Joint Effects of Calcium Carbonate in Domestic Water and the Genetic Risk on Rheumatoid Arthritis

Joint Effects of Calcium Carbonate in Domestic Water and the Genetic Risk on Rheumatoid Arthritis | 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 Joint Effects of Calcium Carbonate in Domestic Water and the Genetic Risk on Rheumatoid Arthritis Dandan Tian, Panlong Li, Lijuan Chen, Wanyue Li, Xirui Zhu, Chun Huang, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5871734/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : The aim of this study was to investigate the relationship between minerals in domestic water and the incidence of rheumatoid arthritis (RA). Methods : This longitudinal observational study included 434,958 British individuals from the United Kingdom. The study population consisted of RA-free participants with complete information on water minerals, genetic data, lifestyle habits, and physical measurements at baseline. The baseline assessment was conducted between 2006 and 2010, and the follow-up period was up to 2024. To assess individual genetic susceptibility, a polygenic risk score (PRS) of RA was calculated for each participant. Cox regression models were employed to examine the associations between water minerals, the PRS, and the occurrence of RA. Results : A total of 5,880 new RA cases were reported during a median follow-up of 15 years. After controlling for multiple covariates, the concentration of calcium carbonate in domestic water was negatively associated with the risk of RA (hazard ratio [HR], 0.93; 95% confidence interval [CI], 0.90 to 0.95; p=1.74×10 -9 ). Additionally, individuals in the highest tertile of the polygenic risk score (PRS) had a 53% to 74% increased risk of RA compared with those in the lowest tertile. Notably, individuals with a high PRS and soft water had a 92% (95% CI: 71% to 115%) increased risk of RA compared with those with a low PRS and very hard water. Conclusions : The results suggest that exposure to soft water increases the risk of RA, especially in people who are genetically susceptible. rheumatoid arthritis water hardness calcium carbonate genetic risk gene‒environment interaction Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disorder that causes the joints to become inflammatory, painful, stiff, and destructively degenerative. Approximately 1% of the global population is affected by RA, making it a significant burden on healthcare systems worldwide [ 1 ]. The etiology of RA is complex and involves both genes and the environment [ 2 ]. While genetic susceptibility is a critical factor in the onset of RA, environmental exposure is also important in triggering and sustaining the inflammatory cascade [ 3 ]. Investigating the interaction between genes and the environment can provide new insights into the mechanism and strategies for the prevention and treatment of RA. Among the environmental factors, domestic water hardness, which is primarily determined by the concentrations of calcium and magnesium ions, is inseparable from human health. Different studies from various regions have shown that hard water is good for preventing cardiovascular disease [ 4 , 5 ], improving glucose tolerance [ 6 , 7 ], and reducing the incidence and mortality of tumors [ 8 , 9 ]. However, to the best of our knowledge, no study has been conducted to estimate the effect of water hardness on RA in humans; all available evidence has been from animal studies. Animal studies have shown that Lake Hévíz water, which is rich in minerals such as calcium and magnesium, improves the symptoms of RA model rats [ 10 , 11 ]. In addition, researchers have reported that a high-calcium diet significantly reduces joint inflammation and bone erosion in TNF-alpha transgenic mice [ 12 ]. Another study revealed that calcium gluconate can significantly alleviate the symptoms of collagen-induced RA in DBA/1J mice[ 13 ]. In humans, calcium carbonate (CaCO3), which is often used as a calcium supplement, has been found to be effective in the treatment of various conditions, including hypocalcemia, gastroesophageal reflux disease, chronic kidney disease, osteoporosis, hypothyroidism, and RA, all of which are associated with low serum calcium levels [ 14 ]. Some of this efficacy can be attributed to the Ca 2+ calmodulin-NFAT signaling pathway, which is implicated in autoimmune diseases [ 11 , 12 ]. Furthermore, some of the latest animal experiments have shown that calcium supplementation can decrease the levels of serum IL-6 and IL-10, which are cytokines that are often elevated during inflammation [ 13 ]. Despite the considerable interest in the relationship between daily mineral intake from water and RA, there is still much that is not known. The UK Biobank is a large-scale, population-based cohort study that offers the perfect platform to investigate the associations between domestic water minerals and the risk of RA. Findings from the UK Biobank support an adverse association between soft water and the incidence of neurodegenerative disease, atherosclerotic cardiovascular disease, and all-cause cancer [ 8 , 15 , 16 ]. Furthermore, the complete information on environmental exposures, genetic data, lifestyle, and physical measures in the UK Biobank allows for a comprehensive assessment of the interplay between genes and air pollution in RA risk [ 17 ]. Therefore, this study was designed to examine the joint effects of domestic water mineral concentration and genetic risk on RA via data from the UK Biobank. The present study contributes to the understanding of the genetic and water mineral factors that combine to increase the risk of this autoimmune disease. Hence, these findings may pave the way for the development of more focused and efficient strategies for preventing and controlling RA. Methods The entire study design is shown in Figure 1. To explore the effects of domestic water minerals on the risk of RA, the UK Biobank cohort with complete genetic information, medical history, and water mineral information was included in this study. Multiple covariates, including 15 lifestyle factors, 8 physical activity factors, and 7 physical measurements, were considered. The names of each factor and the information in the UKB are shown in Supplementary Table 1. Multivariate Cox regression models were applied to calculate the hazard ratio, which represented the effect of exposure on the risk of RA. Stratified analysis was applied to further investigate whether there was an interaction effect between water mineral exposure and risk genes for RA occurrence. Study population In this study, 434958 of the 502359 participants were selected from the UK Biobank cohort. First, 6685 participants were excluded because they were diagnosed with RA before enrollment. A total of 39,940 participants were excluded because their water mineral information was missing, and 20,776 participants were excluded because of missing genetic data or low-quality genetic data. The UK Biobank study was approved by the North West Multicenter Research Ethical Committee. All participants signed informed consent when joining the UK Biobank. Details of the participants in the UK Biobank can be found at https://www.ukbiobank.ac.uk/.. Health outcome of RA Details of the first occurrence of RA can be found at https://biobank.ndph.ox.ac.uk/showcase/ukb/docs/first_occurrences_outcomes.pdf. In brief, the first occurrence of RA was detected and recorded by the algorithms, which were linked with a wide range of health outcomes across self-reports, hospital inpatient data, primary care data, and cancer and death registry data. The detected health outcomes were mapped to a 3-digit code of the International Classification of Disease (ICD-10). The self-reported medical condition codes were reported at the baseline visit or subsequent UK Biobank assessment center visit. During each visit, the participants were instructed to disclose any diagnosed conditions via the touchscreen questionnaire, and these details were further verified through a verbal interview conducted by a nurse. Additionally, hospital admission records, mortality registries, cancer registries, and primary healthcare records were kept up-to-date to provide an accurate portrayal of health outcomes. Measurement of Water Minerals The concentrations of domestic water minerals, including calcium carbonate, calcium ions, and magnesium ions, were obtained by submitting freedom of information requests to local water supply companies in England, Scotland, and Wales. A postal code is assigned to each participant's access instance on the basis of their approximate location, rounded to the nearest kilometer, and the assigned postal code is used as the basis for linking to the survey location, thereby obtaining the water mineral information for each participant. According to the United States Geological Survey, the hardness of water is classified into soft water (0–60 mg/L), moderately hard water (60–120 mg/L), hard water (120–180 mg/L), and very hard water (>180 mg/L) on the basis of the concentration of calcium carbonate in household water. The methods used to determine the concentrations of water minerals are outlined in previous research [18]. Covariates To exclude potential bias from factors other than water minerals, this study utilized the comprehensive data system of the UK Biobank, taking into account a wide range of factors beyond gender and age, including those related to lifestyle, physical activity, and physical measures. Lifestyle encompasses 15 indicators, such as smoking and drinking status, cooked vegetable intake, raw vegetable intake, fresh fruit intake, dried fruit intake, oily fish intake, nonoily fish intake, processed meat intake, poultry intake, beef intake, pork intake, cheese intake, cereal intake, and water intake. Physical activity includes 8 indicators: number of days per week walking for 10+ minutes, number of days per week engaging in moderate physical activity for 10+ minutes, number of days per week engaging in vigorous physical activity for 10+ minutes, frequency of stair climbing in the last 4 weeks, time spent driving per day, time spent using a computer per day, time spent watching television per day, and types of physical activity in the last 4 weeks. The physical indicators consisted of 7 indicators: hand grip strength, systolic blood pressure, diastolic blood pressure, pause rate (presumably referring to heart rate variability or a similar metric), HbA1c, metabolic rate, and body fat percentage. Methods for measuring these covariates can be found at the UK Biobank. Genetic Data Genotype data were determined via two specially designed closely related gene chips via Affymetrix, which is now part of Thermo Fisher Scientific and was chosen for its ability to provide comprehensive and reliable genetic information essential for the study's objectives. Of these, approximately 50,000 participants used the UK BiLEVE Axiom chip resource number 149600, and the remaining approximately 450,000 participants used the UK Biobank Axiom chip resource number 149601. Genotype data undergo rigorous quality control (QC) to ensure accuracy. Details of the specific quality control process information, which ensure the accuracy and reliability of the genotype data, are meticulously recorded in the UK Biobank. The gene dataset underwent phasing processing and was combined with the Haplotype Reference Consortium and UK10K haplotype resources via computationally efficient methods. The position of the SNPs in the genetic data was determined according to the GRCh37 coordinates. Imputation was performed on approximately 96 million genotypes. Details of the methods for genetic data measurement and processing, such as imputation and haplotypes, have been described by Bycroft et al. [19]. Data Processing and Analysis In addition to null values, there are also some meaningless values in variables, such as negative values. For example, in the UKB study, if a participant was asked whether they smoked and preferred not to answer, their smoking status would be coded as -3, which is considered a meaningless value. To address this, the study recoded such negative values as NA. For the recording of the RA event time, if the recorded time was equal to or earlier than the birth date or later than the study's deadline of May 28, 2024, these participants were deleted from the dataset. The specific processing procedure for each variable is shown in Supplementary Table 1. The missing rate of the covariates was subsequently calculated to ensure that the missing rate of the covariates was less than 10%. The toolkit "mice" in R software was used to perform multiple predictive imputations on the dataset consisting of covariates without information such as the onset time and status of RA. The method used for imputation was random forest with the number of trees set to 5000 and the number of imputations set to the default value of 5. Before the polygenic risk score (PRS) was calculated, several quality control (QC) measures were implemented to mitigate potential influences from confounding factors. Specifically, single nucleotide polymorphisms (SNPs) were excluded in the following scenarios: if the imputation information (INFO) score was less than 0.2, if there were significant discrepancies in allele frequency between the genetically inferred ancestry groups in the UK Biobank (UKB) and either the Genome Aggregation Database (Gnomad) or the 1000 Genomes Project (with an absolute allele frequency difference of less than 0.2 in any ancestry group and p values exceeding 1×10 --12 and 1×10 --10 for Gnomad and the 1000 Genomes Project, respectively, in any ancestry group), or if there were substantial deviations from Hardy‒Weinberg equilibrium (p<1×10 --10 ) in any ancestry group. Furthermore, SNPs with a minor allele frequency (MAF) of less than 0.05 were also excluded. To evaluate the cumulative effect of multiple susceptible loci on disease susceptibility, as referred to in previous studies [20, 21], the PRS for each individual was calculated according to the following formula: , where is the statistical coefficient of the i th SNP and where is the number of effective alleles observed in the i th SNP j th individual. The loci of genetic risk and the statistical coefficients were obtained from a recent large-sample RA GWAS with 276,020 samples across five ethnic groups [22]. The cross-ethnic GWAS results enhance genetic mapping accuracy, thereby improving the performance of the PRS for RA by providing a more comprehensive genetic landscape. Rare variants with a minor allele frequency (MAF) < 0.01 were removed from the cross-ethnic GWAS meta-analysis results to reduce the impact of instability on the estimated values in the PRS. A threshold of p<5×10 -8 was applied to extract significant genetic variants. The names and statistical coefficients of the selected SNPs are shown in Supplement Table 2. Five Cox regression models considering different covariates were used to calculate the hazard ratio (HR) value, which was used to evaluate the relationships between water minerals and RA risk. The first model considered only the covariates of age and sex, whereas the second model included 15 lifestyle factors. The third model further incorporated eight factors related to physical activity on the basis of the second model. The fourth model further adds seven physical measurement factors to the third model. The fifth model incorporated PRS, the first ten genetic principal components, and a genotyping batch based on the fourth model. To assess whether the genetic susceptibility of RA affects the association between water hardness and RA, the participants were categorized into 12 groups on the basis of three genetic risk levels (low, middle, and high) and four water hardness levels (soft water, moderately hard water, hard water, and very hard water). The Cox proportional hazards model with multiple variables was used to estimate the HRs of RA in different groups, and the Kruskal‒Wallis test was used to estimate the differences between the three-digit groups of PRS. Multiple sensitivity analyses were conducted to examine the robustness and reliability of our findings. First, to evaluate the potential effect of reverse causality, we removed participants with less than 2 years of follow-up and estimated the effect of water minerals on the risk of RA via multivariable Cox proportional hazards models. Second, to avoid inaccurate assignments of water minerals estimated to be caused by changes in residence, a sensitivity analysis was conducted exclusively on participants who had resided at their current address for at least five years. Third, the tenfold cross-validation method was used to validate the robustness of the results for the associations between water minerals and RA. Results The baseline characteristics of the included participants are shown in Table 1. A total of 5880 of the 434950 participants developed RA within 17.83 years of follow-up. The sources of the 5880 RA courses are shown in Supplement Table 3. Compared with the controls, those who experienced RA were older (59.36±7.30 years vs 56.50±8.09 years), predominantly female (65.66% vs 53.98%), and had lower water calcium carbonate concentrations (124.02±103.63 vs 131.28±103.63). The spatial distributions of water calcium carbonate (CaCO3) and RA events are shown in Figure 2. The associations between water minerals and the risk of RA are shown in Table 2. The concentration of water calcium carbonate had a significant negative (HR<1) effect on the risk of RA in all six models. The water hardness categorized by the concentration of calcium carbonate (CaCO3) also had a negative effect on the risk of RA. Except in Model 3, the concentration of calcium was negatively associated with the risk of RA. There was no significant association between magnesium concentration and the risk of RA. For a one-standard error increase in the water CaCO3 concentration, the risk of RA decreased 3% to 10%. Compared with participants exposed to soft water, those with very hard water intake had a 9% to 20% lower risk of RA (Figure 3A and Supplement Table 4). High PRS, old age, previous or current smoking, a long time spent watching TV, and a high pause rate, metabolic rate and body fat percentage were risk factors (HR>1, P<0.00077 for Brohnier correction) for RA. Cheese intake, drinking, stair climbing, and hand grip were protective factors against RA (HR<1, P<0.00077). The Kaplan‒Meier curves of water hardness and PRS revealed that the probabilities of RA at different water hardness values and genetic risk levels were significantly (P<0.005) different (Figure 3B). In the joint analysis, high PRS and soft water were associated with a greater risk of RA after adjusting for multiple covariates (Figure 4). The participants with high PRSs and soft water had a 92% (95% CI: 71%-115%) greater risk of RA than did the participants with low PRSs and very hard water. The HRs were significantly different (p=0.0073) across the tertile groups of PRS. In addition, the results of the sensitivity analyses revealed that the results did not change appreciably after the removal of participants with a follow-up time of less than 2 years (Supplemental Figure 1). In addition, the results were robust after removing participants who had lived at their current address for less than 5 years (Supplemental Figure 2). There were no significant differences (p<0.05) among the HRs in the main analysis or each fold cross-validation analysis (Supplemental Table 5). Discussion To the best of our knowledge, this was the first prospective cohort study to investigate the effects of exposure to water minerals on the risk of RA while considering the modification effect of genetic risk. After adjusting for the effects of multiple covariates, a negative association between the water CaCO3 concentration and the risk of RA was revealed. Furthermore, genetic susceptibility has the capacity to fortify the association between exposure to soft water and the risk of RA. A positive association between a low CaCO3 concentration (soft water) and a high risk of RA was observed in this study. As an important source of minerals for the body, drinking water with a low mineral content has been shown to be associated with an increased risk of cardiovascular disease, cancer, and neurodegenerative diseases [ 8 , 15 , 23 ]. The domestic water CaCO3 concentration may affect the function of the immune system by affecting the calcium metabolism of the human body. Ca²⁺ influx in the body activates calcineurin, leading to the dephosphorylation and translocation of NFAT transcription factors to the nucleus, thereby promoting the expression of various inflammatory factors, such as IL-2 and TNF-α, as well as regulating macrophage function [ 24 – 26 ]. These factors play important roles in the pathological process of autoimmune diseases [ 27 , 28 ]. Another way in which the level of CaCO3 in drinking water affects RA is by influencing the structure of the gut microbiota community. Animal experiments have shown that drinking water with high levels of CaCO3 increases the absorption of calcium in rats and changes the composition of the gut microbiota, increasing the abundance of certain beneficial bacteria and the enrichment of certain bacterial groups [ 29 – 32 ]. In addition, drinking water CaCO3 can bind with phosphate to form insoluble compounds, preventing excessive absorption of phosphate [ 33 ]. In summary, although preliminary results suggest that calcium carbonate has a positive effect on the intestinal flora and the immune system, the specific molecular mechanisms involved remain unclear. In the future, larger-scale and diverse sample verification as well as more basic research are needed to reveal these mechanisms. The results of the stratified analysis revealed that the association between water intake and the risk of RA was intensified by high genetic risk. Gen-environment interactions have been reported in cardiovascular disease, brain disorders, and cancer [ 34 , 35 ]. In particular, gene‒environment interactions are thought to be important reasons for the failure to distinguish “self” from “nonself” in autoimmune diseases [ 36 , 37 ]. In this study, the functions of certain SNPs associated with RA overlapped with those associated with calcium and the immune response. For example, esv3585367 (PADI4), rs2317231 (FCRL3), rs5019428 (PLCL2), rs3093017 (CCR6) and rs3757387 (IRF5) are associated with both calcium-related signaling and the immune response. These genes have also been reported in previous RA studies [ 38 , 39 ]. Therefore, considering the significant correlation between these genes and RA, as well as their functions in calcium and immunity, it is possible that the calcium in drinking water may work together with these mutations to affect the development of RA. Despite the complexities and uncertainties surrounding the underlying mechanisms, our findings suggest that exposure to domestic water with low calcium levels, combined with genetic predisposition, has a cumulative effect on the risk of developing RA. Additional studies leveraging a blend of advanced methodologies, including genomics, proteomics, and metabolomics, are necessary to delve into the relevant pathways and biological mechanisms, ultimately aiming to offer fresh perspectives and potential targets for RA prevention. Although a large sample size was utilized, multiple covariables were incorporated, cross-validation was conducted, and multiple sensitivity analyses were performed, certain limitations still exist in this study. First, as a prospective study, there was recall bias, and therefore, causality should be interpreted with caution [ 20 ]. Second, some other domestic water minerals, such as potassium, sodium, and selenium, were not available in the UK Biobank, as these minerals may be associated with the risk of RA [ 40 – 42 ]. Third, although multiple sources of health-related outcomes, including death registries, primary care, hospital admissions, and self-reports, were used, some RA cases may not have been admitted and recorded. Fourth, it is important to acknowledge that the study exclusively included White participants. Therefore, it is crucial to validate these findings regarding the interaction between genes and water minerals in other racial or ethnic populations to make them more generalizable. In conclusion, for the first time, our findings revealed a negative connection between the concentration of CaCO3 in domestic water and the risk of RA. This association was influenced by genetic factors. Therefore, our results support the idea that hard water with high levels of calcium carbonate could be beneficial in preventing RA. Declarations Acknowledgments The authors are grateful to all the participants of the study. Sources of Funding This research was supported by grants from the National Natural Science Foundation of China (82270463), the Henan Provincial Medical Science and Technology Research Project (LHGJ20230069), the Key Research and Development Project of Henan Province (231111313400), and the Postdoctoral Research Funding of Henan Province. Disclosures All the authors have no conflicts of interest to disclose. Data and Code availability The data used in this study are available from the UKB after application. The number of patients approved by the UK Biobank for this study was 94885. The summary statistics of the GWAS used in the present study can be downloaded through the following link: https://data.cyverse.org/dav-anon/iplant/home/kazuyoshiishigaki/ra_gwas/ra_gwas-10-28-2021.tar. All the data were processed via R (version R-4.2.3), and the code can be downloaded at https://github.com/LiPanlon/Panlong-Li/blob/main/Delirium%20data%20process.R or email to the corresponding author. Contributions Conceptualization: DT, PL, LC, NL, JS, ML, and YH. Methodology: DT, PL, JS, ML, and YH. Software: PL, XZ, ST, YL, YQ, SC, CC, and LZ. Validation: LC, NL, WL, XZ, CH, JS, ML, and YH. Analysis: PL, XZ, ST, YL, YQ, JY, SC, CC, and LZ. Data curation: PL, XZ, ST, YL, YQ, JY, SC, CC, and LZ. Writing (original draft): DT. Writing (review & editing): DT, PL, LC, NL, WL, XZ, CH, ST, YL, YQ, JY, SC, CC, LZ, JS, ML, and YH. Visualisation: PL and NL. Supervision: JS, ML, and YH. Project administration: JS, ML, and YH. Funding acquisition: DT, PL, ML, and YH. 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Martínez, Y., et al., Effect of Acetic Acid and Sodium Bicarbonate Supplemented to Drinking Water on Water Quality, Growth Performance, Organ Weights, Cecal Traits and Hematological Parameters of Young Broilers. Animals (Basel), 2021. 11 (7).DOI: 10.3390/ani11071865. Wang, M., et al., Joint exposure to various ambient air pollutants and incident heart failure: a prospective analysis in UK Biobank. Eur Heart J, 2021. 42 (16): 1582-1591.DOI: 10.1093/eurheartj/ehaa1031. Herrera-Luis, E., et al., Gene-environment interactions in human health. Nat Rev Genet, 2024. 25 (11): 768-784.DOI: 10.1038/s41576-024-00731-z. Cannas, D., et al., Relevance of Essential Trace Elements in Nutrition and Drinking Water for Human Health and Autoimmune Disease Risk. Nutrients, 2020. 12 (7).DOI: 10.3390/nu12072074. Miller, F.W., Environment, Lifestyles, and Climate Change: The Many Nongenetic Contributors to The Long and Winding Road to Autoimmune Diseases. Arthritis Care Res (Hoboken), 2024.DOI: 10.1002/acr.25423. Díaz-Peña, R., et al., Latin American Genes: The Great Forgotten in Rheumatoid Arthritis. J Pers Med, 2020. 10 (4).DOI: 10.3390/jpm10040196. Padyukov, L., Genetics of rheumatoid arthritis. Semin Immunopathol, 2022. 44 (1): 47-62.DOI: 10.1007/s00281-022-00912-0. Deng, X. and Y. Tan, A national cross-sectional analysis of selenium intake and risk of osteoarthritis: NHANES 2003-2016. Front Public Health, 2022. 10 : 1047605.DOI: 10.3389/fpubh.2022.1047605. Fang, J., et al., Association between magnesium, copper, and potassium intakes with risk of rheumatoid arthritis: a cross-sectional study from National Health and Nutrition Examination Survey (NHANES). BMC Public Health, 2023. 23 (1): 2085.DOI: 10.1186/s12889-023-16906-y. Afsar, B. and R.E. Afsar, Salt Behind the Scenes of Systemic Lupus Erythematosus and Rheumatoid Arthritis. Curr Nutr Rep, 2023. 12 (4): 830-844.DOI: 10.1007/s13668-023-00509-5. Tables Table 1. Baseline characteristics of the participants included in this study Characteristics Incident rheumatoid arthritis Yes (n = 5880) No (n = 429078) Total population (n = 434958) Age [years (mean±SD)] 59.36±7.30 56.50±8.09 56.54±8.09 Follow-up time [months, median (IQR)] 92.59（69） 180.14（16） 178.95（16） Sex[n (%) Female] 3861（65.66） 231616（53.98） 235477 (54.14) Water CaCO3 [mg/L (mean±SD)] 124.02 (103.63) 131.28 (103.63) 131.18 (103.63) Water Ca [mg/L (mean±SD)] 49.91 (39.15) 51.36 (39.15) 51.34 (39.15) Water Mg [mg/L (mean±SD)] 4.61 (3.71) 4.60 (3.76) 4.60 (3.76) Smoking status [n (%)] Never smoking 2682（45.61） 235221（54.82） 237903（54.70） Previous smoking 2398（40.78） 148667（34.65） 151065（34.73） Current smoking 800（13.61） 45190（10.53） 45990（10.57） Drinking status [n (%)] Never 385（6.54） 18272（4.26） 18657（4.29） Previous 325（5.53） 15069（3.51） 15394（3.54） Current 5170（87.93） 395737（92.23） 400907（92.17） Cooked vegetable intake [heaped tablespoons/day (mean±SD)] 2.86 (1.99) 2.77 (1.92) 2.77 (1.92) Raw vegetable intake [heaped tablespoons/day (mean±SD)] 2.29 (2.06) 2.25 (2.06) 2.25 (2.15) Fresh fruit intake [pieces/day (mean±SD)] 2.35 (1.68) 2.28 (1.60) 2.28 (1.60) Dried fruit intake [pieces/day (mean±SD)] 0.92 (2.20) 0.87 (1.80) 0.87 (1.80) Oily fish intake [times/week (mean±SD)] 1.65 (0.98) 1.64 (0.93) 1.64 (0.93) Non oily fish intake [times/week (mean±SD)] 1.81 (0.81) 1.79 (0.79) 1.79 (0.79) Processed meat intake [times/week (mean±SD)] 1.81 (1.05) 1.87 (1.06) 1.87 (1.06) Poultry intake [times/week (mean±SD)] 2.29 (0.89) 2.29 (0.89) 2.29 (0.89) Beef intake [times/week (mean±SD)] 1.44 (0.87) 1.45 (0.85) 1.45 (0.85) Pork intake [times/week (mean±SD)] 1.13 (0.75) 1.12 (0.73) 1.12 (0.73) Cheese intake [times/week (mean±SD)] 2.39 (1.10) 2.52 (1.08) 2.52 (1.08) Cereal intake [times/week (mean±SD)] 4.62 (2.75) 4.68 (2.72) 4.68 (2.72) Water intake [glasses/day (mean±SD)] 2.94 (2.23） 2.86 (2.24) 2.86 (2.24) Walked more than 10 minutes [days/week (mean±SD)] 5.45 (1.97) 5.40 (1.923) 5.40 (1.93) Moderate physical activity [min/d (mean±SD)] 3.65 (2.45) 3.62 (2.33) 3.62 (2.34) Vigorous physical activity [min/d (mean±SD)] 1.66 (2.02) 1.85 (1.97) 1.85 (1.97) Frequency of stair climbing/week (mean±SD) 1.98 (1.34) 2.18 (1.31) 2.18 (1.31) Time watching TV [hours/day(mean±SD)] 3.34 (1.87) 2.90 (1.62) 2.90 (1.63) Time using computer [hours/day(mean±SD)] 1.11 (1.52) 1.23 (1.51) 1.23 (1.51) Time driving [hours/day (mean±SD)] 1.08 (1.38) 1.20 (1.42） 1.20 (1.42) Hand grip [kg (mean±SD)] 25.91 (10.93) 30.91 (10.93) 30.84 (10.97) SBP [mmHg (mean±SD)] 139.27 (18.66) 137.95 (18.65) 137.97 (18.65) DBP [mmHg (mean±SD)] 82.13 (10.07) 82.33 (10.14) 82.33 (10.14) Pause rate [bpm (mean±SD)] 71.15 (11.62) 69.35 (11.25) 69.38 (11.26) Basal metabolic rate [KJ (mean±SD)] 6460.62 (1328.33) 6617.17 (1363.23) 6615.05(1362.88) Body fat percentage (mean±SD) 34.62 (8.72) 31.37 (8.52) 31.41 (8.53) Vitamin supplement [n (%)] None of the bellow 3821 (64.98) 295169 (68.79) 298981 (68.74) Vitamin A 128 (2.18) 8482 (1.98) 8610 (1.97) Vitamin B 208 (3.54) 13307 (3.10) 13515 (3.11) Vitamin C 401 (6.82) 26939 (6.28) 27340 (6.28) Vitamin D 120 (2.04) 7633 (1.78) 7753 (1.78) Vitamin E 60 (1.02) 3096 (0.72) 3156 (0.73) Vitamin B9 (folic acid or folate) 223 (3.79) 3678 (0.86) 3901 (0.90) Multivitamins 928 (15.78) 70744 (16.49) 71702 (16.48) Mineral supplement None of the below 2905 (49.40) 245743 (57.27) 248648 (57.17) Fish oil 2081 (35.39) 134119 (31.26) 136200 (31.31) Glucosamine 515 (8.76) 30006 (6.70) 30521 (7.02) Calcium 241 (4.10) 9906 (2.31) 10147 (2.33) Zinc 36 (0.61) 3541 (0.83) 3577 (0.82) Iron 86 (1.46) 4604 (1.07) 4690 (1.08) Selenium 16 (0.27) 1159 (0.27) 1175 (0.27) SD, standard deviation; IQR, interquartile range; SBP, systolic blood pressure; DBP, diastolic blood pressure. Table 2. The hazard ratio denotes the association between water minerals and rheumatoid arthritis. Water hardness CaCO3 Ca Mg Model 1 0.96 [0.94-0.98] 0.95 [0.92-0.97] 0.97 [0.95-1.00] 1.00 [0.98-1.03] Model 2 0.96 [0.94-0.98] 0.95 [0.92-0.97] 0.97 [0.95-1.00] 1.01 [0.98-1.03] Model 3 0.97 [0.95-0.98] 0.96 [0.93-0.98] 0.98 [0.95-1.01] 1.01 [0.98-1.03] Model 4 0.95 [0.93-0.97] 0.94 [0.91-0.96] 0.96 [0.93-0.98] 0.99 [0.97-1.02] Model 5 0.94 [0.92-0.96] 0.92 [0.90-0.95] 0.95 [0.92-0.98] 0.99 [0.96-1.01] Model6 0.94 [0.92-0.96] 0.93 [0.90-0.95] 0.95 [0.92-0.98] 0.99 [0.96-1.02] In Model 1, age and sex were included as covariates. In Model 2, the covariates were age, sex, smoking status, drinking status, cooked vegetable intake, raw vegetable intake, fresh fruit intake, dried fruit intake, oily fish intake, nonoily fish intake, processed meat intake, poultry intake, beef intake, pork intake, cheese intake, cereal intake, and water intake. In Model 3, except for the covariates in Model 2, physical activity, including the number of days/week walked 10+ minutes, the number of days/week of moderate physical activity 10+ minutes, the number of days/week of vigorous physical activity 10+ minutes, the frequency of stair climbing in the last 4 weeks, the time spent driving, the time spent using computers, the time spent watching television, and the types of physical activity in the last 4 weeks, was included. In mode 4, with the exception of the covariates in mode 3, body physical measures, including hand grip strength, systolic blood pressure, diastolic blood pressure, pause rate, HbA1c, metabolic rate, and body fat percentage, were included. In mode 5, with the exception of the covariates in mode 4, genetic information, including the polygenic risk score, the first ten genetic principal components, and the genotyping batch, was included. In mode 6, the mineral and vitamin supplements were further considered. 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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-5871734","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":405060684,"identity":"633f5dcd-7a38-4a38-8613-08738ea89535","order_by":0,"name":"Dandan Tian","email":"","orcid":"","institution":"Henan Provincial People's Hospital \u0026 Zhengzhou University People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dandan","middleName":"","lastName":"Tian","suffix":""},{"id":405060685,"identity":"ca01f10c-b2e7-4f15-b449-98ebe6ab2da7","order_by":1,"name":"Panlong Li","email":"","orcid":"","institution":"Henan Provincial 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Liu","suffix":""},{"id":405060715,"identity":"a55aeec1-1b4a-4c9f-ad2e-819deacbf965","order_by":15,"name":"Yibin Hao","email":"","orcid":"","institution":"Henan Provincial People's Hospital \u0026 Zhengzhou University People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yibin","middleName":"","lastName":"Hao","suffix":""},{"id":405060716,"identity":"ccdc9e3e-5380-45bb-86c1-eebd9ff106d2","order_by":16,"name":"Ninghua Li","email":"","orcid":"","institution":"Henan Provincial People’s Hospital \u0026 Zhengzhou University People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ninghua","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-01-21 08:39:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5871734/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5871734/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74588446,"identity":"e6a713e5-cfa0-4de3-bae1-5c8f4a6a3b02","added_by":"auto","created_at":"2025-01-23 17:11:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2226533,"visible":true,"origin":"","legend":"\u003cp\u003eThe study design was designed to estimate the associations between domestic water minerals and the risk of rheumatoid arthritis. A total of 434958 participants with 17 years of follow-up from the UK Biobank, which includes water minerals together with genetic data, were included in this study. The polygenic risk score (PRS) for each participant was calculated to evaluate the cumulative effect of multiple susceptible loci on disease susceptibility. Cox regression models considering different covariates were used to calculate the hazard ratio (HR) value, which was used to evaluate the relationships between water minerals and RA risk. Stratified analysis was applied to estimate the interactions between calcium carbonate and genes.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5871734/v1/bd37ce943c26e1106fba9493.png"},{"id":74589740,"identity":"bc8ca447-502f-4872-b1f8-9d42d3f2b1cf","added_by":"auto","created_at":"2025-01-23 17:27:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4261377,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution map of water calcium carbonate, water hardness, polygenic risk score and rheumatoid arthritis. The water hardness was classified into soft water (0–60 mg/L), moderately hard water (60–120 mg/L), hard water (120–180 mg/L) and very hard water (\u0026gt;180 mg/L) on the basis of the water calcium carbonate content according to the United States Geological Survey.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5871734/v1/b706b9dac5b0ce8f948f59b4.png"},{"id":74588471,"identity":"3b228088-2e28-46c3-b462-54bd21ba6ed9","added_by":"auto","created_at":"2025-01-23 17:11:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2637449,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of water hardness and genetic risk with rheumatoid arthritisamong UK Biobank (434958 participants with 50588 rheumatoid arthritis events). A represents the hazard ratio for each variable. B shows the Kaplan‒Meier curves for water hardness, water calcium carbonate, and genetic risk. The genetic risk was classified according to the tertiles of the polygenic risk score. Abbreviations: PRS, polygenic risk score; SBP, systolic blood pressure; DBP, diastolic blood pressure; PCA, principal component analysis.\u003c/p\u003e","description":"","filename":"Figure3new.png","url":"https://assets-eu.researchsquare.com/files/rs-5871734/v1/0c5e12694576073b81aa9acf.png"},{"id":74588468,"identity":"a672a7d5-b0d0-4763-82ce-6a506a874283","added_by":"auto","created_at":"2025-01-23 17:11:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":976105,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of water hardness on the risk of rheumatoid arthritis in different genetic categories. The hazard ratios were adjusted for all covariates. The water hardness was classified into soft water (0–60 mg/L), moderately hard water (60–120 mg/L), hard water (120–180 mg/L) and very hard water (\u0026gt;180 mg/L) on the basis of the water calcium carbonate content according to the United States Geological Survey. The P value represents the p value of the hazard ratio for each subgroup. P.group represents the group differences between low genetic risk, medium genetic risk and high genetic risk.\u003c/p\u003e","description":"","filename":"Figure41.png","url":"https://assets-eu.researchsquare.com/files/rs-5871734/v1/30daad5ceede14b7d0e1391a.png"},{"id":75018127,"identity":"8f226ea2-f371-4885-9a1a-5af5be691329","added_by":"auto","created_at":"2025-01-29 12:54:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10955669,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5871734/v1/21dfbe4b-6d0c-428e-ada1-68c2cf68e8ed.pdf"},{"id":74588448,"identity":"83694504-0931-461d-bb80-896edcfeb005","added_by":"auto","created_at":"2025-01-23 17:11:33","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":576933,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-5871734/v1/0c1b6831988d651223e6affb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Joint Effects of Calcium Carbonate in Domestic Water and the Genetic Risk on Rheumatoid Arthritis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRheumatoid arthritis (RA) is a chronic inflammatory autoimmune disorder that causes the joints to become inflammatory, painful, stiff, and destructively degenerative. Approximately 1% of the global population is affected by RA, making it a significant burden on healthcare systems worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The etiology of RA is complex and involves both genes and the environment [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. While genetic susceptibility is a critical factor in the onset of RA, environmental exposure is also important in triggering and sustaining the inflammatory cascade [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Investigating the interaction between genes and the environment can provide new insights into the mechanism and strategies for the prevention and treatment of RA.\u003c/p\u003e \u003cp\u003eAmong the environmental factors, domestic water hardness, which is primarily determined by the concentrations of calcium and magnesium ions, is inseparable from human health. Different studies from various regions have shown that hard water is good for preventing cardiovascular disease [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], improving glucose tolerance [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and reducing the incidence and mortality of tumors [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, to the best of our knowledge, no study has been conducted to estimate the effect of water hardness on RA in humans; all available evidence has been from animal studies. Animal studies have shown that Lake H\u0026eacute;v\u0026iacute;z water, which is rich in minerals such as calcium and magnesium, improves the symptoms of RA model rats [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In addition, researchers have reported that a high-calcium diet significantly reduces joint inflammation and bone erosion in TNF-alpha transgenic mice [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Another study revealed that calcium gluconate can significantly alleviate the symptoms of collagen-induced RA in DBA/1J mice[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In humans, calcium carbonate (CaCO3), which is often used as a calcium supplement, has been found to be effective in the treatment of various conditions, including hypocalcemia, gastroesophageal reflux disease, chronic kidney disease, osteoporosis, hypothyroidism, and RA, all of which are associated with low serum calcium levels [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Some of this efficacy can be attributed to the Ca\u003csup\u003e2+\u003c/sup\u003e calmodulin-NFAT signaling pathway, which is implicated in autoimmune diseases [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Furthermore, some of the latest animal experiments have shown that calcium supplementation can decrease the levels of serum IL-6 and IL-10, which are cytokines that are often elevated during inflammation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Despite the considerable interest in the relationship between daily mineral intake from water and RA, there is still much that is not known.\u003c/p\u003e \u003cp\u003eThe UK Biobank is a large-scale, population-based cohort study that offers the perfect platform to investigate the associations between domestic water minerals and the risk of RA. Findings from the UK Biobank support an adverse association between soft water and the incidence of neurodegenerative disease, atherosclerotic cardiovascular disease, and all-cause cancer [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Furthermore, the complete information on environmental exposures, genetic data, lifestyle, and physical measures in the UK Biobank allows for a comprehensive assessment of the interplay between genes and air pollution in RA risk [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, this study was designed to examine the joint effects of domestic water mineral concentration and genetic risk on RA via data from the UK Biobank. The present study contributes to the understanding of the genetic and water mineral factors that combine to increase the risk of this autoimmune disease. Hence, these findings may pave the way for the development of more focused and efficient strategies for preventing and controlling RA.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe entire study design is shown in Figure 1. To explore the effects of domestic water minerals on the risk of RA, the UK Biobank cohort with complete genetic information, medical history, and water mineral information was included in this study. Multiple covariates, including 15 lifestyle factors, 8 physical activity factors, and 7 physical measurements, were considered. The names of each factor and the information in the UKB are shown in Supplementary Table 1. Multivariate Cox regression models were applied to calculate the hazard ratio, which represented the effect of exposure on the risk of RA. Stratified analysis was applied to further investigate whether there was an interaction effect between water mineral exposure and risk genes for RA occurrence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, 434958 of the 502359 participants were selected from the UK Biobank cohort. First, 6685 participants were excluded because they were diagnosed with RA before enrollment. A total of 39,940 participants were excluded because their water mineral information was missing, and 20,776 participants were excluded because of missing genetic data or low-quality genetic data. The UK Biobank study was approved by the North West Multicenter Research Ethical Committee. All participants signed informed consent when joining the UK Biobank. Details of the participants in the UK Biobank can be found at https://www.ukbiobank.ac.uk/..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHealth outcome of RA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDetails of the first occurrence of RA can be found at https://biobank.ndph.ox.ac.uk/showcase/ukb/docs/first_occurrences_outcomes.pdf. In brief, the first occurrence of RA was detected and recorded by the algorithms, which were linked with a wide range of health outcomes across self-reports, hospital inpatient data, primary care data, and cancer and death registry data. The detected health outcomes were mapped to a 3-digit code of the International Classification of Disease (ICD-10). The self-reported medical condition codes were reported at the baseline visit or subsequent UK Biobank assessment center visit. During each visit, the participants were instructed to disclose any diagnosed conditions via the touchscreen questionnaire, and these details were further verified through a verbal interview conducted by a nurse. Additionally, hospital admission records, mortality registries, cancer registries, and primary healthcare records were kept up-to-date to provide an accurate portrayal of health outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement of Water Minerals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe concentrations of domestic water minerals, including calcium carbonate, calcium ions, and magnesium ions, were obtained by submitting freedom of information requests to local water supply companies in England, Scotland, and Wales. A postal code is assigned to each participant\u0026apos;s access instance on the basis of their approximate location, rounded to the nearest kilometer, and the assigned postal code is used as the basis for linking to the survey location, thereby obtaining the water mineral information for each participant. According to the United States Geological Survey, the hardness of water is classified into soft water (0\u0026ndash;60 mg/L), moderately hard water (60\u0026ndash;120 mg/L), hard water (120\u0026ndash;180 mg/L), and very hard water (\u0026gt;180 mg/L) on the basis of the concentration of calcium carbonate in household water. The methods used to determine the concentrations of water minerals are outlined in previous research [18].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo exclude potential bias from factors other than water minerals, this study utilized the comprehensive data system of the UK Biobank, taking into account a wide range of factors beyond gender and age, including those related to lifestyle, physical activity, and physical measures. Lifestyle encompasses 15 indicators, such as smoking and drinking status, cooked vegetable intake, raw vegetable intake, fresh fruit intake, dried fruit intake, oily fish intake, nonoily fish intake, processed meat intake, poultry intake, beef intake, pork intake, cheese intake, cereal intake, and water intake. Physical activity includes 8 indicators: number of days per week walking for 10+ minutes, number of days per week engaging in moderate physical activity for 10+ minutes, number of days per week engaging in vigorous physical activity for 10+ minutes, frequency of stair climbing in the last 4 weeks, time spent driving per day, time spent using a computer per day, time spent watching television per day, and types of physical activity in the last 4 weeks. The physical indicators consisted of 7 indicators: hand grip strength, systolic blood pressure, diastolic blood pressure, pause rate (presumably referring to heart rate variability or a similar metric), HbA1c, metabolic rate, and body fat percentage. Methods for measuring these covariates can be found at the UK Biobank.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenotype data were determined via two specially designed closely related gene chips via Affymetrix, which is now part of Thermo Fisher Scientific and was chosen for its ability to provide comprehensive and reliable genetic information essential for the study\u0026apos;s objectives. Of these, approximately 50,000 participants used the UK BiLEVE Axiom chip resource number 149600, and the remaining approximately 450,000 participants used the UK Biobank Axiom chip resource number 149601. Genotype data undergo rigorous quality control (QC) to ensure accuracy. Details of the specific quality control process information, which ensure the accuracy and reliability of the genotype data, are meticulously recorded in the UK Biobank. The gene dataset underwent phasing processing and was combined with the Haplotype Reference Consortium and UK10K haplotype resources via computationally efficient methods. The position of the SNPs in the genetic data was determined according to the GRCh37 coordinates. Imputation was performed on approximately 96 million genotypes. Details of the methods for genetic data measurement and processing, such as imputation and haplotypes, have been described by Bycroft et al. [19].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Processing and Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn addition to null values, there are also some meaningless values in variables, such as negative values. For example, in the UKB study, if a participant was asked whether they smoked and preferred not to answer, their smoking status would be coded as -3, which is considered a meaningless value. To address this, the study recoded such negative values as NA. For the recording of the RA event time, if the recorded time was equal to or earlier than the birth date or later than the study\u0026apos;s deadline of May 28, 2024, these participants were deleted from the dataset. The specific processing procedure for each variable is shown in Supplementary Table 1. The missing rate of the covariates was subsequently calculated to ensure that the missing rate of the covariates was less than 10%. The toolkit \u0026quot;mice\u0026quot; in R software was used to perform multiple predictive imputations on the dataset consisting of covariates without information such as the onset time and status of RA. The method used for imputation was random forest with the number of trees set to 5000 and the number of imputations set to the default value of 5.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Before the polygenic risk score (PRS) was calculated, several quality control (QC) measures were implemented to mitigate potential influences from confounding factors. Specifically, single nucleotide polymorphisms (SNPs) were excluded in the following scenarios: if the imputation information (INFO) score was less than 0.2, if there were significant discrepancies in allele frequency between the genetically inferred ancestry groups in the UK Biobank (UKB) and either the Genome Aggregation Database (Gnomad) or the 1000 Genomes Project (with an absolute allele frequency difference of less than 0.2 in any ancestry group and p values exceeding 1\u0026times;10\u003csup\u003e--12\u003c/sup\u003e and 1\u0026times;10\u003csup\u003e--10\u003c/sup\u003e for Gnomad and the 1000 Genomes Project, respectively, in any ancestry group), or if there were substantial deviations from Hardy‒Weinberg equilibrium (p\u0026lt;1\u0026times;10\u003csup\u003e--10\u003c/sup\u003e) in any ancestry group. Furthermore, SNPs with a minor allele frequency (MAF) of less than 0.05 were also excluded.\u003c/p\u003e\n\u003cp\u003eTo evaluate the cumulative effect of multiple susceptible loci on disease susceptibility, as referred to in previous studies [20, 21], the PRS for each individual was calculated according to the following formula: , where\u0026nbsp;\u0026nbsp;is the statistical coefficient of the \u003cem\u003ei\u003c/em\u003eth SNP and where\u0026nbsp;\u0026nbsp;is the number of effective alleles observed in the \u003cem\u003ei\u003c/em\u003eth SNP \u003cem\u003ej\u003c/em\u003eth individual. The loci of genetic risk and the statistical coefficients were obtained from a recent large-sample RA GWAS with 276,020 samples across five ethnic groups [22]. The cross-ethnic GWAS results enhance genetic mapping accuracy, thereby improving the performance of the PRS for RA by providing a more comprehensive genetic landscape. Rare variants with a minor allele frequency (MAF) \u0026lt; 0.01 were removed from the cross-ethnic GWAS meta-analysis results to reduce the impact of instability on the estimated values in the PRS. A threshold of p\u0026lt;5\u0026times;10\u003csup\u003e-8\u003c/sup\u003e was applied to extract significant genetic variants. The names and statistical coefficients of the selected SNPs are shown in Supplement Table 2.\u003c/p\u003e\n\u003cp\u003eFive Cox regression models considering different covariates were used to calculate the hazard ratio (HR) value, which was used to evaluate the relationships between water minerals and RA risk. The first model considered only the covariates of age and sex, whereas the second model included 15 lifestyle factors. The third model further incorporated eight factors related to physical activity on the basis of the second model. The fourth model further adds seven physical measurement factors to the third model. The fifth model incorporated PRS, the first ten genetic principal components, and a genotyping batch based on the fourth model.\u003c/p\u003e\n\u003cp\u003eTo assess whether the genetic susceptibility of RA affects the association between water hardness and RA, the participants were categorized into 12 groups on the basis of three genetic risk levels (low, middle, and high) and four water hardness levels (soft water, moderately hard water, hard water, and very hard water). The Cox proportional hazards model with multiple variables was used to estimate the HRs of RA in different groups, and the Kruskal‒Wallis test was used to estimate the differences between the three-digit groups of PRS.\u003c/p\u003e\n\u003cp\u003eMultiple sensitivity analyses were conducted to examine the robustness and reliability of our findings. First, to evaluate the potential effect of reverse causality, we removed participants with less than 2 years of follow-up and estimated the effect of water minerals on the risk of RA via multivariable Cox proportional hazards models. Second, to avoid inaccurate assignments of water minerals estimated to be caused by changes in residence, a sensitivity analysis was conducted exclusively on participants who had resided at their current address for at least five years. Third, the tenfold cross-validation method was used to validate the robustness of the results for the associations between water minerals and RA.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe baseline characteristics of the included participants are shown in Table 1. A total of 5880 of the 434950 participants developed RA within 17.83 years of follow-up. The sources of the 5880 RA courses are shown in Supplement Table 3. Compared with the controls, those who experienced RA were older (59.36\u0026plusmn;7.30 years vs 56.50\u0026plusmn;8.09 years), predominantly female (65.66% vs 53.98%), and had lower water calcium carbonate concentrations (124.02\u0026plusmn;103.63 vs 131.28\u0026plusmn;103.63). The spatial distributions of water calcium carbonate (CaCO3) and RA events are shown in Figure 2. The associations between water minerals and the risk of RA are shown in Table 2. The concentration of water calcium carbonate had a significant negative (HR\u0026lt;1) effect on the risk of RA in all six models. The water hardness categorized by the concentration of calcium carbonate (CaCO3) also had a negative effect on the risk of RA. Except in Model 3, the concentration of calcium was negatively associated with the risk of RA. There was no significant association between magnesium concentration and the risk of RA. For a one-standard error increase in the water CaCO3 concentration, the risk of RA decreased 3% to 10%.\u003c/p\u003e\n\u003cp\u003eCompared with participants exposed to soft water, those with very hard water intake had a 9% to 20% lower risk of RA (Figure 3A and Supplement Table 4). High PRS, old age, previous or current smoking, a long time spent watching TV, and a high pause rate, metabolic rate and body fat percentage were risk factors (HR\u0026gt;1, P\u0026lt;0.00077 for Brohnier correction) for RA. Cheese intake, drinking, stair climbing, and hand grip were protective factors against RA (HR\u0026lt;1, P\u0026lt;0.00077). The Kaplan‒Meier curves of water hardness and PRS revealed that the probabilities of RA at different water hardness values and genetic risk levels were significantly (P\u0026lt;0.005) different (Figure 3B).\u003c/p\u003e\n\u003cp\u003eIn the joint analysis, high PRS and soft water were associated with a greater risk of RA after adjusting for multiple covariates (Figure 4). The participants with high PRSs and soft water had a 92% (95% CI: 71%-115%) greater risk of RA than did the participants with low PRSs and very hard water. The HRs were significantly different (p=0.0073) across the tertile groups of PRS.\u003c/p\u003e\n\u003cp\u003eIn addition, the results of the sensitivity analyses revealed that the results did not change appreciably after the removal of participants with a follow-up time of less than 2 years (Supplemental Figure 1). In addition, the results were robust after removing participants who had lived at their current address for less than 5 years (Supplemental Figure 2). There were no significant differences (p\u0026lt;0.05) among the HRs in the main analysis or each fold cross-validation analysis (Supplemental Table 5).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this was the first prospective cohort study to investigate the effects of exposure to water minerals on the risk of RA while considering the modification effect of genetic risk. After adjusting for the effects of multiple covariates, a negative association between the water CaCO3 concentration and the risk of RA was revealed. Furthermore, genetic susceptibility has the capacity to fortify the association between exposure to soft water and the risk of RA.\u003c/p\u003e \u003cp\u003eA positive association between a low CaCO3 concentration (soft water) and a high risk of RA was observed in this study. As an important source of minerals for the body, drinking water with a low mineral content has been shown to be associated with an increased risk of cardiovascular disease, cancer, and neurodegenerative diseases [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The domestic water CaCO3 concentration may affect the function of the immune system by affecting the calcium metabolism of the human body. Ca\u0026sup2;⁺ influx in the body activates calcineurin, leading to the dephosphorylation and translocation of NFAT transcription factors to the nucleus, thereby promoting the expression of various inflammatory factors, such as IL-2 and TNF-α, as well as regulating macrophage function [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These factors play important roles in the pathological process of autoimmune diseases [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Another way in which the level of CaCO3 in drinking water affects RA is by influencing the structure of the gut microbiota community. Animal experiments have shown that drinking water with high levels of CaCO3 increases the absorption of calcium in rats and changes the composition of the gut microbiota, increasing the abundance of certain beneficial bacteria and the enrichment of certain bacterial groups [\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In addition, drinking water CaCO3 can bind with phosphate to form insoluble compounds, preventing excessive absorption of phosphate [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In summary, although preliminary results suggest that calcium carbonate has a positive effect on the intestinal flora and the immune system, the specific molecular mechanisms involved remain unclear. In the future, larger-scale and diverse sample verification as well as more basic research are needed to reveal these mechanisms.\u003c/p\u003e \u003cp\u003eThe results of the stratified analysis revealed that the association between water intake and the risk of RA was intensified by high genetic risk. Gen-environment interactions have been reported in cardiovascular disease, brain disorders, and cancer [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In particular, gene‒environment interactions are thought to be important reasons for the failure to distinguish \u0026ldquo;self\u0026rdquo; from \u0026ldquo;nonself\u0026rdquo; in autoimmune diseases [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In this study, the functions of certain SNPs associated with RA overlapped with those associated with calcium and the immune response. For example, esv3585367 (PADI4), rs2317231 (FCRL3), rs5019428 (PLCL2), rs3093017 (CCR6) and rs3757387 (IRF5) are associated with both calcium-related signaling and the immune response. These genes have also been reported in previous RA studies [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Therefore, considering the significant correlation between these genes and RA, as well as their functions in calcium and immunity, it is possible that the calcium in drinking water may work together with these mutations to affect the development of RA. Despite the complexities and uncertainties surrounding the underlying mechanisms, our findings suggest that exposure to domestic water with low calcium levels, combined with genetic predisposition, has a cumulative effect on the risk of developing RA. Additional studies leveraging a blend of advanced methodologies, including genomics, proteomics, and metabolomics, are necessary to delve into the relevant pathways and biological mechanisms, ultimately aiming to offer fresh perspectives and potential targets for RA prevention.\u003c/p\u003e \u003cp\u003eAlthough a large sample size was utilized, multiple covariables were incorporated, cross-validation was conducted, and multiple sensitivity analyses were performed, certain limitations still exist in this study. First, as a prospective study, there was recall bias, and therefore, causality should be interpreted with caution [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Second, some other domestic water minerals, such as potassium, sodium, and selenium, were not available in the UK Biobank, as these minerals may be associated with the risk of RA [\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Third, although multiple sources of health-related outcomes, including death registries, primary care, hospital admissions, and self-reports, were used, some RA cases may not have been admitted and recorded. Fourth, it is important to acknowledge that the study exclusively included White participants. Therefore, it is crucial to validate these findings regarding the interaction between genes and water minerals in other racial or ethnic populations to make them more generalizable.\u003c/p\u003e \u003cp\u003eIn conclusion, for the first time, our findings revealed a negative connection between the concentration of CaCO3 in domestic water and the risk of RA. This association was influenced by genetic factors. Therefore, our results support the idea that hard water with high levels of calcium carbonate could be beneficial in preventing RA.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to all the participants of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSources of Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by grants from the National Natural Science Foundation of China (82270463), the Henan Provincial Medical Science and Technology Research Project (LHGJ20230069), the Key Research and Development Project of Henan Province (231111313400), and the Postdoctoral Research Funding of Henan Province.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors have no conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and Code availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are available from the UKB after application. The number of patients approved by the UK Biobank for this study was 94885. The summary statistics of the GWAS used in the present study can be downloaded through the following link: https://data.cyverse.org/dav-anon/iplant/home/kazuyoshiishigaki/ra_gwas/ra_gwas-10-28-2021.tar. All the data were processed via R (version R-4.2.3), and the code can be downloaded at https://github.com/LiPanlon/Panlong-Li/blob/main/Delirium%20data%20process.R or email to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: DT, PL, LC, NL, JS, ML, and YH. Methodology: DT, PL, JS, ML, and YH. Software: PL, XZ, ST, YL, YQ, SC, CC, and LZ. Validation: LC, NL, WL, XZ, CH, JS, ML, and YH. Analysis: PL, XZ, ST, YL, YQ, JY, SC, CC, and LZ. Data curation: PL, XZ, ST, YL, YQ, JY, SC, CC, and LZ. Writing (original draft): DT. Writing (review \u0026amp; editing): DT, PL, LC, NL, WL, XZ, CH, ST, YL, YQ, JY, SC, CC, LZ, JS, ML, and YH. Visualisation: PL and NL. Supervision: JS, ML, and YH. Project administration: JS, ML, and YH. Funding acquisition: DT, PL, ML, and YH. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003cem\u003eGlobal, regional, and national burden of rheumatoid arthritis, 1990-2020, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021.\u003c/em\u003e Lancet Rheumatol, 2023. \u003cstrong\u003e5\u003c/strong\u003e(10): e594-e610.DOI: 10.1016/s2665-9913(23)00211-4.\u003c/li\u003e\n\u003cli\u003eMaisha, J.A., H.S. El-Gabalawy, and L.J. 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Tan, \u003cem\u003eA national cross-sectional analysis of selenium intake and risk of osteoarthritis: NHANES 2003-2016.\u003c/em\u003e Front Public Health, 2022. \u003cstrong\u003e10\u003c/strong\u003e: 1047605.DOI: 10.3389/fpubh.2022.1047605.\u003c/li\u003e\n\u003cli\u003eFang, J., et al., \u003cem\u003eAssociation between magnesium, copper, and potassium intakes with risk of rheumatoid arthritis: a cross-sectional study from National Health and Nutrition Examination Survey (NHANES).\u003c/em\u003e BMC Public Health, 2023. \u003cstrong\u003e23\u003c/strong\u003e(1): 2085.DOI: 10.1186/s12889-023-16906-y.\u003c/li\u003e\n\u003cli\u003eAfsar, B. and R.E. Afsar, \u003cem\u003eSalt Behind the Scenes of Systemic Lupus Erythematosus and Rheumatoid Arthritis.\u003c/em\u003e Curr Nutr Rep, 2023. \u003cstrong\u003e12\u003c/strong\u003e(4): 830-844.DOI: 10.1007/s13668-023-00509-5.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Baseline characteristics of the participants included in this study\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"571\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003eIncident\u0026nbsp;rheumatoid arthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eYes (n = 5880)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eNo (n = 429078)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eTotal population (n = 434958)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eAge [years (mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e59.36\u0026plusmn;7.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e56.50\u0026plusmn;8.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e56.54\u0026plusmn;8.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eFollow-up time [months, median (IQR)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e92.59（69）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e180.14（16）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e178.95（16）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eSex[n (%) Female]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e3861（65.66）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e231616（53.98）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e235477 (54.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eWater CaCO3 [mg/L (mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e124.02 (103.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e131.28 (103.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e131.18 (103.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eWater Ca [mg/L (mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e49.91 (39.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e51.36 (39.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e51.34 (39.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eWater Mg [mg/L (mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e4.61 (3.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e4.60 (3.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e4.60 (3.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eSmoking status [n (%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eNever smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e2682（45.61）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e235221（54.82）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e237903（54.70）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003ePrevious smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e2398（40.78）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e148667（34.65）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e151065（34.73）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eCurrent smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e800（13.61）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e45190（10.53）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e45990（10.57）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eDrinking status [n (%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e385（6.54）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e18272（4.26）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e18657（4.29）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003ePrevious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e325（5.53）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e15069（3.51）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e15394（3.54）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e5170（87.93）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e395737（92.23）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e400907（92.17）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eCooked vegetable intake [heaped tablespoons/day (mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e2.86 (1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2.77 (1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.77 (1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eRaw vegetable intake [heaped tablespoons/day (mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e2.29 (2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2.25 (2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.25 (2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eFresh fruit intake [pieces/day (mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e2.35 (1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2.28 (1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.28 (1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eDried fruit intake [pieces/day (mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.92 (2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.87 (1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.87 (1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eOily fish intake [times/week (mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.65 (0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.64 (0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.64 (0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eNon oily fish intake [times/week (mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.81 (0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.79 (0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.79 (0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eProcessed meat intake [times/week (mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.81 (1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.87 (1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.87 (1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003ePoultry intake [times/week (mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e2.29 (0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2.29 (0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.29 (0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eBeef intake [times/week (mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.44 (0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.45 (0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.45 (0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003ePork intake [times/week (mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.13 (0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.12 (0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.12 (0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eCheese intake [times/week (mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e2.39 (1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2.52 (1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.52 (1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eCereal intake [times/week (mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e4.62 (2.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e4.68 (2.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e4.68 (2.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eWater intake [glasses/day (mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e2.94 (2.23）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2.86 (2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.86 (2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eWalked more than 10 minutes [days/week (mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e5.45 (1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e5.40 (1.923)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e5.40 (1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eModerate physical activity \u0026nbsp;[min/d (mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e3.65 (2.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e3.62 (2.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e3.62 (2.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eVigorous physical activity [min/d (mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.66 (2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.85 (1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.85 (1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eFrequency of stair climbing/week (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.98 (1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2.18 (1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.18 (1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eTime watching TV [hours/day(mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e3.34 (1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2.90 (1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.90 (1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eTime using computer \u0026nbsp; \u0026nbsp; [hours/day(mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.11 (1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.23 (1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.23 (1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eTime driving [hours/day (mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.08 (1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.20 (1.42）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.20 (1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eHand grip [kg (mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e25.91 (10.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e30.91 (10.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e30.84 (10.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eSBP [mmHg (mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e139.27 (18.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e137.95 (18.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e137.97 (18.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eDBP [mmHg (mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e82.13 (10.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e82.33 (10.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e82.33 (10.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003ePause rate [bpm (mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e71.15 (11.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e69.35 (11.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e69.38 (11.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eBasal metabolic rate [KJ (mean\u0026plusmn;SD)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e6460.62 (1328.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e6617.17 (1363.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e6615.05(1362.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eBody fat percentage (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e34.62 (8.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e31.37 (8.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e31.41 (8.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eVitamin supplement [n (%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eNone of the bellow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e3821 (64.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e295169 (68.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e298981 (68.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eVitamin A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e128 (2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e8482 (1.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e8610 (1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eVitamin B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e208 (3.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e13307 (3.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e13515 (3.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eVitamin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e401 (6.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e26939 (6.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e27340 (6.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eVitamin D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e120 (2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e7633 (1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e7753 (1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eVitamin E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e60 (1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e3096 (0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e3156 (0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eVitamin B9 (folic acid or folate)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e223 (3.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e3678 (0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e3901 (0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eMultivitamins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e928 (15.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e70744 (16.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e71702 (16.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eMineral supplement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eNone of the below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e2905 (49.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e245743 (57.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e248648 (57.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eFish oil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e2081 (35.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e134119 (31.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e136200 (31.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eGlucosamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e515 (8.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e30006 (6.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e30521 (7.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eCalcium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e241 (4.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e9906 (2.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e10147 (2.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eZinc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e36 (0.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e3541 (0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e3577 (0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eIron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e86 (1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e4604 (1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e4690 (1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eSelenium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e16 (0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1159 (0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1175 (0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSD, standard deviation; IQR, interquartile range; SBP, systolic blood pressure; DBP, diastolic blood pressure.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. The hazard ratio denotes the association between water minerals and rheumatoid arthritis.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eWater hardness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eCaCO3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eCa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003eMg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.96 [0.94-0.98]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.95 [0.92-0.97]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.97 [0.95-1.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e1.00 [0.98-1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.96 [0.94-0.98]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.95 [0.92-0.97]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.97 [0.95-1.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e1.01 [0.98-1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.97 [0.95-0.98]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.96 [0.93-0.98]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.98 [0.95-1.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e1.01 [0.98-1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eModel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.95 [0.93-0.97]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.94 [0.91-0.96]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.96 [0.93-0.98]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.99 [0.97-1.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eModel 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.94 [0.92-0.96]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.92 [0.90-0.95]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.95 [0.92-0.98]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.99 [0.96-1.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eModel6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.94 [0.92-0.96]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.93 [0.90-0.95]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.95 [0.92-0.98]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.99 [0.96-1.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIn Model 1, age and sex were included as covariates. In Model 2, the covariates were age, sex, smoking status, drinking status, cooked vegetable intake, raw vegetable intake, fresh fruit intake, dried fruit intake, oily fish intake, nonoily fish intake, processed meat intake, poultry intake, beef intake, pork intake, cheese intake, cereal intake, and water intake. In Model 3, except for the covariates in Model 2, physical activity, including the number of days/week walked 10+ minutes, the number of days/week of moderate physical activity 10+ minutes, the number of days/week of vigorous physical activity 10+ minutes, the frequency of stair climbing in the last 4 weeks, the time spent driving, the time spent using computers, the time spent watching television, and the types of physical activity in the last 4 weeks, was included. In mode 4, with the exception of the covariates in mode 3, body physical measures, including hand grip strength, systolic blood pressure, diastolic blood pressure, pause rate, HbA1c, metabolic rate, and body fat percentage, were included. In mode 5, with the exception of the covariates in mode 4, genetic information, including the polygenic risk score, the first ten genetic principal components, and the genotyping batch, was included. In mode 6, the mineral and vitamin supplements were further considered.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"rheumatoid arthritis, water hardness, calcium carbonate, genetic risk, gene‒environment interaction","lastPublishedDoi":"10.21203/rs.3.rs-5871734/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5871734/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: The aim of this study was to investigate the relationship between minerals in domestic water and the incidence of rheumatoid arthritis (RA).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: This longitudinal observational study included 434,958 British individuals from the United Kingdom. The study population consisted of RA-free participants with complete information on water minerals, genetic data, lifestyle habits, and physical measurements at baseline. The baseline assessment was conducted between 2006 and 2010, and the follow-up period was up to 2024. To assess individual genetic susceptibility, a polygenic risk score (PRS) of RA was calculated for each participant. Cox regression models were employed to examine the associations between water minerals, the PRS, and the occurrence of RA.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A total of 5,880 new RA cases were reported during a median follow-up of 15 years. After controlling for multiple covariates, the concentration of calcium carbonate in domestic water was negatively associated with the risk of RA (hazard ratio [HR], 0.93; 95% confidence interval [CI], 0.90 to 0.95; p=1.74×10\u003csup\u003e-9\u003c/sup\u003e). Additionally, individuals in the highest tertile of the polygenic risk score (PRS) had a 53% to 74% increased risk of RA compared with those in the lowest tertile. Notably, individuals with a high PRS and soft water had a 92% (95% CI: 71% to 115%) increased risk of RA compared with those with a low PRS and very hard water.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: The results suggest that exposure to soft water increases the risk of RA, especially in people who are genetically susceptible.\u003c/p\u003e","manuscriptTitle":"Joint Effects of Calcium Carbonate in Domestic Water and the Genetic Risk on Rheumatoid Arthritis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-23 17:11:28","doi":"10.21203/rs.3.rs-5871734/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"676dbf0b-b08c-4148-8271-0d06078f706b","owner":[],"postedDate":"January 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-29T12:53:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-23 17:11:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5871734","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5871734","identity":"rs-5871734","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}