The L-shaped Relationship Between Composite Dietary Antioxidant Index and Cognitive Impairment in the American Elderly: A Cross-Sectional Study (NHANES 2011-2014)

The L-shaped Relationship Between Composite Dietary Antioxidant Index and Cognitive Impairment in the American Elderly: A Cross-Sectional Study (NHANES 2011-2014) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The L-shaped Relationship Between Composite Dietary Antioxidant Index and Cognitive Impairment in the American Elderly: A Cross-Sectional Study (NHANES 2011-2014) Hang Yang, Xiaoying Wang, Ye Zhou, Shenyingjie Zhang, Zhenzhen Gao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4384652/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Oxidative stress is involved in the development and progression of cognitive impairment. However, the association between composite dietary antioxidant index (CDAI) and cognitive impairment remains unknown. Objective This cross-sectional study investigated the non-linear relationship between CDAI and cognitive impairment among the American elderly. Methods The public data was available from the United States National Health and Nutrition Examination Survey from 2011 to 2014. Participants aged ≥ 60 years were eligible for cognitive function, including word learning and recall modules from the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD), the animal fluency test (AFT), and the digit symbol substitution test (DSST). A composite cognition score was created to evaluate global cognition. The univariate and multivariate logistic regression analysis, restricted cubic spline, stratified and sensitivity analysis were conducted. Results CDAI was negatively associated with cognitive impairment. For each standard deviation increase in CDAI, the risk of cognitive impairment decreased by 6% for DSST (OR = 0.94, 95% CI: 0.9, 0.97), 7% for AFT (OR = 0.93, 95% CI: 0.9, 0.96), 4% for CERAD (OR = 0.96, 95% CI: 0.93, 0.99), and 7% for global cognition (OR = 0.93, 95% CI: 0.9, 0.96) after adjusting for multiple potential confounders. This significant negative relationship remained consistent when comparing individuals in the highest CDAI tertile with those in the lowest CDAI tertile. Furthermore, a non-linear relationship was observed between CDAI and cognitive impairment on AFT ( P for non-linearity = 0.009) and global cognition ( P for non-linearity = 0.006).These negative correlations between CDAI and cognitive impairment were observed across the stratified age, gender, poverty-to-income ratio, body mass index, hypertension, and diabetes. However, the interaction test revealed significance for education on DSST ( P for interaction = 0.04). Moreover, vitamin E, zinc, selenium, and carotenoids were independently associated with cognitive impairment in this study. The sensitivity analysis for participants with complete covariates yielded a similar finding. Conclusion These findings suggested a negative and L-shaped association between the CDAI and the risk of cognitive impairment among the American elderly. The results have significant implications for public health initiatives to prevent and limit the progression of cognitive impairment through dietary interventions. Health sciences/Risk factors Health sciences/Diseases/Nutrition disorders Biological sciences/Neuroscience/Cognitive ageing Health sciences/Health care/Geriatrics Health sciences/Health care/Nutrition Health sciences/Health care/Public health Health sciences/Health care/Quality of life oxidative stress nutrient composite dietary antioxidant index cognitive impairment cross-sectional study Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Cognitive decline is a classic aging-related change in the elderly, which develops from intact cognition through mild impairment and further dementia [ 1 ]. The prevalence of mild cognitive impairment (MCI) in the global population aged 65 and older is approximately 10–20% [ 2 ]. By the year 2050, approximately 152 million individuals worldwide will be afflicted by dementia [ 3 ]. MCI is a pre-stage of dementia, which is more common than dementia among the elderly [ 4 ]. The progression of dementia is irreversible, yet cognitive impairments bridging the gap between "normal aging" and dementia still demonstrate an approximately 25% reversion rate [ 5 ]. The high prevalence of dementia, coupled with limited strategies, will impose a substantial burden on individuals, families, healthcare systems, and society, underscoring the crucial role of primary prevention and the need for proactive measures in addressing dementia. Oxidative stress is an imbalance between pro-oxidative and antioxidative processes which leads to disruption of the redox circuitry and damage to macromolecules [ 6 ]. The brain's sensitivity to oxidative stress increases with age, leading to the accumulation of reactive oxygen species (ROS), mitochondrial dysfunction, disruption of metal homeostasis, and impairment of synaptic activity and neural transmission in neurons, resulting in cognitive dysfunction [ 7 ]. Dietary nutrients, being one of the modifiable lifestyle-related factors, play a crucial role in the regulation of neurons, cellular aging, and modulation of oxidative stress, and antioxidants are associated with cognitive function [ 8 , 9 ]. Findings from a prospective cohort study in Singapore suggested that a higher dietary total antioxidant capacity (TAC) is associated with a decreased risk of late-life cognitive impairment [ 10 ]. However, a report from the PREADVISE trial revealed that regular antioxidant supplements (vitamin E or selenium) over a period of 5.4 years did not prevent AD among asymptomatic men [ 11 ]. Current studies on antioxidants and dementia or cognitive impairment remain conflicting, possibly due to variations in the sources, dosages or patterns of antioxidant intake. The Composite Dietary Antioxidant Index (CDAI) is established based on the aggregate effects of anti-inflammatory indicators such as interleukin-1 and tumor necrosis factor-alpha [ 12 ], incorporating vitamin A, vitamin C, vitamin E, manganese, selenium, and zinc. It is utilized to measure an individual's dietary antioxidant capacity.Although there have been studies on the relationship between TAC and cognitive function, TAC scores are calculated using plasma tests for ferric-reducing ability, which may only capture one element of the body's antioxidant activity [ 13 ]. The relationship between CDAI and cognitive impairment has not been thoroughly assessed. To address this research gap, our study examined and evaluated the association between CDAI and cognitive impairment in the elderly population of the United States, utilizing data from the National Health and Nutrition Examination Survey (NHANES). Method Data source NHANES database was used in this study, which began in the early 1960s and has been a major and continuous program conducted by the National Center for Health Statistics (NCHS) to assess health and nutritional statistics of the noninstitutionalized American population through a stratified, multistage probability sampling [ 14 ]. The National Center for Health Statistics (NCHS) Ethics Review Board approved the NHANES 2011–2012 (Protocol number: 2011-17) and 2013–2014 (Continuation of Protocol # 2011-17) protocols, which are available on the NHANES website ( https://www.cdc.gov/nchs/nhanes/irba98.htm ). All the methods in this research were performed following the Declaration of Helsinki. Study population Moreover, NHANES has a primary role in collecting extensive examinations for the American elderly to increase the knowledge of the aging population. Particularly after the age of 60 years, the three cognitive functioning assessments were examined, including word learning and recall modules from the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD), the Animal Fluency test (AFT) and the Digit Symbol Substitution test (DSST), which were only available from 2011–2012 and 2013–2014 cycles. The NCHS Ethics Review Board subsequently approved the NHANES protocols, and all the participants provided written informed consent at enrolment. A total of 2,524 participants with complete cognitive functioning assessments and CDAI-related dietary records were enrolled from a data pool of 19,931 participants. The flowchart for participant enrolment is presented in (Fig. 1 ). CDAI measurement All the NHANES dietary intake information was collected through a 24-hour dietary recall interview to estimate the dietary nutrients from foods and beverages. The first dietary recall was an in-person interview conducted in the Mobile Examination Center (MEC) with the question “What do we eat in America” and the second one was a telephone follow-up 3 to 10 days later. The dietary intake of vitamins and minerals was following the United States Department of Agriculture’s Food and Nutrient Database for Dietary Studies. CDAI included six antioxidants, including vitamin A, vitamin C, vitamin E, zinc, selenium, and carotenoids, following the revised version of CDAI developed by Wright and colleagues in 2004 [ 15 ]. To maximize the reliability and effectiveness, each dietary intake used in this study was calculated as the average of the two dietary recalls. The calculation of CDAI was the summary of each standardized z-score of dietary intakes of these six antioxidants. The z-score formula is z = (x - mean)/σ, where x represents the individual intake of each antioxidant, ‘mean’ is the average intake of each antioxidant for the cohort, and σ is the standard deviation. The categorical variable of CDAI was then categorized into tertiles. Cognitive impairment measurement A series of cognitive functioning assessments in NHANES (2011–2014) included CERAD, AFT and DSST, only eligible for participants who are aged ≥ 60 years. The CERAD word learning subtest included three immediate word recalls and a delayed recall. Every participant was suggested to read 10 unrelated words and recall them immediately as many as possible for three trials. The delayed recall occurred after completing AFT and DSST. Each correct recalled word was one point. Participants were instructed to name as many animals as possible in one minute in AFT, which was designed to evaluate the executive function. The AFT score is determined by the number of named animals. The processing speed, sustained attention, and working memory could be evaluated by DSST which is one part of the Wechsler Adult Intelligence Scale (WAIS-III). Participants were asked to perform the symbol-number pair in two minutes, with one point for correctly pairing. NCHS suggested the potential cognitive impairment could be identified as participants were scoring in the lowest 25th percentile of each test [ 16 ]. In our study, individuals with CERAD < 22, AFT < 14, or DSST < 35 could be classified as having cognitive impairment, consistent with previous studies [ 17 , 18 ]. In addition, to mitigate uneven differences and biases among individuals, as well as to address floor and ceiling effects, we created a composite cognition score to represent global cognition [ 19 , 20 ]. The composite cognition score is calculated by summarizing each standardized z-score of CERAD, AFT, and DSST. Individuals scoring < -1 were classified as having cognitive impairment [ 21 ]. Covariates The selection of possible potential confounders was based on clinical practice, previous studies. The sociodemographic included age, gender, race and ethnicity, education level, and poverty-to-income ratios (PIR) collected through a Computer-Assisted Personal Interviewing system. Age was classified as 60–69 years and ≥ 70 years. Gender included male and female. Race and ethnicity included five groups: Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, and other races. Education was classified as less than high school, high school graduate, and college or more. The PIR was classified as low level (< 1.3), medium (1.3 to 3.5) and high (≥ 3.5) [ 22 ]. Smoking status and physical work activity were self-reported. Smoking status was classified as current smokers (smoked at least 100 cigarettes in life and still smoke now), former smokers (smoked at least 100 cigarettes in life and didn’t smoke now) and never smokers ( smoked less than 100 cigarettes in life) [ 23 ]. Physical work activity was classified as vigorous-intensity activity (causes large increases in breathing or heart rate at least 10 minutes) or moderate-intensity (causes small increases in breathing or heart rate) and mild or sedentary [ 24 ]. BMI was measured in the MEC, and was classified as normal (< 25 kg/m 2 ), overweight (25–30 kg/m 2 ), and obese (≥ 30 kg/m 2 ).The self-reported physician diagnosis of hypertension (yes or no), diabetes (yes, no or borderline), and stroke (yes or no) were collected by trained interviewers. The albumin concentration (g/L) was tested by the DcX800 method. Some covariates were missing at random, such as PIR (missing 7.57%), BMI (missing 1.27%), albumin (missing 4.99%), education (missing 0.08%), smoking status (missing 0.08%), physical activity (missing 0.12%), hypertension (missing 0.12%), diabetes (missing 0.08%), and stroke (missing 0.2%). These missing values were imputed using a simple imputation method. Mean imputation was applied for continuous variables with a normal distribution, while median imputation was used for variables with a skewed distribution. Mode imputation was applied for categorical variables. Statistical analysis No a priori calculation of statistical power was performed because the sample size was based on the available data from NHANES. All analyses were performed by R software (version 4.2.3; R Foundation for Statistical Computing; http://www.Rproject.org ) and Free Statistics software (version 1.9.1; Beijing Free Clinical Medical Technology Co., Ltd.). In all analyses, a two-sided p-value < 0.05 indicated statistical significance. Firstly, the Kolmogorov-Smirnov test was used to determine the normality of continuous variables. Normally distributed variables were presented as mean (standard deviation, SD), while skewed variables were presented as median (25–75% interquartile range, IQR). Categorical variables were represented by number (percentage). Statistical tests such as ANOVA, Kruskal–Wallis, and chi-squared tests were applied to compare differences across groups. Next, both univariate and multivariate logistic regression models were employed to investigate the association between CDAI and cognitive impairment in various domains. CDAI was then transformed into tertiles, and the P -value for trend was calculated to validate the results when treating CDAI as a continuous variable. Three models were adjusted based on clinical practice, and previous studies [ 25 , 26 , 27 ] or P < 0.1 in univariate logistic regression analysis. Model 1 was non-adjusted; Model 2 was adjusted for age (continuous variables), gender, race and ethnicity; Model 3 was further adjusted for education, PIR (continuous variable), BMI (continuous variables), hypertension, diabetes, stroke and albumin. The restricted cubic splines (RCS) with four knots (5th, 35th, 65th and 95th percentiles) were performed to explore the non-linearity between CDAI and cognitive impairment on DSST, AFT, CERAD, and global cognition by adjusting the cofounders consistent with model 3. The stratified analysis was additionally performed by age (60–69 years and > = 70 years), gender (male and female), education level (less than high school, high school graduate, and college or more), PIR (low, medium and high), BMI (normal, overweight and obese), hypertension (yes and no), and diabetes (yes/borderline and no), and likelihood test was used for interaction. Finally, in the sensitivity analysis, we examined the association between six components of CDAI and cognitive impairment. Further, we excluded individuals with missing covariate data and conducted multivariate logistic regression and RCS to assess the robustness of the findings. Results Study population and baseline characteristics A total number of 2,524 participants aged ³ 60 years were included in the analysis. The general characteristics of the participants with CDAI tertiles are presented in Table 1. Of those, the mean (SD) age was 69.5 (6.8) years, and 1,356 (49%) were male. Compared with the individuals with low CDAI tertiles, individuals with the highest CDAI score (CDAI ≥ 0.94) tended to be male, non-Hispanic white, high education level, high level of PIR and serum albumin ( P < 0.001). The high score of CDAI (≥ 0.94) was less likely to be current smokers ( P < 0.001), vigorous-intensity workers ( P = 0.007), or those without a history of hypertension ( P < 0.001), diabetes ( P < 0.001), or stroke ( P = 0.014). In addition, participants with the highest level of CDAI scores are more likely to have a low incidence of cognitive impairment on DSST, AF, CERAD, and global cognition. There were no statistical differences regarding age ( P = 0.309) or BMI ( P = 0.132). Univariate logistic regression analysis The univariate analysis indicated that age, race and ethnicity, education level, PIR, physical activity, hypertension, diabetes, stroke, albumin, and CDAI (continuous variables) were correlated with cognitive impairment in different domains ( P < 0.1) (Supplementary Table 1). Multivariate logistic regression analysis between CDAI and cognitive impairment We observed an inverse association between CDAI and cognitive impairment on DSST, AFT, CERAD and global cognition assessments, indicating individuals with higher CDAI scores had a lower incidence of cognitive impairment (Table 2). After adjusting for multiple confounders, a high intake of CDAI was associated with a decreased risk of cognitive impairment, with odds ratios (OR) per 1-SD increase of 0.94 (95% CI: 0.9, 0.97) for DSST, 0.93 (95% CI: 0.9, 0.96) for AFT, 0.96 (95% CI: 0.93, 0.99) for CERAD, and 0.93 (95% CI: 0.9, 0.96) for global cognitive assessment. When comparing the individuals in the lowest tertile of CDAI, the ORs in the highest intake of CDAI was 0.54 (95% CI: 0.41, 0.73) for DSST; 0.52 (95% CI: 0.41, 0.66) for AFT; 0.74 (95% CI: 0.58, 0.95) for CERAD; 0.56 (95% CI: 0.43, 0.72) for global cognition, indicating a 46%, 48%, 26%, and 44% decrease in the risk of cognitive impairment with DSST, AFT, CERAD and global cognition assessments respectively. In addition, the results also presented a similar trend for DSST, AF, CERAD and global cognition ( P for trend < 0.05). Analysis of the non-linear relationship between CDAI and cognitive impairment The RCS suggested that the association between CDAI and cognitive impairment was non-linear for AFT ( P for non-linearity = 0.009, Figure 2a) and global cognition ( P for non-linearity = 0.006, Figure 2b), while it demonstrated a linear relationship between CDAI and cognitive impairment for DSST ( P for non-linearity = 0.16, Figure 2c) and CERAD ( P for non-linearity = 0.189, Figure 2d). Stratified and sensitive analysis The stratified analyses were performed by the age, gender, education, PIR, BMI, hypertension and diabetes to assess the potential effect modification on the relationship between CDAI and cognitive impairment on DSST (Figure 3a), AFT (Figure 3b), CERAD (Figure 3c) and global cognition (Figure 3d). The ORs of DSST, AF, CERAD and global cognition in the stratified groups were stable overall. Yet, the interaction effect for eudcation and DSST cognitive impairment was observed ( P for interaction = 0.04, Figure 3a). Considering the multiple testing, the interaction of education may not be statistically significant. We performed a sensitive analysis on the association of six components of CDAI and cognitive impairment (Table 3). Vitamin E, zinc, selenium, and carotenoids were independently and negatively related to the cognitive impairment on DSST, AFT, CERAD and global cognition (P < 0.05) after adjusting multiple cofounders, yet the statistical association between carotenoids and AFT was marginal (P = 0.048). Interestingly, higher intake of vitamin C was only related to AFT (P = 0.009). Additionally, we conducted a comparison of the baseline characteristics between participants with missing covariates and those with complete values (Supplementary Table 2). Only race and ethnicity, education level, and physical work activity showed statistical differences (P < 0.05). Similar results of multivariable logistic regression (Supplementary Table 3) and RCS analysis (Supplementary Figure 1) were observed when excluding participants with missing covariates (n = 2193). Discussion This population-based cross-sectional study found a negative relationship between CDAI and risk of cognitive impairment among 2523 American elderly. Additionally, we identified a non-linear relationship between CDAI and cognitive impairment, particularly in AFT and global cognition assessments. Additionally, these negative associations were consistent among most stratified groups, providing evidence for the role of dietary antioxidants in cognitive impairment. The previous research on the association between dietary or supplement intake of antioxidants, and cognitive decline or dementia has been inconsistent [ 11 , 20 , 28 ]. Our findings align with a prospective cohort study conducted among Chinese individuals in Singapore. [ 10 ]This study revealed a 16% reduction in the risk of cognitive impairment in the highest quartile of the CDAI group, where the mini-mental state examination was utilized to assess cognitive impairment. Moreover, within the component nutrients, the dietary intake of vitamin C, vitamin E, carotenoids, and flavonoids is inversely correlated with cognitive impairment. [ 25 ]Another study based on the NHANES database calculated the TAC of eight antioxidant vitamins from dietary intake, suggesting the higher dietary antioxidant potential was associated with a reduced risk of cognitive impairment. Additionally, this study observed a subtle non-linear relationship in the dose-response analysis between TAC and impaired cognition. [ 20 ]Another study from the Chicago Health and Aging Project (CHAP), conducted over an average follow-up of 3.2 years, revealed a negative association between cognitive decline and both dietary and supplemental vitamin E intake. However, there was limited evidence of such an association with vitamin C or carotenoids. Our study also found no association between the risk of cognitive impairment and dietary intake of vitamin A or vitamin C based on DSST, CERAD, and global cognition. However, some cohort studies and randomized control trials (RCT) have not identified a correlation between dietary antioxidant capacity and cognition or dementia [ 28 , 29 ]. A cohort study involving 16,010 nurses' health reported that although higher antioxidant capacity (dietary and supplements) was associated with better cognitive performance during the initial interviews, a follow-up after 4 years revealed no relationship between dietary antioxidant scores and cognitive decline [ 28 ]. A sub-experiment of an RCT study [ 29 ] found that supplement intake of Vitamin E and beta-carotene was not associated with slower rates of cognitive change. However, Vitamin C was found to be more protective against cognitive change among women who experienced new cardiovascular events during the trial. Another RCT reported that supplements containing antioxidants with or without zinc and copper did not have a significant effect on cognitive performance [ 30 ]. While the results of many clinical trials are mixed, interestingly, even in RCTs where antioxidant supplements did not yield favorable outcomes, those containing a mix of antioxidants remained proportionally the most successful [ 31 ]. Indeed, what is most beneficial for the brain is not individual nutrients, but rather the dietary pattern and the optimal combination of various essential nutrients [ 8 , 32 ]. CDAI is a commonly used and composite score for assessing the overall level of dietary antioxidants, supplementing the limitations of TAC, which is restricted to a single antioxidant active element in the body. Recently, two cross-sectional studies have found a positive correlation between CDAI and biological aging [ 33 , 34 ]. Several studies have identified a protective role of CDAI in age-related degenerative diseases, such as cardiovascular diseases and osteoporosis[ 35 , 36 ]. Oxidative stress has been widely acknowledged as a significant contributing factor to dementia, serving as a bridge connecting all mechanisms and pathways of dementia [ 37 ]. The imbalance in oxidative stress can lead to the accumulation of ROS, involvement in the amyloid cascade reaction, disruption of mitochondrial function, excessive phosphorylation of tau, formation of neurofibrillary tangles, activation, and release of neuroinflammatory cells, disruption of metal ion homeostasis, ultimately resulting in apoptosis of neurons, leading to cognitive dysfunction or further progression into dementia [ 38 ]. With aging, the efficiency of the endogenous antioxidant system within the body tends to decline, and the brain becomes more sensitive to oxidative stress [ 39 ]. Additionally, the regulation of oxidative stress in the body relies on exogenous antioxidant nutrients [ 40 ], including but not limited to vitamin E, vitamin C, carotenoids, and trace minerals (such as manganese, copper, selenium, and zinc) [ 41 ]. Research has indicated that antioxidant therapy can alleviate oxidative stress, reduce reactive oxygen species production, decrease the release of pro-inflammatory factors, mitigate inflammatory responses, and alleviate cellular damage [ 42 ]. There are some strengths of our study. It is the first study to find an L-shaped relationship between CDAI and cognitive impairment based on the NHANES dataset. Additionally, we considered several possible confounders in this study. Further, a non-linear relationship between CDAI and cognitive impairment was detected, suggesting a suitable range of CDAI for better cognitive performance. Our study provided additional evidence for the association between dietary antioxidants and cognitive impairment. Still, our study has limitations. First, all participants were Americans aged ≥ 60 years and the findings may not be generalizable to younger or populations due to the role of the environment in cognitive impairment. Second, we defined cognitive impairment based on the DSST, AFT, and CERAD, instead of the golden standard (European Consortium Criteria) for screening dementia [ 43 ]. However, DSST, AFT and CERAD were able to evaluate the cognition from multiple domains (processing speed, sustained attention, working memory, executive function, and immediate and delayed memory), and we create a composite z-score to establish a global cognition to prevent the floor and ceiling effects and other sources of measurement error [ 20 ]. Third, the dietary information was obtained from the 24h recall which could result in recall bias. Fourth, we didn’t take into account the bioavailability and oxidative activity of antioxidant nutrients, such as vitamins A and E, which may underestimate the overall efficacy of CDAI. Fifth, some residual confounders may exist, such as antioxidant supplements, and medication interference. However, nutritional supplement surveys were not used because approximately 90% of the participants didn’t report the dietary supplement during the past 30 days. Finally, we could not make any causal inferences due to the cross-sectional study design. In the future, we need more longitudinal or RCT studies to confirm or refute our findings. Conclusion These findings suggested a negative and non-linear association between the CDAI and the risk of cognitive impairment among the American elderly. The results have significant implications for public health initiatives to prevent and limit the progression of cognitive impairment through dietary interventions. Abbreviations CDAI, composite dietary antioxidant index AD, Alzheimer's disease (AD) ADRD, Alzheimer's disease-related dementia MCI, mild cognitive impairment ROS, reactive oxygen species TAC, total antioxidant capacity NHANES, National Health and Nutrition Examination Survey NCHS, National Center for Health Statistics MEC, Mobile Examination Center CERAD, Consortium to Establish a Registry for Alzheimer’s Disease AFT, Animal Fluency test DSST, Digit Symbol Substitution test PIR, poverty-to-income ratios BMI, body mass index RCS, restricted cubic spline OR, odds ratio CI, confidence interval RCT, random control trial Declarations Acknowledgments We need to thank Dr Liren Zeng for checking the language of this manuscript and the team of physician-scientists for the consultation of statistics. Funding This study was supported by grants from Zhejiang Traditional Chinese Medicine Science and Technology Program [2023ZL055]; “Sanying” Talent Development Project 3.0 from the First Affiliated Hospital of Zhejiang Chinese Medical University ( [2023] No. 24) Author information Hang Yang, Department of the Rehabilitation Medicine, the First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China, 310002. Xiaoying Wang, Department of the Rehabilitation Medicine, the First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China, 310002. Ye Zhou, Department of the Rehabilitation Medicine, the First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China, 310002. Shenyingjie Zhang, Department of the Rehabilitation Medicine, the First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China, 310002. ZhenZhen Gao, Department of the Rehabilitation Medicine, the First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China, 310002. Authors’ Contributions Hang Yang (Writing - Original Draft; Conceptualization; Methodology; Formal Analysis; Investigation; funding acquisition) Xiaoying Wang (Writing - Original Draft; Methodology; Formal Analysis) Ye Zhou (Data curation, analysis and interpretation) Shenyingjie Zhang (Data curation and interpretation) Zhenzhen Gao (Writing - Review & Editing; Conceptualization; Funding acquisition) Corresponding author Correspondence to Zhenzhen Gao Ethics approval and consent to participate The National Center for Health Statistics (NCHS) Ethics Review Board approved the NHANES 2011-2012 (Protocol number: 2011-17) and 2013-2014 (Continuation of Protocol # 2011-17) protocols, which are available on the NHANES website (https://www.cdc.gov/nchs/nhanes/irba98.htm). Consent for publication Not applicable. 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Comparison of two commonly used clinical cognitive screening tests to diagnose mild cognitive impairment in heart failure with the golden standard European Consortium Criteria. Int J Cardiol. 2017;228:558-562. doi:10.1016/j.ijcard.2016.11.193 Tables Table 1 Baseline characteristics of participants in NHANES, 2011-2014 Characteristics Participants, CDAI tertiles Total T1 (≤ -1.88) T2 (-1.89 to 0.93) T3 (≥ 0.94) P -value No. 2524 841 841 842 Age, years, mean (SD) 69.4 (6.8) 69.7 (6.7) 69.5 (6.9) 69.2 (6.7) 0.309 Gender (Male), n (%) 1217 (48.2) 304 (36.1) 384 (45.7) 529 (62.8) < 0.001 Race and ethnicity < 0.001 Mexican American 211 ( 8.4) 69 (8.2) 77 (9.2) 65 (7.7) Other Hispanic 244 ( 9.7) 102 (12.1) 83 (9.9) 59 (7) Non-Hispanic White 1269 (50.3) 349 (41.5) 436 (51.8) 484 (57.5) Non-Hispanic Black 594 (23.5) 252 (30) 176 (20.9) 166 (19.7) Others 206 ( 8.2) 69 (8.2) 69 (8.2) 68 (8.1) Education level, n (%) < 0.001 high school 1322 (52.4) 344 (40.9) 440 (52.3) 538 (63.9) PIR, median (IQR) 2.5 (1.3, 4.1) 1.9 (1.1, 2.9) 2.6 (1.4, 4.2) 2.6 (1.5, 4.8) < 0.001 BMI, (kg/m 2 ), mean (SD) 29.2 (6.4) 29.5 (6.7) 29.1 (6.0) 28.9 (6.3) 0.132 Smoke status, n (%) < 0.001 Current 297 (11.8) 134 (15.9) 72 (8.6) 91 (10.8) Former 1249 (49.5) 440 (52.3) 424 (50.4) 385 (45.7) Never 978 (38.7) 267 (31.7) 345 (41) 366 (43.5) Physical activity, n (%) 0.007 Vigorous 274 (10.9) 76 (9) 92 (10.9) 106 (12.6) Moderate 512 (20.3) 147 (17.5) 180 (21.4) 185 (22) Mild or sedentary 1738 (68.9) 618 (73.5) 569 (67.7) 551 (65.4) Hypertension, n (%) < 0.001 Yes 1579 (62.6) 568 (67.5) 513 (61) 498 (59.1) No 945 (37.4) 273 (32.5) 328 (39) 344 (40.9) Diabetes, n (%) < 0.001 Yes 595 (23.6) 237 (28.2) 188 (22.4) 170 (20.2) No 1813 (71.8) 575 (68.4) 615 (73.1) 623 (74) Borderline 116 ( 4.6) 29 (3.4) 38 (4.5) 49 (5.8) Stroke, n (%) 0.014 Yes 169 ( 6.7) 66 (7.8) 39 (4.6) 64 (7.6) No 2355 (93.3) 775 (92.2) 802 (95.4) 778 (92.4) Albumin, (g/L), mean (SD) 41.9 (3.0) 41.5 (3.0) 42.0 (2.9) 42.1 (2.9) < 0.001 Cognitive impairment, n (%) Based on DSST 632 (25.0) 304 (36.1) 181 (21.5) 147 (17.5) < 0.001 Based on AFT 730 (28.9) 335 (39.8) 228 (27.1) 167 (19.8) < 0.001 Based on CERAD 706 (28.0) 276 (32.8) 222 (26.4) 208 (24.7) < 0.001 Based on Global cognition 876 (34.7) 385 (45.8) 266 (31.6) 225 (26.7) < 0.001 Abbreviations: CDAI, composite dietary antioxidant index; PIR, poverty-to-income ratio; BMI, body mass index; DSST, Digit Symbol Substitution Test; AFT, Animal Fluency Test; CERAD, Consortium to Establish a Registry for Alzheimer’s Disease; SD, standard deviation, IQR, interquartile range. T1–T3, Tertiles based on CDAI Table 2 The association between CDAI and cognitive impairment on DSST, AFT, CERAD, and Global cognition (n = 2524) Variables OR (95% CI) No. n (%) Model 1 P- value Model 2 P -value Model 3 P -value DSST a CDAI (per SD) 2524 632 (25) 0.89 (0.86~0.91) <0.001 0.89 (0.86~0.92) <0.001 0.94 (0.9~0.97) <0.001 CDAI tertiles T1 (≤ -1.88) 841 304 (36.1) 1(Ref) 1(Ref) 1(Ref) T2 (-1.89 to 0.93) 841 181 (21.5) 0.48 (0.39~0.6) <0.001 0.49 (0.39~0.63) <0.001 0.68 (0.52~0.89) 0.005 T3 (≥ 0.94) 842 147 (17.5) 0.37 (0.3~0.47) <0.001 0.38 (0.29~0.49) <0.001 0.54 (0.41~0.73) <0.001 P for trend 2524 632 (25) <0.001 <0.001 <0.001 AFT a CDAI (per SD) 2524 730 (28.9) 0.89 (0.87~0.92) <0.001 0.91 (0.88~0.93) <0.001 0.93 (0.9~0.96) <0.001 CDAI tertiles T1 (≤ -1.88) 841 335 (39.8) 1(Ref) 1(Ref) 1(Ref) T2 (-1.89 to 0.93) 841 228 (27.1) 0.56 (0.46~0.69) <0.001 0.62 (0.5~0.77) <0.001 0.72 (0.58~0.9) 0.005 T3 (≥ 0.94) 842 167 (19.8) 0.37 (0.3~0.46) <0.001 0.43 (0.34~0.54) <0.001 0.52 (0.41~0.66) <0.001 P for trend 2524 730 (28.9) <0.001 <0.001 <0.001 CERAD a CDAI (per SD) 2524 706 (28) 0.95 (0.93~0.98) <0.001 0.94 (0.91~0.96) <0.001 0.96 (0.93~0.99) 0.005 CDAI tertiles T1 (≤ -1.88) 841 276 (32.8) 1(Ref) 1(Ref) 1(Ref) T2 (-1.89 to 0.93) 841 222 (26.4) 0.73 (0.59~0.91) 0.004 0.7 (0.56~0.88) 0.002 0.84 (0.67~1.07) 0.159 T3 (≥ 0.94) 842 208 (24.7) 0.67 (0.54~0.83) <0.001 0.59 (0.47~0.75) <0.001 0.74 (0.58~0.95) 0.016 P for trend 2524 706 (28) <0.001 <0.001 0.016 Global cognition a CDAI (per SD) 2524 876 (34.7) 0.9 (0.88~0.93) <0.001 0.9 (0.87~0.92) <0.001 0.93 (0.9~0.96) <0.001 CDAI tertiles T1 (≤ -1.88) 841 385 (45.8) 1(Ref) 1(Ref) 1(Ref) T2 (-1.89 to 0.93) 841 266 (31.6) 0.55 (0.45~0.67) <0.001 0.54 (0.44~0.68) <0.001 0.7 (0.55~0.89) 0.004 T3 (≥ 0.94) 842 225 (26.7) 0.43 (0.35~0.53) <0.001 0.41 (0.32~0.52) <0.001 0.56 (0.43~0.72) <0.001 P for trend 2524 876 (34.7) <0.001 <0.001 <0.001 Abbreviations: CDAI, composite dietary antioxidant index; DSST, Digit Symbol Substitution Test; AFT, Animal Fluency Test; CERAD, Consortium to Establish a Registry for Alzheimer’s Disease; OR, odds ratio; CI, confidence interval; SD, standard deviation; T1–T3, tertiles based on CDAI a CDAI was entered as a continuous variable per change SD. Model 1: non-adjusted Model 2: adjusted for age, gender, race Model 3: adjusted for model 2, additionally adjusted for education, poverty-to-income ratio, body mass index, smoke status, physical activity, hypertension, diabetes, stroke, albumin Table 3 The relationship between CDAI components and Cognitive impairment on DSST, AFT, CERAD, and Global cognition Variable n (%) OR (95% CI) Crude P -value Adjusted P- value DSST 632 (25%) Vitamin A 0.81 (0.7~0.92) 0.002 0.9 (0.78~1.04) 0.165 Vitamin C 0.85 (0.76~0.95) 0.003 0.95 (0.83~1.08) 0.442 Vitamin E 0.57 (0.5~0.65) <0.001 0.76 (0.65~0.88) <0.001 Zinc 0.72 (0.64~0.8) <0.001 0.84 (0.75~0.96) 0.008 Selenium 0.77 (0.69~0.86) <0.001 0.85 (0.74~0.96) 0.012 Carotenoids 0.68 (0.59~0.79) <0.001 0.81 (0.68~0.96) 0.017 AFT 730 (28.9%) Vitamin A 0.87 (0.77~0.97) 0.017 0.96 (0.86~1.07) 0.478 Vitamin C 0.83 (0.75~0.92) <0.001 0.86 (0.77~0.96) 0.009 Vitamin E 0.65 (0.58~0.73) <0.001 0.79 (0.7~0.89) <0.001 Zinc 0.67 (0.61~0.75) <0.001 0.77 (0.69~0.86) <0.001 Selenium 0.72 (0.65~0.8) <0.001 0.8 (0.72~0.9) <0.001 Carotenoids 0.78 (0.68~0.89) <0.001 0.87 (0.75~1) 0.048 CERAD 706 (28%) Vitamin A 1.02 (0.94~1.11) 0.585 1.01 (0.91~1.11) 0.882 Vitamin C 0.97 (0.89~1.06) 0.506 1.01 (0.91~1.12) 0.833 Vitamin E 0.76 (0.68~0.84) <0.001 0.84 (0.75~0.94) 0.003 Zinc 0.89 (0.82~0.98) 0.015 0.89 (0.8~0.99) 0.027 Selenium 0.85 (0.78~0.94) 0.001 0.84 (0.75~0.94) 0.002 Carotenoids 0.73 (0.64~0.84) <0.001 0.8 (0.69~0.92) 0.002 Global cognition 876 (34.7%) Vitamin A 0.9 (0.82~1) 0.057 0.93 (0.83~1.05) 0.241 Vitamin C 0.88 (0.8~0.97) 0.007 0.96 (0.86~1.07) 0.426 Vitamin E 0.64 (0.57~0.71) <0.001 0.78 (0.69~0.88) <0.001 Zinc 0.76 (0.7~0.83) <0.001 0.83 (0.74~0.92) <0.001 Selenium 0.74 (0.67~0.82) <0.001 0.75 (0.67~0.85) <0.001 Carotenoids 0.73 (0.64~0.83) <0.001 0.84 (0.73~0.98) 0.024 Abbreviations: CDAI, composite dietary antioxidant index; DSST, Digit Symbol Substitution Test; AFT, Animal Fluency Test; CERAD, Consortium to Establish a Registry for Alzheimer’s Disease; OR, odds ratio; CI, confidence interval Adjusted for age, gender, race, education, poverty-to-income ratio, body mass index, smoke status, physical activity, hypertension, diabetes, stroke, albumin Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4384652","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":304602564,"identity":"dc7aa0fe-f133-4479-8700-c6663bdcdd69","order_by":0,"name":"Hang Yang","email":"","orcid":"","institution":"the First Affiliated Hospital of Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hang","middleName":"","lastName":"Yang","suffix":""},{"id":304602565,"identity":"8b395662-117c-45e3-885d-7b124f224c0f","order_by":1,"name":"Xiaoying Wang","email":"","orcid":"","institution":"the First Affiliated Hospital of Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoying","middleName":"","lastName":"Wang","suffix":""},{"id":304602566,"identity":"76f4d572-6f93-41f2-9dfe-dd859a7b340f","order_by":2,"name":"Ye Zhou","email":"","orcid":"","institution":"the First Affiliated Hospital of Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ye","middleName":"","lastName":"Zhou","suffix":""},{"id":304602567,"identity":"ddf27add-bcb3-40b2-a39b-09cf53fc5ccc","order_by":3,"name":"Shenyingjie Zhang","email":"","orcid":"","institution":"the First Affiliated Hospital of Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shenyingjie","middleName":"","lastName":"Zhang","suffix":""},{"id":304602568,"identity":"fadc9109-306b-4d14-a908-7c82c2a1d20b","order_by":4,"name":"Zhenzhen Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYJCCDxIgkr2x8eEHInUwzgBr4TncbCxBtBYwJZHeJsBDjHp598MHGyx31Nkb3HzYxiDBYCen20BAi+GZtMQGyTOHEzfcTmx7UMCQbGx2gJCWhhzzB5JtBxLMbie2G0gwHEjcRlBL/xvDBsm2OnuzmwfbJHiI0SIvkQPSwsy47QYjkVoMJJ5B/LL/TCIwkA2I8It8f/LBZklgiEm2H3/48EOFnRxBLQZABcySDXAuAeVgW4CqGT82EFI2CkbBKBgFIxoAAACqR6wcN0uLAAAAAElFTkSuQmCC","orcid":"","institution":"the First Affiliated Hospital of Zhejiang Chinese Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zhenzhen","middleName":"","lastName":"Gao","suffix":""}],"badges":[],"createdAt":"2024-05-07 17:41:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4384652/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4384652/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56889622,"identity":"404632d2-de29-4f00-a5e9-8118ca109905","added_by":"auto","created_at":"2024-05-21 19:12:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":39017,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of the screening and enrolment of study participants\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations: \u003c/strong\u003eNHANES, National Health and Nutrition Examination Survey; CDAI, composite dietary antioxidant index\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4384652/v1/f70064167292e2cbee6f0da7.png"},{"id":56888843,"identity":"66a94151-b3de-4dbd-9156-2879a2e54e4b","added_by":"auto","created_at":"2024-05-21 19:04:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":149334,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline plot of the association between CDAI and risk of cognitive impairment\u003c/p\u003e\n\u003cp\u003e(a), assessed by AFT; (b), assessed by global cognition; (c), assessed by DSST; (d), assessed by CERAD. CDAI, composite dietary antioxidant index; AFT, animal fluency test; DSST, digit symbol substitution test; CERAD, the Consortium to Establish a Registry for Alzheimer’s Disease. Solid and dashed lines represent the predicted value and 95% confidence intervals. Adjusted for age, gender, race, education, poverty-to-income ratio, body mass index, physical work activity, smoking, hypertension, diabetes, stroke, and albumin. Only 99% of the data is presented.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4384652/v1/f50aaf0d9927828bd66e8f19.png"},{"id":56888846,"identity":"764abe2a-ebe7-4a0d-b003-5e7204d811bd","added_by":"auto","created_at":"2024-05-21 19:04:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":659471,"visible":true,"origin":"","legend":"\u003cp\u003eStratified analysis of the relationship between CDAI and cognitive impairment on DSST (a), AFT (b), CERAD (c), and global cognition (d).\u003c/p\u003e\n\u003cp\u003e(a), assessed by DSST; (b), assessed by AFT; (c), assessed by CERAD; (d), assessed by global cognition. CDAI, composite dietary antioxidant index; AFT, animal fluency test; DSST, digit symbol substitution test; CERAD, the Consortium to Establish a Registry for Alzheimer’s Disease. Adjusted for age, gender, race, education, poverty-to-income ratio, body mass index, physical work activity, smoking, hypertension, diabetes, stroke, and albumin\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4384652/v1/37db33679606980b0c33d850.png"},{"id":57984272,"identity":"dc1c15b4-ee9c-4b41-931a-8b99e25b2e1e","added_by":"auto","created_at":"2024-06-08 18:01:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1695718,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4384652/v1/76540fb2-e3b7-4fd4-a426-e097b8fcac3b.pdf"},{"id":56888842,"identity":"19e4cb7d-564d-4c3b-b166-81608fbb26cf","added_by":"auto","created_at":"2024-05-21 19:04:07","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13934,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-4384652/v1/6e2868e0a7cd7ae5f005d594.docx"},{"id":56888845,"identity":"36195d1b-c773-4bb9-9de2-af49e5aaad2f","added_by":"auto","created_at":"2024-05-21 19:04:07","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":34413,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-4384652/v1/91840de70d1ab29a5644d47e.docx"},{"id":56888847,"identity":"dbd4415f-d30b-47cf-bbdb-70e50823d710","added_by":"auto","created_at":"2024-05-21 19:04:07","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":261241,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4384652/v1/e477ee11ea70c6000b558fd5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The L-shaped Relationship Between Composite Dietary Antioxidant Index and Cognitive Impairment in the American Elderly: A Cross-Sectional Study (NHANES 2011-2014)","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eCognitive decline is a classic aging-related change in the elderly, which develops from intact cognition through mild impairment and further dementia [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The prevalence of mild cognitive impairment (MCI) in the global population aged 65 and older is approximately 10\u0026ndash;20% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. By the year 2050, approximately 152\u0026nbsp;million individuals worldwide will be afflicted by dementia [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. MCI is a pre-stage of dementia, which is more common than dementia among the elderly [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The progression of dementia is irreversible, yet cognitive impairments bridging the gap between \"normal aging\" and dementia still demonstrate an approximately 25% reversion rate [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The high prevalence of dementia, coupled with limited strategies, will impose a substantial burden on individuals, families, healthcare systems, and society, underscoring the crucial role of primary prevention and the need for proactive measures in addressing dementia.\u003c/p\u003e \u003cp\u003eOxidative stress is an imbalance between pro-oxidative and antioxidative processes which leads to disruption of the redox circuitry and damage to macromolecules [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The brain's sensitivity to oxidative stress increases with age, leading to the accumulation of reactive oxygen species (ROS), mitochondrial dysfunction, disruption of metal homeostasis, and impairment of synaptic activity and neural transmission in neurons, resulting in cognitive dysfunction [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Dietary nutrients, being one of the modifiable lifestyle-related factors, play a crucial role in the regulation of neurons, cellular aging, and modulation of oxidative stress, and antioxidants are associated with cognitive function [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Findings from a prospective cohort study in Singapore suggested that a higher dietary total antioxidant capacity (TAC) is associated with a decreased risk of late-life cognitive impairment [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, a report from the PREADVISE trial revealed that regular antioxidant supplements (vitamin E or selenium) over a period of 5.4 years did not prevent AD among asymptomatic men [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrent studies on antioxidants and dementia or cognitive impairment remain conflicting, possibly due to variations in the sources, dosages or patterns of antioxidant intake. The Composite Dietary Antioxidant Index (CDAI) is established based on the aggregate effects of anti-inflammatory indicators such as interleukin-1 and tumor necrosis factor-alpha [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], incorporating vitamin A, vitamin C, vitamin E, manganese, selenium, and zinc. It is utilized to measure an individual's dietary antioxidant capacity.Although there have been studies on the relationship between TAC and cognitive function, TAC scores are calculated using plasma tests for ferric-reducing ability, which may only capture one element of the body's antioxidant activity [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe relationship between CDAI and cognitive impairment has not been thoroughly assessed. To address this research gap, our study examined and evaluated the association between CDAI and cognitive impairment in the elderly population of the United States, utilizing data from the National Health and Nutrition Examination Survey (NHANES).\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eNHANES database was used in this study, which began in the early 1960s and has been a major and continuous program conducted by the National Center for Health Statistics (NCHS) to assess health and nutritional statistics of the noninstitutionalized American population through a stratified, multistage probability sampling [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe National Center for Health Statistics (NCHS) Ethics Review Board approved the NHANES 2011\u0026ndash;2012 (Protocol number: 2011-17) and 2013\u0026ndash;2014 (Continuation of Protocol # 2011-17) protocols, which are available on the NHANES website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/irba98.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/irba98.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All the methods in this research were performed following the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eMoreover, NHANES has a primary role in collecting extensive examinations for the American elderly to increase the knowledge of the aging population. Particularly after the age of 60 years, the three cognitive functioning assessments were examined, including word learning and recall modules from the Consortium to Establish a Registry for Alzheimer\u0026rsquo;s Disease (CERAD), the Animal Fluency test (AFT) and the Digit Symbol Substitution test (DSST), which were only available from 2011\u0026ndash;2012 and 2013\u0026ndash;2014 cycles. The NCHS Ethics Review Board subsequently approved the NHANES protocols, and all the participants provided written informed consent at enrolment.\u003c/p\u003e \u003cp\u003eA total of 2,524 participants with complete cognitive functioning assessments and CDAI-related dietary records were enrolled from a data pool of 19,931 participants. The flowchart for participant enrolment is presented in (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCDAI measurement\u003c/h2\u003e \u003cp\u003eAll the NHANES dietary intake information was collected through a 24-hour dietary recall interview to estimate the dietary nutrients from foods and beverages. The first dietary recall was an in-person interview conducted in the Mobile Examination Center (MEC) with the question \u0026ldquo;What do we eat in America\u0026rdquo; and the second one was a telephone follow-up 3 to 10 days later. The dietary intake of vitamins and minerals was following the United States Department of Agriculture\u0026rsquo;s Food and Nutrient Database for Dietary Studies. CDAI included six antioxidants, including vitamin A, vitamin C, vitamin E, zinc, selenium, and carotenoids, following the revised version of CDAI developed by Wright and colleagues in 2004 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. To maximize the reliability and effectiveness, each dietary intake used in this study was calculated as the average of the two dietary recalls. The calculation of CDAI was the summary of each standardized z-score of dietary intakes of these six antioxidants. The z-score formula is z = (x - mean)/σ, where x represents the individual intake of each antioxidant, \u0026lsquo;mean\u0026rsquo; is the average intake of each antioxidant for the cohort, and σ is the standard deviation. The categorical variable of CDAI was then categorized into tertiles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCognitive impairment measurement\u003c/h2\u003e \u003cp\u003eA series of cognitive functioning assessments in NHANES (2011\u0026ndash;2014) included CERAD, AFT and DSST, only eligible for participants who are aged \u0026ge; 60 years.\u003c/p\u003e \u003cp\u003eThe CERAD word learning subtest included three immediate word recalls and a delayed recall. Every participant was suggested to read 10 unrelated words and recall them immediately as many as possible for three trials. The delayed recall occurred after completing AFT and DSST. Each correct recalled word was one point.\u003c/p\u003e \u003cp\u003eParticipants were instructed to name as many animals as possible in one minute in AFT, which was designed to evaluate the executive function. The AFT score is determined by the number of named animals.\u003c/p\u003e \u003cp\u003eThe processing speed, sustained attention, and working memory could be evaluated by DSST which is one part of the Wechsler Adult Intelligence Scale (WAIS-III). Participants were asked to perform the symbol-number pair in two minutes, with one point for correctly pairing.\u003c/p\u003e \u003cp\u003eNCHS suggested the potential cognitive impairment could be identified as participants were scoring in the lowest 25th percentile of each test [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In our study, individuals with CERAD\u0026thinsp;\u0026lt;\u0026thinsp;22, AFT\u0026thinsp;\u0026lt;\u0026thinsp;14, or DSST\u0026thinsp;\u0026lt;\u0026thinsp;35 could be classified as having cognitive impairment, consistent with previous studies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In addition, to mitigate uneven differences and biases among individuals, as well as to address floor and ceiling effects, we created a composite cognition score to represent global cognition [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The composite cognition score is calculated by summarizing each standardized z-score of CERAD, AFT, and DSST. Individuals scoring \u0026lt; -1 were classified as having cognitive impairment [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eThe selection of possible potential confounders was based on clinical practice, previous studies. The sociodemographic included age, gender, race and ethnicity, education level, and poverty-to-income ratios (PIR) collected through a Computer-Assisted Personal Interviewing system. Age was classified as 60\u0026ndash;69 years and \u0026ge; 70 years. Gender included male and female. Race and ethnicity included five groups: Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, and other races. Education was classified as less than high school, high school graduate, and college or more. The PIR was classified as low level (\u0026lt;\u0026thinsp;1.3), medium (1.3 to 3.5) and high (\u0026ge;\u0026thinsp;3.5) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSmoking status and physical work activity were self-reported. Smoking status was classified as current smokers (smoked at least 100 cigarettes in life and still smoke now), former smokers (smoked at least 100 cigarettes in life and didn\u0026rsquo;t smoke now) and never smokers ( smoked less than 100 cigarettes in life) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Physical work activity was classified as vigorous-intensity activity (causes large increases in breathing or heart rate at least 10 minutes) or moderate-intensity (causes small increases in breathing or heart rate) and mild or sedentary [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. BMI was measured in the MEC, and was classified as normal (\u0026lt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (25\u0026ndash;30 kg/m\u003csup\u003e2\u003c/sup\u003e), and obese (\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e).The self-reported physician diagnosis of hypertension (yes or no), diabetes (yes, no or borderline), and stroke (yes or no) were collected by trained interviewers. The albumin concentration (g/L) was tested by the DcX800 method.\u003c/p\u003e \u003cp\u003eSome covariates were missing at random, such as PIR (missing 7.57%), BMI (missing 1.27%), albumin (missing 4.99%), education (missing 0.08%), smoking status (missing 0.08%), physical activity (missing 0.12%), hypertension (missing 0.12%), diabetes (missing 0.08%), and stroke (missing 0.2%). These missing values were imputed using a simple imputation method. Mean imputation was applied for continuous variables with a normal distribution, while median imputation was used for variables with a skewed distribution. Mode imputation was applied for categorical variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eNo a priori calculation of statistical power was performed because the sample size was based on the available data from NHANES. All analyses were performed by R software (version 4.2.3; R Foundation for Statistical Computing; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.Rproject.org\u003c/span\u003e\u003cspan address=\"http://www.Rproject.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Free Statistics software (version 1.9.1; Beijing Free Clinical Medical Technology Co., Ltd.). In all analyses, a two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated statistical significance.\u003c/p\u003e \u003cp\u003eFirstly, the Kolmogorov-Smirnov test was used to determine the normality of continuous variables. Normally distributed variables were presented as mean (standard deviation, SD), while skewed variables were presented as median (25\u0026ndash;75% interquartile range, IQR). Categorical variables were represented by number (percentage). Statistical tests such as ANOVA, Kruskal\u0026ndash;Wallis, and chi-squared tests were applied to compare differences across groups.\u003c/p\u003e \u003cp\u003eNext, both univariate and multivariate logistic regression models were employed to investigate the association between CDAI and cognitive impairment in various domains. CDAI was then transformed into tertiles, and the \u003cem\u003eP\u003c/em\u003e-value for trend was calculated to validate the results when treating CDAI as a continuous variable. Three models were adjusted based on clinical practice, and previous studies [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] or \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1 in univariate logistic regression analysis. Model 1 was non-adjusted; Model 2 was adjusted for age (continuous variables), gender, race and ethnicity; Model 3 was further adjusted for education, PIR (continuous variable), BMI (continuous variables), hypertension, diabetes, stroke and albumin.\u003c/p\u003e \u003cp\u003eThe restricted cubic splines (RCS) with four knots (5th, 35th, 65th and 95th percentiles) were performed to explore the non-linearity between CDAI and cognitive impairment on DSST, AFT, CERAD, and global cognition by adjusting the cofounders consistent with model 3. The stratified analysis was additionally performed by age (60\u0026ndash;69 years and \u0026gt;\u0026thinsp;=\u0026thinsp;70 years), gender (male and female), education level (less than high school, high school graduate, and college or more), PIR (low, medium and high), BMI (normal, overweight and obese), hypertension (yes and no), and diabetes (yes/borderline and no), and likelihood test was used for interaction.\u003c/p\u003e \u003cp\u003eFinally, in the sensitivity analysis, we examined the association between six components of CDAI and cognitive impairment. Further, we excluded individuals with missing covariate data and conducted multivariate logistic regression and RCS to assess the robustness of the findings.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003ch2\u003eStudy population and baseline characteristics\u003c/h2\u003e\n\u003cp\u003eA total number of\u0026nbsp;2,524\u0026nbsp;participants aged\u0026nbsp;\u0026sup3;\u0026nbsp;60 years were included in the analysis. The general characteristics of the participants with CDAI tertiles are presented in Table 1. Of those, the mean (SD) age was 69.5 (6.8) years, and 1,356 (49%) were male. Compared with the individuals with low CDAI tertiles, individuals with the highest CDAI score (CDAI \u0026ge; 0.94) tended to be male, non-Hispanic white, high education level, high level of PIR and serum albumin (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001). The high score of CDAI (\u0026ge; 0.94) was less likely to be current smokers (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), vigorous-intensity workers (\u003cem\u003eP\u003c/em\u003e = 0.007), or those without a history of hypertension (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), diabetes (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), or stroke (\u003cem\u003eP\u003c/em\u003e = 0.014). In addition, participants with the highest level of CDAI scores are more likely to have a low incidence of cognitive impairment on DSST, AF, CERAD, and global cognition. There were no statistical differences regarding age (\u003cem\u003eP\u003c/em\u003e = 0.309) or BMI (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= \u0026nbsp;0.132).\u003c/p\u003e\n\u003ch2\u003eUnivariate logistic regression analysis\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe univariate analysis indicated that age, race and ethnicity, education level, PIR, physical activity, \u0026nbsp;hypertension, diabetes, stroke, albumin, and CDAI (continuous variables) were correlated with cognitive impairment in different domains (\u003cem\u003eP \u0026lt;\u0026nbsp;\u003c/em\u003e0.1) (Supplementary Table 1).\u003c/p\u003e\n\u003ch2\u003eMultivariate logistic regression analysis between CDAI and cognitive impairment\u003c/h2\u003e\n\u003cp\u003eWe observed an inverse association between CDAI \u0026nbsp;and cognitive impairment on DSST, AFT, CERAD and global cognition assessments, indicating individuals with higher CDAI scores had a lower incidence of cognitive impairment (Table 2). After adjusting for multiple confounders, a high intake of CDAI was associated with a decreased risk of cognitive impairment, with odds ratios (OR) per 1-SD increase of 0.94 (95% CI: 0.9, 0.97) for DSST, 0.93 (95% CI: 0.9, 0.96) for AFT, 0.96 (95% CI: 0.93, 0.99) for CERAD, and 0.93 (95% CI: 0.9, 0.96) for global cognitive assessment. When comparing the individuals in the lowest tertile of CDAI, the ORs in the highest intake of CDAI was 0.54 (95% CI: 0.41, 0.73) for DSST; 0.52 (95% CI: 0.41, 0.66) for AFT; 0.74 (95% CI: 0.58, 0.95) for CERAD; 0.56 (95% CI: 0.43, 0.72) for global cognition, indicating a 46%, 48%, 26%, and 44% decrease in the risk of cognitive impairment with DSST, AFT, CERAD and global cognition assessments respectively. In addition, the results also presented a similar trend for DSST, AF, CERAD and global cognition (\u003cem\u003eP\u003c/em\u003e for trend \u0026lt; 0.05).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAnalysis of the non-linear relationship between CDAI and cognitive impairment\u003c/h2\u003e\n\u003cp\u003eThe RCS suggested that the association between CDAI and cognitive impairment was non-linear for AFT (\u003cem\u003eP\u003c/em\u003e for non-linearity = 0.009, Figure 2a) and global cognition (\u003cem\u003eP\u0026nbsp;\u003c/em\u003efor non-linearity = 0.006, Figure 2b), while it demonstrated a linear relationship between CDAI and cognitive impairment for DSST (\u003cem\u003eP\u003c/em\u003e for non-linearity = 0.16, Figure 2c) and CERAD (\u003cem\u003eP\u003c/em\u003e for non-linearity = 0.189, Figure 2d).\u003c/p\u003e\n\u003ch2\u003eStratified and sensitive analysis\u003c/h2\u003e\n\u003cp\u003eThe stratified analyses were performed by the age, gender, education, PIR, BMI, hypertension and diabetes to assess the potential effect modification on the relationship between CDAI and cognitive impairment on DSST (Figure 3a), AFT (Figure 3b), CERAD (Figure 3c) and global cognition (Figure 3d). The ORs of DSST, AF, CERAD and global cognition in the stratified groups were stable overall. Yet, the interaction effect for eudcation and DSST cognitive impairment was observed (\u003cem\u003eP\u003c/em\u003e for interaction = 0.04, Figure 3a). Considering the multiple testing, the interaction of education may not be statistically significant.\u003c/p\u003e\n\u003cp\u003eWe performed a sensitive analysis on the association of six components of CDAI and cognitive impairment (Table 3). Vitamin E, zinc, selenium, and carotenoids were independently and negatively related to the cognitive impairment on DSST, AFT, CERAD and global cognition (P \u0026lt; 0.05) after adjusting multiple cofounders, yet the statistical association between carotenoids and AFT was marginal (P = 0.048). Interestingly, higher intake of vitamin C was only related to AFT (P = 0.009).\u003c/p\u003e\n\u003cp\u003eAdditionally, we conducted a comparison of the baseline characteristics between participants with missing covariates and those with complete values (Supplementary Table 2). Only race and ethnicity, education level, and physical work activity showed statistical differences (P \u0026lt; 0.05). Similar results of multivariable logistic regression (Supplementary Table 3) and RCS analysis (Supplementary Figure 1) \u0026nbsp;were observed when excluding participants with missing covariates (n = 2193).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis population-based cross-sectional study found a negative relationship between CDAI and risk of cognitive impairment among 2523 American elderly. Additionally, we identified a non-linear relationship between CDAI and cognitive impairment, particularly in AFT and global cognition assessments. Additionally, these negative associations were consistent among most stratified groups, providing evidence for the role of dietary antioxidants in cognitive impairment.\u003c/p\u003e \u003cp\u003eThe previous research on the association between dietary or supplement intake of antioxidants, and cognitive decline or dementia has been inconsistent [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Our findings align with a prospective cohort study conducted among Chinese individuals in Singapore. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]This study revealed a 16% reduction in the risk of cognitive impairment in the highest quartile of the CDAI group, where the mini-mental state examination was utilized to assess cognitive impairment. Moreover, within the component nutrients, the dietary intake of vitamin C, vitamin E, carotenoids, and flavonoids is inversely correlated with cognitive impairment. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]Another study based on the NHANES database calculated the TAC of eight antioxidant vitamins from dietary intake, suggesting the higher dietary antioxidant potential was associated with a reduced risk of cognitive impairment. Additionally, this study observed a subtle non-linear relationship in the dose-response analysis between TAC and impaired cognition. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]Another study from the Chicago Health and Aging Project (CHAP), conducted over an average follow-up of 3.2 years, revealed a negative association between cognitive decline and both dietary and supplemental vitamin E intake. However, there was limited evidence of such an association with vitamin C or carotenoids. Our study also found no association between the risk of cognitive impairment and dietary intake of vitamin A or vitamin C based on DSST, CERAD, and global cognition.\u003c/p\u003e \u003cp\u003eHowever, some cohort studies and randomized control trials (RCT) have not identified a correlation between dietary antioxidant capacity and cognition or dementia [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A cohort study involving 16,010 nurses' health reported that although higher antioxidant capacity (dietary and supplements) was associated with better cognitive performance during the initial interviews, a follow-up after 4 years revealed no relationship between dietary antioxidant scores and cognitive decline [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. A sub-experiment of an RCT study [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] found that supplement intake of Vitamin E and beta-carotene was not associated with slower rates of cognitive change. However, Vitamin C was found to be more protective against cognitive change among women who experienced new cardiovascular events during the trial. Another RCT reported that supplements containing antioxidants with or without zinc and copper did not have a significant effect on cognitive performance [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. While the results of many clinical trials are mixed, interestingly, even in RCTs where antioxidant supplements did not yield favorable outcomes, those containing a mix of antioxidants remained proportionally the most successful [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Indeed, what is most beneficial for the brain is not individual nutrients, but rather the dietary pattern and the optimal combination of various essential nutrients [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCDAI is a commonly used and composite score for assessing the overall level of dietary antioxidants, supplementing the limitations of TAC, which is restricted to a single antioxidant active element in the body. Recently, two cross-sectional studies have found a positive correlation between CDAI and biological aging [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Several studies have identified a protective role of CDAI in age-related degenerative diseases, such as cardiovascular diseases and osteoporosis[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Oxidative stress has been widely acknowledged as a significant contributing factor to dementia, serving as a bridge connecting all mechanisms and pathways of dementia [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The imbalance in oxidative stress can lead to the accumulation of ROS, involvement in the amyloid cascade reaction, disruption of mitochondrial function, excessive phosphorylation of tau, formation of neurofibrillary tangles, activation, and release of neuroinflammatory cells, disruption of metal ion homeostasis, ultimately resulting in apoptosis of neurons, leading to cognitive dysfunction or further progression into dementia [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. With aging, the efficiency of the endogenous antioxidant system within the body tends to decline, and the brain becomes more sensitive to oxidative stress [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Additionally, the regulation of oxidative stress in the body relies on exogenous antioxidant nutrients [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], including but not limited to vitamin E, vitamin C, carotenoids, and trace minerals (such as manganese, copper, selenium, and zinc) [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Research has indicated that antioxidant therapy can alleviate oxidative stress, reduce reactive oxygen species production, decrease the release of pro-inflammatory factors, mitigate inflammatory responses, and alleviate cellular damage [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere are some strengths of our study. It is the first study to find an L-shaped relationship between CDAI and cognitive impairment based on the NHANES dataset. Additionally, we considered several possible confounders in this study. Further, a non-linear relationship between CDAI and cognitive impairment was detected, suggesting a suitable range of CDAI for better cognitive performance. Our study provided additional evidence for the association between dietary antioxidants and cognitive impairment.\u003c/p\u003e \u003cp\u003eStill, our study has limitations. First, all participants were Americans aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years and the findings may not be generalizable to younger or populations due to the role of the environment in cognitive impairment. Second, we defined cognitive impairment based on the DSST, AFT, and CERAD, instead of the golden standard (European Consortium Criteria) for screening dementia [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. However, DSST, AFT and CERAD were able to evaluate the cognition from multiple domains (processing speed, sustained attention, working memory, executive function, and immediate and delayed memory), and we create a composite z-score to establish a global cognition to prevent the floor and ceiling effects and other sources of measurement error [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Third, the dietary information was obtained from the 24h recall which could result in recall bias. Fourth, we didn\u0026rsquo;t take into account the bioavailability and oxidative activity of antioxidant nutrients, such as vitamins A and E, which may underestimate the overall efficacy of CDAI. Fifth, some residual confounders may exist, such as antioxidant supplements, and medication interference. However, nutritional supplement surveys were not used because approximately 90% of the participants didn\u0026rsquo;t report the dietary supplement during the past 30 days. Finally, we could not make any causal inferences due to the cross-sectional study design. In the future, we need more longitudinal or RCT studies to confirm or refute our findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThese findings suggested a negative and non-linear association between the CDAI and the risk of cognitive impairment among the American elderly. The results have significant implications for public health initiatives to prevent and limit the progression of cognitive impairment through dietary interventions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCDAI, composite dietary antioxidant index\u003c/p\u003e\n\u003cp\u003eAD, Alzheimer\u0026apos;s disease (AD)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eADRD, Alzheimer\u0026apos;s disease-related dementia\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMCI, mild cognitive impairment\u003c/p\u003e\n\u003cp\u003eROS, reactive oxygen species\u003c/p\u003e\n\u003cp\u003eTAC, total antioxidant capacity\u003c/p\u003e\n\u003cp\u003eNHANES, National Health and Nutrition Examination Survey\u003c/p\u003e\n\u003cp\u003eNCHS, National Center for Health Statistics\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMEC, Mobile Examination Center\u003c/p\u003e\n\u003cp\u003eCERAD, Consortium to Establish a Registry for Alzheimer\u0026rsquo;s Disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAFT, Animal Fluency test\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDSST, Digit Symbol Substitution test\u003c/p\u003e\n\u003cp\u003ePIR, poverty-to-income ratios\u003c/p\u003e\n\u003cp\u003eBMI, body mass index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRCS, restricted cubic spline\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOR, odds ratio\u003c/p\u003e\n\u003cp\u003eCI, confidence interval\u003c/p\u003e\n\u003cp\u003eRCT, random control trial\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgments\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe need to thank Dr Liren Zeng for checking the language of this manuscript and the team of physician-scientists for the consultation of statistics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from Zhejiang Traditional Chinese Medicine Science and Technology Program [2023ZL055]; \u0026ldquo;Sanying\u0026rdquo; Talent Development Project 3.0 from the First Affiliated Hospital of Zhejiang Chinese Medical University ( [2023] No. 24)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor information\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHang Yang, Department of the Rehabilitation Medicine, the First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China, 310002.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eXiaoying Wang, Department of the Rehabilitation Medicine, the First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China, 310002.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYe Zhou, Department of the Rehabilitation Medicine, the First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China, 310002.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eShenyingjie Zhang, Department of the Rehabilitation Medicine, the First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China, 310002.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eZhenZhen Gao, Department of the Rehabilitation Medicine, the First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China, 310002.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026rsquo; Contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHang Yang (Writing - Original Draft; Conceptualization; Methodology; Formal Analysis; Investigation; funding acquisition)\u003c/p\u003e\n\u003cp\u003eXiaoying Wang (Writing - Original Draft; Methodology; Formal Analysis)\u003c/p\u003e\n\u003cp\u003eYe Zhou (Data curation, analysis and interpretation)\u003c/p\u003e\n\u003cp\u003eShenyingjie Zhang (Data curation and interpretation)\u003c/p\u003e\n\u003cp\u003eZhenzhen Gao (Writing - Review \u0026amp; Editing; Conceptualization; Funding acquisition)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCorresponding author\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Zhenzhen Gao\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Center for Health Statistics (NCHS) Ethics Review Board approved the NHANES 2011-2012 (Protocol number: 2011-17) and 2013-2014 (Continuation of Protocol # 2011-17) protocols, which are available on the NHANES website (https://www.cdc.gov/nchs/nhanes/irba98.htm).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors\u0026nbsp;have no conflict of interest to report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData availability\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset supporting the results of this article is available in the NHANES repository conducted by NCHS at [http://www.cdc.gov/ nchs/nhanes.htm].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePetersen RC, Caracciolo B, Brayne C, Gauthier S, Jelic V, Fratiglioni L. 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Published 2023 Oct 12. doi:10.3390/healthcare11202722\u003c/li\u003e\n\u003cli\u003eYang C, Yang Q, Peng X, Li X, Rao G. Associations of composite dietary antioxidant index with cardiovascular disease mortality among patients with type 2 diabetes. Diabetol Metab Syndr. 2023;15(1):131. Published 2023 Jun 20. doi:10.1186/s13098-023-01109-7\u003c/li\u003e\n\u003cli\u003eHan, H., Chen, S., Wang, X. et al. Association of the composite dietary antioxidant index with bone mineral density in the United States general population: data from NHANES 2005\u0026ndash;2010. J Bone Miner Metab 41, 631\u0026ndash;641 (2023). https://doi-org.ezproxy.uwc.ac.za/10.1007/s00774-023-01438-7\u003c/li\u003e\n\u003cli\u003eT\u0026ouml;nnies E, Trushina E. Oxidative Stress, Synaptic Dysfunction, and Alzheimer\u0026apos;s Disease. J Alzheimers Dis. 2017;57(4):1105-1121. doi:10.3233/JAD-161088\u003c/li\u003e\n\u003cli\u003eBai R, Guo J, Ye XY, Xie Y, Xie T. Oxidative stress: The core pathogenesis and mechanism of Alzheimer\u0026apos;s disease. \u003cem\u003eAgeing Res Rev\u003c/em\u003e. 2022;77:101619. doi:10.1016/j.arr.2022.101619\u003c/li\u003e\n\u003cli\u003eGuemouri L, Artur Y, Herbeth B, Jeandel C, Cuny G, Siest G. Biological variability of superoxide dismutase, glutathione peroxidase, and catalase in blood. \u003cem\u003eClin Chem\u003c/em\u003e. 1991;37(11):1932-1937.\u003c/li\u003e\n\u003cli\u003eSardesai VM. Role of antioxidants in health maintenance. \u003cem\u003eNutr Clin Pract\u003c/em\u003e. 1995;10(1):19-25. doi:10.1177/011542659501000119\u003c/li\u003e\n\u003cli\u003eHuang D. Dietary Antioxidants and Health Promotion. Antioxidants (Basel). 2018; 7:9. https://doi.org/10.3390/antiox7010009\u003c/li\u003e\n\u003cli\u003eLiguori I, Russo G, Curcio F, Bulli G, Aran L, Della-Morte D, Gargiulo G, Testa G, Cacciatore F, Bonaduce D, Abete P. Oxidative stress, aging, and diseases. Clin Interv Aging. 2018; 13:757\u0026ndash;72. https://doi.org/10.2147/CIA.S158513\u003c/li\u003e\n\u003cli\u003eAlagiakrishnan K, Mah D, Dyck JR, Senthilselvan A, Ezekowitz J. Comparison of two commonly used clinical cognitive screening tests to diagnose mild cognitive impairment in heart failure with the golden standard European Consortium Criteria. Int J Cardiol. 2017;228:558-562. doi:10.1016/j.ijcard.2016.11.193\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1 Baseline characteristics of participants in NHANES, 2011-2014\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"652\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.02450229709035%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"71.97549770290965%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipants, CDAI tertiles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.89596602972399%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.656050955414013%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026le; -1.88)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.628450106157114%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT2\u0026nbsp;\u003cbr\u003e\u0026nbsp;(-1.89 to 0.93)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.656050955414013%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT3\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026ge; 0.94)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.163481953290871%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e2524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, years, mean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e69.4 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e69.7 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e69.5 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e69.2 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender (Male), n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e1217 (48.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e304 (36.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e384 (45.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e529 (62.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace and ethnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Mexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e211 ( 8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e69 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e77 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e65 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Other Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e244 ( 9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e102 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e83 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e59 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Non-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e1269 (50.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e349 (41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e436 (51.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e484 (57.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Non-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e594 (23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e252 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e176 (20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e166 (19.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e206 ( 8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e69 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e69 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e68 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026lt; high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e603 (23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e284 (33.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e177 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e142 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e599 (23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e213 (25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e224 (26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e162 (19.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026gt; high school\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e1322 (52.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e344 (40.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e440 (52.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e538 (63.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIR, median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e2.5 (1.3, 4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e1.9 (1.1, 2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e2.6 (1.4, 4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e2.6 (1.5, 4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI, (kg/m\u003csup\u003e2\u003c/sup\u003e), mean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e29.2 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e29.5 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e29.1 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e28.9 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoke status, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Current\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e297 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e134 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e72 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e91 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Former\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e1249 (49.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e440 (52.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e424 (50.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e385 (45.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Never\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e978 (38.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e267 (31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e345 (41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e366 (43.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical activity, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Vigorous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e274 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e76 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e92 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e106 (12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e512 (20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e147 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e180 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e185 (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Mild or sedentary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e1738 (68.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e618 (73.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e569 (67.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e551 (65.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e1579 (62.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e568 (67.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e513 (61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e498 (59.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e945 (37.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e273 (32.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e328 (39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e344 (40.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e595 (23.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e237 (28.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e188 (22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e170 (20.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e1813 (71.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e575 (68.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e615 (73.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e623 (74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Borderline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e116 ( 4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e29 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e38 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e49 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStroke, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e169 ( 6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e66 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e39 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e64 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e2355 (93.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e775 (92.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e802 (95.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e778 (92.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlbumin, (g/L), mean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e41.9 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e41.5 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e42.0 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e42.1 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCognitive impairment, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Based on DSST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e632 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e304 (36.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e181 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e147 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Based on AFT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e730 (28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e335 (39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e228 (27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e167 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Based on CERAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e706 (28.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e276 (32.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e222 (26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e208 (24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.98165137614679%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Based on Global cognition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.608562691131498%\" valign=\"top\"\u003e\n \u003cp\u003e876 (34.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e385 (45.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.737003058103976%\" valign=\"top\"\u003e\n \u003cp\u003e266 (31.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.596330275229358%\" valign=\"top\"\u003e\n \u003cp\u003e225 (26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.480122324159021%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e CDAI, composite dietary antioxidant index; PIR, poverty-to-income ratio; BMI, body mass index; DSST, Digit Symbol Substitution Test; AFT, Animal Fluency Test; CERAD, Consortium to Establish a Registry for Alzheimer\u0026rsquo;s Disease; SD, standard deviation, IQR, interquartile range. T1\u0026ndash;T3, Tertiles based on CDAI\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 The association between CDAI and cognitive impairment on DSST, AFT, CERAD, and Global cognition (n = 2524)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"671\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.49925484351714%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84.50074515648286%\" colspan=\"8\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.274647887323944%\" valign=\"top\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.549295774647888%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.901408450704224%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.725352112676056%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel \u0026nbsp;3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDSST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003eCDAI (per SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\n \u003cp\u003e2524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e632 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\n \u003cp\u003e0.89 (0.86~0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.89 (0.86~0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\n \u003cp\u003e0.94 (0.9~0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCDAI tertiles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003eT1 (\u0026le; -1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\n \u003cp\u003e841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e304 (36.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003eT2 (-1.89 to 0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\n \u003cp\u003e841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e181 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\n \u003cp\u003e0.48 (0.39~0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.49 (0.39~0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\n \u003cp\u003e0.68 (0.52~0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003eT3 (\u0026ge; 0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\n \u003cp\u003e842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e147 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\n \u003cp\u003e0.37 (0.3~0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.38 (0.29~0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\n \u003cp\u003e0.54 (0.41~0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\n \u003cp\u003e2524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e632 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAFT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003eCDAI (per SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\n \u003cp\u003e2524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e730 (28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\n \u003cp\u003e0.89 (0.87~0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.91 (0.88~0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\n \u003cp\u003e0.93 (0.9~0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCDAI tertiles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003eT1 (\u0026le; -1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\n \u003cp\u003e841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e335 (39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003eT2 (-1.89 to 0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\n \u003cp\u003e841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e228 (27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\n \u003cp\u003e0.56 (0.46~0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.62 (0.5~0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\n \u003cp\u003e0.72 (0.58~0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003eT3 (\u0026ge; 0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\n \u003cp\u003e842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e167 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\n \u003cp\u003e0.37 (0.3~0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.43 (0.34~0.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\n \u003cp\u003e0.52 (0.41~0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\n \u003cp\u003e2524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e730 (28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCERAD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003eCDAI (per SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\n \u003cp\u003e2524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e706 (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\n \u003cp\u003e0.95 (0.93~0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.94 (0.91~0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\n \u003cp\u003e0.96 (0.93~0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCDAI tertiles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003eT1 (\u0026le; -1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\n \u003cp\u003e841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e276 (32.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003eT2 (-1.89 to 0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\n \u003cp\u003e841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e222 (26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\n \u003cp\u003e0.73 (0.59~0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.7 (0.56~0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\n \u003cp\u003e0.84 (0.67~1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003eT3 (\u0026ge; 0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\n \u003cp\u003e842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e208 (24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\n \u003cp\u003e0.67 (0.54~0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.59 (0.47~0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\n \u003cp\u003e0.74 (0.58~0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\n \u003cp\u003e2524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e706 (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlobal cognition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003eCDAI (per SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\n \u003cp\u003e2524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e876 (34.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\n \u003cp\u003e0.9 (0.88~0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.9 (0.87~0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\n \u003cp\u003e0.93 (0.9~0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCDAI tertiles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003eT1 (\u0026le; -1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\n \u003cp\u003e841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e385 (45.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003eT2 (-1.89 to 0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\n \u003cp\u003e841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e266 (31.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\n \u003cp\u003e0.55 (0.45~0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.54 (0.44~0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\n \u003cp\u003e0.7 (0.55~0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003eT3 (\u0026ge; 0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\n \u003cp\u003e842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e225 (26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\n \u003cp\u003e0.43 (0.35~0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.41 (0.32~0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\n \u003cp\u003e0.56 (0.43~0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.994047619047619%\" valign=\"top\"\u003e\n \u003cp\u003e2524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.821428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e876 (34.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.988095238095237%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.136904761904763%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.482142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e CDAI, composite dietary antioxidant index; DSST, Digit Symbol Substitution Test; AFT, Animal Fluency Test; CERAD, Consortium to Establish a Registry for Alzheimer\u0026rsquo;s Disease; OR, odds ratio; CI, confidence interval; SD, standard deviation; T1\u0026ndash;T3, tertiles based on CDAI\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eCDAI was entered as a continuous variable per change SD.\u003c/p\u003e\n\u003cp\u003eModel 1: non-adjusted\u003c/p\u003e\n\u003cp\u003eModel 2: adjusted for age, gender, race\u003c/p\u003e\n\u003cp\u003eModel 3: adjusted for model 2, additionally adjusted for education, poverty-to-income ratio, body mass index, smoke status, physical activity, hypertension, diabetes, stroke, albumin\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eThe relationship between CDAI components and Cognitive impairment on DSST, AFT, CERAD, and Global cognition\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"652\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.30061349693251%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.566265060240966%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.349397590361445%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.25301204819277%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.83132530120482%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDSST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e632 (25%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.0920245398773%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.042944785276074%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.165644171779142%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" valign=\"top\"\u003e\n \u003cp\u003eVitamin A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.0920245398773%\" valign=\"top\"\u003e\n \u003cp\u003e0.81 (0.7~0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.042944785276074%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.165644171779142%\" valign=\"top\"\u003e\n \u003cp\u003e0.9 (0.78~1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" valign=\"top\"\u003e\n \u003cp\u003eVitamin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.0920245398773%\" valign=\"top\"\u003e\n \u003cp\u003e0.85 (0.76~0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.042944785276074%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.165644171779142%\" valign=\"top\"\u003e\n \u003cp\u003e0.95 (0.83~1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cu\u003eVitamin E\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.0920245398773%\" valign=\"top\"\u003e\n \u003cp\u003e0.57 (0.5~0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.042944785276074%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.165644171779142%\" valign=\"top\"\u003e\n \u003cp\u003e0.76 (0.65~0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cu\u003eZinc\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.0920245398773%\" valign=\"top\"\u003e\n \u003cp\u003e0.72 (0.64~0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.042944785276074%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.165644171779142%\" valign=\"top\"\u003e\n \u003cp\u003e0.84 (0.75~0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cu\u003eSelenium\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.0920245398773%\" valign=\"top\"\u003e\n \u003cp\u003e0.77 (0.69~0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.042944785276074%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.165644171779142%\" valign=\"top\"\u003e\n \u003cp\u003e0.85 (0.74~0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cu\u003eCarotenoids\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.0920245398773%\" valign=\"top\"\u003e\n \u003cp\u003e0.68 (0.59~0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.042944785276074%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.165644171779142%\" valign=\"top\"\u003e\n \u003cp\u003e0.81 (0.68~0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.0920245398773%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.042944785276074%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.165644171779142%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAFT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e730 (28.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.0920245398773%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.042944785276074%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.165644171779142%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" valign=\"top\"\u003e\n \u003cp\u003eVitamin A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.0920245398773%\" valign=\"top\"\u003e\n \u003cp\u003e0.87 (0.77~0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.042944785276074%\" valign=\"top\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.165644171779142%\" valign=\"top\"\u003e\n \u003cp\u003e0.96 (0.86~1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cu\u003eVitamin C\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.0920245398773%\" valign=\"top\"\u003e\n \u003cp\u003e0.83 (0.75~0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.042944785276074%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.165644171779142%\" valign=\"top\"\u003e\n \u003cp\u003e0.86 (0.77~0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cu\u003eVitamin E\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.0920245398773%\" valign=\"top\"\u003e\n \u003cp\u003e0.65 (0.58~0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.042944785276074%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.165644171779142%\" valign=\"top\"\u003e\n \u003cp\u003e0.79 (0.7~0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cu\u003eZinc\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.0920245398773%\" valign=\"top\"\u003e\n \u003cp\u003e0.67 (0.61~0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.042944785276074%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.165644171779142%\" valign=\"top\"\u003e\n \u003cp\u003e0.77 (0.69~0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cu\u003eSelenium\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.0920245398773%\" valign=\"top\"\u003e\n \u003cp\u003e0.72 (0.65~0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.042944785276074%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.165644171779142%\" valign=\"top\"\u003e\n \u003cp\u003e0.8 (0.72~0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" valign=\"top\"\u003e\n \u003cp\u003eCarotenoids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.0920245398773%\" valign=\"top\"\u003e\n \u003cp\u003e0.78 (0.68~0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.042944785276074%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.165644171779142%\" valign=\"top\"\u003e\n \u003cp\u003e0.87 (0.75~1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCERAD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;706 (28%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.0920245398773%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.042944785276074%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.165644171779142%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" valign=\"top\"\u003e\n \u003cp\u003eVitamin A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.0920245398773%\" valign=\"top\"\u003e\n \u003cp\u003e1.02 (0.94~1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.042944785276074%\" valign=\"top\"\u003e\n \u003cp\u003e0.585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.165644171779142%\" valign=\"top\"\u003e\n \u003cp\u003e1.01 (0.91~1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" valign=\"top\"\u003e\n \u003cp\u003eVitamin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.0920245398773%\" valign=\"top\"\u003e\n \u003cp\u003e0.97 (0.89~1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.042944785276074%\" valign=\"top\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.165644171779142%\" valign=\"top\"\u003e\n \u003cp\u003e1.01 (0.91~1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cu\u003eVitamin E\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.0920245398773%\" valign=\"top\"\u003e\n \u003cp\u003e0.76 (0.68~0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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(0.75~0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cu\u003eCarotenoids\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.0920245398773%\" valign=\"top\"\u003e\n \u003cp\u003e0.73 (0.64~0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.042944785276074%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.165644171779142%\" valign=\"top\"\u003e\n \u003cp\u003e0.8 (0.69~0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlobal cognition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e876 (34.7%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.0920245398773%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.042944785276074%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.165644171779142%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" valign=\"top\"\u003e\n \u003cp\u003eVitamin A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.0920245398773%\" valign=\"top\"\u003e\n \u003cp\u003e0.9 (0.82~1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.042944785276074%\" valign=\"top\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.165644171779142%\" valign=\"top\"\u003e\n \u003cp\u003e0.93 (0.83~1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" valign=\"top\"\u003e\n \u003cp\u003eVitamin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.0920245398773%\" valign=\"top\"\u003e\n \u003cp\u003e0.88 (0.8~0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.042944785276074%\" valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.165644171779142%\" valign=\"top\"\u003e\n \u003cp\u003e0.96 (0.86~1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cu\u003eVitamin E\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.0920245398773%\" valign=\"top\"\u003e\n \u003cp\u003e0.64 (0.57~0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.042944785276074%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.165644171779142%\" valign=\"top\"\u003e\n \u003cp\u003e0.78 (0.69~0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cu\u003eZinc\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.0920245398773%\" valign=\"top\"\u003e\n \u003cp\u003e0.76 (0.7~0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.042944785276074%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.165644171779142%\" valign=\"top\"\u003e\n \u003cp\u003e0.83 (0.74~0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cu\u003eSelenium\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.0920245398773%\" valign=\"top\"\u003e\n \u003cp\u003e0.74 (0.67~0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.042944785276074%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.165644171779142%\" valign=\"top\"\u003e\n \u003cp\u003e0.75 (0.67~0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cu\u003eCarotenoids\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.644171779141104%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.0920245398773%\" valign=\"top\"\u003e\n \u003cp\u003e0.73 (0.64~0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.042944785276074%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.165644171779142%\" valign=\"top\"\u003e\n \u003cp\u003e0.84 (0.73~0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969325153374234%\" valign=\"top\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.2269938650306749%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e CDAI, composite dietary antioxidant index; DSST, Digit Symbol Substitution Test; AFT, Animal Fluency Test; CERAD, Consortium to Establish a Registry for Alzheimer\u0026rsquo;s Disease; OR, odds ratio; CI, confidence interval\u003c/p\u003e\n\u003cp\u003eAdjusted for age, gender, race, education, poverty-to-income ratio, body mass index, smoke status, physical activity, hypertension, diabetes, stroke, albumin\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"oxidative stress, nutrient, composite dietary antioxidant index, cognitive impairment, cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-4384652/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4384652/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eOxidative stress is involved in the development and progression of cognitive impairment. However, the association between composite dietary antioxidant index (CDAI) and cognitive impairment remains unknown.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis cross-sectional study investigated the non-linear relationship between CDAI and cognitive impairment among the American elderly.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe public data was available from the United States National Health and Nutrition Examination Survey from 2011 to 2014. Participants aged \u0026ge; 60 years were eligible for cognitive function, including word learning and recall modules from the Consortium to Establish a Registry for Alzheimer\u0026rsquo;s Disease (CERAD), the animal fluency test (AFT), and the digit symbol substitution test (DSST). A composite cognition score was created to evaluate global cognition. The univariate and multivariate logistic regression analysis, restricted cubic spline, stratified and sensitivity analysis were conducted.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCDAI was negatively associated with cognitive impairment. For each standard deviation increase in CDAI, the risk of cognitive impairment decreased by 6% for DSST (OR\u0026thinsp;=\u0026thinsp;0.94, 95% CI: 0.9, 0.97), 7% for AFT (OR\u0026thinsp;=\u0026thinsp;0.93, 95% CI: 0.9, 0.96), 4% for CERAD (OR\u0026thinsp;=\u0026thinsp;0.96, 95% CI: 0.93, 0.99), and 7% for global cognition (OR\u0026thinsp;=\u0026thinsp;0.93, 95% CI: 0.9, 0.96) after adjusting for multiple potential confounders. This significant negative relationship remained consistent when comparing individuals in the highest CDAI tertile with those in the lowest CDAI tertile. Furthermore, a non-linear relationship was observed between CDAI and cognitive impairment on AFT (\u003cem\u003eP\u003c/em\u003e for non-linearity\u0026thinsp;=\u0026thinsp;0.009) and global cognition (\u003cem\u003eP\u003c/em\u003e for non-linearity\u0026thinsp;=\u0026thinsp;0.006).These negative correlations between CDAI and cognitive impairment were observed across the stratified age, gender, poverty-to-income ratio, body mass index, hypertension, and diabetes. However, the interaction test revealed significance for education on DSST (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.04). Moreover, vitamin E, zinc, selenium, and carotenoids were independently associated with cognitive impairment in this study. The sensitivity analysis for participants with complete covariates yielded a similar finding.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese findings suggested a negative and L-shaped association between the CDAI and the risk of cognitive impairment among the American elderly. The results have significant implications for public health initiatives to prevent and limit the progression of cognitive impairment through dietary interventions.\u003c/p\u003e","manuscriptTitle":"The L-shaped Relationship Between Composite Dietary Antioxidant Index and Cognitive Impairment in the American Elderly: A Cross-Sectional Study (NHANES 2011-2014)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-21 19:04:02","doi":"10.21203/rs.3.rs-4384652/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":"faa16680-bc03-4c84-8e67-f3617fb47926","owner":[],"postedDate":"May 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32157822,"name":"Health sciences/Risk factors"},{"id":32157823,"name":"Health sciences/Diseases/Nutrition disorders"},{"id":32157824,"name":"Biological sciences/Neuroscience/Cognitive ageing"},{"id":32157825,"name":"Health sciences/Health care/Geriatrics"},{"id":32157826,"name":"Health sciences/Health care/Nutrition"},{"id":32157827,"name":"Health sciences/Health care/Public health"},{"id":32157828,"name":"Health sciences/Health care/Quality of life"}],"tags":[],"updatedAt":"2024-06-08T17:53:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-21 19:04:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4384652","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4384652","identity":"rs-4384652","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}