The association between the comprehensive dietary antioxidant index and the risk of gastrointestinal cancer: A cross-sectional study based on NHANES

The association between the comprehensive dietary antioxidant index and the risk of gastrointestinal cancer: A cross-sectional study based on NHANES | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The association between the comprehensive dietary antioxidant index and the risk of gastrointestinal cancer: A cross-sectional study based on NHANES Jianhua Wu, Wen Du, Jierui Liu, Zhaohui Liao, Wei Yu, Yu Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7040004/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 Gastrointestinal cancer accounts for approximately one-third of global cancer incidence and mortality. The early screening rates remain low, which leads to a poor prognosis. Identifying modifiable risk factors is therefore a pressing need. Oxidative stress plays a pivotal role in gastrointestinal carcinogenesis. Dietary antioxidants may mitigate this process by neutralizing reactive oxygen species. However, studies focusing on single nutrients have limitations due to their inability to capture the synergistic effects of multiple dietary components. The Composite Dietary Antioxidant Index (CDAI) is a quantitative measure that evaluates the combined impact of various dietary antioxidants. The relationship between CDAI and gastrointestinal cancer risk warrants further investigation. Objective To examine the association between CDAI and the risk of gastrointestinal cancer. Method Cross-sectional data from NHANES 2005–2023 were utilized to calculate CDAI scores. Logistic regression models, restricted cubic splines, and subgroup analyses were employed to comprehensively assess the relationship between CDAI and gastrointestinal cancer risk. Result Among the 21,762 participants included in the study, the high CDAI group consisted predominantly of males, individuals with high socioeconomic status (non-Hispanic whites, high income), and those who engaged in healthy behaviors (low smoking rates, low body mass index). Multivariate analysis revealed that CDAI scores were significantly lower among females, current smokers, and obese individuals, while higher scores were observed in the high-income group. The association analysis demonstrated that for every 1-unit increase in CDAI, the risk of gastrointestinal cancer decreased by 4.55% (OR = 0.9545). This protective effect was more pronounced among individuals with obesity, females, and non-Hispanic whites. No significant association was identified between CDAI and non-gastrointestinal cancers. Conclusion Higher CDAI scores are associated with a diminished risk of gastrointestinal cancer, particularly among females, individuals with obesity, and non-Hispanic whites. These findings highlight the potential preventive role of dietary antioxidants in gastrointestinal tumor development. Composite Dietary Antioxidant Index (CDAI) gastrointestinal cancer non-gastrointestinal cancer NHANES cross-sectional study Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Gastrointestinal cancers, including esophageal, gastric, hepatic, pancreatic, colonic, and rectal cancers, account for one-quarter of global cancer incidence and one-third of cancer-related mortality [ 1 ]. These malignancies are among the most prevalent cancers worldwide. According to data from the International Agency for Research on Cancer (IARC), there were over 5 million new cases of gastrointestinal cancer worldwide in 2022, representing more than 25% of all cancer-related deaths [ 2 ]. Despite the continuous advancements in diagnostic and therapeutic technologies, low early screening rates and poor prognosis remain significant challenges, making gastrointestinal cancer a major public health issue [ 3 ]. In addition to genetic susceptibility, environmental and lifestyle factors, such as dietary habits, smoking, and obesity, have been identified as critical contributors in the development of gastrointestinal cancer [ 4 ]. Of these, dietary patterns, as modifiable risk factors [ 5 ], have garnered increasing scholarly interest in recent years. Excessive accumulation of reactive oxygen species (ROS) and free radicals can induce oxidative stress, leading to DNA damage, lipid peroxidation, and protein dysfunction, thereby promoting carcinogenesis [ 6 ]. Gastrointestinal tissues are particularly vulnerable to oxidative damage due to prolonged exposure to dietary-derived oxidants (e.g., nitrosamines, polycyclic aromatic hydrocarbons) and to endogenous inflammatory environments [ 7 ]. Natural dietary antioxidants, such as vitamin C, vitamin E, carotenoids, and polyphenols, have the capacity to mitigate oxidative stress through mechanisms like free radical scavenging and activation of antioxidant enzyme systems, which may provide protective effects against cancers [ 8 , 9 ]. However, the efficacy of individual antioxidant nutrients is often influenced by dose-dependent effects and synergistic interactions [ 9 ]. Comprehensive indices, such as the Composite Dietary Antioxidant Index (CDAI), evaluate overall dietary antioxidant capacity, which may better reflect the actual biological effects. CDAI quantifies the synergistic impact of multiple dietary antioxidants, and it has been widely used in studies examining the relationship between dietary antioxidant capacity and chronic diseases [ 10 ]. Although numerous cohort studies have investigated the association between dietary antioxidants and cancer risk, the findings are inconsistent. For instance, a hospital-based case-control study demonstrated that dietary antioxidants from high-quality diets (e.g., vegetables, whole grains, fruits), including vitamins A, C, and E, as well as minerals such as zinc, selenium, and manganese, significantly reduced the incidence of colorectal cancer [ 11 ]. A further study supported an inverse correlation between vitamin E intake and colorectal cancer risk [ 12 ]. Conversely, other randomized controlled trials, such as the Selenium and Vitamin E Cancer Prevention Trial (SELECT), failed to identify a substantial protective effect of antioxidant supplementation [ 13 ], instead suggesting their potential roles in promoting intestinal tumor progression [ 14 ]. Consistent with this, a Mendelian randomization study based on the UK Biobank database revealed that elevated levels of circulating antioxidants from dietary sources did not reduce the risk of digestive system tumors [ 15 ]. Such inconsistencies may be attributed to methodological heterogeneity (e.g., single-nutrient analysis, retrospective dietary assessment bias) and population differences. Furthermore, current research on CDAI principally focuses on cardiovascular diseases [ 16 ] or metabolic syndrome [ 17 ]. The epidemiological evidence regarding gastrointestinal cancer is limited, and there are no large-scale cross-sectional studies to support the application of CDAI. The National Health and Nutrition Examination Survey (NHANES) database provides standardized data encompassing diet, lifestyle, and health outcomes, thus offering a robust platform for exploring the relationship between dietary antioxidant capacity and disease. This study represents the first attempt to construct CDAI using NHANES data (2005–2023), systematically analyze its cross-sectional association with the risk of gastrointestinal cancer, and investigate potential modifying factors, including age, gender, and obesity. The findings aim to inform the optimization of dietary intervention strategies and the development of personalized cancer prevention guidelines, thereby advancing the transformation of the "food-antioxidant-disease" research paradigm from single-nutrient perspectives to holistic dietary patterns. Material and methods 1. Data sources The data for this study were derived from the cross-sectional survey of NHANES conducted from 2005 to 2023, including dietary intake, health outcomes, and demographic information. The NHANES is a national health survey conducted by the National Center for Health Statistics (NCHS), covering health examinations, laboratory tests, and dietary interviews for participants of all ages. The NHANES collects data on the health status of adults and children in the United States with the objective of evaluating these populations. The distinguishing characteristic of this survey lies in its combination of interviews and physical examinations. In order to select representative American civilians and non-institutionalized populations, the NHANES excluded all individuals receiving supervised care or guardianship in institutional settings, all active-duty military personnel, overseas active-duty family members, and any other American citizens residing outside the 50 states and the District of Columbia. Detailed survey operation manuals, consent documents, and brochures for each period are available on the NHANES website. The NHANES was approved by the Institutional Review Board of the NCHS, and all participants signed informed consent forms. The survey is released biennially and provides data collected from participants across the United States, who were selected through a complex multi-stage stratified sampling method. This study followed the cross-sectional reporting guidelines of the Strengthened Reporting of Observational Studies in Epidemiology (STROBE). 2. Study population In this study, data from eight NHANES cycles (2005–2006, 2007–2008, 2009–2010, 2011–2012, 2013–2014, 2015–2016, 2017–2020, and 2021–2023) were utilized. Due to the COVID-19 pandemic in 2019, the NHANES program suspended on-site operations in March 2020. Consequently, data collection for the NHANES 2019–2020 cycle remains unfinished, and the collected data is not nationally representative. Thus, the data collected from 2019 to March 2020 were combined with the data from the NHANES 2017–2018 cycle, forming a nationally representative sample of NHANES 2017–2020 March pre-pandemic data. The study involved a total of 88,429 participants. Due to the absence of adequate data necessary for calculating CDAI, 16,121 participants were excluded. Additionally, participants under the age of 20 were excluded from this study, leaving 43,072 adults. 21,310 participants were excluded from the study due to the lack of covariate data. Ultimately, 21,762 participants were enrolled in this study, including 10,707 males and 11,055 females. The enrollment flowchart is shown in Fig. 1 . As referenced in previous NHANES literature [ 18 – 20 ], a complex stratified sampling method was used to analyze the data from the NHANES project. 3. Calculation of the CDAI In order to assess the combined exposure resulting from dietary antioxidant intake, we used the modified CDAI developed by Wright et al. [ 21 ]. The specific steps are as follows: 3.1 Based on the dietary questionnaire survey data provided in the NHANES, we extracted the intake amounts of vitamins A, C, E, selenium, zinc, and carotenoids in the participants' diets. 3.2 Standardize the intake amounts of each antioxidant (Z-score). 3.3 Add the standardized values together to obtain the CDAI. $$\:CDAI=\sum\:_{i=1}^{n=6}\frac{Individual\:Intake-Mean}{SD}$$ 4. Definition of gastrointestinal cancer In this study, gastrointestinal cancer was determined based on responses to the question, "Which type of cancer?" Only responses that clearly indicated the presence of gastrointestinal tumors were considered valid. The present study focused on seven types of gastrointestinal cancers from the NHANES database, namely esophageal cancer, gastric cancer, liver cancer, gallbladder cancer, pancreatic cancer, colon cancer, and rectal cancer. Detailed information on cancer types and the distribution of CDAI can be found in Supplementary Table 1 . For more detailed information, please refer to the official website NHANES 2021–2023 MCQ section (URL: https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2021/DataFiles/MCQ_L.htm#MCQ230a ). 5. Covariates This study utilized several covariates, including confounding factors, to accurately assess the relationship between dietary quality and gastrointestinal cancers. These included gender (defined as male or female) and age (treated as a continuous variable). Other variables included body mass index (BMI), classified as < 20, 20–25, 25–30, and ≥ 30; race/ethnicity, including Mexican Americans, non-Hispanic whites, non-Hispanic blacks, and others; smoking status; poverty income ratio (PIR), divided into < 1.3, 1.3–3.5, and ≥ 3.5; hypertension; diabetes mellitus (DM). PIR is defined as the ratio of family monthly income to the specific poverty line for the family size. Smoking status was assessed as follows: never smokers (smoking < 100 cigarettes), former smokers (currently not smoking but having smoked ≥ 100 cigarettes), or current smokers (having smoked ≥ 100 cigarettes, currently smoking or smoking on certain days). Hypertension was determined through a doctor's diagnosis, elevated average blood pressure [systolic blood pressure (SBP) ≥ 130 mmHg and/or diastolic blood pressure (DBP) ≥ 85 mmHg], or the use of antihypertensive drugs. Diabetes was defined as self-reported doctor-diagnosed diabetes, using insulin or hypoglycemic drugs, fasting blood glucose > 126 mg/dL, glycated hemoglobin ≥ 6.5%, or oral glucose tolerance test results ≥ 200 mg/dL. 6. Statistical Analysis Continuous variables are presented as the mean ± standard error (weighted), while categorical variables are expressed as observed frequencies and weighted percentages. Firstly, a simple statistical description of the baseline characteristics of the participants was conducted. Secondly, weighted univariate and multivariate linear regression analyses were employed to analyze the relationship between CDAI and the study variables, thereby identifying potential factors influencing CDAI. Multivariate logistic regression analysis is used to calculate odds ratios (OR) and 95% confidence intervals (CI). Three covariate models are evaluated, with model 1 not adjusting for variables. Gender is presented as a categorical variable in Model 2. Model 3 further adjusts for race/ethnicity, PIR, marital status, smoking status, BMI, and disease history (including DM and hypertension), while also evaluating the trend of CDAI quartile changes. Finally, the ultimate objective of spline regression is to assess the existence of a linear relationship between CDAI and gastrointestinal tumors/non-gastrointestinal tumors. All statistical analyses and graphs are performed using the R Project for Statistical Computing (version 4.4.2) and RStudio. A P value < 0.05 (two-sided test) is defined as statistically significant. Results 1. Study population characteristics Table 1 summarized the demographic data, anthropometric data, and medical history of the study population. A total of 21,762 participants were finally included, with an average age of 47.41 ± 0.27 years. Among them, there were 196 patients with gastrointestinal tumors (GI group), 1,892 patients with non-gastrointestinal tumors (NGI group) and 19,662 non-tumor patients (NCA group). The average CDAI value was 0.63 ± 4.13 mcg. Table 1 Characteristics of the study participants by the quartiles of composite dietary antioxidant index. Total Q1 (-8.279, -1.886) Q2 (-1.886, -0.086) Q3 (-0.086, 2.257) Q4 (2.257, 134.532) p.overall N = 21762 N = 5441 N = 5441 N = 5439 N = 5441 CDAI, mcg 0.63 (4.13) -3.47 (1.16) -0.85 (0.52) 0.90 (0.68) 5.93 (4.34) Vitamin_A, mcg 621 (572) 303 (170) 431 (228) 683 (310) 1066 (890) Vitamin_C, mcg 43795 (47370) 21177 (23591) 33938 (32424) 46113 (39457) 72122 (63378) Vitamin_E, mcg 4192 (4306) 2078 (1358) 3213 (1960) 4130 (2600) 7112 (6515) Zinc, mcg 5611 (4301) 3299 (1694) 4745 (2058) 5841 (2638) 8404 (6384) Selenium, mcg 111 (52.3) 70.8 (24.3) 98.4 (28.2) 118 (34.2) 156 (62.2) Carotenoids, mcg 9379 (10077) 3627 (3097) 6632 (4527) 9413 (6067) 17222 (14432) Age 49.5 (17.7) 51.4 (18.0) 49.1 (17.9) 49.5 (17.7) 48.2 (17.0) < 0.001 Gender < 0.001 Male 10707 (49.2%) 1817 (33.4%) 2533 (46.6%) 2856 (52.5%) 3501 (64.3%) Female 11055 (50.8%) 3624 (66.6%) 2908 (53.4%) 2583 (47.5%) 1940 (35.7%) Race/ethnicity < 0.001 Mexican American 3163 (14.5%) 781 (14.4%) 846 (15.5%) 791 (14.5%) 745 (13.7%) Non-Hispanic White 10207 (46.9%) 2335 (42.9%) 2428 (44.6%) 2666 (49.0%) 2778 (51.1%) Non-Hispanic Black 4542 (20.9%) 1376 (25.3%) 1160 (21.3%) 1010 (18.6%) 996 (18.3%) Other 3850 (17.7%) 949 (17.4%) 1007 (18.5%) 972 (17.9%) 922 (16.9%) Marital < 0.001 Married/Living with partner 13048 (60.0%) 2937 (54.0%) 3179 (58.4%) 3402 (62.6%) 3530 (64.9%) Widowed/Divorced/Separated 4993 (23.0%) 1569 (28.8%) 1318 (24.2%) 1131 (20.8%) 975 (17.9%) Never Married 3714 (17.1%) 934 (17.2%) 943 (17.3%) 905 (16.6%) 932 (17.1%) PIR < 0.001 ≤ 1.3 6804 (31.3%) 2173 (39.9%) 1809 (33.2%) 1491 (27.4%) 1331 (24.5%) 1.3–3.5 7971 (36.6%) 2045 (37.6%) 2027 (37.3%) 2012 (37.0%) 1887 (34.7%) ≥ 3.5 6987 (32.1%) 1223 (22.5%) 1605 (29.5%) 1936 (35.6%) 2223 (40.9%) Smoke < 0.001 Never 11847 (54.4%) 2857 (52.5%) 2932 (53.9%) 3000 (55.2%) 3058 (56.2%) Former 5459 (25.1%) 1217 (22.4%) 1297 (23.8%) 1457 (26.8%) 1488 (27.3%) Current 4456 (20.5%) 1367 (25.1%) 1212 (22.3%) 982 (18.1%) 895 (16.4%) BMI 29.3 (6.95) 29.8 (7.21) 29.4 (6.94) 29.2 (6.90) 28.8 (6.72) < 0.001 BMI_category < 0.001 < 20 920 (4.23%) 248 (4.56%) 211 (3.88%) 223 (4.10%) 238 (4.37%) 20–25 5263 (24.2%) 1178 (21.7%) 1314 (24.1%) 1338 (24.6%) 1433 (26.3%) 25–30 7150 (32.9%) 1695 (31.2%) 1782 (32.8%) 1807 (33.2%) 1866 (34.3%) ≥ 30 8429 (38.7%) 2320 (42.6%) 2134 (39.2%) 2071 (38.1%) 1904 (35.0%) hypertension < 0.001 Yes 10252 (47.1%) 2832 (52.0%) 2571 (47.3%) 2504 (46.0%) 2345 (43.1%) No 11510 (52.9%) 2609 (48.0%) 2870 (52.7%) 2935 (54.0%) 3096 (56.9%) SBP 124 (18.4) 125 (20.0) 124 (18.5) 123 (17.9) 122 (16.9) < 0.001 DBP 70.1 (12.9) 69.4 (13.2) 70.3 (13.0) 70.3 (12.7) 70.6 (12.4) < 0.001 DM < 0.001 No 17083 (78.5%) 4029 (74.0%) 4229 (77.7%) 4341 (79.8%) 4484 (82.4%) Yes 4679 (21.5%) 1412 (26.0%) 1212 (22.3%) 1098 (20.2%) 957 (17.6%) GI_ca 0.016 NCA 19662 (90.4%) 4899 (90.1%) 4950 (91.0%) 4918 (90.5%) 4895 (90.0%) NGI 1892 (8.70%) 470 (8.64%) 443 (8.14%) 481 (8.85%) 498 (9.16%) GI 196 (0.90%) 69 (1.27%) 46 (0.85%) 37 (0.68%) 44 (0.81%) CDAI, Composite Dietary Antioxidant Index; OR, odds ratio; CI, confidence interval; BMI, body mass index; DM, diabetes mellitus; PIR, poverty income ratio; SBP, systolic blood pressure; DBP, diastolic blood pressure; GI, gastrointestinal tumors; NGI, non-gastrointestinal tumors; NCA, non-cancerous group. The high CDAI group (Q4, Table 1 ) was predominantly male, with a high socioeconomic status (non-Hispanic whites, high PIR), and healthy behaviors (low smoking rate, low BMI), suggesting a synergistic effect of social demographics and lifestyle on dietary quality. 2. Relationship between CDAI and research variables Univariate linear regression analysis indicated that gender, race, marital status, PIR, BMI, smoking status, hypertension, DM, and tumor status were significantly associated with CDAI levels. Gastrointestinal tumors pose a growing challenge to global health, so the identification of modifiable risk factors, such as dietary quality, is essential for developing effective prevention and management strategies. Multivariate regression analysis of factors influencing CDAI revealed that patients with gastrointestinal cancer had significantly lower CDAI levels (β = -0.73, p = 0.004). This may potentially reflecting disease-related anorexia, nutritional metabolic disorders, or dietary restrictions imposed by treatment interventions. Furthermore, multivariate correction analysis demonstrated that female patients (β = -2.11, p < 0.001), current smokers (β = -1.04, p < 0.001), and obese individuals (β = -0.84, p < 0.001) exhibited significantly lower CDAI scores. Conversely, high-income individuals (β = 0.87, p < 0.001) and patients with gastrointestinal cancer (β = -0.73, p = 0.004) showed significantly higher and lower scores, respectively (Table 2 ). Notably, the significant associations between race and marital status observed in the univariate analysis disappeared after multivariate adjustment, suggesting that their effects may be mediated through socio-economic or health-related factors. Table 2 Linear regression analysis was conducted to examine the relationship between CDAI and the study variables. Variables Univariate Multivariate Β (95% CI) P value Β (95% CI) P value Gender Male Ref Ref Female -2.06 (-2.20, -1.92) < 0.001 -2.11 (-2.26, -1.96) < 0.001 Race/ethnicity Mexican American Ref Non-Hispanic white 0.39 (0.13, 0.66) 0.004 0.21 (-0.00, 0.43) 0.054 Non-Hispanic Black -0.50 (-0.78, -0.22) < 0.001 -0.21 (-0.46, 0.03) 0.091 Other Race -0.04 (-0.34, 0.24) 0.743 -0.13 (-0.39, 0.14) 0.347 Marital Married/live with partner Ref Widowed/Divorced/Separated -0.86 (-1.08, -0.64) < 0.001 -0.16 (-0.38, 0.06) 0.155 Never Married -0.22 (-0.45, 0.01) 0.06 -0.14 (-0.38, 0.09) 0.232 PIR ≤ 1.3 Ref Ref 1.3–3.5 0.52 (0.32, 0.71) < 0.001 0.27 (0.07, 0.47) 0.008 ≥ 3.5 1.40 (1.17, 1.62) < 0.001 0.87 (0.66, 1.08) < 0.001 Smoke Never Ref Ref Former 0.15 (-0.05, 0.31) < 0.001 -0.09 (-0.28, 0.10) 0.305 Current -0.93 (-1.15, -0.71) < 0.001 -1.04 (-1.26, -0.83) < 0.001 BMI_category < 20 Ref 20–25 0.06 (-0.38, 0.51) 0.7938 -0.39 (-0.81, 0.02) 0.004 25–30 -0.04 (-0.45, 0.37) 0.8422 -0.75 (-1.14, -0.37) 0.060 ≥ 30 -0.49 (-0.89, -0.08) 0.0196 -0.84 (-1.21, -0.46) < 0.001 hyptersion No Ref Yes -0.35 (-0.51, -0.19) < 0.001 -0.25 (-0.42, -0.08) < 0.001 DM No Ref Yes -0.65 (-0.86, -0.51) < 0.001 -0.44 (-0.63, -0.28) < 0.001 GI_ca NCA Ref NGI 0.0043 (-0.38, 0.38) 0.008626 0.05 (-0.34, 0.44) 0.799 GI -0.91 (-1.44, -0.39) < 0.001 -0.73 (-1.22, -0.24) 0.004 CDAI, Composite Dietary Antioxidant Index; OR, odds ratio; CI, confidence interval; BMI, body mass index; DM, diabetes mellitus; PIR, poverty income ratio; GI, gastrointestinal tumors; NGI, non-gastrointestinal tumors; NCA, non-cancerous group. 3. The association between CDAI and the risk of gastrointestinal cancer In the multivariate linear regression analysis, potential confounding factors were adjusted for (Table 3 , Model 3). The CDAI, expressed as a continuous variable (each 1 unit), was negatively correlated with GI ca (β = -0.051, -0.099 to -0.002, p = 0.0402). In the unadjusted model, compared with the highest quarter (Q4), the lowest quarter (Q1) of CDAI was associated with an increased risk of gastrointestinal cancer (β = 0.692, 0.189 to 1.195, p = 0.0083), but after adjusting for age, gender, and socioeconomic factors, there was no statistical significance (β = 0.529, -0.018 to 1.075, p = 0.0580). These results suggested that the association between CDAI and cancer risk may be driven by confounding factors rather than acting independently. Table 3 The Association between CDAI and gastrointestinal cancers in NHANES 2005–2023. Model Continue CDAI QI Q2 Q3 Q4 Β (95% CI) p-value Β (95% CI) p-value Β (95% CI) p-value β (95% CI) p-value Reference GI ca Model 1 -0.065 (-0.112, -0.019) 0.007 0.692 (0.189, 1.195) 0.0083 -0.015 (-0.528, 0.498) 0.9549 0.330 (-0.258, 0.918) 0.2744 Reference Model 2 -0.046 (-0.090, -0.002) 0.0413 0.483 (-0.018, 0.984) 0.0588 -0.060 (-0.580, 0.459) 0.8175 0.227 (-0.398, 0.852) 0.4729 Reference Model 3 -0.051 (-0.099, -0.002) 0.0402 0.529 (-0.018, 1.075) 0.0580 0.008 (-0.514, 0.531) 0.9748 -0.081 (-0.245, 0.082) 0.4485 Reference NGI ca Model1 0.0006 (-0.0194, 0.0207) 0.95 -0.007 (-0.172, 0.158) 0.933 -0.074 (-0.267, 0.117) 0.443 -0.012 (-0.179, 0.156) 0.892 Reference Model 2 0.0142 (0.0011, 0.0273) 0.0337 -0.219 (-0.403, -0.034) 0.0205 -0.116 (-0.319, 0.0866) 0.2581 -0.106 (-0.267, 0.055) 0.1937 Reference Model 3 0.0065 (-0.0104, 0.0234) 0.4476 -0.100 (-0.293, 0.093) 0.3041 -0.032 (-0.237, 0.173) 0.7558 -0.081 (-0.245, 0.082) 0.3258 Reference Model 1, Unadjusted; Model 2, Adjusted for age and gender; Model 3, Model 2 plus additional adjustment for the race/ethnicity, PIR, marital status, smoke, BMI and disease histories (including DM, hypertension). Q1 (-8.2793693, -1.5519494); Q2 (-1.5519494, 0.1701044); Q3 (0.1701044, 2.6228700); Q4 (2.6228700, 134.532056). CDAI, Composite Dietary Antioxidant Index; OR, odds ratio; CI, confidence interval. There was no significant correlation between CDAI and NGI ca. It is worth noting that in non-gastrointestinal cancers, the medium group (Q2) of CDAI had a reduced risk compared with the highest quarter (Q4), after adjusting for age and gender (β = -0.219, -0.403 to -0.034, p = 0.0205). However, this effect weakened after further correction (p = 0.3041). The restricted cubic spline regression analysis showed that CDAI was weakly positively correlated with the risk of gastrointestinal cancer (P overall = 0.0126, Fig. 2 A). The OR value increased to 1.25 in the high CDAI range (> 30), but the CI crossed 1 (for example, when CDAI = 50, 95% CI: 0.95–1.65). In non-gastrointestinal cancers, there was no significant association between CDAI and risk (P overall = 0.758), and the OR value was always close to 1 (Fig. 2 B). The nonlinear (NL) trends in Figs. 2 A (NL-P value = 0.7372) and 2B (NL-P value = 0.7637) were not significant (NL-P value > 0.7), suggesting that the influence of CDAI is more likely to be linear. The multivariate logistic regression analysis indicated that the CDAI had a significant protective effect on gastrointestinal tumors (GI ca). Individuals with a higher CDAI had a significantly lower risk of gastrointestinal cancer. For every 1 unit increase in CDAI, the probability of GI ca occurrence decreased by 4.55% (OR = 0.9545, 95% CI: 0.917–0.994, p = 0.023), while there was no statistically significant prediction for non-gastrointestinal tumors (NGI ca) (OR = 1.0015, p = 0.79). The overall fitting effect of the model was good (AIC = 15057.55), suggesting that CDAI might be an independent predictor of GI ca risk. In addition, Fig. 3 showed the predictive probabilities of the CDAI for GI, NGI, and NCA. 4. Hierarchical analysis results The subgroup analysis showed that an increase in CDAI was significantly associated with a reduced risk of gastrointestinal tumors in obese individuals (OR = 0.92, p = 0.031), females (OR = 0.89, p = 0.002), and non-Hispanic whites (OR = 0.94, p = 0.019) (Fig. 4 ). Conversely, in non-gastrointestinal tumors, the risk was slightly increased in individuals with normal BMI (OR = 1.02, p = 0.042) and those who were widowed or divorced (OR = 1.02, p = 0.030) (Fig. 5 ). It is noteworthy that current smokers (OR = 0.96, p = 0.008) and Mexican Americans (OR = 0.92, p = 0.007) had significantly lower risks of non-gastrointestinal cancer, suggesting that the effect of CDAI may vary depending on sociodemographic characteristics. Discussion This study examined the correlation between CDAI and the risk of gastrointestinal cancer and non-gastrointestinal cancer, elucidating the potential role of CDAI in cancer prevention and its interactions with socio-demographic and lifestyle factors. Initially, the characteristics of the study population indicated that the high CDAI group was predominantly composed of males, individuals with a high socioeconomic status (non-Hispanic whites, high PIR), and those exhibiting healthy behaviors (low smoking rate, low BMI). These findings suggested that dietary quality might be synergistically influenced by socio-economic and lifestyle factors [ 22 ]. Furthermore, the prevalence of hypertension and diabetes was lower in the high CDAI group, indicating that a diet rich in antioxidants could be associated with better metabolic health [ 23 ]. Multivariate analysis further indicated that women, current smokers, and obese individuals exhibited significantly lower CDAI values, which may be attributed to energy-restricted diets, increased oxidative stress, and metabolic disorders, suggesting that a high-antioxidant diet might be related to metabolic health [ 23 ]. Notably, the high-income group had a higher CDAI, highlighting the crucial role of gender [ 24 ], behavior, and economic factors in dietary antioxidant intake. It is noteworthy that the univariate associations of race and marital status disappeared after multivariate adjustment, suggesting that their effects might be mediated through socio-economic or health behaviors, rather than acting independently. The association analysis of CDAI and the risk of gastrointestinal cancer demonstrated that for each unit increase in CDAI, the risk of gastrointestinal cancer decreased by 4.55%, and the protective effect was more significant in obese individuals, females, and non-Hispanic whites. This result might be attributed to the presence of dietary antioxidants (e.g., vitamin C, carotenoids) of neutralizing free radicals, reducing oxidative stress, and inhibiting inflammatory pathways, thus contributing to a reduction in the risk of gastrointestinal carcinogenesis [ 25 ]. The phenomenon of oxidative stress occurs when there is an imbalance between free radicals and antioxidants within cells and tissues. Free radicals are defined as oxygen-containing molecules with an odd number of electrons, rendering them highly reactive molecules and prone to reacting with other molecules, such as DNA, which can result in DNA damage. The accumulation of DNA damage has the potential to disrupt normal gene expression and thereby initiate carcinogenesis. Antioxidants have been identified as molecules capable of donating electrons to free radicals, thereby stabilizing them and reducing their reactivity. This process, consequently, offers a protective measure against the carcinogenic effects of oxidative stress [ 26 ]. CDAI, a measure of total dietary antioxidant intake, is inversely related to circulating inflammatory cytokines such as interleukin-1β and tumor necrosis factor-α, suggesting that CDAI may exert its effects via anti-inflammatory mechanisms [ 27 ]. Nevertheless, subsequent quartile analysis revealed that the statistical significance between Q1 and Q4 was diminished upon adjustment for confounding variables, indicating that the inverse correlation between CDAI and gastrointestinal cancer may be partially obscured by factors such as age, socio-economic status, or comorbid conditions (including diabetes). Additionally, the findings from restricted cubic spline regression indicated a marginal positive correlation between CDAI and the risk of gastrointestinal cancer (with an odds ratio of 1.25 within the high CDAI interval). However, the CI was wide and included the value of 1, thus necessitating a cautious approach to interpretation. One hypothesis that has been postulated is that exceedingly high antioxidant intake could exert a "biphasic effect," thereby disrupting normal redox equilibrium and potentially facilitating carcinogenesis [ 26 , 28 ]. The role of ROS is twofold: it can either provoke mutations that lead to tumorigenesis or trigger apoptotic signals that suppress tumor growth. In the former instance, antioxidants may impede cancer development by neutralizing ROS; however, in the latter case, they could enhance the survival rate of cancer cells through the same neutralization process, thereby fostering cancer incidence. The behavior of ROS is contingent upon varying conditions. Majumder et al. assert that the impact of antioxidants on carcinogenesis is contingent upon the cellular microenvironment [ 26 ]. There is no significant correlation between CDAI and non-gastrointestinal cancer. However, in a stratified analysis, the risk slightly increased in individuals with a normal BMI and those who are widowed or divorced, while it decreased in current smokers and Mexican Americans. This observed heterogeneity may reflect differences in the etiology of distinct cancers, as well as the impact of social and psychological factors (such as marital status) on cancer risk through stress or behavioral pathways [ 29 ]. For instance, the protective effect of CDAI in smokers may be related to the alleviation of tobacco-related oxidative damage by antioxidants [ 30 ]. Research significance and limitations This study is based on data from the nationally representative NHANES database, thereby enhancing the robustness and generalizability of our research findings. CDAI, a comprehensive indicator used to assess the overall levels of various antioxidant substances in the diet, was utilized to reflect the overall potential of the diet to combat oxidative stress and to effectively distinguish dietary patterns across different population. However, it is necessary to interpret social and economic variables to explain the observed variations. It is hypothesized that improving the dietary quality of low-income groups, controlling smoking, and managing obesity may indirectly enhance antioxidant status. Nevertheless, merely supplementing dietary antioxidants may not result in a reduction of the risk of cancer. The present study has one limitation. Firstly, the cross-sectional design is unable to determine the causal relationship between CDAI and cancer. The disease state may reverse the influence of diet (e.g., reduced intake by cancer patients). It is recommended that future studies include prospective cohort studies to more accurately assess these temporal relationships. A key methodological consideration involves the inherent limitations of the NHANES dietary assessment. Despite the use of validated USDA automated multiple-pass method in the survey, the 24-hour dietary recall method has potential measurement errors and various forms of bias. These biases include recall bias, social desirability bias when reporting healthy or unhealthy foods, and challenges in portion estimation. Although the NHANES implemented a second 24-hour recall for some participants to account for individual differences, this method might not adequately address long-term dietary patterns related to cancer risk. However, confirmatory studies have demonstrated a reasonable correlation between NHANES dietary recall and objective biomarkers of specific nutrients, thereby supporting the practicality of these data in population-level dietary assessment. Additionally, while various factors such as age, gender, race, smoking status, PIR, hypertension, and diabetes were adjusted in our analysis, other potential confounding factors, including exercise, total energy intake, family history, genetic susceptibility, and interactions with the intestinal microbiome, have not been explored. We look forward to addressing these issues in future research. Conclusion This investigation highlights the significance of dietary quality in the prevention and management of gastrointestinal cancers. A heightened CDAI correlates with a diminished risk of gastrointestinal cancer, indicating that augmenting dietary antioxidants could exert a favorable influence on the prevention of gastrointestinal cancer, particularly among women, obese individuals, and non-Hispanic whites, where the protective effect is more pronounced. These findings suggest the potential efficacy of dietary antioxidants in the prevention of gastrointestinal tumors. However, since CDAI is an aggregate indicator of multi-dimensional social ecology and health behaviors, its correlation with gastrointestinal tumors may be influenced by confounding factors and non-independent biological effects. At present, there is insufficient evidence to support the use of CDAI as an independent predictor of cancer. A multi-dimensional risk assessment model is required to be integrated. Additionally, CDAI exhibits no distinct correlation with non-gastrointestinal cancers, and its impact may be modulated by socio-economic and lifestyle factors. Prospective cohort studies are warranted to confirm the causal role of CDAI, investigate the dose-effect relationship and biological mechanisms of specific antioxidants, analyze the biphasic effect of specific antioxidant components (such as selenium, carotenoids) in carcinogenesis (antioxidant/oxidative), and devise comprehensive nutritional intervention strategies for high-risk populations (smokers). It is imperative that healthcare providers and public health initiatives raise awareness of dietary quality and develop comprehensive dietary assessment tools to identify further preventive capabilities. It is acknowledged that dietary habits vary across different cultures, tailored recommendations for different ethnic groups may have a significant impact on compliance. Future health education efforts must promote healthy eating habits through customized strategies. Moreover, in subsequent research, lengthier follow-up periods and larger cohorts will reinforce these findings, while investigating the mechanisms by which diet affects the risk of tumors can guide customized dietary guidelines and policies. Declarations Data availability This study used data from the National Health and Nutrition Examination Survey (NHANES) (https://www.cdc.gov/nchs/nhanes/index.html). Ethics statement The studies involving humans were approved by the Research Ethics Review Board of the National Center for Health Statistics.The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Author contributions JW: Conceptualization, Methodology, Writing, Data curation–original draft. WD: Data curation, Writing – original draft. JL:Investigation, Writing – original draft. ZL: Writing – original draft. WY: Writing – original draft.YZ: Writing – original draft. ZX: Writing – review & editing. Funding The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was financially supported by the National Natural Science Foundation of China (No. 82460128 and No. 82260131). Acknowledgments We would like to acknowledge all the staff and participants of the National Health and Nutrition Examination Survey (NHANES) and for providing data licenses. Ethics approval and consent to participate All data in this study are sourced from public databases, and participants have provided informed consent, eliminating any ethical concerns. Competing interests The authors declare no competing interests. Consent to Participate declaration Every human participant should provide their consent. References Wang S, et al. Global, regional, and national lifetime risks of developing and dying from gastrointestinal cancers in 185 countries: a population-based systematic analysis of GLOBOCAN. Lancet Gastroenterol Hepatol. 2024;9(3):229–37. Bray F, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–63. Mamun TI, Younus S, Rahman MH. Gastric cancer-Epidemiology, modifiable and non-modifiable risk factors, challenges and opportunities: An updated review. Cancer Treat Res Commun. 2024;41:100845. Erben V, et al. Strong associations of a healthy lifestyle with all stages of colorectal carcinogenesis: Results from a large cohort of participants of screening colonoscopy. Int J Cancer. 2019;144(9):2135–43. Vernia F et al. Dietary Factors Modulating Colorectal Carcinogenesis. Nutrients, 2021. 13(1). Sarmiento-Salinas FL, et al. Reactive oxygen species: Role in carcinogenesis, cancer cell signaling and tumor progression. Life Sci. 2021;284:119942. Wang Y, et al. The role of reactive oxygen species in gastric cancer. Cancer Biol Med. 2024;21(9):740–53. Pisoschi AM, et al. Oxidative stress mitigation by antioxidants - An overview on their chemistry and influences on health status. Eur J Med Chem. 2021;209:112891. Luo M et al. Antioxidant Therapy in Cancer: Rationale and Progress. Antioxid (Basel), 2022. 11(6). Pourmontaseri H, et al. Exploring the application of dietary antioxidant index for disease risk assessment: a comprehensive review. Front Nutr. 2024;11:1497364. Vahid F, Rahmani W, Davoodi SH. The association between dietary total antioxidant capacity and quality of nutrients with odds of colorectal cancer: A hospital-based case-control study. Clin Nutr ESPEN. 2022;52:277–84. Jun S, et al. Interaction between vitamin E intake and a COMT gene variant on colorectal cancer risk among Korean adults: a case-control study. Epidemiol Health. 2023;45:e2023100. Lance P, et al. Colorectal Adenomas in Participants of the SELECT Randomized Trial of Selenium and Vitamin E for Prostate Cancer Prevention. Cancer Prev Res (Phila). 2017;10(1):45–54. Zou ZV et al. Antioxidants Promote Intestinal Tumor Progression in Mice. Antioxid (Basel), 2021. 10(2). Yin L et al. Diet-Derived Circulating Antioxidants and Risk of Digestive System Tumors: A Mendelian Randomization Study. Nutrients, 2022. 14(16). Wang R, Tao W, Cheng X. Association of composite dietary antioxidant index with cardiovascular disease in adults: results from 2011 to 2020 NHANES. Front Cardiovasc Med. 2024;11:1379871. Liu W, et al. Composite dietary antioxidant index is associated with the prevalence of metabolic syndrome in females: results from NHANES 2011–2016. Front Nutr. 2025;12:1529332. Wang W, Chang Y, Chen G. Association between Healthy Eating Index-2020, alternative Mediterranean Diet scores, and gastrointestinal cancer risk in NHANES 2005–2018. Sci Rep. 2025;15(1):3983. Zheng M, et al. Association between composite dietary antioxidant index and fatty liver index among US adults. Front Nutr. 2024;11:1466807. Fu Y, et al. Association between the composite dietary antioxidant index and non-alcoholic fatty liver disease: evidence from National Health and Nutrition Examination Survey 2005–2016. Front Nutr. 2025;12:1473487. Wright ME, 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(1):68–76. Mendoza A, et al. Energy density of foods and diets in Mexico and their monetary cost by socioeconomic strata: analyses of ENSANUT data 2012. J Epidemiol Community Health. 2017;71(7):713–21. Xue T, et al. Association Between Composite Dietary Antioxidant Index and Metabolic Syndrome in Normal Weight Population: Evidence From NHANES. Curr Dev Nutr. 2024;8(2):102666. Bennett E, Peters SAE, Woodward M. Sex differences in macronutrient intake and adherence to dietary recommendations: findings from the UK Biobank. BMJ Open. 2018;8(4):e020017. Soto KM et al. Antioxidants in Traditional Mexican Medicine and Their Applications as Antitumor Treatments. Pharmaceuticals (Basel), 2023. 16(4). Majumder D, et al. Understanding the complicated relationship between antioxidants and carcinogenesis. J Biochem Mol Toxicol. 2021;35(2):e22643. Luu HN, et al. Are dietary antioxidant intake indices correlated to oxidative stress and inflammatory marker levels? Antioxid Redox Signal. 2015;22(11):951–9. Hecht F, et al. Regulation of antioxidants in cancer. Mol Cell. 2024;84(1):23–33. Lempesis IG et al. Role of stress in the pathogenesis of cancer (Review). Int J Oncol, 2023. 63(5). Astori E, et al. Antioxidants in smokers. Nutr Res Rev. 2022;35(1):70–97. Additional Declarations No competing interests reported. Supplementary Files STROBE.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7040004","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":484971223,"identity":"ff32e9ef-e75e-4684-a54a-25544c56a219","order_by":0,"name":"Jianhua Wu","email":"","orcid":"","institution":"The Second Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Jianhua","middleName":"","lastName":"Wu","suffix":""},{"id":484971224,"identity":"33ea8b82-482f-424e-b58f-de3650b73fde","order_by":1,"name":"Wen Du","email":"","orcid":"","institution":"People's Hospital of Ji'an City Center","correspondingAuthor":false,"prefix":"","firstName":"Wen","middleName":"","lastName":"Du","suffix":""},{"id":484971225,"identity":"a70ff55b-1aa1-4ec1-b173-625bce0cbbee","order_by":2,"name":"Jierui Liu","email":"","orcid":"","institution":"The Second Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Jierui","middleName":"","lastName":"Liu","suffix":""},{"id":484971226,"identity":"a51a3f56-b15f-4e1a-9195-17f29f3f3181","order_by":3,"name":"Zhaohui Liao","email":"","orcid":"","institution":"The Second Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Zhaohui","middleName":"","lastName":"Liao","suffix":""},{"id":484971228,"identity":"f86c5ca8-d264-4cc8-8910-712b1adac876","order_by":4,"name":"Wei Yu","email":"","orcid":"","institution":"The Second Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Yu","suffix":""},{"id":484971230,"identity":"9475e262-5382-47e7-9056-f9c735b64937","order_by":5,"name":"Yu Zhang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Zhang","suffix":""},{"id":484971231,"identity":"244a5ad9-f5f9-471f-87c6-4428462e9239","order_by":6,"name":"Zhengyuan Xie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIie3RMQrCMBTG8VcC0SHQTV4Q7BUSCuLgYdpdQRDESesSlx6g4uAVnJxbsgZdBZeCF+hYwcE6udmMgvnP32/IC4DL9bvhICAkz6vanoxCuVVxsUvtyTJOjAl1l1psxcUM+UOht8kmlQYGgd/LvxOemFBwhaSD06OejUDu9tF34nupLKVC6mUNyRhE4tZCKGEijxUyuE5KzagF8SmTZaEQwRiwIzylc5mcUTRHFs2Rsf0t4qJPvF6s1gdC7lVVjwO/30IAugK8z3dg2/xdpwR42gxdLpfrb3sBDlZEQQSQnrMAAAAASUVORK5CYII=","orcid":"","institution":"The Second Affiliated Hospital of Nanchang University","correspondingAuthor":true,"prefix":"","firstName":"Zhengyuan","middleName":"","lastName":"Xie","suffix":""}],"badges":[],"createdAt":"2025-07-03 16:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7040004/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7040004/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87266174,"identity":"9ec2a3fb-be38-4231-badf-142a97287aec","added_by":"auto","created_at":"2025-07-22 07:59:47","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":99951,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart for the study population: From NHANES 2005–2023.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7040004/v1/b0a03ffef565c8c761e7ede4.jpg"},{"id":87267710,"identity":"dc4eaaad-bb0e-4498-848b-76ebc3a1f5d6","added_by":"auto","created_at":"2025-07-22 08:07:47","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48399,"visible":true,"origin":"","legend":"\u003cp\u003eThe association between gastrointestinal cancers (A), non-gastrointestinal cancers (B) and CDAI Using the restricted cubic spline regression model. Graphs showed ORs for end according to CDAI adjusted for sex, age, race/ethnicity, PIR, marital status, smoke, BMI and disease histories (including DM, hypertension). Solid lines indicate ORs, and the light orange semi-transparent band-like area represents the 95% CI. CDAI, Composite Dietary Antioxidant Index; OR, odds ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7040004/v1/3638d5b579d3f78403ebc1f6.jpg"},{"id":87266173,"identity":"cb950904-d68a-4add-90fe-559cb9d22a12","added_by":"auto","created_at":"2025-07-22 07:59:47","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":41941,"visible":true,"origin":"","legend":"\u003cp\u003eThe predictive probabilities of the CDAI for tumor types. CDAI, Composite Dietary Antioxidant Index; GI, gastrointestinal tumors; NGI, non-gastrointestinal tumors; NCA, non-cancerous group.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7040004/v1/330d95af3b8508bd500106b2.jpg"},{"id":87267712,"identity":"6900c45e-25ac-4f79-9d07-e7c0a7199fa2","added_by":"auto","created_at":"2025-07-22 08:07:47","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":102239,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses of the association between CDAI and gastrointestinal cancers.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7040004/v1/e17dfb50be426f5e301d4312.jpg"},{"id":87269601,"identity":"df7b0fae-ae20-44b2-b957-90fd5596688a","added_by":"auto","created_at":"2025-07-22 08:15:47","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":97781,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses of the association between CDAI and non-gastrointestinal cancers. CDAI, Composite Dietary Antioxidant Index; OR, odds ratio; CI, confidence interval; BMI, body mass index; DM, diabetes mellitus; PIR, poverty income ratio.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7040004/v1/c2230a37c4ae634f0a052537.jpg"},{"id":93147750,"identity":"d819e51e-0ae6-4900-9c02-af39ef28c7b5","added_by":"auto","created_at":"2025-10-09 14:08:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1550614,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7040004/v1/70e4916a-7425-45e4-9f68-a61d3135e7c7.pdf"},{"id":87269600,"identity":"8df5ab78-3d5e-4c73-925f-beeb5d0d79ce","added_by":"auto","created_at":"2025-07-22 08:15:47","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":30112,"visible":true,"origin":"","legend":"","description":"","filename":"STROBE.docx","url":"https://assets-eu.researchsquare.com/files/rs-7040004/v1/7af23197645b64d482baa9f2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The association between the comprehensive dietary antioxidant index and the risk of gastrointestinal cancer: A cross-sectional study based on NHANES","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastrointestinal cancers, including esophageal, gastric, hepatic, pancreatic, colonic, and rectal cancers, account for one-quarter of global cancer incidence and one-third of cancer-related mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These malignancies are among the most prevalent cancers worldwide. According to data from the International Agency for Research on Cancer (IARC), there were over 5\u0026nbsp;million new cases of gastrointestinal cancer worldwide in 2022, representing more than 25% of all cancer-related deaths [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite the continuous advancements in diagnostic and therapeutic technologies, low early screening rates and poor prognosis remain significant challenges, making gastrointestinal cancer a major public health issue [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In addition to genetic susceptibility, environmental and lifestyle factors, such as dietary habits, smoking, and obesity, have been identified as critical contributors in the development of gastrointestinal cancer [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Of these, dietary patterns, as modifiable risk factors [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], have garnered increasing scholarly interest in recent years.\u003c/p\u003e\u003cp\u003eExcessive accumulation of reactive oxygen species (ROS) and free radicals can induce oxidative stress, leading to DNA damage, lipid peroxidation, and protein dysfunction, thereby promoting carcinogenesis [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Gastrointestinal tissues are particularly vulnerable to oxidative damage due to prolonged exposure to dietary-derived oxidants (e.g., nitrosamines, polycyclic aromatic hydrocarbons) and to endogenous inflammatory environments [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Natural dietary antioxidants, such as vitamin C, vitamin E, carotenoids, and polyphenols, have the capacity to mitigate oxidative stress through mechanisms like free radical scavenging and activation of antioxidant enzyme systems, which may provide protective effects against cancers [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the efficacy of individual antioxidant nutrients is often influenced by dose-dependent effects and synergistic interactions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Comprehensive indices, such as the Composite Dietary Antioxidant Index (CDAI), evaluate overall dietary antioxidant capacity, which may better reflect the actual biological effects. CDAI quantifies the synergistic impact of multiple dietary antioxidants, and it has been widely used in studies examining the relationship between dietary antioxidant capacity and chronic diseases [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough numerous cohort studies have investigated the association between dietary antioxidants and cancer risk, the findings are inconsistent. For instance, a hospital-based case-control study demonstrated that dietary antioxidants from high-quality diets (e.g., vegetables, whole grains, fruits), including vitamins A, C, and E, as well as minerals such as zinc, selenium, and manganese, significantly reduced the incidence of colorectal cancer [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A further study supported an inverse correlation between vitamin E intake and colorectal cancer risk [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Conversely, other randomized controlled trials, such as the Selenium and Vitamin E Cancer Prevention Trial (SELECT), failed to identify a substantial protective effect of antioxidant supplementation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], instead suggesting their potential roles in promoting intestinal tumor progression [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Consistent with this, a Mendelian randomization study based on the UK Biobank database revealed that elevated levels of circulating antioxidants from dietary sources did not reduce the risk of digestive system tumors [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Such inconsistencies may be attributed to methodological heterogeneity (e.g., single-nutrient analysis, retrospective dietary assessment bias) and population differences. Furthermore, current research on CDAI principally focuses on cardiovascular diseases [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] or metabolic syndrome [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The epidemiological evidence regarding gastrointestinal cancer is limited, and there are no large-scale cross-sectional studies to support the application of CDAI.\u003c/p\u003e\u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES) database provides standardized data encompassing diet, lifestyle, and health outcomes, thus offering a robust platform for exploring the relationship between dietary antioxidant capacity and disease. This study represents the first attempt to construct CDAI using NHANES data (2005\u0026ndash;2023), systematically analyze its cross-sectional association with the risk of gastrointestinal cancer, and investigate potential modifying factors, including age, gender, and obesity. The findings aim to inform the optimization of dietary intervention strategies and the development of personalized cancer prevention guidelines, thereby advancing the transformation of the \"food-antioxidant-disease\" research paradigm from single-nutrient perspectives to holistic dietary patterns.\u003c/p\u003e"},{"header":"Material and methods","content":"\n\u003ch3\u003e1. Data sources\u003c/h3\u003e\n\u003cp\u003eThe data for this study were derived from the cross-sectional survey of NHANES conducted from 2005 to 2023, including dietary intake, health outcomes, and demographic information. The NHANES is a national health survey conducted by the National Center for Health Statistics (NCHS), covering health examinations, laboratory tests, and dietary interviews for participants of all ages. The NHANES collects data on the health status of adults and children in the United States with the objective of evaluating these populations. The distinguishing characteristic of this survey lies in its combination of interviews and physical examinations. In order to select representative American civilians and non-institutionalized populations, the NHANES excluded all individuals receiving supervised care or guardianship in institutional settings, all active-duty military personnel, overseas active-duty family members, and any other American citizens residing outside the 50 states and the District of Columbia. Detailed survey operation manuals, consent documents, and brochures for each period are available on the NHANES website. The NHANES was approved by the Institutional Review Board of the NCHS, and all participants signed informed consent forms. The survey is released biennially and provides data collected from participants across the United States, who were selected through a complex multi-stage stratified sampling method. This study followed the cross-sectional reporting guidelines of the Strengthened Reporting of Observational Studies in Epidemiology (STROBE).\u003c/p\u003e\n\u003ch3\u003e2. Study population\u003c/h3\u003e\n\u003cp\u003eIn this study, data from eight NHANES cycles (2005\u0026ndash;2006, 2007\u0026ndash;2008, 2009\u0026ndash;2010, 2011\u0026ndash;2012, 2013\u0026ndash;2014, 2015\u0026ndash;2016, 2017\u0026ndash;2020, and 2021\u0026ndash;2023) were utilized. Due to the COVID-19 pandemic in 2019, the NHANES program suspended on-site operations in March 2020. Consequently, data collection for the NHANES 2019\u0026ndash;2020 cycle remains unfinished, and the collected data is not nationally representative. Thus, the data collected from 2019 to March 2020 were combined with the data from the NHANES 2017\u0026ndash;2018 cycle, forming a nationally representative sample of NHANES 2017\u0026ndash;2020 March pre-pandemic data. The study involved a total of 88,429 participants. Due to the absence of adequate data necessary for calculating CDAI, 16,121 participants were excluded. Additionally, participants under the age of 20 were excluded from this study, leaving 43,072 adults. 21,310 participants were excluded from the study due to the lack of covariate data. Ultimately, 21,762 participants were enrolled in this study, including 10,707 males and 11,055 females. The enrollment flowchart is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. As referenced in previous NHANES literature [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], a complex stratified sampling method was used to analyze the data from the NHANES project.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e3. Calculation of the CDAI\u003c/h3\u003e\n\u003cp\u003eIn order to assess the combined exposure resulting from dietary antioxidant intake, we used the modified CDAI developed by Wright et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The specific steps are as follows:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e3.1 Based on the dietary questionnaire survey data provided in the NHANES, we extracted the intake amounts of vitamins A, C, E, selenium, zinc, and carotenoids in the participants' diets.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e3.2 Standardize the intake amounts of each antioxidant (Z-score).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e3.3 Add the standardized values together to obtain the CDAI.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:CDAI=\\sum\\:_{i=1}^{n=6}\\frac{Individual\\:Intake-Mean}{SD}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003e4. Definition of gastrointestinal cancer\u003c/h3\u003e\n\u003cp\u003eIn this study, gastrointestinal cancer was determined based on responses to the question, \"Which type of cancer?\" Only responses that clearly indicated the presence of gastrointestinal tumors were considered valid. The present study focused on seven types of gastrointestinal cancers from the NHANES database, namely esophageal cancer, gastric cancer, liver cancer, gallbladder cancer, pancreatic cancer, colon cancer, and rectal cancer. Detailed information on cancer types and the distribution of CDAI can be found in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e. For more detailed information, please refer to the official website NHANES 2021\u0026ndash;2023 MCQ section (URL: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2021/DataFiles/MCQ_L.htm#MCQ230a\u003c/span\u003e\u003cspan address=\"https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2021/DataFiles/MCQ_L.htm#MCQ230a\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003e5. Covariates\u003c/h3\u003e\n\u003cp\u003eThis study utilized several covariates, including confounding factors, to accurately assess the relationship between dietary quality and gastrointestinal cancers. These included gender (defined as male or female) and age (treated as a continuous variable). Other variables included body mass index (BMI), classified as \u0026lt;\u0026thinsp;20, 20\u0026ndash;25, 25\u0026ndash;30, and \u0026ge;\u0026thinsp;30; race/ethnicity, including Mexican Americans, non-Hispanic whites, non-Hispanic blacks, and others; smoking status; poverty income ratio (PIR), divided into \u0026lt;\u0026thinsp;1.3, 1.3\u0026ndash;3.5, and \u0026ge;\u0026thinsp;3.5; hypertension; diabetes mellitus (DM). PIR is defined as the ratio of family monthly income to the specific poverty line for the family size. Smoking status was assessed as follows: never smokers (smoking\u0026thinsp;\u0026lt;\u0026thinsp;100 cigarettes), former smokers (currently not smoking but having smoked\u0026thinsp;\u0026ge;\u0026thinsp;100 cigarettes), or current smokers (having smoked\u0026thinsp;\u0026ge;\u0026thinsp;100 cigarettes, currently smoking or smoking on certain days). Hypertension was determined through a doctor's diagnosis, elevated average blood pressure [systolic blood pressure (SBP)\u0026thinsp;\u0026ge;\u0026thinsp;130 mmHg and/or diastolic blood pressure (DBP)\u0026thinsp;\u0026ge;\u0026thinsp;85 mmHg], or the use of antihypertensive drugs. Diabetes was defined as self-reported doctor-diagnosed diabetes, using insulin or hypoglycemic drugs, fasting blood glucose\u0026thinsp;\u0026gt;\u0026thinsp;126 mg/dL, glycated hemoglobin\u0026thinsp;\u0026ge;\u0026thinsp;6.5%, or oral glucose tolerance test results\u0026thinsp;\u0026ge;\u0026thinsp;200 mg/dL.\u003c/p\u003e\n\u003ch3\u003e6. Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eContinuous variables are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error (weighted), while categorical variables are expressed as observed frequencies and weighted percentages. Firstly, a simple statistical description of the baseline characteristics of the participants was conducted. Secondly, weighted univariate and multivariate linear regression analyses were employed to analyze the relationship between CDAI and the study variables, thereby identifying potential factors influencing CDAI. Multivariate logistic regression analysis is used to calculate odds ratios (OR) and 95% confidence intervals (CI). Three covariate models are evaluated, with model 1 not adjusting for variables. Gender is presented as a categorical variable in Model 2. Model 3 further adjusts for race/ethnicity, PIR, marital status, smoking status, BMI, and disease history (including DM and hypertension), while also evaluating the trend of CDAI quartile changes. Finally, the ultimate objective of spline regression is to assess the existence of a linear relationship between CDAI and gastrointestinal tumors/non-gastrointestinal tumors. All statistical analyses and graphs are performed using the R Project for Statistical Computing (version 4.4.2) and RStudio. A P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-sided test) is defined as statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003e1. Study population characteristics\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarized the demographic data, anthropometric data, and medical history of the study population. A total of 21,762 participants were finally included, with an average age of 47.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27 years. Among them, there were 196 patients with gastrointestinal tumors (GI group), 1,892 patients with non-gastrointestinal tumors (NGI group) and 19,662 non-tumor patients (NCA group). The average CDAI value was 0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;4.13 mcg.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of the study participants by the quartiles of composite dietary antioxidant index.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003cp\u003e(-8.279, -1.886)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003cp\u003e(-1.886, -0.086)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003cp\u003e(-0.086, 2.257)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003cp\u003e(2.257, 134.532)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep.overall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;21762\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;5441\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;5441\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;5439\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;5441\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCDAI, mcg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.63 (4.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-3.47 (1.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.85 (0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.90 (0.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.93 (4.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitamin_A, mcg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e621 (572)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e303 (170)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e431 (228)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e683 (310)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1066 (890)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitamin_C, mcg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43795 (47370)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21177 (23591)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33938 (32424)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e46113 (39457)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e72122 (63378)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitamin_E, mcg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4192 (4306)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2078 (1358)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3213 (1960)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4130 (2600)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7112 (6515)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZinc, mcg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5611 (4301)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3299 (1694)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4745 (2058)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5841 (2638)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8404 (6384)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelenium, mcg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e111 (52.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.8 (24.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98.4 (28.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e118 (34.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e156 (62.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCarotenoids, mcg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9379 (10077)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3627 (3097)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6632 (4527)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9413 (6067)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17222 (14432)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49.5 (17.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51.4 (18.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.1 (17.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49.5 (17.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e48.2 (17.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10707 (49.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1817 (33.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2533 (46.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2856 (52.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3501 (64.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11055 (50.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3624 (66.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2908 (53.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2583 (47.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1940 (35.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace/ethnicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3163 (14.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e781 (14.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e846 (15.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e791 (14.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e745 (13.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10207 (46.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2335 (42.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2428 (44.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2666 (49.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2778 (51.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4542 (20.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1376 (25.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1160 (21.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1010 (18.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e996 (18.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3850 (17.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e949 (17.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1007 (18.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e972 (17.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e922 (16.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried/Living with partner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13048 (60.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2937 (54.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3179 (58.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3402 (62.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3530 (64.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed/Divorced/Separated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4993 (23.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1569 (28.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1318 (24.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1131 (20.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e975 (17.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever Married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3714 (17.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e934 (17.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e943 (17.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e905 (16.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e932 (17.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePIR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6804 (31.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2173 (39.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1809 (33.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1491 (27.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1331 (24.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.3\u0026ndash;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7971 (36.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2045 (37.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2027 (37.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2012 (37.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1887 (34.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6987 (32.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1223 (22.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1605 (29.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1936 (35.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2223 (40.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11847 (54.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2857 (52.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2932 (53.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3000 (55.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3058 (56.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5459 (25.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1217 (22.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1297 (23.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1457 (26.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1488 (27.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4456 (20.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1367 (25.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1212 (22.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e982 (18.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e895 (16.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.3 (6.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.8 (7.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.4 (6.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29.2 (6.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e28.8 (6.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI_category\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e920 (4.23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e248 (4.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e211 (3.88%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e223 (4.10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e238 (4.37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5263 (24.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1178 (21.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1314 (24.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1338 (24.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1433 (26.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7150 (32.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1695 (31.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1782 (32.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1807 (33.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1866 (34.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8429 (38.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2320 (42.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2134 (39.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2071 (38.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1904 (35.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10252 (47.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2832 (52.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2571 (47.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2504 (46.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2345 (43.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11510 (52.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2609 (48.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2870 (52.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2935 (54.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3096 (56.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e124 (18.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e124 (18.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e123 (17.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e122 (16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70.1 (12.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69.4 (13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70.3 (13.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e70.3 (12.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e70.6 (12.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17083 (78.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4029 (74.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4229 (77.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4341 (79.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4484 (82.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4679 (21.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1412 (26.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1212 (22.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1098 (20.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e957 (17.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGI_ca\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19662 (90.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4899 (90.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4950 (91.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4918 (90.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4895 (90.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNGI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1892 (8.70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e470 (8.64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e443 (8.14%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e481 (8.85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e498 (9.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e196 (0.90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69 (1.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46 (0.85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37 (0.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e44 (0.81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eCDAI, Composite Dietary Antioxidant Index; OR, odds ratio; CI, confidence interval; BMI, body mass index; DM, diabetes mellitus; PIR, poverty income ratio; SBP, systolic blood pressure; DBP, diastolic blood pressure; GI, gastrointestinal tumors; NGI, non-gastrointestinal tumors; NCA, non-cancerous group.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe high CDAI group (Q4, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was predominantly male, with a high socioeconomic status (non-Hispanic whites, high PIR), and healthy behaviors (low smoking rate, low BMI), suggesting a synergistic effect of social demographics and lifestyle on dietary quality.\u003c/p\u003e\n\u003ch3\u003e2. Relationship between CDAI and research variables\u003c/h3\u003e\n\u003cp\u003eUnivariate linear regression analysis indicated that gender, race, marital status, PIR, BMI, smoking status, hypertension, DM, and tumor status were significantly associated with CDAI levels. Gastrointestinal tumors pose a growing challenge to global health, so the identification of modifiable risk factors, such as dietary quality, is essential for developing effective prevention and management strategies. Multivariate regression analysis of factors influencing CDAI revealed that patients with gastrointestinal cancer had significantly lower CDAI levels (β = -0.73, p\u0026thinsp;=\u0026thinsp;0.004). This may potentially reflecting disease-related anorexia, nutritional metabolic disorders, or dietary restrictions imposed by treatment interventions. Furthermore, multivariate correction analysis demonstrated that female patients (β = -2.11, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), current smokers (β = -1.04, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and obese individuals (β = -0.84, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) exhibited significantly lower CDAI scores. Conversely, high-income individuals (β\u0026thinsp;=\u0026thinsp;0.87, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and patients with gastrointestinal cancer (β = -0.73, p\u0026thinsp;=\u0026thinsp;0.004) showed significantly higher and lower scores, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Notably, the significant associations between race and marital status observed in the univariate analysis disappeared after multivariate adjustment, suggesting that their effects may be mediated through socio-economic or health-related factors.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLinear regression analysis was conducted to examine the relationship between CDAI and the study variables.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnivariate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eMultivariate\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eΒ (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eΒ (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.06 (-2.20, -1.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.11 (-2.26, -1.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eRace/ethnicity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic white\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.39 (0.13, 0.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.21 (-0.00, 0.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.50 (-0.78, -0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.21 (-0.46, 0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.04 (-0.34, 0.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.13 (-0.39, 0.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.347\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eMarital\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried/live with partner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed/Divorced/Separated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.86 (-1.08, -0.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.16 (-0.38, 0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.155\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever Married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.22 (-0.45, 0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.14 (-0.38, 0.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.232\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003ePIR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.3\u0026ndash;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.52 (0.32, 0.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.27 (0.07, 0.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.40 (1.17, 1.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.87 (0.66, 1.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eSmoke\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.15 (-0.05, 0.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.09 (-0.28, 0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.305\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.93 (-1.15, -0.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.04 (-1.26, -0.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eBMI_category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.06 (-0.38, 0.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7938\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.39 (-0.81, 0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.04 (-0.45, 0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.75 (-1.14, -0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.49 (-0.89, -0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.84 (-1.21, -0.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003ehyptersion\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.35 (-0.51, -0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.25 (-0.42, -0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eDM\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.65 (-0.86, -0.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.44 (-0.63, -0.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eGI_ca\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNGI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0043 (-0.38, 0.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.008626\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05 (-0.34, 0.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.799\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.91 (-1.44, -0.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.73 (-1.22, -0.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eCDAI, Composite Dietary Antioxidant Index; OR, odds ratio; CI, confidence interval; BMI, body mass index; DM, diabetes mellitus; PIR, poverty income ratio; GI, gastrointestinal tumors; NGI, non-gastrointestinal tumors; NCA, non-cancerous group.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003e3. The association between CDAI and the risk of gastrointestinal cancer\u003c/h3\u003e\n\u003cp\u003eIn the multivariate linear regression analysis, potential confounding factors were adjusted for (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Model 3). The CDAI, expressed as a continuous variable (each 1 unit), was negatively correlated with GI ca (β = -0.051, -0.099 to -0.002, p\u0026thinsp;=\u0026thinsp;0.0402). In the unadjusted model, compared with the highest quarter (Q4), the lowest quarter (Q1) of CDAI was associated with an increased risk of gastrointestinal cancer (β\u0026thinsp;=\u0026thinsp;0.692, 0.189 to 1.195, p\u0026thinsp;=\u0026thinsp;0.0083), but after adjusting for age, gender, and socioeconomic factors, there was no statistical significance (β\u0026thinsp;=\u0026thinsp;0.529, -0.018 to 1.075, p\u0026thinsp;=\u0026thinsp;0.0580). These results suggested that the association between CDAI and cancer risk may be driven by confounding factors rather than acting independently.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe Association between CDAI and gastrointestinal cancers in NHANES 2005\u0026ndash;2023.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c3\" namest=\"c2\" rowspan=\"2\"\u003e\u003cp\u003eContinue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c10\" namest=\"c4\"\u003e\u003cp\u003eCDAI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eQI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eΒ (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eΒ (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eΒ (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eβ (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003eGI ca\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.065\u003c/p\u003e\u003cp\u003e(-0.112, -0.019)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.692\u003c/p\u003e\u003cp\u003e(0.189, 1.195)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.015\u003c/p\u003e\u003cp\u003e(-0.528, 0.498)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.330\u003c/p\u003e\u003cp\u003e(-0.258, 0.918)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.2744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.046\u003c/p\u003e\u003cp\u003e(-0.090, -0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0413\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.483\u003c/p\u003e\u003cp\u003e(-0.018, 0.984)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0588\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.060\u003c/p\u003e\u003cp\u003e(-0.580, 0.459)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.8175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.227\u003c/p\u003e\u003cp\u003e(-0.398, 0.852)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.4729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.051\u003c/p\u003e\u003cp\u003e(-0.099, -0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0402\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.529 (-0.018, 1.075)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003cp\u003e(-0.514, 0.531)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.081 (-0.245, 0.082)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.4485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003eNGI ca\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0006\u003c/p\u003e\u003cp\u003e(-0.0194, 0.0207)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.007\u003c/p\u003e\u003cp\u003e(-0.172, 0.158)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.074 (-0.267, 0.117)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.012 (-0.179, 0.156)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0142\u003c/p\u003e\u003cp\u003e(0.0011, 0.0273)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.219 (-0.403, -0.034)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.116\u003c/p\u003e\u003cp\u003e(-0.319, 0.0866)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.2581\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.106\u003c/p\u003e\u003cp\u003e(-0.267, 0.055)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.1937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0065\u003c/p\u003e\u003cp\u003e(-0.0104, 0.0234)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.100\u003c/p\u003e\u003cp\u003e(-0.293, 0.093)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.032 (-0.237, 0.173)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.7558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.081 (-0.245, 0.082)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.3258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003eModel 1, Unadjusted; Model 2, Adjusted for age and gender; Model 3, Model 2 plus additional adjustment for the race/ethnicity, PIR, marital status, smoke, BMI and disease histories (including DM, hypertension). Q1 (-8.2793693, -1.5519494); Q2 (-1.5519494, 0.1701044); Q3 (0.1701044, 2.6228700); Q4 (2.6228700, 134.532056).\u003c/p\u003e\u003cp\u003eCDAI, Composite Dietary Antioxidant Index; OR, odds ratio; CI, confidence interval.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThere was no significant correlation between CDAI and NGI ca. It is worth noting that in non-gastrointestinal cancers, the medium group (Q2) of CDAI had a reduced risk compared with the highest quarter (Q4), after adjusting for age and gender (β = -0.219, -0.403 to -0.034, p\u0026thinsp;=\u0026thinsp;0.0205). However, this effect weakened after further correction (p\u0026thinsp;=\u0026thinsp;0.3041).\u003c/p\u003e\u003cp\u003eThe restricted cubic spline regression analysis showed that CDAI was weakly positively correlated with the risk of gastrointestinal cancer (P overall\u0026thinsp;=\u0026thinsp;0.0126, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The OR value increased to 1.25 in the high CDAI range (\u0026gt;\u0026thinsp;30), but the CI crossed 1 (for example, when CDAI\u0026thinsp;=\u0026thinsp;50, 95% CI: 0.95\u0026ndash;1.65). In non-gastrointestinal cancers, there was no significant association between CDAI and risk (P overall\u0026thinsp;=\u0026thinsp;0.758), and the OR value was always close to 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The nonlinear (NL) trends in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA (NL-P value\u0026thinsp;=\u0026thinsp;0.7372) and \u003cb\u003e2B\u003c/b\u003e (NL-P value\u0026thinsp;=\u0026thinsp;0.7637) were not significant (NL-P value\u0026thinsp;\u0026gt;\u0026thinsp;0.7), suggesting that the influence of CDAI is more likely to be linear.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe multivariate logistic regression analysis indicated that the CDAI had a significant protective effect on gastrointestinal tumors (GI ca). Individuals with a higher CDAI had a significantly lower risk of gastrointestinal cancer. For every 1 unit increase in CDAI, the probability of GI ca occurrence decreased by 4.55% (OR\u0026thinsp;=\u0026thinsp;0.9545, 95% CI: 0.917\u0026ndash;0.994, p\u0026thinsp;=\u0026thinsp;0.023), while there was no statistically significant prediction for non-gastrointestinal tumors (NGI ca) (OR\u0026thinsp;=\u0026thinsp;1.0015, p\u0026thinsp;=\u0026thinsp;0.79). The overall fitting effect of the model was good (AIC\u0026thinsp;=\u0026thinsp;15057.55), suggesting that CDAI might be an independent predictor of GI ca risk. In addition, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e showed the predictive probabilities of the CDAI for GI, NGI, and NCA.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e4. Hierarchical analysis results\u003c/h3\u003e\n\u003cp\u003eThe subgroup analysis showed that an increase in CDAI was significantly associated with a reduced risk of gastrointestinal tumors in obese individuals (OR\u0026thinsp;=\u0026thinsp;0.92, p\u0026thinsp;=\u0026thinsp;0.031), females (OR\u0026thinsp;=\u0026thinsp;0.89, p\u0026thinsp;=\u0026thinsp;0.002), and non-Hispanic whites (OR\u0026thinsp;=\u0026thinsp;0.94, p\u0026thinsp;=\u0026thinsp;0.019) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Conversely, in non-gastrointestinal tumors, the risk was slightly increased in individuals with normal BMI (OR\u0026thinsp;=\u0026thinsp;1.02, p\u0026thinsp;=\u0026thinsp;0.042) and those who were widowed or divorced (OR\u0026thinsp;=\u0026thinsp;1.02, p\u0026thinsp;=\u0026thinsp;0.030) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). It is noteworthy that current smokers (OR\u0026thinsp;=\u0026thinsp;0.96, p\u0026thinsp;=\u0026thinsp;0.008) and Mexican Americans (OR\u0026thinsp;=\u0026thinsp;0.92, p\u0026thinsp;=\u0026thinsp;0.007) had significantly lower risks of non-gastrointestinal cancer, suggesting that the effect of CDAI may vary depending on sociodemographic characteristics.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the correlation between CDAI and the risk of gastrointestinal cancer and non-gastrointestinal cancer, elucidating the potential role of CDAI in cancer prevention and its interactions with socio-demographic and lifestyle factors. Initially, the characteristics of the study population indicated that the high CDAI group was predominantly composed of males, individuals with a high socioeconomic status (non-Hispanic whites, high PIR), and those exhibiting healthy behaviors (low smoking rate, low BMI). These findings suggested that dietary quality might be synergistically influenced by socio-economic and lifestyle factors [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Furthermore, the prevalence of hypertension and diabetes was lower in the high CDAI group, indicating that a diet rich in antioxidants could be associated with better metabolic health [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMultivariate analysis further indicated that women, current smokers, and obese individuals exhibited significantly lower CDAI values, which may be attributed to energy-restricted diets, increased oxidative stress, and metabolic disorders, suggesting that a high-antioxidant diet might be related to metabolic health [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Notably, the high-income group had a higher CDAI, highlighting the crucial role of gender [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], behavior, and economic factors in dietary antioxidant intake. It is noteworthy that the univariate associations of race and marital status disappeared after multivariate adjustment, suggesting that their effects might be mediated through socio-economic or health behaviors, rather than acting independently.\u003c/p\u003e\u003cp\u003eThe association analysis of CDAI and the risk of gastrointestinal cancer demonstrated that for each unit increase in CDAI, the risk of gastrointestinal cancer decreased by 4.55%, and the protective effect was more significant in obese individuals, females, and non-Hispanic whites. This result might be attributed to the presence of dietary antioxidants (e.g., vitamin C, carotenoids) of neutralizing free radicals, reducing oxidative stress, and inhibiting inflammatory pathways, thus contributing to a reduction in the risk of gastrointestinal carcinogenesis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe phenomenon of oxidative stress occurs when there is an imbalance between free radicals and antioxidants within cells and tissues. Free radicals are defined as oxygen-containing molecules with an odd number of electrons, rendering them highly reactive molecules and prone to reacting with other molecules, such as DNA, which can result in DNA damage. The accumulation of DNA damage has the potential to disrupt normal gene expression and thereby initiate carcinogenesis. Antioxidants have been identified as molecules capable of donating electrons to free radicals, thereby stabilizing them and reducing their reactivity. This process, consequently, offers a protective measure against the carcinogenic effects of oxidative stress [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. CDAI, a measure of total dietary antioxidant intake, is inversely related to circulating inflammatory cytokines such as interleukin-1β and tumor necrosis factor-α, suggesting that CDAI may exert its effects via anti-inflammatory mechanisms [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNevertheless, subsequent quartile analysis revealed that the statistical significance between Q1 and Q4 was diminished upon adjustment for confounding variables, indicating that the inverse correlation between CDAI and gastrointestinal cancer may be partially obscured by factors such as age, socio-economic status, or comorbid conditions (including diabetes). Additionally, the findings from restricted cubic spline regression indicated a marginal positive correlation between CDAI and the risk of gastrointestinal cancer (with an odds ratio of 1.25 within the high CDAI interval). However, the CI was wide and included the value of 1, thus necessitating a cautious approach to interpretation. One hypothesis that has been postulated is that exceedingly high antioxidant intake could exert a \"biphasic effect,\" thereby disrupting normal redox equilibrium and potentially facilitating carcinogenesis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The role of ROS is twofold: it can either provoke mutations that lead to tumorigenesis or trigger apoptotic signals that suppress tumor growth. In the former instance, antioxidants may impede cancer development by neutralizing ROS; however, in the latter case, they could enhance the survival rate of cancer cells through the same neutralization process, thereby fostering cancer incidence. The behavior of ROS is contingent upon varying conditions. Majumder et al. assert that the impact of antioxidants on carcinogenesis is contingent upon the cellular microenvironment [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThere is no significant correlation between CDAI and non-gastrointestinal cancer. However, in a stratified analysis, the risk slightly increased in individuals with a normal BMI and those who are widowed or divorced, while it decreased in current smokers and Mexican Americans. This observed heterogeneity may reflect differences in the etiology of distinct cancers, as well as the impact of social and psychological factors (such as marital status) on cancer risk through stress or behavioral pathways [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. For instance, the protective effect of CDAI in smokers may be related to the alleviation of tobacco-related oxidative damage by antioxidants [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eResearch significance and limitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study is based on data from the nationally representative NHANES database, thereby enhancing the robustness and generalizability of our research findings. CDAI, a comprehensive indicator used to assess the overall levels of various antioxidant substances in the diet, was utilized to reflect the overall potential of the diet to combat oxidative stress and to effectively distinguish dietary patterns across different population. However, it is necessary to interpret social and economic variables to explain the observed variations. It is hypothesized that improving the dietary quality of low-income groups, controlling smoking, and managing obesity may indirectly enhance antioxidant status. Nevertheless, merely supplementing dietary antioxidants may not result in a reduction of the risk of cancer.\u003c/p\u003e\u003cp\u003eThe present study has one limitation. Firstly, the cross-sectional design is unable to determine the causal relationship between CDAI and cancer. The disease state may reverse the influence of diet (e.g., reduced intake by cancer patients). It is recommended that future studies include prospective cohort studies to more accurately assess these temporal relationships. A key methodological consideration involves the inherent limitations of the NHANES dietary assessment. Despite the use of validated USDA automated multiple-pass method in the survey, the 24-hour dietary recall method has potential measurement errors and various forms of bias. These biases include recall bias, social desirability bias when reporting healthy or unhealthy foods, and challenges in portion estimation. Although the NHANES implemented a second 24-hour recall for some participants to account for individual differences, this method might not adequately address long-term dietary patterns related to cancer risk. However, confirmatory studies have demonstrated a reasonable correlation between NHANES dietary recall and objective biomarkers of specific nutrients, thereby supporting the practicality of these data in population-level dietary assessment. Additionally, while various factors such as age, gender, race, smoking status, PIR, hypertension, and diabetes were adjusted in our analysis, other potential confounding factors, including exercise, total energy intake, family history, genetic susceptibility, and interactions with the intestinal microbiome, have not been explored. We look forward to addressing these issues in future research.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis investigation highlights the significance of dietary quality in the prevention and management of gastrointestinal cancers. A heightened CDAI correlates with a diminished risk of gastrointestinal cancer, indicating that augmenting dietary antioxidants could exert a favorable influence on the prevention of gastrointestinal cancer, particularly among women, obese individuals, and non-Hispanic whites, where the protective effect is more pronounced. These findings suggest the potential efficacy of dietary antioxidants in the prevention of gastrointestinal tumors. However, since CDAI is an aggregate indicator of multi-dimensional social ecology and health behaviors, its correlation with gastrointestinal tumors may be influenced by confounding factors and non-independent biological effects. At present, there is insufficient evidence to support the use of CDAI as an independent predictor of cancer. A multi-dimensional risk assessment model is required to be integrated.\u003c/p\u003e\u003cp\u003eAdditionally, CDAI exhibits no distinct correlation with non-gastrointestinal cancers, and its impact may be modulated by socio-economic and lifestyle factors. Prospective cohort studies are warranted to confirm the causal role of CDAI, investigate the dose-effect relationship and biological mechanisms of specific antioxidants, analyze the biphasic effect of specific antioxidant components (such as selenium, carotenoids) in carcinogenesis (antioxidant/oxidative), and devise comprehensive nutritional intervention strategies for high-risk populations (smokers).\u003c/p\u003e\u003cp\u003eIt is imperative that healthcare providers and public health initiatives raise awareness of dietary quality and develop comprehensive dietary assessment tools to identify further preventive capabilities. It is acknowledged that dietary habits vary across different cultures, tailored recommendations for different ethnic groups may have a significant impact on compliance. Future health education efforts must promote healthy eating habits through customized strategies. Moreover, in subsequent research, lengthier follow-up periods and larger cohorts will reinforce these findings, while investigating the mechanisms by which diet affects the risk of tumors can guide customized dietary guidelines and policies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study used data from the National Health and Nutrition Examination Survey (NHANES) (https://www.cdc.gov/nchs/nhanes/index.html).\u003c/p\u003e\n\u003cp\u003eEthics statement\u003c/p\u003e\n\u003cp\u003eThe studies involving humans were approved by the Research Ethics Review Board of the National Center for Health Statistics.The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eJW: Conceptualization, Methodology, Writing, Data curation–original draft. WD: Data curation, Writing\u0026nbsp;–\u0026nbsp;original draft. JL:Investigation, Writing\u0026nbsp;–\u0026nbsp;original draft. ZL: Writing\u0026nbsp;–\u0026nbsp;original draft. WY: Writing\u0026nbsp;–\u0026nbsp;original draft.YZ: Writing\u0026nbsp;–\u0026nbsp;original draft. ZX: Writing\u0026nbsp;–\u0026nbsp;review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was financially supported by the National Natural Science Foundation of China (No. 82460128 and No. 82260131).\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge all the staff and participants of the National Health and Nutrition Examination Survey (NHANES) and for providing data licenses.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll data in this study are sourced from public databases, and participants have provided informed consent, eliminating any ethical concerns.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eConsent to Participate declaration\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEvery human participant should provide their consent.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWang S, et al. 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Int J Cancer. 2019;144(9):2135\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVernia F et al. Dietary Factors Modulating Colorectal Carcinogenesis. Nutrients, 2021. 13(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSarmiento-Salinas FL, et al. Reactive oxygen species: Role in carcinogenesis, cancer cell signaling and tumor progression. Life Sci. 2021;284:119942.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y, et al. The role of reactive oxygen species in gastric cancer. Cancer Biol Med. 2024;21(9):740\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePisoschi AM, et al. Oxidative stress mitigation by antioxidants - An overview on their chemistry and influences on health status. Eur J Med Chem. 2021;209:112891.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuo M et al. Antioxidant Therapy in Cancer: Rationale and Progress. Antioxid (Basel), 2022. 11(6).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePourmontaseri H, et al. Exploring the application of dietary antioxidant index for disease risk assessment: a comprehensive review. Front Nutr. 2024;11:1497364.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVahid F, Rahmani W, Davoodi SH. The association between dietary total antioxidant capacity and quality of nutrients with odds of colorectal cancer: A hospital-based case-control study. Clin Nutr ESPEN. 2022;52:277\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJun S, et al. Interaction between vitamin E intake and a COMT gene variant on colorectal cancer risk among Korean adults: a case-control study. Epidemiol Health. 2023;45:e2023100.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLance P, et al. Colorectal Adenomas in Participants of the SELECT Randomized Trial of Selenium and Vitamin E for Prostate Cancer Prevention. Cancer Prev Res (Phila). 2017;10(1):45\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZou ZV et al. Antioxidants Promote Intestinal Tumor Progression in Mice. Antioxid (Basel), 2021. 10(2).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYin L et al. Diet-Derived Circulating Antioxidants and Risk of Digestive System Tumors: A Mendelian Randomization Study. Nutrients, 2022. 14(16).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang R, Tao W, Cheng X. Association of composite dietary antioxidant index with cardiovascular disease in adults: results from 2011 to 2020 NHANES. Front Cardiovasc Med. 2024;11:1379871.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu W, et al. Composite dietary antioxidant index is associated with the prevalence of metabolic syndrome in females: results from NHANES 2011\u0026ndash;2016. Front Nutr. 2025;12:1529332.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang W, Chang Y, Chen G. Association between Healthy Eating Index-2020, alternative Mediterranean Diet scores, and gastrointestinal cancer risk in NHANES 2005\u0026ndash;2018. Sci Rep. 2025;15(1):3983.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZheng M, et al. Association between composite dietary antioxidant index and fatty liver index among US adults. Front Nutr. 2024;11:1466807.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFu Y, et al. Association between the composite dietary antioxidant index and non-alcoholic fatty liver disease: evidence from National Health and Nutrition Examination Survey 2005\u0026ndash;2016. Front Nutr. 2025;12:1473487.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWright ME, 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(1):68\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMendoza A, et al. Energy density of foods and diets in Mexico and their monetary cost by socioeconomic strata: analyses of ENSANUT data 2012. J Epidemiol Community Health. 2017;71(7):713\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXue T, et al. Association Between Composite Dietary Antioxidant Index and Metabolic Syndrome in Normal Weight Population: Evidence From NHANES. Curr Dev Nutr. 2024;8(2):102666.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBennett E, Peters SAE, Woodward M. Sex differences in macronutrient intake and adherence to dietary recommendations: findings from the UK Biobank. BMJ Open. 2018;8(4):e020017.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSoto KM et al. Antioxidants in Traditional Mexican Medicine and Their Applications as Antitumor Treatments. Pharmaceuticals (Basel), 2023. 16(4).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMajumder D, et al. Understanding the complicated relationship between antioxidants and carcinogenesis. J Biochem Mol Toxicol. 2021;35(2):e22643.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuu HN, et al. Are dietary antioxidant intake indices correlated to oxidative stress and inflammatory marker levels? Antioxid Redox Signal. 2015;22(11):951\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHecht F, et al. Regulation of antioxidants in cancer. Mol Cell. 2024;84(1):23\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLempesis IG et al. Role of stress in the pathogenesis of cancer (Review). Int J Oncol, 2023. 63(5).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAstori E, et al. Antioxidants in smokers. Nutr Res Rev. 2022;35(1):70\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Composite Dietary Antioxidant Index (CDAI), gastrointestinal cancer, non-gastrointestinal cancer, NHANES, cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-7040004/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7040004/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eGastrointestinal cancer accounts for approximately one-third of global cancer incidence and mortality. The early screening rates remain low, which leads to a poor prognosis. Identifying modifiable risk factors is therefore a pressing need. Oxidative stress plays a pivotal role in gastrointestinal carcinogenesis. Dietary antioxidants may mitigate this process by neutralizing reactive oxygen species. However, studies focusing on single nutrients have limitations due to their inability to capture the synergistic effects of multiple dietary components. The Composite Dietary Antioxidant Index (CDAI) is a quantitative measure that evaluates the combined impact of various dietary antioxidants. The relationship between CDAI and gastrointestinal cancer risk warrants further investigation.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo examine the association between CDAI and the risk of gastrointestinal cancer.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e\u003cp\u003eCross-sectional data from NHANES 2005\u0026ndash;2023 were utilized to calculate CDAI scores. Logistic regression models, restricted cubic splines, and subgroup analyses were employed to comprehensively assess the relationship between CDAI and gastrointestinal cancer risk.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e\u003cp\u003eAmong the 21,762 participants included in the study, the high CDAI group consisted predominantly of males, individuals with high socioeconomic status (non-Hispanic whites, high income), and those who engaged in healthy behaviors (low smoking rates, low body mass index). Multivariate analysis revealed that CDAI scores were significantly lower among females, current smokers, and obese individuals, while higher scores were observed in the high-income group. The association analysis demonstrated that for every 1-unit increase in CDAI, the risk of gastrointestinal cancer decreased by 4.55% (OR\u0026thinsp;=\u0026thinsp;0.9545). This protective effect was more pronounced among individuals with obesity, females, and non-Hispanic whites. No significant association was identified between CDAI and non-gastrointestinal cancers.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eHigher CDAI scores are associated with a diminished risk of gastrointestinal cancer, particularly among females, individuals with obesity, and non-Hispanic whites. These findings highlight the potential preventive role of dietary antioxidants in gastrointestinal tumor development.\u003c/p\u003e","manuscriptTitle":"The association between the comprehensive dietary antioxidant index and the risk of gastrointestinal cancer: A cross-sectional study based on NHANES","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-22 07:59:42","doi":"10.21203/rs.3.rs-7040004/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":"99a1ffdc-befe-49dd-9208-d41af6e9cb14","owner":[],"postedDate":"July 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-09T14:08:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-22 07:59:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7040004","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7040004","identity":"rs-7040004","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}