Dataset Information

Dietary Information Improves Model Performance and Predictive Ability of a Noninvasive Type 2 Diabetes Risk Model.

ABSTRACT: 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.

SUBMITTER: Han T

PROVIDER: S-EPMC5112856 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Dietary Information Improves Model Performance and Predictive Ability of a Noninvasive Type 2 Diabetes Risk Model.

Han Tianshu T Tian Shuang S Wang Li L Liang Xi X Cui Hongli H Du Shanshan S Na Guanqiong G Na Lixin L Sun Changhao C

PloS one 20161116 11

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 ...[more]

PMID: 27851788

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Project description:IntroductionType 2 diabetes (T2D) is a growing public health burden throughout the world. Many people looking for information on how to prevent T2D will search on diabetes websites. Multiple dietary factors have a significant association with T2D risk, such as high intake of added sugars, refined carbohydrates, saturated fat, and red meat or processed meat; and decreased intake of dietary fiber, and fruits/vegetables. Despite this dietary information being available in the scientific literature, it is unclear whether this information is available in gray literature (websites).ObjectiveIn this study, we evaluate the use of specific terms from diabetes websites that are significantly associated with causes/risk factors and preventions for T2D from three term categories: (A) dietary factors, (B) nondietary nongenetic (lifestyle-associated) factors, and (C) genetic (non-modifiable) factors. We also evaluate the effect of website type (business, government, nonprofit) on term usage among websites.MethodsWe used web scraping and coding tools to quantify the use of specific terms from 73 diabetes websites. To determine the effect of term category and website type on the usage of specific terms among 73 websites, a repeated measures general linear model was performed.ResultsWe found that dietary risk factors that are significantly associated with T2D (e.g., sugar, processed carbohydrates, dietary fat, fruits/vegetables, fiber, processed meat/red meat) were mentioned in significantly fewer websites than either nondietary nongenetic factors (e.g., obesity, physical activity, dyslipidemia, blood pressure) or genetic factors (age, family history, ethnicity). Among websites that provided "eat healthy" guidance, one third provided zero dietary factors associated with type 2 diabetes, and only 30% provided more than two specific dietary factors associates with type 2 diabetes. We also observed that mean percent usage of all terms associated with T2D causes/risk factors and preventions was significantly lower among government websites compared to business websites and nonprofit websites.ConclusionDiabetes websites need to increase their usage of dietary factors when discussing causes/risk factors and preventions for T2D; as dietary factors are modifiable and strongly associated with all nondietary nongenetic risk factors, in addition to T2D risk.

Project description:Background Blood metabolic profiles can be used to assess metabolic disorders and to evaluate the health status of dairy cows. Given that these analyses are time-consuming, expensive, and stressful for the cows, there has been increased interest in Fourier transform infrared (FTIR) spectroscopy of milk samples as a rapid, cost-effective alternative for predicting metabolic disturbances. The integration of FTIR data with other layers of information such as genomic and on-farm data (days in milk (DIM) and parity) has been proposed to further enhance the predictive ability of statistical methods. Here, we developed a phenotype prediction approach for a panel of blood metabolites based on a combination of milk FTIR data, on-farm data, and genomic information recorded on 1150 Holstein cows, using BayesB and gradient boosting machine (GBM) models, with tenfold, batch-out and herd-out cross-validation (CV) scenarios. Results The predictive ability of these approaches was measured by the coefficient of determination (R2). The results show that, compared to the model that includes only FTIR data, integration of both on-farm (DIM and parity) and genomic information with FTIR data improves the R2 for blood metabolites across the three CV scenarios, especially with the herd-out CV: R2 values ranged from 5.9 to 17.8% for BayesB, from 8.2 to 16.9% for GBM with the tenfold random CV, from 3.8 to 13.5% for BayesB and from 8.6 to 17.5% for GBM with the batch-out CV, and from 8.4 to 23.0% for BayesB and from 8.1 to 23.8% for GBM with the herd-out CV. Overall, with the model that includes the three sources of data, GBM was more accurate than BayesB with accuracies across the CV scenarios increasing by 7.1% for energy-related metabolites, 10.7% for liver function/hepatic damage, 9.6% for oxidative stress, 6.1% for inflammation/innate immunity, and 11.4% for mineral indicators. Conclusions Our results show that, compared to using only milk FTIR data, a model integrating milk FTIR spectra with on-farm and genomic information improves the prediction of blood metabolic traits in Holstein cattle and that GBM is more accurate in predicting blood metabolites than BayesB, especially for the batch-out CV and herd-out CV scenarios. Supplementary Information The online version contains supplementary material available at 10.1186/s12711-023-00795-1.

Dataset Information

Dietary Information Improves Model Performance and Predictive Ability of a Noninvasive Type 2 Diabetes Risk Model.

Publications

Dietary Information Improves Model Performance and Predictive Ability of a Noninvasive Type 2 Diabetes Risk Model.

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