Association Between the TG/HDL-c Ratio and Hyperuricemia in Women participants in China: A Cross-sectional Study

Association Between the TG/HDL-c Ratio and Hyperuricemia in Women participants in China: A Cross-sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association Between the TG/HDL-c Ratio and Hyperuricemia in Women participants in China: A Cross-sectional Study Shuai Zhang, Hao Liang, Jia You, Ye Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5341007/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Objectives: The study aimed to assess the clinical value of the triglyceride/high-density lipoprotein cholesterol(TG/HDL-c) ratio as a diagnostic marker for hyperuricemia (HUA) in female population in China. Methods: A total of 21,316 eligible female participants, aged 18 years and older, were consecutively recruited during routine medical examinations at Northern Jiangsu People’s Hospital from July 2014 to August 2023. Participants were divided into four groups based on their TG/HDL-c ratio values. Logistic regression analysis models were employed to further investigate the correlation between the prevalence of HUA and TG/HDL-c ratio in this region.Restricted cubic splines (RCS) were used to explore the linear associations of TG/HDL-c and HUA. Results: The average age of participants was 42.68±13.96 years and the overall prevalence of HUA is 6.41%. The mean uric acid level was 265.34±59.72umol/L. The univariate logistic analysis showed that a higher TG/HDL-c ratio was positively correlated with the presence of hyperuricemia (OR (95%CI) =3.601(3.281,3.951), P < 0.001). In different age groups of female participants, we found a statistically significant association between higher levels of TG/HDL-c and HUA( P <0.05). In female participants, there was a nonlinear association between TG/HDL-c and HUA ( P < 0.001).After adjusting for multiple covariates, this study found a significant interaction between TG/HDL-c and BMI, as well as creatinine, with a statistically significant difference ( P < 0.05).The AUC for TG/HDL-c in predicting the occurrence of HUA among female participants was 0.717(95%Cl:0.703-0.731). Conclusions: An elevated TG/HDL-C ratio increases the risk of HUA in females in eastern China, particularly in individuals with low creatinine levels and normal body weight. Monitoring TG/HDL-c levels may be beneficial for preventing HUA in women. Hyperuricaemia triglycerides high-density lipoprotein cholesterol risk factor Figures Figure 1 Figure 2 Figure 3 Introduction In China, hyperuricemia(HUA) has become a common metabolic disease with a significant gender disparity. The occurrence of HUA ranges from 13.3% to 21.6%, with noticeable differences by gender and across various countries or regions, potentially driven by factors like environmental and economic circumstances[1, 2]. Although multiple epidemiological studies have explored the link between serum uric acid (SUA) levels and cardiovascular diseases such as coronary artery disease, hypertension, metabolic syndrome(MetS) and so on. However, these associations have not been uniformly observed across all studies, as shown by the Framingham Heart Study[3]. Research conducted in China reported a rise in the prevalence of hyperuricemia in women from 5.8% in 2010 to 10.1% in 2019[4]. We should attempt to study certain sensitive indicators to predict the occurrence of hyperuricemia among women in the central Jiangsu region. Numerous indicators are identified as independent risk factors for the development of HUA[5]. The triglyceride-to-high-density lipoprotein cholesterol (TG/HDL-c) ratio is an emerging marker of lipid abnormalities, providing a reliable indicator for MetS, insulin resistance and cardiovascular events. A study based on individuals aged 45 and older in China indicates that the TG/HDL-c ratio is positively associated with the risk of HUA in middle-aged and elderly populations, with a stronger predictive power observed in females[6, 7]. However, the association between the TG/HDL-c ratio and the risk of developing HUA in females remains limited[8]. In this study, we aimed to investigate the correlation between the TG/HDL-c ratio and HUA occurrence of female participants. These findings provide valuable insights for the auxiliary diagnosis and prevention of HUA in the local population. Materials and Methods Study population The study population for this cross-sectional study comprised female adults who underwent routine health check-ups at Northern Jiangsu People’s Hospital between July 2014 and August 2023 in Yangzhou. According to the hospital’s standard procedures, participants provided their medical history, underwent blood tests and had abdominal ultrasounds. For research purpose, participants were required to meet the following criteria: females aged 18 years or older who had resided in the selected communities for at least six months before the survey. Exclusion criteria included: (1) mental or cognitive disorders such as dementia or impaired comprehension; (2) physical disabilities like paraplegia; (3) individuals undergoing urate-lowering therapy. Given the retrospective and cross-sectional nature of the study, it was granted an exemption from ethics board approval by the Ethics Committee at Northern Jiangsu People's Hospital. Data Collection A comprehensive physical examination was performed, which included parameters such as age, height, weight, gender, fasting blood glucose(GLU), serum uric acid (SUA), serum creatinine(Scr), triglyceride(TG), blood urea nitrogen (BUN), total cholesterol(TC), high-density lipoprotein (HDL-c), low-density lipoprotein(LDL-c), red blood cells (RBCs), white blood cells (WBCs), hemoglobin (Hb) and platelets (PLTs). Blood samples were obtained and processed following accredited laboratory protocols. Definition of Cohort The diagnostic criterion for HUA was an SUA level above 360µmol/L in women[ 9 ]. Standardized methods were employed to determine both weight and height, from which the Body mass index(BMI) was calculated by dividing the weight(kg) by the squared height (m)[ 10 , 11 ]. The TG/HDL-c ratio was calculated by dividing the TG level by the HDL-c level, and the TG/HDL-c ratio was grouped according to the interquartile range (IQR), which converted TG/HDL-c from a continuous variable to a categorical variable (Q1, Q2, Q3, and Q4)[ 12 ]. Statistical analysis Data analysis was carried out using R version 4.0.3 (R Foundation for Statistical Computing) and SPSS version 27.0. Normally distributed variables were expressed as mean ± standard deviation (SD) and compared using Student’s t tests. Categorical data were presented as frequencies and percentages with the Chi-square test or Fisher’s exact test. Comparisons between multiple groups were made using one-way ANOVA. We used univariate and multivariate logistic regression analyses, restricted cubic spline(RCS) regression analysis, subgroup analysis and interaction analysis to analyze the relationship between the TG/HDL-c ratio and serum uric acid levels. RCS plots were used to test for nonlinear association between TG/HDL-c and HUA in the female population. The area under the receiver operating characteristic (ROC) curve was used to assess TG/HDL-c in predicting the occurrence of HUA among female participants. Statistical significance was determined as a threshold of P < 0.05. Results Clinic characteristics of the participants The study enrolled a total of 21316 female participants and the average age was 42.68 ± 13.96 years, with a mean SUA level of 265.34 ± 59.72 umol/L. The overall prevalence of HUA was 6.41%. Participants were categorized into four groups based on the values of the TG/HDL-c ratio (Table 1 ). The statistical analysis revealed significant differences in UA, TG, and HDL-c levels among the four groups. Specifically, the UA and TG levels exhibited a significant increase with higher TG/HDL-c ratios, while the HDL-c levels showed a significant decrease ( P < 0.001). Further analysis demonstrated a positive association between the prevalence of HUA and increasing TG/HDL-c ratios(Fig. 1 ). Moreover, There are significant differences in age, BMI, blood glucose levels, serum creatinine, TC, LDL-c, WBCs, RBCs, Hb, PLTs, and total protein among females across different TG/HDL-c ratios ( P < 0.001). However, no statistically significant difference was observed in BUN across different TG/HDL-c groups. Table 1 Baseline Data of Female Participants. Variables Total TG/HDL-c ratio (mmol/L) (quartiles) P -value Q1( 0.992) N 21316 5332 5326 5330 5328 Age, years 42.68 ± 13.96 37.33 ± 11.44 40.55 ± 13.06 44.25 ± 14.07 48.59 ± 14.46 <0.001 BMI, kg/m 2 22.58 ± 3.04 21.04 ± 2.28 21.97 ± 2.66 23.01 ± 3.01 24.31 ± 3.12 <0.001 UA, µmol/L 265.34 ± 59.72 245.08 ± 50.06 254.71 ± 53.15 268.15 ± 57.84 293.43 ± 65.36 <0.001 HUA, n(%) 1367(6.41) 109(2.04) 178(3.34) 336(6.31) 744(14.01) <0.001 Glucose, mmol/L 5.03 ± 0.85 4.80 ± 0.51 4.93 ± 0.71 5.07 ± 0.92 5.32 ± 1.08 <0.001 Creatinine, µmol/L 66.71 ± 9.05 65.98 ± 8.22 66.43 ± 8.21 66.87 ± 9.14 67.56 ± 10.38 <0.001 BUN, mmol/L 4.81 ± 1.22 4.83 ± 1.19 4.76 ± 1.19 4.78 ± 1.22 4.84 ± 1.25 0.15 TG, mmol/L 1.07 ± 0.46 0.61 ± 0.12 0.85 ± 0.14 1.13 ± 0.21 1.71 ± 0.35 <0.001 TC, mmol/L 4.56 ± 0.86 4.36 ± 0.77 4.48 ± 0.84 4.62 ± 0.89 4.79 ± 0.89 <0.001 HDL-c, mmol/L 1.51 ± 0.31 1.78 ± 0.25 1.58 ± 0.24 1.43 ± 0.23 1.21 ± 0.21 <0.001 LDL-c, mmol/L 2.81 ± 0.76 2.48 ± 0.66 2.71 ± 0.71 2.91 ± 0.77 3.09 ± 0.78 <0.001 RBC, ×10 9 /L 4.48 ± 0.33 4.41 ± 0.31 4.46 ± 0.32 4.51 ± 0.34 4.56 ± 0.35 <0.001 WBC, ×10 9 /L 5.89 ± 1.51 5.53 ± 1.36 5.82 ± 1.46 6.01 ± 1.52 6.21 ± 1.57 <0.001 Hb, g/L 132.93 ± 11.08 130.92 ± 10.91 132.26 ± 10.97 133.33 ± 11.06 135.21 ± 10.95 <0.001 PLT, ×10 9 /L 239.73 ± 62.41 235.46 ± 58.57 238.61 ± 61.34 241.78 ± 64.15 243.11 ± 65.06 <0.001 TP, g/L 73.66 ± 3.87 73.21 ± 3.79 73.58 ± 3.82 73.71 ± 3.81 74.16 ± 4.01 <0.001 Data presented as mean ± SD. BMI, body mass index;TC,total cholesterol;TG,triglycerides;HDL-c,high-density lipoprotein cholesterol;LDL-c, low-density lipoprotein cholesterol;TP,total protein;BUN,blood urea nitrogen;UA,serum uric acid;Hb, haemoglobin. Correlation between the TG/HDL-c ratio and HUA The univariate logistics analysis, as shown in Table 2 , reported that higher levels of the TG/HDL-c ratio were positively correlated with the presence of HUA [OR (95%CI) = 3.601(3.281,3.951), P < 0.001, Table 2 ]. In addition, age, blood glucose, TC, total protein, serum creatinine, urea, TG, LDL-c, RBC, and WBC are also risk factors for HUA ( P < 0.001), whereas HDL-c serves as a protective factor against the occurrence of HUA. Table 2 Univariate logistic regression analysis of HUA in the female population Variables Statistics of HUA Statistics of non-HUA Coefficient OR(95%Cl) P-value TG/HDL-c 1.12 ± 0.54 * 0.75 ± 0.45 1.281 3.601(3.281,3.951) P<0.001 Age, years 47.95 ± 18.52 * 42.32 ± 13.51 0.026 1.026(1.022,1.029) P<0.001 Glucose, mmol/L 5.31 ± 0.93 ** 5.01 ± 0.85 0.234 1.264(1.210,1.319) P<0.001 Creatinine, µmol/L 74.01 ± 14.46 * 66.21 ± 8.33 0.081 1.083(1.076,1.089) P<0.001 BUN, mmol/L 5.28 ± 1.58 * 4.77 ± 1.18 0.297 1.346(1.293,1.400) P<0.001 TG, mmol/L 1.41 ± 0.51 * 1.05 ± 0.45 1.368 3.931(3.453,4.353) P<0.001 TC, mmol/L 4.83 ± 0.94 * 4.55 ± 0.86 0.342 1.408(1.327,1.493) P<0.001 HDL-c, mmol/L 1.35 ± 0.29 * 1.51 ± 0.31 -1.864 0.155(0.127,0.188) P<0.001 LDL-c, mmol/L 3.09 ± 0.83 * 2.78 ± 0.76 0.481 1.617(1.514,1.725) P<0.001 WBC, ×10 9 /L 6.53 ± 1.74 * 5.85 ± 1.47 0.254 1.289(1.248,1.329) P<0.001 RBC, ×10 9 /L 4.59 ± 0.37 * 4.47 ± 0.33 0.991 2.694(2.297,3.161) P<0.001 Hb, g/L 136.52 ± 10.18 * 132.68 ± 11.11 0.036 1.037(1.031,1.042) P<0.001 PLT, ×10 9 /L 251.59 ± 69.31 * 238.92 ± 61.81 0.003 1.003(1.002,1.004) P<0.001 TP, g/L 75.03 ± 4.28 * 73.57 ± 3.82 0.094 1.099(1.084,1.114) P<0.001 * P < 0.001, versus non-hyperuricaemia group (Student’s t-test). Multivariate logistic regression analysis of TG/HDL-c with HUA We performed logistic regression analysis using both unadjusted and adjusted models to further demonstrate that the TG/HDL-c ratio is an independent predictor of elevated serum levels(Table 3 ). In the unadjusted model, the TG/HDL-c ratio was positively correlated with the incidence of hyperuricemia (OR (95%CI) = 3.601 (3.281, 3.951, P < 0.001). In the fully adjusted model, a higher TG/HDL-c ratio was still positively correlated with HUA prevalence (OR (95%CI) = 2.279(2.036, 2.549, P < 0.001). The Hosmer-Lemeshow test indicated a good model fit ( P = 0.168). Table 3 The multivariate logistic regression analysis on the relationship between TG/HDL-c and HUA among female participants. Variables Model1 Model2 Model3 Coefficient P-value OR(95%Cl) Coefficient P-value OR(95%Cl) Coefficient P-value OR(95%Cl) TG/HDL-c 1.281 <0.001 3.601(3.281,3.951) 0.913 <0.001 2.491(2.243,2.762) 0.824 <0.001 2.279(2.036,2.549) TG/HDL-c group Q1 n = 5332 Ref Ref Ref Q2 n = 5326 0.504 <0.001 1.65(1.303,2.112) 0.298 0.016 1.34(1.057,1.722) 0.218 0.084 1.244(0.999,1.633) Q3 n = 5330 1.17 <0.001 3.22(2.596,4.031) 0.751 <0.001 2.11(1.695,2.664) 0.608 <0.001 1.837(1.543,2.445) Q4 n = 5328 2.051 <0.001 7.77(6.359,9.592) 1.415 <0.001 4.11(3.326,5.134) 1.23 <0.001 3.423(2.881,4.499) TG/HDL-c<0.655 n = 10888 Ref Ref Ref TG/HDL-c ≥ 0.655 n = 10428 1.404 <0.001 4.074(3.571,4.659) 0.941 <0.001 2.562(2.227,2.953) 0.795 <0.001 2.216(1.913,2.570) p for trend <0.001 <0.001 <0.001 Model1, No adjustment for any potential influence factors. Model2, Adjusted for BMI,Age. Model3, Adjusted for BMI,Age,Creatinine,Glucose,Total protein,Total cholesterol,WBC and RBC. We also converted the TG/HDL-c ratio from a continuous variable into a categorical variable (quartiles). In the fully adjusted model, the results for the TG/HDL-c ratio as a categorical variable were consistent with those when the ratio was treated as a continuous variable. By comparing the OR values, we found that in both the unadjusted and adjusted models, the groups with medium-high TG/HDL-c ratios (Q3-Q4) had significantly higher risks of HUA compared to the low ratio groups (Q1-Q2) ( P for trend < 0.001). The multivariate logistic regression analysis on the relationship between TG/HDL-c and HUA among women across different age groups. In different age groups of female participants, we found a statistically significant association between higher TG/HDL-c levels and HUA( P < 0.05). Age < 30years(Q3,OR(95%CI) = 2.016(1.366,2.998);Q4,OR(95%CI) = 4.053(2.712,6.116);Age30-40years(Q3,OR(95%CI) = 1.669(1.068,2.653);Q4,OR(95%CI) = 3.783(2.447,5.977);Age40-50years(Q3,OR(95%CI) = 2.439(1.203,5.479);Q4,OR(95%CI) = 4.978(2.542,10.935);Age ≥ 50years(Q3,OR(95%CI) = 1.964(1.221,3.321);Q4,OR(95%CI) = 3.406(2.153,5.680)(Table 4 ). Table 4 The multivariate logistic regression results of the relationship between TG/HDL-C and HUA among women in different age groups. Variables Case/Total Q1 Q2 Q3 Q4 P-value OR(95%Cl) P-value OR(95%Cl) P-value OR(95%Cl) Age, years < 30 316/4626 Ref 0.248 1.265(0.849, 1.894) < 0.001 2.016(1.366, 2.998) < 0.001 4.053(2.712, 6.116) 30–40 282/6085 Ref 0.627 1.127(0.696, 1.842) 0.026 1.669(1.068, 2.653) < 0.001 3.783(2.447, 5.977) 40–50 150/4596 Ref 0.349 1.468(0.674, 3.445) 0.019 2.439(1.203, 5.479) < 0.001 4.978(2.542, 10.935) ≥ 50 619/6009 Ref 0.198 1.419(0.846, 2.476) 0.007 1.964(1.221, 3.321) < 0.001 3.406(2.153, 5.680) Adjusted for BMI,Age,Creatinine,Glucose,Total protein,Total cholesterol,WBC and RBC. RCS analysis The RCS was used to determine if there was a nonlinear association between TG/HDL-c and HUA in the female population. Among female participants, there was a nonlinear association between TG/HDL-c and HUA ( P < 0.001)(Fig. 2 ). The RCS curve for the TG/HDL-c index initially remained relatively stable, then rapidly increased when the TG/HDL-c index exceeded 0.655, showing a J-shaped association. After adjusting for multiple confounding factors and performing a threshold analysis with TG/HDL-c = 0.655 as the cutoff point, the risk of HUA in female participants with TG/HDL-c ≥ 0.655 was significantly higher than in those with TG/HDL-c < 0.655 (OR (95%CI) = 2.216(1.913,2.570), P < 0.001). Subgroup analysis and interaction of TG/HDL-c and HUA in female participants. After adjusting for multiple covariates, including age, BMI, creatinine, total protein, WBC, RBC, blood glucose, and total cholesterol, we found a significant interaction between TG/HDL-c and both BMI and creatinine levels, with statistical significance ( P < 0.05)(Table 5 ). Further subgroup analysis of TG/HDL-c with BMI and creatinine levels revealed that the association between TG/HDL-c and HUA was stronger among females with a BMI < 23 kg/m² (OR (95% CI) = 3.584 (2.989, 4.280), P < 0.001). Similarly, among females with serum creatinine levels < 70 µmol/L, the association between TG/HDL-c and HUA was also stronger (OR (95% CI) = 3.918 (3.407, 4.499), P 1, P < 0.001). Table 5 Subgroup Analysis and Interaction between TG/HDL-C and HUA in the Female Population. Variables OR(95%Cl) P -value P for interaction Age, years 0.167 < 30 4.207(3.421, 5.171) < 0.001 30–40 4.409(3.625, 5.354) < 0.001 40–50 3.309(2.537, 4.288) < 0.001 ≥ 50 2.832(2.425, 3.306) < 0.001 BMI, kg/m 2 < 0.001 < 23 3.584(2.989, 4.280) < 0.001 ≥ 23 2.555(2.268, 2.874) < 0.001 Glucose, mmol/l 0.286 < 6 3.414(3.083, 3.777) 6 2.953(2.245, 3.900) < 0.001 Creatinine, µmol/l 0.003 < 70 3.918(3.407, 4.499) < 0.001 ≥ 70 3.369(2.951, 3.845) < 0.001 RBC, ×10 9 /l 0.555 < 5 3.524(3.186, 3.982) < 0.001 ≥ 5 3.191(2.415, 4.224) < 0.001 Total protein, g/l 0.174 < 80 3.547(3.212, 3.915) < 0.001 ≥ 80 3.464(2.575, 4.673) < 0.001 WBC, ×10 9 /l 0.86 < 10 3.576(3.251, 3.931) < 0.001 ≥ 10 3.211(1.870, 5.629) < 0.001 Total cholesterol, mmol/l 0.165 < 5.2 3.461(3.105, 3.853) < 0.001 ≥ 5.2 3.768(3.108, 4.567) < 0.001 The ROC Curve of the TG/HDL-c Ratio for predicting the incidence of HUA in women The ROC curve for the TG/HDL-c ratio in predicting the incidence of HUA in women shows an AUC of 0.717 (95% CI: 0.703–0.731), indicating relatively good performance of this index in distinguishing whether female participants develop HUA. Based on the Youden index, the cut-off value is calculated as 0.832. At a TG/HDL-c ratio of 0.832, the sensitivity for predicting HUA in female participants is 67.9%, and the specificity is 65.5%(Fig. 3 ). Discussion In China, HUA has become a common metabolic disorder with significant gender differences. A study conducted in Eastern China in 2015 reported a prevalence rate of hyperuricemia among women of 5.6%, which is slightly higher than the national average and increases with age progressively[ 13 , 14 ]. In our study, the prevalence of hyperuricemia among women in the central Jiangsu region reached 6.41%. HUA may promote the development of atherosclerosis, and increase the risk of hypertension and coronary heart disease[ 15 ]. Therefore, we should investigate sensitive indicators to predict the occurrence of HUA in women in the central Jiangsu region. In our study, we found a significant non-linear association between the TG/HDL-c ratio and the risk of HUA, with the graph showing a J-shape, consistent with previous research results[ 16 , 17 ]. This suggests that we should pay attention to elevated TG/HDL-c ratios in women. TG/HDL-C reveals the disordered state of lipid metabolism, including abnormalities in cholesterol and triglyceride metabolism, which may lead to abnormal production and excretion of blood uric acid (UA). The mechanism might be that when fat synthesized in the body exceeds the metabolic system's capacity, fat accumulates in organs, such as the kidneys. This excessive fat deposition can trigger inflammation, increase oxidative stress, and ultimately contribute to the development of various diseases[ 6 ]. These changes can reduce the excretion of uric acid (UA), significantly increasing the risk of HUA. A cohort study also indicated [ 17 ] that after adjusting for diabetes, hypertension, obesity, stroke history, and heart disease history using a multivariate logistic regression model, the TG/HDL-C ratio was significantly associated with a decline in eGFR, suggesting that TG/HDL-C is an independent risk factor for the decline in eGFR[ 18 ]. Age is a significant factor influencing the prevalence of HUA in women. Based on the OR values for TG/HDL-c index Q3 and Q4, the association between TG/HDL-c and HUA is weaker in women over 50 years old compared to those under 50 years old. Estrogen plays a role in reducing the level of urate reabsorption transporters. After the age of 50, due to menopause, SUA levels rise sharply, likely due to the loss of estrogen’s uricosuric effect[ 19 ]. Additionally, during menopause, declining estrogen levels lead to an increase in TC, LDL-c, and triglyceride (TG) levels, while HDL-c levels decrease [ 20 ]. Beyond the role of estrogen, aging lowers the metabolic rate, potentially altering lipid metabolism patterns. Older adults may also be more prone to insulin resistance and renal dysfunction, both of which can affect serum uric acid levels, thereby altering the strength of the association between the TG/HDL-C ratio and HUA[ 21 ]. In the BMI subgroup of this study, we found that female participants with normal or underweight BMI (< 23) were more strongly associated with the prevalence of HUA. A previous large-scale retrospective study based on a Chinese population indicated a positive correlation between the TG/HDL-C ratio and the risk of HUA, particularly among women and individuals with normal body weight [ 22 ]. Our study also found that, in the female population, being overweight and related factors weakened the association between TG/HDL-C and HUA. Additionally, a study on Hispanic children suggested [ 23 ] that changes in serum uric acid levels are largely influenced by genetic factors, such as genetic variations in the SLC2A9 gene. These genetic variations are closely linked to phenotypic traits associated with obesity. Furthermore, other studies have found that the metabolically healthy obesity (MHO) phenotype is significantly associated with the risk of HUA only among women, not men [ 24 ]. MHO refers to individuals who meet the obesity criteria with a high BMI but do not exhibit related metabolic abnormalities, such as hypertension, dyslipidemia, or insulin resistance[ 25 ]. These studies may help explain why TG/HDL-C serves as an important biomarker for assessing the risk of HUA in women, especially those with normal body weight. In the serum creatinine subgroup of this study, we found a statistically significant interaction between creatinine levels and the TG/HDL-c ratio in influencing the prevalence of HUA among women. Metabolic disorders in the lipid profile may contribute to the development of chronic kidney disease through various mechanisms[ 26 ]. Firstly, hypertriglyceridemia and insulin resistance may lead to increased renal lipid uptake, promoting the development of glomerular fibrosis[ 27 ]. Secondly, hyperlipidemia can trigger inflammatory responses and oxidative stress, both of which can damage renal cells and impair kidney function[ 28 ]. Recent studies[ 29 ] have shown that the Nrf2/ARE signaling pathway mitigates lipid-induced renal damage through its antioxidant and anti-inflammatory effects by downregulating mtROS-mediated NLRP3 inflammasome activation. A prospective study of middle-aged and elderly Chinese individuals also indicated that the TG/HDL-C ratio has a strong independent association with an increased risk of chronic kidney disease (CKD) in the Chinese population and is a better predictor than TG levels alone. These studies may illustrate that in populations with lower creatinine levels (Creatinine < 70µmol/L), the TG/HDL-c ratio better reflects the risk of HUA[ 30 ]. This study still has limitations. It does not explain the causal relationship between changes in the TG/HDL-c ratio and HUA. Secondly, selection bias is inevitable in a single-center study. Moreover, our study did not collect data on other factors that may influence SUA levels, for example drinking and smoking history, dietary habits, menopausal status as well as concomitant medication. Conclusion The studies involving humans were approved by The Ethics Committee of Northern Jiangsu People’s Hospital. The studies were conducted in accordance with the Declaration of Helsinki. Due to the absence of personal identifiers in the database and the retrospective, observational nature of the study design, the requirement for informed consent was waived. Declarations Ethics statement The studies involving humans were approved by The Ethics Committee of Northern Jiangsu People’s Hospital. The studies were conducted in accordance with the Declaration of Helsinki. Due to the absence of personal identifiers in the database and the retrospective, observational nature of the study design, the requirement for informed consent was waived. Acknowledgments We thank the medical staff at the Northern Jiangsu People’s Hospital for support in this study. Author Contributions YZ designed and wrote the article ; H L, S Z and J Ycollected the clinical data . Declaration of conflicting interest The authors declare that there is no conflict of interest. Funding information This work was supported by The National Natural Science Foundation of China (81800250), China Postdoctoral Science Foundation (2022M711417), Jiangsu Province Traditional Chinese Medicine Project (MS2023137), Yangzhou Science and Technology Plan Social Development Project (YZ2023096), Clinical Trials from the Northern Jiangsu People’s Hospital(SBLC23002). Data sharing statement The data are available from the corresponding author upon reasonable request. References Yuanyuan Q, Yunhua H, Qingyun C, Min G, Lujie Z, Peng W, Lin F: The prevalence of hyperuricemia and its correlates in Zhuang nationality, Nanning, Guangxi Province . J Clin Lab Anal 2022, 36 (11):e24711. He W, Yin L, Liu Q, Zhang Y, Zhao Y, Wang L, You L: Influencing factors and predictive model for left atrial appendage emptying velocity in nonvalvular AF patients . Front Cardiovasc Med 2024, 11 :1468379. Lee CK, Chen IC, Lin HJ, Lin CH, Chen YM: Association of the ABCG2 rs2231142 variant with the Framingham Cardiovascular Disease Risk score in the Taiwanese population . Heliyon 2024, 10 (18):e37839. Peng H, Zhang K, Zhang C, Gao J: Knowledge, attitude, and practice toward hyperuricemia among healthcare workers in Shandong, China . PeerJ 2024, 12 :e17926. Zhang Y, Ban C, Su D, Liu Y, Zhou S, Fan J: Xanthine oxidase inhibitors: Virtual screening and mechanism of inhibition studies . Int J Biol Macromol 2024, 281 (Pt 1):136281. Han Y, Zhou Z, Zhang Y, Zhao G, Xu B: The Association of Surrogates of Insulin Resistance with Hyperuricemia among Middle-Aged and Older Individuals: A Population-Based Nationwide Cohort Study . Nutrients 2023, 15 (14). Chang CH, Lin MS, Lin YC, Huang TJ, Chen MY: A novel nomogram for predicting cardiometabolic diseases from modifiable risks in middle-aged adults-implication for health education . Front Endocrinol (Lausanne) 2023, 14 :1291741. Lu X, Wang A, Liu K, Chen Y, Chen W, Mao Y, Ye D: Associations of Dietary Magnesium Intake with All-Cause and Cause-Specific Mortality Among Individuals with Gout and Hyperuricemia . Biol Trace Elem Res 2024. Cao C, Cai D, Liu H, Zhang X, Cai L, Sun C, Wang H, Zhao H, Yue C: Causal relationship between genetic-predicted uric acid and cervical cancer risk: evidence for nutritional intervention on cervical cancer prevention . Front Nutr 2024, 11 :1464046. Wang H, Zheng Y, Yang M, Wang L, Xu Y, You S, Mao N, Fan J, Ren S: Gut microecology: effective targets for natural products to modulate uric acid metabolism . Front Pharmacol 2024, 15 :1446776. Xu X, He M, Zhao G, Liu X, Liu X, Xu H, Cheng Y, Jiang Y, Peng Q, Shi J et al : The Association of Dietary Diversity with Hyperuricemia among Community Inhabitants in Shanghai, China: A Prospective Research . Nutrients 2024, 16 (17). Wakabayashi I, Daimon T: Hematometabolic Index as a New Discriminator of Cardiometabolic Risk in Middle-Aged Men with Polycythemia and High Leukocyte Count in Peripheral Blood . Metab Syndr Relat Disord 2023, 21 (5):267-274. Chao TH, Lin TH, Cheng CI, Wu YW, Ueng KC, Wu YJ, Lin WW, Leu HB, Cheng HM, Huang CC et al : 2024 Guidelines of the Taiwan Society of Cardiology on the Primary Prevention of Atherosclerotic Cardiovascular Disease --- Part I . Acta Cardiol Sin 2024, 40 (5):479-543. Zhang M, Zhu X, Wu J, Huang Z, Zhao Z, Zhang X, Xue Y, Wan W, Li C, Zhang W et al : Prevalence of Hyperuricemia Among Chinese Adults: Findings From Two Nationally Representative Cross-Sectional Surveys in 2015-16 and 2018-19 . Front Immunol 2021, 12 :791983. Zhang Y, Zhu S, Gu Y, Feng Y, Gao B: Network Pharmacology Combined with Experimental Validation to Investigate the Mechanism of the Anti-Hyperuricemia Action of Portulaca oleracea Extract . Nutrients 2024, 16 (20). Oliveri A, Rebernick RJ, Kuppa A, Pant A, Chen Y, Du X, Cushing KC, Bell HN, Raut C, Prabhu P et al : Comprehensive genetic study of the insulin resistance marker TG:HDL-C in the UK Biobank . Nat Genet 2024, 56 (2):212-221. Zhang Y, Zhang M, Yu X, Wei F, Chen C, Zhang K, Feng S, Wang Y, Li WD: Association of hypertension and hypertriglyceridemia on incident hyperuricemia: an 8-year prospective cohort study . J Transl Med 2020, 18 (1):409. Elías-López D, Vedel-Krogh S, Kobylecki CJ, Wadström BN, Nordestgaard BG: Impaired Renal Function With Higher Remnant Cholesterol Related to Risk of Atherosclerotic Cardiovascular Disease: CGPS . Arterioscler Thromb Vasc Biol 2024. Zitt E, Fischer A, Lhotta K, Concin H, Nagel G: Sex- and age-specific variations, temporal trends and metabolic determinants of serum uric acid concentrations in a large population-based Austrian cohort . Sci Rep 2020, 10 (1):7578. Anagnostis P, Bitzer J, Cano A, Ceausu I, Chedraui P, Durmusoglu F, Erkkola R, Goulis DG, Hirschberg AL, Kiesel L et al : Menopause symptom management in women with dyslipidemias: An EMAS clinical guide . Maturitas 2020, 135 :82-88. Zuo YQ, Gao ZH, Yin YL, Yang X, Guan X, Feng PY: Insulin Resistance Surrogates May Predict HTN-HUA in Young, Non-Obese Individuals . Diabetes Metab Syndr Obes 2024, 17 :3593-3601. Liu XY, Wu QY, Chen ZH, Yan GY, Lu Y, Dai HJ, Li Y, Yang PT, Yuan H: Elevated triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio increased risk of hyperuricemia: a 4-year cohort study in China . Endocrine 2020, 68 (1):71-80. Voruganti VS, Laston S, Haack K, Mehta NR, Cole SA, Butte NF, Comuzzie AG: Serum uric acid concentrations and SLC2A9 genetic variation in Hispanic children: the Viva La Familia Study . Am J Clin Nutr 2015, 101 (4):725-732. Tian S, Liu Y, Feng A, Zhang S: Sex-Specific Differences in the Association of Metabolically Healthy Obesity With Hyperuricemia and a Network Perspective in Analyzing Factors Related to Hyperuricemia . Front Endocrinol (Lausanne) 2020, 11 :573452. Zheng X, Tian C, Xu G, Du D, Zhang N, Wang J, Sang Q, Wuyun Q, Chen W, Lian D et al : Prevalence, Risk Factors, and Metabolic Characteristics of Metabolically Healthy Obesity in Patients Seeking Bariatric Surgery: A Cohort Study . Am Surg 2024, 90 (6):1456-1462. Śledziński M, Gołębiewska J, Mika A: The Long-Term Effect of Kidney Transplantation on the Serum Fatty Acid Profile . Nutrients 2024, 16 (19). Han B, Wang N, Chen Y, Li Q, Zhu C, Chen Y, Lu Y: Prevalence of hyperuricaemia in an Eastern Chinese population: a cross-sectional study . BMJ Open 2020, 10 (5):e035614. Mochel JP, Ward JL, Blondel T, Kundu D, Merodio MM, Zemirline C, Guillot E, Giebelhaus RT, de la Mata P, Iennarella-Servantez CA et al : Preclinical modeling of metabolic syndrome to study the pleiotropic effects of novel antidiabetic therapy independent of obesity . Sci Rep 2024, 14 (1):20665. Jiang XS, Liu T, Xia YF, Gan H, Ren W, Du XG: Activation of the Nrf2/ARE signaling pathway ameliorates hyperlipidemia-induced renal tubular epithelial cell injury by inhibiting mtROS-mediated NLRP3 inflammasome activation . Front Immunol 2024, 15 :1342350. Liao S, Lin D, Feng Q, Li F, Qi Y, Feng W, Yang C, Yan L, Ren M, Sun K: Lipid Parameters and the Development of Chronic Kidney Disease: A Prospective Cohort Study in Middle-Aged and Elderly Chinese Individuals . Nutrients 2022, 15 (1). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 20 May, 2026 Reviewers invited by journal 27 Nov, 2024 Editor invited by journal 28 Oct, 2024 Editor assigned by journal 28 Oct, 2024 Submission checks completed at journal 28 Oct, 2024 First submitted to journal 27 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5341007","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":372622772,"identity":"5f71c10e-0b84-4f11-9722-6f93d8cbbcc9","order_by":0,"name":"Shuai Zhang","email":"","orcid":"","institution":"Northern Jiangsu People's Hospital Affiliated to Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Zhang","suffix":""},{"id":372622773,"identity":"a635443b-425b-4242-bbac-be5b1b63f064","order_by":1,"name":"Hao Liang","email":"","orcid":"","institution":"Northern Jiangsu People's Hospital Affiliated to Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Liang","suffix":""},{"id":372622774,"identity":"b6731220-8410-4890-8f10-f4df377e97a8","order_by":2,"name":"Jia You","email":"","orcid":"","institution":"Yangzhou Maternal and Child Health Care Hospital Affiliated to Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"You","suffix":""},{"id":372622775,"identity":"916b61c2-39b9-4ada-b96c-5f308ba9b831","order_by":3,"name":"Ye Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBACeWb+j4//GNTYsbE3EKnFsL3B2ICn4FgyH88BYq05c8BMgucDM+M8iQQidTDOSEiTkDBgY2aTfLzxBkONTTRBLewSCYctDAxk+Nik04otGI6l5TYQtiWx8UYCyBbpHDMJxobDhLUw3EhmkDhgwMzYJnmGWC1njjFJNoC0SPAQqcWwvYfZmMHgWDIbD9AvCcT4RZ6Zh/Exw58aO/n2wxtvfKixIcJhSMCA6KhB0kKqjlEwCkbBKBgZAADbpDiZLn/+4wAAAABJRU5ErkJggg==","orcid":"","institution":"Northern Jiangsu People's Hospital Affiliated to Yangzhou University","correspondingAuthor":true,"prefix":"","firstName":"Ye","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2024-10-27 11:23:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5341007/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5341007/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69889442,"identity":"e3446aaa-13a3-4951-99b9-0f5ba327016b","added_by":"auto","created_at":"2024-11-26 10:09:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":112012,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of HUA patients in Q1 TG/HDL-c\u0026lt;0.441 mmol/L, Q2 TG/HDL-c 0.441~0.645 mmol/L, Q3 TG/HDL-c 0.645~0.992 mmol/L, and Q4 TG/HDL-c \u0026gt;0.992 mmol/L. The numbers on the columns represent the number of HUA patients in each group (*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5341007/v1/cbdb6f10c10a052b5bac2f86.png"},{"id":69889443,"identity":"5fd23340-20f7-4109-b2fe-9e26799e6fbe","added_by":"auto","created_at":"2024-11-26 10:09:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":120115,"visible":true,"origin":"","legend":"\u003cp\u003eThe dose-response curve of TG/HDL-C and HUA in the female population.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5341007/v1/6705fc629d59f7c46cd3360a.png"},{"id":69889441,"identity":"b2a7e482-2ba7-460f-8997-4c2bf4b1c9eb","added_by":"auto","created_at":"2024-11-26 10:09:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":168522,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of the TG/HDL-c ratio for predicting the incidence of HUA in women.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5341007/v1/ad209a961aa3324f9acd7d78.png"},{"id":69889801,"identity":"02399d5b-0dfa-4f9f-87d5-f8ebcaeca8a0","added_by":"auto","created_at":"2024-11-26 10:17:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2192581,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5341007/v1/cd620419-ec6f-452f-9d87-f212b0d39a9a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between the TG/HDL-c Ratio and Hyperuricemia in Women participants in China: A Cross-sectional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn China, hyperuricemia(HUA) has become a common metabolic disease with a significant gender disparity. The occurrence of HUA ranges from 13.3% to 21.6%, with noticeable differences by gender and across various countries or regions, potentially driven by factors like environmental and economic circumstances[1, 2]. Although multiple epidemiological studies have explored the link between serum uric acid (SUA) levels and cardiovascular diseases such as coronary artery disease, hypertension, metabolic syndrome(MetS) and so on. However, these associations have not been uniformly observed across all studies, as shown by the Framingham Heart Study[3]. Research conducted in China reported a rise in the prevalence of hyperuricemia in women from 5.8% in 2010 to 10.1% in 2019[4]. We should attempt to study certain sensitive indicators to predict the occurrence of hyperuricemia among women in the central Jiangsu region.\u003c/p\u003e\n\u003cp\u003eNumerous indicators are identified as independent risk factors for the development of HUA[5]. The triglyceride-to-high-density lipoprotein cholesterol (TG/HDL-c) ratio is an emerging marker of lipid abnormalities, providing a reliable indicator for MetS, insulin resistance and cardiovascular events. A study based on individuals aged 45 and older in China indicates that the TG/HDL-c ratio is positively associated with the risk of HUA in middle-aged and elderly populations, with a stronger predictive power observed in females[6, 7]. However, the association between the TG/HDL-c ratio and the risk of developing HUA in females remains limited[8]. In this study, we aimed to investigate the correlation between the TG/HDL-c ratio and HUA occurrence of female participants. These findings provide valuable insights for the auxiliary diagnosis and prevention of HUA in the local population.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe study population for this cross-sectional study comprised female adults who underwent routine health check-ups at Northern Jiangsu People\u0026rsquo;s Hospital between July 2014 and August 2023 in Yangzhou. According to the hospital\u0026rsquo;s standard procedures, participants provided their medical history, underwent blood tests and had abdominal ultrasounds. For research purpose, participants were required to meet the following criteria: females aged 18 years or older who had resided in the selected communities for at least six months before the survey.\u003c/p\u003e \u003cp\u003eExclusion criteria included: (1) mental or cognitive disorders such as dementia or impaired comprehension; (2) physical disabilities like paraplegia; (3) individuals undergoing urate-lowering therapy.\u003c/p\u003e \u003cp\u003e Given the retrospective and cross-sectional nature of the study, it was granted an exemption from ethics board approval by the Ethics Committee at Northern Jiangsu People's Hospital.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cp\u003eA comprehensive physical examination was performed, which included parameters such as age, height, weight, gender, fasting blood glucose(GLU), serum uric acid (SUA), serum creatinine(Scr), triglyceride(TG), blood urea nitrogen (BUN), total cholesterol(TC), high-density lipoprotein (HDL-c), low-density lipoprotein(LDL-c), red blood cells (RBCs), white blood cells (WBCs), hemoglobin (Hb) and platelets (PLTs). Blood samples were obtained and processed following accredited laboratory protocols.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDefinition of Cohort\u003c/h3\u003e\n\u003cp\u003eThe diagnostic criterion for HUA was an SUA level above 360\u0026micro;mol/L in women[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Standardized methods were employed to determine both weight and height, from which the Body mass index(BMI) was calculated by dividing the weight(kg) by the squared height (m)[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The TG/HDL-c ratio was calculated by dividing the TG level by the HDL-c level, and the TG/HDL-c ratio was grouped according to the interquartile range (IQR), which converted TG/HDL-c from a continuous variable to a categorical variable (Q1, Q2, Q3, and Q4)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData analysis was carried out using R version 4.0.3 (R Foundation for Statistical Computing) and SPSS version 27.0. Normally distributed variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and compared using Student\u0026rsquo;s t tests. Categorical data were presented as frequencies and percentages with the Chi-square test or Fisher\u0026rsquo;s exact test. Comparisons between multiple groups were made using one-way ANOVA. We used univariate and multivariate logistic regression analyses, restricted cubic spline(RCS) regression analysis, subgroup analysis and interaction analysis to analyze the relationship between the TG/HDL-c ratio and serum uric acid levels. RCS plots were used to test for nonlinear association between TG/HDL-c and HUA in the female population. The area under the receiver operating characteristic (ROC) curve was used to assess TG/HDL-c in predicting the occurrence of HUA among female participants. Statistical significance was determined as a threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eClinic characteristics of the participants\u003c/h2\u003e \u003cp\u003eThe study enrolled a total of 21316 female participants and the average age was 42.68\u0026thinsp;\u0026plusmn;\u0026thinsp;13.96 years, with a mean SUA level of 265.34\u0026thinsp;\u0026plusmn;\u0026thinsp;59.72 umol/L. The overall prevalence of HUA was 6.41%. Participants were categorized into four groups based on the values of the TG/HDL-c ratio (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The statistical analysis revealed significant differences in UA, TG, and HDL-c levels among the four groups. Specifically, the UA and TG levels exhibited a significant increase with higher TG/HDL-c ratios, while the HDL-c levels showed a significant decrease (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Further analysis demonstrated a positive association between the prevalence of HUA and increasing TG/HDL-c ratios(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Moreover, There are significant differences in age, BMI, blood glucose levels, serum creatinine, TC, LDL-c, WBCs, RBCs, Hb, PLTs, and total protein among females across different TG/HDL-c ratios (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, no statistically significant difference was observed in BUN across different TG/HDL-c groups.\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 Data of Female 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eTG/HDL-c ratio (mmol/L) (quartiles)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1(\u0026lt;\u0026thinsp;0.441)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2(0.441\u0026ndash;0.645)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3(0.645\u0026ndash;0.992)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4(\u0026gt;\u0026thinsp;0.992)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5328\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\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.68\u0026thinsp;\u0026plusmn;\u0026thinsp;13.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.33\u0026thinsp;\u0026plusmn;\u0026thinsp;11.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.55\u0026thinsp;\u0026plusmn;\u0026thinsp;13.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.25\u0026thinsp;\u0026plusmn;\u0026thinsp;14.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.59\u0026thinsp;\u0026plusmn;\u0026thinsp;14.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.58\u0026thinsp;\u0026plusmn;\u0026thinsp;3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.04\u0026thinsp;\u0026plusmn;\u0026thinsp;2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.97\u0026thinsp;\u0026plusmn;\u0026thinsp;2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.01\u0026thinsp;\u0026plusmn;\u0026thinsp;3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.31\u0026thinsp;\u0026plusmn;\u0026thinsp;3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e265.34\u0026thinsp;\u0026plusmn;\u0026thinsp;59.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e245.08\u0026thinsp;\u0026plusmn;\u0026thinsp;50.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e254.71\u0026thinsp;\u0026plusmn;\u0026thinsp;53.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e268.15\u0026thinsp;\u0026plusmn;\u0026thinsp;57.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e293.43\u0026thinsp;\u0026plusmn;\u0026thinsp;65.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHUA, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1367(6.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109(2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e178(3.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e336(6.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e744(14.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.71\u0026thinsp;\u0026plusmn;\u0026thinsp;9.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.98\u0026thinsp;\u0026plusmn;\u0026thinsp;8.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.43\u0026thinsp;\u0026plusmn;\u0026thinsp;8.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.87\u0026thinsp;\u0026plusmn;\u0026thinsp;9.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.56\u0026thinsp;\u0026plusmn;\u0026thinsp;10.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.81\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-c, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-c, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132.93\u0026thinsp;\u0026plusmn;\u0026thinsp;11.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130.92\u0026thinsp;\u0026plusmn;\u0026thinsp;10.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132.26\u0026thinsp;\u0026plusmn;\u0026thinsp;10.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e133.33\u0026thinsp;\u0026plusmn;\u0026thinsp;11.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e135.21\u0026thinsp;\u0026plusmn;\u0026thinsp;10.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e239.73\u0026thinsp;\u0026plusmn;\u0026thinsp;62.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e235.46\u0026thinsp;\u0026plusmn;\u0026thinsp;58.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e238.61\u0026thinsp;\u0026plusmn;\u0026thinsp;61.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e241.78\u0026thinsp;\u0026plusmn;\u0026thinsp;64.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e243.11\u0026thinsp;\u0026plusmn;\u0026thinsp;65.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.66\u0026thinsp;\u0026plusmn;\u0026thinsp;3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.21\u0026thinsp;\u0026plusmn;\u0026thinsp;3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.58\u0026thinsp;\u0026plusmn;\u0026thinsp;3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73.71\u0026thinsp;\u0026plusmn;\u0026thinsp;3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74.16\u0026thinsp;\u0026plusmn;\u0026thinsp;4.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eData presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eBMI, body mass index;TC,total cholesterol;TG,triglycerides;HDL-c,high-density lipoprotein cholesterol;LDL-c, low-density lipoprotein cholesterol;TP,total protein;BUN,blood urea nitrogen;UA,serum uric acid;Hb, haemoglobin.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between the TG/HDL-c ratio and HUA\u003c/h2\u003e \u003cp\u003eThe univariate logistics analysis, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, reported that higher levels of the TG/HDL-c ratio were positively correlated with the presence of HUA [OR (95%CI)\u0026thinsp;=\u0026thinsp;3.601(3.281,3.951), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e]. In addition, age, blood glucose, TC, total protein, serum creatinine, urea, TG, LDL-c, RBC, and WBC are also risk factors for HUA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas HDL-c serves as a protective factor against the occurrence of HUA.\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\u003eUnivariate logistic regression analysis of HUA in the female population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\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\u003eStatistics of HUA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStatistics of non-HUA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR(95%Cl)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\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\u003eTG/HDL-c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.601(3.281,3.951)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e \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=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e47.95\u0026thinsp;\u0026plusmn;\u0026thinsp;18.52\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e42.32\u0026thinsp;\u0026plusmn;\u0026thinsp;13.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.026(1.022,1.029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.264(1.210,1.319)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e74.01\u0026thinsp;\u0026plusmn;\u0026thinsp;14.46\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e66.21\u0026thinsp;\u0026plusmn;\u0026thinsp;8.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.083(1.076,1.089)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.346(1.293,1.400)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.931(3.453,4.353)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.408(1.327,1.493)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-c, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.155(0.127,0.188)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-c, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.617(1.514,1.725)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.289(1.248,1.329)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.694(2.297,3.161)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e136.52\u0026thinsp;\u0026plusmn;\u0026thinsp;10.18\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e132.68\u0026thinsp;\u0026plusmn;\u0026thinsp;11.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.037(1.031,1.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e251.59\u0026thinsp;\u0026plusmn;\u0026thinsp;69.31\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e238.92\u0026thinsp;\u0026plusmn;\u0026thinsp;61.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.003(1.002,1.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e75.03\u0026thinsp;\u0026plusmn;\u0026thinsp;4.28\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e73.57\u0026thinsp;\u0026plusmn;\u0026thinsp;3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.099(1.084,1.114)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e*\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, versus non-hyperuricaemia group (Student\u0026rsquo;s t-test).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMultivariate logistic regression analysis of TG/HDL-c with HUA\u003c/h3\u003e\n\u003cp\u003eWe performed logistic regression analysis using both unadjusted and adjusted models to further demonstrate that the TG/HDL-c ratio is an independent predictor of elevated serum levels(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the unadjusted model, the TG/HDL-c ratio was positively correlated with the incidence of hyperuricemia (OR (95%CI)\u0026thinsp;=\u0026thinsp;3.601 (3.281, 3.951, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the fully adjusted model, a higher TG/HDL-c ratio was still positively correlated with HUA prevalence (OR (95%CI)\u0026thinsp;=\u0026thinsp;2.279(2.036, 2.549, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The Hosmer-Lemeshow test indicated a good model fit (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.168).\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\u003eThe multivariate logistic regression analysis on the relationship between TG/HDL-c and HUA among female participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR(95%Cl)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR(95%Cl)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOR(95%Cl)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTG/HDL-c\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.281\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.601(3.281,3.951)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.491(2.243,2.762)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.279(2.036,2.549)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTG/HDL-c group\u003c/b\u003e\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003en\u0026thinsp;=\u0026thinsp;5332\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003en\u0026thinsp;=\u0026thinsp;5326\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.504\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.65(1.303,2.112)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.298\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.34(1.057,1.722)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.218\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.084\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.244(0.999,1.633)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003en\u0026thinsp;=\u0026thinsp;5330\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.22(2.596,4.031)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.751\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2.11(1.695,2.664)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.608\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.837(1.543,2.445)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003en\u0026thinsp;=\u0026thinsp;5328\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.051\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e7.77(6.359,9.592)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.415\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e4.11(3.326,5.134)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e3.423(2.881,4.499)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTG/HDL-c\u0026lt;0.655\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003en\u0026thinsp;=\u0026thinsp;10888\u003c/b\u003e\u003c/p\u003e \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\u003cb\u003eRef\u003c/b\u003e\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 \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTG/HDL-c\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;0.655\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003en\u0026thinsp;=\u0026thinsp;10428\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.404\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4.074(3.571,4.659)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.941\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2.562(2.227,2.953)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.795\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e2.216(1.913,2.570)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ep for trend\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eModel1, No adjustment for any potential influence factors.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eModel2, Adjusted for BMI,Age.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eModel3, Adjusted for BMI,Age,Creatinine,Glucose,Total protein,Total cholesterol,WBC and RBC.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe also converted the TG/HDL-c ratio from a continuous variable into a categorical variable (quartiles). In the fully adjusted model, the results for the TG/HDL-c ratio as a categorical variable were consistent with those when the ratio was treated as a continuous variable. By comparing the OR values, we found that in both the unadjusted and adjusted models, the groups with medium-high TG/HDL-c ratios (Q3-Q4) had significantly higher risks of HUA compared to the low ratio groups (Q1-Q2) (\u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe multivariate logistic regression analysis on the relationship between TG/HDL-c and HUA among women across different age groups.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn different age groups of female participants, we found a statistically significant association between higher TG/HDL-c levels and HUA(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Age\u0026thinsp;\u0026lt;\u0026thinsp;30years(Q3,OR(95%CI)\u0026thinsp;=\u0026thinsp;2.016(1.366,2.998);Q4,OR(95%CI)\u0026thinsp;=\u0026thinsp;4.053(2.712,6.116);Age30-40years(Q3,OR(95%CI)\u0026thinsp;=\u0026thinsp;1.669(1.068,2.653);Q4,OR(95%CI)\u0026thinsp;=\u0026thinsp;3.783(2.447,5.977);Age40-50years(Q3,OR(95%CI)\u0026thinsp;=\u0026thinsp;2.439(1.203,5.479);Q4,OR(95%CI)\u0026thinsp;=\u0026thinsp;4.978(2.542,10.935);Age\u0026thinsp;\u0026ge;\u0026thinsp;50years(Q3,OR(95%CI)\u0026thinsp;=\u0026thinsp;1.964(1.221,3.321);Q4,OR(95%CI)\u0026thinsp;=\u0026thinsp;3.406(2.153,5.680)(Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe multivariate logistic regression results of the relationship between TG/HDL-C and HUA among women in different age groups.\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\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase/Total\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eQ4\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR(95%Cl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR(95%Cl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOR(95%Cl)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"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\u003e\u0026lt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e316/4626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.265(0.849, 1.894)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.016(1.366, 2.998)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.053(2.712, 6.116)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e282/6085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.127(0.696, 1.842)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.669(1.068, 2.653)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.783(2.447, 5.977)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150/4596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.468(0.674, 3.445)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.439(1.203, 5.479)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.978(2.542, 10.935)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e619/6009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.419(0.846, 2.476)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.964(1.221, 3.321)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.406(2.153, 5.680)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eAdjusted for BMI,Age,Creatinine,Glucose,Total protein,Total cholesterol,WBC and RBC.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eRCS analysis\u003c/h3\u003e\n\u003cp\u003eThe RCS was used to determine if there was a nonlinear association between TG/HDL-c and HUA in the female population. Among female participants, there was a nonlinear association between TG/HDL-c and HUA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001)(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The RCS curve for the TG/HDL-c index initially remained relatively stable, then rapidly increased when the TG/HDL-c index exceeded 0.655, showing a J-shaped association. After adjusting for multiple confounding factors and performing a threshold analysis with TG/HDL-c\u0026thinsp;=\u0026thinsp;0.655 as the cutoff point, the risk of HUA in female participants with TG/HDL-c\u0026thinsp;\u0026ge;\u0026thinsp;0.655 was significantly higher than in those with TG/HDL-c\u0026thinsp;\u0026lt;\u0026thinsp;0.655 (OR (95%CI)\u0026thinsp;=\u0026thinsp;2.216(1.913,2.570), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSubgroup analysis and interaction of TG/HDL-c and HUA in female participants.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAfter adjusting for multiple covariates, including age, BMI, creatinine, total protein, WBC, RBC, blood glucose, and total cholesterol, we found a significant interaction between TG/HDL-c and both BMI and creatinine levels, with statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)(Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Further subgroup analysis of TG/HDL-c with BMI and creatinine levels revealed that the association between TG/HDL-c and HUA was stronger among females with a BMI\u0026thinsp;\u0026lt;\u0026thinsp;23 kg/m\u0026sup2; (OR (95% CI)\u0026thinsp;=\u0026thinsp;3.584 (2.989, 4.280), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, among females with serum creatinine levels\u0026thinsp;\u0026lt;\u0026thinsp;70 \u0026micro;mol/L, the association between TG/HDL-c and HUA was also stronger (OR (95% CI)\u0026thinsp;=\u0026thinsp;3.918 (3.407, 4.499), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Moreover, a significant positive correlation between TG/HDL-c and HUA was consistently observed across all groups (OR\u0026thinsp;\u0026gt;\u0026thinsp;1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSubgroup Analysis and Interaction between TG/HDL-C and HUA in the Female Population.\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=\"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 \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\u003eOR(95%Cl)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.207(3.421, 5.171)\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.409(3.625, 5.354)\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.309(2.537, 4.288)\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.832(2.425, 3.306)\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.584(2.989, 4.280)\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.555(2.268, 2.874)\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose, mmol/l\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 \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.414(3.083, 3.777)\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.953(2.245, 3.900)\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine, \u0026micro;mol/l\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 \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.918(3.407, 4.499)\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.369(2.951, 3.845)\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/l\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 \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.524(3.186, 3.982)\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.191(2.415, 4.224)\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal protein, g/l\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 \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.547(3.212, 3.915)\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.464(2.575, 4.673)\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/l\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 \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.576(3.251, 3.931)\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.211(1.870, 5.629)\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol, mmol/l\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 \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.461(3.105, 3.853)\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.768(3.108, 4.567)\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe ROC Curve of the TG/HDL-c Ratio for predicting the incidence of HUA in women\u003c/h2\u003e \u003cp\u003eThe ROC curve for the TG/HDL-c ratio in predicting the incidence of HUA in women shows an AUC of 0.717 (95% CI: 0.703\u0026ndash;0.731), indicating relatively good performance of this index in distinguishing whether female participants develop HUA. Based on the Youden index, the cut-off value is calculated as 0.832. At a TG/HDL-c ratio of 0.832, the sensitivity for predicting HUA in female participants is 67.9%, and the specificity is 65.5%(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn China, HUA has become a common metabolic disorder with significant gender differences. A study conducted in Eastern China in 2015 reported a prevalence rate of hyperuricemia among women of 5.6%, which is slightly higher than the national average and increases with age progressively[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In our study, the prevalence of hyperuricemia among women in the central Jiangsu region reached 6.41%. HUA may promote the development of atherosclerosis, and increase the risk of hypertension and coronary heart disease[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, we should investigate sensitive indicators to predict the occurrence of HUA in women in the central Jiangsu region.\u003c/p\u003e \u003cp\u003eIn our study, we found a significant non-linear association between the TG/HDL-c ratio and the risk of HUA, with the graph showing a J-shape, consistent with previous research results[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This suggests that we should pay attention to elevated TG/HDL-c ratios in women. TG/HDL-C reveals the disordered state of lipid metabolism, including abnormalities in cholesterol and triglyceride metabolism, which may lead to abnormal production and excretion of blood uric acid (UA). The mechanism might be that when fat synthesized in the body exceeds the metabolic system's capacity, fat accumulates in organs, such as the kidneys. This excessive fat deposition can trigger inflammation, increase oxidative stress, and ultimately contribute to the development of various diseases[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These changes can reduce the excretion of uric acid (UA), significantly increasing the risk of HUA. A cohort study also indicated [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] that after adjusting for diabetes, hypertension, obesity, stroke history, and heart disease history using a multivariate logistic regression model, the TG/HDL-C ratio was significantly associated with a decline in eGFR, suggesting that TG/HDL-C is an independent risk factor for the decline in eGFR[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAge is a significant factor influencing the prevalence of HUA in women. Based on the OR values for TG/HDL-c index Q3 and Q4, the association between TG/HDL-c and HUA is weaker in women over 50 years old compared to those under 50 years old. Estrogen plays a role in reducing the level of urate reabsorption transporters. After the age of 50, due to menopause, SUA levels rise sharply, likely due to the loss of estrogen\u0026rsquo;s uricosuric effect[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Additionally, during menopause, declining estrogen levels lead to an increase in TC, LDL-c, and triglyceride (TG) levels, while HDL-c levels decrease [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Beyond the role of estrogen, aging lowers the metabolic rate, potentially altering lipid metabolism patterns. Older adults may also be more prone to insulin resistance and renal dysfunction, both of which can affect serum uric acid levels, thereby altering the strength of the association between the TG/HDL-C ratio and HUA[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the BMI subgroup of this study, we found that female participants with normal or underweight BMI (\u0026lt;\u0026thinsp;23) were more strongly associated with the prevalence of HUA. A previous large-scale retrospective study based on a Chinese population indicated a positive correlation between the TG/HDL-C ratio and the risk of HUA, particularly among women and individuals with normal body weight [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Our study also found that, in the female population, being overweight and related factors weakened the association between TG/HDL-C and HUA. Additionally, a study on Hispanic children suggested [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] that changes in serum uric acid levels are largely influenced by genetic factors, such as genetic variations in the SLC2A9 gene. These genetic variations are closely linked to phenotypic traits associated with obesity. Furthermore, other studies have found that the metabolically healthy obesity (MHO) phenotype is significantly associated with the risk of HUA only among women, not men [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. MHO refers to individuals who meet the obesity criteria with a high BMI but do not exhibit related metabolic abnormalities, such as hypertension, dyslipidemia, or insulin resistance[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These studies may help explain why TG/HDL-C serves as an important biomarker for assessing the risk of HUA in women, especially those with normal body weight.\u003c/p\u003e \u003cp\u003eIn the serum creatinine subgroup of this study, we found a statistically significant interaction between creatinine levels and the TG/HDL-c ratio in influencing the prevalence of HUA among women. Metabolic disorders in the lipid profile may contribute to the development of chronic kidney disease through various mechanisms[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Firstly, hypertriglyceridemia and insulin resistance may lead to increased renal lipid uptake, promoting the development of glomerular fibrosis[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Secondly, hyperlipidemia can trigger inflammatory responses and oxidative stress, both of which can damage renal cells and impair kidney function[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Recent studies[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] have shown that the Nrf2/ARE signaling pathway mitigates lipid-induced renal damage through its antioxidant and anti-inflammatory effects by downregulating mtROS-mediated NLRP3 inflammasome activation. A prospective study of middle-aged and elderly Chinese individuals also indicated that the TG/HDL-C ratio has a strong independent association with an increased risk of chronic kidney disease (CKD) in the Chinese population and is a better predictor than TG levels alone. These studies may illustrate that in populations with lower creatinine levels (Creatinine\u0026thinsp;\u0026lt;\u0026thinsp;70\u0026micro;mol/L), the TG/HDL-c ratio better reflects the risk of HUA[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study still has limitations. It does not explain the causal relationship between changes in the TG/HDL-c ratio and HUA. Secondly, selection bias is inevitable in a single-center study. Moreover, our study did not collect data on other factors that may influence SUA levels, for example drinking and smoking history, dietary habits, menopausal status as well as concomitant medication.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe studies involving humans were approved by The Ethics Committee of Northern Jiangsu People\u0026rsquo;s Hospital.\u0026nbsp;The studies were conducted in accordance with the Declaration of Helsinki. Due to the absence of personal identifiers in the database and the retrospective, observational nature of the study design, the requirement for informed consent was waived.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving humans were approved by The Ethics Committee of Northern Jiangsu People’s Hospital.\u0026nbsp;The studies were conducted in accordance with the Declaration of Helsinki. Due to the absence of personal identifiers in the database and the retrospective, observational nature of the study design, the requirement for informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the medical staff at the Northern Jiangsu People’s Hospital for support in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYZ designed and wrote the article ; H L, S Z and J Ycollected the clinical data . \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of conflicting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by The National Natural Science Foundation of China (81800250), China Postdoctoral Science Foundation (2022M711417), Jiangsu Province Traditional Chinese Medicine Project (MS2023137), Yangzhou Science and Technology Plan Social Development Project (YZ2023096), Clinical Trials from the Northern Jiangsu People’s Hospital(SBLC23002).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sharing statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYuanyuan Q, Yunhua H, Qingyun C, Min G, Lujie Z, Peng W, Lin F: \u003cstrong\u003eThe prevalence of hyperuricemia and its correlates in Zhuang nationality, Nanning, Guangxi Province\u003c/strong\u003e. \u003cem\u003eJ Clin Lab Anal \u003c/em\u003e2022, \u003cstrong\u003e36\u003c/strong\u003e(11):e24711.\u003c/li\u003e\n\u003cli\u003eHe W, Yin L, Liu Q, Zhang Y, Zhao Y, Wang L, You L: \u003cstrong\u003eInfluencing factors and predictive model for left atrial appendage emptying velocity in nonvalvular AF patients\u003c/strong\u003e. \u003cem\u003eFront Cardiovasc Med \u003c/em\u003e2024, \u003cstrong\u003e11\u003c/strong\u003e:1468379.\u003c/li\u003e\n\u003cli\u003eLee CK, Chen IC, Lin HJ, Lin CH, Chen YM: \u003cstrong\u003eAssociation of the ABCG2 rs2231142 variant with the Framingham Cardiovascular Disease Risk score in the Taiwanese population\u003c/strong\u003e. \u003cem\u003eHeliyon \u003c/em\u003e2024, \u003cstrong\u003e10\u003c/strong\u003e(18):e37839.\u003c/li\u003e\n\u003cli\u003ePeng H, Zhang K, Zhang C, Gao J: \u003cstrong\u003eKnowledge, attitude, and practice toward hyperuricemia among healthcare workers in Shandong, China\u003c/strong\u003e. \u003cem\u003ePeerJ \u003c/em\u003e2024, \u003cstrong\u003e12\u003c/strong\u003e:e17926.\u003c/li\u003e\n\u003cli\u003eZhang Y, Ban C, Su D, Liu Y, Zhou S, Fan J: \u003cstrong\u003eXanthine oxidase inhibitors: Virtual screening and mechanism of inhibition studies\u003c/strong\u003e. \u003cem\u003eInt J Biol Macromol \u003c/em\u003e2024, \u003cstrong\u003e281\u003c/strong\u003e(Pt 1):136281.\u003c/li\u003e\n\u003cli\u003eHan Y, Zhou Z, Zhang Y, Zhao G, Xu B: \u003cstrong\u003eThe Association of Surrogates of Insulin Resistance with Hyperuricemia among Middle-Aged and Older Individuals: A Population-Based Nationwide Cohort Study\u003c/strong\u003e. \u003cem\u003eNutrients \u003c/em\u003e2023, \u003cstrong\u003e15\u003c/strong\u003e(14).\u003c/li\u003e\n\u003cli\u003eChang CH, Lin MS, Lin YC, Huang TJ, Chen MY: \u003cstrong\u003eA novel nomogram for predicting cardiometabolic diseases from modifiable risks in middle-aged adults-implication for health education\u003c/strong\u003e. \u003cem\u003eFront Endocrinol (Lausanne) \u003c/em\u003e2023, \u003cstrong\u003e14\u003c/strong\u003e:1291741.\u003c/li\u003e\n\u003cli\u003eLu X, Wang A, Liu K, Chen Y, Chen W, Mao Y, Ye D: \u003cstrong\u003eAssociations of Dietary Magnesium Intake with All-Cause and Cause-Specific Mortality Among Individuals with Gout and Hyperuricemia\u003c/strong\u003e. \u003cem\u003eBiol Trace Elem Res \u003c/em\u003e2024.\u003c/li\u003e\n\u003cli\u003eCao C, Cai D, Liu H, Zhang X, Cai L, Sun C, Wang H, Zhao H, Yue C: \u003cstrong\u003eCausal relationship between genetic-predicted uric acid and cervical cancer risk: evidence for nutritional intervention on cervical cancer prevention\u003c/strong\u003e. \u003cem\u003eFront Nutr \u003c/em\u003e2024, \u003cstrong\u003e11\u003c/strong\u003e:1464046.\u003c/li\u003e\n\u003cli\u003eWang H, Zheng Y, Yang M, Wang L, Xu Y, You S, Mao N, Fan J, Ren S: \u003cstrong\u003eGut microecology: effective targets for natural products to modulate uric acid metabolism\u003c/strong\u003e. \u003cem\u003eFront Pharmacol \u003c/em\u003e2024, \u003cstrong\u003e15\u003c/strong\u003e:1446776.\u003c/li\u003e\n\u003cli\u003eXu X, He M, Zhao G, Liu X, Liu X, Xu H, Cheng Y, Jiang Y, Peng Q, Shi J\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eThe Association of Dietary Diversity with Hyperuricemia among Community Inhabitants in Shanghai, China: A Prospective Research\u003c/strong\u003e. \u003cem\u003eNutrients \u003c/em\u003e2024, \u003cstrong\u003e16\u003c/strong\u003e(17).\u003c/li\u003e\n\u003cli\u003eWakabayashi I, Daimon T: \u003cstrong\u003eHematometabolic Index as a New Discriminator of Cardiometabolic Risk in Middle-Aged Men with Polycythemia and High Leukocyte Count in Peripheral Blood\u003c/strong\u003e. \u003cem\u003eMetab Syndr Relat Disord \u003c/em\u003e2023, \u003cstrong\u003e21\u003c/strong\u003e(5):267-274.\u003c/li\u003e\n\u003cli\u003eChao TH, Lin TH, Cheng CI, Wu YW, Ueng KC, Wu YJ, Lin WW, Leu HB, Cheng HM, Huang CC\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003e2024 Guidelines of the Taiwan Society of Cardiology on the Primary Prevention of Atherosclerotic Cardiovascular Disease --- Part I\u003c/strong\u003e. \u003cem\u003eActa Cardiol Sin \u003c/em\u003e2024, \u003cstrong\u003e40\u003c/strong\u003e(5):479-543.\u003c/li\u003e\n\u003cli\u003eZhang M, Zhu X, Wu J, Huang Z, Zhao Z, Zhang X, Xue Y, Wan W, Li C, Zhang W\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003ePrevalence of Hyperuricemia Among Chinese Adults: Findings From Two Nationally Representative Cross-Sectional Surveys in 2015-16 and 2018-19\u003c/strong\u003e. \u003cem\u003eFront Immunol \u003c/em\u003e2021, \u003cstrong\u003e12\u003c/strong\u003e:791983.\u003c/li\u003e\n\u003cli\u003eZhang Y, Zhu S, Gu Y, Feng Y, Gao B: \u003cstrong\u003eNetwork Pharmacology Combined with Experimental Validation to Investigate the Mechanism of the Anti-Hyperuricemia Action of Portulaca oleracea Extract\u003c/strong\u003e. \u003cem\u003eNutrients \u003c/em\u003e2024, \u003cstrong\u003e16\u003c/strong\u003e(20).\u003c/li\u003e\n\u003cli\u003eOliveri A, Rebernick RJ, Kuppa A, Pant A, Chen Y, Du X, Cushing KC, Bell HN, Raut C, Prabhu P\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eComprehensive genetic study of the insulin resistance marker TG:HDL-C in the UK Biobank\u003c/strong\u003e. \u003cem\u003eNat Genet \u003c/em\u003e2024, \u003cstrong\u003e56\u003c/strong\u003e(2):212-221.\u003c/li\u003e\n\u003cli\u003eZhang Y, Zhang M, Yu X, Wei F, Chen C, Zhang K, Feng S, Wang Y, Li WD: \u003cstrong\u003eAssociation of hypertension and hypertriglyceridemia on incident hyperuricemia: an 8-year prospective cohort study\u003c/strong\u003e. \u003cem\u003eJ Transl Med \u003c/em\u003e2020, \u003cstrong\u003e18\u003c/strong\u003e(1):409.\u003c/li\u003e\n\u003cli\u003eEl\u0026iacute;as-L\u0026oacute;pez D, Vedel-Krogh S, Kobylecki CJ, Wadstr\u0026ouml;m BN, Nordestgaard BG: \u003cstrong\u003eImpaired Renal Function With Higher Remnant Cholesterol Related to Risk of Atherosclerotic Cardiovascular Disease: CGPS\u003c/strong\u003e. \u003cem\u003eArterioscler Thromb Vasc Biol \u003c/em\u003e2024.\u003c/li\u003e\n\u003cli\u003eZitt E, Fischer A, Lhotta K, Concin H, Nagel G: \u003cstrong\u003eSex- and age-specific variations, temporal trends and metabolic determinants of serum uric acid concentrations in a large population-based Austrian cohort\u003c/strong\u003e. \u003cem\u003eSci Rep \u003c/em\u003e2020, \u003cstrong\u003e10\u003c/strong\u003e(1):7578.\u003c/li\u003e\n\u003cli\u003eAnagnostis P, Bitzer J, Cano A, Ceausu I, Chedraui P, Durmusoglu F, Erkkola R, Goulis DG, Hirschberg AL, Kiesel L\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eMenopause symptom management in women with dyslipidemias: An EMAS clinical guide\u003c/strong\u003e. \u003cem\u003eMaturitas \u003c/em\u003e2020, \u003cstrong\u003e135\u003c/strong\u003e:82-88.\u003c/li\u003e\n\u003cli\u003eZuo YQ, Gao ZH, Yin YL, Yang X, Guan X, Feng PY: \u003cstrong\u003eInsulin Resistance Surrogates May Predict HTN-HUA in Young, Non-Obese Individuals\u003c/strong\u003e. \u003cem\u003eDiabetes Metab Syndr Obes \u003c/em\u003e2024, \u003cstrong\u003e17\u003c/strong\u003e:3593-3601.\u003c/li\u003e\n\u003cli\u003eLiu XY, Wu QY, Chen ZH, Yan GY, Lu Y, Dai HJ, Li Y, Yang PT, Yuan H: \u003cstrong\u003eElevated triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio increased risk of hyperuricemia: a 4-year cohort study in China\u003c/strong\u003e. \u003cem\u003eEndocrine \u003c/em\u003e2020, \u003cstrong\u003e68\u003c/strong\u003e(1):71-80.\u003c/li\u003e\n\u003cli\u003eVoruganti VS, Laston S, Haack K, Mehta NR, Cole SA, Butte NF, Comuzzie AG: \u003cstrong\u003eSerum uric acid concentrations and SLC2A9 genetic variation in Hispanic children: the Viva La Familia Study\u003c/strong\u003e. \u003cem\u003eAm J Clin Nutr \u003c/em\u003e2015, \u003cstrong\u003e101\u003c/strong\u003e(4):725-732.\u003c/li\u003e\n\u003cli\u003eTian S, Liu Y, Feng A, Zhang S: \u003cstrong\u003eSex-Specific Differences in the Association of Metabolically Healthy Obesity With Hyperuricemia and a Network Perspective in Analyzing Factors Related to Hyperuricemia\u003c/strong\u003e. \u003cem\u003eFront Endocrinol (Lausanne) \u003c/em\u003e2020, \u003cstrong\u003e11\u003c/strong\u003e:573452.\u003c/li\u003e\n\u003cli\u003eZheng X, Tian C, Xu G, Du D, Zhang N, Wang J, Sang Q, Wuyun Q, Chen W, Lian D\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003ePrevalence, Risk Factors, and Metabolic Characteristics of Metabolically Healthy Obesity in Patients Seeking Bariatric Surgery: A Cohort Study\u003c/strong\u003e. \u003cem\u003eAm Surg \u003c/em\u003e2024, \u003cstrong\u003e90\u003c/strong\u003e(6):1456-1462.\u003c/li\u003e\n\u003cli\u003eŚledziński M, Gołębiewska J, Mika A: \u003cstrong\u003eThe Long-Term Effect of Kidney Transplantation on the Serum Fatty Acid Profile\u003c/strong\u003e. \u003cem\u003eNutrients \u003c/em\u003e2024, \u003cstrong\u003e16\u003c/strong\u003e(19).\u003c/li\u003e\n\u003cli\u003eHan B, Wang N, Chen Y, Li Q, Zhu C, Chen Y, Lu Y: \u003cstrong\u003ePrevalence of hyperuricaemia in an Eastern Chinese population: a cross-sectional study\u003c/strong\u003e. \u003cem\u003eBMJ Open \u003c/em\u003e2020, \u003cstrong\u003e10\u003c/strong\u003e(5):e035614.\u003c/li\u003e\n\u003cli\u003eMochel JP, Ward JL, Blondel T, Kundu D, Merodio MM, Zemirline C, Guillot E, Giebelhaus RT, de la Mata P, Iennarella-Servantez CA\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003ePreclinical modeling of metabolic syndrome to study the pleiotropic effects of novel antidiabetic therapy independent of obesity\u003c/strong\u003e. \u003cem\u003eSci Rep \u003c/em\u003e2024, \u003cstrong\u003e14\u003c/strong\u003e(1):20665.\u003c/li\u003e\n\u003cli\u003eJiang XS, Liu T, Xia YF, Gan H, Ren W, Du XG: \u003cstrong\u003eActivation of the Nrf2/ARE signaling pathway ameliorates hyperlipidemia-induced renal tubular epithelial cell injury by inhibiting mtROS-mediated NLRP3 inflammasome activation\u003c/strong\u003e. \u003cem\u003eFront Immunol \u003c/em\u003e2024, \u003cstrong\u003e15\u003c/strong\u003e:1342350.\u003c/li\u003e\n\u003cli\u003eLiao S, Lin D, Feng Q, Li F, Qi Y, Feng W, Yang C, Yan L, Ren M, Sun K: \u003cstrong\u003eLipid Parameters and the Development of Chronic Kidney Disease: A Prospective Cohort Study in Middle-Aged and Elderly Chinese Individuals\u003c/strong\u003e. \u003cem\u003eNutrients \u003c/em\u003e2022, \u003cstrong\u003e15\u003c/strong\u003e(1).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hyperuricaemia, triglycerides, high-density lipoprotein cholesterol, risk factor","lastPublishedDoi":"10.21203/rs.3.rs-5341007/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5341007/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives: \u003c/strong\u003eThe study aimed to assess the clinical value of the triglyceride/high-density lipoprotein cholesterol(TG/HDL-c) ratio as a diagnostic marker for hyperuricemia (HUA) in female population in China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA total of 21,316 eligible female participants, aged 18 years and older, were consecutively recruited during routine medical examinations at Northern Jiangsu People’s Hospital from July 2014 to August 2023. Participants were divided into four groups based on their TG/HDL-c ratio values. Logistic regression analysis models were employed to further investigate the correlation between the prevalence of HUA and TG/HDL-c ratio in this region.Restricted cubic splines (RCS) were used to explore the linear associations of TG/HDL-c and HUA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The average age of participants was 42.68±13.96 years and the overall prevalence of HUA is 6.41%. The mean uric acid level was 265.34±59.72umol/L. The univariate logistic analysis showed that a higher TG/HDL-c ratio was positively correlated with the presence of hyperuricemia (OR (95%CI) =3.601(3.281,3.951), \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001). In different age groups of female participants, we found a statistically significant association between higher levels of TG/HDL-c and HUA(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). In female participants, there was a nonlinear association between TG/HDL-c and HUA (\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001).After adjusting for multiple covariates, this study found a significant interaction between TG/HDL-c and BMI, as well as creatinine, with a statistically significant difference (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).The AUC for TG/HDL-c in predicting the occurrence of HUA among female participants was 0.717(95%Cl:0.703-0.731).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e An elevated TG/HDL-C ratio increases the risk of HUA in females in eastern China, particularly in individuals with low creatinine levels and normal body weight. Monitoring TG/HDL-c levels may be beneficial for preventing HUA in women.\u003c/p\u003e","manuscriptTitle":"Association Between the TG/HDL-c Ratio and Hyperuricemia in Women participants in China: A Cross-sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-26 10:08:46","doi":"10.21203/rs.3.rs-5341007/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"165941345282281156153625853964547213580","date":"2026-05-20T09:56:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-27T12:42:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-10-28T11:30:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-28T10:13:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-28T08:37:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2024-10-27T11:10:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3832a0b8-e871-480d-a07c-4a630c865885","owner":[],"postedDate":"November 26th, 2024","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"165941345282281156153625853964547213580","date":"2026-05-20T09:56:50+00:00","index":125,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-11-26T10:08:46+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-26 10:08:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5341007","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5341007","identity":"rs-5341007","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}