title: Dietary information improves cardiovascular disease risk prediction models.
abstract: OBJECTIVE
  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.
  
  
  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.
authors.affiliation: Department of Foods and Nutrition, College of Natural Sciences, Kookmin University, Seoul, Republic of Korea. ibaik@kookmin.ac.kr
authors.forename: I
authors.initials: I
authors.lastname: Baik
authors.forename: N H
authors.initials: NH
authors.lastname: Cho
authors.forename: S H
authors.initials: SH
authors.lastname: Kim
authors.forename: C
authors.initials: C
authors.lastname: Shin
mesh_terms: D000328:Adult; D000368:Aged; D002138:Calibration; D002318:Cardiovascular Diseases / Q000453:epidemiology / Q000208:ethnology / Q000209:etiology* / Q000517:prevention & control; D015331:Cohort Studies; D004032:Diet / Q000009:adverse effects* / Q000208:ethnology; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D016016:Proportional Hazards Models; D011446:Prospective Studies; D056910:Republic of Korea / Q000453:epidemiology; D012307:Risk Factors; D011795:Surveys and Questionnaires