Prevalence Trends of Diabetes Subgroups in the United States: A Data-driven Analysis Spanning Three Decades from NHANES (1988-2018)

Abstract

Context: Data-driven diabetes subgroups were proposed as an alternative to address diabetes heterogeneity. However, changes in trends for these subgroups have not been reported. Objective: Here, we analyzed trends of diabetes subgroups, stratified by sex, race, education level, age categories, and time since diabetes diagnosis in the United States. Methods: We used data from consecutive NHANES cycles spanning the 1988-2018 period. Diabetes subgroups (mild obesity-related [MOD], severe insulin-deficient [SIDD], severe insulin-resistant [SIRD], and mild age-related diabetes [MARD]) were classified using validated self-normalizing neural networks. Severe autoimmune diabetes (SAID) was assessed for NHANES-III. Prevalence was estimated using examination sample weights considering bicyclic changes (BCs) to evaluate trends and changes over time. Results: Diabetes prevalence in the United States increased from 7.5% (95% CI 7.1-7.9) in 1988-1989 to 13.9% (95% CI 13.4-14.4) in 2016-2018 (BC 1.09%, 95% CI 0.98-1.31, P