Background: Diabetes subgroups are a promising framework for characterizing diabetes heterogeneity but data on the epidemiology and sociodemographic profile of these subgroups in the Mexican population are sparse.
Methods: We conducted a serial cross-sectional analysis of the National Health and Nutrition Survey in Mexico (2016-2021). Diabetes subgroups (obesity related [MOD], severe-insulin deficient [SIID], severe-insulin resistant [SIRD], and age-related [MARD]) were classified using self-normalizing neural networks using HbA1c, time since diabetes diagnosis, HOMA2-IR, HOMA2-B, and BMI. Sociodemographic inequalities were proxied with the social lag index, which captures state-level social disadvantage.
Results: Diabetes prevalence in Mexico increased from 13.3% (95%CI 11.5-15.1) in 2016 to 15.8% (95%CI 13.6-17.9) in 2021, with the highest increases in the Northern-Pacific, Central-Pacific, and Center regions. SIDD was the most prevalent diabetes subgroup in Mexico, followed by MOD, MARD, and SIRD. Increases in diabetes prevalence were primarily attributed to MOD and SIRD. SIID had higher aggregation in states with higher social lag (Fig. 1).
Conclusion: Diabetes subgroups in the Mexican population display geographic and sociodemographic differences, which may help identify populations at higher risk of health disparities and adverse outcomes.
N.Antonio villa: None. A.Caballero: None. J.A.Seiglie: Consultant; BDMT Global, Techspert Expert Network. O.Bello-chavolla: None. D.Ramírez-garcía: None. C.Fermin: None. A.Vargas-vázquez: None. M.R.Basile-alvarez: None. A.Nuñez: None. C.D.Paz cabrera: None. L.Fernández chirino: None.
