The Composite Dietary Antioxidant Index Counteracts the Cognitive Effects of Heavy Metal Exposure

The Composite Dietary Antioxidant Index Counteracts the Cognitive Effects of Heavy Metal Exposure | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Composite Dietary Antioxidant Index Counteracts the Cognitive Effects of Heavy Metal Exposure Lujie Han, Jiawei Ye, Weitao Yu, Haoyue Cheng, Huiyuan Wang, Panpan Shen, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6737663/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective We aimed to explore if a higher composite dietary antioxidant index (CDAI) and exposure to heavy metals including lead and cadmium are associated with cognitive function in the elderly. Additionally, we explore the interaction effects between CDAI and heavy metals on cognitive function. Methods Data from the 2011–2014 US National Health and Nutrition Examination Survey (NHANES) was utilized to calculate the CDAI, based on the intake levels of vitamins A, C, E, α-carotene, β-carotene, β-cryptoxanthin, lycopene, and lutein/zeaxanthin. Blood levels of lead and cadmium were measured to assess heavy metal exposure. Cognitive function was evaluated using a z-score derived from a battery of tests, including the Immediate Recall Score, Digit Symbol Substitution Test, Category Fluency Test, and Delayed Recall Score. We evaluated the level of chronic inflammation using white blood cell count (WBCL) and explored its mediating role in the relationship between CDAI, heavy metals, and cognitive scores. Finally, we evaluated the effects of CDAI and heavy metals exposure on cognitive function, along with their interactions. Results The study included 1745 elderly participants aged 60 and above. CDAI and blood levels of lead and cadmium were each significantly associated with all cognitive scores, including each specific cognitive function score and z-score ( P < 0.05). The mediation analysis shows that WBCL partially mediate the relationship between CDAI and Z-scores, contributing 2.76%, 3.31%, and 7.00% to the total effect. Additionally, WBCL partially mediates the relationship between blood cadmium levels and z-scores, contributing 9.87% and 10.72% to the total effect (all P < 0.05). Interaction analysis confirmed a significant correlation between CDAI and blood cadmium with z-score (all P < 0.05). Conclusion The study manifested the relationship between CDAI, exposure to lead and cadmium, and cognitive function in the US elderly population. An antioxidant diet can combat the cognitive damage caused by cadmium exposure through an anti-inflammatory response. composite dietary antioxidant index cognitive impairment white blood cell levels blood lead blood cadmium Figures Figure 1 Figure 2 Figure 3 Introduction With increasing life expectancy in developed countries, there has been a notable rise in age-related neurological disorders, particularly Alzheimer's disease [ 1 ] , which is the most common form of dementia. By 2030, it is projected that over 70 million elderly individuals worldwide will suffer from dementia, incurring social costs expected to exceed $ 2 trillion [ 2 ] . This growing burden highlights the need for early intervention during the preclinical stages to improve outcomes, making it a critical public health priority to find effective strategies to prevent or delay cognitive decline [ 3 ] . Environmental pollution, particularly heavy metal intake, has emerged as a significant contributor to cognitive dysfunction [ 4 ] . Metals such as lead and cadmium are of particular concern due to their widespread presence and neurotoxic effects, even at low exposure levels [ 5 ] . McGrattan et al. found that heavy metals can disrupt metal ion homeostasis in the brain [ 6 ] , triggering systemic inflammation [ 7 ] , which is central to cognitive decline and dementia development. Diet may provide a promising avenue for mitigating these effects. The Cognitive Dietary Antioxidant Index (CDAI) serves as a metric for assessing the intake of a variety of dietary antioxidants. These include essential nutrients like selenium, zinc, vitamins A, C, E, as well as phytochemicals such as α-carotene, β-carotene, β-cryptoxanthin, lycopene, and lutein/zeaxanthin [ 8 ] . A higher CDAI score, reflecting a more robust intake of antioxidants, has been correlated with a delay in biological aging processes [ 9 ] . Sheng et al. found that a high CDAI is associated with a reduced likelihood of cognitive impairment in later life within a Chinese population in Singapore [ 10 ] . However, the extent to which a diet rich in antioxidants can counteract the cognitive damage caused by heavy metals is not well understood and warrants further investigation. Therefore, we aimed to investigate the impact of heavy metal exposure and CDAI diet on cognitive function, and the interaction between heavy metal exposure and CDAI diet. We utilized the data from the 2011–2014 National Health and Nutrition Examination Survey (NHANES) to examine the effect of heavy metal levels and antioxidant intake on cognition status. We hypothesized that cognitive status was negatively associated with heavy metal exposure level but was positively associated with CDAI assumption level, and antioxidant-rich diets with high CDAI may mitigate cognitive impairment caused by heavy metals exposure. Methods Study Population The National Health and Nutrition Examination Survey (NHANES) conducts biennial cross-sectional surveys among non-institutionalized U.S. civilians. The NHANES 2011–2014 cycles were chosen for our analysis as they uniquely provide laboratory data and cognitive function scores necessary to test our hypothesis. From the overall sample of 19,932 individuals in NHANES 2011–2014, we applied exclusion criteria, resulting in a final study sample of 1,745 participants. Exclusions included participants who did not complete all four cognitive assessments (n = 16,865), had missing or unclear responses for covariates (n = 845), or exhibited abnormal biochemical test values (n = 477). The data, which is publicly available and deidentified, did not require additional ethical approval for this analysis. We extracted relevant sociodemographic, lifestyle, dietary, medical, and biochemical information from the NHANES 2011–2014 dataset. Composite Dietary Antioxidant Index (CDAI) Dietary data in NHANES were collected through two non-consecutive 24-hour dietary recall interviews. The first dietary recall interview was conducted in-person at the Mobile Examination Center (MEC), and the second interview was conducted via telephone 3 to 10 days later. These data were converted into estimated nutrient intake values using the Food and Nutrient Database for Dietary Studies (FNDDS), developed by the United States Department of Agriculture. This study included eight vitamins (vitamins A, C, E, α-carotene, β-carotene, β-cryptoxanthin, lycopene, and lutein-zeaxanthin) and two trace elements (zinc and selenium) related to antioxidants. The intake of each dietary antioxidant was calculated as the average of the two recall interviews. CDAI was calculated using a standardization method based on the formula [ 11 ] : CDAI= \(\:{?}_{n=10}^{}\frac{Antioxidant\:content-mean\left(Antioxidant\:content\right)}{Sd\left(Antioxidant\:content\right)}\) . Assessment of Heavy Metal Exposure Whole blood concentrations of lead and cadmium concentrations were measured using inductively coupled plasma mass spectrometry (ICP-MS). This method involved creating plasma from an argon stream to ionize the sample. Blood samples were converted into an aerosol, passed through the plasma where they were atomized and ionized at high temperatures, and then sent to the mass spectrometer. The mass spectrometer, operating under very low pressure, detected the ions based on their mass-to-charge ratio, enabling the identification of different isotopes of each element. Inflammation markers We selected white blood cell level (WBCL) and LP (lymphocyte percentage) as the two classic inflammation indicators in NHANES. A decrease in these markers indicates a reduction in oxidative stress and inflammatory responses [ 12 ] . Covariates Covariates included age, gender, race (categorized as Mexican American, non-Hispanic white, non-Hispanic black, other Hispanic, and others), marital status (married or unmarried), educational level (below high school or high school and above), body mass index (BMI) categories (underweight, normal, overweight, and obese), smoking status (ever smoked at least 100 cigarettes in their lifetime), drinking status (at least 12 drinks per year or not), and a history of diabetes. Cognitive Function Cognitive function in NHANES was evaluated using four standardized scales: the Immediate Recall Test (IRT) and Delayed Recall Test (DRT) from the Consortium to Establish a Registry for Alzheimer's Disease (CERAD), the Digit Symbol Substitution Test (DSST), and the Animal Fluency Test (AFT). CERAD-IRT and CERAD-DRT: Participants recalled 10 unrelated words over three trials (IRT) and again after 8–10 minutes (DRT), with a maximum score of 40 points. AFT: Participants named as many animals as possible within one minute, scoring one point per correct animal. DSST: A Wechsler Adult Intelligence Scale component where participants matched symbols to numbers, scoring based on correct matches within two minutes. Test specific z-scores (including DSST, CERAD­WL, CERAD­WL, AFT) were created using sample means and SDs of test scores. A standardized global cognition z-score was generated by averaging the test specific z-scores divided by SD [ 13 ] . Statistical Methods Statistical analyses incorporated sample weights from both cycles, utilizing R software version 4.2.1. Continuous variables were presented as mean ± standard error (SE), and categorical variables were reported as percentages. Group differences in participant characteristics were evaluated using independent t-tests for continuous variables and chi-square tests for categorical variables. The z-score was divided into four quartiles (Q1-Q4), with Q1 as the reference group. This research population’s baseline information was represented as mean and standard deviation (SD) for any continuous variable and percentages for any categorical variable. The CDAI and blood lead and blood cadmium were further used to analyze both in continuous variables and quartiles. Bayesian weighted linear regression analyses were applied to compute β and 95% confidential interval (CI) to detect any potential association between the CDAI and blood lead and blood cadmium index (continuous or quartiles) and all cognitive tests scores. Univariate and multivariate weighted linear regression models were used to assess linear trends and the independent correlation between CDAI, blood lead, and cadmium levels and cognitive function. Mediation analysis estimated the total effect, direct effect, and indirect effect, with the mediation percentage (Pm) calculated as the proportion of the indirect effect in the total effect. Within the framework of structural equation modeling (SEM), Bootstrap was applied for significance testing of the mediation analysis using the SEM package in R version 4.2.1 [ 14 ] . Mixed model was tested. Interaction analyses determined the statistical significance of the interaction between CDAI and cadmium levels. Results are presented as beta coefficients, standard errors, and two-tailed P -values, with statistical significance defined as P < 0.05. Results Participant Characteristics Among the 1745 participants, 848 were male (48.6%) and 897 female (51.5%), with a mean age of 69.4 ± 6.7 years (Table 1 ). Non-Hispanic Whites made up the largest racial group, with 877 individuals (50.3%), followed by non-Hispanic Blacks at 408 (23.4%). In this population, 1187 (56.6%) were married, and 903 (51.7%) had attained a college education or higher. The prevalence of obesity, defined by BMI, was 899 (51.5%), with 472 having a history of diabetes (27.0%). There were 886 smokers (50.8%) and 1211 participants (69.4%) who reported alcohol consumption habits. Univariate analysis revealed significant differences in demographic characteristics among the quartiles of z-scores, including age, gender, race, educational attainment, and marital status, as well as lifestyle habits such as smoking and alcohol consumption (all P < 0.05). As the CDAI raised with increasing z-score quartiles, conversely, blood lead and cadmium levels and WBCL decreased gradually with increasing z-score quartiles. Table 1 Characteristics of included participants based on quartile of z-score. Variables a Overall (n = 1745) Q1 (n = 436) Q2 (n = 437) Q3 (n = 436) Q4 (n = 436) P for trend Demographic data Age, years, (mean ± SD) 69.02 ± 6.57 73.32 ± 6.54 71.75 ± 6.65 69.25 ± 6.21 65.81 ± 4.99 < 0.001 Male, n (%) 848 (48.6) 253 (14.5) 231 (13.24) 209 (11.98) 155 (8.88) < 0.001 Race, n (%) < 0.001 Mexican American 137 (7.9) 53 (3.04) 36 (2.06) 28 (1.6) 20 (1.15) Other Hispanic 180 (10.3) 82 (4.7) 49 (2.81) 24 (1.38) 25 (1.43) Non-Hispanic White 877 (50.3) 145 (8.31) 207 (11.86) 233 (13.35) 292 (16.73) Non-Hispanic Black 408 (23.4) 140 (8.02) 105 (6.02) 106 (6.07) 57 (3.27) Non-Hispanic Asian 119 (6.8) 12 (0.69) 29 (1.66) 41 (2.35) 37 (2.12) Other Race 24 (1.4) 4 (0.23) 10 (0.57) 5 (0.29) 5 (0.29) Education, n (%) < 0.001 < 9th grade 196 (11.2) 143 (8.19) 41 (2.35) 9 (0.52) 3 (0.17) 9-11th grade 646 (37.0) 198 (11.35) 194 (11.12) 161 (9.23) 93 (5.33) High school graduate 903 (51.7) 95 (5.44) 201 (11.52) 267 (15.3) 340 (19.48) Marital Status, n (%) < 0.001 Married 985 (56.4) 216 (12.38) 245 (14.04) 259 (14.84) 265 (15.19) Unmarried 760 (43.6) 220 (12.61) 191 (10.95) 178 (10.2) 171 (9.8) BMI, n (%) 0.160 Underweight 23 (1.3) 12 (0.69) 3 (0.17) 3 (0.17) 5 (0.29) Normal 296 (17.0) 75 (4.3) 79 (4.53) 71 (4.07) 71 (4.07) Overweight 527 (30.2) 131 (7.51) 140 (8.02) 126 (7.22) 130 (7.45) Obese 899 (51.5) 218 (12.49) 214 (12.26) 237 (13.58) 230 (13.18) Medical history, n (%) Diabetes 472 (27.0) 153 (8.77) 123 (7.05) 120 (6.88) 76 (4.36) < 0.001 Living habit, n (%) Smoking status 886 (50.8) 232 (13.3) 232 (13.3) 226 (12.95) 196 (11.23) 0.007 Drinking status 1211 (69.4) 285 (16.33) 291 (16.68) 300 (17.19) 335 (19.2) < 0.001 The composition of CDAI (mean ± SD) Vitamin A, ug 1.0 ± 1.2 0.85 ± 0.8 0.98 ± 0.97 1.09 ± 1.89 1.12 ± 0.9 < 0.001 Vitamin C, mg 131.2 ± 129.9 114.02 ± 103.15 132.56 ± 118.39 138.19 ± 170.78 140.12 ± 115.7 0.003 Vitamin E, mg 11.7 ± 7.5 9.31 ± 5.16 11.16 ± 7.13 12.41 ± 7.99 14.2 ± 8.49 < 0.001 α-carotene, mg 0.7 ± 2.8 0.5 ± 1.02 0.58 ± 1.1 0.97 ± 5.34 0.79 ± 1.32 0.043 β-carotene, mg 4.0 ± 10.1 3.14 ± 5.82 3.47 ± 5.01 4.89 ± 17.77 4.66 ± 5.57 0.008 β-cryptoxanthin, mg 0.1 ± 0.4 0.16 ± 0.56 0.14 ± 0.43 0.14 ± 0.21 0.13 ± 0.17 0.095 Lycopene, mg 6.2 ± 10.0 5.84 ± 10.3 5.94 ± 9.4 6.57 ± 10.54 6.62 ± 10 0.180 Lutein Zeaxanthin, mg 2.9 ± 7.6 2.34 ± 5.64 2.59 ± 4.33 3.1 ± 11.82 3.78 ± 6.39 0.003 Zinc, mg 14.9 ± 7.5 12.99 ± 6.62 14.85 ± 7.1 15.83 ± 8.16 16.24 ± 7.57 < 0.001 Selenium, mg 152.9 ± 72.7 137.48 ± 64.18 150.91 ± 80.57 160.24 ± 68.87 163.29 ± 73.81 < 0.001 CDAI 0.1 ± 2.8 -0.8 ± 2.07 -0.12 ± 2.5 0.18 ± 2.28 0.62 ± 2.54 < 0.001 Heavy metal exposure (mean ± SD) blood lead, ug/dL 1.8 ± 1.5 2.02 ± 1.69 2.01 ± 2.01 1.72 ± 1.05 1.64 ± 1.12 < 0.001 blood cadmium, ug/L 0.5 ± 0.4 0.55 ± 0.46 0.52 ± 0.43 0.49 ± 0.43 0.44 ± 0.38 < 0.001 Inflammation markers (mean ± SD) WBCL, 1000cells/UL 6.9 ± 2.4 7.14 ± 3.08 7 ± 2.41 6.86 ± 1.89 6.67 ± 2.13 0.003 LP, % 28.6 ± 9.1 28.49 ± 9.93 28.26 ± 9.62 28.9 ± 8.88 29.02 ± 8.01 0.240 Cognitive function (mean ± SD) CERAD-IRT 18.8 ± 4.5 14.77 ± 3.78 18 ± 3.54 20.18 ± 3.36 22.43 ± 3.23 < 0.001 DSST 46.6 ± 17.0 26.05 ± 8.22 40.81 ± 6.57 52.11 ± 6.75 67.6 ± 8.62 < 0.001 AFT 16.8 ± 5.4 12.43 ± 3.83 15.26 ± 3.68 17.76 ± 4.19 21.67 ± 5 < 0.001 CERAD-DRT 5.9 ± 2.3 4.08 ± 2.05 5.43 ± 1.85 6.46 ± 1.98 7.61 ± 1.61 < 0.001 z-score 88.2 ± 24.1 57.33 ± 9.77 79.5 ± 5.03 96.51 ± 5.02 119.31 ± 10.78 < 0.001 CDAI, the Composite Dietary Antioxidant Index; SD, Standard Deviation; BMI, Body Mass Index; WBCL, the white blood cell level; LP, lymphocyte percentage; CERAD-IRT, the Consortium to Establish a Registry for Alzheimer's Disease's- immediate recall test; DSST, the digit symbol substitution test; AFT, the animal fluency test; CERAD-DRT, the Consortium to Establish a Registry for Alzheimer's Disease's- delayed recall test. Association of CDAI and Blood Metal with Cognitive Function Scores Linear regression analysis showed that, compared to the lowest quartile of CDAI, higher quartiles of CDAI were significantly associated with better cognitive function scores, including four cognitive tests and the z-score (all P < 0.05) (Model 1). These correlations remained significant even after adjusting for potential confounders in Models 2 and 3 (Table 2 ). Specifically, for each one-unit increase in the CDAI score, there was a corresponding improvement of 0.05 points in the z-score. When compared to individuals in the lowest quartile of CDAI (Q1), those in the second (Q2), third (Q3), and fourth (Q4) quartiles experienced increases in their z-scores by 0.11, 0.11, and 0.05 points, respectively. There is a negative correlation between blood lead and cadmium levels and the z-score (β = -0.07, 95% CI = -0.10 to -0.04, P < 0.001; β = -0.25, 95% CI = -0.35 to -0.14, P < 0.001). Through Bayesian linear regression analysis, we found that individuals with lower blood lead and cadmium levels performed better on cognitive function scores (Model 1). After adjusting for potential confounders in Models 2, 3, and 4, the correlation between blood cadmium and CERAD-IRT, DSST, AFT, and the z-score remained significant. Conversely, the correlations between blood lead levels and the various cognitive test scores were not significant (all P > 0.05) (Table 3 ). Table 2 Multivariate linear analysis of the association between CDAI and cognitive function scale scores. Scales CDAI Model 1 Model 2 Model 3 β (95% Cl) P for trend β (95% Cl) P for trend β (95% Cl) P for trend CERAD-IRT Q1 Reference Reference Reference Q2 0.39 (0.08,0.72) < 0.001 0.51 (0.21,0.81) < 0.001 0.39 (0.09,0.68) 0.006 Q3 0.38 (0.19,0.57) 0.43 (0.25,0.61) 0.30 (0.12,0.48) Q4 0.18 (0.10,0.26) 0.21 (0.13,0.28) 0.12 (0.05,0.20) Continuous (per CDAI) 0.22 (0.13,0.21) < 0.001 0.27 (0.19,0.35) < 0.001 0.16 (0.08,0.25) < 0.001 DSST Q1 Reference Reference Reference Q2 2.78 (1.62,3.94) < 0.001 3.18 (2.09,4.28) < 0.001 2.00 (1.04,2.97) < 0.001 Q3 2.89 (2.20,3.59) 3.11 (2.46,3.76) 1.91 (1.33,2.48) Q4 1.03 (0.74,1.31) 1.09 (0.82,1.37) 0.49 (0.24,0.73) Continuous (per CDAI) 1.46 (1.13,1.78) < 0.001 1.60 (1.28,1.92) < 0.001 0.78 (0.51, 1.06) < 0.001 AFT Q1 Reference Reference Reference Q2 0.44 (0.07,0.81) < 0.001 0.45 (0.08,0.81) ** < 0.001 0.24 (-0.11,0.59) < 0.001 Q3 0.58 (0.36,0.80) 0.57 (0.35,0.78) ** 0.32 (0.11,0.53) Q4 0.31 (0.22,0.41) 0.28 (0.19,0.38) ** 0.16 (0.07,0.26) Continuous (per CDAI) 0.44 (0.34,0.54) < 0.001 0.43 (0.33,0.53) < 0.001 0.28 (0.18,0.38) < 0.001 CERAD-DRT Q1 Reference Reference Reference Q2 0.17 (0.01,0.33) < 0.001 0.21 (0.06,0.36) < 0.001 0.16 (0.01,0.31) < 0.001 Q3 0.14 (0.04,0.23) 0.16 (0.06,0.25) 0.09 (0.003,0.19) Q4 0.06 (0.02,0.10) 0.07 (0.03,0.11) 0.04 (0.002,0.08) Continuous (per CDAI) 0.09 (0.05,0.13) < 0.001 0.10 (0.06,0.15) 0.002 0.06 (0.02,0.11) 0.004 z-score Q1 Reference Reference Reference Q2 0.15 (0.09,0.22) < 0.001 0.18 (0.11,0.24) < 0.001 0.11 (0.06,0.17) < 0.001 Q3 0.16 (0.12,0.21) 0.17 (0.14,0.21) 0.11 (0.07,0.14) Q4 0.09 (0.07,0.11) 0.09 (0.07,0.11) 0.05 (0.03,0.07) Continuous (per CDAI) 0.04 (0.06,1.00) < 0.001 0.10 (0.08,0.12) < 0.001 0.05 (0.04,0.07) < 0.001 Model 1: non-adjusted model. Model 2: adjusted for age, gender, race, and BMI. Model 3: adjusted for covariates of model 2, education, and marital status, smoking, alcohol consumption, and diabetes history. CDAI, the Composite Dietary Antioxidant Index; BMI, Body Mass Index; CERAD-IRT, the immediate recall test; DSST, the digit symbol substitution test; AFT, the animal fluency test; CERAD-DRT, the delayed recall test; Cl, Confidence Interval Table 3 Multivariate linear analysis of the association between blood lead and blood cadmium and cognitive function scale scores. Scales Blood heavy metal Model 1 Model 2 Model 3 Model 4 β (95% Cl) P for trend β (95% Cl) P for trend β (95% Cl) P for trend β (95% Cl) P for trend CERAD-IRT blood lead Q1 Reference Reference Reference Reference Q2 -0.68(-1.24, -0.11) < 0.001 -1.45(-0.99,0.09) 0.410 -0.54(-1.07, -0.01) 0.140 -0.54 (-1.29,0.23) 0.270 Q3 -0.78(-1.36, -0.19) -0.31(-0.87,0.24) -0.60(-1.16, -0.05) -0.37 (-0.86,0.12) Q4 -1.26(-1.81, -0.70) -0.43(-0.98,0.12) -0.39(-0.93,0.16) -0.04 (-0.18,0.09) Continuous (per blood lead) -0.25 (-0.38, -0.11) < 0.001 -0.10 (-0.23,0.03) 0.090 -0.07 (-0.20,0.06) 0.310 -0.04 (-0.17,0.09) 0.530 blood cadmium Q1 Reference Reference Reference Reference Q2 -0.70(-1.57,0.16) 0.020 -1.02(-1.86, -0.18) 0.040 -1.13(-2.00, -0.27) 0.020 -1.07 (-1.94, -0.20) 0.040 Q3 -0.57(-1.30,0.15) -0.65(-1.34,0.04) -0.53(-1.25,0.18) -0.43 (-1.15,0.30) Q4 -0.75(-1.36, -0.13) -0.79(-1.38, -0.20) -0.69(-1.29, -0.08) -0.64 (-1.24, -0.01) Continuous (per blood cadmium) -0.59 (-1.08, -0.10) 0.018 -0.70 (-1.17, -0.23) 0.004 -0.57 (-1.06, -0.08) 0.021 -0.56 (-1.04, -0.06) 0.030 DSST blood lead Q1 Reference Reference Reference Reference Q2 -1.21(-3.41,0.98) 0.010 -0.77(-2.87,1.33) 0.850 -1.50(-3.32,0.25) 0.110 0.02 (-2,50,2.50) 0.340 Q3 -0.48(-2.69,1.72) 1.04(-1.07,3.16) -1.15(-2.97,0.66) -0.55 (-2.10,0.99) Q4 -5.07(-7.26, -2.87) -2.56(-4.75, -0.37) -2.09(-3.98, -0.20) -0.33 (-0.80,0.14) Continuous (per blood lead) -1.19 (-1.71, -0.67) < 0.001 -0.78 (-1.29, -0.28) 0.002 -0.51 (-0.95, -0.08) 0.021 -0.35 (-0.77,0.07) 0.110 blood cadmium Q1 Reference Reference Reference Reference Q2 -3.71(-7.04, -0.37) < 0.001 -5.28(-8.53, -2.03) < 0.001 -4.09(-7.02, -1.16) < 0.001 -3.78 (-6.53, -0.95) < 0.001 Q3 -3.25(-5.99, -0.52) -3.86(-6.47, -1.24) -2.64(-4.96, -0.32) -1.93 (-4.17,0.37) Q4 -4.84(-7.24, -2.43) -5.73(-8.05, -3.41) -3.94(-6.03, -1.86) -3.49 (-5.55, -1.44) Continuous (per blood cadmium) -4.26 (-6.12, -2.41) < 0.001 -5.10 (-6.88, -3.32) < 0.001 -3.14 (-4.75, -1.52) < 0.001 -2.86 (-4.45,-1.19) < 0.001 AFT blood lead Q1 Reference Reference Reference Reference Q2 -1.17(-2.24, -0.10) 0.970 -0.88(-1.95,0.19) 0.160 -0.84(-1.92,0.24) 0.510 0.26 (-0.68,1.21) 0.290 Q3 -0.77(-1.63,0.09) -0.50(-1.35,0.35) -0.42(-1.29,0.44) 0.42 (-0.17,1.01) Q4 -0.78(-1.53, -0.02) -0.68(-1.44,0.08) -0.42(-1.19,0.34) 0.05 (-0.11,0.22) Continuous (per blood lead) -0.6 (-0.23,0.10) 0.450 -0.01 (-0.18,0.15) 0.860 0.03 (-0.13,0.19) 0.690 0.08 (-0.08,0.26) 0.330 blood cadmium Q1 Reference Reference Reference Reference Q2 -1.17(-2.24, -0.10) 0.001 -0.88(-1.95,0.19) 0.040 -0.84(-1.92,0.24) 0.030 -0.87 (-1.91,0.18) 0.020 Q3 -0.77(-1.63,0.09) -0.50(-1.35,0.35) -0.42(-1.29,0.44) -0.57 (-1.39,0.30) Q4 -0.78(-1.53, -0.02) -0.68(-1.44,0.08) -0.42(-1.19,0.34) -0.48 (--1.25,0.29) Continuous (per blood cadmium) -0.96 (-1.55, -0.37) 0.001 -0.91 (-1.50, -0.32) 0.003 -0.58 (-1.18, -0.01) 0.040 -0.65 (-1.26, -0.03) 0.040 CERAD-DRT blood lead Q1 Reference Reference Reference Reference Q2 -0.14(-0.43,0.15) < 0.001 -0.03(-0.30,0.24) 0.560 -0.09(-0.36,0.18) 0.230 -0.10 (-0.48,0.30) 0.340 Q3 -0.27(-0.57,0.02) -0.03(0.32,0.26) -0.17(-0.45,0.12) -0.16 (-0.42,0.09) Q4 -0.58(-0.87, -0.28) -0.15(-0.44,0.14) -0.14(-0.43,0.15) -0.03 (-0.10,0.05) Continuous (per blood lead) -0.12 (-0.19, -0.05) < 0.001 -0.04 (-0.11,0.02) 0.180 -0.03 (-0.10,0.03) 0.150 -0.04 (-0.10,0.03) 0.310 blood cadmium Q1 Reference Reference Reference Reference Q2 -0.6 (-0.23,0.10) 0.080 -0.01 (-0.18,0.15) 0.230 0.03 (-0.13,0.19) 0.150 -0.30 (-0.73.0.13) 0.220 Q3 -0.13(-0.57,0.30) -0.26(-0.69,0.16) -0.30(-0.74,0.14) 0.05 (-0.33,0.44) Q4 -0.17(-0.48,0.15) -0.02(-0.38,0.34) 0.001(-0.37,0.37) -0.11 (-0.42,0.20) Continuous (per blood cadmium) -0.11 (-0.36,0.14) 0.380 -0.15 (-0.39,0.09) 0.220 -0.08 (-0.33,0.17) 0.520 -0.06 (-0.31,0.21) 0.660 z-score blood lead Q1 Reference Reference Reference Reference Q2 -0.05 (-0.09,-0.01) < 0.001 -0.02 (-0.05,0.02) 0.010 -0.01 (-0.05,0.02) 0.100 -0.01 (-0.18,0.14) 0.450 Q3 -0.07 (-0.12, -0.02) -0.02 (-0.07,0.02) -0.02 (-0.06,0.02) -0.03 (-0.12,0.07) Q4 -0.09 (-0.13, -0.04) -0.07 (-0.11, -0.02) -0.04 (-0.07, -0.01) -0.01 (-0.04,0.01) Continuous (per blood lead) -0.07 (-0.10, -0.04) < 0.001 -0.04 (-0.07, -0.01) 0.009 -0.02 (-0.05, -0.01) 0.048 -0.01 (-0.04,0.01) 0.270 blood cadmium Q1 Reference Reference Reference Reference Q2 -0.14 (-0.28,0.01) < 0.001 -0.18 (-0.32, -0.04) < 0.001 -0.10 (-0.23,0.03) < 0.001 -0.26 (-0.44, -0.10) < 0.001 Q3 -0.25 (-0.40, -0.11) -0.27 (-0.41, -0.13) -0.18 (-0.31, -0.05) -0.14 (-0.29,0.01) Q4 -0.31 (-0.45, -0.16) -0.35 (-0.49, -0.21) -0.21 (-0.33, -0.08) -0.20 (-0.33, -0.08) Continuous (per blood cadmium) -0.25 (-0.35, -0.14) < 0.001 -0.28 (-0.39, -0.18) < 0.001 -0.18 (-0.28, -0.09) < 0.001 -0.17 (-0.27, -0.07) < 0.001 Model 1: non-adjusted model. Model 2: adjusted for age, gender, race, and BMI. Model 3: adjusted for covariates of model 2, education, and marital status, smoking, alcohol consumption, and diabetes history. Model 4: adjusted for the covariates of model 3using either blood lead or blood cadmium. BMI, Body Mass Index; CERAD-IRT, the immediate recall test; DSST, the digit symbol substitution test; AFT, the animal fluency test; CERAD-DRT, the delayed recall test; CI, Confidence Interval. Mediator Analysis Based on the correlation between WBCL and z-score (Table 1 ), we further conducted mediation analyses to examine whether WBCL mediates the effects of CDAI and heavy metals exposure levels on cognitive function. Mediation analyses revealed that WBCL mediated the relationship between CDAI and z-score, DSST, CERAD-DRT, explaining 2.76%, 3.31%, and 7.00% of the total effects, respectively (Fig. 1 ). However, blood lead was not significantly associated with WBCL ( P = 0.06), and WBCL did not mediate the association between blood lead and z-score. Univariate correlation analysis indicated that blood cadmium was associated with WBCL ( P < 0.001), and WBCL mediated the relationship between blood cadmium and z-score, DSST, explaining 9.87% and 10.72% of the total effects, respectively. There was no evidence that WBCL mediated the relationship between any of the exposure metal level and AFT, CERAD-IRT scores. CDAI, the Composite Dietary Antioxidant Index; WBCL, white blood cell count; CERAD-IRT, the immediate recall test; DSST, the digit symbol substitution test; AFT, the animal fluency test; CERAD-DRT, the delayed recall test. Mixture Associations of CDAI and Blood Cadmium on Cognitive Function. In the mixed model, the mixed effect component weights of CDAI and blood cadmium were significantly correlated with DSST, AFT, CERAD-DRT, and z-scores (all P < 0.05) (Fig. 2 ). Figure 3 illustrated smoothed curve fitting, stratified by metal intake levels, where z-score decreased with an increase in CDAI. When blood cadmium was below 0.29 µg/L, the curve flattened after CDAI exceeded 5.0. In the range where CDAI < 5.0, with Q1 as the reference, when blood cadmium levels were in Q2, the improvement in z-score due to CDAI increased by 0.03 points, but this change was not statistically significant. When blood cadmium levels reached Q3 and Q4, the improvement in z-score due to CDAI decreased by 0.13 and 0.30 points, respectively ( P < 0.05). However, when blood cadmium exceeded 0.29 µg/L, the z-score raised as CDAI increased. Discussion In our study, we discovered a strong correlation between CDAI and cognitive function among the elderly population in the United States. Additionally, exposure to heavy metals, including blood lead and cadmium, was found to be independently associated with cognitive status. WBCL acted as a mediator in the relationship between CDAI, blood cadmium, and cognitive status. Further interaction analysis indicated that a positive correlation between CDAI and cognitive function was maintained even in the presence of blood cadmium. This finding suggests that the consumption of dietary antioxidants might enhance cognitive function by reducing inflammatory responses, an effect that is influenced by the level of exposure to blood cadmium. Our study revealed that a high CDAI, indicative of robust antioxidant intake, was strongly correlated with improved cognitive function. This suggested that a diet rich in antioxidants can mitigate the neurotoxic effects of chronic inflammation on cognition, a risk exacerbated by heavy metals through oxidative stress [ 15 , 16 ] . Our mediation analysis indicated that while higher CDAI scores and reduced cadmium exposure were associated with enhanced cognitive performance by potentially lowering inflammation, lead exposure does not significantly correlate with WBCL, suggesting that WBCL does not mediate the association between lead and cognition impairment. WBCL, serving as a biomarker for chronic inflammation and oxidative stress, was linked to reduced cognitive performance. High WBCL levels, possibly resulting from free radical damage, were consistent with research highlighting the detrimental effects of systemic inflammation on the central nervous system and the increased risk of cognitive decline [ 17 ] . Supported by global research linking inflammatory markers to cognitive abilities [ 18 ] , our findings emphasized the importance of inflammation management in maintaining cognitive health. This underscored the need for dietary interventions that promote antioxidant intake to counteract the cognitive risks posed by heavy metal exposure. Furthermore, we found a significant interaction between CDAI and the blood cadmium, suggesting that the cognitive benefits of antioxidant intake may depend on an individual exposure levels to cadmium. Antioxidant-rich diets may help counteract the adverse cognitive effects of cadmium, with the protective effect possibly varying based on different exposure levels. In our study, we found that when blood cadmium was below 0.29 µg/L, the curve flattened once CDAI exceeds 2.5. however, when blood cadmium levels exceeded 0.29 µg/L, the z-score continued to increase with higher CDAI, suggesting that elevating CDAI lessened cognitive damage caused by blood cadmium. The strengths of this study included a comprehensive approach to dietary antioxidants, a large, nationally representative sample of older adults, and the use of mediation models to understand the complex interactions between inflammation, diet, heavy metal exposure, and cognition. However, limitations included the cross-sectional design, which hindered causal inference, and the restriction of cognitive assessment data to the period between 2011 and 2014 due to database constraints. The lack of direct oxidative stress biomarkers, such as C-reactive protein, also limited a more detailed analysis. Despite these limitations, the study offered valuable insights into the influence of diet and heavy metal exposure on cognitive health in the elderly. Conclusion Among the elderly population in the United States, an antioxidant-rich diet offered an protective effect on cognitive function even in the presence of cadmium exposure. The presence of chronic inflammation, as indicated by WBCL, served as a mediating factor between antioxidant-rich diets, blood cadmium levels, and cognitive function. This finding implies that a antioxidant-rich diets may counteract the cognitive impairments induced by heavy metals by eliciting an anti-inflammatory response. Declarations Author Contributions Concept: LH, SZ,YG, and XJ. Data curation: LH, JY, and HW. Formal analysis: LH, WY and HC. Writing—original draft: LH, HW, PS and XY. Methodology: LH, JY and WY. Project administration and resources: SZ and YG and XJ. Writing—review and editing: JY, HW, PS, XY, PW, YL, QH and WZ. Supervision: SZ and YG. All authors reviewed the manuscript drafts, critically revised the manuscript and approved the final manuscript. Data Sharing Statement The Data is publicly available on the internet throughout the world. (www.cdc.gov/nchs/nhanes/). Acknowledgement None. Conflict of Interest All authors affirm that there are no conflicts of interest. Clinical trial number Not applicable. Ethical Statement Data for this study were sourced from datasets available to the public with the approval of the National Center for Health Statistics’ research ethics review committees. Funding This work was supported by the Medical Science and Technology Project of Zhejiang Province (No. 2022KY600 and No. 2024KY019No. 2024KY019) and Zhejiang Provincial Natural Science Foundation of China (No. LGF22H090020) and Social Development Science and Technology Projects of Wenling City in 2023 (No. 2023S00131) and Social Development Science and Technology Projects of Wenling City in 2024 (No. 2024S00181) References Xu Xiaoxian, Bai W. Global prevalence of mild cognitive impairment [J]. China Rehabilitation. 2023;38(01):8. Jiang, Shixiang. Yang Yanjie. The development, evolution, and identification diagnosis of mild cognitive impairment [J]. 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Association between composite dietary antioxidant index and handgrip strength in American adults: Data from National Health and Nutrition Examination Survey (NHANES, 2011–2014). Front Nutr. 2023;10:1147869. He H, et al. Composite dietary antioxidant index associated with delayed biological aging: a population-based study. Aging. 2024;16(1):15–27. Sheng LT, et al. Dietary Total Antioxidant Capacity and Late-Life Cognitive Impairment: The Singapore Chinese Health Study. J Gerontol Biol Sci Med Sci. 2022;77(3):561–9. McGrattan AM, et al. Diet and Inflammation in Cognitive Ageing and Alzheimer's Disease. Curr Nutr ReP. 2019;8(2):53–65. Huang X, et al. Predictive value of peripheral blood inflammatory markers for the prognosis of newly diagnosed multiple myeloma patients [J]. China Mod Doctor. 2024;62(02):15–20. Wei J, Wang L, Kulshreshtha A, Xu H. Adherence to Life's Simple 7 and Cognitive Function Among Older Adults: The National Health and Nutrition Examination Survey 2011 to 2014. 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Geng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYPACmwQwxUOCljTStRwmQYu8e+/h17xt5/N02w8wPnjbxiBvTkiL4Zlzada8bbeLzc4kMBvObWMw3NlASMuMHDNjoJbEbTcY2KR52xgSDA4Q0jL/DUjLOZAW9t9EaZGX4DF+zNt2AGwLM1FaDHhyzBjnnEtO3HYmsVlyzjkJww0EbWk/Y/zhTZld4rbjhw8CGTbyhG05wMAmBYkOxgYgIUFAPciWBgbmjz8IqxsFo2AUjIKRDADsbkGseyR+mwAAAABJRU5ErkJggg==","orcid":"","institution":"Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College","correspondingAuthor":true,"prefix":"","firstName":"Yu","middleName":"","lastName":"Geng","suffix":""}],"badges":[],"createdAt":"2025-05-24 08:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6737663/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6737663/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85616878,"identity":"8eb502a9-7900-4a46-96df-61c5d0ecb4a8","added_by":"auto","created_at":"2025-06-29 14:39:44","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":187776,"visible":true,"origin":"","legend":"\u003cp\u003eMediation analysis of the single-exposure factor between WBCL and cognitive scores. The proportion of mediation was determined using the formula: (Indirect Effect / Total Effect) × 100%, where the indirect effect is the product of the path coefficients (path B and C) for exposure factor on cognitive functioning through the inflammatory marker, the total effect (path A) represents the unadjusted association between single-exposure factor and cognitive functioning (\u003csup\u003e**\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.001; \u003csup\u003e*\u003c/sup\u003e, \u003cem\u003eP \u003c/em\u003e\u0026lt;0.05)\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6737663/v1/12acc96ca7c50cc16df37c64.jpeg"},{"id":85616881,"identity":"0d256376-c68e-428f-a845-0f8987506aec","added_by":"auto","created_at":"2025-06-29 14:39:44","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":226473,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between mixed models and cognitive scores.\u003c/p\u003e\n\u003cp\u003eCDAI, the Composite Dietary Antioxidant Index, CERAD-IRT, the immediate recall test; DSST, the digit symbol substitution test; AFT, the animal fluency test; CERAD-DRT, the delayed recall test; CI, Confidence Interval.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6737663/v1/80da1683d7d2083a0da86243.jpeg"},{"id":85616882,"identity":"a0af2ad1-3bce-447f-96b1-bb8cdaa18651","added_by":"auto","created_at":"2025-06-29 14:39:44","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":191729,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between CDAI and z-score under different blood cadmium gradients.\u003c/p\u003e\n\u003cp\u003eCDAI, the Composite Dietary Antioxidant Index.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6737663/v1/dd7ef5a6620fc65bbd522116.jpeg"},{"id":90095023,"identity":"f27940ac-97eb-4387-a50e-77375fceaa3b","added_by":"auto","created_at":"2025-08-28 12:08:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1851607,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6737663/v1/81b636a9-e1bc-4865-8d4d-7293bcabad56.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Composite Dietary Antioxidant Index Counteracts the Cognitive Effects of Heavy Metal Exposure","fulltext":[{"header":"Introduction","content":"\u003cp\u003e With increasing life expectancy in developed countries, there has been a notable rise in age-related neurological disorders, particularly Alzheimer's disease \u003csup\u003e [ \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e ] \u003c/sup\u003e , which is the most common form of dementia. By 2030, it is projected that over 70\u0026nbsp;million elderly individuals worldwide will suffer from dementia, incurring social costs expected to exceed \u003cspan\u003e$\u003c/span\u003e2 trillion \u003csup\u003e [ \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e ] \u003c/sup\u003e . This growing burden highlights the need for early intervention during the preclinical stages to improve outcomes, making it a critical public health priority to find effective strategies to prevent or delay cognitive decline \u003csup\u003e [ \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e ] \u003c/sup\u003e . \u003c/p\u003e \u003cp\u003eEnvironmental pollution, particularly heavy metal intake, has emerged as a significant contributor to cognitive dysfunction \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Metals such as lead and cadmium are of particular concern due to their widespread presence and neurotoxic effects, even at low exposure levels \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. McGrattan et al. found that heavy metals can disrupt metal ion homeostasis in the brain \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, triggering systemic inflammation \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, which is central to cognitive decline and dementia development.\u003c/p\u003e \u003cp\u003eDiet may provide a promising avenue for mitigating these effects. The Cognitive Dietary Antioxidant Index (CDAI) serves as a metric for assessing the intake of a variety of dietary antioxidants. These include essential nutrients like selenium, zinc, vitamins A, C, E, as well as phytochemicals such as α-carotene, β-carotene, β-cryptoxanthin, lycopene, and lutein/zeaxanthin \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. A higher CDAI score, reflecting a more robust intake of antioxidants, has been correlated with a delay in biological aging processes \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Sheng et al. found that a high CDAI is associated with a reduced likelihood of cognitive impairment in later life within a Chinese population in Singapore \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. However, the extent to which a diet rich in antioxidants can counteract the cognitive damage caused by heavy metals is not well understood and warrants further investigation.\u003c/p\u003e \u003cp\u003eTherefore, we aimed to investigate the impact of heavy metal exposure and CDAI diet on cognitive function, and the interaction between heavy metal exposure and CDAI diet. We utilized the data from the 2011\u0026ndash;2014 National Health and Nutrition Examination Survey (NHANES) to examine the effect of heavy metal levels and antioxidant intake on cognition status. We hypothesized that cognitive status was negatively associated with heavy metal exposure level but was positively associated with CDAI assumption level, and antioxidant-rich diets with high CDAI may mitigate cognitive impairment caused by heavy metals exposure.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES) conducts biennial cross-sectional surveys among non-institutionalized U.S. civilians. The NHANES 2011\u0026ndash;2014 cycles were chosen for our analysis as they uniquely provide laboratory data and cognitive function scores necessary to test our hypothesis.\u003c/p\u003e \u003cp\u003eFrom the overall sample of 19,932 individuals in NHANES 2011\u0026ndash;2014, we applied exclusion criteria, resulting in a final study sample of 1,745 participants. Exclusions included participants who did not complete all four cognitive assessments (n\u0026thinsp;=\u0026thinsp;16,865), had missing or unclear responses for covariates (n\u0026thinsp;=\u0026thinsp;845), or exhibited abnormal biochemical test values (n\u0026thinsp;=\u0026thinsp;477). The data, which is publicly available and deidentified, did not require additional ethical approval for this analysis. We extracted relevant sociodemographic, lifestyle, dietary, medical, and biochemical information from the NHANES 2011\u0026ndash;2014 dataset.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eComposite Dietary Antioxidant Index (CDAI)\u003c/h3\u003e\n\u003cp\u003eDietary data in NHANES were collected through two non-consecutive 24-hour dietary recall interviews. The first dietary recall interview was conducted in-person at the Mobile Examination Center (MEC), and the second interview was conducted via telephone 3 to 10 days later. These data were converted into estimated nutrient intake values using the Food and Nutrient Database for Dietary Studies (FNDDS), developed by the United States Department of Agriculture. This study included eight vitamins (vitamins A, C, E, α-carotene, β-carotene, β-cryptoxanthin, lycopene, and lutein-zeaxanthin) and two trace elements (zinc and selenium) related to antioxidants. The intake of each dietary antioxidant was calculated as the average of the two recall interviews. CDAI was calculated using a standardization method based on the formula \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e:\u003c/p\u003e \u003cp\u003eCDAI=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{?}_{n=10}^{}\\frac{Antioxidant\\:content-mean\\left(Antioxidant\\:content\\right)}{Sd\\left(Antioxidant\\:content\\right)}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eAssessment of Heavy Metal Exposure\u003c/h3\u003e\n\u003cp\u003eWhole blood concentrations of lead and cadmium concentrations were measured using inductively coupled plasma mass spectrometry (ICP-MS). This method involved creating plasma from an argon stream to ionize the sample. Blood samples were converted into an aerosol, passed through the plasma where they were atomized and ionized at high temperatures, and then sent to the mass spectrometer. The mass spectrometer, operating under very low pressure, detected the ions based on their mass-to-charge ratio, enabling the identification of different isotopes of each element.\u003c/p\u003e\n\u003ch3\u003eInflammation markers\u003c/h3\u003e\n\u003cp\u003eWe selected white blood cell level (WBCL) and LP (lymphocyte percentage) as the two classic inflammation indicators in NHANES. A decrease in these markers indicates a reduction in oxidative stress and inflammatory responses \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eCovariates included age, gender, race (categorized as Mexican American, non-Hispanic white, non-Hispanic black, other Hispanic, and others), marital status (married or unmarried), educational level (below high school or high school and above), body mass index (BMI) categories (underweight, normal, overweight, and obese), smoking status (ever smoked at least 100 cigarettes in their lifetime), drinking status (at least 12 drinks per year or not), and a history of diabetes.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCognitive Function\u003c/h2\u003e \u003cp\u003eCognitive function in NHANES was evaluated using four standardized scales: the Immediate Recall Test (IRT) and Delayed Recall Test (DRT) from the Consortium to Establish a Registry for Alzheimer's Disease (CERAD), the Digit Symbol Substitution Test (DSST), and the Animal Fluency Test (AFT).\u003c/p\u003e \u003cp\u003e CERAD-IRT and CERAD-DRT: Participants recalled 10 unrelated words over three trials (IRT) and again after 8\u0026ndash;10 minutes (DRT), with a maximum score of 40 points. AFT: Participants named as many animals as possible within one minute, scoring one point per correct animal. DSST: A Wechsler Adult Intelligence Scale component where participants matched symbols to numbers, scoring based on correct matches within two minutes.\u003c/p\u003e \u003cp\u003eTest specific z-scores (including DSST, CERAD\u0026shy;WL, CERAD\u0026shy;WL, AFT) were created using sample means and SDs of test scores. A standardized global cognition z-score was generated by averaging the test specific z-scores divided by SD \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistical Methods\u003c/h3\u003e\n\u003cp\u003eStatistical analyses incorporated sample weights from both cycles, utilizing R software version 4.2.1. Continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error (SE), and categorical variables were reported as percentages. Group differences in participant characteristics were evaluated using independent t-tests for continuous variables and chi-square tests for categorical variables.\u003c/p\u003e \u003cp\u003eThe z-score was divided into four quartiles (Q1-Q4), with Q1 as the reference group. This research population\u0026rsquo;s baseline information was represented as mean and standard deviation (SD) for any continuous variable and percentages for any categorical variable. The CDAI and blood lead and blood cadmium were further used to analyze both in continuous variables and quartiles. Bayesian weighted linear regression analyses were applied to compute β and 95% confidential interval (CI) to detect any potential association between the CDAI and blood lead and blood cadmium index (continuous or quartiles) and all cognitive tests scores. Univariate and multivariate weighted linear regression models were used to assess linear trends and the independent correlation between CDAI, blood lead, and cadmium levels and cognitive function. Mediation analysis estimated the total effect, direct effect, and indirect effect, with the mediation percentage (Pm) calculated as the proportion of the indirect effect in the total effect. Within the framework of structural equation modeling (SEM), Bootstrap was applied for significance testing of the mediation analysis using the SEM package in R version 4.2.1 \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Mixed model was tested. Interaction analyses determined the statistical significance of the interaction between CDAI and cadmium levels. Results are presented as beta coefficients, standard errors, and two-tailed \u003cem\u003eP\u003c/em\u003e-values, with statistical significance defined as \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eParticipant Characteristics\u003c/h2\u003e \u003cp\u003eAmong the 1745 participants, 848 were male (48.6%) and 897 female (51.5%), with a mean age of 69.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7 years (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Non-Hispanic Whites made up the largest racial group, with 877 individuals (50.3%), followed by non-Hispanic Blacks at 408 (23.4%). In this population, 1187 (56.6%) were married, and 903 (51.7%) had attained a college education or higher. The prevalence of obesity, defined by BMI, was 899 (51.5%), with 472 having a history of diabetes (27.0%). There were 886 smokers (50.8%) and 1211 participants (69.4%) who reported alcohol consumption habits.\u003c/p\u003e \u003cp\u003eUnivariate analysis revealed significant differences in demographic characteristics among the quartiles of z-scores, including age, gender, race, educational attainment, and marital status, as well as lifestyle habits such as smoking and alcohol consumption (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). As the CDAI raised with increasing z-score quartiles, conversely, blood lead and cadmium levels and WBCL decreased gradually with increasing z-score quartiles.\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\u003eCharacteristics of included participants based on quartile of z-score.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall (n\u0026thinsp;=\u0026thinsp;1745)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1 (n\u0026thinsp;=\u0026thinsp;436)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2 (n\u0026thinsp;=\u0026thinsp;437)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3 (n\u0026thinsp;=\u0026thinsp;436)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4 (n\u0026thinsp;=\u0026thinsp;436)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographic data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years, (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.02\u0026thinsp;\u0026plusmn;\u0026thinsp;6.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.32\u0026thinsp;\u0026plusmn;\u0026thinsp;6.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.75\u0026thinsp;\u0026plusmn;\u0026thinsp;6.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.25\u0026thinsp;\u0026plusmn;\u0026thinsp;6.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65.81\u0026thinsp;\u0026plusmn;\u0026thinsp;4.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e848 (48.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e253 (14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e231 (13.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e209 (11.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e155 (8.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (3.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (2.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 (1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (2.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25 (1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e877 (50.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145 (8.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e207 (11.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e233 (13.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e292 (16.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e408 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140 (8.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105 (6.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106 (6.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57 (3.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Asian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41 (2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37 (2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003e\u0026lt;\u0026thinsp;9th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e196 (11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143 (8.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e646 (37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e198 (11.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e194 (11.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e161 (9.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93 (5.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school graduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e903 (51.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95 (5.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e201 (11.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e267 (15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e340 (19.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e985 (56.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e216 (12.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e245 (14.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e259 (14.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e265 (15.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e760 (43.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220 (12.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e191 (10.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e178 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e171 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e296 (17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79 (4.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71 (4.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71 (4.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e527 (30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131 (7.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140 (8.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e126 (7.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e130 (7.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e899 (51.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e218 (12.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e214 (12.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e237 (13.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e230 (13.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedical history, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e472 (27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153 (8.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123 (7.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120 (6.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76 (4.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiving habit, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e886 (50.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e232 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e232 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e226 (12.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e196 (11.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1211 (69.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e285 (16.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e291 (16.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e300 (17.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e335 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eThe composition of CDAI (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin A, ug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003eVitamin C, mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131.2\u0026thinsp;\u0026plusmn;\u0026thinsp;129.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114.02\u0026thinsp;\u0026plusmn;\u0026thinsp;103.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132.56\u0026thinsp;\u0026plusmn;\u0026thinsp;118.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138.19\u0026thinsp;\u0026plusmn;\u0026thinsp;170.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e140.12\u0026thinsp;\u0026plusmn;\u0026thinsp;115.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin E, mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.31\u0026thinsp;\u0026plusmn;\u0026thinsp;5.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.16\u0026thinsp;\u0026plusmn;\u0026thinsp;7.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.41\u0026thinsp;\u0026plusmn;\u0026thinsp;7.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003eα-carotene, mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97\u0026thinsp;\u0026plusmn;\u0026thinsp;5.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-carotene, mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.14\u0026thinsp;\u0026plusmn;\u0026thinsp;5.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.47\u0026thinsp;\u0026plusmn;\u0026thinsp;5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.89\u0026thinsp;\u0026plusmn;\u0026thinsp;17.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.66\u0026thinsp;\u0026plusmn;\u0026thinsp;5.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-cryptoxanthin, mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLycopene, mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.84\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.94\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.57\u0026thinsp;\u0026plusmn;\u0026thinsp;10.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.62\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLutein Zeaxanthin, mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.34\u0026thinsp;\u0026plusmn;\u0026thinsp;5.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.59\u0026thinsp;\u0026plusmn;\u0026thinsp;4.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.78\u0026thinsp;\u0026plusmn;\u0026thinsp;6.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZinc, mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.99\u0026thinsp;\u0026plusmn;\u0026thinsp;6.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.85\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.83\u0026thinsp;\u0026plusmn;\u0026thinsp;8.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.24\u0026thinsp;\u0026plusmn;\u0026thinsp;7.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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, mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e152.9\u0026thinsp;\u0026plusmn;\u0026thinsp;72.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137.48\u0026thinsp;\u0026plusmn;\u0026thinsp;64.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150.91\u0026thinsp;\u0026plusmn;\u0026thinsp;80.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e160.24\u0026thinsp;\u0026plusmn;\u0026thinsp;68.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e163.29\u0026thinsp;\u0026plusmn;\u0026thinsp;73.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003eCDAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.62\u0026thinsp;\u0026plusmn;\u0026thinsp;2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003e\u003cb\u003eHeavy metal exposure (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eblood lead, ug/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.01\u0026thinsp;\u0026plusmn;\u0026thinsp;2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003eblood cadmium, ug/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInflammation markers (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBCL, 1000cells/UL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.14\u0026thinsp;\u0026plusmn;\u0026thinsp;3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLP, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.49\u0026thinsp;\u0026plusmn;\u0026thinsp;9.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.26\u0026thinsp;\u0026plusmn;\u0026thinsp;9.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.02\u0026thinsp;\u0026plusmn;\u0026thinsp;8.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCognitive function (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCERAD-IRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.77\u0026thinsp;\u0026plusmn;\u0026thinsp;3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u0026thinsp;\u0026plusmn;\u0026thinsp;3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.18\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.43\u0026thinsp;\u0026plusmn;\u0026thinsp;3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003eDSST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.6\u0026thinsp;\u0026plusmn;\u0026thinsp;17.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.05\u0026thinsp;\u0026plusmn;\u0026thinsp;8.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.81\u0026thinsp;\u0026plusmn;\u0026thinsp;6.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.11\u0026thinsp;\u0026plusmn;\u0026thinsp;6.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003eAFT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.43\u0026thinsp;\u0026plusmn;\u0026thinsp;3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.26\u0026thinsp;\u0026plusmn;\u0026thinsp;3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.76\u0026thinsp;\u0026plusmn;\u0026thinsp;4.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.67\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003eCERAD-DRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.08\u0026thinsp;\u0026plusmn;\u0026thinsp;2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003ez-score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.2\u0026thinsp;\u0026plusmn;\u0026thinsp;24.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.33\u0026thinsp;\u0026plusmn;\u0026thinsp;9.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.51\u0026thinsp;\u0026plusmn;\u0026thinsp;5.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e119.31\u0026thinsp;\u0026plusmn;\u0026thinsp;10.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003eCDAI, the Composite Dietary Antioxidant Index; SD, Standard Deviation; BMI, Body Mass Index; WBCL, the white blood cell level; LP, lymphocyte percentage; CERAD-IRT, the Consortium to Establish a Registry for Alzheimer's Disease's- immediate recall test; DSST, the digit symbol substitution test; AFT, the animal fluency test; CERAD-DRT, the Consortium to Establish a Registry for Alzheimer's Disease's- delayed recall test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of CDAI and Blood Metal with Cognitive Function Scores\u003c/h2\u003e \u003cp\u003eLinear regression analysis showed that, compared to the lowest quartile of CDAI, higher quartiles of CDAI were significantly associated with better cognitive function scores, including four cognitive tests and the z-score (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Model 1). These correlations remained significant even after adjusting for potential confounders in Models 2 and 3 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Specifically, for each one-unit increase in the CDAI score, there was a corresponding improvement of 0.05 points in the z-score. When compared to individuals in the lowest quartile of CDAI (Q1), those in the second (Q2), third (Q3), and fourth (Q4) quartiles experienced increases in their z-scores by 0.11, 0.11, and 0.05 points, respectively.\u003c/p\u003e \u003cp\u003eThere is a negative correlation between blood lead and cadmium levels and the z-score (β = -0.07, 95% CI = -0.10 to -0.04, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; β = -0.25, 95% CI = -0.35 to -0.14, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Through Bayesian linear regression analysis, we found that individuals with lower blood lead and cadmium levels performed better on cognitive function scores (Model 1). After adjusting for potential confounders in Models 2, 3, and 4, the correlation between blood cadmium and CERAD-IRT, DSST, AFT, and the z-score remained significant. Conversely, the correlations between blood lead levels and the various cognitive test scores were not significant (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eMultivariate linear analysis of the association between CDAI and cognitive function scale scores.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eScales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCDAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ (95% Cl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ (95% Cl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eβ (95% Cl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eCERAD-IRT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.39 (0.08,0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51 (0.21,0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.39 (0.09,0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38 (0.19,0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.43 (0.25,0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.30 (0.12,0.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18 (0.10,0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21 (0.13,0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.12 (0.05,0.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContinuous (per CDAI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22 (0.13,0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.27 (0.19,0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.16 (0.08,0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eDSST\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.78 (1.62,3.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.18 (2.09,4.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.00 (1.04,2.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.89 (2.20,3.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.11 (2.46,3.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.91 (1.33,2.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03 (0.74,1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.09 (0.82,1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.49 (0.24,0.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContinuous (per CDAI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.46 (1.13,1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.60 (1.28,1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.78 (0.51, 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eAFT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44 (0.07,0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45 (0.08,0.81) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.24 (-0.11,0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.58 (0.36,0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.57 (0.35,0.78) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.32 (0.11,0.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.31 (0.22,0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.28 (0.19,0.38) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.16 (0.07,0.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContinuous (per CDAI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44 (0.34,0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.43 (0.33,0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.28 (0.18,0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eCERAD-DRT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17 (0.01,0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21 (0.06,0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.16 (0.01,0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14 (0.04,0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16 (0.06,0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09 (0.003,0.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06 (0.02,0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07 (0.03,0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04 (0.002,0.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContinuous (per CDAI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09 (0.05,0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10 (0.06,0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06 (0.02,0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003ez-score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15 (0.09,0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.18 (0.11,0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.11 (0.06,0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16 (0.12,0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17 (0.14,0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.11 (0.07,0.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09 (0.07,0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09 (0.07,0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05 (0.03,0.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContinuous (per CDAI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04 (0.06,1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10 (0.08,0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05 (0.04,0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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\u003eModel 1: non-adjusted model.\u003c/p\u003e \u003cp\u003eModel 2: adjusted for age, gender, race, and BMI.\u003c/p\u003e \u003cp\u003eModel 3: adjusted for covariates of model 2, education, and marital status, smoking, alcohol consumption, and diabetes history.\u003c/p\u003e \u003cp\u003eCDAI, the Composite Dietary Antioxidant Index; BMI, Body Mass Index; CERAD-IRT, the immediate recall test; DSST, the digit symbol substitution test; AFT, the animal fluency test; CERAD-DRT, the delayed recall test; Cl, Confidence Interval\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\u003eMultivariate linear analysis of the association between blood lead and blood cadmium and cognitive function scale scores.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eScales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c3\" namest=\"c2\" rowspan=\"2\"\u003e \u003cp\u003eBlood heavy metal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ (95% Cl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ (95% Cl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eβ (95% Cl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eβ (95% Cl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e\u003cb\u003eCERAD-IRT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eblood lead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.68(-1.24, -0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.45(-0.99,0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.54(-1.07, -0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.54 (-1.29,0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.78(-1.36, -0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.31(-0.87,0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.60(-1.16, -0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.37 (-0.86,0.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.26(-1.81, -0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.43(-0.98,0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.39(-0.93,0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.04 (-0.18,0.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous (per blood lead)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.25 (-0.38, -0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.10 (-0.23,0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.07 (-0.20,0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.04 (-0.17,0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eblood cadmium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.70(-1.57,0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.02(-1.86, -0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.13(-2.00, -0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.07 (-1.94, -0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.57(-1.30,0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.65(-1.34,0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.53(-1.25,0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.43 (-1.15,0.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.75(-1.36, -0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.79(-1.38, -0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.69(-1.29, -0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.64 (-1.24, -0.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous (per blood cadmium)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.59 (-1.08, -0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.70 (-1.17, -0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.57 (-1.06, -0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.56 (-1.04, -0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e\u003cb\u003eDSST\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eblood lead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.21(-3.41,0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.77(-2.87,1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.50(-3.32,0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.02 (-2,50,2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.48(-2.69,1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.04(-1.07,3.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.15(-2.97,0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.55 (-2.10,0.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.07(-7.26, -2.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.56(-4.75, -0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-2.09(-3.98, -0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.33 (-0.80,0.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous (per blood lead)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.19 (-1.71, -0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.78 (-1.29, -0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.51 (-0.95, -0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.35 (-0.77,0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eblood cadmium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.71(-7.04, -0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.28(-8.53, -2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-4.09(-7.02, -1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-3.78 (-6.53, -0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.25(-5.99, -0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.86(-6.47, -1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-2.64(-4.96, -0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.93 (-4.17,0.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.84(-7.24, -2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.73(-8.05, -3.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-3.94(-6.03, -1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-3.49 (-5.55, -1.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous (per blood cadmium)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.26 (-6.12, -2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.10 (-6.88, -3.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-3.14 (-4.75, -1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-2.86 (-4.45,-1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e\u003cb\u003eAFT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eblood lead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.17(-2.24, -0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.88(-1.95,0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.84(-1.92,0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.26 (-0.68,1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.77(-1.63,0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.50(-1.35,0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.42(-1.29,0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.42 (-0.17,1.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.78(-1.53, -0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.68(-1.44,0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.42(-1.19,0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.05 (-0.11,0.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous (per blood lead)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.6 (-0.23,0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.01 (-0.18,0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03 (-0.13,0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.08 (-0.08,0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eblood cadmium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.17(-2.24, -0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.88(-1.95,0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.84(-1.92,0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.87 (-1.91,0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.77(-1.63,0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.50(-1.35,0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.42(-1.29,0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.57 (-1.39,0.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.78(-1.53, -0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.68(-1.44,0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.42(-1.19,0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.48 (--1.25,0.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous (per blood cadmium)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.96 (-1.55, -0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.91 (-1.50, -0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.58 (-1.18, -0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.65 (-1.26, -0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e\u003cb\u003eCERAD-DRT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eblood lead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.14(-0.43,0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.03(-0.30,0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.09(-0.36,0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.10 (-0.48,0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.27(-0.57,0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.03(0.32,0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.17(-0.45,0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.16 (-0.42,0.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.58(-0.87, -0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.15(-0.44,0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.14(-0.43,0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.03 (-0.10,0.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous (per blood lead)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.12 (-0.19, -0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.04 (-0.11,0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.03 (-0.10,0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.04 (-0.10,0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eblood cadmium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.6 (-0.23,0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.01 (-0.18,0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03 (-0.13,0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.30 (-0.73.0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.13(-0.57,0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.26(-0.69,0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.30(-0.74,0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.05 (-0.33,0.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.17(-0.48,0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.02(-0.38,0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001(-0.37,0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.11 (-0.42,0.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous (per blood cadmium)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.11 (-0.36,0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.15 (-0.39,0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.08 (-0.33,0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.06 (-0.31,0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e\u003cb\u003ez-score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eblood lead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.05 (-0.09,-0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.02 (-0.05,0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.01 (-0.05,0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.01 (-0.18,0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.07 (-0.12, -0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.02 (-0.07,0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.02 (-0.06,0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.03 (-0.12,0.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.09 (-0.13, -0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.07 (-0.11, -0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.04 (-0.07, -0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.01 (-0.04,0.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous (per blood lead)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.07 (-0.10, -0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.04 (-0.07, -0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.02 (-0.05, -0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.01 (-0.04,0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eblood cadmium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.14 (-0.28,0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.18 (-0.32, -0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.10 (-0.23,0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.26 (-0.44, -0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.25 (-0.40, -0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.27 (-0.41, -0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.18 (-0.31, -0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.14 (-0.29,0.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.31 (-0.45, -0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.35 (-0.49, -0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.21 (-0.33, -0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.20 (-0.33, -0.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous (per blood cadmium)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.25 (-0.35, -0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.28 (-0.39, -0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.18 (-0.28, -0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.17 (-0.27, -0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\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\u003eModel 1: non-adjusted model.\u003c/p\u003e \u003cp\u003eModel 2: adjusted for age, gender, race, and BMI.\u003c/p\u003e \u003cp\u003eModel 3: adjusted for covariates of model 2, education, and marital status, smoking, alcohol consumption, and diabetes history.\u003c/p\u003e \u003cp\u003eModel 4: adjusted for the covariates of model 3using either blood lead or blood cadmium.\u003c/p\u003e \u003cp\u003eBMI, Body Mass Index; CERAD-IRT, the immediate recall test; DSST, the digit symbol substitution test; AFT, the animal fluency test; CERAD-DRT, the delayed recall test; CI, Confidence Interval.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMediator Analysis\u003c/h2\u003e \u003cp\u003eBased on the correlation between WBCL and z-score (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), we further conducted mediation analyses to examine whether WBCL mediates the effects of CDAI and heavy metals exposure levels on cognitive function. Mediation analyses revealed that WBCL mediated the relationship between CDAI and z-score, DSST, CERAD-DRT, explaining 2.76%, 3.31%, and 7.00% of the total effects, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, blood lead was not significantly associated with WBCL (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06), and WBCL did not mediate the association between blood lead and z-score. Univariate correlation analysis indicated that blood cadmium was associated with WBCL (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and WBCL mediated the relationship between blood cadmium and z-score, DSST, explaining 9.87% and 10.72% of the total effects, respectively. There was no evidence that WBCL mediated the relationship between any of the exposure metal level and AFT, CERAD-IRT scores.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCDAI, the Composite Dietary Antioxidant Index; WBCL, white blood cell count; CERAD-IRT, the immediate recall test; DSST, the digit symbol substitution test; AFT, the animal fluency test; CERAD-DRT, the delayed recall test.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMixture Associations of CDAI and Blood Cadmium on Cognitive Function.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn the mixed model, the mixed effect component weights of CDAI and blood cadmium were significantly correlated with DSST, AFT, CERAD-DRT, and z-scores (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrated smoothed curve fitting, stratified by metal intake levels, where z-score decreased with an increase in CDAI. When blood cadmium was below 0.29 \u0026micro;g/L, the curve flattened after CDAI exceeded 5.0. In the range where CDAI\u0026thinsp;\u0026lt;\u0026thinsp;5.0, with Q1 as the reference, when blood cadmium levels were in Q2, the improvement in z-score due to CDAI increased by 0.03 points, but this change was not statistically significant. When blood cadmium levels reached Q3 and Q4, the improvement in z-score due to CDAI decreased by 0.13 and 0.30 points, respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, when blood cadmium exceeded 0.29 \u0026micro;g/L, the z-score raised as CDAI increased.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our study, we discovered a strong correlation between CDAI and cognitive function among the elderly population in the United States. Additionally, exposure to heavy metals, including blood lead and cadmium, was found to be independently associated with cognitive status. WBCL acted as a mediator in the relationship between CDAI, blood cadmium, and cognitive status. Further interaction analysis indicated that a positive correlation between CDAI and cognitive function was maintained even in the presence of blood cadmium. This finding suggests that the consumption of dietary antioxidants might enhance cognitive function by reducing inflammatory responses, an effect that is influenced by the level of exposure to blood cadmium.\u003c/p\u003e \u003cp\u003eOur study revealed that a high CDAI, indicative of robust antioxidant intake, was strongly correlated with improved cognitive function. This suggested that a diet rich in antioxidants can mitigate the neurotoxic effects of chronic inflammation on cognition, a risk exacerbated by heavy metals through oxidative stress \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Our mediation analysis indicated that while higher CDAI scores and reduced cadmium exposure were associated with enhanced cognitive performance by potentially lowering inflammation, lead exposure does not significantly correlate with WBCL, suggesting that WBCL does not mediate the association between lead and cognition impairment. WBCL, serving as a biomarker for chronic inflammation and oxidative stress, was linked to reduced cognitive performance. High WBCL levels, possibly resulting from free radical damage, were consistent with research highlighting the detrimental effects of systemic inflammation on the central nervous system and the increased risk of cognitive decline \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Supported by global research linking inflammatory markers to cognitive abilities \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, our findings emphasized the importance of inflammation management in maintaining cognitive health. This underscored the need for dietary interventions that promote antioxidant intake to counteract the cognitive risks posed by heavy metal exposure.\u003c/p\u003e \u003cp\u003eFurthermore, we found a significant interaction between CDAI and the blood cadmium, suggesting that the cognitive benefits of antioxidant intake may depend on an individual exposure levels to cadmium. Antioxidant-rich diets may help counteract the adverse cognitive effects of cadmium, with the protective effect possibly varying based on different exposure levels. In our study, we found that when blood cadmium was below 0.29 \u0026micro;g/L, the curve flattened once CDAI exceeds 2.5. however, when blood cadmium levels exceeded 0.29 \u0026micro;g/L, the z-score continued to increase with higher CDAI, suggesting that elevating CDAI lessened cognitive damage caused by blood cadmium.\u003c/p\u003e \u003cp\u003eThe strengths of this study included a comprehensive approach to dietary antioxidants, a large, nationally representative sample of older adults, and the use of mediation models to understand the complex interactions between inflammation, diet, heavy metal exposure, and cognition. However, limitations included the cross-sectional design, which hindered causal inference, and the restriction of cognitive assessment data to the period between 2011 and 2014 due to database constraints. The lack of direct oxidative stress biomarkers, such as C-reactive protein, also limited a more detailed analysis. Despite these limitations, the study offered valuable insights into the influence of diet and heavy metal exposure on cognitive health in the elderly.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAmong the elderly population in the United States, an antioxidant-rich diet offered an protective effect on cognitive function even in the presence of cadmium exposure. The presence of chronic inflammation, as indicated by WBCL, served as a mediating factor between antioxidant-rich diets, blood cadmium levels, and cognitive function. This finding implies that a antioxidant-rich diets may counteract the cognitive impairments induced by heavy metals by eliciting an anti-inflammatory response.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConcept: LH, SZ,YG, and XJ. Data curation: LH, JY, and HW. Formal analysis: LH, WY and HC. Writing\u0026mdash;original draft: LH, HW, PS and XY. Methodology: LH, JY and WY. Project administration and resources: SZ and YG and XJ. Writing\u0026mdash;review and editing: JY, HW, PS, XY, PW, YL, QH and WZ. Supervision: SZ and YG. All authors reviewed the manuscript drafts, critically revised the manuscript and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Sharing Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Data is publicly available on the internet throughout the world. (www.cdc.gov/nchs/nhanes/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors affirm that there are no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData for this study were sourced from datasets available to the public with the approval of the National Center for Health Statistics\u0026rsquo; research ethics review committees.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the\u0026nbsp;Medical Science and Technology Project of Zhejiang Province\u0026nbsp;(No. 2022KY600 and No. 2024KY019No. 2024KY019) and Zhejiang Provincial Natural Science Foundation of China (No. LGF22H090020) and Social Development Science and Technology Projects of Wenling City in 2023 (No. 2023S00131) and Social Development Science and Technology Projects of Wenling City in 2024 (No. 2024S00181)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eXu Xiaoxian, Bai W. 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J Am Heart Assoc. 2022;11(6):e022959.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBotelho J, et al. The Role of Inflammatory Diet and Vitamin D on the Link between Periodontitis and Cognitive Function: A Mediation Analysis in Older Adults. Nutrients. 2021;13(3):924.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCsavina, et al. A review on the importance of metals and metalloids in atmospheric dust and aerosol from mining operations. Sci Total Environ. 2012;433:58\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoikkanen, et al. Modification of glutamate-induced oxidative stress by lead: The role of extracellular calcium. Free Radic Biol Med. 1998;24:377\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuP. LeeJ,etal.CognitiveFunctionandCardiometabolic-InflammatoryRiskFactorsA mongOlderIndiansandAmericans.JAmGeriatrSoc.2020Aug;68Suppl3(Suppl3):S36-S44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMichopoulosV,etal. InflammationinFear-andAnxiety-BasedDisorders:PTSD,GAD,and Beyond.Neuropsychopharmacology.2017Jan;42(1):254\u0026ndash;270.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"composite dietary antioxidant index, cognitive impairment, white blood cell levels, blood lead, blood cadmium","lastPublishedDoi":"10.21203/rs.3.rs-6737663/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6737663/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eWe aimed to explore if a higher composite dietary antioxidant index (CDAI) and exposure to heavy metals including lead and cadmium are associated with cognitive function in the elderly. Additionally, we explore the interaction effects between CDAI and heavy metals on cognitive function.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData from the 2011\u0026ndash;2014 US National Health and Nutrition Examination Survey (NHANES) was utilized to calculate the CDAI, based on the intake levels of vitamins A, C, E, α-carotene, β-carotene, β-cryptoxanthin, lycopene, and lutein/zeaxanthin. Blood levels of lead and cadmium were measured to assess heavy metal exposure. Cognitive function was evaluated using a z-score derived from a battery of tests, including the Immediate Recall Score, Digit Symbol Substitution Test, Category Fluency Test, and Delayed Recall Score. We evaluated the level of chronic inflammation using white blood cell count (WBCL) and explored its mediating role in the relationship between CDAI, heavy metals, and cognitive scores. Finally, we evaluated the effects of CDAI and heavy metals exposure on cognitive function, along with their interactions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study included 1745 elderly participants aged 60 and above. CDAI and blood levels of lead and cadmium were each significantly associated with all cognitive scores, including each specific cognitive function score and z-score (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The mediation analysis shows that WBCL partially mediate the relationship between CDAI and Z-scores, contributing 2.76%, 3.31%, and 7.00% to the total effect. Additionally, WBCL partially mediates the relationship between blood cadmium levels and z-scores, contributing 9.87% and 10.72% to the total effect (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Interaction analysis confirmed a significant correlation between CDAI and blood cadmium with z-score (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe study manifested the relationship between CDAI, exposure to lead and cadmium, and cognitive function in the US elderly population. An antioxidant diet can combat the cognitive damage caused by cadmium exposure through an anti-inflammatory response.\u003c/p\u003e","manuscriptTitle":"The Composite Dietary Antioxidant Index Counteracts the Cognitive Effects of Heavy Metal Exposure","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-29 14:39:39","doi":"10.21203/rs.3.rs-6737663/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"45e9cb0f-ba5a-422d-83ce-46451d68eca2","owner":[],"postedDate":"June 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-28T12:08:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-29 14:39:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6737663","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6737663","identity":"rs-6737663","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}