| Summary: | OBJECTIVE:To classify individuals with diabetes mellitus (DM) into DM subtypes using population-based studies. DESIGN:Population-based survey. SETTING:Individuals participated in 2003-2004, 2005-2006, or 2009-2010 the National Health and Nutrition Examination Survey (NHANES), and 2010 Coronary Artery Risk Development in Young Adults (CARDIA) survey (research materials obtained from the National Heart, Lung, and Blood Institute Biologic Specimen and Data Repository Information Coordinating Center). PARTICIPANTS:3084, 3040 and 3318 US adults from the 2003-2004, 2005-2006 and 2009-2010 NHANES samples respectively, and 5,115 US adults in the CARDIA cohort. PRIMARY OUTCOME MEASURES:We proposed the Diabetes Typology Model (DTM) through the use of six composite measures based on the Homeostatic Model Assessment (HOMA-IR, HOMA-%β, high HOMA-%S), insulin and glucose levels, and body mass index and conducted latent class analyses to empirically classify individuals into different classes. RESULTS:Three empirical latent classes consistently emerged across studies (entropy = 0.81-0.998). These three classes were likely Type 1 DM, likely Type 2 DM, and atypical DM. The classification has high sensitivity (75.5%), specificity (83.3%), and positive predictive value (97.4%) when validated against C-peptide level. Correlates of Type 2 DM were significantly associated with model-identified Type 2 DM. Compared to regression analysis on known correlates of Type 2 DM using all diabetes cases as outcomes, using DTM to remove likely Type 1 DM and atypical DM cases results in a 2.5-5.3% r-square improvement in the regression analysis, as well as model fits as indicated by significant improvement in -2 log likelihood (p<0.01). Lastly, model-defined likely Type 2 DM was significantly associated with known correlates of Type 2 DM (e.g., age, waist circumference), which provide additional validation of the DTM-defined classes. CONCLUSIONS:Our Diabetes Typology Model reflects a promising first step toward discerning likely DM types from population-based data. This novel tool will improve how large population-based studies can be used to examine behavioral and environmental factors associated with different types of DM.
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