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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; ENSANUT 2016, n=614?and CAIPaDi, n=1608) to characterize diabetes subgroups in Mexicans according to treatment response, risk for chronic complications and risk factors for the incidence of each subgroup. RESULTS:SNNN yielded four reproducible clinical profiles (obesity related, insulin deficient, insulin resistant, age related) in NHANES and Mexican cohorts even without C-peptide measurements. We observed in a population-based survey a high prevalence of the insulin-deficient form (41.25%, 95%?CI 41.02% to 41.48%), followed by obesity-related (33.60%, 95%?CI 33.40% to 33.79%), age-related (14.72%, 95%?CI 14.63% to 14.82%) and severe insulin-resistant groups. A significant association was found between the SLC16A11 diabetes risk variant and the obesity-related subgroup (OR 1.42, 95%?CI 1.10 to 1.83, p=0.008). Among incident cases, we observed a greater incidence of mild obesity-related diabetes (n=149, 45.0%). In a diabetes outpatient clinic cohort, we observed increased 1-year risk (HR 1.59, 95%?CI 1.01 to 2.51) and 2-year risk (HR 1.94, 95%?CI 1.13 to 3.31) for incident retinopathy in the insulin-deficient group and decreased 2-year diabetic retinopathy risk for the obesity-related subgroup (HR 0.49, 95%?CI 0.27 to 0.89). CONCLUSIONS:Diabetes subgroup phenotypes are reproducible using SNNN; our algorithm is available as web-based tool. Application of these models allowed for better characterization of diabetes subgroups and risk factors in Mexicans that could have clinical applications.

SUBMITTER: Bello-Chavolla OY

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

REPOSITORIES: biostudies-literature

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

Bello-Chavolla Omar Yaxmehen OY Bahena-López Jessica Paola JP Vargas-Vázquez Arsenio A Antonio-Villa Neftali Eduardo NE Márquez-Salinas Alejandro A Fermín-Martínez Carlos A CA Rojas Rosalba R Mehta Roopa R Cruz-Bautista Ivette I Hernández-Jiménez Sergio S García-Ulloa Ana Cristina AC Almeda-Valdes Paloma P Aguilar-Salinas Carlos Alberto CA

BMJ open diabetes research & care 20200701 1

<h4>Introduction</h4>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.<h4>Research design and methods</h4>We trained SNNN and compared it with k-means clustering to classify diabetes subgroups in a multiethnic and representative population ...[more]

PMID: 32699108

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

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

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