<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD Journal Publishing DTD v2.3 20070202//EN" "journalpublishing.dtd">
<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" article-type="research-article" dtd-version="2.3" xml:lang="EN">
<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Endocrinol.</journal-id>
<journal-title>Frontiers in Endocrinology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Endocrinol.</abbrev-journal-title>
<issn pub-type="epub">1664-2392</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fendo.2025.1506964</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Endocrinology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Correlation analysis between serum uric acid and carotid intima-media thickness: a cross sectional study</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Zhang</surname>
<given-names>Ziheng</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/methodology/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
<role content-type="https://credit.niso.org/contributor-roles/conceptualization/"/>
<role content-type="https://credit.niso.org/contributor-roles/software/"/>
<role content-type="https://credit.niso.org/contributor-roles/visualization/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Zhang</surname>
<given-names>Peng</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/validation/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Yu</surname>
<given-names>Xiangli</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/investigation/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Ji</surname>
<given-names>Zhongmin</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/data-curation/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Zhang</surname>
<given-names>Aimei</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/777165/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/funding-acquisition/"/>
<role content-type="https://credit.niso.org/contributor-roles/resources/"/>
<role content-type="https://credit.niso.org/contributor-roles/supervision/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author" equal-contrib="yes" corresp="yes">
<name>
<surname>Wang</surname>
<given-names>Hongjun</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<xref ref-type="author-notes" rid="fn003">
<sup>&#x2020;</sup>
</xref>
<xref ref-type="author-notes" rid="fn004">
<sup>&#x2021;</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/data-curation/"/>
<role content-type="https://credit.niso.org/contributor-roles/resources/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author" equal-contrib="yes" corresp="yes">
<name>
<surname>Li</surname>
<given-names>Daojing</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<xref ref-type="author-notes" rid="fn004">
<sup>&#x2021;</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/817238/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/conceptualization/"/>
<role content-type="https://credit.niso.org/contributor-roles/project-administration/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>Clinical Medical College, Jining Medical University</institution>, <addr-line>Jining</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Department of Neurology, Jining First People&#x2019;s Hospital</institution>, <addr-line>Jining</addr-line>, <country>China</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>Department of Neurology, Affiliated Hospital of Jining Medical University</institution>, <addr-line>Jining</addr-line>, <country>China</country>
</aff>
<aff id="aff4">
<sup>4</sup>
<institution>Department of Medical Imaging, Affiliated Hospital of Jining Medical University</institution>, <addr-line>Jining</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Elettra Mancuso, University Magna Graecia of Catanzaro, Italy</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Carolina Averta, Magna Gr&#xe6;cia University of Catanzaro, Italy</p>
<p>Francesca De Vito, Magna Gr&#xe6;cia University, Italy</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Hongjun Wang, <email xlink:href="mailto:wanghongjunjyfy@163.com">wanghongjunjyfy@163.com</email>; Daojing Li, <email xlink:href="mailto:lidaojing0415@163.com">lidaojing0415@163.com</email>
</p>
</fn>
<fn fn-type="present-address" id="fn003">
<p>&#x2020;Present address: Hongjun Wang, Rizhao International Heart Hospital, Qingdao University, Rizhao, China</p>
</fn>
<fn fn-type="equal" id="fn004">
<p>&#x2021;These authors have contributed equally to this work</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>24</day>
<month>04</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="collection">
<year>2025</year>
</pub-date>
<volume>16</volume>
<elocation-id>1506964</elocation-id>
<history>
<date date-type="received">
<day>06</day>
<month>10</month>
<year>2024</year>
</date>
<date date-type="accepted">
<day>02</day>
<month>04</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2025 Zhang, Zhang, Yu, Ji, Zhang, Wang and Li</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Zhang, Zhang, Yu, Ji, Zhang, Wang and Li</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>
<sec>
<title>Objective</title>
<p>This study aims to investigate the association between serum uric acid (UA) and carotid intima-media thickness (CIMT) in adults undergoing routine health screenings.</p>
</sec>
<sec>
<title>Methods</title>
<p>Clinical data from 375 participants (mean age: 64.26 &#xb1; 9.97 years; 48.53% male) who underwent health examinations at Jining Medical University Affiliated Hospital (January 2022&#x2013;January 2023) were analyzed. &#x200b;Generalized additive models and piecewise linear regression were used to evaluate linear/non-linear relationships and threshold effects.</p>
</sec>
<sec>
<title>Results</title>
<p>The study included a total of 375 individuals, with an average age of 64.26 &#xb1; 9.97 years. The participants consisted of 48.53% males. After adjusting for confounding factors (age, sex, BMI, etc.), a non-linear relationship between UA and CIMT was identified. The threshold occurred at UA = 3.15 mg/dL. &#x200b;When UA &#x2265; 3.15 mg/dL, each 1 mg/dL increase in UA was associated with a 0.061 mm increase in CIMT (&#x3b2; = 0.061, 95% CI: 0.031&#x2013;0.090, p &lt; 0.0001). No significant association was observed when UA &lt; 3.15 mg/dL (&#x3b2; = &#x2212;0.002, 95% CI: &#x2212;0.033&#x2013;0.030, p = 0.9240).</p>
</sec>
<sec>
<title>Conclusion</title>
<p>The study demonstrates a non-linear relationship between UA and CIMT in the health screening population. UA levels &#x2265;3.15 mg/dL are positively correlated with increased CIMT, suggesting that elevated UA may promote carotid atherosclerosis progression.</p>
</sec>
</abstract>
<kwd-group>
<kwd>intima media thickness</kwd>
<kwd>uric acid</kwd>
<kwd>carotid atherosclerosis</kwd>
<kwd>cerebrovascular disease</kwd>
<kwd>cross sectional study</kwd>
</kwd-group>
<counts>
<fig-count count="1"/>
<table-count count="4"/>
<equation-count count="0"/>
<ref-count count="28"/>
<page-count count="8"/>
<word-count count="3931"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-in-acceptance</meta-name>
<meta-value>Cardiovascular Endocrinology</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<p>The global expansion of the elderly population &#x200b;has been accompanied by a rising incidence of stroke. Despite significant advancements in stroke treatment and prevention strategies, &#x200b;stroke-related mortality and disability rates remain persistently high (<xref ref-type="bibr" rid="B1">1</xref>). Atherosclerotic plaques, &#x200b;a predominant precursor of ischemic stroke, predominantly occur in the internal carotid artery distal to the bifurcation of the common carotid artery in Western populations. &#x200b;This anatomical predilection may be associated with reduced shear stress at this arterial segment. &#x200b;Compromised endothelial function, characterized by increased intimal thickness and diminished nitric oxide release under low shear stress conditions, &#x200b;contributes to the susceptibility to cholesterol plaque formation. &#x200b;Since 2015, stroke &#x200b;has emerged as the leading cause of mortality and disability in China, posing substantial threats to public health and socioeconomic stability (<xref ref-type="bibr" rid="B2">2</xref>).</p>
<p>Atherosclerosis serves as the principal pathological foundation for cardiovascular and cerebrovascular diseases (<xref ref-type="bibr" rid="B3">3</xref>, <xref ref-type="bibr" rid="B4">4</xref>). Carotid intima-media thickness (CIMT), &#x200b;a non-invasive ultrasonographic marker, provides reliable assessment of subclinical atherosclerosis and endothelial dysfunction (<xref ref-type="bibr" rid="B5">5</xref>, <xref ref-type="bibr" rid="B6">6</xref>). Growing evidence supports CIMT &#x200b;as a predictive biomarker for cardiovascular events and stroke (<xref ref-type="bibr" rid="B7">7</xref>&#x2013;<xref ref-type="bibr" rid="B9">9</xref>). While traditional risk factors for atherosclerosis &#x200b;are well-characterized, &#x200b;the pathophysiological contributions of certain metabolic parameters &#x200b;require further elucidation.</p>
<p>Urine is the main route for excreting serum uric acid (UA), which is the final byproduct of purine metabolism synthesized in the liver (<xref ref-type="bibr" rid="B10">10</xref>).The elevation of UA concentration in human plasma is influenced by factors such as diet, alcohol consumption, fructose intake, obesity, and ethnicity (<xref ref-type="bibr" rid="B11">11</xref>).Many studies have demonstrated that an excess of uric acid can lead to conditions such as gout, kidney stones, and inflammatory reactions (<xref ref-type="bibr" rid="B12">12</xref>&#x2013;<xref ref-type="bibr" rid="B15">15</xref>).There is an increasing body of evidence supporting the promotive role of UA in atherosclerosis. However, it is noteworthy that some studies have indicated its antioxidative effects in oxidative stress (<xref ref-type="bibr" rid="B16">16</xref>), suggesting a protective role for blood vessels in the human body (<xref ref-type="bibr" rid="B17">17</xref>, <xref ref-type="bibr" rid="B18">18</xref>). Contradictory data (<xref ref-type="bibr" rid="B19">19</xref>&#x2013;<xref ref-type="bibr" rid="B21">21</xref>) characterizes the involvement of UA in the development of atherosclerosis. Furthermore, there is no consensus on the optimal UA level control in healthy populations to manage atherosclerosis. Hence, it is crucial to further explore the connection between UA and CIMT in people undergoing health examinations, providing a basis for future medication interventions that aim to regulate UA levels in order to prevent the onset and progression of atherosclerosis.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<label>2</label>
<title>Materials and methods</title>
<sec id="s2_1">
<label>2.1</label>
<title>Study population</title>
<p>Data from 560 individuals who were undergoing health check-ups at the Health Check Center of the Affiliated Hospital of Jining Medical College between January 2022 and January 2023 were analyzed in this cross-sectional study. Inclusion criteria: (1) age &#x2265; 18 years and (2) signed informed consent. Exclusion criteria: (1) a history of gout or the use of medications affecting uric acid metabolism, (2) significant organ failure in the heart, kidneys, lungs, liver, etc., (3) comorbidities like tumors, rheumatic diseases, or autoimmune diseases, and (4) incomplete data collection for UA and carotid ultrasound. Participants with gout (n = 23), tumors or autoimmune diseases (n = 13), incomplete carotid ultrasound data (n = 86), or missing UA data (n = 63) were excluded from the analysis. The final analysis included 375 participants. Health screenings involved a comprehensive assessment, including UA levels, carotid ultrasound, and other laboratory tests.</p>
</sec>
<sec id="s2_2">
<label>2.2</label>
<title>General information</title>
<p>The hospital&#x2019;s health examination system provided comprehensive details about the participants, encompassing their gender, age, systolic blood pressure, diastolic blood pressure, BMI, blood creatinine, fasting blood sugar, triglycerides, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, homocysteine, neutrophil count, platelet count, albumin, medical history (including diabetes, hypertension, coronary heart disease, and stroke), usage of antiplatelet and statin medications, smoking habits, and alcohol consumption. Blood pressure was measured following the American College of Cardiology (ACC) guidelines.</p>
</sec>
<sec id="s2_3">
<label>2.3</label>
<title>Laboratory measurements</title>
<p>After fasting for 8-12 hours, fasting blood samples were collected for laboratory analysis. An automated biochemical analyzer (Cobas) was used to measure UA levels, TC, HDL-C, LDL-C, TG, and FPG. The concentration of glycated hemoglobin (HbA1c) was determined through high-performance liquid chromatography, while the measurement of plasma glucose was conducted using the hexokinase method.</p>
</sec>
<sec id="s2_4">
<label>2.4</label>
<title>Ultrasound image analysis</title>
<p>Carotid ultrasound examinations were conducted using a portable LOGIQ ultrasound machine (GE, Best, USA). Trained and certified ultrasound physicians followed standard scanning and reading protocols. CIMT measurements were obtained at six different positions near the division point of the common carotid artery, 1 cm above and below the division point on both sides. To enhance reliability and eliminate measurement errors, each of these six locations was measured twice, and the values were averaged.</p>
</sec>
<sec id="s2_5">
<label>2.5</label>
<title>Statistical analysis</title>
<p>Statistical analyses were conducted using Empower Stats and R software version 4.2.0. Descriptive statistics were employed for general information and biochemical variables. Mean (standard deviation) was used to express continuous variables with a normal distribution, whereas the median was used for non-normally distributed continuous variables. Frequencies or percentages were used to present categorical variables. Univariate analysis models were used to examine the correlation of UA and other anthropometric and biochemical variables with CIMT. Following the adjustment for possible confounding variables, a sleek curve fitting technique was utilized to investigate the correlation between UA and CIMT. Multivariate segmented linear regression models were further employed to assess the independent correlation between UA and CIMT based on the smooth curve fit. Threshold effect analysis was used to determine the presence of inflection points. A significance level of less than 0.05 was attributed to the p-value.</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<label>3</label>
<title>Results</title>
<sec id="s3_1">
<label>3.1</label>
<title>Participant characteristics</title>
<p>There are a total of 375 individuals involved in the study, consisting of 182 (46.75%) males and 193 (53.25%) females. Participants had an average age of 64.264 &#xb1; 9.974 years. The mean levels of UA and CIMT were 3.104 &#xb1; 0.781 mg/dL and 0.763 &#xb1; 0.123 mm, respectively. For UA, the median values ranged from 1.298 to 5.148 mg/dL, while for CIMT, it ranged from 0.5 to 1.095 mm. In order to investigate the connection between UA and CIMT, UA was divided into four groups (Q1-Q4) according to quartiles, and the initial characteristics of this group are described in <xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>.</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Comparison of demographic, clinical, and laboratory characteristics in groups. (n=375).</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="left">UA Category</th>
<th valign="middle" align="left">Q1 (N=93) 1.298-2.552</th>
<th valign="middle" align="left">Q2 (N=94) 2.553-3.014</th>
<th valign="middle" align="left">Q3 (N=91) 3.015-3.590</th>
<th valign="middle" align="left">Q4 (N=97) 3.602-5.148</th>
<th valign="middle" align="left">P-value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">Age, y</td>
<td valign="middle" align="left">64.04 &#xb1; 10.34</td>
<td valign="middle" align="left">65.80 &#xb1; 9.60</td>
<td valign="middle" align="left">63.88 &#xb1; 10.70</td>
<td valign="middle" align="left">63.35 &#xb1; 9.23</td>
<td valign="middle" align="left">0.270</td>
</tr>
<tr>
<td valign="middle" align="left">SEX (%)</td>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left">&lt;0.001</td>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Male (%)</td>
<td valign="middle" align="left">25 (26.88%)</td>
<td valign="middle" align="left">38 (40.43%)</td>
<td valign="middle" align="left">56 (61.54%)</td>
<td valign="middle" align="left">63 (64.95%)</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Female (%)</td>
<td valign="middle" align="left">68 (73.12%)</td>
<td valign="middle" align="left">56 (59.57%)</td>
<td valign="middle" align="left">35 (38.46%)</td>
<td valign="middle" align="left">34 (35.05%)</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">SBP, mmHg</td>
<td valign="middle" align="left">134.06 &#xb1; 18.78</td>
<td valign="middle" align="left">135.49 &#xb1; 19.91</td>
<td valign="middle" align="left">133.21 &#xb1; 15.38</td>
<td valign="middle" align="left">135.59 &#xb1; 18.49</td>
<td valign="middle" align="left">0.697</td>
</tr>
<tr>
<td valign="middle" align="left">DBP, mmHg</td>
<td valign="middle" align="left">77.53 &#xb1; 12.28</td>
<td valign="middle" align="left">78.61 &#xb1; 12.47</td>
<td valign="middle" align="left">80.56 &#xb1; 11.17</td>
<td valign="middle" align="left">80.87 &#xb1; 11.56</td>
<td valign="middle" align="left">0.142</td>
</tr>
<tr>
<td valign="middle" align="left">Heart rate, beats per minute</td>
<td valign="middle" align="left">74.903 &#xb1; 9.537</td>
<td valign="middle" align="left">74.691 &#xb1; 11.746</td>
<td valign="middle" align="left">73.736 &#xb1; 11.326</td>
<td valign="middle" align="left">76.237 &#xb1; 13.212</td>
<td valign="middle" align="left">0.522</td>
</tr>
<tr>
<td valign="middle" align="left">BMI, kg/m&#xb2;</td>
<td valign="middle" align="left">24.30 &#xb1; 2.85</td>
<td valign="middle" align="left">24.31 &#xb1; 3.54</td>
<td valign="middle" align="left">24.99 &#xb1; 3.28</td>
<td valign="middle" align="left">26.30 &#xb1; 2.95</td>
<td valign="middle" align="left">&lt;0.001</td>
</tr>
<tr>
<td valign="middle" align="left">CR, &#xb5;mol/L</td>
<td valign="middle" align="left">55.88 &#xb1; 16.99</td>
<td valign="middle" align="left">60.12 &#xb1; 13.69</td>
<td valign="middle" align="left">63.48 &#xb1; 14.72</td>
<td valign="middle" align="left">72.60 &#xb1; 24.63</td>
<td valign="middle" align="left">&lt;0.001</td>
</tr>
<tr>
<td valign="middle" align="left">Cystatin C, mg/L</td>
<td valign="middle" align="left">1.01 &#xb1; 0.22</td>
<td valign="middle" align="left">1.11 &#xb1; 0.45</td>
<td valign="middle" align="left">1.12 &#xb1; 0.34</td>
<td valign="middle" align="left">1.15 &#xb1; 0.25</td>
<td valign="middle" align="left">&lt;0.001</td>
</tr>
<tr>
<td valign="middle" align="left">FPG, mmol/L</td>
<td valign="middle" align="left">5.57 &#xb1; 2.00</td>
<td valign="middle" align="left">5.31 &#xb1; 1.75</td>
<td valign="middle" align="left">5.10 &#xb1; 1.01</td>
<td valign="middle" align="left">5.53 &#xb1; 1.91</td>
<td valign="middle" align="left">0.289</td>
</tr>
<tr>
<td valign="middle" align="left">Triglyceride, mmol/L</td>
<td valign="middle" align="left">1.24 &#xb1; 0.66</td>
<td valign="middle" align="left">1.32 &#xb1; 0.70</td>
<td valign="middle" align="left">1.34 &#xb1; 0.64</td>
<td valign="middle" align="left">1.66 &#xb1; 1.13</td>
<td valign="middle" align="left">0.006</td>
</tr>
<tr>
<td valign="middle" align="left">Total cholesterol, mmol/L</td>
<td valign="middle" align="left">4.22 &#xb1; 0.98</td>
<td valign="middle" align="left">4.15 &#xb1; 1.21</td>
<td valign="middle" align="left">4.02 &#xb1; 0.88</td>
<td valign="middle" align="left">4.25 &#xb1; 1.16</td>
<td valign="middle" align="left">0.424</td>
</tr>
<tr>
<td valign="middle" align="left">HDL-C, mmol/L</td>
<td valign="middle" align="left">1.31 &#xb1; 0.28</td>
<td valign="middle" align="left">1.31 &#xb1; 0.35</td>
<td valign="middle" align="left">1.24 &#xb1; 0.25</td>
<td valign="middle" align="left">1.18 &#xb1; 0.28</td>
<td valign="middle" align="left">0.004</td>
</tr>
<tr>
<td valign="middle" align="left">LDL-C, mmol/L</td>
<td valign="middle" align="left">2.55 &#xb1; 0.79</td>
<td valign="middle" align="left">2.44 &#xb1; 0.93</td>
<td valign="middle" align="left">2.43 &#xb1; 0.77</td>
<td valign="middle" align="left">2.71 &#xb1; 0.87</td>
<td valign="middle" align="left">0.112</td>
</tr>
<tr>
<td valign="middle" align="left">HCY, &#xb5;mol/L</td>
<td valign="middle" align="left">1.24 &#xb1; 0.66</td>
<td valign="middle" align="left">1.32 &#xb1; 0.70</td>
<td valign="middle" align="left">1.34 &#xb1; 0.64</td>
<td valign="middle" align="left">1.66 &#xb1; 1.13</td>
<td valign="middle" align="left">0.006</td>
</tr>
<tr>
<td valign="middle" align="left">Neutrophil count,10^9/L</td>
<td valign="middle" align="left">3.46 &#xb1; 1.16</td>
<td valign="middle" align="left">3.53 &#xb1; 1.46</td>
<td valign="middle" align="left">3.42 &#xb1; 1.01</td>
<td valign="middle" align="left">3.67 &#xb1; 1.11</td>
<td valign="middle" align="left">0.192</td>
</tr>
<tr>
<td valign="middle" align="left">TLC,10^9/L</td>
<td valign="middle" align="left">1.87 &#xb1; 0.52</td>
<td valign="middle" align="left">1.90 &#xb1; 0.63</td>
<td valign="middle" align="left">2.06 &#xb1; 0.66</td>
<td valign="middle" align="left">2.04 &#xb1; 0.55</td>
<td valign="middle" align="left">0.117</td>
</tr>
<tr>
<td valign="middle" align="left">Platelet count,10^9/L</td>
<td valign="middle" align="left">231.91 &#xb1; 52.60</td>
<td valign="middle" align="left">219.55 &#xb1; 62.63</td>
<td valign="middle" align="left">227.45 &#xb1; 52.21</td>
<td valign="middle" align="left">226.16 &#xb1; 53.79</td>
<td valign="middle" align="left">0.691</td>
</tr>
<tr>
<td valign="middle" align="left">Albumin, g/L</td>
<td valign="middle" align="left">40.22 &#xb1; 3.17</td>
<td valign="middle" align="left">40.80 &#xb1; 4.52</td>
<td valign="middle" align="left">41.15 &#xb1; 4.65</td>
<td valign="middle" align="left">42.01 &#xb1; 3.36</td>
<td valign="middle" align="left">0.010</td>
</tr>
<tr>
<td valign="middle" align="left">Diabetes mellitus (%)</td>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left">0.329</td>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;0</td>
<td valign="middle" align="left">70 (75.27%)</td>
<td valign="middle" align="left">73 (77.66%)</td>
<td valign="middle" align="left">78 (85.71%)</td>
<td valign="middle" align="left">78 (80.41%)</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;1</td>
<td valign="middle" align="left">23 (24.73%)</td>
<td valign="middle" align="left">21 (22.34%)</td>
<td valign="middle" align="left">13 (14.29%)</td>
<td valign="middle" align="left">19 (19.59%)</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">Hypertension (%)</td>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left">0.073</td>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;0</td>
<td valign="middle" align="left">47 (50.54%)</td>
<td valign="middle" align="left">45 (47.87%)</td>
<td valign="middle" align="left">40 (43.96%)</td>
<td valign="middle" align="left">32 (32.99%)</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;1</td>
<td valign="middle" align="left">46 (49.46%)</td>
<td valign="middle" align="left">49 (52.13%)</td>
<td valign="middle" align="left">51 (56.04%)</td>
<td valign="middle" align="left">65 (67.01%)</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">CVD (%)</td>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left">0.301</td>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;0</td>
<td valign="middle" align="left">74 (79.57%)</td>
<td valign="middle" align="left">64 (68.09%)</td>
<td valign="middle" align="left">64 (70.33%)</td>
<td valign="middle" align="left">68 (70.10%)</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;1</td>
<td valign="middle" align="left">19 (20.43%)</td>
<td valign="middle" align="left">30 (31.91%)</td>
<td valign="middle" align="left">27 (29.67%)</td>
<td valign="middle" align="left">29 (29.90%)</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">Stroke (%)</td>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left">0.186</td>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;0</td>
<td valign="middle" align="left">82 (88.17%)</td>
<td valign="middle" align="left">74 (78.72%)</td>
<td valign="middle" align="left">73 (80.22%)</td>
<td valign="middle" align="left">74 (76.29%)</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;1</td>
<td valign="middle" align="left">11 (11.83%)</td>
<td valign="middle" align="left">20 (21.28%)</td>
<td valign="middle" align="left">18 (19.78%)</td>
<td valign="middle" align="left">23 (23.71%)</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">Antiplatelet drug use (%)</td>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left">0.048</td>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;0</td>
<td valign="middle" align="left">77 (82.80%)</td>
<td valign="middle" align="left">66 (70.21%)</td>
<td valign="middle" align="left">59 (64.84%)</td>
<td valign="middle" align="left">69 (71.13%)</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;1</td>
<td valign="middle" align="left">16 (17.20%)</td>
<td valign="middle" align="left">28 (29.79%)</td>
<td valign="middle" align="left">32 (35.16%)</td>
<td valign="middle" align="left">28 (28.87%)</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">Statins use (%)</td>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left">0.221</td>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;0</td>
<td valign="middle" align="left">78 (83.87%)</td>
<td valign="middle" align="left">67 (71.28%)</td>
<td valign="middle" align="left">68 (74.73%)</td>
<td valign="middle" align="left">74 (76.29%)</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;1</td>
<td valign="middle" align="left">15 (16.13%)</td>
<td valign="middle" align="left">27 (28.72%)</td>
<td valign="middle" align="left">23 (25.27%)</td>
<td valign="middle" align="left">23 (23.71%)</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">Smoking status (%)</td>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left">&lt;0.001</td>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;0</td>
<td valign="middle" align="left">84 (90.32%)</td>
<td valign="middle" align="left">72 (76.60%)</td>
<td valign="middle" align="left">61 (67.03%)</td>
<td valign="middle" align="left">59 (60.82%)</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;1</td>
<td valign="middle" align="left">9 (9.68%)</td>
<td valign="middle" align="left">22 (23.40%)</td>
<td valign="middle" align="left">30 (32.97%)</td>
<td valign="middle" align="left">38 (39.18%)</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">Alcohol consumption status (%)</td>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left">0.001</td>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;0</td>
<td valign="middle" align="left">83 (89.25%)</td>
<td valign="middle" align="left">77 (81.91%)</td>
<td valign="middle" align="left">64 (70.33%)</td>
<td valign="middle" align="left">66 (68.04%)</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;1</td>
<td valign="middle" align="left">10 (10.75%)</td>
<td valign="middle" align="left">17 (18.09%)</td>
<td valign="middle" align="left">27 (29.67%)</td>
<td valign="middle" align="left">31 (31.96%)</td>
<td valign="middle" align="left"/>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Data are presented as mean &#xb1; standard deviation or n (%).</p>
</fn>
<fn>
<p>BMI, body mass index; CIMT, carotid intima media thickness; Cr, creatinine; CVD, Cardiovascular Disease; DBP, diastolic blood pressure; FPG, fasting plasma glucose; HCY, plasma homocysteine; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; SBP, systolic blood pressure; TC, total cholesterol; TLC, total lymphocyte count; TG, triglyceride; UA, uric acid. &#x201c;0&#x201d; means no, &#x201c;1&#x201d; means yes.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_2">
<label>3.2</label>
<title>Univariate analysis</title>
<p>To investigate the correlation between clinical parameters and CIMT, a single-variable linear regression analysis was conducted. <xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref> shows a noteworthy correlation between UA and CIMT, indicating a positive relationship.UA was categorized into four groups based on quartiles, revealing statistically significant associations in the Q3 and Q4 groups (p = 0.02623, p &#x2264; 0.00001). CIMT exhibited no significant correlation with BMI, triglycerides, and medical history (p &gt; 0.05).</p>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Univariate Analysis of CIMT.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="left">Covariate</th>
<th valign="middle" align="left">Statistics</th>
<th valign="middle" align="left">Effect size</th>
<th valign="middle" align="left">P-value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">Age, y</td>
<td valign="middle" align="left">64.264 &#xb1; 9.974</td>
<td valign="middle" align="left">0.003 (0.002, 0.004)</td>
<td valign="middle" align="left">&lt;0.00001</td>
</tr>
<tr>
<td valign="middle" align="left">Sex, male</td>
<td valign="middle" align="left">182 (48.533%)</td>
<td valign="middle" align="left">Ref</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">Sex, female</td>
<td valign="middle" align="left">193 (51.467%)</td>
<td valign="middle" align="left">-0.061 (-0.085, -0.036)</td>
<td valign="middle" align="left">&lt;0.00001</td>
</tr>
<tr>
<td valign="middle" align="left">SBP, mmHg</td>
<td valign="middle" align="left">134.608 &#xb1; 18.193</td>
<td valign="middle" align="left">0.000 (-0.000, 0.001)</td>
<td valign="middle" align="left">0.47153</td>
</tr>
<tr>
<td valign="middle" align="left">DBP, mmHg</td>
<td valign="middle" align="left">79.397 &#xb1; 11.917</td>
<td valign="middle" align="left">-0.001 (-0.002, -0.000)</td>
<td valign="middle" align="left">0.04462</td>
</tr>
<tr>
<td valign="middle" align="left">BMI, kg/m&#xb2;</td>
<td valign="middle" align="left">24.986 &#xb1; 3.259</td>
<td valign="middle" align="left">0.000 (-0.004, 0.004)</td>
<td valign="middle" align="left">0.97703</td>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">BMI category, kg/m&#xb2;</th>
</tr>
<tr>
<td valign="middle" align="left">&lt;24</td>
<td valign="middle" align="left">146 (38.933%)</td>
<td valign="middle" align="left">Ref</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2265;24, &lt;28</td>
<td valign="middle" align="left">162 (43.200%)</td>
<td valign="middle" align="left">-0.021 (-0.049, 0.006)</td>
<td valign="middle" align="left">0.13068</td>
</tr>
<tr>
<td valign="middle" align="left">&#x2265;28</td>
<td valign="middle" align="left">67 (17.867%)</td>
<td valign="middle" align="left">-0.002 (-0.038, 0.034)</td>
<td valign="middle" align="left">0.91437</td>
</tr>
<tr>
<td valign="middle" align="left">UA, mg/dL</td>
<td valign="middle" align="left">3.104 &#xb1; 0.781</td>
<td valign="middle" align="left">0.037 (0.021, 0.052)</td>
<td valign="middle" align="left">&lt;0.00001</td>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">UA category, mg/dL</th>
</tr>
<tr>
<td valign="middle" align="left">Q1</td>
<td valign="middle" align="left">93 (24.800%)</td>
<td valign="middle" align="left">Ref</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">Q2</td>
<td valign="middle" align="left">94 (25.067%)</td>
<td valign="middle" align="left">0.031 (-0.004, 0.065)</td>
<td valign="middle" align="left">0.08288</td>
</tr>
<tr>
<td valign="middle" align="left">Q3</td>
<td valign="middle" align="left">91 (24.267%)</td>
<td valign="middle" align="left">0.040 (0.005, 0.074)</td>
<td valign="middle" align="left">0.02623</td>
</tr>
<tr>
<td valign="middle" align="left">Q4</td>
<td valign="middle" align="left">97 (25.867%)</td>
<td valign="middle" align="left">0.081 (0.047, 0.115)</td>
<td valign="middle" align="left">&lt;0.00001</td>
</tr>
<tr>
<td valign="middle" align="left">Neutrophil count,10^9/L</td>
<td valign="middle" align="left">3.523 &#xb1; 1.198</td>
<td valign="middle" align="left">0.012 (0.002, 0.022)</td>
<td valign="middle" align="left">0.02213</td>
</tr>
<tr>
<td valign="middle" align="left">Cystatin C, mg/L</td>
<td valign="middle" align="left">1.099 &#xb1; 0.329</td>
<td valign="middle" align="left">0.057 (0.019, 0.095)</td>
<td valign="middle" align="left">0.00333</td>
</tr>
<tr>
<td valign="middle" align="left">TLC,10^9/L</td>
<td valign="middle" align="left">1.967 &#xb1; 0.596</td>
<td valign="middle" align="left">0.002 (-0.019, 0.023)</td>
<td valign="middle" align="left">0.81933</td>
</tr>
<tr>
<td valign="middle" align="left">Platelet count,10^9/L</td>
<td valign="middle" align="left">226.245 &#xb1; 55.445</td>
<td valign="middle" align="left">-0.000 (-0.000, 0.000)</td>
<td valign="middle" align="left">0.38947</td>
</tr>
<tr>
<td valign="middle" align="left">FPG, mmol/L</td>
<td valign="middle" align="left">5.380 &#xb1; 1.724</td>
<td valign="middle" align="left">0.002 (-0.005, 0.010)</td>
<td valign="middle" align="left">0.50670</td>
</tr>
<tr>
<td valign="middle" align="left">Albumin, g/L</td>
<td valign="middle" align="left">41.056 &#xb1; 4.008</td>
<td valign="middle" align="left">-0.003 (-0.007, -0.000)</td>
<td valign="middle" align="left">0.02796</td>
</tr>
<tr>
<td valign="middle" align="left">Triglyceride, mmol/L</td>
<td valign="middle" align="left">1.393 &#xb1; 0.825</td>
<td valign="middle" align="left">0.003 (-0.012, 0.018)</td>
<td valign="middle" align="left">0.68291</td>
</tr>
<tr>
<td valign="middle" align="left">HDL, mmol/L</td>
<td valign="middle" align="left">1.258 &#xb1; 0.297</td>
<td valign="middle" align="left">-0.054 (-0.096, -0.013)</td>
<td valign="middle" align="left">0.01079</td>
</tr>
<tr>
<td valign="middle" align="left">LDL, mmol/L</td>
<td valign="middle" align="left">2.533 &#xb1; 0.848</td>
<td valign="middle" align="left">-0.020 (-0.034, -0.005)</td>
<td valign="middle" align="left">0.00754</td>
</tr>
<tr>
<td valign="middle" align="left">HCY, &#xb5;mol/L</td>
<td valign="middle" align="left">11.289 &#xb1; 4.662</td>
<td valign="middle" align="left">0.006 (0.003, 0.008)</td>
<td valign="middle" align="left">0.00006</td>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">Hypertension (%)</th>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;0</td>
<td valign="middle" align="left">164 (43.733%)</td>
<td valign="middle" align="left">Ref</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;1</td>
<td valign="middle" align="left">211 (56.267%)</td>
<td valign="middle" align="left">0.045 (0.020, 0.070)</td>
<td valign="middle" align="left">0.00042</td>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">Diabetes mellitus (%)</th>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;0</td>
<td valign="middle" align="left">299 (79.733%)</td>
<td valign="middle" align="left">Ref</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;1</td>
<td valign="middle" align="left">76 (20.267%)</td>
<td valign="middle" align="left">0.010 (-0.021, 0.041)</td>
<td valign="middle" align="left">0.51582</td>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">Stroke (%)</th>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;0</td>
<td valign="middle" align="left">303 (80.800%)</td>
<td valign="middle" align="left">Ref</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;1</td>
<td valign="middle" align="left">72 (19.200%)</td>
<td valign="middle" align="left">0.013 (-0.018, 0.045)</td>
<td valign="middle" align="left">0.40582</td>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">CVD (%)</th>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;0</td>
<td valign="middle" align="left">270 (72.000%)</td>
<td valign="middle" align="left">Ref</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;1</td>
<td valign="middle" align="left">105 (28.000%)</td>
<td valign="middle" align="left">0.012 (-0.016, 0.039)</td>
<td valign="middle" align="left">0.40916</td>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">Antiplatelet drug use (%)</th>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;0</td>
<td valign="middle" align="left">271 (72.267%)</td>
<td valign="middle" align="left">Ref</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;1</td>
<td valign="middle" align="left">104 (27.733%)</td>
<td valign="middle" align="left">0.047 (0.020, 0.075)</td>
<td valign="middle" align="left">0.00084</td>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">Statins drug use (%)</th>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;0</td>
<td valign="middle" align="left">287 (76.533%)</td>
<td valign="middle" align="left">Ref</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;1</td>
<td valign="middle" align="left">88 (23.467%)</td>
<td valign="middle" align="left">0.036 (0.007, 0.066)</td>
<td valign="middle" align="left">0.01532</td>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">Smoking status (%)</th>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;0</td>
<td valign="middle" align="left">276 (73.600%)</td>
<td valign="middle" align="left">Ref</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;1</td>
<td valign="middle" align="left">99 (26.400%)</td>
<td valign="middle" align="left">0.048 (0.020, 0.076)</td>
<td valign="middle" align="left">0.00090</td>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">Alcohol consumption status (%)</th>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;0</td>
<td valign="middle" align="left">290 (77.333%)</td>
<td valign="middle" align="left">0</td>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;1</td>
<td valign="middle" align="left">85 (22.667%)</td>
<td valign="middle" align="left">0.036 (0.006, 0.066)</td>
<td valign="middle" align="left">0.01739</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>BMI, body mass index; CIMT, carotid intima media thickness; Cr, creatinine; CVD, Cardiovascular Disease; DBP, diastolic blood pressure; FPG, fasting plasma glucose; HCY, plasma homocysteine; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; SBP, systolic blood pressure; TC, total cholesterol; TLC, total lymphocyte count; TG, triglyceride; UA, uric acid. &#x201c;0&#x201d; means no, &#x201c;1&#x201d; means yes; Ref, reference.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_3">
<label>3.3</label>
<title>Linear regression results of UA and CIMT</title>
<p>In the original model, every 1 mg/dL rise in UA was associated with a 0.037 mm increase in CIMT (&#x3b2;=0.037; 95% CI=0.021&#x2013;0.052, p&lt;0.00001).After making minimal adjustments for age, sex, and BMI, the model still showed a noteworthy association (&#x3b2;=0.031; 95% CI=0.015-0.048, p=0.00017).After controlling for relevant confounding factors such as sex, age, BMI, HCY, CR, HBP, CYC, antiplatelet drug use, smoking status, alcohol consumption status, DLP, HDL, LDL, NEU, ALB, DBP, and removing factors with Variance Inflation Factors greater than 10 from the fully adjusted model, the adjusted model II still demonstrated a significant positive linear association between UA and CIMT (&#x3b2;=0.033; 95% CI=0.015-0.050, p=0.00032).However, a statistically significant association between UA and CIMT was observed only in Q4 when grouped by quartiles (&#x3b2;=0.069; 95% CI=0.031-0.106, p=0.00036).This suggests a potential non-linear relationship between UA and CIMT (<xref ref-type="table" rid="T3">
<bold>Table&#xa0;3</bold>
</xref>).</p>
<table-wrap id="T3" position="float">
<label>Table&#xa0;3</label>
<caption>
<p>Relationship between UA and CIMT in Different Models.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="left">Variable</th>
<th valign="middle" align="left">Crude model</th>
<th valign="middle" align="left">Minimally adjusted model</th>
<th valign="middle" align="left">Fully adjusted model</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">UA</td>
<td valign="middle" align="left">0.037 (0.021, 0.052) &lt;0.00001</td>
<td valign="middle" align="left">0.031 (0.015, 0.048) 0.00017</td>
<td valign="middle" align="left">0.034 (0.016, 0.051) 0.00028</td>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">UA(%) category</th>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;Q1</td>
<td valign="middle" align="left">Ref</td>
<td valign="middle" align="left">Ref</td>
<td valign="middle" align="left">Ref</td>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;Q2</td>
<td valign="middle" align="left">0.031 (-0.004, 0.065) 0.08288</td>
<td valign="middle" align="left">0.019 (-0.014, 0.052) 0.25150</td>
<td valign="middle" align="left">0.020 (-0.014, 0.053) 0.24777</td>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;Q3</td>
<td valign="middle" align="left">0.040 (0.005, 0.074) 0.02623</td>
<td valign="middle" align="left">0.025 (-0.009, 0.060) 0.14450</td>
<td valign="middle" align="left">0.021 (-0.015, 0.056) 0.25315</td>
</tr>
<tr>
<td valign="middle" align="left">&#xa0;Q4</td>
<td valign="middle" align="left">0.081 (0.047, 0.115) &lt;0.00001</td>
<td valign="middle" align="left">0.068 (0.033, 0.103) 0.00015</td>
<td valign="middle" align="left">0.069 (0.031, 0.107) 0.00040</td>
</tr>
<tr>
<td valign="middle" align="left">P for trend</td>
<td valign="middle" align="left">&lt;0.00001</td>
<td valign="middle" align="left">0.00022</td>
<td valign="middle" align="left">0.00098</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Crude model: we did not adjust other covariates.</p>
</fn>
<fn>
<p>Minimally adjusted model: we adjusted SEX; AGE; BMI.</p>
</fn>
<fn>
<p>Fully adjusted model: we adjusted SEX; AGE; BMI; HCY; CR; Hypertension; Cystatin C; Antiplatelet drug use; Smoking status; DLP; HDL-C; LDL-C; Alcohol consumption status; Neutrophil count; Albumin; DBP; SBP; triglyceride.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_4">
<label>3.4</label>
<title>Non-linear relationship between UA and CIMT</title>
<p>
<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref> shows a non-linear relationship between UA and CIMT in the smoothed curve plot, after adjusting for the mentioned confounding factors. <xref ref-type="table" rid="T4">
<bold>Table&#xa0;4</bold>
</xref> revealed the recognition of a pivotal moment in the correlation between UA levels and CIMT. When UA is &lt;3.15 mg/dL, the relationship is not statistically significant (p = 0.9240). Nevertheless, in the UA level range from 3.15 to 5.148 mg/dL, there is a notable and meaningful connection between UA and CIMT, which varies depending on the dosage (adjusted &#x3b2; = 0.061; 95% CI = 0.031-0.090, p &lt; 0.0001).</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Plot Of Piecewise Linear Regression. The relationship between UA and CIMT. A threshold, nonlinear association between UA and CIMT was found in a generalized additive model (GAM). Solid red line represents the smooth curve fit between variables. Dotted line represents the 95% of confidence interval from the fit. All adjusted for SEX; AGE; BMI; HCY; CR; Hypertension; Cystatin C; Antiplatelet drug use; Smoking status; DLP; HDL-C; LDL-C; Alcohol consumption status; Neutrophil count; Albumin; DBP.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fendo-16-1506964-g001.tif"/>
</fig>
<table-wrap id="T4" position="float">
<label>Table&#xa0;4</label>
<caption>
<p>The independent association between UA and CIMT by multivariate piecewise linear regression.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="left">Inflection Point of UA (mg/dL)</th>
<th valign="middle" align="left">Effect Size (&#x3b2;)</th>
<th valign="middle" align="left">95% CI</th>
<th valign="middle" align="left">P-value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">&lt;3.15</td>
<td valign="middle" align="left">-0.002</td>
<td valign="middle" align="left">(-0.033, 0.030)</td>
<td valign="middle" align="left">0.9240</td>
</tr>
<tr>
<td valign="middle" align="left">&#x2265;3.15</td>
<td valign="middle" align="left">0.061</td>
<td valign="middle" align="left">(0.031, 0.090)</td>
<td valign="middle" align="left">&lt;0.0001</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>we adjusted SEX; AGE; BMI; HCY; CR; Hypertension; Cystatin C; Antiplatelet drug use; Smoking status; DLP; HDL-C; LDL-C; Alcohol consumption status; Neutrophil count; Albumin; DBP; SBP; triglyceride.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_5">
<label>3.5</label>
<title>
<italic>post hoc</italic> power analysis</title>
<p>A <italic>post hoc</italic> power analysis was conducted to evaluate the achieved statistical power based on the observed effect size in the multivariate piecewise linear regression model. Power was computed using R (version 4.4.1) with parameters obtained from the primary analysis, assuming a significance level of 0.05 and a critical power threshold of 0.80. The <italic>post hoc</italic> power analysis was conducted using an effect size of 0.0634 and degrees of freedom (19, 320). The estimated power was found to be 0.80.</p>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<label>4</label>
<title>Discussion</title>
<p>Elevated levels of UA in the human body have been associated with multiple pathological processes. &#x200b;Epidemiological studies consistently identify hyperuricemia as an independent risk factor for cardiovascular events. For instance, Bos et&#xa0;al. demonstrated that hyperuricemia independently predicts myocardial infarction and stroke (<xref ref-type="bibr" rid="B22">22</xref>). &#x200b;Epidemiological studies consistently identify hyperuricemia as an independent risk factor for cardiovascular events. For instance, Bos et&#xa0;al. demonstrated that hyperuricemia independently predicts myocardial infarction and stroke (<xref ref-type="bibr" rid="B23">23</xref>).Research conducted on endothelial cells from the human umbilical vein discovered that elevated levels of UA trigger oxidative stress and inflammation by impacting the signaling pathway of HMGB1/RAGE, the pathway of NF-&#x3ba;B, the activation of the renin-angiotensin system, the reduction of NO, and the expression of inflammatory cytokines (<xref ref-type="bibr" rid="B24">24</xref>&#x2013;<xref ref-type="bibr" rid="B26">26</xref>). These mechanisms collectively promote endothelial dysfunction and vascular remodeling, accelerating atherosclerosis.</p>
<p>In our study involving a healthy check-up population, we identified an independent correlation between UA and increased CIMT (&#x3b2; = 0.037; 95% CI = 0.021-0.052, p &lt; 0.00001).The correlation remained significant even after accounting for other variables (&#x3b2; = 0.033; 95% CI = 0.015-0.050, p = 0.00032).The association between increased UA from Q1 to Q4 and CIMT was significant in both minimally adjusted and fully adjusted models (<xref ref-type="table" rid="T3">
<bold>Table&#xa0;3</bold>
</xref>). Significantly, we noticed an inverse correlation between UA and CIMT, exhibiting a critical threshold at 3.15 mg/dL. Below this threshold, UA levels were not statistically associated with CIMT, while above this threshold, a significant positive correlation was observed, suggesting a hormesis phenomenon.</p>
<p>Subsequent analysis using smoothing functions and segmented linear regression models confirmed the threshold effect at 3.15 mg/dL. According to our research, there is a nonlinear correlation between UA and CIMT in the population undergoing regular health check-ups, with a critical value at 3.15 mg/dL. There is no significant correlation between CIMT and UA when UA is less than 3.15 mg/dL, but a significant positive correlation with CIMT is observed when UA is 3.15 mg/dL or higher. Reducing uric acid through treatment can help alleviate factors that cause arterial inflammation and the formation of neointimal lesions in a mouse model induced with carotid atherosclerosis (<xref ref-type="bibr" rid="B27">27</xref>).To reduce the risk of increased CIMT, it is suggested that individuals without symptoms should maintain UA levels below 3.15 mg/dL, taking into account the potential advantages of prolonged non-bisphosphonate therapy in preventing arterial stiffness caused by hyperuricemia (<xref ref-type="bibr" rid="B28">28</xref>).</p>
<p>The innovation of this study is primarily reflected in the following aspects:&#x200b;Revealing the nonlinear relationship between UA and CIMT: While previous studies have investigated the association between hyperuricemia and atherosclerosis, few have systematically analyzed the nonlinear relationship between uric acid (UA) and carotid intima-media thickness (CIMT). By employing generalized additive models (GAMs) and piecewise linear regression models, we successfully demonstrated this nonlinear relationship, overcoming the limitations of traditional linear models. Furthermore, a threshold effect of UA levels was identified, providing a more precise reference for clinical intervention.&#x200b;Highlighting the potential of UA as an early biomarker for arteriosclerosis: Through our analysis, we observed a significant association between elevated UA levels and increased CIMT, particularly when UA concentrations exceeded 3.15 mg/dL. These findings provide a theoretical foundation for the potential application of UA as a biomarker in early arteriosclerosis screening. This discovery may offer new directions for the prevention and intervention of early-stage cardiovascular and cerebrovascular diseases.</p>
<p>Despite these findings, our study has certain limitations. Initially, as a cross-sectional study, it solely illustrates a non-linear correlation between UA and CIMT and cannot establish causation, necessitating prospective studies for verification. Secondly, averaging the measurements of CIMT at six locations on both sides of the neck may not fully reflect the thickness at other locations if there is severe thickening beyond these measured locations. Thirdly, due to collinearity, the use of statin drugs, total cholesterol, and estimated glomerular filtration rate were not adequately adjusted and may influence the results. Additionally, our study population had a high proportion of individuals over 60 years old (67.73%), possibly influenced by factors such as social, economic, and family values, making them more willing to undergo health check-ups. In conclusion, since this study was conducted retrospectively on a population undergoing routine health check-ups, the findings may not be applicable to individuals suffering from different medical conditions.</p>
</sec>
</body>
<back>
<sec id="s5" sec-type="data-availability">
<title>Data availability statement</title>
<p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p>
</sec>
<sec id="s6" sec-type="ethics-statement">
<title>Ethics statement</title>
<p>The studies involving humans were approved by Medical Research Ethics Committee of Affiliated Hospital of Jining Medical University, Affiliated Hospital of Jining Medical University. The studies were conducted in accordance with the local legislation and institutional requirements. The human samples used in this study were acquired from primarily isolated as part of your previous study for which ethical approval was obtained. Written informed consent for participation was not required from the participants or the participants&#x2019; legal guardians/next of kin in accordance with the national legislation and institutional requirements.</p>
</sec>
<sec id="s7" sec-type="author-contributions">
<title>Author contributions</title>
<p>ZZ: Methodology, Writing &#x2013; original draft, Conceptualization, Software, Visualization. PZ: Validation, Writing &#x2013; original draft. XY: Investigation, Writing &#x2013; review &amp; editing. ZJ: Data curation, Writing &#x2013; original draft. AZ: Funding acquisition, Resources, Supervision, Writing &#x2013; review &amp; editing. HW: Data curation, Resources, Writing &#x2013; review &amp; editing. DL: Conceptualization, Project administration, Writing &#x2013; review &amp; editing.</p>
</sec>
<sec id="s8" sec-type="funding-information">
<title>Funding</title>
<p>The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by the following grants: Health Science and Technology Project of Shandong Province (Grant No. 202403070137), Clinical Specialist Group Program of the Affiliated Hospital of Jining Medical University (Contract No. ZZTD-MS-2023-03), Scientific Research Foundation of Jining Medical University (Grant No. JYGC2022FKJ013), and Jining Key Research and Development Program (Grant No. 2022YXNS082).</p>
</sec>
<ack>
<title>Acknowledgments</title>
<p>We appreciate the statistical guidance provided by Dr. Yumo Xue and Dr. Lihua Zhang, as well as the language editing services from Home for Researchers (<ext-link ext-link-type="uri" xlink:href="http://www.home-for-researchers.com">www.home-for-researchers.com</ext-link>).</p>
</ack>
<sec id="s9" sec-type="COI-statement">
<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 id="s10" sec-type="ai-statement">
<title>Generative AI statement</title>
<p>The author(s) declare that no Generative AI was used in the creation of this manuscript.</p>
</sec>
<sec id="s11" sec-type="disclaimer">
<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>
<ref-list>
<title>References</title>
<ref id="B1">
<label>1</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Hankey</surname> <given-names>GJ</given-names>
</name>
</person-group>. <article-title>Stroke</article-title>. <source>Lancet</source>. (<year>2017</year>) <volume>389</volume>:<page-range>641&#x2013;54</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.1016/S0140-6736(16)30962-X</pub-id>
</citation>
</ref>
<ref id="B2">
<label>2</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Tu</surname> <given-names>WJ</given-names>
</name>
<name>
<surname>Wang</surname> <given-names>LD</given-names>
</name>
<collab>R. Special Writing Group of China Stroke Surveillance</collab>
</person-group>. <article-title>China stroke surveillance report 2021</article-title>. <source>Mil Med Res</source>. (<year>2023</year>) <volume>10</volume>:<fpage>33</fpage>. doi:&#xa0;<pub-id pub-id-type="doi">10.1186/s40779-023-00463-x</pub-id>
</citation>
</ref>
<ref id="B3">
<label>3</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Bjorkegren</surname> <given-names>JLM</given-names>
</name>
<name>
<surname>Lusis</surname> <given-names>AJ</given-names>
</name>
</person-group>. <article-title>Atherosclerosis: recent developments</article-title>. <source>Cell</source>. (<year>2022</year>) <volume>185</volume>:<page-range>1630&#x2013;45</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.1016/j.cell.2022.04.004</pub-id>
</citation>
</ref>
<ref id="B4">
<label>4</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Tsao</surname> <given-names>CW</given-names>
</name>
<name>
<surname>Aday</surname> <given-names>AW</given-names>
</name>
<name>
<surname>Almarzooq</surname> <given-names>ZI</given-names>
</name>
<name>
<surname>Anderson</surname> <given-names>CAM</given-names>
</name>
<name>
<surname>Arora</surname> <given-names>P</given-names>
</name>
<name>
<surname>Avery</surname> <given-names>CL</given-names>
</name>
<etal/>
</person-group>. <article-title>Heart disease and stroke statistics-2023 update: A report from the American Heart Association</article-title>. <source>Circulation</source>. (<year>2023</year>) <volume>147</volume>:<fpage>e93</fpage>&#x2013;<lpage>e621</lpage>. doi:&#xa0;<pub-id pub-id-type="doi">10.1161/CIR.0000000000001123</pub-id>
</citation>
</ref>
<ref id="B5">
<label>5</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Antonini-Canterin</surname> <given-names>F</given-names>
</name>
<name>
<surname>Di Nora</surname> <given-names>C</given-names>
</name>
<name>
<surname>Pellegrinet</surname> <given-names>M</given-names>
</name>
<name>
<surname>Vriz</surname> <given-names>O</given-names>
</name>
<name>
<surname>La Carrubba</surname> <given-names>S</given-names>
</name>
<name>
<surname>Carerj</surname> <given-names>S</given-names>
</name>
<etal/>
</person-group>. <article-title>Effect of uric acid serum levels on carotid arterial stiffness and intima-media thickness: A high resolution Echo-Tracking Study</article-title>. <source>Monaldi Arch Chest Dis</source>. (<year>2019</year>) <volume>89</volume>. doi:&#xa0;<pub-id pub-id-type="doi">10.4081/monaldi.2019.1007</pub-id>
</citation>
</ref>
<ref id="B6">
<label>6</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Cismaru</surname> <given-names>G</given-names>
</name>
<name>
<surname>Serban</surname> <given-names>T</given-names>
</name>
<name>
<surname>Tirpe</surname> <given-names>A</given-names>
</name>
</person-group>. <article-title>Ultrasound methods in the evaluation of atherosclerosis: from pathophysiology to clinic</article-title>. <source>Biomedicines</source>. (<year>2021</year>) <volume>9</volume>. doi:&#xa0;<pub-id pub-id-type="doi">10.3390/biomedicines9040418</pub-id>
</citation>
</ref>
<ref id="B7">
<label>7</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Kumar</surname> <given-names>P</given-names>
</name>
<name>
<surname>Sharma</surname> <given-names>R</given-names>
</name>
<name>
<surname>Misra</surname> <given-names>S</given-names>
</name>
<name>
<surname>Kumar</surname> <given-names>A</given-names>
</name>
<name>
<surname>Nath</surname> <given-names>M</given-names>
</name>
<name>
<surname>Nair</surname> <given-names>P</given-names>
</name>
<etal/>
</person-group>. <article-title>CIMT as a risk factor for stroke subtype: A systematic review</article-title>. <source>Eur J Clin Invest</source>. (<year>2020</year>) <volume>50</volume>:<fpage>e13348</fpage>. doi:&#xa0;<pub-id pub-id-type="doi">10.1111/eci.13348</pub-id>
</citation>
</ref>
<ref id="B8">
<label>8</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Willeit</surname> <given-names>P</given-names>
</name>
<name>
<surname>Tschiderer</surname> <given-names>L</given-names>
</name>
<name>
<surname>Allara</surname> <given-names>E</given-names>
</name>
<name>
<surname>Reuber</surname> <given-names>K</given-names>
</name>
<name>
<surname>Seekircher</surname> <given-names>L</given-names>
</name>
<name>
<surname>Gao</surname> <given-names>L</given-names>
</name>
<etal/>
</person-group>. <article-title>Carotid intima-media thickness progression as surrogate marker for cardiovascular risk: meta-analysis of 119 clinical trials involving 100 667 patients</article-title>. <source>Circulation</source>. (<year>2020</year>) <volume>142</volume>:<page-range>621&#x2013;42</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.1161/CIRCULATIONAHA.120.046361</pub-id>
</citation>
</ref>
<ref id="B9">
<label>9</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Qiao</surname> <given-names>T</given-names>
</name>
<name>
<surname>Wu</surname> <given-names>H</given-names>
</name>
<name>
<surname>Peng</surname> <given-names>W</given-names>
</name>
</person-group>. <article-title>The relationship between elevated serum uric acid and risk of stroke in adult: an updated and dose-response meta-analysis</article-title>. <source>Front Neurol</source>. (<year>2021</year>) <volume>12</volume>:<elocation-id>674398</elocation-id>. doi:&#xa0;<pub-id pub-id-type="doi">10.3389/fneur.2021.674398</pub-id>
</citation>
</ref>
<ref id="B10">
<label>10</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Zou</surname> <given-names>F</given-names>
</name>
<name>
<surname>Zhao</surname> <given-names>X</given-names>
</name>
<name>
<surname>Wang</surname> <given-names>F</given-names>
</name>
</person-group>. <article-title>A review on the fruit components affecting uric acid level and their underlying mechanisms</article-title>. <source>J Food Biochem</source>. (<year>2021</year>) <volume>45</volume>:<fpage>e13911</fpage>. doi:&#xa0;<pub-id pub-id-type="doi">10.1111/jfbc.v45.10</pub-id>
</citation>
</ref>
<ref id="B11">
<label>11</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Sieminska</surname> <given-names>E</given-names>
</name>
<name>
<surname>Sobczak</surname> <given-names>P</given-names>
</name>
<name>
<surname>Skibi&#x144;ska</surname> <given-names>N</given-names>
</name>
<name>
<surname>Sikora</surname> <given-names>J</given-names>
</name>
</person-group>. <article-title>The differential role of uric acid - The purpose or cause of cardiovascular diseases</article-title>? <source>Med Hypotheses</source>. (<year>2020</year>) <volume>142</volume>:<fpage>109791</fpage>. doi:&#xa0;<pub-id pub-id-type="doi">10.1016/j.mehy.2020.109791</pub-id>
</citation>
</ref>
<ref id="B12">
<label>12</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Yanai</surname> <given-names>H</given-names>
</name>
<name>
<surname>Adachi</surname> <given-names>H</given-names>
</name>
<name>
<surname>Hakoshima</surname> <given-names>M</given-names>
</name>
<name>
<surname>Katsuyama</surname> <given-names>H</given-names>
</name>
</person-group>. <article-title>Molecular biological and clinical understanding of the pathophysiology and treatments of hyperuricemia and its association with metabolic syndrome, cardiovascular diseases and chronic kidney disease</article-title>. <source>Int J Mol Sci</source>. (<year>2021</year>) <volume>22</volume>. doi:&#xa0;<pub-id pub-id-type="doi">10.3390/ijms22179221</pub-id>
</citation>
</ref>
<ref id="B13">
<label>13</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Copur</surname> <given-names>S</given-names>
</name>
<name>
<surname>Demiray</surname> <given-names>A</given-names>
</name>
<name>
<surname>Kanbay</surname> <given-names>M</given-names>
</name>
</person-group>. <article-title>Uric acid in metabolic syndrome: Does uric acid have a definitive role</article-title>? <source>Eur J Intern Med</source>. (<year>2022</year>) <volume>103</volume>:<fpage>4</fpage>&#x2013;<lpage>12</lpage>. doi:&#xa0;<pub-id pub-id-type="doi">10.1016/j.ejim.2022.04.022</pub-id>
</citation>
</ref>
<ref id="B14">
<label>14</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Xu</surname> <given-names>R</given-names>
</name>
<name>
<surname>Lian</surname> <given-names>D</given-names>
</name>
<name>
<surname>Xie</surname> <given-names>Y</given-names>
</name>
<name>
<surname>Mu</surname> <given-names>L</given-names>
</name>
<name>
<surname>Wu</surname> <given-names>Y</given-names>
</name>
<name>
<surname>Chen</surname> <given-names>Z</given-names>
</name>
<etal/>
</person-group>. <article-title>Relationship between serum uric acid levels and osteoporosis</article-title>. <source>Endocr Connect</source>. (<year>2023</year>) <volume>12</volume>. doi:&#xa0;<pub-id pub-id-type="doi">10.1530/EC-23-0040</pub-id>
</citation>
</ref>
<ref id="B15">
<label>15</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Zhang</surname> <given-names>WZ</given-names>
</name>
</person-group>. <article-title>Uric acid en route to gout</article-title>. <source>Adv Clin Chem</source>. (<year>2023</year>) <volume>116</volume>:<page-range>209&#x2013;75</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.1016/bs.acc.2023.05.003</pub-id>
</citation>
</ref>
<ref id="B16">
<label>16</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Alcaino</surname> <given-names>H</given-names>
</name>
<name>
<surname>Greig</surname> <given-names>D</given-names>
</name>
<name>
<surname>Chiong</surname> <given-names>M</given-names>
</name>
<name>
<surname>Verdejo</surname> <given-names>H</given-names>
</name>
<name>
<surname>Miranda</surname> <given-names>R</given-names>
</name>
<name>
<surname>Concepcion</surname> <given-names>R</given-names>
</name>
<etal/>
</person-group>. <article-title>Serum uric acid correlates with extracellular superoxide dismutase activity in patients with chronic heart failure</article-title>. <source>Eur J Heart Fail</source>. (<year>2008</year>) <volume>10</volume>:<page-range>646&#x2013;51</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.1016/j.ejheart.2008.05.008</pub-id>
</citation>
</ref>
<ref id="B17">
<label>17</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Yu</surname> <given-names>W</given-names>
</name>
<name>
<surname>Cheng</surname> <given-names>JD</given-names>
</name>
</person-group>. <article-title>Uric acid and cardiovascular disease: an update from molecular mechanism to clinical perspective</article-title>. <source>Front Pharmacol</source>. (<year>2020</year>) <volume>11</volume>:<elocation-id>582680</elocation-id>. doi:&#xa0;<pub-id pub-id-type="doi">10.3389/fphar.2020.582680</pub-id>
</citation>
</ref>
<ref id="B18">
<label>18</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Jayachandran</surname> <given-names>M</given-names>
</name>
<name>
<surname>Qu</surname> <given-names>S</given-names>
</name>
</person-group>. <article-title>Harnessing hyperuricemia to atherosclerosis and understanding its mechanistic dependence</article-title>. <source>Med Res Rev</source>. (<year>2021</year>) <volume>41</volume>:<page-range>616&#x2013;29</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.1002/med.21742</pub-id>
</citation>
</ref>
<ref id="B19">
<label>19</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Oikonen</surname> <given-names>M</given-names>
</name>
<name>
<surname>Wendelin-Saarenhovi</surname> <given-names>M</given-names>
</name>
<name>
<surname>Lyytik&#xe4;inen</surname> <given-names>LP</given-names>
</name>
<name>
<surname>Siitonen</surname> <given-names>N</given-names>
</name>
<name>
<surname>Loo</surname> <given-names>BM</given-names>
</name>
<name>
<surname>Jula</surname> <given-names>A</given-names>
</name>
<etal/>
</person-group>. <article-title>Associations between serum uric acid and markers of subclinical atherosclerosis in young adults. The cardiovascular risk in Young Finns study</article-title>. <source>Atherosclerosis</source>. (<year>2012</year>) <volume>223</volume>:<fpage>497</fpage>&#x2013;<lpage>503</lpage>. doi:&#xa0;<pub-id pub-id-type="doi">10.1016/j.atherosclerosis.2012.05.036</pub-id>
</citation>
</ref>
<ref id="B20">
<label>20</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Lau&#x10d;yt&#x117;-Cibulskien&#x117;</surname> <given-names>A</given-names>
</name>
<name>
<surname>Smaliukait&#x117;</surname> <given-names>M</given-names>
</name>
<name>
<surname>Dadonien&#x117;</surname> <given-names>J</given-names>
</name>
<name>
<surname>&#x10c;ypien&#x117;</surname> <given-names>A</given-names>
</name>
<name>
<surname>Mikolaityt&#x117;</surname> <given-names>J</given-names>
</name>
<name>
<surname>Ryli&#x161;kyt&#x117;</surname> <given-names>L</given-names>
</name>
<etal/>
</person-group>. <article-title>Inflammaging and vascular function in metabolic syndrome: the role of hyperuricemia</article-title>. <source>Medicina (Kaunas)</source>. (<year>2022</year>) <volume>58</volume>. doi:&#xa0;<pub-id pub-id-type="doi">10.3390/medicina58030373</pub-id>
</citation>
</ref>
<ref id="B21">
<label>21</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Li</surname> <given-names>L</given-names>
</name>
<name>
<surname>Zhu</surname> <given-names>JX</given-names>
</name>
<name>
<surname>Hou</surname> <given-names>XH</given-names>
</name>
<name>
<surname>Ma</surname> <given-names>YH</given-names>
</name>
<name>
<surname>Xu</surname> <given-names>W</given-names>
</name>
<name>
<surname>Tan</surname> <given-names>CC</given-names>
</name>
<etal/>
</person-group>. <article-title>Serum uric acid levels and risk of intracranial atherosclerotic stenosis: A cross-sectional study</article-title>. <source>Neurotox Res</source>. (<year>2020</year>) <volume>37</volume>:<page-range>936&#x2013;43</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.1007/s12640-020-00171-7</pub-id>
</citation>
</ref>
<ref id="B22">
<label>22</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Bos</surname> <given-names>MJ</given-names>
</name>
<name>
<surname>Koudstaal</surname> <given-names>PJ</given-names>
</name>
<name>
<surname>Hofman</surname> <given-names>A</given-names>
</name>
<name>
<surname>Witteman</surname> <given-names>JCM</given-names>
</name>
<name>
<surname>Breteler</surname> <given-names>MMB</given-names>
</name>
</person-group>. <article-title>Uric acid is a risk factor for myocardial infarction and stroke: the Rotterdam study</article-title>. <source>Stroke</source>. (<year>2006</year>) <volume>37</volume>:<page-range>1503&#x2013;7</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.1161/01.STR.0000221716.55088.d4</pub-id>
</citation>
</ref>
<ref id="B23">
<label>23</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Yu</surname> <given-names>W</given-names>
</name>
<name>
<surname>Liu</surname> <given-names>W</given-names>
</name>
<name>
<surname>Xie</surname> <given-names>D</given-names>
</name>
<name>
<surname>Wang</surname> <given-names>Q</given-names>
</name>
<name>
<surname>Xu</surname> <given-names>C</given-names>
</name>
<name>
<surname>Zhao</surname> <given-names>H</given-names>
</name>
<etal/>
</person-group>. <article-title>High level of uric acid promotes atherosclerosis by targeting NRF2-mediated autophagy dysfunction and ferroptosis</article-title>. <source>Oxid Med Cell Longev</source>. (<year>2022</year>) <volume>2022</volume>:<fpage>9304383</fpage>. doi:&#xa0;<pub-id pub-id-type="doi">10.1155/2022/9304383</pub-id>
</citation>
</ref>
<ref id="B24">
<label>24</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Cai</surname> <given-names>W</given-names>
</name>
<name>
<surname>Duan</surname> <given-names>XM</given-names>
</name>
<name>
<surname>Liu</surname> <given-names>Y</given-names>
</name>
<name>
<surname>Yu</surname> <given-names>J</given-names>
</name>
<name>
<surname>Tang</surname> <given-names>YL</given-names>
</name>
<name>
<surname>Liu</surname> <given-names>ZL</given-names>
</name>
<etal/>
</person-group>. <article-title>Uric acid induces endothelial dysfunction by activating the HMGB1/RAGE signaling pathway</article-title>. <source>BioMed Res Int</source>. (<year>2017</year>) <volume>2017</volume>:<fpage>4391920</fpage>. doi:&#xa0;<pub-id pub-id-type="doi">10.1155/2017/4391920</pub-id>
</citation>
</ref>
<ref id="B25">
<label>25</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Zhen</surname> <given-names>H</given-names>
</name>
<name>
<surname>Gui</surname> <given-names>F</given-names>
</name>
</person-group>. <article-title>The role of hyperuricemia on vascular endothelium dysfunction</article-title>. <source>BioMed Rep</source>. (<year>2017</year>) <volume>7</volume>:<page-range>325&#x2013;30</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.3892/br.2017.966</pub-id>
</citation>
</ref>
<ref id="B26">
<label>26</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Yu</surname> <given-names>W</given-names>
</name>
<name>
<surname>Chen</surname> <given-names>C</given-names>
</name>
<name>
<surname>Zhuang</surname> <given-names>W</given-names>
</name>
<name>
<surname>Wang</surname> <given-names>W</given-names>
</name>
<name>
<surname>Liu</surname> <given-names>W</given-names>
</name>
<name>
<surname>Zhao</surname> <given-names>H</given-names>
</name>
<etal/>
</person-group>. <article-title>Silencing TXNIP ameliorates high uric acid-induced insulin resistance via the IRS2/AKT and Nrf2/HO-1 pathways in macrophages</article-title>. <source>Free Radic Biol Med</source>. (<year>2022</year>) <volume>178</volume>:<fpage>42</fpage>&#x2013;<lpage>53</lpage>. doi:&#xa0;<pub-id pub-id-type="doi">10.1016/j.freeradbiomed.2021.11.034</pub-id>
</citation>
</ref>
<ref id="B27">
<label>27</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Lu</surname> <given-names>J</given-names>
</name>
<name>
<surname>Sun</surname> <given-names>M</given-names>
</name>
<name>
<surname>Wu</surname> <given-names>X</given-names>
</name>
<name>
<surname>Yuan</surname> <given-names>X</given-names>
</name>
<name>
<surname>Liu</surname> <given-names>Z</given-names>
</name>
<name>
<surname>Qu</surname> <given-names>X</given-names>
</name>
<etal/>
</person-group>. <article-title>Urate-lowering therapy alleviates atherosclerosis inflammatory response factors and neointimal lesions in a mouse model of induced carotid atherosclerosis</article-title>. <source>FEBS J</source>. (<year>2019</year>) <volume>286</volume>:<page-range>1346&#x2013;59</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.1111/febs.2019.286.issue-7</pub-id>
</citation>
</ref>
<ref id="B28">
<label>28</label>
<citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname>Shiina</surname> <given-names>K</given-names>
</name>
<name>
<surname>Tomiyama</surname> <given-names>H</given-names>
</name>
<name>
<surname>Tanaka</surname> <given-names>A</given-names>
</name>
<name>
<surname>Yoshida</surname> <given-names>H</given-names>
</name>
<name>
<surname>Eguchi</surname> <given-names>K</given-names>
</name>
<name>
<surname>Kario</surname> <given-names>K</given-names>
</name>
<etal/>
</person-group>. <article-title>Differential effect of a xanthine oxidase inhibitor on arterial stiffness and carotid atherosclerosis: a subanalysis of the PRIZE study</article-title>. <source>Hypertens Res</source>. (<year>2022</year>) <volume>45</volume>:<page-range>602&#x2013;11</page-range>. doi:&#xa0;<pub-id pub-id-type="doi">10.1038/s41440-022-00857-9</pub-id>
</citation>
</ref>
</ref-list>
</back>
</article>