Cargando…

Development of the diabetes typology model for discerning Type 2 diabetes mellitus with national survey data

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 Art...

Descripción completa

Detalles Bibliográficos
Autores principales: Bellatorre, Anna, Jackson, Sharon H., Choi, Kelvin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5333874/
https://www.ncbi.nlm.nih.gov/pubmed/28253317
http://dx.doi.org/10.1371/journal.pone.0173103
_version_ 1782511787849023488
author Bellatorre, Anna
Jackson, Sharon H.
Choi, Kelvin
author_facet Bellatorre, Anna
Jackson, Sharon H.
Choi, Kelvin
author_sort Bellatorre, Anna
collection PubMed
description 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.
format Online
Article
Text
id pubmed-5333874
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-53338742017-03-10 Development of the diabetes typology model for discerning Type 2 diabetes mellitus with national survey data Bellatorre, Anna Jackson, Sharon H. Choi, Kelvin PLoS One Research Article 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. Public Library of Science 2017-03-02 /pmc/articles/PMC5333874/ /pubmed/28253317 http://dx.doi.org/10.1371/journal.pone.0173103 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication.
spellingShingle Research Article
Bellatorre, Anna
Jackson, Sharon H.
Choi, Kelvin
Development of the diabetes typology model for discerning Type 2 diabetes mellitus with national survey data
title Development of the diabetes typology model for discerning Type 2 diabetes mellitus with national survey data
title_full Development of the diabetes typology model for discerning Type 2 diabetes mellitus with national survey data
title_fullStr Development of the diabetes typology model for discerning Type 2 diabetes mellitus with national survey data
title_full_unstemmed Development of the diabetes typology model for discerning Type 2 diabetes mellitus with national survey data
title_short Development of the diabetes typology model for discerning Type 2 diabetes mellitus with national survey data
title_sort development of the diabetes typology model for discerning type 2 diabetes mellitus with national survey data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5333874/
https://www.ncbi.nlm.nih.gov/pubmed/28253317
http://dx.doi.org/10.1371/journal.pone.0173103
work_keys_str_mv AT bellatorreanna developmentofthediabetestypologymodelfordiscerningtype2diabetesmellituswithnationalsurveydata
AT jacksonsharonh developmentofthediabetestypologymodelfordiscerningtype2diabetesmellituswithnationalsurveydata
AT choikelvin developmentofthediabetestypologymodelfordiscerningtype2diabetesmellituswithnationalsurveydata