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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">87</journal-id>
      <journal-id journal-id-type="index">urn:lsid:arphahub.com:pub:A116C711-4C18-5A38-8F1E-5E97753A8A64</journal-id>
      <journal-title-group>
        <journal-title xml:lang="en">Folia Medica</journal-title>
        <abbrev-journal-title xml:lang="en">FM</abbrev-journal-title>
      </journal-title-group>
      <issn pub-type="ppub">0204-8043</issn>
      <issn pub-type="epub">1314-2143</issn>
      <publisher>
        <publisher-name>Plovdiv Medical University</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.3897/folmed.64.e67744</article-id>
      <article-id pub-id-type="publisher-id">67744</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Original Article</subject>
        </subj-group>
        <subj-group subj-group-type="scientific_subject">
          <subject>Internal Diseases</subject>
          <subject>Metabolic disorders</subject>
          <subject>Radiology &amp; Imaging</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Evaluation of risk factors and diseases associated with metabolic and atherosclerotic disorders in different abdominal fat distribution patterns assessed by CT-scan</article-title>
      </title-group>
      <contrib-group content-type="authors">
        <contrib contrib-type="author" corresp="no">
          <name name-style="western">
            <surname>Ehsanbakhsh</surname>
            <given-names>Alireza</given-names>
          </name>
          <xref ref-type="aff" rid="A1">1</xref>
        </contrib>
        <contrib contrib-type="author" corresp="yes">
          <name name-style="western">
            <surname>Abadi</surname>
            <given-names>Javad Mohamadi Taze</given-names>
          </name>
          <email xlink:type="simple">jmohamadi10@yahoo.com</email>
          <uri content-type="orcid">https://orcid.org/0000-0003-2659-0902</uri>
          <xref ref-type="aff" rid="A2">2</xref>
        </contrib>
        <contrib contrib-type="author" corresp="no">
          <name name-style="western">
            <surname>Khorashadizadeh</surname>
            <given-names>Nasrin</given-names>
          </name>
          <xref ref-type="aff" rid="A1">1</xref>
        </contrib>
        <contrib contrib-type="author" corresp="no">
          <name name-style="western">
            <surname>Darabi</surname>
            <given-names>Azadeh</given-names>
          </name>
          <xref ref-type="aff" rid="A2">2</xref>
        </contrib>
      </contrib-group>
      <aff id="A1">
        <label>1</label>
        <addr-line>Department of Radiology, Valiasr Hospital, Birjand University of Medical Sciences, Birjand, Iran</addr-line>
      </aff>
      <aff id="A2">
        <label>2</label>
        <addr-line>Department of Radiology, Emam Reza Hospital, Mashhad University of Medical Sciences, Mashhad, Iran</addr-line>
      </aff>
      <aff id="A3">
        <label>3</label>
        <addr-line>Department of Pediatrics, Ghaem Hospital, Mashhad University of Medical Sciences, Mashhad, Iran</addr-line>
      </aff>
      <author-notes>
        <fn fn-type="corresp">
          <p>Corresponding author: Javad Mohamadi Taze Abadi, Department of Radiology, Emam Reza Hospital, Mashhad University of Medical Sciences, Mashhad, Iran; Email: <email xlink:type="simple">jmohamadi10@yahoo.com</email></p>
        </fn>
      </author-notes>
      <pub-date pub-type="collection">
        <year>2022</year>
      </pub-date>
      <pub-date pub-type="epub">
        <day>31</day>
        <month>10</month>
        <year>2022</year>
      </pub-date>
      <volume>64</volume>
      <issue>5</issue>
      <fpage>754</fpage>
      <lpage>761</lpage>
      <uri content-type="arpha" xlink:href="http://openbiodiv.net/4BFA7779-7D7F-5A7A-A71C-FAFB77A9AFE6">4BFA7779-7D7F-5A7A-A71C-FAFB77A9AFE6</uri>
      <history>
        <date date-type="received">
          <day>22</day>
          <month>04</month>
          <year>2021</year>
        </date>
        <date date-type="accepted">
          <day>04</day>
          <month>11</month>
          <year>2021</year>
        </date>
      </history>
      <permissions>
        <copyright-statement>Alireza Ehsanbakhsh, Javad Mohamadi Taze Abadi, Nasrin Khorashadizadeh, Azadeh Darabi</copyright-statement>
        <license license-type="creative-commons-attribution" xlink:href="http://creativecommons.org/licenses/by/4.0/" xlink:type="simple">
          <license-p>This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p>
        </license>
      </permissions>
      <abstract>
        <label>Abstract</label>
        <p><bold>Introduction</bold>: Obesity is defined as an increase in body fat composition.</p>
        <p><bold>Aim</bold>: The purpose of our study was to evaluate metabolic risk factors and diseases in different patterns of abdominal fat distribution.</p>
        <p><bold>Materials and methods</bold>: This is a cross-sectional study. Among patients aged 15 to 65 years who have had no significant weight loss in the past year and were referred to the Radiology Department to perform an abdominal CT-scan, the visceral and subcutaneous fat area (<abbrev xlink:title="visceral fat area" id="ABBRID0EGE">VFA</abbrev> and <abbrev xlink:title="subcutaneous fat area" id="ABBRID0EKE">SFA</abbrev>) with Hounsfield units -30 to -190 (±2 SD) was calculated at the umbilical level. Based on the <abbrev xlink:title="visceral fat area" id="ABBRID0EOE">VFA</abbrev> and <abbrev xlink:title="subcutaneous fat area" id="ABBRID0ESE">SFA</abbrev>, patients were stratified into four groups, group 1: V(+)S(+); group 2: V(+)S(-); group 3: V(−)S(+); group 4: V(−)S(−). The following parameters were assessed in the groups: anthropometric parameters including body mass index (<abbrev xlink:title="body mass index" id="ABBRID0EWE">BMI</abbrev>), waist circumference (<abbrev xlink:title="waist circumference" id="ABBRID0E1E">WC</abbrev>), waist-to-height ratio (<abbrev xlink:title="waist-to-height ratio" id="ABBRID0E5E">WHtR</abbrev>), waist to hip ratio (<abbrev xlink:title="waist to hip ratio" id="ABBRID0ECF">WH</abbrev>); laboratory parameters, including fasting blood glucose (<abbrev xlink:title="fasting blood glucose" id="ABBRID0EGF">FBG</abbrev>), lipids profile (TG, LDH, LDL, and total cholesterol), creatinine, and liver enzymes (AST, ALT). Additionally, sensitivity, specificity, positive predictive value (<abbrev xlink:title="positive predictive value" id="ABBRID0EKF">PPV</abbrev>), and negative predictive value of study variables were assessed in predicting group 1.</p>
        <p><bold>Results</bold>: The study included 180 individuals (mean age 50±14 years, range 15-65 years). Group 1 was the most, and group 2 was the least prevalent pattern of abdominal fat distribution. Most females (75%) had high percentage of subcutaneous fat tissue. There was a significant association between the abdominal fat distribution pattern and <abbrev xlink:title="body mass index" id="ABBRID0ESF">BMI</abbrev>, <abbrev xlink:title="waist circumference" id="ABBRID0EWF">WC</abbrev>, <abbrev xlink:title="waist-to-height ratio" id="ABBRID0E1F">WHtR</abbrev>, TG, LDL, HDL, total cholesterol, <abbrev xlink:title="fasting blood glucose" id="ABBRID0E5F">FBG</abbrev>, diabetes, and metabolic syndrome (<italic>p</italic>&lt;0.05).</p>
        <p><bold>Conclusions</bold>: Most of the metabolic factors, including <abbrev xlink:title="body mass index" id="ABBRID0EIG">BMI</abbrev>, <abbrev xlink:title="waist circumference" id="ABBRID0EMG">WC</abbrev>, lipid profile, and <abbrev xlink:title="fasting blood glucose" id="ABBRID0EQG">FBG</abbrev>, as well as metabolic syndrome, diabetes, and impaired glucose tolerance, were highly correlated with group 1. However, most of the individuals in group 1 were normal according to the factors mentioned above. Therefore, there is a gap between the main definition of obesity (increasing body fat mass) and parameters that calculated obesity and metabolic disorders.</p>
      </abstract>
      <kwd-group>
        <label>Keywords</label>
        <kwd>abdominal fat distribution</kwd>
        <kwd>association</kwd>
        <kwd>CT-scan</kwd>
        <kwd>metabolic disorders</kwd>
        <kwd>obesity</kwd>
      </kwd-group>
      <funding-group>
        <funding-statement>birjand radiology association</funding-statement>
      </funding-group>
    </article-meta>
    <notes>
      <sec sec-type="Citation" id="SECID0E3G">
        <title>Citation</title>
        <p>Ehsanbakhsh A, Abadi JMT, Khorashadizadeh N, Darabi A. Evaluation of risk factors and diseases associated with metabolic and atherosclerotic disorders in different abdominal fat distribution patterns assessed by CT-scan. Folia Med (Plovdiv) 2022;64(5):754-761. doi: <ext-link xlink:type="simple" ext-link-type="doi" xlink:href="10.3897/folmed.64.e67744">10.3897/folmed.64.e67744</ext-link>.</p>
      </sec>
    </notes>
  </front>
  <body>
    <sec sec-type="Introduction" id="SECID0EIH">
      <title>Introduction</title>
      <p>Obesity is currently recognized as a global issue associated with metabolic disorders and cardiovascular diseases.‌<sup>[<xref ref-type="bibr" rid="B1">1</xref>,<xref ref-type="bibr" rid="B2">2</xref>]</sup> In recent years, it has been shown that accumulated abdominal adipose tissue produces abnormal metabolites that are associated with the increased risk of atherosclerotic and cardiovascular diseases.<sup>[<xref ref-type="bibr" rid="B3">3</xref>]</sup> The abdominal fat tissue comprises two compartments, including visceral fat and subcutaneous fat. Visceral fat is the fat tissue that is stored internally to the abdominal wall muscles, and subcutaneous fat is the fat tissue accumulated externally to these muscles and beneath the skin.</p>
      <p>Visceral fat accumulation is the main component of central obesity, which is essential in developing metabolic disorders such as insulin insensitivity<sup>[<xref ref-type="bibr" rid="B4">4</xref>,<xref ref-type="bibr" rid="B5">5</xref>]</sup>, type 2 diabetes, and metabolic syndrome.<sup>[<xref ref-type="bibr" rid="B4">4</xref>,<xref ref-type="bibr" rid="B6">6</xref>,<xref ref-type="bibr" rid="B7">7</xref>]</sup> Abdominal fat can be quantified using either anthropometric indices such as body mass index (<abbrev xlink:title="body mass index" id="ABBRID0E4AAC">BMI</abbrev>), waist circumference (<abbrev xlink:title="waist circumference" id="ABBRID0EBBAC">WC</abbrev>), waist-to-height ratio (<abbrev xlink:title="waist-to-height ratio" id="ABBRID0EFBAC">WHtR</abbrev>), and waist to hip ratio (<abbrev xlink:title="waist to hip ratio" id="ABBRID0EJBAC">WH</abbrev>) or imaging. However, <abbrev xlink:title="body mass index" id="ABBRID0ENBAC">BMI</abbrev> is not a good candidate because it is not necessarily associated with high visceral fat tissue.<sup>[<xref ref-type="bibr" rid="B8">8</xref>]</sup> Moreover, other anthropometric indices are not entirely reliable and often cause confusion regarding the amount of visceral and subcutaneous fat tissue.<sup>[<xref ref-type="bibr" rid="B8 B9 B10">8–10</xref>]</sup> Therefore, imaging is the most reliable method that can be used to evaluate fat tissue and differentiate visceral from subcutaneous fat.</p>
      <p>Abdominal CT scan is commonly used to measure abdominal fat tissue. Although there is a risk of radiation with this technique, it is widely available and highly reliable.<sup>[<xref ref-type="bibr" rid="B9">9</xref>,<xref ref-type="bibr" rid="B11">11</xref>]</sup> It has been shown that abdominal CT scan at the umbilical level is the most accurate diagnostic method to evaluate abdominal fat tissue.<sup>[<xref ref-type="bibr" rid="B12">12</xref>]</sup></p>
      <p>The intensity range of -30 to -190 Hounsfield units has been defined as the reference standard that indicates abdominal adipose tissue.<sup>[<xref ref-type="bibr" rid="B9">9</xref>]</sup> Quantification of abdominal adipose tissue at the umbilical level has been shown to be highly reliable and quite similar to its quantification at the level of the L3-L4 intervertebral disc. High visceral adipose tissue in diabetic patients with normal <abbrev xlink:title="body mass index" id="ABBRID0E2CAC">BMI</abbrev> was recently reported to be associated with arterial stiffening.<sup>[<xref ref-type="bibr" rid="B6">6</xref>]</sup> This highlights the importance of evaluating and managing visceral adipose tissue to reduce the risk of metabolic and cardiovascular diseases.</p>
    </sec>
    <sec sec-type="Aim" id="SECID0EGDAC">
      <title>Aim</title>
      <p>This study aimed to investigate the association of metabolic and cardiovascular risk factors with the visceral and subcutaneous fat area (<abbrev xlink:title="visceral fat area" id="ABBRID0EMDAC">VFA</abbrev> and <abbrev xlink:title="subcutaneous fat area" id="ABBRID0EQDAC">SFA</abbrev>) measured using abdominal CT scan. Additionally, this study aimed to evaluate diagnostic sensitivity, specificity, positive predictive value (<abbrev xlink:title="positive predictive value" id="ABBRID0EUDAC">PPV</abbrev>), and negative predictive value and its potential to predict radiologically diagnosed obese patients (group 1).</p>
    </sec>
    <sec sec-type="materials|methods" id="SECID0EYDAC">
      <title>Materials And Methods</title>
      <sec sec-type="Settings and patients" id="SECID0E3DAC">
        <title>Settings and patients</title>
        <p>In this cross-sectional study, 180 individuals undergoing abdominal CT scan in the Radiology Department of Valiasr Hospital (Birjand, Iran) were randomly selected. The patients aged 15 to 65 years without a history of significant weight loss (more than 5%) within the past year were included. Patients with history of weight-loss surgeries, history of surgeries causing damage to the abdominal subcutaneous or visceral fat tissue, large abdominal tumors and metastatic tumors to mesenteric and visceral adipose tissue were excluded.</p>
      </sec>
      <sec sec-type="Ethical approval" id="SECID0EBEAC">
        <title>Ethical approval</title>
        <p>Informed written consent was obtained from all participants or their legal guardians (for participants under 18 years of age). Patients were assured that the study would use only the information in the CT scans taken for their primary disease and that they would not receive any extra dose of radiation. This study was approved by the ethics committee of Birjand University of Medical Sciences under the code IR.BUMS.REC.1395.170.</p>
      </sec>
      <sec sec-type="Data collection" id="SECID0EGEAC">
        <title>Data collection</title>
        <p>The demographic data and the history of metabolic disorders were collected using a questionnaire. Fasting blood glucose (<abbrev xlink:title="fasting blood glucose" id="ABBRID0EMEAC">FBG</abbrev>), lipids profile (TG, LDH, LDL, and total cholesterol), creatinine and liver enzymes (AST, ALT) were measured. The height, weight and anthropometric variables including <abbrev xlink:title="body mass index" id="ABBRID0EQEAC">BMI</abbrev>, <abbrev xlink:title="waist circumference" id="ABBRID0EUEAC">WC</abbrev>, <abbrev xlink:title="waist-to-height ratio" id="ABBRID0EYEAC">WHtR</abbrev> and <abbrev xlink:title="waist to hip ratio" id="ABBRID0E3EAC">WH</abbrev> were recorded.</p>
        <p>Based on NCEP.ATP3 criteria, metabolic syndrome was defined as high TG (&gt;150 mg/dL); high <abbrev xlink:title="fasting blood glucose" id="ABBRID0ECFAC">FBG</abbrev> (&gt;100 mg/dL); high <abbrev xlink:title="waist circumference" id="ABBRID0EGFAC">WC</abbrev> (&gt;80 cm for females and &gt;90 cm for males); low HDL (&lt;50 mg/dL in females and &lt;40 mg/dL in males); hypertension (systolic blood pressure ≥130 mmHg, diastolic blood pressure ≥85 mmHg (or receiving anti-hypertensive therapy.<sup>[<xref ref-type="bibr" rid="B13">13</xref>]</sup> Additionally, high LDL (&gt;130 mg/dL) was considered abnormal.<sup>[<xref ref-type="bibr" rid="B14">14</xref>]</sup></p>
        <p>Patients were classified according to the <abbrev xlink:title="body mass index" id="ABBRID0EZFAC">BMI</abbrev> based on WHO classification: obese (≥30 kg/m<sup>2</sup>), overweight (25–29.9 kg/m<sup>2</sup>), normal weight (18.5–24.9 kg/m<sup>2</sup>) and underweight (&lt;18.5 kg/m<sup>2</sup>). Diabetes was defined as two measurements of <abbrev xlink:title="fasting blood glucose" id="ABBRID0EFGAC">FBG</abbrev> &gt;126 mg/dL. <abbrev xlink:title="waist-to-height ratio" id="ABBRID0EJGAC">WHtR</abbrev>≥0.5 indicated central obesity based on previous studies.<sup>[<xref ref-type="bibr" rid="B15">15</xref>]</sup></p>
        <p>The non-contrast abdominal CT scans of the patients were investigated for the signs of fatty liver and renal stones. The border of abdominal skin at the umbilical level parallel to the intervertebral discs L3-L4 and L4-L5 was specified using a tracer. The surface area of regions with the intensities within the range of -30 to -190 Hounsfield units was calculated and recorded as the total abdominal fat area (TFA). The border of abdominal wall muscles and the anterior surface of vertebral bodies were also specified and the visceral fat area was measured. The subcutaneous fat area was calculated by subtracting the <abbrev xlink:title="visceral fat area" id="ABBRID0EVGAC">VFA</abbrev> from TFA <bold>(Fig. <xref ref-type="fig" rid="F1">1</xref>, Table <xref ref-type="table" rid="T1">1</xref>)</bold>.<sup>[<xref ref-type="bibr" rid="B12">12</xref>,<xref ref-type="bibr" rid="B16">16</xref>]</sup></p>
        <table-wrap id="T1" position="float" orientation="portrait">
          <label>Table 1.</label>
          <caption>
            <p>Imaging parameters used to calculate abdominal fat in CT-scan</p>
          </caption>
          <table id="TID0E5DAE" rules="all">
            <tbody>
              <tr>
                <td rowspan="1" colspan="1">Scan position</td>
                <td rowspan="1" colspan="1">Umbilical level parallel to L3-L4 to L4-L5 intervertebral discs</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Attenuation range</td>
                <td rowspan="1" colspan="1">-30 to -90 Hounsfield units</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Tube voltage</td>
                <td rowspan="1" colspan="1">120 kVp</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Slice thickness</td>
                <td rowspan="1" colspan="1">5-10 mm</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Field of view</td>
                <td rowspan="1" colspan="1">Includes complete border of the abdomen without any missing region</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Kidney, liver, iliac bone</td>
                <td rowspan="1" colspan="1">Are not visible at this section to avoid over- or underestimation of abdominal fat</td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
        <fig id="F1" position="float" orientation="portrait">
          <object-id content-type="arpha">6D846FDA-A899-55D7-B505-5CDDF7B9C5B1</object-id>
          <label>Figure 1.</label>
          <caption>
            <p>Axial CT-scan at the umbilical level used for the calculation of <abbrev xlink:title="visceral fat area" id="ABBRID0EMJAC">VFA</abbrev> and <abbrev xlink:title="subcutaneous fat area" id="ABBRID0EQJAC">SFA</abbrev>.</p>
          </caption>
          <graphic xlink:href="foliamedica-64-5-e67744-g001.jpg" position="float" orientation="portrait" xlink:type="simple" id="oo_762851.jpg">
            <uri content-type="original_file">https://binary.pensoft.net/fig/762851</uri>
          </graphic>
        </fig>
        <p>The patients were classified into four groups: 1) V(+)S(+): <abbrev xlink:title="visceral fat area" id="ABBRID0E2JAC">VFA</abbrev> &gt;100 cm<sup>2</sup> and <abbrev xlink:title="subcutaneous fat area" id="ABBRID0EBKAC">SFA</abbrev> &gt;100 cm<sup>2</sup>; 2) V(+)S(−): <abbrev xlink:title="visceral fat area" id="ABBRID0EHKAC">VFA</abbrev> &gt;100 cm<sup>2</sup> and <abbrev xlink:title="subcutaneous fat area" id="ABBRID0ENKAC">SFA</abbrev> &lt;100 cm<sup>2</sup>; 3) V(−)S(+): <abbrev xlink:title="visceral fat area" id="ABBRID0ETKAC">VFA</abbrev> &lt;100 cm<sup>2</sup> and <abbrev xlink:title="subcutaneous fat area" id="ABBRID0EZKAC">SFA</abbrev> &gt;100 cm<sup>2</sup>; 4) V(−)S(−): <abbrev xlink:title="visceral fat area" id="ABBRID0E6KAC">VFA</abbrev> &lt;100 cm<sup>2</sup> and <abbrev xlink:title="subcutaneous fat area" id="ABBRID0EFLAC">SFA</abbrev> &lt;100 cm<sup>2</sup>. We performed this categorization based on a previous study.<sup>[<xref ref-type="bibr" rid="B17">17</xref>]</sup> The diagnostic sensitivity, specificity, positive predictive value (<abbrev xlink:title="positive predictive value" id="ABBRID0ESLAC">PPV</abbrev>), and negative predictive value of study variables were performed to predict group 1.</p>
        <p>It is needed to be acknowledged that the results of history and demographic data for some of our included patients were not available. The study variables were compared among the four groups using the Fisher exact test. Statistical analysis was performed using IBM SPSS 20 (IBM Corp., Armonk, NY, USA). The level of significance was set at <italic>p</italic>&lt;0.05.</p>
      </sec>
    </sec>
    <sec sec-type="Results" id="SECID0E1LAC">
      <title>Results</title>
      <p>In this study, 180 patients were included. The mean age of the patients was 50±14 years, and 30.6% of them were male. The patients were classified into four types of abdominal fat distributions with 38.9% in group 1, 1.6% in group 2, 30.5% in group 3, and 28.8% in group 4.</p>
      <p>There was a significant association between abdominal fat distribution and sex (<italic>p</italic>=0.001) <bold>(Fig. <xref ref-type="fig" rid="F2">2</xref>)</bold>.</p>
      <fig id="F2" position="float" orientation="portrait">
        <object-id content-type="arpha">C54024BF-3A1F-5F49-AFB7-C338A504E30D</object-id>
        <label>Figure 2.</label>
        <caption>
          <p>The association of demographic and anthropometric variables with abdominal fat distributions. <bold>A</bold>: Age (years); <bold>B</bold>: <abbrev xlink:title="body mass index" id="ABBRID0EWMAC">BMI</abbrev> (kg/m<sup>2</sup>); <bold>C</bold>: Sex; <bold>D</bold>: <abbrev xlink:title="waist circumference" id="ABBRID0EANAC">WC</abbrev>; <bold>E</bold>: <abbrev xlink:title="waist-to-height ratio" id="ABBRID0EGNAC">WHtR</abbrev>. Group 1: V(+)S(+); Group 2: V(+)S(−); Group 3: V(−)S(+); Group 4: V(−)S(−). <abbrev xlink:title="body mass index" id="ABBRID0EKNAC">BMI</abbrev>: body mass index; <abbrev xlink:title="waist circumference" id="ABBRID0EONAC">WC</abbrev>: waist circumference; <abbrev xlink:title="waist-to-height ratio" id="ABBRID0ESNAC">WHtR</abbrev>: waist-to-height ratio; Fisher exact test. * <italic>p</italic>&lt;0.05.</p>
        </caption>
        <graphic xlink:href="foliamedica-64-5-e67744-g002.jpg" position="float" orientation="portrait" xlink:type="simple" id="oo_762852.jpg">
          <uri content-type="original_file">https://binary.pensoft.net/fig/762852</uri>
        </graphic>
      </fig>
      <p>High <abbrev xlink:title="subcutaneous fat area" id="ABBRID0E6NAC">SFA</abbrev> was observed in 75% of the females, while 38% of them had high <abbrev xlink:title="visceral fat area" id="ABBRID0EDOAC">VFA</abbrev>, indicating a lack of association between <abbrev xlink:title="subcutaneous fat area" id="ABBRID0EHOAC">SFA</abbrev> and <abbrev xlink:title="visceral fat area" id="ABBRID0ELOAC">VFA</abbrev> among women. High <abbrev xlink:title="subcutaneous fat area" id="ABBRID0EPOAC">SFA</abbrev> and high <abbrev xlink:title="visceral fat area" id="ABBRID0ETOAC">VFA</abbrev> was observed in 56% and 47% of the male patients, respectively. <abbrev xlink:title="subcutaneous fat area" id="ABBRID0EXOAC">SFA</abbrev> and <abbrev xlink:title="visceral fat area" id="ABBRID0E2OAC">VFA</abbrev> among men were highly correlated and only 11% of them had high <abbrev xlink:title="subcutaneous fat area" id="ABBRID0E6OAC">SFA</abbrev> despite a normal <abbrev xlink:title="visceral fat area" id="ABBRID0EDPAC">VFA</abbrev>. No significant association was observed between the age and abdominal fat distribution. <abbrev xlink:title="body mass index" id="ABBRID0EHPAC">BMI</abbrev> was significantly associated with abdominal fat distribution. All obese patients and most of the overweight patients were in group 1, although 44.9% of patients in this group had normal <abbrev xlink:title="body mass index" id="ABBRID0ELPAC">BMI</abbrev><bold>(Fig. <xref ref-type="fig" rid="F2">2</xref>)</bold>.</p>
      <p>Obesity was highly specific for group 1 with a positive predictive value (<abbrev xlink:title="positive predictive value" id="ABBRID0EYPAC">PPV</abbrev>) of 100%. Only 7% of the patients in group 3 and 2% of the patients in group 4 were overweight. <abbrev xlink:title="waist circumference" id="ABBRID0E3PAC">WC</abbrev> was also significantly associated with abdominal fat distribution. Almost all patients with high <abbrev xlink:title="waist circumference" id="ABBRID0EBAAE">WC</abbrev> were in groups one and two, and <abbrev xlink:title="waist circumference" id="ABBRID0EFAAE">WC</abbrev> was highly specific for these two groups. On the other hand, 80.3% of the patients in group 1 had high <abbrev xlink:title="waist circumference" id="ABBRID0EJAAE">WC</abbrev>; therefore, <abbrev xlink:title="waist circumference" id="ABBRID0ENAAE">WC</abbrev> was highly sensitive for this group <bold>(Table <xref ref-type="table" rid="T3">3</xref>)</bold>.</p>
      <table-wrap id="T2" position="float" orientation="portrait">
        <label>Table 2.</label>
        <caption>
          <p>Comorbid diseases in different abdominal fat distributions</p>
        </caption>
        <table id="TID0E5HAE" rules="all">
          <tbody>
            <tr>
              <td rowspan="1" colspan="2"/>
              <td rowspan="1" colspan="1">
                <bold>Group 1</bold>
              </td>
              <td rowspan="1" colspan="1">
                <bold>Group 2</bold>
              </td>
              <td rowspan="1" colspan="1">
                <bold>Group 3</bold>
              </td>
              <td rowspan="1" colspan="1">
                <bold>Group 4</bold>
              </td>
              <td rowspan="1" colspan="1">
                <bold>Chi-square</bold>
              </td>
              <td rowspan="1" colspan="1">
                <bold><italic>P</italic>-value</bold>
              </td>
            </tr>
            <tr>
              <td rowspan="3" colspan="1">Diabetes</td>
              <td rowspan="1" colspan="1">Present (n=27)</td>
              <td rowspan="1" colspan="1">74.1%</td>
              <td rowspan="1" colspan="1">0.0%</td>
              <td rowspan="1" colspan="1">14.8%</td>
              <td rowspan="1" colspan="1">11.1%</td>
              <td rowspan="3" colspan="1">16.181</td>
              <td rowspan="3" colspan="1">0.001</td>
            </tr>
            <tr>
              <td rowspan="1" colspan="1">Absent (n=148)</td>
              <td rowspan="1" colspan="1">33.1%</td>
              <td rowspan="1" colspan="1">2.0%</td>
              <td rowspan="1" colspan="1">33.8%</td>
              <td rowspan="1" colspan="1">31.1%</td>
            </tr>
            <tr>
              <td rowspan="1" colspan="1">Total (n=175)</td>
              <td rowspan="1" colspan="1">39.4%</td>
              <td rowspan="1" colspan="1">1.7%</td>
              <td rowspan="1" colspan="1">30.9%</td>
              <td rowspan="1" colspan="1">28.0%</td>
            </tr>
            <tr>
              <td rowspan="3" colspan="1">Hypertension</td>
              <td rowspan="1" colspan="1">Present (n=94)</td>
              <td rowspan="1" colspan="1">41.5%</td>
              <td rowspan="1" colspan="1">1.1%</td>
              <td rowspan="1" colspan="1">31.9%</td>
              <td rowspan="1" colspan="1">25.5%</td>
              <td rowspan="3" colspan="1">1.703</td>
              <td rowspan="3" colspan="1">0.6</td>
            </tr>
            <tr>
              <td rowspan="1" colspan="1">Absent (n=80)</td>
              <td rowspan="1" colspan="1">37.5%</td>
              <td rowspan="1" colspan="1">2.5%</td>
              <td rowspan="1" colspan="1">27.5%</td>
              <td rowspan="1" colspan="1">32.5%</td>
            </tr>
            <tr>
              <td rowspan="1" colspan="1">Total (n=174)</td>
              <td rowspan="1" colspan="1">39.7%</td>
              <td rowspan="1" colspan="1">1.7%</td>
              <td rowspan="1" colspan="1">29.9%</td>
              <td rowspan="1" colspan="1">28.7%</td>
            </tr>
            <tr>
              <td rowspan="3" colspan="1">Ischemic heart disease</td>
              <td rowspan="1" colspan="1">Present (n=9)</td>
              <td rowspan="1" colspan="1">55.6%</td>
              <td rowspan="1" colspan="1">0.0%</td>
              <td rowspan="1" colspan="1">11.1%</td>
              <td rowspan="1" colspan="1">33.3%</td>
              <td rowspan="3" colspan="1">2.074</td>
              <td rowspan="3" colspan="1">0.5</td>
            </tr>
            <tr>
              <td rowspan="1" colspan="1">Absent (n=167)</td>
              <td rowspan="1" colspan="1">38.3%</td>
              <td rowspan="1" colspan="1">1.8%</td>
              <td rowspan="1" colspan="1">31.7%</td>
              <td rowspan="1" colspan="1">28.1%</td>
            </tr>
            <tr>
              <td rowspan="1" colspan="1">Total (n=176)</td>
              <td rowspan="1" colspan="1">39.2%</td>
              <td rowspan="1" colspan="1">1.7%</td>
              <td rowspan="1" colspan="1">30.7%</td>
              <td rowspan="1" colspan="1">28.4%</td>
            </tr>
            <tr>
              <td rowspan="3" colspan="1">Stroke</td>
              <td rowspan="1" colspan="1">Present (n=14)</td>
              <td rowspan="1" colspan="1">28.6%</td>
              <td rowspan="1" colspan="1">0.0%</td>
              <td rowspan="1" colspan="1">50.0%</td>
              <td rowspan="1" colspan="1">21.4%</td>
              <td rowspan="3" colspan="1">2.809</td>
              <td rowspan="3" colspan="1">0.4</td>
            </tr>
            <tr>
              <td rowspan="1" colspan="1">Absent (n=162)</td>
              <td rowspan="1" colspan="1">40.1%</td>
              <td rowspan="1" colspan="1">1.9%</td>
              <td rowspan="1" colspan="1">29.0%</td>
              <td rowspan="1" colspan="1">29.0%</td>
            </tr>
            <tr>
              <td rowspan="1" colspan="1">Total (n=176)</td>
              <td rowspan="1" colspan="1">39.2%</td>
              <td rowspan="1" colspan="1">1.7%</td>
              <td rowspan="1" colspan="1">30.7%</td>
              <td rowspan="1" colspan="1">28.4%</td>
            </tr>
            <tr>
              <td rowspan="3" colspan="1">Fatty liver</td>
              <td rowspan="1" colspan="1">Present (n=5)</td>
              <td rowspan="1" colspan="1">20.0%</td>
              <td rowspan="1" colspan="1">0.0%</td>
              <td rowspan="1" colspan="1">40.0%</td>
              <td rowspan="1" colspan="1">40.0%</td>
              <td rowspan="3" colspan="1">0.955</td>
              <td rowspan="3" colspan="1">0.8</td>
            </tr>
            <tr>
              <td rowspan="1" colspan="1">Absent (n=171)</td>
              <td rowspan="1" colspan="1">39.8%</td>
              <td rowspan="1" colspan="1">1.8%</td>
              <td rowspan="1" colspan="1">29.8%</td>
              <td rowspan="1" colspan="1">28.7%</td>
            </tr>
            <tr>
              <td rowspan="1" colspan="1">Total (n=176)</td>
              <td rowspan="1" colspan="1">39.2%</td>
              <td rowspan="1" colspan="1">1.7%</td>
              <td rowspan="1" colspan="1">30.1%</td>
              <td rowspan="1" colspan="1">29.0%</td>
            </tr>
            <tr>
              <td rowspan="3" colspan="1">Metabolic syndrome</td>
              <td rowspan="1" colspan="1">Present (n=21)</td>
              <td rowspan="1" colspan="1">85.7%</td>
              <td rowspan="1" colspan="1">0.0%</td>
              <td rowspan="1" colspan="1">9.5%</td>
              <td rowspan="1" colspan="1">4.8%</td>
              <td rowspan="3" colspan="1">21.974</td>
              <td rowspan="3" colspan="1">&lt; 0.01</td>
            </tr>
            <tr>
              <td rowspan="1" colspan="1">Absent (n=156)</td>
              <td rowspan="1" colspan="1">32.7%</td>
              <td rowspan="1" colspan="1">1.9%</td>
              <td rowspan="1" colspan="1">33.3%</td>
              <td rowspan="1" colspan="1">32.1%</td>
            </tr>
            <tr>
              <td rowspan="1" colspan="1">Total (n=177)</td>
              <td rowspan="1" colspan="1">39.0%</td>
              <td rowspan="1" colspan="1">1.7%</td>
              <td rowspan="1" colspan="1">30.5%</td>
              <td rowspan="1" colspan="1">28.8%</td>
            </tr>
            <tr>
              <td rowspan="3" colspan="1">Renal stone</td>
              <td rowspan="1" colspan="1">Present (n=22)</td>
              <td rowspan="1" colspan="1">31.8%</td>
              <td rowspan="1" colspan="1">0.0%</td>
              <td rowspan="1" colspan="1">27.3%</td>
              <td rowspan="1" colspan="1">40.9%</td>
              <td rowspan="3" colspan="1">2.118</td>
              <td rowspan="3" colspan="1">0.5</td>
            </tr>
            <tr>
              <td rowspan="1" colspan="1">Absent (n=155)</td>
              <td rowspan="1" colspan="1">40.0%</td>
              <td rowspan="1" colspan="1">1.9%</td>
              <td rowspan="1" colspan="1">30.0%</td>
              <td rowspan="1" colspan="1">27.1%</td>
            </tr>
            <tr>
              <td rowspan="1" colspan="1">Total (n=177)</td>
              <td rowspan="1" colspan="1">39.0%</td>
              <td rowspan="1" colspan="1">1.7%</td>
              <td rowspan="1" colspan="1">30.5%</td>
              <td rowspan="1" colspan="1">28.8%</td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <fn>
            <p>Group 1: V(+)S(+); Group 2: V(+)S(−); Group 3: V(−)S(+); Group 4: V(−)S(−). <italic>P</italic>&lt;0.05 was considered significant .</p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
      <p>Elevated TG and <abbrev xlink:title="fasting blood glucose" id="ABBRID0EGPAE">FBG</abbrev> levels had a high specificity and <abbrev xlink:title="positive predictive value" id="ABBRID0EKPAE">PPV</abbrev> for group 1, and abnormal total cholesterol, LDL, and HDL had a high specificity for this group. The prevalence of impaired glucose tolerance (IGT) and diabetes in this study was 15.4%, and 74% of them were in group 1. Therefore, IGT and diabetes had a high specificity and <abbrev xlink:title="positive predictive value" id="ABBRID0EOPAE">PPV</abbrev> for group 1. There was no significant association between abdominal distribution patterns and creatinine, AST, and ALT <bold>(Fig. <xref ref-type="fig" rid="F3">3</xref>, Tables <xref ref-type="table" rid="T2">2</xref>, <xref ref-type="table" rid="T3">3</xref>)</bold>.</p>
      <table-wrap id="T3" position="float" orientation="portrait">
        <label>Table 3.</label>
        <caption>
          <p>Diagnostic accuracy of study variables to predict V+S+ group</p>
        </caption>
        <table id="TID0EF3AE" rules="all">
          <tbody>
            <tr>
              <td rowspan="1" colspan="1">
                <bold>Indexes</bold>
              </td>
              <td rowspan="1" colspan="1">
                <bold>Specificity</bold>
              </td>
              <td rowspan="1" colspan="1">
                <bold>Sensitivity</bold>
              </td>
              <td rowspan="1" colspan="1">
                <bold>Positive predictive value</bold>
              </td>
              <td rowspan="1" colspan="1">
                <bold>Negative predictive value</bold>
              </td>
            </tr>
            <tr>
              <td rowspan="1" colspan="1">
                <abbrev xlink:title="body mass index" id="ABBRID0ERRAE">BMI</abbrev>
              </td>
              <td rowspan="1" colspan="1">100%</td>
              <td rowspan="1" colspan="1">18%</td>
              <td rowspan="1" colspan="1">100%</td>
              <td rowspan="1" colspan="1">65%</td>
            </tr>
            <tr>
              <td rowspan="1" colspan="1">
                <abbrev xlink:title="waist circumference" id="ABBRID0EGSAE">WC</abbrev>
              </td>
              <td rowspan="1" colspan="1">75%</td>
              <td rowspan="1" colspan="1">80%</td>
              <td rowspan="1" colspan="1">67%</td>
              <td rowspan="1" colspan="1">86%</td>
            </tr>
            <tr>
              <td rowspan="1" colspan="1">WtHR</td>
              <td rowspan="1" colspan="1">71%</td>
              <td rowspan="1" colspan="1">53%</td>
              <td rowspan="1" colspan="1">58%</td>
              <td rowspan="1" colspan="1">74%</td>
            </tr>
            <tr>
              <td rowspan="1" colspan="1">TG</td>
              <td rowspan="1" colspan="1">96%</td>
              <td rowspan="1" colspan="1">30%</td>
              <td rowspan="1" colspan="1">84%</td>
              <td rowspan="1" colspan="1">68%</td>
            </tr>
            <tr>
              <td rowspan="1" colspan="1">LDL</td>
              <td rowspan="1" colspan="1">89%</td>
              <td rowspan="1" colspan="1">34%</td>
              <td rowspan="1" colspan="1">69%</td>
              <td rowspan="1" colspan="1">32%</td>
            </tr>
            <tr>
              <td rowspan="1" colspan="1">HDL</td>
              <td rowspan="1" colspan="1">90%</td>
              <td rowspan="1" colspan="1">23%</td>
              <td rowspan="1" colspan="1">61%</td>
              <td rowspan="1" colspan="1">64%</td>
            </tr>
            <tr>
              <td rowspan="1" colspan="1">Total cholesterol</td>
              <td rowspan="1" colspan="1">93%</td>
              <td rowspan="1" colspan="1">20%</td>
              <td rowspan="1" colspan="1">66%</td>
              <td rowspan="1" colspan="1">64%</td>
            </tr>
            <tr>
              <td rowspan="1" colspan="1">
                <abbrev xlink:title="fasting blood glucose" id="ABBRID0ELVAE">FBG</abbrev>
              </td>
              <td rowspan="1" colspan="1">98%</td>
              <td rowspan="1" colspan="1">23%</td>
              <td rowspan="1" colspan="1">88%</td>
              <td rowspan="1" colspan="1">66%</td>
            </tr>
            <tr>
              <td rowspan="1" colspan="1">Diabetes and impaired glucose tolerance</td>
              <td rowspan="1" colspan="1">93%</td>
              <td rowspan="1" colspan="1">28%</td>
              <td rowspan="1" colspan="1">74%</td>
              <td rowspan="1" colspan="1">66%</td>
            </tr>
            <tr>
              <td rowspan="1" colspan="1">Metabolic syndrome</td>
              <td rowspan="1" colspan="1">97%</td>
              <td rowspan="1" colspan="1">26%</td>
              <td rowspan="1" colspan="1">85%</td>
              <td rowspan="1" colspan="1">67%</td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <fn>
            <p><abbrev xlink:title="body mass index" id="ABBRID0E6WAE">BMI</abbrev>: body mass index; <abbrev xlink:title="waist circumference" id="ABBRID0EDXAE">WC</abbrev>: waist circumference; <abbrev xlink:title="waist-to-height ratio" id="ABBRID0EHXAE">WHtR</abbrev>: waist-to-height ratio; HDL: high-density lipoprotein; LDL: low-density lipoprotein; TG: triglycerides</p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
      <fig id="F3" position="float" orientation="portrait">
        <object-id content-type="arpha">C95D6302-FB8E-557D-879B-A8C040C517AB</object-id>
        <label>Figure 3.</label>
        <caption>
          <p>The association of laboratory variables with abdominal fat distributions. <bold>A</bold>: ALT; <bold>B</bold>: AST; <bold>C</bold>: creatinine; <bold>D</bold>: <abbrev xlink:title="fasting blood glucose" id="ABBRID0E2XAE">FBG</abbrev>; <bold>E</bold>: HDL; <bold>F</bold>: LDL; <bold>G</bold>: TG; <bold>H</bold>: total cholesterol. Group 1: V(+)S(+); Group 2: V(+)S(−); Group 3: V(−)S(+); Group 4: V(−)S(−). ALT: alanine aminotransferase; AST: aspartate aminotransferase; HDL: high-density lipoprotein; LDL: low-density lipoprotein; TG: triglycerides; <abbrev xlink:title="fasting blood glucose" id="ABBRID0EHYAE">FBG</abbrev>: fasting blood glucose; Fisher exact test. * <italic>p</italic>&lt;0.05.</p>
        </caption>
        <graphic xlink:href="foliamedica-64-5-e67744-g003.jpg" position="float" orientation="portrait" xlink:type="simple" id="oo_762853.jpg">
          <uri content-type="original_file">https://binary.pensoft.net/fig/762853</uri>
        </graphic>
      </fig>
      <p>The history of hypertension, ischemic heart disease, cerebral stroke, and fatty liver was also not associated with abdominal distribution patterns. Metabolic syndrome was present in 11.9% of the study population, and 85% of them were group 1. Therefore, the metabolic syndrome had a high specificity and <abbrev xlink:title="positive predictive value" id="ABBRID0EUYAE">PPV</abbrev> for the this group <bold>(Table <xref ref-type="table" rid="T3">3</xref>)</bold>.</p>
    </sec>
    <sec sec-type="Discussion" id="SECID0E6YAE">
      <title>Discussion</title>
      <p>Abnormalities in abdominal fat distribution, especially excessive amounts of visceral fat, which is considered an integral part of central obesity, have been linked to many adverse metabolic conditions.<sup>[<xref ref-type="bibr" rid="B4 B5 B6 B7">4–7</xref>]</sup> The association of visceral fat accumulation with metabolic syndrome and its major components, such as impaired glucose metabolism and insulin resistance, has been established in the literature.<sup>[<xref ref-type="bibr" rid="B18 B19 B20 B21">18–21</xref>]</sup> The aim of this study was to evaluate metabolic and atherosclerotic risk factors and disorders in different abdominal fat distributions in CT scans. In this study, groups 1 and 2 were the high-risk groups because they had high <abbrev xlink:title="visceral fat area" id="ABBRID0ETZAE">VFA</abbrev>. The prevalence of group 1 was the highest with 38.9%, followed by group 3 with 30.5%, group 4 with 28.4% and group 2 with 6.1% <bold>(Fig. <xref ref-type="fig" rid="F2">2</xref>)</bold>.</p>
      <p>In this study, we showed that gender, probably through the effect of sex hormones, affects the abdominal fat distribution since 75.4% of female patients had high <abbrev xlink:title="subcutaneous fat area" id="ABBRID0EA1AE">SFA</abbrev> and 88.9% of the group 3 patients were women <bold>(Table <xref ref-type="table" rid="T2">2</xref>)</bold>. Moreover, our study showed that, unlike women, there was a correlation between <abbrev xlink:title="subcutaneous fat area" id="ABBRID0EL1AE">SFA</abbrev> and <abbrev xlink:title="visceral fat area" id="ABBRID0EP1AE">VFA</abbrev> among men as 45.4% and 41.8% of them were in groups 1 and 2, respectively.</p>
      <p>Depending on the technique used to measure obesity, different results can be obtained. Based on <abbrev xlink:title="body mass index" id="ABBRID0EV1AE">BMI</abbrev>, we had 13 obese patients, who were all in group 1. Additionally, based on imaging criteria, 69 patients had high <abbrev xlink:title="visceral fat area" id="ABBRID0EZ1AE">VFA</abbrev> and <abbrev xlink:title="subcutaneous fat area" id="ABBRID0E41AE">SFA</abbrev> levels and were considered obese patients. However, 38 (55%) of the patients in this group had low or normal levels of <abbrev xlink:title="body mass index" id="ABBRID0EB2AE">BMI</abbrev>, indicating the inability of <abbrev xlink:title="body mass index" id="ABBRID0EF2AE">BMI</abbrev> to detect obesity. Therefore, it can be inferred that <abbrev xlink:title="body mass index" id="ABBRID0EJ2AE">BMI</abbrev> may not be a suitable index to detect obesity. The gap between the results of radiologically diagnosed obesity (group 1) and obesity diagnosed by other measures, e.g., anthropometric parameters (<abbrev xlink:title="body mass index" id="ABBRID0EN2AE">BMI</abbrev>, <abbrev xlink:title="waist circumference" id="ABBRID0ER2AE">WC</abbrev>, and WtHR) can be referred to as the occult obesity gap (<abbrev xlink:title="occult obesity gap" id="ABBRID0EV2AE">OOG</abbrev>).</p>
      <p>Unlike <abbrev xlink:title="body mass index" id="ABBRID0E22AE">BMI</abbrev>, which was highly correlated only with group one, <abbrev xlink:title="waist circumference" id="ABBRID0E62AE">WC</abbrev> was specific for both groups 1 and 2 and was a better marker of abdominal fat accumulation in the general population. The <abbrev xlink:title="occult obesity gap" id="ABBRID0ED3AE">OOG</abbrev> for <abbrev xlink:title="waist circumference" id="ABBRID0EH3AE">WC</abbrev> was nearly 20%, which is a better marker for treating obesity compared to <abbrev xlink:title="body mass index" id="ABBRID0EL3AE">BMI</abbrev> with an <abbrev xlink:title="occult obesity gap" id="ABBRID0EP3AE">OOG</abbrev> of 55% <bold>(Fig. <xref ref-type="fig" rid="F2">2</xref>)</bold>. This finding was in line with the results reported by Shen and colleagues, who found <abbrev xlink:title="waist circumference" id="ABBRID0E13AE">WC</abbrev> to be a better predictor of visceral obesity in the Caucasian race compared with <abbrev xlink:title="body mass index" id="ABBRID0E53AE">BMI</abbrev>.<sup>[<xref ref-type="bibr" rid="B22">22</xref>]</sup> Several other studies have reported the superiority of <abbrev xlink:title="waist circumference" id="ABBRID0EJ4AE">WC</abbrev> to <abbrev xlink:title="body mass index" id="ABBRID0EN4AE">BMI</abbrev> in detecting visceral obesity and predicting the risk of cardiovascular disorders and metabolic syndrome.<sup>[<xref ref-type="bibr" rid="B23 B24 B25 B26">23–26</xref>]</sup></p>
      <p>In our study, the prevalence of metabolic syndrome was 11.9%, and the prevalence of diabetes and IGT was 15.4%. Metabolic syndrome, diabetes, and impaired glucose metabolism had high specificity and positive predictive value but low sensitivity and negative predictive value for group 1 <bold>(Table <xref ref-type="table" rid="T3">3</xref>)</bold>. Previous studies have also reported similar results, indicating an association between quantities of visceral fat as measured by radiological techniques and impaired glucose metabolism and diabetes.<sup>[<xref ref-type="bibr" rid="B20">20</xref>,<xref ref-type="bibr" rid="B27">27</xref>,<xref ref-type="bibr" rid="B28">28</xref>]</sup></p>
      <p>The prevalence of non-alcoholic fatty liver disease (<abbrev xlink:title="non-alcoholic fatty liver disease" id="ABBRID0EQ5AE">NAFLD</abbrev>), when assessed by ultrasound exam, has been reported to be around 15.3% in Iran while ranging between 2% and 21.5% in different regions.<sup>[<xref ref-type="bibr" rid="B29 B30 B31">29–31</xref>]</sup> However, we found the prevalence of <abbrev xlink:title="non-alcoholic fatty liver disease" id="ABBRID0E25AE">NAFLD</abbrev> to be around 8% in our study, according to CT scans. This inconsistency could be due to the lower sensitivity of CT imaging for diagnosing <abbrev xlink:title="non-alcoholic fatty liver disease" id="ABBRID0E65AE">NAFLD</abbrev> compared to ultrasonography. The prevalence of <abbrev xlink:title="non-alcoholic fatty liver disease" id="ABBRID0ED6AE">NAFLD</abbrev> in our study was similar to the figure reported for Gonabad, which could be due to the geographic proximity of the two regions and the large rural population in both regions.</p>
      <p>This study provides evidence of sensitivity and specificity of different techniques detecting obesity in individuals; however, some limitations need to be acknowledged with regard to the research methods. Firstly, although history and demographic data for some of our patients were not available, we did not exclude them. Secondly, we conducted CT scans on individuals who visited the hospital to do CT scans for any reason; however, it is suggested to perform CT scans on healthy individuals. Therefore, continued efforts are needed to the best method for measuring abdominal fat.</p>
    </sec>
    <sec sec-type="Conclusions" id="SECID0EI6AE">
      <title>Conclusions</title>
      <p>Most of the metabolic factors, including <abbrev xlink:title="body mass index" id="ABBRID0EO6AE">BMI</abbrev>, <abbrev xlink:title="waist circumference" id="ABBRID0ES6AE">WC</abbrev>, lipid profile, and <abbrev xlink:title="fasting blood glucose" id="ABBRID0EW6AE">FBG</abbrev>, as well as metabolic syndrome, diabetes, and impaired glucose tolerance, were specific for group 1 (V+S+). However, most of the individuals in group 1 were normal according to the factors mentioned above. Therefore, there is a gap between the main definition of obesity (increasing body fat mass) and parameters that calculated obesity and metabolic disorders.</p>
    </sec>
    <sec sec-type="Conflict of Interest" id="SECID0E16AE">
      <title>Conflict of Interest</title>
      <p>The authors declare that they have no conflict of interest.</p>
    </sec>
    <sec sec-type="Ethical Approval" id="SECID0EAAAG">
      <title>Ethical Approval</title>
      <p>All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee of Birjand University of Medical Sciences (reference number: ir.bums.rec.1395.170) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.</p>
    </sec>
    <sec sec-type="Data availability" id="SECID0EFAAG">
      <title>Data availability</title>
      <p>The datasets generated during the present study are available from the corresponding author on reasonable request.</p>
    </sec>
    <sec sec-type="Author contributions" id="SECID0EKAAG">
      <title>Author contributions</title>
      <p>Javad Mohamadi Taze Abadi and Alireza Ehsanbakhsh conceived of the presented idea. Javad Mohamadi Taze Abadi developed the theory and performed the computations. Nasrin Khorashadizadeh and Azadeh Darabi verified the analytical methods. Alireza Ehsanbakhsh encouraged Javad Mohamadi Taze Abadi to investigate the correct way to measure abdominal fat and supervised the findings of this work. All authors discussed the results and contributed to the final manuscript.</p>
    </sec>
  </body>
  <back>
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