The association of triglyceride-glucose related indices with benign prostatic hyperplasia: insights from the CHARLS

The association of triglyceride-glucose related indices with benign prostatic hyperplasia: insights from the CHARLS | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The association of triglyceride-glucose related indices with benign prostatic hyperplasia: insights from the CHARLS Xiaoxuan Bai, Yishan Zhang, Yongchen Jin, Zhihan Li, Ziteng Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7968845/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction Benign prostatic hyperplasia (BPH) is a common disease among middle-aged and older men, often linked to obesity, dyslipidemia, and metabolic syndrome. It causes lower urinary tract symptoms and impacts their quality of life. While the triglyceride-glucose (TyG) index is a known marker for metabolic syndrome, its relationship with BPH remains underexplored. This study investigates the association between TyG-related indices and the risk of BPH to uncover potential clinical implications. Methods This study analyzed data from the China Health and Retirement Longitudinal Study (CHARLS), involving 3,460 men aged 45 and older who were free of BPH at baseline. During a 7-year follow-up, new BPH cases were recorded. Baseline data included TyG, Chinese visceral adiposity index (CVAI), and TyG-related indices. Statistical methods including Cox proportional hazards regression, Restricted Cubic Splines, Kaplan-Meier curves, and subgroup analyses were used to assess the relationship between these indices and BPH. Results Over a 7-year follow-up, 745 participants developed BPH. Our analysis revealed that higher values of TyG-related indices were significantly associated with an elevated risk of BPH. TyG and CVAI had a linear relationship with BPH risk, while TyG-BMI, TyG-WC, TyG-WHtR and TyG-CVAI showed a nonlinear association. No significant interaction was observed in the subgroup analysis. Conclusion There is a strong link between TyG-related indices and the risk of BPH, consistent across Chinese populations. The risk of BPH rises with higher metabolic indices, notably CVAI. These findings suggest new avenues for early prevention and intervention. Future research should investigate the mechanisms of these indices. Health sciences/Diseases Health sciences/Medical research Health sciences/Risk factors Health sciences/Urology benign prostatic hyperplasia triglyceride-glucose index triglyceride-glucose related indices metabolic syndrome CHARLS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Benign prostatic hyperplasia (BPH) is a common urological disorder among middle-aged and elderly men. Globally, over 50% of men exhibit some degree of BPH by the age of 60, with prevalence increasing to more than 80% in men aged 70 years and older 1 . The pathogenesis of BPH is not yet fully elucidated, emerging evidence indicates that hormonal alterations are crucial in its development 2 , 3 . Furthermore, imbalances between cellular proliferation and apoptosis 4 , 5 , modifications in the stromal components of the prostate, and chronic inflammatory responses 6 have been identified as potential contributory mechanisms. Despite its non-malignant nature, BPH is a leading cause of lower urinary tract symptoms (LUTS), such as urinary hesitancy, increased frequency, and nocturia. The associated pathological changes and clinical manifestations significantly impair patients’ physical health and quality of life 7 . Common complications, including urinary retention, urinary tract infections, bladder stones, and chronic renal insufficiency, further exacerbate these detrimental effects. Without timely intervention, these complications can result in significant renal damage and a notable decline in quality of life. Symptoms, including frequent nocturnal urination and urgency can greatly disrupt sleep, contribute to mood disturbances, and impair social functioning, thereby exacerbating the psychological burden on affected individuals 8 . Despite advancements in the understanding of BPH pathophysiology and the development of pharmacological and surgical treatments, early detection and intervention remain challenging due to the heterogeneous and multifactorial nature of the disease. Given the rapidly aging global population, the necessity for further research into BPH has become increasingly pressing. BPH is considered to be closely associated with metabolic syndrome and is often accompanied by metabolic disturbances such as obesity and dyslipidemia. However, the pathogenesis of BPH has not yet been fully elucidated, as its complex pathological processes involve multiple factors, thereby increasing the challenges of both research and clinical intervention. Traditional indicators used to evaluate metabolic syndrome have been relatively simple and limited in accuracy, making it difficult to comprehensively capture the true metabolic status of patients. In recent years, the triglyceride-glucose (TyG) index has emerged as a novel marker that more precisely reflects the characteristics of metabolic syndrome and its related risks 9 . Therefore, the present study aims to investigate the potential association between the TyG index and its related indices and BPH, with the goal of providing new insights into the metabolic mechanisms underlying BPH and laying a foundation for optimizing clinical diagnosis and intervention strategies. BPH is a condition characterized by androgen dependence, with insulin resistance and metabolic abnormalities thought to be closely linked to the proliferation of prostate tissue. Previous research has documented associations between BPH and conditions such as obesity 2 , 10 , dyslipidemia 11 , and metabolic syndrome 12 . Nonetheless, investigations into the relationship between TyG index and its related indices with BPH are relatively scarce. Consequently, utilizing data from the China Health and Retirement Longitudinal Study (CHARLS), we conducted a longitudinal cohort study to further examine the association between TyG and its related indices and the risk of developing BPH. Method Study population This study employed data from CHARLS, a comprehensive, nationally representative longitudinal survey encompassing 28 provinces, autonomous regions, and municipalities throughout China, including 150 counties/districts and 450 villages/urban communities. The survey gathered extensive information from participants aged 45 years and older. CHARLS offers a broad spectrum of data, encompassing demographics, family structure, health status, healthcare and insurance, employment and pension, income and expenditures, housing conditions, and laboratory test results. The national baseline survey was conducted in 2011, with subsequent waves in 2013, 2015, and 2018. The study received approval from the Institutional Review Board (IRB) of Peking University (IRB approval number: IRB00001052-11015), and all participants provided written informed consent prior to participation. For the current analysis, the inclusion criteria were as follows: (1) male participants who were free of BPH or LUTS in 2011, (2) availability of complete data on CVAI, TyG index and its related indices, (3) availability of complete data on covariates, and (4) completion of follow-up. Ultimately, a total of 3,460 participants met these criteria (Fig. 1 ). BPH statistics BPH was defined utilizing self-reported diagnoses obtained through standardized questionnaire items. Participants who did not have a BPH diagnosis at the baseline in 2011 were identified by their negative response to the question: " Have you ever been diagnosed with a prostate illness, such as prostate hyperplasia (excluding prostatic cancer)? " During the follow-up periods in 2013, 2015, and 2018, the diagnosis of BPH was evaluated through several questions, including: " Have you ever been diagnosed with a prostate illness, such as prostate hyperplasia (excluding prostatic cancer) since we last spoke (in respondent's last interview month, year/in the last two years)? ", " Are you aware if you have had a prostate illness, such as prostate hyperplasia (excluding prostatic cancer)? ", and " Have you ever been diagnosed with a prostate illness, such as prostate hyperplasia (excluding prostatic cancer) since [ZIWTime]? " Based on the responses to these items, we identified participants who were newly diagnosed with BPH during the follow-up period. Male participants who reported no BPH diagnosis at baseline in 2011 but received a diagnosis in any subsequent follow-up wave were classified as BPH cases. Those who remained free of a BPH diagnosis throughout the entire study period were categorized accordingly. TyG, CVAI and TyG related indexes Fasting venous blood samples were obtained by trained medical professionals adhering to a standardized protocol and subsequently analyzed at a central laboratory. Serum triglycerides (TG) and fasting plasma glucose (FPG) concentrations were determined using an enzymatic colorimetric method. The TyG index was computed using the formula 13 : TyG = ln [TG (mg/dL) × FPG (mg/dL) / 2]. Anthropometric measurements, including height and weight, were conducted using a stadiometer and a calibrated scale, respectively, with participants barefoot and dressed in light clothing. Waist circumference (WC) was measured by trained personnel using a flexible tape at the level of the umbilicus. Each anthropometric parameter—height, weight, and WC—was measured three times, and the mean value was utilized for analysis. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters. The waist-to-height ratio (WHtR) was defined as WC divided by height. Expanding on the visceral adiposity index framework and with the objective of more precisely representing visceral fat distribution and metabolic status within the Chinese population, while considering the impact of ethnicity, age, and other variables on fat distribution, Chinese researchers developed the Chinese Visceral Adiposity Index (CVAI) in 2016 14 .The Chinese Visceral Adiposity Index (CVAI) for males was determined utilizing the formula 15 : CVAI = − 267.93 + 0.68 × age (years) + 0.03 × BMI (kg/m²) + 4.00 × WC (cm) + 22.00 × lg TG (mmol/L) − 16.32 × HDL-C (mmol/L). In this context, HDL-C denotes high-density lipoprotein cholesterol, which was quantified using an enzymatic colorimetric method. The TyG index was further modified by multiplying it with BMI, WC, WHtR and CVAI to derive the TyG-BMI, TyG-WC, TyG-WHtR and TyG-CVAI indices, respectively 16 . Covariates The study incorporated a range of covariates, including sociodemographic characteristics, lifestyle factors, laboratory measurements, and medical history. Data on demographics, lifestyle, and disease history were gathered through standardized questionnaires administered by trained interviewers. Sociodemographic variables comprised age, educational level, marital status, and residential location (rural or urban). Lifestyle factors encompassed smoking status (ever versus never), alcohol consumption (ever versus never), and sleep duration. Medical history was assessed via self-reported diagnoses of conditions such as hypertension, diabetes, pulmonary disease, heart disease, kidney disease, gastrointestinal disorders, arthritis, and asthma. Hypertension was classified as a systolic blood pressure (SBP) of ≥ 140 mmHg and/or a diastolic blood pressure (DBP) of ≥ 90 mmHg, a self-reported history of physician-diagnosed hypertension, or current use of antihypertensive medications. SBP and DBP were each measured three times using an Omron HEM-7200 automatic blood pressure monitor, with the mean values recorded. Diabetes or elevated blood glucose was defined as a fasting plasma glucose level exceeding 110 mg/dL, or a self-reported history of diabetes. Statistical analysis Continuous variables following a normal distribution are presented as mean ± standard deviation (SD), whereas skewed variables are reported as median with interquartile range (IQR). Categorical variables are expressed as percentages. Group comparisons for continuous variables were conducted using either the independent-samples Student’s t-test or the Mann–Whitney U test, contingent upon the normality of the distribution 17 . The chi-square test was employed for the comparison of categorical variables, as appropriate. Three Cox proportional hazards models were utilized to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the risk of BPH/LUTS. The proportional hazards assumption was assessed using Schoenfeld residuals. TyG index, TyG-related indices (TyG-BMI, TyG-WC, TyG-WHtR), and CVAI were analyzed both as continuous variables and as categorical variables based on quartiles. The selection of potential confounders was informed by clinical relevance and evidence from existing literature, with variables demonstrating significant associations in univariate analysis also included in the multivariable models. To investigate potential dose–response relationships between these indices and the risk of BPH/LUTS, restricted cubic spline (RCS) models were employed to produce smoothed curves. For indices demonstrating nonlinear associations, piecewise regression models were utilized for further fitting and segmented analysis. Kaplan–Meier (KM) curves were constructed to depict the cumulative incidence of BPH/LUTS across quartiles of TyG, TyG-related indices, and CVAI. Receiver operating characteristic (ROC) curve analyses were performed to evaluate the discriminatory ability of each index in predicting BPH. The area under the ROC curve (AUC) was calculated with 95% confidence intervals. Participants were stratified into diabetic and non-diabetic groups, allowing for the separate examination of the associations between these indices and BPH risk within each group. Additionally, stratified analyses were performed to assess potential heterogeneity in the associations of TyG, TyG-related indices, and CVAI with BPH risk across various subgroups. All statistical analyses were conducted using R Statistical Software (Version 4.4.3, http://www.R-project.org , The R Foundation) and Free Statistics Analysis Platform (Version 2.2.0, Beijing, China, http://www.clinicalscientists.cn/freestatistics ). Results Baseline Characteristics of Participants In accordance with the established inclusion and exclusion criteria, this study incorporated a total of 3,460 male participants aged 45 years and older, with a mean age of 59.67 ± 8.93 years. Over a follow-up period extending up to 7 years, 745 participants (21.53%) developed symptoms of BPH/LUTS. Table 1 delineates the demographic characteristics of participants, stratified by the incidence of BPH/LUTS during the follow-up period. Participants who developed BPH/LUTS were significantly older than those in the non-BPH/LUTS group (60.65 ± 8.39 vs. 59.41 ± 9.05 years, p < 0.001) and were more likely to reside in urban areas (22.01% vs. 13.41%, p < 0.001). Indicators related to obesity, such as waist circumference, BMI, WHtR, were significantly elevated in the BPH/LUTS group compared to the non-BPH/LUTS group (all p < 0.001). Furthermore, in terms of metabolic diseases, participants with BPH/LUTS exhibited higher prevalence rates of hypertension (45.1% vs. 36.69%, p < 0.001), dyslipidemia (68.51% vs. 60.87%, p < 0.001), and heart disease (15.17% vs. 7.00%, p < 0.001). Furthermore, the incidence of kidney disease, arthritis, pulmonary disease, and asthma was notably elevated in the BPH/LUTS cohort. In terms of metabolic parameters, the BPH/LUTS group exhibited higher triglyceride (TG) levels (median 102.66 (73.46, 156.65) vs. 96.46 (69.03, 143.37) mg/dL, p = 0.002) and reduced high-density lipoprotein cholesterol (HDL-C) levels (49.31 ± 16.42 vs. 51.15 ± 16.23 mg/dL, p = 0.006). The prevalence of smoking was marginally lower in the BPH/LUTS cohort (71.14% vs. 76.69%, p = 0.002), while no significant difference was detected in alcohol consumption between the two cohorts (p = 0.739). Table 1 Baseline characteristics of included subjects stratified by incidence of BPH/LUTS Variables Total No BPH/LUTS BPH/LUTS P value (n = 3,460) (n = 2,715) (n = 745) Age, years 59.67 ± 8.93 59.41 ± 9.05 60.65 ± 8.39 < 0.001 Residence, n (%) < 0.001 Rural 2,932 (84.74) 2,351 (86.59) 581 (77.99) Urban 528 (15.26) 364 (13.41) 164 (22.01) Education, n (%) < 0.001 College or above 62 (1.79) 34 (1.25) 28 (3.76) Senior high school 398 (11.50) 302 (11.12) 96 (12.89) Junior high school 909 (26.27) 713 (26.26) 196 (26.31) Primary or below 1,656 (47.86) 1,291 (47.55) 365 (48.99) Illiterate 435 (12.57) 375 (13.81) 60 (8.05) Marriage, n (%) 0.585 Married 3,191 (92.23) 2,503 (92.19) 688 (92.35) Divorced 33 (0.95) 23 (0.85) 10 (1.34) Widowed 194 (5.61) 156 (5.75) 38 (5.1) Never married 42 (1.21) 33 (1.22) 9 (1.21) Waist, cm 84.18 ± 11.75 83.67 ± 11.40 86.05 ± 12.77 < 0.001 BMI, kg/m^2 23.01 ± 3.60 22.77 ± 3.49 23.87 ± 3.88 < 0.001 WHtR 0.51 ± 0.07 0.51 ± 0.07 0.52 ± 0.08 < 0.001 Sleep, hour 6.48 ± 1.79 6.50 ± 1.79 6.40 ± 1.81 0.153 Smoking, n (%) 2,612 (75.49) 2,082 (76.69) 530 (71.14) 0.002 Drinking, n (%) 2,409 (69.62) 1,894 (69.76) 515 (69.13) 0.739 Hypertension, n (%) 1,332 (38.50) 996 (36.69) 336 (45.10) < 0.001 Dyslipidemia, n (%) 2,155 (62.52) 1,646 (60.87) 509 (68.51) < 0.001 Diabetes, n (%) 1,103 (31.88) 859 (31.64) 244 (32.75) 0.564 Cancer, n (%) 17 (0.49) 14 (0.52) 3 (0.4) 1.000 Lung disease, n (%) 392 (11.33) 280 (10.31) 112 (15.03) < 0.001 Liver disease, n (%) 136 (3.94) 100 (3.69) 36 (4.83) 0.156 Heart disease, n (%) 303 (8.76) 190 (7) 113 (15.17) < 0.001 Stroke, n (%) 66 (1.91) 46 (1.7) 20 (2.68) 0.081 Kidney disease, n (%) 221 (6.39) 136 (5.01) 85 (11.41) < 0.001 Digestive disease, n (%) 686 (19.83) 516 (19.01) 170 (22.82) 0.021 Mental disease, n (%) 22 (0.64) 14 (0.52) 8 (1.08) 0.114 Memory disease, n (%) 42 (1.22) 31 (1.14) 11 (1.48) 0.460 Arthritis, n (%) 1,038 (30.00) 775 (28.55) 263 (35.3) < 0.001 Asthma, n (%) 142 (4.10) 98 (3.61) 44 (5.91) 0.005 Glucose, mg/dl 110.10 ± 35.23 109.73 ± 34.69 111.43 ± 37.10 0.243 TG, mg/dl 97.35 (70.80, 145.14) 96.46 (69.03, 143.37) 102.66 (73.46, 156.65) 0.002 HDL-C, mg/dl 50.75 ± 16.29 51.15 ± 16.23 49.31 ± 16.42 0.006 LDL-C, mg/dl 113.00 ± 34.29 112.22 ± 34.05 115.84 ± 35.03 0.011 BMI: body mass index, WHtR: waist-to-height ratio, TG: serum triglycerides, HDL-C: high-density lipoprotein cholesterol, LDL-C: low-density lipoprotein cholesterol As illustrated in Fig. 2 , participants who developed BPH/LUTS during the follow-up period exhibited elevated levels of TyG index, TyG-related indices, and CVAI in comparison to those who did not develop these symptoms (TyG: mean 8.61 vs. 8.70, p = 0.002, TyG-BMI: mean 196.88 vs. 208.36, p < 0.001, TyG-WC: mean 722.78 vs. 750.84, p < 0.001, TyG-WHtR: mean 4.41 vs. 4.56, p < 0.001, CVAI: mean 91.58 vs. 102.84, p < 0.001, TyG-CVAI: mean 801.15 vs. 907.48, p < 0.001). Associations of TyG, CVAI and TyG-related indices with BPH among all participants In the multivariable Cox regression analysis, three models were constructed to investigate the associations between TyG, CVAI, and TyG-related indices with the risk of developing BPH/LUTS, as presented in Table 2 . Model 1 was unadjusted, while Model 2 was adjusted for sociodemographic characteristics and lifestyle factors, including age, educational level, marital status, residence (urban/rural), smoking status, alcohol consumption, and sleep duration. Model 3 incorporated additional adjustments for a history of chronic diseases, such as hypertension, diabetes, pulmonary disease, heart disease, kidney disease, gastrointestinal disorders, arthritis, and asthma. When analyzed as standardized continuous variables, an increase of one standard deviation in TyG, TyG-BMI, TyG-WC, TyG-WHtR, CVAI and TyG-CVAI was significantly associated with an elevated risk of BPH/LUTS. Among these, CVAI exhibited the strongest association (Model 3: HR = 1.26, 95% CI: 1.16–1.36, p < 0.001). The TyG-related indices also demonstrated substantial risk increases: TyG-BMI (HR = 1.25, 95%CI: 1.17–1.34), TyG-WC (HR = 1.19,95%CI: 1.10–1.29), TyG-WHtR (HR = 1.15, 95%CI: 1.06–1.25), and TyG-CVAI (HR = 1.23, 95%CI: 1.14–1.33), all with p < 0.001. These associations remained statistically significant even after comprehensive adjustment for potential confounders. In a more detailed analysis utilizing quartile classification, a distinct dose-response relationship was identified between each index and the risk of BPH/LUTS. Participants in the highest quartile (Q4) exhibited a significantly elevated risk of BPH/LUTS compared to those in the lowest quartile (Q1) across all models. Specifically, in Model 3, the hazard ratios (HRs) for Q4 were as follows: 1.27 for TyG index, 1.92 for TyG-BMI, 1.87 for TyG-WC, 1.56 for TyG-WHtR, 2.15 for CVAI, 2.03 for TyG-CVAI. The p value for trend was statistically significant for all variables across all models (P < 0.05), indicating a consistent trend. These results suggest that elevated levels of TyG and its related indices, particularly CVAI, may serve as significant predictors of BPH/LUTS incidence. Table 2 The association of TyG, TyG-BMI, TyG-WC, TyG-WHtR and CVAI with BPH/LUTS in all participants Variable Model1 Model2 Model3 HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value TyG 1.11(1.03–1.18) 0.004 1.11(1.03–1.19) 0.004 1.11(1.02–1.20) 0.011 Quartile Q1 1(Ref) 1(Ref) 1(Ref) Q2 1.04(0.84–1.28) 0.712 1.01(0.82–1.24) 0.947 0.99(0.80–1.22) 0.926 Q3 1.07(0.86–1.31) 0.557 1.04(0.85–1.29) 0.692 1.04(0.84–1.29) 0.726 Q4 1.28(1.04–1.56) 0.017 1.26(1.03–1.54) 0.028 1.27(1.02–1.59) 0.035 P for trend 0.018 0.025 0.035 TyG-BMI 1.24(1.17–1.32) < 0.001 1.25(1.17–1.33) < 0.001 1.25(1.17–1.34) < 0.001 Quartile Q1 1(Ref) 1(Ref) 1(Ref) Q2 1.23(0.98–1.54) 0.076 1.26(1.00-1.58) 0.046 1.31(1.04–1.65) 0.020 Q3 1.44(1.16–1.80) 0.001 1.45(1.16–1.81) 0.001 1.52(1.21–1.97) < 0.001 Q4 1.90(1.55–2.34) < 0.001 1.91(1.53–2.37) < 0.001 1.92(1.52–2.42) < 0.001 P for trend < 0.001 < 0.001 < 0.001 TyG-WC 1.21(1.12–1.30) < 0.001 1.20(1.11–1.29) < 0.001 1.19(1.10–1.29) < 0.001 Quartile Q1 1(Ref) 1(Ref) 1(Ref) Q2 1.41(1.13–1.76) 0.003 1.44(1.15–1.81) 0.001 1.47(1.17–1.84) < 0.001 Q3 1.54(1.24–1.92) < 0.001 1.48(1.18–1.85) < 0.001 1.55(1.23–1.94) < 0.001 Q4 1.90(1.54–2.35) < 0.001 1.86(1.50–2.32) < 0.001 1.87(1.48–2.36) < 0.001 P for trend < 0.001 < 0.001 < 0.001 TyG-WHtR 1.18(1.10–1.27) < 0.001 1.17(1.08–1.26) < 0.001 1.15(1.06–1.25) < 0.001 Quartile Q1 1(Ref) 1(Ref) 1(Ref) Q2 1.23(0.99–1.53) 0.068 1.19(0.96–1.49) 0.121 1.20(0.96–1.50) 0.102 Q3 1.45(1.17–1.79) < 0.001 1.37(1.10–1.70) 0.005 1.40(1.13–1.75) 0.003 Q4 1.69(1.37–2.08) < 0.001 1.59(1.29–1.97) < 0.001 1.56(1.24–1.96) < 0.001 P for trend < 0.001 < 0.001 < 0.001 CVAI 1.33(1.24–1.43) < 0.001 1.27(1.18–1.37) < 0.001 1.26(1.16–1.36) < 0.001 Quartile Q1 1(Ref) 1(Ref) 1(Ref) Q2 1.49(1.17–1.88) 0.001 1.42(1.12–1.80) 0.004 1.47(1.17–1.88) 0.001 Q3 1.78(1.42–2.23) < 0.001 1.61(1.27–2.04) < 0.001 1.69(1.33–2.14) < 0.001 Q4 2.46(1.98–3.06) < 0.001 2.17(1.72–2.72) < 0.001 2.15(1.69–2.73) < 0.001 P for trend < 0.001 < 0.001 < 0.001 TyG-CVAI 1.29(1.21–1.38) < 0.001 1.24(1.16–1.33) < 0.001 1.23 (1.14–1.33) < 0.001 Quartile Q1 1(Ref) 1(Ref) 1(Ref) Q2 1.56(1.24–1.97) < 0.001 1.48 (1.17–1.87) 0.001 1.52 (1.20–1.92) < 0.001 Q3 1.67(1.33–2.10) < 0.001 1.49 (1.18–1.89) 0.001 1.58 (1.24-2.00) < 0.001 Q4 2.33(1.88–2.90) < 0.001 2.04 (1.63–2.55) < 0.001 2.03 (1.60–2.58) < 0.001 P for trend < 0.001 < 0.001 < 0.001 Cox proportional hazards models were used to estimate the associations between TyG, TyG-BMI, TyG-WC, TyG-WHtR, CVAI and TyG-CVAI (analyzed both as continuous and categorical variables) and the risk of developing BPH/LUTS. Three models were constructed : Model 1: Unadjusted Model 2: Adjusted for age, educational level, marital status, residence (urban/rural), smoking status, drinking status, and sleep duration Model 3: Further adjusted for hypertension, diabetes, pulmonary disease, heart disease, kidney disease, gastrointestinal disorders, arthritis, and asthma on the basis of Model 2 TyG: Triglyceride-glucose, TyG-BMI: Triglyceride-glucose with body mass index, TyG-WC: Triglyceride-glucose with waist circumference, TyG-WHtR: Triglyceride-glucose with waist-to-height ratio, CVAI: Chinese visceral adiposity index, TyG-CVAI: Triglyceride-glucose with Chinese visceral adiposity index, HR: hazard ratio, CI : confidence interval In order to further explore the dose–response relationship between TyG and its related indices and the risk of BPH/LUTS, we conducted RCS analyses for TyG, TyG-BMI, TyG-WC, TyG-WHtR, CVAI and TyG-CVAI, with comprehensive adjustment for all covariates included in Model 3 of Table 2 . The findings (Fig. 3 ) indicated a linear positive association between TyG, CVAI, TyG-CVAI and the risk of BPH/LUTS, as evidenced by non-significant tests for non-linearity (P > 0.05). Conversely, TyG-BMI, TyG-WC, and TyG-WHtR exhibited nonlinear associations with BPH/LUTS risk (all non-linearity P < 0.05). While the non-linearity test for TyG did not reach statistical significance, its overall association with BPH/LUTS resulted in a p value of 0.065, indicating a potentially weak or borderline statistically significant relationship. Conversely, CVAI exhibited a non-significant non-linear trend but achieved a highly significant overall p value of < 0.001, suggesting a robust and consistent linear association with an increased risk of BPH. In response to the observation that certain indices exhibited nonlinear trends, we utilized piecewise linear models to examine the relationships between TyG-BMI, TyG-WC, TyG-WHtR, and the risk of BPH/LUTS. The results are as follows: For TyG-BMI, within the range of TyG-BMI ≤ 193.25, each standard deviation increase was significantly associated with a heightened risk of BPH/LUTS (HR: 1.68, 95%CI: 1.23–2.29, P = 0.001). When TyG-BMI exceeded 193.25, the association persisted significantly (HR: 1.17, 95% CI: 1.06–1.30, P = 0.003), albeit with a slightly reduced effect size. In terms of TyG-WC, no significant association with the risk of BPH was observed for TyG-WC 714.68, each SD increase significantly increased the risk (HR: 1.21, 95% CI: 1.05–1.40, P = 0.008). Regarding TyG-WHtR, no significant association was identified for values ≤ 4.38 (P = 0.750), but for values > 4.38, higher levels were significantly correlated with an increased risk of BPH/LUTS (HR: 1.17, 95% CI: 1.01–1.35, P = 0.033). These findings further corroborate the nonlinear dose–response relationship between specific TyG-related indices and the risk of BPH/LUTS. Table 3 Associations of TyG-BMI, TyG-WC, and TyG-WHtR with BPH/LUTS across different subgroups among all participants subgroup HR 95% CI p value TyG-BMI ≤ 193.25 1.68 1.23–2.29 0.001 TyG-BMI>193.25 1.17 1.06–1.30 0.003 TyG-WC ≤ 714.68 1.03 0.85–1.24 0.780 TyG-WC>714.68 1.21 1.05–1.40 0.008 TyG-WHtR ≤ 4.38 0.97 0.82–1.15 0.750 TyG-WHtR>4.38 1.17 1.01–1.35 0.033 The models are adjusted for age, educational level, marital status, residence (urban/rural), smoking status, drinking status, sleep duration, hypertension, diabetes, pulmonary disease, heart disease, kidney disease, gastrointestinal disorders, arthritis, and asthma. To further elucidate the cumulative incidence of BPH/LUTS in relation to varying levels of TyG, CVAI, and TyG-correlation indices, we stratified these indices into two distinct groups based on the inflection point values derived from the RCS curves. Subsequently, we generated Kaplan-Meier curves for each group (Fig. 4 ). The analysis reveals that the groups with elevated CVAI and TyG-correlation indices exhibit a significantly higher cumulative incidence (P < 0.001) compared to those with lower values. This finding substantiates the dose-response relationship between CVAI and TyG-related indices and the incidence of BPH/LUTS. Predictive performance of TyG-related indices versus traditional measures for BPH To evaluate the predictive efficacy of traditional indices (BMI, WC, WHtR) versus novel TyG-related indices (TyG-BMI, TyG-WC, TyG-WHtR) in identifying BPH, ROC curve analyses were conducted for each index (Fig. 5 ). Comparisons of AUC values between traditional indices and novel TyG-related indices were conducted using DeLong’s test. The TyG-related indices exhibited superior predictive performance for BPH compared to traditional measures. Specifically, the AUC values for TyG-BMI, TyG-WC, and TyG-WHtR were 0.670, 0.659, and 0.665, respectively, whereas the corresponding AUC values for BMI, WC, and WHtR were 0.590, 0.577, and 0.568. Pairwise comparisons revealed that the predictive value of each TyG-related index was significantly greater than that of its traditional counterpart (all p < 0.001). Among the novel indices, TyG-BMI demonstrated the highest AUC (0.670), indicating its potential as the most effective predictor of BPH risk. Furthermore, ROC curve analysis indicated that TyG-related indices consistently achieved a superior sensitivity-specificity balance across various cutoff values compared to conventional measures. Associations of TyG, CVAI and TyG-related indices with BPH among participants with and without type 2 diabetes The study population was further stratified according to diabetes status into a non-diabetic cohort (n = 2,357) and a diabetic cohort, which included individuals with elevated blood glucose levels (n = 1,103). Following adjustments for all covariates in Model 4 (refer to Table 4 ), we evaluated the associations of TyG, CVAI and TyG-related indices with the risk of BPH/LUTS within each cohort. The findings indicated that, within the non-diabetic cohort, each standard deviation increase in TyG was significantly correlated with an elevated risk of BPH/LUTS (HR: 1.15, 95% CI: 1.02–1.29, P = 0.023). Conversely, in the diabetic cohort, this association did not reach statistical significance (HR: 1.05, 95% CI: 0.94–1.18, P = 0.390). The indices TyG-BMI, TyG-WC, TyG-WHtR, CVAI and TyG-CVAI demonstrated varying degrees of significant associations with BPH/LUTS across both groups. In the non-diabetic cohort, an increase of one standard deviation in these indices was significantly correlated with an elevated risk of BPH/LUTS (TyG-BMI: HR: 1.31, TyG-WC: HR: 1.21, TyG-WHtR: HR: 1.14, CVAI: HR: 1.28, TyG-CVAI: HR:1.37, all P < 0.05). Moreover, the P for trend across quartiles was highly significant for all indices (P < 0.01), indicating a clear dose–response relationship. In the diabetic cohort, the associations for TyG-BMI and TyG-WC were slightly attenuated and did not consistently achieve statistical significance (TyG-BMI: P = 0.018, P for trend = 0.068; TyG-WC: P = 0.025, P for trend = 0.051). Conversely, TyG-WHtR, CVAI and TyG-CVAI remained significantly associated with BPH/LUTS risk (P = 0.042, 0.006 and < 0.001, respectively), with TyG-CVAI in the highest quartile exhibiting a significantly increased risk (HR = 2.13, P = 0.001). In summary, the correlations between the TyG index and its associated indices with the risk of BPH/LUTS were more significant in the non-diabetic population, while their predictive efficacy was diminished among individuals with diabetes. This attenuation may be due to the intricate metabolic pathophysiology characteristic of diabetes, which could obscure or alter the impact of markers related to insulin resistance. These findings indicate that in diabetic patients, the onset of BPH/LUTS may be influenced by multiple interacting factors, thereby limiting the effectiveness of TyG-related indices for risk assessment within this subgroup. Table 4 The association of TyG, TyG-BMI, TyG-WC, TyG-WHtR CVAI and TyG-CVAI with BPH/LUTS in diabetes patients and non-diabetes patients Variable Non-diabetes Diabetes n.total n.event(%) HR (95% CI) P value n.total n.event(%) HR (95% CI) P value TyG 2,357 501 (21.3) 1.15 (1.02–1.29) 0.023 1,103 244 (22.1) 1.05 (0.94–1.18) 0.390 Quartile Q1 773 153 (19.8) 1(Ref) 92 18 (19.6) 1(Ref) Q2 669 141 (21.1) 0.99 (0.79–1.25) 0.928 196 38 (19.4) 0.93 (0.53–1.65) 0.814 Q3 582 123 (21.1) 1.02 (0.81–1.30) 0.841 283 58 (20.5) 1.00 (0.59–1.72) 0.987 Q4 333 84 (25.2) 1.29 (0.98–1.69) 0.070 532 130 (24.4) 1.16 (0.70–1.93) 0.559 P for trend 0.126 0.249 TyG-BMI 2,357 501 (21.3) 1.31 (1.19–1.43) < 0.001 1,103 244 (22.1) 1.15 (1.02–1.29) 0.018 Quartile Q1 710 108 (15.2) 1(Ref) 155 30 (19.4) 1(Ref) Q2 654 135 (20.6) 1.51 (1.17–1.95) 0.002 211 32 (15.2) 0.75 (0.46–1.24) 0.267 Q3 565 130 (23.0) 1.65 (1.26–2.15) < 0.001 300 63 (21.0) 1.09 (0.69–1.70) 0.714 Q4 428 128 (29.9) 2.12 (1.60–2.79) < 0.001 437 119 (27.2) 1.25 (0.81–1.93) 0.318 P for trend < 0.001 0.068 TyG-WC 2,357 501 (21.3) 1.21 (1.08–1.35) 0.001 1,103 244 (22.1) 1.15 (1.02–1.30) 0.025 Quartile Q1 716 108 (15.1) 1(Ref) 149 24 (16.1) 1(Ref) Q2 663 141 (21.3) 1.54 (1.20–1.99) 0.001 202 40 (19.8) 1.19 (0.71-2.00) 0.501 Q3 565 139 (24.6) 1.69 (1.30–2.19) < 0.001 300 56 (18.7) 1.16 (0.71–1.90) 0.546 Q4 413 113 (27.4) 1.92 (1.45–2.54) < 0.001 452 124 (27.4) 1.53 (0.96–2.44) 0.075 P for trend < 0.001 0.051 TyG-WHtR 2,357 501 (21.3) 1.14 (1.03–1.28) 0.015 1,103 244 (22.1) 1.14 (1.00-1.28) 0.042 Quartile Q1 723 122 (16.9) 1(Ref) 142 21 (14.8) 1(Ref) Q2 676 137 (20.3) 1.20 (0.94–1.54) 0.136 189 36 (19.0) 1.20 (0.70–2.07) 0.512 Q3 557 136 (24.4) 1.44 (1.12–1.85) 0.004 308 63 (20.5) 1.34 (0.81–2.21) 0.252 Q4 401 106 (26.4) 1.51 (1.15–1.98) 0.003 464 124 (26.7) 1.55 (0.96–2.52) 0.074 P for trend 0.001 0.045 CVAI 2,357 501 (21.3) 1.28 (1.16–1.41) < 0.001 1,103 244 (22.1) 1.20 (1.05–1.36) 0.006 Quartile Q1 683 91 (13.3) 1(Ref) 182 27 (14.8) 1(Ref) Q2 637 126 (19.8) 1.53 (1.17–2.02) 0.002 228 41 (18) 1.32 (0.81–2.15) 0.272 Q3 580 140 (24.1) 1.84 (1.39–2.42) < 0.001 285 58 (20.4) 1.30 (0.81–2.07) 0.273 Q4 457 144 (31.5) 2.31 (1.74–3.08) < 0.001 408 118 (28.9) 1.68 (1.08–2.63) 0.023 P for trend < 0.001 0.021 TyG-CVAI 2,357 501(21.3) 1.37 (1.24–1.50) < 0.001 1,103 244(22.1) 1.26 (1.14 ~ 1.4) < 0.001 Quartile Q1 698 96(13.8) 1(Ref) 167 24(14.4) 1(Ref) Q2 654 139(21.3) 1.63(1.25–2.11) < 0.001 211 40(19.0) 1.37(0.83–2.27) 0.223 Q3 578 133(23.0) 1.78(1.37–2.31) < 0.001 287 57(19.9) 1.45(0.90–2.34) 0.127 Q4 427 501(21.3) 2.49(1.92–3.24) < 0.001 438 123(28.1) 2.13(1.37–3.30) 0.001 P for trend < 0.001 < 0.001 Cox proportional hazards models were used to estimate the associations between TyG, TyG-BMI, TyG-WC, TyG-WHtR, CVAI and TyG-CVAI (analyzed both as continuous and categorical variables) and the risk of developing BPH/LUTS. The models are adjusted for age, educational level, marital status, residence (urban/rural), smoking status, drinking status, sleep duration, hypertension, pulmonary disease, heart disease, kidney disease, gastrointestinal disorders, arthritis, and asthma. Subgroup analysis To further explore the applicability of TyG index, CVAI and TyG related indices (TyG-BMI TyG-WC, TyG-WHtR, TyG-CVAI) across diverse populations and their reliability in predicting BPH risk, this study conducted stratified analyses across various subgroups: age (≤ 65 years vs. >65 years), residence (urban vs. rural), education level, marital status, smoking status, drinking status, sleep duration (≤ 7 hours vs. >7 hours), and hypertension status. For each subgroup, Cox regression models were employed, and likelihood ratio tests were utilized to evaluate the statistical significance of interaction terms (Fig. 6 ). In the exploratory subgroup analysis conducted on multiple variables, we found that smoking status may have an interaction with CVAI (P for interaction = 0.049). Given that multiple interaction tests were performed, this finding may be at risk of false positivity and requires Bonferroni correction, meaning a p-value < 0.006 would indicate statistical significance. Beyond this, no significant interactions were observed for other indicators. Discussion In this comprehensive nationwide cohort study, we identified several factors significantly associated with BPH/LUTS, including age, residential location, obesity, and metabolic disorders. With respect to obesity-related metrics, the BPH/LUTS group exhibited significantly elevated waist circumference, BMI, and waist-to-height ratio compared to the non-BPH/LUTS group, indicating that obesity may constitute a critical risk factor for LUTS. Furthermore, metabolic disorders, including hypertension, diabetes, and dyslipidemia, were more prevalent among individuals with BPH/LUTS. This observation aligns with existing literature, which suggests that factors related to metabolic syndrome are significantly correlated with the onset of BPH/LUTS 12 . For example, previous studies have demonstrated that hypertension and dyslipidemia may aggravate BPH/LUTS by influencing microvascular circulation and inflammatory responses in the prostate 18 . Our findings provide essential data for future investigations and establish a theoretical framework for the formulation of preventive strategies against BPH/LUTS. Our study utilized prospective follow-up data to systematically assess the association between TyG index, CVAI and TyG-related indices with the risk of developing BPH/LUTS. Our findings indicate that elevated levels of TyG, TyG-BMI, TyG-WC, TyG-WHtR CVAI and TyG-CVAI are significantly correlated with an increased risk of BPH/LUTS. These associations persisted even after adjusting for a range of demographic variables, lifestyle factors, and histories of chronic diseases, underscoring the potential of these indices as predictive markers for BPH/LUTS. Restricted cubic splines demonstrated variability in the dose-response relationship between the various metabolic indices and BPH/LUTS risk. Specifically, TyG, CVAI and TyG-CVAI exhibited a linear positive correlation with BPH/LUTS risk, suggesting that as these indices rise, the risk of BPH/LUTS correspondingly increases. This linear relationship may show the continuous influence of insulin resistance and visceral fat accumulation on prostate enlargement. Previous research has indicated that insulin resistance activates the IGF-1 and PI3K/Akt signaling pathways, which promote prostate cell proliferation and inhibit apoptosis, thereby contributing to an increase in prostate volume 19 – 21 . CVAI, a composite measure of visceral fat accumulation, demonstrated a significant linear association, implying that imbalanced fat distribution may serve as a critical metabolic foundation for BPH 22 . Conversely, the indices TyG-BMI, TyG-WC, and TyG-WHtR displayed nonlinear relationships with the risk of BPH/LUTS, indicating the presence of threshold effects at varying levels of these indices. Further analysis using a piecewise linear model revealed that when these indices exceeded certain levels (e.g., TyG-BMI > 193.25, TyG-WC > 714.68, TyG-WHtR > 4.38), their influence on the risk of BPH/LUTS became more pronounced. This finding suggests that once individuals surpass a specific metabolic load threshold, the prostate may become more vulnerable to systemic metabolic disturbances, leading to structural and functional alterations. Such nonlinear trend has substantial clinical implications, indicating that early interventions should prioritize high-risk populations, especially individuals exhibiting both obesity and insulin resistance. In this large, nationally representative study based on CHARLS data, we observed that TyG-related indices provided better discriminatory ability for benign prostatic hyperplasia compared with conventional indicators. While BMI, WC, and WHtR have been widely used to reflect general and central obesity, their predictive value for BPH was modest, with AUC values below 0.60. In contrast, the incorporation of the TyG index, a surrogate marker of insulin resistance, substantially enhanced predictive accuracy. The underlying mechanisms may be explained by the pivotal role of insulin resistance and related metabolic disturbances in the pathogenesis of BPH. Accumulating evidence indicates that insulin resistance contributes to hyperinsulinemia, which can stimulate sympathetic activity, increase growth factor expression, and promote prostate cell proliferation. Moreover, metabolic dysregulation associated with insulin resistance often coexists with systemic inflammation and oxidative stress, further driving prostate enlargement. Traditional measures such as BMI or WC capture only anthropometric aspects of obesity but fail to account for metabolic heterogeneity. By combining these indices with TyG, the novel indicators simultaneously reflect adiposity and insulin resistance, thereby providing a more comprehensive assessment of metabolic health status. Our findings are consistent with recent studies reporting the clinical utility of TyG-related indices in predicting cardiovascular diseases, diabetes, and chronic kidney disease. Extending this evidence, we demonstrate that these indices are also relevant to urological outcomes, specifically BPH. Among the three novel indices, TyG-BMI showed the strongest predictive power, suggesting that the interaction between overall adiposity and insulin resistance may have a greater impact on BPH development than central obesity alone. In the diabetes subgroup analysis of this study, we identified more pronounced and consistent associations between TyG index and its related metabolic indices with the risk of BPH/LUTS in the non-diabetic cohort. Conversely, although most indices within the diabetic group exhibited an upward trend in hazard ratios, their statistical significance diminished, and several quartile trend tests failed to achieve significance. This disparity indicates that diabetic status may influence the relationship between TyG-related indices and BPH/LUTS risk. The TyG index, a well-established surrogate marker for insulin resistance, has been extensively utilized to evaluate metabolic abnormalities. In non-diabetic individuals, elevated TyG levels frequently indicate underlying metabolic dysfunction and lipid metabolism disorders, which may contribute to the development of BPH/LUTS through mechanisms such as chronic low-grade prostate inflammation 23 , activation of androgen synthesis pathways 24 , and enhanced endothelial dysfunction 25 . Conversely, in individuals with diabetes, the metabolic condition is more intricate, characterized by prevalent insulin resistance, hyperinsulinemia, and pathological processes resulting from chronic hyperglycemia, including oxidative stress 26 , 27 , vascular dysfunction 28 , and disturbances in hormonal axes 29 , 30 . These factors may obscure or reduce the efficacy of the TyG index in risk identification. In the subgroup analyses, we observed a potential interaction between CVAI and smoking status in relation to BPH risk (P = 0.049). Although this finding may suggest that smoking could modify the association between visceral adiposity and BPH, caution is warranted in its interpretation. Given that multiple interaction tests were performed, the possibility of a chance finding cannot be excluded. In particular, after applying a Bonferroni correction for multiple comparisons, a p-value threshold of < 0.006 would be required to achieve statistical significance, and the observed association does not meet this more stringent criterion. Therefore, the interaction between CVAI and smoking should be regarded as exploratory and hypothesis-generating rather than conclusive. Future studies with larger sample sizes, independent cohorts, and pre-specified subgroup analyses will be necessary to confirm whether smoking truly modifies the relationship between visceral adiposity and BPH risk. The clinical implications of our findings are noteworthy. TyG-related indices are simple to calculate using routinely available laboratory and anthropometric data, making them practical tools for risk stratification in primary care and large-scale population screening. Their superior predictive performance may enable earlier identification of high-risk individuals, thereby facilitating timely lifestyle modification or pharmacological intervention. Given that BPH substantially impairs quality of life in aging men, improved predictive tools could contribute to preventive strategies and alleviate healthcare burden. Our study also has several notable strengths. The large sample size and the nationally representative nature of the CHARLS cohort enhance the generalizability of the results. The standardized calculation of TyG and related indices, coupled with the use of multiple adjusted models, bolsters the stability and credibility of our findings. Moreover, the application of diverse statistical approaches, including restricted cubic splines, stratified analyses, and Kaplan–Meier curves, further reinforces the robustness of the results. Nevertheless, some limitations must be acknowledged. Firstly, despite efforts to adjust for relevant confounders in the multivariable models, the possibility of unmeasured or unknown residual confounders cannot be entirely excluded, potentially leading to either an overestimation or underestimation of the observed associations. Secondly, the generalizability of CVAI and TyG as indices across diverse populations necessitates further validation. Lastly, additional mechanistic studies are required to elucidate the biological basis underlying the association between these metabolic indices and the occurrence of BPH. Conclusion In this study, we evaluated the association between the triglyceride-glucose index (TyG) and its related metabolic indices (TyG-BMI, TyG-WC, TyG-WHtR, CVAI, and TyG-CVAI) and the risk of BPH/LUTS. Our findings indicate that higher levels of these metabolic indices are associated with an increased risk of BPH/LUTS. Notably, TyG, CVAI, and TyG-CVAI showed a linear relationship with BPH/LUTS risk, whereas TyG-BMI, TyG-WC, and TyG-WHtR exhibited nonlinear associations. Stratified analyses suggested a potential interaction between CVAI and smoking status; however, given multiple testing and the need for Bonferroni correction, this finding should be interpreted cautiously and considered exploratory. Overall, these results highlight the relevance of TyG-related metabolic indices as predictive markers for BPH/LUTS risk and underscore their potential utility in clinical practice for early identification, risk stratification, and the development of targeted monitoring or intervention strategies to mitigate the incidence of BPH/LUTS. Declarations Funding: This work was funded by National Natural Science Foundation of China (No. 82272864 and No.82573601) and Capital’s Funds for Health Improvement and Research (No.2024-2-2059). Declaration of any potential financial and non-financial conflicts of interest: The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this study. Data Availability Statement Data are available on request to the authors. The manuscript has been read and approved by all the authors. The requirements for authorship have been met, and each author believes that the manuscript represents honest work. Author Contributions Statement X.X.B. and Y.S.Z. collected the data, conducted background research, and drafted the initial version of the manuscript. Y.C.J. performed the statistical analyses. Z.H.L. prepared the figures, and Z.T.Z. organized and formatted the tables. Y.X.L. and H.P. provided critical professional guidance and contributed to the revision and final approval of the manuscript. All authors reviewed and approved the final version of the manuscript. References Ng, M., Leslie, S. W. & Baradhi, K. M. Benign Prostatic Hyperplasia. in StatPearls (StatPearls Publishing, Treasure Island (FL), 2025). Bratchikov, O. I., Tyuzikov, I. A., Artishchev, S. O. & Shumakova, E. A. [The role of obesity in the pathogenesis of benign prostatic hyperplasia]. Urologiia 101–106 (2020). Wang, X. et al. 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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-7968845","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":546153196,"identity":"67b109b5-4462-459f-afea-f4010f024941","order_by":0,"name":"Xiaoxuan Bai","email":"","orcid":"","institution":"Beijing Tongren Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoxuan","middleName":"","lastName":"Bai","suffix":""},{"id":546153197,"identity":"9166cf6d-79b2-4547-985a-b12897654764","order_by":1,"name":"Yishan Zhang","email":"","orcid":"","institution":"Beijing Tongren 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10:11:12","extension":"xml","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":163668,"visible":true,"origin":"","legend":"","description":"","filename":"9f7aa1b65eb54e37bf8fc1a9516353b61structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7968845/v1/0f23a462941a096fdeca6156.xml"},{"id":96694388,"identity":"a810c535-ee50-4d04-833b-b9533655af41","added_by":"auto","created_at":"2025-11-25 07:17:02","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":171940,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7968845/v1/c8783c729f47f11520938f16.html"},{"id":96711327,"identity":"dbdd797c-be5a-46c7-bacd-3776fbba978e","added_by":"auto","created_at":"2025-11-25 10:11:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":183513,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFlow chart of the study population inclusion\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7968845/v1/32aeffb6f02e228c765999c3.png"},{"id":96694374,"identity":"ecc28452-71ed-4572-9eea-74b6a2712da7","added_by":"auto","created_at":"2025-11-25 07:17:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":464513,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDifference in distribution of TyG (A), TyG-BMI (B), TyG-WC (C), TyG-WHtR (D), CVAI(E), TyG-CVAI(F) in BPH/LUTS and no BPH/LUTS groups. The solid gray line in the middle of the Box Plot represents the median, and the upper and lower edge lines of the box plot represent the first quartile and the third quartile. The confluence of the two ends of the Violin Plot represents the lower adjusted value and upper adjacent value. The linear parts of the Violin Plot at the upper and lower ends reflect the outside points. The scatter plots in the red and blue sections visually reflect the distribution of data. TyG: triglyceride-glucose, TyG-BMI: triglyceride-glucose with body mass index, TyG-WC: triglyceride-glucose with waist circumference, TyG-WHtR: triglyceride-glucose with waist-to-height ratio, CVAI: Chinese visceral adiposity index, TyG-CVAI: triglyceride-glucose with Chinese visceral adiposity index.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7968845/v1/b310cd73c94248a55fcd17ee.png"},{"id":96711554,"identity":"929376d5-547e-4514-a417-675551c418e7","added_by":"auto","created_at":"2025-11-25 10:12:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":444635,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRestricted cubic spline curves for BPH/LUTS by TyG (A), TyG-BMI (B), TyG-WC (C), TyG-WHtR (D), CVAI(E) and TyG-CVAI (F) in all participants after covariate adjustment. Heavy central line represents the estimated adjusted hazard ratio, with shaded ribbons denoting 95% confidence interval. The models are adjusted for age, educational level, marital status, residence (urban/rural), smoking status, drinking status, sleep duration, hypertension, diabetes, pulmonary disease, heart disease, kidney disease, gastrointestinal disorders, arthritis, and asthma.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7968845/v1/395c7ad3b11deec769823922.png"},{"id":96694373,"identity":"2f1581fe-4b89-4a57-a814-520a42c37554","added_by":"auto","created_at":"2025-11-25 07:17:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":431164,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe cumulative incidence of BPH/LUTS with different TyG, CVAI and TyG-related indices.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7968845/v1/c6ab3ae0c427ad20c14e8b9c.png"},{"id":96694379,"identity":"181fe597-c8aa-4556-8603-32c65012d326","added_by":"auto","created_at":"2025-11-25 07:17:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":961862,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eROC curves for traditional indices and TyG-related indices in predicting BPH. The ROC curves demonstrate that the novel TyG-related indices (TyG-BMI, TyG-WC, TyG-WHtR) show superior predictive performance compared with traditional anthropometric measures (BMI, WC, WHtR). The AUCs for TyG-BMI, TyG-WC, and TyG-WHtR were 0.670, 0.659, and 0.665, respectively, whereas the AUCs for BMI, WC, and WHtR were 0.590, 0.577, and 0.568.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7968845/v1/5be28ed9f7d058ff925af626.png"},{"id":96694380,"identity":"61b00544-d17b-4d63-883a-d72c08cfc17e","added_by":"auto","created_at":"2025-11-25 07:17:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3462724,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSubgroup analyses for the association of TyG (\u003c/em\u003e\u003cem\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e), TyG-BMI (\u003c/em\u003e\u003cem\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e), TyG-WC (\u003c/em\u003e\u003cem\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e), TyG-WHtR (\u003c/em\u003e\u003cem\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e), CVAI(E) and TyG-CVAI(F) with risk of BPH in age, residence, education, marriage, smoking, drinking, sleep time and hypertension subgroup. The HR was calculated using Cox proportional hazards model with the adjustments including age, educational level, marital status, residence (urban/rural), smoking status, alcohol consumption, sleep duration, hypertension, diabetes, pulmonary disease, heart disease, kidney disease, gastrointestinal disorders, arthritis, and asthma. The P values for the interaction effect that using the likelihood ratio test are annotated to the right of A, B, C, D, E and F. HR: hazard ratio, CI: confidence interval\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7968845/v1/464c5d4367a8ae54268fe149.png"},{"id":100421933,"identity":"0d43e9f9-2d99-4450-bcdd-70f8289b9174","added_by":"auto","created_at":"2026-01-16 14:03:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7376981,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7968845/v1/0bb2899a-3a84-4119-9804-7a92dc7ef6fe.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The association of triglyceride-glucose related indices with benign prostatic hyperplasia: insights from the CHARLS","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBenign prostatic hyperplasia (BPH) is a common urological disorder among middle-aged and elderly men. Globally, over 50% of men exhibit some degree of BPH by the age of 60, with prevalence increasing to more than 80% in men aged 70 years and older\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The pathogenesis of BPH is not yet fully elucidated, emerging evidence indicates that hormonal alterations are crucial in its development\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Furthermore, imbalances between cellular proliferation and apoptosis\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, modifications in the stromal components of the prostate, and chronic inflammatory responses\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e have been identified as potential contributory mechanisms. Despite its non-malignant nature, BPH is a leading cause of lower urinary tract symptoms (LUTS), such as urinary hesitancy, increased frequency, and nocturia. The associated pathological changes and clinical manifestations significantly impair patients\u0026rsquo; physical health and quality of life\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Common complications, including urinary retention, urinary tract infections, bladder stones, and chronic renal insufficiency, further exacerbate these detrimental effects. Without timely intervention, these complications can result in significant renal damage and a notable decline in quality of life. Symptoms, including frequent nocturnal urination and urgency can greatly disrupt sleep, contribute to mood disturbances, and impair social functioning, thereby exacerbating the psychological burden on affected individuals\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Despite advancements in the understanding of BPH pathophysiology and the development of pharmacological and surgical treatments, early detection and intervention remain challenging due to the heterogeneous and multifactorial nature of the disease. Given the rapidly aging global population, the necessity for further research into BPH has become increasingly pressing.\u003c/p\u003e\u003cp\u003eBPH is considered to be closely associated with metabolic syndrome and is often accompanied by metabolic disturbances such as obesity and dyslipidemia. However, the pathogenesis of BPH has not yet been fully elucidated, as its complex pathological processes involve multiple factors, thereby increasing the challenges of both research and clinical intervention. Traditional indicators used to evaluate metabolic syndrome have been relatively simple and limited in accuracy, making it difficult to comprehensively capture the true metabolic status of patients. In recent years, the triglyceride-glucose (TyG) index has emerged as a novel marker that more precisely reflects the characteristics of metabolic syndrome and its related risks\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Therefore, the present study aims to investigate the potential association between the TyG index and its related indices and BPH, with the goal of providing new insights into the metabolic mechanisms underlying BPH and laying a foundation for optimizing clinical diagnosis and intervention strategies.\u003c/p\u003e\u003cp\u003eBPH is a condition characterized by androgen dependence, with insulin resistance and metabolic abnormalities thought to be closely linked to the proliferation of prostate tissue. Previous research has documented associations between BPH and conditions such as obesity\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, dyslipidemia\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, and metabolic syndrome\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Nonetheless, investigations into the relationship between TyG index and its related indices with BPH are relatively scarce. Consequently, utilizing data from the China Health and Retirement Longitudinal Study (CHARLS), we conducted a longitudinal cohort study to further examine the association between TyG and its related indices and the risk of developing BPH.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy population\u003c/h2\u003e\u003cp\u003eThis study employed data from CHARLS, a comprehensive, nationally representative longitudinal survey encompassing 28 provinces, autonomous regions, and municipalities throughout China, including 150 counties/districts and 450 villages/urban communities. The survey gathered extensive information from participants aged 45 years and older. CHARLS offers a broad spectrum of data, encompassing demographics, family structure, health status, healthcare and insurance, employment and pension, income and expenditures, housing conditions, and laboratory test results. The national baseline survey was conducted in 2011, with subsequent waves in 2013, 2015, and 2018. The study received approval from the Institutional Review Board (IRB) of Peking University (IRB approval number: IRB00001052-11015), and all participants provided written informed consent prior to participation. For the current analysis, the inclusion criteria were as follows: (1) male participants who were free of BPH or LUTS in 2011, (2) availability of complete data on CVAI, TyG index and its related indices, (3) availability of complete data on covariates, and (4) completion of follow-up. Ultimately, a total of 3,460 participants met these criteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eBPH statistics\u003c/h3\u003e\n\u003cp\u003eBPH was defined utilizing self-reported diagnoses obtained through standardized questionnaire items. Participants who did not have a BPH diagnosis at the baseline in 2011 were identified by their negative response to the question: \"\u003cem\u003eHave you ever been diagnosed with a prostate illness, such as prostate hyperplasia (excluding prostatic cancer)?\u003c/em\u003e\" During the follow-up periods in 2013, 2015, and 2018, the diagnosis of BPH was evaluated through several questions, including: \"\u003cem\u003eHave you ever been diagnosed with a prostate illness, such as prostate hyperplasia (excluding prostatic cancer) since we last spoke (in respondent's last interview month, year/in the last two years)?\u003c/em\u003e\", \"\u003cem\u003eAre you aware if you have had a prostate illness, such as prostate hyperplasia (excluding prostatic cancer)?\u003c/em\u003e\", and \"\u003cem\u003eHave you ever been diagnosed with a prostate illness, such as prostate hyperplasia (excluding prostatic cancer) since [ZIWTime]?\u003c/em\u003e\" Based on the responses to these items, we identified participants who were newly diagnosed with BPH during the follow-up period. Male participants who reported no BPH diagnosis at baseline in 2011 but received a diagnosis in any subsequent follow-up wave were classified as BPH cases. Those who remained free of a BPH diagnosis throughout the entire study period were categorized accordingly.\u003c/p\u003e\n\u003ch3\u003eTyG, CVAI and TyG related indexes\u003c/h3\u003e\n\u003cp\u003eFasting venous blood samples were obtained by trained medical professionals adhering to a standardized protocol and subsequently analyzed at a central laboratory. Serum triglycerides (TG) and fasting plasma glucose (FPG) concentrations were determined using an enzymatic colorimetric method. The TyG index was computed using the formula\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e:\u003c/p\u003e\u003cp\u003eTyG\u0026thinsp;=\u0026thinsp;ln [TG (mg/dL) \u0026times; FPG (mg/dL) / 2].\u003c/p\u003e\u003cp\u003eAnthropometric measurements, including height and weight, were conducted using a stadiometer and a calibrated scale, respectively, with participants barefoot and dressed in light clothing. Waist circumference (WC) was measured by trained personnel using a flexible tape at the level of the umbilicus. Each anthropometric parameter\u0026mdash;height, weight, and WC\u0026mdash;was measured three times, and the mean value was utilized for analysis. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters. The waist-to-height ratio (WHtR) was defined as WC divided by height. Expanding on the visceral adiposity index framework and with the objective of more precisely representing visceral fat distribution and metabolic status within the Chinese population, while considering the impact of ethnicity, age, and other variables on fat distribution, Chinese researchers developed the Chinese Visceral Adiposity Index (CVAI) in 2016\u003csup\u003e14\u003c/sup\u003e.The Chinese Visceral Adiposity Index (CVAI) for males was determined utilizing the formula\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e:\u003c/p\u003e\u003cp\u003eCVAI\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;267.93\u0026thinsp;+\u0026thinsp;0.68 \u0026times; age (years)\u0026thinsp;+\u0026thinsp;0.03 \u0026times; BMI (kg/m\u0026sup2;)\u0026thinsp;+\u0026thinsp;4.00 \u0026times; WC (cm)\u0026thinsp;+\u0026thinsp;22.00 \u0026times; lg TG (mmol/L)\u0026thinsp;\u0026minus;\u0026thinsp;16.32 \u0026times; HDL-C (mmol/L).\u003c/p\u003e\u003cp\u003eIn this context, HDL-C denotes high-density lipoprotein cholesterol, which was quantified using an enzymatic colorimetric method. The TyG index was further modified by multiplying it with BMI, WC, WHtR and CVAI to derive the TyG-BMI, TyG-WC, TyG-WHtR and TyG-CVAI indices, respectively\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eThe study incorporated a range of covariates, including sociodemographic characteristics, lifestyle factors, laboratory measurements, and medical history. Data on demographics, lifestyle, and disease history were gathered through standardized questionnaires administered by trained interviewers. Sociodemographic variables comprised age, educational level, marital status, and residential location (rural or urban). Lifestyle factors encompassed smoking status (ever versus never), alcohol consumption (ever versus never), and sleep duration. Medical history was assessed via self-reported diagnoses of conditions such as hypertension, diabetes, pulmonary disease, heart disease, kidney disease, gastrointestinal disorders, arthritis, and asthma. Hypertension was classified as a systolic blood pressure (SBP) of \u0026ge;\u0026thinsp;140 mmHg and/or a diastolic blood pressure (DBP) of \u0026ge;\u0026thinsp;90 mmHg, a self-reported history of physician-diagnosed hypertension, or current use of antihypertensive medications. SBP and DBP were each measured three times using an Omron HEM-7200 automatic blood pressure monitor, with the mean values recorded. Diabetes or elevated blood glucose was defined as a fasting plasma glucose level exceeding 110 mg/dL, or a self-reported history of diabetes.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eContinuous variables following a normal distribution are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), whereas skewed variables are reported as median with interquartile range (IQR). Categorical variables are expressed as percentages. Group comparisons for continuous variables were conducted using either the independent-samples Student\u0026rsquo;s t-test or the Mann\u0026ndash;Whitney U test, contingent upon the normality of the distribution\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The chi-square test was employed for the comparison of categorical variables, as appropriate. Three Cox proportional hazards models were utilized to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the risk of BPH/LUTS. The proportional hazards assumption was assessed using Schoenfeld residuals. TyG index, TyG-related indices (TyG-BMI, TyG-WC, TyG-WHtR), and CVAI were analyzed both as continuous variables and as categorical variables based on quartiles. The selection of potential confounders was informed by clinical relevance and evidence from existing literature, with variables demonstrating significant associations in univariate analysis also included in the multivariable models.\u003c/p\u003e\u003cp\u003eTo investigate potential dose\u0026ndash;response relationships between these indices and the risk of BPH/LUTS, restricted cubic spline (RCS) models were employed to produce smoothed curves. For indices demonstrating nonlinear associations, piecewise regression models were utilized for further fitting and segmented analysis. Kaplan\u0026ndash;Meier (KM) curves were constructed to depict the cumulative incidence of BPH/LUTS across quartiles of TyG, TyG-related indices, and CVAI. Receiver operating characteristic (ROC) curve analyses were performed to evaluate the discriminatory ability of each index in predicting BPH. The area under the ROC curve (AUC) was calculated with 95% confidence intervals. Participants were stratified into diabetic and non-diabetic groups, allowing for the separate examination of the associations between these indices and BPH risk within each group. Additionally, stratified analyses were performed to assess potential heterogeneity in the associations of TyG, TyG-related indices, and CVAI with BPH risk across various subgroups.\u003c/p\u003e\u003cp\u003eAll statistical analyses were conducted using R Statistical Software (Version 4.4.3, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, The R Foundation) and Free Statistics Analysis Platform (Version 2.2.0, Beijing, China, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.clinicalscientists.cn/freestatistics\u003c/span\u003e\u003cspan address=\"http://www.clinicalscientists.cn/freestatistics\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eBaseline Characteristics of Participants\u003c/h2\u003e\u003cp\u003eIn accordance with the established inclusion and exclusion criteria, this study incorporated a total of 3,460 male participants aged 45 years and older, with a mean age of 59.67\u0026thinsp;\u0026plusmn;\u0026thinsp;8.93 years. Over a follow-up period extending up to 7 years, 745 participants (21.53%) developed symptoms of BPH/LUTS. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e delineates the demographic characteristics of participants, stratified by the incidence of BPH/LUTS during the follow-up period. Participants who developed BPH/LUTS were significantly older than those in the non-BPH/LUTS group (60.65\u0026thinsp;\u0026plusmn;\u0026thinsp;8.39 vs. 59.41\u0026thinsp;\u0026plusmn;\u0026thinsp;9.05 years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and were more likely to reside in urban areas (22.01% vs. 13.41%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Indicators related to obesity, such as waist circumference, BMI, WHtR, were significantly elevated in the BPH/LUTS group compared to the non-BPH/LUTS group (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, in terms of metabolic diseases, participants with BPH/LUTS exhibited higher prevalence rates of hypertension (45.1% vs. 36.69%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), dyslipidemia (68.51% vs. 60.87%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and heart disease (15.17% vs. 7.00%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, the incidence of kidney disease, arthritis, pulmonary disease, and asthma was notably elevated in the BPH/LUTS cohort. In terms of metabolic parameters, the BPH/LUTS group exhibited higher triglyceride (TG) levels (median 102.66 (73.46, 156.65) vs. 96.46 (69.03, 143.37) mg/dL, p\u0026thinsp;=\u0026thinsp;0.002) and reduced high-density lipoprotein cholesterol (HDL-C) levels (49.31\u0026thinsp;\u0026plusmn;\u0026thinsp;16.42 vs. 51.15\u0026thinsp;\u0026plusmn;\u0026thinsp;16.23 mg/dL, p\u0026thinsp;=\u0026thinsp;0.006). The prevalence of smoking was marginally lower in the BPH/LUTS cohort (71.14% vs. 76.69%, p\u0026thinsp;=\u0026thinsp;0.002), while no significant difference was detected in alcohol consumption between the two cohorts (p\u0026thinsp;=\u0026thinsp;0.739).\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 included subjects stratified by incidence of BPH/LUTS\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo BPH/LUTS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBPH/LUTS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3,460)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2,715)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;745)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59.67\u0026thinsp;\u0026plusmn;\u0026thinsp;8.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.41\u0026thinsp;\u0026plusmn;\u0026thinsp;9.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.65\u0026thinsp;\u0026plusmn;\u0026thinsp;8.39\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\u003eResidence, 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\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,932 (84.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,351 (86.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e581 (77.99)\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\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e528 (15.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e364 (13.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e164 (22.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation, 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\u003eCollege or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62 (1.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28 (3.76)\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\u003eSenior high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e398 (11.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e302 (11.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96 (12.89)\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\u003eJunior high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e909 (26.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e713 (26.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e196 (26.31)\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\u003ePrimary or below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,656 (47.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,291 (47.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e365 (48.99)\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\u003eIlliterate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e435 (12.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e375 (13.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60 (8.05)\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\u003eMarriage, 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\u003e0.585\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,191 (92.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,503 (92.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e688 (92.35)\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\u003eDivorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (0.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (0.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (1.34)\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\u003eWidowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e194 (5.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e156 (5.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38 (5.1)\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\u003eNever married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42 (1.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (1.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (1.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWaist, cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84.18\u0026thinsp;\u0026plusmn;\u0026thinsp;11.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83.67\u0026thinsp;\u0026plusmn;\u0026thinsp;11.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86.05\u0026thinsp;\u0026plusmn;\u0026thinsp;12.77\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, kg/m^2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.01\u0026thinsp;\u0026plusmn;\u0026thinsp;3.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.77\u0026thinsp;\u0026plusmn;\u0026thinsp;3.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.87\u0026thinsp;\u0026plusmn;\u0026thinsp;3.88\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\u003eWHtR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\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\u003eSleep, hour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.153\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,612 (75.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,082 (76.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e530 (71.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrinking, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,409 (69.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,894 (69.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e515 (69.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.739\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,332 (38.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e996 (36.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e336 (45.10)\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\u003eDyslipidemia, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,155 (62.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,646 (60.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e509 (68.51)\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\u003eDiabetes, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,103 (31.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e859 (31.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e244 (32.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.564\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCancer, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (0.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLung disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e392 (11.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e280 (10.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e112 (15.03)\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\u003eLiver disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e136 (3.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100 (3.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36 (4.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.156\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e303 (8.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e190 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e113 (15.17)\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\u003eStroke, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66 (1.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 (2.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKidney disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e221 (6.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e136 (5.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e85 (11.41)\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\u003eDigestive disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e686 (19.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e516 (19.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e170 (22.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMental disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (0.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (1.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMemory disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42 (1.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31 (1.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (1.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.460\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArthritis, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,038 (30.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e775 (28.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e263 (35.3)\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\u003eAsthma, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e142 (4.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98 (3.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44 (5.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucose, mg/dl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e110.10\u0026thinsp;\u0026plusmn;\u0026thinsp;35.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109.73\u0026thinsp;\u0026plusmn;\u0026thinsp;34.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e111.43\u0026thinsp;\u0026plusmn;\u0026thinsp;37.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.243\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG, mg/dl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97.35 (70.80, 145.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96.46 (69.03, 143.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e102.66 (73.46, 156.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C, mg/dl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50.75\u0026thinsp;\u0026plusmn;\u0026thinsp;16.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51.15\u0026thinsp;\u0026plusmn;\u0026thinsp;16.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.31\u0026thinsp;\u0026plusmn;\u0026thinsp;16.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C, mg/dl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113.00\u0026thinsp;\u0026plusmn;\u0026thinsp;34.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e112.22\u0026thinsp;\u0026plusmn;\u0026thinsp;34.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e115.84\u0026thinsp;\u0026plusmn;\u0026thinsp;35.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.011\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\u003cem\u003eBMI: body mass index, WHtR: waist-to-height ratio, TG: serum triglycerides, HDL-C: high-density lipoprotein cholesterol, LDL-C: low-density lipoprotein cholesterol\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, participants who developed BPH/LUTS during the follow-up period exhibited elevated levels of TyG index, TyG-related indices, and CVAI in comparison to those who did not develop these symptoms (TyG: mean 8.61 vs. 8.70, p\u0026thinsp;=\u0026thinsp;0.002, TyG-BMI: mean 196.88 vs. 208.36, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, TyG-WC: mean 722.78 vs. 750.84, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, TyG-WHtR: mean 4.41 vs. 4.56, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, CVAI: mean 91.58 vs. 102.84, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, TyG-CVAI: mean 801.15 vs. 907.48, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAssociations of TyG, CVAI and TyG-related indices with BPH among all participants\u003c/h3\u003e\n\u003cp\u003eIn the multivariable Cox regression analysis, three models were constructed to investigate the associations between TyG, CVAI, and TyG-related indices with the risk of developing BPH/LUTS, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Model 1 was unadjusted, while Model 2 was adjusted for sociodemographic characteristics and lifestyle factors, including age, educational level, marital status, residence (urban/rural), smoking status, alcohol consumption, and sleep duration. Model 3 incorporated additional adjustments for a history of chronic diseases, such as hypertension, diabetes, pulmonary disease, heart disease, kidney disease, gastrointestinal disorders, arthritis, and asthma. When analyzed as standardized continuous variables, an increase of one standard deviation in TyG, TyG-BMI, TyG-WC, TyG-WHtR, CVAI and TyG-CVAI was significantly associated with an elevated risk of BPH/LUTS. Among these, CVAI exhibited the strongest association (Model 3: HR\u0026thinsp;=\u0026thinsp;1.26, 95% CI: 1.16\u0026ndash;1.36, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The TyG-related indices also demonstrated substantial risk increases: TyG-BMI (HR\u0026thinsp;=\u0026thinsp;1.25, 95%CI: 1.17\u0026ndash;1.34), TyG-WC (HR\u0026thinsp;=\u0026thinsp;1.19,95%CI: 1.10\u0026ndash;1.29), TyG-WHtR (HR\u0026thinsp;=\u0026thinsp;1.15, 95%CI: 1.06\u0026ndash;1.25), and TyG-CVAI (HR\u0026thinsp;=\u0026thinsp;1.23, 95%CI: 1.14\u0026ndash;1.33), all with p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. These associations remained statistically significant even after comprehensive adjustment for potential confounders.\u003c/p\u003e\u003cp\u003eIn a more detailed analysis utilizing quartile classification, a distinct dose-response relationship was identified between each index and the risk of BPH/LUTS. Participants in the highest quartile (Q4) exhibited a significantly elevated risk of BPH/LUTS compared to those in the lowest quartile (Q1) across all models. Specifically, in Model 3, the hazard ratios (HRs) for Q4 were as follows: 1.27 for TyG index, 1.92 for TyG-BMI, 1.87 for TyG-WC, 1.56 for TyG-WHtR, 2.15 for CVAI, 2.03 for TyG-CVAI. The p value for trend was statistically significant for all variables across all models (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating a consistent trend. These results suggest that elevated levels of TyG and its related indices, particularly CVAI, may serve as significant predictors of BPH/LUTS incidence.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe association of TyG, TyG-BMI, TyG-WC, TyG-WHtR and CVAI with BPH/LUTS in all participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.11(1.03\u0026ndash;1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.11(1.03\u0026ndash;1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.11(1.02\u0026ndash;1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuartile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.04(0.84\u0026ndash;1.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.01(0.82\u0026ndash;1.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.947\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.99(0.80\u0026ndash;1.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.926\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.07(0.86\u0026ndash;1.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.557\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.04(0.85\u0026ndash;1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.04(0.84\u0026ndash;1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.726\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.28(1.04\u0026ndash;1.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.26(1.03\u0026ndash;1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.27(1.02\u0026ndash;1.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG-BMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.24(1.17\u0026ndash;1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.25(1.17\u0026ndash;1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.25(1.17\u0026ndash;1.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuartile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.23(0.98\u0026ndash;1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.26(1.00-1.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG-WC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.21(1.12\u0026ndash;1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.20(1.11\u0026ndash;1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.19(1.10\u0026ndash;1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuartile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.41(1.13\u0026ndash;1.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.44(1.15\u0026ndash;1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.47(1.17\u0026ndash;1.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.54(1.24\u0026ndash;1.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.48(1.18\u0026ndash;1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.55(1.23\u0026ndash;1.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG-WHtR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.18(1.10\u0026ndash;1.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.17(1.08\u0026ndash;1.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.15(1.06\u0026ndash;1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuartile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.23(0.99\u0026ndash;1.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.19(0.96\u0026ndash;1.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.20(0.96\u0026ndash;1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.45(1.17\u0026ndash;1.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.37(1.10\u0026ndash;1.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.40(1.13\u0026ndash;1.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.69(1.37\u0026ndash;2.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.59(1.29\u0026ndash;1.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.56(1.24\u0026ndash;1.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.33(1.24\u0026ndash;1.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.27(1.18\u0026ndash;1.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.26(1.16\u0026ndash;1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuartile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.49(1.17\u0026ndash;1.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.42(1.12\u0026ndash;1.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.47(1.17\u0026ndash;1.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.78(1.42\u0026ndash;2.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.61(1.27\u0026ndash;2.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.69(1.33\u0026ndash;2.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.46(1.98\u0026ndash;3.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.17(1.72\u0026ndash;2.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.15(1.69\u0026ndash;2.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG-CVAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.29(1.21\u0026ndash;1.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.24(1.16\u0026ndash;1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.23 (1.14\u0026ndash;1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuartile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.56(1.24\u0026ndash;1.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.48 (1.17\u0026ndash;1.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.52 (1.20\u0026ndash;1.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.67(1.33\u0026ndash;2.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.49 (1.18\u0026ndash;1.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.58 (1.24-2.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.33(1.88\u0026ndash;2.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.04 (1.63\u0026ndash;2.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.03 (1.60\u0026ndash;2.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\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\u003cem\u003eCox proportional hazards models were used to estimate the associations between TyG, TyG-BMI, TyG-WC, TyG-WHtR, CVAI and TyG-CVAI (analyzed both as continuous and categorical variables) and the risk of developing BPH/LUTS. Three models were constructed\u003c/em\u003e:\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eModel 1: Unadjusted\u003c/h2\u003e\u003cp\u003e\u003cem\u003eModel 2: Adjusted for age, educational level, marital status, residence (urban/rural), smoking status, drinking status, and sleep duration\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eModel 3: Further adjusted for hypertension, diabetes, pulmonary disease, heart disease, kidney disease, gastrointestinal disorders, arthritis, and asthma on the basis of Model 2\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eTyG: Triglyceride-glucose, TyG-BMI: Triglyceride-glucose with body mass index, TyG-WC: Triglyceride-glucose with waist circumference, TyG-WHtR: Triglyceride-glucose with waist-to-height ratio, CVAI: Chinese visceral adiposity index, TyG-CVAI: Triglyceride-glucose with Chinese visceral adiposity index, HR: hazard ratio, CI\u003c/em\u003e: confidence interval\u003c/p\u003e\u003cp\u003eIn order to further explore the dose\u0026ndash;response relationship between TyG and its related indices and the risk of BPH/LUTS, we conducted RCS analyses for TyG, TyG-BMI, TyG-WC, TyG-WHtR, CVAI and TyG-CVAI, with comprehensive adjustment for all covariates included in Model 3 of Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) indicated a linear positive association between TyG, CVAI, TyG-CVAI and the risk of BPH/LUTS, as evidenced by non-significant tests for non-linearity (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Conversely, TyG-BMI, TyG-WC, and TyG-WHtR exhibited nonlinear associations with BPH/LUTS risk (all non-linearity P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). While the non-linearity test for TyG did not reach statistical significance, its overall association with BPH/LUTS resulted in a p value of 0.065, indicating a potentially weak or borderline statistically significant relationship. Conversely, CVAI exhibited a non-significant non-linear trend but achieved a highly significant overall p value of \u0026lt;\u0026thinsp;0.001, suggesting a robust and consistent linear association with an increased risk of BPH.\u003c/p\u003e\u003cp\u003eIn response to the observation that certain indices exhibited nonlinear trends, we utilized piecewise linear models to examine the relationships between TyG-BMI, TyG-WC, TyG-WHtR, and the risk of BPH/LUTS. The results are as follows: For TyG-BMI, within the range of TyG-BMI\u0026thinsp;\u0026le;\u0026thinsp;193.25, each standard deviation increase was significantly associated with a heightened risk of BPH/LUTS (HR: 1.68, 95%CI: 1.23\u0026ndash;2.29, P\u0026thinsp;=\u0026thinsp;0.001). When TyG-BMI exceeded 193.25, the association persisted significantly (HR: 1.17, 95% CI: 1.06\u0026ndash;1.30, P\u0026thinsp;=\u0026thinsp;0.003), albeit with a slightly reduced effect size. In terms of TyG-WC, no significant association with the risk of BPH was observed for TyG-WC\u0026thinsp;\u0026lt;\u0026thinsp;714.68 (P\u0026thinsp;=\u0026thinsp;0.780); however, for TyG-WC\u0026thinsp;\u0026gt;\u0026thinsp;714.68, each SD increase significantly increased the risk (HR: 1.21, 95% CI: 1.05\u0026ndash;1.40, P\u0026thinsp;=\u0026thinsp;0.008). Regarding TyG-WHtR, no significant association was identified for values\u0026thinsp;\u0026le;\u0026thinsp;4.38 (P\u0026thinsp;=\u0026thinsp;0.750), but for values\u0026thinsp;\u0026gt;\u0026thinsp;4.38, higher levels were significantly correlated with an increased risk of BPH/LUTS (HR: 1.17, 95% CI: 1.01\u0026ndash;1.35, P\u0026thinsp;=\u0026thinsp;0.033). These findings further corroborate the nonlinear dose\u0026ndash;response relationship between specific TyG-related indices and the risk of BPH/LUTS.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociations of TyG-BMI, TyG-WC, and TyG-WHtR with BPH/LUTS across different subgroups among all participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003esubgroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG-BMI\u0026thinsp;\u0026le;\u0026thinsp;193.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.23\u0026ndash;2.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG-BMI\u0026gt;193.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.06\u0026ndash;1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG-WC\u0026thinsp;\u0026le;\u0026thinsp;714.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.85\u0026ndash;1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.780\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG-WC\u0026gt;714.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.05\u0026ndash;1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG-WHtR\u0026thinsp;\u0026le;\u0026thinsp;4.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.82\u0026ndash;1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.750\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG-WHtR\u0026gt;4.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.01\u0026ndash;1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.033\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\u003cem\u003eThe models are adjusted for age, educational level, marital status, residence (urban/rural), smoking status, drinking status, sleep duration, hypertension, diabetes, pulmonary disease, heart disease, kidney disease, gastrointestinal disorders, arthritis, and asthma.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTo further elucidate the cumulative incidence of BPH/LUTS in relation to varying levels of TyG, CVAI, and TyG-correlation indices, we stratified these indices into two distinct groups based on the inflection point values derived from the RCS curves. Subsequently, we generated Kaplan-Meier curves for each group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The analysis reveals that the groups with elevated CVAI and TyG-correlation indices exhibit a significantly higher cumulative incidence (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to those with lower values. This finding substantiates the dose-response relationship between CVAI and TyG-related indices and the incidence of BPH/LUTS.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003ePredictive performance of TyG-related indices versus traditional measures for BPH\u003c/h2\u003e\u003cp\u003eTo evaluate the predictive efficacy of traditional indices (BMI, WC, WHtR) versus novel TyG-related indices (TyG-BMI, TyG-WC, TyG-WHtR) in identifying BPH, ROC curve analyses were conducted for each index (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Comparisons of AUC values between traditional indices and novel TyG-related indices were conducted using DeLong\u0026rsquo;s test. The TyG-related indices exhibited superior predictive performance for BPH compared to traditional measures. Specifically, the AUC values for TyG-BMI, TyG-WC, and TyG-WHtR were 0.670, 0.659, and 0.665, respectively, whereas the corresponding AUC values for BMI, WC, and WHtR were 0.590, 0.577, and 0.568. Pairwise comparisons revealed that the predictive value of each TyG-related index was significantly greater than that of its traditional counterpart (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among the novel indices, TyG-BMI demonstrated the highest AUC (0.670), indicating its potential as the most effective predictor of BPH risk. Furthermore, ROC curve analysis indicated that TyG-related indices consistently achieved a superior sensitivity-specificity balance across various cutoff values compared to conventional measures.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssociations of TyG, CVAI and TyG-related indices with BPH among participants with and without type 2 diabetes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study population was further stratified according to diabetes status into a non-diabetic cohort (n\u0026thinsp;=\u0026thinsp;2,357) and a diabetic cohort, which included individuals with elevated blood glucose levels (n\u0026thinsp;=\u0026thinsp;1,103). Following adjustments for all covariates in Model 4 (refer to Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), we evaluated the associations of TyG, CVAI and TyG-related indices with the risk of BPH/LUTS within each cohort. The findings indicated that, within the non-diabetic cohort, each standard deviation increase in TyG was significantly correlated with an elevated risk of BPH/LUTS (HR: 1.15, 95% CI: 1.02\u0026ndash;1.29, P\u0026thinsp;=\u0026thinsp;0.023). Conversely, in the diabetic cohort, this association did not reach statistical significance (HR: 1.05, 95% CI: 0.94\u0026ndash;1.18, P\u0026thinsp;=\u0026thinsp;0.390).\u003c/p\u003e\u003cp\u003eThe indices TyG-BMI, TyG-WC, TyG-WHtR, CVAI and TyG-CVAI demonstrated varying degrees of significant associations with BPH/LUTS across both groups. In the non-diabetic cohort, an increase of one standard deviation in these indices was significantly correlated with an elevated risk of BPH/LUTS (TyG-BMI: HR: 1.31, TyG-WC: HR: 1.21, TyG-WHtR: HR: 1.14, CVAI: HR: 1.28, TyG-CVAI: HR:1.37, all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Moreover, the P for trend across quartiles was highly significant for all indices (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating a clear dose\u0026ndash;response relationship. In the diabetic cohort, the associations for TyG-BMI and TyG-WC were slightly attenuated and did not consistently achieve statistical significance (TyG-BMI: P\u0026thinsp;=\u0026thinsp;0.018, P for trend\u0026thinsp;=\u0026thinsp;0.068; TyG-WC: P\u0026thinsp;=\u0026thinsp;0.025, P for trend\u0026thinsp;=\u0026thinsp;0.051). Conversely, TyG-WHtR, CVAI and TyG-CVAI remained significantly associated with BPH/LUTS risk (P\u0026thinsp;=\u0026thinsp;0.042, 0.006 and \u0026lt;\u0026thinsp;0.001, respectively), with TyG-CVAI in the highest quartile exhibiting a significantly increased risk (HR\u0026thinsp;=\u0026thinsp;2.13, P\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eIn summary, the correlations between the TyG index and its associated indices with the risk of BPH/LUTS were more significant in the non-diabetic population, while their predictive efficacy was diminished among individuals with diabetes. This attenuation may be due to the intricate metabolic pathophysiology characteristic of diabetes, which could obscure or alter the impact of markers related to insulin resistance. These findings indicate that in diabetic patients, the onset of BPH/LUTS may be influenced by multiple interacting factors, thereby limiting the effectiveness of TyG-related indices for risk assessment within this subgroup.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe association of TyG, TyG-BMI, TyG-WC, TyG-WHtR CVAI and TyG-CVAI with BPH/LUTS in diabetes patients and non-diabetes patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eNon-diabetes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en.total\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en.event(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003en.total\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003en.event(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e501 (21.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.15 (1.02\u0026ndash;1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e244 (22.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.05 (0.94\u0026ndash;1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.390\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuartile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e153 (19.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18 (19.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e669\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e141 (21.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.99 (0.79\u0026ndash;1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e38 (19.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.93 (0.53\u0026ndash;1.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.814\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123 (21.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.02 (0.81\u0026ndash;1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.841\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e58 (20.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.00 (0.59\u0026ndash;1.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84 (25.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.29 (0.98\u0026ndash;1.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e532\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e130 (24.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.16 (0.70\u0026ndash;1.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.559\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.249\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG-BMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e501 (21.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.31 (1.19\u0026ndash;1.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e244 (22.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.15 (1.02\u0026ndash;1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuartile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e710\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e63 (21.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.09 (0.69\u0026ndash;1.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.714\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e128 (29.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.12 (1.60\u0026ndash;2.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e119 (27.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.25 (0.81\u0026ndash;1.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.318\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG-WC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e501 (21.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.21 (1.08\u0026ndash;1.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e244 (22.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.15 (1.02\u0026ndash;1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuartile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd 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align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.54 (1.20\u0026ndash;1.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e40 (19.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.19 (0.71-2.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.501\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e565\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e139 (24.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.69 (1.30\u0026ndash;2.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e56 (18.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.16 (0.71\u0026ndash;1.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.546\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e413\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e113 (27.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.92 (1.45\u0026ndash;2.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e124 (27.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.53 (0.96\u0026ndash;2.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG-WHtR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e501 (21.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.14 (1.03\u0026ndash;1.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e244 (22.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.14 (1.00-1.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuartile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e723\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e122 (16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e21 (14.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e137 (20.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.20 (0.94\u0026ndash;1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e36 (19.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.20 (0.70\u0026ndash;2.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.512\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e557\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e136 (24.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.44 (1.12\u0026ndash;1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e63 (20.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.34 (0.81\u0026ndash;2.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.252\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e106 (26.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.51 (1.15\u0026ndash;1.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e124 (26.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.55 (0.96\u0026ndash;2.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e501 (21.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.28 (1.16\u0026ndash;1.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e244 (22.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.20 (1.05\u0026ndash;1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuartile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e683\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e91 (13.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e27 (14.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e637\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e126 (19.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.53 (1.17\u0026ndash;2.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e41 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.32 (0.81\u0026ndash;2.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.272\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140 (24.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.84 (1.39\u0026ndash;2.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e58 (20.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.30 (0.81\u0026ndash;2.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.273\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e457\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e144 (31.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.31 (1.74\u0026ndash;3.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e118 (28.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.68 (1.08\u0026ndash;2.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG-CVAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e501(21.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.37 (1.24\u0026ndash;1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e244(22.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.26 (1.14\u0026thinsp;~\u0026thinsp;1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\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\u003eQuartile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96(13.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e24(14.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e139(21.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.63(1.25\u0026ndash;2.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e40(19.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.37(0.83\u0026ndash;2.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.223\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e578\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e133(23.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.78(1.37\u0026ndash;2.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e57(19.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.45(0.90\u0026ndash;2.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.127\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e501(21.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.49(1.92\u0026ndash;3.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e123(28.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.13(1.37\u0026ndash;3.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\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\u003cem\u003eCox proportional hazards models were used to estimate the associations between TyG, TyG-BMI, TyG-WC, TyG-WHtR, CVAI and TyG-CVAI (analyzed both as continuous and categorical variables) and the risk of developing BPH/LUTS. The models are adjusted for age, educational level, marital status, residence (urban/rural), smoking status, drinking status, sleep duration, hypertension, pulmonary disease, heart disease, kidney disease, gastrointestinal disorders, arthritis, and asthma.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eSubgroup analysis\u003c/h2\u003e\u003cp\u003eTo further explore the applicability of TyG index, CVAI and TyG related indices (TyG-BMI TyG-WC, TyG-WHtR, TyG-CVAI) across diverse populations and their reliability in predicting BPH risk, this study conducted stratified analyses across various subgroups: age (\u0026le;\u0026thinsp;65 years vs. \u0026gt;65 years), residence (urban vs. rural), education level, marital status, smoking status, drinking status, sleep duration (\u0026le;\u0026thinsp;7 hours vs. \u0026gt;7 hours), and hypertension status. For each subgroup, Cox regression models were employed, and likelihood ratio tests were utilized to evaluate the statistical significance of interaction terms (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In the exploratory subgroup analysis conducted on multiple variables, we found that smoking status may have an interaction with CVAI (P for interaction\u0026thinsp;=\u0026thinsp;0.049). Given that multiple interaction tests were performed, this finding may be at risk of false positivity and requires Bonferroni correction, meaning a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.006 would indicate statistical significance. Beyond this, no significant interactions were observed for other indicators.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this comprehensive nationwide cohort study, we identified several factors significantly associated with BPH/LUTS, including age, residential location, obesity, and metabolic disorders. With respect to obesity-related metrics, the BPH/LUTS group exhibited significantly elevated waist circumference, BMI, and waist-to-height ratio compared to the non-BPH/LUTS group, indicating that obesity may constitute a critical risk factor for LUTS. Furthermore, metabolic disorders, including hypertension, diabetes, and dyslipidemia, were more prevalent among individuals with BPH/LUTS. This observation aligns with existing literature, which suggests that factors related to metabolic syndrome are significantly correlated with the onset of BPH/LUTS\u003csup\u003e12\u003c/sup\u003e. For example, previous studies have demonstrated that hypertension and dyslipidemia may aggravate BPH/LUTS by influencing microvascular circulation and inflammatory responses in the prostate\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Our findings provide essential data for future investigations and establish a theoretical framework for the formulation of preventive strategies against BPH/LUTS.\u003c/p\u003e\u003cp\u003eOur study utilized prospective follow-up data to systematically assess the association between TyG index, CVAI and TyG-related indices with the risk of developing BPH/LUTS. Our findings indicate that elevated levels of TyG, TyG-BMI, TyG-WC, TyG-WHtR CVAI and TyG-CVAI are significantly correlated with an increased risk of BPH/LUTS. These associations persisted even after adjusting for a range of demographic variables, lifestyle factors, and histories of chronic diseases, underscoring the potential of these indices as predictive markers for BPH/LUTS. Restricted cubic splines demonstrated variability in the dose-response relationship between the various metabolic indices and BPH/LUTS risk. Specifically, TyG, CVAI and TyG-CVAI exhibited a linear positive correlation with BPH/LUTS risk, suggesting that as these indices rise, the risk of BPH/LUTS correspondingly increases. This linear relationship may show the continuous influence of insulin resistance and visceral fat accumulation on prostate enlargement. Previous research has indicated that insulin resistance activates the IGF-1 and PI3K/Akt signaling pathways, which promote prostate cell proliferation and inhibit apoptosis, thereby contributing to an increase in prostate volume\u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. CVAI, a composite measure of visceral fat accumulation, demonstrated a significant linear association, implying that imbalanced fat distribution may serve as a critical metabolic foundation for BPH\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Conversely, the indices TyG-BMI, TyG-WC, and TyG-WHtR displayed nonlinear relationships with the risk of BPH/LUTS, indicating the presence of threshold effects at varying levels of these indices. Further analysis using a piecewise linear model revealed that when these indices exceeded certain levels (e.g., TyG-BMI\u0026thinsp;\u0026gt;\u0026thinsp;193.25, TyG-WC\u0026thinsp;\u0026gt;\u0026thinsp;714.68, TyG-WHtR\u0026thinsp;\u0026gt;\u0026thinsp;4.38), their influence on the risk of BPH/LUTS became more pronounced. This finding suggests that once individuals surpass a specific metabolic load threshold, the prostate may become more vulnerable to systemic metabolic disturbances, leading to structural and functional alterations. Such nonlinear trend has substantial clinical implications, indicating that early interventions should prioritize high-risk populations, especially individuals exhibiting both obesity and insulin resistance.\u003c/p\u003e\u003cp\u003eIn this large, nationally representative study based on CHARLS data, we observed that TyG-related indices provided better discriminatory ability for benign prostatic hyperplasia compared with conventional indicators. While BMI, WC, and WHtR have been widely used to reflect general and central obesity, their predictive value for BPH was modest, with AUC values below 0.60. In contrast, the incorporation of the TyG index, a surrogate marker of insulin resistance, substantially enhanced predictive accuracy. The underlying mechanisms may be explained by the pivotal role of insulin resistance and related metabolic disturbances in the pathogenesis of BPH. Accumulating evidence indicates that insulin resistance contributes to hyperinsulinemia, which can stimulate sympathetic activity, increase growth factor expression, and promote prostate cell proliferation. Moreover, metabolic dysregulation associated with insulin resistance often coexists with systemic inflammation and oxidative stress, further driving prostate enlargement. Traditional measures such as BMI or WC capture only anthropometric aspects of obesity but fail to account for metabolic heterogeneity. By combining these indices with TyG, the novel indicators simultaneously reflect adiposity and insulin resistance, thereby providing a more comprehensive assessment of metabolic health status. Our findings are consistent with recent studies reporting the clinical utility of TyG-related indices in predicting cardiovascular diseases, diabetes, and chronic kidney disease. Extending this evidence, we demonstrate that these indices are also relevant to urological outcomes, specifically BPH. Among the three novel indices, TyG-BMI showed the strongest predictive power, suggesting that the interaction between overall adiposity and insulin resistance may have a greater impact on BPH development than central obesity alone.\u003c/p\u003e\u003cp\u003eIn the diabetes subgroup analysis of this study, we identified more pronounced and consistent associations between TyG index and its related metabolic indices with the risk of BPH/LUTS in the non-diabetic cohort. Conversely, although most indices within the diabetic group exhibited an upward trend in hazard ratios, their statistical significance diminished, and several quartile trend tests failed to achieve significance. This disparity indicates that diabetic status may influence the relationship between TyG-related indices and BPH/LUTS risk. The TyG index, a well-established surrogate marker for insulin resistance, has been extensively utilized to evaluate metabolic abnormalities. In non-diabetic individuals, elevated TyG levels frequently indicate underlying metabolic dysfunction and lipid metabolism disorders, which may contribute to the development of BPH/LUTS through mechanisms such as chronic low-grade prostate inflammation\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, activation of androgen synthesis pathways\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, and enhanced endothelial dysfunction\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Conversely, in individuals with diabetes, the metabolic condition is more intricate, characterized by prevalent insulin resistance, hyperinsulinemia, and pathological processes resulting from chronic hyperglycemia, including oxidative stress\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, vascular dysfunction\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, and disturbances in hormonal axes\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. These factors may obscure or reduce the efficacy of the TyG index in risk identification.\u003c/p\u003e\u003cp\u003eIn the subgroup analyses, we observed a potential interaction between CVAI and smoking status in relation to BPH risk (P\u0026thinsp;=\u0026thinsp;0.049). Although this finding may suggest that smoking could modify the association between visceral adiposity and BPH, caution is warranted in its interpretation. Given that multiple interaction tests were performed, the possibility of a chance finding cannot be excluded. In particular, after applying a Bonferroni correction for multiple comparisons, a p-value threshold of \u0026lt;\u0026thinsp;0.006 would be required to achieve statistical significance, and the observed association does not meet this more stringent criterion. Therefore, the interaction between CVAI and smoking should be regarded as exploratory and hypothesis-generating rather than conclusive. Future studies with larger sample sizes, independent cohorts, and pre-specified subgroup analyses will be necessary to confirm whether smoking truly modifies the relationship between visceral adiposity and BPH risk.\u003c/p\u003e\u003cp\u003eThe clinical implications of our findings are noteworthy. TyG-related indices are simple to calculate using routinely available laboratory and anthropometric data, making them practical tools for risk stratification in primary care and large-scale population screening. Their superior predictive performance may enable earlier identification of high-risk individuals, thereby facilitating timely lifestyle modification or pharmacological intervention. Given that BPH substantially impairs quality of life in aging men, improved predictive tools could contribute to preventive strategies and alleviate healthcare burden. Our study also has several notable strengths. The large sample size and the nationally representative nature of the CHARLS cohort enhance the generalizability of the results. The standardized calculation of TyG and related indices, coupled with the use of multiple adjusted models, bolsters the stability and credibility of our findings. Moreover, the application of diverse statistical approaches, including restricted cubic splines, stratified analyses, and Kaplan\u0026ndash;Meier curves, further reinforces the robustness of the results.\u003c/p\u003e\u003cp\u003eNevertheless, some limitations must be acknowledged. Firstly, despite efforts to adjust for relevant confounders in the multivariable models, the possibility of unmeasured or unknown residual confounders cannot be entirely excluded, potentially leading to either an overestimation or underestimation of the observed associations. Secondly, the generalizability of CVAI and TyG as indices across diverse populations necessitates further validation. Lastly, additional mechanistic studies are required to elucidate the biological basis underlying the association between these metabolic indices and the occurrence of BPH.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we evaluated the association between the triglyceride-glucose index (TyG) and its related metabolic indices (TyG-BMI, TyG-WC, TyG-WHtR, CVAI, and TyG-CVAI) and the risk of BPH/LUTS. Our findings indicate that higher levels of these metabolic indices are associated with an increased risk of BPH/LUTS. Notably, TyG, CVAI, and TyG-CVAI showed a linear relationship with BPH/LUTS risk, whereas TyG-BMI, TyG-WC, and TyG-WHtR exhibited nonlinear associations. Stratified analyses suggested a potential interaction between CVAI and smoking status; however, given multiple testing and the need for Bonferroni correction, this finding should be interpreted cautiously and considered exploratory. Overall, these results highlight the relevance of TyG-related metabolic indices as predictive markers for BPH/LUTS risk and underscore their potential utility in clinical practice for early identification, risk stratification, and the development of targeted monitoring or intervention strategies to mitigate the incidence of BPH/LUTS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by National Natural Science Foundation of China (No. 82272864 and No.82573601) and Capital\u0026rsquo;s Funds for Health Improvement and Research (No.2024-2-2059).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eDeclaration of any potential financial and non-financial conflicts of interest:\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available on request to the authors.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The manuscript has been read and approved by all the authors. The requirements for authorship have been met, and each author believes that the manuscript represents honest work.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor Contributions Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX.X.B. and Y.S.Z. collected the data, conducted background research, and drafted the initial version of the manuscript. Y.C.J. performed the statistical analyses. Z.H.L. prepared the figures, and Z.T.Z. organized and formatted the tables. Y.X.L. and H.P. provided critical professional guidance and contributed to the revision and final approval of the manuscript. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNg, M., Leslie, S. W. \u0026amp; Baradhi, K. M. Benign Prostatic Hyperplasia. in \u003cem\u003eStatPearls\u003c/em\u003e (StatPearls Publishing, Treasure Island (FL), 2025).\u003c/li\u003e\n\u003cli\u003eBratchikov, O. 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Molecular Events Linking Oxidative Stress and Inflammation to Insulin Resistance and \u0026beta;-Cell Dysfunction. \u003cem\u003eOxid Med Cell Longev\u003c/em\u003e \u003cstrong\u003e2015\u003c/strong\u003e, 181643 (2015).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"benign prostatic hyperplasia, triglyceride-glucose index, triglyceride-glucose related indices, metabolic syndrome, CHARLS","lastPublishedDoi":"10.21203/rs.3.rs-7968845/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7968845/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBenign prostatic hyperplasia (BPH) is a common disease among middle-aged and older men, often linked to obesity, dyslipidemia, and metabolic syndrome. It causes lower urinary tract symptoms and impacts their quality of life. While the triglyceride-glucose (TyG) index is a known marker for metabolic syndrome, its relationship with BPH remains underexplored. This study investigates the association between TyG-related indices and the risk of BPH to uncover potential clinical implications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study analyzed data from the China Health and Retirement Longitudinal Study (CHARLS), involving 3,460 men aged 45 and older who were free of BPH at baseline. During a 7-year follow-up, new BPH cases were recorded. Baseline data included TyG, Chinese visceral adiposity index (CVAI), and TyG-related indices. Statistical methods including Cox proportional hazards regression, Restricted Cubic Splines, Kaplan-Meier curves, and subgroup analyses were used to assess the relationship between these indices and BPH.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOver a 7-year follow-up, 745 participants developed BPH. Our analysis revealed that higher values of TyG-related indices were significantly associated with an elevated risk of BPH. TyG and CVAI had a linear relationship with BPH risk, while TyG-BMI, TyG-WC, TyG-WHtR and TyG-CVAI showed a nonlinear association. No significant interaction was observed in the subgroup analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is a strong link between TyG-related indices and the risk of BPH, consistent across Chinese populations. The risk of BPH rises with higher metabolic indices, notably CVAI. These findings suggest new avenues for early prevention and intervention. Future research should investigate the mechanisms of these indices.\u003c/p\u003e","manuscriptTitle":"The association of triglyceride-glucose related indices with benign prostatic hyperplasia: insights from the CHARLS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-25 07:16:57","doi":"10.21203/rs.3.rs-7968845/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e7e16c54-6042-42d9-8124-ecc9f4afea78","owner":[],"postedDate":"November 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58096936,"name":"Health sciences/Diseases"},{"id":58096937,"name":"Health sciences/Medical research"},{"id":58096938,"name":"Health sciences/Risk factors"},{"id":58096939,"name":"Health sciences/Urology"}],"tags":[],"updatedAt":"2026-01-16T11:40:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-25 07:16:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7968845","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7968845","identity":"rs-7968845","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}