25-hydroxyvitamin D3 mediates the associations between urinary heavy metals and non- melanoma skin cancer: Insights from a population-based study

25-hydroxyvitamin D3 mediates the associations between urinary heavy metals and non- melanoma skin cancer: Insights from a population-based study | 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 25-hydroxyvitamin D3 mediates the associations between urinary heavy metals and non- melanoma skin cancer: Insights from a population-based study Kexun Zhang, Ke Zhang, Hua Wang, Wei Zhang, Feng Hong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6504354/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 impact of combined heavy metal exposure on non-melanoma skin cancer (NMSC) in the general population remains poorly understood. Objective : To investigate the associations of metals with NMSC and examine the potential mediating effect of 25-hydroxyvitamin D3 [25(OH)D3]. Methods : We extracted data from the National Health and Nutrition Examination Survey, comprising 9835 participants with nine urinary metal concentrations, which were mercury (Hg), cesium (Cs), thallium (Tl), Ionium (Io), cobalt (Co), molybdenum (Mo), lead (Pb), barium (Ba), and arsenic (As). Multivariable logistic regression and weighted quantile sum regression were employed to estimate the associations of independent and combined metals with NMSC. Additionally, mediation analyses were conducted to explore the mediated effects of serum 25(OH)D3 on these associations. Results : Urinary levels of Hg, Io, Co, and mixed metals were positively correlated with NMSC. Serum 25(OH)D3 exhibited significant associations with NMSC, as did Hg, Cs, Tl, Io, Co, Mo, Pb, Ba, and combined metals with 25(OH)D3. Furthermore, the associations between single metals (primarily Hg and Io) and mixed metals with NMSC were partially mediated by 25(OH)D3. Limitations : Lack of validation in longitudinal study and independent populations. Conclusion : Metal exposure heightens NMSC risk, with a portion of this risk being mediated by 25(OH)D3. Heavy metals Non-melanoma skin cancer Combined exposure 25-hydroxyvitamin D3 Mediation analysis Figures Figure 1 Figure 2 Introduction Skin cancer is among the most prevalent carcinomas, affecting individuals across all geographic regions, races, and socioeconomic groups. 1 , 2 It is broadly classified into melanoma and non-melanoma skin cancer (NMSC), with basal-cell carcinoma (BCC) and squamous-cell carcinoma (SCC) being the primary histologic subtypes of NMSC. The incidence and prevalence of NMSC are increasing globally. From 1990 to 2019, the age-standardized incidence rates of non-melanoma skin cancer (NMSC) significantly increased across most of the five Socio-demographic Index (SDI) categories and 21 geographical regions. The largest increases were observed in East Asia for SCC and in high-income North America for BCC. In the United States alone, the age-standardized incidence rates of NMSC rose by 1.9 times, while prevalence increased by 1.2 times during the same period. 2 , 3 Prevention and early detection are crucial in alleviating the burden of NMSC, which can be achieved through a deeper understanding of risk factors. Heavy metals are ubiquitously present in various environmental media, such as air, soil, drinking water, and food, posing significant health concerns for human populations. 4 , 5 Widespread exposure to heavy metals has been linked to an increased risk of several types of cancers, such as lung, breast, bladder, kidney, liver, and skin cancers. 6 , 7 Previous study has shown that arsenic-related NMSC is prevalent in regions with high arsenic content in drinking water. 7 – 9 A consistent dose-response relationship has been observed between water arsenic levels and skin lesions, indicating increased risk even at low- to moderate-dose exposures. 9 Recent study has revealed that elevated blood levels of total and methylmercury correlate with increased prevalence of NMSC. 10 Furthermore, blood mercury concentration has been positively correlated with the risk of skin cancer in individuals who consume alcohol. In contrast, higher blood concentrations of manganese (Mn) have been associated with a decreased risk of skin cancer. 11 However, many of these studies have primarily focused on the detrimental effects of individual heavy metals. Heavy metals typically coexist in the environment, and their effects rely on their cooperation and interaction. Yet, the impact of combined exposure to heavy metals on NMSC in the general population remains poorly understood. The effect of 25-hydroxyvitamin D3 (25(OH)D3), a major metabolite of vitamin D3 in the body, on skin cancer is controversial. Emerging evidence suggests that serum 25-hydroxyvitamin D3 plays a protective role against skin melanoma. 12 , 13 However, findings from epidemiologic studies investigating the association between vitamin D and risk of skin cancer remain inconsistent. Some studies suggested an increased risk of melanoma and NMSC, while others have reported inverse or null associations. 14 – 19 This suggests a complex relationship where both deficient and excessively high levels of 25(OH)D3 might be associated with an increased risk of skin cancer, 14 , 16 involving considerations of sun exposure, vitamin D synthesis, and individual genetic factors. 14 , 20 Interestingly, higher levels of 25(OH)D 3 have been linked to enhanced absorption of toxic elements such as aluminum, cadmium, cobalt and lead as well as radioactive isotopes including cesium and radioactive strontium. 21 Conversely, heavy metals like lead, cadmium and mercury can have various impacts on the body's vitamin D metabolism and function. 22 Therefore, it is hypothesized that heavy metal exposure may elevate the risk of NMSC by interacting with 25(OH)D3. Here, we conducted a population-based study to investigate the associations between 9 urinary metals with NMSC risk based on the National Health and Nutrition Examination Survey (NHANES) 2007–2018. Additionally, we explored the mediated effects of 25(OH)D3 on the relationship between heavy metals and NMSC risk. Methods Study design and participants The NHANES, a nationally representative survey provided by the United States Centers for Disease Control and Prevention (CDC) website (http://www.cdc.gov/nchs/nhanes.htm), collected extensive health data. Ethical approval was obtained from the National Center for Health Statistics Research Ethics Review Board, with written consent from all participants. Trained NHANES interviewers conducted surveys and physical examinations. This study analyzed 59,842 participants from six survey periods (2007-2018), measuring urinary metal concentrations in 17,225 individuals. Exclusions were made for missing data on urinary heavy metals (N = 759), 25(OH)D3 (N = 2,049), and key covariates such as BMI (N = 113), education level (N = 264), smoking (N = 4,057), and urinary creatinine (N = 8). Participants with unknown skin cancer (N= 77) and melanoma (N = 63) were also excluded. Ultimately, 9,835 individuals were included for further analyses (Fig. 1). Diagnosis of NMSC in NHANES In the NHANES program, the diagnosis of NMSC was self-reported through a structured questionnaire survey. 10 A study found a 91% agreement rate between self-reports and clinical confirmations for NMSC, indicating the reliability of patient-reported data. 23 Specifically, participants were asked two related questions in the medical conditions section. First, they were asked, "Have you been told by a doctor or other health professional that you had cancer or other malignancy?" If yes, they were further required to select one or more cancers from 38 listed types, including melanoma, non-melanoma skin cancer, and unknown type. Determination of Metals and 25(OH)D3 Spot urine samples from the NHANES study were detected for nine heavy metals (mercury (Hg), cesium (Cs), thallium (Tl), Ionium (Io), cobalt (Co), molybdenum (Mo), lead (Pb), barium (Ba), and arsenic (As)) using inductively coupled plasma mass spectrometry (ICP-MS). Serum 25(OH)D3 levels were quantified using ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Values below the detection limit (LOD) were substituted using the LOD divided by the square root of two. 24 Covariates NHANES questionnaire data included key demographic details: age, sex, race/ethnicity, educational attainment, marital status, and family income to poverty ratio (PIR). Cigarette smoking was defined by whether participants had smoked at least 100 cigarettes in their lifetime. Body mass index (BMI, kg/m 2 ) was measured by health technicians. In our analysis, we adjusted for several covariates: age (20-39, 40-59, and ≥60), sex (male or female), race/ethnicity (Mexican American, non-Hispanic White, non-Hispanic Black, and others), educational level (less than high school, high school or equivalent, and above high school), BMI categories (underweight [<18.5 kg/m 2 ], normal weight [18.5-24.9 kg/m 2 ], overweight [25.0-29.9 kg/m 2 ], and obesity [≥30.0 kg/m 2 ]), smoking status (no, yes), and urine creatinine levels obtained from laboratory tests (μmol/L). Statistical analyses Urinary metal and 25(OH)D3 concentrations were ln-transformed for normal distribution. Metal levels were scaled to obtain per 1-SD ln-transformed increase or categorized into quartiles (Q1-Q4). Chi-square tests and Kruskal-Wallis tests were adopted to assess participants' demographic features by NMSC. To quantify their association with NMSC probability, multivariable logistic regression was employed to calculate odds ratios (ORs) and 95% confidence intervals (CIs). Multivariable linear regression was used to explore associations of single and combined heavy metals with 25(OH)D3. The trend test took quartiles as integer values 1-4. All analyses took into account complex sampling weights. Model 1 was adjusted for age, sex, and race/ethnicity, while Model 2 was further adjusted for educational level, smoking, BMI, and urine creatinine. Spearman correlation analysis was adopted to examine correlations among ln-transformed heavy metals. Weighted Quantile Sum (WQS) regression, known for its robustness in analyzing multiple exposures, was applied to investigate the combined effect of heavy metals on NMSC. 25 Each metal category received a weight that summed to 1, and further formed a WQS index. Both the continuous and quartering WQS index were used to examine their relationships of combined metals with NMSC and 25(OH)D3 concentrations. Mediation analysis was used to assess the mediating effect of 25(OH)D3 between heavy metals, WQS index, and NMSC, including indirect effect (IE), direct effect (DE), and mediation proportion. The mediation analyses relied on a nonparametric bootstrap procedure, setting a simulated Monte Carlo approach with 1000 runs. Random seeds were set before performing mediation analyses. Statistical analyses, conducted in R program, used packages like “gWQS”, “mediation”, “rcssci”, and “psych”. Level of Significance was set at P < 0.05 (R version 3.5.3). Sensitivity analysis We included marital status (married/living with partner, widowed/divorced/separated, or never married) and PIR (continuous) as covariates to test associations' robustness between metals, 25(OH)D3, and NMSC. Additionally, considering the potential nonlinear associations, we conducted restricted cubic splines (RCS) analysis, with optimizing knots based on the Akaike information criterion minimum. Results Characteristics of participants and metals distribution The demographic characteristics of our population were shown in Table 1. Among 9,835 participants, the mean age was 49 years, and 50.7% were female. 40.0% were non-Hispanic white, and 20.7% were non-Hispanic black. The median values of serum 25(OH)D3 concentrations were 58.10 nmol/L. The median of the nine urinary heavy metals ranged from 0.16 µg/L to 130.70 µg/L. A total of 149 participants reported a diagnosis of NMSC, 96% of whom were non-Hispanic white. Among these, 72.5% were aged 60 years or older, 64.4% had education beyond high school, and 57% were smokers. On the whole, age, ethnicity, education level, smoking, 25(OH)D3, Hg, Tl, Io, Pb, and Ba were statistically different between NMSC and other participants (Table 1). The Spearman’s correlation coefficients between any two of nine urinary heavy metals were showed in Fig. S1. The correlation coefficients between metals were all positive, with the highest between Cs and Tl. Other urinary metals showed varying degrees of correlation. Associations between heavy metal, 25(OH)D3 concentrations and NMSC The associations between metal concentrations, 25(OH)D3, and NMSC were examined using complex survey-weighted adjusted logistic regression models, considering covariates under two models (Table 2). The fourth quartile of Hg (OR 2.51, 95%CI 1.39 to 4.52), Io (OR 2.59, 95%CI 1.18 to 5.68), Co (OR 2.33, 95%CI 1.27 to 4.26), and 25(OH)D3 (OR 2.57, 95%CI 1.14 to 5.81) increased the odds of NMSC compared to the first quartile (all P for trend < 0.05). These associations were also observed in per 1-SD increase in ln-transformed metals and NMSC (all P < 0.05). No significant differences were noted with other single heavy metals. Regarding combined metals, mixed urinary metals were positively associated with NMSC in both models (all P for trend < 0.05) (Table 2 and Fig. S2). The associations of Hg, Io, Co, combined metals, and 25(OH)D3 with NMSC remained statistically significant after further adjusting for PIR and marriage status on basis of Model 2 (Table S1 and S2). Associations of single and combined metals levels with 25(OH)D3 The associations between metal concentrations and 25(OH)D3 levels were displayed in Table 3. In Model 2, compared to the first quartile, the highest quartile of Hg, Tl, Io, and Mo showed increased levels of 25(OH)D3 (all P for trend < 0.001). Conversely, Cs, Co, Pb, and Ba exhibited negative associations with 25(OH)D3 in Model 1 (all P for trend < 0.05). Additionally, mixed metals were positively associated with 25(OH)D3 levels (P for trend < 0.001) in Model 2. These associations were also observed with per 1-SD increase in ln-transformed metals and 25(OH)D3 (all P < 0.05). However, no significant associations were found between As concentrations and 25(OH)D3 levels. Mediation analyses Furthermore, mediation analyses were conducted to evaluate the potential mediation effects of 25(OH)D3 on the associations of metals with NMSC. 25(OH)D3 exhibited significant mediated effects on the associations of Hg, Io, and combined metals with NMSC, with the proportions of mediation being 7.056%, 13.879%, and 5.815%, respectively (all P < 0.05) (Fig. 2 and Table S3). Discussion The skin, being a primary target organ exposed to environmental pollutants like UV radiation, heavy metals, and volatile organic compounds, is subject to increased incidence of skin diseases. 4 , 26 There are limited data on heavy metals exposure and NMSC incidence. Previous studies have mostly focused on individual heavy metals' association with skin cancer. A recent cross-sectional study assessed associations between blood cadmium (Cd), mercury (Hg), lead (Pb), manganese (Mn), and selenium (Se) concentrations and skin cancer. 11 It found a positive association between blood Hg and skin cancer, consistent with other studies and our findings. 10 Additionally, higher blood Mn concentration was negatively associated with skin cancer in participants who consumed alcohol. 11 Although NHANES has employed the same technique to detect some heavy metals in blood and urine samples since 2003, our study included a wider range of toxic metals in urine. Ionium (Io), also known as thorium-230, is an isotope of thorium. It was discovered in the early 20th century and named for its propensity to readily form ions. 27 Although the association between Io and the risk of NMSC is seldom reported, there have been instances of basal cell carcinoma following the application of topical thorium X. 28 , 29 Our study contributes epidemiological evidence supporting the positive and significant associations between Io and NMSC risk. Most studies have focused on the increased cancer risk associated with occupational Co exposure, such as lung cancer and non-Hodgkin's lymphoma. 30 , 31 Epidemiological evidence related to skin cancer, however, is limited. Our study targeted the general population in the USA, encompassing representative racial/ethnic groups without restricting participants to specific occupations. We identified a significant association between Co exposure and NMSC risk. Additionally, experimental studies have shown that Co metal and metallic carbides interact to produce selective lung toxicity, indicating a potential association between mixed metals exposure and NMSC risk. 32 Heavy metals typically coexist in the environment, and their effects depend on their cooperation and interaction. In our study, the correlations between any two of nine urinary heavy metals between metals were all positive, with the highest correlation between Cs and Tl. Moreover, mixed heavy metals were positively associated with NMSC risk, suggesting that mixed metal exposure may contribute to NMSC progression. Interestingly, we observed significant mediated effects of 25(OH)D3 on the associations of Hg, Io, and combined metals with NMSC risk. The association between 25(OH)D3 and NMSC is acknowledged, yet not fully understood. Some studies suggest a potential link between sufficient vitamin D3 intake and reduced skin cancer incidence, attributing this to its role in skin cell regulation, growth, and inflammation suppression. 19 , 33 However, research results are inconsistent, with some studies finding a positive correlation between high concentrations of vitamin D3 and the incidence of skin cancer. 14 , 19 In our study, we observed a positive association between 25(OH)D3 levels and NMSC risk. Additionally, 25(OH)D3 exhibited significant mediated effects on the associations between heavy metals and NMSC risk. In this study, we employed various methods to explore metal-NMSC risk relationships in a large population. However, our research has limitations. Firstly, self-reported NMSC diagnoses may impact result credibility. Secondly, single-point metal measurements may not fully represent individual exposure. Thirdly, we only analyzed 9 metals, necessitating exploration of others linked to NMSC risk. Finally, residual confounders, unmeasured variables, and measurement errors, along with the wide analysis time span despite survey cycle adjustments, may bias our analyses. These findings should be cautiously interpreted, and further investigations are warranted. Conclusions In summary, our findings demonstrate a positive association between both single and mixed metals exposure and increased NMSC risk, notably influenced by Hg, Io, and Co. Moreover, we observed a relationship between metal exposure and 25(OH)D3 levels, with 25(OH)D3 also associated with NMSC risk. Mediation analyses revealed that the association between metals and NMSC risk may be mediated by 25(OH)D3. These results highlight risk factors for NMSC and suggest 25(OH)D3 as a potential underlying mechanism for the adverse effects of metals on NMSC. Declarations Funding sources: This study was supported by the National Natural Science Foundation of China (Grant Number: 82360458), and Natural Science Foundation of Guizhou Province (Grant Number: ZK2022449). Conflicts of Interest: None declared. Data availability statement: The data used to support the findings of this study were extracted from the National Health and Nutrition Examination Survey (NHANES), which is freely available at the United States Centers for Disease Control and Prevention (CDC) website (http://www.cdc.gov/nchs/nhanes.htm). Patient consent: Not applicable (Patients or the public were not involved in the design, data collection, analyses, or interpretation of this research.) Ethics statement: Ethical approval was obtained from the National Center for Health Statistics Research Ethics Review Board, with written consent from all participants. Contributor statement: Wei Zhang, Kexun Zhang: Study design, Manuscript writing. Kexun Zhang, Ke Zhang, Hua Wang: Data collection. Kexun Zhang, Wei Zhang, Ke Zhang, Hua Wang: Data analyses. All authors: The results interpretations. Wei Zhang, Feng Hong: Manuscript proofing. Acknowledgment : We extend our gratitude for the contributions of the National Health and Nutrition Examination Survey. References Kocarnik JM, Compton K, Dean FE, Fu W, Gaw BL, Harvey JD et al. Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life Years for 29 Cancer Groups From 2010 to 2019: A Systematic Analysis for the Global Burden of Disease Study 2019. JAMA Oncol 2022;8:420-44. Zhang W, Zeng W, Jiang A, He Z, Shen X, Dong X et al. Global, regional and national incidence, mortality and disability-adjusted life-years of skin cancers and trend analysis from 1990 to 2019: An analysis of the Global Burden of Disease Study 2019. Cancer Med 2021. Aggarwal P, Knabel P , Fleischer AB. 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Klasson M, Bryngelsson I-L, Pettersson C, Husby B, Arvidsson H , Westberg H. Occupational Exposure to Cobalt and Tungsten in the Swedish Hard Metal Industry: Air Concentrations of Particle Mass, Number, and Surface Area. Ann Occup Hyg 2016;60:684-99. Wang R, Zhang Y, Lan Q, Holford TR, Leaderer B, Hoar Zahm S et al. Occupational Exposure to Solvents and Risk of Non-Hodgkin Lymphoma in Connecticut Women. American Journal of Epidemiology 2008;169:176-85. Lison D. Human toxicity of cobalt-containing dust and experimental studies on the mechanism of interstitial lung disease (hard metal disease). Crit Rev Toxicol 1996;26:585-616. Feldman D, Krishnan AV, Swami S, Giovannucci E , Feldman BJ. The role of vitamin D in reducing cancer risk and progression. Nat Rev Cancer 2014;14:342-57. Tables Table 1. Basic features of the study population Characteristics Overall Non-skin cancer Skin cancer P value N=9835 N=9686 N=149 Age, years, N (%) <0.001 20-39 3364 (34.2) 3356 (34.6) 8 (5.4) 40-59 3235 (32.9) 3202 (33.1) 33 (22.1) ≥60 3236 (32.9) 3128 (32.3) 108 (72.5) Gender, N (%) 0.099 Male 4852 (49.3) 4768 (49.2) 84 (56.4) Female 4983 (50.7) 4918 (50.8) 65 (43.6) Ethnicity/race, N (%) <0.001 Mexican American 1522 (15.5) 1520 (15.7) 2 (1.3) Non-Hispanic White 3930 (40.0) 3787 (39.1) 143 (96.0) Non-Hispanic Black 2036 (20.7) 2036 (21.0) 0 (0.0) Others 2347 (23.9) 2343 (24.2) 4 (2.7) Education level, N (%) 0.004 Less than high school 2424 (24.6) 2403 (24.8) 21 (14.1) High school or equivalent 2243 (22.8) 2211 (22.8) 32 (21.5) More than high school 5168 (52.5) 5072 (52.4) 96 (64.4) Smoking, N (%) 0.002 No 5513 (56.1) 5449 (56.3) 64 (43.0) Yes 4322 (43.9) 4237 (43.7) 85 (57.0) BMI, N (%) 0.492 Underweight 161 (1.6) 159 (1.6) 2 (1.3) Normal 2655 (27.0) 2622 (27.1) 33 (22.1) Overweight 3237 (32.9) 3181 (32.8) 56 (37.6) Obesity 3782 (38.5) 3724 (38.4) 58 (38.9) Urine creatinine, umol/L, median (IQR) 9282.0(5215.6,14320.8) 9282.0 (5215.6, 14320.8) 7956.0(5215.6,12287.6) 0.061 25-hydroxyvitamin D3, nmol/L, median (IQR) 58.10 (40.60, 76.60) 57.80 (40.47, 76.00) 80.10 (62.00, 93.00) <0.001 Hg, ug/L, median (IQR) 0.28 (0.09, 0.63) 0.28 (0.09, 0.62) 0.41 (0.14, 0.86) 0.008 Cs, ug/L, median (IQR) 4.40 (2.65, 6.56) 4.40 (2.65, 6.56) 4.52 (2.98, 7.16) 0.257 Tl, ug/L, median (IQR) 0.16 (0.09, 0.24) 0.16 (0.09, 0.24) 0.14 (0.09, 0.20) 0.041 Io, ug/L, median (IQR) 130.70 (73.80, 233.05) 130.10 (73.40, 231.88) 190.80 (109.60, 315.20) <0.001 Co, ug/L, median (IQR) 0.36 (0.22, 0.58) 0.36 (0.22, 0.58) 0.37 (0.25, 0.63) 0.185 Mo, ug/L, median (IQR) 39.50 (20.50, 68.00) 39.54 (20.50, 68.19) 38.40 (21.45, 61.60) 0.619 Pb, ug/L, median (IQR) 0.41 (0.22, 0.73) 0.41 (0.22, 0.73) 0.46 (0.30, 0.86) 0.014 Ba, ug/L, median (IQR) 1.10 (0.54, 2.15) 1.09 (0.54, 2.15) 1.40 (0.69, 2.53) 0.004 As, ug/L, median (IQR) 7.51 (3.76, 16.14) 7.50 (3.76, 16.17) 7.89 (4.15, 14.49) 0.956 Table 2. Associations of single, combined metals, and 25(OH)D3 levels with skin cancer Heavy metals Model Continuous OR (95% CI) Quartiles of single, combined metals, and 25(OH)D3 [OR (95% CI)] P for trend Q1 Q2 Q3 Q4 Hg Model 1 1.26 (1.06, 1.50) 1.00 (Ref.) 1.38 (0.72, 2.65) 1.53 (0.76, 3.10) 2.28 (1.36, 3.81) 0.002 Model 2 1.30 (1.05, 1.62) 1.00 (Ref.) 1.42 (0.72, 2.78) 1.64 (0.76, 3.54) 2.51 (1.39, 4.52) 0.003 Cs Model 1 1.13 (0.87, 1.46) 1.00 (Ref.) 1.38 (0.70, 2.73) 0.96 (0.51, 1.82) 1.36 (0.73, 2.53) 0.537 Model 2 1.27 (0.91, 1.78) 1.00 (Ref.) 1.46 (0.70, 3.06) 1.09 (0.54, 2.18) 1.63 (0.74, 3.59) 0.303 Tl Model 1 1.10 (0.90, 1.36) 1.00 (Ref.) 1.10 (0.65, 1.85) 1.41 (0.83, 2.38) 1.13 (0.59, 2.19) 0.405 Model 2 1.19 (0.89, 1.58) 1.00 (Ref.) 1.17 (0.69, 1.98) 1.58 (0.91, 2.75) 1.35 (0.62, 2.95) 0.252 Io Model 1 1.20 (1.03, 1.40) 1.00 (Ref.) 1.32 (0.67, 2.62) 1.63 (0.86, 3.10) 1.86 (1.00, 3.45) 0.049 Model 2 1.30 (1.09, 1.55) 1.00 (Ref.) 1.56 (0.75, 3.25) 2.06 (0.99, 4.29) 2.59 (1.18, 5.68) 0.018 Co Model 1 1.21 (0.99, 1.48) 1.00 (Ref.) 1.64 (0.93, 2.89) 1.61 (0.85, 3.04) 1.68 (0.99, 2.83) 0.090 Model 2 1.34 (1.06, 1.68) 1.00 (Ref.) 1.91 (1.06, 3.46) 2.05 (1.06, 3.96) 2.33 (1.27, 4.26) 0.011 Mo Model 1 1.04 (0.89, 1.23) 1.00 (Ref.) 1.37 (0.82, 2.31) 1.57 (0.94, 2.63) 0.79 (0.44, 1.42) 0.808 Model 2 1.12 (0.89, 1.41) 1.00 (Ref.) 1.42 (0.80, 2.51) 1.70 (0.96, 3.01) 0.90 (0.43, 1.87) 0.847 Pb Model 1 1.09 (0.90, 1.33) 1.00 (Ref.) 1.55 (0.87, 2.74) 1.62 (0.88, 3.00) 1.29 (0.72, 2.30) 0.406 Model 2 1.20 (0.94, 1.52) 1.00 (Ref.) 1.67 (0.95, 2.96) 1.93 (1.02, 3.66) 1.62 (0.88, 3.00) 0.132 Ba Model 1 1.08 (0.87, 1.34) 1.00 (Ref.) 1.30 (0.67, 2.52) 1.79 (0.88, 3.66) 1.53 (0.81, 2.89) 0.159 Model 2 1.11 (0.89, 1.40) 1.00 (Ref.) 1.33 (0.69, 2.57) 1.94 (0.93, 4.05) 1.75 (0.91, 3.36) 0.080 As Model 1 1.17 (0.93, 1.48) 1.00 (Ref.) 1.52 (0.89, 2.60) 1.68 (0.97, 2.92) 1.47 (0.73, 2.97) 0.202 Model 2 1.21 (0.94, 1.55) 1.00 (Ref.) 1.70 (0.96, 3.02) 1.95 (1.06, 3.57) 1.66 (0.77, 3.55) 0.152 Combined metals Model 1 1.31 (1.01, 1.70) 1.00 (Ref.) 1.95 (1.04, 3.63) 2.55 (1.34, 4.85) 1.88 (0.97, 3.65) 0.042 Model 2 2.06 (1.36, 3.11) 1.00 (Ref.) 2.47 (1.26, 4.83) 4.22 (2.00, 8.90) 4.04 (1.67, 9.80) 0.001 25(OH)D3 Model 1 1.95 (1.26, 3.04) 1.00 (Ref.) 0.99 (0.36, 2.72) 2.21 (0.95, 5.10) 2.53 (1.11, 5.75) 0.003 Model 2 1.94 (1.23, 3.04) 1.00 (Ref.) 1.00 (0.37, 2.74) 2.22 (0.97, 5.09) 2.57 (1.14, 5.81) 0.003 Note: Model 1 was adjusted for age, sex, and race/ethnicity. Model 2 was additionally adjusted for educational level, smoking, BMI, and urine creatinine. Table 3. Associations of single and combined metals levels with 25(OH)D3 Heavy metals Model Continuous β (95% CI) Quartiles of single and combined metals [β (95% CI)] P for trend Q1 Q2 Q3 Q4 Hg Model 1 0.014 (0.002, 0.025) 0 (Ref.) 0.006 (-0.026, 0.038) 0.009 (-0.024, 0.042) 0.034 (0.001, 0.068) 0.054 Model 2 0.022 (0.010, 0.035) 0 (Ref.) 0.015 (-0.016, 0.045) 0.022 (-0.012, 0.056) 0.053 (0.018, 0.088) 0.005 Cs Model 1 -0.02(-0.032, -0.008) 0 (Ref.) -0.02 (-0.052, 0.011) -0.042 (-0.074, -0.01) -0.047 (-0.078, -0.016) 0.002 Model 2 0.003(-0.013, 0.019) 0 (Ref.) 0.008 (-0.023, 0.039) 0.007 (-0.030, 0.044) 0.016 (-0.024, 0.056) 0.493 Tl Model 1 -0.009(-0.021,0.003) 0 (Ref.) -0.017 (-0.046, 0.013) -0.03 (-0.06, -0.001) -0.021 (-0.055, 0.012) 0.151 Model 2 0.020 (0.005, 0.035) 0 (Ref.) 0.010 (-0.020, 0.041) 0.023 (-0.010, 0.057) 0.053 (0.010, 0.096) 0.018 Io Model 1 0.011(-0.001, 0.023) 0 (Ref.) -0.007 (-0.041, 0.027) 0.013 (-0.02, 0.045) 0.035 (0.004, 0.067) 0.013 Model 2 0.042 (0.028, 0.056 ) 0 (Ref.) 0.036 (-0.001, 0.072) 0.080 (0.040, 0.120) 0.117 (0.078, 0.156) <0.001 Co Model 1 -0.021(-0.033,0.008) 0 (Ref.) -0.014 (-0.044, 0.016) -0.035 (-0.064, -0.006) -0.053 (-0.084, -0.022) 0.001 Model 2 0.001(-0.015, 0.017) 0 (Ref.) 0.014 (-0.017, 0.045) 0.008 (-0.025, 0.042) 0.008 (-0.031, 0.046) 0.806 Mo Model 1 -0.002(-0.015,0.010) 0 (Ref.) -0.031 (-0.064, 0.003) -0.02 (-0.055, 0.016) -0.007 (-0.042, 0.028) 0.793 Model 2 0.030 (0.014, 0.046) 0 (Ref.) 0.003 (-0.030, 0.035) 0.044 (0.003, 0.085) 0.074 (0.028, 0.120) 0.002 Pb Model 1 -0.034(-0.046,0.022) 0 (Ref.) -0.052 (-0.084, -0.02) -0.057 (-0.087, -0.027) -0.08 (-0.114, -0.046) <0.001 Model 2 - 0.024(-0.041,0.008) 0 (Ref.) -0.036 (-0.068, -0.004) -0.03 (-0.066, 0.007) -0.046 (-0.09, -0.003) 0.063 Ba Model 1 -0.013(-0.024,0.002) 0 (Ref.) 0.004 (-0.035, 0.044) -0.003 (-0.036, 0.029) -0.033 (-0.065, -0.001) 0.034 Model 2 0.005(-0.008, 0.018) 0 (Ref.) 0.016 (-0.024, 0.056) 0.023 (-0.011, 0.057) 0.012 (-0.025, 0.050) 0.485 As Model 1 0.001(-0.011, 0.013) 0 (Ref.) -0.024 (-0.058, 0.009) -0.006 (-0.037, 0.025) -0.001 (-0.034, 0.031) 0.867 Model 2 0.012(-0.001, 0.025) 0 (Ref.) 0.001 (-0.030, 0.033) 0.029 (-0.005, 0.062) 0.025 (-0.009, 0.059) 0.068 Combined metals Model 1 -0.011(-0.026,0.003) 0 (Ref.) -0.038 (-0.077, 0.000) -0.051 (-0.084, -0.018) -0.018 (-0.051, 0.014) 0.204 Model 2 0.043 (0.018, 0.068) 0 (Ref.) 0.003 (-0.036, 0.041) 0.023 (-0.015, 0.060) 0.092 (0.043, 0.14) <0.001 Note: Model 1 was adjusted for age, sex, and race/ethnicity. Model 2 was additionally adjusted for educational level, smoking, BMI, and urine creatinine. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6504354","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":451969762,"identity":"b785d17b-3fff-41f5-bb25-5c8637e65a05","order_by":0,"name":"Kexun Zhang","email":"","orcid":"","institution":"Kunshan Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Kexun","middleName":"","lastName":"Zhang","suffix":""},{"id":451969764,"identity":"813793c5-d5e6-47eb-abf0-df06f3b8167a","order_by":1,"name":"Ke Zhang","email":"","orcid":"","institution":"Affiliated Hospital of Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Zhang","suffix":""},{"id":451969766,"identity":"48c11c12-833c-4788-86bb-2c82bb58ee2e","order_by":2,"name":"Hua Wang","email":"","orcid":"","institution":"Kunshan Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Hua","middleName":"","lastName":"Wang","suffix":""},{"id":451969768,"identity":"3e7d1965-e6f3-4bf7-b2e9-ba3f5f692866","order_by":3,"name":"Wei Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYFACxgYQkoNw2EjQYkyKFoiuxAaitRgcb27+8HPHnfT5YYcfMHwoO8zAP7uBgJYzBxsMe888y914O82Acca5wwwSdw7g12J2I7EhgbftcO7G2TkMzEAGg4FEAgEt9x82HPzbdjjdEKTlL1FabjA2NgMNT5CXBmphJEaL/ZnEZmbZtsOGG6TTDA72nEvnkbhBQItk+/HHH9+2HZaXn5388MGPMms5/hkEtMCBwQEGBiBi4CFSPRDINxCvdhSMglEwCkYYAADaqklLyye5AgAAAABJRU5ErkJggg==","orcid":"","institution":"Affiliated Hospital of Guizhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Zhang","suffix":""},{"id":451969769,"identity":"da18d742-43bf-4a8e-b0b4-ea5abf622593","order_by":4,"name":"Feng Hong","email":"","orcid":"","institution":"Ministry of Education, Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Hong","suffix":""}],"badges":[],"createdAt":"2025-04-22 12:38:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6504354/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6504354/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82275090,"identity":"989fdb44-7ff2-4ee6-9aa3-2b2bc5f5fd24","added_by":"auto","created_at":"2025-05-08 14:37:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":493961,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of participants selection (2007-2018). NHANES, National Health and Nutrition Examination Survey; BMI, body mass index.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6504354/v1/8be1f2926701d2402db3a8a2.png"},{"id":82275091,"identity":"fe824c91-eed5-4f13-8b49-dcb7ad98cdaa","added_by":"auto","created_at":"2025-05-08 14:37:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5208288,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated proportion of the association between single or mixed metals and non-melanoma skin cancer mediated by 25(OH)D3.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6504354/v1/07a070778a67376d73b12ed2.png"},{"id":107870139,"identity":"5f2bdd35-f890-41d8-91eb-250b3e876bd0","added_by":"auto","created_at":"2026-04-27 07:38:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2483700,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6504354/v1/f8a9af79-eb95-463f-8617-9dd6d6d74ed0.pdf"},{"id":82273516,"identity":"409f1a2d-f3b5-4afa-8ab1-108c39d0be8d","added_by":"auto","created_at":"2025-05-08 14:29:18","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":308994,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6504354/v1/a876ba317fa270bcf615f413.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"25-hydroxyvitamin D3 mediates the associations between urinary heavy metals and non- melanoma skin cancer: Insights from a population-based study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSkin cancer is among the most prevalent carcinomas, affecting individuals across all geographic regions, races, and socioeconomic groups.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e It is broadly classified into melanoma and non-melanoma skin cancer (NMSC), with basal-cell carcinoma (BCC) and squamous-cell carcinoma (SCC) being the primary histologic subtypes of NMSC. The incidence and prevalence of NMSC are increasing globally. From 1990 to 2019, the age-standardized incidence rates of non-melanoma skin cancer (NMSC) significantly increased across most of the five Socio-demographic Index (SDI) categories and 21 geographical regions. The largest increases were observed in East Asia for SCC and in high-income North America for BCC. In the United States alone, the age-standardized incidence rates of NMSC rose by 1.9 times, while prevalence increased by 1.2 times during the same period.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Prevention and early detection are crucial in alleviating the burden of NMSC, which can be achieved through a deeper understanding of risk factors.\u003c/p\u003e \u003cp\u003eHeavy metals are ubiquitously present in various environmental media, such as air, soil, drinking water, and food, posing significant health concerns for human populations.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Widespread exposure to heavy metals has been linked to an increased risk of several types of cancers, such as lung, breast, bladder, kidney, liver, and skin cancers.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Previous study has shown that arsenic-related NMSC is prevalent in regions with high arsenic content in drinking water.\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e A consistent dose-response relationship has been observed between water arsenic levels and skin lesions, indicating increased risk even at low- to moderate-dose exposures.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Recent study has revealed that elevated blood levels of total and methylmercury correlate with increased prevalence of NMSC.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Furthermore, blood mercury concentration has been positively correlated with the risk of skin cancer in individuals who consume alcohol. In contrast, higher blood concentrations of manganese (Mn) have been associated with a decreased risk of skin cancer.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e However, many of these studies have primarily focused on the detrimental effects of individual heavy metals. Heavy metals typically coexist in the environment, and their effects rely on their cooperation and interaction. Yet, the impact of combined exposure to heavy metals on NMSC in the general population remains poorly understood.\u003c/p\u003e \u003cp\u003eThe effect of 25-hydroxyvitamin D3 (25(OH)D3), a major metabolite of vitamin D3 in the body, on skin cancer is controversial. Emerging evidence suggests that serum 25-hydroxyvitamin D3 plays a protective role against skin melanoma.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e However, findings from epidemiologic studies investigating the association between vitamin D and risk of skin cancer remain inconsistent. Some studies suggested an increased risk of melanoma and NMSC, while others have reported inverse or null associations. \u003csup\u003e\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e This suggests a complex relationship where both deficient and excessively high levels of 25(OH)D3 might be associated with an increased risk of skin cancer,\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e involving considerations of sun exposure, vitamin D synthesis, and individual genetic factors.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Interestingly, higher levels of 25(OH)D\u003csub\u003e3\u003c/sub\u003e have been linked to enhanced absorption of toxic elements such as aluminum, cadmium, cobalt and lead as well as radioactive isotopes including cesium and radioactive strontium.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Conversely, heavy metals like lead, cadmium and mercury can have various impacts on the body's vitamin D metabolism and function.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Therefore, it is hypothesized that heavy metal exposure may elevate the risk of NMSC by interacting with 25(OH)D3.\u003c/p\u003e \u003cp\u003eHere, we conducted a population-based study to investigate the associations between 9 urinary metals with NMSC risk based on the National Health and Nutrition Examination Survey (NHANES) 2007\u0026ndash;2018. Additionally, we explored the mediated effects of 25(OH)D3 on the relationship between heavy metals and NMSC risk.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and participants\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES, a nationally representative survey provided by the United States Centers for Disease Control and Prevention (CDC) website (http://www.cdc.gov/nchs/nhanes.htm), collected extensive health data. Ethical approval was obtained from the National Center for Health Statistics Research Ethics Review Board, with written consent from all participants. Trained NHANES interviewers conducted surveys and physical examinations. This study analyzed 59,842 participants from six survey periods (2007-2018), measuring urinary metal concentrations in 17,225 individuals. Exclusions were made for missing data on urinary heavy metals (N = 759), 25(OH)D3 (N = 2,049), and key covariates such as BMI (N = 113), education level (N = 264), smoking (N = 4,057), and urinary creatinine (N = 8). Participants with unknown skin cancer (N= 77) and melanoma (N = 63) were also excluded. Ultimately, 9,835 individuals were included for further analyses (Fig. 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnosis of NMSC in NHANES\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the NHANES program, the \u0026nbsp;diagnosis of NMSC was self-reported through a structured questionnaire survey.\u003csup\u003e10\u003c/sup\u003e A study found a 91% agreement rate between self-reports and clinical confirmations for NMSC, indicating the reliability of patient-reported data.\u003csup\u003e23\u003c/sup\u003e Specifically, participants were asked two related questions in the medical conditions section. First, they were asked, \u0026quot;Have you been told by a doctor or other health professional that you had cancer or other malignancy?\u0026quot; If yes, they were further required to select one or more cancers from 38 listed types, including melanoma, non-melanoma skin cancer, and unknown type.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetermination of Metals and 25(OH)D3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpot urine samples from the NHANES study were detected for nine heavy metals (mercury (Hg), cesium (Cs), thallium (Tl), Ionium (Io), cobalt (Co), molybdenum (Mo), lead (Pb), barium (Ba), and arsenic (As)) using inductively coupled plasma mass spectrometry (ICP-MS). Serum 25(OH)D3 levels were quantified using ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Values below the detection limit (LOD) were substituted using the LOD divided by the square root of two.\u003csup\u003e24\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNHANES questionnaire data included key demographic details: age, sex, race/ethnicity, educational attainment, marital status, and family income to poverty ratio (PIR). Cigarette smoking was defined by whether participants had smoked at least 100 cigarettes in their lifetime. Body mass index (BMI, kg/m\u003csup\u003e2\u003c/sup\u003e) was measured by health technicians. In our analysis, we adjusted for several covariates: age (20-39, 40-59, and\u0026nbsp;\u0026ge;60), sex (male or female), race/ethnicity (Mexican American, non-Hispanic White, non-Hispanic Black, and others), educational level (less than high school, high school or equivalent, and above high school), BMI categories (underweight [\u0026lt;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e], normal weight [18.5-24.9 kg/m\u003csup\u003e2\u003c/sup\u003e], overweight [25.0-29.9 kg/m\u003csup\u003e2\u003c/sup\u003e], and obesity [\u0026ge;30.0 kg/m\u003csup\u003e2\u003c/sup\u003e]), smoking status (no, yes), and urine creatinine levels obtained from laboratory tests (\u0026mu;mol/L).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUrinary metal and 25(OH)D3 concentrations were ln-transformed for normal distribution. Metal levels were scaled to obtain per 1-SD ln-transformed increase or categorized into quartiles (Q1-Q4). Chi-square tests and Kruskal-Wallis tests were adopted to assess participants\u0026apos; demographic features by NMSC.\u003c/p\u003e\n\u003cp\u003eTo quantify their association with NMSC probability, multivariable logistic regression was employed to calculate odds ratios (ORs) and 95% confidence intervals (CIs). Multivariable linear regression was used to explore associations of single and combined heavy metals with 25(OH)D3. The trend test took quartiles as integer values 1-4. All analyses took into account complex sampling weights. Model 1 was adjusted for age, sex, and race/ethnicity, while Model 2 was further adjusted for educational level, smoking, BMI, and urine creatinine. Spearman correlation analysis was adopted to examine correlations among ln-transformed heavy metals.\u003c/p\u003e\n\u003cp\u003eWeighted Quantile Sum (WQS) regression, known for its robustness in analyzing multiple exposures, was applied to investigate the combined effect of heavy metals on NMSC.\u003csup\u003e25\u003c/sup\u003e Each metal category received a weight that summed to 1, and further formed a WQS index. Both the continuous and quartering WQS index were used to examine their relationships of combined metals with NMSC and 25(OH)D3 concentrations.\u003c/p\u003e\n\u003cp\u003eMediation analysis was used to assess the mediating effect of 25(OH)D3 between heavy metals, WQS index, and NMSC, including indirect effect (IE), direct effect (DE), and mediation proportion. The mediation analyses relied on a nonparametric bootstrap procedure, setting a simulated Monte Carlo approach with 1000 runs. Random seeds were set before performing mediation analyses. Statistical analyses, conducted in R program, used packages like \u0026ldquo;gWQS\u0026rdquo;, \u0026ldquo;mediation\u0026rdquo;, \u0026ldquo;rcssci\u0026rdquo;, and \u0026ldquo;psych\u0026rdquo;. \u0026nbsp;Level of Significance was set at P \u0026lt; 0.05 (R version 3.5.3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;We included marital status (married/living with partner, widowed/divorced/separated, or never married) and PIR (continuous) as covariates to test associations\u0026apos; robustness between metals, 25(OH)D3, and NMSC. Additionally, considering the potential nonlinear associations, we conducted restricted cubic splines (RCS) analysis, with optimizing knots based on the Akaike information criterion minimum.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCharacteristics of\u0026nbsp;participants and\u0026nbsp;metals distribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe demographic characteristics of our population were shown in Table 1. Among 9,835 participants, the mean age was 49 years, and 50.7% were female. 40.0% were non-Hispanic white, and 20.7% were non-Hispanic black. The median values of serum 25(OH)D3 concentrations were 58.10 nmol/L. The median of the nine urinary heavy metals ranged from 0.16 \u0026micro;g/L to 130.70 \u0026micro;g/L. A total of 149 participants reported a diagnosis of NMSC, 96% of whom were non-Hispanic white. Among these, 72.5% were aged 60 years or older, 64.4% had education beyond high school, and 57% were smokers. On the whole, age, ethnicity, education level, smoking, 25(OH)D3, Hg, Tl, Io, Pb, and Ba were statistically different between NMSC and other participants (Table 1). The Spearman\u0026rsquo;s correlation coefficients between any two of nine urinary heavy metals were showed in Fig. S1. The correlation coefficients between metals were all positive, with the highest between Cs and Tl. Other urinary metals showed varying degrees of correlation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociations between heavy metal, 25(OH)D3 concentrations and NMSC\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe associations between metal concentrations, 25(OH)D3, and NMSC were examined using complex survey-weighted adjusted logistic regression models, considering covariates under two models (Table 2). The fourth quartile of Hg (OR 2.51, 95%CI 1.39 to 4.52), Io (OR 2.59, 95%CI 1.18 to 5.68), Co (OR 2.33, 95%CI 1.27 to 4.26), and 25(OH)D3 (OR 2.57, 95%CI 1.14 to 5.81) increased the odds of NMSC compared to the first quartile (all P for trend \u0026lt; 0.05). These associations were also observed in per 1-SD increase in ln-transformed metals and NMSC (all P \u0026lt; 0.05). No significant differences were noted with other single heavy metals. Regarding combined metals, mixed urinary metals were positively associated with NMSC in both models (all P for trend \u0026lt; 0.05) (Table 2 and Fig. S2). The associations of Hg, Io, Co, combined metals, and 25(OH)D3 with NMSC remained statistically significant after further adjusting for PIR and marriage status on basis of Model 2 (Table S1 and S2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociations of single and combined metals levels with 25(OH)D3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe associations between metal concentrations and 25(OH)D3 levels were displayed in Table 3. In Model 2, compared to the first quartile, the highest quartile of Hg, Tl, Io, and Mo showed increased levels of 25(OH)D3 (all P for trend \u0026lt; 0.001). Conversely, Cs, Co, Pb, and Ba exhibited negative associations with 25(OH)D3 in Model 1 (all P for trend \u0026lt; 0.05). Additionally, mixed metals were positively associated with 25(OH)D3 levels (P for trend \u0026lt; 0.001) in Model 2. These associations were also observed with per 1-SD increase in ln-transformed metals and 25(OH)D3 (all P \u0026lt; 0.05). However, no significant associations were found between As concentrations and 25(OH)D3 levels.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMediation analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurthermore, mediation analyses were conducted to evaluate the potential mediation effects of 25(OH)D3 on the associations of metals with NMSC. 25(OH)D3 exhibited significant mediated effects on the associations of Hg, Io, and combined metals with NMSC, with the proportions of mediation being 7.056%, 13.879%, and 5.815%, respectively (all P \u0026lt; 0.05) (Fig. 2 and Table S3).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe skin, being a primary target organ exposed to environmental pollutants like UV radiation, heavy metals, and volatile organic compounds, is subject to increased incidence of skin diseases.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e There are limited data on heavy metals exposure and NMSC incidence. Previous studies have mostly focused on individual heavy metals' association with skin cancer. A recent cross-sectional study assessed associations between blood cadmium (Cd), mercury (Hg), lead (Pb), manganese (Mn), and selenium (Se) concentrations and skin cancer.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e It found a positive association between blood Hg and skin cancer, consistent with other studies and our findings.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Additionally, higher blood Mn concentration was negatively associated with skin cancer in participants who consumed alcohol.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Although NHANES has employed the same technique to detect some heavy metals in blood and urine samples since 2003, our study included a wider range of toxic metals in urine. Ionium (Io), also known as thorium-230, is an isotope of thorium. It was discovered in the early 20th century and named for its propensity to readily form ions.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Although the association between Io and the risk of NMSC is seldom reported, there have been instances of basal cell carcinoma following the application of topical thorium X.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Our study contributes epidemiological evidence supporting the positive and significant associations between Io and NMSC risk. Most studies have focused on the increased cancer risk associated with occupational Co exposure, such as lung cancer and non-Hodgkin's lymphoma.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Epidemiological evidence related to skin cancer, however, is limited. Our study targeted the general population in the USA, encompassing representative racial/ethnic groups without restricting participants to specific occupations. We identified a significant association between Co exposure and NMSC risk. Additionally, experimental studies have shown that Co metal and metallic carbides interact to produce selective lung toxicity, indicating a potential association between mixed metals exposure and NMSC risk.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eHeavy metals typically coexist in the environment, and their effects depend on their cooperation and interaction. In our study, the correlations between any two of nine urinary heavy metals between metals were all positive, with the highest correlation between Cs and Tl. Moreover, mixed heavy metals were positively associated with NMSC risk, suggesting that mixed metal exposure may contribute to NMSC progression. Interestingly, we observed significant mediated effects of 25(OH)D3 on the associations of Hg, Io, and combined metals with NMSC risk. The association between 25(OH)D3 and NMSC is acknowledged, yet not fully understood. Some studies suggest a potential link between sufficient vitamin D3 intake and reduced skin cancer incidence, attributing this to its role in skin cell regulation, growth, and inflammation suppression. \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e However, research results are inconsistent, with some studies finding a positive correlation between high concentrations of vitamin D3 and the incidence of skin cancer.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e In our study, we observed a positive association between 25(OH)D3 levels and NMSC risk. Additionally, 25(OH)D3 exhibited significant mediated effects on the associations between heavy metals and NMSC risk.\u003c/p\u003e \u003cp\u003eIn this study, we employed various methods to explore metal-NMSC risk relationships in a large population. However, our research has limitations. Firstly, self-reported NMSC diagnoses may impact result credibility. Secondly, single-point metal measurements may not fully represent individual exposure. Thirdly, we only analyzed 9 metals, necessitating exploration of others linked to NMSC risk. Finally, residual confounders, unmeasured variables, and measurement errors, along with the wide analysis time span despite survey cycle adjustments, may bias our analyses. These findings should be cautiously interpreted, and further investigations are warranted.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, our findings demonstrate a positive association between both single and mixed metals exposure and increased NMSC risk, notably influenced by Hg, Io, and Co. Moreover, we observed a relationship between metal exposure and 25(OH)D3 levels, with 25(OH)D3 also associated with NMSC risk. Mediation analyses revealed that the association between metals and NMSC risk may be mediated by 25(OH)D3. These results highlight risk factors for NMSC and suggest 25(OH)D3 as a potential underlying mechanism for the adverse effects of metals on NMSC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding sources: \u003c/strong\u003eThis study was supported by the National Natural Science Foundation of China (Grant Number: 82360458), and Natural Science Foundation of Guizhou Province (Grant Number: ZK2022449).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e None declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u003c/strong\u003e The data used to support the findings of this study were extracted from the National Health and Nutrition Examination Survey (NHANES), which is freely available at the United States Centers for Disease Control and Prevention (CDC) website (http://www.cdc.gov/nchs/nhanes.htm).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent:\u003c/strong\u003e Not applicable (Patients or the public were not involved in the design, data collection, analyses, or interpretation of this research.)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement:\u003c/strong\u003e Ethical approval was obtained from the National Center for Health Statistics Research Ethics Review Board, with written consent from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributor statement:\u003c/strong\u003e Wei Zhang, Kexun Zhang: Study design, Manuscript writing. Kexun Zhang, Ke Zhang, Hua Wang: Data collection. Kexun Zhang, Wei Zhang, Ke Zhang, Hua Wang: Data analyses. All authors: The results interpretations. Wei Zhang, Feng Hong: Manuscript proofing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e: We extend our gratitude for the contributions of the National Health and Nutrition Examination Survey.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKocarnik JM, Compton K, Dean FE, Fu W, Gaw BL, Harvey JD et al. Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life Years for 29 Cancer Groups From 2010 to 2019: A Systematic Analysis for the Global Burden of Disease Study 2019. JAMA Oncol 2022;8:420-44.\u003c/li\u003e\n\u003cli\u003eZhang W, Zeng W, Jiang A, He Z, Shen X, Dong X et al. 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Biological aging mediates the associations between urinary metals and osteoarthritis among U.S. adults. BMC Med 2022;20:207.\u003c/li\u003e\n\u003cli\u003eCarrico C, Gennings C, Wheeler DC , Factor-Litvak P. Characterization of Weighted Quantile Sum Regression for Highly Correlated Data in a Risk Analysis Setting. J Agric Biol Environ Stat 2015;20:100-20.\u003c/li\u003e\n\u003cli\u003eBaudouin C, Charveron M, Tarroux R , Gall Y. Environmental pollutants and skin cancer. Cell Biol Toxicol 2002;18:341-8.\u003c/li\u003e\n\u003cli\u003eMiyake Y , Sugimura Y. Ionium-Thorium Chronology of Deep-Sea Sediments of the Western North Pacific Ocean. Science 1961;133:1823-4.\u003c/li\u003e\n\u003cli\u003eScerri L , Navaratnam AE. Basal cell carcinoma presenting as a delayed complication of thorium X used for treating a congenital hemangioma. Journal of the American Academy of Dermatology 1994;31:796-7.\u003c/li\u003e\n\u003cli\u003eRajaratnam R, Balasubramaniam P , Marsden JR. Thorium X and skin cancer: still a problem in the 21st century. Clin Exp Dermatol 2007;32:125-6.\u003c/li\u003e\n\u003cli\u003eKlasson M, Bryngelsson I-L, Pettersson C, Husby B, Arvidsson H , Westberg H. Occupational Exposure to Cobalt and Tungsten in the Swedish Hard Metal Industry: Air Concentrations of Particle Mass, Number, and Surface Area. Ann Occup Hyg 2016;60:684-99.\u003c/li\u003e\n\u003cli\u003eWang R, Zhang Y, Lan Q, Holford TR, Leaderer B, Hoar Zahm S et al. Occupational Exposure to Solvents and Risk of Non-Hodgkin Lymphoma in Connecticut Women. American Journal of Epidemiology 2008;169:176-85.\u003c/li\u003e\n\u003cli\u003eLison D. Human toxicity of cobalt-containing dust and experimental studies on the mechanism of interstitial lung disease (hard metal disease). Crit Rev Toxicol 1996;26:585-616.\u003c/li\u003e\n\u003cli\u003eFeldman D, Krishnan AV, Swami S, Giovannucci E , Feldman BJ. The role of vitamin D in reducing cancer risk and progression. Nat Rev Cancer 2014;14:342-57.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Basic features of the study population\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"699\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNon-skin cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSkin cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP \u0026nbsp;value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;N=9835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eN=9686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eN=149\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eAge, years, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003e20-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e3364 (34.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e3356 (34.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e8 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\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: 203px;\"\u003e\n \u003cp\u003e40-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e3235 (32.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e3202 (33.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e33 (22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\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: 203px;\"\u003e\n \u003cp\u003e\u0026ge;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e3236 (32.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e3128 (32.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e108 (72.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\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: 203px;\"\u003e\n \u003cp\u003eGender, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e4852 (49.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e4768 (49.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e84 (56.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e4983 (50.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e4918 (50.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e65 (43.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eEthnicity/race, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e1522 (15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e1520 (15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e2 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e3930 (40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e3787 (39.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e143 (96.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e2036 (20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e2036 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e2347 (23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e2343 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e4 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eEducation level, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eLess than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e2424 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e2403 (24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e21 (14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eHigh school or equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e2243 (22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e2211 (22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e32 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eMore than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e5168 (52.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e5072 (52.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e96 (64.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eSmoking, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e5513 (56.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e5449 (56.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e64 (43.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e4322 (43.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e4237 (43.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e85 (57.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eBMI, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e161 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e159 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e2 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e2655 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e2622 (27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e33 (22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e3237 (32.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e3181 (32.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e56 (37.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e3782 (38.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e3724 (38.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e58 (38.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eUrine creatinine, umol/L, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e9282.0(5215.6,14320.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e9282.0 (5215.6, 14320.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e7956.0(5215.6,12287.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003e25-hydroxyvitamin D3, nmol/L, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e58.10 (40.60, 76.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e57.80 (40.47, 76.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e80.10 (62.00, 93.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eHg, ug/L, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e0.28 (0.09, 0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0.28 (0.09, 0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0.41 (0.14, 0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; 0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eCs, ug/L, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e4.40 (2.65, 6.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e4.40 (2.65, 6.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e4.52 (2.98, 7.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eTl, ug/L, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e0.16 (0.09, 0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0.16 (0.09, 0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0.14 (0.09, 0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; 0.041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eIo, ug/L, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;130.70 (73.80, 233.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e130.10 (73.40, 231.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e190.80 (109.60, 315.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eCo, ug/L, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e0.36 (0.22, 0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0.36 (0.22, 0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0.37 (0.25, 0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp; 0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eMo, ug/L, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 39.50 (20.50, 68.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e39.54 (20.50, 68.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e38.40 (21.45, 61.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003ePb, ug/L, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e0.41 (0.22, 0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0.41 (0.22, 0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0.46 (0.30, 0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;0.014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eBa, ug/L, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e1.10 (0.54, 2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e1.09 (0.54, 2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e1.40 (0.69, 2.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eAs, ug/L, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp; 7.51 (3.76, 16.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e7.50 (3.76, 16.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e7.89 (4.15, 14.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. \u0026nbsp;Associations of single, combined metals, and 25(OH)D3 levels with skin cancer\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"701\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHeavy metals\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 361px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eQuartiles of single, combined metals, and 25(OH)D3 [OR (95% CI)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.26 (1.06, 1.50)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.38 (0.72, 2.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.53 (0.76, 3.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.28 (1.36, 3.81)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.30 (1.05, 1.62)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.42 (0.72, 2.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.64 (0.76, 3.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.51 (1.39, 4.52)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eCs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.13 (0.87, 1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.38 (0.70, 2.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.96 (0.51, 1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.36 (0.73, 2.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.537\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.27 (0.91, 1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.46 (0.70, 3.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.09 (0.54, 2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.63 (0.74, 3.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.303\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eTl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.10 (0.90, 1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.10 (0.65, 1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.41 (0.83, 2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.13 (0.59, 2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.405\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.19 (0.89, 1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.17 (0.69, 1.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.58 (0.91, 2.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.35 (0.62, 2.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.252\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eIo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.20 (1.03, 1.40)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.32 (0.67, 2.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.63 (0.86, 3.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.86 (1.00, 3.45)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.049\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.30 (1.09, 1.55)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.56 (0.75, 3.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e2.06 (0.99, 4.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.59 (1.18, 5.68)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eCo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.21 (0.99, 1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.64 (0.93, 2.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.61 (0.85, 3.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.68 (0.99, 2.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.090\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.34 (1.06, 1.68)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.91 (1.06, 3.46)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.05 (1.06, 3.96)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.33 (1.27, 4.26)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eMo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.04 (0.89, 1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.37 (0.82, 2.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.57 (0.94, 2.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.79 (0.44, 1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.808\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.12 (0.89, 1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.42 (0.80, 2.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.70 (0.96, 3.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.90 (0.43, 1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.847\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003ePb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.09 (0.90, 1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.55 (0.87, 2.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.62 (0.88, 3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.29 (0.72, 2.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.406\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.20 (0.94, 1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.67 (0.95, 2.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.93 (1.02, 3.66)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.62 (0.88, 3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.132\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eBa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.08 (0.87, 1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.30 (0.67, 2.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.79 (0.88, 3.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.53 (0.81, 2.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.159\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.11 (0.89, 1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.33 (0.69, 2.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.94 (0.93, 4.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.75 (0.91, 3.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.080\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eAs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.17 (0.93, 1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.52 (0.89, 2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.68 (0.97, 2.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.47 (0.73, 2.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.202\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.21 (0.94, 1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.70 (0.96, 3.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.95 (1.06, 3.57)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.66 (0.77, 3.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.152\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eCombined metals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.31 (1.01, 1.70)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.95 (1.04, 3.63)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.55 (1.34, 4.85)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.88 (0.97, 3.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.042\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.06 (1.36, 3.11)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.47 (1.26, 4.83)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.22 (2.00, 8.90)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.04 (1.67, 9.80)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e25(OH)D3\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.95 (1.26, 3.04)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.99 (0.36, 2.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e2.21 (0.95, 5.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.53 (1.11, 5.75)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.94 (1.23, 3.04)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.00 (0.37, 2.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e2.22 (0.97, 5.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.57 (1.14, 5.81)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Model 1 was adjusted for age, sex, and race/ethnicity. Model 2 was additionally adjusted for educational level, smoking, BMI, and urine creatinine.\u003c/p\u003e\n\u003cp\u003eTable 3. Associations of single and combined metals levels with 25(OH)D3\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"701\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHeavy metals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 361px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eQuartiles of single and combined metals [\u0026beta; (95% CI)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.014 (0.002, 0.025)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.006 (-0.026, 0.038)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.009 (-0.024, 0.042)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.034 (0.001, 0.068)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.054\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022 (0.010, 0.035)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.015 (-0.016, 0.045)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.022 (-0.012, 0.056)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.053 (0.018, 0.088)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eCs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.02(-0.032, -0.008)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.02 (-0.052, 0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.042 (-0.074, -0.01)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.047 (-0.078, -0.016)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.003(-0.013, 0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.008 (-0.023, 0.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.007 (-0.030, 0.044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.016 (-0.024, 0.056)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.493\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eTl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e-0.009(-0.021,0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.017 (-0.046, 0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.03 (-0.06, -0.001)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.021 (-0.055, 0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.151\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.020 (0.005, 0.035)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.010 (-0.020, 0.041)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.023 (-0.010, 0.057)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.053 (0.010, 0.096)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eIo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.011(-0.001, 0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.007 (-0.041, 0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.013 (-0.02, 0.045)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.035 (0.004, 0.067)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.042 (0.028, 0.056\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.036 (-0.001, 0.072)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.080 (0.040, 0.120)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.117 (0.078, 0.156)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eCo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.021(-0.033,0.008)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.014 (-0.044, 0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.035 (-0.064, -0.006)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.053 (-0.084, -0.022)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.001(-0.015, 0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.014 (-0.017, 0.045)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.008 (-0.025, 0.042)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.008 (-0.031, 0.046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.806\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eMo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.002(-0.015,0.010)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.031 (-0.064, 0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.02 (-0.055, 0.016)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.007 (-0.042, 0.028)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.793\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.030 (0.014, 0.046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.003 (-0.030, 0.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.044 (0.003, 0.085)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.074 (0.028, 0.120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003ePb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.034(-0.046,0.022)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.052 (-0.084, -0.02)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.057 (-0.087, -0.027)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.08 (-0.114, -0.046)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e-\u003cstrong\u003e0.024(-0.041,0.008)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.036 (-0.068, -0.004)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.03 (-0.066, 0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.046 (-0.09, -0.003)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.063\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eBa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.013(-0.024,0.002)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.004 (-0.035, 0.044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.003 (-0.036, 0.029)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.033 (-0.065, -0.001)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.034\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.005(-0.008, 0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.016 (-0.024, 0.056)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.023 (-0.011, 0.057)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.012 (-0.025, 0.050)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.485\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eAs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.001(-0.011, 0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.024 (-0.058, 0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.006 (-0.037, 0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.001 (-0.034, 0.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.867\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.012(-0.001, 0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.001 (-0.030, 0.033)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.029 (-0.005, 0.062)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.025 (-0.009, 0.059)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.068\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eCombined metals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e-0.011(-0.026,0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.038 (-0.077, 0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.051 (-0.084, -0.018)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.018 (-0.051, 0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.204\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.043 (0.018, 0.068)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.003 (-0.036, 0.041)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.023 (-0.015, 0.060)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.092 (0.043, 0.14)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Model 1 was adjusted for age, sex, and race/ethnicity. Model 2 was additionally adjusted for educational level, smoking, BMI, and urine creatinine.\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":"Heavy metals, Non-melanoma skin cancer, Combined exposure, 25-hydroxyvitamin D3, Mediation analysis ","lastPublishedDoi":"10.21203/rs.3.rs-6504354/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6504354/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/em\u003e: The impact of combined heavy metal exposure on non-melanoma skin cancer (NMSC) in the general population remains poorly understood.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/em\u003e: To investigate the associations of metals with NMSC and examine the potential mediating effect of 25-hydroxyvitamin D3 [25(OH)D3].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/em\u003e: We extracted data from the National Health and Nutrition Examination Survey, comprising 9835 participants with nine urinary metal concentrations, which were mercury (Hg), cesium (Cs), thallium (Tl), Ionium (Io), cobalt (Co), molybdenum (Mo), lead (Pb), barium (Ba), and arsenic (As). Multivariable logistic regression and weighted quantile sum regression were employed to estimate the associations of independent and combined metals with NMSC. Additionally, mediation analyses were conducted to explore the mediated effects of serum 25(OH)D3 on these associations.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/em\u003e: Urinary levels of Hg, Io, Co, and mixed metals were positively correlated with NMSC. Serum 25(OH)D3 exhibited significant associations with NMSC, as did Hg, Cs, Tl, Io, Co, Mo, Pb, Ba, and combined metals with 25(OH)D3. Furthermore, the associations between single metals (primarily Hg and Io) and mixed metals with NMSC were partially mediated by 25(OH)D3.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/em\u003e: Lack of validation in longitudinal study and independent populations.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/em\u003e: Metal exposure heightens NMSC risk, with a portion of this risk being mediated by 25(OH)D3.\u003c/p\u003e","manuscriptTitle":"25-hydroxyvitamin D3 mediates the associations between urinary heavy metals and non- melanoma skin cancer: Insights from a population-based study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-08 14:29:13","doi":"10.21203/rs.3.rs-6504354/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":"1ce2d737-81a4-4b0f-8e5f-1f030770b501","owner":[],"postedDate":"May 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T00:09:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-08 14:29:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6504354","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6504354","identity":"rs-6504354","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}