Negative association of composite dietary antioxidant index and peripheral artery disease in US participants :a cross-sectional study

Negative association of composite dietary antioxidant index and peripheral artery disease in US participants :a cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Negative association of composite dietary antioxidant index and peripheral artery disease in US participants :a cross-sectional study Qiang Liu, Xing Wu, Jun Yan, Yigang He, Yun Wang, Jianjun Shi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6089133/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 : There is currently insufficient evidence regarding the relationship between the composite dietary antioxidant index (CDAI) and peripheral artery disease (PAD). This association is of significant importance for both individual and public health. Understanding the correlation between CDAI and PAD is an increasingly relevant topic of research. Objective: This study aimed to investigate the correlation between CDAI and the occurrence of PAD. Methods : A retrospective cross-sectional study was conducted, participants from the National Health and Nutrition Examination Survey of the United States during the period 1999–2004. Data on demographic factors such as age, gender, race, education level, marital status, poverty income ratio, as well as health-related variables including physical activity, body mass index, smoking status, total cholesterol, C-reactive protein (CRP), glycosylated hemoglobin (HbA1c), history of cardiovascular disease, hypertension, and diabetes were collected. Logistic regression analysis, smooth curve fitting, and assessment of interaction effects were used to support the research objectives. Results : A total of 6,018 participants were included, of whom, 5.9% (358/6,018) reported having PAD. After adjusting for all covariates, CDAI remained negatively associated with PAD (OR=0.96, 95% CI: 0.92–1). When CDAI was divided into tertiles, the T2 group participants exhibited a reduced probability of PAD compared to those in the T1 group(OR=0.74;95% CI=0.56–0.98), the T3 group also showed a lower probability of PAD than the Q1 group(OR=0.93;95% CI=0.69–1.24), while considering potential confounding variables.Subgroup analysis showed similar patterns of association, with all P values for interaction being >0.05. Conclusions :. Our study provides evidence that CDAI is negatively associated with the incidence of PAD. Further exploration is needed to understand the relationship between CDAI and PAD. Health sciences/Diseases Health sciences/Health care composite dietary antioxidant index peripheral arterial disease ankle-brachial index National Health and Nutrition Examination Survey cross-sectional study Figures Figure 1 Figure 2 1 Introduction Peripheral arterial disease (PAD) is a type of atherosclerosis characterized by the buildup of fatty deposits in the arteries of the lower limbs and feet[ 1 ]. It affects more than 200 million individuals globally and over 8.5 million adults aged 40 and above in the United States, with similar proportions impacting both males and females[ 2 , 3 ]. The economic and health burdens associated with PAD are significant, highlighting the need to identify its causes and implement appropriate measures. Previous research has highlighted the significant roles played by markers of inflammation, dysfunction in the inner lining of blood vessels, and oxidative stress in the development of PAD[ 4 – 9 ]. While smoking, diabetes, hypertension, and dyslipidemia are well-known risk factors for PAD[ 10 ], there is limited documentation regarding the impact of diet. Further investigation is necessary to enhance our understanding of preventive strategies and management approaches for PAD due to insufficient knowledge about effective preventive factors. PAD is a prominent expression of systemic atherosclerosis, marked by the blockage of arteries in the lower extremities[ 11 ]. Research from both experimental and epidemiological perspectives has underscored the vital role of oxidative stress in the onset and advancement of the atherosclerotic process[ 12 – 15 ]. The body's antioxidant system greatly benefits from the consumption of exogenous antioxidants through diet[ 16 ]. Previous studies have suggested that a higher intake of dietary antioxidants, such as carotenoids, vitamin C, and vitamin E, is associated with a lower risk of PAD[ 17 – 19 ]. However, these studies have primarily focused on specific dietary antioxidants, leaving uncertainty regarding the impact of overall dietary antioxidant capacity on PAD risk[ 17 , 18 , 20 – 22 ]. To address this knowledge gap, researchers[ 23 , 24 ] developed the composite dietary antioxidant index (CDAI), which evaluates the potential antioxidant capacity of daily diets. Nevertheless, its relationship with PAD in adults has yet to be established. Therefore, our study aimed to investigate the role of CDAI in relation to PAD and explore the implications arising from this association. We conducted a retrospective cross-sectional study involving 6018 participants residing in the United States. 2 Materials and Methods 2.1 Study population During a retrospective cross-sectional analysis, we analyzed data from 6,018 participants who participated in the National Health and Nutrition Examination Survey (NHANES) of the United States between 1999 and 2004. The NHANES collects various health-related information including demographic details, physical examination results, laboratory findings, and dietary patterns[ 25 ]. This information was collected by the National Center for Health Statistics with approval from its ethics review board. Prior to participating in the NHANES, all individuals provided written informed consent[ 26 ]. Access to NHANES data is publicly available through their website ( http://www.cdc.gov/nchs/nhanes.htm ). From a total of 31,126 individuals who underwent an in-home interview, we excluded those below 40 years old (n = 21,156) as ABI measurements were not taken for this age group. Participants were also excluded if they had missing bilateral ABI data (n = 2399), ABI values exceeding 1.4 (n = 73)[ 27 , 28 ], or lacked diet information (n = 169). Individuals with missing covariates were also removed from the study sample resulting in a final analysis sample of 6,018 participants. 2.2 Peripheral artery disease The ABI measurements were conducted on participants aged 40 years and above, employing a specific methodology as described in previous research studies. Subsequent to a brief period of rest, systolic blood pressure readings were obtained from the right arm and both ankles. In cases where data for the right arm was unavailable, measurements were acquired from the left arm instead. Participants between the ages of 40 and 59 underwent systolic blood pressure assessment on two occasions, while those aged 60 years and older had it evaluated once. To calculate the ABI, the average systolic blood pressure of the ankle was divided by that of the arm. PAD was defined as an ABI value below 0.9[ 19 , 20 ]. 2.3 Composite dietary antioxidant index Data related to diets was gathered from the NHANES 1999 to 2004 first-day dietary interview examination files. A 24-hour dietary recall interview was conducted via the NHANES computer-assisted dietary data interview system to acquire detailed dietary intake data for all participants. This interview captured details of all foods and beverages consumed within a 24-hour duration, such as consumption time, eating occasion, food descriptions, portion sizes, food sources, and location of consumption. Following the dietary recall, a set of health-related inquiries were made. The data collection approach utilized the Automated Multiple Pass Method, which is the dietary data collection tool of the US Department of Agriculture (USDA).Detailed methodologies for the dietary survey can be found in the NHANES Dietary Interviewer’s Procedure Manual[ 31 ]. While 24-h dietary recalls possess inherent limitations in terms of reliability and effectiveness, they offer a greater level of specificity regarding the varieties and amounts of ingested foods than food frequency questionnaires[ 32 , 33 ]. In this study, the CDAI, a composite score measuring the combined consumption of six antioxidants (selenium, magnesium, zinc, vitamin A, vitamin C, and vitamin E), was utilized[ 34 ]. To determine the CDAI value for each individual's intake levels of vitamins and minerals were standardized by subtracting the global mean and dividing it by the global standard deviation. Finally, these standardized intakes were aggregated with equal weighting to calculate the overall CDAI score as described below[ 35 – 37 ]: \(\:DAI={\sum\:}_{i=1}^{n=6}\:\frac{Individual\:Intake-Mean}{SD}\) 2.4 Covariates We administered standardized surveys to collect socio-demographic and lifestyle data. The Mobile Examination Center provided the results of the examinations, which included measurements such as body mass index (BMI), blood pressure, and various biochemical parameters. We considered different potential factors based on existing literature[ 13 , 17 , 17 – 19 , 22 , 32 , 33 , 37 – 41 ], including age, gender, ethnicity, education level, marital status, family income level, smoking habits, physical activity levels, BMI values,and laboratory test results (such as total cholesterol,C-reactive protein and glycated hemoglobin A1c). Additionally, we accounted for any pre-existing medical conditions like diabetes,hypertension,and cardiovascular disease (CVD). For more detailed information about these variables - including measurement methods used in questionnaires and a complete list of variables - please refer to the official NHANES website ( www.cdc.gov/nchs/nhanes/ accessed on 1 May 2022). Family income was categorized into three groups based on poverty income ratio (PIR): low (PIR ≤ 1.3), medium (PIR > 1.3–3.5),and high(PIR > 3.5). Smoking status was classified according to established definitions from previous studies: never smoked,current smoker,and former smoker.To assess physical activity levels,the participants were asked about intense exercises that significantly increased breathing and heart rate(such as swimming or high-speed cycling)as well as moderate exercises causing mild-to-moderate increases in these physiological parameters(for example,golfing or leisurely biking). Each activity should have lasted at least 10 minutes within the last month.Physical activity levels were divided into three categories: below-moderate(lacking both moderate and intense activities),moderate(absence of intense exercise but inclusion of at least one moderately active pursuit),and high-intensity(presence of at least one episode involving rigorous exertion). Hypertension was defined as having an average systolic blood pressure (ASBP) or average diastolic blood pressure (ADBP) of at least 140/90 mmHg, currently using antihypertensive drugs, having received a diagnosis from a healthcare professional, or any combination of these factors. Diabetes was defined by a fasting glucose level greater than 7 mmol/L, a random glucose level of 11.1 mmol/L or higher, a glycated hemoglobin A1c of 6.5% or more, the use of medications to lower blood sugar, or a documented history of diabetes. The occurrence of cardiovascular diseases was assessed based on individuals' self-reported experiences with congestive heart failure, coronary heart disease, angina pectoris, myocardial infarction, and stroke. BMI was calculated using a conventional method that takes into account an individual's weight and height. 2.5 Statistical analysis This study conducted a secondary analysis of publicly available datasets. Categorical variables were presented as proportions (%), while continuous variables were reported as either the mean (with standard deviation, SD) or the median (with interquartile range, IQR), as appropriate. To evaluate differences among groups, one-way analyses of variance were utilized for normally distributed data, Kruskal-Wallis tests for skewed distributions, and chi-square tests for categorical variables. Furthermore, logistic regression models were employed to calculate odds ratios (OR) and 95% confidence intervals (95% CIs) to investigate the relationship between oxidant CDAI and the occurrence of PAD.Our selection of confounding factors was based on their clinical relevance, existing scientific literature, significant covariates identified in the initial analysis, or their impact on the outcomes of interest by more than 10%. Four models were developed: Model 1 had no adjustments. Model 2 included additional adjustments for age, gender, race, education level, MS, PIR, and physical activity. Model 3 further accounted for age, gender, race ,education level, MS, PIR, physical activity, BMI, total cholesterol, CPR, HbA1c. Lastly,model4 incorporated all relevant covariates. To ensure the robustness of our findings, we assessed various factors that might influence the relationship between CDAI and PAD. These factors included: sex, physical activity levels (sedentary compared to moderate or vigorous), family income (low versus medium or high), smoking status (never smoked versus former or current smoker), and the presence of a history of cardiovascular disease, hypertension, and diabetes (no versus yes). We evaluated heterogeneity among subgroups using multivariate logistic regression, and we analyzed interactions between subgroups and CDAI through likelihood ratio testing. Our statistical analysis did not utilize sampling weights or incorporate the complex probability sample design of NHANES. Therefore, the estimates and standard errors presented in the study were not weighted. Statistical analyses were performed using R Statistical Software (Version 4.2.2, http://www.R-project.org , The R Foundation) and the Free Statistics Analysis Platform (Version 1.9, Beijing, China, http://www.clinicalscientists.cn/freestatistics ). A detailed explanation of their functionalities ensured transparency and reproducibility of the analytical methods used in this study. Overall, the statistical analysis appears to be comprehensive, well executed, and supported by appropriate software tools, contributing to the strength of the study's findings. 3 Results 3.1 Characteristics of the study population In total, 31,126 individuals participated in the in-home interviews. Finally, 6,018 individuals were included in the study. (Fig 1). The study included eligible individuals aged 40 years or older and had a prevalence rate of PAD at 5.9%. Table 1 presents the basic characteristics of the participants categorized based on their CDAI scores. The three groups exhibited significant differences in terms of sex, age, education level, race, multiple sclerosis status, poverty income ratio level physical activity level HbA1c levels CRP levels smoking status hypertension presence cardiovascular disease presence and diabetes mellitus presence(P <0.05). CDAI (T3) was associated with a range of demographic and health factors. Participants were younger, with an average age of 58.1 years compared to 61.3 years in T1 (p < 0.001). There was a higher proportion of males in T3 (66.4% vs. 37.9% in T1, p < 0.001) and a greater percentage of non-Hispanic white individuals (60.7% vs. 48% in T1, p < 0.001). Additionally, T3 participants had higher education levels, with 52% having post-high school education compared to 35.9% in T1, and higher income levels, with 45.8% classified as high income versus 27.8% in T1 (p < 0.001). Marital status was also more favorable in T3, with 72.4% being married compared to 59.6% in T1. Vigorous physical activity was more common among T3 participants (30% vs. 17.1% in T1, p < 0.001), and they had lower CRP levels (median 0.2 mg/dL vs. 0.3 mg/dL in T1, p < 0.001). No significant differences were found in BMI (p = 0.846) or total cholesterol (p = 0.249). Furthermore, T3 participants exhibited lower rates of hypertension (31.9% vs. 39.4% in T1), cardiovascular disease (13.3% vs. 17.7%), and smoking (17.3% vs. 22.5% in T1, p < 0.001). However, the prevalence of diabetes did not differ significantly (p = 0.127). Table 1 . Baseline characteristics stratified by the composite dietary antioxidant index (CDAI) tertiles (T). Variables Total CDAI p -Value (n = 6018) T1 (n = 2006) T2 (n = 2006) T3 (n = 2006) Age (years) 59.9 ± 13.0 61.3 ± 12.9 60.2 ± 13.1 58.1 ± 12.8 < 0.001 Gender, n (%) < 0.001 Male 3081 (51.2) 760 (37.9) 989 (49.3) 1332 (66.4) Female 2937 (48.8) 1246 (62.1) 1017 (50.7) 674 (33.6) Race, n (%) < 0.001 Mexican American 1239 (20.6) 442 (22) 394 (19.6) 403 (20.1) Other Hispanic 234 ( 3.9) 86 (4.3) 76 (3.8) 72 (3.6) Non-Hispanic white 3356 (55.8) 962 (48) 1177 (58.7) 1217 (60.7) Non-Hispanic black 1016 (16.9) 454 (22.6) 295 (14.7) 267 (13.3) Other races 173 ( 2.9) 62 (3.1) 64 (3.2) 47 (2.3) Education level, n (%) < 0.001 Less than high school 1013 (16.8) 454 (22.6) 303 (15.1) 256 (12.8) High school diploma or GED 2331 (38.7) 832 (41.5) 792 (39.5) 707 (35.2) More than high school 2674 (44.4) 720 (35.9) 911 (45.4) 1043 (52) MS, n (%) < 0.001 Married/Living with partner 4004 (66.5) 1195 (59.6) 1356 (67.6) 1453 (72.4) Widowed/Divorced/Separated 1679 (27.9) 701 (34.9) 545 (27.2) 433 (21.6) Never married 335 ( 5.6) 110 (5.5) 105 (5.2) 120 (6) PIR, n (%) < 0.001 Low income 1497 (24.9) 655 (32.7) 453 (22.6) 389 (19.4) Medium income 2285 (38.0) 794 (39.6) 792 (39.5) 699 (34.8) High income 2236 (37.2) 557 (27.8) 761 (37.9) 918 (45.8) Physical activity, n (%) < 0.001 Sedentary 2708 (45.0) 1085 (54.1) 856 (42.7) 767 (38.2) Moderate 1909 (31.7) 578 (28.8) 694 (34.6) 637 (31.8) Vigorous 1401 (23.3) 343 (17.1) 456 (22.7) 602 (30) BMI, kg/m 2 28.5 ± 5.6 28.5 ± 5.7 28.4 ± 5.4 28.5 ± 5.6 0.846 Total cholesterol, mg/dL 209.2 ± 41.6 210.4 ± 42.4 208.9 ± 40.1 208.3 ± 42.1 0.249 HbA1c, % 5.8 ± 1.1 5.8 ± 1.2 5.8 ± 1.1 5.7 ± 1.1 0.015 CRP, mg/dL 0.2 (0.1, 0.5) 0.3 (0.1, 0.6) 0.2 (0.1, 0.5) 0.2 (0.1, 0.4) < 0.001 Hypertension, n (%) < 0.001 No 3859 (64.1) 1216 (60.6) 1276 (63.6) 1367 (68.1) Yes 2159 (35.9) 790 (39.4) 730 (36.4) 639 (31.9) Diabetes, n (%) 0.127 No 5246 (87.2) 1727 (86.1) 1749 (87.2) 1770 (88.2) Yes 772 (12.8) 279 (13.9) 257 (12.8) 236 (11.8) CVD, n (%) < 0.001 No 5093 (84.6) 1651 (82.3) 1703 (84.9) 1739 (86.7) Yes 925 (15.4) 355 (17.7) 303 (15.1) 267 (13.3) Smoking, n (%) < 0.001 Never 2788 (46.3) 924 (46.1) 962 (48) 902 (45) Former 2079 (34.5) 631 (31.5) 691 (34.4) 757 (37.7) Current 1151 (19.1) 451 (22.5) 353 (17.6) 347 (17.3) PAD, n (%) < 0.001 No 5660 (94.1) 1842 (91.8) 1906 (95) 1912 (95.3) Yes 358 ( 5.9) 164 (8.2) 100 (5) 94 (4.7) T,tertile,values are given as mean ± standard deviation or numbers and percentages. MS, marital status; PIR, poverty income ratio; BMI, body mass index; CVD, cardiovascular disease; CRP, C-reactive protein; GED, general educational development; HbA1c, glycosylated hemoglobin; CDAI, composite dietary antioxidant index ; PAD, peripheral arterial disease. 3.2 Association between CDAI and PAD The univariate analysis revealed significant associations between various factors, including age, gender, marital status, race, smoking habits, family income, physical activity level, total cholesterol levels, CRP levels,HbA1c levels,CVD presence,hypertension,and diabetes with the occurrence of PAD (Table 2). In the multivariable logistic regression analyses adjusting for potential confounders (Table 3,model 4), a continuous variable representation of CDAI (Per 1 unit) demonstrated an inverse correlation with the likelihood of developing PAD (OR=0.96;95% CI=0.92–1; P=0.033). Furthermore,the T2 group participants exhibited a reduced probability of PAD compared to those in the T1 group(OR=0.74;95% CI=0.56–0.98; P=0.037), and the T3 group also showed a lower probability of PAD than the T1 group(OR=0.93;95% CI=0.69–1.24; P=0.611), while considering potential confounding variables(Table 3,model 4). Table 2 . Association of covariates and PAD risk. Variable OR-95CI P -value Age (years) 1.08 (1.07~1.09) <0.001 Gender, n (%) Male 1 (reference) Female 1.02 (0.82~1.26) 0.889 Race, n (%) Mexican American 1 (reference) Other Hispanic 0.81 (0.4~1.67) 0.575 Non-Hispanic white 1.31 (0.97~1.77) 0.076 Non-Hispanic black 1.81 (1.28~2.56) 0.001 Other races 0.61 (0.24~1.53) 0.29 Education level, n (%) Less than high school 1 (reference) High school diploma or GED 0.69 (0.53~0.9) 0.007 More than high school 0.44 (0.33~0.58) <0.001 MS, n (%) Married/Living with partner 1 (reference) Widowed/Divorced/Separated 2.01 (1.61~2.5) <0.001 Never married 0.61 (0.32~1.16) 0.13 PIR, n (%) Low income 1 (reference) Medium income 0.8 (0.63~1.02) 0.076 High income 0.41 (0.31~0.55) <0.001 Physical activity, n (%) Sedentary 1 (reference) Moderate 0.61 (0.48~0.77) <0.001 Vigorous 0.28 (0.2~0.41) <0.001 BMI, kg/m 2 0.99 (0.97~1.01) 0.186 Total cholesterol, mg/dL 1 (1~1) 0.096 CRP, mg/dL 1.23 (1.13~1.34) <0.001 HbA1c, % 1.21 (1.13~1.3) <0.001 Hypertension, n (%) No 1 (reference) Yes 2.84 (2.28~3.53) <0.001 Diabetes, n (%) No 1 (reference) Yes 2.45 (1.91~3.15) <0.001 CVD, n (%) No 1 (reference) Yes 3.21 (2.55~4.05) <0.001 Smoking, n (%) Never 1 (reference) Former 1.92 (1.49~2.46) <0.001 Current 1.92 (1.44~2.56) <0.001 CDAI 0.9 (0.87~0.93) <0.001 Table 3 . Association between CDAI and PAD. CADI Table 3 . Association between CDAI and PAD. Model 1 Model 2 Model 3 Model 4 OR(95% CI) p-value OR(95% CI) p-value OR(95% CI) p-value OR(95% CI) p-value Continuous 0.9 (0.87~0.93) <0.001 0.95 (0.91~0.98) 0.004 0.95 (0.91~0.98) 0.005 0.96 (0.92~1) 0.033 Categorical T1(<-7.29) Ref Ref Ref Ref T2(-1.94-0.78) 0.59 (0.46~0.76) <0.001 0.7 (0.53~0.91) 0.009 0.84 (0.63~1.12) 0.23 0.74 (0.56~0.98) 0.037 T3(0.78-39.73) 0.55 (0.43~0.72) <0.001 0.82 (0.61~1.09) 0.168 0.9 (0.77~1.04) 0.147 0.93 (0.69~1.24) 0.611 T, tertiles; OR, odds ratio; CI, confidence interval; Ref: reference. Model 1 adjusted for none. Model 2 adjusted for age,gender,race, education level,MS,PIR,physical activity. Model 3 adjusted for age,gender,race, education level,MS, PIR,physical activity,BMI,total cholesterol, CRP,HbA1c. Model 4 adjusted for all covariates. 3.3 Stratification analysis In this study, we conducted a subgroup analysis considering various factors including gender, presence of hypertension or diabetes, CVD, PIR, physical activity level, and smoking habits. (Fig. 2). The findings were consistent across all subgroups. The results from the subgroup analysis did not provide significant evidence of effect modification or interaction based on common risk factors for PAD (all P values for interaction were greater than 0.05). 4 Discussion The present study investigated the association between the CDAI and the prevalence of PAD in a large, nationally representative sample of U.S. adults. Our findings revealed a significant negative association between CDAI and PAD, suggesting that higher dietary antioxidant intake, as measured by CDAI, may be protective against PAD. This association remained robust even after adjusting for a wide range of potential confounders, including demographic factors, lifestyle behaviors, and clinical risk factors. The results of this study contribute to the growing body of evidence supporting the role of dietary antioxidants in cardiovascular health, particularly in the context of PAD. The negative association between CDAI and PAD observed in this study aligns with previous research that has highlighted the protective effects of individual antioxidants, such as vitamins A, C, and E, as well as trace minerals like selenium, magnesium, and zinc, against cardiovascular diseases.Asghar Z. Naqvi et al.[ 19 ] discovered significant inverse relationships between PAD and the consumption of vitamins A, C, and E, even after adjusting for factors such as age, gender, hypertension, diabetes, and smoking. Expanding on these findings, a retrospective cross-sectional analysis using NHANES data revealed that higher intake of specific nutrients like vitamin A (OR = 0.79; 95%CI = 0.63–0.99; P = 0.036), C (OR = 0.84; 95%CI = 0.76–0.92; P < 0 .001), and E (OR = 0.78; 95%CI = 0.65–094 ; P = 011) had a notable protective effect against PAD irrespective of traditional cardiovascular risk factors[ 18 ].Furthermore,Antonelli-Incalzi et al.[ 17 ]conducted a study involving 1251 individuals residing in their own homes as part of the InCHIANTI research project.The average age of participants was 68 years.They also examined whether there was a potential reduction in PAD risk associated with a daily intake equal to or greater than 7.726 mg/d of Vitamin E.(OR:0.037;95%CI:0.16–0.84).Additionally,the Rotterdam Study[ 42 ] found interesting associations regarding vitamin consumption and PAD.In men,vitamin E intake showed an inverse correlation with PAD(OR = 0.67;95%CI:0.44–1.03).On the other hand,in women,vitamin C intake demonstrated significant negative association with PAD(OR = 0.64;95%CI:0.48–0.89). A recent study has confirmed a significant association between higher dietary magnesium intake and a lower incidence of PAD[ 22 ]. Previous studies conducted on cells and animals have also demonstrated the protective effects of zinc against cardiovascular risk factors, such as the development of atherosclerosis[ 43 ]. Zhang et al. [ 44 ]performed a comprehensive analysis by combining data from 16 prospective observational studies and nine randomized controlled trials (RCTs) up until 2013. The results from these observational studies indicated an inverse relationship between selenium status and the risk of cardiovascular disease, particularly when baseline circulating selenium levels ranged between 55 to 145 µg/L. This study provides novel insights into the association between CDAI levels and PAD risk using NHANES data collected from 1999–2004, which is significant for its contribution to existing research conducted on participants in the United States' NHANES survey. Our findings confirm and expand upon previous research, revealing a negative association between CDAI levels and PAD risk. CDAI as a continuous variable, after adjusting all covariates, each unit increase in CDAI corresponds to a 4% reduction in the odds of PAD (OR = 0.96; 95%CI = 0.92–1). Additionally, participants in the T2 group demonstrated a decreased likelihood of having PAD compared to those in the T1 group (OR = 0.74; 95% CI = 0.56–0.98). Similarly, the T3 group exhibited a lower likelihood of PAD than the T1 group (OR = 0.93; 95% CI = 0.69–1.24), after accounting for potential confounding factors. Subgroup analysis showed consistent patterns of association without any statistically significant interactions observed (P > 0.05). Further investigations are warranted to validate our findings and explore potential underlying mechanisms. The protective effects of antioxidants against PAD may be mediated through several mechanisms[ 45 , 46 ]. Oxidative stress and inflammation are key contributors to the pathogenesis of atherosclerosis, the underlying cause of PAD[ 47 ]. Antioxidants such as vitamin C and vitamin E are known to neutralize reactive oxygen species (ROS) and reduce oxidative damage to lipids, proteins, and DNA, thereby mitigating the progression of atherosclerosis[ 13 , 15 , 18 ]. Vitamin A, on the other hand, plays a crucial role in maintaining endothelial function and immune regulation, which are critical for vascular health[ 48 ]. Magnesium, a trace mineral included in the CDAI, has been shown to modulate vascular tone and reduce inflammation[ 49 , 50 ], while zinc and selenium contribute to the maintenance of redox balance and protection against oxidative stress[ 43 , 51 – 53 ]. The synergistic effects of these antioxidants, as captured by the CDAI, may explain the observed reduction in PAD risk. One of the key strengths of this study is the use of CDAI to assess overall antioxidant intake, which provides a more comprehensive measure of dietary antioxidant exposure compared to individual nutrient analyses. Additionally, the study utilized data from the NHANES, a large, nationally representative survey, which enhances the generalizability of our findings. The inclusion of a wide range of covariates in the analysis also strengthens the validity of the results by minimizing the potential for confounding. However,this study had several limitations. Firstly, due to its cross-sectional and observational design, causal relationships between CDAI, covariates, and PAD could not be definitively established. It is important to note that our analysis did not account for potential energy-related confounders, a methodological choice aligned with certain precedents in the field[ 17 , 25 , 54 , 50 ]. While this approach ensures direct comparability with prior studies employing similar frameworks, future investigations could benefit from incorporating energy metrics to enhance the generalizability of results.Despite efforts made to control for relevant confounding factors in the multivariate model, there may still exist unmeasured or unknown residual confounders such as dietary habits and family income that might have potentially resulted in an overestimation of the observed associations. Additionally, the lack of weighted calculations limits the generalizability of the study's conclusions beyond the sample data, warranting further exploration in populations beyond US adults.Further research involving diverse populations is necessary to obtain a more comprehensive understanding of this topic. 5 Conclusions The prevalence of PAD among adults in the United States was found to have an inverse correlation with higher CDAI scores. These findings hold significant implications for healthcare providers when making decisions regarding PAD treatment, highlighting the need for further research to validate these results considering potential confounding factors. Declarations A cknowledgement We express our gratitude to Jie Liu from the Department of Vascular and Endovascular Surgery at Chinese PLA General Hospital for providing valuable assistance in statistical analysis, study design consultations, and offering insightful feedback on the manuscript. Statement of Ethics Study approval statement: The study did not require any additional institutional review board approval for the secondary analysis, thus ethical review and approval were exempted. Consent to participate statement: The NHANES study was approved by the Ethics Review Committee of the National Center for Health Statistics (NCHS), and prior to their involvement, all participants provided written consent after being fully informed. Conflict of Interest Statement The authors have no conflicts of interest to declare. Funding Sources This study was not financially supported by any funding agencies in the public, commercial, or non-profit sectors. Author Contributions Conceptualization, Qiang Liu and Jianjun Shi; Data curation, Xing Wu; Formal analysis and Methodology, Qiang Liu, Jun Yan; Funding acquisition, Jianjun Shi;Writing—original draft, Qiang Liu and Xing Wu; Writing, Yigang He and Yun Wang; All the authors have carefully reviewed and given their consent to the final version of the manuscript. Data Availability Statement Publicly accessible datasets for this study can be found online. The name of the repository/repositories and their corresponding accession numbers are provided at http://www.cdc.gov/nchs/nhanes/ References Poledniczek M, Neumayer C, Kopp CW, Schlager O, Gremmel T, Jozkowicz A, et al. Micro- and Macrovascular Effects of Inflammation in Peripheral Artery Disease—Pathophysiology and Translational Therapeutic Approaches. Biomedicines. 2023;11:2284. Shu J, Santulli G. Update on peripheral artery disease: Epidemiology and evidence-based facts. Atherosclerosis. 2018;275:379–81. Martin SS, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, et al. 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. Circulation. 2024;149:e347–913. Krzyzanowska K, Mittermayer F, Krugluger W, Schnack C, Hofer M, Wolzt M, et al. Asymmetric dimethylarginine is associated with macrovascular disease and total homocysteine in patients with type 2 diabetes. Atherosclerosis. 2006;189:236–40. Tzoulaki I, Murray GD, Lee AJ, Rumley A, Lowe GDO, Fowkes FGR. C-reactive protein, interleukin-6, and soluble adhesion molecules as predictors of progressive peripheral atherosclerosis in the general population: Edinburgh Artery Study. Circulation. 2005;112:976–83. Ding N, Yang C, Ballew SH, Kalbaugh CA, McEvoy JW, Salameh M, et al. Fibrosis and Inflammatory Markers and Long-Term Risk of Peripheral Artery Disease: The ARIC Study. Arterioscler Thromb Vasc Biol. 2020;40:2322–31. Hayfron-Benjamin CF, Mosterd C, Maitland - Van Der Zee AH, Van Raalte DH, Amoah AGB, Agyemang C, et al. Inflammation and its associations with aortic stiffness, coronary artery disease and peripheral artery disease in different ethnic groups: The HELIUS Study. eClinicalMedicine. 2021;38:101012. Loffredo L, Marcoccia A, Pignatelli P, Andreozzi P, Borgia MC, Cangemi R, et al. Oxidative-stress-mediated arterial dysfunction in patients with peripheral arterial disease. Eur Heart J. 2007;28:608–12. Selvaggio S, Abate A, Brugaletta G, Musso C, Di Guardo M, Di Guardo C, et al. Platelet‑to‑lymphocyte ratio, neutrophil‑to‑lymphocyte ratio and monocyte‑to‑HDL cholesterol ratio as markers of peripheral artery disease in elderly patients. Int J Mol Med. 2020;46:1210–6. Joosten MM, Pai JK, Bertoia ML, Rimm EB, Spiegelman D, Mittleman MA, et al. Associations between conventional cardiovascular risk factors and risk of peripheral artery disease in men. JAMA. 2012;308:1660–7. Violi F, Loffredo L, Mancini A, Marcoccia A. Antioxidants in peripheral arterial disease. Curr Drug Targets. 2003;4:651–5. Garg A, Lee JC-Y. Vitamin E: Where Are We Now in Vascular Diseases? Life. 2022;12:310. Kumar M, Deshmukh P, Kumar M, Bhatt A, Sinha AH, Chawla P. Vitamin E Supplementation and Cardiovascular Health: A Comprehensive Review. Cureus [Internet]. 2023 [cited 2024 Mar 10]; Available from: https://www.cureus.com/articles/184881-vitamin-e-supplementation-and-cardiovascular-health-a-comprehensive-review Kleijnen J, Knipschild P, ter Riet G. Vitamin E and cardiovascular disease. Eur J Clin Pharmacol. 1989;37:541–4. Podmore ID, Griffiths HR, Herbert KE, Mistry N, Mistry P, Lunec J. Vitamin C exhibits pro-oxidant properties. Nature. 1998;392:559. Chen Y, Tang W, Li H, Lv J, Chang L, Chen S. Composite dietary antioxidant index negatively correlates with osteoporosis among middle-aged and older US populations. Antonelli-Incalzi R, Pedone C, McDermott MM, Bandinelli S, Miniati B, Lova RM, et al. Association between nutrient intake and peripheral artery disease: Results from the InCHIANTI study. Atherosclerosis. 2006;186:200–6. Lane JS, Magno CP, Lane KT, Chan T, Hoyt DB, Greenfield S. Nutrition impacts the prevalence of peripheral arterial disease in the United States. Journal of Vascular Surgery. 2008;48:897-904.e1. Naqvi AZ, Davis RB, Mukamal KJ. Nutrient intake and peripheral artery disease in adults: key considerations in cross-sectional studies. Clin Nutr. 2014;33:443–7. Bleys J, Navas-Acien A, Laclaustra M, Pastor-Barriuso R, Menke A, Ordovas J, et al. Serum selenium and peripheral arterial disease: results from the national health and nutrition examination survey, 2003-2004. Am J Epidemiol. 2009;169:996–1003. Klipstein-Grobusch K. Dietary Antioxidants and Peripheral Arterial Disease : The Rotterdam Study. American Journal of Epidemiology. 2001;154:145–9. Wu Z, Ruan Z, Liang G, Wang X, Wu J, Wang B. Association between dietary magnesium intake and peripheral arterial disease: Results from NHANES 1999-2004. PLoS One. 2023;18:e0289973. Tur JA, Romaguera D, Pons A. Does the diet of the Balearic population, a Mediterranean-type diet, ensure compliance with nutritional objectives for the Spanish population? Public Health Nutr. 2005;8:275–83. Wright ME, Mayne ST, Stolzenberg-Solomon RZ, Li Z, Pietinen P, Taylor PR, et al. Development of a comprehensive dietary antioxidant index and application to lung cancer risk in a cohort of male smokers. Am J Epidemiol. 2004;160:68–76. Fan H, Zhou J, Huang Y, Feng X, Dang P, Li G, et al. A Proinflammatory Diet Is Associated with Higher Risk of Peripheral Artery Disease. Nutrients. 2022;14. Zipf G, Chiappa M, Porter KS, Ostchega Y, Lewis BG, Dostal J. National health and nutrition examination survey: plan and operations, 1999-2010. Vital Health Stat 1. 2013;1–37. Zalawadiya SK, Veeranna V, Panaich SS, Afonso L. Red cell distribution width and risk of peripheral artery disease: analysis of National Health and Nutrition Examination Survey 1999-2004. Vasc Med. 2012;17:155–63. Berger JS, Eraso LH, Xie D, Sha D, Mohler ER 3rd. Mean platelet volume and prevalence of peripheral artery disease, the National Health and Nutrition Examination Survey, 1999-2004. Atherosclerosis. 2010;213:586–91. Min J-Y, Cho J-S, Lee K-J, Park J-B, Park S-G, Kim JY, et al. Potential role for organochlorine pesticides in the prevalence of peripheral arterial diseases in obese persons: results from the National Health and Nutrition Examination Survey 1999-2004. Atherosclerosis. 2011;218:200–6. Selvin E, Köttgen A, Coresh J. Kidney function estimated from serum creatinine and cystatin C and peripheral arterial disease in NHANES 1999-2002. Eur Heart J. 2009;30:1918–25. Hicks CW, Wang D, Matsushita K, McEvoy JW, Christenson R, Selvin E. Glycated albumin and HbA1c as markers of lower extremity disease inUS adults with and without diabetes. Diabetes Res Clin Pract. 2022;184:109212. Mazidi M, Mikhailidis DP, Banach M. Higher dietary acid load is associated with higher likelihood of peripheral arterial disease among American adults. J Diabetes Complications. 2018;32:565–9. Mattioli AV, Francesca C, Mario M, Alberto F. Fruit and vegetables in hypertensive women with asymptomatic peripheral arterial disease. Clin Nutr ESPEN. 2018;27:110–2. Wright ME. Development of a Comprehensive Dietary Antioxidant Index and Application to Lung Cancer Risk in a Cohort of Male Smokers. American Journal of Epidemiology. 2004;160:68–76. Luu HN, Wen W, Li H, Dai Q, Yang G, Cai Q, et al. Are dietary antioxidant intake indices correlated to oxidative stress and inflammatory marker levels? Antioxid Redox Signal. 2015;22:951–9. Kolarzyk E, Pietrzycka A, Zając J, Morawiecka-Baranek J. Relationship between dietary antioxidant index (DAI) and antioxidants level in plasma of Kraków inhabitants. Adv Clin Exp Med. 2017;26:393–9. Rivas A, Romero A, Mariscal-Arcas M, Monteagudo C, López G, Lorenzo ML, et al. Association between dietary antioxidant quality score (DAQs) and bone mineral density in Spanish women. Nutr Hosp. 2012;27:1886–93. Donnan P, Thomson M, Fowkes F, Prescott R, Housley E. Diet as a risk factor for peripheral arterial disease in the general population: The Edinburgh Artery Study. The American Journal of Clinical Nutrition. 1993;57:917–21. Ogilvie RP, Lutsey PL, Heiss G, Folsom AR, Steffen LM. Dietary intake and peripheral arterial disease incidence in middle-aged adults: the Atherosclerosis Risk in Communities (ARIC) Study. Am J Clin Nutr. 2017;105:651–9. Zhuang X, Ni A, Liao L, Guo Y, Dai W, Jiang Y, et al. Environment-wide association study to identify novel factors associated with peripheral arterial disease: Evidence from the National Health and Nutrition Examination Survey (1999-2004). Atherosclerosis. 2018;269:172–7. Amrock SM, Weitzman M. Multiple biomarkers for mortality prediction in peripheral arterial disease. Vasc Med. 2016;21:105–12. Klipstein-Grobusch K. Dietary Antioxidants and Peripheral Arterial Disease : The Rotterdam Study. American Journal of Epidemiology. 2001;154:145–9. Strand TA, Mathisen M. Zinc - a scoping review for Nordic Nutrition Recommendations 2023. Food Nutr Res. 2023;67. Zhang X, Liu C, Guo J, Song Y. Selenium status and cardiovascular diseases: meta-analysis of prospective observational studies and randomized controlled trials. Eur J Clin Nutr. 2016;70:162–9. Signorelli SS, Scuto S, Marino E, Xourafa A, Gaudio A. Oxidative Stress in Peripheral Arterial Disease (PAD) Mechanism and Biomarkers. Antioxidants (Basel). 2019;8:367. Steven S, Daiber A, Dopheide JF, Münzel T, Espinola-Klein C. Peripheral artery disease, redox signaling, oxidative stress - Basic and clinical aspects. Redox Biol. 2017;12:787–97. Koutakis P, Ismaeel A, Farmer P, Purcell S, Smith RS, Eidson JL, et al. Oxidative stress and antioxidant treatment in patients with peripheral artery disease. Physiol Rep. 2018;6:e13650. Naqvi AZ, Davis RB, Mukamal KJ. Nutrient intake and peripheral artery disease in adults: key considerations in cross-sectional studies. Clin Nutr. 2014;33:443–7. Kostov K, Halacheva L. Role of Magnesium Deficiency in Promoting Atherosclerosis, Endothelial Dysfunction, and Arterial Stiffening as Risk Factors for Hypertension. Int J Mol Sci. 2018;19:1724. Wu Z, Ruan Z, Liang G, Wang X, Wu J, Wang B. Association between dietary magnesium intake and peripheral arterial disease: Results from NHANES 1999-2004. PLoS One. 2023;18:e0289973. Huang J, Hu L, Yang J. Dietary zinc intake and body mass index as modifiers of the association between household pesticide exposure and infertility among US women: a population-level study. Environ Sci Pollut Res. 2022;30:20327–36. Alexander J, Olsen A-K. Selenium - a scoping review for Nordic Nutrition Recommendations 2023. Food Nutr Res. 2023;67. Klipstein-Grobusch K, den Breeijen JH, Grobbee DE, Boeing H, Hofman A, Witteman JC. Dietary antioxidants and peripheral arterial disease : the Rotterdam Study. Am J Epidemiol. 2001;154:145–9. Naqvi AZ, Davis RB, Mukamal KJ. Dietary fatty acids and peripheral artery disease in adults. Atherosclerosis. 2012;222:545–50. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-6089133","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":422238762,"identity":"9983d5f0-47ed-4699-a629-293c57827493","order_by":0,"name":"Qiang Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYBACNv7mww8kKmzk5NkbGx8kVNQQ1sIncSzNwOJMmrFhz+FmgwdnjhHWIseQYyBR2XY4seGGe5vkwxZmIhzGcMbA4AZbGmPjDMa2isQGNgb+9u4E/FqY2woezuCxYWaXbmy7kbhDhkHizNkNBGw5vMFYQiKNjXHOQaCWM2wMBhK5hLQkGEj/MTjMw3Ajsa0gsY2ZGC0pBhISCYclQFoYiNMCCmSJA2kGhj0HmyUSzhzjIegX+X5gVEr+s6mfz97+8OOPiho5/vZe/FowAA9pykfBKBgFo2AUYAUAtKpN+RdJ/DcAAAAASUVORK5CYII=","orcid":"","institution":"Taiyuan Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Liu","suffix":""},{"id":422238763,"identity":"c82270a8-4fe0-4ef9-b5a1-77268da2d4da","order_by":1,"name":"Xing Wu","email":"","orcid":"","institution":"Taiyuan Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xing","middleName":"","lastName":"Wu","suffix":""},{"id":422238764,"identity":"60af066d-6eb0-4461-9bf5-17cf090ed93a","order_by":2,"name":"Jun Yan","email":"","orcid":"","institution":"Taiyuan Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Yan","suffix":""},{"id":422238765,"identity":"de320123-9212-474d-9e90-5dcb73b56c1c","order_by":3,"name":"Yigang He","email":"","orcid":"","institution":"Taiyuan Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yigang","middleName":"","lastName":"He","suffix":""},{"id":422238766,"identity":"70c54958-8106-4d7b-8c74-e379dff67855","order_by":4,"name":"Yun Wang","email":"","orcid":"","institution":"Taiyuan Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"Wang","suffix":""},{"id":422238767,"identity":"28520a65-1f5f-446e-866f-086abf06f1ea","order_by":5,"name":"Jianjun Shi","email":"","orcid":"","institution":"Taiyuan Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jianjun","middleName":"","lastName":"Shi","suffix":""}],"badges":[],"createdAt":"2025-02-23 09:08:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6089133/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6089133/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77681821,"identity":"ca82b05c-3a84-4e57-99b8-86900922275d","added_by":"auto","created_at":"2025-03-04 08:43:03","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":59019,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe flow chart of the study.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6089133/v1/de4baddff631953cf3bfa61f.jpeg"},{"id":77681827,"identity":"0b20f23d-0d3f-4e51-90ce-899a5746181d","added_by":"auto","created_at":"2025-03-04 08:43:04","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":281850,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect CDAI on PAD in each subgroup.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6089133/v1/ecc829b5ffbcec6d33705d65.jpeg"},{"id":78167268,"identity":"dd60d150-9287-4e83-a9b8-447a367372e3","added_by":"auto","created_at":"2025-03-10 14:09:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1850403,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6089133/v1/79cdde24-bd38-459a-8634-c539fc7f50ee.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Negative association of composite dietary antioxidant index and peripheral artery disease in US participants :a cross-sectional study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003ePeripheral arterial disease (PAD) is a type of atherosclerosis characterized by the buildup of fatty deposits in the arteries of the lower limbs and feet[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It affects more than 200\u0026nbsp;million individuals globally and over 8.5\u0026nbsp;million adults aged 40 and above in the United States, with similar proportions impacting both males and females[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The economic and health burdens associated with PAD are significant, highlighting the need to identify its causes and implement appropriate measures. Previous research has highlighted the significant roles played by markers of inflammation, dysfunction in the inner lining of blood vessels, and oxidative stress in the development of PAD[\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. While smoking, diabetes, hypertension, and dyslipidemia are well-known risk factors for PAD[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], there is limited documentation regarding the impact of diet. Further investigation is necessary to enhance our understanding of preventive strategies and management approaches for PAD due to insufficient knowledge about effective preventive factors.\u003c/p\u003e \u003cp\u003ePAD is a prominent expression of systemic atherosclerosis, marked by the blockage of arteries in the lower extremities[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Research from both experimental and epidemiological perspectives has underscored the vital role of oxidative stress in the onset and advancement of the atherosclerotic process[\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The body's antioxidant system greatly benefits from the consumption of exogenous antioxidants through diet[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Previous studies have suggested that a higher intake of dietary antioxidants, such as carotenoids, vitamin C, and vitamin E, is associated with a lower risk of PAD[\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, these studies have primarily focused on specific dietary antioxidants, leaving uncertainty regarding the impact of overall dietary antioxidant capacity on PAD risk[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. To address this knowledge gap, researchers[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] developed the composite dietary antioxidant index (CDAI), which evaluates the potential antioxidant capacity of daily diets. Nevertheless, its relationship with PAD in adults has yet to be established. Therefore, our study aimed to investigate the role of CDAI in relation to PAD and explore the implications arising from this association. We conducted a retrospective cross-sectional study involving 6018 participants residing in the United States.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study population\u003c/h2\u003e\n \u003cp\u003eDuring a retrospective cross-sectional analysis, we analyzed data from 6,018 participants who participated in the National Health and Nutrition Examination Survey (NHANES) of the United States between 1999 and 2004. The NHANES collects various health-related information including demographic details, physical examination results, laboratory findings, and dietary patterns[\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. This information was collected by the National Center for Health Statistics with approval from its ethics review board. Prior to participating in the NHANES, all individuals provided written informed consent[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. Access to NHANES data is publicly available through their website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cdc.gov/nchs/nhanes.htm\u003c/span\u003e\u003c/span\u003e). From a total of 31,126 individuals who underwent an in-home interview, we excluded those below 40 years old (n\u0026thinsp;=\u0026thinsp;21,156) as ABI measurements were not taken for this age group. Participants were also excluded if they had missing bilateral ABI data (n\u0026thinsp;=\u0026thinsp;2399), ABI values exceeding 1.4 (n\u0026thinsp;=\u0026thinsp;73)[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e], or lacked diet information (n\u0026thinsp;=\u0026thinsp;169). Individuals with missing covariates were also removed from the study sample resulting in a final analysis sample of 6,018 participants.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Peripheral artery disease\u003c/h2\u003e\n \u003cp\u003eThe ABI measurements were conducted on participants aged 40 years and above, employing a specific methodology as described in previous research studies. Subsequent to a brief period of rest, systolic blood pressure readings were obtained from the right arm and both ankles. In cases where data for the right arm was unavailable, measurements were acquired from the left arm instead. Participants between the ages of 40 and 59 underwent systolic blood pressure assessment on two occasions, while those aged 60 years and older had it evaluated once. To calculate the ABI, the average systolic blood pressure of the ankle was divided by that of the arm. PAD was defined as an ABI value below 0.9[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cstrong\u003e2.3 Composite dietary antioxidant index\u003c/strong\u003e\u003c/h2\u003e\n \u003cp\u003eData related to diets was gathered from the NHANES 1999 to 2004 first-day dietary interview examination files. A 24-hour dietary recall interview was conducted via the NHANES computer-assisted dietary data interview system to acquire detailed dietary intake data for all participants. This interview captured details of all foods and beverages consumed within a 24-hour duration, such as consumption time, eating occasion, food descriptions, portion sizes, food sources, and location of consumption. Following the dietary recall, a set of health-related inquiries were made. The data collection approach utilized the Automated Multiple Pass Method, which is the dietary data collection tool of the US Department of Agriculture (USDA).Detailed methodologies for the dietary survey can be found in the NHANES Dietary Interviewer\u0026rsquo;s Procedure Manual[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. While 24-h dietary recalls possess inherent limitations in terms of reliability and effectiveness, they offer a greater level of specificity regarding the varieties and amounts of ingested foods than food frequency questionnaires[\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. In this study, the CDAI, a composite score measuring the combined consumption of six antioxidants (selenium, magnesium, zinc, vitamin A, vitamin C, and vitamin E), was utilized[\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. To determine the CDAI value for each individual\u0026apos;s intake levels of vitamins and minerals were standardized by subtracting the global mean and dividing it by the global standard deviation. Finally, these standardized intakes were aggregated with equal weighting to calculate the overall CDAI score as described below[\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:DAI={\\sum\\:}_{i=1}^{n=6}\\:\\frac{Individual\\:Intake-Mean}{SD}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Covariates\u003c/h2\u003e\n \u003cp\u003eWe administered standardized surveys to collect socio-demographic and lifestyle data. The Mobile Examination Center provided the results of the examinations, which included measurements such as body mass index (BMI), blood pressure, and various biochemical parameters. We considered different potential factors based on existing literature[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e], including age, gender, ethnicity, education level, marital status, family income level, smoking habits, physical activity levels, BMI values,and laboratory test results (such as total cholesterol,C-reactive protein and glycated hemoglobin A1c). Additionally, we accounted for any pre-existing medical conditions like diabetes,hypertension,and cardiovascular disease (CVD). For more detailed information about these variables - including measurement methods used in questionnaires and a complete list of variables - please refer to the official NHANES website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003c/span\u003e accessed on 1 May 2022). Family income was categorized into three groups based on poverty income ratio (PIR): low (PIR\u0026thinsp;\u0026le;\u0026thinsp;1.3), medium (PIR\u0026thinsp;\u0026gt;\u0026thinsp;1.3\u0026ndash;3.5),and high(PIR\u0026thinsp;\u0026gt;\u0026thinsp;3.5). Smoking status was classified according to established definitions from previous studies: never smoked,current smoker,and former smoker.To assess physical activity levels,the participants were asked about intense exercises that significantly increased breathing and heart rate(such as swimming or high-speed cycling)as well as moderate exercises causing mild-to-moderate increases in these physiological parameters(for example,golfing or leisurely biking). Each activity should have lasted at least 10 minutes within the last month.Physical activity levels were divided into three categories: below-moderate(lacking both moderate and intense activities),moderate(absence of intense exercise but inclusion of at least one moderately active pursuit),and high-intensity(presence of at least one episode involving rigorous exertion).\u003c/p\u003e\n \u003cp\u003eHypertension was defined as having an average systolic blood pressure (ASBP) or average diastolic blood pressure (ADBP) of at least 140/90 mmHg, currently using antihypertensive drugs, having received a diagnosis from a healthcare professional, or any combination of these factors. Diabetes was defined by a fasting glucose level greater than 7 mmol/L, a random glucose level of 11.1 mmol/L or higher, a glycated hemoglobin A1c of 6.5% or more, the use of medications to lower blood sugar, or a documented history of diabetes. The occurrence of cardiovascular diseases was assessed based on individuals\u0026apos; self-reported experiences with congestive heart failure, coronary heart disease, angina pectoris, myocardial infarction, and stroke. BMI was calculated using a conventional method that takes into account an individual\u0026apos;s weight and height.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e\n \u003cp\u003eThis study conducted a secondary analysis of publicly available datasets. Categorical variables were presented as proportions (%), while continuous variables were reported as either the mean (with standard deviation, SD) or the median (with interquartile range, IQR), as appropriate. To evaluate differences among groups, one-way analyses of variance were utilized for normally distributed data, Kruskal-Wallis tests for skewed distributions, and chi-square tests for categorical variables. Furthermore, logistic regression models were employed to calculate odds ratios (OR) and 95% confidence intervals (95% CIs) to investigate the relationship between oxidant CDAI and the occurrence of PAD.Our selection of confounding factors was based on their clinical relevance, existing scientific literature, significant covariates identified in the initial analysis, or their impact on the outcomes of interest by more than 10%. Four models were developed: Model 1 had no adjustments. Model 2 included additional adjustments for age, gender, race, education level, MS, PIR, and physical activity. Model 3 further accounted for age, gender, race ,education level, MS, PIR, physical activity, BMI, total cholesterol, CPR, HbA1c. Lastly,model4 incorporated all relevant covariates. To ensure the robustness of our findings, we assessed various factors that might influence the relationship between CDAI and PAD. These factors included: sex, physical activity levels (sedentary compared to moderate or vigorous), family income (low versus medium or high), smoking status (never smoked versus former or current smoker), and the presence of a history of cardiovascular disease, hypertension, and diabetes (no versus yes). We evaluated heterogeneity among subgroups using multivariate logistic regression, and we analyzed interactions between subgroups and CDAI through likelihood ratio testing. Our statistical analysis did not utilize sampling weights or incorporate the complex probability sample design of NHANES. Therefore, the estimates and standard errors presented in the study were not weighted.\u003c/p\u003e\n \u003cp\u003eStatistical analyses were performed using R Statistical Software (Version 4.2.2, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003c/span\u003e, The R Foundation) and the Free Statistics Analysis Platform (Version 1.9, Beijing, China, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.clinicalscientists.cn/freestatistics\u003c/span\u003e\u003c/span\u003e). A detailed explanation of their functionalities ensured transparency and reproducibility of the analytical methods used in this study. Overall, the statistical analysis appears to be comprehensive, well executed, and supported by appropriate software tools, contributing to the strength of the study\u0026apos;s findings.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003e\u003cstrong\u003e3.1\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCharacteristics of the study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn total, 31,126 individuals participated in the in-home interviews. Finally, 6,018 individuals were included in the study. (Fig 1). The study included eligible individuals aged 40 years or older and had a prevalence rate of PAD at 5.9%. Table 1 presents the basic characteristics of the participants categorized based on their CDAI scores. The three groups exhibited significant differences in terms of sex, age, education level, race, multiple sclerosis status, poverty income ratio level physical activity level HbA1c levels CRP levels smoking status hypertension presence cardiovascular disease presence and diabetes mellitus presence(P \u0026lt;0.05). CDAI (T3) was associated with a range of demographic and health factors. Participants were younger, with an average age of 58.1 years compared to 61.3 years in T1 (p \u0026lt; 0.001). There was a higher proportion of males in T3 (66.4% vs. 37.9% in T1, p \u0026lt; 0.001) and a greater percentage of non-Hispanic white individuals (60.7% vs. 48% in T1, p \u0026lt; 0.001). Additionally, T3 participants had higher education levels, with 52% having post-high school education compared to 35.9% in T1, and higher income levels, with 45.8% classified as high income versus 27.8% in T1 (p \u0026lt; 0.001). Marital status was also more favorable in T3, with 72.4% being married compared to 59.6% in T1. Vigorous physical activity was more common among T3 participants (30% vs. 17.1% in T1, p \u0026lt; 0.001), and they had lower CRP levels (median 0.2 mg/dL vs. 0.3 mg/dL in T1, p \u0026lt; 0.001). No significant differences were found in BMI (p = 0.846) or total cholesterol (p = 0.249). Furthermore, T3 participants exhibited lower rates of hypertension (31.9% vs. 39.4% in T1), cardiovascular disease (13.3% vs. 17.7%), and smoking (17.3% vs. 22.5% in T1, p \u0026lt; 0.001). However, the prevalence of diabetes did not differ significantly (p = 0.127).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e. Baseline characteristics stratified by the composite dietary antioxidant index (CDAI) tertiles (T).\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCDAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" 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 valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 6018)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1 (n = 2006)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT2 (n = 2006)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT3 (n = 2006)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59.9 \u0026plusmn; 13.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61.3 \u0026plusmn; 12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60.2 \u0026plusmn; 13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58.1 \u0026plusmn; 12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Male\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3081 (51.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e760 (37.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e989 (49.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1332 (66.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Female\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2937 (48.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1246 (62.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1017 (50.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e674 (33.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Mexican American\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1239 (20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e442 (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e394 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e403 (20.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Other Hispanic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e234 ( 3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e86 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Non-Hispanic white\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3356 (55.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e962 (48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1177 (58.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1217 (60.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Non-Hispanic black\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1016 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e454 (22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e295 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e267 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Other races\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e173 ( 2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Less than high school\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1013 (16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e454 (22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e303 (15.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e256 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; High school diploma or GED\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2331 (38.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e832 (41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e792 (39.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e707 (35.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; More than high school\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2674 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e720 (35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e911 (45.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1043 (52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMS, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Married/Living with partner\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4004 (66.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1195 (59.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1356 (67.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1453 (72.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Widowed/Divorced/Separated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1679 (27.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e701 (34.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e545 (27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e433 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Never married\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e335 ( 5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e110 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e105 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e120 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIR, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Low income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1497 (24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e655 (32.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e453 (22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e389 (19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Medium income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2285 (38.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e794 (39.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e792 (39.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e699 (34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; High income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2236 (37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e557 (27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e761 (37.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e918 (45.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical activity, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Sedentary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2708 (45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1085 (54.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e856 (42.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e767 (38.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Moderate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1909 (31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e578 (28.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e694 (34.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e637 (31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Vigorous\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1401 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e343 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e456 (22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e602 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.5 \u0026plusmn; 5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.5 \u0026plusmn; 5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.4 \u0026plusmn; 5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.5 \u0026plusmn; 5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal cholesterol, mg/dL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e209.2 \u0026plusmn; 41.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e210.4 \u0026plusmn; 42.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e208.9 \u0026plusmn; 40.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e208.3 \u0026plusmn; 42.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHbA1c, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.8 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.8 \u0026plusmn; 1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.8 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.7 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP, mg/dL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2 (0.1, 0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3 (0.1, 0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2 (0.1, 0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2 (0.1, 0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3859 (64.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1216 (60.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1276 (63.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1367 (68.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Yes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2159 (35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e790 (39.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e730 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e639 (31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5246 (87.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1727 (86.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1749 (87.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1770 (88.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Yes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e772 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e279 (13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e257 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e236 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCVD, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5093 (84.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1651 (82.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1703 (84.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1739 (86.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Yes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e925 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e355 (17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e303 (15.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e267 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Never\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2788 (46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e924 (46.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e962 (48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e902 (45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Former\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2079 (34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e631 (31.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e691 (34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e757 (37.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Current\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1151 (19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e451 (22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e353 (17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e347 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePAD, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5660 (94.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1842 (91.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1906 (95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1912 (95.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Yes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e358 ( 5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e164 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e94 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\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\u003eT,tertile,values are given as mean \u0026plusmn; standard deviation or numbers and percentages. MS, marital status; PIR, poverty income ratio; BMI, body mass index; CVD, cardiovascular disease; CRP, C-reactive protein; GED, general educational development; HbA1c, glycosylated hemoglobin; CDAI,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ecomposite dietary antioxidant index ; PAD, peripheral arterial disease.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAssociation between CDAI and PAD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe univariate analysis revealed significant associations between various factors, including age, gender, marital status, race, smoking habits, family income, physical activity level, total cholesterol levels, CRP levels,HbA1c levels,CVD presence,hypertension,and diabetes with the occurrence of PAD (Table 2). In the multivariable logistic regression analyses adjusting for potential confounders (Table 3,model 4), a continuous variable representation of CDAI (Per 1 unit) demonstrated an inverse correlation with the likelihood of developing PAD (OR=0.96;95% CI=0.92\u0026ndash;1; P=0.033). Furthermore,the T2 group participants exhibited a reduced probability of PAD compared to those in the T1 group(OR=0.74;95% CI=0.56\u0026ndash;0.98; P=0.037), and the T3 group also showed a lower probability of PAD than the T1 group(OR=0.93;95% CI=0.69\u0026ndash;1.24; P=0.611), while considering potential confounding variables(Table 3,model 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e.\u003cstrong\u003eAssociation of covariates and PAD risk.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR-95CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.08 (1.07~1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Male\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Female\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.02 (0.82~1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.889\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Mexican American\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Other Hispanic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.81 (0.4~1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Non-Hispanic white\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.31 (0.97~1.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Non-Hispanic black\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.81 (1.28~2.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Other races\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.61 (0.24~1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Less than high school\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; High school diploma or GED\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.69 (0.53~0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; More than high school\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.44 (0.33~0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMS, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Married/Living with partner\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Widowed/Divorced/Separated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.01 (1.61~2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Never married\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.61 (0.32~1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIR, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Low income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Medium income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8 (0.63~1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; High income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.41 (0.31~0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical activity, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Sedentary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Moderate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.61 (0.48~0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Vigorous\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.28 (0.2~0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.99 (0.97~1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal cholesterol, mg/dL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (1~1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP, mg/dL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.23 (1.13~1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHbA1c, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.21 (1.13~1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Yes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.84 (2.28~3.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Yes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.45 (1.91~3.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCVD, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Yes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.21 (2.55~4.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Never\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Former\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.92 (1.49~2.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Current\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.92 (1.44~2.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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 valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCDAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9 (0.87~0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e. Association between CDAI and PAD.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCADI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e. Association between CDAI and PAD.\u003c/strong\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContinuous\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.9 (0.87~0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.95 (0.91~0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.95 (0.91~0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.96 (0.92~1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategorical\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1(\u0026lt;-7.29)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT2(-1.94-0.78)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.59 (0.46~0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.7 (0.53~0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.84 (0.63~1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.74 (0.56~0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT3(0.78-39.73)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.55 (0.43~0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.82 (0.61~1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.9 (0.77~1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.93 (0.69~1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eT, tertiles; OR, odds ratio; CI, confidence interval; Ref: reference.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel 1 adjusted for none.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel 2 adjusted for age,gender,race, education level,MS,PIR,physical activity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel 3 adjusted for age,gender,race, education level,MS, PIR,physical activity,BMI,total cholesterol, CRP,HbA1c.\u003c/p\u003e\n\u003cp\u003eModel 4 adjusted for all covariates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eStratification analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we conducted a subgroup analysis considering various factors including gender, presence of hypertension or diabetes, CVD, PIR, physical activity level, and smoking habits. (Fig. 2). The findings were consistent across all subgroups. The results from the subgroup analysis did not provide significant evidence of effect modification or interaction based on common risk factors for PAD (all P values for interaction were greater than 0.05).\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe present study investigated the association between the CDAI and the prevalence of PAD in a large, nationally representative sample of U.S. adults. Our findings revealed a significant negative association between CDAI and PAD, suggesting that higher dietary antioxidant intake, as measured by CDAI, may be protective against PAD. This association remained robust even after adjusting for a wide range of potential confounders, including demographic factors, lifestyle behaviors, and clinical risk factors. The results of this study contribute to the growing body of evidence supporting the role of dietary antioxidants in cardiovascular health, particularly in the context of PAD.\u003c/p\u003e \u003cp\u003eThe negative association between CDAI and PAD observed in this study aligns with previous research that has highlighted the protective effects of individual antioxidants, such as vitamins A, C, and E, as well as trace minerals like selenium, magnesium, and zinc, against cardiovascular diseases.Asghar Z. Naqvi et al.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] discovered significant inverse relationships between PAD and the consumption of vitamins A, C, and E, even after adjusting for factors such as age, gender, hypertension, diabetes, and smoking. Expanding on these findings, a retrospective cross-sectional analysis using NHANES data revealed that higher intake of specific nutrients like vitamin A (OR\u0026thinsp;=\u0026thinsp;0.79; 95%CI\u0026thinsp;=\u0026thinsp;0.63\u0026ndash;0.99; P\u0026thinsp;=\u0026thinsp;0.036), C (OR\u0026thinsp;=\u0026thinsp;0.84; 95%CI\u0026thinsp;=\u0026thinsp;0.76\u0026ndash;0.92; P\u0026thinsp;\u0026lt;\u0026thinsp;0 .001), and E (OR\u0026thinsp;=\u0026thinsp;0.78; 95%CI\u0026thinsp;=\u0026thinsp;0.65\u0026ndash;094 ; P\u0026thinsp;=\u0026thinsp;011) had a notable protective effect against PAD irrespective of traditional cardiovascular risk factors[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].Furthermore,Antonelli-Incalzi et al.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]conducted a study involving 1251 individuals residing in their own homes as part of the InCHIANTI research project.The average age of participants was 68 years.They also examined whether there was a potential reduction in PAD risk associated with a daily intake equal to or greater than 7.726 mg/d of Vitamin E.(OR:0.037;95%CI:0.16\u0026ndash;0.84).Additionally,the Rotterdam Study[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] found interesting associations regarding vitamin consumption and PAD.In men,vitamin E intake showed an inverse correlation with PAD(OR\u0026thinsp;=\u0026thinsp;0.67;95%CI:0.44\u0026ndash;1.03).On the other hand,in women,vitamin C intake demonstrated significant negative association with PAD(OR\u0026thinsp;=\u0026thinsp;0.64;95%CI:0.48\u0026ndash;0.89). A recent study has confirmed a significant association between higher dietary magnesium intake and a lower incidence of PAD[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Previous studies conducted on cells and animals have also demonstrated the protective effects of zinc against cardiovascular risk factors, such as the development of atherosclerosis[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Zhang et al. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]performed a comprehensive analysis by combining data from 16 prospective observational studies and nine randomized controlled trials (RCTs) up until 2013. The results from these observational studies indicated an inverse relationship between selenium status and the risk of cardiovascular disease, particularly when baseline circulating selenium levels ranged between 55 to 145 \u0026micro;g/L. This study provides novel insights into the association between CDAI levels and PAD risk using NHANES data collected from 1999\u0026ndash;2004, which is significant for its contribution to existing research conducted on participants in the United States' NHANES survey. Our findings confirm and expand upon previous research, revealing a negative association between CDAI levels and PAD risk. CDAI as a continuous variable, after adjusting all covariates, each unit increase in CDAI corresponds to a 4% reduction in the odds of PAD (OR\u0026thinsp;=\u0026thinsp;0.96; 95%CI\u0026thinsp;=\u0026thinsp;0.92\u0026ndash;1). Additionally, participants in the T2 group demonstrated a decreased likelihood of having PAD compared to those in the T1 group (OR\u0026thinsp;=\u0026thinsp;0.74; 95% CI\u0026thinsp;=\u0026thinsp;0.56\u0026ndash;0.98). Similarly, the T3 group exhibited a lower likelihood of PAD than the T1 group (OR\u0026thinsp;=\u0026thinsp;0.93; 95% CI\u0026thinsp;=\u0026thinsp;0.69\u0026ndash;1.24), after accounting for potential confounding factors. Subgroup analysis showed consistent patterns of association without any statistically significant interactions observed (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Further investigations are warranted to validate our findings and explore potential underlying mechanisms.\u003c/p\u003e \u003cp\u003eThe protective effects of antioxidants against PAD may be mediated through several mechanisms[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Oxidative stress and inflammation are key contributors to the pathogenesis of atherosclerosis, the underlying cause of PAD[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Antioxidants such as vitamin C and vitamin E are known to neutralize reactive oxygen species (ROS) and reduce oxidative damage to lipids, proteins, and DNA, thereby mitigating the progression of atherosclerosis[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Vitamin A, on the other hand, plays a crucial role in maintaining endothelial function and immune regulation, which are critical for vascular health[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Magnesium, a trace mineral included in the CDAI, has been shown to modulate vascular tone and reduce inflammation[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], while zinc and selenium contribute to the maintenance of redox balance and protection against oxidative stress[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The synergistic effects of these antioxidants, as captured by the CDAI, may explain the observed reduction in PAD risk.\u003c/p\u003e \u003cp\u003eOne of the key strengths of this study is the use of CDAI to assess overall antioxidant intake, which provides a more comprehensive measure of dietary antioxidant exposure compared to individual nutrient analyses. Additionally, the study utilized data from the NHANES, a large, nationally representative survey, which enhances the generalizability of our findings. The inclusion of a wide range of covariates in the analysis also strengthens the validity of the results by minimizing the potential for confounding.\u003c/p\u003e \u003cp\u003eHowever,this study had several limitations. Firstly, due to its cross-sectional and observational design, causal relationships between CDAI, covariates, and PAD could not be definitively established. It is important to note that our analysis did not account for potential energy-related confounders, a methodological choice aligned with certain precedents in the field[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. While this approach ensures direct comparability with prior studies employing similar frameworks, future investigations could benefit from incorporating energy metrics to enhance the generalizability of results.Despite efforts made to control for relevant confounding factors in the multivariate model, there may still exist unmeasured or unknown residual confounders such as dietary habits and family income that might have potentially resulted in an overestimation of the observed associations. Additionally, the lack of weighted calculations limits the generalizability of the study's conclusions beyond the sample data, warranting further exploration in populations beyond US adults.Further research involving diverse populations is necessary to obtain a more comprehensive understanding of this topic.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eThe prevalence of PAD among adults in the United States was found to have an inverse correlation with higher CDAI scores. These findings hold significant implications for healthcare providers when making decisions regarding PAD treatment, highlighting the need for further research to validate these results considering potential confounding factors.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003ecknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our gratitude to Jie Liu from the Department of Vascular and Endovascular Surgery at Chinese PLA General Hospital for providing valuable assistance in statistical analysis, study design consultations, and offering insightful feedback on the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement of Ethics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy approval statement: The study did not require any additional institutional review board approval for the secondary analysis, thus ethical review and approval were exempted.\u003c/p\u003e\n\u003cp\u003eConsent to participate statement: The NHANES study was approved by the Ethics Review Committee of the National Center for Health Statistics (NCHS), and prior to their involvement, all participants provided written consent after being fully informed.\u003c/p\u003e\n\u003cp id=\"_Toc472330565\"\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp id=\"_Toc472330566\"\u003e\u003cstrong\u003eFunding Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp id=\"_Toc472330568\"\u003eThis study was not financially supported by any funding agencies in the public, commercial, or non-profit sectors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, Qiang Liu and Jianjun Shi; Data curation, \u0026nbsp;Xing Wu; Formal analysis and Methodology, Qiang Liu, Jun Yan; Funding acquisition, \u0026nbsp;Jianjun Shi;Writing\u0026mdash;original draft, Qiang Liu and Xing Wu; Writing, Yigang He and Yun Wang; All the authors have carefully reviewed and given their consent to the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly accessible datasets for this study can be found online. The name of the repository/repositories and their corresponding accession numbers are provided at http://www.cdc.gov/nchs/nhanes/\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePoledniczek M, Neumayer C, Kopp CW, Schlager O, Gremmel T, Jozkowicz A, et al. Micro- and Macrovascular Effects of Inflammation in Peripheral Artery Disease\u0026mdash;Pathophysiology and Translational Therapeutic Approaches. Biomedicines. 2023;11:2284. \u003c/li\u003e\n\u003cli\u003eShu J, Santulli G. Update on peripheral artery disease: Epidemiology and evidence-based facts. Atherosclerosis. 2018;275:379\u0026ndash;81. \u003c/li\u003e\n\u003cli\u003eMartin SS, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, et al. 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. 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Red cell distribution width and risk of peripheral artery disease: analysis of National Health and Nutrition Examination Survey 1999-2004. Vasc Med. 2012;17:155\u0026ndash;63. \u003c/li\u003e\n\u003cli\u003eBerger JS, Eraso LH, Xie D, Sha D, Mohler ER 3rd. Mean platelet volume and prevalence of peripheral artery disease, the National Health and Nutrition Examination Survey, 1999-2004. Atherosclerosis. 2010;213:586\u0026ndash;91. \u003c/li\u003e\n\u003cli\u003eMin J-Y, Cho J-S, Lee K-J, Park J-B, Park S-G, Kim JY, et al. Potential role for organochlorine pesticides in the prevalence of peripheral arterial diseases in obese persons: results from the National Health and Nutrition Examination Survey 1999-2004. Atherosclerosis. 2011;218:200\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eSelvin E, K\u0026ouml;ttgen A, Coresh J. Kidney function estimated from serum creatinine and cystatin C and peripheral arterial disease in NHANES 1999-2002. 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Dietary zinc intake and body mass index as modifiers of the association between household pesticide exposure and infertility among US women: a population-level study. Environ Sci Pollut Res. 2022;30:20327\u0026ndash;36. \u003c/li\u003e\n\u003cli\u003eAlexander J, Olsen A-K. Selenium - a scoping review for Nordic Nutrition Recommendations 2023. Food Nutr Res. 2023;67. \u003c/li\u003e\n\u003cli\u003eKlipstein-Grobusch K, den Breeijen JH, Grobbee DE, Boeing H, Hofman A, Witteman JC. Dietary antioxidants and peripheral arterial disease : the Rotterdam Study. Am J Epidemiol. 2001;154:145\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eNaqvi AZ, Davis RB, Mukamal KJ. Dietary fatty acids and peripheral artery disease in adults. Atherosclerosis. 2012;222:545\u0026ndash;50. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"composite dietary antioxidant index, peripheral arterial disease, ankle-brachial index, National Health and Nutrition Examination Survey, cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-6089133/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6089133/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: There is currently insufficient evidence regarding the relationship between the composite dietary antioxidant index (CDAI) and peripheral artery disease (PAD). This association is of significant importance for both individual and public health. Understanding the correlation between CDAI and PAD is an increasingly relevant topic of research.\u003c/p\u003e\n\u003cp\u003eObjective: This study aimed to investigate the correlation between CDAI and the occurrence of PAD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A retrospective cross-sectional study was conducted, participants from the National Health and Nutrition Examination Survey of the United States during the period 1999–2004. Data on demographic factors such as age, gender, race, education level, marital status, poverty income ratio, as well as health-related variables including physical activity, body mass index, smoking status, total cholesterol, C-reactive protein (CRP), glycosylated hemoglobin (HbA1c), history of cardiovascular disease, hypertension, and diabetes were collected. Logistic regression analysis, smooth curve fitting, and assessment of interaction effects were used to support the research objectives.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A total of 6,018 participants were included, of whom, 5.9% (358/6,018) reported having PAD. After adjusting for all covariates, CDAI remained negatively associated with PAD (OR=0.96, 95% CI: 0.92–1). When CDAI was divided into tertiles, the T2 group participants exhibited a reduced probability of PAD compared to those in the T1 group(OR=0.74;95% CI=0.56–0.98), the T3 group also showed a lower probability of PAD than the Q1 group(OR=0.93;95% CI=0.69–1.24), while considering potential confounding variables.Subgroup analysis showed similar patterns of association, with all P values for interaction being \u0026gt;0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e:. Our study provides evidence that CDAI is negatively associated with the incidence of PAD. Further exploration is needed to understand the relationship between CDAI and PAD.\u003c/p\u003e","manuscriptTitle":"Negative association of composite dietary antioxidant index and peripheral artery disease in US participants :a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-04 08:42:59","doi":"10.21203/rs.3.rs-6089133/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":"e2fb9616-fa38-477f-bd29-77e4634e755d","owner":[],"postedDate":"March 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":45008410,"name":"Health sciences/Diseases"},{"id":45008411,"name":"Health sciences/Health care"}],"tags":[],"updatedAt":"2025-03-10T14:08:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-04 08:42:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6089133","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6089133","identity":"rs-6089133","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}