作者
Omar Yaxmehen Bello-Chavolla, Jessica Paola Bahena-López, Arsenio Vargas-Vázquez, Neftali Eduardo Antonio-Villa, Alejandro Márquez-Salinas, Carlos A Fermín-Martínez, Rosalba Rojas, Roopa Mehta, Ivette Cruz-Bautista, Sergio Hernández-Jiménez, Ana Cristina García-Ulloa, Paloma Almeda-Valdes, Carlos Alberto Aguilar-Salinas, Metabolic Syndrome Study Group
发表日期
2020/7/1
期刊
BMJ Open Diabetes Research and Care
卷号
8
期号
1
页码范围
e001550
出版商
BMJ Specialist Journals
简介
Introduction
Previous reports in European populations demonstrated the existence of five data-driven adult-onset diabetes subgroups. Here, we use self-normalizing neural networks (SNNN) to improve reproducibility of these data-driven diabetes subgroups in Mexican cohorts to extend its application to more diverse settings.
Research design and methods
We trained SNNN and compared it with k-means clustering to classify diabetes subgroups in a multiethnic and representative population-based National Health and Nutrition Examination Survey (NHANES) datasets with all available measures (training sample: NHANES-III, n=1132; validation sample: NHANES 1999–2006, n=626). SNNN models were then applied to four Mexican cohorts (SIGMA-UIEM, n=1521; Metabolic Syndrome cohort, n=6144; ENSANUT 2016, n=614 and CAIPaDi, n=1608) to characterize diabetes subgroups in Mexicans according to …
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OY Bello-Chavolla, JP Bahena-López… - BMJ Open Diabetes Research and Care, 2020