Role of composite dietary antioxidant index in staging and mortality risk of cardiovascular-kidney-metabolic syndrome: a study from NHANES 2001-2018

Role of composite dietary antioxidant index in staging and mortality risk of cardiovascular-kidney-metabolic syndrome: a study from NHANES 2001-2018 | 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 Role of composite dietary antioxidant index in staging and mortality risk of cardiovascular-kidney-metabolic syndrome: a study from NHANES 2001-2018 yupeng wang, Xintong Gao, Fudong Wen, Simeng Yu, Yubing Yang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7380717/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 This study investigated the role of the Composite Dietary Antioxidant Index (CDAI) in cardiovascular-kidney-metabolic (CKM) syndrome staging and mortality using NHANES 2001–2018 data from 25,155 U.S. adults. Higher CDAI quartiles demonstrated progressively reduced odds of advanced CKM stages versus Stage 0 (Q4 vs. Q1 ORs: Stage 1: 0.71 (0.56–0.91); Stage 2: 0.58 (0.45–0.74); Stage 3: 0.30 (0.20–0.47); Stage 4: 0.46 (0.35–0.60); all P < 0.05). Weighted Quantile Sum regression identified a protective effect of the antioxidant mixture against advanced CKM (OR: 0.82 (0.76–0.88); P < 0.001), primarily driven by vitamins A (weight = 0.357), C (0.290), and selenium (0.212). In CKM patients, higher CDAI was associated with significantly lower all-cause (Q4 vs. Q1 HR: 0.63 (0.57–0.70)), cardiovascular (HR: 0.63 (0.51–0.78)), and non-cardiovascular mortality (HR: 0.63 (0.56–0.72); all P < 0.001). Nonlinear analyses revealed threshold effects for all-cause and non-CVD mortality at CDAI ≈ 0. These findings indicate that elevated CDAI is robustly associated with less severe CKM staging and reduced mortality, supporting dietary antioxidant optimization for CKM management and risk stratification. Cardiovascular-kidney-metabolic syndrome Composite dietary antioxidant index Oxidative stress Mortality NHANES Figures Figure 1 Introduction The intertwined global burdens of cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders continue to escalate 1 . CVD remains the leading cause of mortality worldwide, accounting for approximately 17.9 million deaths in 2019 2 . Concurrently, CKD affects an estimated 10% of the global population (~ 850 million individuals) 3 , while metabolic syndrome—a key driver of both CVD and CKD—has a global prevalence of ~ 25% 4 . Recognizing the interplay among these conditions, the American Heart Association (AHA) introduced cardiovascular-kidney-metabolic (CKM) syndrome in 2023. CKM syndrome represents a progressive, systemic health condition characterized by the co-occurrence of cardiovascular disease, diabetes, obesity, chronic kidney disease, and other metabolic derangements 5 . Notably, nearly 90% of US adults met CKM criteria (2011–2020), with 15% having advanced-stage disease 6 . Beyond its clinical prevalence, the molecular pathogenesis of CKM syndrome involves interconnected pathways, prominently featuring oxidative stress as a central driver 7 . Characterized by excessive reactive oxygen species (ROS) production overwhelming endogenous antioxidant defenses, it induces cellular damage to proteins, lipids, and nucleic acids 8 . This process underpins metabolic dysregulation by disrupting energy metabolism, cell signaling, and genomic integrity 9 . Furthermore, oxidative stress accelerates cardiovascular and renal pathophysiology: ROS directly promotes cardiac dysfunction 10 and exacerbates CKD progression through interactions with immune dysregulation, hypertension, and metabolic perturbations 11 . Given the central role of oxidative stress in CKM pathogenesis 7 , endogenous antioxidant defenses are critical for maintaining redox homeostasis by eliminating ROS and mitigating cellular damage 12 . Compromised antioxidant capacity contributes to multi-organ pathology. Dietary antioxidants represent a modifiable source of support, with evidence showing that adequate intake elevates plasma antioxidant levels and reduces systemic oxidative stress 13 . Consequently, dietary modification presents a viable preventive approach. The Composite Dietary Antioxidant Index (CDAI) was developed to holistically assess dietary antioxidant potential, incorporating six key antioxidants: vitamins A, C, E, zinc, selenium, and carotenoids 14 . Higher CDAI scores consistently show inverse associations with risk/mortality of CKM-implicated conditions, including hypertension 15 , cancer 16 , and cardiovascular disease 17 . While higher CDAI scores are associated with reduced risk/mortality in isolated CKM-related conditions (hypertension, cancer, CVD) 15 – 17 , the integrated pathophysiology of CKM syndrome—driven by oxidative stress 7 – 9 —demands assessment of dietary antioxidants' role in disease staging and prognosis. This study uniquely evaluates CDAI's relationship with CKM staging across metabolic, renal, and cardiovascular domains, and its association with mortality in established CKM. Using NHANES (2001–2018) data, we investigate CDAI as both a predictor of CKM severity and modulator of survival outcomes. Methods 2.1 Study population This study utilized data from the NHANES, a nationally representative program that assesses the health and nutritional status of non-institutionalized U.S. civilians through structured household interviews and standardized physical examinations conducted biennially by the National Center for Health Statistics (NCHS). The NHANES database provides comprehensive demographic, dietary, clinical, laboratory, and psychosocial variables, enabling investigation of disease risk factors and nutrition-health relationships. The NCHS Research Ethics Review Board approved all NHANES protocols, and written informed consent was obtained from all participants 18 . From the initial 91,351 NHANES 2001–2018 participants, we applied the following exclusion criteria: age < 20 years (n = 41,150), pregnancy (n = 1,258), lack of dietary information (n = 5,590), missing CKM syndrome status (n = 4,018), missing survival status (n = 69), and missing covariate data (n = 14,111). After these exclusions, 25,155 eligible participants were enrolled in the final analytical cohort (Supplementary Figure S1 ). 2.2 Measurement of composite dietary antioxidant index Dietary intake data were obtained from two non-consecutive 24-hour dietary recall interviews administered to participants within the NHANES 2001–2018 cycles. The initial interview was conducted in person at a mobile examination center, and the second recall was performed via telephone 3 to 10 days later. Participants were instructed to report all foods and beverages, including dietary supplements, consumed within the preceding 24-hour period, detailing types, amounts, and preparation methods. The average intake derived from these two recalls was utilized to represent the participants' usual dietary intake, a validated approach for estimating habitual intake in large epidemiological studies 19 . Total daily intake of six dietary antioxidants (vitamins A, C, E, zinc, selenium, and carotenoids) was derived from dietary data using USDA nutrient composition databases 18 , 20 . The CDAI was calculated exclusively from food sources, excluding supplements or other non-dietary contributions. The CDAI was calculated according to the established methodology described by Wright et al 14 . For each of the six antioxidants, the population mean ( \(\:{\mu\:}_{i}\) ) and standard deviation ( \(\:{s}_{i}\) ) of daily intake were determined. An individual's standardized intake (z-score) for each antioxidant ( \(\:i\) ) was computed as the difference between their daily intake ( \(\:{x}_{i}\) ) and the population mean ( \(\:{\mu\:}_{i}\) ), divided by the population standard deviation ( \(\:{s}_{i}\) ). The CDAI represents the sum of these standardized scores across all six antioxidants: $$\:CDAI=\sum\:_{i=1}^{6}\frac{{x}_{i}-{\mu\:}_{i}}{{s}_{i}}$$ Thus, the CDAI provides a single, integrated measure reflecting an individual's overall dietary antioxidant intake relative to the study population, with higher values indicating higher combined antioxidant intake. 2.3 Cardiovascular-Kidney-Metabolic Syndrome Assessment CKM syndrome was defined and staged according to the AHA framework 5 , which integrates metabolic risk factors, CKD, and subclinical/clinical CVD into a progressive staging system (Stages 0–4). This classification reflects the pathophysiological continuum linking metabolic dysregulation, renal impairment, and cardiovascular damage. Staging implementation: Stage 0: Absence of CKM risk factors. Stage 1: Presence of excess adiposity, dysmetabolism, or prediabetes. Stage 2: Metabolic risk factors (hypertension, diabetes, dyslipidemia) and/or moderate-to-high risk CKD 21 . Stage 3: Very high-risk CKD or high predicted 10-year CVD risk (≥ 20%) calculated using AHA PREVENT equations 22 . Stage 4: Established clinical CVD. Detailed diagnostic thresholds for metabolic parameters (waist circumference, blood pressure, laboratory values) and CKD classification criteria are provided in Supplementary Tables S1-S3. The staging algorithm prioritizes the highest qualifying stage when multiple conditions coexist 5 . 2.4 Survival Outcome Assessment The primary survival outcomes assessed in this study were all-cause mortality, CVD mortality, and non-CVD mortality. Mortality status (alive or deceased) and underlying cause of death were ascertained through linkage of participant records to the National Death Index database, maintained by the Centers for Disease Control and Prevention. Data on vital status and cause of death were available through December 31, 2019, ensuring a minimum follow-up duration for analysis. Causes of death were classified according to the Tenth Revision of the International Statistical Classification of Diseases and Related Health Problems. 2.5 Covariates To account for potential confounding influences on the relationship between CDAI, CKM staging, and mortality, we identified and included a comprehensive set of covariates based on established associations with these outcomes. Covariates were categorized as follows: Sociodemographic Factors: Age (years); Gender (male or female); Race (Mexican American, Non-Hispanic White, Non-Hispanic Black, Other); Education level (Less than high school, High school graduate, More than high school); Marital status (Married/Living with partner, Living alone); Poverty Income Ratio (PIR) (< 1.0, ≥ 1.0), representing the ratio of family income to the federal poverty threshold. Behavioral and Lifestyle Factors: Smoking status (Never smoked, Former smoker, Current smoker); Alcohol consumption status (Non-drinker, Mild-to-moderate drinker, Heavy drinker); Physical activity intensity (Low, Medium, High). Activity intensity was classified based on self-reported participation in vigorous or moderate activities over the past 30 days. Participants who answered "No", "Unable to do activity", "Refuse", or "Don't know" to both the vigorous and moderate activity questions were categorized as having "Low" intensity. Those reporting either moderate or vigorous activity were classified as "Medium" or "High" based on standard NHANES definitions applied within our analysis. Clinical and Anthropometric Measurements: Body Mass Index (BMI, kg/m²); Systolic Blood Pressure (SBP, mmHg); Diastolic Blood Pressure (DBP, mmHg). Laboratory Parameters: Total Cholesterol (TC, mg/dL); High-Density Lipoprotein Cholesterol (HDL-C, mg/dL); Estimated Glomerular Filtration Rate (eGFR, mL/min/1.73m²; calculated using the CKD-EPI equation); Urine Albumin-to-Creatinine Ratio (UACR, mg/g); Glycohemoglobin (HbA1c, %). 2.6 Statistical analysis Descriptive Statistics Baseline characteristics of the study population, stratified by CKM stage and CDAI quartiles, were summarized. Categorical variables were presented as frequencies and percentages. Continuous variables, assessed for normality and found to be non-normally distributed, were presented as medians with interquartile ranges (IQRs). Group comparisons used chi-square tests for categorical variables and Kruskal-Wallis tests for continuous variables. Association between CDAI and CKM Staging (Cross-sectional) : The relationship between CDAI (modeled both continuously and categorically in quartiles: Q1-lowest to Q4-highest) and CKM staging was assessed using multinomial logistic regression. Initially, ordered logistic regression was considered for the ordinal outcome (Stages 0–4). However, the proportional odds assumption was violated (significant parallel lines test, P < 0.05). Therefore, multinomial logistic regression was employed, treating CKM stage as a nominal outcome. The non-CKM group (Stage 0) served as the reference. Models were adjusted for age, gender, race, education level, PIR, smoking status, alcohol consumption status, and physical activity intensity. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Component Analysis of CDAI and CKM : To evaluate the joint effect and relative contribution of the six individual dietary antioxidants (vitamins A, C, E, zinc, selenium, carotenoids) comprising the CDAI on advanced CKM (Stages 3–4 vs. Stages 1–2), Weighted quantile sum (WQS) regression was employed. This method estimates an overall mixture effect index and assigns weights to each component reflecting their relative contribution to the association. WQS regression was performed in three progressively adjusted models: Model 1(Unadjusted); Model 2 (Adjusted for age, gender, and race); Model 3 (Adjusted for age, gender, race, education level, PIR, smoking status, alcohol consumption status, and physical activity intensity). Additionally, multivariable logistic regression models (using the same adjustment sets as WQS models) were fitted for each antioxidant component to assess their independent associations with advanced CKM. Association between CDAI and Mortality in CKM Patients (Longitudinal) Survival analyses were restricted to participants with CKM syndrome (Stages 1–4). The primary endpoints were all-cause mortality, CVD mortality, and non-CVD mortality. Kaplan-Meier Analysis: Cumulative survival probabilities across CDAI quartiles were visualized using Kaplan-Meier curves. Statistical differences between quartile groups were assessed using the log-rank test. Cox Proportional Hazards Models: Multivariable Cox proportional hazards regression was used to quantify the association between CDAI quartiles and mortality risk. The lowest quartile (Q1) served as the reference group. Models were adjusted for age, gender, race, education level, PIR, smoking status, alcohol consumption status, and physical activity intensity. Hazard ratios (HRs) and 95% CIs were calculated. Nonlinearity Assessment: The potential nonlinear relationship between continuous CDAI and mortality risk was explored using restricted cubic splines (RCS) with 3 knots (selected based on the lowest Bayesian Information Criterion; see Supplementary Table S4). The reference point for HR calculation was set at the median CDAI value. The P-value for nonlinearity was reported. Stratified Analysis: Cox models were also performed stratified by CKM stage (Stages 1–2 vs. Stages 3–4) to explore potential effect modification. Optimal Risk Stratification Cut-off: For each mortality outcome, the optimal cut-off point for CDAI to stratify mortality risk in CKM patients was identified using the maximum selected rank statistics method implemented in the “survminer” package. Kaplan-Meier survival curves based on these optimal cut-offs were generated. Sensitivity Analyses Two sensitivity analyses were conducted to evaluate the robustness of the primary mortality findings. First, to mitigate potential reverse causality, CKM patients who died within the first two years of follow-up were excluded, and the Cox regression models were re-run. Second, given the strong influence of cancer on mortality, CKM patients with a history of cancer were excluded, and the Cox regression models were re-analyzed. All analyses were performed using R software (version 4.4.3), with statistical significance defined as a two-sided P -value < 0.05. Result 3.1 Participant characteristics This study included 25,155 eligible participants (median [IQR] age: 49.0 [35.0, 63.0] years; 51.1% male; median [IQR] follow-up: 105.0 [58.0, 154.0] months). Among them, 650 (2.6%) were without CKM syndrome (Stage 0), while 24,505 (97.4%) had CKM syndrome (Stage 1: n = 6,881; Stage 2: n = 14,850; Stage 3: n = 314; Stage 4: n = 2,460). Baseline characteristics stratified by CKM stage are summarized in Table 1 . Significant differences (all P < 0.001) were observed across CKM stages for all assessed variables, including sociodemographic factors (age, gender, race, education, PIR, marital status), lifestyle factors (smoking, alcohol consumption, physical activity), clinical parameters (BMI, SBP, DBP), and laboratory measures (TC, HDL-C, eGFR, UACR, glycohemoglobin). Notably, the CDAI progressively decreased with advancing CKM stage (median [IQR]: Stage 0: 0.6 [− 1.6, 3.2]; Stage 1: 0.3 [− 1.9, 2.9]; Stage 2: −0.1 [− 2.1, 2.6]; Stage 3: −1.2 [− 3.0, 1.2]; Stage 4: −0.5 [− 2.5, 2.1]; P < 0.001). Characteristics of CKM patients according to CDAI quartiles and CKM stage severity (early: Stages 1–2; advanced: Stages 3–4) are detailed in Supplementary Tables S5 and S6, respectively. Table 1 Characteristics of the Study Population Stratified by CKM Stage Characteristic ALL (N = 25155) Non-CKM (N = 650) CKM Stage P Stage 1 (N = 6881) Stage 2 (N = 14850) Stage 3 (N = 314) Stage 4 (N = 2460) Age 49.0 (35.0, 63.0) 41.0 (34.0, 49.0) 37.0 (28.0, 48.0) 52.0 (39.0, 64.0) 76.0 (65.0, 80.0) 68.0 (58.0, 77.0) < 0.001 Gender (%) < 0.001 Male 12856 (51.1) 231 (35.5) 3277 (47.6) 7737 (52.1) 193 (61.5) 1418 (57.6) Female 12299 (48.9) 419 (64.5) 3604 (52.4) 7113 (47.9) 121 (38.5) 1042 (42.4) Race (%) < 0.001 Mexican American 4214 (16.8) 60 (9.2) 1375 (20.0) 2482 (16.7) 42 (13.4) 255 (10.4) Non-Hispanic White 11699 (46.5) 415 (63.8) 2924 (42.5) 6776 (45.6) 141 (44.9) 1443 (58.7) Non-Hispanic Black 4992 (19.8) 76 (11.7) 1278 (18.6) 3086 (20.8) 81 (25.8) 471 (19.1) Other 4250 (16.9) 99 (15.2) 1304 (19.0) 2506 (16.9) 50 (15.9) 291 (11.8) Education level (%) < 0.001 Below high school 2363 (9.4) 35 (5.4) 468 (6.8) 1472 (9.9) 71 (22.6) 317 (12.9) High school 3299 (13.1) 58 (8.9) 855 (12.4) 1940 (13.1) 50 (15.9) 396 (16.1) Above high school 19493 (77.5) 557 (85.7) 5558 (80.8) 11438 (77.0) 193 (61.5) 1747 (71.0) PIR (%) < 0.001 < 1 4605 (18.3) 65 (10.0) 1321 (19.2) 2649 (17.8) 70 (22.3) 500 (20.3) ≥ 1 20550 (81.7) 585 (90.0) 5560 (80.8) 12201 (82.2) 244 (77.7) 1960 (79.7) Marital status (%) < 0.001 Married 19061 (75.8) 508 (78.2) 4469 (64.9) 11610 (78.2) 283 (90.1) 2191 (89.1) Living with a partner 1976 (7.9) 49 (7.5) 736 (10.7) 1082 (7.3) 12 (3.8) 97 (3.9) Living alone 4118 (16.4) 93 (14.3) 1676 (24.4) 2158 (14.5) 19 (6.1) 172 (7.0) Smoke status (%) < 0.001 Never smoked 13780 (54.8) 386 (59.4) 4128 (60.0) 8103 (54.6) 151 (48.1) 1012 (41.1) Previously smoked 6075 (24.2) 109 (16.8) 1294 (18.8) 3615 (24.3) 119 (37.9) 938 (38.1) Now smoking 5300 (21.1) 155 (23.8) 1459 (21.2) 3132 (21.1) 44 (14.0) 510 (20.7) Alcohol status (%) < 0.001 Non-drinker 4152 (16.5) 70 (10.8) 957 (13.9) 2516 (16.9) 98 (31.2) 511 (20.8) Mild to moderate 18629 (74.1) 545 (83.8) 5194 (75.5) 10870 (73.2) 204 (65.0) 1816 (73.8) Heavy 2374 (9.4) 35 (5.4) 730 (10.6) 1464 (9.9) 12 (3.8) 133 (5.4) Activity intensity (%) < 0.001 High 6120 (24.3) 194 (29.8) 1943 (28.2) 3577 (24.1) 33 (10.5) 373 (15.2) Medium 6229 (24.8) 159 (24.5) 1629 (23.7) 3708 (25.0) 76 (24.2) 657 (26.7) Low 12806 (50.9) 297 (45.7) 3309 (48.1) 7565 (50.9) 205 (65.3) 1430 (58.1) BMI 28.5 (25.3, 32.8) 22.3 (20.8, 23.7) 28.2 (25.9, 31.8) 29.0 (25.4, 33.5) 28.1 (25.4, 32.1) 29.1 (25.4, 33.4) < 0.001 SBP 123.0 (113.0, 135.0) 109.0 (102.0, 115.0) 113.0 (107.0, 119.0) 129.0 (118.0, 140.0) 145.0 (129.0, 161.8) 130.0 (117.0, 145.0) < 0.001 DBP 71.0 (64.0, 79.0) 67.0 (61.0, 72.0) 68.0 (63.0, 73.0) 75.0 (67.0, 83.0) 69.0 (58.0, 79.0) 68.0 (60.0, 77.0) < 0.001 TC 194.0 (168.0, 222.0) 187.0 (166.2, 209.0) 190.0 (166.0, 215.0) 199.0 (173.0, 227.0) 194.0 (169.0, 226.8) 180.0 (153.0, 211.0) < 0.001 HDL-C 50.0 (41.0, 61.0) 64.0 (54.0, 74.0) 51.0 (43.0, 61.0) 49.0 (41.0, 61.0) 46.0 (38.0, 56.0) 48.0 (40.0, 60.0) < 0.001 eGFR 96.1 (80.1, 109.8) 101.2 (89.7, 112.6) 105.0 (91.4, 117.2) 94.8 (79.4, 107.7) 41.2 (28.5, 56.6) 77.4 (60.4, 93.0) < 0.001 UACR 6.8 (4.4, 13.3) 5.6 (3.9, 8.3) 5.2 (3.7, 7.8) 7.6 (4.7, 15.7) 94.7 (25.8, 529.0) 10.7 (5.9, 29.9) < 0.001 Glycohemoglobin 5.5 (5.2, 5.8) 5.2 (5.0, 5.4) 5.3 (5.1, 5.6) 5.6 (5.3, 5.9) 6.0 (5.5, 7.1) 5.8 (5.4, 6.3) < 0.001 CDAI 0.0 (− 2.1, 2.7) 0.6 (− 1.6, 3.2) 0.3 (− 1.9, 2.9) -0.1 (− 2.1, 2.6) -1.2 (-3.0, 1.2) -0.5 (-2.5, 2.1) < 0.001 Abbreviation: CKM, cardiovascular-kidney-metabolic syndrome; PIR, poverty income ratio; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; UACR, urinary albumin to creatinine ratio; CDAI, composite dietary antioxidant index. Table 2 Inverse Association Between CDAI Quartiles and Progression to CKM Syndrome Stages CKM Stage (Ref: Stage 0) CDAI Quartile OR (95%CI) P Stage 1 Q1 Ref - Q2 0.96 (0.74–1.23) 0.726 Q3 0.77 (0.61–0.98) 0.034 Q4 0.71 (0.56–0.91) 0.007 Stage 2 Q1 Ref - Q2 0.91 (0.71–1.16) 0.455 Q3 0.64 (0.51–0.81) < 0.001 Q4 0.58 (0.45–0.74) < 0.001 Stage 3 Q1 Ref - Q2 0.72 (0.49–1.06) 0.094 Q3 0.44 (0.29–0.65) < 0.001 Q4 0.30 (0.20–0.47) < 0.001 Stage 4 Q1 Ref - Q2 0.79 (0.60–1.03) 0.085 Q3 0.52 (0.40–0.68) < 0.001 Q4 0.46 (0.35–0.60) < 0.001 P values from the multinomial logistic regression models adjusted for age, gender, race, education level, poverty income ratio, smoking status, alcohol status, and physical activity. Q1 (lowest) to Q4 (highest) CDAI quartiles. Abbreviation: OR, odds ratios; CI, confidence interval; CDAI, composite dietary antioxidant index; CKM, cardiovascular-kidney-metabolic syndrome. 3.2 Relationship between CDAI and CKM syndrome staging Multinomial logistic regression revealed a significant inverse association between CDAI quartiles and progression to advanced CKM stages relative to the non-CKM reference group (Stage 0). Compared to the lowest CDAI quartile (Q1), participants in higher quartiles demonstrated progressively reduced odds of advancing to the CKM stage after full covariate adjustment. Specifically, the highest quartile (Q4) exhibited substantially lower odds ratios across all stages: Stage 1 (OR: 0.71; 95% CI: 0.56–0.91; P = 0.007), Stage 2 (OR: 0.58; 95% CI: 0.45–0.74; P < 0.001), Stage 3 (OR: 0.30; 95% CI: 0.20–0.47; P < 0.001), and Stage 4 (OR: 0.46; 95% CI: 0.35–0.60; P < 0.001). A dose-dependent protective relationship was evident, with the intermediate quartiles (Q2-Q3) showing attenuated risk reduction. Specifically, Q3 demonstrated statistically significant risk reduction, whereas Q2 did not reach statistical significance across all stages. These findings demonstrate that elevated dietary antioxidant intake, as quantified by CDAI, is robustly associated with decreased likelihood of CKM syndrome development and progression. 3.3 Weighted Quantile Sum Regression Model WQS regression analysis revealed a significant protective association between the overall dietary antioxidant mixture (vitamins A, C, E, zinc, selenium, and carotenoids) and advanced CKM syndrome (Stages 3–4 vs. Stages 1–2). This inverse relationship was consistent and highly significant ( P < 0.001) across all models, even after sequential adjustment for potential confounders (Table 3 ). The fully adjusted Model 3 yielded an OR of 0.82 (95% CI: 0.76–0.88) per unit increase in the overall dietary antioxidant mixture. Component weight analysis revealed that vitamin A (weight = 0.357), vitamin C (weight = 0.290), and selenium (weight = 0.212) contributed most substantially to this protective effect, followed by vitamin E (0.073), zinc (0.050), and carotenoids (0.019; Fig. 1 ). Table 3 Odds Ratios for Advanced CKM Syndrome from the Overall Dietary Antioxidant Mixture Effect in WQS Regression Model OR (95% CI) P Model 1 0.77 (0.73, 0.81) < 0.001 Model 2 0.76(0.71, 0.82) < 0.001 Model 3 0.82(0.76, 0.88) < 0.001 Model 1: the unadjusted WQS regression. Model 2: the WQS regression adjusted for age, gender, and race. Model 3: the WQS regression adjusted for age, gender, race, education level, poverty income ratio, smoking status, alcohol status, and physical activity. Note: The outcome variable, Advanced CKM Syndrome, was defined as CKM Stages 3-4 versus Stages 1-2. Abbreviation: OR, odds ratios; CI, confidence interval; CKM, cardiovascular-kidney-metabolic syndrome; WQS, weighted quantile sum. To validate these findings, multivariable logistic regression was performed for each antioxidant component (Supplementary Table S7). In Model 3, vitamin C (OR: 0.95, 95% CI: 0.90–0.99; P = 0.021), vitamin E (OR: 0.92, 95% CI: 0.88–0.96; P < 0.001), and selenium (OR: 0.92, 95% CI: 0.88–0.97; P = 0.001) retained statistically significant inverse associations with advanced CKM syndrome. Vitamin A, zinc, and carotenoids did not reach significance in the fully adjusted model. For mortality outcomes in CKM patients, WQS regression similarly indicated protective effects of a higher overall dietary antioxidant mixture against all-cause (HR: 0.78, 95% CI: 0.73–0.82), CVD (HR: 0.79, 95% CI: 0.70–0.88), and non-CVD mortality (HR: 0.82, 95% CI: 0.77–0.88; all P < 0.001; Supplementary Table S8). Component weights for mortality outcomes are detailed in Supplementary Figure S2. 3.4 Associations between CDAI and mortality outcomes in CKM syndrome patients Kaplan-Meier survival analyses demonstrated significant differences in cumulative survival probabilities across CDAI quartiles for all mortality endpoints (all log-rank P < 0.001; Supplementary Figure S3). Participants in the highest quartile (Q4) exhibited the highest survival probability, while those in the lowest quartile (Q1) showed the poorest survival outcomes. RCS analyses revealed nonlinear relationships between continuous CDAI and mortality risk (Supplementary Figure S4). For all-cause mortality ( P-nonlinear < 0.001) and non-CVD mortality ( P-nonlinear < 0.001), a threshold effect was observed: mortality risk decreased linearly with increasing CDAI until a node point (CDAI ≈ 0), beyond which the association plateaued. In contrast, CVD mortality exhibited a linear inverse association ( P-nonlinear = 0.408). Multivariable Cox proportional hazards models confirmed robust, dose-dependent reductions in mortality risk with higher CDAI quartiles (Table 4 ). Compared to Q1 (reference), Q4 was associated with significantly lower risks of all-cause mortality (HR = 0.63; 95% CI: 0.57–0.70), CVD mortality (HR = 0.63; 95% CI: 0.51–0.78), and non-CVD mortality (HR = 0.63; 95% CI: 0.56–0.72; all P < 0.001). Intermediate quartiles (Q2–Q3) also showed progressively reduced risks, though Q2 did not reach significance for CVD mortality (HR = 1.04; 95% CI: 0.86–1.25). Table 4 Associations of CDAI Quartiles with Mortality in Patients with CKM Syndrome Mortality Outcome CDAI Quartile HR (95%CI) P All-cause Q1 Ref - Q2 0.86 (0.78–0.95) 0.003 Q3 0.73 (0.66–0.80) < 0.001 Q4 0.63 (0.57–0.70) < 0.001 Cardiovascular Q1 Ref - Q2 1.04 (0.86–1.25) 0.686 Q3 0.81 (0.66–0.98) 0.033 Q4 0.63 (0.51–0.78) < 0.001 Non-cardiovascular Q1 Ref - Q2 0.81 (0.72–0.90) < 0.001 Q3 0.70 (0.62–0.78) < 0.001 Q4 0.63 (0.56–0.72) < 0.001 P values from the multivariable Cox proportional hazards models adjusted for age, gender, race and ethnicity, education level, poverty income ratio, smoking status, alcohol status, and physical activity. Q1 (lowest) to Q4 (highest) CDAI quartiles. Abbreviation: CI, confidence interval; HR, hazard ratio; CDAI, composite dietary antioxidant index; CKM, cardiovascular-kidney-metabolic syndrome. Stratified analyses by CKM stage severity revealed differential protective effects (Supplementary Table S9). In early-stage CKM (Stages 1–2), higher CDAI quartiles (Q3–Q4) were strongly associated with reduced all-cause, CVD, and non-CVD mortality (e.g., Q4 all-cause HR = 0.64; 95% CI: 0.57–0.73). Conversely, in advanced stages (Stages 3–4), only Q4 conferred significant protection against all-cause (HR = 0.83; 95% CI: 0.70–0.99) and non-CVD mortality (HR = 0.78; 95% CI: 0.63–0.96), with no significant association for CVD mortality. 3.5 Optimal risk stratification cut-off points for CDAI on mortality outcomes in CKM patients Optimal CDAI cut-offs for mortality risk stratification were identified as − 0.20 (all-cause), 0.68 (CVD), and − 1.43 (non-CVD; Supplementary Figure S5). Survival curves based on these thresholds further validated the discriminative capacity of CDAI, with significantly higher survival in patients above versus below each cut-off (all log-rank P < 0.001; Supplementary Figure S6). 3.6 Sensitivity analysis After excluding early deaths (≤ 2 years follow-up; Supplementary Table S10) or participants with a cancer history (Supplementary Table S11), the association between higher CDAI quartiles and significantly reduced risks of all-cause, CVD, and non-CVD mortality was still observed in fully adjusted models. Discussion This study demonstrates a significant inverse association between higher CDAI scores and both reduced severity of CKM staging and lower all-cause, CVD, and non-CVD mortality risk in individuals with CKM syndrome. We observed a clear dose-response relationship, where ascending CDAI quartiles correlated with progressively lower odds of advanced CKM stages and reduced mortality. WQS regression confirmed the protective effect of the overall dietary antioxidant mixture against advanced CKM, primarily driven by vitamins A, C, and selenium. Nonlinear relationships and optimal risk-stratification thresholds for mortality were also identified. These findings underscore CDAI as a valuable tool for risk stratification and highlight the protective role of antioxidant-rich diets in CKM management, particularly during early disease stages. The observed inverse associations between higher CDAI and both CKM staging severity and mortality risk are mechanistically plausible through the mitigation of oxidative stress, a central pathophysiological driver of CKM progression and complications 7 – 9 . Constituent antioxidants within CDAI counteract oxidative damage via synergistic pathways: zinc serves as a critical cofactor for superoxide dismutase 23 , selenium is incorporated into selenoproteins like glutathione peroxidase to prevent lipid peroxidation 24 , vitamins A, C, and E act as key non-enzymatic antioxidants, scavenging free radicals and protecting cellular structures 25 , and carotenoids activate the Nrf2 pathway, inducing detoxifying and antioxidant enzymes 26 . Crucially, under pathological states inherent in CKM (metabolic dysregulation, CKD, CVD), excessive ROS production overwhelms endogenous defenses, leading to oxidative macromolecular damage that promotes inflammation, apoptosis, tissue injury, and adverse outcomes 7 – 9 . The integrated protective effect captured by the CDAI – addressing antioxidant defense through enzymatic, non-enzymatic, and transcriptional regulation pathways – provides a comprehensive countermeasure to this multifaceted oxidative burden, more effectively than assessments of individual nutrients. Our findings align with previous evidence linking higher CDAI scores to lower risks of individual CKM components, including hypertension 15 , metabolic disorders 27 , CKD 28 , and CVD 29 , supporting dietary antioxidant optimization as a strategy against CKM pathogenesis. Notably, WQS analysis identified vitamins A, C, and selenium as the primary contributors to the protective effect against advanced CKM, whereas multivariable regression showed significant independent associations only for vitamins C, E, and selenium. This discrepancy likely reflects methodological differences: WQS captures synergistic interactions within the antioxidant mixture [e.g., vitamin C regenerating vitamin E 30 ], where vitamin A—often co-consumed with vitamin C-rich foods 31 , 32 — may amplify effects without strong independent associations after covariate adjustment. Multivariable regression, conversely, isolates individual effects potentially attenuated by collinearity or residual confounding [e.g., bioavailability variations 33 ]. Thus, WQS highlights the importance of the dietary pattern, while regression underscores the non-redundant roles of vitamins C/E and selenium. Stratified analysis revealed attenuated protection against CVD mortality in advanced CKM (Stages 3–4), consistent with the syndrome’s pathophysiology. In advanced stages, extensive irreversible organ damage (e.g., myocardial fibrosis, renal sclerosis, vascular calcification) driven by chronic oxidative stress likely dominates clinical outcomes 7 , 10 , 11 . Dietary antioxidants exert greater effects in early disease by modulating reversible pathways like endothelial dysfunction and metabolic dysregulation 8 , 9 . However, once severe structural cardiovascular damage is established, their capacity to offset CVD-specific mortality risk may be limited 10 . This underscores the critical need for early intervention: optimizing CDAI in Stages 1–2 may delay progression, whereas Stages 3–4 require combinatorial approaches (e.g., pharmacotherapy alongside dietary modifications) 5 , 34 . Based on these findings, several evidence-based recommendations for clinical practice emerge. Healthcare providers should prioritize dietary counseling for patients at risk of or diagnosed with CKM, emphasizing the adoption of antioxidant-rich diets focused on key sources like fruits (especially berries and citrus fruits rich in vitamin C), vegetables (particularly leafy greens and colorful vegetables rich in carotenoids, vitamin C, and vitamin E), nuts (rich in vitamin E and selenium), and whole grains 35 . Furthermore, leveraging CDAI scores, or dietary patterns reflecting them, can help develop individualized dietary plans, and regular monitoring of dietary habits allows for dynamic tailoring of interventions. In addition, public health initiatives should raise awareness of the critical role dietary antioxidants play in mitigating CKM risk, highlighting the benefits of balanced diets rich in these compounds and educating the public about oxidative stress. A multidisciplinary care model integrating dietitians or nutritionists is essential to synergize these targeted dietary modifications with other evidence-based lifestyle strategies, such as physical activity and stress management 34 . Finally, the identified robust dose-response relationship and protective thresholds support the potential utility of the CDAI as a valuable tool for risk stratification in CKM; consequently, existing clinical guidelines for CKM management should be updated to incorporate recommendations for assessing and optimizing dietary antioxidant status (reflected by the CDAI or its components) as a routine part of risk assessment and preventive care for both affected patients and high-risk individuals. This study has several methodological limitations that warrant consideration. Primarily, the cross-sectional design inherently precludes inferring temporal sequence or establishing causality regarding the association between CDAI and CKM staging; confirming the etiopathogenic link and assessing the impact of dietary interventions requires future prospective longitudinal cohort studies or randomized clinical trials. Furthermore, while a comprehensive set of potential confounders was meticulously controlled for, the possibility of residual confounding due to unmeasured or unknown variables remains inherent to observational studies. The reliance on self-reported dietary data (24-hour recalls) for calculating the CDAI introduces susceptibility to recall bias, and these estimates may inadequately capture variations in nutrient bioavailability or losses during food preparation; future studies should incorporate objective biochemical markers of antioxidant micronutrient status for more quantitative exposure assessment. Additionally, the CDAI itself represents a simplification, as it does not account for potential synergistic or antagonistic effects with non-antioxidant dietary components or inter-individual variations in metabolic responses that modulate antioxidant impact. The characteristics of the NHANES U.S. population also limit the generalizability of findings to other ethnicities or populations with distinct dietary patterns and genetic backgrounds, making replication in more diverse cohorts essential. Finally, further mechanistic research is warranted to elucidate the precise pathways through which the CDAI confers protection, as such insights could inform the development of novel targeted therapies or refined dietary supplement strategies. Conclusion Higher dietary antioxidant intake, quantified by CDAI, is robustly associated with less severe CKM staging and significantly reduced mortality risk. CDAI shows promise as a tool for risk stratification, supporting the importance of antioxidant-rich diets in CKM management, particularly for early intervention. Abbreviations CVD Cardiovascular disease CKD Chronic kidney disease AHA American Heart Association CKM Cardiovascular-kidney-metabolic Syndrome ROS Reactive oxygen species CDAI Composite Dietary Antioxidant Index NHANES National Health and Nutrition Examination Survey NCHS National Center for Health Statistics PIR Poverty income ratio BMI Body mass index SBP Systolic blood pressure DBP Diastolic blood pressure TC Total Cholesterol HDL-C High-density lipoprotein cholesterol eGFR Estimated glomerular filtration rate UACR Urinary albumin to creatinine ratio IQRs Interquartile ranges ORs Odds ratios CIs Confidence intervals WQS Weighted Quantile Sum HRs Hazard ratios RCS Restricted cubic splines Declarations Ethics approval and consent to participate The National Center for Health Statistics Research Ethics Review Board approved all National Health and Nutrition Examination Survey protocols, and written informed consent was obtained from all participants. Consent for publication Not applicable. Availability of data and materials The datasets used for this study are available in the National Health and Nutrition Examination Survey (https://wwwn.cdc.gov/nchs/nhanes). Competing interests All authors declare no competing interests. Funding This study was funded by the National Natural Science Youth Foundation of China (Grant Number 82003556). Authors' Contributions FDW designed the study; XTG and FDW drafted the manuscript; XTG and SMY carried out data analyses; YBY, YY, and QM advised on data interpretation; YPW and DL supervised the study. All authors read and approved the final manuscript. Acknowledgments We sincerely thank the National Health and Nutrition Examination Survey participants and the survey, development, and management teams for contributing to this project. 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The American journal of clinical nutrition . May 2010;91(5):1468s-1473s. doi:10.3945/ajcn.2010.28674G Taherkhani S, Suzuki K, Castell L. A Short Overview of Changes in Inflammatory Cytokines and Oxidative Stress in Response to Physical Activity and Antioxidant Supplementation. Antioxidants (Basel, Switzerland) . Sep 18 2020;9(9)doi:10.3390/antiox9090886 Li S, Chen G, Zhang C, Wu M, Wu S, Liu Q. Research progress of natural antioxidants in foods for the treatment of diseases. Food Science and Human Wellness . 2014;3(3-4):110-116. doi:10.1016/j.fshw.2014.11.002 Supplementary Files SupplementaryInformation.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7380717","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502070123,"identity":"aa4fe37e-8ce9-4997-8577-e38fae4b786d","order_by":0,"name":"yupeng wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYHAD5mMMjA1glgGxWtjSSNbCY0acFoPjZw+/5m27I2fOv+bb48od2xIb2Ju3STDU3MGt5UxemjVv2zNjyxlvtxuePXM7sYHnWJkEw7FnOLWYHcgxM+ZtO5y44cbZbZKNbUAtEjlmEowNh3FrOf8GrKV+w40zzyBa5N8Q0HIjx/gxUEuCwfkeNqgtPPi12N94Y8Y459wzww032MwkG8/cNm7jSSu2SDiGW4tkf47xhzdld+QNzh8GOmzHbdl+9sMbb3yowa0FCNikeBgOMDBIJEC5ICIBnwZgQvn4A6SF/wB+ZaNgFIyCUTByAQArGGCwyGqjOQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-4624-679X","institution":"Harbin Medical University","correspondingAuthor":true,"prefix":"","firstName":"yupeng","middleName":"","lastName":"wang","suffix":""},{"id":502070124,"identity":"b887a943-26d8-437a-a92e-3b3092820212","order_by":1,"name":"Xintong Gao","email":"","orcid":"","institution":"Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xintong","middleName":"","lastName":"Gao","suffix":""},{"id":502070125,"identity":"9d5ddd53-6f84-4155-8766-714eced71b55","order_by":2,"name":"Fudong Wen","email":"","orcid":"","institution":"Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fudong","middleName":"","lastName":"Wen","suffix":""},{"id":502070126,"identity":"e1da7bbd-3208-4c53-92b0-72be7db1f604","order_by":3,"name":"Simeng Yu","email":"","orcid":"","institution":"Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Simeng","middleName":"","lastName":"Yu","suffix":""},{"id":502070127,"identity":"29324494-ed59-4ab0-9e28-34b921c3ada0","order_by":4,"name":"Yubing Yang","email":"","orcid":"","institution":"Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yubing","middleName":"","lastName":"Yang","suffix":""},{"id":502070128,"identity":"85a22f44-92e1-4610-877d-5ff7e1e1d021","order_by":5,"name":"Yi Yang","email":"","orcid":"","institution":"Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Yang","suffix":""},{"id":502070129,"identity":"fa226e85-391d-490c-93a0-eb898a59a00a","order_by":6,"name":"Qiang Ma","email":"","orcid":"","institution":"The 2nd Affiliated Hospital of Harbin Medical University: Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Ma","suffix":""},{"id":502070130,"identity":"49179dea-0c84-469d-99b8-546005dbbc57","order_by":7,"name":"Dan Liu","email":"","orcid":"","institution":"Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Dan","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-08-15 10:52:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7380717/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7380717/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89987646,"identity":"cfabfe5b-1b67-4edb-87e2-545acefbcecb","added_by":"auto","created_at":"2025-08-27 07:00:43","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":272089,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eContribution of CDAI Components to Advanced CKM Syndrome in WQS Regression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e The Weights were from the WQS regression adjusted for age, gender, race, education level, poverty income ratio, smoking status, alcohol status, and physical activity. The outcome variable, Advanced CKM Syndrome, was defined as CKM Stages 3-4 versus Stages 1-2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviation:\u003c/strong\u003e CDAI, composite dietary antioxidant index; CKM, cardiovascular-kidney-metabolic syndrome; WQS, weighted quantile sum.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7380717/v1/b0f37d48a2c8b48a70ad5112.jpeg"},{"id":90043022,"identity":"d6fe842b-4135-4d82-b23f-9e71ff2d70df","added_by":"auto","created_at":"2025-08-27 17:29:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1789868,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7380717/v1/731b45c6-b8a9-4308-9cde-0cd7a7a8bc2d.pdf"},{"id":89989317,"identity":"ca806873-ef8f-42f9-91fc-673e55d500b8","added_by":"auto","created_at":"2025-08-27 07:08:44","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1242671,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7380717/v1/5ee147bff68e39c3da1f61ff.pdf"}],"financialInterests":"","formattedTitle":"Role of composite dietary antioxidant index in staging and mortality risk of cardiovascular-kidney-metabolic syndrome: a study from NHANES 2001-2018","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe intertwined global burdens of cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders continue to escalate\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. CVD remains the leading cause of mortality worldwide, accounting for approximately 17.9\u0026nbsp;million deaths in 2019\u003csup\u003e2\u003c/sup\u003e. Concurrently, CKD affects an estimated 10% of the global population (~\u0026thinsp;850\u0026nbsp;million individuals)\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, while metabolic syndrome\u0026mdash;a key driver of both CVD and CKD\u0026mdash;has a global prevalence of ~\u0026thinsp;25%\u003csup\u003e4\u003c/sup\u003e. Recognizing the interplay among these conditions, the American Heart Association (AHA) introduced cardiovascular-kidney-metabolic (CKM) syndrome in 2023. CKM syndrome represents a progressive, systemic health condition characterized by the co-occurrence of cardiovascular disease, diabetes, obesity, chronic kidney disease, and other metabolic derangements\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Notably, nearly 90% of US adults met CKM criteria (2011\u0026ndash;2020), with 15% having advanced-stage disease \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBeyond its clinical prevalence, the molecular pathogenesis of CKM syndrome involves interconnected pathways, prominently featuring oxidative stress as a central driver\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Characterized by excessive reactive oxygen species (ROS) production overwhelming endogenous antioxidant defenses, it induces cellular damage to proteins, lipids, and nucleic acids\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. This process underpins metabolic dysregulation by disrupting energy metabolism, cell signaling, and genomic integrity\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Furthermore, oxidative stress accelerates cardiovascular and renal pathophysiology: ROS directly promotes cardiac dysfunction\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e and exacerbates CKD progression through interactions with immune dysregulation, hypertension, and metabolic perturbations\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGiven the central role of oxidative stress in CKM pathogenesis\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, endogenous antioxidant defenses are critical for maintaining redox homeostasis by eliminating ROS and mitigating cellular damage\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Compromised antioxidant capacity contributes to multi-organ pathology. Dietary antioxidants represent a modifiable source of support, with evidence showing that adequate intake elevates plasma antioxidant levels and reduces systemic oxidative stress\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Consequently, dietary modification presents a viable preventive approach. The Composite Dietary Antioxidant Index (CDAI) was developed to holistically assess dietary antioxidant potential, incorporating six key antioxidants: vitamins A, C, E, zinc, selenium, and carotenoids\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Higher CDAI scores consistently show inverse associations with risk/mortality of CKM-implicated conditions, including hypertension\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, cancer\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, and cardiovascular disease\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWhile higher CDAI scores are associated with reduced risk/mortality in isolated CKM-related conditions (hypertension, cancer, CVD)\u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, the integrated pathophysiology of CKM syndrome\u0026mdash;driven by oxidative stress\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e \u0026mdash;demands assessment of dietary antioxidants' role in disease staging and prognosis. This study uniquely evaluates CDAI's relationship with CKM staging across metabolic, renal, and cardiovascular domains, and its association with mortality in established CKM. Using NHANES (2001\u0026ndash;2018) data, we investigate CDAI as both a predictor of CKM severity and modulator of survival outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study population\u003c/h2\u003e\u003cp\u003eThis study utilized data from the NHANES, a nationally representative program that assesses the health and nutritional status of non-institutionalized U.S. civilians through structured household interviews and standardized physical examinations conducted biennially by the National Center for Health Statistics (NCHS). The NHANES database provides comprehensive demographic, dietary, clinical, laboratory, and psychosocial variables, enabling investigation of disease risk factors and nutrition-health relationships. The NCHS Research Ethics Review Board approved all NHANES protocols, and written informed consent was obtained from all participants\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFrom the initial 91,351 NHANES 2001\u0026ndash;2018 participants, we applied the following exclusion criteria: age\u0026thinsp;\u0026lt;\u0026thinsp;20 years (n\u0026thinsp;=\u0026thinsp;41,150), pregnancy (n\u0026thinsp;=\u0026thinsp;1,258), lack of dietary information (n\u0026thinsp;=\u0026thinsp;5,590), missing CKM syndrome status (n\u0026thinsp;=\u0026thinsp;4,018), missing survival status (n\u0026thinsp;=\u0026thinsp;69), and missing covariate data (n\u0026thinsp;=\u0026thinsp;14,111). After these exclusions, 25,155 eligible participants were enrolled in the final analytical cohort (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Measurement of composite dietary antioxidant index\u003c/h2\u003e\u003cp\u003eDietary intake data were obtained from two non-consecutive 24-hour dietary recall interviews administered to participants within the NHANES 2001\u0026ndash;2018 cycles. The initial interview was conducted in person at a mobile examination center, and the second recall was performed via telephone 3 to 10 days later. Participants were instructed to report all foods and beverages, including dietary supplements, consumed within the preceding 24-hour period, detailing types, amounts, and preparation methods. The average intake derived from these two recalls was utilized to represent the participants' usual dietary intake, a validated approach for estimating habitual intake in large epidemiological studies\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTotal daily intake of six dietary antioxidants (vitamins A, C, E, zinc, selenium, and carotenoids) was derived from dietary data using USDA nutrient composition databases\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The CDAI was calculated exclusively from food sources, excluding supplements or other non-dietary contributions.\u003c/p\u003e\u003cp\u003eThe CDAI was calculated according to the established methodology described by Wright et al\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. For each of the six antioxidants, the population mean (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e) and standard deviation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{s}_{i}\\)\u003c/span\u003e\u003c/span\u003e) of daily intake were determined. An individual's standardized intake (z-score) for each antioxidant (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e) was computed as the difference between their daily intake (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{i}\\)\u003c/span\u003e\u003c/span\u003e) and the population mean (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e), divided by the population standard deviation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{s}_{i}\\)\u003c/span\u003e\u003c/span\u003e). The CDAI represents the sum of these standardized scores across all six antioxidants:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:CDAI=\\sum\\:_{i=1}^{6}\\frac{{x}_{i}-{\\mu\\:}_{i}}{{s}_{i}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThus, the CDAI provides a single, integrated measure reflecting an individual's overall dietary antioxidant intake relative to the study population, with higher values indicating higher combined antioxidant intake.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Cardiovascular-Kidney-Metabolic Syndrome Assessment\u003c/h2\u003e\u003cp\u003eCKM syndrome was defined and staged according to the AHA framework\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, which integrates metabolic risk factors, CKD, and subclinical/clinical CVD into a progressive staging system (Stages 0\u0026ndash;4). This classification reflects the pathophysiological continuum linking metabolic dysregulation, renal impairment, and cardiovascular damage.\u003c/p\u003e\u003cp\u003eStaging implementation:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eStage 0: Absence of CKM risk factors.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStage 1: Presence of excess adiposity, dysmetabolism, or prediabetes.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStage 2: Metabolic risk factors (hypertension, diabetes, dyslipidemia) and/or moderate-to-high risk CKD \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStage 3: Very high-risk CKD or high predicted 10-year CVD risk (\u0026ge;\u0026thinsp;20%) calculated using AHA PREVENT equations\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStage 4: Established clinical CVD.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eDetailed diagnostic thresholds for metabolic parameters (waist circumference, blood pressure, laboratory values) and CKD classification criteria are provided in Supplementary Tables S1-S3. The staging algorithm prioritizes the highest qualifying stage when multiple conditions coexist\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Survival Outcome Assessment\u003c/h2\u003e\u003cp\u003eThe primary survival outcomes assessed in this study were all-cause mortality, CVD mortality, and non-CVD mortality. Mortality status (alive or deceased) and underlying cause of death were ascertained through linkage of participant records to the National Death Index database, maintained by the Centers for Disease Control and Prevention. Data on vital status and cause of death were available through December 31, 2019, ensuring a minimum follow-up duration for analysis. Causes of death were classified according to the Tenth Revision of the International Statistical Classification of Diseases and Related Health Problems.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Covariates\u003c/h2\u003e\u003cp\u003eTo account for potential confounding influences on the relationship between CDAI, CKM staging, and mortality, we identified and included a comprehensive set of covariates based on established associations with these outcomes. Covariates were categorized as follows:\u003c/p\u003e\u003cp\u003eSociodemographic Factors: Age (years); Gender (male or female); Race (Mexican American, Non-Hispanic White, Non-Hispanic Black, Other); Education level (Less than high school, High school graduate, More than high school); Marital status (Married/Living with partner, Living alone); Poverty Income Ratio (PIR) (\u0026lt;\u0026thinsp;1.0, \u0026ge;\u0026thinsp;1.0), representing the ratio of family income to the federal poverty threshold.\u003c/p\u003e\u003cp\u003eBehavioral and Lifestyle Factors: Smoking status (Never smoked, Former smoker, Current smoker); Alcohol consumption status (Non-drinker, Mild-to-moderate drinker, Heavy drinker); Physical activity intensity (Low, Medium, High). Activity intensity was classified based on self-reported participation in vigorous or moderate activities over the past 30 days. Participants who answered \"No\", \"Unable to do activity\", \"Refuse\", or \"Don't know\" to both the vigorous and moderate activity questions were categorized as having \"Low\" intensity. Those reporting either moderate or vigorous activity were classified as \"Medium\" or \"High\" based on standard NHANES definitions applied within our analysis.\u003c/p\u003e\u003cp\u003eClinical and Anthropometric Measurements: Body Mass Index (BMI, kg/m\u0026sup2;); Systolic Blood Pressure (SBP, mmHg); Diastolic Blood Pressure (DBP, mmHg).\u003c/p\u003e\u003cp\u003eLaboratory Parameters: Total Cholesterol (TC, mg/dL); High-Density Lipoprotein Cholesterol (HDL-C, mg/dL); Estimated Glomerular Filtration Rate (eGFR, mL/min/1.73m\u0026sup2;; calculated using the CKD-EPI equation); Urine Albumin-to-Creatinine Ratio (UACR, mg/g); Glycohemoglobin (HbA1c, %).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eDescriptive Statistics\u003c/strong\u003e\u003cp\u003eBaseline characteristics of the study population, stratified by CKM stage and CDAI quartiles, were summarized. Categorical variables were presented as frequencies and percentages. Continuous variables, assessed for normality and found to be non-normally distributed, were presented as medians with interquartile ranges (IQRs). Group comparisons used chi-square tests for categorical variables and Kruskal-Wallis tests for continuous variables.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssociation between CDAI and CKM Staging (Cross-sectional)\u003c/b\u003e: The relationship between CDAI (modeled both continuously and categorically in quartiles: Q1-lowest to Q4-highest) and CKM staging was assessed using multinomial logistic regression. Initially, ordered logistic regression was considered for the ordinal outcome (Stages 0\u0026ndash;4). However, the proportional odds assumption was violated (significant parallel lines test, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Therefore, multinomial logistic regression was employed, treating CKM stage as a nominal outcome. The non-CKM group (Stage 0) served as the reference. Models were adjusted for age, gender, race, education level, PIR, smoking status, alcohol consumption status, and physical activity intensity. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated.\u003c/p\u003e\u003cp\u003e\u003cb\u003eComponent Analysis of CDAI and CKM\u003c/b\u003e: To evaluate the joint effect and relative contribution of the six individual dietary antioxidants (vitamins A, C, E, zinc, selenium, carotenoids) comprising the CDAI on advanced CKM (Stages 3\u0026ndash;4 vs. Stages 1\u0026ndash;2), Weighted quantile sum (WQS) regression was employed. This method estimates an overall mixture effect index and assigns weights to each component reflecting their relative contribution to the association. WQS regression was performed in three progressively adjusted models: Model 1(Unadjusted); Model 2 (Adjusted for age, gender, and race); Model 3 (Adjusted for age, gender, race, education level, PIR, smoking status, alcohol consumption status, and physical activity intensity). Additionally, multivariable logistic regression models (using the same adjustment sets as WQS models) were fitted for each antioxidant component to assess their independent associations with advanced CKM.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAssociation between CDAI and Mortality in CKM Patients (Longitudinal)\u003c/strong\u003e\u003cp\u003eSurvival analyses were restricted to participants with CKM syndrome (Stages 1\u0026ndash;4). The primary endpoints were all-cause mortality, CVD mortality, and non-CVD mortality.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eKaplan-Meier Analysis: Cumulative survival probabilities across CDAI quartiles were visualized using Kaplan-Meier curves. Statistical differences between quartile groups were assessed using the log-rank test.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCox Proportional Hazards Models: Multivariable Cox proportional hazards regression was used to quantify the association between CDAI quartiles and mortality risk. The lowest quartile (Q1) served as the reference group. Models were adjusted for age, gender, race, education level, PIR, smoking status, alcohol consumption status, and physical activity intensity. Hazard ratios (HRs) and 95% CIs were calculated.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eNonlinearity Assessment: The potential nonlinear relationship between continuous CDAI and mortality risk was explored using restricted cubic splines (RCS) with 3 knots (selected based on the lowest Bayesian Information Criterion; see Supplementary Table S4). The reference point for HR calculation was set at the median CDAI value. The P-value for nonlinearity was reported.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStratified Analysis: Cox models were also performed stratified by CKM stage (Stages 1\u0026ndash;2 vs. Stages 3\u0026ndash;4) to explore potential effect modification.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eOptimal Risk Stratification Cut-off: For each mortality outcome, the optimal cut-off point for CDAI to stratify mortality risk in CKM patients was identified using the maximum selected rank statistics method implemented in the \u0026ldquo;survminer\u0026rdquo; package. Kaplan-Meier survival curves based on these optimal cut-offs were generated.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSensitivity Analyses\u003c/strong\u003e\u003cp\u003eTwo sensitivity analyses were conducted to evaluate the robustness of the primary mortality findings. First, to mitigate potential reverse causality, CKM patients who died within the first two years of follow-up were excluded, and the Cox regression models were re-run. Second, given the strong influence of cancer on mortality, CKM patients with a history of cancer were excluded, and the Cox regression models were re-analyzed.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eAll analyses were performed using R software (version 4.4.3), with statistical significance defined as a two-sided \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Participant characteristics\u003c/h2\u003e\u003cp\u003eThis study included 25,155 eligible participants (median [IQR] age: 49.0 [35.0, 63.0] years; 51.1% male; median [IQR] follow-up: 105.0 [58.0, 154.0] months). Among them, 650 (2.6%) were without CKM syndrome (Stage 0), while 24,505 (97.4%) had CKM syndrome (Stage 1: n\u0026thinsp;=\u0026thinsp;6,881; Stage 2: n\u0026thinsp;=\u0026thinsp;14,850; Stage 3: n\u0026thinsp;=\u0026thinsp;314; Stage 4: n\u0026thinsp;=\u0026thinsp;2,460). Baseline characteristics stratified by CKM stage are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Significant differences (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were observed across CKM stages for all assessed variables, including sociodemographic factors (age, gender, race, education, PIR, marital status), lifestyle factors (smoking, alcohol consumption, physical activity), clinical parameters (BMI, SBP, DBP), and laboratory measures (TC, HDL-C, eGFR, UACR, glycohemoglobin). Notably, the CDAI progressively decreased with advancing CKM stage (median [IQR]: Stage 0: 0.6 [\u0026minus;\u0026thinsp;1.6, 3.2]; Stage 1: 0.3 [\u0026minus;\u0026thinsp;1.9, 2.9]; Stage 2: \u0026minus;0.1 [\u0026minus;\u0026thinsp;2.1, 2.6]; Stage 3: \u0026minus;1.2 [\u0026minus;\u0026thinsp;3.0, 1.2]; Stage 4: \u0026minus;0.5 [\u0026minus;\u0026thinsp;2.5, 2.1]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Characteristics of CKM patients according to CDAI quartiles and CKM stage severity (early: Stages 1\u0026ndash;2; advanced: Stages 3\u0026ndash;4) are detailed in Supplementary Tables S5 and S6, respectively.\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 Population Stratified by CKM Stage\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eALL (N\u0026thinsp;=\u0026thinsp;25155)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNon-CKM (N\u0026thinsp;=\u0026thinsp;650)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003eCKM Stage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStage 1 (N\u0026thinsp;=\u0026thinsp;6881)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStage 2 (N\u0026thinsp;=\u0026thinsp;14850)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStage 3 (N\u0026thinsp;=\u0026thinsp;314)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eStage 4 (N\u0026thinsp;=\u0026thinsp;2460)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49.0 (35.0, 63.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41.0 (34.0, 49.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37.0 (28.0, 48.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e52.0 (39.0, 64.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e76.0 (65.0, 80.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e68.0 (58.0, 77.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12856 (51.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e231 (35.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3277 (47.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7737 (52.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e193 (61.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1418 (57.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12299 (48.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e419 (64.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3604 (52.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7113 (47.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e121 (38.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1042 (42.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4214 (16.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60 (9.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1375 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2482 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e42 (13.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e255 (10.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11699 (46.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e415 (63.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2924 (42.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6776 (45.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e141 (44.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1443 (58.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4992 (19.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76 (11.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1278 (18.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3086 (20.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e81 (25.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e471 (19.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4250 (16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e99 (15.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1304 (19.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2506 (16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e50 (15.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e291 (11.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation level (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelow high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2363 (9.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35 (5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e468 (6.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1472 (9.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e71 (22.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e317 (12.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3299 (13.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58 (8.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e855 (12.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1940 (13.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e50 (15.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e396 (16.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbove high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19493 (77.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e557 (85.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5558 (80.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11438 (77.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e193 (61.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1747 (71.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePIR (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4605 (18.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1321 (19.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2649 (17.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e70 (22.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e500 (20.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20550 (81.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e585 (90.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5560 (80.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12201 (82.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e244 (77.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1960 (79.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital status (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19061 (75.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e508 (78.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4469 (64.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11610 (78.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e283 (90.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2191 (89.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiving with a partner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1976 (7.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49 (7.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e736 (10.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1082 (7.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12 (3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97 (3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiving alone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4118 (16.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e93 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1676 (24.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2158 (14.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19 (6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e172 (7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoke status (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever smoked\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13780 (54.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e386 (59.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4128 (60.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8103 (54.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e151 (48.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1012 (41.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreviously smoked\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6075 (24.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e109 (16.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1294 (18.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3615 (24.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e119 (37.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e938 (38.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNow smoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5300 (21.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e155 (23.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1459 (21.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3132 (21.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e44 (14.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e510 (20.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAlcohol status (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-drinker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4152 (16.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70 (10.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e957 (13.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2516 (16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e98 (31.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e511 (20.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMild to moderate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18629 (74.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e545 (83.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5194 (75.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10870 (73.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e204 (65.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1816 (73.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeavy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2374 (9.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35 (5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e730 (10.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1464 (9.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12 (3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e133 (5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eActivity intensity (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6120 (24.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e194 (29.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1943 (28.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3577 (24.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e33 (10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e373 (15.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6229 (24.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e159 (24.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1629 (23.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3708 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e76 (24.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e657 (26.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12806 (50.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e297 (45.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3309 (48.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7565 (50.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e205 (65.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1430 (58.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28.5 (25.3, 32.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.3 (20.8, 23.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.2 (25.9, 31.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29.0 (25.4, 33.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e28.1 (25.4, 32.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e29.1 (25.4, 33.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSBP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e123.0 (113.0, 135.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e109.0 (102.0, 115.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e113.0 (107.0, 119.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e129.0 (118.0, 140.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e145.0 (129.0, 161.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e130.0 (117.0, 145.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDBP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e71.0 (64.0, 79.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e67.0 (61.0, 72.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e68.0 (63.0, 73.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e75.0 (67.0, 83.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e69.0 (58.0, 79.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e68.0 (60.0, 77.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e194.0 (168.0, 222.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e187.0 (166.2, 209.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e190.0 (166.0, 215.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e199.0 (173.0, 227.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e194.0 (169.0, 226.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e180.0 (153.0, 211.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHDL-C\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50.0 (41.0, 61.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64.0 (54.0, 74.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.0 (43.0, 61.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e49.0 (41.0, 61.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e46.0 (38.0, 56.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e48.0 (40.0, 60.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eeGFR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e96.1 (80.1, 109.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e101.2 (89.7, 112.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e105.0 (91.4, 117.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e94.8 (79.4, 107.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e41.2 (28.5, 56.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e77.4 (60.4, 93.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUACR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.8 (4.4, 13.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.6 (3.9, 8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.2 (3.7, 7.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.6 (4.7, 15.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e94.7 (25.8, 529.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10.7 (5.9, 29.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGlycohemoglobin\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.5 (5.2, 5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.2 (5.0, 5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.3 (5.1, 5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.6 (5.3, 5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.0 (5.5, 7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.8 (5.4, 6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCDAI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0 (\u0026minus;\u0026thinsp;2.1, 2.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.6 (\u0026minus;\u0026thinsp;1.6, 3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.3 (\u0026minus;\u0026thinsp;1.9, 2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.1 (\u0026minus;\u0026thinsp;2.1, 2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1.2 (-3.0, 1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.5 (-2.5, 2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAbbreviation:\u0026nbsp;\u003c/strong\u003eCKM, cardiovascular-kidney-metabolic syndrome; PIR, poverty income ratio; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; UACR, urinary albumin to creatinine ratio; CDAI, composite dietary antioxidant index.\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\u003eInverse Association Between CDAI Quartiles and Progression to CKM Syndrome Stages\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCKM Stage (Ref: Stage 0)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCDAI Quartile\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eStage 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.96 (0.74\u0026ndash;1.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.726\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.77 (0.61\u0026ndash;0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.71 (0.56\u0026ndash;0.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eStage 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.91 (0.71\u0026ndash;1.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.455\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.64 (0.51\u0026ndash;0.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.58 (0.45\u0026ndash;0.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eStage 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.72 (0.49\u0026ndash;1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.094\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.44 (0.29\u0026ndash;0.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.30 (0.20\u0026ndash;0.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eStage 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.79 (0.60\u0026ndash;1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.52 (0.40\u0026ndash;0.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.46 (0.35\u0026ndash;0.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eP\u003c/b\u003e \u003cb\u003evalues\u003c/b\u003e from the multinomial logistic regression models adjusted for age, gender, race, education level, poverty income ratio, smoking status, alcohol status, and physical activity. Q1 (lowest) to Q4 (highest) CDAI quartiles.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAbbreviation:\u003c/strong\u003e OR, odds ratios; CI, confidence interval; CDAI, composite dietary antioxidant index; CKM, cardiovascular-kidney-metabolic syndrome.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Relationship between CDAI and CKM syndrome staging\u003c/h2\u003e\u003cp\u003eMultinomial logistic regression revealed a significant inverse association between CDAI quartiles and progression to advanced CKM stages relative to the non-CKM reference group (Stage 0). Compared to the lowest CDAI quartile (Q1), participants in higher quartiles demonstrated progressively reduced odds of advancing to the CKM stage after full covariate adjustment. Specifically, the highest quartile (Q4) exhibited substantially lower odds ratios across all stages: Stage 1 (OR: 0.71; 95% CI: 0.56\u0026ndash;0.91; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), Stage 2 (OR: 0.58; 95% CI: 0.45\u0026ndash;0.74; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Stage 3 (OR: 0.30; 95% CI: 0.20\u0026ndash;0.47; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Stage 4 (OR: 0.46; 95% CI: 0.35\u0026ndash;0.60; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A dose-dependent protective relationship was evident, with the intermediate quartiles (Q2-Q3) showing attenuated risk reduction. Specifically, Q3 demonstrated statistically significant risk reduction, whereas Q2 did not reach statistical significance across all stages. These findings demonstrate that elevated dietary antioxidant intake, as quantified by CDAI, is robustly associated with decreased likelihood of CKM syndrome development and progression.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Weighted Quantile Sum Regression Model\u003c/h2\u003e\u003cp\u003eWQS regression analysis revealed a significant protective association between the overall dietary antioxidant mixture (vitamins A, C, E, zinc, selenium, and carotenoids) and advanced CKM syndrome (Stages 3\u0026ndash;4 vs. Stages 1\u0026ndash;2). This inverse relationship was consistent and highly significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) across all models, even after sequential adjustment for potential confounders (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The fully adjusted Model 3 yielded an OR of 0.82 (95% CI: 0.76\u0026ndash;0.88) per unit increase in the overall dietary antioxidant mixture. Component weight analysis revealed that vitamin A (weight\u0026thinsp;=\u0026thinsp;0.357), vitamin C (weight\u0026thinsp;=\u0026thinsp;0.290), and selenium (weight\u0026thinsp;=\u0026thinsp;0.212) contributed most substantially to this protective effect, followed by vitamin E (0.073), zinc (0.050), and carotenoids (0.019; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eOdds Ratios for Advanced CKM Syndrome from the Overall Dietary Antioxidant Mixture Effect in WQS Regression\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.77 (0.73, 0.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.76(0.71, 0.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.82(0.76, 0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eModel 1:\u003c/strong\u003e the\u0026nbsp;unadjusted WQS regression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 2:\u003c/strong\u003e the WQS regression adjusted for age, gender, and race.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 3:\u003c/strong\u003e the WQS regression adjusted for age, gender, race, education level, poverty income ratio, smoking status, alcohol status, and physical activity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e The outcome variable, Advanced CKM Syndrome, was defined as CKM Stages 3-4 versus Stages 1-2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviation:\u003c/strong\u003e OR, odds ratios; CI, confidence interval; CKM, cardiovascular-kidney-metabolic syndrome; WQS, weighted quantile sum.\u003c/p\u003e\u003cp\u003eTo validate these findings, multivariable logistic regression was performed for each antioxidant component (Supplementary Table S7). In Model 3, vitamin C (OR: 0.95, 95% CI: 0.90\u0026ndash;0.99; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021), vitamin E (OR: 0.92, 95% CI: 0.88\u0026ndash;0.96; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and selenium (OR: 0.92, 95% CI: 0.88\u0026ndash;0.97; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) retained statistically significant inverse associations with advanced CKM syndrome. Vitamin A, zinc, and carotenoids did not reach significance in the fully adjusted model.\u003c/p\u003e\u003cp\u003eFor mortality outcomes in CKM patients, WQS regression similarly indicated protective effects of a higher overall dietary antioxidant mixture against all-cause (HR: 0.78, 95% CI: 0.73\u0026ndash;0.82), CVD (HR: 0.79, 95% CI: 0.70\u0026ndash;0.88), and non-CVD mortality (HR: 0.82, 95% CI: 0.77\u0026ndash;0.88; all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Supplementary Table S8). Component weights for mortality outcomes are detailed in Supplementary Figure S2.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Associations between CDAI and mortality outcomes in CKM syndrome patients\u003c/h2\u003e\u003cp\u003eKaplan-Meier survival analyses demonstrated significant differences in cumulative survival probabilities across CDAI quartiles for all mortality endpoints (all log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Supplementary Figure S3). Participants in the highest quartile (Q4) exhibited the highest survival probability, while those in the lowest quartile (Q1) showed the poorest survival outcomes.\u003c/p\u003e\u003cp\u003eRCS analyses revealed nonlinear relationships between continuous CDAI and mortality risk (Supplementary Figure S4). For all-cause mortality (\u003cem\u003eP-nonlinear\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and non-CVD mortality (\u003cem\u003eP-nonlinear\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), a threshold effect was observed: mortality risk decreased linearly with increasing CDAI until a node point (CDAI\u0026thinsp;\u0026asymp;\u0026thinsp;0), beyond which the association plateaued. In contrast, CVD mortality exhibited a linear inverse association (\u003cem\u003eP-nonlinear\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.408).\u003c/p\u003e\u003cp\u003eMultivariable Cox proportional hazards models confirmed robust, dose-dependent reductions in mortality risk with higher CDAI quartiles (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Compared to Q1 (reference), Q4 was associated with significantly lower risks of all-cause mortality (HR\u0026thinsp;=\u0026thinsp;0.63; 95% CI: 0.57\u0026ndash;0.70), CVD mortality (HR\u0026thinsp;=\u0026thinsp;0.63; 95% CI: 0.51\u0026ndash;0.78), and non-CVD mortality (HR\u0026thinsp;=\u0026thinsp;0.63; 95% CI: 0.56\u0026ndash;0.72; all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Intermediate quartiles (Q2\u0026ndash;Q3) also showed progressively reduced risks, though Q2 did not reach significance for CVD mortality (HR\u0026thinsp;=\u0026thinsp;1.04; 95% CI: 0.86\u0026ndash;1.25).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociations of CDAI Quartiles with Mortality in Patients with CKM Syndrome\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMortality Outcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCDAI Quartile\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eAll-cause\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.86 (0.78\u0026ndash;0.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.73 (0.66\u0026ndash;0.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.63 (0.57\u0026ndash;0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eCardiovascular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.04 (0.86\u0026ndash;1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.686\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.81 (0.66\u0026ndash;0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.63 (0.51\u0026ndash;0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eNon-cardiovascular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.81 (0.72\u0026ndash;0.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.70 (0.62\u0026ndash;0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.63 (0.56\u0026ndash;0.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eP\u003c/b\u003e \u003cb\u003evalues\u003c/b\u003e from the multivariable Cox proportional hazards models adjusted for age, gender, race and ethnicity, education level, poverty income ratio, smoking status, alcohol status, and physical activity. Q1 (lowest) to Q4 (highest) CDAI quartiles.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAbbreviation:\u003c/strong\u003e CI, confidence interval; HR, hazard ratio; CDAI, composite dietary antioxidant index; CKM, cardiovascular-kidney-metabolic syndrome.\u003c/p\u003e\u003cp\u003eStratified analyses by CKM stage severity revealed differential protective effects (Supplementary Table S9). In early-stage CKM (Stages 1\u0026ndash;2), higher CDAI quartiles (Q3\u0026ndash;Q4) were strongly associated with reduced all-cause, CVD, and non-CVD mortality (e.g., Q4 all-cause HR\u0026thinsp;=\u0026thinsp;0.64; 95% CI: 0.57\u0026ndash;0.73). Conversely, in advanced stages (Stages 3\u0026ndash;4), only Q4 conferred significant protection against all-cause (HR\u0026thinsp;=\u0026thinsp;0.83; 95% CI: 0.70\u0026ndash;0.99) and non-CVD mortality (HR\u0026thinsp;=\u0026thinsp;0.78; 95% CI: 0.63\u0026ndash;0.96), with no significant association for CVD mortality.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Optimal risk stratification cut-off points for CDAI on mortality outcomes in CKM patients\u003c/h2\u003e\u003cp\u003eOptimal CDAI cut-offs for mortality risk stratification were identified as \u0026minus;\u0026thinsp;0.20 (all-cause), 0.68 (CVD), and \u0026minus;\u0026thinsp;1.43 (non-CVD; Supplementary Figure S5). Survival curves based on these thresholds further validated the discriminative capacity of CDAI, with significantly higher survival in patients above versus below each cut-off (all log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Supplementary Figure S6).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Sensitivity analysis\u003c/h2\u003e\u003cp\u003eAfter excluding early deaths (\u0026le;\u0026thinsp;2 years follow-up; Supplementary Table S10) or participants with a cancer history (Supplementary Table S11), the association between higher CDAI quartiles and significantly reduced risks of all-cause, CVD, and non-CVD mortality was still observed in fully adjusted models.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates a significant inverse association between higher CDAI scores and both reduced severity of CKM staging and lower all-cause, CVD, and non-CVD mortality risk in individuals with CKM syndrome. We observed a clear dose-response relationship, where ascending CDAI quartiles correlated with progressively lower odds of advanced CKM stages and reduced mortality. WQS regression confirmed the protective effect of the overall dietary antioxidant mixture against advanced CKM, primarily driven by vitamins A, C, and selenium. Nonlinear relationships and optimal risk-stratification thresholds for mortality were also identified. These findings underscore CDAI as a valuable tool for risk stratification and highlight the protective role of antioxidant-rich diets in CKM management, particularly during early disease stages.\u003c/p\u003e\u003cp\u003eThe observed inverse associations between higher CDAI and both CKM staging severity and mortality risk are mechanistically plausible through the mitigation of oxidative stress, a central pathophysiological driver of CKM progression and complications\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Constituent antioxidants within CDAI counteract oxidative damage via synergistic pathways: zinc serves as a critical cofactor for superoxide dismutase\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, selenium is incorporated into selenoproteins like glutathione peroxidase to prevent lipid peroxidation\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, vitamins A, C, and E act as key non-enzymatic antioxidants, scavenging free radicals and protecting cellular structures\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, and carotenoids activate the Nrf2 pathway, inducing detoxifying and antioxidant enzymes\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Crucially, under pathological states inherent in CKM (metabolic dysregulation, CKD, CVD), excessive ROS production overwhelms endogenous defenses, leading to oxidative macromolecular damage that promotes inflammation, apoptosis, tissue injury, and adverse outcomes\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The integrated protective effect captured by the CDAI \u0026ndash; addressing antioxidant defense through enzymatic, non-enzymatic, and transcriptional regulation pathways \u0026ndash; provides a comprehensive countermeasure to this multifaceted oxidative burden, more effectively than assessments of individual nutrients. Our findings align with previous evidence linking higher CDAI scores to lower risks of individual CKM components, including hypertension\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, metabolic disorders\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, CKD\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, and CVD\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, supporting dietary antioxidant optimization as a strategy against CKM pathogenesis.\u003c/p\u003e\u003cp\u003eNotably, WQS analysis identified vitamins A, C, and selenium as the primary contributors to the protective effect against advanced CKM, whereas multivariable regression showed significant independent associations only for vitamins C, E, and selenium. This discrepancy likely reflects methodological differences: WQS captures synergistic interactions within the antioxidant mixture [e.g., vitamin C regenerating vitamin E\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e], where vitamin A\u0026mdash;often co-consumed with vitamin C-rich foods \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e \u0026mdash; may amplify effects without strong independent associations after covariate adjustment. Multivariable regression, conversely, isolates individual effects potentially attenuated by collinearity or residual confounding [e.g., bioavailability variations\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e]. Thus, WQS highlights the importance of the dietary pattern, while regression underscores the non-redundant roles of vitamins C/E and selenium.\u003c/p\u003e\u003cp\u003eStratified analysis revealed attenuated protection against CVD mortality in advanced CKM (Stages 3\u0026ndash;4), consistent with the syndrome\u0026rsquo;s pathophysiology. In advanced stages, extensive irreversible organ damage (e.g., myocardial fibrosis, renal sclerosis, vascular calcification) driven by chronic oxidative stress likely dominates clinical outcomes\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Dietary antioxidants exert greater effects in early disease by modulating reversible pathways like endothelial dysfunction and metabolic dysregulation\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. However, once severe structural cardiovascular damage is established, their capacity to offset CVD-specific mortality risk may be limited\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. This underscores the critical need for early intervention: optimizing CDAI in Stages 1\u0026ndash;2 may delay progression, whereas Stages 3\u0026ndash;4 require combinatorial approaches (e.g., pharmacotherapy alongside dietary modifications)\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBased on these findings, several evidence-based recommendations for clinical practice emerge. Healthcare providers should prioritize dietary counseling for patients at risk of or diagnosed with CKM, emphasizing the adoption of antioxidant-rich diets focused on key sources like fruits (especially berries and citrus fruits rich in vitamin C), vegetables (particularly leafy greens and colorful vegetables rich in carotenoids, vitamin C, and vitamin E), nuts (rich in vitamin E and selenium), and whole grains\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Furthermore, leveraging CDAI scores, or dietary patterns reflecting them, can help develop individualized dietary plans, and regular monitoring of dietary habits allows for dynamic tailoring of interventions. In addition, public health initiatives should raise awareness of the critical role dietary antioxidants play in mitigating CKM risk, highlighting the benefits of balanced diets rich in these compounds and educating the public about oxidative stress. A multidisciplinary care model integrating dietitians or nutritionists is essential to synergize these targeted dietary modifications with other evidence-based lifestyle strategies, such as physical activity and stress management\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Finally, the identified robust dose-response relationship and protective thresholds support the potential utility of the CDAI as a valuable tool for risk stratification in CKM; consequently, existing clinical guidelines for CKM management should be updated to incorporate recommendations for assessing and optimizing dietary antioxidant status (reflected by the CDAI or its components) as a routine part of risk assessment and preventive care for both affected patients and high-risk individuals.\u003c/p\u003e\u003cp\u003eThis study has several methodological limitations that warrant consideration. Primarily, the cross-sectional design inherently precludes inferring temporal sequence or establishing causality regarding the association between CDAI and CKM staging; confirming the etiopathogenic link and assessing the impact of dietary interventions requires future prospective longitudinal cohort studies or randomized clinical trials. Furthermore, while a comprehensive set of potential confounders was meticulously controlled for, the possibility of residual confounding due to unmeasured or unknown variables remains inherent to observational studies. The reliance on self-reported dietary data (24-hour recalls) for calculating the CDAI introduces susceptibility to recall bias, and these estimates may inadequately capture variations in nutrient bioavailability or losses during food preparation; future studies should incorporate objective biochemical markers of antioxidant micronutrient status for more quantitative exposure assessment. Additionally, the CDAI itself represents a simplification, as it does not account for potential synergistic or antagonistic effects with non-antioxidant dietary components or inter-individual variations in metabolic responses that modulate antioxidant impact. The characteristics of the NHANES U.S. population also limit the generalizability of findings to other ethnicities or populations with distinct dietary patterns and genetic backgrounds, making replication in more diverse cohorts essential. Finally, further mechanistic research is warranted to elucidate the precise pathways through which the CDAI confers protection, as such insights could inform the development of novel targeted therapies or refined dietary supplement strategies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eHigher dietary antioxidant intake, quantified by CDAI, is robustly associated with less severe CKM staging and significantly reduced mortality risk. CDAI shows promise as a tool for risk stratification, supporting the importance of antioxidant-rich diets in CKM management, particularly for early intervention.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCVD\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCardiovascular disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCKD\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eChronic kidney disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eAHA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAmerican Heart Association\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCKM\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCardiovascular-kidney-metabolic Syndrome\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eROS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReactive oxygen species\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCDAI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eComposite Dietary Antioxidant Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eNHANES\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNational Health and Nutrition Examination Survey\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eNCHS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNational Center for Health Statistics\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ePIR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePoverty income ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody mass index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eSBP\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSystolic blood pressure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eDBP\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDiastolic blood pressure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eTC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTotal Cholesterol\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eHDL-C\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHigh-density lipoprotein cholesterol\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eeGFR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEstimated glomerular filtration rate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eUACR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUrinary albumin to creatinine ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eIQRs\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInterquartile ranges\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eORs\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOdds ratios\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCIs\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence intervals\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eWQS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWeighted Quantile Sum\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eHRs\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHazard ratios\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eRCS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRestricted cubic splines\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Center for Health Statistics Research Ethics Review Board approved all National Health and Nutrition Examination Survey protocols, and written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets used for this study are available in the National Health and Nutrition Examination Survey (https://wwwn.cdc.gov/nchs/nhanes).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was funded by the National Natural Science Youth Foundation of China (Grant Number 82003556).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFDW\u0026nbsp;designed the study; XTG and FDW drafted the manuscript; XTG and SMY carried out data analyses; YBY, YY, and QM advised on data interpretation; YPW and DL supervised the study. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank the National Health and Nutrition Examination Survey participants and the survey, development, and management teams for contributing to this project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGlobal burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. \u003cem\u003eLancet (London, England)\u003c/em\u003e. May 18 2024;403(10440):2100-2132. doi:10.1016/s0140-6736(24)00367-2\u003c/li\u003e\n\u003cli\u003eCardiovascular diseases (CVDs). World Health Organization Web. 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Association of the Composite dietary antioxidant index with all-cause and cardiovascular mortality: A prospective cohort study. \u003cem\u003eFrontiers in cardiovascular medicine\u003c/em\u003e. 2022;9:993930. doi:10.3389/fcvm.2022.993930\u003c/li\u003e\n\u003cli\u003eAhluwalia N, Dwyer J, Terry A, Moshfegh A, Johnson C. Update on NHANES Dietary Data: Focus on Collection, Release, Analytical Considerations, and Uses to Inform Public Policy. \u003cem\u003eAdvances in nutrition (Bethesda, Md)\u003c/em\u003e. Jan 2016;7(1):121-34. doi:10.3945/an.115.009258\u003c/li\u003e\n\u003cli\u003eBasiotis PP, Welsh SO, Cronin FJ, Kelsay JL, Mertz W. Number of days of food intake records required to estimate individual and group nutrient intakes with defined confidence. \u003cem\u003eThe Journal of nutrition\u003c/em\u003e. Sep 1987;117(9):1638-41. doi:10.1093/jn/117.9.1638\u003c/li\u003e\n\u003cli\u003eMoshfegh AJ, Rhodes DG, Baer DJ, et al. 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Sep 2024;34(9):2165-2172. doi:10.1016/j.numecd.2024.06.002\u003c/li\u003e\n\u003cli\u003eTraber MG, Stevens JF. Vitamins C and E: beneficial effects from a mechanistic perspective. \u003cem\u003eFree radical biology \u0026amp; medicine\u003c/em\u003e. Sep 1 2011;51(5):1000-13. doi:10.1016/j.freeradbiomed.2011.05.017\u003c/li\u003e\n\u003cli\u003eJacobs DR, Jr., Gross MD, Tapsell LC. Food synergy: an operational concept for understanding nutrition. \u003cem\u003eThe American journal of clinical nutrition\u003c/em\u003e. May 2009;89(5):1543s-1548s. doi:10.3945/ajcn.2009.26736B\u003c/li\u003e\n\u003cli\u003eLiu RH. Health-promoting components of fruits and vegetables in the diet. \u003cem\u003eAdvances in nutrition (Bethesda, Md)\u003c/em\u003e. May 1 2013;4(3):384s-92s. doi:10.3945/an.112.003517\u003c/li\u003e\n\u003cli\u003eTang G. Bioconversion of dietary provitamin A carotenoids to vitamin A in humans. \u003cem\u003eThe American journal of clinical nutrition\u003c/em\u003e. May 2010;91(5):1468s-1473s. doi:10.3945/ajcn.2010.28674G\u003c/li\u003e\n\u003cli\u003eTaherkhani S, Suzuki K, Castell L. A Short Overview of Changes in Inflammatory Cytokines and Oxidative Stress in Response to Physical Activity and Antioxidant Supplementation. \u003cem\u003eAntioxidants (Basel, Switzerland)\u003c/em\u003e. Sep 18 2020;9(9)doi:10.3390/antiox9090886\u003c/li\u003e\n\u003cli\u003eLi S, Chen G, Zhang C, Wu M, Wu S, Liu Q. Research progress of natural antioxidants in foods for the treatment of diseases. \u003cem\u003eFood Science and Human Wellness\u003c/em\u003e. 2014;3(3-4):110-116. doi:10.1016/j.fshw.2014.11.002\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cardiovascular-kidney-metabolic syndrome, Composite dietary antioxidant index, Oxidative stress, Mortality, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-7380717/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7380717/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigated the role of the Composite Dietary Antioxidant Index (CDAI) in cardiovascular-kidney-metabolic (CKM) syndrome staging and mortality using NHANES 2001\u0026ndash;2018 data from 25,155 U.S. adults. Higher CDAI quartiles demonstrated progressively reduced odds of advanced CKM stages versus Stage 0 (Q4 vs. Q1 ORs: Stage 1: 0.71 (0.56\u0026ndash;0.91); Stage 2: 0.58 (0.45\u0026ndash;0.74); Stage 3: 0.30 (0.20\u0026ndash;0.47); Stage 4: 0.46 (0.35\u0026ndash;0.60); all \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). Weighted Quantile Sum regression identified a protective effect of the antioxidant mixture against advanced CKM (OR: 0.82 (0.76\u0026ndash;0.88); \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001), primarily driven by vitamins A (weight\u0026thinsp;=\u0026thinsp;0.357), C (0.290), and selenium (0.212). In CKM patients, higher CDAI was associated with significantly lower all-cause (Q4 vs. Q1 HR: 0.63 (0.57\u0026ndash;0.70)), cardiovascular (HR: 0.63 (0.51\u0026ndash;0.78)), and non-cardiovascular mortality (HR: 0.63 (0.56\u0026ndash;0.72); all \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001). Nonlinear analyses revealed threshold effects for all-cause and non-CVD mortality at CDAI\u0026thinsp;\u0026asymp;\u0026thinsp;0. These findings indicate that elevated CDAI is robustly associated with less severe CKM staging and reduced mortality, supporting dietary antioxidant optimization for CKM management and risk stratification.\u003c/p\u003e","manuscriptTitle":"Role of composite dietary antioxidant index in staging and mortality risk of cardiovascular-kidney-metabolic syndrome: a study from NHANES 2001-2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 07:00:38","doi":"10.21203/rs.3.rs-7380717/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":"055fb6af-741b-4c51-9c68-fc9742d6d721","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-27T17:21:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-27 07:00:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7380717","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7380717","identity":"rs-7380717","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}