Título
Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach
Autor
OMAR YAXMEHEN BELLO CHAVOLLA
JESSICA PAOLA BAHENA LOPEZ
ARSENIO VARGAS VAZQUEZ
Neftali Eduardo Antonio Villa
Alejandro Márquez Salinas
Carlos Alberto Fermín Martínez
MARIA ROSALBA ROJAS MARTINEZ
ROOPA PRAVIN MEHTA
IVETTE CRUZ BAUTISTA
MIGUEL SERGIO HERNANDEZ JIMENEZ
ANA CRISTINA GARCIA ULLOA
PALOMA ALMEDA VALDES
CARLOS ALBERTO AGUILAR SALINAS
Nivel de Acceso
Acceso Abierto
Materias
MEDICINA Y CIENCIAS DE LA SALUD - (CTI) Ciencias médicas - ([BMJ Open Diabetes Research and Care (2052-4897) Vol. 8 (2020)]) Ciencias clínicas - ([BMJ Open Diabetes Research and Care (2052-4897) Vol. 8 (2020)]) Geriatría - ([BMJ Open Diabetes Research and Care (2052-4897) Vol. 8 (2020)]) Diabetes - ([BMJ Open Diabetes Research and Care (2052-4897) Vol. 8 (2020)]) Diabetes mellitus - ([BMJ Open Diabetes Research and Care (2052-4897) Vol. 8 (2020)]) Obesidad - ([BMJ Open Diabetes Research and Care (2052-4897) Vol. 8 (2020)]) Obesity - ([BMJ Open Diabetes Research and Care (2052-4897) Vol. 8 (2020)]) Encuesta de Salud Nacional y Examen de Nutrición - ([BMJ Open Diabetes Research and Care (2052-4897) Vol. 8 (2020)]) National Health and Nutrition Examination Survey - ([BMJ Open Diabetes Research and Care (2052-4897) Vol. 8 (2020)]) México - ([BMJ Open Diabetes Research and Care (2052-4897) Vol. 8 (2020)]) Mexico - ([BMJ Open Diabetes Research and Care (2052-4897) Vol. 8 (2020)])
Resumen o descripción
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.
Editor
BMJ Publishing Group
Fecha de publicación
2020
Tipo de publicación
Artículo
Recurso de información
Formato
Adobe PDF
application/pdf
Fuente
BMJ Open Diabetes Research and Care (2052-4897) Vol. 8 (2020)
Idioma
Inglés
Relación
https://drc.bmj.com/content/8/1/e001550.long
Repositorio Orígen
INSTITUTO NACIONAL DE GERIATRIA
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