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dc.contributor.author Zulueta, Mirella
dc.contributor.author Gallardo Rincón, Héctor
dc.contributor.author Martinez Juarez, Luis Alberto
dc.contributor.author Lomelin Gascon, Julieta
dc.contributor.author Ortega Montiel, Janinne
dc.contributor.author Montoya, Alejandra
dc.contributor.author Mendizabal, Leire
dc.contributor.author Arregi, Maddi
dc.contributor.author Martínez Martínez, María de los Angeles
dc.contributor.author Camarillo Romero, Eneida del Socorro
dc.contributor.author Mendieta Zerón, Hugo
dc.contributor.author Garduño García, José de Jesús
dc.contributor.author Simón, Laureano
dc.contributor.author Tapia Conyer, Roberto
dc.date.accessioned 2024-10-15T02:35:16Z
dc.date.available 2024-10-15T02:35:16Z
dc.date.issued 2023-04
dc.identifier.issn 2052-4897
dc.identifier.uri http://hdl.handle.net/20.500.11799/141346
dc.description.abstract Introduction: Gestational diabetes mellitus (GDM) is underdiagnosed in Mexico. Early GDM risk stratification through prediction modeling is expected to improve preventative care. We developed a GDM risk assessment model that integrates both genetic and clinical variables. Research design and methods: Data from pregnant Mexican women enrolled in the ‘Cuido mi Embarazo’ (CME) cohort were used for development (107 cases, 469 controls) and data from the ‘Mónica Pretelini Sáenz’ Maternal Perinatal Hospital (HMPMPS) cohort were used for external validation (32 cases, 199 controls). A 2-hour oral glucose tolerance test (OGTT) with 75 g glucose performed at 24–28 gestational weeks was used to diagnose GDM. A total of 114 single-nucleotide polymorphisms (SNPs) with reported predictive power were selected for evaluation. Blood samples collected during the OGTT were used for SNP analysis. The CME cohort was randomly divided into training (70% of the cohort) and testing datasets (30% of the cohort). The training dataset was divided into 10 groups, 9 to build the predictive model and 1 for validation. The model was further validated using the testing dataset and the HMPMPS cohort. Results: Nineteen attributes (14 SNPs and 5 clinical variables) were significantly associated with the outcome; 11 SNPs and 4 clinical variables were included in the GDM prediction regression model and applied to the training dataset. The algorithm was highly predictive, with an area under the curve (AUC) of 0.7507, 79% sensitivity, and 71% specificity and adequately powered to discriminate between cases and controls. On further validation, the training dataset and HMPMPS cohort had AUCs of 0.8256 and 0.8001, respectively. Conclusions: We developed a predictive model using both genetic and clinical factors to identify Mexican women at risk of developing GDM. These findings may contribute to a greater understanding of metabolic functions that underlie elevated GDM risk and support personalized patient recommendations. es
dc.description.sponsorship This study was funded by Patia Europe and by the Carlos Slim Foundation. es
dc.language.iso eng es
dc.publisher BMJ Open Diabetes Research Care es
dc.rights openAccess es
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0 es
dc.subject Genotype es
dc.subject Gestational diabetes es
dc.subject Prediction es
dc.subject Algorithm es
dc.subject.classification MEDICINA Y CIENCIAS DE LA SALUD es
dc.title Development and validation of a multivariable genotype-informed gestational diabetes prediction algorithm for clinical use in the Mexican population: insights into susceptibility mechanisms es
dc.type Artículo es
dc.provenance Científica es
dc.road Dorada es
dc.organismo Medicina es
dc.ambito Internacional es
dc.relation.vol 11
dc.relation.no 2
dc.validacion.itt No es


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  • Título
  • Development and validation of a multivariable genotype-informed gestational diabetes prediction algorithm for clinical use in the Mexican population: insights into susceptibility mechanisms
  • Autor
  • Zulueta, Mirella
  • Gallardo Rincón, Héctor
  • Martinez Juarez, Luis Alberto
  • Lomelin Gascon, Julieta
  • Ortega Montiel, Janinne
  • Montoya, Alejandra
  • Mendizabal, Leire
  • Arregi, Maddi
  • Martínez Martínez, María de los Angeles
  • Camarillo Romero, Eneida del Socorro
  • Mendieta Zerón, Hugo
  • Garduño García, José de Jesús
  • Simón, Laureano
  • Tapia Conyer, Roberto
  • Fecha de publicación
  • 2023-04
  • Editor
  • BMJ Open Diabetes Research Care
  • Tipo de documento
  • Artículo
  • Palabras clave
  • Genotype
  • Gestational diabetes
  • Prediction
  • Algorithm
  • Los documentos depositados en el Repositorio Institucional de la Universidad Autónoma del Estado de México se encuentran a disposición en Acceso Abierto bajo la licencia Creative Commons: Atribución-NoComercial-SinDerivar 4.0 Internacional (CC BY-NC-ND 4.0)

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