Associations between gallbladder surgery and kidney stones: The mediating roles of hypertension and diabetes

Associations between gallbladder surgery and kidney stones: The mediating roles of hypertension and diabetes | 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 Associations between gallbladder surgery and kidney stones: The mediating roles of hypertension and diabetes Youjian Li, Yongshan Li, Zuhong Ji, Jie Liu, Kai Li, Yetao Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6560456/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 : Kidney stones are a prevalent urological condition that affects a considerable number of individuals worldwide. However, the connection between gallbladder surgery and the formation of kidney stones, along with the potential mediating effect ofhypertension and diabetes on this relationship, is not fully elucidated. Materials and methods : Data from 6,579 adults aged 20 years or older in the National Health and Nutrition Examination Survey (NHANES) from 2017 to 2020 were analyzed. Gallbladder surgery and kidney stone history were determined through questionnaires. Weighted multivariate logistic regression was used to assess associations, subgroup analyses were conducted, and mediation analysis evaluated the mediating effects of metabolic syndrome (MetS), diabetes, hypertension, and estimated glucose disposal rate (eGDR). Results : The prevalence of kidney stones was 10.36%, with significantly higher rates in the gallbladder surgery group (18.88% compared to 9.21%). After adjusting for confounding factors, gallbladder surgery remained independently associated with kidney stones (OR = 1.94, 95% CI: 1.35–2.79). Subgroup analyses indicated stronger associations among vigorous activity individuals (OR = 2.786) and those without MetS (OR = 2.675). Mediation analysis revealed that diabetes, hypertension, and eGDR mediated 3.04%, 5.34%, and 10.54% of the effect, respectively, whereas MetS did not mediate the association. Conclusion : Gallbladder surgery is closely associated with kidney stones, and hypertension and diabetes exert a partial mediating effect. Special attention should be given to the management of hypertension and diabetes in patients who have undergone gallbladder surgery to prevent the formation of kidney stones. Further investigation is necessary to better understand the mechanisms and enhance preventive measures. Gallbladder surgery Kidney stones Diabetes Hypertension NHANES Mediation analysis Figures Figure 1 Figure 2 Figure 3 Introduction Kidney stones represent a significant global health burden, affecting approximately 10% of the population worldwide[ 1 , 2 ]. While traditional risk factors such as dietary habits and dehydration are well-documented[ 3 ], emerging evidence suggests complex interactions between metabolic disorders, surgical history, and lithogenesis[ 4 – 6 ]. Gallbladder surgery, a procedure performed over 1.2 million times annually in the US, has recently been associated with changes in metabolic profiles that may predispose individuals to kidney stones[ 7 , 8 ]. Specifically, post-cholecystectomy patients exhibit elevated risks of obesity, insulin resistance, and dyslipidemia – all components of metabolic syndrome (MetS)[ 9 , 10 ]. These metabolic disturbances could theoretically affect urinary crystallization processes by mechanisms that involve altered bile acid metabolism and systemic inflammation. The National Health and Nutrition Examination Survey (NHANES) offers a unique opportunity to examine these associations, providing nationally representative data with comprehensive measurements of both surgical histories and metabolic parameters. Previous studies based on NHANES data have established connections between gallbladder surgery and cardiovascular risks, but its relationship with kidney stones has been less explored[ 11 ]. Notably, while components of metabolic syndrome such as hypertension and diabetes are recognized lithogenic factors, their potential role as mediators in the gallbladder surgery-kidney stone pathway has not been systematically examined. This study aims to analyze NHANES data from 2017 to 2020 to assess the association between gallbladder surgery and the occurrence of kidney stones, while investigating whether metabolic factors mediate this relationship. By elucidating these associations, we hope to contribute valuable insights into the long-term health implications of gallbladder surgery. Materials and methods Study population The NHANES 2017–2020 dataset was used. Participants aged ≥ 20 years with complete information on gallbladder surgery, kidney stones, and relevant covariates were included. A total of 6579 participants met the inclusion criteria (Fig. 1 ). The study was exempt from ethical approval as it used publicly available de-identified data, and all participants provided informed consent in the original NHANES survey. Kidney stone history Kidney stone history was determined through the Kidney Conditions-Urology survey within the questionnaire data. The kidney stone survey was carried out by trained interviewers at the participants' homes, and a computer-assisted personal interview (CAPI) system was employed. The participants were queried with “ Have you ever had a kidney stone? ”, and if the response was “ Yes ”, the participant was regarded as having a history of kidney stones. Gallbladder surgery history Gallbladder surgery history was identified from the Medical Conditions in the questionnaire data. The participants were asked “ Have you ever had gallbladder surgery? ”, and if the answer was “ Yes ”, the participant was considered to have the history of gallbladder surgery. Covariates In this study, a comprehensive collection of covariate data was conducted, focusing on variables potentially associated with the occurrence of kidney stones. The dataset encompassed a wide range of demographic and lifestyle factors, including age, sex, race, education level, marital status, poverty income ratio (PIR), and body mass index (BMI). Additionally, smoking history, engagement in moderate and vigorous physical activities, and the presence of hypertension and diabetes were included as potential risk factors. Furthermore, two comprehensive indices reflecting metabolism and insulin resistance were taken into account: MetS and estimated glucose disposal rate (eGDR). MetS was determined based on the NCEP ATPIII criteria. An individual was classified as having MetS when three or more of the following conditions were met[ 9 ]: central obesity, elevated fasting blood glucose levels, high serum triglyceride concentration, low serum high density lipoprotein cholesterol levels, and hypertension. The eGDR was calculated using the formula: eGDR = 21.158 - (0.09 * WC) - (3.407 * HT) - (0.551 * HbA1c). WC = waist circumference (cm), HT = hypertension (yes = 1/no = 0) and HbA1c = HbA1c (%)[ 12 , 13 ]. A comprehensive overview of these covariates is detailed in Supplementary Table 1. Statistical analysis We followed standard statistical procedures for all analyses, taking into account the complex survey design of NHANES, including sampling weights, stratification, and clustering. Descriptive statistics were used to summarize participant characteristics. Continuous variables were presented as medians (interquartile range, IQR), and categorical variables were presented as percentages. The differences between groups were evaluated using weighted chi-square tests for categorical variables. For continuous variables, given their non-normal distribution, weighted non-parametric tests were applied instead of weighted linear regression. Specifically, the weighted Mann-Whitney U test was utilized for comparisons between two groups. The association between gallbladder surgery and kidney stones was assessed using multivariable logistic regression models. Four models were constructed: Model 1: Unadjusted. Model 2: Adjusted for demographic factors (age, sex, race, education level, PIR, marital status). Model 3: Further adjusted for BMI, smoking status, physical activity, diabetes and hypertension. Model 4: Fully adjusted for all covariates, including Mets and eGDR. Subgroup analyses were conducted to examine the consistency of the association between gallbladder surgery and kidney stones across various strata. The subgroups were defined by variables such as age, sex, race, PIR, BMI, marital status, education level, smoking status, diabetes, hypertension, moderate activity, vigorous activity, and MetS. Interaction terms were included to test for potential effect modification by these variables. Mediation analysis was conducted to investigate the roles of diabetes, hypertension, MetS, and eGDR in the association between gallbladder surgery and kidney stones. The generalized additive model was used to smooth the mediation effect on the outcome. The analysis was adjusted for sex, age, race, education level, PIR, marital status, BMI, smoking history, moderate activity and vigorous activity. 1,000 bootstraps were utilized in this analysis, and the results presented the size of the indirect path effect, the proportion of the mediation effect, and the p-value of the mediation effect. Statistical software R version 4.4.1 was used for statistical analysis. A two-tailed p-value < 0.05 was considered statistically significant in all analyses. Results Study population characteristics The weighted characteristics of participants, stratified by kidney stones and gallbladder surgery, are detailed in Tables 1 and 2 . The overall prevalence of kidney stones was 10.36%. Demographically, significant differences were observed between the kidney stones group and the non-kidney stones group in age, race, and marital status (all p < 0.05). Among the variables related to metabolism, the kidney stones group exhibited: Higher prevalence of BMI ≥ 25 (82.17% vs. 73.46%, p < 0.001); Increased rates of diabetes (31.45% vs. 22.05%, p = 0.002); Elevated hypertension incidence (57.91% vs. 37.04%, p < 0.001); Greater proportion of MetS (35.33% vs. 25.07%, p < 0.001). Notably, eGDR was significantly lower in the kidney stones group compared to controls (median: 6.13 mg/kg/min vs. 8.27 mg/kg/min, p < 0.001). A striking association was identified in surgical history: the kidney stones group had a substantially higher rate of prior gallbladder surgery (21.66% vs. 10.75%, p < 0.001). Table 1 Characteristics of study participants by categories of kidney stones: NHANES 2017–2020, weighted. Characteristic Overall Non-kidney stones (89.64%) Kidney stones (10.36%) P -value Age (year, %) =60 29.53 28.59 37.71 Sex (%) 0.077 Male 48.62 47.86 55.16 Female 51.38 52.14 44.84 Race (%) < 0.001 Mexican American 7.85 8.04 6.23 Non-Hispanic Black 10.36 10.87 6.01 Non-Hispanic White 65.42 64.74 71.35 Other 16.37 16.36 16.41 Education level (%) 0.149 Below high school 9.83 9.59 11.93 High school or above 90.17 90.41 88.07 Marital status (%) 0.031 No 36.85 37.42 31.94 Yes 63.15 62.58 68.06 PIR (%) 0.461 =1 87.78 87.66 88.77 BMI (kg/m 2 , %) < 0.001 =25 74.36 73.46 82.17 Smoking history (%) 0.192 Never-smoker 57.09 57.57 52.90 Former-smoker 26.35 25.81 31.02 Now-smoker 16.56 16.62 16.08 Moderate activity (%) 0.876 No 49.85 49.90 49.42 Yes 50.16 50.11 50.58 Vigorous activity (%) 0.145 No 72.49 72.87 69.25 Yes 27.51 27.14 30.76 Diabetes (%) 0.002 No 76.97 77.95 68.55 Yes 23.03 22.05 31.45 Hypertension (%) < 0.001 No 60.80 62.96 42.09 Yes 39.20 37.04 57.91 MetS (%) < 0.001 No 73.87 74.93 64.67 Yes 26.13 25.07 35.33 eGDR[mg/kg/min,median(IQR)] 8.08(5.44,9.91) 8.27(5.56,9.97) 6.13(4.49,8.73) < 0.001 Gallbladder surgery (%) < 0.001 No 88.12 89.25 78.34 Yes 11.88 10.75 21.66 Median (IQR) for continuous variables, P < 0.05 presents significant difference. Table 2 Characteristics of study participants by categories of gallbladder surgery: NHANES 2017–2020, weighted. Characteristic Overall Non-gallbladder surgery (88.12%) Gallbladder surgery (11.88%) P -value Age (year, %) =60 29.53 26.64 51.01 Sex (%) < 0.001 Male 48.62 52.17 22.29 Female 51.38 47.83 77.71 Race (%) < 0.001 Mexican American 7.85 8.04 6.42 Non-Hispanic Black 10.36 11.06 5.15 Non-Hispanic White 65.42 64.02 75.81 Other 16.37 16.87 12.62 Education level (%) 0.897 Below high school 9.83 9.81 9.99 High school or above 90.17 90.19 90.01 Marital status (%) 0.567 No 36.85 37.07 35.22 Yes 63.15 62.93 64.78 PIR (%) 0.271 =1 87.78 87.60 89.11 BMI (kg/m 2 , %) < 0.001 =25 74.36 72.63 87.19 Smoking history (%) 0.004 Never-smoker 57.09 57.98 50.46 Former-smoker 26.35 25.33 33.91 Now-smoker 16.56 16.69 15.63 Moderate activity (%) 0.102 No 49.85 49.32 53.78 Yes 50.16 50.69 46.22 Vigorous activity (%) 0.009 No 72.49 71.47 80.06 Yes 27.51 28.53 19.94 Diabetes (%) < 0.001 No 76.97 78.82 63.30 Yes 23.03 21.18 36.70 Hypertension (%) < 0.001 No 60.80 63.67 39.56 Yes 39.20 36.33 60.44 MetS (%) < 0.001 No 73.87 76.42 54.96 Yes 26.13 23.58 45.04 eGDR[mg/kg/min,median(IQR)] 8.08(5.44,9.91) 8.36(5.64,10.03) 5.85(4.10,8.33) < 0.001 Kidney stones (%) < 0.001 No 89.64 90.79 81.12 Yes 10.36 9.21 18.88 Median (IQR) for continuous variables, P < 0.05 presents significant difference. Abbreviations: IQR, interquartile range; BMI, body mass index; PIR, poverty income ratio; MetS, metabolic syndrome; eGDR, estimated glucose disposal rate. Significant differences were observed between the gallbladder surgery group and the non-gallbladder surgery group in age, sex, race, smoking history, and vigorous activity (all p < 0.05). Metabolic comparisons revealed consistent disparities: Higher prevalence of BMI ≥ 25 in the gallbladder surgery group (87.19% vs. 72.63%, p < 0.001); Elevated diabetes incidence (36.70% vs. 21.18%, p < 0.001); Increased hypertension rates (60.44% vs. 36.33%, p < 0.001); Greater proportion of MetS (45.04% vs. 23.58%, p < 0.001). The gallbladder surgery group demonstrated significantly lower eGDR compared to controls (median: 5.85 mg/kg/min vs. 8.36 mg/kg/min, p < 0.001). Association between gallbladder surgery and kidney stones Multivariable logistic regression revealed a consistent positive association between gallbladder surgery and kidney stones across sequential adjustment models (Table 3 ). In the fully adjusted model (Model 4), gallbladder surgery remained independently associated with kidney stones (OR = 1.94, 95% CI: 1.35–2.79, p = 0.004), after accounting for demographic, socioeconomic, lifestyle, and metabolic confounders. Table 3 Multivariable logistic regression analysis of the relationship between gallbladder surgery with kidney stones, weighted. gallbladder surgery Model 1 Model 2 Model 3 Model 4 OR (95% CI ) P -value OR (95% CI ) P -value OR (95% CI ) P -value OR (95% CI ) P -value No ref ref ref ref ref ref ref ref Yes 2.30(1.68,3.13) < 0.001 2.25(1.58,3.20) < 0.001 1.99(1.40,2.83) 0.002 1.94(1.35,2.79) 0.004 Model 1: Unadjusted. Model 2: Adjust for sex, age, race, education level, PIR and marital status. Model 3: Adjust for sex, age, race, education level, PIR, marital status, BMI, smoking history, moderate activity, vigorous activity, hypertension and diabetes. Model 4: Adjust for sex, age, race, education level, PIR, marital status, BMI, smoking history, moderate activity, vigorous activity, hypertension, diabetes, eGDR and MetS. Abbreviations: OR , odds ratio; CI , confidence interval; BMI, body mass index; PIR, poverty income ratio; MetS, metabolic syndrome; eGDR, estimated glucose disposal rate. Subgroup analyses (Fig. 2 ) revealed significant effect modifications by both vigorous activity and MetS status. Stratification by vigorous activity indicated heterogeneity in the association (p for interaction = 0.041), with stronger effects seen in physically active individuals (OR = 2.786, 95% CI: 1.386–5.599) compared to their inactive counterparts (OR = 1.816, 95% CI: 1.220–2.703). Furthermore, stratification by MetS status (p for interaction = 0.029) demonstrated a more pronounced association in participants without MetS (OR = 2.675, 95% CI: 1.678–4.262) than in those with MetS (OR = 1.344, 95% CI: 0.714–2.526). Mediation analysis Mediation models identified diabetes, hypertension, and eGDR as significant mediators (Table 4 , Fig. 3 ). In adjusted analyses, diabetes accounted for 3.04% (95% CI: 0.46–7.23%, p = 0.012), hypertension for 5.34% (95% CI: 2.43–11.08%, p < 0.001), and eGDR for 10.54% (95% CI: 5.67–20.23%, p < 0.001) of the total effect. MetS showed no mediating role (p = 0.87). Table 4 Various mediators in the association of gallbladder surgery with kidney stones. Mediators ACME ADE Total effect Proportion mediated Estimate (95% CI ), P Estimate (95% CI ), P Estimate (95% CI ), P Estimate (95% CI ), P Diabetes Unadjusted analyses 0.0052(0.0025,0.0124),<0.001 0.0819(0.0524,0.1114),<0.001 0.0871(0.0576,0.1216),<0.001 0.0599(0.0265,0.1126),<0.001 Adjusted analyses 0.0022(0.0003,0.0045),0.012 0.0716(0.0427,0.1025),<0.001 0.0738(0.0451,0.1041),<0.001 0.0304(0.0046,0.0723),0.012 Hypertension Unadjusted analyses 0.0093(0.0063,0.0122),<0.001 0.0778(0.0512,0.1132),<0.001 0.0871(0.0602,0.1241),<0.001 0.1067(0.0655,0.1721),<0.001 Adjusted analyses 0.0039(0.0020,0.0068),<0.001 0.0699(0.0395,0.1003),<0.001 0.0738(0.0439,0.1123),<0.001 0.0534(0.0243,0.1108),<0.001 eGDR Unadjusted analyses 0.0158(0.0114,0.0202),<0.001 0.0713(0.0415,0.1012),<0.001 0.0871(0.0565,0.1206),<0.001 0.1818(0.1184,0.3024),<0.001 Adjusted analyses 0.0078(0.0045,0.0113),<0.001 0.0661(0.0375,0.1026),<0.001 0.0738(0.0447,0.1043),<0.001 0.1054(0.0567,0.2023),<0.001 MetS Unadjusted analyses 0(0,0),0.87 0.08714(0.0595,0.1203),<0.001 0.08714(0.0595,0.1203),<0.001 0(0,0),0.87 Adjusted analyses - - - - Generalized additive model was used to smooth mediator effect on outcome. Adjusted analyses adjusted for sex, age, race, education level, PIR, marital status, BMI, smoking history, moderate activity, vigorous activity. Abbreviations: ACME, average causal mediation effect; ADE, average direct effect; CI , confidence interval; BMI, body mass index; PIR, poverty income ratio; MetS, metabolic syndrome; eGDR, estimated glucose disposal rate. Discussion Our study has provided robust evidence of the significant association between gallbladder surgery and kidney stones. Through multivariate logistic regression analyses, it has been clearly demonstrated that gallbladder surgery is an independent risk factor for the formation of kidney stones. The stratified analysis further supports this finding, revealing that the association persists across different subgroups, albeit with some variations in significance. The mediation analysis has introduced a crucial dimension to our understanding of this relationship by revealing that diabetes and hypertension play significant mediating roles. The eGDR accounted for a larger proportion in the mediation analysis. Given that the eGDR formula incorporates parameters related to diabetes and hypertension, it more comprehensively reflects the mediating roles of these metabolic disorders in the association between gallbladder surgery and kidney stones. In comparison with previous research in this area, which has been relatively limited, our findings offer novel insights. Some earlier studies have hinted at a possible link between gallbladder and kidney disorders[ 14 – 16 ], yet the underlying mechanisms and the specific role of diabetes and hypertension have not been fully elucidated. Our study, in contrast, employs a comprehensive approach utilizing a large, nationally representative dataset, which enables a more detailed examination of multiple factors simultaneously. This allows us to better account for potential confounding variables and provides a more accurate depiction of the complex interplay between gallbladder surgery and kidney stones. The findings from our study also lend support to the hypothesis that gallbladder surgery contributes to MetS. Previous studies have reported that individuals who undergo cholecystectomy are at a higher risk of developing conditions such as non-alcoholic fatty liver disease (NAFLD) and hypertension. For instance, a large cohort study[ 17 ] indicated that cholecystectomized patients had significantly elevated serum levels of liver enzymes and a higher prevalence of NAFLD compared to those without gallbladder disease. The metabolic changes subsequent to gallbladder removal, such as alterations in bile acid metabolism and changes in gut microbiota[ 18 , 19 ], are implicated in the association with kidney stone risk. Bile acids play a crucial role in lipid digestion and metabolism, their absence can lead to dysregulation of metabolic pathways, contributing to insulin resistance[ 20 ]. Moreover, changes in gut microbiota composition post-cholecystectomy can influence systemic inflammation and metabolic health[ 18 ]. Recent research has suggested that the gut microbiome can impact stone formation through mechanisms involving oxalate metabolism and inflammation[ 21 ], further highlighting the complexity of these interactions. Regarding specific metabolic indicators, both the gallbladder surgery group and the kidney stone group showed a significantly higher prevalence of MetS, suggesting a potential link between MetS and the relationship between gallbladder surgery and kidney stones[ 22 , 23 ]. However, the mediation analysis revealed no significant mediating effect of MetS in this association, indicating that although MetS co-occurs with both conditions, its syndromic complexity may obscure component-specific mediation pathways rather than acting as a unified intermediary. Hypertension plays a crucial role as a mediator in the formation of kidney stones by connecting various metabolic and hemodynamic pathways. Previous research has shown that hypertension is linked to disorders in calcium metabolism, resulting in increased calcium excretion, secondary activation of the parathyroid glands, enhanced mobilization of bone calcium, and an elevated risk of urinary tract stones[ 24 ]. Diabetes, as a mediator, exacerbates the process through its impact on glucose and lipid metabolism. Hyperglycemia and insulin resistance associated with diabetes can promote oxidative stress, inflammation, and changes in renal tubular function[ 25 ]. These alterations enhance the reabsorption of substances like calcium and oxalate in the kidneys, increasing the likelihood of crystal precipitation and stone formation. Furthermore, diabetes-induced endothelial dysfunction and vascular damage can also play a role in the development of kidney stones by disrupting the normal filtration and clearance mechanisms in the kidneys[ 26 , 27 ]. The interaction between gallbladder surgery-induced changes in bile metabolism and diabetes-related metabolic derangements creates a synergistic effect that further increases the risk of kidney stones. The stratified analyses revealed unexpected patterns of effect modification. The association was stronger in individuals engaging in vigorous activity (OR = 2.786) than in those not engaging in vigorous activity (OR = 1.816). Gallbladder surgery patients often experience a reduction in the bile acid pool and impaired fat absorption. Those who engage in vigorous activity typically require a higher caloric intake, particularly in fat and protein, which may result in increased urinary calcium excretion and elevated uric acid production, thereby increasing the risk of kidney stones. The attenuated association in individuals with MetS (OR = 1.344 vs 2.675) suggests that metabolic overload may overshadow the lithogenic effects of surgical history, creating a "ceiling effect"[ 28 ]where additional risk factors contribute less and less. Despite the valuable insights gained from our study, it is not without limitations. The cross-sectional design of our study precludes us from establishing a causal relationship between gallbladder surgery and kidney stones. Although we have adjusted for a wide range of covariates, there may still be unmeasured confounding factors that could influence the results. The reliance on self-reported data for gallbladder surgery and kidney stones history is another potential source of bias. Recall bias may lead to inaccurate reporting, particularly for events that occurred in the past. Additionally, the dataset we used does not provide detailed information on certain aspects, such as the type and indication of gallbladder surgery, which could have implications for the interpretation of the results. The lack of information on dietary factors, such as specific nutrient intake and fluid consumption, is also a limitation. These factors are known to play a significant role in kidney stone formation and could potentially modify the relationship between gallbladder surgery and kidney stones. Conclusion In conclusion, our study has reinforced the significant association between gallbladder surgery and kidney stone formation, highlighting diabetes and hypertension as critical mediators in this relationship. These findings call for heightened awareness among clinicians regarding the potential long-term risks associated with cholecystectomy, particularly concerning metabolic health and renal outcomes. Further research is essential to develop targeted strategies aimed at reducing these risks in affected populations. Future studies should aim to address the limitations of the current study by incorporating more detailed dietary data and using longitudinal study designs to better establish causality. Abbreviations IQR interquartile range BMI body mass index PIR poverty income ratio MetS metabolic syndrome eGDR estimated glucose disposal rate. Declarations Acknowledgments We are very grateful to all the participants in this research project. Author c ontributions Youjian Li: study design, data interpretation, and manuscript writing; Haoli Yin: study concept, data analysis, and revision of the manuscript; Yongshan Li, Zuhong Ji and Kai Li: study design and data analysis; Jie Liu, Yetao Zhang and Kai Zhou: data analysis and data interpretation; Qingyi Zhu.: study concept and revision of the manuscript. All authors read and approved the final manuscript. Funding This study did not receive specific funding. Availability of data and material The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Ethics approval and consent to participate The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). All participants were fully informed and gave consent.This study used previously collected deidentified data, which had been reviewed and approved by the National Center for Health Statistics (NCHS) Research Ethics Review Committee and found to be exempt from review by the Ethics Committee of The Second Affiliated Hospital of Nanjing Medical University. Clinical trial number Not applicable. Consent for publication : Participants in the NHANES survey provided informed consent for data collection. Co mpeting i ntere st s None of the authors declare a competing interest, and the results presented in this paper have not been published previously in whole or part. References Romero V, Akpinar H, Assimos DG. Kidney stones: a global picture of prevalence, incidence, and associated risk factors. Rev Urol. 2010;12(2–3):e86–96. Hill AJ, Basourakos SP, Lewicki P, Wu X, Arenas-Gallo C, Chuang D, Bodner D, Jaeger I, Nevo A, Zell M, et al. Incidence of Kidney Stones in the United States: The Continuous National Health and Nutrition Examination Survey. J Urol. 2022;207(4):851–6. Ferraro PM, Bargagli M, Trinchieri A, Gambaro G. Risk of Kidney Stones: Influence of Dietary Factors, Dietary Patterns, and Vegetarian-Vegan Diets. Nutrients 2020, 12(3). Wong Y, Cook P, Roderick P, Somani BK. Metabolic Syndrome and Kidney Stone Disease: A Systematic Review of Literature. J Endourol. 2016;30(3):246–53. Devarajan A. Cross-talk between renal lithogenesis and atherosclerosis: an unveiled link between kidney stone formation and cardiovascular diseases. Clinical science (London, England : 1979) 2018, 132(6):615–626. Yuan Y, Tan W, Huang Y, Huang H, Li Y, Gou Y, Zeng S, Hu Z. Association between hysterectomy and kidney stone disease: results from the National Health and Nutrition Examination Survey 2007–2018 and Mendelian randomization analysis. World J Urol. 2023;41(8):2133–9. Pal SC, Castillo-Castañeda SM, Díaz-Orozco LE, Ramírez-Mejía MM, Dorantes-Heredia R, Alonso-Morales R, Eslam M, Lammert F. Méndez-Sánchez N: Molecular Mechanisms Involved in MAFLD in Cholecystectomized Patients: A Cohort Study. Genes 2023, 14(10). Chew BH, Miller LE, Eisner B, Bhattacharyya S, Bhojani NJJOP. Prevalence, Incidence, and Determinants of Kidney Stones in a Nationally Representative Sample of US Adults. 2024. Smiley A, King D, Bidulescu A. The Association between Sleep Duration and Metabolic Syndrome: The NHANES 2013/2014. Nutrients 2019, 11(11). Lange AH, Pedersen MG, Ellegaard AM, Nerild HH, Brønden A, Sonne DP, Knop FK. The bile-gut axis and metabolic consequences of cholecystectomy. Eur J Endocrinol. 2024;190(4):R1–9. Park S, Jeong S, Park SJ, Song J, Kim SM, Chang J, Choi S, Cho Y, Oh YH, Kim JS, et al. Associations of cholecystectomy with metabolic health changes and incident cardiovascular disease: a retrospective cohort study. Sci Rep. 2024;14(1):3195. Nyström T, Holzmann MJ, Eliasson B, Svensson AM, Sartipy U. Estimated glucose disposal rate predicts mortality in adults with type 1 diabetes. Diabetes Obes Metab. 2018;20(3):556–63. Lu Z, Xiong Y, Feng X, Yang K, Gu H, Zhao X, Meng X, Wang Y. Insulin resistance estimated by estimated glucose disposal rate predicts outcomes in acute ischemic stroke patients. Cardiovasc Diabetol. 2023;22(1):225. Lai SW, Liao KF, Lai HC, Chou CY, Cheng KC, Lai YM. The prevalence of gallbladder stones is higher among patients with chronic kidney disease in Taiwan. Medicine. 2009;88(1):46–51. Kharazmi E, Scherer D, Boekstegers F, Liang Q, Sundquist K, Sundquist J, Fallah M, Lorenzo Bermejo J. Gallstones, Cholecystectomy, and Kidney Cancer: Observational and Mendelian Randomization Results Based on Large Cohorts. Gastroenterology. 2023;165(1):218–e227218. Ahmed MH, Barakat S, Almobarak AO. The association between renal stone disease and cholesterol gallstones: the easy to believe and not hard to retrieve theory of the metabolic syndrome. Ren Fail. 2014;36(6):957–62. Ruhl CE, Everhart JE. Relationship of non-alcoholic fatty liver disease with cholecystectomy in the US population. Am J Gastroenterol. 2013;108(6):952–8. Xu F, Chen R, Zhang C, Wang H, Ding Z, Yu L, Tian F, Chen W, Zhou Y, Zhai Q. Cholecystectomy Significantly Alters Gut Microbiota Homeostasis and Metabolic Profiles: A Cross-Sectional Study. Nutrients 2023, 15(20). Wang Q, Lu Q, Shao W, Jiang Z, Hu H. Dysbiosis of gut microbiota after cholecystectomy is associated with non-alcoholic fatty liver disease in mice. FEBS open bio. 2021;11(8):2329–39. Ahmad TR, Haeusler RA. Bile acids in glucose metabolism and insulin signalling - mechanisms and research needs. Nat reviews Endocrinol. 2019;15(12):701–12. Ticinesi A, Nouvenne A, Chiussi G, Castaldo G, Guerra A, Meschi T. Calcium Oxalate Nephrolithiasis and Gut Microbiota: Not just a Gut-Kidney Axis. A Nutritional Perspective. Nutrients 2020, 12(2). Ye Z, Wu C, Xiong Y, Zhang F, Luo J, Xu L, Wang J, Bai Y. Obesity, metabolic dysfunction, and risk of kidney stone disease: a national cross-sectional study. aging male: official J Int Soc Study Aging Male. 2023;26(1):2195932. Dassanayake SN, Lafont T, Somani BK. Association and risk of metabolic syndrome and kidney stone disease: outcomes from a systematic review and meta-analysis. Current opinion in urology 2024. Cappuccio FP, Kalaitzidis R, Duneclift S, Eastwood JB. Unravelling the links between calcium excretion, salt intake, hypertension, kidney stones and bone metabolism. J Nephrol. 2000;13(3):169–77. Wang Y, Jin M, Cheng CK, Li Q. Tubular injury in diabetic kidney disease: molecular mechanisms and potential therapeutic perspectives. Front Endocrinol. 2023;14:1238927. Yang DR, Wang MY, Zhang CL, Wang Y. Endothelial dysfunction in vascular complications of diabetes: a comprehensive review of mechanisms and implications. Front Endocrinol. 2024;15:1359255. Saenz-Medina J, Muñoz M, Rodriguez C, Sanchez A, Contreras C, Carballido-Rodríguez J, Prieto D. Endothelial Dysfunction: An Intermediate Clinical Feature between Urolithiasis and Cardiovascular Diseases. Int J Mol Sci 2022, 23(2). Bell KR, Oliver WM, White TO, Molyneux SG, Clement ND, Duckworth AD. QuickDASH and PRWE Are Not Optimal Patient-Reported Outcome Measures After Distal Radial Fracture Due to Ceiling Effect: Potential Implications for Future Research. J bone joint Surg Am volume. 2023;105(16):1270–9. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6560456","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":464898907,"identity":"4378dd88-0842-4310-81eb-7bac0273d765","order_by":0,"name":"Youjian Li","email":"","orcid":"","institution":"The Second Affiliated Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Youjian","middleName":"","lastName":"Li","suffix":""},{"id":464898908,"identity":"1c0720a5-57fc-4711-97f7-3d79077022fa","order_by":1,"name":"Yongshan Li","email":"","orcid":"","institution":"The Second Affiliated Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yongshan","middleName":"","lastName":"Li","suffix":""},{"id":464898909,"identity":"c7d8a631-1d2b-469d-b2e0-3b2a9c7bc180","order_by":2,"name":"Zuhong Ji","email":"","orcid":"","institution":"The Second Affiliated Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zuhong","middleName":"","lastName":"Ji","suffix":""},{"id":464898910,"identity":"4594fa83-c348-43ea-8142-fa1d8fa17af5","order_by":3,"name":"Jie Liu","email":"","orcid":"","institution":"Jiangning Hospital Affiliated to Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Liu","suffix":""},{"id":464898911,"identity":"985e1dc3-da60-4a41-ac12-a2fdb883b986","order_by":4,"name":"Kai Li","email":"","orcid":"","institution":"The Second Affiliated Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Li","suffix":""},{"id":464898912,"identity":"8b730d1e-0741-44c0-a6d1-5d98a2dc2c15","order_by":5,"name":"Yetao Zhang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yetao","middleName":"","lastName":"Zhang","suffix":""},{"id":464898913,"identity":"a7041549-5aca-43e8-b62d-ae42e9da0dbe","order_by":6,"name":"Kai Zhou","email":"","orcid":"","institution":"The Second Affiliated Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Zhou","suffix":""},{"id":464898914,"identity":"2bbc97cd-b5b4-40ca-be51-960af4d455c1","order_by":7,"name":"Haoli Yin","email":"","orcid":"","institution":"Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan","correspondingAuthor":false,"prefix":"","firstName":"Haoli","middleName":"","lastName":"Yin","suffix":""},{"id":464898915,"identity":"fa428acc-15cc-4aec-8c53-08734db25eb4","order_by":8,"name":"Qingyi Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYDACCRBhYFPfxt7AYAAWOUCUloI0xj6eAyRp+XCYcZ5EAlSEkBb52T2Gt3kMDjOzSb49UHSzjUGO70YC4+cCPFoY55wxtuYxSGdjk85LMM5tYzCWvJHALD0DjxZmiRwzaR4Dax426RwDkJbEDTcS2Jh58Ghhg2hhlmCTPAPWUk9QCw9Ei7MBmwQPWEuCASEtEhJpxZZzDNIS2HiADss5J2E488zDZml8WuRnJG+88eaPTYJ8+xkz45wyG3m+48kHP+PTAgJSUAVsBpBoYmwgoIGBQfIHhGZ+QFDpKBgFo2AUjEgAAI56QSTQ8dJIAAAAAElFTkSuQmCC","orcid":"","institution":"The Second Affiliated Hospital of Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Qingyi","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2025-04-30 02:53:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6560456/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6560456/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83899827,"identity":"b93ade7e-e0be-48ce-9d3c-1f326a8a71db","added_by":"auto","created_at":"2025-06-04 09:21:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":75508,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of participant screening. NHANES, National Health and Nutrition Examination Survey.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6560456/v1/bc00ece5277e62879837ab0d.png"},{"id":83899832,"identity":"e37683fe-6824-49c4-b3be-cb57fb6e75b8","added_by":"auto","created_at":"2025-06-04 09:21:59","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":113779,"visible":true,"origin":"","legend":"\u003cp\u003eThe subgroup analysis of the relationship between gallbladder surgery with kidney stones was conducted. Adjustments were made for sex, age, race, education level, PIR, marital status, BMI, smoking history, moderate activity, vigorous activity, hypertension, diabetes and MetS. It should be noted that the model was not adjusted for the factor itself in each stratification.\u003c/p\u003e\n\u003cp\u003eAbbreviations: OR, odds ratio; CI, confidence interval; BMI, body mass index; PIR, poverty income ratio; MetS, metabolic syndrome.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6560456/v1/2cafbe2821474ad759a45b5b.jpeg"},{"id":83899830,"identity":"ff2da438-69d7-448b-b61b-dc9bf75f4509","added_by":"auto","created_at":"2025-06-04 09:21:59","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":43934,"visible":true,"origin":"","legend":"\u003cp\u003eMediational models. Figure demonstrating mediation models with the independent variable of gallbladder surgery, mediators being diabetes, hypertension and eGDR, and the dependent variable of kidney stones. *indicates p \u0026lt; 0.05; \u0026nbsp;**indicates p \u0026lt; 0.01; **-indicates p \u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003eAbbreviations: ACME, average causal mediation effect; eGDR, estimated glucose disposal rate.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6560456/v1/e396a8a919588624c21a690e.jpeg"},{"id":88752303,"identity":"3bf58229-6d14-455b-a5f1-81008b10706b","added_by":"auto","created_at":"2025-08-11 06:31:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1358678,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6560456/v1/a153926e-49b2-4a7b-8134-1ca2ed8b98bb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations between gallbladder surgery and kidney stones: The mediating roles of hypertension and diabetes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eKidney stones represent a significant global health burden, affecting approximately 10% of the population worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. While traditional risk factors such as dietary habits and dehydration are well-documented[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], emerging evidence suggests complex interactions between metabolic disorders, surgical history, and lithogenesis[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Gallbladder surgery, a procedure performed over 1.2\u0026nbsp;million times annually in the US, has recently been associated with changes in metabolic profiles that may predispose individuals to kidney stones[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Specifically, post-cholecystectomy patients exhibit elevated risks of obesity, insulin resistance, and dyslipidemia \u0026ndash; all components of metabolic syndrome (MetS)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These metabolic disturbances could theoretically affect urinary crystallization processes by mechanisms that involve altered bile acid metabolism and systemic inflammation.\u003c/p\u003e \u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES) offers a unique opportunity to examine these associations, providing nationally representative data with comprehensive measurements of both surgical histories and metabolic parameters. Previous studies based on NHANES data have established connections between gallbladder surgery and cardiovascular risks, but its relationship with kidney stones has been less explored[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Notably, while components of metabolic syndrome such as hypertension and diabetes are recognized lithogenic factors, their potential role as mediators in the gallbladder surgery-kidney stone pathway has not been systematically examined.\u003c/p\u003e \u003cp\u003eThis study aims to analyze NHANES data from 2017 to 2020 to assess the association between gallbladder surgery and the occurrence of kidney stones, while investigating whether metabolic factors mediate this relationship. By elucidating these associations, we hope to contribute valuable insights into the long-term health implications of gallbladder surgery.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe NHANES 2017\u0026ndash;2020 dataset was used. Participants aged\u0026thinsp;\u0026ge;\u0026thinsp;20 years with complete information on gallbladder surgery, kidney stones, and relevant covariates were included. A total of 6579 participants met the inclusion criteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The study was exempt from ethical approval as it used publicly available de-identified data, and all participants provided informed consent in the original NHANES survey.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eKidney stone history\u003c/h3\u003e\n\u003cp\u003eKidney stone history was determined through the Kidney Conditions-Urology survey within the questionnaire data. The kidney stone survey was carried out by trained interviewers at the participants' homes, and a computer-assisted personal interview (CAPI) system was employed. The participants were queried with \u0026ldquo; Have you ever had a kidney stone? \u0026rdquo;, and if the response was \u0026ldquo; Yes \u0026rdquo;, the participant was regarded as having a history of kidney stones.\u003c/p\u003e\n\u003ch3\u003eGallbladder surgery history\u003c/h3\u003e\n\u003cp\u003eGallbladder surgery history was identified from the Medical Conditions in the questionnaire data. The participants were asked \u0026ldquo; Have you ever had gallbladder surgery? \u0026rdquo;, and if the answer was \u0026ldquo; Yes \u0026rdquo;, the participant was considered to have the history of gallbladder surgery.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eIn this study, a comprehensive collection of covariate data was conducted, focusing on variables potentially associated with the occurrence of kidney stones. The dataset encompassed a wide range of demographic and lifestyle factors, including age, sex, race, education level, marital status, poverty income ratio (PIR), and body mass index (BMI). Additionally, smoking history, engagement in moderate and vigorous physical activities, and the presence of hypertension and diabetes were included as potential risk factors. Furthermore, two comprehensive indices reflecting metabolism and insulin resistance were taken into account: MetS and estimated glucose disposal rate (eGDR). MetS was determined based on the NCEP ATPIII criteria. An individual was classified as having MetS when three or more of the following conditions were met[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]: central obesity, elevated fasting blood glucose levels, high serum triglyceride concentration, low serum high density lipoprotein cholesterol levels, and hypertension. The eGDR was calculated using the formula: eGDR\u0026thinsp;=\u0026thinsp;21.158 - (0.09 * WC) - (3.407 * HT) - (0.551 * HbA1c). WC\u0026thinsp;=\u0026thinsp;waist circumference (cm), HT\u0026thinsp;=\u0026thinsp;hypertension (yes\u0026thinsp;=\u0026thinsp;1/no\u0026thinsp;=\u0026thinsp;0) and HbA1c\u0026thinsp;=\u0026thinsp;HbA1c (%)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. A comprehensive overview of these covariates is detailed in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003e We followed standard statistical procedures for all analyses, taking into account the complex survey design of NHANES, including sampling weights, stratification, and clustering. Descriptive statistics were used to summarize participant characteristics. Continuous variables were presented as medians (interquartile range, IQR), and categorical variables were presented as percentages. The differences between groups were evaluated using weighted chi-square tests for categorical variables. For continuous variables, given their non-normal distribution, weighted non-parametric tests were applied instead of weighted linear regression. Specifically, the weighted Mann-Whitney U test was utilized for comparisons between two groups. The association between gallbladder surgery and kidney stones was assessed using multivariable logistic regression models. Four models were constructed: Model 1: Unadjusted. Model 2: Adjusted for demographic factors (age, sex, race, education level, PIR, marital status). Model 3: Further adjusted for BMI, smoking status, physical activity, diabetes and hypertension. Model 4: Fully adjusted for all covariates, including Mets and eGDR. Subgroup analyses were conducted to examine the consistency of the association between gallbladder surgery and kidney stones across various strata. The subgroups were defined by variables such as age, sex, race, PIR, BMI, marital status, education level, smoking status, diabetes, hypertension, moderate activity, vigorous activity, and MetS. Interaction terms were included to test for potential effect modification by these variables. Mediation analysis was conducted to investigate the roles of diabetes, hypertension, MetS, and eGDR in the association between gallbladder surgery and kidney stones. The generalized additive model was used to smooth the mediation effect on the outcome. The analysis was adjusted for sex, age, race, education level, PIR, marital status, BMI, smoking history, moderate activity and vigorous activity. 1,000 bootstraps were utilized in this analysis, and the results presented the size of the indirect path effect, the proportion of the mediation effect, and the p-value of the mediation effect. Statistical software R version 4.4.1 was used for statistical analysis. A two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant in all analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy population characteristics\u003c/h2\u003e\n \u003cp\u003eThe weighted characteristics of participants, stratified by kidney stones and gallbladder surgery, are detailed in Tables \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The overall prevalence of kidney stones was 10.36%. Demographically, significant differences were observed between the kidney stones group and the non-kidney stones group in age, race, and marital status (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among the variables related to metabolism, the kidney stones group exhibited: Higher prevalence of BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 (82.17% vs. 73.46%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); Increased rates of diabetes (31.45% vs. 22.05%, p\u0026thinsp;=\u0026thinsp;0.002); Elevated hypertension incidence (57.91% vs. 37.04%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); Greater proportion of MetS (35.33% vs. 25.07%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, eGDR was significantly lower in the kidney stones group compared to controls (median: 6.13 mg/kg/min vs. 8.27 mg/kg/min, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A striking association was identified in surgical history: the kidney stones group had a substantially higher rate of prior gallbladder surgery (21.66% vs. 10.75%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of study participants by categories of kidney stones: NHANES 2017\u0026ndash;2020, weighted.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-kidney stones (89.64%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKidney stones (10.36%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (year, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;=60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation level (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBelow high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital status (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePIR (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.461\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;=1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;=25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking history (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNow-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate activity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVigorous activity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetS (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeGDR[mg/kg/min,median(IQR)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.08(5.44,9.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.27(5.56,9.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.13(4.49,8.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGallbladder surgery (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eMedian (IQR) for continuous variables, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 presents significant difference.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of study participants by categories of gallbladder surgery: NHANES 2017\u0026ndash;2020, weighted.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-gallbladder surgery (88.12%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGallbladder surgery (11.88%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (year, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;=60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation level (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBelow high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital status (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePIR (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.271\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;=1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;=25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking history (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNow-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate activity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVigorous activity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetS (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeGDR[mg/kg/min,median(IQR)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.08(5.44,9.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.36(5.64,10.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.85(4.10,8.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKidney stones (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eMedian (IQR) for continuous variables, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 presents significant difference.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eAbbreviations: IQR, interquartile range; BMI, body mass index; PIR, poverty income ratio; MetS, metabolic syndrome; eGDR, estimated glucose disposal rate.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eSignificant differences were observed between the gallbladder surgery group and the non-gallbladder surgery group in age, sex, race, smoking history, and vigorous activity (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Metabolic comparisons revealed consistent disparities: Higher prevalence of BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 in the gallbladder surgery group (87.19% vs. 72.63%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); Elevated diabetes incidence (36.70% vs. 21.18%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); Increased hypertension rates (60.44% vs. 36.33%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); Greater proportion of MetS (45.04% vs. 23.58%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The gallbladder surgery group demonstrated significantly lower eGDR compared to controls (median: 5.85 mg/kg/min vs. 8.36 mg/kg/min, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eAssociation between gallbladder surgery and kidney stones\u003c/h3\u003e\n\u003cp\u003eMultivariable logistic regression revealed a consistent positive association between gallbladder surgery and kidney stones across sequential adjustment models (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). In the fully adjusted model (Model 4), gallbladder surgery remained independently associated with kidney stones (OR\u0026thinsp;=\u0026thinsp;1.94, 95% CI: 1.35\u0026ndash;2.79, p\u0026thinsp;=\u0026thinsp;0.004), after accounting for demographic, socioeconomic, lifestyle, and metabolic confounders.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultivariable logistic regression analysis of the relationship between gallbladder surgery with kidney stones, weighted.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"12\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003egallbladder surgery\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 4\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.30(1.68,3.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.25(1.58,3.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.99(1.40,2.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.94(1.35,2.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eModel 1: Unadjusted.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eModel 2: Adjust for sex, age, race, education level, PIR and marital status.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eModel 3: Adjust for sex, age, race, education level, PIR, marital status, BMI, smoking history, moderate activity, vigorous activity, hypertension and diabetes.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eModel 4: Adjust for sex, age, race, education level, PIR, marital status, BMI, smoking history, moderate activity, vigorous activity, hypertension, diabetes, eGDR and MetS.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eAbbreviations: \u003cem\u003eOR\u003c/em\u003e, odds ratio; \u003cem\u003eCI\u003c/em\u003e, confidence interval; BMI, body mass index; PIR, poverty income ratio; MetS, metabolic syndrome; eGDR, estimated glucose disposal rate.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSubgroup analyses (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) revealed significant effect modifications by both vigorous activity and MetS status. Stratification by vigorous activity indicated heterogeneity in the association (p for interaction\u0026thinsp;=\u0026thinsp;0.041), with stronger effects seen in physically active individuals (OR\u0026thinsp;=\u0026thinsp;2.786, 95% CI: 1.386\u0026ndash;5.599) compared to their inactive counterparts (OR\u0026thinsp;=\u0026thinsp;1.816, 95% CI: 1.220\u0026ndash;2.703). Furthermore, stratification by MetS status (p for interaction\u0026thinsp;=\u0026thinsp;0.029) demonstrated a more pronounced association in participants without MetS (OR\u0026thinsp;=\u0026thinsp;2.675, 95% CI: 1.678\u0026ndash;4.262) than in those with MetS (OR\u0026thinsp;=\u0026thinsp;1.344, 95% CI: 0.714\u0026ndash;2.526).\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMediation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMediation models identified diabetes, hypertension, and eGDR as significant mediators (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). In adjusted analyses, diabetes accounted for 3.04% (95% CI: 0.46\u0026ndash;7.23%, p\u0026thinsp;=\u0026thinsp;0.012), hypertension for 5.34% (95% CI: 2.43\u0026ndash;11.08%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and eGDR for 10.54% (95% CI: 5.67\u0026ndash;20.23%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) of the total effect. MetS showed no mediating role (p\u0026thinsp;=\u0026thinsp;0.87).\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eVarious mediators in the association of gallbladder surgery with kidney stones.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMediators\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eACME\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eADE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal effect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProportion mediated\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEstimate (95% \u003cem\u003eCI\u003c/em\u003e),\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEstimate (95% \u003cem\u003eCI\u003c/em\u003e),\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEstimate (95% \u003cem\u003eCI\u003c/em\u003e),\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEstimate (95% \u003cem\u003eCI\u003c/em\u003e),\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnadjusted analyses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0052(0.0025,0.0124),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0819(0.0524,0.1114),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0871(0.0576,0.1216),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0599(0.0265,0.1126),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted analyses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0022(0.0003,0.0045),0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0716(0.0427,0.1025),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0738(0.0451,0.1041),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0304(0.0046,0.0723),0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnadjusted analyses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0093(0.0063,0.0122),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0778(0.0512,0.1132),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0871(0.0602,0.1241),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1067(0.0655,0.1721),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted analyses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0039(0.0020,0.0068),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0699(0.0395,0.1003),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0738(0.0439,0.1123),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0534(0.0243,0.1108),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeGDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnadjusted analyses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0158(0.0114,0.0202),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0713(0.0415,0.1012),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0871(0.0565,0.1206),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1818(0.1184,0.3024),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted analyses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0078(0.0045,0.0113),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0661(0.0375,0.1026),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0738(0.0447,0.1043),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1054(0.0567,0.2023),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnadjusted analyses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0,0),0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08714(0.0595,0.1203),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08714(0.0595,0.1203),\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0,0),0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted analyses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eGeneralized additive model was used to smooth mediator effect on outcome. Adjusted analyses adjusted for sex, age, race, education level, PIR, marital status, BMI, smoking history, moderate activity, vigorous activity.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eAbbreviations: ACME, average causal mediation effect; ADE, average direct effect; \u003cem\u003eCI\u003c/em\u003e, confidence interval; BMI, body mass index; PIR, poverty income ratio; MetS, metabolic syndrome; eGDR, estimated glucose disposal rate.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study has provided robust evidence of the significant association between gallbladder surgery and kidney stones. Through multivariate logistic regression analyses, it has been clearly demonstrated that gallbladder surgery is an independent risk factor for the formation of kidney stones. The stratified analysis further supports this finding, revealing that the association persists across different subgroups, albeit with some variations in significance. The mediation analysis has introduced a crucial dimension to our understanding of this relationship by revealing that diabetes and hypertension play significant mediating roles. The eGDR accounted for a larger proportion in the mediation analysis. Given that the eGDR formula incorporates parameters related to diabetes and hypertension, it more comprehensively reflects the mediating roles of these metabolic disorders in the association between gallbladder surgery and kidney stones.\u003c/p\u003e \u003cp\u003eIn comparison with previous research in this area, which has been relatively limited, our findings offer novel insights. Some earlier studies have hinted at a possible link between gallbladder and kidney disorders[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], yet the underlying mechanisms and the specific role of diabetes and hypertension have not been fully elucidated. Our study, in contrast, employs a comprehensive approach utilizing a large, nationally representative dataset, which enables a more detailed examination of multiple factors simultaneously. This allows us to better account for potential confounding variables and provides a more accurate depiction of the complex interplay between gallbladder surgery and kidney stones.\u003c/p\u003e \u003cp\u003eThe findings from our study also lend support to the hypothesis that gallbladder surgery contributes to MetS. Previous studies have reported that individuals who undergo cholecystectomy are at a higher risk of developing conditions such as non-alcoholic fatty liver disease (NAFLD) and hypertension. For instance, a large cohort study[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] indicated that cholecystectomized patients had significantly elevated serum levels of liver enzymes and a higher prevalence of NAFLD compared to those without gallbladder disease. The metabolic changes subsequent to gallbladder removal, such as alterations in bile acid metabolism and changes in gut microbiota[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], are implicated in the association with kidney stone risk. Bile acids play a crucial role in lipid digestion and metabolism, their absence can lead to dysregulation of metabolic pathways, contributing to insulin resistance[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Moreover, changes in gut microbiota composition post-cholecystectomy can influence systemic inflammation and metabolic health[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Recent research has suggested that the gut microbiome can impact stone formation through mechanisms involving oxalate metabolism and inflammation[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], further highlighting the complexity of these interactions.\u003c/p\u003e \u003cp\u003eRegarding specific metabolic indicators, both the gallbladder surgery group and the kidney stone group showed a significantly higher prevalence of MetS, suggesting a potential link between MetS and the relationship between gallbladder surgery and kidney stones[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, the mediation analysis revealed no significant mediating effect of MetS in this association, indicating that although MetS co-occurs with both conditions, its syndromic complexity may obscure component-specific mediation pathways rather than acting as a unified intermediary. Hypertension plays a crucial role as a mediator in the formation of kidney stones by connecting various metabolic and hemodynamic pathways. Previous research has shown that hypertension is linked to disorders in calcium metabolism, resulting in increased calcium excretion, secondary activation of the parathyroid glands, enhanced mobilization of bone calcium, and an elevated risk of urinary tract stones[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Diabetes, as a mediator, exacerbates the process through its impact on glucose and lipid metabolism. Hyperglycemia and insulin resistance associated with diabetes can promote oxidative stress, inflammation, and changes in renal tubular function[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These alterations enhance the reabsorption of substances like calcium and oxalate in the kidneys, increasing the likelihood of crystal precipitation and stone formation. Furthermore, diabetes-induced endothelial dysfunction and vascular damage can also play a role in the development of kidney stones by disrupting the normal filtration and clearance mechanisms in the kidneys[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The interaction between gallbladder surgery-induced changes in bile metabolism and diabetes-related metabolic derangements creates a synergistic effect that further increases the risk of kidney stones.\u003c/p\u003e \u003cp\u003eThe stratified analyses revealed unexpected patterns of effect modification. The association was stronger in individuals engaging in vigorous activity (OR\u0026thinsp;=\u0026thinsp;2.786) than in those not engaging in vigorous activity (OR\u0026thinsp;=\u0026thinsp;1.816). Gallbladder surgery patients often experience a reduction in the bile acid pool and impaired fat absorption. Those who engage in vigorous activity typically require a higher caloric intake, particularly in fat and protein, which may result in increased urinary calcium excretion and elevated uric acid production, thereby increasing the risk of kidney stones. The attenuated association in individuals with MetS (OR\u0026thinsp;=\u0026thinsp;1.344 vs 2.675) suggests that metabolic overload may overshadow the lithogenic effects of surgical history, creating a \"ceiling effect\"[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]where additional risk factors contribute less and less.\u003c/p\u003e \u003cp\u003eDespite the valuable insights gained from our study, it is not without limitations. The cross-sectional design of our study precludes us from establishing a causal relationship between gallbladder surgery and kidney stones. Although we have adjusted for a wide range of covariates, there may still be unmeasured confounding factors that could influence the results. The reliance on self-reported data for gallbladder surgery and kidney stones history is another potential source of bias. Recall bias may lead to inaccurate reporting, particularly for events that occurred in the past. Additionally, the dataset we used does not provide detailed information on certain aspects, such as the type and indication of gallbladder surgery, which could have implications for the interpretation of the results. The lack of information on dietary factors, such as specific nutrient intake and fluid consumption, is also a limitation. These factors are known to play a significant role in kidney stone formation and could potentially modify the relationship between gallbladder surgery and kidney stones.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study has reinforced the significant association between gallbladder surgery and kidney stone formation, highlighting diabetes and hypertension as critical mediators in this relationship. These findings call for heightened awareness among clinicians regarding the potential long-term risks associated with cholecystectomy, particularly concerning metabolic health and renal outcomes. Further research is essential to develop targeted strategies aimed at reducing these risks in affected populations. Future studies should aim to address the limitations of the current study by incorporating more detailed dietary data and using longitudinal study designs to better establish causality.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePIR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epoverty income ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMetS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emetabolic syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eeGDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eestimated glucose disposal rate.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are very grateful to all the participants in this research project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003eontributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYoujian Li: study design, data interpretation, and manuscript writing;\u003c/p\u003e\n\u003cp\u003eHaoli Yin: study concept, data analysis, and revision of the manuscript;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYongshan Li,\u0026nbsp;Zuhong Ji and\u0026nbsp;Kai Li: study design and data analysis;\u003c/p\u003e\n\u003cp\u003eJie Liu,\u0026nbsp;Yetao Zhang and Kai Zhou: data analysis and data interpretation;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQingyi Zhu.: study concept and revision of the manuscript.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not receive specific funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eof data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). All participants were fully informed and gave consent.This study used previously collected deidentified data, which had been reviewed and approved by the National Center for Health Statistics (NCHS) Research Ethics Review Committee and found to be exempt from review by the Ethics Committee of\u0026nbsp;The Second Affiliated Hospital of Nanjing Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eParticipants in the NHANES survey provided informed consent for data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCo\u003c/strong\u003e\u003cstrong\u003empeting\u003c/strong\u003e\u003cstrong\u003ei\u003c/strong\u003e\u003cstrong\u003entere\u003c/strong\u003e\u003cstrong\u003est\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone of the authors declare a competing interest, and the results presented in this paper have not been published previously in whole or part.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRomero V, Akpinar H, Assimos DG. Kidney stones: a global picture of prevalence, incidence, and associated risk factors. Rev Urol. 2010;12(2\u0026ndash;3):e86\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHill AJ, Basourakos SP, Lewicki P, Wu X, Arenas-Gallo C, Chuang D, Bodner D, Jaeger I, Nevo A, Zell M, et al. Incidence of Kidney Stones in the United States: The Continuous National Health and Nutrition Examination Survey. J Urol. 2022;207(4):851\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerraro PM, Bargagli M, Trinchieri A, Gambaro G. Risk of Kidney Stones: Influence of Dietary Factors, Dietary Patterns, and Vegetarian-Vegan Diets. Nutrients 2020, 12(3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong Y, Cook P, Roderick P, Somani BK. Metabolic Syndrome and Kidney Stone Disease: A Systematic Review of Literature. J Endourol. 2016;30(3):246\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDevarajan A. Cross-talk between renal lithogenesis and atherosclerosis: an unveiled link between kidney stone formation and cardiovascular diseases. \u003cem\u003eClinical science (London, England\u003c/em\u003e: 1979) 2018, 132(6):615\u0026ndash;626.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan Y, Tan W, Huang Y, Huang H, Li Y, Gou Y, Zeng S, Hu Z. Association between hysterectomy and kidney stone disease: results from the National Health and Nutrition Examination Survey 2007\u0026ndash;2018 and Mendelian randomization analysis. World J Urol. 2023;41(8):2133\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePal SC, Castillo-Casta\u0026ntilde;eda SM, D\u0026iacute;az-Orozco LE, Ram\u0026iacute;rez-Mej\u0026iacute;a MM, Dorantes-Heredia R, Alonso-Morales R, Eslam M, Lammert F. M\u0026eacute;ndez-S\u0026aacute;nchez N: Molecular Mechanisms Involved in MAFLD in Cholecystectomized Patients: A Cohort Study. Genes 2023, 14(10).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChew BH, Miller LE, Eisner B, Bhattacharyya S, Bhojani NJJOP. Prevalence, Incidence, and Determinants of Kidney Stones in a Nationally Representative Sample of US Adults. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmiley A, King D, Bidulescu A. The Association between Sleep Duration and Metabolic Syndrome: The NHANES 2013/2014. Nutrients 2019, 11(11).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLange AH, Pedersen MG, Ellegaard AM, Nerild HH, Br\u0026oslash;nden A, Sonne DP, Knop FK. The bile-gut axis and metabolic consequences of cholecystectomy. Eur J Endocrinol. 2024;190(4):R1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark S, Jeong S, Park SJ, Song J, Kim SM, Chang J, Choi S, Cho Y, Oh YH, Kim JS, et al. Associations of cholecystectomy with metabolic health changes and incident cardiovascular disease: a retrospective cohort study. Sci Rep. 2024;14(1):3195.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNystr\u0026ouml;m T, Holzmann MJ, Eliasson B, Svensson AM, Sartipy U. Estimated glucose disposal rate predicts mortality in adults with type 1 diabetes. Diabetes Obes Metab. 2018;20(3):556\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu Z, Xiong Y, Feng X, Yang K, Gu H, Zhao X, Meng X, Wang Y. Insulin resistance estimated by estimated glucose disposal rate predicts outcomes in acute ischemic stroke patients. Cardiovasc Diabetol. 2023;22(1):225.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLai SW, Liao KF, Lai HC, Chou CY, Cheng KC, Lai YM. The prevalence of gallbladder stones is higher among patients with chronic kidney disease in Taiwan. Medicine. 2009;88(1):46\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKharazmi E, Scherer D, Boekstegers F, Liang Q, Sundquist K, Sundquist J, Fallah M, Lorenzo Bermejo J. Gallstones, Cholecystectomy, and Kidney Cancer: Observational and Mendelian Randomization Results Based on Large Cohorts. Gastroenterology. 2023;165(1):218\u0026ndash;e227218.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed MH, Barakat S, Almobarak AO. The association between renal stone disease and cholesterol gallstones: the easy to believe and not hard to retrieve theory of the metabolic syndrome. Ren Fail. 2014;36(6):957\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuhl CE, Everhart JE. Relationship of non-alcoholic fatty liver disease with cholecystectomy in the US population. Am J Gastroenterol. 2013;108(6):952\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu F, Chen R, Zhang C, Wang H, Ding Z, Yu L, Tian F, Chen W, Zhou Y, Zhai Q. Cholecystectomy Significantly Alters Gut Microbiota Homeostasis and Metabolic Profiles: A Cross-Sectional Study. Nutrients 2023, 15(20).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Q, Lu Q, Shao W, Jiang Z, Hu H. Dysbiosis of gut microbiota after cholecystectomy is associated with non-alcoholic fatty liver disease in mice. FEBS open bio. 2021;11(8):2329\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmad TR, Haeusler RA. Bile acids in glucose metabolism and insulin signalling - mechanisms and research needs. Nat reviews Endocrinol. 2019;15(12):701\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTicinesi A, Nouvenne A, Chiussi G, Castaldo G, Guerra A, Meschi T. Calcium Oxalate Nephrolithiasis and Gut Microbiota: Not just a Gut-Kidney Axis. A Nutritional Perspective. Nutrients 2020, 12(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe Z, Wu C, Xiong Y, Zhang F, Luo J, Xu L, Wang J, Bai Y. Obesity, metabolic dysfunction, and risk of kidney stone disease: a national cross-sectional study. aging male: official J Int Soc Study Aging Male. 2023;26(1):2195932.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDassanayake SN, Lafont T, Somani BK. Association and risk of metabolic syndrome and kidney stone disease: outcomes from a systematic review and meta-analysis. \u003cem\u003eCurrent opinion in urology\u003c/em\u003e 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCappuccio FP, Kalaitzidis R, Duneclift S, Eastwood JB. Unravelling the links between calcium excretion, salt intake, hypertension, kidney stones and bone metabolism. J Nephrol. 2000;13(3):169\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Jin M, Cheng CK, Li Q. Tubular injury in diabetic kidney disease: molecular mechanisms and potential therapeutic perspectives. Front Endocrinol. 2023;14:1238927.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang DR, Wang MY, Zhang CL, Wang Y. Endothelial dysfunction in vascular complications of diabetes: a comprehensive review of mechanisms and implications. Front Endocrinol. 2024;15:1359255.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaenz-Medina J, Mu\u0026ntilde;oz M, Rodriguez C, Sanchez A, Contreras C, Carballido-Rodr\u0026iacute;guez J, Prieto D. Endothelial Dysfunction: An Intermediate Clinical Feature between Urolithiasis and Cardiovascular Diseases. Int J Mol Sci 2022, 23(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBell KR, Oliver WM, White TO, Molyneux SG, Clement ND, Duckworth AD. QuickDASH and PRWE Are Not Optimal Patient-Reported Outcome Measures After Distal Radial Fracture Due to Ceiling Effect: Potential Implications for Future Research. J bone joint Surg Am volume. 2023;105(16):1270\u0026ndash;9.\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":"Gallbladder surgery, Kidney stones, Diabetes, Hypertension, NHANES, Mediation analysis","lastPublishedDoi":"10.21203/rs.3.rs-6560456/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6560456/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Kidney stones are a prevalent urological condition that affects a considerable number of individuals worldwide. However, the connection between gallbladder surgery and the formation of kidney stones, along with the potential mediating effect ofhypertension and diabetes on this relationship, is not fully elucidated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and methods\u003c/strong\u003e: Data from 6,579 adults aged 20 years or older in the National Health and Nutrition Examination Survey (NHANES) from 2017 to 2020 were analyzed. Gallbladder surgery and kidney stone history were determined through questionnaires. Weighted multivariate logistic regression was used to assess associations, subgroup analyses were conducted, and mediation analysis evaluated the mediating effects of metabolic syndrome (MetS), diabetes, hypertension, and estimated glucose disposal rate (eGDR).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The prevalence of kidney stones was 10.36%, with significantly higher rates in the gallbladder surgery group (18.88% compared to 9.21%). After adjusting for confounding factors, gallbladder surgery remained independently associated with kidney stones (OR = 1.94, 95% CI: 1.35–2.79). Subgroup analyses indicated stronger associations among vigorous activity individuals (OR = 2.786) and those without MetS (OR = 2.675). Mediation analysis revealed that diabetes, hypertension, and eGDR mediated 3.04%, 5.34%, and 10.54% of the effect, respectively, whereas MetS did not mediate the association.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Gallbladder surgery is closely associated with kidney stones, and hypertension and diabetes exert a partial mediating effect. Special attention should be given to the management of hypertension and diabetes in patients who have undergone gallbladder surgery to prevent the formation of kidney stones. Further investigation is necessary to better understand the mechanisms and enhance preventive measures.\u003c/p\u003e","manuscriptTitle":"Associations between gallbladder surgery and kidney stones: The mediating roles of hypertension and diabetes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-04 09:21:54","doi":"10.21203/rs.3.rs-6560456/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":"c0ed98b9-c316-4863-91b6-3f57767ad199","owner":[],"postedDate":"June 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[{"value":"featured","date":"2025-06-04 15:37:40"}],"updatedAt":"2025-08-11T06:23:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-04 09:21:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6560456","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6560456","identity":"rs-6560456","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}