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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Nutr.</journal-id>
<journal-title>Frontiers in Nutrition</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Nutr.</abbrev-journal-title>
<issn pub-type="epub">2296-861X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fnut.2025.1606769</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Nutrition</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Association of composite dietary antioxidant index and peripheral artery disease: a national cross-sectional study</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Fan</surname>
<given-names>Zhi</given-names>
</name>
<uri xlink:href="https://loop.frontiersin.org/people/3002251/overview"/>
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<role content-type="https://credit.niso.org/contributor-roles/data-curation/"/>
<role content-type="https://credit.niso.org/contributor-roles/formal-analysis/"/>
<role content-type="https://credit.niso.org/contributor-roles/methodology/"/>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Zhu</surname>
<given-names>Rongrong</given-names>
</name>
<uri xlink:href="https://loop.frontiersin.org/people/3129319/overview"/>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Guo</surname>
<given-names>Shuang</given-names>
</name>
<uri xlink:href="https://loop.frontiersin.org/people/3084865/overview"/>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Wu</surname>
<given-names>Qi</given-names>
</name>
<role content-type="https://credit.niso.org/contributor-roles/data-curation/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Li</surname>
<given-names>Jiazheng</given-names>
</name>
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<contrib contrib-type="author">
<name>
<surname>Liu</surname>
<given-names>Shibiao</given-names>
</name>
<uri xlink:href="https://loop.frontiersin.org/people/1663687/overview"/>
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<contrib contrib-type="author">
<name>
<surname>Su</surname>
<given-names>Zhixiang</given-names>
</name>
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</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Wu</surname>
<given-names>Weiwei</given-names>
</name>
<xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref>
<role content-type="https://credit.niso.org/contributor-roles/funding-acquisition/"/>
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<aff><institution>Department of Vascular Surgery, Beijing Tsinghua Changgung Hospital, School of Medicine, Tsinghua University</institution>, <addr-line>Beijing</addr-line>, <country>China</country></aff>
<author-notes>
<fn fn-type="edited-by" id="fn0001">
<p>Edited by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1028970/overview">Gentaro Ikeda</ext-link>, Stanford University, United States</p>
</fn>
<fn fn-type="edited-by" id="fn0002">
<p>Reviewed by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/566500/overview">Xin Chen</ext-link>, Tongji University, China</p>
<p><ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/2853981/overview">Yazareni Jos&#x00E9; Mercadante Urqu&#x00ED;a</ext-link>, Universidade Federal do Esp&#x00ED;rito Santo, Brazil</p>
</fn>
<corresp id="c001">&#x002A;Correspondence: Weiwei Wu, <email>wwwa00906@btch.edu.cn</email></corresp>
</author-notes>
<pub-date pub-type="epub">
<day>10</day>
<month>09</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="collection">
<year>2025</year>
</pub-date>
<volume>12</volume>
<elocation-id>1606769</elocation-id>
<history>
<date date-type="received">
<day>06</day>
<month>04</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>26</day>
<month>08</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2025 Fan, Zhu, Guo, Wu, Li, Liu, Su and Wu.</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Fan, Zhu, Guo, Wu, Li, Liu, Su and Wu</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p>The Composite Dietary Antioxidant Index (CDAI), a comprehensive measure of dietary antioxidant intake, quantifies the combined effects of key micronutrients, including vitamins A, C, and E, zinc (Zn), and selenium (Se), to evaluate overall antioxidant capacity. Existing evidence suggests that CDAI is inversely associated with cardiovascular diseases, including myocardial infarction and stroke. This study aims to investigate the relationship between CDAI and peripheral artery disease (PAD), which remains unclear in the current literature. In this study, we analyzed data from 2,332 participants with available ankle-brachial index (ABI) measurements from the NHANES database. Multivariable logistic regression and smooth curve fitting were employed to evaluate the association between CDAI and PAD. Additionally, subgroup analyses and interaction tests were conducted to assess the generalizability and stability of these relationships. Our findings revealed a significant inverse association between CDAI and PAD. In the fully adjusted model, each one-unit increase in CDAI was associated with a 12% reduction in PAD prevalence (OR&#x202F;=&#x202F;0.88, 95% CI: 0.81&#x2013;0.95). Moreover, participants in the highest quartile of CDAI had a 53% lower likelihood of developing PAD (OR&#x202F;=&#x202F;0.47, 95% CI: 0.24&#x2013;0.93) compared with those in the lowest quartile. These results demonstrate a strong correlation between CDAI and PAD risk, suggesting that diets rich in antioxidants (reflected by higher CDAI scores) may play a role in PAD prevention. However, further comprehensive research and prospective cohort studies are needed to explore causal relationships and validate these findings.</p>
</abstract>
<kwd-group>
<kwd>oxidative stress</kwd>
<kwd>antioxidant</kwd>
<kwd>CDAI</kwd>
<kwd>PAD</kwd>
<kwd>NHANES</kwd>
</kwd-group>
<counts>
<fig-count count="4"/>
<table-count count="3"/>
<equation-count count="1"/>
<ref-count count="53"/>
<page-count count="10"/>
<word-count count="6355"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Clinical Nutrition</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="sec1">
<label>1</label>
<title>Introduction</title>
<p>PAD, the manifestation of atherosclerosis in lower extremity arteries, is a common cardiovascular condition linked to increased risks of amputation, myocardial infarction, stroke, and death (<xref ref-type="bibr" rid="ref1">1</xref>). PAD is an established predictor of all-cause mortality and cardiovascular death. Current epidemiological evidence demonstrates that PAD significantly raises the risk of all-cause mortality, cardiovascular death, coronary artery disease, and cerebrovascular disease (<xref ref-type="bibr" rid="ref2">2</xref>). The global burden of PAD affects more than 200 million people, corresponding to over 3% of the world&#x2019;s population (<xref ref-type="bibr" rid="ref3">3</xref>).</p>
<p>The CDAI developed by Wright et al. (<xref ref-type="bibr" rid="ref4">4</xref>) is an integrative scoring system that measures cumulative antioxidant intake (including vitamins A, C, E, selenium, zinc, and carotenoids) from dietary sources. A higher CDAI has been associated with a reduced risk of multiple chronic conditions, including diabetes, chronic obstructive pulmonary disease (COPD), low back pain, and sleep disorders (<xref ref-type="bibr" rid="ref5 ref6 ref7 ref8">5&#x2013;8</xref>). Two additional studies have, respectively, identified inverse relationships between the CDAI and the probability of cardiovascular disease in general adult populations and postmenopausal women (<xref ref-type="bibr" rid="ref9">9</xref>, <xref ref-type="bibr" rid="ref10">10</xref>).</p>
<p>However, no studies have investigated the association between CDAI and the prevalence of PAD. Therefore, we initiated cross-sectional research to explore the relationship between CDAI and PAD using data from the 1999&#x2013;2004 NHANES.</p>
</sec>
<sec sec-type="materials|methods" id="sec2">
<label>2</label>
<title>Materials and methods</title>
<sec id="sec3">
<label>2.1</label>
<title>Study population</title>
<p>NHANES is an extensive health assessment initiative conducted by the Centers for Disease Control and Prevention (CDC). The research design and associated materials obtained formal authorization from the designated regulatory authorities and were approved through ethical review. Because NHANES data on the ABI questionnaire were only available from 1999 through 2004, our study primarily focused on this period, and 31,125 people were included in this research. The exclusion criteria were as follows: (a) no ABI data (23,555); (b) no CDAI data (5,236); and (c) CDAI outliers (<xref ref-type="bibr" rid="ref2">2</xref>). Based on the above criteria, 2,332 participants were enrolled. The baseline information of the participants is illustrated in <xref ref-type="fig" rid="fig1">Figure 1</xref>.</p>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p>A detailed flow chart of participant recruitment.</p>
</caption>
<graphic xlink:href="fnut-12-1606769-g001.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Flowchart illustrating participant selection process from NHANES 1999-2004. Starting with 31,125 participants, 23,555 were excluded due to missing ABI data, leaving 7,570 participants. Of these, 5,236 were excluded due to missing CDAI data, resulting in 2,334 participants. Finally, 2 CDAI outliers were excluded, resulting in 2,332 participants.</alt-text>
</graphic>
</fig>
</sec>
<sec id="sec4">
<label>2.2</label>
<title>Composite dietary antioxidant index</title>
<p>CDAI served as the primary exposure factor in this study. It was derived from the NHANES Dietary Interview Questionnaire and calculated using a specific formula. Existing literature has validated the reliability of this methodological approach (<xref ref-type="bibr" rid="ref11 ref12 ref13">11&#x2013;13</xref>). The questionnaire collects dietary intake data over any two consecutive days within a calendar year, and calculates the intake of various nutrients and trace elements. The data were collected using two different methods: the first time at the Mobile Examination Center (MEC), a specially designed bus, and the second time via telephone follow-up. The specific dietary intake collection items and the proportion of nutritional components are defined by the USDA&#x2019;s (U. S. Department of Agriculture) Dietary Research Food and Nutrition Database (FNDDS). To calculate CDAI, we selected the daily intake of six dietary antioxidants (vitamins A, C, E, selenium, zinc, and carotenoids) obtained from the Dietary Interview Questionnaire (Days 1 and 2). The CDAI is computed as the sum of standardized values for each antioxidant using the formula:</p>
<disp-formula id="E1">
<mml:math id="M1">
<mml:mtext mathvariant="italic">CDAI</mml:mtext>
<mml:mo>=</mml:mo>
<mml:munderover>
<mml:mo movablelimits="false">&#x2211;</mml:mo>
<mml:mrow>
<mml:mi>i</mml:mi>
<mml:mo>=</mml:mo>
<mml:mn>1</mml:mn>
</mml:mrow>
<mml:mn>6</mml:mn>
</mml:munderover>
<mml:mfrac>
<mml:mrow>
<mml:mtext mathvariant="italic">each intake</mml:mtext>
<mml:mo>&#x2212;</mml:mo>
<mml:mtext mathvariant="italic">mean</mml:mtext>
</mml:mrow>
<mml:mi mathvariant="italic">SD</mml:mi>
</mml:mfrac>
</mml:math>
</disp-formula>
</sec>
<sec id="sec5">
<label>2.3</label>
<title>Peripheral artery disease</title>
<p>We evaluated the ankle-brachial blood pressure index (ABI) as an indicator for assessing lower extremity disease using the NHANES examination data component. During the measurement, participants were required to lie on the examination table in the supine position. A cuff tonometer was placed on the right arm and both ankles, and blood pressure was measured at each site. If the subject&#x2019;s right upper extremity had skin lesions, amputation, or contraindications to compression, the left arm was used. According to the protocol, measurements were taken twice at each site, while for participants older than 65&#x202F;years, only one measurement was taken. Finally, the ABI was calculated by the ratio of ankle systolic blood pressure to brachial systolic blood pressure. Based on clinical guidelines, participants with an ABI&#x202F;&#x2264;&#x202F;0.9 in either leg were defined as suffering PAD (<xref ref-type="bibr" rid="ref14">14</xref>).</p>
</sec>
<sec id="sec6">
<label>2.4</label>
<title>Covariables</title>
<p>In this study, covariates were rigorously selected based on two principles: (1) documented associations with PAD in previous research, or (2) recognition as established clinical risk factors supported by epidemiological studies or consensus guidelines. The included covariates encompassed demographic characteristics (age, gender, race), socioeconomic factors (education level, marital status, income-to-poverty ratio), anthropometric and lifestyle measures&#x2014;including body mass index, physical activity levels, dietary factors (excluding antioxidants such as total energy, calcium, and iron intake), and adverse health behaviors (smoking status and alcohol consumption). Additionally, comorbid conditions (diabetes mellitus, hypertension, and hypercholesterolemia) were incorporated due to their well-established role in PAD pathogenesis, pro-inflammatory and pro-oxidative effects, and potential confounding influence on the relationship between CDAI and PAD outcomes. Adjustment for these covariates enhances the robustness of our analyses by minimizing residual confounding, thereby improving the validity and interpretability of the observed associations.</p>
</sec>
<sec id="sec7">
<label>2.5</label>
<title>Statistical analysis</title>
<p>The baseline characteristics of the participants were evaluated using the chi-square test and t-test. The effect sizes of exposure factors on outcomes were examined using logistic regression analyses. In this study, three distinct multivariate logistic regression models were employed to investigate the relationship between CDAI and PAD, while progressively adjusting for and controlling potential confounders to more accurately assess the independent associations between the two variables. These models differed in their covariate adjustments: Model 1 was unadjusted for any covariates, while Model 2 included age, gender, race, and educational status. In contrast, Model 3 combined covariates including gender, age, race, marital status, education level, household poverty rate, BMI, physical activity, energy intake, calcium intake, iron intake, drinking status, smoking status, diabetes, hypertension, and high cholesterol. Additionally, CDAI was divided into quartiles of continuous measurements to allow for trend analysis to determine potential correlations with PAD, with values categorized as follows: quartile 1 (Q1): &#x2212;5.62 to &#x2212;2.26; quartile 2 (Q2): &#x2212;2.25 to &#x2212;0.75; quartile 3 (Q3): &#x2212;0.74 to 1.18; and quartile 4 (Q4): 1.19 to 29.17. Smoothed curve fitting methods were used to evaluate nonlinear correlations. Stratified multiple regression analysis with interaction terms was conducted to assess heterogeneity and stability across subgroups, including sex, age, race, education, PIR, BMI, drinking, smoking, diabetes, hypertension, and hypercholesterolemia. All statistical analyses were conducted utilizing R software (version 3.4.3) in conjunction with EmpowerStats (version 2.0); statistical significance was defined as <italic>p</italic>&#x202F;&#x003C;&#x202F;0.05.</p>
</sec>
</sec>
<sec sec-type="results" id="sec8">
<label>3</label>
<title>Results</title>
<sec id="sec9">
<label>3.1</label>
<title>Sociodemographic and clinical characteristics</title>
<p>Baseline sociodemographic and clinical characteristics of the participants are presented in <xref ref-type="table" rid="tab1">Table 1</xref>. A total of 2,332 participants aged over 40&#x202F;years were included in this analysis. The mean age was 60.27&#x202F;years (SD&#x202F;=&#x202F;12.74). Among the participants, 50.21% were male, and 47.60% identified as non-Hispanic White. The mean CDAI score for the overall cohort was 0.02 (SD&#x202F;=&#x202F;3.46). Participants with PAD had significantly lower CDAI values than those without PAD (&#x2212;1.08 vs. 0.06, <italic>p</italic> &#x003C;&#x202F;0.001). 7.12% of participants were diagnosed with PAD. Participants with PAD were generally older and more likely to be non-Hispanic Black, smokers, or have hypertension, diabetes mellitus, or hypercholesterolemia. They were also more likely to have family PIR&#x202F;&#x2264;&#x202F;100% and lower energy intake. In addition, patients with PAD had lower educational levels. Significant differences appeared in marital status, with higher proportions of widowed/divorced/separated PAD participants and lower proportions of married/living with a partner. Individuals with PAD tended to have a BMI in the normal or overweight range.</p>
<table-wrap position="float" id="tab1">
<label>Table 1</label>
<caption>
<p>Clinical characteristics of study population.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Variables</th>
<th align="center" valign="top">Overall</th>
<th align="center" valign="top">ABI &#x003E;0.9</th>
<th align="center" valign="top">ABI &#x2264;0.9</th>
<th align="center" valign="top"><italic>p</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">N, %</td>
<td align="center" valign="middle">2,332(100%)</td>
<td align="center" valign="middle">2,166(92.88%)</td>
<td align="center" valign="middle">166(7.12%)</td>
<td align="center" valign="top"><bold>&#x003C;0.001</bold></td>
</tr>
<tr>
<td align="left" valign="middle">Age (years)</td>
<td align="center" valign="middle">60.27&#x202F;&#x00B1;&#x202F;12.74</td>
<td align="center" valign="middle">59.49&#x202F;&#x00B1;&#x202F;12.50</td>
<td align="center" valign="middle">70.49&#x202F;&#x00B1;&#x202F;11.29</td>
<td align="center" valign="top"><bold>&#x003C;0.001</bold></td>
</tr>
<tr>
<td align="left" valign="middle" colspan="4">Gender, %</td>
<td align="center" valign="top">0.670</td>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;Male</td>
<td align="center" valign="top">1,171 (50.21%)</td>
<td align="center" valign="top">1,085 (50.09%)</td>
<td align="center" valign="top">86 (51.81%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;Female</td>
<td align="center" valign="middle">1,161 (49.79%)</td>
<td align="center" valign="middle">1,081 (49.91%)</td>
<td align="center" valign="middle">80 (48.19%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle" colspan="4">Race/Ethnicity, %</td>
<td align="center" valign="top"><bold>0.008</bold></td>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;Mexican American</td>
<td align="center" valign="middle">628 (26.93%)</td>
<td align="center" valign="middle">596 (27.52%)</td>
<td align="center" valign="middle">32 (19.28%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;Other Hispanic</td>
<td align="center" valign="middle">125 (5.36%)</td>
<td align="center" valign="middle">118 (5.45%)</td>
<td align="center" valign="middle">7 (4.22%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;Non-Hispanic White</td>
<td align="center" valign="middle">1,110 (47.60%)</td>
<td align="center" valign="middle">1,026 (47.37%)</td>
<td align="center" valign="middle">84 (50.60%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;Non-Hispanic Black</td>
<td align="center" valign="top">408 (17.50%)</td>
<td align="center" valign="top">366 (16.90%)</td>
<td align="center" valign="top">42 (25.30%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;Other</td>
<td align="center" valign="middle">61 (2.62%)</td>
<td align="center" valign="middle">60 (2.77%)</td>
<td align="center" valign="middle">1 (0.60%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle" colspan="4">Education level, %</td>
<td align="center" valign="top"><bold>0.002</bold></td>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;&#x003C;9th grade</td>
<td align="center" valign="top">541 (23.20%)</td>
<td align="center" valign="top">492 (22.71%)</td>
<td align="center" valign="top">49 (29.52%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;9&#x2013;11th grade</td>
<td align="center" valign="top">445 (19.08%)</td>
<td align="center" valign="top">407 (18.79%)</td>
<td align="center" valign="top">38 (22.89%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;High school graduate</td>
<td align="center" valign="top">513 (22.00%)</td>
<td align="center" valign="top">469 (21.65%)</td>
<td align="center" valign="top">44 (26.51%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;Some college or AA degree</td>
<td align="center" valign="top">461 (19.77%)</td>
<td align="center" valign="top">440 (20.31%)</td>
<td align="center" valign="top">21 (12.65%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;College graduate or above</td>
<td align="center" valign="middle">372 (15.95%)</td>
<td align="center" valign="middle">358 (16.53%)</td>
<td align="center" valign="middle">14 (8.43%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle" colspan="4">Family PIR, %</td>
<td align="center" valign="top"><bold>0.029</bold></td>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;&#x2264;100</td>
<td align="center" valign="top">367 (15.74%)</td>
<td align="center" valign="top">331 (15.28%)</td>
<td align="center" valign="top">36 (21.69%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;&#x003E;100</td>
<td align="center" valign="middle">1965 (84.26%)</td>
<td align="center" valign="middle">1835 (84.72%)</td>
<td align="center" valign="middle">130 (78.31%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle" colspan="4">BMI (kg/m<sup>2</sup>), %</td>
<td align="center" valign="top"><bold>0.004</bold></td>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;&#x003C;18.5</td>
<td align="center" valign="middle">25 (1.07%)</td>
<td align="center" valign="middle">23 (1.06%)</td>
<td align="center" valign="middle">2 (1.20%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;18.5&#x2013;24.9</td>
<td align="center" valign="top">624 (26.76%)</td>
<td align="center" valign="top">565 (26.08%)</td>
<td align="center" valign="top">59 (35.54%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;25.0&#x2013;29.9</td>
<td align="center" valign="middle">916 (39.28%)</td>
<td align="center" valign="middle">846 (39.06%)</td>
<td align="center" valign="middle">70 (42.17%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;&#x2265;30.0</td>
<td align="center" valign="middle">767 (32.89%)</td>
<td align="center" valign="middle">732 (33.80%)</td>
<td align="center" valign="middle">35 (21.08%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle" colspan="4">Physical activity, %</td>
<td align="center" valign="top">0.714</td>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;Active</td>
<td align="center" valign="middle">1962 (84.13%)</td>
<td align="center" valign="middle">1824 (84.21%)</td>
<td align="center" valign="middle">138 (83.13%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;Moderate</td>
<td align="center" valign="middle">370 (15.87%)</td>
<td align="center" valign="middle">342 (15.79%)</td>
<td align="center" valign="middle">28 (16.87%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle" colspan="4">Smoking, %</td>
<td align="center" valign="top"><bold>&#x003C;0.001</bold></td>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;Yes</td>
<td align="center" valign="middle">1,238 (53.09%)</td>
<td align="center" valign="middle">1,122 (51.80%)</td>
<td align="center" valign="middle">116 (69.88%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;No</td>
<td align="center" valign="middle">1,094 (46.91%)</td>
<td align="center" valign="middle">1,044 (48.20%)</td>
<td align="center" valign="middle">50 (30.12%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle" colspan="4">Drinking, %</td>
<td align="center" valign="top">0.410</td>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;Yes</td>
<td align="center" valign="middle">1,543 (66.17%)</td>
<td align="center" valign="middle">1,438 (66.39%)</td>
<td align="center" valign="middle">105 (63.25%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;No</td>
<td align="center" valign="middle">789 (33.83%)</td>
<td align="center" valign="middle">728 (33.61%)</td>
<td align="center" valign="middle">61 (36.75%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle" colspan="4">Diabetes, %</td>
<td align="center" valign="top"><bold>&#x003C;0.001</bold></td>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;Yes</td>
<td align="center" valign="middle">298 (12.78%)</td>
<td align="center" valign="middle">260 (12.00%)</td>
<td align="center" valign="middle">38 (22.89%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;No</td>
<td align="center" valign="middle">2034 (87.22%)</td>
<td align="center" valign="middle">1906 (88.00%)</td>
<td align="center" valign="middle">128 (77.11%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle" colspan="4">Hypertension, %</td>
<td align="center" valign="top"><bold>&#x003C;0.001</bold></td>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;Yes</td>
<td align="center" valign="middle">930 (39.88%)</td>
<td align="center" valign="middle">837 (38.64%)</td>
<td align="center" valign="middle">93 (56.02%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;No</td>
<td align="center" valign="middle">1,402 (60.12%)</td>
<td align="center" valign="middle">1,329 (61.36%)</td>
<td align="center" valign="middle">73 (43.98%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle" colspan="4">Hypercholesterolemia, %</td>
<td align="center" valign="top"><bold>0.009</bold></td>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;Yes</td>
<td align="center" valign="middle">796 (34.13%)</td>
<td align="center" valign="middle">724 (33.43%)</td>
<td align="center" valign="middle">72 (43.37%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;No</td>
<td align="center" valign="middle">1,536 (65.87%)</td>
<td align="center" valign="middle">1,442 (66.57%)</td>
<td align="center" valign="middle">94 (56.63%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">CDAI</td>
<td align="center" valign="top">0.02&#x202F;&#x00B1;&#x202F;3.46</td>
<td align="center" valign="top">0.06&#x202F;&#x00B1;&#x202F;3.50</td>
<td align="center" valign="top">&#x2212;1.08&#x202F;&#x00B1;&#x202F;2.67</td>
<td align="center" valign="top"><bold>&#x003C;0.001</bold></td>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;Energy Intake (kcal)</td>
<td align="center" valign="middle">1923.99&#x202F;&#x00B1;&#x202F;888.95</td>
<td align="center" valign="middle">1944.31&#x202F;&#x00B1;&#x202F;901.60</td>
<td align="center" valign="middle">1658.80&#x202F;&#x00B1;&#x202F;649.33</td>
<td align="center" valign="top"><bold>&#x003C;0.001</bold></td>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;Calcium Intake (mg)</td>
<td align="center" valign="top">748.20&#x202F;&#x00B1;&#x202F;519.36</td>
<td align="center" valign="top">753.80&#x202F;&#x00B1;&#x202F;522.61</td>
<td align="center" valign="top">675.16&#x202F;&#x00B1;&#x202F;470.26</td>
<td align="center" valign="top">0.060</td>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;Iron Intake (mg)</td>
<td align="center" valign="middle">14.62&#x202F;&#x00B1;&#x202F;9.24</td>
<td align="center" valign="middle">14.73&#x202F;&#x00B1;&#x202F;9.21</td>
<td align="center" valign="middle">13.32&#x202F;&#x00B1;&#x202F;9.52</td>
<td align="center" valign="top">0.059</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Continuous data were presented as the mean and 95% confidence interval, category data were presented as the proportion and 95% confidence interval, CDAI, composite dietary antioxidant index, BMI, body mass index, PIR, poverty income ratio. Bold values indicate statistical significance (<italic>p</italic> &#x003C; 0.05).</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec10">
<label>3.2</label>
<title>Association between CDAI and PAD</title>
<p>The association between CDAI and PAD was assessed using logistic regression models. The findings from logistic regression on PAD are summarized in <xref ref-type="table" rid="tab2">Table 2</xref>. After adjusting for all covariates, a one-unit increment in CDAI was linked to a 14% reduction in PAD prevalence [OR&#x202F;=&#x202F;0.86 (95% CI: 0.78&#x2013;0.95)]. After categorizing the CDAI into four groups (Q1-4) based on its values, the inverse association remained statistically significant (<italic>p</italic> &#x003C;&#x202F;0.05). Compared to participants in Q1 (reference group), those in Q4 had a 53% reduction in PAD risk [OR&#x202F;=&#x202F;0.47 (95% CI: 0.24&#x2013;0.93)]. The smoothed curve fitting visualized the relationship between CDAI and PAD, confirming a negative correlation between the two variates (<xref ref-type="fig" rid="fig2">Figure 2</xref>). The P for trend indicated a significant dose&#x2013;response relationship (unadjusted model: <italic>p</italic> =&#x202F;0.001; model I: <italic>p</italic> =&#x202F;0.005; model II: <italic>p</italic> =&#x202F;0.025). Threshold effect analysis also revealed a significant nonlinear relationship between CDAI and PAD prevalence (p for nonlinearity&#x202F;=&#x202F;0.008). Using a two-piecewise linear regression model, an inflection point was identified at CDAI&#x202F;=&#x202F;&#x2212;1.32. This result demonstrated distinct dose&#x2013;response patterns on either side of this threshold. Below this value, each unit increase in CDAI corresponded to a 35% reduction in PAD probability (adjusted OR&#x202F;=&#x202F;0.65, 95% CI: 0.51&#x2013;0.82, <italic>p</italic> &#x003C;&#x202F;0.001). However, beyond this threshold, the protective association plateaued (adjusted OR&#x202F;=&#x202F;0.92, 95% CI: 0.83&#x2013;1.02, <italic>p</italic> =&#x202F;0.097). No further clinical benefit was seen for increasing CDAI levels above this point (<xref ref-type="table" rid="tab3">Table 3</xref>).</p>
<table-wrap position="float" id="tab2">
<label>Table 2</label>
<caption>
<p>Results from logistic regression analysis on PAD.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" rowspan="2">Result</th>
<th align="center" valign="top" colspan="2">Non-adjusted model</th>
<th align="center" valign="top" colspan="2">Model I</th>
<th align="center" valign="top" colspan="2">Model II</th>
</tr>
<tr>
<th align="center" valign="top">OR [95% CI]</th>
<th align="center" valign="top"><italic>p</italic> value</th>
<th align="center" valign="top">OR [95% CI]</th>
<th align="center" valign="top"><italic>P</italic> value</th>
<th align="center" valign="top">OR [95% CI]</th>
<th align="center" valign="top"><italic>P</italic> value</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">Continuous CDAI</td>
<td align="center" valign="middle">0.87 (0.82, 0.93)</td>
<td align="center" valign="middle">&#x003C;0.0001</td>
<td align="center" valign="middle">0.89 (0.83, 0.95)</td>
<td align="center" valign="middle">0.0011</td>
<td align="center" valign="middle">0.86 (0.78, 0.95)</td>
<td align="center" valign="middle">0.0030</td>
</tr>
<tr>
<td align="left" valign="middle">CDAI-Q1 (&#x2212;5.62 to &#x2212;2.26)</td>
<td align="center" valign="middle">1 (ref)</td>
<td align="center" valign="middle">&#x2014;</td>
<td align="center" valign="middle">1 (ref)</td>
<td align="center" valign="middle">&#x2014;</td>
<td align="center" valign="middle">1 (ref)</td>
<td align="center" valign="middle">&#x2014;</td>
</tr>
<tr>
<td align="left" valign="middle">CDAI-Q2 (&#x2212;2.25 to &#x2212;0.75)</td>
<td align="center" valign="middle">0.69 (0.46, 1.05)</td>
<td align="center" valign="middle">0.0806</td>
<td align="center" valign="middle">0.63 (0.41, 0.98)</td>
<td align="center" valign="middle">0.0411</td>
<td align="center" valign="middle">0.57 (0.35, 0.92)</td>
<td align="center" valign="middle">0.0203</td>
</tr>
<tr>
<td align="left" valign="middle">CDAI-Q3 (&#x2212;0.74 to 1.18)</td>
<td align="center" valign="middle">0.57 (0.37, 0.88)</td>
<td align="center" valign="middle">0.0114</td>
<td align="center" valign="middle">0.55 (0.35, 0.87)</td>
<td align="center" valign="middle">0.0110</td>
<td align="center" valign="middle">0.52 (0.30, 0.91)</td>
<td align="center" valign="middle">0.0212</td>
</tr>
<tr>
<td align="left" valign="middle">CDAI-Q4 (1.19 to 29.17)</td>
<td align="center" valign="middle">0.42 (0.26, 0.68)</td>
<td align="center" valign="middle">0.0003</td>
<td align="center" valign="middle">0.51 (0.31, 0.84)</td>
<td align="center" valign="middle">0.0090</td>
<td align="center" valign="middle">0.47 (0.24, 0.93)</td>
<td align="center" valign="middle">0.0303</td>
</tr>
<tr>
<td align="left" valign="middle"><italic>P</italic> for trend</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td/>
<td align="center" valign="middle">0.005</td>
<td/>
<td align="center" valign="middle">0.0025</td>
<td/>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Model 1: no covariates were adjusted. Model 2: adjusted for age, sex, race/ethnicity, and education levels. Model 3: adjusted for age, sex, race/ethnicity, education levels, PIR, BMI, physical activity, energy intake, calcium intake, iron intake, smoking, drinking, hypertension, diabetes and hypercholesterolemia.</p>
</table-wrap-foot>
</table-wrap>
<fig position="float" id="fig2">
<label>Figure 2</label>
<caption>
<p>Results from smooth curve fitting.</p>
</caption>
<graphic xlink:href="fnut-12-1606769-g002.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Graph depicting the odds ratio of peripheral artery disease (PAD) with 95% confidence intervals plotted against the clinical disease activity index (CDAI). The x-axis represents CDAI values from -5 to 30, while the y-axis shows odds ratios from 0 to 1.0. A red line indicates the odds ratio trend, initially decreasing, then stabilizing around zero, with a sharp increase beyond CDAI 25. Dotted blue lines represent confidence intervals. A rug plot at the bottom denotes data distribution.</alt-text>
</graphic>
</fig>
<table-wrap position="float" id="tab3">
<label>Table 3</label>
<caption>
<p>Threshold effect analysis of CDAI on PAD using a two-piecewise linear regression model.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Inflection point</th>
<th align="center" valign="top">Adjusted OR (95%CI)</th>
<th align="center" valign="top"><italic>P</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle" colspan="3">Model I</td>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;One line effect</td>
<td align="center" valign="middle">0.86 (0.78, 0.95)</td>
<td align="center" valign="middle">0.0032</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="3">Model II</td>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;Turning point (k)</td>
<td align="center" valign="middle">&#x2212;1.32</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;&#x003C; K effect 1</td>
<td align="center" valign="middle">0.65 (0.51, 0.82)</td>
<td align="center" valign="middle">0.0002</td>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;&#x003E; K effect 2</td>
<td align="center" valign="middle">0.92 (0.83, 1.02)</td>
<td align="center" valign="middle">0.0968</td>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;Effect 2&#x2013;1</td>
<td align="center" valign="middle">1.42 (1.10, 1.83)</td>
<td align="center" valign="middle">0.0073</td>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;Model fit value at K</td>
<td align="center" valign="middle">&#x2212;2.60 (&#x2212;2.86, &#x2212;2.35)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x2003;Log-likelihood ratio</td>
<td/>
<td align="center" valign="middle">0.0080</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="sec11">
<label>3.3</label>
<title>Subgroup analysis</title>
<p>Analyses were stratified by sex, age, race, education, PIR, BMI, energy intake, calcium intake, iron intake, smoking, drinking, hypertension, diabetes, and hypercholesterolemia. The forest plot (<xref ref-type="fig" rid="fig3">Figure 3</xref>) shows a consistent and statistically significant inverse association between CDAI and PAD across all genders, age &#x2265;65&#x202F;years, Non-Hispanic Black, participants with family PIR above 100, 9th grade to high school graduates, widowed/divorced/separated participants, BMI 25.0&#x2013;29.9, smokers, non-smokers, drinkers, non-drinkers, participants with hypertension, participants without diabetes, and participants with hypercholesterolemia. In all subgroup analyses, the interaction tests were statistically insignificant (<italic>P</italic> for interaction &#x003E; 0.05), indicating the negative association has good robustness and generalizability. Smoothing curves (<xref ref-type="fig" rid="fig4">Figure 4</xref>) suggested steeper negative slopes in females and participants aged &#x003C;65&#x202F;years. However, formal interaction tests remained non-significant. The underlying mechanisms warrant further investigation.</p>
<fig position="float" id="fig3">
<label>Figure 3</label>
<caption>
<p>Results from subgroup analyses. Age, sex, race/ethnicity, education levels, PIR, BMI, physical activity, energy intake, calcium intake, iron intake, smoking, drinking, hypertension, diabetes and hypercholesterolemia were adjusted. Abbreviation: CDAI, composite dietary antioxidant index, BMI, body mass index, PIR, poverty Income Ratio.</p>
</caption>
<graphic xlink:href="fnut-12-1606769-g003.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Forest plot displaying odds ratios (OR) with 95% confidence intervals for various subgroups, including age, gender, marital status, race/ethnicity, education level, family PIR, BMI, smoking, drinking, diabetes, hypertension, and hypercholesterolemia. ORs are plotted against a reference line at 1. Significant differences are noted for age over 60, non-Hispanic Black ethnicity, BMI 25 to 29.9, and absence of diabetes, hypertension, and hypercholesterolemia. P-values for interaction range from 0.160 to 0.888.</alt-text>
</graphic>
</fig>
<fig position="float" id="fig4">
<label>Figure 4</label>
<caption>
<p>Results from subgroups smooth curve fitting analyses. <bold>(A)</bold> sex (male and female), <bold>(B)</bold> age (&#x003C; 65&#x202F;years, &#x2265; 65&#x202F;years). Age, sex, race/ethnicity, education levels, PIR, BMI, physical activity, energy intake, calcium intake, iron intake, smoking, drinking, hypertension, diabetes and hypercholesterolemia were adjusted.</p>
</caption>
<graphic xlink:href="fnut-12-1606769-g004.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Two line graphs compare LogOR against CDAI. Chart A shows data by gender, with red dots for one gender and blue circles for the other. Chart B shows data by age, using red dots for age under sixty-five and blue circles for age sixty-five and older. Both graphs show decreasing trends.</alt-text>
</graphic>
</fig>
</sec>
</sec>
<sec sec-type="discussion" id="sec12">
<label>4</label>
<title>Discussion</title>
<p>In the cross-sectional study, we enrolled 2,332 representative participants and observed the negative relationship between CDAI and PAD. This suggests that higher levels of dietary antioxidants may be linked to a lower risk of developing PAD, particularly in high-risk populations with antioxidant deficiency.</p>
<p>Our results suggest that CDAI may have potential clinical value in the prevention of PAD. To our knowledge, this is the first population-based study to evaluate the association between CDAI and PAD, independent of traditional cardiovascular risk factors. By analyzing the NHANES database, Teng et al. (<xref ref-type="bibr" rid="ref11">11</xref>) demonstrated a negative association between CDAI and stroke, with the antioxidant-based nomogram model showing strong predictive capability for stroke risk. Analogously, Ma et al. (<xref ref-type="bibr" rid="ref12">12</xref>) demonstrated that the CDAI exhibited an L-shaped inverse association with the risk of coronary heart disease (CHD), reducing it by up to 65%. A study has also found that CDAI improves Atherosclerosis, Maugeri et al. (<xref ref-type="bibr" rid="ref15">15</xref>) using the Kardiovize Brno 2030 study found that CDAI reduced carotid intima-media thickness (cIMT) in women. Consistent with the results of the previous study, CDAI is a protective factor against cardiovascular diseases. In this study, we found a negative relationship between the CDAI and PAD, indicating that individuals with high levels of dietary antioxidants may have a lower risk of developing PAD.</p>
<p>Atherosclerosis is a major cause of stenosis or occlusion of the arteries lumen in the lower extremities, affecting the blood supply to the lower extremities. Oxidative stress, inflammatory response, and lipid infiltration are the primary causes of atherosclerosis formation (<xref ref-type="bibr" rid="ref16">16</xref>). Of these, reactive oxygen species (ROS), the primary byproducts of oxidative stress, initiate atherosclerosis by inducing endothelial dysfunction, chronic inflammation, and dysregulated lipid metabolism (<xref ref-type="bibr" rid="ref17">17</xref>). Moreover, oxidative stress critically influences PAD progression by promoting vascular inflammation and endothelial dysfunction (<xref ref-type="bibr" rid="ref18">18</xref>, <xref ref-type="bibr" rid="ref19">19</xref>). Oxidative stress results from the overproduction of reactive oxygen species (ROS) combined with impaired or inadequate antioxidant defense systems (<xref ref-type="bibr" rid="ref20">20</xref>, <xref ref-type="bibr" rid="ref21">21</xref>). Animal and clinical studies indicate that oxidative stress drives the initiation and progression of PAD via diverse mechanisms. In animal models, experimental hindlimb ischemia (HLI) studies have shown that the generation of ROS is closely associated with angiogenesis, endothelial dysfunction, and inflammatory responses (<xref ref-type="bibr" rid="ref22">22</xref>). Increased muscle ROS formation was found after ligation of unilateral iliac and femoral arteries (<xref ref-type="bibr" rid="ref23">23</xref>). Moreover, ROS interferes with vascular endothelial growth factor (VEGF) mediated angiogenesis by decreasing nitric oxide (NO) bioavailability, thereby affecting the recovery of ischemic tissues (<xref ref-type="bibr" rid="ref24">24</xref>). In addition, animal studies of cigarette smoke exposure have demonstrated that major risk factors for PAD, such as smoking and diabetes, further exacerbate inflammation by enhancing oxidative stress and decreasing NO bioactivity (<xref ref-type="bibr" rid="ref25">25</xref>, <xref ref-type="bibr" rid="ref26">26</xref>). In terms of clinical studies, a cross-sectional analysis comparing blood markers in patients with type 2 diabetes with or without PAD found higher levels of AGEs and MDA (two biomarkers closely linked to oxidative stress), and lower levels of vitamin E and total reactive antioxidant potentials (TRAPs) in patients with PAD compared to those without PAD (<xref ref-type="bibr" rid="ref27">27</xref>). A small clinical study (<italic>n</italic>&#x202F;=&#x202F;74,12 months duration) noted that propionyl L-carnitine improved ABI, maximum walking distance, pre-morbid walking distance, and quality of life in patients (<xref ref-type="bibr" rid="ref28">28</xref>).</p>
<p>The key antioxidants included in the CDAI&#x2014;such as carotenoids, vitamins A, C, E, and essential trace metals (e.g., Zn, Se)&#x2014;exert protective effects against oxidative stress through diverse mechanisms. Vitamin C is a water-soluble compound generated through glucose metabolism (<xref ref-type="bibr" rid="ref29">29</xref>, <xref ref-type="bibr" rid="ref30">30</xref>). It synthesizes the reducing agents needed for collagen fibers, and it also protects the body from free radicals. Moreover, Vitamin C has been demonstrated to ameliorate oxidative stress through the modulation of key signaling pathways (<xref ref-type="bibr" rid="ref31">31</xref>). Carotenoids are structurally similar to vitamin A (<xref ref-type="bibr" rid="ref32">32</xref>). They may be involved in antioxidant protection against oxidative free radical attack (<xref ref-type="bibr" rid="ref33">33</xref>). Some studies indicate that vitamin A and carotenoids are potent antioxidants that may inhibit the development of cardiovascular diseases and enhance visual health (<xref ref-type="bibr" rid="ref34">34</xref>, <xref ref-type="bibr" rid="ref35">35</xref>). They can impact both free radicals and peroxyl radicals. Vitamin E, as an essential nutrient that can only be ingested exogenously, is a fat-soluble vitamin group present in high concentrations throughout the body, including cell membranes and cytoplasmic proteins, and is involved in regulating the body&#x2019;s redox balance (<xref ref-type="bibr" rid="ref36">36</xref>). Numerous studies have demonstrated that Vitamin E exhibits anti-atherosclerotic and anti-cardiovascular effects (<xref ref-type="bibr" rid="ref37">37</xref>, <xref ref-type="bibr" rid="ref38">38</xref>). The trace element selenium (Se) is regarded as a beneficial dietary supplement due to its significant antioxidant capabilities and function of enhancing health (<xref ref-type="bibr" rid="ref39">39</xref>). Amino acids, peptides, and enzymes are selenium-containing compounds, also playing crucial biological roles in the human body (<xref ref-type="bibr" rid="ref40">40</xref>). Selenium and its derivatives, especially selenoproteins containing selenocysteine form selenium, have been shown to have antioxidant properties (<xref ref-type="bibr" rid="ref41">41</xref>). Zn(II), Cu(I), and iron, the metal thiolate groups, also function as redox switches (<xref ref-type="bibr" rid="ref42">42</xref>), serving as crucial and irreplaceable elements in the reaction pathway.</p>
<p>However, the US Preventive Services Task Force (USPSTF) found through a systematic review of RCTs that current evidence is insufficient to evaluate the balance of benefits and risks of using single or combined nutritional supplements for the prevention of certain diseases (<xref ref-type="bibr" rid="ref43">43</xref>). Findings from retrospective research also support similar conclusions (<xref ref-type="bibr" rid="ref44">44</xref>, <xref ref-type="bibr" rid="ref45">45</xref>).</p>
<p>Although the effects of single nutrients are not significant or insufficiently supported by evidence, some studies have found that the Mediterranean diet, rich in antioxidant components, is linked to a lower risk of cardiovascular disease and PAD (<xref ref-type="bibr" rid="ref46">46</xref>). The Mediterranean Diet is predominantly plant-based, particularly in fruits, vegetables, legumes, and nuts, which are abundant in antioxidants such as vitamin A, C, E, polyphenols and others (<xref ref-type="bibr" rid="ref47">47</xref>). The Mediterranean diet has been used for the prevention of cardiovascular diseases, including peripheral arterial disease and ischemic stroke. The Mediterranean diet was found to be superior to a low-fat diet in the prevention of cardiovascular events, including peripheral arterial disease, myocardial infarction, and ischemic stroke, in a 1,000-person RCT, supporting the secondary prevention of cardiovascular disease through the Mediterranean diet (<xref ref-type="bibr" rid="ref48">48</xref>). Additionally, an exploratory, non-prespecified analysis of a large-scale randomized controlled trial revealed that the Mediterranean diet, characterized by an unrestricted energy intake and rich in antioxidants, significantly reduces the prevalence of PAD, underscoring its value in cardiovascular disease prevention (<xref ref-type="bibr" rid="ref49">49</xref>, <xref ref-type="bibr" rid="ref50">50</xref>).</p>
<p>Interestingly, as depicted in <xref ref-type="fig" rid="fig4">Figure 4</xref>, our study revealed a potential differential benefit of antioxidant dietary intake across sex and age groups. Specifically, female participants and individuals under 65&#x202F;years of age exhibited a more pronounced reduction in PAD risk with increasing CDAI levels. Accumulating evidence indicates that female sex hormones, particularly estrogen, exert multifaceted atheroprotective effects (<xref ref-type="bibr" rid="ref51">51</xref>). A prospective study on oxidative balance scores and hypertension also observed similar sex-specific differences and demonstrated that it may be mediated by oxidative stress mechanisms (<xref ref-type="bibr" rid="ref52">52</xref>). In elderly individuals, advanced age drives vascular aging process. It includes oxidative stress, endothelial dysfunction, and progressive arterial stiffening, which collectively may compromise the efficacy of antioxidant interventions (<xref ref-type="bibr" rid="ref53">53</xref>). Therefore, this may raise different guidelines for the prevention of PAD in people of different genders and ages. For instance, premenopausal women or middle-aged people at risk of PAD might benefit from a diet plan that emphasizes foods high in antioxidants, while older populations may require adjunct therapies to overcome age-related declines in antioxidant absorption or utilization.</p>
<p>This study has several advantages. Firstly, this study is grounded in data extracted from the NHANES, which employs a randomized and scientifically rigorous sampling methodology. This approach ensures that the selected cohort reflects the demographic diversity of the US population, providing a robust representation of the US population&#x2019;s health and nutritional status. Additionally, this study benefits from a substantial sample size, enhancing statistical precision and reliability. Additionally, we also adjusted for a large number of covariates to make the results more independent and more credible. Nevertheless, the limitations of this study must be acknowledged. Firstly, due to the cross-sectional study design, the authors were unable to establish clear causal relationships. Secondly, dietary information was self-reported, which may introduce inaccuracies due to recall bias. Moreover, due to the NHANES study protocol, which excluded individuals aged below 40&#x202F;years from screening for these variables, the association between CDAI and PAD could not be comprehensively evaluated across a broader demographic spectrum.</p>
</sec>
<sec sec-type="conclusions" id="sec13">
<label>5</label>
<title>Conclusion</title>
<p>In conclusion, our study demonstrates that CDAI is inversely associated with PAD in U. S. adults aged &#x2265;40&#x202F;years. Moreover, this association remained consistent across all subgroups, supporting the potential role of antioxidant-rich diets (reflected by higher CDAI scores) in PAD prevention in the general population. However, further comprehensive research and prospective cohort studies are needed to explore causal relationships and validate these findings.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="sec14">
<title>Data availability statement</title>
<p>Publicly available datasets were analyzed in this study. This data can be found at: <ext-link xlink:href="http://wwwn.cdc.gov/nchs/nhanes/" ext-link-type="uri">wwwn.cdc.gov/nchs/nhanes/</ext-link>.</p>
</sec>
<sec sec-type="ethics-statement" id="sec15">
<title>Ethics statement</title>
<p>Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the [patients/ participants OR patients/participants legal guardian/next of kin] was not required to participate in this study in accordance with the national legislation and the institutional requirements.</p>
</sec>
<sec sec-type="author-contributions" id="sec16">
<title>Author contributions</title>
<p>ZF: Conceptualization, Data curation, Formal analysis, Methodology, Software, Visualization, Writing &#x2013; original draft, Writing &#x2013; review &#x0026; editing. RZ: Data curation, Supervision, Writing &#x2013; review &#x0026; editing. SG: Data curation, Writing &#x2013; review &#x0026; editing, Writing &#x2013; original draft. QW: Data curation, Writing &#x2013; review &#x0026; editing. JL: Data curation, Writing &#x2013; original draft. SL: Data curation, Writing &#x2013; review &#x0026; editing. ZS: Data curation, Writing &#x2013; review &#x0026; editing. WW: Funding acquisition, Supervision, Writing &#x2013; review &#x0026; editing.</p>
</sec>
<sec sec-type="funding-information" id="sec17">
<title>Funding</title>
<p>The author(s) declare that financial support was received for the research and/or publication of this article. The study was funded by the Beijing Tsinghua Changgung Hospital Fund (Grant No.12022C6020).</p>
</sec>
<sec sec-type="COI-statement" id="sec18">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="ai-statement" id="sec19">
<title>Generative AI statement</title>
<p>The authors declare that no Gen AI was used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p>
</sec>
<sec sec-type="disclaimer" id="sec20">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
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