Cargando…
Dietary Information Improves Model Performance and Predictive Ability of a Noninvasive Type 2 Diabetes Risk Model
There is no diabetes risk model that includes dietary predictors in Asia. We sought to develop a diet-containing noninvasive diabetes risk model in Northern China and to evaluate whether dietary predictors can improve model performance and predictive ability. Cross-sectional data for 9,734 adults ag...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5112856/ https://www.ncbi.nlm.nih.gov/pubmed/27851788 http://dx.doi.org/10.1371/journal.pone.0166206 |
_version_ | 1782468089172983808 |
---|---|
author | Han, Tianshu Tian, Shuang Wang, Li Liang, Xi Cui, Hongli Du, Shanshan Na, Guanqiong Na, Lixin Sun, Changhao |
author_facet | Han, Tianshu Tian, Shuang Wang, Li Liang, Xi Cui, Hongli Du, Shanshan Na, Guanqiong Na, Lixin Sun, Changhao |
author_sort | Han, Tianshu |
collection | PubMed |
description | There is no diabetes risk model that includes dietary predictors in Asia. We sought to develop a diet-containing noninvasive diabetes risk model in Northern China and to evaluate whether dietary predictors can improve model performance and predictive ability. Cross-sectional data for 9,734 adults aged 20–74 years old were used as the derivation data, and results obtained for a cohort of 4,515 adults with 4.2 years of follow-up were used as the validation data. We used a logistic regression model to develop a diet-containing noninvasive risk model. Akaike’s information criterion (AIC), area under curve (AUC), integrated discrimination improvements (IDI), net classification improvement (NRI) and calibration statistics were calculated to explicitly assess the effect of dietary predictors on a diabetes risk model. A diet-containing type 2 diabetes risk model was developed. The significant dietary predictors including the consumption of staple foods, livestock, eggs, potato, dairy products, fresh fruit and vegetables were included in the risk model. Dietary predictors improved the noninvasive diabetes risk model with a significant increase in the AUC (delta AUC = 0.03, P<0.001), an increase in relative IDI (24.6%, P-value for IDI <0.001), an increase in NRI (category-free NRI = 0.155, P<0.001), an increase in sensitivity of the model with 7.3% and a decrease in AIC (delta AIC = 199.5). The results of the validation data were similar to the derivation data. The calibration of the diet-containing diabetes risk model was better than that of the risk model without dietary predictors in the validation data. Dietary information improves model performance and predictive ability of noninvasive type 2 diabetes risk model based on classic risk factors. Dietary information may be useful for developing a noninvasive diabetes risk model. |
format | Online Article Text |
id | pubmed-5112856 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-51128562016-12-08 Dietary Information Improves Model Performance and Predictive Ability of a Noninvasive Type 2 Diabetes Risk Model Han, Tianshu Tian, Shuang Wang, Li Liang, Xi Cui, Hongli Du, Shanshan Na, Guanqiong Na, Lixin Sun, Changhao PLoS One Research Article There is no diabetes risk model that includes dietary predictors in Asia. We sought to develop a diet-containing noninvasive diabetes risk model in Northern China and to evaluate whether dietary predictors can improve model performance and predictive ability. Cross-sectional data for 9,734 adults aged 20–74 years old were used as the derivation data, and results obtained for a cohort of 4,515 adults with 4.2 years of follow-up were used as the validation data. We used a logistic regression model to develop a diet-containing noninvasive risk model. Akaike’s information criterion (AIC), area under curve (AUC), integrated discrimination improvements (IDI), net classification improvement (NRI) and calibration statistics were calculated to explicitly assess the effect of dietary predictors on a diabetes risk model. A diet-containing type 2 diabetes risk model was developed. The significant dietary predictors including the consumption of staple foods, livestock, eggs, potato, dairy products, fresh fruit and vegetables were included in the risk model. Dietary predictors improved the noninvasive diabetes risk model with a significant increase in the AUC (delta AUC = 0.03, P<0.001), an increase in relative IDI (24.6%, P-value for IDI <0.001), an increase in NRI (category-free NRI = 0.155, P<0.001), an increase in sensitivity of the model with 7.3% and a decrease in AIC (delta AIC = 199.5). The results of the validation data were similar to the derivation data. The calibration of the diet-containing diabetes risk model was better than that of the risk model without dietary predictors in the validation data. Dietary information improves model performance and predictive ability of noninvasive type 2 diabetes risk model based on classic risk factors. Dietary information may be useful for developing a noninvasive diabetes risk model. Public Library of Science 2016-11-16 /pmc/articles/PMC5112856/ /pubmed/27851788 http://dx.doi.org/10.1371/journal.pone.0166206 Text en © 2016 Han et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Han, Tianshu Tian, Shuang Wang, Li Liang, Xi Cui, Hongli Du, Shanshan Na, Guanqiong Na, Lixin Sun, Changhao Dietary Information Improves Model Performance and Predictive Ability of a Noninvasive Type 2 Diabetes Risk Model |
title | Dietary Information Improves Model Performance and Predictive Ability of a Noninvasive Type 2 Diabetes Risk Model |
title_full | Dietary Information Improves Model Performance and Predictive Ability of a Noninvasive Type 2 Diabetes Risk Model |
title_fullStr | Dietary Information Improves Model Performance and Predictive Ability of a Noninvasive Type 2 Diabetes Risk Model |
title_full_unstemmed | Dietary Information Improves Model Performance and Predictive Ability of a Noninvasive Type 2 Diabetes Risk Model |
title_short | Dietary Information Improves Model Performance and Predictive Ability of a Noninvasive Type 2 Diabetes Risk Model |
title_sort | dietary information improves model performance and predictive ability of a noninvasive type 2 diabetes risk model |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5112856/ https://www.ncbi.nlm.nih.gov/pubmed/27851788 http://dx.doi.org/10.1371/journal.pone.0166206 |
work_keys_str_mv | AT hantianshu dietaryinformationimprovesmodelperformanceandpredictiveabilityofanoninvasivetype2diabetesriskmodel AT tianshuang dietaryinformationimprovesmodelperformanceandpredictiveabilityofanoninvasivetype2diabetesriskmodel AT wangli dietaryinformationimprovesmodelperformanceandpredictiveabilityofanoninvasivetype2diabetesriskmodel AT liangxi dietaryinformationimprovesmodelperformanceandpredictiveabilityofanoninvasivetype2diabetesriskmodel AT cuihongli dietaryinformationimprovesmodelperformanceandpredictiveabilityofanoninvasivetype2diabetesriskmodel AT dushanshan dietaryinformationimprovesmodelperformanceandpredictiveabilityofanoninvasivetype2diabetesriskmodel AT naguanqiong dietaryinformationimprovesmodelperformanceandpredictiveabilityofanoninvasivetype2diabetesriskmodel AT nalixin dietaryinformationimprovesmodelperformanceandpredictiveabilityofanoninvasivetype2diabetesriskmodel AT sunchanghao dietaryinformationimprovesmodelperformanceandpredictiveabilityofanoninvasivetype2diabetesriskmodel |