title: Development of the diabetes typology model for discerning Type 2 diabetes mellitus with national survey data.
abstract: OBJECTIVE
  To classify individuals with diabetes mellitus (DM) into DM subtypes using population-based studies.
  
  
  METHODS
  Population-based survey.
  
  
  METHODS
  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).
  
  
  METHODS
  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.
  
  
  METHODS
  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.
authors.affiliation: Division of Intramural Research, National Institute on Minority Health and Health Disparities, Bethesda, Maryland, United States of America.
authors.forename: Anna
authors.initials: A
authors.lastname: Bellatorre
authors.affiliation: Division of Intramural Research, National Institute on Minority Health and Health Disparities, Bethesda, Maryland, United States of America.
authors.forename: Sharon H
authors.initials: SH
authors.lastname: Jackson
authors.affiliation: Division of Intramural Research, National Institute on Minority Health and Health Disparities, Bethesda, Maryland, United States of America.
authors.forename: Kelvin
authors.identifier: http://orcid.org/0000-0002-6753-2883
authors.initials: K
authors.lastname: Choi
mesh_terms: D003924:Diabetes Mellitus, Type 2 / Q000473:pathology*; D006801:Humans; D008962:Models, Theoretical; D011795:Surveys and Questionnaires