Abstract
This study investigates the relationship between the composite dietary antioxidant index (CDAI) and the risk of all-cause and cardiovascular mortality in individuals with hypertension, aiming to provide dietary recommendations to reduce mortality. Data were sourced from the National Health and Nutrition Examination Survey (NHANES) database. The best cut-off for CDAI in relation to survival outcomes was determined using the maximally selected rank statistics method. Multivariate Cox regression analysis was used to examine the association between CDAI and mortality risks, and hazard ratios with 95% confidence intervals were calculated. Restricted cubic spline (RCS) curves were used to visualize the correlations. After analyzing data from 22,591 hypertensive adults, the optimal CDAI cut-off was found to be −1.23, categorizing participants into high and low CDAI groups. The results showed that the high CDAI group had a lower risk of cardiovascular and all-cause mortality (HR [95% CI]: 0.73 [0.61–0.87] and 0.79 [0.71–0.88], respectively). RCS analysis indicated a negative linear relationship with cardiovascular mortality and a nonlinear relationship with all-cause mortality. Subgroup and sensitivity analyses confirmed these findings. In conclusion, CDAI is a valuable predictor of long-term mortality risks in hypertensive individuals and can serve as a cost-effective tool for assessing their prognosis.
Keywords: antioxidant diet, cardiovascular disease, cox regression analysis, mortality risk
1. Introduction
One of the main global risk factors for cardiovascular disease (CVD) is hypertension,[1,2] accounting for 14% of global deaths.[3] Finding practical ways to stop or postpone negative effects in hypertension individuals is essential. A healthy lifestyle is linked to lower cardiovascular risk, better vascular health, and the prevention or delay of hypertension.[4,5] Additionally, research suggests that for patients with moderate hypertension, lifestyle modifications may be more crucial than medicine.[6] Increased vascular oxidative stress in hypertension causes vascular smooth muscle cells to proliferate, grow, and deposit collagen, which thickens the vascular media and narrows the lumen. Additionally, endothelium damage and impairment of endothelium-dependent vasodilation can occur due to oxidative stress, increasing vascular contraction.[7] Research by Dikalova et al found that treatment with mitochondrial-targeted antioxidants reduced blood pressure in mouse models of hypertension induced by angiotensin II and deoxycorticosterone acetate salt.[8]
A study by Daneshzad et al[9] suggests that the consumption of dietary antioxidants can increase their plasma concentrations to reduce oxidative stress. Therefore, it is believed that adjusting dietary composition can decrease oxidative stress in the body, thereby regulating blood pressure levels. The A person’s total antioxidant capacity, which is determined by their dietary intake of important antioxidants such Vitamins A, C, and E as well as the minerals zinc and selenium, is summarized by the composite dietary antioxidant index (CDAI).[10,11] A higher CDAI can enhance conditions like heart failure, hypertension, depression, and atherosclerotic cardiovascular diseases, while also lowering the risk of morbidity.[11–14] However, no studies to date have investigated the relationship between CDAI and the risks of all-cause mortality and cardiovascular mortality in individuals with hypertension.
In our study, we are the first to utilize NHANES data with a large sample cohort to examine the association between CDAI and the risk of hypertension-related mortality in adults aged 20 and older, while also determining the optimal cut-off value for CDAI. Our goal is to assess how dietary modifications can impact the risk of death from hypertension, which carries important clinical implications for the early prevention, treatment, and prognosis of patients with hypertension.
2. Methods
2.1. Study design and population
Data was utilized from the National Health and Nutrition Examination Survey (NHANES) in this study, which is a large cross-sectional study assessing the nutritional status of non-institutionalized U.S. residents. Structured house interviews, medical examinations, and laboratory tests carried out in mobile centers were used to gather data. The study design employed multistage probability sampling to assess the health status of a representative U.S. population, with surveys conducted every 2 years and follow-ups every 4 years. Detailed methodology can be accessed from the official website (http://www.cdc.gov/nchs/nhanes.htm, accessed October 1, 2023).
This cohort study analyzed NHANES data from 1999 to 2018. Patients with missing data, those under 20 years old, pregnant women, individuals diagnosed with tumors, and those with missing covariates were excluded. Ultimately, 22,591 hypertensive patients aged 20 and older met the inclusion criteria after applying the specified inclusion and exclusion standards and were included in this study. The inclusion and exclusion criteria flowchart is shown in Figure 1.
Figure 1.
Flowchart of data inclusion and exclusion criteria.
2.2. Ethics statement
The CDC’s Institutional Review Board gave the initial survey procedure approval after it underwent a thorough ethical review. The studies were carried out in compliance with institutional guidelines and local laws. The participants’ legal guardians or next of kin gave written, informed consent for them to take part in this study.
2.3. Dietary assessment and definition of hypertension
The evaluation of the CDAI utilizes the index established by Wright et al.[15] Each participant’s food and nutrient intake information was gathered using 24-hour dietary recall interviews using the NHANES dataset. A second interview was done over the phone 3 to 10 days after the first in-person interview. The USDA’s Dietary Studies Food and Nutrient database was used to quantify the intake of antioxidants, micronutrients, and total energy.[16] According to the questionnaire interview results, we recorded dietary supplement intake over the past month, including dosage, frequency, and duration.[17] Six antioxidants were taken at regulated levels: zinc, manganese, selenium, and Vitamins A, C, and E. This was done by deducting the mean and dividing the result by the standard deviation. Ultimately, the total of these standardized intake levels is the CDAI.
To identify hypertension, the following criteria were used: participants self-reported having hypertension or were currently taking medication to lower blood pressure. Systolic and diastolic blood pressure readings were measured 3 times to ensure accuracy; if they exceeded the standard reference range, the participant was considered hypertensive.
2.4. The risk of mortality in the study population
Mortality linkage files (MLF), which capture the risks of death linked with any cause of death, were used to establish the primary endpoint, which was the risk of all-cause mortality. Through a CDC link, one can access the National Death Index (NDI) database, which contains mortality data used in this study. Mortality-public.htm, https://www.cdc.gov/nchs/data-linkage The duration of the patient follow-up was December 31, 2019, which corresponded to the most recent NDI update, or the day of death. The 10th Edition of the International Statistical Classification of Diseases (ICD-10) codes (I00–I09, I11, I13, and I20–I51) were used to identify deaths from CVD.
2.5. Definition of primary variates
The covariates include demographic characteristics: age, sex, race, education level, family income-poverty ratio (PIR), household insurance, smoking status, and drinking status; physical examination parameters: body mass index (BMI); medical conditions: diabetes. This study obtained data from the NHANES, which included demographic information and health questionnaires
Age was categorized into 40 to 54 years and 55 to 65 years; sex was categorized into male and female; race was divided into White and other races; education level was classified as middle school or below, high school or vocational school, and college or above; PIR was defined as the ratio of family income to the poverty threshold and categorized into low income (<1%), middle income (1% ≤ PIR ≤ 3%), and high income (>3%)[18,19]; household insurance was determined based on whether the respondents had purchased household insurance according to the questionnaire; the 3 categories for smoking status were: never (defined as <100 cigarettes smoked), former (more than 100 cigarettes smoked but not presently smoking at all), and current (more than 100 cigarettes smoked and currently smoking either some days or every day)[20]; there were 2 categories for drinking status: never drinking (defined as consuming <12 drinks in a lifetime) and drinking (12 or more drinks in a lifetime)[21]; weight (kg) divided by height (m2) yielded the BMI, which was then separated into 3 categories: normal (< 25 kg/m2), overweight (25 ≤ BMI < 30 kg/m2), and obese (≥ 30 kg/m²)[22]; when any of the following conditions were satisfied, diabetes was diagnosed: a previous diagnosis; a HbA1C level of 6.5% or higher; 7.0 mmol/L fasting blood glucose, 11.1 mmol/L random plasma glucose, 11.1 mmol/L 2-hour OGTT, or the use of hypoglycemic drugs. Impaired glucose control was identified when standard reference values were exceeded but the previously indicated conditions were not satisfied.[23]
2.6. Statistical analysis
The complicated sampling design and sampling weights were taken into account during the analysis in accordance with the NHANES analysis and reporting criteria. All continuous variables were converted into categorical variables, expressed as percentages, and the Chi-squared test was used to determine how the groups differed from one another. The maximally selected rank statistics approach, which is based on the “maxstat” package (https://CRAN.R-project.org/package=maxstat), was used to determine the ideal CDAI cut-off point that corresponded to the most significant correlation with survival outcomes.[24,25] Participants were then divided into high CDAI and low CDAI groups.
Using survey-weighted Cox regression analysis, the relationship between CDAI and the risk of cardiovascular and all-cause mortality in hypertensive patients was assessed. Three models were developed in order to account for possible confounding variables. Model 1 took into account confounders such as age, gender, and race; Model 2 added variables such as education level, smoking, PIR, insurance, and drinking status; Model 3 added variables such as BMI and diabetes on top of Model 2’s variables.
To visually assess the association between continuous CDAI and the risk of cardiovascular and all-cause death, a restricted cubic spline (RCS) model was built. To investigate the robustness of the findings, subgroup analysis, interaction analysis, and sensitivity analysis were performed. Statistical significance was indicated by a 2-tailed P < .05.
3. Results
3.1. The characteristics of the study population
The study included a total of 22,591 hypertensive patients. Using the maximally selected rank statistics method, −1.23 was identified as the optimal CDAI cut-off value significantly associated with survival. Participants were then classified into higher and lower CDAI groups based on this threshold (Fig. 2).
Figure 2.
Cut-off points calculated using the “maxstat” package, computed using standardized log-rank statistics.
The baseline characteristics of the participants are summarized in Table 1. Significant differences were observed in age, race, education status, marital status, smoking status, PIR, household insurance, diabetes, and CDAI among hypertensive patients. Among the surviving hypertensive patients, a relatively higher proportion were married, had a higher education level, purchased health insurance, never smoked, and consumed alcohol. Additionally, individuals with higher CDAI scores had a relatively higher survival rate.
Table 1.
Basic characteristics.
| Characteristic | Overall | Survivor | Death | P value |
|---|---|---|---|---|
| n = 22591(%) | n = 19471(%) | n = 3120(%) | ||
| Age group | ||||
| ≤45 yr | 8441 (40.96) | 8239 (44.46) | 202 (9.28) | <.01 |
| 46–64 yr | 8685 (41.44) | 7890 (42.40) | 795 (32.77) | |
| ≥65 yr | 5465 (17.60) | 3342 (13.13) | 2123 (57.95) | |
| Sex | ||||
| Female | 11,340 (50.60) | 9912 (50.75) | 1428 (49.19) | .18 |
| Male | 11,251 (49.40) | 9559 (49.25) | 1692 (50.81) | |
| Race/ethnicity | ||||
| White race | 10,370 (70.02) | 8468 (69.06) | 1902 (78.67) | <.01 |
| Other race | 12,221 (29.98) | 11,003 (30.94) | 1218 (21.33) | |
| Marital status | ||||
| Married/with spouse | 421 (68.40) | 300 (70.05) | 121 (64.95) | <.01 |
| Divorced/widowed/living alone | 295 (31.60) | 208 (29.95) | 87 (35.05) | |
| Educational attainment | ||||
| Junior high school or below | 5930 (16.35) | 4754 (14.94) | 1176 (29.05) | <.01 |
| High school or technical secondary school | 5426 (25.21) | 4576 (24.68) | 850 (29.99) | |
| College or above | 11,235 (58.44) | 10,141 (60.38) | 1094 (40.97) | |
| Poverty income ratio group | ||||
| 1 | 6874 (21.05) | 5746 (20.25) | 1128 (28.27) | <.01 |
| 2 | 8611 (35.51) | 7254 (34.55) | 1357 (44.23) | |
| 3 | 7106 (43.44) | 6471 (45.21) | 635 (27.49) | |
| Health insurance | ||||
| Insured | 17,859 (83.01) | 15,031 (82.21) | 2828 (90.24) | <.01 |
| Uninsured | 4732 (16.99) | 4440 (17.79) | 292 (9.76) | |
| Smoking status | ||||
| Non-smoker | 11,880 (52.55) | 10,657 (54.21) | 1223 (37.58) | <.01 |
| Former smoker | 5825 (25.32) | 4606 (23.98) | 1219 (37.39) | |
| Current smoker | 4886 (22.13) | 4208 (21.81) | 678 (25.03) | |
| Alcohol consumption status | ||||
| Yes | 19,415 (88.89) | 16,839 (89.34) | 2576 (84.77) | <.01 |
| No | 3176 (11.11) | 2632 (10.66) | 544 (15.23) | |
| BMI (kg/m2) | ||||
| Normal (<25) | 5074 (23.08) | 4266 (22.85) | 808 (25.13) | .06 |
| Overweight (25–30) | 7969 (34.83) | 6815 (34.80) | 1154 (35.06) | |
| Obese (≥30) | 9548 (42.09) | 8390 (42.34) | 1158 (39.81) | |
| Diabetes | ||||
| DM | 4689 (15.58) | 3554 (13.66) | 1135 (32.82) | <.01 |
| IFG | 1143 (4.82) | 965 (4.72) | 178 (5.76) | |
| IGT | 771 (3.06) | 664 (2.98) | 107 (3.75) | |
| No | 15,988 (76.55) | 14,288 (78.64) | 1700 (57.67) | |
| CQ | ||||
| Q1 | 8603 (34.40) | 7094 (33.14) | 1509 (45.77) | <.01 |
| Q2 | 13,988 (65.60) | 12,377 (66.86) | 1611 (54.23) | |
BMI = body mass index.
3.2. Association between CDAI and mortality risk
To explore the relationship between CDAI and the risk of all-cause mortality in hypertensive patients, 3 models were constructed using Cox proportional hazards regression (Table 2). When CDAI was evaluated as a continuous variable, in Model 1, the hazard ratio (HR) and 95% confidence interval (CI) were (HR [95% CI]: 0.95 [0.93–0.97]), indicating that for every unit increase in CDAI, the mortality risk of hypertension decreased. This relationship persisted in Model 2 (HR [95% CI]: 0.97 [0.96–0.99]) and Model 3 (HR [95% CI]: 0.98 [0.96–0.99]). When CDAI was assessed as a categorical variable, in Model 1, the HR and 95% CI were (HR [95% CI]: 0.67 [0.60–0.75]), in Model 2 (HR [95% CI]: 0.77 [0.69–0.86]), and in Model 3 (HR [95% CI]: 0.79 [0.71–0.88]). These results suggest that whether CDAI is evaluated as a continuous or categorical variable, lower CDAI is consistently associated with a higher risk of all-cause mortality.
Table 2.
Association of CDAI with mortality risk in adults with hypertension.
| Characteristic | Model 1 | P | Model 2 | P | Model 3 | P |
|---|---|---|---|---|---|---|
| HR (95% CI) | HR (95% CI) | HR (95% CI) | ||||
| All-cause mortality | ||||||
| CDAI (continuous variable) | 0.95 (0.93–0.97) | <.01 | 0.97 (0.96–0.99) | <.01 | 0.98 (0.96–0.99) | <.01 |
| Higher CDAI* | 0.67 (0.60–0.75) | <.01 | 0.77 (0.69–0.86) | <.01 | 0.79 (0.71–0.88) | <.01 |
| Cardiovascular mortality | ||||||
| CDAI (continuous variable) | 0.93 (0.91–0.95) | <.01 | 0.95 (0.93–0.98) | <.01 | 0.96 (0.94–0.98) | <.01 |
| Higher CDAI* | 0.61 (0.51–0.72) | <.01 | 0.70 (0.59–0.83) | <.01 | 0.73 (0.61–0.87) | <.01 |
Model 1, confounders were adjusted for gender, race, age only; Model 2 was adjusted based on gender, race, age, education level, smoking, insurance, alcohol consumption, and PIR; Model 3 adjusted for gender, race, age, education level, smoking, PIR, BMI, insurance, alcohol consumption, diabetes.
BMI = body mass index, CADI = composite dietary antioxidant index, HR = hazard ratio, PIR = income-poverty ratio.
Risk of mortality was analyzed compared to the lower CDAI group.
In exploring the relationship between CDAI and cardiovascular mortality risk in hypertensive patients, when CDAI was treated as a continuous variable, the hazard ratios and 95% confidence intervals (CIs) in the 3 models were 0.93 (0.91–0.95), 0.95 (0.93–0.98), and 0.96 (0.94–0.98), respectively. When CDAI was assessed as a categorical variable, in Model 1, the HR and 95% CI were (HR [95% CI]: 0.61 [0.51–0.72]), in Model 2 (HR [95% CI]: 0.70 [0.59–0.83]), and in Model 3 (HR [95% CI]: 0.73 [0.61–0.87]).
RCS curve showed a nonlinear relationship between CDAI and all-cause mortality risk in hypertension (Pnon-linear < .01) (Fig. 3) and a negative linear correlation with cardiovascular mortality risk (Pnon-linear = .5511) (Fig. 4).
Figure 3.
RCS showing the relationship between CDAI and all-cause mortality in hypertension patients. CDAI = composite dietary antioxidant index, RCS = restricted cubic spline.
Figure 4.
RCS showing the relationship between CDAI and cardiovascular mortality in hypertension patients. CDAI = composite dietary antioxidant index, RCS = restricted cubic spline.
3.3. Subgroup and sensitivity analyses
In the subgroup analysis, we found that CDAI was significantly negatively correlated with all-cause mortality and cardiovascular mortality risk in most subgroups. However, the statistical significance of this correlation requires further investigation in individuals aged ≤ 45 and those who are currently smoking. Additionally, the negative correlation in other racial groups regarding cardiovascular mortality also needs further clarification. In the subgroup analysis of all-cause and cardiovascular mortality, gender was identified as an interacting factor, and this interaction warrants further exploration. These results suggest that in subgroups with potential confounding factors, the relationship between higher CDAI and lower mortality risk persists, but the gender differences in the association between CDAI and both all-cause and cardiovascular mortality risks need further examination (Table 3)
Table 3.
Subgroup analyses of CDAI and mortality risk in hypertension.
| Variable | All-cause mortality Higher CDAI (≥2.56) | P value | P for interaction | Cardiovascular mortality higher CDAI (≥2.56) | P value | P for interaction |
|---|---|---|---|---|---|---|
| HR (95% CI) | HR (95% CI) | |||||
| Sex | ||||||
| Male | 0.76 (0.63–0.92) | <.01 | <.01 | 0.51 (0.39–0.65) | <.01 | <.01 |
| Female | 0.57 (0.47–0.69) | <.01 | 0.76 (0.62–0.92) | <.01 | ||
| Age group | ||||||
| ≤45 yr | 0.59 (0.45–0.77) | <.01 | .06 | 0.59 (0.33–1.07) | .08 | .46 |
| 46–64 yr | 1.19 (0.66–2.15) | .56 | 0.71 (0.60–0.84) | <.01 | ||
| ≥65 yr | 0.76 (0.62–0.92) | <.01 | 0.64 (0.45–0.91) | <.01 | ||
| Race/ethnicity | ||||||
| White race | 0.66 (0.51–0.84) | <.01 | .95 | 0.56 (0.46–0.68) | <.01 | .14 |
| Other race | 0.66 (0.55–0.79) | <.01 | 0.75 (0.55–1.03) | .08 | ||
| Smoking status | ||||||
| Non-smoker | 0.66 (0.53–0.83) | <.01 | .09 | 0.53 (0.42–0.69) | <.01 | .45 |
| Former smoker | 0.86 (0.65–1.13) | .28 | 0.67 (0.54–0.83) | <.01 | ||
| Current smoker | 0.56 (0.44–0.72) | <.01 | 0.69 (0.45–1.05) | .08 | ||
| BMI (kg/m2) | ||||||
| Normal (<25) | 0.61 (0.48–0.76) | <.01 | .07 | 0.62 (0.48–0.78) | <.01 | .79 |
| Overweight (25–30) | 0.79 (0.62–1.00) | .05 | 0.65 (0.51–0.84) | <.01 | ||
| Obese (≥30) | 0.54 (0.42–0.68) | <.01 | 0.58 (0.41–0.81) | <.01 | ||
BMI = body mass index, CADI = composite dietary antioxidant index, HR = hazard ratio.
To evaluate the findings’ robustness, a sensitivity analysis was performed, and the results showed that there was a consistent relationship between CDAI and the risks of cardiovascular and all-cause mortality as well as all-cause mortality (Table 4).
Table 4.
Sensitivity analysis of CDAI and mortality risk in hypertension.
| Characteristic | Model 1 | P | Model 2 | P | Model 3 | P |
|---|---|---|---|---|---|---|
| HR (95% CI) | HR (95% CI) | HR (95% CI) | ||||
| All-cause mortality | ||||||
| CDAI (continuous variable) | 0.94 (0.91–0.96) | <.01 | 0.96 (0.94–0.98) | <.01 | 0.96 (0.94–0.98) | <.01 |
| Higher CDAI* | 0.67 (0.56–0.79) | <.01 | 0.77 (0.66–0.89) | <.01 | 0.78 (0.67–0.90) | <.01 |
| Cardiovascular mortality | ||||||
| CDAI (continuous variable) | 0.92 (0.89–0.96) | <.01 | 0.95 (0.92–0.98) | <.01 | 0.95 (0.92–0.98) | <.01 |
| Higher CDAI* | 0.60 (0.46–0.77) | <.01 | 0.71 (0.55–0.92) | <.01 | 0.73 (0.57–0.94) | <.01 |
In Model 1, confounders were adjusted for gender, marital status, race, age, and education level only; Model 2 was adjusted based on gender, marital status, race, age, education level, smoking, insurance, alcohol consumption, and PIR; Model 3 adjusted for gender, race, marital status, age, education level, smoking, PIR, BMI, insurance, hypertension, alcohol consumption, diabetes, and CVD.
BMI = body mass index, CADI = composite dietary antioxidant index, CVD = cardiovascular disease, HR = hazard ratio, PIR = income-poverty ratio.
Risk of mortality was analyzed compared to the lower CDAI group.
4. Discussion
This cohort study looked into the connection between mortality risk among hypertensive individuals in the United States and dietary antioxidants, specifically CDAI. Higher CDAI was linked, among hypertensive people, to a lower risk of all-cause and cardiovascular mortality after controlling for several possible factors in Cox models. These results imply that dietary modifications and higher consumption of antioxidants may reduce the risk of death in this population. While prior studies have examined the effects of individual antioxidants like Vitamins A, E, and zinc on hypertension, daily diets typically contain a variety of foods and composite antioxidants. Most research has focused on individual antioxidants without considering their combined effects or their influence on mortality risk. Thus, exploring the link between composite dietary antioxidant intake and mortality risk in hypertensive populations is essential.
Numerous cardiovascular disorders, such as heart failure, ischemic heart disease, stroke, hypertensive heart disease, and renal events are significantly increased by hypertension.[26] The 2017 Global Burden of Disease, Injuries, and Risk Factors Study’s comparative risk assessment found that the main risk factor for adult mortality is hypertension.[27] Existing studies suggest that the risk factors for hypertension are primarily environmental factors, including diet and lifestyle, such as sodium and potassium intake, alcohol consumption, weight, physical activity, socioeconomic status, and genetic factors. Improving these factors can reduce the risk of hypertension.[28,29]
Dietary techniques for managing hypertension have been shown to successfully lower blood pressure in both normotensive and hypertensive people, according to a comprehensive review and meta-analysis of randomized controlled trials.[30] The study also showed that the DASH diet’s blood pressure-lowering effects were more pronounced in individuals under 50 and those consuming more than 2400 mg of sodium daily.[30] Another review and dose-response meta-analysis by Soltani et al found that even low adherence to the DASH diet was linked to reduced all-cause, cardiovascular, and cancer mortality.[31] Previous research has identified positive correlations between oxidative stress biomarkers like methylmalonic acid and malondialdehyde and increased risk of CVD and hypertension.[32,33] Dietary antioxidants, widely found in various diets, strengthen the body’s defense against oxidative stress by neutralizing excess free radicals, thus reducing oxidative stress..
CDAI, as a comprehensive tool for evaluating the overall antioxidant capacity of a diet, can identify and classify potential antioxidant sources from complex dietary components.[15] This method makes it possible to categorize dietary patterns of individuals according to their intake of antioxidants, which promotes the adoption of healthy eating habits and lifestyles. Studies in the past have demonstrated a connection between antioxidant consumption and the emergence of hypertension.[28,34] Tan et al, in a study based on the NHANES database, found that higher CDAI levels were negatively correlated with all-cause mortality and cancer mortality in cancer survivors but had no significant association with cardiovascular mortality. There was no significant correlation found between different CDAI levels and cardiovascular mortality, but Q4 (HR: 0.34, 95% CI:0.16–0.74, P＜0.01) was the only CDAI level associated with reduced cancer mortality. The study divided CDAI levels into 4 quartiles (Q1 [−6.695, −2.188], Q2 [−2.188, −0.308], Q3 [−0.308, 2.029], and Q4 [2.029, 78.016]).[35] Zhang’s research identified a negative linear relationship between CDAI and all-cause mortality risk in patients with rheumatoid arthritis, with physical activity significantly influencing this association. This suggests that increasing dietary antioxidant intake could help lower the risk of all-cause mortality in U.S. patients with rheumatoid arthritis.[36] A cohort study suggested a linear negative correlation between CDAI and hypertension after adjusting for potential confounding factors. Zhu et al showed that greater serum carotenoid concentrations were related with decreased all-cause and cardiovascular mortality risk in hypertensive adults,[37] but the exploration of CDAI and mortality risk in hypertensive populations remains incomplete.
Our study looked at the relationship between hypertension patients’ CDAI and the risks of cardiovascular and all-cause death. It showed that dietary factors — especially CDAI — can be very important in the management of chronic diseases in clinical settings. Our study highlights the significance of dietary antioxidants in lowering mortality risk in hypertensive populations, whereas conventional medical research has mostly concentrated on medications and clinical interventions for hypertension prevention and treatment. We also examined the effect of CDAI on mortality risk across several subgroups using subgroup analysis and interaction tests, demonstrating the validity and generalizability of our results.
This study provides important new information for public health campaigns and the clinical treatment of hypertension by highlighting the critical impact that diets high in antioxidants play in reducing both overall and cause-specific mortality in hypertensive individuals.
This study is not without limits, though. First, residual or unmeasured confounders may still persist even after several potential confounding factors were taken into account. Second, recall bias and differences between reported and real circumstances may be introduced by using dietary questionnaires and self-reported disease status from the NHANES database. Lastly, the results may not apply to other groups because the study only included hypertensive individuals in the United States. To corroborate these findings, more research with a wider range of populations is required. However, the large sample size, long-term follow-up, and thorough adjustment for confounders, among other characteristics of the study, improve the reliability of the results.
5. Conclusion
In conclusion, this cross-sectional study based on the NHANE database identified an optimal CDAI cutoff value of −1.23. After adjusting for potential confounding factors, it was found that there was no linear relationship between CDAI and all-cause mortality risk in adults with hypertension in the United States. However, there was a linear negative correlation with cardiovascular mortality risk. This study offers a new approach for early intervention in hypertension to reduce its mortality risk. Further cohort studies or randomized controlled trials are desperately needed to confirm these results in the future and offer more accurate and practical methods for controlling, preventing, and lowering the mortality risk associated with hypertension.
Author contributions
Conceptualization: Zhao Chen.
Data curation: Qian Huang, Peng Zhou, Qian He.
Methodology: Wenqiang Li.
Supervision: Zhiping Deng.
Writing – original draft: Zhao Chen, Qian Huang, Qian He, Zhiping Deng.
Writing – review & editing: Zhao Chen, Wenqiang Li, Peng Zhou, Zhiping Deng.
Abbreviations:
- BMI
- body mass index
- CDAI
- composite dietary antioxidant index
- CIs
- confidence intervals
- CVD
- cardiovascular disease
- HRs
- hazard ratios
- PIR
- income-poverty ratio
- RCS
- restricted cubic spline.
The datasets generated during and/or analyzed during the current study are publicly available.
The studies involving human participants were reviewed and approved by The National Center for Health Statistics (NCHS) Research Ethics Review Board. The patients/participants provided their written informed consent to participate in this study.
How to cite this article: Chen Z, Huang Q, Li W, Zhou P, He Q, Deng Z. Association of an antioxidant-rich diet with all-cause and cardiovascular mortality in hypertensive patients: A prospective study. Medicine 2025;104:29(e43443).
This work was supported by the Zigong Science and Technology Program Project (2023YKY07).
The authors have no conflicts of interest to disclose.
Contributor Information
Zhao Chen, Email: chenzhao0427@163.com.
Qian Huang, Email: hqhxedu@163.com.
Wenqiang Li, Email: lwq1858320@163.com.
Peng Zhou, Email: 76104448@qq.com.
Qian He, Email: hqhxedu@163.com.
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