Visceral adipose tissue and nephrolithiasis risk: Evidence from National Health and Nutrition Examination Survey and Mendelian randomization analysis

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Methods: Multivariable logistic regression models were used to estimate the association between nephrolithiasis risk and VAT quartiles. Restricted cubic splines (RCSs) were employed to investigate potential nonlinear associations between visceral adipose tissue (VAT) and the likelihood of developing nephrolithiasis. A Mendelian randomization analysis was conducted to assess the causal relationship between VAT volume and nephrolithiasis risk. Results: Participants in the highest VAT quartile demonstrated a significantly greater risk of nephrolithiasis than did those in the lowest quartile across all the models: crude mode (OR [95% CI], 3.00 [1.78, 5.07]), model 1 (OR [95% CI], 2.24 [1.28, 3.92]), model 2 (OR [95% CI], 2.18 [1.24, 3.83]), and model 3 (OR [95% CI], 1.95 [0.99, 3.82]). The RCS analysis revealed a linear relationship between VAT volume and nephrolithiasis (P-nonlinear=0.443). Mendelian randomization analysis provided consistent evidence that higher VAT volume was causally associated with increased nephrolithiasis risk (OR [95% CI], 1.03 [1.02, 1.04]; P<0.001). Conclusions: This study demonstrated a positive linear causal association between VAT volume and nephrolithiasis risk. Health sciences/Diseases/Metabolic disorders Health sciences/Diseases/Urogenital diseases Figures Figure 1 Figure 2 Introduction Nephrolithiasis, commonly known as kidney stones, are common recurrent kidney conditions that are a significant public health concern. Considerable attention has been given to the high prevalence and costs associated with nephrolithiasis 1 . Studies have shown that the prevalence of nephrolithiasis has increased to 8.8% in North America 2 . Given the substantial negative impacts of nephrolithiasis on both society and individuals, identifying modifiable risk factors is crucial for developing effective interventions to reduce the burden of this widespread health problem. Numerous factors, including age, sex, race, obesity, cardiovascular disease, and metabolic changes, have been implicated in the development of nephrolithiasis 2–4 . Among these factors, obesity is a significant and modifiable contributor to nephrolithiasis formation. However, the literature on the association between obesity and nephrolithiasis is limited. Specifically, the assessment of obesity in previous studies was primarily based on body mass index (BMI), which might not accurately distinguish between adipose tissue and nonadipose tissue 5 . The VAT, referring to the fat located within the abdominal cavity and surrounding organs, has been increasingly linked to various health conditions, including nephrolithiasis 6–9 . Nevertheless, the majority of studies on the association between obesity and nephrolithiasis have utilized less representative samples 10 . Additionally, the dose‒response relationship between VAT and nephrolithiasis remains unclear. Retrospective studies are prone to confounding factors and interference from reverse causality 11,12 . To address these limitations, we utilized data from the National Health and Nutrition Examination Survey (NHANES), which provides a representative sample of the U.S. population. Furthermore, we employed Mendelian randomization (MR) analysis to investigate the causal relationship between VAT and nephrolithiasis. MR analysis utilizes genetic variation obtained from large-scale genome-wide association studies (GWASs) as an instrumental variable, effectively avoiding confounding factors and reverse causal relationships present in observational studies. 13 The primary objective of this study was to investigate the association, particularly the dose‒response relationship, between VAT and nephrolithiasis using data from the NHANES. We aimed to determine any potential causal relationship between VAT and nephrolithiasis. By employing Mendelian randomization analysis, we aimed to provide further insights into the mechanisms underlying the observed associations. Materials and Methods Observational study Data sources and participants This cross-sectional study utilized data collected during the 2011–2018 cycle of the NHANES, a nationally representative survey that focuses on the noninstitutionalized population of the U.S. Approval for the study was obtained from the Ethics Review Board of the National Center for Health Statistics. The research encompassed a total of 8,570 samples. Collection and definition of variables The primary outcome of this observational study was to determine whether individuals had a history of nephrolithiasis. Specifically, participants who answered "yes" to the question "Have you/Has sample person (SP) ever had kidney stones?" identified in the NHANES questionnaire. The primary exposure factor was visceral adipose tissue (VAT). To assess this possibility, dual-energy X-ray scans were obtained from all eligible participants aged 8–59 years who met the inclusion criteria and had no contraindications as part of the NHANES study. Hologic APEX software was used to define VAT in the scan analysis. The VAT volume, which refers to the amount of fat located inside the abdominal cavity, was measured at the approximate interspace between the L4 and L5 vertebrae. The researchers also considered various covariates, including age; gender (male or female); race (Mexican American, Other Hispanic, Non-Hispanic White Black, Non-Hispanic Black or Others); education level (less than 9th grade, 9–11th grade, high school graduate/General Educational Development or equivalent, some college or Associate of Arts degree, or college graduate or above); marital status (married, widowed, divorced, separated, never married or living with partner); family PIR (PIR = ratio of family income to poverty threshold) 3.5); body mass index (BMI, normal or low weight; overweight, Obese); total cholesterol (TC, mg/dL); HDL-Cholesterol (HDL, mg/dL); hypertension (NO or YES); diabetes (NO,Borderline or YES); alcohol (NO or YES);Smoke( Never, smoke (Never, Former or Current); subcutaneous adipose tissue (SAT); and total abdominal adipose tissue (TAT). Statistical analysis Sample weights were adjusted to present nationally representative estimates in all analyses according to the stratified, multistage probability sampling design. Continuous variables are presented as the mean ± standard error, while categorical variables are presented as frequencies (percentages). Differences between groups for continuous and categorical variables were calculated using the weighted t test and chi-square test (χ2), respectively. To control for potential confounding variables, VAT was categorized into quarters and treated as both a continuous variable and a categorical variable. Multivariate logistic regression was conducted with or without adjustment for possible confounding variables to further explore the association between VAT and nephrolithiasis. The crude model was not adjusted, while Model 1 was adjusted for race, hypertension, and diabetes. Model 2 was adjusted for race, hypertension, diabetes status, and alcohol consumption. Model 3 was adjusted for age, gender, race, education level, marital status, family PIR, BMI, total cholesterol (TC), high-density lipoprotein cholesterol (HDL), hypertension, diabetes, alcohol consumption, smoking status, SAT, and TAT. Trend tests (p value for trend) were performed by entering VAT (quartile-categorical) as a continuous variable, with the first quartile of VAT serving as the reference group. Furthermore, an analysis of possible nonlinear relationships between VAT volume and the risk of nephrolithiasis was carried out using restricted cubic splines (RCSs). MR analyses Data sources and SNP selection for VAT and nephrolithiasis. The primary genetic data were obtained from a recent GWAS on VAT involving 325,153 European males (n = 164,004) and females (n = 161,149). Summary data for the predicted VAT from the GWAS are accessible at https://www.ebi.ac.uk/gwas/search?query=GCST008744 . SNPs that exhibited a significant association with VAT (p < 5 × 10 − 8 , LD r2 10) 14 . To ascertain whether there were SNPs linked to confounders, the Phenoscanner database was examined for SNPs associated with exposure. The MR-PRESSO test was used to eliminate outliers 15 . The GWAS summary data for nephrolithiasis were selected from a UK Biobank-based GWAS that included individuals of 388,508 controls and 6536 stone formers 16 . Statistical analysis To evaluate the causal relationship between VAT and nephrolithiasis through the combination of multiple SNPs, a two-sample Mendelian randomization analysis was conducted using three primary methods: the inverse variance weighted (IVW) method, weighted median, and MR‒Egger regression 14,17,18 . To further validate the stability of the Mendelian randomization results, additional sensitivity analyses were performed. First, Cochran's Q statistic was utilized to quantify heterogeneity, followed by the MR‒Egger intercept test to evaluate the presence of horizontal pleiotropy 15 . Additionally, a leave-one-out analysis was conducted to verify the consistency of the findings by removing each SNP individually 19 . All the statistical analyses were performed using R software (version 4.0.3), and the MR analyses were performed using the two-sample MR package (version 0.5.6). All p values were considered two-sided, and a p value < 0.05 indicated statistical significance. Results Observational results between VAT and nephrolithiasis in the NHANES Participant characteristics The overall weighted incidence of nephrolithiasis was 9.1%. The characteristics of the study subjects stratified by nephrolithiasis are presented in Table 1 . There were statistically significant differences in age, race, marital status, BMI, HDL cholesterol, diabetes status, hypertension status, alcohol consumption, smoking status, SAT, TAT, and VAT between the nephrolithiasis group and the control group (p < 0.05). Table 1 Characteristics of the study subjects stratified by nephrolithiasis. Characteristic Overall, N = 8570 (100%) 1 Control, N = 7847 (91%) 1 Nephrolithiasis, N = 723 (9.1%) 1 p value 2 Gender 0.7 Male 4,547 (53%) 4,174 (53%) 373 (52%) Female 4,023 (47%) 3,673 (47%) 350 (48%) Age(years) 40 (12) 39 (12) 43 (11) < 0.001 Race < 0.001 Mexican American 1,179 (9.2%) 1,081 (9.3%) 98 (8.0%) Other Hispanic 819 (6.4%) 731 (6.4%) 88 (6.6%) Non-Hispanic White 3,383 (66%) 3,020 (65%) 363 (73%) Non-Hispanic Black 1,795 (10%) 1,696 (11%) 99 (6.2%) Others 1,394 (8.3%) 1,319 (8.6%) 75 (6.1%) Education level 0.2 Less than 9th grade 375 (2.7%) 347 (2.7%) 28 (2.3%) 9-11th grade 988 (8.6%) 892 (8.5%) 96 (9.5%) High school graduate/GED 1,869 (21%) 1,712 (21%) 157 (23%) Some college or AA degree 2,934 (34%) 2,638 (34%) 296 (37%) College graduate or above 2,404 (33%) 2,258 (34%) 146 (28%) Marital status < 0.001 Married 4,113 (52%) 3,735 (51%) 378 (58%) Widowed 121 (1.2%) 112 (1.2%) 9 (0.9%) Divorced 846 (9.8%) 746 (9.4%) 100 (14%) Separated 300 (2.5%) 268 (2.5%) 32 (2.6%) Never married 2,190 (23%) 2,060 (24%) 130 (15%) Living with partner 1,000 (11%) 926 (11%) 74 (8.9%) family PIR 0.5 3.5 2,843 (45%) 2,612 (45%) 231 (42%) BMI (kg/m2) < 0.001 Normal or low weight 2,575 (30%) 2,422 (31%) 153 (21%) Overweight 2,699 (32%) 2,483 (33%) 216 (31%) Obese 3,296 (38%) 2,942 (37%) 354 (49%) TC (mg/dL) 193 (41) 192 (40) 196 (51) 0.2 HDL (mg/dL) 53 (16) 53 (16) 50 (15) < 0.001 Diabetes < 0.001 NO 7,771 (92%) 7,176 (93%) 595 (83%) Borderline 154 (1.7%) 129 (1.4%) 25 (4.0%) YES 645 (6.1%) 542 (5.4%) 103 (13%) Hypertension < 0.001 NO 6,445 (76%) 5,990 (78%) 455 (63%) YES 2,125 (24%) 1,857 (22%) 268 (37%) Alcohol < 0.001 NO 1,134 (12%) 990 (11%) 144 (18%) YES 7,436 (88%) 6,857 (89%) 579 (82%) Smoke 0.006 Never 4,758 (55%) 4,416 (56%) 342 (48%) Former 1,676 (22%) 1,507 (22%) 169 (25%) Current 2,136 (23%) 1,924 (22%) 212 (27%) SAT (cm3) 1,775 (883) 1,755 (883) 1,965 (862) < 0.001 TAT (cm3) 2,329 (1,089) 2,295 (1,083) 2,667 (1,089) < 0.001 VAT (cm3) 555 (311) 540 (302) 702 (353) < 0.001 1 n (unweighted) (%); Mean (SD) 2 chi-squared test with Rao & Scott's second-order correction; Wilcoxon rank-sum test for complex survey samples family PIR, ratio of family income to poverty threshold; BMI, Body Mass Index; TC, Total Cholesterol; HDL, HDL-Cholesterol; VAT, Visceral adipose tissue; SAT, Subcutaneous adipose tissue; TAT, Total abdominal adipose tissue. Multivariate regression analysis As summarized in Table 2 , individuals in the highest quartile (Quartile 4) of VAT exhibited a markedly elevated risk of developing nephrolithiasis compared to those in the lowest quartile (Quartile 1). This elevated risk was statistically significant in the crude model (OR [95% CI], 3.00 [1.78, 5.07]), as well as in Model 1 (OR [95% CI], 2.24 [1.28, 3.92]), Model 2 (OR [95% CI], 2.18 [1.24, 3.83]), and Model 3 (OR [95% CI], 1.95 [0.99, 3.82]). Furthermore, the p value for the trend analysis was less than 0.05 in all the models. Table 2 Multivariate regression analysis between VAT and nephrolithiasis Variable Crude Model1 Model2 Model3 OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) VAT (cm 3 ) Quartile 1 reference reference reference reference Quartile 2 1.41(0.92, 2.17) 1.35(0.88, 2.09) 1.33(0.86, 2.05) 1.28(0.79, 2.08) Quartile 3 1.54(0.93, 2.54) 1.31(0.78, 2.21) 1.3(0.77, 2.18) 1.19(0.66, 2.12) Quartile 4 3.00(1.78, 5.07) 2.24(1.28, 3.92) 2.18(1.24, 3.83) 1.95(0.99, 3.82) p for trend < 0.001 0.003 0.005 0.029 The crude material was not adjusted. Model 1 was adjusted for race, hypertension, and diabetes, and Model 2 was adjusted for race, hypertension, diabetes, and alcohol. Model3 for age, gender, race, education level, marital status, family PIR, BMI, total cholesterol (TC), high-density lipoprotein cholesterol (HDL), hypertension, diabetes, alcohol, smoking, SAT, and TAT. VAT, Visceral adipose tissue. Restricted cubic splines (RCSs) analysis As Fig. 1 illustrates, the analysis conducted using regression calibration spline (RCSs) revealed a linear relationship between visceral adipose tissue (VAT) and nephrolithiasis, even after accounting for potential confounders (p-nonlinear = 0.443). Two-sample MR analysis In this study, we analyzed 225 statistically significant VAT-associated SNPs (with p < 5 × 10 − 8 and LD r 2 < 0.001), 27 of which were present in the nephrolithiasis dataset. After searching for additional VAT-associated SNPs in the PhenoScanner database, we identified two SNPs (rs11776713 and rs56094641) linked to treatment with bendroflumethiazide, a drug considered protective against nephrolithiasis 20 . The F-statistics for the IVs of VAT exceeded the threshold of 10, indicating that these IVs were robust and likely to minimize estimation bias. We further refined our analysis using the MR-PRESSO test to remove outliers, resulting in the exclusion of two SNPs (rs10423928 and rs7165759) and the inclusion of 23 SNPs as IVs in our nephrolithiasis analysis. As Fig. 2 illustrates, Cochran's Q test revealed the presence of heterogeneity (P < 0.05). Consequently, we employed a random-effects model using the inverse variance weighted (IVW) method. Our findings suggest a causal association between genetically elevated VAT and nephrolithiasis (OR [95% CI], 1.03 [1.02, 1.04]; P < 0.001). The results obtained from the weighted median model were consistent with those from IVW (OR [95% CI], 1.03 [1.02, 1.04]; P < 0.001). However, the MR‒Egger method indicated no causal effects of genetically predicted VAT on nephrolithiasis (OR [95% CI], 0.99 [0.96, 1.03]; P = 0.64). Furthermore, the MR‒Egger analysis did not suggest any directional pleiotropy for the IVs (P for intercept = 0.06). Finally, our leave-one-out sensitivity analysis demonstrated that no single SNP had a significant impact on the overall pooled results. Discussion The results of our study indicate a positive linear relationship between visceral adipose tissue (VAT) and nephrolithiasis, suggesting that as VAT increases, the risk of developing nephrolithiasis also increases. To further investigate this potential causal relationship, we utilized a two-sample Mendelian randomization (MR) approach, which provided evidence supporting a causal role of genetically predicted VAT in nephrolithiasis risk. To the best of our knowledge, this represents the first study to explore the causal link between VAT and nephrolithiasis using large-scale observational data in conjunction with MR analyses. The relationship between VAT and nephrolithiasis, while not fully understood, is gaining recognition. Our findings align with previous research identifying VAT as a risk factor for nephrolithiasis 11,12,21,22 . However, one study reported conflicting results, suggesting a stronger association between subcutaneous fat and nephrolithiasis risk 23 . Key differences in study design, including sample size, age group, and study type, may account for this discrepancy. To enhance reliability, we treated VAT volume as a continuous variable and employed restricted cubic splines (RCSs) to confirm that VAT volume was linearly related to nephrolithiasis. Subsequently, a two-sample Mendelian randomization approach was employed to explore the causal association between VAT and nephrolithiasis. Notably, our study revealed a positive causal link between VAT and nephrolithiasis. The underlying mechanisms linking VAT to nephrolithiasis are incompletely understood but are likely multifaceted. VAT may contribute to nephrolithiasis development through several pathways, including the secretion of proinflammatory cytokines such as tumor necrosis factor-alpha (TNF-α), which can trigger chronic inflammation and kidney dysfunction 8 . Additionally, insulin resistance in VAT may lead to elevated blood glucose and insulin levels, affecting urine composition and crystal formation 24 . VAT may also influence kidney handling of salt, as adipocytes can secrete aldosterone, a hormone that regulates sodium and water reabsorption 25 . Finally, VAT may disrupt calcium homeostasis, a known factor in nephrolithiasis formation 26 . Our study has several strengths. First, the large-scale data provided robust statistical power to detect associations between VAT and nephrolithiasis. Second, exploring the potential linear relationship between these variables offers new insights into this complex association. Third, compared with traditional observational studies, the MR approach employs genetic variants as proxies for VAT, minimizing biases associated with reverse causation and confounding factors. Despite these strengths, several limitations should be noted. Self-reported nephrolithiasis information in the NHANES dataset may have introduced recall or reporting biases. Additionally, the lack of stone composition data prevented stratified analyses by stone type. Our findings are also primarily applicable to European and American populations, limiting generalizability to other ethnic groups. Conclusion In conclusion, our findings point to a positive and linear association between VAT volume and nephrolithiasis risk. The Mendelian randomization (MR) approach has provided evidence supporting a causal role of genetically predicted VAT in nephrolithiasis risk. Future research should therefore prioritize the elucidation of the underlying mechanisms involved and the confirmation of these findings in diverse populations. Declarations Author Contribution: The authors contributed to the conception and design of the study, data collection and analysis, interpretation of the findings, and writing and revision of the manuscript. The author read and approved the final manuscript. Ethical Statement: The data used in this study were obtained from a previous study in which informed consent was obtained from all participants. No additional informed consent was required for the present study. Conflicts of interest: The authors declare no conflicts of interest. Data availability: The data presented in this article can be accessed in the main text and supplementary materials. References Raheem, O. A., Khandwala, Y. S., Sur, R. L., Ghani, K. R. & Denstedt, J. D. Burden of Urolithiasis: Trends in Prevalence, Treatments, and Costs. European Urology Focus 3 , 18–26 (2017). Scales, C. D., Smith, A. C., Hanley, J. M. & Saigal, C. S. Prevalence of Kidney Stones in the United States. European Urology 62 , 160–165 (2012). Xu, Z., Yao, X., Duan, C., Liu, H. & Xu, H. Metabolic changes in kidney stone disease. Front. Immunol. 14 , 1142207 (2023). Devarajan, A. Cross-talk between renal lithogenesis and atherosclerosis: an unveiled link between kidney stone formation and cardiovascular diseases. Clinical Science 132 , 615–626 (2018). Lavie, C. J. et al. Body composition and survival in stable coronary heart disease: impact of lean mass index and body fat in the ‘obesity paradox’. J Am Coll Cardiol 60 , 1374–1380 (2012). Tchernof, A. & Després, J.-P. Pathophysiology of human visceral obesity: an update. Physiol Rev 93 , 359–404 (2013). Neeland, I. J. et al. Visceral and ectopic fat, atherosclerosis, and cardiometabolic disease: a position statement. Lancet Diabetes Endocrinol 7 , 715–725 (2019). Kolb, H. Obese visceral fat tissue inflammation: from protective to detrimental? BMC Med 20 , 494 (2022). Jensen, M. D. Visceral Fat: Culprit or Canary? Endocrinol Metab Clin North Am 49 , 229–237 (2020). Choi, C., Kim, J. K., Han, K., Lee, Y. G. & Han, J. H. Effect of obesity and metabolic health on urolithiasis: A nationwide population-based study. Investig Clin Urol 63 , 63–70 (2022). Bartani, Z., Heydarpour, B., Alijani, A. & Sadeghi, M. The Relationship Between Nephrolithiasis Risk with Body Fat Measured by Body Composition Analyzer in Obese People. Acta Inform Med 25 , 126–129 (2017). Akarken, I. et al. Visceral obesity: A new risk factor for stone disease. Can Urol Assoc J 9 , E795-799 (2015). Sanderson, E. et al. Mendelian randomization. Nat Rev Methods Primers 2 , 6 (2022). Jack Bowden et al. Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption. Int J Epidemiol 48 , 728–742 (2019). Verbanck, M., Chen, C.-Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 50 , 693–698 (2018). Howles, S. A. et al. Genetic variants of calcium and vitamin D metabolism in kidney stone disease. Nat Commun 10 , 5175 (2019). Slob, E. A. W. & Burgess, S. A comparison of robust Mendelian randomization methods using summary data. Genet Epidemiol 44 , 313–329 (2020). Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol 40 , 304–314 (2016). Geroldinger, A., Lusa, L., Nold, M. & Heinze, G. Leave-one-out cross-validation, penalization, and differential bias of some prediction model performance measures-a simulation study. Diagn Progn Res 7 , 9 (2023). Triozzi, J. L. et al. Mendelian Randomization Analysis of Genetic Proxies of Thiazide Diuretics and the Reduction of Kidney Stone Risk. JAMA Netw Open 6 , e2343290 (2023). Tastemur, S., Senel, S., Olcucuoglu, E. & Uzun, E. Evaluation of the Relationship between Fat Volume and Nephrolithiasis. Curr Med Imaging 18 , 398–403 (2022). Hou, B. et al. Is the visceral adiposity index a potential indicator for the risk of kidney stones? Front. Endocrinol. 13 , 1065520 (2022). Ye, Z. et al. Subcutaneous Adipose Tissue Accumulation Is an Independent Risk Factor of Urinary Stone in Young People. Front Endocrinol (Lausanne) 13 , 865930 (2022). Patel, P. & Abate, N. Body fat distribution and insulin resistance. Nutrients 5 , 2019–2027 (2013). Hall, J. E., do Carmo, J. M., da Silva, A. A., Wang, Z. & Hall, M. E. Obesity-induced hypertension: interaction of neurohumoral and renal mechanisms. Circ Res 116 , 991–1006 (2015). Lovegrove, C. E. et al. Central Adiposity Increases Risk of Kidney Stone Disease through Effects on Serum Calcium Concentrations. J Am Soc Nephrol 34 , 1991–2011 (2023). Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3902291","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":271492636,"identity":"46ccc61c-d69f-4509-bc83-a02887882320","order_by":0,"name":"Tianen Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYDAC9v7nPz/+s5FjY28/QKQWnjMM0hJsacZ8PGcSiNQikcMgwcN2OHGehIMBcToMzpw9YCDBczi9TYIhgeFHxTYitBzvS0gokEjPbZNuPMDYc+Y2YS1mZw4YHJAwsM5tkzmQwMzYRoyWGwmGDTwJzOlsEgkGxGrJMWbgOeCcQLwW+zPH0pglG9IM24CBfJAov0i2Nx9j/NhgIy/f3n7wwY8KIrSggAMkqh8Fo2AUjIJRgAsAANVwPYlWrvZ0AAAAAElFTkSuQmCC","orcid":"","institution":"Jinjiang Municipal Hospital","correspondingAuthor":true,"prefix":"","firstName":"Tianen","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2024-01-27 07:47:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3902291/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3902291/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50936643,"identity":"b9659be6-922f-4d73-9e14-f76fc91ffacc","added_by":"auto","created_at":"2024-02-09 21:09:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33330,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between visceral adipose tissue (VAT) levels and the prevalence of nephrolithiasis determined using restricted cubic splines.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3902291/v1/d05a2a5178c7b999c8ec568e.png"},{"id":50936644,"identity":"f56c2e97-9c34-40c8-b97b-2eb453664af0","added_by":"auto","created_at":"2024-02-09 21:09:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":26583,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of genetic proxies of VAT with risk of nephrolithiasis. OR, odds ratio; CI, confidence interval; VAT, visceral adipose tissue.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3902291/v1/c48afacef2701ffec35bf501.png"},{"id":52739371,"identity":"36f83900-9697-4db0-a21d-ca14a1c7f351","added_by":"auto","created_at":"2024-03-15 07:20:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":466632,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3902291/v1/36e53825-44e5-45fa-8e5e-a7a2d93fbab6.pdf"},{"id":50936645,"identity":"4087b463-3b7f-4d7f-802d-85726308f8b4","added_by":"auto","created_at":"2024-02-09 21:09:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":398365,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3902291/v1/b159c00b215c8cca2f9c9fa8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Visceral adipose tissue and nephrolithiasis risk: Evidence from National Health and Nutrition Examination Survey and Mendelian randomization analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNephrolithiasis, commonly known as kidney stones, are common recurrent kidney conditions that are a significant public health concern. Considerable attention has been given to the high prevalence and costs associated with nephrolithiasis\u003csup\u003e1\u003c/sup\u003e. Studies have shown that the prevalence of nephrolithiasis has increased to 8.8% in North America \u003csup\u003e2\u003c/sup\u003e. Given the substantial negative impacts of nephrolithiasis on both society and individuals, identifying modifiable risk factors is crucial for developing effective interventions to reduce the burden of this widespread health problem. Numerous factors, including age, sex, race, obesity, cardiovascular disease, and metabolic changes, have been implicated in the development of nephrolithiasis\u003csup\u003e2\u0026ndash;4\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAmong these factors, obesity is a significant and modifiable contributor to nephrolithiasis formation. However, the literature on the association between obesity and nephrolithiasis is limited. Specifically, the assessment of obesity in previous studies was primarily based on body mass index (BMI), which might not accurately distinguish between adipose tissue and nonadipose tissue \u003csup\u003e5\u003c/sup\u003e. The VAT, referring to the fat located within the abdominal cavity and surrounding organs, has been increasingly linked to various health conditions, including nephrolithiasis \u003csup\u003e6\u0026ndash;9\u003c/sup\u003e. Nevertheless, the majority of studies on the association between obesity and nephrolithiasis have utilized less representative samples \u003csup\u003e10\u003c/sup\u003e. Additionally, the dose‒response relationship between VAT and nephrolithiasis remains unclear. Retrospective studies are prone to confounding factors and interference from reverse causality \u003csup\u003e11,12\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo address these limitations, we utilized data from the National Health and Nutrition Examination Survey (NHANES), which provides a representative sample of the U.S. population. Furthermore, we employed Mendelian randomization (MR) analysis to investigate the causal relationship between VAT and nephrolithiasis. MR analysis utilizes genetic variation obtained from large-scale genome-wide association studies (GWASs) as an instrumental variable, effectively avoiding confounding factors and reverse causal relationships present in observational studies.\u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe primary objective of this study was to investigate the association, particularly the dose‒response relationship, between VAT and nephrolithiasis using data from the NHANES. We aimed to determine any potential causal relationship between VAT and nephrolithiasis. By employing Mendelian randomization analysis, we aimed to provide further insights into the mechanisms underlying the observed associations.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eObservational study\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eData sources and participants\u003c/h2\u003e \u003cp\u003eThis cross-sectional study utilized data collected during the 2011\u0026ndash;2018 cycle of the NHANES, a nationally representative survey that focuses on the noninstitutionalized population of the U.S. Approval for the study was obtained from the Ethics Review Board of the National Center for Health Statistics. The research encompassed a total of 8,570 samples.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCollection and definition of variables\u003c/h2\u003e \u003cp\u003eThe primary outcome of this observational study was to determine whether individuals had a history of nephrolithiasis. Specifically, participants who answered \"yes\" to the question \"Have you/Has sample person (SP) ever had kidney stones?\" identified in the NHANES questionnaire.\u003c/p\u003e \u003cp\u003eThe primary exposure factor was visceral adipose tissue (VAT). To assess this possibility, dual-energy X-ray scans were obtained from all eligible participants aged 8\u0026ndash;59 years who met the inclusion criteria and had no contraindications as part of the NHANES study. Hologic APEX software was used to define VAT in the scan analysis. The VAT volume, which refers to the amount of fat located inside the abdominal cavity, was measured at the approximate interspace between the L4 and L5 vertebrae.\u003c/p\u003e \u003cp\u003eThe researchers also considered various covariates, including age; gender (male or female); race (Mexican American, Other Hispanic, Non-Hispanic White Black, Non-Hispanic Black or Others); education level (less than 9th grade, 9\u0026ndash;11th grade, high school graduate/General Educational Development or equivalent, some college or Associate of Arts degree, or college graduate or above); marital status (married, widowed, divorced, separated, never married or living with partner); family PIR (PIR\u0026thinsp;=\u0026thinsp;ratio of family income to poverty threshold)\u0026thinsp;\u0026lt;\u0026thinsp;1.3, 1.3\u0026ndash;3.5, \u0026gt;\u0026thinsp;3.5); body mass index (BMI, normal or low weight; overweight, Obese); total cholesterol (TC, mg/dL); HDL-Cholesterol (HDL, mg/dL); hypertension (NO or YES); diabetes (NO,Borderline or YES); alcohol (NO or YES);Smoke( Never, smoke (Never, Former or Current); subcutaneous adipose tissue (SAT); and total abdominal adipose tissue (TAT).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003e Sample weights were adjusted to present nationally representative estimates in all analyses according to the stratified, multistage probability sampling design. Continuous variables are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error, while categorical variables are presented as frequencies (percentages). Differences between groups for continuous and categorical variables were calculated using the weighted t test and chi-square test (χ2), respectively.\u003c/p\u003e \u003cp\u003eTo control for potential confounding variables, VAT was categorized into quarters and treated as both a continuous variable and a categorical variable. Multivariate logistic regression was conducted with or without adjustment for possible confounding variables to further explore the association between VAT and nephrolithiasis. The crude model was not adjusted, while Model 1 was adjusted for race, hypertension, and diabetes. Model 2 was adjusted for race, hypertension, diabetes status, and alcohol consumption. Model 3 was adjusted for age, gender, race, education level, marital status, family PIR, BMI, total cholesterol (TC), high-density lipoprotein cholesterol (HDL), hypertension, diabetes, alcohol consumption, smoking status, SAT, and TAT. Trend tests (p value for trend) were performed by entering VAT (quartile-categorical) as a continuous variable, with the first quartile of VAT serving as the reference group. Furthermore, an analysis of possible nonlinear relationships between VAT volume and the risk of nephrolithiasis was carried out using restricted cubic splines (RCSs).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMR analyses\u003c/h2\u003e \u003cp\u003e \u003cb\u003eData sources and SNP selection for VAT and nephrolithiasis.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe primary genetic data were obtained from a recent GWAS on VAT involving 325,153 European males (n\u0026thinsp;=\u0026thinsp;164,004) and females (n\u0026thinsp;=\u0026thinsp;161,149). Summary data for the predicted VAT from the GWAS are accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/gwas/search?query=GCST008744\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/gwas/search?query=GCST008744\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. SNPs that exhibited a significant association with VAT (p\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e, LD r2\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were selected. F-statistics were computed to identify strong IVs (F\u0026thinsp;\u0026gt;\u0026thinsp;10)\u003csup\u003e14\u003c/sup\u003e. To ascertain whether there were SNPs linked to confounders, the Phenoscanner database was examined for SNPs associated with exposure. The MR-PRESSO test was used to eliminate outliers\u003csup\u003e15\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe GWAS summary data for nephrolithiasis were selected from a UK Biobank-based GWAS that included individuals of 388,508 controls and 6536 stone formers\u003csup\u003e16\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eTo evaluate the causal relationship between VAT and nephrolithiasis through the combination of multiple SNPs, a two-sample Mendelian randomization analysis was conducted using three primary methods: the inverse variance weighted (IVW) method, weighted median, and MR‒Egger regression\u003csup\u003e14,17,18\u003c/sup\u003e. To further validate the stability of the Mendelian randomization results, additional sensitivity analyses were performed. First, Cochran's Q statistic was utilized to quantify heterogeneity, followed by the MR‒Egger intercept test to evaluate the presence of horizontal pleiotropy\u003csup\u003e15\u003c/sup\u003e. Additionally, a leave-one-out analysis was conducted to verify the consistency of the findings by removing each SNP individually\u003csup\u003e19\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAll the statistical analyses were performed using R software (version 4.0.3), and the MR analyses were performed using the two-sample MR package (version 0.5.6). All p values were considered two-sided, and a p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eObservational results between VAT and nephrolithiasis in the NHANES\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003eParticipant characteristics\u003c/h2\u003e \u003cp\u003eThe overall weighted incidence of nephrolithiasis was 9.1%. The characteristics of the study subjects stratified by nephrolithiasis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There were statistically significant differences in age, race, marital status, BMI, HDL cholesterol, diabetes status, hypertension status, alcohol consumption, smoking status, SAT, TAT, and VAT between the nephrolithiasis group and the control group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the study subjects stratified by nephrolithiasis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;8570 (100%)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;7847 (91%)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNephrolithiasis, N\u0026thinsp;=\u0026thinsp;723 (9.1%)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,547 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,174 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e373 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,023 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,673 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,179 (9.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,081 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98 (8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e819 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e731 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88 (6.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,383 (66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,020 (65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e363 (73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,795 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,696 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,394 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,319 (8.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 9th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e375 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e347 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9-11th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e988 (8.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e892 (8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school graduate/GED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,869 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,712 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e157 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome college or AA degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,934 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,638 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e296 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege graduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,404 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,258 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e146 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,113 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,735 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e378 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e846 (9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e746 (9.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e268 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,190 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,060 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,000 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e926 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (8.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efamily PIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,600 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,366 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e234 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.3\u0026ndash;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,127 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,869 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e258 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,843 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,612 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e231 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal or low weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,575 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,422 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e153 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,699 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,483 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e216 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,296 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,942 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e354 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e193 (41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192 (40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e196 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,771 (92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,176 (93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e595 (83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorderline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e154 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e645 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e542 (5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,445 (76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,990 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e455 (63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,125 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,857 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e268 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,134 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e990 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,436 (88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,857 (89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e579 (82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,758 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,416 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e342 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,676 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,507 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e169 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,136 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,924 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e212 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAT (cm3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,775 (883)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,755 (883)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,965 (862)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAT (cm3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,329 (1,089)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,295 (1,083)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,667 (1,089)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAT (cm3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e555 (311)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e540 (302)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e702 (353)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003en (unweighted) (%); Mean (SD)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e2\u003c/sup\u003echi-squared test with Rao \u0026amp; Scott's second-order correction; Wilcoxon rank-sum test for complex survey samples\u003c/p\u003e \u003cp\u003efamily PIR, ratio of family income to poverty threshold; BMI, Body Mass Index; TC, Total Cholesterol; HDL, HDL-Cholesterol; VAT, Visceral adipose tissue; SAT, Subcutaneous adipose tissue; TAT, Total abdominal adipose tissue.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate regression analysis\u003c/h2\u003e \u003cp\u003eAs summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, individuals in the highest quartile (Quartile 4) of VAT exhibited a markedly elevated risk of developing nephrolithiasis compared to those in the lowest quartile (Quartile 1). This elevated risk was statistically significant in the crude model (OR [95% CI], 3.00 [1.78, 5.07]), as well as in Model 1 (OR [95% CI], 2.24 [1.28, 3.92]), Model 2 (OR [95% CI], 2.18 [1.24, 3.83]), and Model 3 (OR [95% CI], 1.95 [0.99, 3.82]). Furthermore, the p value for the trend analysis was less than 0.05 in all the models.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate regression analysis between VAT and nephrolithiasis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAT (cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.41(0.92, 2.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.35(0.88, 2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.33(0.86, 2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.28(0.79, 2.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.54(0.93, 2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.31(0.78, 2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3(0.77, 2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19(0.66, 2.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.00(1.78, 5.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.24(1.28, 3.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.18(1.24, 3.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.95(0.99, 3.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eThe crude material was not adjusted. Model 1 was adjusted for race, hypertension, and diabetes, and Model 2 was adjusted for race, hypertension, diabetes, and alcohol. Model3 for age, gender, race, education level, marital status, family PIR, BMI, total cholesterol (TC), high-density lipoprotein cholesterol (HDL), hypertension, diabetes, alcohol, smoking, SAT, and TAT. VAT, Visceral adipose tissue.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRestricted cubic splines (RCSs) analysis\u003c/h2\u003e \u003cp\u003eAs Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates, the analysis conducted using regression calibration spline (RCSs) revealed a linear relationship between visceral adipose tissue (VAT) and nephrolithiasis, even after accounting for potential confounders (p-nonlinear\u0026thinsp;=\u0026thinsp;0.443).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTwo-sample MR analysis\u003c/h2\u003e \u003cp\u003eIn this study, we analyzed 225 statistically significant VAT-associated SNPs (with p\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e and LD r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 27 of which were present in the nephrolithiasis dataset. After searching for additional VAT-associated SNPs in the PhenoScanner database, we identified two SNPs (rs11776713 and rs56094641) linked to treatment with bendroflumethiazide, a drug considered protective against nephrolithiasis\u003csup\u003e20\u003c/sup\u003e. The F-statistics for the IVs of VAT exceeded the threshold of 10, indicating that these IVs were robust and likely to minimize estimation bias. We further refined our analysis using the MR-PRESSO test to remove outliers, resulting in the exclusion of two SNPs (rs10423928 and rs7165759) and the inclusion of 23 SNPs as IVs in our nephrolithiasis analysis.\u003c/p\u003e \u003cp\u003eAs Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates, Cochran's Q test revealed the presence of heterogeneity (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Consequently, we employed a random-effects model using the inverse variance weighted (IVW) method. Our findings suggest a causal association between genetically elevated VAT and nephrolithiasis (OR [95% CI], 1.03 [1.02, 1.04]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The results obtained from the weighted median model were consistent with those from IVW (OR [95% CI], 1.03 [1.02, 1.04]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, the MR‒Egger method indicated no causal effects of genetically predicted VAT on nephrolithiasis (OR [95% CI], 0.99 [0.96, 1.03]; P\u0026thinsp;=\u0026thinsp;0.64). Furthermore, the MR‒Egger analysis did not suggest any directional pleiotropy for the IVs (P for intercept\u0026thinsp;=\u0026thinsp;0.06). Finally, our leave-one-out sensitivity analysis demonstrated that no single SNP had a significant impact on the overall pooled results.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results of our study indicate a positive linear relationship between visceral adipose tissue (VAT) and nephrolithiasis, suggesting that as VAT increases, the risk of developing nephrolithiasis also increases. To further investigate this potential causal relationship, we utilized a two-sample Mendelian randomization (MR) approach, which provided evidence supporting a causal role of genetically predicted VAT in nephrolithiasis risk. To the best of our knowledge, this represents the first study to explore the causal link between VAT and nephrolithiasis using large-scale observational data in conjunction with MR analyses.\u003c/p\u003e \u003cp\u003eThe relationship between VAT and nephrolithiasis, while not fully understood, is gaining recognition. Our findings align with previous research identifying VAT as a risk factor for nephrolithiasis\u003csup\u003e11,12,21,22\u003c/sup\u003e. However, one study reported conflicting results, suggesting a stronger association between subcutaneous fat and nephrolithiasis risk\u003csup\u003e23\u003c/sup\u003e. Key differences in study design, including sample size, age group, and study type, may account for this discrepancy. To enhance reliability, we treated VAT volume as a continuous variable and employed restricted cubic splines (RCSs) to confirm that VAT volume was linearly related to nephrolithiasis. Subsequently, a two-sample Mendelian randomization approach was employed to explore the causal association between VAT and nephrolithiasis. Notably, our study revealed a positive causal link between VAT and nephrolithiasis.\u003c/p\u003e \u003cp\u003eThe underlying mechanisms linking VAT to nephrolithiasis are incompletely understood but are likely multifaceted. VAT may contribute to nephrolithiasis development through several pathways, including the secretion of proinflammatory cytokines such as tumor necrosis factor-alpha (TNF-α), which can trigger chronic inflammation and kidney dysfunction\u003csup\u003e8\u003c/sup\u003e. Additionally, insulin resistance in VAT may lead to elevated blood glucose and insulin levels, affecting urine composition and crystal formation\u003csup\u003e24\u003c/sup\u003e. VAT may also influence kidney handling of salt, as adipocytes can secrete aldosterone, a hormone that regulates sodium and water reabsorption \u003csup\u003e25\u003c/sup\u003e. Finally, VAT may disrupt calcium homeostasis, a known factor in nephrolithiasis formation \u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur study has several strengths. First, the large-scale data provided robust statistical power to detect associations between VAT and nephrolithiasis. Second, exploring the potential linear relationship between these variables offers new insights into this complex association. Third, compared with traditional observational studies, the MR approach employs genetic variants as proxies for VAT, minimizing biases associated with reverse causation and confounding factors.\u003c/p\u003e \u003cp\u003eDespite these strengths, several limitations should be noted. Self-reported nephrolithiasis information in the NHANES dataset may have introduced recall or reporting biases. Additionally, the lack of stone composition data prevented stratified analyses by stone type. Our findings are also primarily applicable to European and American populations, limiting generalizability to other ethnic groups.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our findings point to a positive and linear association between VAT volume and nephrolithiasis risk. The Mendelian randomization (MR) approach has provided evidence supporting a causal role of genetically predicted VAT in nephrolithiasis risk. Future research should therefore prioritize the elucidation of the underlying mechanisms involved and the confirmation of these findings in diverse populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contribution:\u003c/strong\u003e The authors contributed to the conception and design of the study, data collection and analysis, interpretation of the findings, and writing and revision of the manuscript. The author read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Statement:\u0026nbsp;\u003c/strong\u003eThe data used in this study were obtained from a previous study in which informed consent was obtained from all participants. No additional informed consent was required for the present study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e The data presented in this article can be accessed in the main text and supplementary materials.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRaheem, O. A., Khandwala, Y. S., Sur, R. L., Ghani, K. R. \u0026amp; Denstedt, J. D. 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E., do Carmo, J. M., da Silva, A. A., Wang, Z. \u0026amp; Hall, M. E. Obesity-induced hypertension: interaction of neurohumoral and renal mechanisms. \u003cem\u003eCirc Res\u003c/em\u003e \u003cstrong\u003e116\u003c/strong\u003e, 991\u0026ndash;1006 (2015).\u003c/li\u003e\n\u003cli\u003eLovegrove, C. E. \u003cem\u003eet al.\u003c/em\u003e Central Adiposity Increases Risk of Kidney Stone Disease through Effects on Serum Calcium Concentrations. \u003cem\u003eJ Am Soc Nephrol\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 1991\u0026ndash;2011 (2023).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3902291/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3902291/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThis study aimed to investigate the dose‒response association and potential causal effect between VAT volume and nephrolithiasis risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eMultivariable logistic regression models were used to estimate the association between nephrolithiasis risk and VAT quartiles. Restricted cubic splines (RCSs) were employed to investigate potential nonlinear associations between visceral adipose tissue (VAT) and the likelihood of developing nephrolithiasis. A Mendelian randomization analysis was conducted to assess the causal relationship between VAT volume and nephrolithiasis risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eParticipants in the highest VAT quartile demonstrated a significantly greater risk of nephrolithiasis than did those in the lowest quartile across all the models: crude mode (OR [95% CI], 3.00 [1.78, 5.07]), model 1 (OR [95% CI], 2.24 [1.28, 3.92]), model 2 (OR [95% CI], 2.18 [1.24, 3.83]), and model 3 (OR [95% CI], 1.95 [0.99, 3.82]). The RCS analysis revealed a linear relationship between VAT volume and nephrolithiasis (P-nonlinear=0.443). Mendelian randomization analysis provided consistent evidence that higher VAT volume was causally associated with increased nephrolithiasis risk (OR [95% CI], 1.03 [1.02, 1.04]; P\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThis study demonstrated a positive linear causal association between VAT volume and nephrolithiasis risk.\u003c/p\u003e","manuscriptTitle":"Visceral adipose tissue and nephrolithiasis risk: Evidence from National Health and Nutrition Examination Survey and Mendelian randomization analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-09 21:09:44","doi":"10.21203/rs.3.rs-3902291/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":"28964948-069e-49b7-a7ec-5bea8f6111ea","owner":[],"postedDate":"February 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28621124,"name":"Health sciences/Diseases/Metabolic disorders"},{"id":28621125,"name":"Health sciences/Diseases/Urogenital diseases"}],"tags":[],"updatedAt":"2024-03-15T07:12:08+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-09 21:09:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3902291","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3902291","identity":"rs-3902291","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}