Metabolic syndrome, obesity-related indicators, and incident urinary tract infection: a UK Biobank cohort study with TyG-related indices as core mediators

Metabolic syndrome, obesity-related indicators, and incident urinary tract infection: a UK Biobank cohort study with TyG-related indices as core mediators | 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 Metabolic syndrome, obesity-related indicators, and incident urinary tract infection: a UK Biobank cohort study with TyG-related indices as core mediators Zhicheng Cong, Mulun Xiao, Zhengyan Tang, Junyi Sun, Li Huang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9109439/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objective To explore the causal association of metabolic syndrome (MetS) and obesity-related indicators with incident urinary tract infection (UTI), and the underlying mediating mechanisms. Methods A total of 382,791 participants free of baseline UTI from the UK Biobank were included, with a median follow-up of 13.05 years. Exposures were MetS and obesity-related indicators, outcome was incident UTI. IPW-adjusted and multivariable Cox proportional hazards models were used to assess the exposure-outcome association, RCS for nonlinear dose-response analysis, and counterfactual-based mediation analysis to explore the mediating effect of TyG-related indices. Results During follow-up, 19,097 participants developed incident UTI. MetS was identified as an independent risk factor for UTI, with a significant cumulative effect of increasing nMetS. RCS analysis demonstrated a significant L-shaped inverse association between LDL-C/TC levels and UTI risk (all P for nonlinearity < 0.05). TyG-derived indices were confirmed as the core mediators, with a mediated proportion ranging from 14.69% to 69.62%. Conclusions MetS is independently associated with an increased risk of incident UTI, mainly driven by central obesity and insulin resistance. Metabolic control and early intervention in high-risk individuals represent a promising public health approach for UTI prevention, rational antibiotic use and antimicrobial resistance mitigation. Metabolic Syndrome Urinary Tract Infection Insulin Resistance Obesity Mediation Effect Analysis Inverse Probability Weighting Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Urinary tract infection (UTI) rank among the most prevalent bacterial infectious diseases worldwide, affecting individuals of all ages and genders [ 1 ]. Recurrent UTI can lead to severe complications such as renal impairment and urosepsis, which not only seriously threaten patients’ health but also impose a heavy burden on public health resources due to the resulting antimicrobial resistance [ 2 , 3 ]. The pathogenesis of UTI depends on the interaction between pathogen virulence and the host’s urinary tract defense mechanisms [ 4 , 5 ]. Currently, local metabolic abnormalities in the urinary tract have been identified as a core risk factor that influences the host’s urinary tract microenvironment and impairs local urinary immune defense [ 6 ]. Metabolic syndrome (MetS) is characterized by the clustering of several cardiometabolic abnormalities, which is diagnosed based on the presence of at least three of the following: elevated (1) waist circumference (WC), (2) triglycerides(TG), (3) blood pressure (BP), (4) Glycosylated Hemoglobin, Type A1C (HbA1c) or blood glucose and (5) reduced high-density lipoprotein cholesterol (HDL-C) [ 7 ]. Obesity, which is closely linked to metabolic dysregulation, has also become highly prevalent worldwide and has a significant impact on public health [ 8 ]. Similar to UTI, the prevalence of MetS and obesity has been rising continuously in recent years along with global population aging [ 9 , 10 ]. Owing to the substantial health burden and wide range of comorbidities it causes, MetS and obesity have become a major public health challenge worldwide [ 11 , 12 ]. Existing research has confirmed that the occurrence, recurrence, and adverse progression of UTI are closely related to metabolic disorders of the local urinary tract microenvironment and imbalance of immune defense mechanisms [ 13 , 14 ]. Previous studies have demonstrated that obesity can increase susceptibility to UTI by disrupting bladder focal adhesion kinase signaling, disturbing the urethral microenvironment and weakening the local immune defense of host [ 15 ]. Collectively, these findings suggest that systemic metabolic abnormalities may also be closely associated with susceptibility to UTI, yet the precise causal association and underlying molecular mechanisms remain to be fully elucidated [ 6 ]. In particular, no large‑scale prospective cohort study has yet clarified the causal link between overall MetS, its components, or obesity and incident UTI in the general population, nor identified the core mediating pathways and actionable intervention targets. Against the backdrop of the rising prevalence of UTI, the global crisis of antibiotic resistance, and the modifiable nature of MetS, this study was conducted to provide evidence for potential and promising strategies in the prevention and metabolic management of UTI. Methods Assessment of Study Population The UK Biobank is a large-scale, population-based prospective multicenter cohort study. For the current analysis, a total of 382791 participants were ultimately included. The participant inclusion and exclusion flowchart is presented in Figure S1 . Participants were excluded if they had missing key baseline metabolic data (n = 88928), prevalent UTI at baseline (n = 6322), or follow-up duration ≤ 180 days (n = 23896). Assessment of MetS and Obesity MetS was defined per the 2009 Harmonized Criteria by the International Diabetes Federation (IDF) and American Heart Association/National Heart, Lung, and Blood Institute (AHA/NHLBI) [ 7 ], with diagnosis requiring ≥ 3 of 5 components: (1) abdominal obesity (elevated WC: ≥102 cm males, ≥ 88 cm females); (2) elevated TG (≥ 150 mg/dL or 1.7 mmol/L); (3) elevated BP (≥ 130/85 mmHg) or antihypertensive use; (4) elevated fasting glucose (≥ 100 mg/dL or ≥ 5.6 mmol/L) or glucose-lowering use; (5) reduced HDL-cholesterol (< 40 mg/dL males/<50 mg/dL females) or lipid‐modifying use. Given variable fasting times skewing glucose measurements, HbA1c ≥ 5.7% (39 mmol/mol) was used as a hyperglycemia surrogate per American Diabetes Association (ADA) recommendations [ 16 ]. To assess obesity, those following indicators were included: body mass index (BMI), waist-to-hip ratio (WHR), body fat percentage (BFP), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), as well as WC and HDL-C, which are core components of MetS definition. Assessment of UTI The outcome of this study was the diagnosis of incident UTI. Participants with UTI were identified using the World Health Organization (WHO) International Classification of Diseases, Tenth Revision (ICD-10) codes, and a total of 5 ICD-10 codes (N10, N12, N30.0, N30.9 and N39.0) were employed for UTI case ascertainment. UTI cases were further classified into three subgroups according to infection site: upper UTI (UUTI), lower UTI (LUTI) and UTI, not otherwise specified (UTI, NOS) based on corresponding ICD-10 codes. (Table S1 ) Assessment of Covariates and Missing Data Handling Potential confounders were selected based on prior epidemiologic evidence and their plausible roles in the association between MetS and UTI. The following 12 covariates showed in Table S2 were included for adjustment in inverse probability weighting (IPW) and Cox regression models. Missing values for key exposure variables and covariates with a low proportion of missing data were imputed using the random forest algorithm. Statistical Analysis IPW was applied to account for confounding in the association between MetS and UTI. A logistic regression model was constructed to estimate the propensity score (PS) for MetS exposure, with the 12 prespecified baseline covariates as predictors. Stabilized IPW weights were derived from the PS and trimmed at the 1st and 99th percentiles to reduce extreme weight bias. The balance of covariates before and after weighting was assessed using standardized mean differences (SMDs). Model performance was further validated by PS distribution, weight distribution, calibration curves, and ROC curve analysis. Continuous variables were presented as mean ± standard deviation (SD), and categorical variables presented as frequencies and percentages. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Three models were constructed: Model 1 was unadjusted; Model 2 was adjusted for age, sex, ethnicity, employment status, educational attainment, TDI, and household income; Model 3 was further adjusted for smoking status, alcohol drinker status, alcohol consumption, physical activity, and healthy diet score. Cumulative incidence of UTI was estimated using IPW and Model 3 adjustment, stratified by MetS status and nMetS (the number of MetS components). Survival curves were plotted over the 15-year follow-up. Stratified analyses by follow-up period (0–5, 5–10, ≥ 10 years) and a weighted Cox trend test were performed to test robustness. BMI, WC, WHR, BFP, TG, HDL-C, LDL-C, and TC were categorized into quartiles for forest plot analyses. Restricted cubic splines (RCS) were used to evaluate nonlinear exposure-response associations with UTI risk. Subgroup analyses were conducted across key sociodemographic, lifestyle, and socioeconomic strata, with adjustment using both IPW and Model 3. FDR correction was applied for interaction, with Bonferroni correction for sensitivity testing. All statistical analyses were performed using R 4.5.1. A two-sided P < 0.05 was considered statistically significant. Mediation Effect As a core metabolic disorder and obesity-related metabolic trait, Insulin resistance (IR) was assessed using the Triglyceride-Glucose (TyG) index and its composite indices: TyG-ABSI (TyG index combined with A Body Shape Index), TyG-WWI (TyG index combined with Waist-to-Weight Index), TyG-BMI (TyG index combined with Body Mass Index), TyG-WC (TyG index combined with Waist Circumference) and TyG-WHtR (TyG index combined with Waist-to-Height Ratio). These indices have been widely validated for evaluating IR and related metabolic disorders [ 17 – 19 ]. Biomarkers reflecting inflammation, renal function, liver function, and red blood cell status were also included as potential mediators. Mediation effects were quantified using standardized effect sizes according to established methods [ 20 ]. All metabolic indices were standardized after log-transformation with 0.01 added to non-zero values. Cause-specific Cox regression was applied to assess the association between MetS and UTI. Mediation analyses were performed using the R “mediation” package (version 4.5.1) under the counterfactual framework with 1000 bootstrap replications. Natural direct effects, natural indirect effects, and proportion mediated (PM) were estimated with FDR correction for multiple testing. Result Baseline Characteristics A total of 382,791 participants free of baseline UTI were enrolled, among whom 19,097 (4.99%) developed incident UTI during follow-up. IPW-adjusted baseline characteristics of the total cohort, non-UTI group, and incident UTI group are summarized in Table 1 . Following IPW adjustment, participants with incident UTI were significantly older and exhibited higher obesity-related indices compare to those without UTI (all P < 0.001). Socioeconomically, the incident UTI group presented greater deprivation, lower educational attainment and household income, and a higher proportion of non-employment (all P < 0.001). Unfavorable lifestyle factors, including smoking, insufficient physical activity, abnormal sleep duration, and frequent insomnia, were also more common in the incident UTI group (all P < 0.001). Table 1 Baseline characteristics of the study population (IPW-weighted percentages and P-values) Variables Overall (n = 382791) No UTI (n = 363694) UTI (n = 19097) P Sex, n(%) < 0.001 Female 204730 (51.75) 195227 (51.92) 9503 (48.42) Male 178061 (48.25) 168467 (48.08) 9594 (51.58) Age at recruitment, Mean ± SD 57.27 ± 7.97 57.07 ± 7.97 61.00 ± 6.97 < 0.001 BMI, Mean ± SD 26.91 ± 4.49 26.87 ± 4.46 27.60 ± 4.98 < 0.001 Whole body fat mass, Mean ± SD 23.62 ± 9.13 23.56 ± 9.08 24.73 ± 10.11 < 0.001 Body fat percentage, Mean ± SD 30.40 ± 8.68 30.37 ± 8.66 31.00 ± 9.11 < 0.001 Waist-to-Hip Ratio, Mean ± SD 0.87 ± 0.09 0.87 ± 0.09 0.89 ± 0.09 < 0.001 Townsend deprivation index at recruitment, n(%) < 0.001 1 (least deprived) 95121 (24.36) 90987 (24.50) 4134 (21.58) 2 96139 (24.88) 91601 (24.94) 4538 (23.68) 3 95876 (25.06) 91343 (25.14) 4533 (23.55) 4 (most deprived) 95655 (25.70) 89763 (25.42) 5892 (31.19) Educational attainment, n(%) < 0.001 Degree 125082 (30.88) 120556 (31.29) 4526 (22.97) A-level or equivalent 42842 (10.93) 41200 (11.06) 1642 (8.36) O-level/GCSE/CSE 100607 (26.35) 95977 (26.47) 4630 (24.07) Vocational/Professional 45198 (12.28) 42642 (12.22) 2556 (13.40) No qualification/Unknown 69062 (19.56) 63319 (18.96) 5743 (31.19) Ethnic background, n(%) 0.008 White 365082 (95.21) 346848 (95.19) 18234 (95.47) Mixed 2094 (0.53) 2009 (0.53) 85 (0.45) Asian or Asian British 6826 (1.89) 6479 (1.90) 347 (1.78) Black or Black British 4713 (1.30) 4471 (1.30) 242 (1.27) Chinese 1068 (0.27) 1037 (0.27) 31 (0.15) Other/Unknown 3008 (0.81) 2850 (0.81) 158 (0.88) Average total household income before tax, n(%) < 0.001 Less than 18,000 97872 (27.47) 89809 (26.66) 8063 (43.23) 18,000 to 30,999 96790 (25.85) 91654 (25.79) 5136 (27.04) 31,000 to 51,999 99764 (25.42) 96180 (25.79) 3584 (18.29) 52,000 to 100,000 70445 (17.08) 68561 (17.48) 1884 (9.41) Greater than 100,000 17920 (4.17) 17490 (4.28) 430 (2.03) Employment Status, n(%) < 0.001 Other/Unknown 3775 (1.00) 3552 (0.99) 223 (1.13) Employed 208507 (52.25) 202106 (53.28) 6401 (32.31) Retired 136762 (37.67) 126599 (36.82) 10163 (54.13) Homemaker 9515 (2.31) 9206 (2.35) 309 (1.56) Sick/Disabled 14721 (4.24) 13164 (4.02) 1557 (8.43) Unemployed 6860 (1.87) 6519 (1.87) 341 (1.94) Unpaid or voluntary work 1716 (0.43) 1639 (0.43) 77 (0.39) Full or part-time student 935 (0.23) 909 (0.23) 26 (0.12) Smoking status, n(%) < 0.001 Never 207033 (52.84) 198240 (53.21) 8793 (45.70) Previous 134483 (35.88) 126761 (35.67) 7722 (40.06) Current 39760 (10.86) 37332 (10.73) 2428 (13.43) Alcohol drinker status, n(%) < 0.001 Never 16042 (4.47) 14887 (4.38) 1155 (6.08) Previous 13577 (3.80) 12463 (3.70) 1114 (5.80) Current 352339 (91.49) 335598 (91.69) 16741 (87.57) Physical activity level, n(%) < 0.001 Non-enough physical activity 99905 (27.47) 93748 (27.20) 6157 (32.70) Enough physical activity 282886 (72.53) 269946 (72.80) 12940 (67.30) Alcohol consumption, n(%) < 0.001 0–14 UK units/week 311587 (81.43) 295794 (81.38) 15793 (82.32) ≥14 UK units/week 71204 (18.57) 67900 (18.62) 3304 (17.68) The sum of healthy diet, n(%) < 0.001 0–5 servings 90876 (24.25) 86116 (24.16) 4760 (25.87) ≥5 servings 291915 (75.75) 277578 (75.84) 14337 (74.13) Sleep duration, n(%) < 0.001 ≤6 or ≥ 9 hour/d 123475 (32.31) 116161 (32.03) 7314 (37.89) 7–8 hour/d 259316 (67.69) 247533 (67.97) 11783 (62.11) Chronotype, n(%) 0.812 Tend to be a "morning person" 253028 (66.46) 240457 (66.46) 12571 (66.38) Tend to be a "evening person" 129763 (33.54) 123237 (33.54) 6526 (33.62) Insomnia, n(%) < 0.001 Low-Freq (Never, rarely, sometimes) 274109 (71.71) 261544 (71.98) 12565 (66.48) High-Freq (Usually) 108682 (28.29) 102150 (28.02) 6532 (33.52) Snoring, n(%) 0.808 No 137962 (34.87) 130861 (34.86) 7101 (34.95) Yes 244829 (65.13) 232833 (65.14) 11996 (65.05) Daytime dozing, n(%) < 0.001 Low-Freq (Never, rarely, sometimes) 372132 (97.28) 353957 (97.37) 18175 (95.47) High-Freq (Often, all the time) 10659 (2.72) 9737 (2.63) 922 (4.53) LDL Cholesterol, Mean ± SD 1.75 ± 0.34 1.75 ± 0.34 1.71 ± 0.36 < 0.001 Total Cholesterol, Mean ± SD 4.65 ± 0.70 4.65 ± 0.70 4.57 ± 0.75 < 0.001 Elevated WC, n(%) 129930 (26.96) 121121 (26.63) 8809 (33.37) < 0.001 Elevated TG, n(%) 154495 (34.26) 145607 (34.19) 8888 (35.68) < 0.001 Elevated BP or blood pressure medication, n (%) 286032 (73.07) 269983 (72.70) 16049 (80.42) < 0.001 Elevated HbA1c or insulin use, n(%) 73639 (14.04) 67662 (13.71) 5977 (20.53) < 0.001 Reduced HDL or cholesterol lowering medication, n(%) 127416 (26.59) 118457 (26.18) 8959 (34.44) < 0.001 Table 1. Baseline characteristics of the study population (IPW-weighted percentages and P-values) BMI, body mass index; HDL, high-density lipoprotein; LDL, low-density lipoprotein; WC, waist circumference; TG, triglyceride; BP, blood pressure; HbA1c, Glycosylated Hemoglobin A1c 1. Continuous data are presented as mean ± standard deviation (Mean ± SD), and categorical data as n (%). 2. P-values were calculated as follows: (1) For continuous variables: the weighted t-test was used for two-group comparisons. (2) For categorical variables: the weighted Pearson chi-squared test was used. All P-values are reported to 3 decimal places. For metabolic and obesity-related traits, the incident UTI group showed a significantly higher prevalence of all MetS components at baseline, along with lower LDL-C and TC levels (all P < 0.001). Briefly, unweighted characteristics by incident UTI are presented in Table S3, while IPW-adjusted characteristics stratified by UTI subtype, MetS status, and nMetS are shown in Tables S4–S6, respectively. Evaluation of IPW Model Performance We rigorously assessed PS model performance and IPW covariate balancing efficacy to validate the robustness of causal inference for the MetS-UTI association, with detailed results presented in Figure S2 and S3. The PS model yielded an AUC of 0.6737 (within the optimal 0.6–0.8 range), demonstrating moderate-to-high discriminative power with minimal overfitting risk. A calibration error of 0.1369 indicated acceptable model consistency, aligned with IPW’s core priority of covariate balance over predictive accuracy. Notably, 100% of participants fell within the common support region, requiring no sample exclusion (superior to most analogous studies that exclude 5–10% of out-of-range participants). IPW weight statistics (mean 1.093, range 0.43–2.82) confirmed no extreme weights that would bias effect estimates. Critically, SMDs for all 12 covariates were < 0.1 post-weighting, indicating excellent intergroup balance. These findings confirmed effective control of baseline confounding, strengthening the reliability of our causal inference for the MetS-UTI association. IPW weight distributions across nMetS-stratified subgroups followed a systematic pattern consistent with stabilized weighting principles (Figure S4, S5). Crude weights were overestimated in 0–2 nMetS subgroups (1.13–1.42) and underestimated in 3–5 nMetS subgroups (0.62–0.71), aligning with differential exposure probabilities across subgroups. The maximum weight across all subgroups ranged from 2.45 to 2.82, within acceptable limits. Subgroup weight efficiency ranged from 89.7% to 92.3%, with a relatively low information loss rate of 7.7%–10.3% due to weight dispersion and redistribution of statistical contribution (Figure S6). Cumulative Incidence Risk of UTI IPW-adjusted cumulative risk of UTI among participants with and without MetS is presented in Figure S7. Participants with MetS had a significantly higher cumulative risk of UTI over the 15-year follow-up period, with the between-group difference in risk widening progressively over time. This trend was further validated in analyses using Model 3 (Figure S8). At the 3rd year of follow-up, the cumulative incidence of UTI was 0.83% in the MetS group and 0.59% in the non-MetS group, with a between-group difference of only 0.24%. By the 15th year of follow-up, the cumulative incidence of UTI in the MetS group reached 8.60%, which was markedly higher than the 6.03% observed in the non-MetS group. Consistent results were obtained from IPW-adjusted and Model 3 analyses for the three UTI subtypes, and MetS group had a significantly higher cumulative incidence risk of all subtypes over the 15-year follow-up compared with non-MetS group, and the between-group risk difference became more pronounced with extended follow-up (Figure S9 and Figure S10). IPW-weighted cumulative incidence risk analyses revealed an overall stepwise increasing trend in the cumulative risk of UTI across all follow-up time points with rising nMetS over the 15-year follow-up period, with one exception: participants with nMetS = 3 had marginally lower cumulative incidence risk of UTI at all follow-up time points than those with nMetS = 2 (Fig. 1 ). The overall statistical significance of this trend was confirmed by the weighted Cox global test (P < 0.001). A similar increasing trend in risk was observed in the Model 3-based analysis, where the cumulative incidence risk of UTI presented a strictly monotonic increase with rising nMetS, without the exception shown in the IPW-weighted analysis (Figure S11). Association Between MetS and UTI Risk Across Follow-Up Duration Both Model 3 adjustment and IPW analyses consistently confirmed an independent association between MetS and elevated UTI risk, as illustrated in Figure S12. Subgroup stratification by follow-up duration (0–5, 5–10, ≥ 10 years) verified the robustness of this association across all time intervals, with HRs showing a mild incremental trend with prolonged follow-up overall. The only exception was that HR estimates in the IPW analysis were largely comparable between the 5–10 and ≥ 10-year subgroups. Association Between nMetS and UTI Risk The association between nMetS and UTI risk is presented in Figure S13. IPW-weighted analysis showed an overall significant positive trend: higher nMetS was associated with elevated incident UTI risk, except that HR for nMetS = 3 was marginally lower than those for nMetS = 2. Consistently, a significant positive association between increasing nMetS and UTI risk was observed across all three models, as detailed in Table S7 and Table S8. Findings from adjusted analyses of nMetS and UTI subtype risks are presented in Figure S14 and Figure S15, adjusted by IPW and Model 3 respectively. Potentially limited by size, IPW analysis showed no significant association of MetS with either UUTI and LUTI. In Model 3-adjusted analysis, no significant association was found for UUTI; only a weak threshold-dependent association was detected for LUTI, with increased UTI risk solely observed in participants with nMetS = 4–5. In contrast, analyses for UTI, NOS under both models yielded results highly consistent with the overall UTI analysis. Associations of Individual Metabolic and Anthropometric Indicators with UTI Risk We assessed the associations of individual metabolic and anthropometric parameters with UTI risk, evaluating both continuous per-unit increments and categorical quartile distributions of each exposure variable (Fig. 2 , Figure S16). Consistent findings were observed across IPW and Model 3 adjustment analyses in the study cohort: elevated values of most core metabolic and anthropometric indicators were independently linked to higher risk of UTI. Conversely, higher concentrations of HDL-C, LDL-C and TC were associated with reduced UTI risk, representing a clinically counterintuitive trend for LDL-C and TC. Dose-response analyses further verified graded associations: HRs elevated incrementally with increasing levels of most parameters, while HDL-C, LDL-C and TC exhibited inverse dose-response relationships with UTI risk (all P for trend < 0.001). Non-Linear Dose-Response Relationships Assessed by RCS Analysis RCS analysis was performed to explore non-linear dose-response associations between metabolic and anthropometric parameters and UTI risk, with evaluations conducted in two adjusted models. IPW-adjusted RCS analysis (Fig. 3 ) detected significant non-linear relationships for all assessed indicators (all P for non-linearity < 0.001), with varied curve patterns across parameters. BMI, WC, whole body fat mass and TG showed J-shaped associations with UTI risk, marked by a sharper HR increase beyond a certain threshold; WHR and BFP presented complex non-linear trends, with elevated HRs at both low and high levels and steep rises in the moderate range; HDL-C, LDL-C and TC exhibited L-shaped associations, featuring rapid HR declines at low concentrations followed by a weakened downward trend. Corresponding inflection points were estimated as critical thresholds for UTI risk changes. Model 3-adjusted RCS analysis (Figure S17) verified these non-linear associations, with all P for non-linearity < 0.05 and highly consistent curve trends compared with the IPW-adjusted model. Subgroup Analyses of MetS-UTI Association Subgroup analyses were performed to investigate the association between MetS and incident UTI, based on IPW adjustment and Model 3 adjustment (Figure S18 and S19). IPW analysis identified effect modifications of age at recruitment, TDI, smoking status, alcohol consumption status and physical activity level on the MetS-UTI association, whereas no significant effect modification was detected for sex, alcohol intake and healthy diet. Both IPW and Model 3 analyses consistently validated a positive association between MetS and UTI incidence across all prespecified subgroups (all FDR-adjusted P < 0.001). The significant effect modifications detected in IPW analysis were replicated in Model 3 analysis, with all interaction terms reaching statistical significance. Additionally, both models consistently indicated a non-significant alcohol intake-MetS interaction regarding UTI risk. Sensitivity Analyses Sensitivity analyses via IPW and Model 3 verified result robustness, with detailed findings in Table S9-S12. For the IPW model, positive associations between key indicators and UTI risk were consistent across all scenarios: exclusion of early UTI cases (first 2 follow-up years), exclusion of 1st–99th percentile outliers, 5th–95th percentile IPW trimming, and the three-method combination. For Model 3, core association trends remained stable after excluding early UTI cases and outliers, as well as the two-method combination. Both models consistently linked BMI, WC, WHR, BFP, whole body fat mass, TG, elevated BP/BP medication and elevated HbA1c/insulin use to higher UTI risk; all associations were significant (P < 0.01) except specific IPW model subgroups. HDL-C and TC were inversely associated with UTI risk in both models, while the negative LDL-C association was stronger in the IPW model (P < 0.01 in most analyses). Clear between-model differences emerged for MetS trait count. In Model 3, 1 MetS trait correlated with marginally lower UTI risk (HR = 0.91–0.92, 95% CI 0.89–0.94, P < 0.001) vs. no traits, and ≥ 3 traits showed dose-dependent elevated risk (HR = 1.08–1.42, 95% CI 1.06–1.47, P < 0.001). In contrast, the IPW model revealed consistent dose-dependent UTI risk elevation with increasing MetS traits: HR = 1.12–1.15 (95% CI 1.04–1.23) for 1 trait, rising to HR = 2.18–2.43 (95% CI 1.99–2.63) for 5 traits (all P < 0.01). Mediation analyses Mediation analyses via IPW and Model 3 confirmed multiple biomarkers mediated the MetS-UTI association (Fig. 4 , Figure S20). In the plots, β coefficient reflects MetS-biomarker associations, HR denotes biomarker-UTI associations, direct HR represents the direct effect of MetS on UTI after mediator adjustment, and PM indicates the mediated proportion of total effect. In IPW model, all biomarkers showed significant associations with MetS and UTI (all FDR-P < 0.001) except TyG-ABSI (FDR-P = 0.297) and TyG-WWI (FDR-P = 0.023). Most biomarkers had positive PM values, while several (dark gray in plots) showed negative PM, suggesting competitive or suppressive mediation. Brightly highlighted biomarkers corresponded to stronger direct effects and higher mediation proportions; lymphocyte percentage and albumin (light gray) presented inverse direct effects and negative mediation patterns. Model 3 findings were generally consistent with the IPW model, but no negative PM values were observed. Moreover, several TyG-related biomarkers showed slightly attenuated significance and weaker direct effect estimates in Model 3 versus the IPW model. Discussion To date, epidemiological studies directly investigating the associations of MetS and obesity with incident UTI risk remain scarce. Existing studies are predominantly small-sample, single-center cross-sectional analyses, merely focus on the correlation between individual MetS component and UTI risk, and are mostly conducted in specific populations with postoperative UTI events [ 21 , 22 ]. This study is the first to systematically explore the associations of MetS, its components and obesity with incident UTI risk in a large-scale cohort. Using IPW and model 3 adjustment, we first confirmed that MetS serves as an independent risk factor for incident UTI in the general population, with a distinct cumulative effect of its components. As a highly prevalent infectious disease worldwide, UTI has driven massive global antibiotic consumption and a persistent upward trend in antimicrobial resistance. Notably, MetS is a readily modifiable clinical phenotype. Accordingly, our findings provide robust evidence for the primary prevention of UTI and the comprehensive management of UTI. Increased obesity has been widely associated with elevated UTI risk. However, the majority of clinical studies have solely used BMI to assess obesity status. Semins et al. [ 23 ] and Taramian et al. [ 24 ] demonstrated that elevated BMI correlates with an increased UTI risk in patients; Nseir et al. reported that high BMI is linked to recurrent UTI among premenopausal women [ 25 ]. Building on prior research, the present study incorporated a comprehensive panel of adiposity-related indicators, and confirmed that BMI, WHR, BFP, whole body fat mass, HDL-C, TG, and WC were all positively associated with UTI risk. Intriguingly, the present study revealed that LDL-C and TC were inversely associated with incident UTI risk, which runs counter to the conventional clinical view. These findings were robustly validated via quartile-based trend and RCS analyses in both IPW model and Model 3. RCS curves illustrated a typical L-shaped dose-response relationship: rising LDL-C and TC levels were accompanied by a sharp reduction in UTI risk, while the protective effect plateaued markedly beyond a critical threshold. This implies that an optimal lipid level is a prerequisite for sustaining urinary tract immune defense, and both excessively low and high lipid profiles are unfavorable. This phenomenon has also been reported in previous infection-related studies; for instance, Chen et al. documented that low serum LDL-C levels are associated with a higher risk of post-stroke infection in patients with acute ischemic stroke [ 26 ]. Thus, excessive lipid lowering should be avoided in lipid-modifying therapy for MetS patients, with due consideration of the physiological protective roles of lipids. The potential mechanism underlying this observation is that LDL-C can bind bacterial toxins secreted by pathogenic microorganisms and activate the bactericidal activity of phagocytes, thereby enhancing host anti-infective defense [ 26 , 27 ]. Meanwhile, TC is a core structural component of the urothelial cell membrane and a pivotal material basis for maintaining urinary mucosal barrier integrity and innate immune function [ 28 ], which may explain the correlation between low TC levels and elevated UTI risk. These findings challenge traditional clinical consensus, though further investigations are warranted to clarify the exact mechanism. Previous studies have confirmed that the Integrated Stress Response (INSR) is implicated in urothelial barrier function and modulates host defense against UTI [ 29 , 30 ], while IR can increase infection susceptibility by suppressing the INSR signaling pathway. Grounded in these findings, the present study included the TyG index and its derived indicators (TyG-BMI, TyG-WC, TyG-WHtR, TyG-WWI, TyG-ABSI) — which exhibit stronger correlations with visceral fat accumulation and chronic low-grade inflammation[ 18 , 19 ] — to systematically explore their mediating effects in the association between MetS and UTI. Mediation analyses indicated that the mediated proportion of TyG-derived indicators was significantly higher than TyG index and other biomarkers. This provides clinical guidance for monitoring TyG-derived indicators, rather than relying solely on traditional metrics, to identify high-risk subgroups among MetS patients and halt the progression of MetS-related UTI. Subgroup analyses in this study further validated the robustness and population generalizability of the positive association between MetS and UTI. Of note, both models consistently demonstrated no significant effect modification of alcohol intake on the MetS-UTI association, indicating that alcohol consumption behavior warrants greater clinical attention than alcohol dosage. Nevertheless, several limitations of this study merit acknowledgment. First, most UTI cases in this study were UTI, NOS, which may stem from the challenges of clinical UTI subclassification and warrants attention; second, UTI cases were only identified from inpatient medical records, and future studies could integrate outpatient data for further validation; third, MetS and obesity-related indicators were only assessed at baseline, and follow-up research may explore the association between dynamic changes in MetS status and long-term UTI risk. In summary, MetS and obesity are independent risk factors for UTI onset, and the pathogenesis may be associated with metabolic disorders and central obesity, though the detailed mechanism remains to be further elucidated. Strengthening surveillance of patients with metabolic abnormalities and obesity, and implementing targeted dietary guidance and lifestyle interventions at early stage, are conducive to the prevention of UTI. Declarations Ethics approval and consent to participate This study was conducted in strict adherence to the ethical principles set out in the World Medical Association Declaration of Helsinki (2024 Revision). The study protocol was approved by the UK Biobank under application number 529233. The UK Biobank has obtained full ethical approval for its establishment and operation from the National Health Service Northwest Multicenter Research Ethics Committee (reference number: 11/NW/0382). All study participants provided written, free and full informed consent prior to enrollment, fully meeting the Declaration’s ethical requirements for secondary research using human-derived data. Consent for publication All participants of the UK Biobank provided informed consent for the use of their anonymized data in health-related research and for the publication of the results. Therefore, no additional consent for publication is required for this study. ​​Availability of data and materials The data that support the findings of this study are available from the UK Biobank ( www.ukbiobank.ac.uk ), but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the UK Biobank. Competing of interest statement All authors declare that they have no competing interests. ​ ​​ Funding This study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. ​​ Authors' contributions Z.C and M.X conceived and designed the study. Z.C, L.H and J.S performed the statistical analysis. M.X and J.S drafted the manuscript. Z.C, Z.T and Z.W critically revised the manuscript for important intellectual content. All authors read and approved the final manuscript. Acknowledgements We express our gratitude to the participants and researchers of the UK Biobank. References Mancuso G, Midiri A, Gerace E, Marra M, Zummo S, Biondo C. Urinary Tract Infections: The Current Scenario and Future Prospects. Pathogens. 2023;12(4). Yang DC, Chao JY, Hsiao CY, Tseng CT, Lin WH, Kuo TH, et al. Impact of urinary tract infection requiring hospital admission on short-term, mid-term and long-term renal outcomes in adult CKD patients - A potentially modifiable factor for CKD progression. J Infect Public Health. 2025;18(5):102712. Kaye KS, Gupta V, Mulgirigama A, Joshi AV, Scangarella-Oman NE, Yu K, et al. Co-resistance Among Escherichia coli and Klebsiella pneumoniae Urine Isolates from Female Outpatients with Presumed UTI: A Retrospective US Cohort Study. Infect Dis Ther. 2024;13(7):1715–22. Josephs-Spaulding J, Rettig HC, Zimmermann J, Chkonia M, Mischnik A, Franzenburg S, et al. Metatranscriptomics-based metabolic modeling of patient-specific urinary microbiome during infection. NPJ Biofilms Microbiomes. 2025;11(1):183. Hou Y, Lv Z, Hu Q, Zhu A, Niu H. The immune mechanisms of the urinary tract against infections. Front Cell Infect Microbiol. 2025;15:1540149. Neugent ML, Hulyalkar NV, Ghosh D, Saenz CN, Zimmern PE, Shulaev V, et al. Urinary biochemical ecology reveals microbiome-metabolite interactions and metabolic markers of recurrent urinary tract infection. NPJ Biofilms Microbiomes. 2025;11(1):216. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120(16):1640–5. Riggen-Bueno V, Del Toro-Arreola S, Baltazar-Díaz TA, Vega-Magaña AN, Peña-Rodríguez M, Castaño-Jiménez PA et al. Intestinal Dysbiosis in Subjects with Obesity from Western Mexico and Its Association with a Proinflammatory Profile and Disturbances of Folate (B9) and Carbohydrate Metabolism. Metabolites. 2024;14(2). Zhao Q, Tan X, Su Z, Manzi HP, Su L, Tang Z et al. The Relationship between the Dietary Inflammatory Index (DII) and Metabolic Syndrome (MetS) in Middle-Aged and Elderly Individuals in the United States. Nutrients. 2023;15(8). Zhang X, Wang Y, Li Y, Gui J, Mei Y, Yang X, et al. Four-years change of BMI and waist circumference are associated with metabolic syndrome in middle-aged and elderly Chinese. Sci Rep. 2024;14(1):10220. Shin D, Kongpakpaisarn K, Bohra C. Trends in the prevalence of metabolic syndrome and its components in the United States 2007–2014. Int J Cardiol. 2018;259:216–9. Guembe MJ, Fernandez-Lazaro CI, Sayon-Orea C, Toledo E, Moreno-Iribas C. Risk for cardiovascular disease associated with metabolic syndrome and its components: a 13-year prospective study in the RIVANA cohort. Cardiovasc Diabetol. 2020;19(1):195. Hou Y, Lv Z, Hu Q, Zhu A, Niu H. The immune mechanisms of the urinary tract against infections. Front Cell Infect Microbiol. 2025;Volume 15–2025. Garcia MA, Nelson WJ, Chavez N. Cell-Cell Junctions Organize Structural and Signaling Networks. Cold Spring Harb Perspect Biol. 2018;10(4). Schwartz L, Salamon K, Simoni A, Cotzomi-Ortega I, Sanchez-Zamora Y, Linn-Peirano S, et al. Obesity promotes urinary tract infection by disrupting bladder focal adhesion kinase signaling. iScience. 2025;28(11):113862. Standards of medical care in. diabetes–2010. Diabetes Care. 2010;33(Suppl 1):S11–61. Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord. 2008;6(4):299–304. Wei X, Min Y, Song G, Ye X, Liu L. Association between triglyceride-glucose related indices with the all-cause and cause-specific mortality among the population with metabolic syndrome. Cardiovasc Diabetol. 2024;23(1):134. Wang C, He S, Xie G, Zhang S, Xiong Z, Lu H, et al. Associations of longitudinal trajectories of triglyceride-glucose index combined with classical and novel obesity indices and cardiovascular disease: evidence from a nationwide prospective cohort study in China. Cardiovasc Diabetol. 2025;24(1):431. Miočević M, O'Rourke HP, MacKinnon DP, Brown HC. Statistical properties of four effect-size measures for mediation models. Behav Res Methods. 2018;50(1):285–301. Kobayashi M, Uematsu T, Nakamura G, Kokubun H, Mizuno T, Betsunoh H, et al. The Predictive Value of Glycated Hemoglobin and Albumin for the Clinical Course Following Hospitalization of Patients with Febrile Urinary Tract Infection. Infect Chemother. 2018;50(3):228–37. Pugliese G, Liccardi A, Graziadio C, Barrea L, Muscogiuri G, Colao A. Obesity and infectious diseases: pathophysiology and epidemiology of a double pandemic condition. Int J Obes (Lond). 2022;46(3):449–65. Semins MJ, Shore AD, Makary MA, Weiner J, Matlaga BR. The impact of obesity on urinary tract infection risk. Urology. 2012;79(2):266–9. Taramian S, Joukar F, Maroufizadeh S, Hassanipour S, Sheida F, Mansour-Ghanaei F. Association between body mass index and urinary tract infections: A cross-sectional investigation of the PERSIAN Guilan cohort study. Obes Sci Pract. 2024;10(5):e70013. Nseir W, Farah R, Mahamid M, Sayed-Ahmad H, Mograbi J, Taha M, et al. Obesity and recurrent urinary tract infections in premenopausal women: a retrospective study. Int J Infect Dis. 2015;41:32–5. Chen ZM, Gu HQ, Mo JL, Yang KX, Jiang YY, Yang X, et al. U-shaped association between low-density lipoprotein cholesterol levels and risk of all-cause mortality mediated by post-stroke infection in acute ischemic stroke. Sci Bull (Beijing). 2023;68(12):1327–35. Benn M, Emanuelsson F, Tybjærg-Hansen A, Nordestgaard BG. Low LDL cholesterol and risk of bacterial and viral infections: observational and Mendelian randomization studies. Eur Heart J Open. 2025;5(1):oeaf009. Resnik N, Baraga D, Glažar P, Jokhadar Zemljič Š, Derganc J, Sepčić K, et al. Molecular, morphological and functional properties of tunnelling nanotubes between normal and cancer urothelial cells: New insights from the in vitro model mimicking the situation after surgical removal of the urothelial tumor. Front Cell Dev Biol. 2022;10:934684. Schwartz L, Salamon K, Simoni A, Eichler T, Jackson AR, Murtha M, et al. Insulin receptor signaling engages bladder urothelial defenses that limit urinary tract infection. Cell Rep. 2024;43(4):114007. Mohanty S, Kamolvit W, Scheffschick A, Björklund A, Tovi J, Espinosa A, et al. Diabetes downregulates the antimicrobial peptide psoriasin and increases E. coli burden in the urinary bladder. Nat Commun. 2022;13(1):4983. Additional Declarations No competing interests reported. Supplementary Files Summaryofsupplementmaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 24 Mar, 2026 Editor invited by journal 20 Mar, 2026 Editor assigned by journal 19 Mar, 2026 Submission checks completed at journal 19 Mar, 2026 First submitted to journal 12 Mar, 2026 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9109439","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611703411,"identity":"8a3f0c1e-86e7-4300-ad82-35796b5cffb3","order_by":0,"name":"Zhicheng Cong","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Zhicheng","middleName":"","lastName":"Cong","suffix":""},{"id":611703412,"identity":"b72f482f-4101-424c-9966-8b794871394c","order_by":1,"name":"Mulun Xiao","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Mulun","middleName":"","lastName":"Xiao","suffix":""},{"id":611703413,"identity":"87c24568-992e-4afe-956b-448d46c02207","order_by":2,"name":"Zhengyan Tang","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Zhengyan","middleName":"","lastName":"Tang","suffix":""},{"id":611703414,"identity":"af0addf6-d707-4018-9110-af543bb364b9","order_by":3,"name":"Junyi Sun","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Junyi","middleName":"","lastName":"Sun","suffix":""},{"id":611703415,"identity":"084d5baa-fac9-47f0-b223-a4bfc4603776","order_by":4,"name":"Li Huang","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Huang","suffix":""},{"id":611703416,"identity":"73a04747-74e4-4d81-91bc-9b4d761107e4","order_by":5,"name":"Zhao Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYBADHvvjjY0PP5CiRY7hzOFmYwlStBgz3EhvE+AhRqnBjRzDzwW/6hIbZz5sY5BgsJPTbSCgRXJGjrH0zD62xGbpxLYHBQzJxmYHCGjhl8jdIM3bw5PYJp3YbiDBcCBxGyEtbBK5m3/z9kgk9kgebJPgIUYL0JZt0jw/DIwlJBiJ1CLZ8/6bNW9DgpwBTyIwkA2I8IvB8bTk2zx/6ngM2I8/fPihwk6OoBYGgQQGBsY2uAmElIMAP8jQP8SoHAWjYBSMghELAG+HQN9g6Ol3AAAAAElFTkSuQmCC","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":true,"prefix":"","firstName":"Zhao","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-03-13 02:39:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9109439/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9109439/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105565771,"identity":"b63471c8-fe67-44bb-8355-775a551a9a2c","added_by":"auto","created_at":"2026-03-27 12:54:21","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1354833,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative risk of UTI by nMetS (0–5) (stabilized IPW weights)\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9109439/v1/7fcc1be2f73e40b5bffb91a0.jpeg"},{"id":105477317,"identity":"0b5bc948-c903-43bf-b986-7fa6ca21577b","added_by":"auto","created_at":"2026-03-26 13:06:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":712723,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of Metabolic, Anthropometric, and Biochemical Parameters with UTI Risk: Per-Unit Increment Analyses (stabilized IPW weights)\u003c/p\u003e\n\u003cp\u003eBMI, body mass index.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9109439/v1/fce43a5946bc7814c83acd66.png"},{"id":105477320,"identity":"8b79e7d2-bc29-4187-bae0-7f6e361ea8b6","added_by":"auto","created_at":"2026-03-26 13:06:52","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":383865,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of Metabolic, Anthropometric, and Biochemical Parameters with UTI Risk: Restricted Cubic Spline Analyses (stabilized IPW weights)\u003c/p\u003e\n\u003cp\u003eBMI, body mass index; BFP, body fat percentage; WC, waist circumference; HDL, high-density lipoprotein; LDL, low-density lipoprotein.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9109439/v1/23fd0782690ecc88c90a22f7.jpeg"},{"id":105477319,"identity":"3372b09a-c29a-434c-99af-b8d1ee364f07","added_by":"auto","created_at":"2026-03-26 13:06:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":695596,"visible":true,"origin":"","legend":"\u003cp\u003eMediation Analyses of MetS Associated Metabolic and Biochemical Parameters with UTI Risk: IPW Adjusted HR\u003c/p\u003e\n\u003cp\u003eNLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; TyG, triglyceride-glucose index; TyG-BMI, triglyceride glucose-body mass index; TyG-WC, triglyceride glucose-waist circumference; TyG-WHtR, triglyceride glucose-waist to height ratio; TyG-ABSI, triglyceride glucose-a body shape index; TyG-WWI, triglyceride glucose-waist to weight ratio.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9109439/v1/55a01357e5ccd816b66f7071.png"},{"id":105569261,"identity":"83f4c730-e61b-4bef-8c3b-35807a2a68fc","added_by":"auto","created_at":"2026-03-27 13:11:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4301181,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9109439/v1/ce765050-1e77-48ec-9e7a-d15bf493fca1.pdf"},{"id":105477336,"identity":"c58a3af7-816e-42b2-bb7c-007bb8f1a7c4","added_by":"auto","created_at":"2026-03-26 13:06:52","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18500580,"visible":true,"origin":"","legend":"","description":"","filename":"Summaryofsupplementmaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-9109439/v1/d506293763f55d45dfbdcf31.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metabolic syndrome, obesity-related indicators, and incident urinary tract infection: a UK Biobank cohort study with TyG-related indices as core mediators","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUrinary tract infection (UTI) rank among the most prevalent bacterial infectious diseases worldwide, affecting individuals of all ages and genders [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Recurrent UTI can lead to severe complications such as renal impairment and urosepsis, which not only seriously threaten patients\u0026rsquo; health but also impose a heavy burden on public health resources due to the resulting antimicrobial resistance [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The pathogenesis of UTI depends on the interaction between pathogen virulence and the host\u0026rsquo;s urinary tract defense mechanisms [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Currently, local metabolic abnormalities in the urinary tract have been identified as a core risk factor that influences the host\u0026rsquo;s urinary tract microenvironment and impairs local urinary immune defense [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMetabolic syndrome (MetS) is characterized by the clustering of several cardiometabolic abnormalities, which is diagnosed based on the presence of at least three of the following: elevated (1) waist circumference (WC), (2) triglycerides(TG), (3) blood pressure (BP), (4) Glycosylated Hemoglobin, Type A1C (HbA1c) or blood glucose and (5) reduced high-density lipoprotein cholesterol (HDL-C) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Obesity, which is closely linked to metabolic dysregulation, has also become highly prevalent worldwide and has a significant impact on public health [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Similar to UTI, the prevalence of MetS and obesity has been rising continuously in recent years along with global population aging [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Owing to the substantial health burden and wide range of comorbidities it causes, MetS and obesity have become a major public health challenge worldwide [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExisting research has confirmed that the occurrence, recurrence, and adverse progression of UTI are closely related to metabolic disorders of the local urinary tract microenvironment and imbalance of immune defense mechanisms [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Previous studies have demonstrated that obesity can increase susceptibility to UTI by disrupting bladder focal adhesion kinase signaling, disturbing the urethral microenvironment and weakening the local immune defense of host [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Collectively, these findings suggest that systemic metabolic abnormalities may also be closely associated with susceptibility to UTI, yet the precise causal association and underlying molecular mechanisms remain to be fully elucidated [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn particular, no large‑scale prospective cohort study has yet clarified the causal link between overall MetS, its components, or obesity and incident UTI in the general population, nor identified the core mediating pathways and actionable intervention targets. Against the backdrop of the rising prevalence of UTI, the global crisis of antibiotic resistance, and the modifiable nature of MetS, this study was conducted to provide evidence for potential and promising strategies in the prevention and metabolic management of UTI.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of Study Population\u003c/h2\u003e \u003cp\u003eThe UK Biobank is a large-scale, population-based prospective multicenter cohort study. For the current analysis, a total of 382791 participants were ultimately included. The participant inclusion and exclusion flowchart is presented in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Participants were excluded if they had missing key baseline metabolic data (n\u0026thinsp;=\u0026thinsp;88928), prevalent UTI at baseline (n\u0026thinsp;=\u0026thinsp;6322), or follow-up duration\u0026thinsp;\u0026le;\u0026thinsp;180 days (n\u0026thinsp;=\u0026thinsp;23896).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssessment of MetS and Obesity\u003c/h3\u003e\n\u003cp\u003eMetS was defined per the 2009 Harmonized Criteria by the International Diabetes Federation (IDF) and American Heart Association/National Heart, Lung, and Blood Institute (AHA/NHLBI) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], with diagnosis requiring\u0026thinsp;\u0026ge;\u0026thinsp;3 of 5 components: (1) abdominal obesity (elevated WC: \u0026ge;102 cm males, \u0026ge;\u0026thinsp;88 cm females); (2) elevated TG (\u0026ge;\u0026thinsp;150 mg/dL or 1.7 mmol/L); (3) elevated BP (\u0026ge;\u0026thinsp;130/85 mmHg) or antihypertensive use; (4) elevated fasting glucose (\u0026ge;\u0026thinsp;100 mg/dL or \u0026ge;\u0026thinsp;5.6 mmol/L) or glucose-lowering use; (5) reduced HDL-cholesterol (\u0026lt;\u0026thinsp;40 mg/dL males/\u0026lt;50 mg/dL females) or lipid‐modifying use. Given variable fasting times skewing glucose measurements, HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;5.7% (39 mmol/mol) was used as a hyperglycemia surrogate per American Diabetes Association (ADA) recommendations [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo assess obesity, those following indicators were included: body mass index (BMI), waist-to-hip ratio (WHR), body fat percentage (BFP), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), as well as WC and HDL-C, which are core components of MetS definition.\u003c/p\u003e\n\u003ch3\u003eAssessment of UTI\u003c/h3\u003e\n\u003cp\u003eThe outcome of this study was the diagnosis of incident UTI. Participants with UTI were identified using the World Health Organization (WHO) International Classification of Diseases, Tenth Revision (ICD-10) codes, and a total of 5 ICD-10 codes (N10, N12, N30.0, N30.9 and N39.0) were employed for UTI case ascertainment.\u003c/p\u003e \u003cp\u003eUTI cases were further classified into three subgroups according to infection site: upper UTI (UUTI), lower UTI (LUTI) and UTI, not otherwise specified (UTI, NOS) based on corresponding ICD-10 codes. (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/p\u003e\n\u003ch3\u003eAssessment of Covariates and Missing Data Handling\u003c/h3\u003e\n\u003cp\u003ePotential confounders were selected based on prior epidemiologic evidence and their plausible roles in the association between MetS and UTI. The following 12 covariates showed in Table S2 were included for adjustment in inverse probability weighting (IPW) and Cox regression models. Missing values for key exposure variables and covariates with a low proportion of missing data were imputed using the random forest algorithm.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eIPW was applied to account for confounding in the association between MetS and UTI. A logistic regression model was constructed to estimate the propensity score (PS) for MetS exposure, with the 12 prespecified baseline covariates as predictors. Stabilized IPW weights were derived from the PS and trimmed at the 1st and 99th percentiles to reduce extreme weight bias. The balance of covariates before and after weighting was assessed using standardized mean differences (SMDs). Model performance was further validated by PS distribution, weight distribution, calibration curves, and ROC curve analysis.\u003c/p\u003e \u003cp\u003eContinuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and categorical variables presented as frequencies and percentages. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Three models were constructed: Model 1 was unadjusted; Model 2 was adjusted for age, sex, ethnicity, employment status, educational attainment, TDI, and household income; Model 3 was further adjusted for smoking status, alcohol drinker status, alcohol consumption, physical activity, and healthy diet score.\u003c/p\u003e \u003cp\u003eCumulative incidence of UTI was estimated using IPW and Model 3 adjustment, stratified by MetS status and nMetS (the number of MetS components). Survival curves were plotted over the 15-year follow-up. Stratified analyses by follow-up period (0\u0026ndash;5, 5\u0026ndash;10, \u0026ge;\u0026thinsp;10 years) and a weighted Cox trend test were performed to test robustness. BMI, WC, WHR, BFP, TG, HDL-C, LDL-C, and TC were categorized into quartiles for forest plot analyses. Restricted cubic splines (RCS) were used to evaluate nonlinear exposure-response associations with UTI risk.\u003c/p\u003e \u003cp\u003eSubgroup analyses were conducted across key sociodemographic, lifestyle, and socioeconomic strata, with adjustment using both IPW and Model 3. FDR correction was applied for interaction, with Bonferroni correction for sensitivity testing. All statistical analyses were performed using R 4.5.1. A two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMediation Effect\u003c/h2\u003e \u003cp\u003eAs a core metabolic disorder and obesity-related metabolic trait, Insulin resistance (IR) was assessed using the Triglyceride-Glucose (TyG) index and its composite indices: TyG-ABSI (TyG index combined with A Body Shape Index), TyG-WWI (TyG index combined with Waist-to-Weight Index), TyG-BMI (TyG index combined with Body Mass Index), TyG-WC (TyG index combined with Waist Circumference) and TyG-WHtR (TyG index combined with Waist-to-Height Ratio). These indices have been widely validated for evaluating IR and related metabolic disorders [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Biomarkers reflecting inflammation, renal function, liver function, and red blood cell status were also included as potential mediators. Mediation effects were quantified using standardized effect sizes according to established methods [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAll metabolic indices were standardized after log-transformation with 0.01 added to non-zero values. Cause-specific Cox regression was applied to assess the association between MetS and UTI. Mediation analyses were performed using the R \u0026ldquo;mediation\u0026rdquo; package (version 4.5.1) under the counterfactual framework with 1000 bootstrap replications. Natural direct effects, natural indirect effects, and proportion mediated (PM) were estimated with FDR correction for multiple testing.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics\u003c/h2\u003e \u003cp\u003eA total of 382,791 participants free of baseline UTI were enrolled, among whom 19,097 (4.99%) developed incident UTI during follow-up. IPW-adjusted baseline characteristics of the total cohort, non-UTI group, and incident UTI group are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Following IPW adjustment, participants with incident UTI were significantly older and exhibited higher obesity-related indices compare to those without UTI (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Socioeconomically, the incident UTI group presented greater deprivation, lower educational attainment and household income, and a higher proportion of non-employment (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Unfavorable lifestyle factors, including smoking, insufficient physical activity, abnormal sleep duration, and frequent insomnia, were also more common in the incident UTI group (all 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 characteristics of the study population (IPW-weighted percentages and P-values)\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\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;382791)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo UTI\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;363694)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUTI\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;19097)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e204730 (51.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e195227 (51.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9503 (48.42)\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\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e178061 (48.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e168467 (48.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9594 (51.58)\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\u003eAge at recruitment, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57.27\u0026thinsp;\u0026plusmn;\u0026thinsp;7.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.07\u0026thinsp;\u0026plusmn;\u0026thinsp;7.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.00\u0026thinsp;\u0026plusmn;\u0026thinsp;6.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.91\u0026thinsp;\u0026plusmn;\u0026thinsp;4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.87\u0026thinsp;\u0026plusmn;\u0026thinsp;4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.60\u0026thinsp;\u0026plusmn;\u0026thinsp;4.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhole body fat mass, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.62\u0026thinsp;\u0026plusmn;\u0026thinsp;9.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.56\u0026thinsp;\u0026plusmn;\u0026thinsp;9.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.73\u0026thinsp;\u0026plusmn;\u0026thinsp;10.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody fat percentage, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.40\u0026thinsp;\u0026plusmn;\u0026thinsp;8.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.37\u0026thinsp;\u0026plusmn;\u0026thinsp;8.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.00\u0026thinsp;\u0026plusmn;\u0026thinsp;9.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist-to-Hip Ratio, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTownsend deprivation index at recruitment, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 \u003cem\u003e(least deprived)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95121 (24.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90987 (24.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4134 (21.58)\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96139 (24.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91601 (24.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4538 (23.68)\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\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95876 (25.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91343 (25.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4533 (23.55)\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\u003e4 \u003cem\u003e(most deprived)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95655 (25.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89763 (25.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5892 (31.19)\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\u003eEducational attainment, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e125082 (30.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120556 (31.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4526 (22.97)\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\u003eA-level or equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42842 (10.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41200 (11.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1642 (8.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO-level/GCSE/CSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100607 (26.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95977 (26.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4630 (24.07)\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\u003eVocational/Professional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45198 (12.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42642 (12.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2556 (13.40)\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\u003eNo qualification/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69062 (19.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63319 (18.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5743 (31.19)\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\u003eEthnic background, n(%)\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\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e365082 (95.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e346848 (95.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18234 (95.47)\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\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2094 (0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2009 (0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85 (0.45)\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\u003eAsian or Asian British\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6826 (1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6479 (1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e347 (1.78)\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\u003eBlack or Black British\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4713 (1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4471 (1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e242 (1.27)\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\u003eChinese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1068 (0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1037 (0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31 (0.15)\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/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3008 (0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2850 (0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e158 (0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage total household income before tax, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 18,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97872 (27.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89809 (26.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8063 (43.23)\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\u003e18,000 to 30,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96790 (25.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91654 (25.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5136 (27.04)\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\u003e31,000 to 51,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99764 (25.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96180 (25.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3584 (18.29)\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\u003e52,000 to 100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70445 (17.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68561 (17.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1884 (9.41)\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\u003eGreater than 100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17920 (4.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17490 (4.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e430 (2.03)\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\u003eEmployment Status, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3775 (1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3552 (0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e223 (1.13)\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\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e208507 (52.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e202106 (53.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6401 (32.31)\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\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e136762 (37.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126599 (36.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10163 (54.13)\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\u003eHomemaker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9515 (2.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9206 (2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e309 (1.56)\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\u003eSick/Disabled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14721 (4.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13164 (4.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1557 (8.43)\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\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6860 (1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6519 (1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e341 (1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnpaid or voluntary work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1716 (0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1639 (0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77 (0.39)\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\u003eFull or part-time student\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e935 (0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e909 (0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26 (0.12)\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 status, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e207033 (52.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e198240 (53.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8793 (45.70)\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\u003ePrevious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e134483 (35.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126761 (35.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7722 (40.06)\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\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39760 (10.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37332 (10.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2428 (13.43)\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\u003eAlcohol drinker status, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16042 (4.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14887 (4.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1155 (6.08)\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\u003ePrevious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13577 (3.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12463 (3.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1114 (5.80)\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\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e352339 (91.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e335598 (91.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16741 (87.57)\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\u003ePhysical activity level, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-enough physical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99905 (27.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93748 (27.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6157 (32.70)\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\u003eEnough physical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e282886 (72.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e269946 (72.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12940 (67.30)\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 consumption, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;14 UK units/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e311587 (81.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e295794 (81.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15793 (82.32)\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;14 UK units/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71204 (18.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67900 (18.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3304 (17.68)\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\u003eThe sum of healthy diet, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;5 servings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90876 (24.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86116 (24.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4760 (25.87)\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;5 servings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e291915 (75.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e277578 (75.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14337 (74.13)\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\u003eSleep duration, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;6 or \u0026ge;\u0026thinsp;9 hour/d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e123475 (32.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116161 (32.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7314 (37.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u0026ndash;8 hour/d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e259316 (67.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e247533 (67.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11783 (62.11)\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\u003eChronotype, n(%)\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\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTend to be a \"morning person\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e253028 (66.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e240457 (66.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12571 (66.38)\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\u003eTend to be a \"evening person\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e129763 (33.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e123237 (33.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6526 (33.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsomnia, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-Freq (Never, rarely, sometimes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e274109 (71.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e261544 (71.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12565 (66.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-Freq (Usually)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108682 (28.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102150 (28.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6532 (33.52)\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\u003eSnoring, n(%)\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\u003e0.808\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\u003e137962 (34.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e130861 (34.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7101 (34.95)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e244829 (65.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e232833 (65.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11996 (65.05)\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\u003eDaytime dozing, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-Freq (Never, rarely, sometimes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e372132 (97.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e353957 (97.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18175 (95.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-Freq (Often, all the time)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10659 (2.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9737 (2.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e922 (4.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL Cholesterol, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Cholesterol, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevated WC, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e129930 (26.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e121121 (26.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8809 (33.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevated TG, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e154495 (34.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e145607 (34.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8888 (35.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevated BP or blood pressure medication, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e286032 (73.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e269983 (72.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16049 (80.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevated HbA1c or insulin use, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73639 (14.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67662 (13.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5977 (20.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReduced HDL or cholesterol lowering medication, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e127416 (26.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118457 (26.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8959 (34.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Baseline characteristics of the study population (IPW-weighted percentages and P-values)\u003c/p\u003e\n\u003cp\u003eBMI, body mass index; HDL, high-density lipoprotein; LDL, low-density lipoprotein; WC, waist circumference; TG, triglyceride; BP, blood pressure;\u0026nbsp;HbA1c, Glycosylated Hemoglobin A1c\u003c/p\u003e\n\u003cp\u003e1. Continuous data are presented as mean ± standard deviation (Mean ± SD), and categorical data as n (%).\u003c/p\u003e\n\u003cp\u003e2. P-values were calculated as follows:\u003c/p\u003e\n\u003cp\u003e(1) For continuous variables: the weighted t-test was used for two-group comparisons.\u003c/p\u003e\n\u003cp\u003e(2) For categorical variables: the weighted Pearson chi-squared test was used.\u003c/p\u003e\n\u003cp\u003eAll P-values are reported to 3 decimal places.\u003c/p\u003e\u003cp\u003eFor metabolic and obesity-related traits, the incident UTI group showed a significantly higher prevalence of all MetS components at baseline, along with lower LDL-C and TC levels (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Briefly, unweighted characteristics by incident UTI are presented in Table S3, while IPW-adjusted characteristics stratified by UTI subtype, MetS status, and nMetS are shown in Tables S4\u0026ndash;S6, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of IPW Model Performance\u003c/h2\u003e \u003cp\u003eWe rigorously assessed PS model performance and IPW covariate balancing efficacy to validate the robustness of causal inference for the MetS-UTI association, with detailed results presented in Figure S2 and S3.\u003c/p\u003e \u003cp\u003eThe PS model yielded an AUC of 0.6737 (within the optimal 0.6\u0026ndash;0.8 range), demonstrating moderate-to-high discriminative power with minimal overfitting risk. A calibration error of 0.1369 indicated acceptable model consistency, aligned with IPW\u0026rsquo;s core priority of covariate balance over predictive accuracy. Notably, 100% of participants fell within the common support region, requiring no sample exclusion (superior to most analogous studies that exclude 5\u0026ndash;10% of out-of-range participants). IPW weight statistics (mean 1.093, range 0.43\u0026ndash;2.82) confirmed no extreme weights that would bias effect estimates. Critically, SMDs for all 12 covariates were \u0026lt;\u0026thinsp;0.1 post-weighting, indicating excellent intergroup balance. These findings confirmed effective control of baseline confounding, strengthening the reliability of our causal inference for the MetS-UTI association.\u003c/p\u003e \u003cp\u003eIPW weight distributions across nMetS-stratified subgroups followed a systematic pattern consistent with stabilized weighting principles (Figure S4, S5). Crude weights were overestimated in 0\u0026ndash;2 nMetS subgroups (1.13\u0026ndash;1.42) and underestimated in 3\u0026ndash;5 nMetS subgroups (0.62\u0026ndash;0.71), aligning with differential exposure probabilities across subgroups. The maximum weight across all subgroups ranged from 2.45 to 2.82, within acceptable limits. Subgroup weight efficiency ranged from 89.7% to 92.3%, with a relatively low information loss rate of 7.7%\u0026ndash;10.3% due to weight dispersion and redistribution of statistical contribution (Figure S6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCumulative Incidence Risk of UTI\u003c/h2\u003e \u003cp\u003eIPW-adjusted cumulative risk of UTI among participants with and without MetS is presented in Figure S7. Participants with MetS had a significantly higher cumulative risk of UTI over the 15-year follow-up period, with the between-group difference in risk widening progressively over time. This trend was further validated in analyses using Model 3 (Figure S8). At the 3rd year of follow-up, the cumulative incidence of UTI was 0.83% in the MetS group and 0.59% in the non-MetS group, with a between-group difference of only 0.24%. By the 15th year of follow-up, the cumulative incidence of UTI in the MetS group reached 8.60%, which was markedly higher than the 6.03% observed in the non-MetS group. Consistent results were obtained from IPW-adjusted and Model 3 analyses for the three UTI subtypes, and MetS group had a significantly higher cumulative incidence risk of all subtypes over the 15-year follow-up compared with non-MetS group, and the between-group risk difference became more pronounced with extended follow-up (Figure S9 and Figure S10).\u003c/p\u003e \u003cp\u003eIPW-weighted cumulative incidence risk analyses revealed an overall stepwise increasing trend in the cumulative risk of UTI across all follow-up time points with rising nMetS over the 15-year follow-up period, with one exception: participants with nMetS\u0026thinsp;=\u0026thinsp;3 had marginally lower cumulative incidence risk of UTI at all follow-up time points than those with nMetS\u0026thinsp;=\u0026thinsp;2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The overall statistical significance of this trend was confirmed by the weighted Cox global test (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A similar increasing trend in risk was observed in the Model 3-based analysis, where the cumulative incidence risk of UTI presented a strictly monotonic increase with rising nMetS, without the exception shown in the IPW-weighted analysis (Figure S11).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociation Between MetS and UTI Risk Across Follow-Up Duration\u003c/h2\u003e \u003cp\u003eBoth Model 3 adjustment and IPW analyses consistently confirmed an independent association between MetS and elevated UTI risk, as illustrated in Figure S12. Subgroup stratification by follow-up duration (0\u0026ndash;5, 5\u0026ndash;10, \u0026ge;\u0026thinsp;10 years) verified the robustness of this association across all time intervals, with HRs showing a mild incremental trend with prolonged follow-up overall. The only exception was that HR estimates in the IPW analysis were largely comparable between the 5\u0026ndash;10 and \u0026ge;\u0026thinsp;10-year subgroups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAssociation Between nMetS and UTI Risk\u003c/h2\u003e \u003cp\u003eThe association between nMetS and UTI risk is presented in Figure S13. IPW-weighted analysis showed an overall significant positive trend: higher nMetS was associated with elevated incident UTI risk, except that HR for nMetS\u0026thinsp;=\u0026thinsp;3 was marginally lower than those for nMetS\u0026thinsp;=\u0026thinsp;2. Consistently, a significant positive association between increasing nMetS and UTI risk was observed across all three models, as detailed in Table S7 and Table S8.\u003c/p\u003e \u003cp\u003eFindings from adjusted analyses of nMetS and UTI subtype risks are presented in Figure S14 and Figure S15, adjusted by IPW and Model 3 respectively. Potentially limited by size, IPW analysis showed no significant association of MetS with either UUTI and LUTI. In Model 3-adjusted analysis, no significant association was found for UUTI; only a weak threshold-dependent association was detected for LUTI, with increased UTI risk solely observed in participants with nMetS\u0026thinsp;=\u0026thinsp;4\u0026ndash;5. In contrast, analyses for UTI, NOS under both models yielded results highly consistent with the overall UTI analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAssociations of Individual Metabolic and Anthropometric Indicators with UTI Risk\u003c/h2\u003e \u003cp\u003eWe assessed the associations of individual metabolic and anthropometric parameters with UTI risk, evaluating both continuous per-unit increments and categorical quartile distributions of each exposure variable (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Figure S16). Consistent findings were observed across IPW and Model 3 adjustment analyses in the study cohort: elevated values of most core metabolic and anthropometric indicators were independently linked to higher risk of UTI. Conversely, higher concentrations of HDL-C, LDL-C and TC were associated with reduced UTI risk, representing a clinically counterintuitive trend for LDL-C and TC. Dose-response analyses further verified graded associations: HRs elevated incrementally with increasing levels of most parameters, while HDL-C, LDL-C and TC exhibited inverse dose-response relationships with UTI risk (all P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eNon-Linear Dose-Response Relationships Assessed by RCS Analysis\u003c/h2\u003e \u003cp\u003eRCS analysis was performed to explore non-linear dose-response associations between metabolic and anthropometric parameters and UTI risk, with evaluations conducted in two adjusted models. IPW-adjusted RCS analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e) detected significant non-linear relationships for all assessed indicators (all P for non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with varied curve patterns across parameters. BMI, WC, whole body fat mass and TG showed J-shaped associations with UTI risk, marked by a sharper HR increase beyond a certain threshold; WHR and BFP presented complex non-linear trends, with elevated HRs at both low and high levels and steep rises in the moderate range; HDL-C, LDL-C and TC exhibited L-shaped associations, featuring rapid HR declines at low concentrations followed by a weakened downward trend. Corresponding inflection points were estimated as critical thresholds for UTI risk changes.\u003c/p\u003e \u003cp\u003eModel 3-adjusted RCS analysis (Figure S17) verified these non-linear associations, with all P for non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and highly consistent curve trends compared with the IPW-adjusted model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup Analyses of MetS-UTI Association\u003c/h2\u003e \u003cp\u003eSubgroup analyses were performed to investigate the association between MetS and incident UTI, based on IPW adjustment and Model 3 adjustment (Figure S18 and S19). IPW analysis identified effect modifications of age at recruitment, TDI, smoking status, alcohol consumption status and physical activity level on the MetS-UTI association, whereas no significant effect modification was detected for sex, alcohol intake and healthy diet.\u003c/p\u003e \u003cp\u003eBoth IPW and Model 3 analyses consistently validated a positive association between MetS and UTI incidence across all prespecified subgroups (all FDR-adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The significant effect modifications detected in IPW analysis were replicated in Model 3 analysis, with all interaction terms reaching statistical significance. Additionally, both models consistently indicated a non-significant alcohol intake-MetS interaction regarding UTI risk.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity Analyses\u003c/h2\u003e \u003cp\u003eSensitivity analyses via IPW and Model 3 verified result robustness, with detailed findings in Table S9-S12. For the IPW model, positive associations between key indicators and UTI risk were consistent across all scenarios: exclusion of early UTI cases (first 2 follow-up years), exclusion of 1st\u0026ndash;99th percentile outliers, 5th\u0026ndash;95th percentile IPW trimming, and the three-method combination. For Model 3, core association trends remained stable after excluding early UTI cases and outliers, as well as the two-method combination.\u003c/p\u003e \u003cp\u003eBoth models consistently linked BMI, WC, WHR, BFP, whole body fat mass, TG, elevated BP/BP medication and elevated HbA1c/insulin use to higher UTI risk; all associations were significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) except specific IPW model subgroups. HDL-C and TC were inversely associated with UTI risk in both models, while the negative LDL-C association was stronger in the IPW model (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 in most analyses).\u003c/p\u003e \u003cp\u003eClear between-model differences emerged for MetS trait count. In Model 3, 1 MetS trait correlated with marginally lower UTI risk (HR\u0026thinsp;=\u0026thinsp;0.91\u0026ndash;0.92, 95% CI 0.89\u0026ndash;0.94, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) vs. no traits, and \u0026ge;\u0026thinsp;3 traits showed dose-dependent elevated risk (HR\u0026thinsp;=\u0026thinsp;1.08\u0026ndash;1.42, 95% CI 1.06\u0026ndash;1.47, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, the IPW model revealed consistent dose-dependent UTI risk elevation with increasing MetS traits: HR\u0026thinsp;=\u0026thinsp;1.12\u0026ndash;1.15 (95% CI 1.04\u0026ndash;1.23) for 1 trait, rising to HR\u0026thinsp;=\u0026thinsp;2.18\u0026ndash;2.43 (95% CI 1.99\u0026ndash;2.63) for 5 traits (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eMediation analyses\u003c/h2\u003e \u003cp\u003eMediation analyses via IPW and Model 3 confirmed multiple biomarkers mediated the MetS-UTI association (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Figure S20). In the plots, β coefficient reflects MetS-biomarker associations, HR denotes biomarker-UTI associations, direct HR represents the direct effect of MetS on UTI after mediator adjustment, and PM indicates the mediated proportion of total effect.\u003c/p\u003e \u003cp\u003eIn IPW model, all biomarkers showed significant associations with MetS and UTI (all FDR-P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) except TyG-ABSI (FDR-P\u0026thinsp;=\u0026thinsp;0.297) and TyG-WWI (FDR-P\u0026thinsp;=\u0026thinsp;0.023). Most biomarkers had positive PM values, while several (dark gray in plots) showed negative PM, suggesting competitive or suppressive mediation. Brightly highlighted biomarkers corresponded to stronger direct effects and higher mediation proportions; lymphocyte percentage and albumin (light gray) presented inverse direct effects and negative mediation patterns.\u003c/p\u003e \u003cp\u003eModel 3 findings were generally consistent with the IPW model, but no negative PM values were observed. Moreover, several TyG-related biomarkers showed slightly attenuated significance and weaker direct effect estimates in Model 3 versus the IPW model.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo date, epidemiological studies directly investigating the associations of MetS and obesity with incident UTI risk remain scarce. Existing studies are predominantly small-sample, single-center cross-sectional analyses, merely focus on the correlation between individual MetS component and UTI risk, and are mostly conducted in specific populations with postoperative UTI events [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study is the first to systematically explore the associations of MetS, its components and obesity with incident UTI risk in a large-scale cohort. Using IPW and model 3 adjustment, we first confirmed that MetS serves as an independent risk factor for incident UTI in the general population, with a distinct cumulative effect of its components. As a highly prevalent infectious disease worldwide, UTI has driven massive global antibiotic consumption and a persistent upward trend in antimicrobial resistance. Notably, MetS is a readily modifiable clinical phenotype. Accordingly, our findings provide robust evidence for the primary prevention of UTI and the comprehensive management of UTI.\u003c/p\u003e \u003cp\u003eIncreased obesity has been widely associated with elevated UTI risk. However, the majority of clinical studies have solely used BMI to assess obesity status. Semins et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and Taramian et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] demonstrated that elevated BMI correlates with an increased UTI risk in patients; Nseir et al. reported that high BMI is linked to recurrent UTI among premenopausal women [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Building on prior research, the present study incorporated a comprehensive panel of adiposity-related indicators, and confirmed that BMI, WHR, BFP, whole body fat mass, HDL-C, TG, and WC were all positively associated with UTI risk.\u003c/p\u003e \u003cp\u003eIntriguingly, the present study revealed that LDL-C and TC were inversely associated with incident UTI risk, which runs counter to the conventional clinical view. These findings were robustly validated via quartile-based trend and RCS analyses in both IPW model and Model 3. RCS curves illustrated a typical L-shaped dose-response relationship: rising LDL-C and TC levels were accompanied by a sharp reduction in UTI risk, while the protective effect plateaued markedly beyond a critical threshold. This implies that an optimal lipid level is a prerequisite for sustaining urinary tract immune defense, and both excessively low and high lipid profiles are unfavorable. This phenomenon has also been reported in previous infection-related studies; for instance, Chen et al. documented that low serum LDL-C levels are associated with a higher risk of post-stroke infection in patients with acute ischemic stroke [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Thus, excessive lipid lowering should be avoided in lipid-modifying therapy for MetS patients, with due consideration of the physiological protective roles of lipids. The potential mechanism underlying this observation is that LDL-C can bind bacterial toxins secreted by pathogenic microorganisms and activate the bactericidal activity of phagocytes, thereby enhancing host anti-infective defense [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Meanwhile, TC is a core structural component of the urothelial cell membrane and a pivotal material basis for maintaining urinary mucosal barrier integrity and innate immune function [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], which may explain the correlation between low TC levels and elevated UTI risk. These findings challenge traditional clinical consensus, though further investigations are warranted to clarify the exact mechanism.\u003c/p\u003e \u003cp\u003ePrevious studies have confirmed that the Integrated Stress Response (INSR) is implicated in urothelial barrier function and modulates host defense against UTI [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], while IR can increase infection susceptibility by suppressing the INSR signaling pathway. Grounded in these findings, the present study included the TyG index and its derived indicators (TyG-BMI, TyG-WC, TyG-WHtR, TyG-WWI, TyG-ABSI) \u0026mdash; which exhibit stronger correlations with visceral fat accumulation and chronic low-grade inflammation[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] \u0026mdash; to systematically explore their mediating effects in the association between MetS and UTI. Mediation analyses indicated that the mediated proportion of TyG-derived indicators was significantly higher than TyG index and other biomarkers. This provides clinical guidance for monitoring TyG-derived indicators, rather than relying solely on traditional metrics, to identify high-risk subgroups among MetS patients and halt the progression of MetS-related UTI.\u003c/p\u003e \u003cp\u003eSubgroup analyses in this study further validated the robustness and population generalizability of the positive association between MetS and UTI. Of note, both models consistently demonstrated no significant effect modification of alcohol intake on the MetS-UTI association, indicating that alcohol consumption behavior warrants greater clinical attention than alcohol dosage.\u003c/p\u003e \u003cp\u003eNevertheless, several limitations of this study merit acknowledgment. First, most UTI cases in this study were UTI, NOS, which may stem from the challenges of clinical UTI subclassification and warrants attention; second, UTI cases were only identified from inpatient medical records, and future studies could integrate outpatient data for further validation; third, MetS and obesity-related indicators were only assessed at baseline, and follow-up research may explore the association between dynamic changes in MetS status and long-term UTI risk.\u003c/p\u003e \u003cp\u003eIn summary, MetS and obesity are independent risk factors for UTI onset, and the pathogenesis may be associated with metabolic disorders and central obesity, though the detailed mechanism remains to be further elucidated. Strengthening surveillance of patients with metabolic abnormalities and obesity, and implementing targeted dietary guidance and lifestyle interventions at early stage, are conducive to the prevention of UTI.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e This study was conducted in strict adherence to the ethical principles set out in the World Medical Association Declaration of Helsinki (2024 Revision). The study protocol was approved by the UK Biobank under application number 529233. The UK Biobank has obtained full ethical approval for its establishment and operation from the National Health Service Northwest Multicenter Research Ethics Committee (reference number: 11/NW/0382). All study participants provided written, free and full informed consent prior to enrollment, fully meeting the Declaration\u0026rsquo;s ethical requirements for secondary research using human-derived data.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eAll participants of the UK Biobank provided informed consent for the use of their anonymized data in health-related research and for the publication of the results. Therefore, no additional consent for publication is required for this study.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003e​​Availability of data and materials\u003c/h2\u003e \u003cp\u003eThe data that support the findings of this study are available from the UK Biobank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.ukbiobank.ac.uk\u003c/span\u003e\u003cspan address=\"http://www.ukbiobank.ac.uk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the UK Biobank.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting of interest statement\u003c/strong\u003e \u003cp\u003eAll authors declare that they have no competing interests.\u003c/p\u003e \u003cp\u003e​\u003c/p\u003e \u003cp\u003e​​\u003cb\u003eFunding\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e \u003cp\u003e​​\u003cb\u003eAuthors' contributions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eZ.C and M.X conceived and designed the study. Z.C, L.H and J.S performed the statistical analysis. M.X and J.S drafted the manuscript. Z.C, Z.T and Z.W critically revised the manuscript for important intellectual content. All authors read and approved the final manuscript.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe express our gratitude to the participants and researchers of the UK Biobank.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMancuso G, Midiri A, Gerace E, Marra M, Zummo S, Biondo C. Urinary Tract Infections: The Current Scenario and Future Prospects. Pathogens. 2023;12(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang DC, Chao JY, Hsiao CY, Tseng CT, Lin WH, Kuo TH, et al. Impact of urinary tract infection requiring hospital admission on short-term, mid-term and long-term renal outcomes in adult CKD patients - A potentially modifiable factor for CKD progression. J Infect Public Health. 2025;18(5):102712.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaye KS, Gupta V, Mulgirigama A, Joshi AV, Scangarella-Oman NE, Yu K, et al. Co-resistance Among Escherichia coli and Klebsiella pneumoniae Urine Isolates from Female Outpatients with Presumed UTI: A Retrospective US Cohort Study. Infect Dis Ther. 2024;13(7):1715\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJosephs-Spaulding J, Rettig HC, Zimmermann J, Chkonia M, Mischnik A, Franzenburg S, et al. Metatranscriptomics-based metabolic modeling of patient-specific urinary microbiome during infection. NPJ Biofilms Microbiomes. 2025;11(1):183.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHou Y, Lv Z, Hu Q, Zhu A, Niu H. The immune mechanisms of the urinary tract against infections. Front Cell Infect Microbiol. 2025;15:1540149.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeugent ML, Hulyalkar NV, Ghosh D, Saenz CN, Zimmern PE, Shulaev V, et al. Urinary biochemical ecology reveals microbiome-metabolite interactions and metabolic markers of recurrent urinary tract infection. NPJ Biofilms Microbiomes. 2025;11(1):216.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120(16):1640\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiggen-Bueno V, Del Toro-Arreola S, Baltazar-D\u0026iacute;az TA, Vega-Maga\u0026ntilde;a AN, Pe\u0026ntilde;a-Rodr\u0026iacute;guez M, Casta\u0026ntilde;o-Jim\u0026eacute;nez PA et al. Intestinal Dysbiosis in Subjects with Obesity from Western Mexico and Its Association with a Proinflammatory Profile and Disturbances of Folate (B9) and Carbohydrate Metabolism. Metabolites. 2024;14(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Q, Tan X, Su Z, Manzi HP, Su L, Tang Z et al. The Relationship between the Dietary Inflammatory Index (DII) and Metabolic Syndrome (MetS) in Middle-Aged and Elderly Individuals in the United States. Nutrients. 2023;15(8).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Wang Y, Li Y, Gui J, Mei Y, Yang X, et al. Four-years change of BMI and waist circumference are associated with metabolic syndrome in middle-aged and elderly Chinese. Sci Rep. 2024;14(1):10220.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShin D, Kongpakpaisarn K, Bohra C. Trends in the prevalence of metabolic syndrome and its components in the United States 2007\u0026ndash;2014. Int J Cardiol. 2018;259:216\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuembe MJ, Fernandez-Lazaro CI, Sayon-Orea C, Toledo E, Moreno-Iribas C. Risk for cardiovascular disease associated with metabolic syndrome and its components: a 13-year prospective study in the RIVANA cohort. Cardiovasc Diabetol. 2020;19(1):195.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHou Y, Lv Z, Hu Q, Zhu A, Niu H. The immune mechanisms of the urinary tract against infections. Front Cell Infect Microbiol. 2025;Volume 15\u0026ndash;2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarcia MA, Nelson WJ, Chavez N. Cell-Cell Junctions Organize Structural and Signaling Networks. Cold Spring Harb Perspect Biol. 2018;10(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchwartz L, Salamon K, Simoni A, Cotzomi-Ortega I, Sanchez-Zamora Y, Linn-Peirano S, et al. Obesity promotes urinary tract infection by disrupting bladder focal adhesion kinase signaling. iScience. 2025;28(11):113862.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStandards of medical care in. diabetes\u0026ndash;2010. Diabetes Care. 2010;33(Suppl 1):S11\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimental-Mend\u0026iacute;a LE, Rodr\u0026iacute;guez-Mor\u0026aacute;n M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord. 2008;6(4):299\u0026ndash;304.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei X, Min Y, Song G, Ye X, Liu L. Association between triglyceride-glucose related indices with the all-cause and cause-specific mortality among the population with metabolic syndrome. Cardiovasc Diabetol. 2024;23(1):134.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang C, He S, Xie G, Zhang S, Xiong Z, Lu H, et al. Associations of longitudinal trajectories of triglyceride-glucose index combined with classical and novel obesity indices and cardiovascular disease: evidence from a nationwide prospective cohort study in China. Cardiovasc Diabetol. 2025;24(1):431.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiočević M, O'Rourke HP, MacKinnon DP, Brown HC. Statistical properties of four effect-size measures for mediation models. Behav Res Methods. 2018;50(1):285\u0026ndash;301.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKobayashi M, Uematsu T, Nakamura G, Kokubun H, Mizuno T, Betsunoh H, et al. The Predictive Value of Glycated Hemoglobin and Albumin for the Clinical Course Following Hospitalization of Patients with Febrile Urinary Tract Infection. Infect Chemother. 2018;50(3):228\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePugliese G, Liccardi A, Graziadio C, Barrea L, Muscogiuri G, Colao A. Obesity and infectious diseases: pathophysiology and epidemiology of a double pandemic condition. Int J Obes (Lond). 2022;46(3):449\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSemins MJ, Shore AD, Makary MA, Weiner J, Matlaga BR. The impact of obesity on urinary tract infection risk. Urology. 2012;79(2):266\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaramian S, Joukar F, Maroufizadeh S, Hassanipour S, Sheida F, Mansour-Ghanaei F. Association between body mass index and urinary tract infections: A cross-sectional investigation of the PERSIAN Guilan cohort study. Obes Sci Pract. 2024;10(5):e70013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNseir W, Farah R, Mahamid M, Sayed-Ahmad H, Mograbi J, Taha M, et al. Obesity and recurrent urinary tract infections in premenopausal women: a retrospective study. 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Molecular, morphological and functional properties of tunnelling nanotubes between normal and cancer urothelial cells: New insights from the in vitro model mimicking the situation after surgical removal of the urothelial tumor. Front Cell Dev Biol. 2022;10:934684.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchwartz L, Salamon K, Simoni A, Eichler T, Jackson AR, Murtha M, et al. Insulin receptor signaling engages bladder urothelial defenses that limit urinary tract infection. Cell Rep. 2024;43(4):114007.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohanty S, Kamolvit W, Scheffschick A, Bj\u0026ouml;rklund A, Tovi J, Espinosa A, et al. Diabetes downregulates the antimicrobial peptide psoriasin and increases E. coli burden in the urinary bladder. Nat Commun. 2022;13(1):4983.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Metabolic Syndrome, Urinary Tract Infection, Insulin Resistance, Obesity, Mediation Effect Analysis, Inverse Probability Weighting","lastPublishedDoi":"10.21203/rs.3.rs-9109439/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9109439/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo explore the causal association of metabolic syndrome (MetS) and obesity-related indicators with incident urinary tract infection (UTI), and the underlying mediating mechanisms.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 382,791 participants free of baseline UTI from the UK Biobank were included, with a median follow-up of 13.05 years. Exposures were MetS and obesity-related indicators, outcome was incident UTI. IPW-adjusted and multivariable Cox proportional hazards models were used to assess the exposure-outcome association, RCS for nonlinear dose-response analysis, and counterfactual-based mediation analysis to explore the mediating effect of TyG-related indices.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDuring follow-up, 19,097 participants developed incident UTI. MetS was identified as an independent risk factor for UTI, with a significant cumulative effect of increasing nMetS. RCS analysis demonstrated a significant L-shaped inverse association between LDL-C/TC levels and UTI risk (all P for nonlinearity\u0026thinsp;\u0026lt;\u0026thinsp;0.05). TyG-derived indices were confirmed as the core mediators, with a mediated proportion ranging from 14.69% to 69.62%.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eMetS is independently associated with an increased risk of incident UTI, mainly driven by central obesity and insulin resistance. Metabolic control and early intervention in high-risk individuals represent a promising public health approach for UTI prevention, rational antibiotic use and antimicrobial resistance mitigation.\u003c/p\u003e","manuscriptTitle":"Metabolic syndrome, obesity-related indicators, and incident urinary tract infection: a UK Biobank cohort study with TyG-related indices as core mediators","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-26 13:06:35","doi":"10.21203/rs.3.rs-9109439/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-24T11:32:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-20T11:45:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-19T10:45:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-19T10:45:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2026-03-13T02:33:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7d431da9-b1a3-41be-8ac4-f4fe6dbc626b","owner":[],"postedDate":"March 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-26T13:06:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-26 13:06:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9109439","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9109439","identity":"rs-9109439","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}