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, Ana Cristina García-Ulloa, Paloma Almeda-Valdes, Carlos Alberto Aguilar-Salinas, Olimpia Arellano-Campos, Donaji V Gómez-Velasco, César Lam-Chung, Marco A Melgarejo-Hernandez, Alexandro J Martagón, Liliana Muñoz-Hernandez, Luz E Guillén, José de Jesús Garduño-García, Ulices Alvirde, Yukiko Ono-Yoshikawa, Ricardo Choza-Romero, Leobardo Sauque-Reyna, Ma Eugenia Garay-Sevilla, Juan M Malacara-Hernandez, María Teresa Tusié-Luna, Luis Miguel Gutierrez-Robledo, Francisco J Gómez-Pérez, Cristina García-Ulloa, Eder Patiño-Rivera, Denise Arcila-Martínez, Rodrigo Arizmendi-Rodríguez, Oswaldo Briseño-González, Humberto Del Valle-Ramírez, Arturo Flores-García, Fernanda Garnica-Carrillo, Eduardo González-Flores, Mariana Granados-Arcos, Héctor Infanzón-Talango, Victoria Landa-Anell, Claudia Lechuga-Fonseca, Arely López-Reyes, Marco Melgarejo-Hernández, Palacios-Vargas Angélica, Liliana Pérez-Peralta, Alberto Ramírez-García, David Rivera de la Parra, Sofía Ríos-Villavicencio, Francis Rojas-Torres, Marcela Ruiz-Cervantes, Sandra Sainos-Muñoz, Alejandra Sierra-Esquivel, Erendi Tinoco-Ventura, Luz Elena Urbina-Arronte, María Luisa Velasco-Pérez, Héctor Velázquez-Jurado, Andrea Villegas-Narváez, Verónica Zurita-Cortés, Enrique Graue-Hernández Aída Jiménez-Corona, and Carlos A. Aguilar-Salinas
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.