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         article-type="Research Paper"
         xml:lang="en">
  <front>
    <journal-meta>
      <journal-title-group>
        <journal-title>Advances in Obesity, Endocrinology, and Diabetes</journal-title>
        <abbrev-journal-title abbrev-type="publisher">AOEDS</abbrev-journal-title>
      </journal-title-group>
      <issn pub-type="epub">3049-0715</issn>
      <publisher>
        <publisher-name>Dr Lakshmi Nagendra</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="publisher-id">aoeds-00000040</article-id>
      <title-group>
        <article-title>Metabolic Phenotyping for Early Diabetes Prevention: A Web-Based Clinical Tool</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Thalange</surname>
            <given-names>Nandu KS</given-names>
          </name>
          <xref ref-type="aff" rid="aff1"/>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Alsaffar</surname>
            <given-names>Hussain</given-names>
          </name>
          <xref ref-type="aff" rid="aff2"/>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Ghanim</surname>
            <given-names>Reham</given-names>
          </name>
          <xref ref-type="aff" rid="aff3"/>
        </contrib>
      </contrib-group>
      <aff id="aff1">Clinical Professor of Pediatrics, Mohamed Bin Rashid University of Medicine &amp; Health Sciences, &amp; Consultant Pediatric Endocrinologist, Genesis Healthcare, Dubai, UAE</aff>
      <aff id="aff2">Consultant Pediatric Endocrinologist, Sultan Qaboos University Hospital, Muscat, Oman &amp; Associate Professor of Pediatric Endocrinology, College of Medicine, Al-Ameed University, Karbala, Iraq</aff>
      <aff id="aff3">Pediatric Endocrinology Specialist, Glucare.Health, Dubai, UAE</aff>
      <pub-date pub-type="epub" iso-8601-date="2026-06-20">
        <month>06</month>
        <day>20</day>
        <year>2026</year>
      </pub-date>
      <volume>3</volume>
      <issue>1</issue>
      <fpage>1</fpage>
      <lpage>11</lpage>
      <permissions>
        <license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/">
          <license-p>This article is published under the terms of the Creative Commons license.</license-p>
        </license>
      </permissions>
      <abstract>
        <p>Background: The countries of the Gulf Cooperation Council (GCC) face a significant diabetes burden. Preventing, or at least delaying onset of Type 2 diabetes mellitus (T2DM) requires accurate and timely identification of at-risk individuals with prediabetes. Traditional screening focuses on glucose and HbA1c alone and does not include other readily available data that can better identify risk and guide intervention.
Methods: We developed a comprehensive web-based cardiometabolic risk calculator combining the classic homeostasis model assessment (HOMA1), triglyceride-glucose (TyG) index, metabolic syndrome evaluation (ATP III and IDF criteria), diabetes risk prediction adapted from validated models including QDiabetes-2018 and UK Biobank cohort studies, and cardiovascular risk trajectory with Lipoprotein(a) integration. The calculator uses ethnicity-specific thresholds for Middle Eastern, South Asian, and other populations, and provides AI-generated clinical summaries with multi-language patient education materials.
Case: An 18-year-old Emirati male with obesity, prediabetes, severe insulin resistance, and compensatory beta-cell hypersecretion had a calculated 10-year diabetes risk exceeding 55%. This quantitative risk estimate motivated intensive lifestyle modification combined with tirzepatide and metformin. After six months, he achieved 17% weight loss, normalization of insulin resistance and beta-cell function, prediabetes resolution and diabetes risk reduction to under 10%.
Conclusion: Comprehensive metabolic phenotyping with quantitative risk communication can identify high-risk individuals, characterize their metabolic dysfunction, and motivate behavioral change. This freely available tool addresses the need to identify individuals at high 10-year risk of progression to T2DM and elevated lifetime cardiovascular risk, thereby allowing more timely intervention.</p>
      </abstract>
      <kwd-group kwd-group-type="author">
        <kwd>HOMA-IR</kwd>
        <kwd>insulin resistance</kwd>
        <kwd>beta-cell function</kwd>
        <kwd>TyG index</kwd>
        <kwd>diabetes risk prediction</kwd>
        <kwd>metabolic syndrome</kwd>
        <kwd>cardiovascular risk,</kwd>
        <kwd>Lipoprotein(a)</kwd>
        <kwd>prediabetes</kwd>
      </kwd-group>
    </article-meta>
  </front>
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