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	<front>
		<journal-meta>
			<journal-id journal-id-type="publisher-id">rac</journal-id>
			<journal-title-group>
				<journal-title>Revista argentina de cardiología</journal-title>
				<abbrev-journal-title abbrev-type="publisher">Rev Argent Cardiol</abbrev-journal-title>
			</journal-title-group>
			<issn pub-type="ppub">0034-7000</issn>
			<issn pub-type="epub">1850-3748</issn>
			<publisher>
				<publisher-name>Sociedad Argentina de Cardiología</publisher-name>
			</publisher>
		</journal-meta>
		<article-meta>
			<article-id pub-id-type="doi">10.7775/rac.es.v93.i1.20855</article-id>
			<article-id pub-id-type="publisher-id">00008</article-id>
			<article-categories>
				<subj-group subj-group-type="heading">
					<subject>ARTÍCULO BREVE</subject>
				</subj-group>
			</article-categories>
			<title-group>
				<article-title>Glucemia basal y HbA1c asociados a placas carotídeas en no diabéticos: un enfoque con árboles CHAID</article-title>
				<trans-title-group xml:lang="en">
					<trans-title>Basal Glucose and HbA1c Associated with Carotid Plaques in non-Diabetics: an Approach with CHAID Trees</trans-title>
				</trans-title-group>
			</title-group>
			<contrib-group>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0001-7536-7884</contrib-id>
					<name>
						<surname>Guevara-Tirado</surname>
						<given-names>Alberto</given-names>
					</name>
					<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
				</contrib>
					</contrib-group>
				<aff id="aff1">
					<label>1</label>
					<institution content-type="original">Universidad Científica del Sur, Lima, Perú</institution>
					<institution content-type="normalized">Universidad Científica del Sur</institution>
					<institution content-type="orgname">Universidad Científica del Sur</institution>
					<addr-line>
						 <named-content content-type="city">Lima</named-content>
					</addr-line>
					<country country="PE">Peru</country>
					<email>albertoguevara1986@gmail.com</email>
				</aff>
			<author-notes>
				<corresp id="c1"><italic>Dirección para correspondencia:</italic> Calle Doña Delmira manzana E lote 4 Urbanización Los Rosales, Santiago de Surco, Lima, Perú (15048). Correo electrónico: <email>albertoguevara1986@gmail.com</email> Teléfono: 978459469</corresp>
				<fn fn-type="conflict" id="fn1">
					<label>Declaración de conflicto de intereses</label>
					<p> Los autores declaran no tener conflicto de intereses. (Véase formularios de conflictos de interés de los autores en la Web).</p>
				</fn>
			</author-notes>
			<!--<pub-date date-type="pub" publication-format="electronic">
				<day>26</day>
				<month>02</month>
				<year>2025</year>
			</pub-date>
			<pub-date date-type="collection" publication-format="electronic">
				<season>Jan-Feb</season>
				<year>2025</year>
			</pub-date>-->
			<pub-date pub-type="epub-ppub">
				<season>Jan-Feb</season>
				<year>2025</year>
			</pub-date>
			<volume>93</volume>
			<issue>1</issue>
			<fpage>50</fpage>
			<lpage>54</lpage>
			<history>
				<date date-type="received">
					<day>13</day>
					<month>11</month>
					<year>2024</year>
				</date>
				<date date-type="accepted">
					<day>12</day>
					<month>02</month>
					<year>2025</year>
				</date>
			</history>
			<permissions>
				<license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by-nc/4.0/" xml:lang="es">
					<license-p>Este es un artículo publicado en acceso abierto bajo una licencia Creative Commons</license-p>
				</license>
			</permissions>
			<abstract>
				<title>RESUMEN</title>
				<sec>
					<title>Introducción:</title>
					<p> La presencia de placas carotídeas es un importante marcador de riesgo de accidente cerebrovascular (ACV).</p>
				</sec>
				<sec>
					<title>Objetivo:</title>
					<p> Analizar la asociación entre valores de glucemia basal y hemoglobina glicosilada (HbA1c) con la presencia de placas carotídeas en adultos no diabéticos.</p>
				</sec>
				<sec>
					<title>Material y métodos:</title>
					<p> Estudio analítico de corte transversal con la utilización de una base de datos secundaria. Las variables consideradas incluyeron: puntaje de placas carotídeas, glucemia basal y HbA1c. Se empleó el árbol de decisiones CHAID.</p>
				</sec>
				<sec>
					<title>Resultados:</title>
					<p> El árbol CHAID identificó que una glucosa basal de 104 mg/dL con HbA1c &gt;6% es la característica principal asociada a presencia de placas carotídeas de riesgo para ACV. Los pacientes con estas características tuvieron 3,69 veces más a menudo placas carotídeas de riesgo que aquellos con valores menores de glucosa y HbA1c (OR 3,69; IC95% 2,59-5,28). Los valores citados de glucemia basal y HbA1c tuvieron probabilidad significativamente mayor de corresponder a un verdadero positivo que a un falso positivo para señalar la presencia de placas de riesgo (LR+ 3,29; IC95% 2,38-4,54). La mediana de puntaje de placas carotídeas fue de 1,60 en estos pacientes, comparado con 0,25 en aquellos con valores menores (p=0,001). También tuvieron una mediana mayor de número de placas (1,20 vs. 0,30; p&lt;0,001). </p>
				</sec>
				<sec>
					<title>Conclusiones:</title>
					<p> Valores de glucemia basal &gt;104 mg/dL con HbA1c &gt;6% se asociaron a mayor presencia de placas carotídeas de riesgo en pacientes no diabéticos. </p>
				</sec>
			</abstract>
			<trans-abstract xml:lang="en">
				<title>ABSTRACT</title>
				<sec>
					<title>Background: </title>
					<p>Carotid plaques are significant markers of risk for stroke.</p>
				</sec>
				<sec>
					<title>Objective: </title>
					<p>The aim of this research was to analyze the association between baseline glycemia and glycated hemoglobin (HbA<sub>1c</sub>) with the presence of carotid plaques in non-diabetic adults.</p>
				</sec>
				<sec>
					<title>Methods: </title>
					<p>We conducted a cross-sectional analytical study using a secondary database. The variables considered included carotid plaque score, baseline glycemia and HbA<sub>1c</sub>. The CHAID decision tree was utilized in this analysis.</p>
				</sec>
				<sec>
					<title>Results: </title>
					<p>The CHAID tree classified baseline blood glucose levels &gt; 104 mg/dL along with HbA<sub>1c</sub> values &gt; 6%. as the most decisive variables associated with the presence of carotid plaques at risk for stroke. The odds of presenting with a high-risk carotid plaque score was 3.69 times higher for these patients when compared to those with lower glucose and HbA<sub>1c</sub> levels (OR 3.69; 95% CI, 2.59-5.28). Patients with the aforementioned blood glucose levels and HbA<sub>1c</sub> had greater probability of a true positive result for high-risk carotid plaque (LR+ 3.29; 95% CI, 2.38-4.54). In these patients, the median carotid plaque score was 1.60 compared to 0.25 in those with lower values (p=0.001). The median number of plaques was also greater (1.20 vs. 0.30; p &lt; 0.001). </p>
				</sec>
				<sec>
					<title>Conclusions: </title>
					<p>Baseline blood glucose levels &gt;104 mg/dL and HbA<sub>1c</sub> &gt;6% were associated with the presence of high-risk carotid plaques in non-diabetic patients. </p>
				</sec>
			</trans-abstract>
			<kwd-group xml:lang="es">
				<title>Palabras clave:</title>
				<kwd>Placa Aterosclerótica</kwd>
				<kwd>Ultrasonografía carotídea</kwd>
				<kwd>Hemoglobina Glicosilada</kwd>
				<kwd>Glucemia</kwd>
				<kwd>Riesgo cardiovascular</kwd>
			</kwd-group>
			<kwd-group xml:lang="en">
				<title>Keywords:</title>
				<kwd>Plaque</kwd>
				<kwd>Atherosclerotic</kwd>
				<kwd>Ultrasonography</kwd>
				<kwd>Carotid Arteries</kwd>
				<kwd>Glycated Hemoglobin</kwd>
				<kwd>Blood Glucose</kwd>
				<kwd>Primary Health Care</kwd>
			</kwd-group>
			<counts>
				<fig-count count="1"/>
				<table-count count="4"/>
				<equation-count count="0"/>
				<ref-count count="20"/>
				<page-count count="5"/>
			</counts>
		</article-meta>
	</front>
	<body>
		<sec sec-type="intro">
			<title>INTRODUCCIÓN</title>
			<p>Las placas ateromatosas son acumulaciones en la capa interna de las arterias, compuestas por macrófagos, linfocitos T, células dendríticas, calcio, lípidos y tejido conectivo fibroso. (<xref ref-type="bibr" rid="B1">1</xref>) Estas placas pueden estrechar el lumen arterial y causar micro rupturas, con aumento del riesgo de eventos tromboembólicos e isquémicos como infarto de miocardio y accidente cerebrovascular. (<xref ref-type="bibr" rid="B2">2</xref>) La aterosclerosis, una enfermedad inflamatoria crónica, está influenciada por factores como la dislipidemia, la disglucemia, el tabaquismo, una dieta rica en grasas y azúcares, el sedentarismo, la edad y la predisposición genética. (<xref ref-type="bibr" rid="B3">3</xref>)</p>
			<p>La estenosis aterosclerótica de las arterias carótidas causa alrededor del 10-20% de todos los accidentes cerebrovasculares (ACV) isquémicos a través de dos mecanismos principales: deterioro hemodinámico en caso de estenosis significativa, y tromboembolia a partir de una placa aterosclerótica independientemente del grado de estenosis. (<xref ref-type="bibr" rid="B4">4</xref>) El ACV es la segunda causa de muerte y la tercera de morbimortalidad a nivel mundial, (<xref ref-type="bibr" rid="B5">5</xref>) especialmente el ACV isquémico que representa el 85% de los casos. (<xref ref-type="bibr" rid="B6">6</xref>) La hiperglucemia, inducida principalmente por diabetes mellitus tipo 1 y 2, promueve la aterosclerosis a través de mecanismos como la formación de productos de glicosilación avanzada, el estrés oxidativo y la alteración del factor de crecimiento endotelial. (<xref ref-type="bibr" rid="B7">7</xref>) </p>
			<p>Dado que la relación entre glucemia y aterosclerosis no se limita a pacientes con diabetes o prediabetes, es crucial conocer el perfil glucémico asociado a la aterosclerosis carotídea en individuos sin disglucemia. El uso del árbol de clasificación de detección automática de interacciones basado en Chi-cuadrado, por sus siglas CHAID (<italic>Chi-squared Automatic Interaction Detection</italic>) puede ayudar a identificar este perfil. El objetivo de esta investigación fue analizar la asociación entre glucemia basal y HbA1c con la presencia de placas carotídeas en adultos no diabéticos.</p>
		</sec>
		<sec sec-type="materials|methods">
			<title>MATERIAL Y MÉTODOS</title>
			<sec>
				<title>Diseño y población de estudio</title>
				<p>Estudio analítico y transversal, basado en una base de datos internacional registrada en el repositorio “Dryada” (<ext-link ext-link-type="uri" xlink:href="https://datadryad.org">https://datadryad.org</ext-link>). La investigación se originó a partir de la lectura del artículo <italic>Mathematical modeling for the prediction of cerebral white matter lesions based on clinical examination data</italic>, (<xref ref-type="bibr" rid="B8">8</xref>) que se refiere a la predicción de lesiones en la sustancia blanca mediante exámenes médicos rutinarios y algoritmos matemáticos complejos. (<xref ref-type="bibr" rid="B9">9</xref>)</p>
				<p>La población total fue de 1904 adultos, de los que se seleccionó intencionalmente a 1775. Se excluyó a pacientes que toman medicamentos antidiabéticos y aquellos con glucemia basal ≥ 126 mg/dL. </p>
				<p>Se obtuvieron los valores basales de presión arterial sistólica (PAS) y diastólica (PAD), la glucosa basal en mg/dL y la hemoglobina glicosilada (HbA1c). </p>
				<p>Se evaluó en la ecografía carotídea la presencia de placas en la carótida común, su bifurcación y la carótida interna. La variable dependiente en el árbol de decisiones CHAID fue el puntaje de placas carotídeas, evaluado por ultrasonido y dicotomizado en valores &gt; 1,20 mm y ≤ 1,20 mm. (<xref ref-type="bibr" rid="B10">10</xref>) El puntaje de placa carotídea es una medida utilizada para evaluar la presencia y gravedad de placas ateroscleróticas en las arterias carótidas. Se obtiene mediante una ecografía carotídea, que mide el grosor máximo de las placas en las paredes arteriales; si este grosor supera un umbral (generalmente &gt;1,20 mm), se considera de riesgo cardiovascular. </p>
			</sec>
			<sec>
				<title>Análisis estadístico</title>
				<p>Se empleó el árbol de decisiones CHAID, una técnica basada en el estadístico chi-cuadrado que permite segmentar los datos en grupos homogéneos y construir un modelo predictivo. Este método identifica patrones en la relación entre una variable dependiente y múltiples variables independientes, utilizando tanto datos cuantitativos como categóricos. (<xref ref-type="bibr" rid="B11">11</xref>)</p>
				<p>CHAID divide iterativamente la muestra en nodos, creando ramas basadas en las categorías de las variables explicativas. En cada división, se seleccionan los puntos de corte más significativos según el test de chi-cuadrado, generando subgrupos mutuamente excluyentes. (<xref ref-type="bibr" rid="B12">12</xref>) En el caso del puntaje de placa, por ejemplo, los nodos representan categorías diferenciadas según su asociación con la variable de interés.</p>
				<p>Finalmente, se seleccionó el nodo terminal con la mayor fuerza de asociación con el nodo principal, el cual fue la presencia o ausencia de placas carotídeas mayores a 1,20 mm.</p>
				<p>Se realizaron pruebas diagnósticas para estimar la razón de probabilidades (Odds Ratio), grado de asociación (coeficiente Phi), sensibilidad (S), especificidad (E), valores predictivos positivos (VPP) y negativos (VPN), y cocientes de probabilidad (<italic>likelihood ratio</italic>). El análisis se realizó con SPSS statistics 25TM. (<xref ref-type="bibr" rid="B13">13</xref>) </p>
			</sec>
			<sec>
				<title>Consideraciones éticas</title>
				<p>La base de datos fue colocada voluntariamente en Dryada (https://datadryad.org/) bajo licencia Creative Commons (CC0). (<xref ref-type="bibr" rid="B14">14</xref>) Se respetó la declaración de Helsinki. La base de datos está disponible en: <ext-link ext-link-type="uri" xlink:href="https://datadryad.org/stash/dataset/doi:10.5061/dryad.73bh2q8">https://datadryad.org/stash/dataset/doi:10.5061/dryad.73bh2q8 </ext-link>.</p>
			</sec>
		</sec>
		<sec sec-type="results">
			<title>RESULTADOS</title>
			<p>Las características de la población seleccionada se detallan en la <xref ref-type="table" rid="t1">Tabla 1</xref>.</p>
			<p>
				<table-wrap id="t1">
					<label>Tabla 1</label>
					<caption>
						<title>Características basales de la población (n=1775)</title>
					</caption>
					<table frame="hsides" rules="groups">
						<colgroup>
							<col/>
							<col/>
						</colgroup>
						<thead>
							<tr style="border: 0; background-color:#ab0534;color:#ffffff;">
								<th align="left">Variable </th>
								<th align="center"> </th>
							</tr>
						</thead>
						<tbody>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">Sexo masculino, n (%) </td>
								<td align="center">891 (50,2 %) </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left" colspan="2">Grupo etario, n (%) </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">18-39 años </td>
								<td align="center">164 (9,2 %) </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">40-59 años </td>
								<td align="center">838 (47,2 %) </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">≥ 60 años </td>
								<td align="center">773 (43,5 %) </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left" colspan="2">Puntaje de placa carotídea, n (%) </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">De riesgo (&gt; 1,2 mm) </td>
								<td align="center">565 (31,8 %) </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">No patológico </td>
								<td align="center">1210 (68,2 %) </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">Puntaje placas carotídeas, mediana (RIC) </td>
								<td align="center">0,40 (0,10-1,00) </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">Glucemia (mg/dL), media ± DE </td>
								<td align="center">99,86 ± 8,98 </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">HbA1c (%), media ± DE </td>
								<td align="center">5,61 ± 0,38 </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">IMC (kg/m2), media ± DE </td>
								<td align="center">22,91 ± 3,28 </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">PAS (mm Hg), media ± DE </td>
								<td align="center">123,14 ± 18,29 </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">PAD (mm Hg), media ± DE </td>
								<td align="center">73,74 ± 12,17 </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">Número de placas, mediana (RIC) </td>
								<td align="center">0 (0-1) </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">TG (mg/dL), media± DE </td>
								<td align="center">111,82 ± 69,55 </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">HDL (mg/L), media± DE </td>
								<td align="center">61,12 ± 15,39 </td>
							</tr>
						</tbody>
					</table>
					<table-wrap-foot>
						<fn id="TFN1">
							<p>DE: desviación estándar; HbA1c: hemoglobina A1 glicosilada; HDL: lipoproteínas de alta densidad; IMC: índice de masa corporal; PAD: presión arterial diastólica; PAS: presión arterial sistólica; RIC: rango intercuartílico</p>
						</fn>
					</table-wrap-foot>
				</table-wrap>
			</p>
			<p>De los 1775 pacientes, 565 (31,8%) presentaron placa carotídea con puntaje de riesgo. Estos pacientes tuvieron valores significativamente más altos de número de placas, HbA1c, glucosa basal y presión arterial sistólica y diastólica en comparación con aquellos sin riesgo (p &lt; 0,001). Sin embargo, no se encontró una diferencia significativa en el IMC entre ambos grupos (p = 0,071). Esto resalta la asociación entre el riesgo de placa carotídea y marcadores metabólicos y cardiovasculares elevados (<xref ref-type="table" rid="t2">Tabla 2</xref>).</p>
			<p>
				<table-wrap id="t2">
					<label>Tabla 2</label>
					<caption>
						<title>Marcadores biológicos en pacientes con placa carotídea con puntaje de riesgo</title>
					</caption>
					<table frame="hsides" rules="groups">
						<colgroup>
							<col/>
							<col/>
							<col/>
							<col/>
						</colgroup>
						<thead>
							<tr style="border: 0; background-color:#ab0534;color:#ffffff;">
								<th align="center"> </th>
								<th align="center">Puntaje de riesgo (n=565) </th>
								<th align="center">Puntaje no patológico (n=1210) </th>
								<th align="center">p </th>
							</tr>
						</thead>
						<tbody>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">Número de placas, mediana (RIC) </td>
								<td align="center">2(0,50 - 2,50) </td>
								<td align="center">0,35(0,10 - 1,12) </td>
								<td align="center">&lt;0,001 </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">HbA1c (%), media ± DE </td>
								<td align="center">5,67 ± 0,30 </td>
								<td align="center">5,58 ± 0,29 </td>
								<td align="center">&lt;0,001 </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">Glucemia (mg/dL), media ± DE </td>
								<td align="center">101,33 ± 9,02 </td>
								<td align="center">99,22 ± 8,23 </td>
								<td align="center">&lt;0,001 </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">PAS (mm Hg), media ± DE </td>
								<td align="center">128,64 ± 18,34 </td>
								<td align="center">120,76 ± 17,72 </td>
								<td align="center">&lt;0,001 </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">PAD (mm Hg), media ± DE </td>
								<td align="center">75,49 ± 12,19 </td>
								<td align="center">72,98 ± 12,18 </td>
								<td align="center">&lt;0,001 </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">IMC (kg/m2), media ± DE </td>
								<td align="center">23,13 ± 3,18 </td>
								<td align="center">22,82 ± 3,26 </td>
								<td align="center">0,0710 </td>
							</tr>
						</tbody>
					</table>
					<table-wrap-foot>
						<fn id="TFN2">
							<p>DE: desviación estándar; HbA1c: hemoglobina A1 glicosilada; IMC: índice de masa corporal; PAD: presión arterial diastólica; PAS: presión arterial sistólica; RIC: rango intercuartílico</p>
						</fn>
					</table-wrap-foot>
				</table-wrap>
			</p>
			<p>El árbol de clasificación, con el puntaje de placa carotídea como variable dependiente, tuvo una profundidad de 2, con un total de 8 nodos, de los cuales 5 fueron nodos terminales. El árbol clasificó como característica principal asociada a presencia de placas carotídeas de riesgo un valor de glucemia basal mayor a 104 mg/dL, junto a valores de HbA1c mayores de 6%. Como característica principal asociada a la ausencia de placas carotídeas de riesgo señala la presencia de valores de HbA1c ≤ 5,30% (<xref ref-type="fig" rid="f1">Figura 1</xref>)</p>
			<p>
				<fig id="f1">
					<label>Figura 1</label>
					<caption>
						<title>Árbol de decisiones CHAID para perfil glucémico asociado a placas carotideas en adultos no diabéticos</title>
					</caption>
					<graphic xlink:href="1850-3748-rac-93-01-50-gf1.jpg"/>
				</fig>
			</p>
			<p>La asociación entre la presencia de glucemia basal &gt;104 mg/dL y HbA1c &gt;6 % (nodo 7 del árbol CHAID) con el puntaje de placas carotídeas &gt; 1,20 mm fue moderada (Phi=0,182). Los pacientes con glucosa &gt;104 mg/dL y HbA1c &gt;6 % tuvieron placas carotídeas de riesgo 3,69 veces más a menudo que los pacientes con valores menores de glucosa y HbA1c. La sensibilidad fue baja (15%), la especificidad alta (95%), el valor predictivo positivo de 61%, el valor predictivo negativo de 71%. Según la determinación del <italic>likelihood ratio</italic> (cociente de probabilidades), los pacientes con glucosa &gt;104 mg/dL y HbA1c&gt;6 % tuvieron una chance 3,29 veces mayor de corresponder a un verdadero positivo para placas carotídeas de riesgo que a un falso positivo (<xref ref-type="table" rid="t3">Tabla 3</xref>)</p>
			<p>
				<table-wrap id="t3">
					<label>Tabla 3</label>
					<caption>
						<title>Medidas de asociación entre el nodo 7 del árbol CHAID y la presencia de placas carotídeas detectadas según ecografía en adultos no diabéticos</title>
					</caption>
					<table frame="hsides" rules="groups">
						<colgroup>
							<col/>
							<col/>
						</colgroup>
						<tbody>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">Phi </td>
								<td align="center">0,182 </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">OR (IC95%) </td>
								<td align="center">3,69 (2,59-5,28) </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">S </td>
								<td align="center">15 % </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">E </td>
								<td align="center">95 % </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">VPP </td>
								<td align="center">61 % </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">VPN </td>
								<td align="center">71 % </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">LR+ (IC95%) </td>
								<td align="center">3,29 (2,38-4,54) </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">LR- (IC95%) </td>
								<td align="center">0,89 (0,85-0,93) </td>
							</tr>
						</tbody>
					</table>
					<table-wrap-foot>
						<fn id="TFN3">
							<p>E: especificidad; LR: <italic>likelihood ratio</italic> (cociente de probabilidades); OR: Odds Ratio; Phi= coeficiente Phi; S: sensibilidad; VPN: valor predictivo negativo; VPP: valor predictivo positivo</p>
						</fn>
					</table-wrap-foot>
				</table-wrap>
			</p>
			<p>Los 112 pacientes con la característica principal del nodo 7 del árbol CHAID (glucosa &gt;104 mg/dL y HbA1c &gt;6%), tuvieron una mediana de puntaje de placas carotídeas de 1,60, mientras que los pacientes pertenecientes a otros nodos tuvieron un valor de 0,25 (p=0,001). Asimismo, tuvieron una mediana de número de placas de 1,20, mientras que los pacientes de los otros nodos tuvieron una mediana de 0,30 (p&lt;0,001) (<xref ref-type="table" rid="t4">Tabla 4</xref>)</p>
			<p>
				<table-wrap id="t4">
					<label>Tabla 4</label>
					<caption>
						<title>Comparación de promedios antropométricos, hemodinámicos y de laboratorio según la presencia de glucemia basal &gt;104 mg/dL y HbA1c &gt;6% en adultos no diabéticos (nodo 7 de árbol CHAID)</title>
					</caption>
					<table frame="hsides" rules="groups">
						<colgroup>
							<col/>
							<col/>
							<col/>
							<col/>
						</colgroup>
						<thead>
							<tr style="border: 0; background-color:#ab0534;color:#ffffff;">
								<th align="left">Variable </th>
								<th align="center">Nodo 7 (n=142) </th>
								<th align="center">Nodos 1 a 6 (n=1633) </th>
								<th align="center">p </th>
							</tr>
						</thead>
						<tbody>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">Puntaje de placas carotídeas, mediana (RIC) </td>
								<td align="center">1,60 (0,50- 3,70) </td>
								<td align="center">0,25 (0,10-0,50) </td>
								<td align="center">0,001 </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">Número de placas, mediana (RIC) </td>
								<td align="center">1,20 (0,80-1,55) </td>
								<td align="center">0,30 (0,10-0,50) </td>
								<td align="center">&lt;0,001 </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">IMC (kg/m2), media ± DE </td>
								<td align="center">24,36 ± 4,18 </td>
								<td align="center">22,85 ± 3,17 </td>
								<td align="center">&lt;0,001 </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">PAS, mmHg, media ± DE </td>
								<td align="center">127,34 ± 18,64 </td>
								<td align="center">123,03 ± 18,22 </td>
								<td align="center">0,007 </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">PAD, mmHg, media ± DE </td>
								<td align="center">73,65 ± 11,85 </td>
								<td align="center">73,70 ±12,20 </td>
								<td align="center">0,962 </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">HbA1c (%), media ± DE </td>
								<td align="center">6,46 ± 0,45 </td>
								<td align="center">5,59 ± 0,28 </td>
								<td align="center">&lt;0,001 </td>
							</tr>
							<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
								<td align="left">Glucemia (mg/dL), media ± DE </td>
								<td align="center">114,58 ± 5,86 </td>
								<td align="center">99,21 ± 8,11 </td>
								<td align="center">&lt;0,001 </td>
							</tr>
						</tbody>
					</table>
					<table-wrap-foot>
						<fn id="TFN4">
							<p>DE: desviación estándar; HbA1c: hemoglobina A1 glicosilada; IMC: índice de masa corporal; PAD: presión arterial diastólica; PAS: presión arterial sistólica; RIC: rango intercuartílico </p>
						</fn>
					</table-wrap-foot>
				</table-wrap>
			</p>
		</sec>
		<sec sec-type="discussion">
			<title>DISCUSIÓN</title>
			<p>El árbol de decisiones CHAID clasificó y segmentó las variables continuas HbA1c y glucosa basal. Una glucemia basal &gt;104 mg/dL y HbA1c &gt;6% se asocian a la presencia de placas carotídeas de riesgo, compatibles con prediabetes. Esto se corroboró con pruebas diagnósticas, mostrando una influencia significativa en el grosor carotídeo, similar a los hallazgos de Zhou et al. (<xref ref-type="bibr" rid="B15">15</xref>) La relación entre la HbA1c y las placas carotídeas se ha observado en pacientes con enfermedades crónicas, como en los estudios de Dodos et al. (<xref ref-type="bibr" rid="B16">16</xref>) y Cheng et al. (<xref ref-type="bibr" rid="B17">17</xref>) </p>
			<p>La HbA1c elevada influye en la aterosclerosis carotídea a través de mecanismos como la inducción de productos de glicosilación avanzada, estrés oxidativo y disfunción endotelial. (<xref ref-type="bibr" rid="B18">18</xref>,<xref ref-type="bibr" rid="B19">19</xref>,<xref ref-type="bibr" rid="B20">20</xref>) Estos mecanismos pueden afectar a adultos no diabéticos con valores de HbA1c y glucemia basal en rango de prediabetes, sugiriendo que la formación de placas carotídeas puede ser un signo prodrómico.</p>
			<p>El árbol de decisiones también identificó que un valor de HbA1c ≤5,30% se asocia a la ausencia de placas carotideas de riesgo.Aunque los hallazgos sugieren una asociación entre HbA1c y la presencia de placas carotídeas de riesgo, se requieren más estudios y evidencia adicional para establecer una recomendación firme sobre la adopción de objetivos de HbA1c o su medición rutinaria en la prevención de eventos cerebrovasculares relacionados con la aterosclerosis carotídea.</p>
			<p>Las limitaciones del estudio incluyen el tamaño de la muestra (n=1775), que puede no ser representativo de poblaciones más amplias, y posibles sesgos de información debido a la recopilación retrospectiva de los datos. Además, se carece de información sobre otros factores de riesgo clave, como hábitos alimenticios, actividad física y antecedentes familiares, que podrían influir en la glucemia y la aterosclerosis. La falta de seguimiento longitudinal impide establecer una relación causal definitiva, y el estudio no considera la variabilidad individual ni el uso de múltiples biomarcadores, lo que podría ofrecer una evaluación más completa del riesgo cardiovascular.</p>
		</sec>
		<sec sec-type="conclusions">
			<title>CONCLUSIONES</title>
			<p>Valores de glucemia basal &gt;104 mg/dL y HbA1c &gt;6% se asociaron a mayor presencia de placas carotídeas de riesgo en adultos no diabéticos. Aunque el árbol de decisiones CHAID descartó los triglicéridos y HDL como factores relevantes, y valores de glucemia basal &gt;104 mg/dL y HbA1c &gt;6% se asociaron con una mayor presencia de placas carotídeas de riesgo en adultos no diabéticos, la ausencia de datos sobre otros posibles factores de riesgo como el colesterol total o la genética limita la interpretación de estos hallazgos. A pesar de esto, la medición regular de HbA1c en adultos no diabéticos podría ser útil para identificar el riesgo de aterosclerosis carotídea, pero se requieren estudios adicionales que controlen por otros factores confundidores para confirmar esta relación y su impacto en la prevención de la enfermedad.</p>
		</sec>
	</body>
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		<front-stub>
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				<subj-group subj-group-type="heading">
					<subject>BRIEF ARTICLE</subject>
				</subj-group>
			</article-categories>
			<title-group>
				<article-title>Basal Glucose and HbA1c Associated with Carotid Plaques in non-Diabetics: an Approach with CHAID Trees</article-title>
			</title-group>
			<contrib-group>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0001-7536-7884</contrib-id>
					<name>
						<surname>Guevara-Tirado</surname>
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					</name>
					<xref ref-type="aff" rid="aff2"><sup>1</sup></xref>
				</contrib>
				<aff id="aff2">
					<label>1</label>
					<institution content-type="original">Universidad Científica del Sur, Lima, Perú</institution>
					<institution content-type="normalized">Universidad Científica del Sur</institution>
					<institution content-type="orgname">Universidad Científica del Sur</institution>
					<addr-line>
						<city>Lima</city>
					</addr-line>
					<country country="PE">Peru</country>
				</aff>
			</contrib-group>
			<author-notes>
				<corresp id="c2"><italic>Correspondence:</italic> Calle Doña Delmira manzana E lote 4 Urbanización Los Rosales, Santiago de Surco, Lima, Perú (15048). <italic>E-mail:</italic><email>albertoguevara1986@gmail.com</email>
				</corresp>
				<fn fn-type="conflict" id="fn10">
					<p>Conflicts of interest None declared. (See authors conflicts of interest forms in the website).</p>
				</fn>
			</author-notes>
			<abstract>
				<title>ABSTRACT</title>
				<sec>
					<title>Background: </title>
					<p>Carotid plaques are significant markers of risk for stroke.</p>
				</sec>
				<sec>
					<title>Objective: </title>
					<p>The aim of this research was to analyze the association between baseline glycemia and glycated hemoglobin (HbA<sub>1c</sub>) with the presence of carotid plaques in non-diabetic adults.</p>
				</sec>
				<sec>
					<title>Methods: </title>
					<p>We conducted a cross-sectional analytical study using a secondary database. The variables considered included carotid plaque score, baseline glycemia and HbA<sub>1c</sub>. The CHAID decision tree was utilized in this analysis.</p>
				</sec>
				<sec>
					<title>Results: </title>
					<p>The CHAID tree classified baseline blood glucose levels &gt; 104 mg/dL along with HbA<sub>1c</sub> values &gt; 6%. as the most decisive variables associated with the presence of carotid plaques at risk for stroke. The odds of presenting with a high-risk carotid plaque score was 3.69 times higher for these patients when compared to those with lower glucose and HbA<sub>1c</sub> levels (OR 3.69; 95% CI, 2.59-5.28). Patients with the aforementioned blood glucose levels and HbA<sub>1c</sub> had greater probability of a true positive result for high-risk carotid plaque (LR+ 3.29; 95% CI, 2.38-4.54). In these patients, the median carotid plaque score was 1.60 compared to 0.25 in those with lower values (p=0.001). The median number of plaques was also greater (1.20 vs. 0.30; p &lt; 0.001). </p>
				</sec>
				<sec>
					<title>Conclusions: </title>
					<p>Baseline blood glucose levels &gt;104 mg/dL and HbA<sub>1c</sub> &gt;6% were associated with the presence of high-risk carotid plaques in non-diabetic patients. </p>
				</sec>
			</abstract>
			<kwd-group xml:lang="en">
				<title>Keywords:</title>
				<kwd>Plaque</kwd>
				<kwd>Atherosclerotic</kwd>
				<kwd>Ultrasonography</kwd>
				<kwd>Carotid Arteries</kwd>
				<kwd> Glycated Hemoglobin</kwd>
				<kwd>Blood Glucose</kwd>
				<kwd> Primary Health Care</kwd>
			</kwd-group>
		</front-stub>
		<body>
			<sec sec-type="intro">
				<title>INTRODUCTION </title>
				<p>Atheromatous plaques are accumulations of macrophages, T cells, dendritic cells, calcium, lipids, and fibrous connective tissue in the inner layer of arteries. (<xref ref-type="bibr" rid="B21">1</xref>) These plaques narrow the arterial lumen and cause micro-ruptures, increasing the risk of thromboembolic and ischemic events such as myocardial infarction and stroke. (<xref ref-type="bibr" rid="B22">2</xref>) Atherosclerosis, a chronic inflammatory disease, is influenced by factors such as dyslipidemia, dysglycemia, smoking, diets high in fat and carbohydrates, sedentary lifestyle, age and genetic predisposition. (<xref ref-type="bibr" rid="B23">3</xref>)</p>
				<p>Carotid artery stenosis due to atherosclerosis causes about 10-20% of all ischemic strokes by two main mechanisms: hemodynamic impairment in case of significant stenosis, and thromboembolism from an atherosclerotic plaque regardless of the degree of stenosis. (<xref ref-type="bibr" rid="B24">4</xref>) Stroke is the second leading cause of death and the third leading cause of morbidity and mortality worldwide, (<xref ref-type="bibr" rid="B25">5</xref>) especially ischemic stroke, which accounts for 85% of cases. (<xref ref-type="bibr" rid="B26">6</xref>) Hyperglycemia, mainly in type 1 and 2 diabetes mellitus, promotes atherosclerosis through mechanisms such as the formation of advanced glycation end-products, oxidative stress and alteration of endothelial growth factor. (<xref ref-type="bibr" rid="B27">7</xref>) </p>
				<p>Given that the relationship between glycemia and atherosclerosis is not limited to patients with diabetes or prediabetes, it is crucial to understand the glycemic profile associated with carotid artery atherosclerosis in subjects without dysglycemia. The use of the chi-squared automatic interaction detection (CHAID) decision tree analysis can help to identify this profile. The aim of this research was to analyze the association between baseline blood glucose levels and HbA<sub>1c</sub> with the presence of carotid plaques in non-diabetic adults.</p>
			</sec>
			<sec sec-type="methods">
				<title>METHODS</title>
				<sec>
					<title>Study design and population</title>
					<p>We conducted an analytical and cross-sectional study, based on an international database registered in the Dryad repository (https://datadryad.org). The research was triggered after reading the article called “Mathematical modeling for the prediction of cerebral white matter lesions based on clinical examination data”, (<xref ref-type="bibr" rid="B28">8</xref>) which refers to the prediction of white matter lesions using routine medical examinations and complex mathematical algorithms. (<xref ref-type="bibr" rid="B29">9</xref>)</p>
					<p>From a total population of 1904 adults, 1775 were intentionally selected. Patients taking antidiabetic medications and those with baseline blood glucose levels ≥ 126 mg/dL were excluded.</p>
					<p>Baseline determinations included systolic blood pressure (SBP), diastolic blood pressure (DBP), baseline blood glucose levels in mg/dL and glycated hemoglobin (HbA1c). </p>
					<p>During the carotid ultrasound procedure, the presence of plaques in the common carotid artery, its bifurcation and, the internal carotid artery was evaluated. Carotid intima-media thickness was measured.</p>
					<p>The dependent variable in the CHAID decision tree was the carotid plaque score, assessed by ultrasound and dichotomized into values &gt; 1.20 mm and ≤ 1.20 mm. (<xref ref-type="bibr" rid="B30">10</xref>) The carotid plaque score is a measurement used to assess the presence and severity of atherosclerotic plaques in the carotid arteries. This score is obtained by carotid artery ultrasound, which measures the maximum carotid plaque thickness. A cut-off value of &gt;1.20 mm is associated with increased cardiovascular risk. </p>
					<p/>
				</sec>
				<sec>
					<title>Statistical analysis</title>
					<p>We used the CHAID decision tree technique, based on a chi-square measurement metric, to segment the data into homogeneous groups and construct a predictive model. This method identifies patterns in the relationship between a dependent variable and multiple independent variables, using both quantitative and categorical data. (<xref ref-type="bibr" rid="B31">11</xref>)</p>
					<p>CHAID iteratively divides the sample into nodes, creating branches based on the categories of the explanatory variables. In each branch the most significant cut-off points are selected according to the chi-square test, generating mutually exclusive subgroups (<xref ref-type="bibr" rid="B32">12</xref>) For example, in the case of the carotid plaque score the nodes represent differentiated categories according to their association with the variable of interest.</p>
					<p>Finally, the terminal node with the strongest strength of association with the root node was selected, which was the presence or absence of carotid plaques &gt; 1.20 mm.</p>
					<p>Diagnostic tests were performed to estimate the odds ratio (OR), degree of association (Phi coefficient), sensitivity (S), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), and likelihood ratio (LR). All the statistical calculations were performed using SPSS Statistics 25.0® software package. <xref ref-type="bibr" rid="B33"><sup>13</sup></xref>
					</p>
				</sec>
				<sec>
					<title>Ethical considerations</title>
					<p>The database was voluntarily uploaded to Dryad (<ext-link ext-link-type="uri" xlink:href="https://datadryad.org/">https://datadryad.org/</ext-link>) under Creative Commons license (CC0). (<xref ref-type="bibr" rid="B34">14</xref>) The study was conducted following the recommendations of the Declaration of Helsinki. The database is available at: <ext-link ext-link-type="uri" xlink:href="https://datadryad.org/stash/dataset/doi:10.5061/dryad.73bh2q8">https://datadryad.org/stash/dataset/doi:10.5061/dryad.73bh2q8 </ext-link>
					</p>
				</sec>
			</sec>
			<sec sec-type="results">
				<title>RESULTS</title>
				<p>The characteristics of the selected population are described in <xref ref-type="table" rid="t5">Table 1</xref>.</p>
				<p>
					<table-wrap id="t5">
						<label>Table 1</label>
						<caption>
							<title>Baseline characteristics of the population (n=1775)</title>
						</caption>
						<table frame="hsides" rules="groups">
							<colgroup>
								<col/>
								<col/>
							</colgroup>
							<thead>
								<tr style="border: 0; background-color:#ab0534;color:#ffffff;">
									<th align="left">Variable </th>
									<th align="left"> </th>
								</tr>
							</thead>
							<tbody>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">Male sex, n (%) </td>
									<td align="center">891 (50.2%) </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left" colspan="2">Age group, n (%) </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">18-39 years </td>
									<td align="center">164 (9.2%) </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">40-59 years </td>
									<td align="center">838 (47.2%) </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">≥ 60 years </td>
									<td align="center">773 (43.5%) </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left" colspan="2">Carotid plaque score, n (%) </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">High risk (&gt; 1.2 mm) </td>
									<td align="center">565 (31.8%) </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">No risk </td>
									<td align="center">1210 (68.2%) </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">Carotid plaque score, median (IQR) </td>
									<td align="center">0.40 (0.10-1.00) </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">Blood glucose levels (mg/dL), mean ± SD </td>
									<td align="center">99.86 ± 8.98 </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">HbA1c (%), mean ± SD </td>
									<td align="center">5.61± 0.38 </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">BMI (kg/m2), mean ± SD </td>
									<td align="center">22.91± 3.28 </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">SBP (mm Hg), mean ± SD </td>
									<td align="center">123.14± 18.29 </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">DBP (mm Hg), mean ± SD </td>
									<td align="center">73.74± 12.17 </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">Number of plaques, median (IQR) </td>
									<td align="center">0 (0-1) </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">TG (mg/dL), mean ± SD </td>
									<td align="center">111.82 ± 69.55 </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">HDL (mg/L), mean ± SD </td>
									<td align="center">61.12 ± 15.39 </td>
								</tr>
							</tbody>
						</table>
						<table-wrap-foot>
							<fn id="TFN5">
								<p>BMI: body mass index; DBP: diastolic blood pressure; HbA1c: glycated hemoglobin; HDL: high-density lipoprotein; IQR: interquartile range; SD: standard deviation; SBP: systolic blood pressure; TG: triglycerides </p>
							</fn>
						</table-wrap-foot>
					</table-wrap>
				</p>
				<p>Of the 1775 patients, 565 (31.8%) had high-risk carotid plaque score. These patients had significantly higher number of plaques, higher HbA<sub>1c</sub> and baseline blood glucose levels, and higher systolic and diastolic blood pressure values compared with those without high-risk carotid plaque score (p &lt; 0.001). However, there were no significant differences in BMI between both groups (p = 0.071). This highlights the association between high-risk carotid plaque score and elevated metabolic and cardiovascular markers (<xref ref-type="table" rid="t6">Table 2</xref>).</p>
				<p>
					<table-wrap id="t6">
						<label>Table 2</label>
						<caption>
							<title>Biological markers in patients with high-risk carotid plaque score </title>
						</caption>
						<table frame="hsides" rules="groups">
							<colgroup>
								<col/>
								<col/>
								<col/>
								<col/>
							</colgroup>
							<thead>
								<tr style="border: 0; background-color:#ab0534;color:#ffffff;">
									<th align="left"> </th>
									<th align="left">High risk score (n=565) </th>
									<th align="left">No pathological risk score (n=1210) </th>
									<th align="left">p </th>
								</tr>
							</thead>
							<tbody>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">Number of plaques, median (IQR) </td>
									<td align="center">2 (0.50-2.50) </td>
									<td align="center">0.35 (0.10-1.12) </td>
									<td align="center">&lt;0.001 </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">HbA1c (%), mean ± SD </td>
									<td align="center">5.67 ± 0.30 </td>
									<td align="center">5.58 ± 0.29 </td>
									<td align="center">&lt;0.001 </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">Blood glucose levels (mg/dL), mean ± SD </td>
									<td align="center">101.33 ± 9.02 </td>
									<td align="center">99.22 ± 8.23 </td>
									<td align="center">&lt;0.001 </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">SBP (mm Hg), mean ± SD </td>
									<td align="center">128.64 ± 18.34 </td>
									<td align="center">120.76 ± 17.72 </td>
									<td align="center">&lt;0.001 </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">DBP (mm Hg), mean ± SD </td>
									<td align="center">75.49 ±12.19 </td>
									<td align="center">72.98 ± 12.18 </td>
									<td align="center">&lt;0.001 </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">BMI (kg/m2), mean ± SD </td>
									<td align="center">23.13 ± 3.18 </td>
									<td align="center">22.82 ± 3.26 </td>
									<td align="center">0.0710 </td>
								</tr>
							</tbody>
						</table>
						<table-wrap-foot>
							<fn id="TFN6">
								<p>BMI: body mass index; DBP: diastolic blood pressure; HbA1c: glycated hemoglobin; IQR: interquartile range; SBP: systolic blood pressure; SD: standard deviation </p>
							</fn>
						</table-wrap-foot>
					</table-wrap>
				</p>
				<p>The decision tree with the carotid plaque score as the dependent variable included 2 depth levels and a total of 8 nodes of which 5 were terminal nodes. The tree classified baseline blood glucose levels &gt; 104 mg/dL along with HbA1c values &gt; 6%. as the most decisive variables associated with the presence of high-risk carotid plaques. The main characteristic associated with the absence of high-risk carotid plaque score was the presence of HbA1c values ≤ 5.30% (<xref ref-type="fig" rid="f2">Figure 1</xref>).</p>
				<p>
					<fig id="f2">
						<label>Figure 1</label>
						<caption>
							<title>CHAID decision tree for glycemic profile associated with carotid plaques in non-diabetic adults</title>
						</caption>
						<graphic xlink:href="1850-3748-rac-93-01-50-gf2.jpg"/>
					</fig>
				</p>
				<p>The association between the presence of baseline blood glucose levels &gt;104 mg/dL and HbA1c &gt;6 % (node 7 of the CHAID decision tree) with carotid plaque score &gt;1.2 mm was moderate (Phi = 0.182). The odds of presenting with high-risk carotid plaque score was 3.69 times higher for patients with elevated blood glucose levels and HbA1c when compared to those with lower blood glucose levels and HbA1c. The sensitivity was low (15%), the specificity high (95%), and the positive and negative predictive values were 61%, and 71%, respectively. The likelihood ratio indicates that patients with blood glucose levels &gt; 104 mg/dL and HbA1c &gt; 6% exhibited a 3.29 times greater probability of a true positive result for high-risk carotid plaque score in comparison to a false positive result (<xref ref-type="table" rid="t7">Table 3</xref>).</p>
				<p>
					<table-wrap id="t7">
						<label>Table 3</label>
						<caption>
							<title>Measurements of association between node 7 of the CHAID decision tree and the presence of carotid plaques detected by ultrasound in non-diabetic adults </title>
						</caption>
						<table frame="hsides" rules="groups">
							<colgroup>
								<col/>
								<col/>
							</colgroup>
							<tbody>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">Phi </td>
									<td align="center">0.182 </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">OR (95% CI) </td>
									<td align="center">3.69 (2.59-5.28) </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">S </td>
									<td align="center">15% </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">Sp </td>
									<td align="center">95% </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">PPV </td>
									<td align="center">61% </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">NPV </td>
									<td align="center">71% </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">LR+ (95% CI) </td>
									<td align="center">3.29 (2.38-4.54) </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">LR- (95% CI) </td>
									<td align="center">0.89 (0.85-0.93) </td>
								</tr>
							</tbody>
						</table>
						<table-wrap-foot>
							<fn id="TFN7">
								<p>LR: likelihood ratio; NPV: negative predictive value; OR: odds ratio; Phi: Phi coefficient; PPV: positive predictive value; S: sensitivity; Sp: specificity</p>
							</fn>
						</table-wrap-foot>
					</table-wrap>
				</p>
				<p>Patients with the predominant feature of node 7 of the CHAID decision tree (blood glucose levels &gt; 104 mg/dL and HbA1c &gt; 6%), had a median carotid plaque score of 1.60, while patients in the other nodes had a median carotid plaque score of 0.25 (p = 0.001). In addition, the median number plaques in patients in node 7 was 1.20, while the median number plaques in patients in the other nodes was 0.30 (p &lt; 0.001) (<xref ref-type="table" rid="t8">Table 4</xref>).</p>
				<p>
					<table-wrap id="t8">
						<label>Table 4</label>
						<caption>
							<title>Comparison of mean anthropometric, hemodynamic and laboratory tests values according to the presence of baseline blood glucose levels &gt;104 mg/dL and HbA1c &gt;6% in non-diabetic adults (node 7 of the CHAID decision tree).</title>
						</caption>
						<table frame="hsides" rules="groups">
							<colgroup>
								<col/>
								<col/>
								<col/>
								<col/>
							</colgroup>
							<thead>
								<tr style="border: 0; background-color:#ab0534;color:#ffffff;">
									<th align="left">Variable </th>
									<th align="center">Node 7 (n=142) </th>
									<th align="center">Nodes 1 to 6 (n= 1633) </th>
									<th align="center">p </th>
								</tr>
							</thead>
							<tbody>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">Carotid plaque score, median (IQR) </td>
									<td align="center">1.60 (0.50- 3.70) </td>
									<td align="center">0.25 (0.10-0.50) </td>
									<td align="center">0.001 </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">Number of plaques, median (IQR) </td>
									<td align="center">1.20 (0.80-1.55) </td>
									<td align="center">0.30 (0.10-0.50) </td>
									<td align="center">&lt; 0.001 </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">BMI (kg/m2), mean ± SD </td>
									<td align="center">24.36 ± 4.18 </td>
									<td align="center">22.85 ± 3.17 </td>
									<td align="center">&lt; 0.001 </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">SBP (mm Hg), mean ± SD </td>
									<td align="center">127.34 ± 18.64 </td>
									<td align="center">123.03 ± 18.22 </td>
									<td align="center">0.007 </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">DBP (mm Hg), mean ± SD </td>
									<td align="center">73.65 ± 11.85 </td>
									<td align="center">73.70 ±12.20 </td>
									<td align="center">0.962 </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">HbA1c (%), mean ± SD </td>
									<td align="center">6.46 ± 0.45 </td>
									<td align="center">5.59 ± 0.28 </td>
									<td align="center">&lt;0.001 </td>
								</tr>
								<tr style="border-bottom: 2px solid white; background-color: #e3aea9;">
									<td align="left">Blood glucose levels (mg/dL), mean ± SD </td>
									<td align="center">114.58 ± 5.86 </td>
									<td align="center">99.21 ± 8.11 </td>
									<td align="center">&lt;0.001 </td>
								</tr>
							</tbody>
						</table>
						<table-wrap-foot>
							<fn id="TFN8">
								<p>BMI: body mass index; DBP: diastolic blood pressure; HbA<sub>1c</sub>: glycated hemoglobin; IQR: interquartile range; SBP: systolic blood pressure; SD: standard deviation</p>
							</fn>
						</table-wrap-foot>
					</table-wrap>
				</p>
			</sec>
			<sec sec-type="discussion">
				<title>DISCUSSION</title>
				<p>The CHAID decision tree classified and divided the continuous variables HbA<sub>1c</sub> and baseline glucose levels into segments. Baseline blood glucose levels &gt;104 mg/dL and HbA<sub>1c</sub> &gt;6% were associated with the presence of high-risk carotid plaque score, consistent with prediabetes. This was confirmed with diagnostic tests which demonstrated a significant influence on carotid intima-media thickness, similar to the findings by Zhou et al. (<xref ref-type="bibr" rid="B35">15</xref>) The association between HbA1c and carotid plaques has been observed in patients with chronic diseases, as in the studies by Dodos et al. (<xref ref-type="bibr" rid="B36">16</xref>) and Cheng et al. (<xref ref-type="bibr" rid="B37">17</xref>) </p>
				<p>Elevated HbA1c promotes carotid artery atherosclerosis through mechanisms such as the formation of advanced glycation end-products, oxidative stress and endothelial dysfunction. (<xref ref-type="bibr" rid="B38">18</xref>,<xref ref-type="bibr" rid="B39">19</xref>,<xref ref-type="bibr" rid="B40">20</xref>) These mechanisms may have an impact on non-diabetic adults who exhibit HbA<sub>1c</sub> and baseline blood glucose values indicative of prediabetes. suggesting that carotid plaque formation may be a prodromal sign.</p>
				<p>The decision tree also identified that HbA1c ≤ 5.30% is associated with absence of high-risk carotid plaques. While these findings suggest an association between HbA1c and the presence of high-risk carotid plaques, further studies and additional evidence are needed to make a strong recommendation for the adoption of HbA1c targets or the routine measurement of HbA1c to prevent cerebrovascular events related to carotid atherosclerosis.</p>
				<p>The limitations of this study include the sample size (n=1775), which may not be representative of larger populations, and potential reporting biases due to the retrospective nature of the data collection. In addition, information on other key risk factors, such as dietary habits, physical activity, and family history, which could influence glycemia and atherosclerosis, is lacking. The absence of longitudinal follow-up precludes the establishment of a definitive causal relationship, and the study does not consider individual variability or the use of multiple biomarkers, which could provide a more complete assessment of cardiovascular risk.</p>
			</sec>
			<sec sec-type="conclusions">
				<title>CONCLUSIONS</title>
				<p>Baseline blood glucose levels &gt;104 mg/dL and HbA1c &gt;6% were associated with the presence of high-risk carotid plaques in non-diabetic adults. Despite the CHAID decision tree excluded triglycerides and HDL as relevant factors, and the observation that baseline blood glucose levels &gt;104 mg/dL and HbA1c levels &gt;6% were associated with a higher prevalence of high-risk carotid plaques in nondiabetic adults, the absence of data on additional potential risk factors, such as total cholesterol or genetics, hinders the interpretation of these findings. Nevertheless, the routine assessment of HbA1c in nondiabetic adults could be useful in identifying the risk of carotid atherosclerosis, though further research is necessary to ascertain the validity of this association and its implications for disease prevention.</p>
			</sec>
		</body>
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