Uncovering the Relationship between Heavy Metal Exposure, Cognitive Function, and Dietary Inflammation Index in Elderly Americans from the National Health and Nutrition Examination Survey 2011-2014

Uncovering the Relationship between Heavy Metal Exposure, Cognitive Function, and Dietary Inflammation Index in Elderly Americans from the National Health and Nutrition Examination Survey 2011-2014 | 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 Uncovering the Relationship between Heavy Metal Exposure, Cognitive Function, and Dietary Inflammation Index in Elderly Americans from the National Health and Nutrition Examination Survey 2011-2014 Chunlan Tang, Min Shen, Hang Hong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3806622/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Sep, 2024 Read the published version in BMC Public Health → Version 1 posted 12 You are reading this latest preprint version Abstract Background The consequences of heavy metal exposure on cognitive function in elderly adults have been recognized as primarily attributed to the inflammatory response. It is noteworthy that diet can either exacerbate or reduce the inflammatory response. Despite this, there has been limited study about the effects of diet on the relationship between heavy metal exposure and cognitive function. Methods A cross-sectional study was conducted utilizing data from the 2011–2014 NHANES survey to explore the role of the dietary inflammation index in the association between metal exposure and cognitive function in elderly adults. The study enrolled 1726 participants and generalized linear regression model(GLM), Bayesian kernel machine regression model(BKMR), weighted quantile sum regression(WQS), and quantile g-computation regression analysis(Qg-comp) was conducted to assess the impact of five heavy metals in blood on cognitive function under the anti-inflammatory and pro-inflammatory diet. Results The GLM analysis showed a positive correlation between selenium (Se) and both the instant recall test (IRT) and digit symbol substitution test (DSST), whether taken as continuous or quartile variables.Conversely, cadmium (Cd) was negatively associated with DSST. For IRT, Cd in the highest quartile was negatively associated compared to the lowest quartile. Subgroup analysis revealed the effects of Cd on IRT and DSST and Se on DSST under the pro-inflammatory diet. Furthermore, The BKMR analysis showed an inverted U-shaped curve with the negative effect of metal mixtures and DSST and a linearly negative trend with IRT in the pro-inflammatory diet. Among them, Cd was emphasized as the most potent risk factor, and Se was the most vital protective factor for IRT and DSST in WQS and Qg-comp analysis. Conclusions The study suggests that a high-quality diet could alleviate the adverse effects of Cd on IRT and DSST. Additionally, high Se levels improved IRT and DSST in the inflammatory diet. These findings provide valuable insights into the connection between diet, heavy metal exposure, and cognitive function in elderly adults. dietary inflammatory index heavy metals cognitive function NHANES Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Background The rise in global life expectancy has given rise to a significant global public health concern regarding the cognitive health of elderly adults. With the number of older adults experiencing cognitive impairment or dementia on the rise, it is estimated that this population will reach 78 million by 2030 and a staggering 139 million by 2050 [ 1 ]. A decline in various mental abilities, including memory loss, poor concentration, information processing, confusion or memory loss, and semantic comprehension errors, characterizes impaired cognitive function. It can also lead to abnormal reasoning and decision-making processes [ 2 , 3 ]. This decline can profoundly impact the daily activities of elderly adults, ultimately reducing their quality of life. Numerous studies indicate that cognitive decline is closely associated with physical, psychological, social, and lifestyle risk factors [ 4 – 6 ]. Furthermore, several investigations have demonstrated that exposure to metal pollution, both natural and manufactured, can impact cognitive function in older adults [ 7 , 8 ]. The precise mechanisms by which this occurs remain unclear; however, heavy metals are known to trigger neuroinflammation [ 9 , 10 ], which is a complex process involving various factors such as direct toxic effects, immune response, oxidative stress, and neuronal apoptosis. These processes interact and promote the development of neuroinflammation. Heavy metal ions can bind to neuronal proteins, causing neuronal damage, neuroinflammation, and nervous system dysfunction [ 11 , 12 ]. Additionally, they can stimulate the immune system to produce an inflammatory response, resulting in neuroinflammation [ 13 , 14 ]. Heavy metal exposure promotes the production of reactive oxygen species (ROS), which can attack cell components and cause cell damage and inflammation [ 15 , 16 ]. They can also induce neuronal apoptosis, exacerbating neuroinflammation and damaging the nervous system [ 17 ]. The constituents of daily food intake are complex, necessitating an exploration of specific dietary conditions to understand their impact on inflammation and cognitive impairment. The Dietary Inflammatory Index (DII) is a recognized indicator of overall dietary inflammation, calculated by merging diverse food components. Research has demonstrated that a higher DII score positively correlates with cognitive function [ 18 , 19 ]. However, limited studies have evaluated whether dietary intake of antioxidants and anti-inflammatory compounds can modify the effect of metal exposure on markers of neuroinflammation. It is hypothesized that an anti-inflammatory or pro-inflammatory diet may attenuate or enhance inflammation and subsequently reduce or increase metals-induced neurotoxicity. The present study explores whether the association between blood metal and cognitive function is modified by overall diet quality measured by the DII score. The study will employ three statistical models, namely the Bayesian kernel machine regression (BKMR) model, weighted quantile sum (WQS) model, and quantile g-computation (Qg-comp) model, to analyze data from the National Health and Nutrition Examination Survey (NHANES). The primary objective is to investigate whether the overall quality of an individual's diet can strengthen or weaken the relationship between blood metal and cognitive function. 2. Methods 2.1. Study population The present study primarily analyzed data from two cycles (2011–2012 and 2013–2014) of NHANES, a cross-sectional survey conducted by the Centers for Disease Control and Prevention (CDC) to evaluate the health and nutrition status of the non-institutionalized population in the US. The study received approval from the National Center for Health Statistics (NCHS), and all participants provided their informed consent. Out of 19,931 respondents, data were collected from 3,632 participants aged 60 or older. Participants with incomplete information on blood metal levels, cognitive tests, and covariates were excluded from the analysis. Ultimately, the study comprised 1,726 individuals aged 60 or older (Fig. 1 ). (Fig. 1 ) 2.2. Measurement 2.2.1 Cognitive Function The cognitive abilities of the individuals were assessed using four tests: the Instant Recall Test (IRT), the Delayed Recall Test (DRT), the Animal Fluency Test (AFT), and the Digit Symbol Substitution Test (DSST). IRT and DRT were used to evaluate the acquisition of new linguistic material immediately and after a certain period of delay. The AFT was used to assess executive function, while the DSST was used to determine the participants' cognitive abilities, such as reaction time, sustained attention, and working memory. All test scores were standardized to ensure consistency with the composite score. The resulting scores serve as a measure of cognitive function, with higher scores indicating superior performance. 2.2.2 Heavy metal The CDC's Division of Laboratory Sciences at the National Center for Environmental Health processed, stored, and analyzed whole blood specimens for heavy metal concentrations. Specifically, the attention of lead (Pb), cadmium (Cd), total mercury (Hg), manganese (Mn), and selenium (Se) were analyzed using inductively coupled plasma mass spectrometry (ICP-MS). In cases where the blood heavy metal levels fall below the lower limit of detection (LLOD), they were replaced by the result of dividing the limit by the square root of 2 (LLOD/sqrt[ 2 ]). The concentration of all metals in the blood was transformed using a natural logarithm to obtain nearly normal distributions. 2.2.3 Dietary Inflammatory Index The Dietary Inflammatory Index (DII) was derived from data obtained through two 24-hour dietary recall interviews. The calculation of a Z-score involves the subtraction of the global per capita daily intake from the daily intake of a specific nutrient. This result was then divided by the standard deviation of the global per capita daily intake. The resulting Z-score was converted into a percentage, which fell from 0 to 1. This percentage was doubled and subtracted by 1, resulting in a symmetrical distribution centered at "0". Finally, the total inflammation score was obtained by multiplying the percentage received for each dietary component by the total inflammation score. This calculation was done for twenty-seven nutrients, including alcohol, β-carotene, caffeine, carbohydrate, cholesterol, folic acid, fiber, iron, magnesium, n-3 fatty acids, n-6 fatty acids, total monounsaturated fatty acids, niacin, total polyunsaturated fatty acids, protein, total saturated fatty acids, Se, total fat, riboflavin, thiamin, vitamin A, B12, B6, C, D, E, and zinc. Participants were categorized into DII ≥ 0 and DII < 0, signifying pro-inflammatory and anti-inflammatory diets, respectively. 2.3. Covariate assessment The study incorporated several demographic covariates in the analysis, specifically sex, age (60–69, 70–79, or ≥ 80), race (American, Mexican other Hispanic, non-Hispanic white, non-Hispanic black, or other), marital status (never married, married/living with partner, or widowed/divorced/separated), family income to poverty ratio (≤ 1.3, 1.3–3.5, or > 3.5), drinking status (having at least 12 alcohol drinks per year or not), body mass index (BMI) (< 18.5 kg/cm², ≥ 18.5 to < 25 kg/cm², ≥ 25 to < 30 kg/cm², or ≥ 30 kg/cm²), smoking status (having at least 100 cigarettes in their lifetime or not), and education level (less than 9th grade, 9-11th grade, high school grad/GED, some college or AA degree, or college graduate or above). Hyperlipidemia was diagnosed when a person had either low-density cholesterol levels of 160 mg/dL or higher, total cholesterol levels of 240 mg/dL or higher, or was taking lipid-lowering medication. Hypertension was diagnosed by a physician or a systolic blood pressure of ≥ 130 mmHg, a diastolic blood pressure of ≥ 80 mmHg, or current use of hypertension medications. Individuals with diabetes were identified based on any of the following criteria: (a) a hemoglobin A1C concentration of 6.5% or higher or a fasting plasma glucose level of 126 mg/dL or higher; (b) individuals who answered positively to the question: "Has a doctor told you that you have diabetes?" or "Are you currently taking insulin?" Physical activity was measured using a unit called Metabolic Equivalent of Task (MET) scores. The scores were assigned as follows: 8 for vigorous work-related exercise, 4 for moderate work-related activity, 4 for walking or bicycling, 8 for vigorous leisure-time physical activity, and 4 for moderate leisure-time physical activity. To determine the total amount of physical activity, weekly minutes were added and multiplied by each activity's MET score. The US physical activity guidelines defined a high level of physical activity as ≥ 600 MET*min/week and a low level of physical activity as < 600 MET*min/week. 2.4. Statistical analysis The categorical variables of four cognitive test scores were presented as medians, accompanied by their respective interquartile ranges (IQR). The Kruskal-Wallis test was employed for comparison. The correlation between the five metals was evaluated using Pearson correlation coefficients. Cognitive function scores were analyzed for association with metal exposure using generalized linear regression models (GLM). Furthermore, we used quartiles of metal exposure and a trend test across increasing exposure groups in multivariable models, then rerunning the related regression model. Subgroup analysis was conducted to determine any variations in the correlation between metal exposure and cognitive function, stratified by the DII group (anti-inflammatory and pro-inflammatory diets), using stratified linear regression models. Likelihood-ratio tests inspected the modifications and interactions of subgroups. The potential interactions and collective impacts of metal exposure on cognitive function were analyzed using the BKMR statistical modeling. The researchers investigated the nonlinear relationship between exposure and outcome by employing exposure-response cross-sections for a single independent variable and outcome. The study further controlled for other variables by keeping them constant at the median. Bivariate exposure-response profiles can be used to represent the interaction between different mixture compositions. This interaction can be understood as the potential impact of one chemical's slope curve at various modifications of another chemical (25th, 50th, and 75th) while the remaining variables are held constant at the median. The association plot offered a comprehensive analysis of the impact of the mixture on the outcome. It displayed how the outcome was expected to change based on different percentiles of exposure variables, compared to when they are at the media. The iteration was performed using the Markov Chain Monte Carlo method with a fixed value 30,000. The WQS regression model is a statistical approach that uses mixed-effects strategies to analyze the relationship between multiple chemical exposures and outcomes. It evaluates this relationship by computing a weighted index and determining the proportional impact of each exposure. The weights assigned to each exposure are between 0 and 1, and the sum of all weights equals 1. The potential association between metal exposure and cognitive function was investigated by the WQS model while controlling for all relevant covariates. The present study involved the random partitioning of original data into a training set (40%) and a validation set (60%) to derive weight values for each metal exposure. Subsequently, the bootstrap method with a sample size of 3000 was applied to the data to infer the statistical properties of the derived weight values. We used the Qg-comp model to address the WQS regression model's limitations in determining the association's direction. Qg-comp model provides a reliable means of predicting the impact of exposure mixtures. However, in large samples where the assumptions of WQS regression hold, Qg-comp can also yield equivalent estimates. The qgcomp.noboot function was applied to establish a linear model for cognitive function. We applied positive or negative weighting indices to each heavy metal in the blood and subsequently segmented each metal into quartiles to determine its overall effect. We developed five models in sensitivity analysis to examine the association between various factors and the outcome. Model 1 had no adjustments for any confounding variables, while Model 2 adjusted for sex, age, race, education level, income, and marital status. Model 3 added adjustments for physical activity, drinking, smoking habits, BMI, and DII. Model 4 accounted for diabetes, hypertension, and hyperlipidemia. Lastly, Model 5 adjusted for four other metals. All statistical analyses were performed using the R software (version 4.3.2) and packages “survey”,“bkmr”, “gWQS”, and “Qgcomp”. A significance level of P < 0.05 was considered. 3. Results 3.1 Descriptive statistics The study comprised 1726 participants, of which 853 were male and 873 were female. Sociodemographic data, such as sex, age, ethnicity, education level, income, marital status, drinking and smoking habits, BMI, and physical activity, was collected and subsequently categorized as presented in Table 1 . The results indicated that women who were non-Hispanic Whites, highly educated, and had high incomes had higher scores on cognitive function tests. Furthermore, poor cognitive performance was more prevalent in adults with diabetes and hypertension than in those with normal cognitive performance. It is noteworthy that poor cognitive performance in the pro-inflammatory diet were observed compared to that in the anti-inflammatory diet. The study also revealed that older adults with high blood Cd and Pd levels had low cognitive function scores on IRT, DRT, AFT, and DDST. Conversely, high blood Hg and Se levels presented high cognitive function scores. Weak correlations among the five metals were observed, as shown in Figure S1 . Table S1 provides information about the distribution of five metals across various populations. Table 1 Demographic characteristics of study participants Variables Total number(%) IRT DRT AFT DSST Total (n = 1726) 19.0 (16.0, 22.0) 6.0 (4.0, 8.0) 16.0 (13.0, 20.0) 46.0 (34.0, 59.0) Sex, n (%) female 873 (50.6) 20.0 (17.0, 23.0) 7.0 (5.0, 8.0) 16.0 (13.0, 20.0) 49.0 (36.0, 62.0) male 853 (49.4) 18.0 (15.0, 21.0) 6.0 (4.0, 7.0) 16.0 (13.0, 20.0) 43.0 (32.0, 54.0) P < 0.001 < 0.001 0.549 < 0.001 Age group, n (%) 60–69 years 885 (51.3) 20.0 (17.0, 23.0) 7.0 (5.0, 8.0) 17.0 (14.0, 21.0) 51.0 (37.0, 63.0) 70–79 years 477 (27.6) 19.0 (16.0, 22.0) 6.0 (4.0, 8.0) 16.0 (12.0, 20.0) 44.0 (32.0, 55.0) 80 + years 364 (21.1) 17.0 (14.0, 20.0) 5.0 (3.0, 6.0) 15.0 (12.0, 18.0) 40.0 (30.0, 50.0) P < 0.001 < 0.001 < 0.001 < 0.001 Race, n (%) Mexican American 134 ( 7.8) 18.0 (15.0, 21.0) 6.0 (4.0, 7.0) 16.0 (13.0, 19.0) 38.0 (26.0, 53.0) Non-Hispanic Black 403 (23.3) 19.0 (16.0, 22.0) 6.0 (4.0, 8.0) 15.0 (11.0, 18.0) 39.0 (29.0, 52.0) Non-Hispanic White 874 (50.6) 19.0 (16.0, 22.0) 6.0 (4.0, 8.0) 18.0 (14.0, 22.0) 50.0 (39.2, 63.0) Other Hispanic 174 (10.1) 18.0 (15.0, 21.0) 6.0 (4.0, 7.0) 15.0 (12.0, 18.8) 35.0 (25.0, 49.0) Other/multiracial 141 ( 8.2) 20.0 (16.0, 23.0) 7.0 (5.0, 8.0) 15.0 (12.0, 18.0) 50.0 (42.0, 63.0) P < 0.001 < 0.001 < 0.001 < 0.001 Education attainment, n (%) Less Than 9th Grade 191 (11.1) 16.0 (14.0, 19.0) 5.0 (3.0, 6.0) 14.0 (11.0, 17.0) 25.0 (20.0, 33.0) 9-11th Grade 216 (12.5) 18.0 (15.0, 22.0) 6.0 (4.0, 7.0) 15.0 (11.0, 18.0) 37.0 (29.0, 46.0) High School Grad/GED 411 (23.8) 19.0 (16.0, 21.5) 6.0 (4.0, 7.0) 16.0 (12.0, 19.0) 45.0 (34.0, 54.0) Some College or AA degree 503 (29.1) 19.0 (16.0, 21.5) 6.0 (4.0, 7.0) 16.0 (12.0, 19.0) 45.0 (34.0, 54.0) College Graduate or above 405 (23.5) 21.0 (17.0, 23.0) 7.0 (5.0, 8.0) 19.0 (15.0, 22.0) 56.0 (45.0, 67.0) P < 0.001 < 0.001 < 0.001 < 0.001 Income status, n (%) <= 1.30 498 (28.9) 18.0 (15.0, 21.0) 6.0 (4.0, 7.0) 15.0 (12.0, 18.0) 36.0 (25.0, 48.0) 1.31to 3.5 556 (32.2) 20.0 (17.0, 23.0) 7.0 (5.0, 8.0) 18.0 (14.0, 22.0) 55.0 (45.0, 67.0) P < 0.001 < 0.001 < 0.001 < 0.001 Marital status, n (%) Never Married 101 ( 5.9) 20.0 (16.0, 23.0) 7.0 (5.0, 8.0) 15.0 (13.0, 20.0) 47.0 (35.0, 59.0) Married/Living with partner 1010 (58.5) 19.0 (16.0, 22.0) 6.0 (4.0, 8.0) 17.0 (13.0, 21.0) 48.0 (36.0, 60.0) Widowed/Divorced/ Separated 615 (35.6) 19.0 (16.0, 22.0) 6.0 (4.0, 7.0) 16.0 (12.0, 19.0) 43.0 (30.0, 57.0) P 0.21 0.056 0.002 < 0.001 Drinking, n (%) Non-drinker 521 (30.2) 19.0 (15.0, 22.0) 6.0 (4.0, 8.0) 15.0 (12.0, 19.0) 44.0 (31.0, 55.0) drinker 1205 (69.8) 19.0 (16.0, 22.0) 6.0 (4.0, 8.0) 17.0 (13.0, 21.0) 48.0 (35.0, 60.0) P 0.496 0.989 < 0.001 < 0.001 Smoke, n (%) Never smoker 851 (49.3) 19.0 (16.0, 22.0) 6.0 (4.0, 8.0) 16.0 (13.0, 20.0) 48.0 (34.0, 60.5) smoker 875 (50.7) 19.0 (16.0, 22.0) 6.0 (4.0, 7.0) 16.0 (13.0, 20.0) 45.0 (33.0, 57.0) P 0.115 0.145 0.583 0.005 BMI, n (%) Underweight(< 18.5) 23 ( 1.3) 21.0 (11.5, 23.0) 5.0 (3.0, 7.5) 14.0 (12.0, 19.0) 37.0 (29.5, 51.0) Normal(18.5 to < 25) 435 (25.2) 19.0 (16.0, 22.0) 6.0 (4.0, 8.0) 16.0 (13.0, 20.0) Overweight(25 to < 30) 591 (34.2) 19.0 (16.0, 22.0) 6.0 (4.0, 7.0) 16.0 (13.0, 20.0) 46.0 (34.0, 60.0) Obese(30 or greater) 677 (39.2) 19.0 (16.0, 22.0) 6.0 (5.0, 8.0) 16.0 (13.0, 20.0) 47.0 (34.0, 59.0) P 0.564 0.042 0.615 0.368 Physical activity, n (%) Low 866 (50.2) 19.0 (15.0, 22.0) 6.0 (4.0, 7.0) 15.0 (12.0, 19.0) 43.0 (31.0, 55.0) High 860 (49.8) 19.0 (17.0, 22.0) 6.0 (5.0, 8.0) 17.0 (14.0, 21.0) 49.5 (37.0, 62.0) P < 0.001 < 0.001 < 0.001 < 0.001 Diabetes, n (%) No 1224 (70.9) 19.0 (16.0, 22.0) 6.0 (4.0, 8.0) 17.0 (13.0, 21.0) 48.5 (36.0, 61.0) Yes 502 (29.1) 19.0 (15.0, 22.0) 6.0 (4.0, 7.0) 15.0 (12.0, 19.0) 42.0 (29.0, 52.0) P 0.007 < 0.001 < 0.001 < 0.001 Hypertension, n (%) No 328 (19.0) 20.0 (17.0, 23.0) 6.0 (5.0, 8.0) 17.0 (14.0, 22.0) 50.0 (37.0, 63.0) Yes 1398 (81.0) 19.0 (16.0, 22.0) 6.0 (4.0, 8.0) 16.0 (13.0, 20.0) 45.0 (33.0, 57.0) P 0.003 0.003 < 0.001 < 0.001 Hyperlipidemia, n (%) No 555 (32.2) 19.0 (16.0, 22.0) 6.0 (4.0, 8.0) 16.0 (13.0, 20.0) 46.0 (33.5, 58.0) Yes 1171 (67.8) 19.0 (16.0, 22.0) 6.0 (4.0, 8.0) 16.0 (13.0, 20.0) 46.0 (34.0, 59.0) P 0.651 0.63 0.884 0.893 Cadmium, Mean ± SD Q1 424 (24.6) 20.0 (16.0, 22.0) 6.0 (4.0, 8.0) 17.0 (13.0, 20.0) 49.0 (36.0, 61.2) Q2 424 (24.6) 19.0 (16.0, 22.0) 6.0 (4.0, 8.0) 17.0 (13.0, 21.0) 48.0 (35.0, 60.0) Q3 436 (25.3) 19.0 (16.0, 22.0) 6.0 (4.0, 8.0) 16.0 (13.0, 20.0) 46.0 (34.0, 59.0) Q4 442 (25.6) 18.0 (16.0, 22.0) 6.0 (4.0, 8.0) 15.0 (12.0, 19.0) 42.0 (32.0, 55.0) P 0.028 0.532 < 0.001 < 0.001 Lead, Mean ± SD Q1 431 (25.0) 19.0 (17.0, 22.0) 6.0 (5.0, 8.0) 16.0 (13.0, 20.0) 48.0 (34.0, 61.0) Q2 426 (24.7) 20.0 (16.0, 23.0) 6.0 (4.0, 8.0) 17.0 (13.0, 20.0) 49.0 (36.2, 59.0) Q3 436 (25.3) 19.0 (16.0, 22.0) 6.0 (4.0, 8.0) 16.0 (13.0, 20.0) 47.0 (36.0, 60.0) Q4 433 (25.1) 18.0 (15.0, 21.0) 6.0 (4.0, 7.0) 16.0 (13.0, 20.0) 42.0 (31.0, 54.0) P < 0.001 0.003 0.663 < 0.001 Manganese, Mean ± SD Q1 431 (25.0) 19.0 (15.0, 22.0) 6.0 (4.0, 8.0) 17.0 (13.0, 20.0) 44.0 (32.0, 56.0) Q2 432 (25.0) 19.0 (16.0, 22.0) 6.0 (5.0, 8.0) 16.0 (13.0, 20.0) 46.0 (36.0, 59.0) Q3 429 (24.9) 19.0 (16.0, 22.0) 6.0 (4.0, 8.0) 16.0 (13.0, 20.0) 47.0 (33.0, 60.0) Q4 434 (25.1) 19.0 (16.0, 22.0) 6.0 (5.0, 8.0) 16.0 (12.2, 20.0) 48.0 (36.0, 60.0) P 0.402 0.066 0.495 0.01 Mercury, Mean ± SD Q1 421 (24.4) 18.0 (15.0, 21.0) 6.0 (4.0, 7.0) 15.0 (12.0, 19.0) 41.0 (30.0, 54.0) Q2 441 (25.6) 19.0 (16.0, 22.0) 6.0 (4.0, 7.0) 17.0 (13.0, 20.0) 46.0 (33.0, 58.0) Q3 432 (25.0) 19.0 (16.0, 22.0) 6.0 (4.0, 8.0) 17.0 (13.0, 20.0) 48.0 (34.0, 60.0) Q4 432 (25.0) 20.0 (16.0, 23.0) 6.0 (5.0, 8.0) 17.0 (13.0, 21.0) 50.0 (38.0, 63.0) P < 0.001 < 0.001 0.004 < 0.001 Selenium, Mean ± SD Q1 432 (25.0) 18.0 (15.0, 21.0) 6.0 (4.0, 7.0) 15.0 (12.0, 19.0) 40.0 (28.0, 53.0) Q2 431 (25.0) 20.0 (16.0, 22.0) 6.0 (4.0, 8.0) 17.0 (13.0, 20.0) 49.0 (34.0, 61.0) Q3 431 (25.0) 19.0 (16.0, 22.0) 6.0 (5.0, 8.0) 17.0 (13.0, 20.0) 48.0 (37.0, 60.0) Q4 432 (25.0) 20.0 (16.0, 23.0) 6.0 (4.8, 8.0) 17.0 (13.0, 20.0) 48.0 (35.8, 60.0) P < 0.001 < 0.001 0.003 < 0.001 DII group, n (%) pro-inflammatory diet 1011 (58.6) 19.0 (15.0, 22.0) 6.0 (4.0, 8.0) 16.0 (12.0, 19.0) 44.0 (31.0, 56.0) anti-inflammatory diet 715 (41.4) 19.0 (16.5, 23.0) 6.0 (5.0, 8.0) 17.0 (14.0, 21.0) 50.0 (39.0, 63.0) P 0.002 0.008 < 0.001 < 0.001 (Table 1 ) 3.2 Associations between blood metals mixture and cognitive performance scores Table 2 presents the correlation between blood metal concentration and cognitive function. The study revealed a positive correlation between blood Se and IRT and DDST scores (β(95%Cl): 2.06 (0.7 ~ 3.41), P < 0.01; 6.41 (2.35 ~ 10.46), P < 0.01), respectively. The GLM analysis based on quartiles of the exposure variables demonstrated that blood Se at Q2 (β(95%Cl): 0.89 (0.33 ~ 1.45), P < 0.01) and Q3 (β(95%Cl): 3.14 (1.46 ~ 4.81), P < 0.001) had the most significant positive impact on cognitive function of IRT and DDST, respectively. Conversely, blood Pb had a negative correlation with IRT scores (β(95% CI): -0.47 (-0.82~-0.11), P < 0.05), and the Q3 (β(95%Cl): -0.82 (-1.39~-0.25), P < 0.01) had the most negative effect. Blood Cd was negatively associated with DSST (β(95% CI): -1.17 (-2.13~-0.22), P < 0.01), and the Q3 (β(95%Cl): 3.14 (1.46 ~ 4.81), P < 0.001) showed the most significant negative impact. Table 2 Univariate linear regression analysis of the association between metal exposure and cognitive function scores Variable IRT(β(95%CI)) DRT(β(95%CI)) AFT(β(95%CI)) DDST(β(95%CI)) Cadmium Cont -0.31 (-0.64 ~ 0.01) -0.05 (-0.22 ~ 0.11) -0.11 (-0.49 ~ 0.27) -1.17 (-2.13~-0.22) P 0.054 0.533 0.569 0.016 Q1 0(Ref) 0(Ref) 0(Ref) 0(Ref) Q2 -0.52 (-1.08 ~ 0.05) -0.06 (-0.35 ~ 0.23) 0.12 (-0.55 ~ 0.78) -1.91 (-3.59~-0.23) Q3 -0.23 (-0.8 ~ 0.35) -0.24 (-0.54 ~ 0.05) -0.17 (-0.84 ~ 0.51) -1.87 (-3.57~-0.16) Q4 -0.72 (-1.34~-0.1) -0.1 (-0.42 ~ 0.22) -0.37 (-1.11 ~ 0.36) -2.33 (-4.18~-0.48) P -t 0.069 0.317 0.244 0.019 Lead Cont -0.47 (-0.82~-0.11) -0.12 (-0.3 ~ 0.07) 0.19 (-0.24 ~ 0.61) -0.3 (-1.37 ~ 0.77) P 0.011 0.207 0.389 0.579 Q1 0(Ref) 0(Ref) 0(Ref) 0(Ref) Q2 -0.2 (-0.76 ~ 0.36) -0.02 (-0.31 ~ 0.27) 0.06 (-0.6 ~ 0.72) -0.34 (-2.02 ~ 1.34) Q3 -0.82 (-1.39~-0.25) -0.19 (-0.48 ~ 0.1) 0.15 (-0.52 ~ 0.82) -0.09 (-1.78 ~ 1.6) Q4 -0.46 (-1.05 ~ 0.14) -0.14 (-0.45 ~ 0.17) 0.3 (-0.4 ~ 1) -0.93 (-2.7 ~ 0.84) P -t 0.034 0.222 0.378 0.385 Manganese Cont -0.4 (-0.97 ~ 0.16) 0.07 (-0.22 ~ 0.37) -0.59 (-1.26 ~ 0.08) 0.39 (-1.3 ~ 2.09) P 0.164 0.622 0.083 0.649 Q1 0(Ref) 0(Ref) 0(Ref) 0(Ref) Q2 0.06 (-0.49 ~ 0.62) 0.14 (-0.14 ~ 0.43) -0.65 (-1.31 ~ 0) 0.46 (-1.21 ~ 2.12) Q3 0.04 (-0.52 ~ 0.6) 0.16 (-0.13 ~ 0.46) -0.51 (-1.18 ~ 0.15) -0.34 (-2.02 ~ 1.35) Q4 -0.1 (-0.68 ~ 0.48) 0.12 (-0.18 ~ 0.42) -0.81 (-1.49~-0.13) 0.6 (-1.13 ~ 2.32) P -t 0.728 0.422 0.038 0.729 Mercury Cont 0.1 (-0.12 ~ 0.31) 0.02 (-0.09 ~ 0.13) 0.12 (-0.14 ~ 0.37) 0.44 (-0.21 ~ 1.08) P 0.377 0.716 0.37 0.183 Q1 0(Ref) 0(Ref) 0(Ref) 0(Ref) Q2 0.06 (-0.5 ~ 0.62) 0.03 (-0.26 ~ 0.32) 0.43 (-0.23 ~ 1.09) 0.22 (-1.46 ~ 1.9) Q3 0.28 (-0.3 ~ 0.85) 0.09 (-0.21 ~ 0.39) 0.3 (-0.38 ~ 0.98) 1.34 (-0.38 ~ 3.06) Q4 0.18 (-0.43 ~ 0.78) 0.18 (-0.13 ~ 0.49) 0.4 (-0.31 ~ 1.11) 1.2 (-0.6 ~ 3) P -t 0.445 0.235 0.353 0.103 Selenium Cont 2.06 (0.7 ~ 3.41) 0.58 (-0.12 ~ 1.28) 0.83 (-0.77 ~ 2.43) 6.41 (2.35 ~ 10.46) P 0.003 0.106 0.311 0.002 Q1 0(Ref) 0(Ref) 0(Ref) 0(Ref) Q2 0.89 (0.33 ~ 1.45) 0.31 (0.02 ~ 0.6) 0.29 (-0.37 ~ 0.95) 2.87 (1.2 ~ 4.53) Q3 0.49 (-0.07 ~ 1.05) 0.27 (-0.02 ~ 0.56) 0.27 (-0.4 ~ 0.93) 3.14 (1.46 ~ 4.81) Q4 0.76 (0.19 ~ 1.32) 0.25 (-0.05 ~ 0.54) 0.45 (-0.22 ~ 1.12) 2.54 (0.85 ~ 4.22) P -t 0.042 0.14 0.222 0.004 (Table 2 ) 3.3 Associations between blood metals and cognitive performance scores differed by Dietary Inflammatory Index Subgroup analyses assessed whether an anti-inflammatory or pro-inflammatory diet influenced the relationship between blood metal and cognitive function (Figure S2). In the population with a pro-inflammatory diet, blood Cd and IRT showed a negative correlation (β (95% CI): -0.53 (-0.96~-0.11)). The Q4 equates (β (95% CI): -1.15 (-1.97~-0.32)) demonstrated the most negative effect. The same results were observed between Cd and DSST (β (95% CI): -1.31 (-2.59~-0.03)), with all equates showing a negative effect. Conversely, blood Se was highly positively associated with DSST as a continuous variable (β (95% CI): 8.57 (2.79 ~ 14.36)) or quadripartite variable. For IRT, the Q2 equates of blood Se was positively associated. Additionally, the population with an anti-inflammatory diet exhibited negative and positive correlations between blood Pb and Se with IRT, respectively, with β (95% CI) values of -0.72 (-1.28~-0.17) and 2.21 (0.28 ~ 4.15). And the Q3 equates to Pb, and the Q2 equates to Se, showing the association. 3.4 Multi-Metal Exposures and Cognitive Function 3.4.1 Multi-Metal Exposures and Cognitive Function in the Whole Population The BKMR model investigated the association between cognitive function and co-exposure to five blood heavy metals. The results showed that the overall effect of the five metal co-exposure on DRT tended to be positive. However, for IRT, AFT, and DSST, the impact of heavy metal co-exposure was first reduced and then increased, presenting a negative effect overall (Figure S3). Blood Se emerged as a significant component for improved cognitive performance on IRT and DSST (IRT: 0.401; DSST: 0.617) (Table 3 ) and presented a positive relationship when all other chemicals were at the 25th, 50th, and 75th percentile levels (Figure S4). Conversely, blood Cd was found to have a negative correlation with DSST and presented vital components for cognitive impairment (PIP: 0.543). The IRT scores of blood Se showed a linear positive curve, whereas an inverted U-shaped curve was observed in DSST scores. It was a linear negative curve for blood Cd on IRT and DSST (Figure S5). Additionally, the study found that the slope of the dose-response relationship between Cd and IRT when Se was at the 25th, 50th, and 75th percentile levels indicated the interaction between Cd and DSST. No other interaction was observed between blood Se/Cd and IRT or DSST (Figure S6-9). Table 3 The index from BKMR, WQS and Qg-comp analysis in the whole population,anti-inflammatory diet population and pro-inflammatory diet population. BKMR model Variable In the whole population In anti-inflammatory diet population In pro-inflammatory diet population IRT DRT AFT DSST IRT DRT AFT DSST IRT DRT AFT DSST Cadmium 0.052 0.035 0.892 0.543 0.513 0.062 0.399 0.413 0.466 0.089 0.943 0.925 Lead 0.117 0.098 0.696 0.819 0.777 0.141 0.250 0.384 0.019 0.416 0.520 0.421 Manganese 0.013 0.014 0.177 0.127 0.175 0.079 0.159 0.209 0.019 0.016 0.472 0.096 Mercury 0.051 0.023 0.888 0.097 0.800 0.247 0.285 0.986 0.002 0.023 0.464 0.013 Selenium 0.401 0.010 0.615 0.617 0.509 0.112 0.209 0.327 0.525 0.058 0.507 0.795 Qg-comp model Variable In the whole population In anti-inflammatory diet population In pro-inflammatory diet population IRT DRT AFT DSST IRT DRT AFT DSST IRT DRT AFT DSST Cadmium -0.402 -0.438 -0.404 -0.837 0.064 0.323 -0.145 0.606 -0.767 -0.885 -0.671 -1.000 Lead -0.546 -0.562 0.343 -0.163 -0.851 -1.000 0.490 0.394 -0.100 -0.115 0.040 0.041 Manganese -0.053 0.248 -0.596 0.107 -0.149 0.205 -0.353 0.023 -0.133 0.110 -0.302 0.081 Mercury 0.293 0.368 0.314 0.327 0.475 0.055 -0.502 0.371 0.148 0.486 0.960 0.295 Selenium 0.707 0.385 0.342 0.565 0.462 0.417 0.510 0.606 0.852 0.404 -0.028 0.583 WQS model Variable In the whole population In anti-inflammatory diet population In pro-inflammatory diet population IRT(+) DRT(+) AFT(+) DSST(+) IRT(+) DRT(+) AFT(+) DSST(+) IRT(+) DRT(+) AFT(+) DSST(+) Cadmium 0.035 0.039 0.019 0.004 0.235 0.289 0.074 0.133 0.001 0.018 0.000 0.002 Lead 0.012 0.122 0.531 0.171 0.018 0.006 0.360 0.015 0.127 0.232 0.280 0.200 Manganese 0.156 0.453 0.032 0.184 0.039 0.096 0.030 0.032 0.009 0.020 0.326 0.023 Mercury 0.053 0.087 0.139 0.058 0.584 0.453 0.054 0.516 0.095 0.454 0.294 0.379 Selenium 0.744 0.299 0.279 0.583 0.124 0.156 0.482 0.304 0.769 0.275 0.101 0.396 Index 0.379(0.073~ 0.686) 0.207(-0.004~ 0.417) 0.151(-0.296 0.597) 0.805(-0.269 1.879) 0.094(-0.471 ~ 0.660) -0.068(-0.384~ 0.249) 0.764(0.055~ 1.473) -0.311(-2.012 ~ 1.390) 0.320(-0.062~ 0.701) 0.190(-0.086~ 0.467) 0.048(-0.642~ 0.739) 1.752( 0.231 3.274) Variable In the whole population In anti-inflammatory diet population In pro-inflammatory diet population IRT(-) DRT(-) AFT(-) DSST(-) IRT(-) DRT(-) AFT(-) DSST(-) IRT(-) DRT(-) AFT(-) DSST(-) Cadmium 0.340 0.347 0.434 0.716 0.075 0.035 0.254 0.085 0.500 0.441 0.701 0.590 Lead 0.236 0.189 0.016 0.052 0.428 0.685 0.055 0.550 0.104 0.113 0.052 0.070 Manganese 0.142 0.049 0.321 0.039 0.315 0.154 0.333 0.324 0.240 0.302 0.047 0.287 Mercury 0.246 0.308 0.152 0.190 0.021 0.014 0.326 0.003 0.130 0.054 0.040 0.023 Selenium 0.037 0.108 0.077 0.003 0.162 0.112 0.031 0.037 0.026 0.090 0.160 0.030 Index -0.029( -0.482~ 0.424) 0.137(-0.095 ~ 0.368) -0.316(-0.797~ 0.165) -0.613(-1.609~ 0.382) 0.319 (-0.934 ~ 0.297) -0.053(-0.305~ 0.198) 0.223(-0.57 ~ 1.015) -0.070(-1.678~ 1.538) -0.043 ( -0.604 0.518) 0.058–0.239~ 0.355) -0.046(-1.358~ 0.475) -0.676 (-2.163~ 0.810) Furthermore, the WQS index was calculated to evaluate the overall effect of metal mixture and cognitive function (Table 3 ). The fully adjusted WQS model revealed a significant positive association between the WQS index and IRT (0.379 (0.073 ~ 0.686)). Se had the highest weight (0.744) amongst all chemicals in the WQS index. Results from the favorable WQS model analysis further supported the beneficial effect of Se on IRT. In contrast, the research revealed no statistically significant discrepancies in the negative WQS model and other cognitive function scores. The weight study confirmed that Cd was most negatively correlated with IRT and DSST, while Se was most positively correlated with DSST (Figure S10). Qg-comp model (Figure S11) also supported these results. Se had the highest positive correlation with IRT and DSST, with weights of 0.707 and 0.565, respectively. Cd and Pb showed the highest negative correlation with IRT with weights of 0.402 and 0.546, respectively, whereas Cd weighted 0.837 on DSST. (Table 3 ) 3.4.2 Multi-Metal Exposures and cognitive function in an anti-inflammatory diet and pro-inflammatory diet population The influence of diet on metals and cognitive function was studied using the BKMR model. Specifically, the study examined the correlation between blood Cd and Se with IRT and DSST. The results revealed that the effects of metal co-exposure on IRT and DSST were significantly different under anti-inflammatory and pro-inflammatory diets. Under an anti-inflammatory diet, metal co-exposure's overall impact on IRT was opposite to a positive effect, while DSST showed a linear positive correlation (Fig. 2 A). In contrast, under a pro-inflammatory diet, the overall impact of metal co-exposure on IRT was negatively correlated, while for DSST, it was the same as that of the whole population, which was reduced firstly and then increased and presented a negative effect overall (Fig. 2 B). Blood Cd emerged as the significant negative contributor to IRT and DSST in the pro-inflammatory diet, with PIP values of 0.466 and 0.925 (Table 3 ). Blood Se was a substantial component for enhancing cognitive performance on IRT and DSST in the pro-inflammatory diet, not in the anti-inflammatory diet (Figure S12-S13). The exposure-response trends for each metal are shown in Fig. 3 . The IRT scores of blood Se showed a linear positive curve in the anti-inflammatory diet, whereas an inverted U-shaped curve in the pro-inflammatory diet. The inverted U-shaped curve was also observed in DSST scores of blood Se in both anti-inflammatory and pro-inflammatory diets. Blood Cd on DSST exhibited a linear positive curve in anti-inflammatory or pro-inflammatory diets. Furthermore, the interactions between Cd and Mn, Pb and Hg were observed in the pro-inflammatory diet (Figure S14-S21). The WQS and Qg-comp models were used to determine the correlation between co-exposure metals and cognitive function. Positive and negative contributors were identified. The WQS index was significantly associated with DSST (1.752(0.231–3.274)) in the pro-inflammatory diet. The chemical with the highest weight was Se, with a weight of 0.396. The pro-inflammatory diet further supported the beneficial effects of blood Se on DSST. However, no meaningful variations were found in the other WQS index. The negative contributor for IRT and DSST was Cd (WQS index: 0.500 for IRT, 0.590 for DSST), while the positive contributor was Se (WQS index: 0.769 for IRT, 0.396 for DSST). Cd showed the most negative correlation with IRT and DSST, while Se showed the most positive correlation with IRT and DSST in the pro-inflammatory diet (Fig. 4 ). The Qg-comp model confirmed the positive correlation between Se and IRT or DSST and the negative association between Cd and IRT or DSST in the pro-inflammatory diet (Fig. 5 ). 3.5 Sensitivity analysis The sensitivity analyses conducted in this study have demonstrated that blood Se remains significantly associated with IRT and DSST, while blood Cd exhibits a negative correlation with DSST across three models that adjust for different confounding variables. These findings suggested the robustness and reliability of our results. Furthermore, the consistency of the results was maintained even after adjusting for the confounding effect of the other four metals, as evidenced by the data presented in Table S2. 4. Discussion Heavy metals have been identified as potent neurotoxins that could cause acute and chronic neurotoxicity, resulting in cognitive function impairment. On the other hand, diet is a crucial factor in the development of many neurological disorders. Our study aimed to determine if dietary modifications could affect the association between blood metal concentrations and cognitive performance test scores in older adults. Our study represents a pioneering epidemiological investigation characterized by its large-scale nature and the utilization of diverse statistical models to explore the effect of heavy metals on cognitive function, both individually and in combination with one another. The study focuses on modifying dietary intake to elucidate the extent of the influence of heavy metal exposure on cognitive function. To the best of our knowledge, no other study has employed such an approach, making our research significantly contribute to the existing literature on the subject. This study employed four models, GLM, BKMR, WQS, and Qg-comp, to assess the impact of five heavy metals in blood on cognitive function under the anti-inflammatory and pro-inflammatory diet. The GLM models demonstrated a positive correlation between blood Se levels and IRT and DSST in the whole population. Notably, the pro-inflammatory diet group exhibited a significant association between blood Se and DSST, indicating the pro-inflammatory diet could fully exploit blood Se's action to enhance the DSST. Conversely, blood Cd levels negatively correlated with IRT and DSST in the whole population. However, the correlation was only observed among individuals with low diet quality. No associations were found among those with anti-inflammatory diets, implying that an anti-inflammatory diet could mitigate Cd toxicity's effects on IRT and DSST. Therefore, dietary patterns are essential to the association between metals and cognitive function. Recent research has identified inflammation as a potential underlying mechanism contributing to cognitive impairment [ 20 ]. Specifically, studies have found that cognitive function score is negatively associated with the concentration of Cd in the blood [ 21 , 22 ]. Cd exhibits neurotoxic effects by inducing oxidative stress and neuroinflammation [ 23 ]. Exposure to Cd, whether at high levels for a short duration or low levels for a prolonged period, stimulates the production of reactive oxygen species by disrupting mitochondrial function and depleting antioxidants. The phenomenon leads to the onset of oxidative stress in neuronal and brain endothelial cells, resulting in impaired neurodevelopment and oxidative stress-dependent neuroinflammation [ 24 ]. On the other hand, Se has been shown to have beneficial effects in reducing inflammation by inhibiting mitogen-activated protein kinase (MAPK) pathways, changing arachidonic acid metabolism, and decreasing nuclear factor-kappa B (NF-κB) activation [ 25 ]. Studies have demonstrated that both Se-enriched brown rice protein hydrolysates and Se-enriched oolong tea extract exhibit excellent anti-inflammatory properties through the NF-κB/MAPK signaling pathway. Additionally, Se-enriched Cordyceps militaris effectively reduces inflammation in LPS-injured mice by increasing anti-inflammatory cytokine levels and inhibiting pro-inflammatory mediator production [ 26 ]. It is worth noting that inflammation has been confirmed as one of the possible mechanisms contributing to cognitive impairment. Hence, it is plausible that a diet that promotes inflammation may exacerbate or amplify metal toxicity. Several studies have studied the relationship between diet and cognitive health, and different diets can increase or decrease the risk of cognitive impairment [ 27 , 28 ]. DII evaluates dietary inflammation potential by calculating nutrients that contribute to inflammation in food. A pro-inflammatory diet (higher DII) was positively associated with cognitive impairment [ 29 ]. Given the above, it is essential to be mindful of the potential implications of dietary inflammation on cognitive health. An inflammatory diet may trigger systemic inflammation, which could further exacerbate Cd toxicity, and it has been reported that nutritional factors might be associated with decreased blood lead levels [ 30 ]. Conversely, Se presents a protective effect when the body is inflammatory under a pro-inflammatory diet. Hence, promoting an anti-inflammatory diet may help reduce the risk of cognitive impairment and related health issues. The present study utilized three models to investigate the dietary modification of cognitive function in individuals exposed to heavy metals. Using multiple models enhanced the reliability of the results compared with that of a single model [ 31 ]. Furthermore, the model was meticulously adjusted for covariates that could influence both metals and cognitive function, thereby minimizing the risk of confounding bias. A comprehensive assessment of cognitive function was undertaken by employing four tests, namely IRT, DRT, AFT, and DSST, to analyze the relationship between heavy metal exposure and cognitive function under different diet statuses. However, this study has certain limitations. Firstly, it is a cross-sectional study, thus lacking longitudinal follow-up of cognitive status and heavy metal concentrations, and hence, cannot establish a causal relationship between mixed metals and cognitive function under DII. Secondly, Fixed values used to replace levels of heavy metals below the detection limit may have underestimated the association effect. Therefore, while the study provides valuable insights, it's necessary to conduct more prospective cohort studies in the future to confirm the association. 5. Conclusion The current study provides evidence that a high-quality diet can mitigate the adverse effects of Cd on cognitive function, as measured by the IRT and DSST tests. Moreover, the study found that a high level of Se confers a protective effect on IRT and DSST, particularly under a pro-inflammatory diet. Additional cohort studies are necessary to corroborate further the association between diet and cognitive function in the presence of heavy metal exposure. These studies should be designed to measure the general population's annual blood metal concentrations and cognitive function. The findings of such studies will provide reliable evidence to inform policy development concerning diet, heavy metals, and health. Declarations Ethics approval and consent to participate The research in question involved the analysis of de-identified information that was downloaded from the public database of the National Health and Nutrition Examination Survey. Ethics approval for the research was granted by the Ethics Review Committee of the National Center for Health Statistics, and all methods used during the research were in accordance with relevant guidelines and regulations (including the Declaration of Helsinki). Additionally, all individuals who participated in the study provided written informed consent. Consent for publication Not applicable. Declaration of interest The authors have no relevant interests to declare. Funding This work was supported by the Open-ended Fund of Key Laboratory (Grant No.: KFJJ-202101). Author Contribution CLT and HH contributed to the study design. CLT and HH preformed the data analysis. CT wrote the manuscript. CLT, HH and MS critically revised and edited the manuscript for important intellectual content. All authors reviewed and approved the final manuscript. Acknowledgments Not applicable. 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Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 16 Sep, 2024 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 09 Jul, 2024 Reviews received at journal 06 Jul, 2024 Reviewers agreed at journal 04 Jul, 2024 Reviewers agreed at journal 04 Jul, 2024 Reviewers agreed at journal 04 Jul, 2024 Reviews received at journal 13 Jun, 2024 Reviewers agreed at journal 06 Jun, 2024 Reviewers invited by journal 23 May, 2024 Editor assigned by journal 16 May, 2024 Editor invited by journal 28 Jan, 2024 Submission checks completed at journal 28 Jan, 2024 First submitted to journal 26 Dec, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3806622","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":269725468,"identity":"efb5c4ad-ae64-4cb2-9003-e2452366e74c","order_by":0,"name":"Chunlan Tang","email":"","orcid":"","institution":"Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Chunlan","middleName":"","lastName":"Tang","suffix":""},{"id":269725469,"identity":"b1ae5717-e74b-459c-bdcd-51600bfed850","order_by":1,"name":"Min Shen","email":"","orcid":"","institution":"Medical System Biotechnology Co., Ltd. Ningbo","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Shen","suffix":""},{"id":269725470,"identity":"b01dbda0-abef-4f2f-bab3-063b2464e3ac","order_by":2,"name":"Hang Hong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACAyBmBjH42ZsPHPjwgxQtkj3HEg/O7CFFi8GNHOPDHGxEaDFn7z38uqDmjl3DmTMfDjPwMMjzix3Ar8Wy51ya9Yxjz5Ib23s3HC6wYDCcOTuBgMNu5JgZ87AdTmbmObvh8AwehgSD20Rp+Xc4mU0i58FhHjbitBg/5m07bMcjkcNApJYzZ8yYefsOJ0jwHDMABrIEEX453mP8mefbYXv7482PP3z4YSPPL01ACxCwSQCJxAYIR4KgchBg/gAk7IlSOgpGwSgYBSMTAAAOjktiY32qtgAAAABJRU5ErkJggg==","orcid":"","institution":"Ningbo University","correspondingAuthor":true,"prefix":"","firstName":"Hang","middleName":"","lastName":"Hong","suffix":""}],"badges":[],"createdAt":"2023-12-26 06:44:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3806622/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3806622/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-024-20060-4","type":"published","date":"2024-09-16T15:57:56+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":50441373,"identity":"5948eb35-6a6c-4776-9532-e663dbcc0d9f","added_by":"auto","created_at":"2024-01-31 15:18:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":182617,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the selection of eligible subjects.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3806622/v1/dfaf7004815c77a070a69ae7.png"},{"id":50443324,"identity":"a0525022-ad40-4eb3-a3cf-f0982edb2ba2","added_by":"auto","created_at":"2024-01-31 15:26:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":250037,"visible":true,"origin":"","legend":"\u003cp\u003eJoint effect (95% CI) of the metals on IRT, DRT, AFT, and DSST when all the metals at particular percentiles were compared to those at their 50th percentile in the anti-inflammatory (A) and pro-inflammatory diet (B) by the BKMR model. The model was adjusted for adjusted by sex, age, race, education, income, marital status, physical activity, drinking, smoking, BMI, diabetes, hypertension, and hyperlipidemia.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3806622/v1/9bdac58207fa61979a1047c7.png"},{"id":50441375,"identity":"91db72b5-38fa-48fd-870e-0c3d56ee427b","added_by":"auto","created_at":"2024-01-31 15:18:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":511889,"visible":true,"origin":"","legend":"\u003cp\u003eUnivariate exposure-response relationships (95% CI) between the metal and cognitive performance tests (IRT, DRT, AFT, and DSST) while fixing at the median for other four metal concentrations in the anti-inflammatory (A) and pro-inflammatory diet (B) by the BKMR model. The model was adjusted for sex, age, race, education, income, marital status, physical activity, drinking, smoking, BMI, diabetes, hypertension, and hyperlipidemia.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3806622/v1/6fde689287fed699e616301f.png"},{"id":50441376,"identity":"42e7036e-9fb8-40b1-8425-fa600aa958bc","added_by":"auto","created_at":"2024-01-31 15:18:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":357513,"visible":true,"origin":"","legend":"\u003cp\u003eWQS model regression index weights for blood heavy metals and cognitive function in the anti-inflammatory (A) and pro-inflammatory diet (B). The model was adjusted for sex, age, race, education, income, marital status, physical activity, drinking, smoking, BMI, diabetes, hypertension, and hyperlipidemia.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3806622/v1/5878a7fee12739495953bfbf.png"},{"id":50441374,"identity":"deaef391-908c-4af2-afce-309eaf23e323","added_by":"auto","created_at":"2024-01-31 15:18:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":152008,"visible":true,"origin":"","legend":"\u003cp\u003eQg-comp model regression index weights for blood heavy metals and cognitive function in the anti-inflammatory (A) and pro-inflammatory diet (B). The model was adjusted for sex, age, race, education, income, marital status, physical activity, drinking, smoking, BMI, diabetes, hypertension, and hyperlipidemia.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3806622/v1/fd1006ffb0b9474fb849056a.png"},{"id":65104448,"identity":"7dfbd29c-5053-4860-93ab-79212f44025c","added_by":"auto","created_at":"2024-09-23 16:13:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3565477,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3806622/v1/ca97992d-2904-4b0a-b4a6-ca60915c454b.pdf"},{"id":50441378,"identity":"298445e6-4960-4e7e-8b6e-6e382775e84f","added_by":"auto","created_at":"2024-01-31 15:18:18","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":2613291,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3806622/v1/d4b48042c4533cc79d2a69db.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Uncovering the Relationship between Heavy Metal Exposure, Cognitive Function, and Dietary Inflammation Index in Elderly Americans from the National Health and Nutrition Examination Survey 2011-2014","fulltext":[{"header":"1. Background","content":"\u003cp\u003eThe rise in global life expectancy has given rise to a significant global public health concern regarding the cognitive health of elderly adults. With the number of older adults experiencing cognitive impairment or dementia on the rise, it is estimated that this population will reach 78\u0026nbsp;million by 2030 and a staggering 139\u0026nbsp;million by 2050 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A decline in various mental abilities, including memory loss, poor concentration, information processing, confusion or memory loss, and semantic comprehension errors, characterizes impaired cognitive function. It can also lead to abnormal reasoning and decision-making processes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This decline can profoundly impact the daily activities of elderly adults, ultimately reducing their quality of life. Numerous studies indicate that cognitive decline is closely associated with physical, psychological, social, and lifestyle risk factors [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, several investigations have demonstrated that exposure to metal pollution, both natural and manufactured, can impact cognitive function in older adults [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The precise mechanisms by which this occurs remain unclear; however, heavy metals are known to trigger neuroinflammation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], which is a complex process involving various factors such as direct toxic effects, immune response, oxidative stress, and neuronal apoptosis. These processes interact and promote the development of neuroinflammation. Heavy metal ions can bind to neuronal proteins, causing neuronal damage, neuroinflammation, and nervous system dysfunction [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, they can stimulate the immune system to produce an inflammatory response, resulting in neuroinflammation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Heavy metal exposure promotes the production of reactive oxygen species (ROS), which can attack cell components and cause cell damage and inflammation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. They can also induce neuronal apoptosis, exacerbating neuroinflammation and damaging the nervous system [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe constituents of daily food intake are complex, necessitating an exploration of specific dietary conditions to understand their impact on inflammation and cognitive impairment. The Dietary Inflammatory Index (DII) is a recognized indicator of overall dietary inflammation, calculated by merging diverse food components. Research has demonstrated that a higher DII score positively correlates with cognitive function [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, limited studies have evaluated whether dietary intake of antioxidants and anti-inflammatory compounds can modify the effect of metal exposure on markers of neuroinflammation. It is hypothesized that an anti-inflammatory or pro-inflammatory diet may attenuate or enhance inflammation and subsequently reduce or increase metals-induced neurotoxicity. The present study explores whether the association between blood metal and cognitive function is modified by overall diet quality measured by the DII score. The study will employ three statistical models, namely the Bayesian kernel machine regression (BKMR) model, weighted quantile sum (WQS) model, and quantile g-computation (Qg-comp) model, to analyze data from the National Health and Nutrition Examination Survey (NHANES). The primary objective is to investigate whether the overall quality of an individual's diet can strengthen or weaken the relationship between blood metal and cognitive function.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study population\u003c/h2\u003e \u003cp\u003eThe present study primarily analyzed data from two cycles (2011\u0026ndash;2012 and 2013\u0026ndash;2014) of NHANES, a cross-sectional survey conducted by the Centers for Disease Control and Prevention (CDC) to evaluate the health and nutrition status of the non-institutionalized population in the US. The study received approval from the National Center for Health Statistics (NCHS), and all participants provided their informed consent. Out of 19,931 respondents, data were collected from 3,632 participants aged 60 or older. Participants with incomplete information on blood metal levels, cognitive tests, and covariates were excluded from the analysis. Ultimately, the study comprised 1,726 individuals aged 60 or older (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Measurement\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Cognitive Function\u003c/h2\u003e \u003cp\u003eThe cognitive abilities of the individuals were assessed using four tests: the Instant Recall Test (IRT), the Delayed Recall Test (DRT), the Animal Fluency Test (AFT), and the Digit Symbol Substitution Test (DSST). IRT and DRT were used to evaluate the acquisition of new linguistic material immediately and after a certain period of delay. The AFT was used to assess executive function, while the DSST was used to determine the participants' cognitive abilities, such as reaction time, sustained attention, and working memory. All test scores were standardized to ensure consistency with the composite score. The resulting scores serve as a measure of cognitive function, with higher scores indicating superior performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Heavy metal\u003c/h2\u003e \u003cp\u003eThe CDC's Division of Laboratory Sciences at the National Center for Environmental Health processed, stored, and analyzed whole blood specimens for heavy metal concentrations. Specifically, the attention of lead (Pb), cadmium (Cd), total mercury (Hg), manganese (Mn), and selenium (Se) were analyzed using inductively coupled plasma mass spectrometry (ICP-MS). In cases where the blood heavy metal levels fall below the lower limit of detection (LLOD), they were replaced by the result of dividing the limit by the square root of 2 (LLOD/sqrt[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]). The concentration of all metals in the blood was transformed using a natural logarithm to obtain nearly normal distributions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Dietary Inflammatory Index\u003c/h2\u003e \u003cp\u003eThe Dietary Inflammatory Index (DII) was derived from data obtained through two 24-hour dietary recall interviews. The calculation of a Z-score involves the subtraction of the global per capita daily intake from the daily intake of a specific nutrient. This result was then divided by the standard deviation of the global per capita daily intake. The resulting Z-score was converted into a percentage, which fell from 0 to 1. This percentage was doubled and subtracted by 1, resulting in a symmetrical distribution centered at \"0\". Finally, the total inflammation score was obtained by multiplying the percentage received for each dietary component by the total inflammation score. This calculation was done for twenty-seven nutrients, including alcohol, β-carotene, caffeine, carbohydrate, cholesterol, folic acid, fiber, iron, magnesium, n-3 fatty acids, n-6 fatty acids, total monounsaturated fatty acids, niacin, total polyunsaturated fatty acids, protein, total saturated fatty acids, Se, total fat, riboflavin, thiamin, vitamin A, B12, B6, C, D, E, and zinc. Participants were categorized into DII\u0026thinsp;\u0026ge;\u0026thinsp;0 and DII\u0026thinsp;\u0026lt;\u0026thinsp;0, signifying pro-inflammatory and anti-inflammatory diets, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Covariate assessment\u003c/h2\u003e \u003cp\u003eThe study incorporated several demographic covariates in the analysis, specifically sex, age (60\u0026ndash;69, 70\u0026ndash;79, or \u0026ge;\u0026thinsp;80), race (American, Mexican other Hispanic, non-Hispanic white, non-Hispanic black, or other), marital status (never married, married/living with partner, or widowed/divorced/separated), family income to poverty ratio (\u0026le;\u0026thinsp;1.3, 1.3\u0026ndash;3.5, or \u0026gt;\u0026thinsp;3.5), drinking status (having at least 12 alcohol drinks per year or not), body mass index (BMI) (\u0026lt;\u0026thinsp;18.5 kg/cm\u0026sup2;, \u0026ge;\u0026thinsp;18.5 to \u0026lt;\u0026thinsp;25 kg/cm\u0026sup2;, \u0026ge;\u0026thinsp;25 to \u0026lt;\u0026thinsp;30 kg/cm\u0026sup2;, or \u0026ge;\u0026thinsp;30 kg/cm\u0026sup2;), smoking status (having at least 100 cigarettes in their lifetime or not), and education level (less than 9th grade, 9-11th grade, high school grad/GED, some college or AA degree, or college graduate or above). Hyperlipidemia was diagnosed when a person had either low-density cholesterol levels of 160 mg/dL or higher, total cholesterol levels of 240 mg/dL or higher, or was taking lipid-lowering medication. Hypertension was diagnosed by a physician or a systolic blood pressure of \u0026ge;\u0026thinsp;130 mmHg, a diastolic blood pressure of \u0026ge;\u0026thinsp;80 mmHg, or current use of hypertension medications. Individuals with diabetes were identified based on any of the following criteria: (a) a hemoglobin A1C concentration of 6.5% or higher or a fasting plasma glucose level of 126 mg/dL or higher; (b) individuals who answered positively to the question: \"Has a doctor told you that you have diabetes?\" or \"Are you currently taking insulin?\" Physical activity was measured using a unit called Metabolic Equivalent of Task (MET) scores. The scores were assigned as follows: 8 for vigorous work-related exercise, 4 for moderate work-related activity, 4 for walking or bicycling, 8 for vigorous leisure-time physical activity, and 4 for moderate leisure-time physical activity. To determine the total amount of physical activity, weekly minutes were added and multiplied by each activity's MET score. The US physical activity guidelines defined a high level of physical activity as \u0026ge;\u0026thinsp;600 MET*min/week and a low level of physical activity as \u0026lt;\u0026thinsp;600 MET*min/week.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical analysis\u003c/h2\u003e \u003cp\u003eThe categorical variables of four cognitive test scores were presented as medians, accompanied by their respective interquartile ranges (IQR). The Kruskal-Wallis test was employed for comparison. The correlation between the five metals was evaluated using Pearson correlation coefficients. Cognitive function scores were analyzed for association with metal exposure using generalized linear regression models (GLM). Furthermore, we used quartiles of metal exposure and a trend test across increasing exposure groups in multivariable models, then rerunning the related regression model. Subgroup analysis was conducted to determine any variations in the correlation between metal exposure and cognitive function, stratified by the DII group (anti-inflammatory and pro-inflammatory diets), using stratified linear regression models. Likelihood-ratio tests inspected the modifications and interactions of subgroups.\u003c/p\u003e \u003cp\u003eThe potential interactions and collective impacts of metal exposure on cognitive function were analyzed using the BKMR statistical modeling. The researchers investigated the nonlinear relationship between exposure and outcome by employing exposure-response cross-sections for a single independent variable and outcome. The study further controlled for other variables by keeping them constant at the median. Bivariate exposure-response profiles can be used to represent the interaction between different mixture compositions. This interaction can be understood as the potential impact of one chemical's slope curve at various modifications of another chemical (25th, 50th, and 75th) while the remaining variables are held constant at the median. The association plot offered a comprehensive analysis of the impact of the mixture on the outcome. It displayed how the outcome was expected to change based on different percentiles of exposure variables, compared to when they are at the media. The iteration was performed using the Markov Chain Monte Carlo method with a fixed value 30,000.\u003c/p\u003e \u003cp\u003eThe WQS regression model is a statistical approach that uses mixed-effects strategies to analyze the relationship between multiple chemical exposures and outcomes. It evaluates this relationship by computing a weighted index and determining the proportional impact of each exposure. The weights assigned to each exposure are between 0 and 1, and the sum of all weights equals 1. The potential association between metal exposure and cognitive function was investigated by the WQS model while controlling for all relevant covariates. The present study involved the random partitioning of original data into a training set (40%) and a validation set (60%) to derive weight values for each metal exposure. Subsequently, the bootstrap method with a sample size of 3000 was applied to the data to infer the statistical properties of the derived weight values.\u003c/p\u003e \u003cp\u003eWe used the Qg-comp model to address the WQS regression model's limitations in determining the association's direction. Qg-comp model provides a reliable means of predicting the impact of exposure mixtures. However, in large samples where the assumptions of WQS regression hold, Qg-comp can also yield equivalent estimates. The qgcomp.noboot function was applied to establish a linear model for cognitive function. We applied positive or negative weighting indices to each heavy metal in the blood and subsequently segmented each metal into quartiles to determine its overall effect.\u003c/p\u003e \u003cp\u003eWe developed five models in sensitivity analysis to examine the association between various factors and the outcome. Model 1 had no adjustments for any confounding variables, while Model 2 adjusted for sex, age, race, education level, income, and marital status. Model 3 added adjustments for physical activity, drinking, smoking habits, BMI, and DII. Model 4 accounted for diabetes, hypertension, and hyperlipidemia. Lastly, Model 5 adjusted for four other metals. All statistical analyses were performed using the R software (version 4.3.2) and packages \u0026ldquo;survey\u0026rdquo;,\u0026ldquo;bkmr\u0026rdquo;, \u0026ldquo;gWQS\u0026rdquo;, and \u0026ldquo;Qgcomp\u0026rdquo;. A significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Descriptive statistics\u003c/h2\u003e \u003cp\u003eThe study comprised 1726 participants, of which 853 were male and 873 were female. Sociodemographic data, such as sex, age, ethnicity, education level, income, marital status, drinking and smoking habits, BMI, and physical activity, was collected and subsequently categorized as presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The results indicated that women who were non-Hispanic Whites, highly educated, and had high incomes had higher scores on cognitive function tests. Furthermore, poor cognitive performance was more prevalent in adults with diabetes and hypertension than in those with normal cognitive performance. It is noteworthy that poor cognitive performance in the pro-inflammatory diet were observed compared to that in the anti-inflammatory diet. The study also revealed that older adults with high blood Cd and Pd levels had low cognitive function scores on IRT, DRT, AFT, and DDST. Conversely, high blood Hg and Se levels presented high cognitive function scores. Weak correlations among the five metals were observed, as shown in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e provides information about the distribution of five metals across various populations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics of study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal number(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIRT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDRT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAFT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDSST\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;1726)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46.0 (34.0, 59.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e873 (50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.0 (17.0, 23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.0 (5.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49.0 (36.0, 62.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e853 (49.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.0 (15.0, 21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e43.0 (32.0, 54.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;69 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e885 (51.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.0 (17.0, 23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.0 (5.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.0 (14.0, 21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51.0 (37.0, 63.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;79 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e477 (27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0 (12.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44.0 (32.0, 55.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e364 (21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.0 (14.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.0 (3.0, 6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.0 (12.0, 18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40.0 (30.0, 50.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e134 ( 7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.0 (15.0, 21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0 (13.0, 19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38.0 (26.0, 53.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e403 (23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.0 (11.0, 18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39.0 (29.0, 52.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e874 (50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.0 (14.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.0 (39.2, 63.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e174 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.0 (15.0, 21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.0 (12.0, 18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35.0 (25.0, 49.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther/multiracial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e141 ( 8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.0 (16.0, 23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.0 (5.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.0 (12.0, 18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.0 (42.0, 63.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation attainment, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess Than 9th Grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e191 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.0 (14.0, 19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.0 (3.0, 6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.0 (11.0, 17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.0 (20.0, 33.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9-11th Grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e216 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.0 (15.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.0 (11.0, 18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37.0 (29.0, 46.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh School Grad/GED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e411 (23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0 (12.0, 19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45.0 (34.0, 54.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome College or AA degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e503 (29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0 (12.0, 19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45.0 (34.0, 54.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege Graduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e405 (23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.0 (17.0, 23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.0 (5.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.0 (15.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e56.0 (45.0, 67.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;= 1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e498 (28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.0 (15.0, 21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.0 (12.0, 18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e36.0 (25.0, 48.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.31to\u0026thinsp;\u0026lt;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e672 (38.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45.0 (34.0, 57.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e556 (32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.0 (17.0, 23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.0 (5.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.0 (14.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e55.0 (45.0, 67.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever Married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e101 ( 5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.0 (16.0, 23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.0 (5.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47.0 (35.0, 59.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/Living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1010 (58.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.0 (13.0, 21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.0 (36.0, 60.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/Divorced/ Separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e615 (35.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0 (12.0, 19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e43.0 (30.0, 57.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e521 (30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (15.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.0 (12.0, 19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44.0 (31.0, 55.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edrinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1205 (69.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.0 (13.0, 21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.0 (35.0, 60.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e851 (49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.0 (34.0, 60.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e875 (50.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45.0 (33.0, 57.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight(\u0026lt;\u0026thinsp;18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23 ( 1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.0 (11.5, 23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.0 (3.0, 7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.0 (12.0, 19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37.0 (29.5, 51.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal(18.5 to \u0026lt;\u0026thinsp;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e435 (25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight(25 to \u0026lt;\u0026thinsp;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e591 (34.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46.0 (34.0, 60.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese(30 or greater)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e677 (39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (5.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47.0 (34.0, 59.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e866 (50.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (15.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.0 (12.0, 19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e43.0 (31.0, 55.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e860 (49.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (17.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (5.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.0 (14.0, 21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49.5 (37.0, 62.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1224 (70.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.0 (13.0, 21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.5 (36.0, 61.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e502 (29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (15.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.0 (12.0, 19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42.0 (29.0, 52.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e328 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.0 (17.0, 23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (5.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.0 (14.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.0 (37.0, 63.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1398 (81.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45.0 (33.0, 57.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e555 (32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46.0 (33.5, 58.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1171 (67.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46.0 (34.0, 59.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCadmium, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e424 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49.0 (36.0, 61.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e424 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.0 (13.0, 21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.0 (35.0, 60.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e436 (25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46.0 (34.0, 59.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e442 (25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.0 (12.0, 19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42.0 (32.0, 55.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLead, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e431 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (17.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (5.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.0 (34.0, 61.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e426 (24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.0 (16.0, 23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49.0 (36.2, 59.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e436 (25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47.0 (36.0, 60.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e433 (25.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.0 (15.0, 21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42.0 (31.0, 54.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManganese, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e431 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (15.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44.0 (32.0, 56.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e432 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (5.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46.0 (36.0, 59.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e429 (24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47.0 (33.0, 60.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e434 (25.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (5.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0 (12.2, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.0 (36.0, 60.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMercury, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e421 (24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.0 (15.0, 21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.0 (12.0, 19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41.0 (30.0, 54.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e441 (25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46.0 (33.0, 58.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e432 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.0 (34.0, 60.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e432 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.0 (16.0, 23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (5.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.0 (13.0, 21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.0 (38.0, 63.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelenium, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e432 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.0 (15.0, 21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.0 (12.0, 19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40.0 (28.0, 53.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e431 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49.0 (34.0, 61.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e431 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (5.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.0 (37.0, 60.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e432 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.0 (16.0, 23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.8, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.0 (13.0, 20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.0 (35.8, 60.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDII group, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epro-inflammatory diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1011 (58.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (15.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0 (12.0, 19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44.0 (31.0, 56.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eanti-inflammatory diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e715 (41.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0 (16.5, 23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0 (5.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.0 (14.0, 21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.0 (39.0, 63.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Associations between blood metals mixture and cognitive performance scores\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the correlation between blood metal concentration and cognitive function. The study revealed a positive correlation between blood Se and IRT and DDST scores (β(95%Cl): 2.06 (0.7\u0026thinsp;~\u0026thinsp;3.41), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; 6.41 (2.35\u0026thinsp;~\u0026thinsp;10.46), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), respectively. The GLM analysis based on quartiles of the exposure variables demonstrated that blood Se at Q2 (β(95%Cl): 0.89 (0.33\u0026thinsp;~\u0026thinsp;1.45), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and Q3 (β(95%Cl): 3.14 (1.46\u0026thinsp;~\u0026thinsp;4.81), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had the most significant positive impact on cognitive function of IRT and DDST, respectively. Conversely, blood Pb had a negative correlation with IRT scores (β(95% CI): -0.47 (-0.82~-0.11), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the Q3 (β(95%Cl): -0.82 (-1.39~-0.25), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) had the most negative effect. Blood Cd was negatively associated with DSST (β(95% CI): -1.17 (-2.13~-0.22), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and the Q3 (β(95%Cl): 3.14 (1.46\u0026thinsp;~\u0026thinsp;4.81), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) showed the most significant negative impact.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate linear regression analysis of the association between metal exposure and cognitive function scores\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIRT(β(95%CI))\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDRT(β(95%CI))\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAFT(β(95%CI))\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDDST(β(95%CI))\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCadmium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCont\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.31 (-0.64\u0026thinsp;~\u0026thinsp;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.05 (-0.22\u0026thinsp;~\u0026thinsp;0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.11 (-0.49\u0026thinsp;~\u0026thinsp;0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-1.17 (-2.13~-0.22)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(Ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.52 (-1.08\u0026thinsp;~\u0026thinsp;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.06 (-0.35\u0026thinsp;~\u0026thinsp;0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12 (-0.55\u0026thinsp;~\u0026thinsp;0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-1.91 (-3.59~-0.23)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.23 (-0.8\u0026thinsp;~\u0026thinsp;0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.24 (-0.54\u0026thinsp;~\u0026thinsp;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.17 (-0.84\u0026thinsp;~\u0026thinsp;0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-1.87 (-3.57~-0.16)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.72 (-1.34~-0.1)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1 (-0.42\u0026thinsp;~\u0026thinsp;0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.37 (-1.11\u0026thinsp;~\u0026thinsp;0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.33 (-4.18~-0.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-t\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCont\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.47 (-0.82~-0.11)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.12 (-0.3\u0026thinsp;~\u0026thinsp;0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19 (-0.24\u0026thinsp;~\u0026thinsp;0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.3 (-1.37\u0026thinsp;~\u0026thinsp;0.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(Ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2 (-0.76\u0026thinsp;~\u0026thinsp;0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02 (-0.31\u0026thinsp;~\u0026thinsp;0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06 (-0.6\u0026thinsp;~\u0026thinsp;0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.34 (-2.02\u0026thinsp;~\u0026thinsp;1.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.82 (-1.39~-0.25)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.19 (-0.48\u0026thinsp;~\u0026thinsp;0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15 (-0.52\u0026thinsp;~\u0026thinsp;0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.09 (-1.78\u0026thinsp;~\u0026thinsp;1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.46 (-1.05\u0026thinsp;~\u0026thinsp;0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.14 (-0.45\u0026thinsp;~\u0026thinsp;0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3 (-0.4\u0026thinsp;~\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.93 (-2.7\u0026thinsp;~\u0026thinsp;0.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-t\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManganese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCont\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.4 (-0.97\u0026thinsp;~\u0026thinsp;0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07 (-0.22\u0026thinsp;~\u0026thinsp;0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.59 (-1.26\u0026thinsp;~\u0026thinsp;0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39 (-1.3\u0026thinsp;~\u0026thinsp;2.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(Ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06 (-0.49\u0026thinsp;~\u0026thinsp;0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14 (-0.14\u0026thinsp;~\u0026thinsp;0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.65 (-1.31\u0026thinsp;~\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.46 (-1.21\u0026thinsp;~\u0026thinsp;2.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04 (-0.52\u0026thinsp;~\u0026thinsp;0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16 (-0.13\u0026thinsp;~\u0026thinsp;0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.51 (-1.18\u0026thinsp;~\u0026thinsp;0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.34 (-2.02\u0026thinsp;~\u0026thinsp;1.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1 (-0.68\u0026thinsp;~\u0026thinsp;0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12 (-0.18\u0026thinsp;~\u0026thinsp;0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.81 (-1.49~-0.13)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6 (-1.13\u0026thinsp;~\u0026thinsp;2.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-t\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.729\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMercury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCont\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1 (-0.12\u0026thinsp;~\u0026thinsp;0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02 (-0.09\u0026thinsp;~\u0026thinsp;0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12 (-0.14\u0026thinsp;~\u0026thinsp;0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.44 (-0.21\u0026thinsp;~\u0026thinsp;1.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(Ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06 (-0.5\u0026thinsp;~\u0026thinsp;0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03 (-0.26\u0026thinsp;~\u0026thinsp;0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43 (-0.23\u0026thinsp;~\u0026thinsp;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22 (-1.46\u0026thinsp;~\u0026thinsp;1.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.28 (-0.3\u0026thinsp;~\u0026thinsp;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09 (-0.21\u0026thinsp;~\u0026thinsp;0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3 (-0.38\u0026thinsp;~\u0026thinsp;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.34 (-0.38\u0026thinsp;~\u0026thinsp;3.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.18 (-0.43\u0026thinsp;~\u0026thinsp;0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18 (-0.13\u0026thinsp;~\u0026thinsp;0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4 (-0.31\u0026thinsp;~\u0026thinsp;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.2 (-0.6\u0026thinsp;~\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-t\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelenium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCont\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.06 (0.7\u0026thinsp;~\u0026thinsp;3.41)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.58 (-0.12\u0026thinsp;~\u0026thinsp;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83 (-0.77\u0026thinsp;~\u0026thinsp;2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e6.41 (2.35\u0026thinsp;~\u0026thinsp;10.46)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(Ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.89 (0.33\u0026thinsp;~\u0026thinsp;1.45)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.31 (0.02\u0026thinsp;~\u0026thinsp;0.6)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29 (-0.37\u0026thinsp;~\u0026thinsp;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.87 (1.2\u0026thinsp;~\u0026thinsp;4.53)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.49 (-0.07\u0026thinsp;~\u0026thinsp;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27 (-0.02\u0026thinsp;~\u0026thinsp;0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27 (-0.4\u0026thinsp;~\u0026thinsp;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.14 (1.46\u0026thinsp;~\u0026thinsp;4.81)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.76 (0.19\u0026thinsp;~\u0026thinsp;1.32)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25 (-0.05\u0026thinsp;~\u0026thinsp;0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.45 (-0.22\u0026thinsp;~\u0026thinsp;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.54 (0.85\u0026thinsp;~\u0026thinsp;4.22)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-t\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Associations between blood metals and cognitive performance scores differed by Dietary Inflammatory Index\u003c/h2\u003e \u003cp\u003eSubgroup analyses assessed whether an anti-inflammatory or pro-inflammatory diet influenced the relationship between blood metal and cognitive function (Figure S2). In the population with a pro-inflammatory diet, blood Cd and IRT showed a negative correlation (β (95% CI): -0.53 (-0.96~-0.11)). The Q4 equates (β (95% CI): -1.15 (-1.97~-0.32)) demonstrated the most negative effect. The same results were observed between Cd and DSST (β (95% CI): -1.31 (-2.59~-0.03)), with all equates showing a negative effect. Conversely, blood Se was highly positively associated with DSST as a continuous variable (β (95% CI): 8.57 (2.79\u0026thinsp;~\u0026thinsp;14.36)) or quadripartite variable. For IRT, the Q2 equates of blood Se was positively associated. Additionally, the population with an anti-inflammatory diet exhibited negative and positive correlations between blood Pb and Se with IRT, respectively, with β (95% CI) values of -0.72 (-1.28~-0.17) and 2.21 (0.28\u0026thinsp;~\u0026thinsp;4.15). And the Q3 equates to Pb, and the Q2 equates to Se, showing the association.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Multi-Metal Exposures and Cognitive Function\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Multi-Metal Exposures and Cognitive Function in the Whole Population\u003c/h2\u003e \u003cp\u003eThe BKMR model investigated the association between cognitive function and co-exposure to five blood heavy metals. The results showed that the overall effect of the five metal co-exposure on DRT tended to be positive. However, for IRT, AFT, and DSST, the impact of heavy metal co-exposure was first reduced and then increased, presenting a negative effect overall (Figure S3). Blood Se emerged as a significant component for improved cognitive performance on IRT and DSST (IRT: 0.401; DSST: 0.617) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and presented a positive relationship when all other chemicals were at the 25th, 50th, and 75th percentile levels (Figure S4). Conversely, blood Cd was found to have a negative correlation with DSST and presented vital components for cognitive impairment (PIP: 0.543). The IRT scores of blood Se showed a linear positive curve, whereas an inverted U-shaped curve was observed in DSST scores. It was a linear negative curve for blood Cd on IRT and DSST (Figure S5). Additionally, the study found that the slope of the dose-response relationship between Cd and IRT when Se was at the 25th, 50th, and 75th percentile levels indicated the interaction between Cd and DSST. No other interaction was observed between blood Se/Cd and IRT or DSST (Figure S6-9).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe index from BKMR, WQS and Qg-comp analysis in the whole population,anti-inflammatory diet population and pro-inflammatory diet population.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eBKMR\u003c/p\u003e \u003cp\u003emodel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eIn the whole population\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eIn anti-inflammatory diet population\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c14\" namest=\"c11\"\u003e \u003cp\u003eIn pro-inflammatory diet population\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIRT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDRT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAFT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDSST\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIRT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDRT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAFT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDSST\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eIRT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eDRT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eAFT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eDSST\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCadmium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLead\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManganese\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMercury\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelenium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.401\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003eQg-comp model\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eIn the whole population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eIn anti-inflammatory diet population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c14\" namest=\"c11\"\u003e \u003cp\u003eIn pro-inflammatory diet population\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAFT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDSST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAFT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDSST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eIRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eDRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eAFT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eDSST\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCadmium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManganese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMercury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelenium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"14\" rowspan=\"15\"\u003e \u003cp\u003e\u003cb\u003eWQS\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003emodel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eIn the whole population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eIn anti-inflammatory diet population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c14\" namest=\"c11\"\u003e \u003cp\u003eIn pro-inflammatory diet population\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIRT(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDRT(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAFT(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDSST(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIRT(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDRT(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAFT(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDSST(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eIRT(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eDRT(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eAFT(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eDSST(+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCadmium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManganese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMercury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelenium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.379(0.073~ 0.686)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.207(-0.004~ 0.417)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.151(-0.296 0.597)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.805(-0.269 1.879)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.094(-0.471\u0026thinsp;~\u0026thinsp;0.660)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.068(-0.384~ 0.249)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.764(0.055~ 1.473)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.311(-2.012 ~ 1.390)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.320(-0.062~ 0.701)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.190(-0.086~ 0.467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.048(-0.642~ 0.739)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.752( 0.231 3.274)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eIn the whole population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eIn anti-inflammatory diet population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c14\" namest=\"c11\"\u003e \u003cp\u003eIn pro-inflammatory diet population\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIRT(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDRT(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAFT(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDSST(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIRT(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDRT(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAFT(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDSST(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eIRT(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eDRT(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eAFT(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eDSST(-)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCadmium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.590\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManganese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMercury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelenium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.029( -0.482~ 0.424)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.137(-0.095\u0026thinsp;~\u0026thinsp;0.368)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.316(-0.797~ 0.165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.613(-1.609~ 0.382)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.319 (-0.934\u0026thinsp;~\u0026thinsp;0.297)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.053(-0.305~ 0.198)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.223(-0.57\u0026thinsp;~\u0026thinsp;1.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.070(-1.678~ 1.538)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.043 ( -0.604 0.518)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.058\u0026ndash;0.239~ 0.355)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.046(-1.358~ 0.475)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.676 (-2.163~ 0.810)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFurthermore, the WQS index was calculated to evaluate the overall effect of metal mixture and cognitive function (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The fully adjusted WQS model revealed a significant positive association between the WQS index and IRT (0.379 (0.073\u0026thinsp;~\u0026thinsp;0.686)). Se had the highest weight (0.744) amongst all chemicals in the WQS index. Results from the favorable WQS model analysis further supported the beneficial effect of Se on IRT. In contrast, the research revealed no statistically significant discrepancies in the negative WQS model and other cognitive function scores. The weight study confirmed that Cd was most negatively correlated with IRT and DSST, while Se was most positively correlated with DSST (Figure S10). Qg-comp model (Figure S11) also supported these results. Se had the highest positive correlation with IRT and DSST, with weights of 0.707 and 0.565, respectively. Cd and Pb showed the highest negative correlation with IRT with weights of 0.402 and 0.546, respectively, whereas Cd weighted 0.837 on DSST.\u003c/p\u003e \u003cp\u003e(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Multi-Metal Exposures and cognitive function in an anti-inflammatory diet and pro-inflammatory diet population\u003c/h2\u003e \u003cp\u003eThe influence of diet on metals and cognitive function was studied using the BKMR model. Specifically, the study examined the correlation between blood Cd and Se with IRT and DSST. The results revealed that the effects of metal co-exposure on IRT and DSST were significantly different under anti-inflammatory and pro-inflammatory diets. Under an anti-inflammatory diet, metal co-exposure's overall impact on IRT was opposite to a positive effect, while DSST showed a linear positive correlation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In contrast, under a pro-inflammatory diet, the overall impact of metal co-exposure on IRT was negatively correlated, while for DSST, it was the same as that of the whole population, which was reduced firstly and then increased and presented a negative effect overall (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Blood Cd emerged as the significant negative contributor to IRT and DSST in the pro-inflammatory diet, with PIP values of 0.466 and 0.925 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Blood Se was a substantial component for enhancing cognitive performance on IRT and DSST in the pro-inflammatory diet, not in the anti-inflammatory diet (Figure S12-S13). The exposure-response trends for each metal are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The IRT scores of blood Se showed a linear positive curve in the anti-inflammatory diet, whereas an inverted U-shaped curve in the pro-inflammatory diet. The inverted U-shaped curve was also observed in DSST scores of blood Se in both anti-inflammatory and pro-inflammatory diets. Blood Cd on DSST exhibited a linear positive curve in anti-inflammatory or pro-inflammatory diets. Furthermore, the interactions between Cd and Mn, Pb and Hg were observed in the pro-inflammatory diet (Figure S14-S21).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe WQS and Qg-comp models were used to determine the correlation between co-exposure metals and cognitive function. Positive and negative contributors were identified. The WQS index was significantly associated with DSST (1.752(0.231\u0026ndash;3.274)) in the pro-inflammatory diet. The chemical with the highest weight was Se, with a weight of 0.396. The pro-inflammatory diet further supported the beneficial effects of blood Se on DSST. However, no meaningful variations were found in the other WQS index. The negative contributor for IRT and DSST was Cd (WQS index: 0.500 for IRT, 0.590 for DSST), while the positive contributor was Se (WQS index: 0.769 for IRT, 0.396 for DSST). Cd showed the most negative correlation with IRT and DSST, while Se showed the most positive correlation with IRT and DSST in the pro-inflammatory diet (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The Qg-comp model confirmed the positive correlation between Se and IRT or DSST and the negative association between Cd and IRT or DSST in the pro-inflammatory diet (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Sensitivity analysis\u003c/h2\u003e \u003cp\u003eThe sensitivity analyses conducted in this study have demonstrated that blood Se remains significantly associated with IRT and DSST, while blood Cd exhibits a negative correlation with DSST across three models that adjust for different confounding variables. These findings suggested the robustness and reliability of our results. Furthermore, the consistency of the results was maintained even after adjusting for the confounding effect of the other four metals, as evidenced by the data presented in Table S2.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eHeavy metals have been identified as potent neurotoxins that could cause acute and chronic neurotoxicity, resulting in cognitive function impairment. On the other hand, diet is a crucial factor in the development of many neurological disorders. Our study aimed to determine if dietary modifications could affect the association between blood metal concentrations and cognitive performance test scores in older adults. Our study represents a pioneering epidemiological investigation characterized by its large-scale nature and the utilization of diverse statistical models to explore the effect of heavy metals on cognitive function, both individually and in combination with one another. The study focuses on modifying dietary intake to elucidate the extent of the influence of heavy metal exposure on cognitive function. To the best of our knowledge, no other study has employed such an approach, making our research significantly contribute to the existing literature on the subject.\u003c/p\u003e \u003cp\u003eThis study employed four models, GLM, BKMR, WQS, and Qg-comp, to assess the impact of five heavy metals in blood on cognitive function under the anti-inflammatory and pro-inflammatory diet. The GLM models demonstrated a positive correlation between blood Se levels and IRT and DSST in the whole population. Notably, the pro-inflammatory diet group exhibited a significant association between blood Se and DSST, indicating the pro-inflammatory diet could fully exploit blood Se's action to enhance the DSST. Conversely, blood Cd levels negatively correlated with IRT and DSST in the whole population. However, the correlation was only observed among individuals with low diet quality. No associations were found among those with anti-inflammatory diets, implying that an anti-inflammatory diet could mitigate Cd toxicity's effects on IRT and DSST. Therefore, dietary patterns are essential to the association between metals and cognitive function.\u003c/p\u003e \u003cp\u003eRecent research has identified inflammation as a potential underlying mechanism contributing to cognitive impairment [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Specifically, studies have found that cognitive function score is negatively associated with the concentration of Cd in the blood [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Cd exhibits neurotoxic effects by inducing oxidative stress and neuroinflammation [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Exposure to Cd, whether at high levels for a short duration or low levels for a prolonged period, stimulates the production of reactive oxygen species by disrupting mitochondrial function and depleting antioxidants. The phenomenon leads to the onset of oxidative stress in neuronal and brain endothelial cells, resulting in impaired neurodevelopment and oxidative stress-dependent neuroinflammation [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the other hand, Se has been shown to have beneficial effects in reducing inflammation by inhibiting mitogen-activated protein kinase (MAPK) pathways, changing arachidonic acid metabolism, and decreasing nuclear factor-kappa B (NF-κB) activation [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Studies have demonstrated that both Se-enriched brown rice protein hydrolysates and Se-enriched oolong tea extract exhibit excellent anti-inflammatory properties through the NF-κB/MAPK signaling pathway. Additionally, Se-enriched Cordyceps militaris effectively reduces inflammation in LPS-injured mice by increasing anti-inflammatory cytokine levels and inhibiting pro-inflammatory mediator production [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. It is worth noting that inflammation has been confirmed as one of the possible mechanisms contributing to cognitive impairment. Hence, it is plausible that a diet that promotes inflammation may exacerbate or amplify metal toxicity. Several studies have studied the relationship between diet and cognitive health, and different diets can increase or decrease the risk of cognitive impairment [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. DII evaluates dietary inflammation potential by calculating nutrients that contribute to inflammation in food. A pro-inflammatory diet (higher DII) was positively associated with cognitive impairment [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven the above, it is essential to be mindful of the potential implications of dietary inflammation on cognitive health. An inflammatory diet may trigger systemic inflammation, which could further exacerbate Cd toxicity, and it has been reported that nutritional factors might be associated with decreased blood lead levels [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Conversely, Se presents a protective effect when the body is inflammatory under a pro-inflammatory diet. Hence, promoting an anti-inflammatory diet may help reduce the risk of cognitive impairment and related health issues.\u003c/p\u003e \u003cp\u003eThe present study utilized three models to investigate the dietary modification of cognitive function in individuals exposed to heavy metals. Using multiple models enhanced the reliability of the results compared with that of a single model [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Furthermore, the model was meticulously adjusted for covariates that could influence both metals and cognitive function, thereby minimizing the risk of confounding bias. A comprehensive assessment of cognitive function was undertaken by employing four tests, namely IRT, DRT, AFT, and DSST, to analyze the relationship between heavy metal exposure and cognitive function under different diet statuses. However, this study has certain limitations. Firstly, it is a cross-sectional study, thus lacking longitudinal follow-up of cognitive status and heavy metal concentrations, and hence, cannot establish a causal relationship between mixed metals and cognitive function under DII. Secondly, Fixed values used to replace levels of heavy metals below the detection limit may have underestimated the association effect. Therefore, while the study provides valuable insights, it's necessary to conduct more prospective cohort studies in the future to confirm the association.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe current study provides evidence that a high-quality diet can mitigate the adverse effects of Cd on cognitive function, as measured by the IRT and DSST tests. Moreover, the study found that a high level of Se confers a protective effect on IRT and DSST, particularly under a pro-inflammatory diet. Additional cohort studies are necessary to corroborate further the association between diet and cognitive function in the presence of heavy metal exposure. These studies should be designed to measure the general population's annual blood metal concentrations and cognitive function. The findings of such studies will provide reliable evidence to inform policy development concerning diet, heavy metals, and health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThe research in question involved the analysis of de-identified information that was downloaded from the public database of the National Health and Nutrition Examination Survey. Ethics approval for the research was granted by the Ethics Review Committee of the National Center for Health Statistics, and all methods used during the research were in accordance with relevant guidelines and regulations (including the Declaration of Helsinki). Additionally, all individuals who participated in the study provided written informed consent.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDeclaration of interest\u003c/strong\u003e \u003cp\u003eThe authors have no relevant interests to declare.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Open-ended Fund of Key Laboratory (Grant No.: KFJJ-202101).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eCLT and HH contributed to the study design. CLT and HH preformed the data analysis. CT wrote the manuscript. CLT, HH and MS critically revised and edited the manuscript for important intellectual content. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe datasets generated and/or analysed during the current study are available in the NHANES (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Global status report on the public health response to dementia. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrito DVC, Esteves F, Rajado AT, Silva N, Ara\u0026uacute;jo I, Bragan\u0026ccedil;a J, et al. 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Chem Biol Interact. 2022;366:110143.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu Y, Hong H, Lin X, Tong T, Zhang J, He H, et al. Chronic cadmium exposure induces Parkinson-like syndrome by eliciting sphingolipid disturbance and neuroinflammation in the midbrain of C57BL/6J mice. Environ Pollut. 2023;337:122606.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKern JK, Geier DA, Audhya T, King PG, Sykes LK, Geier MR. Evidence of parallels between mercury intoxication and the brain pathology in autism. Acta Neurobiol Exp (Wars). 2012;72:113\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurumulla L, Bandaru LJM, Challa S. Heavy Metal Mediated Progressive Degeneration and Its Noxious Effects on Brain Microenvironment. Biol Trace Elem Res. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12011-023-03778-x\u003c/span\u003e\u003cspan address=\"10.1007/s12011-023-03778-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKirkley KS, Popichak KA, Afzali MF, Legare ME, Tjalkens RB. Microglia amplify inflammatory activation of astrocytes in manganese neurotoxicity. J Neuroinflammation. 2017;14:99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu P, Zhang J, Wu J, Chen H, Luo W, Hu M. TREM2 expression on the microglia resolved lead exposure-induced neuroinflammation by promoting anti-inflammatory activities. Ecotoxicol Environ Saf. 2023;260:115058.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMathys ZK, White AR. Copper and Alzheimer\u0026rsquo;s Disease. Adv Neurobiol. 2017;18:199\u0026ndash;216.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatrick L. Lead toxicity part II: the role of free radical damage and the use of antioxidants in the pathology and treatment of lead toxicity. Altern Med Rev. 2006;11:114\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang P, Wang Z-Y. Metal ions influx is a double edged sword for the pathogenesis of Alzheimer\u0026rsquo;s disease. Ageing Res Rev. 2017;35:265\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing T, Aimaiti M, Cui S, Shen J, Lu M, Wang L et al. Meta-analysis of the association between dietary inflammatory index and cognitive health. Front Nutr. 2023;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVicente BM, Lucio Dos Santos Quaresma MV, Maria de Melo C, Lima Ribeiro SM. The dietary inflammatory index (DII\u0026reg;) and its association with cognition, frailty, and risk of disabilities in older adults: A systematic review. Clin Nutr ESPEN. 2020;40:7\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIadecola C. The overlap between neurodegenerative and vascular factors in the pathogenesis of dementia. Acta Neuropathol. 2010;120:287\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang G, Ren G. Interaction between ω-6 fatty acids intake and blood cadmium on the risk of low cognitive performance in older adults from National Health and Nutrition Examination Survey (NHANES) 2011\u0026ndash;2014. BMC Geriatr. 2022;22:292.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMin J-Y, Min K-B. Blood cadmium levels and Alzheimer\u0026rsquo;s disease mortality risk in older US adults. Environ Health. 2016;15:69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoroszkiewicz J, Farhan JA, Mroczko J, Winkel I, Perkowski M, Mroczko B. Common and Trace Metals in Alzheimer\u0026rsquo;s and Parkinson\u0026rsquo;s Diseases. Int J Mol Sci. 2023;24:15721.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBakulski KM, Seo YA, Hickman RC, Brandt D, Vadari HS, Hu H, et al. Heavy Metals Exposure and Alzheimer\u0026rsquo;s Disease and Related Dementias. J Alzheimers Dis. 2020;76:1215\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGholami A, Amirkalali B, Baradaran HR, Hariri M. A systematic review and dose-response meta-analysis of the effect of selenium supplementation on serum concentration of C-reactive protein. J Trace Elem Med Biol. 2023;80:127273.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Z, Lu Y, Dun X, Wang X, Wang H. Research Progress of Selenium-Enriched Foods. Nutrients. 2023;15:4189.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu Y, Manly JJ, Mayeux RP, Brickman AM. An Inflammation-related Nutrient Pattern is Associated with Both Brain and Cognitive Measures in a Multiethnic Elderly Population. Curr Alzheimer Res. 2018;15:493\u0026ndash;501.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHayden KM, Beavers DP, Steck SE, Hebert JR, Tabung FK, Shivappa N, et al. The association between an inflammatory diet and global cognitive function and incident dementia in older women: The Women\u0026rsquo;s Health Initiative Memory Study. Alzheimers Dement. 2017;13:1187\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrith E, Shivappa N, Mann JR, H\u0026eacute;bert JR, Wirth MD, Loprinzi PD. Dietary inflammatory index and memory function: population-based national sample of elderly Americans. Br J Nutr. 2018;119:552\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark S, Lee B-K. Inverse relationship between fat intake and blood lead levels in the Korean adult population in the KNHANES 2007\u0026ndash;2009. Sci Total Environ. 2012;430:161\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaouali N, Benmarhnia T, Lanphear BP, Weuve J, Mascari M, Boutron-Ruault M-C, et al. Association between blood metals mixtures concentrations and cognitive performance, and effect modification by diet in older US adults. Environ Epidemiol. 2022;6:e192.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"dietary inflammatory index, heavy metals, cognitive function, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-3806622/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3806622/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe consequences of heavy metal exposure on cognitive function in elderly adults have been recognized as primarily attributed to the inflammatory response. It is noteworthy that diet can either exacerbate or reduce the inflammatory response. Despite this, there has been limited study about the effects of diet on the relationship between heavy metal exposure and cognitive function.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted utilizing data from the 2011\u0026ndash;2014 NHANES survey to explore the role of the dietary inflammation index in the association between metal exposure and cognitive function in elderly adults. The study enrolled 1726 participants and generalized linear regression model(GLM), Bayesian kernel machine regression model(BKMR), weighted quantile sum regression(WQS), and quantile g-computation regression analysis(Qg-comp) was conducted to assess the impact of five heavy metals in blood on cognitive function under the anti-inflammatory and pro-inflammatory diet.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe GLM analysis showed a positive correlation between selenium (Se) and both the instant recall test (IRT) and digit symbol substitution test (DSST), whether taken as continuous or quartile variables.Conversely, cadmium (Cd) was negatively associated with DSST. For IRT, Cd in the highest quartile was negatively associated compared to the lowest quartile. Subgroup analysis revealed the effects of Cd on IRT and DSST and Se on DSST under the pro-inflammatory diet. Furthermore, The BKMR analysis showed an inverted U-shaped curve with the negative effect of metal mixtures and DSST and a linearly negative trend with IRT in the pro-inflammatory diet. Among them, Cd was emphasized as the most potent risk factor, and Se was the most vital protective factor for IRT and DSST in WQS and Qg-comp analysis.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe study suggests that a high-quality diet could alleviate the adverse effects of Cd on IRT and DSST. Additionally, high Se levels improved IRT and DSST in the inflammatory diet. These findings provide valuable insights into the connection between diet, heavy metal exposure, and cognitive function in elderly adults.\u003c/p\u003e","manuscriptTitle":"Uncovering the Relationship between Heavy Metal Exposure, Cognitive Function, and Dietary Inflammation Index in Elderly Americans from the National Health and Nutrition Examination Survey 2011-2014","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-31 15:18:13","doi":"10.21203/rs.3.rs-3806622/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-09T10:33:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-06T19:51:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78531044275875912981254258294649422747","date":"2024-07-04T18:23:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28646345080599567300135849435783068836","date":"2024-07-04T14:49:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"237354428743397546638012187240499809525","date":"2024-07-04T10:25:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-13T17:19:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"142316376585183026408136154120339372910","date":"2024-06-06T07:54:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-23T04:48:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-16T07:05:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-01-28T16:19:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-28T16:17:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2023-12-26T06:30:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a2132dd6-b8db-4cd9-8064-d337d7325b38","owner":[],"postedDate":"January 31st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-23T16:08:55+00:00","versionOfRecord":{"articleIdentity":"rs-3806622","link":"https://doi.org/10.1186/s12889-024-20060-4","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2024-09-16 15:57:56","publishedOnDateReadable":"September 16th, 2024"},"versionCreatedAt":"2024-01-31 15:18:13","video":"","vorDoi":"10.1186/s12889-024-20060-4","vorDoiUrl":"https://doi.org/10.1186/s12889-024-20060-4","workflowStages":[]},"version":"v1","identity":"rs-3806622","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3806622","identity":"rs-3806622","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}