Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach.

ABSTRACT:

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; ENSAN

SUBMITTER: Bello-Chavolla OY 

PROVIDER: S-EPMC7380860 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature