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Trends in prevalence of diabetes subgroups in U.S. adults: A data-driven cluster analysis spanning three decades from NHANES (1988-2018)

AIMS: Data-driven diabetes subgroups have been proposed as an alternative to address diabetes heterogeneity; changes in trends for these subgroups have not previously been reported. Here, we analyzed trends of diabetes subgroups, stratified by sex, race, education level, and age categories in the U.S. METHODS: We used data from consecutive NHANES cycles spanning the 1988-2018 period. Diabetes subgroups (mild obesity-related [MOD], severe-insulin deficient [SIID], severe-insulin resistant [SIRD], and age-related diabetes [MARD]) were classified using self-normalizing neural networks. SAID was assessed for NHANES-III cycles. Prevalence was estimated using examination sample weights considering 2-year cycles (biannual change [B.C.]) to evaluate trends. RESULTS: Diabetes prevalence in the US 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<0.001). Non-Hispanic Blacks had the highest prevalence. Overall, MOD, MARD, and SIID had the highest increase during the studied period. Non-Hispanic Blacks had a sharp increase in MARD and SIID, Mexican Americans in SIID, and non-Hispanic Whites in MARD. Males, subjects with primary school/no-education, and adults aged 40-64 years had the highest increase in MOD prevalence. Trends in diabetes subgroups sustained after stratification by body-mass index categories. CONCLUSIONS: The prevalence of diabetes and its data-driven subgroups in the U.S. has increased from 1988-2018. These trends were different across sex, ethnicities, education, and age categories, indicating significant heterogeneity in diabetes within the U.S. Sex-specific factors, population aging, and socioeconomic aspects, together with obesity prevalence increase, could be implicated in the uprising trends of diabetes in the U.S.