ratio between household income and the federally defined poverty threshold, was dichotomized as < 1.3 or ≥ 1.3. PIR values were derived in accordance with the poverty guidelines issued by the U.S. Department of Health and Human Services (DHHS) 16 . Smoking status was determined based on serum cotinine concentration, with individuals exhibiting levels of ≥ 10 ng/mL considered smokers, and those with concentrations below this threshold not categorized as such 17 . Alcohol consumption was operationally defined as the intake of no fewer than 12 alcoholic beverages within the preceding 12 months 18 . 2.3 Exposure and Outcome Definitions UI was assessed using the standardized questionnaire from NHANES. According to NHANES criteria 19 , SUI was defined as involuntary urine leakage during activities such as coughing, lifting, or exercising within the past 12 months (KIQ042). UUI was defined as urine leakage due to a sudden urge to urinate or pressure, occurring before the individual could reach the toilet (KIQ044). Participants exhibiting both SUI and UUI symptoms were classified as having MUI. In addition, unspecified UI was defined as involuntary urine leakage not associated with coughing, lifting, exercising, or urgency (KIQ046). Participants who responded "yes" to any of these items were considered to have UI. The SHR was calculated using the formula: [FPG (mmol/L)] / [1.59 × HbA1c (%) – 2.59] 14 . All participants were categorized into quartiles based on SHR values (Q1, Q2, Q3, and Q4), with Q1 serving as the reference group. 2.4 Mortality Assessment The main outcome measure in this study was all-cause mortality. Vital status during the follow-up period was ascertained through linkage between data provided by the National Center for Health Statistics (NCHS) and the National Death Index (NDI). Based on the information obtained from the NDI, participants were classified as deceased or alive. Mortality causes were classified according to the 10th edition of the International Classification of Diseases (ICD-10). All-cause mortality encompassed deaths attributable to any condition, such as cancer (codes 019–043), diabetes (046), and cardiovascular diseases (054–068), cerebrovascular disease (070), accidents (unintentional injuries, 112–123), and other causes (010). The follow-up period was calculated from the baseline interview to the date of death or December 31, 2019. 2.5 Statistical Analysis All statistical analyses were performed using R software (version 4.0.2) and IBM SPSS Statistics (version 28.0; Armonk, NY, USA). To account for the complex multistage sampling framework of NHANES, which incorporates stratification and clustering, appropriate survey weights, strata variables, and primary sampling units were applied to produce nationally representative estimates. SHR calculated using admission blood glucose and glycated hemoglobin (HbA1c). Participants were grouped into quartiles according to their SHR values. Continuous variables were described using means and standard deviations, while categorical variables were summarized as frequencies with corresponding percentages. Differences across SHR quartiles were evaluated using one-way analysis of variance (ANOVA) for continuous variables and chi-square tests for categorical variables. To assess the association between SHR and mortality outcomes (including all-cause and cardiovascular mortality), Cox proportional hazards regression models were constructed with three progressive levels of covariate adjustment: Model 1 was unadjusted; Model 2 adjusted for age, sex, and body weight; and Model 3 additionally included adjustments for race, BMI, PIR, educational attainment, smoking behavior, and alcohol consumption. For covariates with less than 20% missing data, multiple imputation based on regression was employed. To explore potential nonlinear associations, restricted cubic spline (RCS) functions and smooth curve fitting were applied; when nonlinearity was identified, a two-piecewise Cox regression model was used to estimate threshold effects. Furthermore, subgroup analyses were performed by stratifying participants based on sex, age group (65 vs. ≥65 years), BMI category (24 vs. ≥24 kg/m²), PIR, race, education level, smoking status, and alcohol use. A two-sided p-value < 0.05 was considered statistically significant. To ensure transparent and standardized reporting, we followed the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist for cross-sectional studies when preparing this manuscript 20 . 3. Results 3.1 Baseline Characteristics A total of 5,933 individuals diagnosed with UI were included in the present analysis. Baseline characteristics according to SHR quartiles are summarized in Table 1 . Based on SHR values, participants were classified into four quartile groups: Q1 (0.16–0.83), Q2 (0.83–0.90), Q3 (0.90–0.98), and Q4 (0.98–2.79). The average age of the study population was 55.7 years, and women comprised 75.0% of the cohort. An upward trend in all-cause mortality risk was identified across increasing SHR quartiles. In the highest SHR quartile, the proportion of male participants showed a significant upward trend, increasing by 13.4% compared with the lowest quartile (20.2% vs. 33.6%, P < 0.001), while the proportion of female participants declined accordingly by 13.4% (79.8% vs. 66.4%, P < 0.001). Notably, the proportion of females remained higher than that of males across all quartiles. Other indicators showed that individuals in the highest quartile had a 3.31% higher mean BMI compared to those in the lowest quartile (31.1 vs. 30.1, P < 0.001). From 15.0% to 17.3%, the proportion of Mexican Americans increased by 2.3%, while the proportion of non-Hispanic Whites increased by 13.0% (42.2% vs. 55.2%) and that of non-Hispanic Blacks decreased by 13.7% (29.3% vs. 15.6%) (P < 0.001 for all). In addition, in the highest SHR quartile, the proportion of individuals with a high school education or below was 1.3% higher than in the lowest quartile and accounted for the majority (52.1%, P = 0.007). Moreover, in the highest quartile, the proportion of non-drinkers decreased by 9.7% (P < 0.001), while the proportion of alcohol consumers increased by 9.7% (P < 0.001). Alcohol consumers significantly outnumbered non-drinkers (69.2% vs. 30.8%, P < 0.001). Table 1 Baseline demographic and clinical data of four groups Characteristic Overall (N = 5933) Q1 (N = 1483) Q2 (N = 1483) Q3, (N = 1483) Q4 (N = 1484) p-value 1 Sex, n (%) < 0.001 Male 1483 (25.0) 299 (20.2) 316 (21.3) 369 (24.9) 499 (33.6) Female 4450 (75.0) 1184 (79.8) 1167 (78.7) 1114 (75.1) 985 (66.4) Age (years) 55.7 ± 16.8 56.5 ± 16.5 55.1 ± 16.7 54.4 ± 17.2 56.7 ± 16.8 < 0.001 Body Mass Index (kg/m 2 ) 30.2 ± 7.3 30.1 ± 7.5 29.6 ± 7.2 30.2 ± 7.0 31.1 ± 7.6 < 0.001 Race, n (unweighted) (%) < 0.001 Mexican American 982 (16.6) 222 (15) 230 (15.5) 274 (18.5) 256 (17.3) Other Hispanic 434 ( 7.3) 109 (7.3) 112 (7.6) 102 (6.9) 111 (7.5) Non-Hispanic White 3066 (51.7) 626 (42.2) 782 (52.7) 839 (56.6) 819 (55.2) Non-Hispanic Black 1109 (18.7) 435 (29.3) 252 (17) 191 (12.9) 231 (15.6) Other races 342 ( 5.8) 91 (6.1) 107 (7.2) 77 (5.2) 67 (4.5) Education, n (%) 0.007 High school or low 2952 (49.8) 754 (50.8) 683 (46.1) 742 (50) 773 (52.1) College or high 2981 (50.2) 729 (49.2) 800 (53.9) 741 (50) 711 (47.9) Poverty, n (%) 0.002 <1.3 1866 (31.5) 495 (33.4) 410 (27.6) 467 (31.5) 494 (33.3) ≥ 1.3 4067 (68.5) 988 (66.6) 1073 (72.4) 1016 (68.5) 990 (66.7) Alcohol, n (%) < 0.001 No 2049 (34.5) 601 (40.5) 517 (34.9) 474 (32) 457 (30.8) Yes 3884 (65.5) 882 (59.5) 966 (65.1) 1009 (68) 1027 (69.2) Smoking, n (%) 0.788 No 4546 (76.6) 1129 (76.1) 1150 (77.5) 1130 (76.2) 1137 (76.6) Yes 1387 (23.4) 354 (23.9) 333 (22.5) 353 (23.8) 347 (23.4) 1 chi-squared test with Rao & Scott's second-order correction; Wilcoxon rank-sum test for complex survey samples 3.2 Association Between SHR and Mortality Figures 2 illustrate the all-cause and CVD mortality distribution during follow-up, with 1,172 all-cause and 302 CVD-related deaths recorded. Cox proportional hazards analysis using three progressively adjusted models revealed SHR as a significant mortality predictor in UI patients. In the unadjusted model (Model 1), the highest SHR quartile (Q4) showed a 25% increased all-cause mortality risk versus Q1 (HR = 1.25, 95%CI:1.03–1.53, P < 0.001). This association persisted after adjusting for age, sex and weight (Model 2: HR = 1.22, 95%CI:1.01–1.46, P < 0.001) and remained significant in the fully-adjusted model incorporating demographic, socioeconomic and behavioral factors (Model 3: HR = 1.27, 95%CI:1.05–1.53, P < 0.001). Across all three models, individuals in the highest SHR category consistently exhibited increased vulnerability to all-cause mortality, as indicated by statistically significant trend tests (P for trend < 0.05). Importantly, this strong association was not observed for cardiovascular mortality, as no significant difference in risk was found between participants in Q4 and those in Q1 Instead, participants in the second SHR quartile (Q2) consistently showed substantially lower CVD mortality risk compared to Q1, with hazard ratios indicating a 56% decrease in Model 1, a 54% decrease in Model 2, and a 52% decrease in Model 3 (all P < 0.05). These findings were corroborated by Kaplan–Meier survival curves (Fig. 3 ), which illustrated a clear separation between groups, showing significantly poorer survival among those in Q4. The difference in all-cause mortality across SHR quartiles was statistically significant over the follow-up period, as confirmed by the log-rank test (P < 0.001). 3.3 Nonlinear Association Between SHR and Mortality We assessed the SHR–mortality relationship in UI patients using Cox regression models with restricted cubic splines (RCS), which revealed a significant U-shaped association for both all-cause and cardiovascular (CVD) mortality (Figs. 4 A–B). Two-piecewise Cox models identified inflection points at SHR = 0.829 for all-cause mortality and SHR = 0.850 for CVD mortality, with threshold effects reaching statistical significance (P < 0.001). Below these thresholds, each unit increase in SHR was associated with a sharply reduced risk of mortality (all-cause: HR = 0.02, 95% CI: 0.01–0.06; CVD: HR = 0.05, 95% CI: 0.00–0.06; both P < 0.001). In contrast, when SHR exceeded these inflection points, mortality risks increased markedly (all-cause: HR = 7.20, 95% CI: 4.36–11.90; CVD: HR = 8.90, 95% CI: 4.55–17.41; both P < 0.001). These associations remained robust after full adjustment for demographic, socioeconomic, and lifestyle-related covariates (Table 2 ). Table 2 Threshold effect analysis of SHR on all-cause and cardiovascular mortality in patients with UI. All-cause mortality HR (95%CI) P-value Total 3.21(1.95, 5.30) < 0.001 Fitting by two-piecewise Cox proportional risk model Inflection point 0.829 SHR index < 0.829 0.02(0.02, 0.24) < 0.001 SHR index ≥ 0.829 5.93(3.54, 9.94) < 0.001 CVD mortality Total 2.05(0.56, 7.54) 0.280 Fitting by two-piecewise Cox proportional risk model Inflection point 0.850 SHR index < 0.850 0.03(0.002, 0.26) 0.002 SHR index ≥ 0.850 7.20(3.35, 15.44) < 0.001 Cox proportional hazards models were used to estimate HR and 95% CI. Adjusted for age, sex, race, BMI, PIR, education level, smoking status, and alcohol consumption. HR Hazard ratio; CI Confidence interval 3.4 Stratified Analysis To assess the robustness of the SHR-mortality association, we performed subgroup analyses by demographic (sex, age, race), socioeconomic (education, income), and behavioral factors (smoking, alcohol use). Individuals with SHR above the threshold (≥ 0.829 for all-cause mortality; ≥0.850 for cardiovascular mortality) had uniformly increased mortality risks across all subgroups (Tables 3 – 4 ). The lack of significant interaction effects (P > 0.05 for all) further supported the consistency of this association. Table 3 Subgroup analyses of the association between the SHR and all-cause mortality Characteristic HR (95%CI) [SHR < 0.829] HR (95%CI) [SHR ≥ 0.829] P value P for interaction Overall 1 0.90(0.76, 1.07) 0.244 Sex 0.316 Male 1 0.73(0.54,0.99) 0.04 Female 1 0.88(0.73,1.07) 0.21 Age (years) 0.675 < 65 1 0.96(0.70,1.30) 0.77 ≥ 65 1 0.96(0.80,1.16) 0.69 BMI (Kg/m2) 0.532 < 24 1 1.06(0.72,1.57) 0.75 ≥ 24 1 0.93(0.79,1.11) 0.43 Race 0.620 Mexican American 1 0.81(0.49,1.34) 0.41 Other Hispanic 1 0.46(0.23,0.94) 0.03 Non-Hispanic White 1 0.97(0.81,1.17) 0.75 Non-Hispanic Black 1 1.24(0.89,1.73) 0.21 other 1 1.10(0.40,2.99) 0.85 Education 0.992 High school or less 1 1.04(0.87,1.24) 0.69 college or more 1 0.95(0.72,1.25) 0.73 Poverty 0.616 ≤ 1.3 1 0.99(0.79,1.25) 0.95 > 1.3 1 0.98(0.81,1.18) 0.82 Alcohol 0.093 NO 1 1.23(0.96,1.57) 0.10 YES 1 0.88(0.72,1.09) 0.24 Smoking 0.889 NO 1 1.03(0.86,1.24) 0.74 YES 1 0.96(0.67,1.38) 0.83 Table 4 Subgroup analyses of the association between the SHR and cardiovascular mortality Characteristic HR (95%CI) [SHR < 0.829] HR (95%CI) [SHR ≥ 0.829] P value P for interaction Overall 1 0.77(0.56, 1.05) 0.096 Sex 0.88 Male 1 0.67(0.40,1.13) 0.13 Female 1 0.70(0.49,0.99) 0.05 Age (years) 0.781 < 65 1 0.68(0.39,1.20) 0.18 ≥ 65 1 0.81(0.57,1.14) 0.23 BMI (Kg/m2) 0.409 < 24 1 0.64(0.33,1.24) 0.18 ≥ 24 1 0.81(0.59,1.13) 0.22 Race 0.306 Mexican American 1 0.87(0.34,2.24) 0.77 Other Hispanic 1 1.54(0.33,7.19) 0.58 Non-Hispanic White 1 0.72(0.50,1.04) 0.08 Non-Hispanic Black 1 1.29(0.78,2.14) 0.31 other 1 1.36(0.21,8.86) 0.75 Education 0.071 High school or less 1 1.11(0.76,1.62) 0.60 College or more 1 0.57(0.36,0.90) 0.02 Poverty 0.172 ≤ 1.3 1 1.01(0.61,1.67) 0.96 > 1.3 1 0.70(0.49,1.02) 0.06 Alcohol 0.453 NO 1 0.96(0.66,1.42) 0.85 YES 1 0.71(0.47,1.09) 0.12 Smoking 0.349 NO 1 0.91(0.62,1.32) 0.61 YES 1 0.65(0.34,1.22) 0.18 Subgroup analyses revealed distinct patterns in the association between SHR and mortality. For all-cause mortality, a significantly stronger association with elevated SHR was observed among male participants (P = 0.04) and individuals categorized as other Hispanic (P = 0.03). In contrast, for CVD mortality, higher SHR levels were significantly associated with lower mortality risk in females (P = 0.05) and in participants with a college education or higher (P = 0.02). These associations persisted across all subgroups, though modest differences in effect estimates were noted among various demographic and socioeconomic groups. 4. Discussion This study is the first to demonstrate a U-shaped relationship between the SHR and both all-cause and cardiovascular mortality in individuals with UI. Restricted cubic spline analyses identified optimal thresholds at SHR levels of 0.829 for all-cause mortality and 0.850 for cardiovascular mortality, with mortality risk increasing at both lower and higher values. Notably, this nonlinear association remained statistically significant after comprehensive multivariable adjustment (all P < 0.001), establishing SHR as an independent prognostic marker in this patient population. Subgroup analyses revealed important demographic variations in the SHR-mortality relationship. The association was particularly pronounced among male participants (P = 0.04) and individuals of other Hispanic ethnicity (P = 0.03). These findings suggest that the predictive value of SHR may be modified by both biological (sex) and sociocultural (ethnicity) factors, providing clinically relevant insights for risk stratification in UI patients 21 , 22 . According to recent studies, SHR is a more sensitive indicator of glycemic alterations under acute stress conditions compared to traditional markers such as fasting blood glucose (FBG), HbA1c, and oral glucose tolerance test (OGTT). By comparing admission blood glucose with the estimated average glucose derived from HbA1c, SHR accounts for individual long-term glycemic history and enables a more accurate risk prediction in patients with varying baseline glucose levels. In contrast, absolute glucose levels may be confounded by prior glycemic control, potentially leading to inaccurate assessment of critical illness risk 14 . A growing body of evidence has demonstrated that elevated SHR is positively associated with mortality in a variety of acute medical conditions [20–22] . Additionally, SHR has been shown to be closely linked to a range of diseases, including acute coronary syndrome, sepsis, nonalcoholic fatty liver disease, and chronic kidney disease 24 – 27 . However, no prior studies have reported on the association between SHR and all-cause or CVD mortality specifically in patients with UI. UI is a common condition that severely impacts the quality of life, particularly among women. Data from NHANES indicate that 17.1% of women aged 20 years and older experience moderate to severe UI, with prevalence rising markedly with age—reaching as high as 30–40% among elderly women—highlighting its significance as a major public health concern 2 . Although previous research has identified several predictors of UI 19,28 , there remains a lack of reliable glycemic markers capable of predicting all-cause and CVD mortality among patients with UI. Our study represents the first investigation to establish a U-shaped association between SHR and mortality specifically in UI patients. Analyzing nationally representative data from 5,933 U.S. adults with UI (2001–2018), we consistently observed this nonlinear relationship through both Cox regression and RCS analyses. The identified inflection points (SHR = 0.829 for all-cause mortality; SHR = 0.850 for CVD mortality) demarcated distinct risk patterns, with mortality increasing sharply above these thresholds. These robust findings position SHR as a potentially valuable prognostic indicator for UI patients. While prior research has focused on UI risk factors, our study uniquely characterizes mortality predictors in this vulnerable population. For instance, Li et al 19 observed a positive association between the triglyceride-glucose index combined with BMI (TyG-BMI) and the risk of UI. Similarly, Cao et al 28 found a positive correlation between the metabolic score for insulin resistance (METS-IR) and the risk of UI. Our study expands on this body of evidence by demonstrating that SHR not only reflects metabolic stress but also has prognostic value in predicting mortality risk among UI patients, thereby providing additional clinical relevance. Moreover, similar findings have been reported in related populations. For example, in an observational study, Ding et al 29 found that SHR could predict mortality risk in patients with diabetes or prediabetes. Our study is the first to extend this prognostic utility of SHR to the UI population, further emphasizing its potential as a superior biomarker. Given that SHR has been linked to various pathophysiological changes in previous studies, these alterations may underlie the increased mortality observed in patients with UI. In patients with UI, metabolic dysregulation and chronic inflammatory responses may synergistically contribute to systemic dysfunction. He et al 30 reported that metabolic syndrome may significantly influence the development of UI by inducing inflammation and oxidative stress. This systemic inflammation, driven by metabolic abnormalities, can exacerbate lower urinary tract dysfunction and potentially worsen UI symptoms. Additionally, UI has been strongly associated with sarcopenia 31 , 32 , and stress hyperglycemia has been shown to correlate with impaired muscle function, increasing the risk of falls and reduced mobility in affected individuals 33 . Elevated SHR levels are also known to promote systemic inflammation, cardiovascular dysfunction, and myocardial energy metabolism disturbances. These pathophysiological alterations not only increase the risk of mortality in patients with atrial fibrillation but also resemble the chronic inflammatory and oxidative stress responses seen in individuals with UI, thereby further amplifying the risk of cardiovascular events 34 . these findings collectively underscore the close relationship between metabolic and physiological imbalances and the risk and severity of UI. They also highlight the clinical relevance of SHR as a marker associated with adverse outcomes. In line with our findings, both abnormally high and low SHR values may have detrimental health effects, reinforcing the importance of maintaining metabolic homeostasis in this population. This study drew upon data from the National Health and NHANES to explore the relationship between the SHR and both all-cause and cardiovascular mortality among individuals with UI, using a large-scale, nationally representative cohort of U.S. adults. To enhance the applicability of the findings to the general U.S. population, weighted Cox proportional hazards models were employed in the analysis. However, several limitations should be acknowledged. First, NHANES relies heavily on self-reported data for numerous variables, including lifestyle factors, healthcare utilization, and medical history. As a result, recall bias may be present. Second, the findings are based solely on data from the U.S. population. Therefore, additional studies in diverse populations are needed to validate the observed associations between SHR and mortality outcomes in patients with UI. 5. Conclusion In conclusion, our findings suggest that the SHR may have valuable applications in patients with urinary incontinence. Specifically, SHR could serve as a potential marker for long-term mortality risk stratification in this population. Abbreviations SHR (Stress hyperglycemia ratio ) UI (Urinary incontinence) CVD (Cardiovascular Diseas) NHANES (National Health and Nutrition Examination Survey) BMI (Bdy mass index) PIR (Poverty income ratio) RCS (Restricted cubic spline ) Declarations Ethics approval and consent to participate The NHANES is approved by the National Center for Health Statistics Research Ethics Review Board, and all participants provide informed consent. Consent for publication Not applicable. Author contributions All authors thank the NHANES 2001–2018 participants for their invaluable contributions. The study was conceived and designed by Yuqing Huang, Heqian Liu, Yong Liu, and Yong Xu. Yuqing Huang and Heqian Liu contributed equally to this work and drafted the main manuscript. Xia Fang, ChenHao Deng, and Jia Feng were responsible for data preparation, statistical analysis, and visualization. Mengting Huang and Yuling Yang contributed equally and verified and repeated the statistical analysis. Yong Liu and Yong Xu supervised the overall study. All authors have read and approved the final version of this manuscript. Sources of Funding This work was supported by the Natural Science Foundation of China (NO.82470854).This work was supported by the Scientific research project of Southwest Medical University(NO.2024LCYXZX02) Competing interests The authors declare no competing interests. Acknowledgments The authors are grateful to all the participants for their participation. Data availability The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author. References Haylen, B. 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Association between stress hyperglycemia ratio and all-cause mortality in critically ill patients with atrial fibrillation: insights from a MIMIC-IV study. Front. Endocrinol. 15 , 1412159 (2024). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":119685,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of study participants\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7685623/v1/7b2b539cfc548c9ee97f10a7.png"},{"id":94474057,"identity":"ec595fdf-f0b6-447d-a8f8-b2e865d674e4","added_by":"auto","created_at":"2025-10-27 15:47:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":108641,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Forest plot of hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause mortality according to SHR index quartiles\u003c/p\u003e\n\u003cp\u003e(B) Forest plot of hazard ratios (HRs) and 95% confidence intervals (CIs) for cardiovascular mortality according to SHR index quartiles\u003c/p\u003e\n\u003cp\u003eBMI body mass index; CI confidence interval; HR hazard ratio; SHR stress hyperglycemia ratio; PIRpoverty income ratio\u003c/p\u003e\n\u003cp\u003eModel 1: no covariates were adjusted for\u003c/p\u003e\n\u003cp\u003eModel 2: Adjusted for age, sex and body weight\u003c/p\u003e\n\u003cp\u003eModel 3: Adjusted for age, sex, race, BMI, PIR, education level, smoking status, and alcohol consumption\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7685623/v1/4912fbb37ebedb7df305a337.png"},{"id":94474561,"identity":"070b7ec9-54e9-43a6-95b0-075e3a8bef74","added_by":"auto","created_at":"2025-10-27 15:49:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":129875,"visible":true,"origin":"","legend":"\u003cp\u003eK-M analyses for all-cause mortality among the four groups.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7685623/v1/d40c608f63c2244c67dd7c50.png"},{"id":94473866,"identity":"0735ade3-b139-4539-a896-f6d54d1b4f22","added_by":"auto","created_at":"2025-10-27 15:46:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":70344,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between SHR and (A) all-cause mortality and (B) cardiovascular mortality in patients with UI in the unadjusted model. The solid line and shaded blue area represent the estimated hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs), respectively.\u003c/p\u003e\n\u003cp\u003eSHR, stress hyperglycemia ratio; UI, urinary incontinence\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7685623/v1/e5123f98e363d2966542bb9e.png"},{"id":99790451,"identity":"14140e0f-0daa-4674-9516-d112f2cd63ea","added_by":"auto","created_at":"2026-01-08 12:58:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1619170,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7685623/v1/89b5d525-66b5-4206-b680-4b643c2a3758.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Stress Hyperglycemia Ratio as a Predictor of All-Cause and Cardiovascular Mortality in Patients With Urinary Incontinence: Evidence From NHANES 2001–2018","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eUrinary incontinence (UI) is a common pathological condition characterized by the involuntary leakage of urine \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. According to the International Continence Society, UI is mainly classified into three types\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e: mixed urinary incontinence (MUI), which involves involuntary leakage associated with both increased abdominal pressure and urgency; stress urinary incontinence (SUI), defined as unintentional urine leakage during activities such as coughing, lifting, or exercising; and urgency urinary incontinence (UUI), which is characterized by a sudden, intense urge to urinate that is difficult to control. UI is more prevalent among women\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and significantly impairs quality of life. It has been reported that 61.8% of adult women experience UI, corresponding to approximately 78,297,094 individuals, with 32.4% reporting at least one episode of UI per month \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Meanwhile, the prevalence of UI in men has shown a marked upward trend. According to epidemiological data, the prevalence of UI in men aged 45 years and older was 4.5% in 1992 and increased to 10.5% by 2003\u003csup\u003e4\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGlobally, studies from various countries have demonstrated considerable variation in the prevalence of UI, ranging from approximately 5% to 70%, with most studies reporting a prevalence between 25% and 45%\u003csup\u003e5\u003c/sup\u003e. The prevalence of UI increases with age, and this trend is particularly pronounced among women\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Although UI is not a life-threatening condition, it can profoundly disrupt social interactions and impose significant burdens on both patients and their families\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In recent years, an increasing number of studies have indicated a potential association between UI and mortality\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Therefore, early detection of UI is crucial for improving patients' quality of life and psychological well-being.\u003c/p\u003e\u003cp\u003eAdvancing age\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, body mass index (BMI)\u0026thinsp;\u0026ge;\u0026thinsp;25\u003csup\u003e3\u003c/sup\u003e, glycated hemoglobin (HbA1c)\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, and blood glucose levels\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e have been reported as risk factors associated with UI. However, the direct relationship between diabetes and UI remains controversial. While some studies have found no significant association between diabetes and the incidence of UI\u003csup\u003e3\u003c/sup\u003e, others have reported that diabetes may increase the risk of developing UI\u003csup\u003e12\u003c/sup\u003e. In hospitalized patients, hyperglycemia has been linked to higher morbidity and mortality. SHR, defined as a relative elevation in blood glucose caused by inflammation or neurohormonal dysregulation\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, is considered to reflect the severity of illness. Traditional absolute measures of hyperglycemia may be insufficient for accurately predicting outcomes in critically ill patients. SHR, calculated using admission blood glucose and glycated hemoglobin (HbA1c), represents a patient's relative hyperglycemic state, thereby minimizing the influence of baseline glucose variability. Research suggests that SHR may better identify patients at risk from relative hyperglycemia and provide a more individualized risk assessment compared to absolute hyperglycemia, especially in populations with varying baseline glucose levels\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. However, the potential association between SHR and all-cause mortality among patients with UI has not yet been reported.\u003c/p\u003e\u003cp\u003eAccordingly, this study sought to examine the relationship between the stress hyperglycemia ratio (SHR) and all-cause mortality among individuals with urinary incontinence, utilizing data from the 2001 to 2018 cycles of the National Health and Nutrition Examination Survey (NHANES).\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Population\u003c/h2\u003e\u003cp\u003eThis study employed data from the National Health and Nutrition Examination Survey (NHANES) covering the years 2001 to 2018. The National Health and NHANES is a nationally representative health surveillance initiative collaboratively conducted by the Centers for Disease Control and Prevention (CDC) and the National Center for Health Statistics (NCHS), aimed at providing comprehensive information on the health and nutritional status of the civilian, non-institutionalized U.S. population. The program applies a complex, multistage, stratified probability sampling design to ensure that its findings are representative at the national level, thereby supporting public health monitoring and policymaking. Data collection in NHANES integrates standardized interviews, physical examinations, and laboratory assessments, allowing for the examination of a wide range of health indicators, including metabolic, cardiovascular, and behavioral risk factors. All NHANES procedures are conducted in alignment with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, ensuring methodological rigor and transparency. Ethical oversight is provided by the NCHS Research Ethics Review Board, and all participants provide written informed consent prior to enrollment. The breadth and quality of NHANES data make it an invaluable resource for epidemiological research, particularly for examining health disparities and long-term outcomes across diverse subpopulations in the United States.\u003c/p\u003e\u003cp\u003eThe NHANES database contains comprehensive information, including sociodemographic characteristics (e.g., age, sex, race), socioeconomic status (e.g., education level and poverty income ratio [PIR]), lifestyle behaviors (e.g., smoking, alcohol use), as well as clinical examinations, laboratory tests, and physiological measurements. For this analysis, we accessed publicly available data covering nine continuous NHANES cycles (2001\u0026ndash;2018), which were downloaded from the official NHANES website.\u003c/p\u003e\u003cp\u003eInitially, 91,351 individuals were enrolled in the study. Exclusions were made for participants with missing SHR values (n\u0026thinsp;=\u0026thinsp;63,102), incomplete or unavailable UI data (n\u0026thinsp;=\u0026thinsp;7,981 and n\u0026thinsp;=\u0026thinsp;12,658, respectively), and those lacking data on key covariates including BMI, PIR, alcohol consumption, and serum cotinine levels (n\u0026thinsp;=\u0026thinsp;1,677). After applying all exclusion criteria, the final analytic sample consisted of 5,933 participants, as illustrated in Fig.\u0026nbsp;1. These individuals had complete and valid data on SHR, UI status, and relevant covariates, making them eligible for the subsequent analyses examining the relationship between SHR and mortality risk.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Covariates of Interest\u003c/h2\u003e\u003cp\u003eThis study assessed several essential covariates, including chronological age, biological sex, race, and educational level (classified as high school education or less versus college education or higher). The PIR, representing the ratio between household income and the federally defined poverty threshold, was dichotomized as \u0026lt;\u0026thinsp;1.3 or \u0026ge;\u0026thinsp;1.3. PIR values were derived in accordance with the poverty guidelines issued by the U.S. Department of Health and Human Services (DHHS)\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Smoking status was determined based on serum cotinine concentration, with individuals exhibiting levels of \u0026ge;\u0026thinsp;10 ng/mL considered smokers, and those with concentrations below this threshold not categorized as such\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Alcohol consumption was operationally defined as the intake of no fewer than 12 alcoholic beverages within the preceding 12 months\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Exposure and Outcome Definitions\u003c/h2\u003e\u003cp\u003eUI was assessed using the standardized questionnaire from NHANES. According to NHANES criteria\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, SUI was defined as involuntary urine leakage during activities such as coughing, lifting, or exercising within the past 12 months (KIQ042). UUI was defined as urine leakage due to a sudden urge to urinate or pressure, occurring before the individual could reach the toilet (KIQ044). Participants exhibiting both SUI and UUI symptoms were classified as having MUI. In addition, unspecified UI was defined as involuntary urine leakage not associated with coughing, lifting, exercising, or urgency (KIQ046). Participants who responded \"yes\" to any of these items were considered to have UI.\u003c/p\u003e\u003cp\u003eThe SHR was calculated using the formula: [FPG (mmol/L)] / [1.59 \u0026times; HbA1c (%) \u0026ndash; 2.59]\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. All participants were categorized into quartiles based on SHR values (Q1, Q2, Q3, and Q4), with Q1 serving as the reference group.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Mortality Assessment\u003c/h2\u003e\u003cp\u003eThe main outcome measure in this study was all-cause mortality. Vital status during the follow-up period was ascertained through linkage between data provided by the National Center for Health Statistics (NCHS) and the National Death Index (NDI). Based on the information obtained from the NDI, participants were classified as deceased or alive. Mortality causes were classified according to the 10th edition of the International Classification of Diseases (ICD-10). All-cause mortality encompassed deaths attributable to any condition, such as cancer (codes 019\u0026ndash;043), diabetes (046), and cardiovascular diseases (054\u0026ndash;068), cerebrovascular disease (070), accidents (unintentional injuries, 112\u0026ndash;123), and other causes (010). The follow-up period was calculated from the baseline interview to the date of death or December 31, 2019.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using R software (version 4.0.2) and IBM SPSS Statistics (version 28.0; Armonk, NY, USA). To account for the complex multistage sampling framework of NHANES, which incorporates stratification and clustering, appropriate survey weights, strata variables, and primary sampling units were applied to produce nationally representative estimates. SHR calculated using admission blood glucose and glycated hemoglobin (HbA1c). Participants were grouped into quartiles according to their SHR values. Continuous variables were described using means and standard deviations, while categorical variables were summarized as frequencies with corresponding percentages. Differences across SHR quartiles were evaluated using one-way analysis of variance (ANOVA) for continuous variables and chi-square tests for categorical variables. To assess the association between SHR and mortality outcomes (including all-cause and cardiovascular mortality), Cox proportional hazards regression models were constructed with three progressive levels of covariate adjustment: Model 1 was unadjusted; Model 2 adjusted for age, sex, and body weight; and Model 3 additionally included adjustments for race, BMI, PIR, educational attainment, smoking behavior, and alcohol consumption. For covariates with less than 20% missing data, multiple imputation based on regression was employed. To explore potential nonlinear associations, restricted cubic spline (RCS) functions and smooth curve fitting were applied; when nonlinearity was identified, a two-piecewise Cox regression model was used to estimate threshold effects. Furthermore, subgroup analyses were performed by stratifying participants based on sex, age group (65 vs. \u0026ge;65 years), BMI category (24 vs. \u0026ge;24 kg/m\u0026sup2;), PIR, race, education level, smoking status, and alcohol use. A two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003cp\u003eTo ensure transparent and standardized reporting, we followed the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist for cross-sectional studies when preparing this manuscript\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Baseline Characteristics\u003c/h2\u003e\u003cp\u003eA total of 5,933 individuals diagnosed with UI were included in the present analysis. Baseline characteristics according to SHR quartiles are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Based on SHR values, participants were classified into four quartile groups: Q1 (0.16\u0026ndash;0.83), Q2 (0.83\u0026ndash;0.90), Q3 (0.90\u0026ndash;0.98), and Q4 (0.98\u0026ndash;2.79). The average age of the study population was 55.7 years, and women comprised 75.0% of the cohort. An upward trend in all-cause mortality risk was identified across increasing SHR quartiles. In the highest SHR quartile, the proportion of male participants showed a significant upward trend, increasing by 13.4% compared with the lowest quartile (20.2% vs. 33.6%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the proportion of female participants declined accordingly by 13.4% (79.8% vs. 66.4%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, the proportion of females remained higher than that of males across all quartiles. Other indicators showed that individuals in the highest quartile had a 3.31% higher mean BMI compared to those in the lowest quartile (31.1 vs. 30.1, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). From 15.0% to 17.3%, the proportion of Mexican Americans increased by 2.3%, while the proportion of non-Hispanic Whites increased by 13.0% (42.2% vs. 55.2%) and that of non-Hispanic Blacks decreased by 13.7% (29.3% vs. 15.6%) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all). In addition, in the highest SHR quartile, the proportion of individuals with a high school education or below was 1.3% higher than in the lowest quartile and accounted for the majority (52.1%, P\u0026thinsp;=\u0026thinsp;0.007). Moreover, in the highest quartile, the proportion of non-drinkers decreased by 9.7% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the proportion of alcohol consumers increased by 9.7% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Alcohol consumers significantly outnumbered non-drinkers (69.2% vs. 30.8%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline demographic and clinical data of four groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;5933)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1483)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1483)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ3,\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1483)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1484)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1483 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e299 (20.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e316 (21.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e369 (24.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e499 (33.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4450 (75.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1184 (79.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1167 (78.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1114 (75.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e985 (66.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55.7\u0026thinsp;\u0026plusmn;\u0026thinsp;16.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.5\u0026thinsp;\u0026plusmn;\u0026thinsp;16.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55.1\u0026thinsp;\u0026plusmn;\u0026thinsp;16.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54.4\u0026thinsp;\u0026plusmn;\u0026thinsp;17.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e56.7\u0026thinsp;\u0026plusmn;\u0026thinsp;16.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBody Mass Index (kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e31.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace, n (unweighted) (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e982 (16.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e222 (15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e230 (15.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e274 (18.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e256 (17.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e434 ( 7.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109 (7.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e112 (7.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e102 (6.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e111 (7.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3066 (51.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e626 (42.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e782 (52.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e839 (56.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e819 (55.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1109 (18.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e435 (29.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e252 (17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e191 (12.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e231 (15.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther races\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e342 ( 5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e91 (6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e107 (7.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e77 (5.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e67 (4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school or low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2952 (49.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e754 (50.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e683 (46.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e742 (50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e773 (52.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege or high\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2981 (50.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e729 (49.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e800 (53.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e741 (50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e711 (47.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePoverty, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1866 (31.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e495 (33.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e410 (27.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e467 (31.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e494 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4067 (68.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e988 (66.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1073 (72.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1016 (68.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e990 (66.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAlcohol, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2049 (34.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e601 (40.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e517 (34.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e474 (32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e457 (30.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3884 (65.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e882 (59.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e966 (65.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1009 (68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1027 (69.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.788\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4546 (76.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1129 (76.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1150 (77.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1130 (76.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1137 (76.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1387 (23.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e354 (23.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e333 (22.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e353 (23.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e347 (23.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e1\u003c/sup\u003echi-squared test with Rao \u0026amp; Scott's second-order correction; Wilcoxon rank-sum test for complex survey samples\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Association Between SHR and Mortality\u003c/h2\u003e\u003cp\u003eFigures \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrate the all-cause and CVD mortality distribution during follow-up, with 1,172 all-cause and 302 CVD-related deaths recorded. Cox proportional hazards analysis using three progressively adjusted models revealed SHR as a significant mortality predictor in UI patients. In the unadjusted model (Model 1), the highest SHR quartile (Q4) showed a 25% increased all-cause mortality risk versus Q1 (HR\u0026thinsp;=\u0026thinsp;1.25, 95%CI:1.03\u0026ndash;1.53, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This association persisted after adjusting for age, sex and weight (Model 2: HR\u0026thinsp;=\u0026thinsp;1.22, 95%CI:1.01\u0026ndash;1.46, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and remained significant in the fully-adjusted model incorporating demographic, socioeconomic and behavioral factors (Model 3: HR\u0026thinsp;=\u0026thinsp;1.27, 95%CI:1.05\u0026ndash;1.53, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Across all three models, individuals in the highest SHR category consistently exhibited increased vulnerability to all-cause mortality, as indicated by statistically significant trend tests (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Importantly, this strong association was not observed for cardiovascular mortality, as no significant difference in risk was found between participants in Q4 and those in Q1 Instead, participants in the second SHR quartile (Q2) consistently showed substantially lower CVD mortality risk compared to Q1, with hazard ratios indicating a 56% decrease in Model 1, a 54% decrease in Model 2, and a 52% decrease in Model 3 (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings were corroborated by Kaplan\u0026ndash;Meier survival curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e), which illustrated a clear separation between groups, showing significantly poorer survival among those in Q4. The difference in all-cause mortality across SHR quartiles was statistically significant over the follow-up period, as confirmed by the log-rank test (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Nonlinear Association Between SHR and Mortality\u003c/h2\u003e\u003cp\u003eWe assessed the SHR\u0026ndash;mortality relationship in UI patients using Cox regression models with restricted cubic splines (RCS), which revealed a significant U-shaped association for both all-cause and cardiovascular (CVD) mortality (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u0026ndash;B). Two-piecewise Cox models identified inflection points at SHR\u0026thinsp;=\u0026thinsp;0.829 for all-cause mortality and SHR\u0026thinsp;=\u0026thinsp;0.850 for CVD mortality, with threshold effects reaching statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Below these thresholds, each unit increase in SHR was associated with a sharply reduced risk of mortality (all-cause: HR\u0026thinsp;=\u0026thinsp;0.02, 95% CI: 0.01\u0026ndash;0.06; CVD: HR\u0026thinsp;=\u0026thinsp;0.05, 95% CI: 0.00\u0026ndash;0.06; both P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, when SHR exceeded these inflection points, mortality risks increased markedly (all-cause: HR\u0026thinsp;=\u0026thinsp;7.20, 95% CI: 4.36\u0026ndash;11.90; CVD: HR\u0026thinsp;=\u0026thinsp;8.90, 95% CI: 4.55\u0026ndash;17.41; both P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These associations remained robust after full adjustment for demographic, socioeconomic, and lifestyle-related covariates (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThreshold effect analysis of SHR on all-cause and cardiovascular mortality in patients with UI.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll-cause mortality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.21(1.95, 5.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFitting by two-piecewise Cox proportional risk model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInflection point\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSHR index\u0026thinsp;\u0026lt;\u0026thinsp;0.829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.02(0.02, 0.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSHR index\u0026thinsp;\u0026ge;\u0026thinsp;0.829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.93(3.54, 9.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVD mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.05(0.56, 7.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.280\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFitting by two-piecewise Cox proportional risk model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInflection point\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSHR index\u0026thinsp;\u0026lt;\u0026thinsp;0.850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.03(0.002, 0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSHR index\u0026thinsp;\u0026ge;\u0026thinsp;0.850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.20(3.35, 15.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eCox proportional hazards models were used to estimate HR and 95% CI.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eAdjusted for age, sex, race, BMI, PIR, education level, smoking status, and alcohol consumption. HR Hazard ratio; CI Confidence interval\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Stratified Analysis\u003c/h2\u003e\u003cp\u003eTo assess the robustness of the SHR-mortality association, we performed subgroup analyses by demographic (sex, age, race), socioeconomic (education, income), and behavioral factors (smoking, alcohol use). Individuals with SHR above the threshold (\u0026ge;\u0026thinsp;0.829 for all-cause mortality; \u0026ge;0.850 for cardiovascular mortality) had uniformly increased mortality risks across all subgroups (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The lack of significant interaction effects (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all) further supported the consistency of this association.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSubgroup analyses of the association between the SHR and all-cause mortality\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003cp\u003e[SHR\u0026thinsp;\u0026lt;\u0026thinsp;0.829]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003cp\u003e[SHR\u0026thinsp;\u0026ge;\u0026thinsp;0.829]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP for interaction\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.90(0.76, 1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.316\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.73(0.54,0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.88(0.73,1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.675\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.96(0.70,1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.96(0.80,1.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (Kg/m2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.532\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.06(0.72,1.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.93(0.79,1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.620\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.81(0.49,1.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.46(0.23,0.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.97(0.81,1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.24(0.89,1.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eother\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.10(0.40,2.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.992\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school or less\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.04(0.87,1.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecollege or more\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.95(0.72,1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoverty\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.616\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.99(0.79,1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.98(0.81,1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.23(0.96,1.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.88(0.72,1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.889\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.03(0.86,1.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.96(0.67,1.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSubgroup analyses of the association between the SHR and cardiovascular mortality\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003cp\u003e[SHR\u0026thinsp;\u0026lt;\u0026thinsp;0.829]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003cp\u003e[SHR\u0026thinsp;\u0026ge;\u0026thinsp;0.829]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP for interaction\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.77(0.56, 1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.67(0.40,1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.70(0.49,0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.781\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.68(0.39,1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.81(0.57,1.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (Kg/m2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.64(0.33,1.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.81(0.59,1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.306\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.87(0.34,2.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.54(0.33,7.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.72(0.50,1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.29(0.78,2.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eother\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.36(0.21,8.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school or less\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.11(0.76,1.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege or more\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.57(0.36,0.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoverty\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.172\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.01(0.61,1.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.70(0.49,1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.453\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.96(0.66,1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.71(0.47,1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.349\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.91(0.62,1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.65(0.34,1.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSubgroup analyses revealed distinct patterns in the association between SHR and mortality. For all-cause mortality, a significantly stronger association with elevated SHR was observed among male participants (P\u0026thinsp;=\u0026thinsp;0.04) and individuals categorized as other Hispanic (P\u0026thinsp;=\u0026thinsp;0.03). In contrast, for CVD mortality, higher SHR levels were significantly associated with lower mortality risk in females (P\u0026thinsp;=\u0026thinsp;0.05) and in participants with a college education or higher (P\u0026thinsp;=\u0026thinsp;0.02). These associations persisted across all subgroups, though modest differences in effect estimates were noted among various demographic and socioeconomic groups.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study is the first to demonstrate a U-shaped relationship between the SHR and both all-cause and cardiovascular mortality in individuals with UI. Restricted cubic spline analyses identified optimal thresholds at SHR levels of 0.829 for all-cause mortality and 0.850 for cardiovascular mortality, with mortality risk increasing at both lower and higher values. Notably, this nonlinear association remained statistically significant after comprehensive multivariable adjustment (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), establishing SHR as an independent prognostic marker in this patient population.\u003c/p\u003e\u003cp\u003eSubgroup analyses revealed important demographic variations in the SHR-mortality relationship. The association was particularly pronounced among male participants (P\u0026thinsp;=\u0026thinsp;0.04) and individuals of other Hispanic ethnicity (P\u0026thinsp;=\u0026thinsp;0.03). These findings suggest that the predictive value of SHR may be modified by both biological (sex) and sociocultural (ethnicity) factors, providing clinically relevant insights for risk stratification in UI patients\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAccording to recent studies, SHR is a more sensitive indicator of glycemic alterations under acute stress conditions compared to traditional markers such as fasting blood glucose (FBG), HbA1c, and oral glucose tolerance test (OGTT). By comparing admission blood glucose with the estimated average glucose derived from HbA1c, SHR accounts for individual long-term glycemic history and enables a more accurate risk prediction in patients with varying baseline glucose levels. In contrast, absolute glucose levels may be confounded by prior glycemic control, potentially leading to inaccurate assessment of critical illness risk\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. A growing body of evidence has demonstrated that elevated SHR is positively associated with mortality in a variety of acute medical conditions\u003csup\u003e[20\u0026ndash;22]\u003c/sup\u003e. Additionally, SHR has been shown to be closely linked to a range of diseases, including acute coronary syndrome, sepsis, nonalcoholic fatty liver disease, and chronic kidney disease \u003csup\u003e\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. However, no prior studies have reported on the association between SHR and all-cause or CVD mortality specifically in patients with UI. UI is a common condition that severely impacts the quality of life, particularly among women. Data from NHANES indicate that 17.1% of women aged 20 years and older experience moderate to severe UI, with prevalence rising markedly with age\u0026mdash;reaching as high as 30\u0026ndash;40% among elderly women\u0026mdash;highlighting its significance as a major public health concern\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Although previous research has identified several predictors of UI \u003csup\u003e19,28\u003c/sup\u003e, there remains a lack of reliable glycemic markers capable of predicting all-cause and CVD mortality among patients with UI.\u003c/p\u003e\u003cp\u003eOur study represents the first investigation to establish a U-shaped association between SHR and mortality specifically in UI patients. Analyzing nationally representative data from 5,933 U.S. adults with UI (2001\u0026ndash;2018), we consistently observed this nonlinear relationship through both Cox regression and RCS analyses. The identified inflection points (SHR\u0026thinsp;=\u0026thinsp;0.829 for all-cause mortality; SHR\u0026thinsp;=\u0026thinsp;0.850 for CVD mortality) demarcated distinct risk patterns, with mortality increasing sharply above these thresholds. These robust findings position SHR as a potentially valuable prognostic indicator for UI patients. While prior research has focused on UI risk factors, our study uniquely characterizes mortality predictors in this vulnerable population. For instance, Li et al\u003csup\u003e19\u003c/sup\u003e observed a positive association between the triglyceride-glucose index combined with BMI (TyG-BMI) and the risk of UI. Similarly, Cao et al\u003csup\u003e28\u003c/sup\u003e found a positive correlation between the metabolic score for insulin resistance (METS-IR) and the risk of UI. Our study expands on this body of evidence by demonstrating that SHR not only reflects metabolic stress but also has prognostic value in predicting mortality risk among UI patients, thereby providing additional clinical relevance. Moreover, similar findings have been reported in related populations. For example, in an observational study, Ding et al\u003csup\u003e29\u003c/sup\u003e found that SHR could predict mortality risk in patients with diabetes or prediabetes. Our study is the first to extend this prognostic utility of SHR to the UI population, further emphasizing its potential as a superior biomarker. Given that SHR has been linked to various pathophysiological changes in previous studies, these alterations may underlie the increased mortality observed in patients with UI. In patients with UI, metabolic dysregulation and chronic inflammatory responses may synergistically contribute to systemic dysfunction. He et al\u003csup\u003e30\u003c/sup\u003e reported that metabolic syndrome may significantly influence the development of UI by inducing inflammation and oxidative stress. This systemic inflammation, driven by metabolic abnormalities, can exacerbate lower urinary tract dysfunction and potentially worsen UI symptoms. Additionally, UI has been strongly associated with sarcopenia\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, and stress hyperglycemia has been shown to correlate with impaired muscle function, increasing the risk of falls and reduced mobility in affected individuals \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Elevated SHR levels are also known to promote systemic inflammation, cardiovascular dysfunction, and myocardial energy metabolism disturbances. These pathophysiological alterations not only increase the risk of mortality in patients with atrial fibrillation but also resemble the chronic inflammatory and oxidative stress responses seen in individuals with UI, thereby further amplifying the risk of cardiovascular events\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. these findings collectively underscore the close relationship between metabolic and physiological imbalances and the risk and severity of UI. They also highlight the clinical relevance of SHR as a marker associated with adverse outcomes. In line with our findings, both abnormally high and low SHR values may have detrimental health effects, reinforcing the importance of maintaining metabolic homeostasis in this population.\u003c/p\u003e\u003cp\u003eThis study drew upon data from the National Health and NHANES to explore the relationship between the SHR and both all-cause and cardiovascular mortality among individuals with UI, using a large-scale, nationally representative cohort of U.S. adults. To enhance the applicability of the findings to the general U.S. population, weighted Cox proportional hazards models were employed in the analysis.\u003c/p\u003e\u003cp\u003eHowever, several limitations should be acknowledged. First, NHANES relies heavily on self-reported data for numerous variables, including lifestyle factors, healthcare utilization, and medical history. As a result, recall bias may be present. Second, the findings are based solely on data from the U.S. population. Therefore, additional studies in diverse populations are needed to validate the observed associations between SHR and mortality outcomes in patients with UI.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, our findings suggest that the SHR may have valuable applications in patients with urinary incontinence. Specifically, SHR could serve as a potential marker for long-term mortality risk stratification in this population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eSHR (Stress hyperglycemia ratio )\u003c/p\u003e\n\u003cp\u003eUI (Urinary incontinence)\u003c/p\u003e\n\u003cp\u003eCVD (Cardiovascular Diseas)\u003c/p\u003e\n\u003cp\u003eNHANES (National Health and Nutrition Examination Survey)\u003c/p\u003e\n\u003cp\u003eBMI (Bdy mass index)\u003c/p\u003e\n\u003cp\u003ePIR (Poverty income ratio)\u003c/p\u003e\n\u003cp\u003eRCS (Restricted cubic spline )\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES is approved by the National Center for Health Statistics Research Ethics Review Board, and all participants provide informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors thank the NHANES 2001\u0026ndash;2018 participants for their invaluable contributions. The study was conceived and designed by Yuqing Huang, Heqian Liu, Yong Liu, and Yong Xu. Yuqing Huang and Heqian Liu contributed equally to this work and drafted the main manuscript. Xia Fang, ChenHao Deng, and Jia Feng were responsible for data preparation, statistical analysis, and visualization. Mengting Huang and Yuling Yang contributed equally and verified and repeated the statistical analysis. Yong Liu and Yong Xu supervised the overall study. All authors have read and approved the final version of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSources of Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Science Foundation of China (NO.82470854).This work was supported by the Scientific research project of Southwest Medical University(NO.2024LCYXZX02)\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\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to all the participants for their participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHaylen, B. T., de Ridder, D., Freeman, R. M., Swift, S. E., Berghmans, B., Lee, J., \u003cem\u003eet al.\u003c/em\u003e An International Urogynecological Association (IUGA)/International Continence Society (ICS) joint report on the terminology for female pelvic floor dysfunction. \u003cem\u003eInt Urogynecol J\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 5\u0026ndash;26 (2010).\u003c/li\u003e\n\u003cli\u003eAoki, Y., Brown, H. W., Brubaker, L., Cornu, J. N., Daly, J. O. \u0026amp; Cartwright, R. Urinary incontinence in women. \u003cem\u003eNat Rev Dis Primers\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 17042 (2017).\u003c/li\u003e\n\u003cli\u003ePatel, U. J., Godecker, A. L., Giles, D. L. \u0026amp; Brown, H. W. 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Med.\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 923\u0026ndash;929 (2019).\u003c/li\u003e\n\u003cli\u003eJang, H. C. Diabetes and muscle dysfunction in older adults. \u003cem\u003eAnn. Geriatr. Med. Res.\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 160\u0026ndash;164 (2019).\u003c/li\u003e\n\u003cli\u003eLiu, L., Zhu, Z., Yu, K., Zhang, W., Pu, J., Lv, Y., \u003cem\u003eet al.\u003c/em\u003e Association between stress hyperglycemia ratio and all-cause mortality in critically ill patients with atrial fibrillation: insights from a MIMIC-IV study. \u003cem\u003eFront. Endocrinol.\u003c/em\u003e\u003cstrong\u003e15\u003c/strong\u003e, 1412159 (2024).\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":"SHR, All-Cause Mortality, Cardiovascular Mortality, Urinary Incontinence, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-7685623/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7685623/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eThe stress hyperglycemia ratio (SHR) is a potential marker of stress-related glucose dysregulation and adverse health outcomes. However, its association with mortality risk in individuals with urinary incontinence (UI) remains poorly understood.\u003c/p\u003e\u003ch2\u003eObjective:\u003c/h2\u003e\u003cp\u003eThis study aimed to evaluate the relationship between SHR and mortality risk in adults with UI, specifically examining its role as a predictor of all-cause and cardiovascular mortality.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eWe analyzed data from 5,933 adults with UI from the NHANES 2001\u0026ndash;2018 cohort, with mortality follow-up through 2019. SHR was categorized into quartiles. Survival differences were assessed using Kaplan-Meier curves and log-rank tests. Cox proportional hazards models were applied with stepwise adjustments for potential confounders. Nonlinear trends were explored using restricted cubic spline regression, and subgroup analyses evaluated potential effect modifications.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eA U-shaped association was observed between SHR and both all-cause and cardiovascular mortality. Optimal thresholds for SHR were 0.829 for all-cause mortality and 0.850 for cardiovascular mortality. Participants in the highest SHR quartile had a 27% increased risk of all-cause mortality (HR\u0026thinsp;=\u0026thinsp;1.27, 95% CI: 1.05\u0026ndash;1.53) after full adjustment. Mortality risk escalated sharply beyond these thresholds (7.20-fold for all-cause mortality, 95% CI: 4.36\u0026ndash;11.90; 8.90-fold for cardiovascular mortality, 95% CI: 4.55\u0026ndash;17.41). The association was stronger in males (P\u0026thinsp;=\u0026thinsp;0.04) and other Hispanic subgroups (P\u0026thinsp;=\u0026thinsp;0.03).\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e\u003cp\u003eSHR was an independent predictor of mortality risk in adults with UI. This finding suggests that SHR may serve as a useful biomarker for identifying high-risk individuals and guiding early interventions to reduce mortality in this population.\u003c/p\u003e","manuscriptTitle":"Stress Hyperglycemia Ratio as a Predictor of All-Cause and Cardiovascular Mortality in Patients With Urinary Incontinence: Evidence From NHANES 2001–2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 14:35:20","doi":"10.21203/rs.3.rs-7685623/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":"9d003e71-6f7c-4cd4-ae08-3b7fb2c21395","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-04T16:38:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-27 14:35:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7685623","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7685623","identity":"rs-7685623","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}