The association between a body shape index and kidney stones: A cross-sectional study of NHANES 2007−2018

The association between a body shape index and kidney stones: A cross-sectional study of NHANES 2007−2018 | 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 The association between a body shape index and kidney stones: A cross-sectional study of NHANES 2007−2018 Bo Wang, Shiwei Huang, Juefei Dong, Guangming Yin, Long Wang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5348201/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 stone disease (KSD) is a pervasive urological health problem, which affects the quality of life of older men. However, the relationship between the A Body Shape Index (ABSI) and KSD has rarely been studied in American populations. Method We used data from the National Health and Nutrition Examination Survey (NHANES) spanning 2007 to 2018. A self-report questionnaire identified KSD. We investigated the correlation using multiple linear regression, subgroup analyses, and smooth curve fitting. Result The study included 31,235 participants, with 2,924 having kidney stones and 28,311 without. The results of the study showed a significant association between ABSI and kidney stones, that is, an increase in ABSI was associated with an increased risk of kidney stones (Q4 vs Q1, OR = 1.19, 95% CI= (1.03–1.37), p = 0.0040). After subgroup analyses, it was found that the association between ABSI and kidney stones was not significantly correlated between specific subgroups. Conclusions The present study shows that elevated ABSI is associated with an increased likelihood of developing kidney stones. Kidney stone disease A Body Shape Index Cross-sectional study United States. Figures Figure 1 Figure 2 Figure 3 Introduction Kidney stone disease(KSD), also known as kidney stones or urolithiasis, constitutes an aggregation of crystals within the urinary system [ 1 ] . It is a benign condition caused by abnormal deposition of mineral crystals including calcium oxalate, uric acid, and calcium phosphate in the renal pelvis, calyces, and the junction of the renal pelvis and ureter [ 2 ] . KSD, whose prevalence as well as recurrence rates have increased globally, is a pervasive urological health problem, and the main reason for its general emergency room visit is the onset of symptoms of urological issues such as renal colic [ 3 , 4 ] . In addition to the symptoms of renal colic that lead to emergency room visits, the disease also often leads to urinary tract infections and renal impairment, posing a severe risk to public health [ 5 ] . In recent studies, there has been a gradual rise in kidney stones in Chinese adults, with about 5.8 percent of the adult population now affected. In contrast, in the United States, the cumulative prevalence of KSD has risen by 0.3 percent over the decade [ 6 , 7 ] . Many factors have been shown to increase the incidence of kidney stones, including environmental factors, lifestyle, and several risk factors including age, race, high blood pressure, and obesity [ 8 – 10 ] . With ongoing improvements in living standards, people's eating habits have significantly changed, leading to a gradual rise in the number of overweight and obese individuals [ 11 ] . Obesity is a very complex chronic disease state, and many studies have shown a strong association between kidney stone disease and obesity, i.e., when BMI increases there is a strong association with an increased risk of kidney stone disease [ 12 , 13 ] . Waist circumference (WC) is the gold standard for abdominal obesity indices. It is also used to diagnose metabolic syndrome and is epidemiologically almost identical to BMI. However, they do not always accurately represent an individual's obesity level or abdominal fat distribution [ 14 , 15 ] . Therefore, a better and innovative abdominal obesity index is needed to enhance the assessment of visceral fat and abdominal obesity. The Body Shape Index (ABSI) is an innovative composite index designed to assess an individual's body shape and fat distribution more effectively [ 16 ] . It is derived from waist circumference, height, and weight, offering insights beyond BMI by specifically examining adipose tissue distribution [ 17 ] . However, research on the association between ABSI and the prevalence of KSD in U.S. adults is limited. Therefore, we aimed to explore their potential connection. This study is a cross-sectional analysis using representative data from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2018. It also examined the association between different ABSI levels and KSD prevalence by controlling for relevant covariates. We also performed subgroup analyses based on several important KSD factors and explored potential correlations. Materials and methods Study Population We used data from six cycles of the NHANES to study the association between the ABSI and KSD. Subjects with missing data on KSD (n = 25163) and missing data on ABSI (n = 3444) were excluded. Conducted by the CDC’s National Center for Health Statistics, NHANES collects data on the health and nutritional status of the civilian, non-institutionalized U.S. population. As a national, cross-sectional survey, NHANES structures a series of physical examinations, laboratory tests, and questionnaires to acquire sampling probability-based information. Simultaneously, these data represent the U.S.; all participants have signed informed consent. The data are publicly accessible on the NHANES website, and the program has been approved by the Research Ethics Review Board (ERB) of the National Center for Health Statistics (NCHS) [ 18 ] . Outcome variable and Exposure variable In the study, we included the men and female who had participated in the questionnaires about kidney stone disease from 31235 American civilians. Among these candidates with data on KSD, they were all asked questions related to kidney stones. Question: "Have you ever had a kidney stone?" When participants answered "no", they were not considered to have ever had a kidney stone, while when participants answered "yes", they were considered to have ever had a kidney stone. At the same time, this study also excluded missing data on kidney stones. Experienced examiners use standardized techniques and equipment to measure anthropometric data, including body height (BH), waist circumference (WC), and body weight (BW), and the body mass index (BMI) is calculated using the formula: BMI = BW (kg)/BH 2 (m). For the innovative data index ABSI, the calculation formula is as follows [ 19 ] : $$\:ABSI=\text{W}\text{C}\:\left(\text{c}\text{m}\right)/\text{B}\text{M}\text{I}2/3\:(\text{k}\text{g}/\text{m}2)\:\times\:\:\text{B}\text{H}1/2\:\left(\text{m}\right)$$ Covariates We set the following variables as covariates. Sociodemographic factors included age, race, education level, marital status, and economic status. Lifestyle factors included body mass index (BMI), alcohol consumption, smoking, hypertension, and diabetes. In addition to these, we included uric acid levels as a covariate. For the characteristics of BMI, this study was divided into three groups: normal, overweight, and obese groups. The races can be distinguished as Mexican American, non-Hispanic white, non-Hispanic black, other Hispanic, or other races. Education level was classified into two levels: less than high school; and high school or above. Marital status was separated into two levels: married or living with a partner; widowed, divorced, separated, or never married. The family income-to-poverty ratio (PIR) is calculated by comparing household income to the poverty threshold set by the U.S. Department of Health and Human Services, with higher values indicating better household income [ 20 ] . BMI (< 25/ ≥25, < 30/ ≥30) was equal to weight (kilogram) divided by height (meter) squared. Alcohol consumption was categorized into two classes: those who averaged <2 drinks/day on days they drank in the past twelve months were light drinkers; those who averaged ≥ 2 drinks/day were heavy drinkers. Smokers were also categorized into two groups: those who had not smoked more than 100 cigarettes in their lifetime were non-smokers; while those who had smoked more than 100 cigarettes were defined as smokers. Participants were identified as hypertensive if they had a previous diagnosis of hypertension. Candidates were identified as diabetic if they had a prior history of diabetes. Statistical analysis Data was analyzed using the statistical packages R (The R Foundation; http://r-project.org ; version 3.4.3) and Empower Stats ( www.empowerstats.com ; X&Y solution inc). The statistical analyses accounted for the complex sampling design recommended by NHANES, with a significance level set at P ≤ 0.05. Baseline characteristics were compared using linear regression models for continuous variables (mean ± standard deviation) and chi-square tests for categorical variables (counting number, %). Multivariate linear regression analysis assessed the correlation between ABSI and KSD. We constructed three main models. We did not adjust for variables in Model 1; in Model 2, we adjusted for gender, age, and race. In Model 3, it was regulated for all covariates, which in addition to the covariates in Model 2, included educational status, marital status, household economic status (PIR), smoking, alcohol consumption, hypertension, diabetes, and Uric acid levels. By subgroup analysis, we explored the correlation between ABSI and KSD in different subgroups. Stratification factors included gender, age (< 60/ ≥60 years), race, educational status, marital status, BMI status (< 25/ ≥25, < 30/ ≥30), hypertension, diabetes, smoking, and alcohol consumption. In addition, smoothed curve fitting models were used to verify whether there was a linear or nonlinear correlation between ABSI and KSD. P < 0.05 was considered statistically significant. To avoid a reduction in statistical power and potential bias from directly excluding missing values, we used multivariate multiple imputations with chained equations to impute missing values, maximizing statistical power and minimizing bias if people with missing data were excluded from analyses [ 21 , 22 ] . We repeated all analyses with the complete data cohort for comparison. Results Cohort characteristics Between 2007 and 2018, researchers collected detailed information on 59,842 American adults. After exclusion, 31,235 adults were included in the main analysis, which examined the association between ABSI and KSD. Table 1 details the baseline characteristics of the study participants, where the number of KSDs was 2924 and the number of non-KSDs was 28311, with a prevalence of 9.36%. There were significant differences between the KSD group and the non-KSD group in terms of age, gender, race, educational status, marital status, BMI, smoking status, alcohol consumption, hypertension, diabetes mellitus, uric acid levels, and ABSI. Table 1 Basic Characteristics of Participants in NHANES, 2007–2018 (n = 31235) Variables Non-KSD (n = 28311) KSD (n = 2924) P value Age, years 48.68 ± 17.54 55.71 ± 16.15 < 0.001 PIR 2.47 ± 1.63 2.48 ± 1.61 0.777 UA, umol/L 322.88 ± 85.36 334.45 ± 89.61 < 0.001 ABSI 13.62 ± 1.16 13.94 ± 1.23 < 0.001 Gender, % < 0.001 Male 13587 (47.99%) 1628 (55.68%) Female 14724 (52.01%) 1296 (44.32%) Race/ethnicity, % < 0.001 Mexican American 4346 (15.35%) 378 (12.93%) Other Hispanic 2964 (10.47%) 339 (11.59%) Non-Hispanic White 11110 (39.24%) 1560 (53.35%) Non-Hispanic Black 6287 (22.21%) 391 (13.37%) Other Race 3604 (12.73%) 256 (8.76%) Education, % 0.291 ≤High school 6896 (24.36%) 738 (25.24%) >High school 21415 (75.64%) 2186 (74.76%) Marital status, % < 0.001 Married/living with partner 16752 (59.17%) 1867 (63.85%) Live alone 11559 (40.83%) 1057 (36.15%) Smoking status, % < 0.001 Non-smoker 12283 (43.39%) 1477 (50.51%) smoker 16028 (56.61%) 1447 (49.49%) Alcohol intake, % < 0.001 Light drinker 10062 (35.54%) 1206 (41.24%) Heavy drinker 18249 (64.46%) 1718 (58.76%) BMI status, % < 0.001 BMI < 25 8439 (29.81%) 591 (20.21%) 25 ≤ BMI < 30 9302 (32.86%) 993 (33.96%) BMI ≥ 30 10570 (37.34%) 1340 (45.83%) Hypertension, % < 0.001 Yes 9689 (34.22%) 1460 (49.93%) No 18622 (65.78%) 1464 (50.07%) Diabetes, % < 0.001 Yes 3369 (11.90%) 639 (21.85%) No 24311 (85.87%) 2187 (74.79%) Borderline 631 (2.23%) 98 (3.35%) Association between ABSI and KSD Table 2 shows the results of the multivariable regression analysis between ABSI and KSD. Participants were subdivided into four groups according to ABSI quartiles (Q1: 8.58–12.81; Q2: 12.82–13.61; Q3: 13.62–14.44; Q4: 14.45–19.67). These associations were significant in Model 1(OR = 1.26, 95%CI: (1.22, 1.30), P < 0.0001), Model 2(OR = 1.11, 95%CI: (1.07, 1.16), P < 0.0001), and Model 3(OR = 1.06, 95%CI: (1.02, 1.11), P = 0.0039). In Model 1, the third quartile (OR(Q3vs1) = 1.44, 95%CI: (1.29–1.61), p < 0.0001) and the fourth quartile (OR(Q4vs1) = 1.93, 95%CI: (1.73–2.15), p < 0.0001) was positively associated with KSD compared to the first quartile. In Model 2, the third (OR(Q3vs1) = 1.20, 95%CI: (1.06–1.36), p = 0.0039) and the fourth quartile (OR(Q4vs1) = 1.35, 95%CI: (1.18–1.55), p < 0.0001) compared to the first quartile positively correlated with ABSI. Analyses were performed using the ABSI quartiles. In the fully adjusted model of Model 3, participants in quartile 4(OR = 1.19, 95%CI: (1.03, 1.37), P = 0.0152) were associated with a 19% increased risk of developing KSD compared with quartile 1(p for trend = 0.004). Table 2 Association between ABSI and KSD. Model 1 Model 2 Model 3 ABSI 1.26 (1.22, 1.30) < 0.0001 1.11 (1.07, 1.16) < 0.0001 1.06 (1.02, 1.11) 0.0039 Q1 Reference Reference Reference Q2 1.07 (0.95, 1.21) 0.2427 0.99 (0.88, 1.12) 0.8917 0.98 (0.87, 1.11) 0.7533 Q3 1.44 (1.29, 1.61) < 0.0001 1.20 (1.06, 1.36) 0.0039 1.13 (1.00, 1.28) 0.0583 Q4 1.93 (1.73, 2.15) < 0.0001 1.35 (1.08, 1.19) < 0.0001 1.19 (1.03, 1.37) 0.0152 P for trend < 0.0001 < 0.0001 0.0040 Smooth curve fitting and threshold effect This study investigated the nonlinear association between ABSI and the risk of kidney stones, as shown in Fig. 2 . The solid red line represents the smooth curve fit between variables. Blue bands represent the 95% confidence interval from the fit. By careful analysis of the smoothed curves, this study reveals a nonlinear association between the occurrence of kidney stones and ABSI. ABSI was significantly and positively correlated with KSD. Approximate significance of the smoothing term p-value = 0.0084. The study then derived an inflection point of 12.61 for the ABSI in the survey using a biphasic linear model and a recursive algorithm. The odds of KSD prevalence increased by 10% for each unit increase in the ABSI when the ABSI was higher than 12.61 (OR:1.10, 95%CI: 1.05–1.16). Conversely, when the ABSI was below 12.61, no significant change was observed in the risk of developing KSD. The study was adjusted for gender, age, race, education, marital status, BMI, hypertension, diabetes, alcohol, and smoking habits. Table 3 The analysis of threshold effects between ABSI and KSD. Adjusted OR (95% CI) P value KSD Fitting by standard linear model 1.06 (1.02, 1.11) 0.0042 Fitting by two-piecewise linear model 12.61 1.10 (1.05, 1.16) 0.0001 Log-likelihood ratio 0.007 Subgroup analysis This study used subgroup analyses and interaction tests to verify whether kidney stones are consistently associated with ABSI in the general population. It aims to identify possible differences in specific demographic scenarios based on factors such as BMI, gender, diabetes, and hypertension. As shown in Fig. 3 , the ABSI was significantly and positively associated with KSD in the male group, the group < 60 years of age, the group with an education greater than high school, the non-Hispanic white group, the group living with a partner, the group with a body mass index of ≥ 25, the group with a body mass index of < 30, the group that smoked, the group with no hypertension, the group with diabetes mellitus, and the group of light drinkers. Interaction tests showed that the relationship between ABSI and KSD was not statistically different between any of the strata except the hypertension stratum (P > 0.05 for interaction tests), implying that age, race, body mass index, diabetes mellitus, etc., and alcohol consumption did not have a significant effect on the positive association between ABSI and KSD. Discussion In this cross-sectional study including 31,235 candidates, we found that higher levels of the ABSI index were associated with a higher prevalence of KSD. This association was similar in subgroups stratified by sex, age, race, education, marriage, body mass index, diabetes mellitus, smoking, and alcohol consumption, suggesting that this association may be applicable across population settings. We can therefore hypothesize that clinical attention and management of ABSI levels may contribute to the prevention and treatment of patients with kidney stones. To the best of our knowledge, studies examining the relationship between ABSI and kidney stones in the U.S. population are very sparse. For kidney stone disease, many studies have reported the relationship between kidney stones and several other clinicopathological factors. Risk factors for kidney stones have long been associated with body size measurements, the most commonly used of which tend to be body mass index and abdominal circumference, which may be related to the ability of these metrics to respond to the increased visceral fat that leads to metabolic disorders [ 13 ] . Studies are now showing that being overweight or obese, combined with an unhealthy metabolic state, significantly increases the risk of KSD in the general U.S. population [ 23 ] . There is also a causal relationship between high BMI and an increased risk of kidney stone prevalence demonstrated utilizing Mendelian randomization analysis by Yuan et al [ 24 ] . However, BMI does not accurately reflect the fat distribution and its impact on health, especially visceral fat accumulation [ 15 ] . ABSI provides a more accurate assessment of body size by integrating waist circumference with BMI and height [ 25 ] . Therefore, studying the relationship between ABSI and kidney stones may reveal a more accurate method of assessing kidney stone risk than BMI. ABSI may be more effective than BMI in assessing the risk of these diseases [ 26 ] . In recent years, a growing body of research points to a possible relationship between ABSI and KSD, specifically, Lin et al [ 27 ] . They have concluded that there is a linear relationship between ABSI and the prevalence of KSD. Recently, according to Jeong et al [ 28 ] , metabolic syndrome increases the incidence of kidney stones. At the same time, other studies have shown that an increase in ABSI is positively associated with the development of metabolic syndrome [ 29 ] . This may suggest a close relationship between ABSI and kidney stones. The exact mechanism by which ABSI is positively associated with KSD is unknown. The following explanations may support our results. High ABSI values reflect excessive abdominal visceral fat accumulation, closely associated with insulin resistance and metabolic syndrome, and these metabolic disorders may promote kidney stone formation by increasing urinary calcium and uric acid excretion or altering urinary pH [ 30 ] . Obesity, diabetes mellitus, hypertension, and hyperlipidemia have been reported in some of the many studies and are often thought to increase the prevalence of kidney stones by causing metabolic syndrome [ 31 , 32 ] . In addition, insulin resistance is frequently associated with visceral adiposity, leading to increased renal calcium reabsorption and decreased urinary citrate excretion, which may be a key factor in stone development [ 33 ] . Some studies suggest that urinary citrate plays a crucial role in preventing kidney stone formation, and they also indicate that visceral fat might lower urinary citrate levels by promoting renal calcium reabsorption, which, in turn, could contribute to an increased risk of kidney stone development [ 34 ] . Furthermore, the excessive accumulation of abdominal fat represented by an increased ABSI leads to chronic inflammation and elevated levels of oxidative stress in the body, factors that may impair kidney function and further increase the risk of kidney stone formation [ 35 ] . Immediately following this, people with higher ABSI may have unhealthy dietary habits (high salt, protein, sugar, etc.), all of which are associated with kidney stone formation [ 11 , 36 , 37 ] . This study has several strengths. First, the study is based on data from NHANES, a national population-based sample database obtained using standardized protocols, making the study sample more representative. Second, we adjusted for various confounding covariates to make the results more robust. Despite some of the strengths of this study, it couldn't avoid having some flaws. Firstly, we used data from the past. Secondly, the diagnosis of kidney stones was mainly from questionnaires and the specifics of the disease were not known. Since this was a cross-sectional study without follow-up data, it was impossible to establish a clear causal relationship between ABSI and kidney stone formation. In addition, despite adjusting for several potential covariates, the influence of other possible confounding factors, such as the use of medications and some other comorbidities, could not be completely excluded. Immediately after, the specific application of ABSI in predicting renal stone risk still needs to be validated by more clinical studies. Future studies should consider epidemiological investigations with larger sample sizes and mechanistic studies to clarify the causal relationship between ABSI and kidney stones. Conclusion The present study demonstrated that elevated ABSI is associated with an increased chance of developing kidney stones. We hypothesized that controlling ABSI might correspondingly reduce the occurrence and recurrence of kidney stones. However, further studies are needed to validate our findings. Abbreviations KSD kidney stone disease ABSI A body shape index NHANES National Health and Nutrition Examination Survey WC waist circumference BH body height BW body weight BMI Body mass index CDC Centers for Disease Control and Prevention ERB Research Ethics Review Board NCHS National Center for Health Statistics PIR Ratio of family income to poverty Declarations Acknowledgements Thanks to all the volunteers who took part in the NHANES. Author contributions BW, and JT contributed to the hypothesis development and to the drafting of the manuscript; JD, and SH were responsibility for the data analysis. LW, and GY contributed to the data interpretation and revision of the manuscript. All authors read and approved the final manuscript. Funding Not applicable. Availability of data and materials Survey data are available for data consumers and researchers all across the globe on the internet (https://www.cdc.gov/nchs/nhanes/). Statements and Declarations Ethics approval and consent to participate All NHANES participants provided written informed consent and the National Center for Health Statistics obtained institutional review board approval prior to data collection. Because NHANES data are de-identifed and publicly available, the analysis presented here was exempt from IRB review. Consent for publication Not applicable. 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University","correspondingAuthor":false,"prefix":"","firstName":"Shiwei","middleName":"","lastName":"Huang","suffix":""},{"id":375329946,"identity":"6321cb6c-5c63-486f-809d-296fb1860bf3","order_by":2,"name":"Juefei Dong","email":"","orcid":"","institution":"The Third Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Juefei","middleName":"","lastName":"Dong","suffix":""},{"id":375329947,"identity":"ca1898b3-4fcf-43cb-ac95-446253327be4","order_by":3,"name":"Guangming Yin","email":"","orcid":"","institution":"The Third Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Guangming","middleName":"","lastName":"Yin","suffix":""},{"id":375329948,"identity":"b7f327d9-3766-45b9-8f40-1089c53aecd2","order_by":4,"name":"Long Wang","email":"","orcid":"","institution":"The Third Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Long","middleName":"","lastName":"Wang","suffix":""},{"id":375329949,"identity":"23e9ae0c-5497-4532-a7f3-a76102e80925","order_by":5,"name":"Jinrong Wang","email":"","orcid":"","institution":"The Third Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Jinrong","middleName":"","lastName":"Wang","suffix":""},{"id":375329950,"identity":"4c6ef946-8c19-4b01-8d22-17bbeec78320","order_by":6,"name":"Jianye Liu","email":"","orcid":"","institution":"The Third Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Jianye","middleName":"","lastName":"Liu","suffix":""},{"id":375329951,"identity":"9906bde1-e820-4e17-b297-ca5ee7bf27e7","order_by":7,"name":"Jing Tan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYJACgwQGBjkIk40I5TxQLcakaQGBxAaitdiznz1Q8HBHbXp//xkDhg9lhxn4ZzcQsIUnL8Eg8czx3Bk3cgwYZ5w7zCBx5wAhh+UYGCS2HcvdIMFjwMzbdpjBQCKBgBb+N2At6Qb8ZwyY/xKlRQJsS02CAdA6ZkaitNwA23LAcMaNtIKDPefSeSRuENDC3p9jZvizrU6ev//wxgc/yqzl+GcQ0AIEbAYMDIfBrAMMiIjCC5gfMDDUEaNwFIyCUTAKRioAAIgBP7T+bO/JAAAAAElFTkSuQmCC","orcid":"","institution":"The Third Xiangya Hospital of Central South University","correspondingAuthor":true,"prefix":"","firstName":"Jing","middleName":"","lastName":"Tan","suffix":""}],"badges":[],"createdAt":"2024-10-28 15:08:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5348201/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5348201/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69438571,"identity":"16a62242-4063-48bd-bbec-d12cc4bec0ce","added_by":"auto","created_at":"2024-11-20 10:53:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1189563,"visible":true,"origin":"","legend":"\u003cp\u003eA flow chart of the screening process for participants is included in this study.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5348201/v1/5bda86a372e4c240c77b2ab7.png"},{"id":69438581,"identity":"2c582d9d-88b2-4133-a04b-a7e214d4f2b5","added_by":"auto","created_at":"2024-11-20 10:54:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":278513,"visible":true,"origin":"","legend":"\u003cp\u003eThe association between ABSI and KSD\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5348201/v1/9fb32e7bdd151d711572a6f2.png"},{"id":69438582,"identity":"91e62a09-c722-4af5-8e94-acef83da623f","added_by":"auto","created_at":"2024-11-20 10:54:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2650094,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis for the association between KSD and ABSI.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5348201/v1/0cbc9d342fe28525c0b38164.png"},{"id":69440397,"identity":"17bb9eb4-f00d-41ce-b436-083882da9730","added_by":"auto","created_at":"2024-11-20 11:10:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4427065,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5348201/v1/55c85add-1cb5-4f26-ad39-6d940767acca.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The association between a body shape index and kidney stones: A cross-sectional study of NHANES 2007−2018","fulltext":[{"header":"Introduction","content":"\u003cp\u003eKidney stone disease(KSD), also known as kidney stones or urolithiasis, constitutes an aggregation of crystals within the urinary system \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. It is a benign condition caused by abnormal deposition of mineral crystals including calcium oxalate, uric acid, and calcium phosphate in the renal pelvis, calyces, and the junction of the renal pelvis and ureter \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. KSD, whose prevalence as well as recurrence rates have increased globally, is a pervasive urological health problem, and the main reason for its general emergency room visit is the onset of symptoms of urological issues such as renal colic \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. In addition to the symptoms of renal colic that lead to emergency room visits, the disease also often leads to urinary tract infections and renal impairment, posing a severe risk to public health \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. In recent studies, there has been a gradual rise in kidney stones in Chinese adults, with about 5.8 percent of the adult population now affected. In contrast, in the United States, the cumulative prevalence of KSD has risen by 0.3 percent over the decade \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Many factors have been shown to increase the incidence of kidney stones, including environmental factors, lifestyle, and several risk factors including age, race, high blood pressure, and obesity \u003csup\u003e[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWith ongoing improvements in living standards, people's eating habits have significantly changed, leading to a gradual rise in the number of overweight and obese individuals \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Obesity is a very complex chronic disease state, and many studies have shown a strong association between kidney stone disease and obesity, i.e., when BMI increases there is a strong association with an increased risk of kidney stone disease \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Waist circumference (WC) is the gold standard for abdominal obesity indices. It is also used to diagnose metabolic syndrome and is epidemiologically almost identical to BMI. However, they do not always accurately represent an individual's obesity level or abdominal fat distribution \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Therefore, a better and innovative abdominal obesity index is needed to enhance the assessment of visceral fat and abdominal obesity. The Body Shape Index (ABSI) is an innovative composite index designed to assess an individual's body shape and fat distribution more effectively \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. It is derived from waist circumference, height, and weight, offering insights beyond BMI by specifically examining adipose tissue distribution \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. However, research on the association between ABSI and the prevalence of KSD in U.S. adults is limited. Therefore, we aimed to explore their potential connection.\u003c/p\u003e \u003cp\u003eThis study is a cross-sectional analysis using representative data from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2018. It also examined the association between different ABSI levels and KSD prevalence by controlling for relevant covariates. We also performed subgroup analyses based on several important KSD factors and explored potential correlations.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eStudy Population\u003c/p\u003e \u003cp\u003eWe used data from six cycles of the NHANES to study the association between the ABSI and KSD. Subjects with missing data on KSD (n\u0026thinsp;=\u0026thinsp;25163) and missing data on ABSI (n\u0026thinsp;=\u0026thinsp;3444) were excluded. Conducted by the CDC\u0026rsquo;s National Center for Health Statistics, NHANES collects data on the health and nutritional status of the civilian, non-institutionalized U.S. population. As a national, cross-sectional survey, NHANES structures a series of physical examinations, laboratory tests, and questionnaires to acquire sampling probability-based information. Simultaneously, these data represent the U.S.; all participants have signed informed consent. The data are publicly accessible on the NHANES website, and the program has been approved by the Research Ethics Review Board (ERB) of the National Center for Health Statistics (NCHS) \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOutcome variable and Exposure variable\u003c/p\u003e \u003cp\u003eIn the study, we included the men and female who had participated in the questionnaires about kidney stone disease from 31235 American civilians. Among these candidates with data on KSD, they were all asked questions related to kidney stones. Question: \"Have you ever had a kidney stone?\" When participants answered \"no\", they were not considered to have ever had a kidney stone, while when participants answered \"yes\", they were considered to have ever had a kidney stone. At the same time, this study also excluded missing data on kidney stones.\u003c/p\u003e \u003cp\u003eExperienced examiners use standardized techniques and equipment to measure anthropometric data, including body height (BH), waist circumference (WC), and body weight (BW), and the body mass index (BMI) is calculated using the formula: BMI\u0026thinsp;=\u0026thinsp;BW (kg)/BH\u003csup\u003e2\u003c/sup\u003e (m). For the innovative data index ABSI, the calculation formula is as follows \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:ABSI=\\text{W}\\text{C}\\:\\left(\\text{c}\\text{m}\\right)/\\text{B}\\text{M}\\text{I}2/3\\:(\\text{k}\\text{g}/\\text{m}2)\\:\\times\\:\\:\\text{B}\\text{H}1/2\\:\\left(\\text{m}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eCovariates\u003c/p\u003e \u003cp\u003eWe set the following variables as covariates. Sociodemographic factors included age, race, education level, marital status, and economic status. Lifestyle factors included body mass index (BMI), alcohol consumption, smoking, hypertension, and diabetes. In addition to these, we included uric acid levels as a covariate. For the characteristics of BMI, this study was divided into three groups: normal, overweight, and obese groups. The races can be distinguished as Mexican American, non-Hispanic white, non-Hispanic black, other Hispanic, or other races. Education level was classified into two levels: less than high school; and high school or above. Marital status was separated into two levels: married or living with a partner; widowed, divorced, separated, or never married. The family income-to-poverty ratio (PIR) is calculated by comparing household income to the poverty threshold set by the U.S. Department of Health and Human Services, with higher values indicating better household income \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. BMI (\u0026lt;\u0026thinsp;25/ \u0026ge;25, \u0026lt;\u0026thinsp;30/ \u0026ge;30) was equal to weight (kilogram) divided by height (meter) squared. Alcohol consumption was categorized into two classes: those who averaged \u0026lt;2 drinks/day on days they drank in the past twelve months were light drinkers; those who averaged\u0026thinsp;\u0026ge;\u0026thinsp;2 drinks/day were heavy drinkers. Smokers were also categorized into two groups: those who had not smoked more than 100 cigarettes in their lifetime were non-smokers; while those who had smoked more than 100 cigarettes were defined as smokers. Participants were identified as hypertensive if they had a previous diagnosis of hypertension. Candidates were identified as diabetic if they had a prior history of diabetes.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData was analyzed using the statistical packages R (The R Foundation; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://r-project.org\u003c/span\u003e\u003cspan address=\"http://r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; version 3.4.3) and Empower Stats (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://r-project.org\" target=\"_blank\"\u003ewww.empowerstats.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.empowerstats.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; X\u0026amp;Y solution inc). The statistical analyses accounted for the complex sampling design recommended by NHANES, with a significance level set at P\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eBaseline characteristics were compared using linear regression models for continuous variables (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation) and chi-square tests for categorical variables (counting number, %). Multivariate linear regression analysis assessed the correlation between ABSI and KSD. We constructed three main models. We did not adjust for variables in Model 1; in Model 2, we adjusted for gender, age, and race. In Model 3, it was regulated for all covariates, which in addition to the covariates in Model 2, included educational status, marital status, household economic status (PIR), smoking, alcohol consumption, hypertension, diabetes, and Uric acid levels.\u003c/p\u003e \u003cp\u003eBy subgroup analysis, we explored the correlation between ABSI and KSD in different subgroups. Stratification factors included gender, age (\u0026lt;\u0026thinsp;60/ \u0026ge;60 years), race, educational status, marital status, BMI status (\u0026lt;\u0026thinsp;25/ \u0026ge;25, \u0026lt;\u0026thinsp;30/ \u0026ge;30), hypertension, diabetes, smoking, and alcohol consumption.\u003c/p\u003e \u003cp\u003eIn addition, smoothed curve fitting models were used to verify whether there was a linear or nonlinear correlation between ABSI and KSD. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eTo avoid a reduction in statistical power and potential bias from directly excluding missing values, we used multivariate multiple imputations with chained equations to impute missing values, maximizing statistical power and minimizing bias if people with missing data were excluded from analyses \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. We repeated all analyses with the complete data cohort for comparison.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eCohort characteristics\u003c/p\u003e \u003cp\u003eBetween 2007 and 2018, researchers collected detailed information on 59,842 American adults. After exclusion, 31,235 adults were included in the main analysis, which examined the association between ABSI and KSD. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e details the baseline characteristics of the study participants, where the number of KSDs was 2924 and the number of non-KSDs was 28311, with a prevalence of 9.36%. There were significant differences between the KSD group and the non-KSD group in terms of age, gender, race, educational status, marital status, BMI, smoking status, alcohol consumption, hypertension, diabetes mellitus, uric acid levels, and ABSI.\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\u003eBasic Characteristics of Participants in NHANES, 2007\u0026ndash;2018 (n\u0026thinsp;=\u0026thinsp;31235)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-KSD (n\u0026thinsp;=\u0026thinsp;28311)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKSD (n\u0026thinsp;=\u0026thinsp;2924)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.68\u0026thinsp;\u0026plusmn;\u0026thinsp;17.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.71\u0026thinsp;\u0026plusmn;\u0026thinsp;16.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA, umol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e322.88\u0026thinsp;\u0026plusmn;\u0026thinsp;85.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e334.45\u0026thinsp;\u0026plusmn;\u0026thinsp;89.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13587 (47.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1628 (55.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14724 (52.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1296 (44.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/ethnicity, %\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4346 (15.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e378 (12.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2964 (10.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e339 (11.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11110 (39.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1560 (53.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6287 (22.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e391 (13.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3604 (12.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e256 (8.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, %\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;High school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6896 (24.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e738 (25.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;High school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21415 (75.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2186 (74.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16752 (59.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1867 (63.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLive alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11559 (40.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1057 (36.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking 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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12283 (43.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1477 (50.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16028 (56.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1447 (49.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol intake, %\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10062 (35.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1206 (41.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18249 (64.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1718 (58.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI 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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8439 (29.81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e591 (20.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9302 (32.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e993 (33.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10570 (37.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1340 (45.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9689 (34.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1460 (49.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18622 (65.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1464 (50.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3369 (11.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e639 (21.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24311 (85.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2187 (74.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorderline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e631 (2.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 (3.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAssociation between ABSI and KSD\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the results of the multivariable regression analysis between ABSI and KSD. Participants were subdivided into four groups according to ABSI quartiles (Q1: 8.58\u0026ndash;12.81; Q2: 12.82\u0026ndash;13.61; Q3: 13.62\u0026ndash;14.44; Q4: 14.45\u0026ndash;19.67). These associations were significant in Model 1(OR\u0026thinsp;=\u0026thinsp;1.26, 95%CI: (1.22, 1.30), P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), Model 2(OR\u0026thinsp;=\u0026thinsp;1.11, 95%CI: (1.07, 1.16), P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and Model 3(OR\u0026thinsp;=\u0026thinsp;1.06, 95%CI: (1.02, 1.11), P\u0026thinsp;=\u0026thinsp;0.0039). In Model 1, the third quartile (OR(Q3vs1)\u0026thinsp;=\u0026thinsp;1.44, 95%CI: (1.29\u0026ndash;1.61), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and the fourth quartile (OR(Q4vs1)\u0026thinsp;=\u0026thinsp;1.93, 95%CI: (1.73\u0026ndash;2.15), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) was positively associated with KSD compared to the first quartile. In Model 2, the third (OR(Q3vs1)\u0026thinsp;=\u0026thinsp;1.20, 95%CI: (1.06\u0026ndash;1.36), p\u0026thinsp;=\u0026thinsp;0.0039) and the fourth quartile (OR(Q4vs1)\u0026thinsp;=\u0026thinsp;1.35, 95%CI: (1.18\u0026ndash;1.55), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) compared to the first quartile positively correlated with ABSI. Analyses were performed using the ABSI quartiles. In the fully adjusted model of Model 3, participants in quartile 4(OR\u0026thinsp;=\u0026thinsp;1.19, 95%CI: (1.03, 1.37), P\u0026thinsp;=\u0026thinsp;0.0152) were associated with a 19% increased risk of developing KSD compared with quartile 1(p for trend\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between ABSI and KSD.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26 (1.22, 1.30)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11 (1.07, 1.16)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 (1.02, 1.11) 0.0039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07 (0.95, 1.21) 0.2427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99 (0.88, 1.12) 0.8917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.87, 1.11) 0.7533\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.44 (1.29, 1.61)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20 (1.06, 1.36) 0.0039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13 (1.00, 1.28) 0.0583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.93 (1.73, 2.15)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.35 (1.08, 1.19)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19 (1.03, 1.37) 0.0152\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;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSmooth curve fitting and threshold effect\u003c/p\u003e \u003cp\u003eThis study investigated the nonlinear association between ABSI and the risk of kidney stones, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The solid red line represents the smooth curve fit between variables. Blue bands represent the 95% confidence interval from the fit. By careful analysis of the smoothed curves, this study reveals a nonlinear association between the occurrence of kidney stones and ABSI. ABSI was significantly and positively correlated with KSD. Approximate significance of the smoothing term p-value\u0026thinsp;=\u0026thinsp;0.0084.\u003c/p\u003e \u003cp\u003eThe study then derived an inflection point of 12.61 for the ABSI in the survey using a biphasic linear model and a recursive algorithm. The odds of KSD prevalence increased by 10% for each unit increase in the ABSI when the ABSI was higher than 12.61 (OR:1.10, 95%CI: 1.05\u0026ndash;1.16). Conversely, when the ABSI was below 12.61, no significant change was observed in the risk of developing KSD.\u003c/p\u003e \u003cp\u003eThe study was adjusted for gender, age, race, education, marital status, BMI, hypertension, diabetes, alcohol, and smoking habits.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe analysis of threshold effects between ABSI and KSD.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKSD\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFitting by standard linear model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06 (1.02, 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFitting by two-piecewise linear model\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;12.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87 (0.75, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0631\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;12.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10 (1.05, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog-likelihood ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSubgroup analysis\u003c/p\u003e \u003cp\u003eThis study used subgroup analyses and interaction tests to verify whether kidney stones are consistently associated with ABSI in the general population. It aims to identify possible differences in specific demographic scenarios based on factors such as BMI, gender, diabetes, and hypertension. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the ABSI was significantly and positively associated with KSD in the male group, the group\u0026thinsp;\u0026lt;\u0026thinsp;60 years of age, the group with an education greater than high school, the non-Hispanic white group, the group living with a partner, the group with a body mass index of \u0026ge;\u0026thinsp;25, the group with a body mass index of \u0026lt;\u0026thinsp;30, the group that smoked, the group with no hypertension, the group with diabetes mellitus, and the group of light drinkers. Interaction tests showed that the relationship between ABSI and KSD was not statistically different between any of the strata except the hypertension stratum (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for interaction tests), implying that age, race, body mass index, diabetes mellitus, etc., and alcohol consumption did not have a significant effect on the positive association between ABSI and KSD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this cross-sectional study including 31,235 candidates, we found that higher levels of the ABSI index were associated with a higher prevalence of KSD. This association was similar in subgroups stratified by sex, age, race, education, marriage, body mass index, diabetes mellitus, smoking, and alcohol consumption, suggesting that this association may be applicable across population settings. We can therefore hypothesize that clinical attention and management of ABSI levels may contribute to the prevention and treatment of patients with kidney stones.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, studies examining the relationship between ABSI and kidney stones in the U.S. population are very sparse. For kidney stone disease, many studies have reported the relationship between kidney stones and several other clinicopathological factors. Risk factors for kidney stones have long been associated with body size measurements, the most commonly used of which tend to be body mass index and abdominal circumference, which may be related to the ability of these metrics to respond to the increased visceral fat that leads to metabolic disorders \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Studies are now showing that being overweight or obese, combined with an unhealthy metabolic state, significantly increases the risk of KSD in the general U.S. population \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. There is also a causal relationship between high BMI and an increased risk of kidney stone prevalence demonstrated utilizing Mendelian randomization analysis by Yuan et al \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. However, BMI does not accurately reflect the fat distribution and its impact on health, especially visceral fat accumulation \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. ABSI provides a more accurate assessment of body size by integrating waist circumference with BMI and height \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Therefore, studying the relationship between ABSI and kidney stones may reveal a more accurate method of assessing kidney stone risk than BMI. ABSI may be more effective than BMI in assessing the risk of these diseases \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. In recent years, a growing body of research points to a possible relationship between ABSI and KSD, specifically, Lin et al \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. They have concluded that there is a linear relationship between ABSI and the prevalence of KSD. Recently, according to Jeong et al \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e, metabolic syndrome increases the incidence of kidney stones. At the same time, other studies have shown that an increase in ABSI is positively associated with the development of metabolic syndrome \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. This may suggest a close relationship between ABSI and kidney stones.\u003c/p\u003e \u003cp\u003eThe exact mechanism by which ABSI is positively associated with KSD is unknown. The following explanations may support our results. High ABSI values reflect excessive abdominal visceral fat accumulation, closely associated with insulin resistance and metabolic syndrome, and these metabolic disorders may promote kidney stone formation by increasing urinary calcium and uric acid excretion or altering urinary pH \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Obesity, diabetes mellitus, hypertension, and hyperlipidemia have been reported in some of the many studies and are often thought to increase the prevalence of kidney stones by causing metabolic syndrome \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. In addition, insulin resistance is frequently associated with visceral adiposity, leading to increased renal calcium reabsorption and decreased urinary citrate excretion, which may be a key factor in stone development \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Some studies suggest that urinary citrate plays a crucial role in preventing kidney stone formation, and they also indicate that visceral fat might lower urinary citrate levels by promoting renal calcium reabsorption, which, in turn, could contribute to an increased risk of kidney stone development \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Furthermore, the excessive accumulation of abdominal fat represented by an increased ABSI leads to chronic inflammation and elevated levels of oxidative stress in the body, factors that may impair kidney function and further increase the risk of kidney stone formation \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Immediately following this, people with higher ABSI may have unhealthy dietary habits (high salt, protein, sugar, etc.), all of which are associated with kidney stone formation \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study has several strengths. First, the study is based on data from NHANES, a national population-based sample database obtained using standardized protocols, making the study sample more representative. Second, we adjusted for various confounding covariates to make the results more robust. Despite some of the strengths of this study, it couldn't avoid having some flaws. Firstly, we used data from the past. Secondly, the diagnosis of kidney stones was mainly from questionnaires and the specifics of the disease were not known. Since this was a cross-sectional study without follow-up data, it was impossible to establish a clear causal relationship between ABSI and kidney stone formation. In addition, despite adjusting for several potential covariates, the influence of other possible confounding factors, such as the use of medications and some other comorbidities, could not be completely excluded. Immediately after, the specific application of ABSI in predicting renal stone risk still needs to be validated by more clinical studies. Future studies should consider epidemiological investigations with larger sample sizes and mechanistic studies to clarify the causal relationship between ABSI and kidney stones.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study demonstrated that elevated ABSI is associated with an increased chance of developing kidney stones. We hypothesized that controlling ABSI might correspondingly reduce the occurrence and recurrence of kidney stones. However, further studies are needed to validate our findings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ekidney stone disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eABSI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eA body shape index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNHANES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Health and Nutrition Examination Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ewaist circumference\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody height\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody weight\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\"\u003eCDC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCenters for Disease Control and Prevention\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eERB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eResearch Ethics Review Board\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCHS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Center for Health Statistics\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\u003eRatio of family income to poverty\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks to all the volunteers who took part in the NHANES.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBW, and JT contributed to the hypothesis development and to the drafting of the manuscript; JD, and SH were responsibility for the data analysis. LW, and GY contributed to the data interpretation and revision of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSurvey data are available for data consumers and researchers all across the globe on the internet (https://www.cdc.gov/nchs/nhanes/).\u003c/p\u003e\n\u003ch4\u003eStatements and Declarations\u003c/h4\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll NHANES participants provided written informed consent and the National Center for Health Statistics obtained institutional review board approval prior to data collection. Because NHANES data are de-identifed and publicly available, the analysis presented here was exempt from IRB review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBishop K, Momah T, Ricks J (2020) Nephrolithiasis[J]. Prim Care: Clin Office Pract 47(4):661\u0026ndash;671\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSorokin I, Mamoulakis C, Miyazawa K et al (2017) Epidemiology of stone disease across the world[J]. World J Urol 35(9):1301\u0026ndash;1320\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorbo J, Wang J (2019) Kidney and Ureteral Stones[J]. Emerg Med Clin North Am 37(4):637\u0026ndash;648\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan SR, Pearle MS, Robertson WG et al (2016) Kidney stones[J]. Nat Rev Dis Primers 2:16008\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbufaraj M, Xu T, Cao C et al (2021) Prevalence and Trends in Kidney Stone Among Adults in the USA: Analyses of National Health and Nutrition Examination Survey 2007\u0026ndash;2018 Data[J]. Eur Urol Focus 7(6):1468\u0026ndash;1475\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng G, Mai Z, Xia S et al (2017) Prevalence of kidney stones in China: an ultrasonography based cross-sectional study[J]. BJU Int 120(1):109\u0026ndash;116\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHill AJ, Basourakos SP, Lewicki P et al (2022) Incidence of Kidney Stones in the United States: The Continuous National Health and Nutrition Examination Survey[J]. J Urol 207(4):851\u0026ndash;856\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZisman AL, Coe FL, Cohen AJ et al (2020) Racial Differences in Risk Factors for Kidney Stone Formation[J]. Clin J Am Soc Nephrol 15(8):1166\u0026ndash;1173\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZiemba JB, Matlaga BR (2017) Epidemiology and economics of nephrolithiasis[J]. Investig Clin Urol 58(5):299\u0026ndash;306\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbeywickarama B, Ralapanawa U, Chandrajith R (2016) Geoenvironmental factors related to high incidence of human urinary calculi (kidney stones) in Central Highlands of Sri Lanka[J]. Environ Geochem Health 38(5):1203\u0026ndash;1214\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiener R (2021) Nutrition and Kidney Stone Disease[J]. Nutrients, 13(6)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong W, Hu H, Ni J et al (2023) The relationship between ethylene oxide levels in hemoglobin and the prevalence of kidney stones in US adults: an exposure-response analysis from NHANES 2013\u0026ndash;2016[J]. Environ Sci Pollut Res Int 30(10):26357\u0026ndash;26366\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChou YH, Su CM, Li CC et al (2011) Difference in urinary stone components between obese and non-obese patients[J]. Urol Res 39(4):283\u0026ndash;287\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagayama D, Watanabe Y, Yamaguchi T et al (2020) New index of abdominal obesity, a body shape index, is BMI-independently associated with systemic arterial stiffness in real-world Japanese population[J]. Int J Clin Pharmacol Ther 58(12):709\u0026ndash;717\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePiqueras P, Ballester A, Dur\u0026aacute;-Gil JV et al (2021) Anthropometric Indicators as a Tool for Diagnosis of Obesity and Other Health Risk Factors: A Literature Review[J]. Front Psychol 12:631179\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrakauer NY, Krakauer JC (2020) Association of Body Shape Index (ABSI) with Hand Grip Strength[J]. Int J Environ Res Public Health, 17(18)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagayama D, Fujishiro K, Watanabe Y et al (2022) A Body Shape Index (ABSI) as a Variant of Conicity Index Not Affected by the Obesity Paradox: A Cross-Sectional Study Using Arterial Stiffness Parameter[J]. J Pers Med, 12(12)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZipf G, Chiappa M, Porter KS et al (2013) National health and nutrition examination survey: plan and operations, 1999\u0026ndash;2010[J]. Vital Health Stat 1(56):1\u0026ndash;37\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu WY, Chen IH, Gau YC et al (2022) Metabolic Syndrome and Obesity-Related Indices Are Associated with Rapid Renal Function Decline in a Large Taiwanese Population Follow-Up Study[J]. Biomedicines, 10(7)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith J, Jain N, Normington J et al (2022) Associations of Ready-to-Eat Cereal Consumption and Income With Dietary Outcomes: Results From the National Health and Nutrition Examination Survey 2015\u0026ndash;2018[J]. Front Nutr 9:816548\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarrar D, Fairley L, Santorelli G et al (2015) Association between hyperglycaemia and adverse perinatal outcomes in south Asian and white British women: analysis of data from the Born in Bradford cohort[J]. Lancet Diabetes Endocrinol 3(10):795\u0026ndash;804\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhite IR, Royston P, Wood AM (2011) Multiple imputation using chained equations: Issues and guidance for practice[J]. Stat Med 30(4):377\u0026ndash;399\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe Z, Wu C, Xiong Y et al (2023) Obesity, metabolic dysfunction, and risk of kidney stone disease: a national cross-sectional study[J]. Aging Male 26(1):2195932\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan S, Larsson SC (2021) Assessing causal associations of obesity and diabetes with kidney stones using Mendelian randomization analysis[J]. Mol Genet Metab 134(1\u0026ndash;2):212\u0026ndash;215\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu X, Li X, Ye N et al (2024) Association of novel anthropometric indices with prevalence of kidney stone disease: a population-based cross-sectional study[J]. Eur J Med Res 29(1):204\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLv G, Li X, Zhou X et al (2024) Predictive ability of novel and traditional anthropometric measurement indices for kidney stone disease: a cross-sectional study[J]. World J Urol 42(1):339\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin G, Zhan F, Ren W et al (2023) Association between novel anthropometric indices and prevalence of kidney stones in US adults[J]. World J Urol 41(11):3105\u0026ndash;3111\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJeong IG, Kang T, Bang JK et al (2011) Association between metabolic syndrome and the presence of kidney stones in a screened population[J]. Am J Kidney Dis 58(3):383\u0026ndash;388\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStefanescu A, Revilla L, Lopez T et al (2020) Using A Body Shape Index (ABSI) and Body Roundness Index (BRI) to predict risk of metabolic syndrome in Peruvian adults[J]. J Int Med Res 48(1):300060519848854\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarbone A, Al Salhi Y, Tasca A et al (2018) Obesity and kidney stone disease: a systematic review[J]. Minerva Urol Nefrol 70(4):393\u0026ndash;400\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKohjimoto Y, Sasaki Y, Iguchi M et al (2013) Association of metabolic syndrome traits and severity of kidney stones: results from a nationwide survey on urolithiasis in Japan[J]. Am J Kidney Dis 61(6):923\u0026ndash;929\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang CW, Ke HL, Lee JI et al (2021) Metabolic Syndrome Increases the Risk of Kidney Stone Disease: A Cross-Sectional and Longitudinal Cohort Study[J]. J Pers Med, 11(11)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMallio CA, Cea L, D'andrea V et al (2024) Visceral Adiposity and Its Impact on Nephrolithiasis: A Narrative Review[J]. J Clin Med, 13(14)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCianci R, Franza L, Massaro MG et al (2022) The Crosstalk between Gut Microbiota, Intestinal Immunological Niche and Visceral Adipose Tissue as a New Model for the Pathogenesis of Metabolic and Inflammatory Diseases: The Paradigm of Type 2 Diabetes Mellitus[J]. Curr Med Chem 29(18):3189\u0026ndash;3201\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKolb H (2022) Obese visceral fat tissue inflammation: from protective to detrimental?[J]. BMC Med 20(1):494\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchwingshackl L, Hoffmann G (2014) Comparison of high vs. normal/low protein diets on renal function in subjects without chronic kidney disease: a systematic review and meta-analysis[J]. PLoS ONE 9(5):e97656\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin BB, Lin ME, Huang RH et al (2020) Dietary and lifestyle factors for primary prevention of nephrolithiasis: a systematic review and meta-analysis[J]. BMC Nephrol 21(1):267\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":"Kidney stone disease, A Body Shape Index, Cross-sectional study, United States.","lastPublishedDoi":"10.21203/rs.3.rs-5348201/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5348201/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eKidney stone disease (KSD) is a pervasive urological health problem, which affects the quality of life of older men. However, the relationship between the A Body Shape Index (ABSI) and KSD has rarely been studied in American populations.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eWe used data from the National Health and Nutrition Examination Survey (NHANES) spanning 2007 to 2018. A self-report questionnaire identified KSD. We investigated the correlation using multiple linear regression, subgroup analyses, and smooth curve fitting.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eThe study included 31,235 participants, with 2,924 having kidney stones and 28,311 without. The results of the study showed a significant association between ABSI and kidney stones, that is, an increase in ABSI was associated with an increased risk of kidney stones (Q4 vs Q1, OR\u0026thinsp;=\u0026thinsp;1.19, 95% CI= (1.03\u0026ndash;1.37), p\u0026thinsp;=\u0026thinsp;0.0040). After subgroup analyses, it was found that the association between ABSI and kidney stones was not significantly correlated between specific subgroups.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe present study shows that elevated ABSI is associated with an increased likelihood of developing kidney stones.\u003c/p\u003e","manuscriptTitle":"The association between a body shape index and kidney stones: A cross-sectional study of NHANES 2007−2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-20 10:53:44","doi":"10.21203/rs.3.rs-5348201/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":"d09e4dfc-b926-4c32-b69f-1f4422f62925","owner":[],"postedDate":"November 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-17T12:38:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-20 10:53:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5348201","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5348201","identity":"rs-5348201","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}