<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0016-3813</journal-id>
<journal-title><![CDATA[Gaceta médica de México]]></journal-title>
<abbrev-journal-title><![CDATA[Gac. Méd. Méx]]></abbrev-journal-title>
<issn>0016-3813</issn>
<publisher>
<publisher-name><![CDATA[Academia Nacional de Medicina de México A.C.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0016-38132025000500012</article-id>
<article-id pub-id-type="doi">10.24875/gmm.m25001040</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Modelo predictivo de aprendizaje automático para identificar el estado metabólico en niños mexicanos, utilizando HOMA-IR y la actividad enzimática de la amilasa]]></article-title>
<article-title xml:lang="en"><![CDATA[Machine learning predictive model to identify metabolic status in Mexican children, using HOMA-IR and amylase enzymatic activity]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Villagrana-Bañuelos]]></surname>
<given-names><![CDATA[Karen E.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Galván-Tejada]]></surname>
<given-names><![CDATA[Carlos E.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[García-Domínguez]]></surname>
<given-names><![CDATA[Antonio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Acosta-Cruz]]></surname>
<given-names><![CDATA[Erika]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vázquez-Moreno]]></surname>
<given-names><![CDATA[Miguel A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cruz-López]]></surname>
<given-names><![CDATA[Miguel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma de Zacatecas Unidad Académica de Ingeniería Eléctrica ]]></institution>
<addr-line><![CDATA[Zacatecas ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Autónoma de Coahuila Departamento de Biotecnología ]]></institution>
<addr-line><![CDATA[Saltillo Coahuila]]></addr-line>
<country>México</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Instituto Mexicano del Seguro Social Centro Médico Nacional Siglo XXI Hospital de Especialidades "Dr. Bernardo Sepúlveda Gutiérrez"]]></institution>
<addr-line><![CDATA[Ciudad de México ]]></addr-line>
<country>México</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>10</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>10</month>
<year>2025</year>
</pub-date>
<volume>161</volume>
<numero>5</numero>
<fpage>565</fpage>
<lpage>572</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0016-38132025000500012&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0016-38132025000500012&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0016-38132025000500012&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Antecedentes: La obesidad infantil es un problema mundial de salud, por ser un factor de riesgo para desarrollar enfermedades como el síndrome metabólico y diabetes. Actualmente el identificar estas enfermedades ya establecidas, es relativamente fácil para los profesionales de la salud con el apoyo de estudios de laboratorio, la tendencia mundial en cuanto a salud implica actuar antes de que la enfermedad se establezca.  Objetivo: El objetivo de este estudio es identificar si la actividad de amilasa total es útil para predecir que pacientes desarrollaran síndrome metabólico o diabetes.  Material y métodos: Utilizando una base de datos con 101 pacientes mexicanos, considerando el valor del modelo de evaluación de insulinorresistencia (HOMA-IR) como variable diagnóstica en tres grupos menor de 2 normal, entre 2 y 5 con riesgo metabólico y mayor de 5 como diabetes, así como el valor de la actividad enzimática de la amilasa. Se utilizó Random forest (RF) como método de aprendizaje automático.  Resultados: El modelo RF obtuvo los siguientes resultados: AUC 0.7075, especificidad 0.7619, sensibilidad 0.7142 y exactitud 0.7500.  Conclusiones: Se concluye que es factible con estas variables y RF, contar con un modelo de predicción que contribuya a identificar este tipo de pacientes en el periodo prepatogénico.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Background: Childhood obesity is a global health problem, as it is a risk factor for developing diseases such as metabolic syndrome and diabetes. At present, identifying these already established diseases is relatively easy for health professionals with the support of laboratory studies. The global trend in health involves acting before the disease is established.  Objectives: The objective of this study is to identify whether total amylase activity is useful to predict which patients will develop metabolic syndrome or diabetes.  Material and methods: Using a database with 101 Mexican patients, considering the value of the homeostasis model assessment insulin resistance as a diagnostic variable in three groups &lt; 2 normal, between 2 and 5 with metabolic risk and &gt; 5 as diabetes, as well as the value of the amylase enzymatic activity. Random forest (RF) was used as a machine learning method.  Results: The RF model obtained the following results: area under the curve 0.7075, specificity 0.7619, sensitivity 0.7142, and accuracy 0.7500.  Conclusions: It is concluded that with these variables and RF, it is feasible to have a prediction model that contributes to identifying this type of patients in the prepathogenic period.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Diabetes]]></kwd>
<kwd lng="es"><![CDATA[HOMA-IR]]></kwd>
<kwd lng="es"><![CDATA[Niños mexicanos]]></kwd>
<kwd lng="es"><![CDATA[Estado metabólico]]></kwd>
<kwd lng="es"><![CDATA[Aprendizaje automático]]></kwd>
<kwd lng="en"><![CDATA[Diabetes]]></kwd>
<kwd lng="en"><![CDATA[Homeostasis model assessment insulin resistance]]></kwd>
<kwd lng="en"><![CDATA[Mexican children]]></kwd>
<kwd lng="en"><![CDATA[Metabolic status]]></kwd>
<kwd lng="en"><![CDATA[Machine learning]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<label>1</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Colmenarejo]]></surname>
<given-names><![CDATA[G]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Machine learning models to predict childhood and adolescent obesity:a review]]></article-title>
<source><![CDATA[Nutrients]]></source>
<year>2020</year>
<volume>12</volume>
<page-range>2466</page-range></nlm-citation>
</ref>
<ref id="B2">
<label>2</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ferreira]]></surname>
<given-names><![CDATA[AP]]></given-names>
</name>
<name>
<surname><![CDATA[Oliveira]]></surname>
<given-names><![CDATA[CE]]></given-names>
</name>
<name>
<surname><![CDATA[França]]></surname>
<given-names><![CDATA[NM]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Metabolic syndrome and risk factors for cardiovascular disease in obese children:the relationship with insulin resistance (HOMA-IR)]]></article-title>
<source><![CDATA[J Pediatr (Rio J)]]></source>
<year>2007</year>
<volume>83</volume>
<page-range>21-6</page-range></nlm-citation>
</ref>
<ref id="B3">
<label>3</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ros Pérez]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Medina-Gómez]]></surname>
<given-names><![CDATA[G]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Obesidad, adipogénesis y resistencia a la insulina]]></article-title>
<source><![CDATA[Endocrinol Nutr]]></source>
<year>2011</year>
<volume>58</volume>
<page-range>360-9</page-range></nlm-citation>
</ref>
<ref id="B4">
<label>4</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Almeda-Valdés]]></surname>
<given-names><![CDATA[P]]></given-names>
</name>
<name>
<surname><![CDATA[Bello-Chavolla]]></surname>
<given-names><![CDATA[OY]]></given-names>
</name>
<name>
<surname><![CDATA[Caballeros-Barragán]]></surname>
<given-names><![CDATA[CR]]></given-names>
</name>
<name>
<surname><![CDATA[Gómez-Velasco]]></surname>
<given-names><![CDATA[DV]]></given-names>
</name>
<name>
<surname><![CDATA[Viveros-Ruiz]]></surname>
<given-names><![CDATA[T]]></given-names>
</name>
<name>
<surname><![CDATA[Vargas-Vázquez]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Índices para la evaluación de la resistencia a la insulina en individuos mexicanos sin diabetes]]></article-title>
<source><![CDATA[Gac Med Mex]]></source>
<year>2018</year>
<volume>154</volume>
<numero>Suppl 2</numero>
<issue>Suppl 2</issue>
<page-range>S50-5</page-range></nlm-citation>
</ref>
<ref id="B5">
<label>5</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Asmasary]]></surname>
<given-names><![CDATA[AA]]></given-names>
</name>
<name>
<surname><![CDATA[Artati]]></surname>
<given-names><![CDATA[RD]]></given-names>
</name>
<name>
<surname><![CDATA[Ganda]]></surname>
<given-names><![CDATA[IJ]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Analysis of glycosylated hemoglobin (HbA1c) level and homeostasis model assessment insulin resistance (Homa-IR) value in obese children]]></article-title>
<source><![CDATA[Int J Health Sci Res]]></source>
<year>2023</year>
<volume>13</volume>
<page-range>212-6</page-range></nlm-citation>
</ref>
<ref id="B6">
<label>6</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Pelin]]></surname>
<given-names><![CDATA[AM]]></given-names>
</name>
<name>
<surname><![CDATA[Balan]]></surname>
<given-names><![CDATA[G]]></given-names>
</name>
<name>
<surname><![CDATA[Stefanescu]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
<name>
<surname><![CDATA[Rosca]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Busila]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[New criteria in defining the metabolic syndrome in children - an analysis of the relationship between the hepatic enzymes and the insulin resistance, HOMA-IR, glucose tolerance test in the obese children]]></article-title>
<source><![CDATA[Progr Nutr]]></source>
<year>2022</year>
<volume>23</volume>
</nlm-citation>
</ref>
<ref id="B7">
<label>7</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Palomino]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
<name>
<surname><![CDATA[Motta]]></surname>
<given-names><![CDATA[E]]></given-names>
</name>
<name>
<surname><![CDATA[Chipayo]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
<name>
<surname><![CDATA[Cornejo]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Paredes]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Borda]]></surname>
<given-names><![CDATA[Á]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Correlación entre la glucosa salival con la glucosa de ayuno, la hemoglobina glicada y el péptido-C en personas con diabetes mellitus tipo 2]]></article-title>
<source><![CDATA[Acta Méd Peru]]></source>
<year>2023</year>
<volume>40</volume>
<page-range>7-14</page-range></nlm-citation>
</ref>
<ref id="B8">
<label>8</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Locia-Morales]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
<name>
<surname><![CDATA[Vázquez-Moreno]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[González-Dzib]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Domínguez-Hernández]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
<name>
<surname><![CDATA[Pérez-Herrera]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Robles-Ramírez]]></surname>
<given-names><![CDATA[RJ]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Association of total and pancreatic serum amylase enzymatic activity with insulin resistance and the glucose and insulin responses to oral starch test in Mexican children]]></article-title>
<source><![CDATA[Pediatr Obes]]></source>
<year>2022</year>
<volume>17</volume>
</nlm-citation>
</ref>
<ref id="B9">
<label>9</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Breiman]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Random forests]]></article-title>
<source><![CDATA[Mach Learn]]></source>
<year>2001</year>
<volume>45</volume>
<page-range>5-32</page-range></nlm-citation>
</ref>
<ref id="B10">
<label>10</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
<name>
<surname><![CDATA[Ishwaran]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Random forests for genomic data analysis]]></article-title>
<source><![CDATA[Genomics]]></source>
<year>2012</year>
<volume>99</volume>
<page-range>323-9</page-range></nlm-citation>
</ref>
<ref id="B11">
<label>11</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Su]]></surname>
<given-names><![CDATA[Q]]></given-names>
</name>
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[Q]]></given-names>
</name>
<name>
<surname><![CDATA[Lau]]></surname>
<given-names><![CDATA[RI]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Xu]]></surname>
<given-names><![CDATA[Z]]></given-names>
</name>
<name>
<surname><![CDATA[Yeoh]]></surname>
<given-names><![CDATA[YK]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Faecal microbiome- based machine learning for multi-class disease diagnosis]]></article-title>
<source><![CDATA[Nat Commun]]></source>
<year>2022</year>
<volume>13</volume>
<page-range>6818</page-range></nlm-citation>
</ref>
<ref id="B12">
<label>12</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ko]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Cho]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Petrov]]></surname>
<given-names><![CDATA[MS]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Low serum amylase, lipase, and trypsin as biomarkers of metabolic disorders:a systematic review and meta-analysis]]></article-title>
<source><![CDATA[Diabetes Res Clin Pract]]></source>
<year>2020</year>
<volume>159</volume>
<page-range>107974</page-range></nlm-citation>
</ref>
<ref id="B13">
<label>13</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Faradibah]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Widyawati]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
<name>
<surname><![CDATA[Syahar]]></surname>
<given-names><![CDATA[AU]]></given-names>
</name>
<name>
<surname><![CDATA[Jabir]]></surname>
<given-names><![CDATA[SR]]></given-names>
</name>
<name>
<surname><![CDATA[Belluano]]></surname>
<given-names><![CDATA[PL]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Comparison analysis of random forest classifier, support vector machine, and artificial neural network performance in multiclass brain tumor classification]]></article-title>
<source><![CDATA[Indones J Data Sci]]></source>
<year>2023</year>
<volume>4</volume>
<page-range>55-63</page-range></nlm-citation>
</ref>
<ref id="B14">
<label>14</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Vohra]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Hussain]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Dudyala]]></surname>
<given-names><![CDATA[AK]]></given-names>
</name>
<name>
<surname><![CDATA[Pahareeya]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Khan]]></surname>
<given-names><![CDATA[W]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Multi-class classification algorithms for the diagnosis of anemia in an outpatient clinical setting]]></article-title>
<source><![CDATA[PLoS One]]></source>
<year>2022</year>
<volume>17</volume>
</nlm-citation>
</ref>
<ref id="B15">
<label>15</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Erickson]]></surname>
<given-names><![CDATA[BJ]]></given-names>
</name>
<name>
<surname><![CDATA[Kitamura]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Magician's corner:9. Performance metrics for machine learning models]]></article-title>
<source><![CDATA[Radiol Artif Intell]]></source>
<year>2021</year>
<volume>3</volume>
</nlm-citation>
</ref>
<ref id="B16">
<label>16</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Shreffler]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Huecker]]></surname>
<given-names><![CDATA[MR]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Diagnostic testing accuracy:Sensitivity, specificity, predictive values and likelihood ratios]]></article-title>
<source><![CDATA[Statpearls]]></source>
<year>2020</year>
<publisher-loc><![CDATA[Treasure Island, FL ]]></publisher-loc>
<publisher-name><![CDATA[Statpearls]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B17">
<label>17</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Hicks]]></surname>
<given-names><![CDATA[SA]]></given-names>
</name>
<name>
<surname><![CDATA[Strümke]]></surname>
<given-names><![CDATA[I]]></given-names>
</name>
<name>
<surname><![CDATA[Thambawita]]></surname>
<given-names><![CDATA[V]]></given-names>
</name>
<name>
<surname><![CDATA[Hammou]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Riegler]]></surname>
<given-names><![CDATA[MA]]></given-names>
</name>
<name>
<surname><![CDATA[Halvorsen]]></surname>
<given-names><![CDATA[P]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[On evaluation metrics for medical applications of artificial intelligence]]></article-title>
<source><![CDATA[Sci Rep]]></source>
<year>2022</year>
<volume>12</volume>
<page-range>5979</page-range></nlm-citation>
</ref>
<ref id="B18">
<label>18</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Alaminos-Fernández]]></surname>
<given-names><![CDATA[AF]]></given-names>
</name>
</person-group>
<source><![CDATA[Árboles De Decisión En R Con Random Forest]]></source>
<year>2022</year>
<publisher-loc><![CDATA[Alicante ]]></publisher-loc>
<publisher-name><![CDATA[Limencop]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B19">
<label>19</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Resnik]]></surname>
<given-names><![CDATA[DB]]></given-names>
</name>
<name>
<surname><![CDATA[Hosseini]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Kim]]></surname>
<given-names><![CDATA[JJ]]></given-names>
</name>
<name>
<surname><![CDATA[Epiphaniou]]></surname>
<given-names><![CDATA[G]]></given-names>
</name>
<name>
<surname><![CDATA[Maple]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[GenAI synthetic data create ethical challenges for scientists. Here's how to address them]]></article-title>
<source><![CDATA[Proc Natl Acad Sci U S A]]></source>
<year>2025</year>
<volume>122</volume>
</nlm-citation>
</ref>
<ref id="B20">
<label>20</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Pérez-Ros]]></surname>
<given-names><![CDATA[P]]></given-names>
</name>
<name>
<surname><![CDATA[Navarro-Flores]]></surname>
<given-names><![CDATA[E]]></given-names>
</name>
<name>
<surname><![CDATA[Julián-Rochina]]></surname>
<given-names><![CDATA[I]]></given-names>
</name>
<name>
<surname><![CDATA[Martínez-Arnau]]></surname>
<given-names><![CDATA[FM]]></given-names>
</name>
<name>
<surname><![CDATA[Cauli]]></surname>
<given-names><![CDATA[O]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Changes in salivary amylase and glucose in diabetes:a scoping review]]></article-title>
<source><![CDATA[Diagnostics (Basel)]]></source>
<year>2021</year>
<volume>11</volume>
<page-range>453</page-range></nlm-citation>
</ref>
<ref id="B21">
<label>21</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chaudhari]]></surname>
<given-names><![CDATA[UK]]></given-names>
</name>
<name>
<surname><![CDATA[Hansen]]></surname>
<given-names><![CDATA[BC]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Amylase and lipase levels in the metabolic syndrome and type 2 diabetes:a longitudinal study in rhesus monkeys]]></article-title>
<source><![CDATA[Physiol Rep]]></source>
<year>2024</year>
<volume>12</volume>
</nlm-citation>
</ref>
<ref id="B22">
<label>22</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zhan]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Yan]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Zhao]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Association of serum amylase activity and the copy number variation of AMY1/2A/2B with metabolic syndrome in Chinese adults]]></article-title>
<source><![CDATA[Diabetes Metab Syndr Obes]]></source>
<year>2021</year>
<volume>14</volume>
<page-range>4705-14</page-range></nlm-citation>
</ref>
</ref-list>
</back>
</article>
