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  1. Artikel: Gestational Diabetes Mellitus Subtypes Classified by Oral Glucose Tolerance Test and Maternal and Perinatal Outcomes: Results of a Mexican Multicenter Prospective Cohort Study "Cuido Mi Embarazo".

    Ortega-Montiel, Janinne / Martinez-Juarez, Luis A / Montoya, Alejandra / Morales-Juárez, Linda / Gallardo-Rincón, Héctor / Galicia-Hernández, Victoria / Garcia-Cerde, Rodrigo / Ríos-Blancas, María Jesus / Álvarez-Hernández, Diego-Abelardo / Lomelin-Gascon, Julieta / Martínez-Silva, Gisela / Illescas-Correa, Lucía M / Diaz Martinez, Daniel A / Magos Vázquez, Francisco Javier / Vargas Ávila, Edwin / Carmona-Ramos, Ma Concepción / Mújica-Rosales, Ricardo / Reyes-Muñoz, Enrique / Tapia-Conyer, Roberto

    Diabetes, metabolic syndrome and obesity : targets and therapy

    2024  Band 17, Seite(n) 1491–1502

    Abstract: Purpose: This study explores the impact of gestational diabetes mellitus (GDM) subtypes classified by oral glucose tolerance test (OGTT) values on maternal and perinatal outcomes.: Patients and methods: This multicenter prospective cohort study (May ... ...

    Abstract Purpose: This study explores the impact of gestational diabetes mellitus (GDM) subtypes classified by oral glucose tolerance test (OGTT) values on maternal and perinatal outcomes.
    Patients and methods: This multicenter prospective cohort study (May 2019-December 2022) included participants from the Mexican multicenter cohort study
    Results: Of 2,056 Mexican pregnant women in the CME cohort, 294 (14.3%) had GDM; 53.7%, 34.4%, and 11.9% were classified as GDM-Sensitivity, GDM-Secretion, and GDM-Mixed subtypes, respectively. Women with GDM were older (p = 0.0001) and more often multiparous (p = 0.119) vs without GDM. Cesarean delivery (63.3%; p = 0.02) and neonate LGA (10.7%; p = 0.078) were higher in the GDM-Mixed group than the overall GDM group (55.6% and 8.4%, respectively). Pre-term birth was more common in the GDM-Sensitivity group than in the overall GDM group (10.2% vs 8.5%, respectively; p=0.022). At 6 months postpartum, prediabetes was more frequent in the GDM-Sensitivity group than in the overall GDM group (31.6% vs 25.5%). Type 2 diabetes was more common in the GDM-Mixed group than in the overall GDM group (10.0% vs 3.3%).
    Conclusion: GDM subtypes effectively stratified maternal and perinatal risks. GDM-Mixed subtype increased the risk of cesarean delivery, LGA, and type 2 diabetes postpartum. GDM subtypes may help personalize clinical interventions and optimize maternal and perinatal outcomes.
    Sprache Englisch
    Erscheinungsdatum 2024-03-26
    Erscheinungsland New Zealand
    Dokumenttyp Journal Article
    ZDB-ID 2494854-8
    ISSN 1178-7007
    ISSN 1178-7007
    DOI 10.2147/DMSO.S450939
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: MIDO GDM: an innovative artificial intelligence-based prediction model for the development of gestational diabetes in Mexican women.

    Gallardo-Rincón, Héctor / Ríos-Blancas, María Jesús / Ortega-Montiel, Janinne / Montoya, Alejandra / Martinez-Juarez, Luis Alberto / Lomelín-Gascón, Julieta / Saucedo-Martínez, Rodrigo / Mújica-Rosales, Ricardo / Galicia-Hernández, Victoria / Morales-Juárez, Linda / Illescas-Correa, Lucía Marcela / Ruiz-Cabrera, Ixel Lorena / Díaz-Martínez, Daniel Alberto / Magos-Vázquez, Francisco Javier / Ávila, Edwin Oswaldo Vargas / Benitez-Herrera, Alejandro Efraín / Reyes-Gómez, Diana / Carmona-Ramos, María Concepción / Hernández-González, Laura /
    Romero-Islas, Oscar / Muñoz, Enrique Reyes / Tapia-Conyer, Roberto

    Scientific reports

    2023  Band 13, Heft 1, Seite(n) 6992

    Abstract: Given the barriers to early detection of gestational diabetes mellitus (GDM), this study aimed to develop an artificial intelligence (AI)-based prediction model for GDM in pregnant Mexican women. Data were retrieved from 1709 pregnant women who ... ...

    Abstract Given the barriers to early detection of gestational diabetes mellitus (GDM), this study aimed to develop an artificial intelligence (AI)-based prediction model for GDM in pregnant Mexican women. Data were retrieved from 1709 pregnant women who participated in the multicenter prospective cohort study 'Cuido mi embarazo'. A machine-learning-driven method was used to select the best predictive variables for GDM risk: age, family history of type 2 diabetes, previous diagnosis of hypertension, pregestational body mass index, gestational week, parity, birth weight of last child, and random capillary glucose. An artificial neural network approach was then used to build the model, which achieved a high level of accuracy (70.3%) and sensitivity (83.3%) for identifying women at high risk of developing GDM. This AI-based model will be applied throughout Mexico to improve the timing and quality of GDM interventions. Given the ease of obtaining the model variables, this model is expected to be clinically strategic, allowing prioritization of preventative treatment and promising a paradigm shift in prevention and primary healthcare during pregnancy. This AI model uses variables that are easily collected to identify pregnant women at risk of developing GDM with a high level of accuracy and precision.
    Mesh-Begriff(e) Child ; Pregnancy ; Female ; Humans ; Infant, Newborn ; Diabetes, Gestational/diagnosis ; Prospective Studies ; Diabetes Mellitus, Type 2 ; Artificial Intelligence ; Mexico/epidemiology ; Risk Factors
    Sprache Englisch
    Erscheinungsdatum 2023-04-28
    Erscheinungsland England
    Dokumenttyp Multicenter Study ; Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-023-34126-7
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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