WBCs as a mediator between the TG/HDL ratio and gallstone disease across sex differences: NHANES 2017–2020

WBCs as a mediator between the TG/HDL ratio and gallstone disease across sex differences: NHANES 2017–2020 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article WBCs as a mediator between the TG/HDL ratio and gallstone disease across sex differences: NHANES 2017–2020 Fei Zuo, Qian jiang, Xiaowei Huang, Wenjun Mao, Yunyan Tan, Tianping Luo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6330540/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Gallstone disease (GSD) is associated with insulin resistance (IR) and systemic inflammation, yet the quantitative relationships among these factors remain underexplored. This study investigated the association between IR surrogate indices and the GSD, with a focus on the mediating role of inflammation and potential sex-based differences. Methods Insulin resistance was assessed via biomarkers, including the triglyceride-to-high-density lipoprotein cholesterol (TG/HDL) ratio; TyG, METS-IR, and HOMA-IR, and inflammatory markers, such as white blood cells (WBCs). The associations between TG/HDL and GSD were assessed through logistic regression models and restricted cubic spline (RCS) analysis. Subgroup analyses were conducted on basis of age, sex, marital status, education, poverty-to-income ratio (PIR) and body mass index (BMI). Furthermore, a key focus of the analysis was to investigate the mediating role of WBC count in the relationship between TG/HDL and incident GSD. Additionally, interactions between sex and TG/HDL were tested on both multiplicative and additive scales. Results Among the 3,624 included participants (383 with gallstones and 3,241 without), the mean age was 50.8 ± 17.2 years. Among those diagnosed with gallstone disease, the female-to-male ratio was 2.52:1. The highest quartile (Q4) of TG/HDL was significantly associated with increased GSD risk in the fully adjusted model (OR = 1.63; 95% CI: 1.07–2.49; P = 0.022), whereas TyG, METS-IR, and HOMA-IR did not have significant associations with Q4 (all P > 0.05). RCS analysis revealed a nonlinear, reverse L-shaped relationship between TG/HDL and GSD risk (P = 0.049). Mediation analysis revealed that in the unadjusted model, the WBC count fully mediated the association between the TG/HDL ratio and GSD, accounting for 28.6% of the total effect. After adjusting for sex and age, the WBC count partially mediated this relationship, explaining 17.2% of the effect. Interaction analysis demonstrated a significant additive interaction effect between sex and the TG/HDL ratio (P 0.05), suggesting increased GSD risk in females. No significant interactions were observed between WBC count and TG/HDL. Conclusions The TG/HDL ratio is strongly associated with GSD risk, exhibiting a nonlinear relationship partially mediated by inflammation, as indexed by the WBC count. Sex exerts an additive effect on this association, highlighting potential hormonal contributions to gallstone formation. white wlood cells triglyceride-to-high-density lipoprotein ratio gallstones inflammation insulin resistance NHANES Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Gallstone disease (GSD) is a prevalent and economically burdensome condition that significantly contributes to global morbidity( 1 ). The worldwide prevalence of GSD is estimated at approximately 6.1%, with notably higher rates of approximately 15% observed in populations within the United States and Europe( 2 ). In the United States alone, nearly 800,000 cholecystectomies are performed annually, resulting in costs approaching $ 6 billion and imposing substantial health and economic burdens( 3 ). Consequently, GSD has become a focal point of medical research. The etiology of GSD is multifactorial, with cholesterol gallstones being the most common type, accounting for approximately 75% of cases( 4 ). The formation of cholesterol gallstones is intricately linked to abnormalities in lipid metabolism, which are closely associated with insulin resistance (IR)( 4 , 5 ). Furthermore, IR is strongly related to chronic inflammation, a condition that plays a critical role in various metabolic disorders( 6 ). Obesity and chronic inflammation are the principal determinants of insulin resistance( 7 ). Despite these associations, the interplay between insulin resistance, chronic inflammation, and the development of gallstones remains underexplored, suggesting a promising direction for future research. Previous studies have investigated the direct relationship between hepatic insulin resistance and GSD, revealing potential mechanistic links( 5 , 8 , 9 ). However, these approaches often involve invasive and costly methods, such as the hyperinsulinaemic-euglycaemic clamp (HIEC), thereby limiting their clinical applicability( 8 ). To address these limitations, the present study aimed to examine the relationships of inflammatory indices and insulin resistance indices with the incidence of gallstone disease. By utilizing more accessible and less invasive measures( 10 ), such as insulin resistance indicators and inflammatory scores, this research aims to increase the predictive accuracy for GSD risk and identify high-risk populations, ultimately contributing to more effective prevention and management strategies. Method Research design The National Health and Nutrition Examination Survey (NHANES) is a comprehensive nationwide survey conducted by the National Center for Health Statistics (NCHS). Data on demographics, physical measurements, medical history, laboratory tests, and health behaviors were systematically collected. The program has received ethical approval from the NCHS review board. All participants were recruited voluntarily and provided informed consent. Data collection occurs biennially. For this study, participants were specifically asked to respond to questionnaires regarding their history of gallstones during the 2017–2020 cycle. For further information and data access, please visit the NHANES website ( https://www.cdc.gov/nchs/nhanes/index.htm ). Definition of gallstones To ascertain the presence of gallstones among participants, each individual was asked the following question: "Has a doctor or other health professional ever told you that you had gallstones?" Participants who responded affirmatively were categorized as having gallbladder stones, whereas those who responded negatively were classified as not having gallstones. This self-reported method provided a straightforward means of identifying individuals with and without gallstone disease for the purposes of the study. Identification of covariates A comprehensive assessment of potential covariates was conducted on basis of the literature( 11 – 13 ). The variables included in this analysis were extracted from the NHANES database and included demographic factors (sex, age, race, education level categorized as less than high school, high school, or higher than high school), socioeconomic indicators (marital status, family poverty-to-income ratio [PIR]), health conditions (hypertension, diabetes), lifestyle factors (physical activity, smoking status), anthropometric measurements (body mass index [BMI], waist circumference, hip circumference), and laboratory parameters (complete blood count, lipid profile, liver function tests). The diagnoses of hypertension and diabetes were ascertained through a combination of questionnaire responses, physical examinations, and laboratory test results. Physical activity status was classified via data from the Physical Activity Questionnaire (PAQ), whereas smoking status was determined on the basis of survey responses. Insulin resistance replacement index Four surrogate indices for IR were incorporated into this study: the triglyceride-glucose (TyG) index, METS-IR, triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL), and homeostatic model assessment of insulin resistance (HOMA-IR). The TyG index was calculated via the following formula: TyG index = Ln[triglycerides (mg/dL) × glycemia (mg/ dL)/2]( 14 ). ETS-IR was determined as follows: METS-IR = Ln[2 × glycemia (mg/dL) + triglycerides (mg/dL)] × BMI/Ln HDL (mg/dL)( 15 ). The TG/HDL ratio was computed as: TG/HDL, which was calculated as triglycerides (mg/dL)/HDL (mg/ dL)( 16 ). HOMA-IR was derived via the following equation: HOMA-IR = FPG (mmol/L) × FINS (mIU/L) /22.5( 17 ). Results Study population A total of 15,560 individuals were initially enrolled and completed the baseline interview. A total of 5,867 participants aged < 18 years and 87 pregnant women were excluded from the analysis. Additionally, participants with missing data on gallstone status (n = 482), educational level (n = 14), marital status (n = 7), hypertension (n = 25), diabetes mellitus (n = 4), smoking status (n = 4), and variables related to insulin resistance (IR) such as body mass index (BMI), waist circumference (WC), hip circumference, total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), fasting plasma glucose (FPG), and insulin levels (n = 5,437) were also excluded. Furthermore, participants with missing data on inflammatory biomarkers, including high-sensitivity C-reactive protein (hs-CRP), white blood cell count (WBC), platelet count (PLT), neutrophil, lymphocytes, and monocyte counts (n = 9), were excluded. Ultimately, 3,624 participants were included in the analysis: 383 with gallstones and 3,241 without gallstones. The detailed inclusion and exclusion process is illustrated in Fig. 1 . Baseline characteristics Table 1 shows the baseline characteristics of the 3,624 participants, who had a mean age of 50.8 ± 17.2 years. Among those diagnosed with gallstone disease, the female-to-male ratio was 2.52:1. The cohort diagnosed with gallstones was predominantly Non-Hispanic White. The mean ± SD values for insulin resistance (IR)-related biomarkers were as follows: TyG = 8.7 ± 0.6, METS-IR = 49.8 ± 14.8, TG/HDL = 2.5 ± 1.7, and HOMA-IR = 6.2 ± 13.5. These biomarkers were significantly elevated in participants with gallstones compared with those without gallstones (all p < 0.05). Additionally, the levels of HbA1c, insulin, BMI, FPG, TG, WBC count, and neutrophils were significantly higher in participants with gallstones than in those without gallstones (all p < 0.05). Table 1 Baseline characteristics of the participants Variables Total (n = 3624) Non-gallstone (n = 3241) Gallstone (n = 383) P value Age, Mean ± SD 50.8 ± 17.2 50.0 ± 17.2 57.6 ± 15.4 < 0.001 Sex, n (%) < 0.001 Male 1768 (48.8) 1659 (51.2) 109 (28.5) Female 1856 (51.2) 1582 (48.8) 274 (71.5) Race, n (%) < 0.001 Non-Hispanic White 1228 (33.9) 1076 (33.2) 152 (39.7) Non-Hispanic Black 917 (25.3) 853 (26.3) 64 (16.7) Mexican American 460 (12.7) 404 (12.5) 56 (14.6) Other race 1019 (28.1) 908 ( 28 ) 111 ( 29 ) Marital, n (%) 0.973 Married 2145 (59.2) 1918 (59.2) 227 (59.3) Living alone 1479 (40.8) 1323 (40.8) 156 (40.7) Education level (year), n (%) 0.336 12 2077 (57.3) 1865 (57.5) 212 (55.4) PIR, Mean ± SD 0.05 ≤ 1.3 843 (26.8) 759 ( 27 ) 84 (24.9) > 1.3, < 3.5 1264 (40.2) 1108 (39.5) 156 (46.3) ≥ 3.5 1037 (33.0) 940 (33.5) 97 (28.8) Smoking status, n (%) 0.002 Never 2064 (57.0) 1854 (57.2) 210 (54.8) Former 877 (24.2) 759 (23.4) 118 (30.8) Curren 683 (18.8) 628 (19.4) 55 (14.4) Moderate activity, n (%) 0.93 No 2025 (55.9) 1810 (55.9) 215 (56.1) Yes 1596 (44.1) 1428 (44.1) 168 (43.9) hypertension, n (%) < 0.001 No 2231 (61.6) 2055 (63.4) 176 (46) Yes 1393 (38.4) 1186 (36.6) 207 (54) DM, n (%) < 0.001 No 3053 (84.2) 2770 (85.5) 283 (73.9) Yes 571 (15.8) 471 (14.5) 100 (26.1) BMI (kg/m²) 29.8 ± 7.3 29.4 ± 7.1 33.1 ± 8.5 < 0.001 Waist circumference (cm) 100.7 ± 17.2 99.9 ± 17.0 108.0 ± 17.3 < 0.001 Hip circumference (cm) 107.4 ± 14.8 106.6 ± 14.2 114.3 ± 17.3 < 0.001 FPG (mmol/L) 6.3 ± 2.1 6.2 ± 2.0 6.6 ± 2. 0.001 HbA1c (%) 5.6 (5.3, 6.0) 5.6 (5.3, 6.0) 5.7 (5.4, 6.3) < 0.001 Insulin (µU/mL) 14.6 ± 22.6 14.1 ± 22.2 18.8 ± 25.9 < 0.001 TC (mg/dL) 183.7 ± 40.6 183.9 ± 40.1 182.0 ± 44.3 0.384 HDL (mg/dL) 53.7 ± 15.9 53.9 ± 16.1 52.3 ± 13.8 0.061 TG (mg/dL) 105.0 ± 62.7 103.7 ± 62.8 116.0 ± 61.0 < 0.001 LDL (mmol/L) 2.8 ± 0.9 2.8 ± 0.9 2.8 ± 1.0 0.154 ALT (U/L) 22.2 ± 19.3 22.3 ± 19.9 21.4 ± 13.7 0.391 AST (U/L) 21.8 ± 15.0 22.0 ± 15.4 20.8 ± 10.0 0.148 TyG, Mean ± SD 8.5 ± 0.7 8.5 ± 0.7 8.7 ± 0.6 < 0.001 METSIR, Mean ± SD 44.2 ± 13.1 43.5 ± 12.7 49.8 ± 14.8 < 0.001 TG/HDL, Mean ± SD 2.3 ± 1.8 2.2 ± 1.9 2.5 ± 1.7 0.018 HOMAIR, Mean ± SD 4.4 ± 8.8 4.2 ± 8.1 6.2 ± 13.5 < 0.001 hs-CRP (mg/L) 4.1 ± 7.8 4.0 ± 8.0 4.6 ± 6.1 0.193 WBC (10⁹/L) 6.7 ± 2.1 6.7 ± 2.1 7.1 ± 2.1 < 0.001 PLT (10⁹/L) 241.7 ± 64.4 241.6 ± 63.9 242.8 ± 68.7 0.740 Neutrophils (10⁹/L) 3.9 ± 1.6 3.9 ± 1.6 4.2 ± 1.6 < 0.001 Lymphocytes (10⁹/L) 2.0 ± 0.9 2.0 ± 0.9 2.1 ± 0.8 0.176 Monocytes (10⁹/L) 0.5 ± 0.2 0.5 ± 0.2 0.6 ± 0.2 0.436 Abbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; DM, diabetes mellitus; FPG, fasting plasma glucose; HbA1c, glycated hemoglobin A1c; HDL, high-density lipoprotein; HOMA‐IR, homeostasis model assessment‐insulin resistance; hs‐CRP, high-sensitivity C‐reactive protein; LDL, low‐density lipoprotein ; METSIR, metabolic score for insulin resistance; PLT, platelet count; TC, total cholesterol; TG, triglyceride; TyG, triglyceride glucose index;WBC, white blood cell. Association between IR surrogate indices and the risk of gallstones Univariate analysis revealed several demographic and clinical factors significantly associated with the presence of gallstones (Table 2 ). Specifically, age, sex, race, and smoking status were found to be significantly related to gallstone occurrence. Notably, non-Hispanic White participants presented a greater prevalence of gallstones than other racial groups did. Additionally, female sex was associated with an increased likelihood of gallstone development. Clinical parameters related to cholesterol metabolism were also significantly associated with gallstones. These included HbA1c, hypertension, diabetes mellitus, BMI, waist circumference, hip circumference, FPG, and TG. Furthermore, insulin resistance indices—TyG, METS-IR, TG/HDL, and HOMA-IR—were significantly elevated in participants with gallstones compared with those without gallstones (all p < 0.05). Additionally, the levels of inflammatory markers such as white blood cells and neutrophils were significantly greater in individuals with gallstones. Table 2 Association between covariates and the risk of gallstones Variables OR(95% CI) P value Variables OR(95% CI) P value Age, Mean ± SD 1.03 (1.02 ~ 1.03) < 0.001 Moderate activity, n (%) Sex, n (%) No 1 (reference) Male 1 (reference) Yes 0.99 (0.8 ~ 1.23) 0.93 Female 2.64 (2.09 ~ 3.33) < 0.001 hypertension, n (%) Race, n (%) No 1 (reference) Non-Hispanic White 1 (reference) Yes 2.04 (1.65 ~ 2.52) < 0.001 Non-Hispanic Black 0.53 (0.39 ~ 0.72) < 0.001 DM, n (%) Mexican American 0.98 (0.71 ~ 1.36) 0.91 No 1 (reference) Other race 0.87 (0.67 ~ 1.12) 0.276 Yes 2.08 (1.62 ~ 2.66) < 0.001 Marital, n (%) BMI (kg/m²) 1.06 (1.05 ~ 1.07) < 0.001 Married 1 (reference) Waist circumference (cm) 1.03 (1.02 ~ 1.03) < 0.001 Living alone 1 (0.8 ~ 1.24) 0.973 Hip circumference (cm) 1.03 (1.02 ~ 1.04) < 0.001 Education (year), n (%) FPG (mmol/L) 1.07 (1.03 ~ 1.12) 0.001 12 1.16 (0.74 ~ 1.8) 0.522 HDL (mg/dL) 0.99 (0.99 ~ 1) 0.061 PIR, Mean ± SD TG (mg/dL) 1 (1 ~ 1) 1.3, < 3.5 1.27 (0.96 ~ 1.68) 0.093 ALT (U/L) 1 (0.99 ~ 1) 0.382 ≥ 3.5 0.93 (0.69 ~ 1.27) 0.655 AST (U/L) 0.99 (0.98 ~ 1) 0.131 Smoking status, n (%) TyG, Mean ± SD 1.54 (1.32 ~ 1.8) < 0.001 Never 1 (reference) METSIR, Mean ± SD 1.03 (1.02 ~ 1.04) < 0.001 Former 1.37 (1.08 ~ 1.75) 0.01 TG/HDL, Mean ± SD 1.06 (1.01 ~ 1.12) 0.019 Curren 0.77 (0.57 ~ 1.05) 0.104 HOMAIR, Mean ± SD 1.02 (1.01 ~ 1.02) < 0.001 HbA1c (%) 1.17 (1.08 ~ 1.26) < 0.001 WBC (10⁹/L) 1.09 (1.04 ~ 1.14) < 0.001 hs-CRP (mg/L) 1.01 (1 ~ 1.02) 0.196 Neutrophils (10⁹/L) 1.12 (1.05 ~ 1.19) < 0.001 PLT (10⁹/L) 1 (1 ~ 1) 0.74 Lymphocytes (10⁹/L) 1.06 (0.97 ~ 1.17) 0.195 A total of three logistic regression models were developed to evaluate the associations of the TG/HDL ratio and other insulin resistance indices with the presence of gallstones (Table 3 ). According to the fully adjusted Model 3, the highest quartile of TG/HDL (Q4) remained significantly associated with an increased risk of gallstones (odds ratio [OR] = 1.63; 95% confidence interval [CI]: 1.07–2.49; P = 0.022). In contrast, other insulin resistance indices—TyG (OR = 1.34; 95% CI: 0.88–2.04), METS-IR (OR = 1.41; 95% CI: 0.75–2.63), and HOMA-IR (OR = 1.26; 95% CI: 0.81–1.97)—did not exhibit a statistically significant association with gallstones in Q4 of Model 3 (all P > 0.05). Additionally, in both Model 1 and Model 2, TyG, METS-IR, and HOMA-IR demonstrated positive correlations with the risk of gallstones across all quartiles (Q1, Q2, Q3, and Q4). However, these associations were attenuated and lost statistical significance in the fully adjusted Model 3. This attenuation suggests that the relationship between TG/HDL and gallstone risk is more robust after controlling for potential confounding factors, potentially revealing the true association independent of other metabolic variables. Table 3 Association between insulin resistance indices and gallstones. Exposure Model 1 Model 2 Model 3 Trend.test OR(95%CI) P OR(95%CI) P OR(95%CI) P TyG (quartile) Q1 1.00(Reference) 1.00(Reference) 1.00(Reference) Q2 1.94 (1.37 ~ 2.76) < 0.001 1.76 (1.23 ~ 2.52) 0.002 1.53 (1.02 ~ 2.27) 0.038 Q3 2.15 (1.52 ~ 3.04) < 0.001 1.9 (1.33 ~ 2.71) < 0.001 1.27 (0.84 ~ 1.91) 0.258 Q4 2.75 (1.96 ~ 3.85) < 0.001 2.42 (1.71 ~ 3.43) < 0.001 1.34 (0.88 ~ 2.04) 0.176 Trend.test 1.34 (1.21 ~ 1.47) < 0.001 1.29 (1.16 ~ 1.43) < 0.001 1.05 (0.92 ~ 1.19) 0.474 METSIR (quartile) Q1 1.00(Reference) 1.00(Reference) 1.00(Reference) Q2 1.84 (1.27 ~ 2.67) 0.001 1.79 (1.23 ~ 2.61) 0.003 1.49 (0.97 ~ 2.3) 0.069 Q3 2.4 (1.68 ~ 3.42) < 0.001 2.43 (1.69 ~ 3.5) < 0.001 1.50 (0.94 ~ 2.39) 0.092 Q4 3.57 (2.53 ~ 5.03) < 0.001 3.84 (2.7 ~ 5.44) < 0.001 1.4 1(0.75 ~ 2.63) 0.284 Trend.test 1.48 (1.34 ~ 1.64) < 0.001 1.53 (1.38 ~ 1.7) < 0.001 1.1 (0.91 ~ 1.34) 0.311 TG/HDL (quartile) Q1 1.00(Reference) 1.00(Reference) 1.00(Reference) Q2 2.06 (1.47 ~ 2.9) < 0.001 2.16 (1.53 ~ 3.06) < 0.001 2.05 (1.38 ~ 3.04) < 0.001 Q3 2.18 (1.55 ~ 3.06) < 0.001 2.23 (1.58 ~ 3.16) < 0.001 1.68 (1.12 ~ 2.53) 0.012 Q4 2.27 (1.62 ~ 3.18) < 0.001 2.64 (1.86 ~ 3.74) < 0.001 1.63 (1.07 ~ 2.49) 0.022 Trend.test 1.24 (1.13 ~ 1.37) < 0.001 1.3 (1.18 ~ 1.44) < 0.001 1.09 (0.96 ~ 1.23) 0.171 HOMAIR (quartile) Q1 1.00(Reference) 1.00(Reference) 1.00(Reference) Q2 1.8 (1.25 ~ 2.58) 0.002 1.62 (1.12 ~ 2.34) 0.01 1.14 (0.76 ~ 1.7) 0.530 Q3 2.29 (1.62 ~ 3.25) < 0.001 2.08 (1.45 ~ 2.96) < 0.001 1.23 (0.82 ~ 1.85) 0.311 Q4 3.13 (2.23 ~ 4.38) < 0.001 2.96 (2.1 ~ 4.17) < 0.001 1.26 (0.81 ~ 1.97) 0.299 Trend.test 1.42 (1.29 ~ 1.57) < 0.001 1.41 (1.27 ~ 1.56) < 0.001 1.08 (0.94 ~ 1.24) 0.293 Note: Model 1 was the crude model; Model 2 was adjusted for sex and age; Model 3 was adjusted for sex, age, race, education level, PIR, BMI, smoking status, hypertension, DM, physical activities status, ALT, and AST. Investigating the dose-response relationship between TG/HDL and gallstones Despite the consistent significance across models, the trend test in Model 3 of TG/HDL (Q4) did not reach statistical significance (p = 0.171) (Table 3 ), indicating the absence of a linear dose-response relationship between TG/HDL levels and gallstone risk. To explore potential nonlinear associations, restricted cubic spline (RCS) analysis was conducted. The RCS revealed a reverse L-shaped curve in the relationship between TG/HDL and gallstone occurrence, supporting a nonlinear association (p = 0.049) (Fig. 2 ). In the threshold analysis, the inflection point at a TG/HDL ratio of 2.794 did not reach statistical significance (likelihood ratio test; p = 0.106) (Supplementary Table S1 ). This lack of significance may indicate that while a nonlinear relationship exists, the precise point at which the association changes direction is not definitively established within this dataset. Stratified Analyses Based on Additional Variables To further assess the robustness of the association between the TG/HDL ratio and the incidence of gallstones, subgroup analyses were conducted. These analyses aimed to identify potential effect modifications across various demographic and socioeconomic strata. Across all the examined subgroups, no significant interactions were observed within any subgroup after stratification by sex, age, education level, PIR, or BMI (Fig. 3 ). Furthermore, when considering the issue of multiple testing, an observed p-value of less than 0.05 for the interaction with marital status may not be deemed statistically significant. The mediating effect of WBCs on the association between TG/HDL and gallstones To elucidate the underlying mechanisms by which the TG/HDL ratio influences gallstone formation, mediation analyses were conducted to evaluate the potential mediating role of the WBC count in this association. In the unadjusted mediation model, WBC count was found to fully mediate the relationship between the TG/HDL ratio and gallstone risk, accounting for 28.6% of the fully mediated effects (Fig. 4 ). Upon adjusting for both sex and age, the mediating effect of the WBC count remained statistically significant but was attenuated to 17.2% (Fig. 4 ). In this adjusted model, the WBC count had both direct and indirect effects on the association, suggesting partial mediation. When age was included as a single covariate, the WBC count continued to fully mediate the relationship, which was consistent with the crude model. In contrast, when sex was adjusted for as a single covariate, the WBC count had both direct and indirect effects on the association between the TG/HDL ratio and gallstone risk (Fig. 4 ). These fingdings suggest that sex as a covariate may modify the mediating effect of the WBC count. Interaction analysis between WBC count, sex, and the TG/HDL ratio in relation to gallstone risk The analyses revealed that WBCs do not exhibit multiplicative or additive interactions with TG/HDL in relation to gallstone risk (Supplementary Table S2 ). In contrast, significant additive interactions were identified between sex and the TG/HDL ratio in relation to gallstone risk (P 0.05) (Table 4 ). the WBC count functions solely as a mediator, facilitating the pathway through which the TG/HDL ratio influences gallstone risk. Conversely, sex exhibits an additive interaction, augmenting the overall risk associated with the TG/HDL ratio without multiplicatively altering the relationship. Table 4 Multiplicative and additive interactions between sex and TG/HDL in relation to gallstone risk Risk of gallstone TG/HDL Sex OR(95% CI) P for interaction Measures of additive interaction RERI (95% CI) p AP (95% CI) p gallstone Low Male 1.00 0.21 1.4(0.83 ~ 2.35) 0.01 0.33(0.11 ~ 0.55) < 0.001 High 1.11(0.72 ~ 1.71) Low Female 3.01 (2.1 ~ 4.3) High 4.66 (3.18 ~ 6.83) Note: Adjusted for age, race, education level, PIR, BMI, smoking status, hypertension, DM, and physical activity status. Discussion This study utilized data from the NHANES to explore the relationship between gallstones and TG /HDL levels, with a particular focus on the mediating role of the WBC count. The results from the 2017–2020 NHANES indicate that individuals diagnosed with gallstones were predominantly Non-Hispanic White individuals rather than Non-Hispanic White individuals (Table 2 ). In contrast to earlier studies conducted in the 1990s( 18 , 19 ), which reported markedly higher GSD prevalence in indigenous and Mexican American populations than in White populations in the United States, our current findings suggest a shift in the epidemiological distribution of gallstones across different racial and ethnic groups. Numerous studies have indicated that females exhibit a greater propensity for gallstone development than males do, largely attributable to the effects of sex hormones such as estrogen and progesterone( 20 , 21 ). Consistent with these findings, our research also demonstrated that females are at a greater risk for gallstone formation. Compared with males, females have different eating habits and lifestyles and may be more inclined to consume a high-sugar, high- fat diet, which can lead to a higher TyG index and thus an increased risk of GSD( 22 )。 Approximately 80% of gallstones in Western populations are composed of cholesterol, a consequence of disrupted cholesterol homeostasis involving the liver, gallbladder, and intestine against a genetic background( 23 ). Cholesterol gallstones are closely associated with components of metabolic syndrome, particularly cholesterol metabolism, including obesity, type 2 diabetes, and insulin resistance( 23 – 25 ). These findings suggest that metabolic dysregulation, particularly in cholesterol metabolism, significantly contributes to gallstone formation. Multiple studies have shown that individuals with MetS have a significantly greater risk of developing GSD, with the risk proportionately increasing with the number of MetS components present( 13 , 26 ). In line with the findings of the present study, logistic multivariate regression analysis suggested a synergistic effect of BMI, waist circumference, hip circumference, hypertension, diabetes, and TG/HDL on the formation of gallstones. Specifically, the TG/HDL ratio is positively correlated with BMI, waist circumference, blood pressure, and fasting plasma glucose levels( 27 ). Numerous studies have consistently demonstrated a significant association between TG/HDL and various metabolic factors( 17 , 28 , 29 ). Furthermore, the TG/HDL ratio has been identified as an independent predictor of insulin resistance, even after adjusting for waist circumference( 30 ). This finding is consistent with studies that have highlighted the TG/HDL ratio as a surrogate biomarker for insulin resistance, with specific thresholds enhancing accuracy across different ethnicities and sexes( 31 ). The present study demonstrated that the TG/HDL ratio is closely associated with GSD, exhibiting a nonlinear relationship that is particularly pronounced in females. The key factors contributing to this association include obesity, insulin resistance, and abnormal glucose metabolism( 32 ). Hepatic insulin resistance directly promotes gallstone formation by altering bile acid synthesis and transport, mechanisms essential for cholesterol solubilization and excretion( 33 ). Cholecystolithiasis is frequently associated with inflammation caused by gallstones( 34 ), which exacerbates metabolic disturbances and fosters an environment conducive to gallstone nucleation and growth( 35 ). Several studies on cytokines and gallstones have identified four circulating interleukins—IL-6, IL-10, IL-12, and IL-13—that are linked to the presence of gallstones( 36 , 37 ). Additionally, gallstones are closely associated with insulin resistance and obesity( 7 ). Furthermore, insulin resistance is frequently concomitant with obesity, which fosters a state of chronic, low-grade inflammation. The interplay among insulin resistance, chronic inflammation, and gallstone development is complex. These observations are consistent with our analysis. Multivariate analysis indicated that TyG, METS-IR, HOMA-IR, TG/HDL, and WBC are significantly associated with gallstone disease. Among the insulin resistance surrogate indices evaluated, the TG/HDL ratio demonstrated a robust association with gallstone risk, remaining significant in the fully adjusted Model 3 of TG/HDL (Q4) (odds ratio [OR] = 1.63; 95% confidence interval [CI]: 1.07–2.49; P = 0.022). In contrast, other indices such as TyG, METS-IR, and HOMA-IR, did not retain statistical significance after full adjustment, suggesting that the TG/HDL ratio may serve as a more reliable biomarker for assessing gallstone risk within this population. Furthermore, various stratified analyses further confirmed the robustness of these associations. The dose-response analysis revealed a nonlinear, reverse L-shaped association between the TG/HDL ratio and gallstone occurrence. This nonlinearity suggests that the relationship between TG/HDL and GSD risk may be influenced by threshold effects or diminishing returns at higher levels of TG/HDL. The absence of a statistically significant inflection point in the threshold analysis indicates that further research is necessary to precisely characterize the nature of this nonlinear relationship. One study directly utilized isotopic glucose tracers to estimate whole-body insulin resistance( 8 ). However, owing to the time-consuming nature of this technique and the associated potential risks, we have opted for alternative methods that offer advantages such as low cost and easy accessibility for assessing individual insulin resistance and inflammatory markers. Certain inflammatory factors can directly interfere with the signalling pathway through which insulin exerts its biological effects, exacerbating IR( 38 , 39 ), and IR is typically associated with a systemic low-grade inflammatory state( 40 ). While the relationships among inflammation, IR, and GSD are well established, there is limited human evidence directly linking insulin resistance to gallstone disease. Few studies have quantitatively examined the associations among these three factors. In the present study, the TG/HDL ratio was utilized to assess patients' IR status, and mediation analysis was employed to quantify the relationships among inflammation, IR, and gallstone risk. The mediation analysis revealed that the WBC count significantly mediated the relationship between the TG/HDL ratio and gallstone risk. The findings reveal that the WBC count serves as a significant mediator, accounting for 28.6% of the association in the unadjusted model and 17.2% after adjusting for sex and age, thereby indicating a substantial yet moderated mediating role. This attenuation suggests that while inflammation, as indicated by the WBC count, partially explains the link between dyslipidemia and GSD, other factors may also contribute to this association. The involvement of the WBC count underscores the potential inflammatory pathways through which metabolic disturbances may facilitate gallstone formation, thereby aligning with theories that inflammation plays a critical role in the pathogenesis of cholesterol gallstone disease( 4 ). When sex and age were adjusted for as a single covariate( 41 ), sex appeared to modify the mediating effect of the WBC count through the TG/HDL ratio. To further explore the interaction between sex and the TG/HDL ratio, interaction analysis revealed a significant additive interaction between sex and the TG/HDL ratio in relation to gallstone disease risk, although no multiplicative interaction was observed. This interaction indicates that the effect of the TG/HDL ratio on GSD risk is more pronounced in females than in males, potentially explaining the higher prevalence of gallstones observed in females. Additionally, interaction analyses demonstrated that the WBC count did not exhibit either additive or multiplicative interactions. In summary, the WBC count functions solely as a mediator, facilitating the pathway through which the TG/HDL ratio influences gallstone risk. However, sex exhibits an additive interaction, increasing the overall risk associated with the TG/HDL ratio without multiplicatively altering the relationship. Limitations Despite its strengths, this study is subject to several limitations. The cross-sectional design precludes the establishment of causality between the TG/HDL ratio and gallstone risk. Longitudinal studies are warranted to ascertain temporal relationships and causal pathways. Additionally, the exclusion of a substantial number of participants due to missing data may introduce selection bias, potentially affecting the generalizability of the findings. The reliance on self-reported data for certain variables, such as smoking status and medical history, may also be susceptible to reporting inaccuracies. Moreover, studies that focus on specific IR indices and inflammatory markers may overlook other relevant biological factors influencing gallstone development. Conclusion In conclusion, the TG/HDL ratio emerges as a significant and independent predictor of gallstone risk, outperforming other IR surrogate indices in this regard. The partial mediating role of the WBC count underscores the intersection between metabolic dysregulation and inflammatory processes in gallstone pathogenesis. Furthermore, the additive interaction with sex highlights the necessity for sex-specific approaches in gallstone prevention and management. These findings provide valuable insights into the complex interplay of metabolic and inflammatory factors in gallstone disease and pave the way for targeted interventions aimed at reducing its burden. Declarations Acknowledgements We would like to thank the NHANES database for providing the data source for this study. Author contributions T.P.L designed the study; T.P.L and F.Z wrote the main manuscript text; W.J.M, Y.Y.T, and X.W.H collected biochemical data; T.P.L and Q.J prepared figures 1-4. T.P.L and Q.J prepared tables 1-4. All authors reviewed and approved the final manuscript. Funding No financial support was received for this study Competing interests The authors declare no competing interests. Data availability The datasets generated and analysis during the current study are available in the NHANES, www.cdc.gov/nchs/NHANEs/. Ethics approval and consent to participate The National Center for Health Statistics Ethics Review Board has approved the implementation of NHANES, and every participant signed informed consent. Consent for publication Not applicable. Clinical trial number Not applicable. Competing interests The authors declare that they have no competing interests. References Hotineanu V, Moraru V, Bujor P, Bujor S. Cholelithiasis - Epidemiology, Risk Factors and Etiopathogenic Aspects: Up-to-Date. J Surg. 2014;10:1–5. Wang X, Yu W, Jiang G, Li H, Li S, Xie L, et al. Global Epidemiology of Gallstones in the 21st Century: A Systematic Review and Meta-Analysis. Clin Gastroenterol Hepatol. 2024;22(8):1586–95. Sandler RS, Everhart JE, Donowitz M, Adams E, Cronin K, Goodman C, et al. The burden of selected digestive diseases in the United States. Gastroenterology. 2002;122(5):1500–11. Di Ciaula A, Wang DQ, Portincasa P. An update on the pathogenesis of cholesterol gallstone disease. Curr Opin Gastroenterol. 2018;34(2):71–80. Zanlungo S, Miquel JF, Rigotti A, Nervi F. Insulin and cholesterol gallstones: new insights for a complex pathogenic relationship. Hepatology. 2008;48(6):2078–80. Tilg H, Moschen AR. Inflammatory mechanisms in the regulation of insulin resistance. Mol Med. 2008;14(3–4):222–31. Baker RG, Hayden MS, Ghosh S. NF-kappaB, inflammation, and metabolic disease. Cell Metab. 2011;13(1):11–22. Aydin BN, Stinson EJ, Hanson RL, Looker HC, Cabeza De Baca T, Krakoff J, et al. Hepatic Insulin Resistance Increases Risk of Gallstone Disease in Indigenous Americans in the Southwestern United States. Clin Transl Gastroenterol. 2024;15(11):e00763. Diehl AK. Cholelithiasis and the insulin resistance syndrome. Hepatology. 2000;31(2):528–30. Chang F, Junhong C, Yongxin W, Yibo Y, Xiaocong L, Kai L. Association between complete blood cell count-derived inflammatory biomarkers and gallstones prevalence in American adults under 60 years of age. Front Immunol. 2025;15(0). Lin H, Shi K, Luo S, Ye W, Cai X. Elevated metabolic score for visceral fat was associated with increased prevalence of gallstones in American adults: a cross-sectional study. Front Med (Lausanne). 2024;11:1474368. Zuopu X, Xianpei C, Chunming X, Qi Y, Hao L. Association between ZJU index and gallstones in US adult: a cross-sectional study of NHANES 2017–2020. BMC Gastroenterol. 2024;24(1). Hongyu L, Guoheng J, Wenqian Y, Jing L, Shiyi L, Linjun X et al. Association Between Triglyceride-Glucose Index and Risk of Gallstone Disease: A Prospective Cohort Study of 395 391 Individuals. J Gastroenterol Hepatol. 2024;40(2). Ramdas Nayak VK, Satheesh P, Shenoy MT, Kalra S. Triglyceride Glucose (TyG) Index: A surrogate biomarker of insulin resistance. J Pak Med Assoc. 2022;72(5):986–8. Widjaja NA, Irawan R, Hanindita MH, Ugrasena I, Handajani R. METS-IR vs. HOMA-AD and Metabolic Syndrome in Obese Adolescents. J Med Invest. 2023;70(12):7–16. Abbasi F, Reaven G. Comparison of two methods using plasma triglyceride concentration as a surrogate estimate of insulin action in nondiabetic subjects: triglycerides × glucose versus triglyceride/high-density lipoprotein cholesterol. Metab Clin Exp. 2011;60 12:1673–6. Duan M, Zhao X, Li S, Miao G, Bai L, Zhang Q, et al. Metabolic score for insulin resistance (METS-IR) predicts all-cause and cardiovascular mortality in the general population: evidence from NHANES 2001–2018. Cardiovasc Diabetol. 2024;23(1):243. Diehl AK, Schwesinger WH, Holleman DR Jr., Chapman JB, Kurtin WE. Gallstone characteristics in Mexican Americans and non-Hispanic whites. Dig Dis Sci. 1994;39(10):2223–8. Diehl AK, Rosenthal M, Hazuda HP, Comeaux PJ, Stern MP. Socioeconomic status and the prevalence of clinical gallbladder disease. J Chronic Dis. 1985;38(12):1019–26. Unisa S, Jagannath P, Dhir V, Khandelwal C, Sarangi L, Roy TK. Population-based study to estimate prevalence and determine risk factors of gallbladder diseases in the rural Gangetic basin of North India. HPB (Oxford). 2011;13(2):117–25. Sood S, Winn T, Ibrahim SM, Gobindram A, Arumugam AA, Razali NC, et al. Natural history of asymptomatic gallstones: differential behaviour in male and female subjects. Med J Malay. 2015;70 6:341–5. Liang D, Liu C, Wang Y. The association between triglyceride-glucose index and the likelihood of cardiovascular disease in the U.S. population of older adults aged >/= 60 years: a population-based study. Cardiovasc Diabetol. 2024;23(1):151. Portincasa P, Di Ciaula A, Bonfrate L, Stella A, Garruti G, Lamont JT. Metabolic dysfunction-associated gallstone disease: expecting more from critical care manifestations. Intern Emerg Med. 2023;18(7):1897–918. Di Ciaula A, Wang DQ, Bonfrate L, Portincasa P. Current views on genetics and epigenetics of cholesterol gallstone disease. Cholesterol. 2013;2013:298421. Wang HH, Portincasa P, Wang DQ. Molecular pathophysiology and physical chemistry of cholesterol gallstones. Front Biosci. 2008;13:401–23. Chen LY, Qiao QH, Zhang SC, Chen YH, Chao GQ, Fang LZ. Metabolic syndrome and gallstone disease. World J Gastroenterol. 2012;18(31):4215–20. Ozturk MA. Association between cardiovascular risk factors and triglyceride to high-density lipoprotein ratio: a single-center experience. Arch Med Sci Atheroscler Dis. 2019;4:e196–200. Shin HG, Kim YK, Kim YH, Jung YH, Kang HC. The Relationship between the Triglyceride to High-Density Lipoprotein Cholesterol Ratio and Metabolic Syndrome. Korean J Fam Med. 2017;38(6):352–7. Yang M, Rigdon J, Tsai SA. Association of triglyceride to HDL cholesterol ratio with cardiometabolic outcomes. J Investig Med. 2019;67(3):663–8. Kang HT, Yoon JH, Kim JY, Ahn SK, Linton JA, Koh SB, et al. The association between the ratio of triglyceride to HDL-C and insulin resistance according to waist circumference in a rural Korean population. Nutr Metab Cardiovasc Dis. 2012;22(12):1054–60. Baneu P, Vacarescu C, Dragan SR, Cirin L, Lazar-Hocher AI, Cozgarea A et al. The Triglyceride/HDL Ratio as a Surrogate Biomarker for Insulin Resistance. Biomedicines. 2024;12(7). Cojocaru C, Pandele GI. [Clinical and paraclinical features in diabetic patients cholecystectomized for gallstones]. Rev Med Chir Soc Med Nat Iasi. 2010;114(4):998–1004. Biddinger SB, Haas JT, Yu BB, Bezy O, Jing E, Zhang W, et al. Hepatic insulin resistance directly promotes formation of cholesterol gallstones. Nat Med. 2008;14(7):778–82. Littlefield A, Lenahan C. Cholelithiasis: Presentation and Management. J Midwifery Womens Health. 2019;64(3):289–97. Shoelson SE, Herrero L, Naaz A. Obesity, inflammation, and insulin resistance. Gastroenterology. 2007;132(6):2169–80. Su DQ, Tian XF. Causal associations of cytokines and growth factors with cholelithiasis: a bidirectional Mendelian randomization study. Postgrad Med J. 2024;100(1180):84–90. Liu T, Siyin ST, Yao N, Duan N, Xu G, Li W, et al. Relationship between high-sensitivity C reactive protein and the risk of gallstone disease: results from the Kailuan cohort study. BMJ Open. 2020;10(9):e035880. Greenberg AS, Mcdaniel ML. Identifying the links between obesity, insulin resistance and β-cell function: potential role of adipocyte‐derived cytokines in the pathogenesis of type 2 diabetes. Eur J Clin Invest. 2002;32. Al-Mansoori L, Al-Jaber H, Prince MS, Elrayess MA. Role of Inflammatory Cytokines, Growth Factors and Adipokines in Adipogenesis and Insulin Resistance. Inflammation. 2022;45(1):31–44. Hotamisligil GS. Inflammation and metabolic disorders. Nature. 2006;444(7121):860–7. Thompson ML. Selection of Variables in Multiple Regression: Part I. A Review and Evaluation. Int Stat Rev. 1978;46:1. Additional Declarations No competing interests reported. Supplementary Files TableS1.docx TableS2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6330540","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":448680284,"identity":"bb074487-7f33-43ca-a9c5-4142a795bde2","order_by":0,"name":"Fei Zuo","email":"","orcid":"","institution":"Changzhou Traditional Chinese Medicine Hospital, Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Zuo","suffix":""},{"id":448680285,"identity":"5548bacc-4dba-4b60-bda1-fa9cd0664db8","order_by":1,"name":"Qian jiang","email":"","orcid":"","institution":"Changzhou Traditional Chinese Medicine Hospital, Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"jiang","suffix":""},{"id":448680286,"identity":"57a6732e-0d26-4dbe-b554-78d44cb26487","order_by":2,"name":"Xiaowei Huang","email":"","orcid":"","institution":"Changzhou Traditional Chinese Medicine Hospital, Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaowei","middleName":"","lastName":"Huang","suffix":""},{"id":448680287,"identity":"493c7bf5-8619-46f7-99c8-a83c5f27e434","order_by":3,"name":"Wenjun Mao","email":"","orcid":"","institution":"Changzhou Traditional Chinese Medicine Hospital, Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Wenjun","middleName":"","lastName":"Mao","suffix":""},{"id":448680288,"identity":"a50773b1-4deb-4d8a-99e8-74920f89a37a","order_by":4,"name":"Yunyan Tan","email":"","orcid":"","institution":"Changzhou Traditional Chinese Medicine Hospital, Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yunyan","middleName":"","lastName":"Tan","suffix":""},{"id":448680289,"identity":"c77767c4-d454-4652-9c44-cce9a79a136a","order_by":5,"name":"Tianping Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYFACHgaGByCavbHx4QeitSSA6cPNxhKkaZFIbxPgIUaDeXvvwQ8JNXfk+SUftjFIMNjJ6TYQ0CJz5lyyRMKxZ4YzZye2PShgSDY2O0BAi4REjoFEAtvhBIPbie0GEgwHErcRocX4R8K/wwn2Nw+2SfAQqcVMIrENaIsEI7FaeM6lWST2HTaccSYRGMgGxPiFvffwjQ/fDsvztx9/+PBDhZ0cQS1owIA05aNgFIyCUTAKcAAAjsZCPof9Z/sAAAAASUVORK5CYII=","orcid":"","institution":"Changzhou Traditional Chinese Medicine Hospital, Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Tianping","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2025-03-28 19:53:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6330540/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6330540/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82125209,"identity":"85cde182-952d-45a5-8517-451d44611bc7","added_by":"auto","created_at":"2025-05-07 03:45:35","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":394033,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart for study population.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6330540/v1/8314514634988e623ec9668f.jpeg"},{"id":82124018,"identity":"ae51abb9-383c-4198-807f-d3cbc8cc213a","added_by":"auto","created_at":"2025-05-07 03:37:35","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":361356,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between TG/HDL and gallstones odds ratio. Solid and dashed lines represent the predicted value and 95% confidence intervals. They were adjusted for sex, age, race, education level, PIR, BMI, smoking status, hypertension, DM, physical activities status, ALT, and AST. Only 99.5% of the data is displayed.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6330540/v1/070837eebcec7ff4685f8b02.jpeg"},{"id":82125211,"identity":"4804d098-44c9-46f5-a608-29b3cdaa8b28","added_by":"auto","created_at":"2025-05-07 03:45:35","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":663130,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between TG/HDL and gallstones according to basic features. Except for the stratification component itself, each stratification factor was adjusted for all other variables (sex, age, race, education level, PIR, BMI, smoking status, hypertension, DM, physical activities status, ALT, and AST).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6330540/v1/d5c8c07fdddbe46c6812709b.jpeg"},{"id":82124028,"identity":"dce20e00-0582-4d96-b4f1-2aa6c11e699f","added_by":"auto","created_at":"2025-05-07 03:37:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":172531,"visible":true,"origin":"","legend":"\u003cp\u003eMediation analysis for WBC in the association between TG/HDL and gallstones. Total effect: the overall impact of TG/HDL (X) on gallstones (Y), without considering the mediating effect of WBC (M); Direct effect: the direct impact of TG/HDL (X) on gallstones (Y) after controlling for the effect of WBC (M); Indirect effect: the indirect impact of TG/HDL (X) on gallstones (Y) through WBC (M); Percent mediation: the proportion of the indirect effect in the total effect, reflecting the importance of WBC (M) in the relationship between TG/HDL (X) on gallstones (Y). Model 1 was the crude model; Model 2 was adjusted for sex, age. Model 3 was adjusted for age. Model 4 was adjusted for sex.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6330540/v1/1b9e8479788cc29020dd56bd.png"},{"id":85634660,"identity":"f1deecb2-d7e9-4be6-9a50-80dfb0d236f7","added_by":"auto","created_at":"2025-06-30 05:32:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2883977,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6330540/v1/cf8c946e-af53-4981-8492-f7d50855159f.pdf"},{"id":82124017,"identity":"9723aadc-a4e0-4a05-af25-4bb556cf600c","added_by":"auto","created_at":"2025-05-07 03:37:35","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12654,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6330540/v1/f91f5490bc5138ebaa1475d0.docx"},{"id":82124019,"identity":"696c63cc-6a95-4743-bd0d-041bb2bf23cf","added_by":"auto","created_at":"2025-05-07 03:37:35","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":13226,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6330540/v1/5e6d382a26969efd52dcbf4d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"WBCs as a mediator between the TG/HDL ratio and gallstone disease across sex differences: NHANES 2017–2020","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGallstone disease (GSD) is a prevalent and economically burdensome condition that significantly contributes to global morbidity(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The worldwide prevalence of GSD is estimated at approximately 6.1%, with notably higher rates of approximately 15% observed in populations within the United States and Europe(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In the United States alone, nearly 800,000 cholecystectomies are performed annually, resulting in costs approaching \u003cspan\u003e$\u003c/span\u003e6\u0026nbsp;billion and imposing substantial health and economic burdens(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Consequently, GSD has become a focal point of medical research. The etiology of GSD is multifactorial, with cholesterol gallstones being the most common type, accounting for approximately 75% of cases(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The formation of cholesterol gallstones is intricately linked to abnormalities in lipid metabolism, which are closely associated with insulin resistance (IR)(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Furthermore, IR is strongly related to chronic inflammation, a condition that plays a critical role in various metabolic disorders(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Obesity and chronic inflammation are the principal determinants of insulin resistance(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Despite these associations, the interplay between insulin resistance, chronic inflammation, and the development of gallstones remains underexplored, suggesting a promising direction for future research. Previous studies have investigated the direct relationship between hepatic insulin resistance and GSD, revealing potential mechanistic links(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). However, these approaches often involve invasive and costly methods, such as the hyperinsulinaemic-euglycaemic clamp (HIEC), thereby limiting their clinical applicability(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). To address these limitations, the present study aimed to examine the relationships of inflammatory indices and insulin resistance indices with the incidence of gallstone disease. By utilizing more accessible and less invasive measures(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), such as insulin resistance indicators and inflammatory scores, this research aims to increase the predictive accuracy for GSD risk and identify high-risk populations, ultimately contributing to more effective prevention and management strategies.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch design\u003c/h2\u003e \u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES) is a comprehensive nationwide survey conducted by the National Center for Health Statistics (NCHS). Data on demographics, physical measurements, medical history, laboratory tests, and health behaviors were systematically collected. The program has received ethical approval from the NCHS review board. All participants were recruited voluntarily and provided informed consent. Data collection occurs biennially. For this study, participants were specifically asked to respond to questionnaires regarding their history of gallstones during the 2017\u0026ndash;2020 cycle. For further information and data access, please visit the NHANES website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/index.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/index.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDefinition of gallstones\u003c/h3\u003e\n\u003cp\u003eTo ascertain the presence of gallstones among participants, each individual was asked the following question: \"Has a doctor or other health professional ever told you that you had gallstones?\" Participants who responded affirmatively were categorized as having gallbladder stones, whereas those who responded negatively were classified as not having gallstones. This self-reported method provided a straightforward means of identifying individuals with and without gallstone disease for the purposes of the study.\u003c/p\u003e\n\u003ch3\u003eIdentification of covariates\u003c/h3\u003e\n\u003cp\u003eA comprehensive assessment of potential covariates was conducted on basis of the literature(\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The variables included in this analysis were extracted from the NHANES database and included demographic factors (sex, age, race, education level categorized as less than high school, high school, or higher than high school), socioeconomic indicators (marital status, family poverty-to-income ratio [PIR]), health conditions (hypertension, diabetes), lifestyle factors (physical activity, smoking status), anthropometric measurements (body mass index [BMI], waist circumference, hip circumference), and laboratory parameters (complete blood count, lipid profile, liver function tests). The diagnoses of hypertension and diabetes were ascertained through a combination of questionnaire responses, physical examinations, and laboratory test results. Physical activity status was classified via data from the Physical Activity Questionnaire (PAQ), whereas smoking status was determined on the basis of survey responses.\u003c/p\u003e\n\u003ch3\u003eInsulin resistance replacement index\u003c/h3\u003e\n\u003cp\u003eFour surrogate indices for IR were incorporated into this study: the triglyceride-glucose (TyG) index, METS-IR, triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL), and homeostatic model assessment of insulin resistance (HOMA-IR). The TyG index was calculated via the following formula: TyG index\u0026thinsp;=\u0026thinsp;Ln[triglycerides (mg/dL) \u0026times; glycemia (mg/ dL)/2](\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). ETS-IR was determined as follows: METS-IR\u0026thinsp;=\u0026thinsp;Ln[2 \u0026times; glycemia (mg/dL)\u0026thinsp;+\u0026thinsp;triglycerides (mg/dL)] \u0026times; BMI/Ln HDL (mg/dL)(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The TG/HDL ratio was computed as: TG/HDL, which was calculated as triglycerides (mg/dL)/HDL (mg/ dL)(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). HOMA-IR was derived via the following equation: HOMA-IR\u0026thinsp;=\u0026thinsp;FPG (mmol/L) \u0026times; FINS (mIU/L) /22.5(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eA total of 15,560 individuals were initially enrolled and completed the baseline interview. A total of 5,867 participants aged\u0026thinsp;\u0026lt;\u0026thinsp;18 years and 87 pregnant women were excluded from the analysis. Additionally, participants with missing data on gallstone status (n\u0026thinsp;=\u0026thinsp;482), educational level (n\u0026thinsp;=\u0026thinsp;14), marital status (n\u0026thinsp;=\u0026thinsp;7), hypertension (n\u0026thinsp;=\u0026thinsp;25), diabetes mellitus (n\u0026thinsp;=\u0026thinsp;4), smoking status (n\u0026thinsp;=\u0026thinsp;4), and variables related to insulin resistance (IR) such as body mass index (BMI), waist circumference (WC), hip circumference, total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), fasting plasma glucose (FPG), and insulin levels (n\u0026thinsp;=\u0026thinsp;5,437) were also excluded. Furthermore, participants with missing data on inflammatory biomarkers, including high-sensitivity C-reactive protein (hs-CRP), white blood cell count (WBC), platelet count (PLT), neutrophil, lymphocytes, and monocyte counts (n\u0026thinsp;=\u0026thinsp;9), were excluded. Ultimately, 3,624 participants were included in the analysis: 383 with gallstones and 3,241 without gallstones. The detailed inclusion and exclusion process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBaseline characteristics\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the baseline characteristics of the 3,624 participants, who had a mean age of 50.8\u0026thinsp;\u0026plusmn;\u0026thinsp;17.2 years. Among those diagnosed with gallstone disease, the female-to-male ratio was 2.52:1. The cohort diagnosed with gallstones was predominantly Non-Hispanic White. The mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD values for insulin resistance (IR)-related biomarkers were as follows: TyG\u0026thinsp;=\u0026thinsp;8.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6, METS-IR\u0026thinsp;=\u0026thinsp;49.8\u0026thinsp;\u0026plusmn;\u0026thinsp;14.8, TG/HDL\u0026thinsp;=\u0026thinsp;2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7, and HOMA-IR\u0026thinsp;=\u0026thinsp;6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5. These biomarkers were significantly elevated in participants with gallstones compared with those without gallstones (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, the levels of HbA1c, insulin, BMI, FPG, TG, WBC count, and neutrophils were significantly higher in participants with gallstones than in those without gallstones (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;3624)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNon-gallstone (n\u0026thinsp;=\u0026thinsp;3241)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGallstone (n\u0026thinsp;=\u0026thinsp;383)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e50.8\u0026thinsp;\u0026plusmn;\u0026thinsp;17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e50.0\u0026thinsp;\u0026plusmn;\u0026thinsp;17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.6\u0026thinsp;\u0026plusmn;\u0026thinsp;15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1768 (48.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1659 (51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e109 (28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1856 (51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1582 (48.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e274 (71.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1228 (33.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1076 (33.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e152 (39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e917 (25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e853 (26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e460 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e404 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1019 (28.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e908 (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e111 (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2145 (59.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1918 (59.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e227 (59.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1479 (40.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1323 (40.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e156 (40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level (year), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e268 ( 7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e244 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1279 (35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1132 (34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e147 (38.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2077 (57.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1865 (57.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e212 (55.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIR, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e843 (26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e759 (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e84 (24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1.3, \u0026lt;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1264 (40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1108 (39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e156 (46.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1037 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e940 (33.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97 (28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2064 (57.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1854 (57.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e210 (54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e877 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e759 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e118 (30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e683 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e628 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate activity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2025 (55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1810 (55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e215 (56.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1596 (44.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1428 (44.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e168 (43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2231 (61.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2055 (63.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e176 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1393 (38.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1186 (36.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e207 (54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3053 (84.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2770 (85.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e283 (73.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e571 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e471 (14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100 (26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e29.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e29.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e100.7\u0026thinsp;\u0026plusmn;\u0026thinsp;17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e99.9\u0026thinsp;\u0026plusmn;\u0026thinsp;17.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e108.0\u0026thinsp;\u0026plusmn;\u0026thinsp;17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHip circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e107.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e106.6\u0026thinsp;\u0026plusmn;\u0026thinsp;14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e114.3\u0026thinsp;\u0026plusmn;\u0026thinsp;17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.6 (5.3, 6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e5.6 (5.3, 6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e5.7 (5.4, 6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin (\u0026micro;U/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e14.6\u0026thinsp;\u0026plusmn;\u0026thinsp;22.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.1\u0026thinsp;\u0026plusmn;\u0026thinsp;22.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e18.8\u0026thinsp;\u0026plusmn;\u0026thinsp;25.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e183.7\u0026thinsp;\u0026plusmn;\u0026thinsp;40.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e183.9\u0026thinsp;\u0026plusmn;\u0026thinsp;40.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e182.0\u0026thinsp;\u0026plusmn;\u0026thinsp;44.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e53.7\u0026thinsp;\u0026plusmn;\u0026thinsp;15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e53.9\u0026thinsp;\u0026plusmn;\u0026thinsp;16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.3\u0026thinsp;\u0026plusmn;\u0026thinsp;13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e105.0\u0026thinsp;\u0026plusmn;\u0026thinsp;62.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e103.7\u0026thinsp;\u0026plusmn;\u0026thinsp;62.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e116.0\u0026thinsp;\u0026plusmn;\u0026thinsp;61.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e22.2\u0026thinsp;\u0026plusmn;\u0026thinsp;19.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e22.3\u0026thinsp;\u0026plusmn;\u0026thinsp;19.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.4\u0026thinsp;\u0026plusmn;\u0026thinsp;13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e21.8\u0026thinsp;\u0026plusmn;\u0026thinsp;15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e22.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMETSIR, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e44.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e43.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49.8\u0026thinsp;\u0026plusmn;\u0026thinsp;14.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG/HDL, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMAIR, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e6.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e6.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT (10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e241.7\u0026thinsp;\u0026plusmn;\u0026thinsp;64.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e241.6\u0026thinsp;\u0026plusmn;\u0026thinsp;63.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e242.8\u0026thinsp;\u0026plusmn;\u0026thinsp;68.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophils (10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes (10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocytes (10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; DM, diabetes mellitus; FPG, fasting plasma glucose; HbA1c, glycated hemoglobin A1c; HDL, high-density lipoprotein; HOMA‐IR, homeostasis model assessment‐insulin resistance; hs‐CRP, high-sensitivity C‐reactive protein; LDL, low‐density lipoprotein ; METSIR, metabolic score for insulin resistance; PLT, platelet count; TC, total cholesterol; TG, triglyceride; TyG, triglyceride glucose index;WBC, white blood cell.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eAssociation between IR surrogate indices and the risk of gallstones\u003c/h3\u003e\n\u003cp\u003eUnivariate analysis revealed several demographic and clinical factors significantly associated with the presence of gallstones (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Specifically, age, sex, race, and smoking status were found to be significantly related to gallstone occurrence. Notably, non-Hispanic White participants presented a greater prevalence of gallstones than other racial groups did. Additionally, female sex was associated with an increased likelihood of gallstone development. Clinical parameters related to cholesterol metabolism were also significantly associated with gallstones. These included HbA1c, hypertension, diabetes mellitus, BMI, waist circumference, hip circumference, FPG, and TG. Furthermore, insulin resistance indices\u0026mdash;TyG, METS-IR, TG/HDL, and HOMA-IR\u0026mdash;were significantly elevated in participants with gallstones compared with those without gallstones (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, the levels of inflammatory markers such as white blood cells and neutrophils were significantly greater in individuals with gallstones.\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\u003eAssociation between covariates and the risk of gallstones\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=\"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 \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% CI)\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\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03 (1.02\u0026thinsp;~\u0026thinsp;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate activity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99 (0.8\u0026thinsp;~\u0026thinsp;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.64 (2.09\u0026thinsp;~\u0026thinsp;3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.04 (1.65\u0026thinsp;~\u0026thinsp;2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.53 (0.39\u0026thinsp;~\u0026thinsp;0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDM, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.71\u0026thinsp;~\u0026thinsp;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87 (0.67\u0026thinsp;~\u0026thinsp;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.08 (1.62\u0026thinsp;~\u0026thinsp;2.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003eMarital, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06 (1.05\u0026thinsp;~\u0026thinsp;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWaist circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03 (1.02\u0026thinsp;~\u0026thinsp;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003eLiving alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.8\u0026thinsp;~\u0026thinsp;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHip circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03 (1.02\u0026thinsp;~\u0026thinsp;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003eEducation (year), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFPG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07 (1.03\u0026thinsp;~\u0026thinsp;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInsulin (\u0026micro;U/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01 (1\u0026thinsp;~\u0026thinsp;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.32 (0.84\u0026thinsp;~\u0026thinsp;2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTC (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1\u0026thinsp;~\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.16 (0.74\u0026thinsp;~\u0026thinsp;1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHDL (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99 (0.99\u0026thinsp;~\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIR, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\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\u003eTG (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1\u0026thinsp;~\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92 (0.82\u0026thinsp;~\u0026thinsp;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1.3, \u0026lt;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.27 (0.96\u0026thinsp;~\u0026thinsp;1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0.99\u0026thinsp;~\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93 (0.69\u0026thinsp;~\u0026thinsp;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAST (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99 (0.98\u0026thinsp;~\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTyG, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.54 (1.32\u0026thinsp;~\u0026thinsp;1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMETSIR, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03 (1.02\u0026thinsp;~\u0026thinsp;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37 (1.08\u0026thinsp;~\u0026thinsp;1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTG/HDL, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06 (1.01\u0026thinsp;~\u0026thinsp;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77 (0.57\u0026thinsp;~\u0026thinsp;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHOMAIR, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02 (1.01\u0026thinsp;~\u0026thinsp;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.17 (1.08\u0026thinsp;~\u0026thinsp;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWBC (10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.09 (1.04\u0026thinsp;~\u0026thinsp;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003ehs-CRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1\u0026thinsp;~\u0026thinsp;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNeutrophils (10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.12 (1.05\u0026thinsp;~\u0026thinsp;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003ePLT (10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1\u0026thinsp;~\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLymphocytes (10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06 (0.97\u0026thinsp;~\u0026thinsp;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA total of three logistic regression models were developed to evaluate the associations of the TG/HDL ratio and other insulin resistance indices with the presence of gallstones (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). According to the fully adjusted Model 3, the highest quartile of TG/HDL (Q4) remained significantly associated with an increased risk of gallstones (odds ratio [OR]\u0026thinsp;=\u0026thinsp;1.63; 95% confidence interval [CI]: 1.07\u0026ndash;2.49; P\u0026thinsp;=\u0026thinsp;0.022). In contrast, other insulin resistance indices\u0026mdash;TyG (OR\u0026thinsp;=\u0026thinsp;1.34; 95% CI: 0.88\u0026ndash;2.04), METS-IR (OR\u0026thinsp;=\u0026thinsp;1.41; 95% CI: 0.75\u0026ndash;2.63), and HOMA-IR (OR\u0026thinsp;=\u0026thinsp;1.26; 95% CI: 0.81\u0026ndash;1.97)\u0026mdash;did not exhibit a statistically significant association with gallstones in Q4 of Model 3 (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Additionally, in both Model 1 and Model 2, TyG, METS-IR, and HOMA-IR demonstrated positive correlations with the risk of gallstones across all quartiles (Q1, Q2, Q3, and Q4). However, these associations were attenuated and lost statistical significance in the fully adjusted Model 3. This attenuation suggests that the relationship between TG/HDL and gallstone risk is more robust after controlling for potential confounding factors, potentially revealing the true association independent of other metabolic variables.\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\u003eAssociation between insulin resistance indices and gallstones.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eModel 3\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\u003eTrend.test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTyG (quartile)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.94 (1.37\u0026thinsp;~\u0026thinsp;2.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.76 (1.23\u0026thinsp;~\u0026thinsp;2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.53 (1.02\u0026thinsp;~\u0026thinsp;2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.15 (1.52\u0026thinsp;~\u0026thinsp;3.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9 (1.33\u0026thinsp;~\u0026thinsp;2.71)\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\u003e1.27 (0.84\u0026thinsp;~\u0026thinsp;1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.75 (1.96\u0026thinsp;~\u0026thinsp;3.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.42 (1.71\u0026thinsp;~\u0026thinsp;3.43)\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\u003e1.34 (0.88\u0026thinsp;~\u0026thinsp;2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTrend.test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.34 (1.21\u0026thinsp;~\u0026thinsp;1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.29 (1.16\u0026thinsp;~\u0026thinsp;1.43)\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\u003e1.05 (0.92\u0026thinsp;~\u0026thinsp;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMETSIR (quartile)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.84 (1.27\u0026thinsp;~\u0026thinsp;2.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.79 (1.23\u0026thinsp;~\u0026thinsp;2.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.49 (0.97\u0026thinsp;~\u0026thinsp;2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4 (1.68\u0026thinsp;~\u0026thinsp;3.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.43 (1.69\u0026thinsp;~\u0026thinsp;3.5)\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\u003e1.50 (0.94\u0026thinsp;~\u0026thinsp;2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.57 (2.53\u0026thinsp;~\u0026thinsp;5.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.84 (2.7\u0026thinsp;~\u0026thinsp;5.44)\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\u003e1.4 1(0.75\u0026thinsp;~\u0026thinsp;2.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTrend.test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48 (1.34\u0026thinsp;~\u0026thinsp;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.53 (1.38\u0026thinsp;~\u0026thinsp;1.7)\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\u003e1.1 (0.91\u0026thinsp;~\u0026thinsp;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTG/HDL (quartile)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.06 (1.47\u0026thinsp;~\u0026thinsp;2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.16 (1.53\u0026thinsp;~\u0026thinsp;3.06)\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\u003e2.05 (1.38\u0026thinsp;~\u0026thinsp;3.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.18 (1.55\u0026thinsp;~\u0026thinsp;3.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.23 (1.58\u0026thinsp;~\u0026thinsp;3.16)\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\u003e1.68 (1.12\u0026thinsp;~\u0026thinsp;2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.27 (1.62\u0026thinsp;~\u0026thinsp;3.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.64 (1.86\u0026thinsp;~\u0026thinsp;3.74)\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\u003e1.63 (1.07\u0026thinsp;~\u0026thinsp;2.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTrend.test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24 (1.13\u0026thinsp;~\u0026thinsp;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.3 (1.18\u0026thinsp;~\u0026thinsp;1.44)\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\u003e1.09 (0.96\u0026thinsp;~\u0026thinsp;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHOMAIR (quartile)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8 (1.25\u0026thinsp;~\u0026thinsp;2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.62 (1.12\u0026thinsp;~\u0026thinsp;2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.14 (0.76\u0026thinsp;~\u0026thinsp;1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.29 (1.62\u0026thinsp;~\u0026thinsp;3.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.08 (1.45\u0026thinsp;~\u0026thinsp;2.96)\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\u003e1.23 (0.82\u0026thinsp;~\u0026thinsp;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.13 (2.23\u0026thinsp;~\u0026thinsp;4.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.96 (2.1\u0026thinsp;~\u0026thinsp;4.17)\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\u003e1.26 (0.81\u0026thinsp;~\u0026thinsp;1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTrend.test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42 (1.29\u0026thinsp;~\u0026thinsp;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.41 (1.27\u0026thinsp;~\u0026thinsp;1.56)\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\u003e1.08 (0.94\u0026thinsp;~\u0026thinsp;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eNote: Model 1 was the crude model; Model 2 was adjusted for sex and age; Model 3 was adjusted for sex, age, race, education level, PIR, BMI, smoking status, hypertension, DM, physical activities status, ALT, and AST.\u003c/p\u003e \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\u003eInvestigating the dose-response relationship between TG/HDL and gallstones\u003c/h2\u003e \u003cp\u003eDespite the consistent significance across models, the trend test in Model 3 of TG/HDL (Q4) did not reach statistical significance (p\u0026thinsp;=\u0026thinsp;0.171) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), indicating the absence of a linear dose-response relationship between TG/HDL levels and gallstone risk. To explore potential nonlinear associations, restricted cubic spline (RCS) analysis was conducted. The RCS revealed a reverse L-shaped curve in the relationship between TG/HDL and gallstone occurrence, supporting a nonlinear association (p\u0026thinsp;=\u0026thinsp;0.049) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the threshold analysis, the inflection point at a TG/HDL ratio of 2.794 did not reach statistical significance (likelihood ratio test; p\u0026thinsp;=\u0026thinsp;0.106) (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This lack of significance may indicate that while a nonlinear relationship exists, the precise point at which the association changes direction is not definitively established within this dataset.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStratified Analyses Based on Additional Variables\u003c/h2\u003e \u003cp\u003eTo further assess the robustness of the association between the TG/HDL ratio and the incidence of gallstones, subgroup analyses were conducted. These analyses aimed to identify potential effect modifications across various demographic and socioeconomic strata. Across all the examined subgroups, no significant interactions were observed within any subgroup after stratification by sex, age, education level, PIR, or BMI (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Furthermore, when considering the issue of multiple testing, an observed p-value of less than 0.05 for the interaction with marital status may not be deemed statistically significant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe mediating effect of WBCs on the association between TG/HDL and gallstones\u003c/h2\u003e \u003cp\u003eTo elucidate the underlying mechanisms by which the TG/HDL ratio influences gallstone formation, mediation analyses were conducted to evaluate the potential mediating role of the WBC count in this association. In the unadjusted mediation model, WBC count was found to fully mediate the relationship between the TG/HDL ratio and gallstone risk, accounting for 28.6% of the fully mediated effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Upon adjusting for both sex and age, the mediating effect of the WBC count remained statistically significant but was attenuated to 17.2% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In this adjusted model, the WBC count had both direct and indirect effects on the association, suggesting partial mediation. When age was included as a single covariate, the WBC count continued to fully mediate the relationship, which was consistent with the crude model. In contrast, when sex was adjusted for as a single covariate, the WBC count had both direct and indirect effects on the association between the TG/HDL ratio and gallstone risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These fingdings suggest that sex as a covariate may modify the mediating effect of the WBC count.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eInteraction analysis between WBC count, sex, and the TG/HDL ratio in relation to gallstone risk\u003c/h2\u003e \u003cp\u003eThe analyses revealed that WBCs do not exhibit multiplicative or additive interactions with TG/HDL in relation to gallstone risk (Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). In contrast, significant additive interactions were identified between sex and the TG/HDL ratio in relation to gallstone risk (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas no significant multiplicative interactions were observed (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). the WBC count functions solely as a mediator, facilitating the pathway through which the TG/HDL ratio influences gallstone risk. Conversely, sex exhibits an additive interaction, augmenting the overall risk associated with the TG/HDL ratio without multiplicatively altering the relationship.\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\u003eMultiplicative and additive interactions between sex and TG/HDL in relation to gallstone risk\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRisk of gallstone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTG/HDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOR(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP for interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eMeasures of additive interaction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRERI (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAP (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003egallstone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.4(0.83\u0026thinsp;~\u0026thinsp;2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.33(0.11\u0026thinsp;~\u0026thinsp;0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11(0.72\u0026thinsp;~\u0026thinsp;1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.01 (2.1\u0026thinsp;~\u0026thinsp;4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.66 (3.18\u0026thinsp;~\u0026thinsp;6.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: Adjusted for age, race, education level, PIR, BMI, smoking status, hypertension, DM, and physical activity status.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study utilized data from the NHANES to explore the relationship between gallstones and TG /HDL levels, with a particular focus on the mediating role of the WBC count. The results from the 2017\u0026ndash;2020 NHANES indicate that individuals diagnosed with gallstones were predominantly Non-Hispanic White individuals rather than Non-Hispanic White individuals (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast to earlier studies conducted in the 1990s(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), which reported markedly higher GSD prevalence in indigenous and Mexican American populations than in White populations in the United States, our current findings suggest a shift in the epidemiological distribution of gallstones across different racial and ethnic groups. Numerous studies have indicated that females exhibit a greater propensity for gallstone development than males do, largely attributable to the effects of sex hormones such as estrogen and progesterone(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Consistent with these findings, our research also demonstrated that females are at a greater risk for gallstone formation. Compared with males, females have different eating habits and lifestyles and may be more inclined to consume a high-sugar, high- fat diet, which can lead to a higher TyG index and thus an increased risk of GSD(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)。\u003c/p\u003e \u003cp\u003eApproximately 80% of gallstones in Western populations are composed of cholesterol, a consequence of disrupted cholesterol homeostasis involving the liver, gallbladder, and intestine against a genetic background(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Cholesterol gallstones are closely associated with components of metabolic syndrome, particularly cholesterol metabolism, including obesity, type 2 diabetes, and insulin resistance(\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). These findings suggest that metabolic dysregulation, particularly in cholesterol metabolism, significantly contributes to gallstone formation. Multiple studies have shown that individuals with MetS have a significantly greater risk of developing GSD, with the risk proportionately increasing with the number of MetS components present(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In line with the findings of the present study, logistic multivariate regression analysis suggested a synergistic effect of BMI, waist circumference, hip circumference, hypertension, diabetes, and TG/HDL on the formation of gallstones. Specifically, the TG/HDL ratio is positively correlated with BMI, waist circumference, blood pressure, and fasting plasma glucose levels(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Numerous studies have consistently demonstrated a significant association between TG/HDL and various metabolic factors(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Furthermore, the TG/HDL ratio has been identified as an independent predictor of insulin resistance, even after adjusting for waist circumference(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). This finding is consistent with studies that have highlighted the TG/HDL ratio as a surrogate biomarker for insulin resistance, with specific thresholds enhancing accuracy across different ethnicities and sexes(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The present study demonstrated that the TG/HDL ratio is closely associated with GSD, exhibiting a nonlinear relationship that is particularly pronounced in females. The key factors contributing to this association include obesity, insulin resistance, and abnormal glucose metabolism(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Hepatic insulin resistance directly promotes gallstone formation by altering bile acid synthesis and transport, mechanisms essential for cholesterol solubilization and excretion(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Cholecystolithiasis is frequently associated with inflammation caused by gallstones(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), which exacerbates metabolic disturbances and fosters an environment conducive to gallstone nucleation and growth(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Several studies on cytokines and gallstones have identified four circulating interleukins\u0026mdash;IL-6, IL-10, IL-12, and IL-13\u0026mdash;that are linked to the presence of gallstones(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Additionally, gallstones are closely associated with insulin resistance and obesity(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Furthermore, insulin resistance is frequently concomitant with obesity, which fosters a state of chronic, low-grade inflammation. The interplay among insulin resistance, chronic inflammation, and gallstone development is complex. These observations are consistent with our analysis. Multivariate analysis indicated that TyG, METS-IR, HOMA-IR, TG/HDL, and WBC are significantly associated with gallstone disease. Among the insulin resistance surrogate indices evaluated, the TG/HDL ratio demonstrated a robust association with gallstone risk, remaining significant in the fully adjusted Model 3 of TG/HDL (Q4) (odds ratio [OR]\u0026thinsp;=\u0026thinsp;1.63; 95% confidence interval [CI]: 1.07\u0026ndash;2.49; P\u0026thinsp;=\u0026thinsp;0.022). In contrast, other indices such as TyG, METS-IR, and HOMA-IR, did not retain statistical significance after full adjustment, suggesting that the TG/HDL ratio may serve as a more reliable biomarker for assessing gallstone risk within this population.\u003c/p\u003e \u003cp\u003eFurthermore, various stratified analyses further confirmed the robustness of these associations. The dose-response analysis revealed a nonlinear, reverse L-shaped association between the TG/HDL ratio and gallstone occurrence. This nonlinearity suggests that the relationship between TG/HDL and GSD risk may be influenced by threshold effects or diminishing returns at higher levels of TG/HDL. The absence of a statistically significant inflection point in the threshold analysis indicates that further research is necessary to precisely characterize the nature of this nonlinear relationship.\u003c/p\u003e \u003cp\u003eOne study directly utilized isotopic glucose tracers to estimate whole-body insulin resistance(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). However, owing to the time-consuming nature of this technique and the associated potential risks, we have opted for alternative methods that offer advantages such as low cost and easy accessibility for assessing individual insulin resistance and inflammatory markers. Certain inflammatory factors can directly interfere with the signalling pathway through which insulin exerts its biological effects, exacerbating IR(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), and IR is typically associated with a systemic low-grade inflammatory state(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). While the relationships among inflammation, IR, and GSD are well established, there is limited human evidence directly linking insulin resistance to gallstone disease. Few studies have quantitatively examined the associations among these three factors. In the present study, the TG/HDL ratio was utilized to assess patients' IR status, and mediation analysis was employed to quantify the relationships among inflammation, IR, and gallstone risk. The mediation analysis revealed that the WBC count significantly mediated the relationship between the TG/HDL ratio and gallstone risk. The findings reveal that the WBC count serves as a significant mediator, accounting for 28.6% of the association in the unadjusted model and 17.2% after adjusting for sex and age, thereby indicating a substantial yet moderated mediating role. This attenuation suggests that while inflammation, as indicated by the WBC count, partially explains the link between dyslipidemia and GSD, other factors may also contribute to this association. The involvement of the WBC count underscores the potential inflammatory pathways through which metabolic disturbances may facilitate gallstone formation, thereby aligning with theories that inflammation plays a critical role in the pathogenesis of cholesterol gallstone disease(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen sex and age were adjusted for as a single covariate(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), sex appeared to modify the mediating effect of the WBC count through the TG/HDL ratio. To further explore the interaction between sex and the TG/HDL ratio, interaction analysis revealed a significant additive interaction between sex and the TG/HDL ratio in relation to gallstone disease risk, although no multiplicative interaction was observed. This interaction indicates that the effect of the TG/HDL ratio on GSD risk is more pronounced in females than in males, potentially explaining the higher prevalence of gallstones observed in females. Additionally, interaction analyses demonstrated that the WBC count did not exhibit either additive or multiplicative interactions. In summary, the WBC count functions solely as a mediator, facilitating the pathway through which the TG/HDL ratio influences gallstone risk. However, sex exhibits an additive interaction, increasing the overall risk associated with the TG/HDL ratio without multiplicatively altering the relationship.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eDespite its strengths, this study is subject to several limitations. The cross-sectional design precludes the establishment of causality between the TG/HDL ratio and gallstone risk. Longitudinal studies are warranted to ascertain temporal relationships and causal pathways. Additionally, the exclusion of a substantial number of participants due to missing data may introduce selection bias, potentially affecting the generalizability of the findings. The reliance on self-reported data for certain variables, such as smoking status and medical history, may also be susceptible to reporting inaccuracies. Moreover, studies that focus on specific IR indices and inflammatory markers may overlook other relevant biological factors influencing gallstone development.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the TG/HDL ratio emerges as a significant and independent predictor of gallstone risk, outperforming other IR surrogate indices in this regard. The partial mediating role of the WBC count underscores the intersection between metabolic dysregulation and inflammatory processes in gallstone pathogenesis. Furthermore, the additive interaction with sex highlights the necessity for sex-specific approaches in gallstone prevention and management. These findings provide valuable insights into the complex interplay of metabolic and inflammatory factors in gallstone disease and pave the way for targeted interventions aimed at reducing its burden.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the NHANES database for providing the data source for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eT.P.L designed the study; T.P.L and \u0026nbsp;F.Z wrote the main manuscript text; \u0026nbsp;W.J.M, Y.Y.T, \u0026nbsp; and X.W.H collected biochemical data; T.P.L and Q.J prepared figures 1-4. T.P.L and Q.J prepared tables 1-4. All authors reviewed and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo financial support was received for this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysis during the current study are available in the NHANES, www.cdc.gov/nchs/NHANEs/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Center for Health Statistics Ethics Review Board has approved the implementation of NHANES, and every participant signed informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHotineanu V, Moraru V, Bujor P, Bujor S. 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Identifying the links between obesity, insulin resistance and β-cell function: potential role of adipocyte‐derived cytokines in the pathogenesis of type 2 diabetes. Eur J Clin Invest. 2002;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Mansoori L, Al-Jaber H, Prince MS, Elrayess MA. Role of Inflammatory Cytokines, Growth Factors and Adipokines in Adipogenesis and Insulin Resistance. Inflammation. 2022;45(1):31\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHotamisligil GS. Inflammation and metabolic disorders. Nature. 2006;444(7121):860\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThompson ML. Selection of Variables in Multiple Regression: Part I. A Review and Evaluation. Int Stat Rev. 1978;46:1.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"white wlood cells, triglyceride-to-high-density lipoprotein ratio, gallstones, inflammation, insulin resistance, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-6330540/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6330540/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGallstone disease (GSD) is associated with insulin resistance (IR) and systemic inflammation, yet the quantitative relationships among these factors remain underexplored. This study investigated the association between IR surrogate indices and the GSD, with a focus on the mediating role of inflammation and potential sex-based differences.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eInsulin resistance was assessed via biomarkers, including the triglyceride-to-high-density lipoprotein cholesterol (TG/HDL) ratio; TyG, METS-IR, and HOMA-IR, and inflammatory markers, such as white blood cells (WBCs). The associations between TG/HDL and GSD were assessed through logistic regression models and restricted cubic spline (RCS) analysis. Subgroup analyses were conducted on basis of age, sex, marital status, education, poverty-to-income ratio (PIR) and body mass index (BMI). Furthermore, a key focus of the analysis was to investigate the mediating role of WBC count in the relationship between TG/HDL and incident GSD. Additionally, interactions between sex and TG/HDL were tested on both multiplicative and additive scales.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e Among the 3,624 included participants (383 with gallstones and 3,241 without), the mean age was 50.8\u0026thinsp;\u0026plusmn;\u0026thinsp;17.2 years. Among those diagnosed with gallstone disease, the female-to-male ratio was 2.52:1. The highest quartile (Q4) of TG/HDL was significantly associated with increased GSD risk in the fully adjusted model (OR\u0026thinsp;=\u0026thinsp;1.63; 95% CI: 1.07\u0026ndash;2.49; P\u0026thinsp;=\u0026thinsp;0.022), whereas TyG, METS-IR, and HOMA-IR did not have significant associations with Q4 (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). RCS analysis revealed a nonlinear, reverse L-shaped relationship between TG/HDL and GSD risk (P\u0026thinsp;=\u0026thinsp;0.049). Mediation analysis revealed that in the unadjusted model, the WBC count fully mediated the association between the TG/HDL ratio and GSD, accounting for 28.6% of the total effect. After adjusting for sex and age, the WBC count partially mediated this relationship, explaining 17.2% of the effect. Interaction analysis demonstrated a significant additive interaction effect between sex and the TG/HDL ratio (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), without a significant multiplicative interaction effect (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting increased GSD risk in females. No significant interactions were observed between WBC count and TG/HDL.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe TG/HDL ratio is strongly associated with GSD risk, exhibiting a nonlinear relationship partially mediated by inflammation, as indexed by the WBC count. Sex exerts an additive effect on this association, highlighting potential hormonal contributions to gallstone formation.\u003c/p\u003e","manuscriptTitle":"WBCs as a mediator between the TG/HDL ratio and gallstone disease across sex differences: NHANES 2017–2020","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 03:37:30","doi":"10.21203/rs.3.rs-6330540/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"607e78c1-a1a9-4b39-9094-f1be3b5f990b","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-30T05:23:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 03:37:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6330540","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6330540","identity":"rs-6330540","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}