The Association between the Presence of Kidney Stones and the Risk of Developing Osteoporosis: A NHANES-based Cross-sectional Study and Mendelian Randomization Analysis

The Association between the Presence of Kidney Stones and the Risk of Developing Osteoporosis: A NHANES-based Cross-sectional Study and Mendelian Randomization Analysis | 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 the Presence of Kidney Stones and the Risk of Developing Osteoporosis: A NHANES-based Cross-sectional Study and Mendelian Randomization Analysis Juefei Dong, Weibin Hou, Guangming Yin, Jinrong Wang, Long Wang, and 3 more This is a preprint; it has not been peer reviewed by a journal.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-4748828/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Background Kidney stone disease is thought to be a risk factor for osteoporosis, but the causality between the conditions remains unknown.. Thus, we implemented a NHANES-based cross-sectional study and mendelian randomization to investigate whether the presence of kidney stones increases the risk of developing osteoporosis. Methods First, we performed an observational study on the basis of data from the National Health and Nutrition Examination Survey (NHANES; 2007–2020). Kidney stone patients were identified on the basis of their affirmative response to the question "Have you ever experienced kidney stones?" (KIQ026). Participants whose T score at the femoral neck was <-2.5 were defined as osteoporosis patients. Multivariable-adjusted logistic regression was used to assess the correlation between the presence of kidney stones and the risk of developing osteoporosis. Second, Mendelian randomization (MR) was applied to further investigate the causal relationship between the presence of kidney stones and the risk of developing osteoporosis. Genetic instruments were obtained from large genome-wide association studies (GWASs) from the UK Biobank and FinnGen Biobank. Inverse-variance weighting (IVW) was the primary analytical method used. Results After adjustment for demographic and other covariates, a significant association between the presence of kidney stones and the risk of developing osteoporosis was detected (OR 1.778, CI: 1.345–2.351, P < 0.001). The MR results further revealed that genetically speaking, the presence of kidney stones was causally associated with a greater risk of developing osteoporosis (IVW: OR 1.088, CI: 1.015–1.167, P < 0.05). Conclusion The presence of kidney stones is associated with an increased risk of developing osteoporosis. Further prospective cohort studies are needed to validate our results. Osteoporosis Kidney stones National Health and Nutrition Examination Survey Mendelian randomization Figures Figure 1 Figure 2 Background Kidney stones are highly prevalent diseases that affect approximately 10% of the population worldwide, placing a tremendous burden on public health resources . According to earlier research, in the year 2000, the US spent $ 2.1 billion managing kidney stones; by 2030, costs are predicted to rise to $ 5 billion . The etiology of kidney stones is complex. It is believed that genetic variation, metabolic disorders, and nutritional variables are prevalent pathogeneses involved in the development of nephrolithiasis , . Osteoporosis is a systemic skeletal disease characterized by low bone mass and degradation of the bone microstructure . Owing to its high prevalence and associated increased risk of experiencing fracture, osteoporosis is a global concern. Numerous investigations on the pathophysiology of osteoporosis have been conducted throughout the years in an effort to improve osteoporosis diagnosis and treatment. The main causes of osteoporosis are aging and hormone abnormalities, whereas other factors, such as weight, serum calcium concentrations and smoking status, may also be correlated with the risk of developing osteoporosis . Considering that both diseases are associated with the processes of aging and metabolic dysfunction, we hypothesized that the two diseases share a common etiopathogenesis. Multiple clinical and epidemiological studies have demonstrated that patients with urolithiasis exhibit increased bone turnover and reduced bone mass , . Research has also indicated that these two diseases exhibit comparable natural progressions, including morbidity, disease development indicators, and negative outcomes in the absence of appropriate treatment . These findings suggest that the presence of kidney stones may be a vital risk factor for the development of osteoporosis. Mendelian randomization (MR) is a novel epidemiological technique that involves the use of genetic traits as substitutes for exposure (such as the presence of kidney stones) to evaluate the causal impacts on a certain outcome (such as the development of osteoporosis) . Compared with observational research, this approach is less vulnerable to biases caused by confounding factors and reverse causality. To our knowledge, no comprehensive National Health and Nutrition Examination Survey (NHANES) cross-sectional study or MR analysis has been conducted to determine whether individuals with nephrolithiasis are at greater risk of experiencing decreased bone mineral density (BMD) and fractures than control individuals are. In our study, data from the NHANES (2007–2020) were analyzed to investigate whether the presence of kidney stones is associated with the risk of developing osteoporosis. An MR study was subsequently conducted to further explore the causal effect of the presence of kidney stones on the risk of developing osteoporosis. Materials and methods Study population in NHANES The NHANES is a comprehensive and wide-ranging study conducted in the U.S. to examine the health and nutritional status of the U.S. population. This survey involves annual assessments on approximately 5000 persons via interviews, physical examinations, and laboratory tests. The NHANES was approved by the National Center for Health Statistics Research Ethics Review Board, and all participants provided written informed consent . We included data from seven cycles (2007–2020) because a full kidney stone history was provided during these cycles. Nephrolithiasis assessment in NHANES A history of kidney stones was available in the personal interview section for participants over 20 years old in the NHANES data. According to previous studies, individuals who answered “yes” to the question “Have you ever had kidney stones?” (KIQ026) were classified as having former kidney stone status . Osteoporosis assessment in NHANES Dual-energy X-ray absorptiometry (DXA) was used to measure BMD during the examination of the NHANES data. It is widely accepted that osteoporosis is defined as a T score<-2.5 . In our investigation, a femoral neck T score<-2.5 was considered to indicate the presence of osteoporosis because the NHANES provides a larger dataset for femoral neck BMD than for mean lumbar spine BMD and total femoral BMD. BMD was converted into a T score via the following formula: T score = (BMD respondent-mean BMD reference group)/SD reference group . Covariates used in the NHANES The variables were chosen from the previously identified factors that have an impact on the risk of developing osteoporosis . We included the following covariates in our multivariable models: age, race, sex, marital status, education level, family income, obesity status, serum calcium level, serum cholesterol level, smoking status, drinking status, hypertension status and diabetes status. Sample source for MR analysis Genome-wide association study (GWAS) data on kidney stones were obtained from the UK Biobank website ( https://biobank.ctsu.ox.ac.uk/ ). A total of 462,933 patients with 9,851,867 single-nucleotide polymorphisms (SNPs) were included in these GWAS data. The GWAS data for osteoporosis was obtained from the FinnGen Biobank ( https://storage.googleapis.com/fnngen-public-datar10/summary_stats/ ). The GWAS included 398,337 individuals (7300 osteoporosis patients and 391037 control participants) and 20,167,428 SNPs. All the participants were of European descent. Selection of genetic instrumental variables (IVs) All MR analyses follow three key assumptions: (I) the genetic variants applied as IVs are robustly correlated with exposure; (II) the genetic variants applied are not linked to any confounders; and (III) the genetic variants selected affect the risk of developing osteoporosis only via the presence of kidney stones rather than other pathways . We conducted several procedures to identify eligible SNPs via publicly accessible GWAS data on nephrolithiasis. First, we identified SNPs highly related to nephrolithiasis with genome-wide significance (p < 5E-6) to meet the first assumption. Second, the significant SNPs were then clumped on the basis of linkage disequilibrium (LD) with an r 2 < 0.01 and clump distance (kb) = 5,000 to obtain independent IVs. Moreover, F statistics for each IV were computed via the formula F = β 2 exposure/SE 2 exposure to assess the IV's strength. F values < 10 were considered to indicate weak IVs. Statistical analysis For the cross-sectional study using data from the NHANES, continuous variables are represented as the means (standard deviations) and were compared via Student's t test (normal distribution). Categorical variables are presented as absolute values (percentages), and the chi-square test was used for comparisons. Three successive multivariate logistic regression models were used to evaluate the associations between the presence of kidney stones and the risk of developing osteoporosis. Model 1 was unadjusted. Model 2 was adjusted for age, sex, and race. Model 3 was based on Model 2 and was additionally adjusted for marital status, education level, family income, obesity status, serum calcium levels, serum cholesterol levels, smoking status (yes or no), drinking status, hypertension status, and diabetes status. The survey sampling weight was considered in this part of the analysis. For the MR analysis, the inverse-variance weighting (IVW), weighted median, MR‒Egger, weighted mode, and simple mode methods were used to assess correlations between the presence of kidney stones and the risk of developing osteoporosis. IVW was our primary analytical method because it has a weighted scoring method, which can estimate genetic variants individually, making it currently the most statistically efficient MR method . The other 4 MR methods could test the stability and reliability of the IVW results. If the results of IVW were statistically efficient and the results of the other 4 methods were in accordance with those of the IVW method (e.g., all OR values > 1 or < 1), a causal correlation was considered. In addition, we used Cochrane's Q test to examine possible SNP heterogeneity, and P < 0.05 was considered to indicate the presence of heterogeneity among the IVs. For sensitivity analysis, the “leave-one-out” process was applied . By removing each SNP one-by-one, the cumulative impact of the remaining SNPs on the outcome was demonstrated, allowing for the evaluation of the individual effects of each SNP. In addition, horizontal pleiotropy was assessed via MR‒Egger. We performed all the data analyses via R 4.3.1. P < 0.05 was considered to indicate statistical significance. Results Baseline characteristics of NHANES participants A total of 75402 participants from NHANES 2007–2020 were included in the study. After removing those younger than 20, those who did not have data on kidney stones and femoral neck BMD, and those with missing data on confounders, a total of 14,539 eligible participants were enrolled (Fig. 1 ). Table 1 presents the baseline characteristics of the enrolled participants. Compared with patients without osteoporosis, osteoporosis patients were older, had lower body masses, and had elevated levels of cholesterol. In addition, they were more likely to be non-Hispanic white, women, and have medical conditions such as hypertension, diabetes, and kidney stones (P < 0.05). Table 1 Baseline characteristics of the participants included in the analysis of the associations between the presence of kidney stones and the risk of developing osteoporosis. Osteoporosis Nonosteoporosis p Total number of participants 609 13930 Age (mean (SD)) 68.60 (10.44) 51.24 (15.73) < 0.001 Race (%) < 0.001 Mexican American 52 (8.5) 2286 (16.4) Non-Hispanic Black 47 (7.7) 2678 (19.2) Non-Hispanic White 372 (61.1) 6356 (45.6) Other 84 (13.8) 1122 (8.1) Other Hispanic 54 (8.9) 1488 (10.7) Marital status (%) < 0.001 No 332 (54.5) 5210 (37.4) Yes 277 (45.5) 8720 (63.6) Education level (%) 0.041 Below high school 175 (28.7) 3572 (25.6) High School or above 434 (71.3) 10358 (74.4) Poverty–income ratio (PIR, %) 0.603 Not poor 515 (84.6) 11503(82.6) Poor 94 (16.4) 2427(17.4) Calcium (%) 0.016 >2.7 mmol/L 4 (0.7) 43 (0.3) 2.2–2.7 mmol/L 563 (92.4) 13165 (94.5) 2.2 mmol/L< 42 (6.9) 722(5.2) Obesity (%) < 0.001 No 514 (84.4) 8784 (63.1) Yes 95 (15.6) 5146 (36.9) Sex (%) < 0.001 Male 132 (21.7) 7309 (52.5) Female 477 (78.3) 6621 (47.5) Smoking status (%) 0.794 No 510 (83.7) 11107 (79.3) Yes 99 (16.3) 2823 (20.7) Alcohol consumption status (%) < 0.001 No 513 (84.2) 10454 (75.0) Yes 96 (15.8) 3476 (25.0) Diabetes status (%) 0.182 No 488 (80.1) 11487 (82.5) Yes 121 (19.9) 2443 (17.5) Hypertension status (%) 6.18 mmol/L 109(17.9) 1959 (14.1) 5.18–6.18 mmol/L 172(28.2) 3823 (27.4) 5.18 mmol/L< 328 (53.9) 8148 (58.5) Kidney stone status (%) < 0.001 No 529 (86.9) 12522 (89.9) Yes 80(13.1) 1408(10.1) Association between kidney stone status and the risk of developing osteoporosis According to the multivariable analyses, the presence of kidney stones was positively associated with the risk of developing osteoporosis (OR: 1.539; CI: 1.231–2.061; P < 0.001). After adjustment for age, sex and race, the presence of kidney stones was significantly associated with a greater risk of developing osteoporosis (OR 1.581, CI: 1.211–2.063; P < 0.001). After controlling for all the covariates, a strong correlation between the presence of kidney stones and the risk of developing osteoporosis persisted(OR 1.778, CI: 1.345–2.351, P < 0.001). MR analysis of the causal relationship between the presence of kidney stones and the risk of developing osteoporosis We conducted a bidirectional two-sample MR analysis to further investigate the causal association between the presence of kidney stones and the risk of developing osteoporosis. After selection, 50 SNPs were included in our MR analysis. The IWV results suggested a causal association between the presence of kidney stones and risk of developing osteoporosis (OR 1.088, CI: 1.015–1.167, P = 0.018), which was validated by the weighted median (OR 1.138, CI: 1.046–1.237, P = 0.002), weighted mode (OR 1.181, CI: 1.024–1.362, P = 0.027) and simple mode methods (OR 1.195, CI: 1.012–1.410, P = 0.041). However, no significant correlation was found via MR‒Egger (OR 1.179, CI: 0.984–1.412, P = 0.080). A scatter plot of the connection between the presence of kidney stones and the risk of developing osteoporosis at the causal level as assessed by MR techniques is shown in Fig. 2. The MR‒Egger intercept is close to 0, suggesting the absence of horizontal pleiotropy in MR analysis. The Q value of Cochrane's Q test was 10.76 (P = 0.63), which indicates that no horizontal heterogeneity existed in the MR analysis. The results of the "leave-one-out" analysis indicated that the SNPs included in this study were unlikely to be influenced by potential bias, thereby demonstrating the stability and reliability of our study.Funnel plots, plots of the “MR-effect” and “Leave-one-out” sensitivity analysis for two groups were offered in Additional File: Figures S1 , S2 and S3. To investigate the potential inverse relationship between osteoporosis status and the risk of developing kidney stones, we conducted reverse-MR analysis, with the IVW method as the primary analysis. The fixed-effects IVW technique(Additional File 2: Figure S4,S5,S6 and S7), did not reveal any causal influences of osteoporosis status on the risk of developing kidney stones in the reverse-MR analysis (IVW OR = 1.03, 95% CI = 0.95–1.11, p = 0.476). Table 2 Association between the presence of kidney stones and the risk of developing osteoporosis (n = 14,593) Model OR 95% CI P value Lower limit Upper limit Model Ⅰ 1.59 1.23 2.06 < 0.001 Model Ⅱ 1.58 1.21 2.06 < 0.001 Model Ⅲ 1.78 1.34 2.35 < 0.001 Model I: unadjusted Model II: adjusted for age, sex and race Model III: adjusted for age, sex, race, marital status, education level, family income, obesity status, serum calcium level, serum cholesterol level, smoking status, drinking status, hypertension status and diabetes status. Table 3 MR estimates for the association between the presence of kidney stones and risk of developing osteoporosis. Analytical methods OR 95% CI P value Lower limit Upper limit MR–Egger 1.18 0.98 1.41 0.080 Weighted median 1.14 1.05 1.24 0.002 IVW 1.09 1.01 1.17 0.018 Simple mode 1.19 1.01 1.41 0.041 Weighted mode 1.18 1.02 1.36 0.027 Discussion In recent years, researchers have identified a potential correlation between the occurrence of kidney stones and a decrease in BMD in patients , , . In 2021, Ganesan et al reported that 20% of 531,431 patients with kidney stone disease were subsequently diagnosed with osteoporosis . In a more recent comprehensive analysis that included nine case‒control or cohort studies with a total of 454,464 people, researchers reported a notably greater prevalence of osteoporosis in patients with nephrolithiasis and vice versa 9 . Another 2011–2018 NHANES cross-sectional study revealed that a low femoral-neck BMD T score was significantly associated with an increased risk of developing kidney stones . However, the main limitation of these studies is that they cannot say whether osteoporosis was a cause or consequence of nephrolithiasis. Figure 2 Scatter plot of the MR analysis of the effects of kidney stones on the risk of developing osteoporosis. To our knowledge, this is the first combined NHANES and MR study to investigate whether the presence of kidney stones is causally associated with the risk of developing osteoporosis. The large, nationally representative NHANES-based observational study revealed that the presence of kidney stones was associated with a greater risk of developing osteoporosis. To further explore the causal effect of the presence of kidney stones on the risk of developing osteoporosis, an MR analysis was carried out on the basis of the genetic information from two GWASs. The two-sample bidirectional MR study established a causal relationship between the presence of kidney stones and the risk of developing osteoporosis. This finding provides more support for the results obtained from prior epidemiological investigations 20,21,22 . The reverse-MR analysis did not provide evidence to support the hypothesis that osteoporosis status could increase the risk of developing kidney stones. The results obtained from both the observational study and MR analysis were in agreement, indicating a high level of reliability and robustness in this discovery. The observational study included the examination of a group of individuals from the United States, whereas the MR analysis was primarily concentrated on individuals from Europe. These findings suggest that there may indeed be an association between the presence of kidney stones and the risk of developing osteoporosis among various populations. Nephrolithiasis is a systemic metabolic disorder, and its main pathogenic factors include metabolic abnormalities, urinary tract infections, inflammatory reactions and drug factors. Osteoporosis is a long-lasting, metabolic condition affecting the bones of older individuals. It is characterized by a decrease in BMD and a decline in the structure of the bones . Although the exact mechanism underlying the link between the presence of kidney stones and the risk of developing osteoporosis is still unclear, calcium‒phosphate balance and bone metabolism might be key etiopathogeneses A prior study conducted on a population from Taiwan revealed that persons with kidney stones had a greater likelihood of exhibiting allelic variations in the ALPL gene than did the general population . ALPL encodes a tissue-specific alkaline phosphatase (TNSALP) that hydrolyses phosphate substrates such as pyrophosphate (PPi) and phosphorylated glycoproteins such as osteopontin and releases inorganic phosphate, promoting appropriate calcification . Therefore, individuals with nephrolithiasis may experience inadequate calcification, leading to reduced bone mineralization and an elevated risk of developing osteoporosis. Similarly, multiple investigations have shown the presence of mutations in the CYP24A1 gene among patients diagnosed with hypercalcemia and nephrolithiasis , , . CYP24A1 encodes the enzyme 25(OH)D-24-hydroxylase, which inactivates vitamin D metabolites through the C-24 oxidation pathway. The mutant CYP24A1 enzymes presented a significant reduction in their functional capacity, resulting in the stimulation of osteoclasts because of high levels of 1,25(OH)2D, ultimately leading to a decrease in bone mass . Inflammatory reactions may also increase the incidence of osteoporosis in kidney stone patients. Patients with nephrolithiasis have significantly increased levels of inflammatory markers, such as interleukin-1 (IL-1), IL-6, and tumor necrosis factor-α (TNF-α), which have been investigated for their potential role in bone reabsorption . IL-1, also known as "osteoclast-activating factor", has the potential to stimulate osteoclast lineage cells, resulting in increases in both the viability and resorptive capacity of osteoclasts . By increasing both the differentiation and activation of osteoclasts and by stimulating the release of degradative products such as matrix metalloproteinases, IL-1 increases bone resorption. In addition, IL-1 synergistically interacts with other cytokines, such as TNF, IL-6, IL-17, and IL-31, which promote bone resorption and inhibit osteoblasts according to previous studies , . Moreover, kidney stone patients’ with certain dietary habits (low calcium intake) , low physical activity , smoking status and inadequate vitamin D status may increase their chance of developing osteoporosis. The strengths of this study include the diverse groups of participants and the large sample size, which allowed for the adjustment of potential confounders. Another advantage of our study is the combination of an observational study with an MR analysis. The sole use of an observational study is susceptible to unmeasured confounding and reverse causality. When MR is used alone, the rate of false-negative results is relatively high, despite the ability to control for confounding conditions. Therefore, the combination of the two methods makes the results more convincing. Our study also has several limitations. First, kidney stone information in the NHANES was self-reported and could not be treated as a continuous variable; therefore, the P-trend and the threshold effect of parameters were missing in our study. Second, the composition of kidney stones was unknown in both the NHANES and the UK Biobank. Calcium-free stones such as uric acid stones, which make up 10–20% of kidney stones , may lead to potential bias. Third, we excluded a significant proportion of patients who lacked information on kidney stones, femoral neck BMD data, or other variables. This exclusion may have introduced possible selection bias. Fourth, potential heterogeneity and pleiotropy should also be noted for MR. Finally, our study is based on Americans and Europeans, which may limit the generalizability of our findings to other populations. Conclusion The results of this combined NHANES-based observational study with an MR analysis reveal genetic evidence supporting the causal influence of genetically predicted kidney stones on the risk of developing osteoporosis. Further prospective cohort studies are needed to validate our results. In addition, the molecular mechanisms underlying this association should be explored in the future. Declarations Ethics approval and consent to participan t Not applicable. Since all GWAS and NHANES data used in this paper are from original, published studies, they were ethically reviewed and approved by the appropriate institutions at the time of publication. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding This research is supported by the Natural Science Foundation of Hunan Province, China(Grant No. 2024JJ5527) Author Contribution DJF, HWB and TJ designed the research. DJF, HWB and TJ conducted the formal analysis and wrote the original draft. DJF and HWB verified the data results. DJF, HWB, YGM, WJR,WL,LJY,JXZ and TJ edited and modified the article. YGM and TJ obtained funding support for the research. All authors have read and endorsed the final manuscript version for publication Acknowledgement We thank all patients who provided tissue samples and the UK Biobank, FinnGen and the NHANES for making the data publicly available. Data availability All GWAS summary data involved in this study are publicly available in the UK Biobank databases ( https://biobank.ctsu.ox.ac.uk/ ) and FinnGen Biobank ( https://storage.googleapis.com/fnngen-public-datar10/summary_stats/ ). All the NHANES data involved can be accessed in the website of NHANES ( https://www.cdc.gov/nchs/nhanes/index.htm ) References Thongprayoon C, Krambeck AE, Rule AD. Determining the true burden of kidney stone disease. Nat Rev Nephrol. 2020 Dec;16(12):736–746. 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Stress oxidative: nephrolithiasis and chronic kidney diseases. Minerva Med. 2013 Feb;104(1):23–30. Amrein K, Scherkl M, Hoffmann M, et al. Vitamin D deficiency 2.0: an update on the current status worldwide. Eur J Clin Nutr. 2020 Nov;74(11):1498–1513. Ma Q, Fang L, Su R,et al. Uric acid stones, clinical manifestations and therapeutic considerations. Postgrad Med J. 2018 Aug;94(1114):458–462. Additional Declarations No competing interests reported. Supplementary Files Additionalfiles1.docx Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 18 Jul, 2024 Submission checks completed at journal 18 Jul, 2024 First submitted to journal 16 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4748828","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":328853587,"identity":"407f97ae-48a3-49cb-b75e-cbb89fa86f8a","order_by":0,"name":"Juefei Dong","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Juefei","middleName":"","lastName":"Dong","suffix":""},{"id":328853588,"identity":"d2fde382-3807-41cb-b4d5-589a81161fe2","order_by":1,"name":"Weibin Hou","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Weibin","middleName":"","lastName":"Hou","suffix":""},{"id":328853589,"identity":"2b762dd9-0453-407b-a4ae-2b26626f7241","order_by":2,"name":"Guangming Yin","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Guangming","middleName":"","lastName":"Yin","suffix":""},{"id":328853590,"identity":"29b33a50-ac60-4d0e-a69e-a61ceba4dc44","order_by":3,"name":"Jinrong Wang","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Jinrong","middleName":"","lastName":"Wang","suffix":""},{"id":328853591,"identity":"b1e9510b-46eb-40cc-9ccf-253afcbc48a0","order_by":4,"name":"Long Wang","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Long","middleName":"","lastName":"Wang","suffix":""},{"id":328853592,"identity":"729b96bc-86be-4dd3-b07e-7062b1672b9e","order_by":5,"name":"Jianye Liu","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Jianye","middleName":"","lastName":"Liu","suffix":""},{"id":328853593,"identity":"5d1e0a6d-db7d-4ffe-8f7c-f1323319fc89","order_by":6,"name":"Xianzhen Jiang","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Xianzhen","middleName":"","lastName":"Jiang","suffix":""},{"id":328853594,"identity":"9b45e71e-000a-4300-8f51-d373c5954ebb","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":"Central South University","correspondingAuthor":true,"prefix":"","firstName":"Jing","middleName":"","lastName":"Tan","suffix":""}],"badges":[],"createdAt":"2024-07-16 10:07:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4748828/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4748828/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62658574,"identity":"ac91fb4f-f8fb-48ac-bec8-14cf24923e73","added_by":"auto","created_at":"2024-08-17 02:17:52","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":383229,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of participant selection from the NHANES (2007–2020).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4748828/v1/a389c771d468de9d83ea8fbb.jpeg"},{"id":62658575,"identity":"68690bbb-3bee-40f1-8d3d-a3e1900700de","added_by":"auto","created_at":"2024-08-17 02:17:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73514,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of the MR analysis of the effects of kidney stones on the risk of developing osteoporosis.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4748828/v1/5467522dae1853fdb5ee69c3.png"},{"id":62659399,"identity":"7c6c8522-b36e-4cb9-9bb3-11ae3fdc62bd","added_by":"auto","created_at":"2024-08-17 02:25:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1163141,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4748828/v1/d8a71cfb-87c8-4316-a4b2-0ba9efa08a08.pdf"},{"id":62658577,"identity":"a8dabcac-a5d2-4212-8312-d06f2fdb1d5e","added_by":"auto","created_at":"2024-08-17 02:17:52","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":594602,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfiles1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4748828/v1/fae38e11a8010e99f3b24bda.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Association between the Presence of Kidney Stones and the Risk of Developing Osteoporosis: A NHANES-based Cross-sectional Study and Mendelian Randomization Analysis","fulltext":[{"header":"Background","content":"\u003cp\u003eKidney stones are highly prevalent diseases that affect approximately 10% of the population worldwide, placing a tremendous burden on public health resources\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e. According to earlier research, in the year 2000, the US spent \u003cspan\u003e$\u003c/span\u003e2.1\u0026nbsp;billion managing kidney stones; by 2030, costs are predicted to rise to \u003cspan\u003e$\u003c/span\u003e5 billion\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e. The etiology of kidney stones is complex. It is believed that genetic variation, metabolic disorders, and nutritional variables are prevalent pathogeneses involved in the development of nephrolithiasis\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e \u003csup\u003e,\u003c/sup\u003e \u003ca class=\"FNLink\" href=\"#Fn4\" id=\"#FNLinkFn4\"\u003e\u003c/a\u003e.\u003c/p\u003e \u003cp\u003eOsteoporosis is a systemic skeletal disease characterized by low bone mass and degradation of the bone microstructure\u003ca class=\"FNLink\" href=\"#Fn5\" id=\"#FNLinkFn5\"\u003e\u003c/a\u003e. Owing to its high prevalence and associated increased risk of experiencing fracture, osteoporosis is a global concern. Numerous investigations on the pathophysiology of osteoporosis have been conducted throughout the years in an effort to improve osteoporosis diagnosis and treatment. The main causes of osteoporosis are aging and hormone abnormalities, whereas other factors, such as weight, serum calcium concentrations and smoking status, may also be correlated with the risk of developing osteoporosis\u003ca class=\"FNLink\" href=\"#Fn6\" id=\"#FNLinkFn6\"\u003e\u003c/a\u003e.\u003c/p\u003e \u003cp\u003eConsidering that both diseases are associated with the processes of aging and metabolic dysfunction, we hypothesized that the two diseases share a common etiopathogenesis. Multiple clinical and epidemiological studies have demonstrated that patients with urolithiasis exhibit increased bone turnover and reduced bone mass\u003ca class=\"FNLink\" href=\"#Fn7\" id=\"#FNLinkFn7\"\u003e\u003c/a\u003e\u003csup\u003e,\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn8\" id=\"#FNLinkFn8\"\u003e\u003c/a\u003e. Research has also indicated that these two diseases exhibit comparable natural progressions, including morbidity, disease development indicators, and negative outcomes in the absence of appropriate treatment\u003ca class=\"FNLink\" href=\"#Fn9\" id=\"#FNLinkFn9\"\u003e\u003c/a\u003e. These findings suggest that the presence of kidney stones may be a vital risk factor for the development of osteoporosis.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is a novel epidemiological technique that involves the use of genetic traits as substitutes for exposure (such as the presence of kidney stones) to evaluate the causal impacts on a certain outcome (such as the development of osteoporosis)\u003ca class=\"FNLink\" href=\"#Fn10\" id=\"#FNLinkFn10\"\u003e\u003c/a\u003e. Compared with observational research, this approach is less vulnerable to biases caused by confounding factors and reverse causality.\u003c/p\u003e \u003cp\u003eTo our knowledge, no comprehensive National Health and Nutrition Examination Survey (NHANES) cross-sectional study or MR analysis has been conducted to determine whether individuals with nephrolithiasis are at greater risk of experiencing decreased bone mineral density (BMD) and fractures than control individuals are.\u003c/p\u003e \u003cp\u003eIn our study, data from the NHANES (2007\u0026ndash;2020) were analyzed to investigate whether the presence of kidney stones is associated with the risk of developing osteoporosis. An MR study was subsequently conducted to further explore the causal effect of the presence of kidney stones on the risk of developing osteoporosis.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population in NHANES\u003c/h2\u003e \u003cp\u003eThe NHANES is a comprehensive and wide-ranging study conducted in the U.S. to examine the health and nutritional status of the U.S. population. This survey involves annual assessments on approximately 5000 persons via interviews, physical examinations, and laboratory tests. The NHANES was approved by the National Center for Health Statistics Research Ethics Review Board, and all participants provided written informed consent\u003ca class=\"FNLink\" href=\"#Fn11\" id=\"#FNLinkFn11\"\u003e\u003c/a\u003e. We included data from seven cycles (2007\u0026ndash;2020) because a full kidney stone history was provided during these cycles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eNephrolithiasis assessment in NHANES\u003c/h2\u003e \u003cp\u003eA history of kidney stones was available in the personal interview section for participants over 20 years old in the NHANES data. According to previous studies, individuals who answered \u0026ldquo;yes\u0026rdquo; to the question \u0026ldquo;Have you ever had kidney stones?\u0026rdquo; (KIQ026) were classified as having former kidney stone status\u003ca class=\"FNLink\" href=\"#Fn12\" id=\"#FNLinkFn12\"\u003e\u003c/a\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eOsteoporosis assessment in NHANES\u003c/h2\u003e \u003cp\u003eDual-energy X-ray absorptiometry (DXA) was used to measure BMD during the examination of the NHANES data. It is widely accepted that osteoporosis is defined as a T score\u0026lt;-2.5\u003ca class=\"FNLink\" href=\"#Fn13\" id=\"#FNLinkFn13\"\u003e\u003c/a\u003e. In our investigation, a femoral neck T score\u0026lt;-2.5 was considered to indicate the presence of osteoporosis because the NHANES provides a larger dataset for femoral neck BMD than for mean lumbar spine BMD and total femoral BMD. BMD was converted into a T score via the following formula: T score = (BMD respondent-mean BMD reference group)/SD reference group\u003ca class=\"FNLink\" href=\"#Fn14\" id=\"#FNLinkFn14\"\u003e\u003c/a\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCovariates used in the NHANES\u003c/h2\u003e \u003cp\u003eThe variables were chosen from the previously identified factors that have an impact on the risk of developing osteoporosis\u003ca class=\"FNLink\" href=\"#Fn15\" id=\"#FNLinkFn15\"\u003e\u003c/a\u003e. We included the following covariates in our multivariable models: age, race, sex, marital status, education level, family income, obesity status, serum calcium level, serum cholesterol level, smoking status, drinking status, hypertension status and diabetes status.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSample source for MR analysis\u003c/h2\u003e \u003cp\u003eGenome-wide association study (GWAS) data on kidney stones were obtained from the UK Biobank website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biobank.ctsu.ox.ac.uk/\u003c/span\u003e\u003cspan address=\"https://biobank.ctsu.ox.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A total of 462,933 patients with 9,851,867 single-nucleotide polymorphisms (SNPs) were included in these GWAS data. The GWAS data for osteoporosis was obtained from the FinnGen Biobank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://storage.googleapis.com/fnngen-public-datar10/summary_stats/\u003c/span\u003e\u003cspan address=\"https://storage.googleapis.com/fnngen-public-datar10/summary_stats/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The GWAS included 398,337 individuals (7300 osteoporosis patients and 391037 control participants) and 20,167,428 SNPs. All the participants were of European descent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSelection of genetic instrumental variables (IVs)\u003c/h2\u003e \u003cp\u003eAll MR analyses follow three key assumptions: (I) the genetic variants applied as IVs are robustly correlated with exposure; (II) the genetic variants applied are not linked to any confounders; and (III) the genetic variants selected affect the risk of developing osteoporosis only via the presence of kidney stones rather than other pathways\u003ca class=\"FNLink\" href=\"#Fn16\" id=\"#FNLinkFn16\"\u003e\u003c/a\u003e. We conducted several procedures to identify eligible SNPs via publicly accessible GWAS data on nephrolithiasis. First, we identified SNPs highly related to nephrolithiasis with genome-wide significance (p\u0026thinsp;\u0026lt;\u0026thinsp;5E-6) to meet the first assumption. Second, the significant SNPs were then clumped on the basis of linkage disequilibrium (LD) with an r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and clump distance (kb)\u0026thinsp;=\u0026thinsp;5,000 to obtain independent IVs. Moreover, F statistics for each IV were computed via the formula F\u0026thinsp;=\u0026thinsp;β\u003csup\u003e2\u003c/sup\u003e exposure/SE\u003csup\u003e2\u003c/sup\u003e exposure to assess the IV's strength. F values\u0026thinsp;\u0026lt;\u0026thinsp;10 were considered to indicate weak IVs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFor the cross-sectional study using data from the NHANES, continuous variables are represented as the means (standard deviations) and were compared via Student's t test (normal distribution). Categorical variables are presented as absolute values (percentages), and the chi-square test was used for comparisons. Three successive multivariate logistic regression models were used to evaluate the associations between the presence of kidney stones and the risk of developing osteoporosis. Model 1 was unadjusted. Model 2 was adjusted for age, sex, and race. Model 3 was based on Model 2 and was additionally adjusted for marital status, education level, family income, obesity status, serum calcium levels, serum cholesterol levels, smoking status (yes or no), drinking status, hypertension status, and diabetes status. The survey sampling weight was considered in this part of the analysis.\u003c/p\u003e \u003cp\u003eFor the MR analysis, the inverse-variance weighting (IVW), weighted median, MR‒Egger, weighted mode, and simple mode methods were used to assess correlations between the presence of kidney stones and the risk of developing osteoporosis. IVW was our primary analytical method because it has a weighted scoring method, which can estimate genetic variants individually, making it currently the most statistically efficient MR method\u003ca class=\"FNLink\" href=\"#Fn17\" id=\"#FNLinkFn17\"\u003e\u003c/a\u003e. The other 4 MR methods\u003c/p\u003e \u003cp\u003ecould test the stability and reliability of the IVW results. If the results of IVW were statistically efficient and the results of the other 4 methods were in accordance with those of the IVW method (e.g., all OR values\u0026thinsp;\u0026gt;\u0026thinsp;1 or \u0026lt;\u0026thinsp;1), a causal correlation was considered. In addition, we used Cochrane's Q test to examine possible SNP heterogeneity, and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to indicate the presence of heterogeneity among the IVs. For sensitivity analysis, the \u0026ldquo;leave-one-out\u0026rdquo; process was applied\u003ca class=\"FNLink\" href=\"#Fn18\" id=\"#FNLinkFn18\"\u003e\u003c/a\u003e. By removing each SNP one-by-one, the cumulative impact of the remaining SNPs on the outcome was demonstrated, allowing for the evaluation of the individual effects of each SNP. In addition, horizontal pleiotropy was assessed via MR‒Egger.\u003ca class=\"FNLink\" href=\"#Fn19\" id=\"#FNLinkFn19\"\u003e\u003c/a\u003e\u003c/p\u003e \u003cp\u003eWe performed all the data analyses via R 4.3.1. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to indicate statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of NHANES participants\u003c/h2\u003e \u003cp\u003eA total of 75402 participants from NHANES 2007\u0026ndash;2020 were included in the study. After removing those younger than 20, those who did not have data on kidney stones and femoral neck BMD, and those with missing data on confounders, a total of 14,539 eligible participants were enrolled (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline characteristics of the enrolled participants. Compared with patients without osteoporosis, osteoporosis patients were older, had lower body masses, and had elevated levels of cholesterol. In addition, they were more likely to be non-Hispanic white, women, and have medical conditions such as hypertension, diabetes, and kidney stones (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the participants included in the analysis of the associations between the presence of kidney stones and the risk of developing osteoporosis.\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOsteoporosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNonosteoporosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of\u003c/p\u003e \u003cp\u003eparticipants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13930\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\u003eAge (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.60 (10.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.24 (15.73)\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\u003eRace (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"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\u003e52 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2286 (16.4)\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\u003e47 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2678 (19.2)\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\u003e372 (61.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6356 (45.6)\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\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1122 (8.1)\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\u003e54 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1488 (10.7)\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e332 (54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5210 (37.4)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e277 (45.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8720 (63.6)\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 level (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelow high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e175 (28.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3572 (25.6)\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\u003eHigh School or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e434 (71.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10358 (74.4)\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\u003ePoverty\u0026ndash;income ratio (PIR, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e515 (84.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11503(82.6)\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\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94 (16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2427(17.4)\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\u003eCalcium (%)\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\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;2.7 mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (0.3)\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\u003e2.2\u0026ndash;2.7 mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e563 (92.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13165 (94.5)\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\u003e2.2 mmol/L\u0026lt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e722(5.2)\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\u003eObesity (%)\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e514 (84.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8784 (63.1)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5146 (36.9)\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\u003eSex (%)\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\u003e132 (21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7309 (52.5)\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\u003e477 (78.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6621 (47.5)\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\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e510 (83.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11107 (79.3)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99 (16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2823 (20.7)\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 consumption 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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e513 (84.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10454 (75.0)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3476 (25.0)\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 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\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e488 (80.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11487 (82.5)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121 (19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2443 (17.5)\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 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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e279 (45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8360 (60.0)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e330 (54.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5570 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal serum cholesterol concentration (%)\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\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;6.18 mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109(17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1959 (14.1)\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\u003e5.18\u0026ndash;6.18 mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e172(28.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3823 (27.4)\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\u003e5.18 mmol/L\u0026lt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e328 (53.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8148 (58.5)\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\u003eKidney stone 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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e529 (86.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12522 (89.9)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80(13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1408(10.1)\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 \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between kidney stone status and the risk of developing osteoporosis\u003c/h2\u003e \u003cp\u003eAccording to the multivariable analyses, the presence of kidney stones was positively associated with the risk of developing osteoporosis (OR: 1.539; CI: 1.231\u0026ndash;2.061; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After adjustment for age, sex and race, the presence of kidney stones was significantly associated with a greater risk of developing osteoporosis (OR 1.581, CI: 1.211\u0026ndash;2.063; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After controlling for all the covariates, a strong correlation between the presence of kidney stones and the risk of developing osteoporosis persisted(OR 1.778, CI: 1.345\u0026ndash;2.351, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cb\u003eMR analysis of the causal relationship between the presence of kidney stones and the risk of developing osteoporosis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe conducted a bidirectional two-sample MR analysis to further investigate the causal association between the presence of kidney stones and the risk of developing osteoporosis. After selection, 50 SNPs were included in our MR analysis. The IWV results suggested a causal association between the presence of kidney stones and risk of developing osteoporosis (OR 1.088, CI: 1.015\u0026ndash;1.167, P\u0026thinsp;=\u0026thinsp;0.018), which was validated by the weighted median (OR 1.138, CI: 1.046\u0026ndash;1.237, P\u0026thinsp;=\u0026thinsp;0.002), weighted mode (OR 1.181, CI: 1.024\u0026ndash;1.362, P\u0026thinsp;=\u0026thinsp;0.027) and simple mode methods (OR 1.195, CI: 1.012\u0026ndash;1.410, P\u0026thinsp;=\u0026thinsp;0.041). However, no significant correlation was found via MR‒Egger (OR 1.179, CI: 0.984\u0026ndash;1.412, P\u0026thinsp;=\u0026thinsp;0.080). A scatter plot of the connection between the presence of kidney stones and the risk of developing osteoporosis at the causal level as assessed by MR techniques is shown in Fig.\u0026nbsp;2. The MR‒Egger intercept is close to 0, suggesting the absence of horizontal pleiotropy in MR analysis. The Q value of Cochrane's Q test was 10.76 (P\u0026thinsp;=\u0026thinsp;0.63), which indicates that no horizontal heterogeneity existed in the MR analysis. The results of the \"leave-one-out\" analysis indicated that the SNPs included in this study were unlikely to be influenced by potential bias, thereby demonstrating the stability and reliability of our study.Funnel plots, plots of the \u0026ldquo;MR-effect\u0026rdquo; and \u0026ldquo;Leave-one-out\u0026rdquo; sensitivity analysis for two groups were offered in Additional File: Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, S2 and S3.\u003c/p\u003e \u003cp\u003eTo investigate the potential inverse relationship between osteoporosis status and the risk of developing kidney stones, we conducted reverse-MR analysis, with the IVW method as the primary analysis. The fixed-effects IVW technique(Additional File 2: Figure S4,S5,S6 and S7), did not reveal any causal influences of osteoporosis status on the risk of developing kidney stones in the reverse-MR analysis (IVW OR\u0026thinsp;=\u0026thinsp;1.03, 95% CI\u0026thinsp;=\u0026thinsp;0.95\u0026ndash;1.11, p\u0026thinsp;=\u0026thinsp;0.476).\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 the presence of kidney stones and the risk of developing osteoporosis (n\u0026thinsp;=\u0026thinsp;14,593)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eLower limit Upper limit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel Ⅰ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel Ⅱ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel Ⅲ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel I: unadjusted\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel II: adjusted for age, sex and race\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eModel III: adjusted for age, sex, race, marital status, education level, family income, obesity status, serum calcium level, serum cholesterol level, smoking status, drinking status, hypertension status and diabetes status.\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\u003eMR estimates for the association between the presence of kidney stones and risk of developing osteoporosis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAnalytical methods\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eLower limit Upper limit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMR\u0026ndash;Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn recent years, researchers have identified a potential correlation between the occurrence of kidney stones and a decrease in BMD in patients\u003ca class=\"FNLink\" href=\"#Fn20\" id=\"#FNLinkFn20\"\u003e\u003c/a\u003e\u003csup\u003e,\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn21\" id=\"#FNLinkFn21\"\u003e\u003c/a\u003e\u003csup\u003e,\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn22\" id=\"#FNLinkFn22\"\u003e\u003c/a\u003e. In 2021, Ganesan et al reported that 20% of 531,431 patients with kidney stone disease were subsequently diagnosed with osteoporosis\u003ca class=\"FNLink\" href=\"#Fn23\" id=\"#FNLinkFn23\"\u003e\u003c/a\u003e. In a more recent comprehensive analysis that included nine case‒control or cohort studies with a total of 454,464 people, researchers reported a notably greater prevalence of osteoporosis in patients with nephrolithiasis and vice versa\u003csup\u003e9\u003c/sup\u003e. Another 2011\u0026ndash;2018 NHANES cross-sectional study revealed that a low femoral-neck BMD T score was significantly associated with an increased risk of developing kidney stones\u003ca class=\"FNLink\" href=\"#Fn24\" id=\"#FNLinkFn24\"\u003e\u003c/a\u003e. However, the main limitation of these studies is that they cannot say whether osteoporosis was a cause or consequence of nephrolithiasis.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;2 Scatter plot of the MR analysis of the effects of kidney stones on the risk of developing osteoporosis.\u003c/p\u003e \u003cp\u003eTo our knowledge, this is the first combined NHANES and MR study to investigate whether the presence of kidney stones is causally associated with the risk of developing osteoporosis. The large, nationally representative NHANES-based observational study revealed that the presence of kidney stones was associated with a greater risk of developing osteoporosis. To further explore the causal effect of the presence of kidney stones on the risk of developing osteoporosis, an MR analysis was carried out on the basis of the genetic information from two GWASs. The two-sample bidirectional MR study established a causal relationship between the presence of kidney stones and the risk of developing osteoporosis. This finding provides more support for the results obtained from prior epidemiological investigations\u003csup\u003e20,21,22\u003c/sup\u003e. The reverse-MR analysis did not provide evidence to support the hypothesis that osteoporosis status could increase the risk of developing kidney stones. The results obtained from both the observational study and MR analysis were in agreement, indicating a high level of reliability and robustness in this discovery. The observational study included the examination of a group of individuals from the United States, whereas the MR analysis was primarily concentrated on individuals from Europe. These findings suggest that there may indeed be an association between the presence of kidney stones and the risk of developing osteoporosis among various populations.\u003c/p\u003e \u003cp\u003eNephrolithiasis is a systemic metabolic disorder, and its main pathogenic factors include metabolic abnormalities, urinary tract infections, inflammatory reactions and drug factors. Osteoporosis is a long-lasting, metabolic condition affecting the bones of older individuals. It is characterized by a decrease in BMD and a decline in the structure of the bones\u003ca class=\"FNLink\" href=\"#Fn25\" id=\"#FNLinkFn25\"\u003e\u003c/a\u003e.\u003c/p\u003e \u003cp\u003eAlthough the exact mechanism underlying the link between the presence of kidney stones and the risk of developing osteoporosis is still unclear, calcium‒phosphate balance and bone metabolism might be key etiopathogeneses\u003ca class=\"FNLink\" href=\"#Fn26\" id=\"#FNLinkFn26\"\u003e\u003c/a\u003e A prior study conducted on a population from Taiwan revealed that persons with kidney stones had a greater likelihood of exhibiting allelic variations in the ALPL gene than did the general population\u003ca class=\"FNLink\" href=\"#Fn27\" id=\"#FNLinkFn27\"\u003e\u003c/a\u003e. ALPL encodes a tissue-specific alkaline phosphatase (TNSALP) that hydrolyses phosphate substrates such as pyrophosphate (PPi) and phosphorylated glycoproteins such as osteopontin and releases inorganic phosphate, promoting appropriate calcification\u003ca class=\"FNLink\" href=\"#Fn28\" id=\"#FNLinkFn28\"\u003e\u003c/a\u003e. Therefore, individuals with nephrolithiasis may experience inadequate calcification, leading to reduced bone mineralization and an elevated risk of developing osteoporosis. Similarly, multiple investigations have shown the presence of mutations in the CYP24A1 gene among patients diagnosed with hypercalcemia and nephrolithiasis\u003ca class=\"FNLink\" href=\"#Fn29\" id=\"#FNLinkFn29\"\u003e\u003c/a\u003e\u003csup\u003e,\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn30\" id=\"#FNLinkFn30\"\u003e\u003c/a\u003e\u003csup\u003e,\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn31\" id=\"#FNLinkFn31\"\u003e\u003c/a\u003e. CYP24A1 encodes the enzyme 25(OH)D-24-hydroxylase, which inactivates vitamin D metabolites through the C-24 oxidation pathway. The mutant CYP24A1 enzymes presented a significant reduction in their functional capacity, resulting in the stimulation of osteoclasts because of high levels of 1,25(OH)2D, ultimately leading to a decrease in bone mass\u003ca class=\"FNLink\" href=\"#Fn32\" id=\"#FNLinkFn32\"\u003e\u003c/a\u003e.\u003c/p\u003e \u003cp\u003eInflammatory reactions may also increase the incidence of osteoporosis in kidney stone patients. Patients with nephrolithiasis have significantly increased levels of inflammatory markers, such as interleukin-1 (IL-1), IL-6, and tumor necrosis factor-α (TNF-α), which have been investigated for their potential role in bone reabsorption\u003ca class=\"FNLink\" href=\"#Fn33\" id=\"#FNLinkFn33\"\u003e\u003c/a\u003e. IL-1, also known as \"osteoclast-activating factor\", has the potential to stimulate osteoclast lineage cells, resulting in increases in both the viability and resorptive capacity of osteoclasts\u003ca class=\"FNLink\" href=\"#Fn34\" id=\"#FNLinkFn34\"\u003e\u003c/a\u003e. By increasing both the differentiation and activation of osteoclasts and by stimulating the release of degradative products such as matrix metalloproteinases, IL-1 increases bone resorption. In addition, IL-1 synergistically interacts with other cytokines, such as TNF, IL-6, IL-17, and IL-31, which promote bone resorption and inhibit osteoblasts according to previous studies\u003ca class=\"FNLink\" href=\"#Fn35\" id=\"#FNLinkFn35\"\u003e\u003c/a\u003e\u003csup\u003e,\u003c/sup\u003e\u003ca class=\"FNLink\" href=\"#Fn36\" id=\"#FNLinkFn36\"\u003e\u003c/a\u003e.\u003c/p\u003e \u003cp\u003eMoreover, kidney stone patients\u0026rsquo; with certain dietary habits (low calcium intake)\u003ca class=\"FNLink\" href=\"#Fn37\" id=\"#FNLinkFn37\"\u003e\u003c/a\u003e, low physical activity\u003ca class=\"FNLink\" href=\"#Fn38\" id=\"#FNLinkFn38\"\u003e\u003c/a\u003e, smoking status\u003ca class=\"FNLink\" href=\"#Fn39\" id=\"#FNLinkFn39\"\u003e\u003c/a\u003e and inadequate vitamin D status\u003ca class=\"FNLink\" href=\"#Fn40\" id=\"#FNLinkFn40\"\u003e\u003c/a\u003e may increase their chance of developing osteoporosis.\u003c/p\u003e \u003cp\u003eThe strengths of this study include the diverse groups of participants and the large sample size, which allowed for the adjustment of potential confounders. Another advantage of our study is the combination of an observational study with an MR analysis. The sole use of an observational study is susceptible to unmeasured confounding and reverse causality. When MR is used alone, the rate of false-negative results is relatively high, despite the ability to control for confounding conditions. Therefore, the combination of the two methods makes the results more convincing.\u003c/p\u003e \u003cp\u003eOur study also has several limitations. First, kidney stone information in the NHANES was self-reported and could not be treated as a continuous variable; therefore, the P-trend and the threshold effect of parameters were missing in our study. Second, the composition of kidney stones was unknown in both the NHANES and the UK Biobank. Calcium-free stones such as uric acid stones, which make up 10\u0026ndash;20% of kidney stones\u003ca class=\"FNLink\" href=\"#Fn41\" id=\"#FNLinkFn41\"\u003e\u003c/a\u003e, may lead to potential bias. Third, we excluded a significant proportion of patients who lacked information on kidney stones, femoral neck BMD data, or other variables. This exclusion may have introduced possible selection bias. Fourth, potential heterogeneity and pleiotropy should also be noted for MR. Finally, our study is based on Americans and Europeans, which may limit the generalizability of our findings to other populations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe results of this combined NHANES-based observational study with an MR analysis reveal genetic evidence supporting the causal influence of genetically predicted kidney stones on the risk of developing osteoporosis. Further prospective cohort studies are needed to validate our results. In addition, the molecular mechanisms underlying this association should be explored in the future.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003e \u003cb\u003eEthics approval and consent to participan\u003c/b\u003et\u003c/strong\u003e \u003cp\u003eNot applicable. Since all GWAS and NHANES data used in this paper are from original, published studies, they were ethically reviewed and approved by the appropriate institutions at the time of publication.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research is supported by the Natural Science Foundation of Hunan Province, China(Grant No. 2024JJ5527)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDJF, HWB and TJ designed the research. DJF, HWB and TJ conducted the formal analysis and wrote the original draft. DJF and HWB verified the data results. DJF, HWB, YGM, WJR,WL,LJY,JXZ and TJ edited and modified the article. YGM and TJ obtained funding support for the research. All authors have read and endorsed the final manuscript version for publication\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank all patients who provided tissue samples and the UK Biobank, FinnGen and the NHANES for making the data publicly available.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eAll GWAS summary data involved in this study are publicly available in the UK Biobank databases (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biobank.ctsu.ox.ac.uk/\u003c/span\u003e\u003cspan address=\"https://biobank.ctsu.ox.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and FinnGen Biobank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://storage.googleapis.com/fnngen-public-datar10/summary_stats/\u003c/span\u003e\u003cspan address=\"https://storage.googleapis.com/fnngen-public-datar10/summary_stats/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All the NHANES data involved can be accessed in the website of NHANES (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/index.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/index.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e \u003cspan\u003e Thongprayoon C, Krambeck AE, Rule AD. Determining the true burden of kidney stone disease. Nat Rev Nephrol. 2020 Dec;16(12):736\u0026ndash;746.\u003c/span\u003e \u003c/li\u003e\u003cli\u003e \u003cspan\u003e Antonelli JA, Maalouf NM, Pearle MS,et al. Use of the National Health and Nutrition Examination Survey to calculate the impact of obesity and diabetes on cost and prevalence of urolithiasis in 2030. Eur Urol. 2014 Oct;66(4):724-9.\u003c/span\u003e \u003c/li\u003e\u003cli\u003e \u003cspan\u003e Wang M, Jian Z, Ma Y, et al. 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Postgrad Med J. 2018 Aug;94(1114):458\u0026ndash;462.\u003c/span\u003e \u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-urology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"buro","sideBox":"Learn more about [BMC Urology](http://bmcurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/buro/default.aspx","title":"BMC Urology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Osteoporosis, Kidney stones, National Health and Nutrition Examination Survey, Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-4748828/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4748828/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground Kidney stone disease is thought to be a risk factor for osteoporosis, but the causality between the conditions remains unknown.. Thus, we implemented a NHANES-based cross-sectional study and mendelian randomization\u003c/h2\u003e \u003cp\u003eto investigate whether the presence of kidney stones increases the risk of developing osteoporosis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eFirst, we performed an observational study on the basis of data from the National Health and Nutrition Examination Survey (NHANES; 2007\u0026ndash;2020). Kidney stone patients were identified on the basis of their affirmative response to the question \"Have you ever experienced kidney stones?\" (KIQ026). Participants whose T score at the femoral neck was \u0026lt;-2.5 were defined as osteoporosis patients. Multivariable-adjusted logistic regression was used to assess the correlation between the presence of kidney stones and the risk of developing osteoporosis. Second, Mendelian randomization (MR) was applied to further investigate the causal relationship between the presence of kidney stones and the risk of developing osteoporosis. Genetic instruments were obtained from large genome-wide association studies (GWASs) from the UK Biobank and FinnGen Biobank. Inverse-variance weighting (IVW) was the primary analytical method used.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAfter adjustment for demographic and other covariates, a significant association between the presence of kidney stones and the risk of developing osteoporosis was detected (OR 1.778, CI: 1.345\u0026ndash;2.351, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The MR results further revealed that genetically speaking, the presence of kidney stones was causally associated with a greater risk of developing osteoporosis (IVW: OR 1.088, CI: 1.015\u0026ndash;1.167, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe presence of kidney stones is associated with an increased risk of developing osteoporosis. Further prospective cohort studies are needed to validate our results.\u003c/p\u003e","manuscriptTitle":"The Association between the Presence of Kidney Stones and the Risk of Developing Osteoporosis: A NHANES-based Cross-sectional Study and Mendelian Randomization Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-17 02:17:48","doi":"10.21203/rs.3.rs-4748828/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-07-18T18:05:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-18T18:05:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Urology","date":"2024-07-16T10:05:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-urology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"buro","sideBox":"Learn more about [BMC Urology](http://bmcurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/buro/default.aspx","title":"BMC Urology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d09e4dfc-b926-4c32-b69f-1f4422f62925","owner":[],"postedDate":"August 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-08-30T10:51:36+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-17 02:17:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4748828","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4748828","identity":"rs-4748828","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}