Abstract
Background/objectives: Data are limited on cardiovascular disease (CVD) risk prediction models that include dietary predictors. Using known risk factors and dietary information, we constructed and evaluated CVD risk prediction models. Subjects/methods: Data for modeling were from population-based prospective cohort studies comprised of 9026 men and women aged 40-69 years. At baseline, all were free of known CVD and cancer, and were followed up for CVD incidence during an 8-year period. We used Cox proportional hazard regression analysis to construct a traditional risk factor model, an office-based model, and two diet-containing models and evaluated these models by calculating Akaike information criterion (AIC), C-statistics, integrated discrimination improvement (IDI), net reclassification improvement (NRI) and calibration statistic. Results: We constructed diet-containing models with significant dietary predictors such as poultry, legumes, carbonated soft drinks or green tea consumption. Adding dietary predictors to the traditional model yielded a decrease in AIC (delta AIC=15), a 53% increase in relative IDI (P-value for IDI <0.001) and an increase in NRI (category-free NRI=0.14, P <0.001). The simplified diet-containing model also showed a decrease in AIC (delta AIC=14), a 38% increase in relative IDI (P-value for IDI <0.001) and an increase in NRI (category-free NRI=0.08, P<0.01) compared with the office-based model. The calibration plots for risk prediction demonstrated that the inclusion of dietary predictors contributes to better agreement in persons at high risk for CVD. C-statistics for the four models were acceptable and comparable. Conclusions: We suggest that dietary information may be useful in constructing CVD risk prediction models.
| Original language | English |
|---|---|
| Pages (from-to) | 25-30 |
| Number of pages | 6 |
| Journal | European Journal of Clinical Nutrition |
| Volume | 67 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2013 Jan 1 |
Fingerprint
Keywords
- Cardiovascular disease
- Dietary predictors
- Prospective cohort study
- Risk prediction
ASJC Scopus subject areas
- Medicine (miscellaneous)
- Nutrition and Dietetics
Cite this
Dietary information improves cardiovascular disease risk prediction models. / Baik, I.; Cho, N. H.; Kim, Seong Hwan; Shin, Chol.
In: European Journal of Clinical Nutrition, Vol. 67, No. 1, 01.01.2013, p. 25-30.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Dietary information improves cardiovascular disease risk prediction models
AU - Baik, I.
AU - Cho, N. H.
AU - Kim, Seong Hwan
AU - Shin, Chol
PY - 2013/1/1
Y1 - 2013/1/1
N2 - Background/objectives: Data are limited on cardiovascular disease (CVD) risk prediction models that include dietary predictors. Using known risk factors and dietary information, we constructed and evaluated CVD risk prediction models. Subjects/methods: Data for modeling were from population-based prospective cohort studies comprised of 9026 men and women aged 40-69 years. At baseline, all were free of known CVD and cancer, and were followed up for CVD incidence during an 8-year period. We used Cox proportional hazard regression analysis to construct a traditional risk factor model, an office-based model, and two diet-containing models and evaluated these models by calculating Akaike information criterion (AIC), C-statistics, integrated discrimination improvement (IDI), net reclassification improvement (NRI) and calibration statistic. Results: We constructed diet-containing models with significant dietary predictors such as poultry, legumes, carbonated soft drinks or green tea consumption. Adding dietary predictors to the traditional model yielded a decrease in AIC (delta AIC=15), a 53% increase in relative IDI (P-value for IDI <0.001) and an increase in NRI (category-free NRI=0.14, P <0.001). The simplified diet-containing model also showed a decrease in AIC (delta AIC=14), a 38% increase in relative IDI (P-value for IDI <0.001) and an increase in NRI (category-free NRI=0.08, P<0.01) compared with the office-based model. The calibration plots for risk prediction demonstrated that the inclusion of dietary predictors contributes to better agreement in persons at high risk for CVD. C-statistics for the four models were acceptable and comparable. Conclusions: We suggest that dietary information may be useful in constructing CVD risk prediction models.
AB - Background/objectives: Data are limited on cardiovascular disease (CVD) risk prediction models that include dietary predictors. Using known risk factors and dietary information, we constructed and evaluated CVD risk prediction models. Subjects/methods: Data for modeling were from population-based prospective cohort studies comprised of 9026 men and women aged 40-69 years. At baseline, all were free of known CVD and cancer, and were followed up for CVD incidence during an 8-year period. We used Cox proportional hazard regression analysis to construct a traditional risk factor model, an office-based model, and two diet-containing models and evaluated these models by calculating Akaike information criterion (AIC), C-statistics, integrated discrimination improvement (IDI), net reclassification improvement (NRI) and calibration statistic. Results: We constructed diet-containing models with significant dietary predictors such as poultry, legumes, carbonated soft drinks or green tea consumption. Adding dietary predictors to the traditional model yielded a decrease in AIC (delta AIC=15), a 53% increase in relative IDI (P-value for IDI <0.001) and an increase in NRI (category-free NRI=0.14, P <0.001). The simplified diet-containing model also showed a decrease in AIC (delta AIC=14), a 38% increase in relative IDI (P-value for IDI <0.001) and an increase in NRI (category-free NRI=0.08, P<0.01) compared with the office-based model. The calibration plots for risk prediction demonstrated that the inclusion of dietary predictors contributes to better agreement in persons at high risk for CVD. C-statistics for the four models were acceptable and comparable. Conclusions: We suggest that dietary information may be useful in constructing CVD risk prediction models.
KW - Cardiovascular disease
KW - Dietary predictors
KW - Prospective cohort study
KW - Risk prediction
UR - http://www.scopus.com/inward/record.url?scp=84872125436&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872125436&partnerID=8YFLogxK
U2 - 10.1038/ejcn.2012.175
DO - 10.1038/ejcn.2012.175
M3 - Article
C2 - 23149979
AN - SCOPUS:84872125436
VL - 67
SP - 25
EP - 30
JO - European Journal of Clinical Nutrition
JF - European Journal of Clinical Nutrition
SN - 0954-3007
IS - 1
ER -