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<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.2022.1086936</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>Elevated visceral adiposity index is associated with increased stroke prevalence and earlier age at first stroke onset: Based on a national cross-sectional study</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Chen</surname>
<given-names>Qingjie</given-names>
</name>
<uri xlink:href="https://loop.frontiersin.org/people/2079267"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Zhang</surname>
<given-names>Ziwen</given-names>
</name>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Luo</surname>
<given-names>Ning</given-names>
</name>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Qi</surname>
<given-names>Yilong</given-names>
</name>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
</contrib>
</contrib-group>
<aff id="aff1">
<institution>Department of Neurosurgery, The 902nd Hospital of The Chinese People&#x2019;s Liberation Army</institution>, <addr-line>Bengbu</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Carmine Izzo, University of Salerno, Italy</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Yasith Mathangasinghe, University of Colombo, Sri Lanka; Han-Yeong Jeong, Seoul National University Hospital, Republic of Korea</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Yilong Qi, <email xlink:href="mailto:qiyilong32111@aliyun.com">qiyilong32111@aliyun.com</email>
</p>
</fn>
<fn fn-type="other" id="fn002">
<p>This article was submitted to Cardiovascular Endocrinology, a section of the journal Frontiers in Endocrinology</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>16</day>
<month>01</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="collection">
<year>2022</year>
</pub-date>
<volume>13</volume>
<elocation-id>1086936</elocation-id>
<history>
<date date-type="received">
<day>01</day>
<month>11</month>
<year>2022</year>
</date>
<date date-type="accepted">
<day>28</day>
<month>12</month>
<year>2022</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2023 Chen, Zhang, Luo and Qi</copyright-statement>
<copyright-year>2023</copyright-year>
<copyright-holder>Chen, Zhang, Luo and Qi</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>The purpose of this study was to examine the association between the VAI (visceral adiposity index) and stroke prevalence and age at stroke in US adults.</p>
</sec>
<sec>
<title>Methods</title>
<p>We examined the association between VAI and stroke prevalence and age at stroke using logistic regression, subgroup analysis, and dose-response curves using participants from the National Health and Nutrition Examination Survey (NHANES) database from 2007-2018.</p>
</sec>
<sec>
<title>Results</title>
<p>This study ultimately included 29,337 participants aged &gt;20 years, of whom 1022 self-reported a history of stroke, and after adjusting for all confounders, each unit increase in corrected VAI was associated with a 12% increase in the prevalence of stroke (OR= 1.12, 95% CI: 1.01, 1.24) along with an earlier age at stroke 1.64 years (&#x3b2;= -1.64, 95% CI: -2.84, -0.45), stratified analysis showed that the prevalence of stroke was 20% higher in the female group (OR= 1.20, 95% CI: 1.04, 1.39), black group (OR= 1.22, 95% CI: 1.01, 1.48), age &#x2264;60 years group (OR= 1.25, 95% CI: 1.05, 1.48), hypertensive group (OR=1.15, 95% CI:1.01, 1.31), and diabetic group (OR=1.23, 95% CI:1.02, 1.48) VAI increase was positively correlated with stroke prevalence increase. The dose-response curves showed a positive linear correlation between increased VAI and stroke prevalence, while a negative linear correlation was observed between increased VAI and age at stroke.</p>
</sec>
<sec>
<title>Conclusion</title>
<p>Although a causal relationship cannot be proven, higher VAI was positively associated with stroke prevalence and can lead to earlier stroke onset.</p>
</sec>
</abstract>
<kwd-group>
<kwd>stroke prevalence</kwd>
<kwd>stroke onset age</kwd>
<kwd>VAI</kwd>
<kwd>cross-sectional study</kwd>
<kwd>metabolic syndrome</kwd>
</kwd-group>
<counts>
<fig-count count="3"/>
<table-count count="4"/>
<equation-count count="0"/>
<ref-count count="44"/>
<page-count count="12"/>
<word-count count="4671"/>
</counts>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<p>Globally, stroke is the third leading cause of death and the leading contributor to persistent and acquired disability in adults. Approximately 70%-80% of strokes are ischemic strokes, with hemorrhagic strokes accounting for the remainder (<xref ref-type="bibr" rid="B1">1</xref>, <xref ref-type="bibr" rid="B2">2</xref>). From 2009 to 2012, a survey of adults aged 20 years or older showed that the overall prevalence was about 2.6% (<xref ref-type="bibr" rid="B3">3</xref>). Strokes occur in about 17.8% of people over 45 years old, and asymptomatic cerebral infarction occurs in 6%-28% of those over 45 years old (<xref ref-type="bibr" rid="B4">4</xref>). Strokes cost the country and individuals an estimated $45.5 billion each year in 2014-2015 (<xref ref-type="bibr" rid="B5">5</xref>), which is a serious economic burden. Public health must take stroke prevention seriously because stroke is a major public health issue.</p>
<p>At present, there is evidence that metabolic syndrome is a combination of risk factors for stroke development, including atherosclerotic dyslipidemia, hypertension, insulin resistance, and obesity, all of which contribute to atherosclerotic vascular disease (<xref ref-type="bibr" rid="B6">6</xref>). There is a high risk of stroke and recurrent stroke for people with metabolic syndrome, according to several studies (<xref ref-type="bibr" rid="B7">7</xref>&#x2013;<xref ref-type="bibr" rid="B9">9</xref>). With obesity on the rise, the prevalence of metabolic syndrome is expected to rise substantially as obesity increases in the future (<xref ref-type="bibr" rid="B10">10</xref>). In turn, this increase in stroke prevalence may place a heavier burden on society due to the close association between metabolic syndrome and obesity. There are limited reliable indicators of obesity that can be used to predict and assess stroke risk despite obesity being strongly associated with stroke.</p>
<p>Adipocytes store triglycerides in adipose tissue, which controls lipid metabolism and glucose homeostasis (<xref ref-type="bibr" rid="B11">11</xref>). In addition to storing energy, adipose tissue performs an active endocrine function. Numerous bioactive substances are produced in the body by fat cells, lipid-resident immune cells, and endothelial cells (<xref ref-type="bibr" rid="B12">12</xref>). Diabetes, hypertension, cardiovascular disease, and cardiometabolic risk factors are more closely linked to visceral adipose tissue than subcutaneous adipose tissue (<xref ref-type="bibr" rid="B13">13</xref>&#x2013;<xref ref-type="bibr" rid="B15">15</xref>). Body fat can be assessed with a variety of methods, including densitometry (dual-energy X-ray absorptiometry, DXA), magnetic resonance imaging (MRI), computed tomography (CT), and mechanical methods. These methods have a high degree of accuracy in assessing body fat, and the first three methods also provide fat imaging and location within the body (<xref ref-type="bibr" rid="B16">16</xref>). The costs and time involved in these procedures make them unsuitable for routine use in clinical practice because they are technically complex and expensive.</p>
<p>An adipose tissue function indicator, the visceral adiposity index (VAI) measures the distribution of abdominal fat. Based on waist circumference (WC), body mass index (BMI), triglycerides (TG), and high density lipoprotein (HDL) cholesterol, it is a novel and specific index that indirectly measures visceral adipose function (<xref ref-type="bibr" rid="B17">17</xref>). Compared with traditional parameters such as waist circumference and body mass index, the VAI is said to be more sensitive and specific. There has been some progress made in the use of VAI in cardiovascular disease risk assessment associated with obesity (<xref ref-type="bibr" rid="B17">17</xref>&#x2013;<xref ref-type="bibr" rid="B19">19</xref>). While it has been reported that VAI is associated with stroke, a study by Zhang et&#xa0;al. (<xref ref-type="bibr" rid="B20">20</xref>) found that VAI was positively associated with angina pectoris, heart attack, stroke, hypertension, and coronary artery disease, and a study by Cui et&#xa0;al. (<xref ref-type="bibr" rid="B21">21</xref>) found an association between VAI and sudden stroke in Chinese people. However, fewer covariates were included in Zhang and Cui&#x2019;s study, and more covariates need to be included to assess the relationship. Furthermore, VAI and stroke onset age were included in the study, which has not yet been published. As a result, in this study, we set out to determine if VAI was useful in predicting stroke onset and stroke age in the US adult population.</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>Based on National Health and Nutrition Examination Survey (NHANES) data collected between 2007 and 2018, we evaluated baseline clinical data from only participants over 20 years old who completed the stroke questionnaire, and we analyzed data on participants who explicitly responded to whether they had suffered a stroke. The questionnaire was completed by 59842 people. Exclusion criteria were as follows (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref>). Finally, 29337 cases, including 1022 self-reported stroke cases, were included in this study.</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Sample selection process flow chart.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fendo-13-1086936-g001.tif"/>
</fig>
</sec>
<sec id="s2_2">
<label>2.2</label>
<title>Data collection and definition</title>
<p>As an exposure variable, VAI was developed. The following sex-specific equations were used to calculate VAI, where the units for WC, BMI, and TG and HDL are cm kg/m2, and mmol/L (<xref ref-type="bibr" rid="B22">22</xref>). A biochemical analysis used an enzyme-based method to determine triglyceride concentrations. With the Roche Cobas 6000 chemistry analyzer and Roche Modular P chemistry analyzer, serum triglyceride concentrations were measured. Stroke presence or absence and age at stroke onset were assessed by questionnaires. The presence or absence of stroke and the age at stroke were designed as outcome variables.</p>
<p>Multivariate adjusted models summarized potential covariates that may confound the association between VAI and stroke. Covariates in our study included sex (male/female), age (years), race, education level, poverty to income ratio (PIR), marital status (married or living with partner/single), alcohol consumption (drinking or not), physical activity (vigorous/moderate/below moderate), cholesterol level (mg/dl), fasting glucose (mg/dl), urine protein creatinine ratio (mg/g), smoking status (smoking or not), hypertension (smoking or not), diabetes (smoking or not), coronary heart disease (smoking or not), cancers (yes or not), and dietary intake factors, including energy intake, fat intake, sugar intake, and water intake. All participants in years 2007-2018 with two 24-hour dietary recalls will have their consumption averaged based on the two recalls. The numerical variables with more missing data were converted to categorical variables, and the lowest dichotomous was used as the benchmark. The CDC has posted all detailed measurements of the study variables online at <uri xlink:href="http://www.cdc.gov/nchs/nhanes/">www.cdc.gov/nchs/nhanes/</uri>. All NHANES protocols were implemented in accordance with the U.S. Department of Health and Human Services (HHS) Human Research Subject Protection Policy and were reviewed and standardized annually by the NCHS Research Ethics Review Committee. All subjects who participated in the survey signed an informed consent form. All data in this study were released free of charge by NHANES without additional authorization or ethical review.</p>
<p>Smoking status (SMQ020 - Smoked at least 100 cigarettes in life), diabetes (DIQ010 - Doctor told you have diabetes), coronary heart disease (MCQ160C - Ever told you had coronary heart disease), and cancer (MCQ220 - Ever told you had cancer or malignancy) were obtained from the questionnaire data. Participants were considered to have the disease when they answered &#x201c;yes&#x201d;. For hypertension using blood pressure monitoring data from the physical examination, NHANES obtained three consecutive blood pressure readings after participants rested quietly in a seated position for 5 minutes and determined the maximum inflation level (MIL), we took the average of the three tests and converted them into categorical variables according to 140/90 mmHg, with missing values forming their own dummy variable group. The activity data was obtained from the activity questionnaire(PAQ605 - Vigorous work activity, PAQ620 - Moderate work activity, PAQ650 - Vigorous recreational activities, PAQ665 - Moderate recreational activities), when there was strenuous work or recreational activity was identified as the strenuous activity group, when there was moderate work or recreational activity was identified as the moderate activity group, and when there was none of the above activities was considered as the inactive group.</p>
<p>When continuous variables have a large number of missing values, we convert them to categorical variables (<xref ref-type="bibr" rid="B23">23</xref>, <xref ref-type="bibr" rid="B24">24</xref>), where the missing values form their own group as a dummy variable group.</p>
</sec>
<sec id="s2_3">
<label>2.3</label>
<title>Statistical methods</title>    <p>To demonstrate the complex, multi-stage sampling design used in selecting a representative U.S. non-institutionalized population, all statistical analyses were conducted using the sampling weights, stratification, and clustering provided in the NHANES study. A weighted survey mean and 95% confidence intervals are used to express continuous variables, and a weighted survey mean and 95% confidence intervals are used to express categorical variables. Due to the skewed distributions of VAI, LN transformations are applied to transform them into normal distributions. All covariates were screened for variance inflation factor (VIF) covariance, and if the VIF value exceeded 5, the covariate was removed. As per the guidelines, multiple logistic regression models were used to explore the VAI, different VAI triplet groups, and stroke prevalence in three different models based on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement (<xref ref-type="bibr" rid="B25">25</xref>). As far as model 1 is concerned, no adjustment for covariates was made. Several factors were adjusted in model 2, including gender, age, race, marital status, and education. Adjustments were made to all variables in model 3. To further clarify the relationship between VAI and stroke, we used a propensity score method and performed sensitivity analyses. Smoothed curve fitting (penalized spline method) and generalized additive model regression (GAM) were carried out. When a nonlinear relationship was determined to exist, likelihood ratio tests were used to determine inflection point values. Multiple regression analyses were next performed stratified by sex, age, race, hypertension, and diabetes. p &lt; 0.05 was considered statistically significant. All analyses were performed using Empower software <uri xlink:href="https://www.empowerstats.com">www.empowerstats.com</uri> (X&amp;Y Solutions, Inc., Boston, Massachusetts, USA) and R version 4.0.2 (<uri xlink:href="http://www.R-project.org">http://www.R-project.org</uri>, The R Foundation).</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<label>3</label>
<title>Results</title>
<p>The demographic characteristics of the included participants are shown in <xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>. The VAI was 0.72 (0.66,0.78) in the stroke group, higher than 0.56 (0.54,0.58) in the normal group, p &lt; 0.0001. There was a significant difference between the stroke group and the normal group in the proportion of blacks, age, prevalence of hypertension, and prevalence of diabetes.</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Baseline characteristics of participants, weighted.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="left">Characteristic</th>
<th valign="top" align="center">Non-stroke formers<break/>(<italic>n</italic> = 28315)</th>
<th valign="top" align="center">Stroke formers<break/>(<italic>n</italic> = 1022)</th>
<th valign="middle" align="center">P-value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Age(years)</td>
<td valign="top" align="center">46.78 (46.34,47.23)</td>
<td valign="top" align="center">63.53 (62.32,64.74)</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">Serum Cholesterol (mg/dl)</td>
<td valign="top" align="center">194.20 (193.19,195.22)</td>
<td valign="top" align="center">184.53 (180.65,188.41)</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">Ln(VAI)</td>
<td valign="top" align="center">0.56 (0.54,0.58)</td>
<td valign="top" align="center">0.72 (0.66,0.78)</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">Serum Creatinine(mg/dl)</td>
<td valign="top" align="center">0.87 (0.87,0.88)</td>
<td valign="top" align="center">1.05 (1.01,1.10)</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">Serum Glucose(mg/dl)</td>
<td valign="top" align="center">99.12 (98.56,99.67)</td>
<td valign="top" align="center">112.35 (109.10,115.61)</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">Gender(%)</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">0.0133</td>
</tr>
<tr>
<td valign="top" align="left">Male</td>
<td valign="top" align="center">48.66 (48.06,49.26)</td>
<td valign="top" align="center">43.21 (38.98,47.55)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Female</td>
<td valign="top" align="center">51.34 (50.74,51.94)</td>
<td valign="top" align="center">56.79 (52.45,61.02)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Race(%)</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">Mexican American</td>
<td valign="top" align="center">14.82 (12.92,16.94)</td>
<td valign="top" align="center">7.83 (6.21,9.83)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">White</td>
<td valign="top" align="center">66.66 (63.79,69.41)</td>
<td valign="top" align="center">69.36 (64.97,73.42)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Black</td>
<td valign="top" align="center">10.49 (9.19,11.94)</td>
<td valign="top" align="center">15.48 (12.88,18.49)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Other Race</td>
<td valign="top" align="center">8.03 (7.19,8.96)</td>
<td valign="top" align="center">7.33 (5.31,10.05)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Education Level(%)</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">Less than high school</td>
<td valign="top" align="center">20.02 (18.65,21.46)</td>
<td valign="top" align="center">34.03 (30.25,38.02)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">High school</td>
<td valign="top" align="center">28.89 (27.76,30.05)</td>
<td valign="top" align="center">30.46 (26.63,34.58)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">More than high school</td>
<td valign="top" align="center">51.09 (49.22,52.96)</td>
<td valign="top" align="center">35.52 (31.08,40.22)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Marital Status(%)</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">0.0199</td>
</tr>
<tr>
<td valign="top" align="left">Cohabitation</td>
<td valign="top" align="center">64.14 (62.88,65.39)</td>
<td valign="top" align="center">58.83 (54.15,63.35)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Solitude</td>
<td valign="top" align="center">35.86 (34.61,37.12)</td>
<td valign="top" align="center">41.17 (36.65,45.85)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Alcohol(%)</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">0.0009</td>
</tr>
<tr>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">61.23 (59.76,62.68)</td>
<td valign="top" align="center">53.91 (49.87,57.89)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">No</td>
<td valign="top" align="center">18.47 (17.39,19.60)</td>
<td valign="top" align="center">23.41 (19.97,27.24)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Unclear</td>
<td valign="top" align="center">20.30 (19.21,21.43)</td>
<td valign="top" align="center">22.69 (19.39,26.36)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Diabetes(%)</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">8.87 (8.41,9.35)</td>
<td valign="top" align="center">29.42 (26.14,32.93)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">No</td>
<td valign="top" align="center">91.13 (90.65,91.59)</td>
<td valign="top" align="center">70.58 (67.07,73.86)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Smoked(%)</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">43.77 (42.56,44.99)</td>
<td valign="top" align="center">59.92 (56.02,63.69)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">No</td>
<td valign="top" align="center">56.23 (55.01,57.44)</td>
<td valign="top" align="center">40.08 (36.31,43.98)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Physical Activity(%)</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">Never</td>
<td valign="top" align="center">25.77 (24.83,26.75)</td>
<td valign="top" align="center">48.58 (44.45,52.73)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Moderate</td>
<td valign="top" align="center">31.90 (30.99,32.82)</td>
<td valign="top" align="center">32.45 (28.65,36.50)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Vigorous</td>
<td valign="top" align="center">42.33 (41.24,43.42)</td>
<td valign="top" align="center">18.96 (15.84,22.54)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Asthma(%)</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">No</td>
<td valign="top" align="center">85.52 (84.85,86.17)</td>
<td valign="top" align="center">75.82 (72.52,78.85)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">14.48 (13.83,15.15)</td>
<td valign="top" align="center">24.18 (21.15,27.48)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">Coronary Artery Disease</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">2.98 (2.64,3.35)</td>
<td valign="top" align="center">19.20 (15.86,23.05)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">No</td>
<td valign="top" align="center">97.02 (96.65,97.36)</td>
<td valign="top" align="center">80.80 (76.95,84.14)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Cancers</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">9.63 (9.17,10.10)</td>
<td valign="top" align="center">23.08 (19.11,27.59)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">No</td>
<td valign="top" align="center">90.37 (89.90,90.83)</td>
<td valign="top" align="center">76.92 (72.41,80.89)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Ratio of Family Income to Poverty</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">&lt;1.3</td>
<td valign="top" align="center">19.66 (18.48,20.90)</td>
<td valign="top" align="center">31.17 (27.34,35.27)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">&#x2265;1.3&lt;3.5</td>
<td valign="top" align="center">32.60 (31.35,33.87)</td>
<td valign="top" align="center">39.59 (35.19,44.17)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">&#x2265;3.5</td>
<td valign="top" align="center">40.45 (38.59,42.33)</td>
<td valign="top" align="center">22.31 (18.49,26.65)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Unclear</td>
<td valign="top" align="center">7.29 (6.67,7.96)</td>
<td valign="top" align="center">6.93 (5.27,9.07)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Total Kcal(%)</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">Lower</td>
<td valign="top" align="center">38.86 (38.07,39.65)</td>
<td valign="top" align="center">52.72 (48.30,57.10)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Higher</td>
<td valign="top" align="center">46.40 (45.47,47.33)</td>
<td valign="top" align="center">33.97 (30.25,37.89)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Unclear</td>
<td valign="top" align="center">14.75 (13.94,15.59)</td>
<td valign="top" align="center">13.31 (10.59,16.61)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Total Sugar(%)</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">0.0444</td>
</tr>
<tr>
<td valign="top" align="left">Lower</td>
<td valign="top" align="center">36.40 (35.58,37.22)</td>
<td valign="top" align="center">40.86 (37.04,44.79)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Higher</td>
<td valign="top" align="center">37.22 (36.31,38.13)</td>
<td valign="top" align="center">36.63 (32.36,41.12)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Unclear</td>
<td valign="top" align="center">26.38 (25.61,27.17)</td>
<td valign="top" align="center">22.51 (19.25,26.15)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Total Fat(%)</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">Lower</td>
<td valign="top" align="center">38.42 (37.49,39.35)</td>
<td valign="top" align="center">50.85 (47.00,54.70)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Higher</td>
<td valign="top" align="center">46.83 (45.89,47.78)</td>
<td valign="top" align="center">35.83 (32.50,39.31)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Unclear</td>
<td valign="top" align="center">14.75 (13.94,15.59)</td>
<td valign="top" align="center">13.31 (10.59,16.61)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">High Blood Pressure(%)</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">No</td>
<td valign="top" align="center">78.26 (77.24,79.24)</td>
<td valign="top" align="center">62.32 (57.94,66.51)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">12.70 (12.09,13.33)</td>
<td valign="top" align="center">24.13 (21.04,27.52)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Unclear</td>
<td valign="top" align="center">9.04 (8.23,9.94)</td>
<td valign="top" align="center">13.55 (10.34,17.55)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Urine Albumin Creatinine Ratio(%)</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">Lower</td>
<td valign="top" align="center">54.86 (53.90,55.82)</td>
<td valign="top" align="center">32.04 (27.12,37.40)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Higher</td>
<td valign="top" align="center">44.59 (43.64,45.54)</td>
<td valign="top" align="center">65.28 (60.10,70.12)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Unclear</td>
<td valign="top" align="center">0.55 (0.46,0.67)</td>
<td valign="top" align="center">2.68 (1.87,3.83)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Total Water(%)</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">Lower</td>
<td valign="top" align="center">38.42 (37.49,39.35)</td>
<td valign="top" align="center">50.85 (47.00,54.70)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Higher</td>
<td valign="top" align="center">46.83 (45.89,47.78)</td>
<td valign="top" align="center">35.83 (32.50,39.31)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Unclear</td>
<td valign="top" align="center">14.75 (13.94,15.59)</td>
<td valign="top" align="center">13.31 (10.59,16.61)</td>
<td valign="top" align="center"/>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Data of continuous variables are shown as survey-weighted mean(95%CI), P value was calculated by survey-weighted linear regression.Data of categorical variables are shown as survey-weighted percentage (95%CI), P value was calculated by survey-weighted Chi-square test.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<sec id="s3_1">
<label>3.1</label>
<title>A higher VAI is associated with a higher prevalence of stroke</title>
<p>In the final adjusted model, all variables were included if the VIF for each indice was below 5. For stroke, a positive correlation was observed between the VAI and stroke prevalence. Based on the fully adjusted model (model 3), we found a positive association of 1.12 (95% confidence interval: 1.01, 1.21) between the LN-transformed VAI and stroke prevalence of 12%. Furthermore, in order to analyze sensitivity, the VAI was transformed into a categorical variable (dichotomous). Increased prevalence of stroke in high dichotomies compared to the lowest VAI dichotomous group, but there was no effect value (OR=1.10, 95% CI: 0.95, 1.28). (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>). Each unit increase in VAI value after propensity matching was associated with a 29% increase in adjusted stroke prevalence (OR=1.29,95% CI:1.14,1.47) (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Tables&#xa0;1</bold>
</xref>, <xref ref-type="supplementary-material" rid="SM1">
<bold>2</bold>
</xref>).</p>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Analysis between VAI with stroke prevalence.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="left">Characteristic</th>
<th valign="middle" align="center">Model 1 OR(95%CI)</th>
<th valign="middle" align="center">Model 2 OR(95%CI)</th>
<th valign="middle" align="center">Model 3 OR(95%CI)</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">Ln(VAI)</td>
<td valign="middle" align="center">1.20 (1.11, 1.29)</td>
<td valign="middle" align="center">1.22 (1.12, 1.33)</td>
<td valign="middle" align="center">1.12 (1.01, 1.24)</td>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">Categories</th>
</tr>
<tr>
<td valign="middle" align="left">Lower</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center">1</td>
</tr>
<tr>
<td valign="middle" align="left">Higher</td>
<td valign="middle" align="center">1.30 (1.14, 1.47)</td>
<td valign="middle" align="center">1.25 (1.10, 1.43)</td>
<td valign="middle" align="center">1.10 (0.95, 1.28)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Model 1=no covariates were adjusted.</p>
</fn>
<fn>
<p>Model 2=Model 1+age, gender, race education, marital status were adjusted.</p>
</fn>
<fn>
<p>Model 3=Model 2+, diabetes, blood pressure, asthma, PIR, total water, total kcal, total sugar, smoked, physical activity, alcohol use, serum cholesterol, coronary artery disease, serum creatinine urine albumin creatinine ratio, cancers and serum glucose were adjusted.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_2">
<label>3.2</label>
<title>Analysis of the dose-response and threshold effects of VAI on stroke prevalence</title>
<p>A generalized additive model and smoothed curve fitting were used to investigate the relationship between the VAI and stroke prevalence. According to our findings (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref>), stroke prevalence was linearly related to the VAI.</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>Density dose-response relationship between VAI with stroke prevalence. The area between the upper and lower dashed lines is represented as 95% CI. Each point shows the magnitude of the VAI and is connected to form a continuous line. Adjusted for all covariates except effect modifier.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fendo-13-1086936-g002.tif"/>
</fig>
<sec id="s3_2_1">
<label>3.2.1</label>
<title>Subgroup analysis</title>
<p>Subgroup analyses were performed to assess the robustness of the association between VAI and stroke prevalence. The results showed that in the subgroup analysis the VAI indices in the female group (OR=1.15, 95% CI: 1.01, 1.24), black group (OR=1.22, 95% CI:1.01, 1.48), age &#x2264;60 years group (OR=1.25, 95% CI:1.05, 1.48), hypertension group (OR=1.15, 95% CI:1.01, 1.31), and diabetes group (OR=1.23, 95% CI:1.02, 1.48) VAI increase were all positively associated with increased prevalence of stroke. (<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>Subgroup analysis between VAI with stroke prevalence.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="left">Characteristic</th>
<th valign="middle" align="center">Model 1 OR(95%CI)</th>
<th valign="middle" align="center">Model 2 OR(95%CI)</th>
<th valign="middle" align="center">Model 3 OR(95%CI)</th>
</tr>
</thead>
<tbody>
<tr>
<th valign="middle" colspan="4" align="left">Stratified by gender</th>
</tr>
<tr>
<td valign="middle" align="left">Male</td>
<td valign="middle" align="center">1.01 (0.91, 1.13)</td>
<td valign="middle" align="center">1.13 (1.00, 1.27)</td>
<td valign="middle" align="center">1.06 (0.92, 1.22)</td>
</tr>
<tr>
<td valign="middle" align="left">Female</td>
<td valign="middle" align="center">1.43 (1.29, 1.60)</td>
<td valign="middle" align="center">1.35 (1.20, 1.53)</td>
<td valign="middle" align="center">1.20 (1.04, 1.39)</td>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">Stratified by race</th>
</tr>
<tr>
<td valign="top" align="left">Mexican American</td>
<td valign="middle" align="center">1.10 (0.90, 1.34)</td>
<td valign="middle" align="center">1.01 (0.82, 1.26)</td>
<td valign="middle" align="center">0.98 (0.75, 1.28)</td>
</tr>
<tr>
<td valign="top" align="left">White</td>
<td valign="middle" align="center">1.36 (1.22, 1.52)</td>
<td valign="middle" align="center">1.30 (1.15, 1.46)</td>
<td valign="middle" align="center">1.14 (0.99, 1.32)</td>
</tr>
<tr>
<td valign="top" align="left">Black</td>
<td valign="middle" align="center">1.37 (1.18, 1.59)</td>
<td valign="middle" align="center">1.29 (1.10, 1.52)</td>
<td valign="middle" align="center">1.22 (1.01, 1.48)</td>
</tr>
<tr>
<td valign="top" align="left">Other Race</td>
<td valign="middle" align="center">1.04 (0.79, 1.38)</td>
<td valign="middle" align="center">0.92 (0.68, 1.25)</td>
<td valign="middle" align="center">0.67 (0.44, 1.01)</td>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">Stratified by age</th>
</tr>
<tr>
<td valign="middle" align="left">&lt;60</td>
<td valign="middle" align="center">1.39 (1.22, 1.58)</td>
<td valign="middle" align="center">1.52 (1.33, 1.74)</td>
<td valign="middle" align="center">1.25 (1.05, 1.48)</td>
</tr>
<tr>
<td valign="middle" align="left">&#x2265;60</td>
<td valign="middle" align="center">1.04 (0.94, 1.15)</td>
<td valign="middle" align="center">1.10 (0.99, 1.22)</td>
<td valign="middle" align="center">1.01 (0.89, 1.14)</td>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">Stratified by hypertension</th>
</tr>
<tr>
<td valign="middle" align="left">YES</td>
<td valign="middle" align="center">1.28 (1.16, 1.41)</td>
<td valign="middle" align="center">1.25 (1.12, 1.39)</td>
<td valign="middle" align="center">1.15 (1.01, 1.31)</td>
</tr>
<tr>
<td valign="middle" align="left">NO</td>
<td valign="middle" align="center">0.95 (0.82, 1.09)</td>
<td valign="middle" align="center">1.11 (0.95, 1.30)</td>
<td valign="middle" align="center">1.02 (0.84, 1.22)</td>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">Stratified by diabetes</th>
</tr>
<tr>
<td valign="middle" align="left">YES</td>
<td valign="middle" align="center">1.07 (0.93, 1.23)</td>
<td valign="middle" align="center">1.19 (1.02, 1.39)</td>
<td valign="middle" align="center">1.23 (1.02, 1.48)</td>
</tr>
<tr>
<td valign="middle" align="left">NO</td>
<td valign="middle" align="center">1.08 (0.98, 1.18)</td>
<td valign="middle" align="center">1.10 (1.00, 1.22)</td>
<td valign="middle" align="center">1.08 (0.95, 1.21)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Model 1=no covariates were adjusted.</p>
</fn>
<fn>
<p>Model 2=Model 1+age, gender, race education, marital status were adjusted.</p>
</fn>
<fn>
<p>Model 3=adjusted for all covariates except effect modifier.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
</sec>
<sec id="s3_3">
<label>3.3</label>
<title>Elevated VAI and earlier age of stroke onset</title>
<p>Using the fully adjusted model 3, for every one unit increase in Ln (VAI), stroke onset age was 1.64 years earlier (OR=-1.64, 95% CI: -2.84, -0.45) (<xref ref-type="table" rid="T4">
<bold>Table&#xa0;4</bold>
</xref>).</p>
<table-wrap id="T4" position="float">
<label>Table&#xa0;4</label>
<caption>
<p>Analysis between VAI with stroke age onset.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="left">Characteristic</th>
<th valign="middle" align="center">Model 1 &#x3b2;(95%CI)</th>
<th valign="middle" align="center">Model 2 &#x3b2;(95%CI)</th>
<th valign="middle" align="center">Model 3 &#x3b2;(95%CI)</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">Ln(VAI)</td>
<td valign="middle" align="center">-0.79 (-1.92, 0.35)</td>
<td valign="middle" align="center">-1.34 (-2.51, -0.18)</td>
<td valign="middle" align="center">-1.64 (-2.84, -0.45)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Model 1=no covariates were adjusted.</p>
</fn>
<fn>
<p>Model 2=Model 1+gender, race education, marital status were adjusted.</p>
</fn>
<fn>
<p>Model 3=Model 2+, diabetes, blood pressure, asthma, PIR, total water, total kcal, total sugar, smoked, physical activity, alcohol use, serum cholesterol, coronary artery disease, serum creatinine urine albumin creatinine ratio, cancers and serum glucose were adjusted.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_4">
<label>3.4</label>
<title>Analysis of the dose response and threshold effect of VAI on age at stroke onset</title>
<p>A generalized additive model and smoothed curve fitting were used to examine the relationship between the VAI and age at stroke onset. Based on our results, VAI increased with increasing age at stroke onset in a negative linear relationship (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref>).</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Density dose-response relationship between VAI with onset age of stroke. The area between the upper and lower dashed lines is represented as 95% CI. Each point shows the magnitude of the VAI and is connected to form a continuous line. Adjusted for all covariates except effect modifier.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fendo-13-1086936-g003.tif"/>
</fig>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<label>4</label>
<title>Discussion</title>
<p>As a result of industrialization and urbanization, there has been an increase in the consumption of food and lifestyle, suggesting that these changes are risk factors for stroke development (<xref ref-type="bibr" rid="B26">26</xref>, <xref ref-type="bibr" rid="B27">27</xref>) . Thus, epidemiological studies of stroke onset associated with metabolic syndrome are reasonable. As well, VAI is more sensitive and specific than traditional waist circumference and BMI for obesity (<xref ref-type="bibr" rid="B18">18</xref>, <xref ref-type="bibr" rid="B19">19</xref>, <xref ref-type="bibr" rid="B28">28</xref>) . Therefore, in this study, we investigated the relationship between VAI levels and stroke in a large U.S. population and found that after adjusting for all confounders, increased VAI levels were positively correlated with stroke prevalence, and age at stroke was negatively correlated with increased VAI levels. The age of first stroke onset was 1.64 years earlier with each unit increase in VAI after correction, and the prevalence of stroke increased 12% after correction.</p>
<p>Stroke affects both physical and mental health severely, placing an immense burden on our society in terms of morbidity, quality of life, and healthcare costs. These pressures continue to rise throughout the world, making stroke prevention particularly crucial. The VAI can also be used to find specific populations adapted to the index and prevent strokes from occurring more often. Consequently, we performed subgroup analyses on females, blacks, those aged &gt;60, hypertensives, and diabetics and found elevated VAI levels were positively associated with increased stroke prevalence. According to several previous related studies, we suspect this finding to be accurate. As previously reported, VAI differences have been found in correlation studies of atherosclerosis, cardiovascular disease, and asymptomatic cerebral infarction (<xref ref-type="bibr" rid="B17">17</xref>, <xref ref-type="bibr" rid="B21">21</xref>, <xref ref-type="bibr" rid="B29">29</xref>&#x2013;<xref ref-type="bibr" rid="B31">31</xref>) . A study by Li et&#xa0;al (<xref ref-type="bibr" rid="B32">32</xref>) showed that women with VAI were associated with intracranial atherosclerosis, but not men. The findings of Nakagomi (<xref ref-type="bibr" rid="B33">33</xref>) also indicate that VAI increases atherosclerosis in women and the association is stronger. As well as predicting cardiometabolic disease in older women, VAI has been found useful in cardiovascular disease studies -&#xa0;a score of 2.71 can be used to identify high-risk women (<xref ref-type="bibr" rid="B34">34</xref>). Based on the same study by Mohammadreza (<xref ref-type="bibr" rid="B35">35</xref>), women were independently at an increased risk of cardiovascular disease after VAI. According to a Korean study published in 2020, high VAI levels were associated with an increased risk of asymptomatic cerebral infarction in healthy females, particularly (<xref ref-type="bibr" rid="B36">36</xref>). Nakagomi (<xref ref-type="bibr" rid="B33">33</xref>) speculates that the possible explanation for the above findings is either differences in hormone levels between men and women or differences in the composition of visceral adipose tissue and subcutaneous adipose tissue. However, the etiologic mechanism behind these findings remains unclear. As a result of our study, VAI has a greater effect on stroke risk among younger people than those older than 60 years of age. So far, it seems that young and middle-aged individuals have different risk factors for stroke compared to elderly individuals. Atherosclerosis (including atrial fibrillation), hypertension and diabetes mellitus are the three most common risk factors in the elderly (<xref ref-type="bibr" rid="B37">37</xref>). Among young and middle-aged stroke patients, dyslipidemia, smoking and hypertension are the most prevalent vascular risk factors (<xref ref-type="bibr" rid="B38">38</xref>&#x2013;<xref ref-type="bibr" rid="B40">40</xref>), while VAI levels are also affected by dyslipidemia, which makes it possible to predict stroke prevalence in young and middle-aged individuals. It is well known that stroke risk differs by race and ethnicity, and in younger populations these differences are even more pronounced. It is important to note that variations in prevalence are largely determined by the racial composition of the study population. There was an increased prevalence of strokes among young Hispanics and blacks in the Northern Manhattan study (<xref ref-type="bibr" rid="B41">41</xref>). The hospitalization rate for stroke was significantly higher among young blacks and Hispanics in another Florida study (<xref ref-type="bibr" rid="B42">42</xref>). A second study from American Point found that young and middle-aged blacks had up to five times the stroke risk, compared to young and middle-aged whites. Blacks had an increased prevalence of stroke due to the elevated VAI found in our study, which may explain the increased prevalence of stroke among blacks. The increased prevalence of stroke in populations with hypertension and diabetes is not surprising given that both of these factors are known to be risk factors for stroke (<xref ref-type="bibr" rid="B43">43</xref>).</p>
<p>In terms of mortality, stroke is one of the top three causes of death worldwide, as well as one of the leading causes of disability. If stroke continues for a longer period of time, the risk of a second stroke increases, as does the poorer prognosis. The long-term socioeconomic consequences of stroke in young patients are also significant. According to a recent study, young stroke patients spend an average of $34,886 in hospitalization for ischemic stroke, $150,307 for subarachnoid hemorrhage, and $94,482 for cerebral hemorrhage (<xref ref-type="bibr" rid="B44">44</xref>). The correlation between VAI and age at first stroke was another important finding in this study. As a consequence of our results, every unit increase in VAI will result in a 1.64 year increase in the age of stroke onset. VAI was linearly correlated negatively with age at first stroke even when smoothing curves were fitted. This finding is heartening and has not yet been reported. It is hypothesized that treating and managing VAI levels at younger ages can reduce the risk of stroke. However, the veracity of the present results may be limited by the sample size and needs to be further confirmed by a multicenter prospective study with a large sample.</p>
<p>Several advantages are associated with our study. An extensive quality assurance and quality control process is followed by the NHANES 2007-2018 sample, which represents a representative sample of the U.S. population. Secondly, we adjusted for confounding covariates to ensure our results were reliable and applicable to a wide range of individuals. Our study does, however, have some limitations. We were unable to establish a causal relationship between VAI and stroke due to the fact that we used the NHANES database, a cross-sectional study. In addition, the data on medication history and stroke type classification were not disclosed in the database, which may have contributed to recall bias. Third, the diagnosis of stroke was made by means of a questionnaire, which can be subject to recall bias. It is noteworthy that the present study showed that VAI is associated with stroke onset and, for the first time, evaluated its role in age at first stroke onset.</p>
</sec>
<sec id="s5">
<label>5</label>
<title>Summary</title>
<p>The VAI is associated with higher stroke prevalence and a younger age at first stroke. Although the causal relationship between VAI management and stroke occurrence cannot be clearly established, we hypothesize that managing VAI levels at a younger age may reduce the occurrence of strokes and delay stroke onset.</p>
</sec>
<sec id="s6" sec-type="data-availability">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Materials</bold>
</xref>, further inquiries can be directed to the corresponding author.</p>
</sec>
<sec id="s7" sec-type="ethics-statement">
<title>Ethics statement</title>
<p>The studies involving human participants were reviewed and approved by NCHS Research Ethics Review Committee. The patients/participants provided their written informed consent to participate in this study.</p>
</sec>
<sec id="s8" sec-type="author-contributions">
<title>Author contributions</title>
<p>QC: Conceptualization, Methodology, Software. ZZ: Data curation, Writing original draft. NL: Visualization, Investigation. YQ: Writing - review &amp; editing. All authors contributed to the article and approved the submitted version.</p>
</sec>
</body>
<back>
<ack>
<title>Acknowledgments</title>
<p>We would like to thank all NHANES participants and staff.</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="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>
<sec id="s11" sec-type="supplementary-material">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fendo.2022.1086936/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fendo.2022.1086936/full#supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="Table_1.docx" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document"/>
</sec>
<fn-group>
<title>Abbreviations</title>
<fn fn-type="abbr">
<p>NHANES, National Health and Nutrition Examination Survey; BMI, body mass index; PIR, ratio of family income to poverty; NCHS, National Center for Health Statistics; CI, confidence interval; OR, odds ratio; MetS, metabolic syndrome; IR, insulin resistance; TG, triglyceride; TC, Cholesterol; FPG, fasting plasma glucose, VAI, visceral adiposity index.</p>
</fn>
</fn-group>
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