Document Detail


Dietary information improves cardiovascular disease risk prediction models.
MedLine Citation:
PMID:  23149979     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
I Baik; N H Cho; S H Kim; C Shin
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Publication Detail:
Type:  Comparative Study; Journal Article; Research Support, Non-U.S. Gov't     Date:  2012-11-14
Journal Detail:
Title:  European journal of clinical nutrition     Volume:  67     ISSN:  1476-5640     ISO Abbreviation:  Eur J Clin Nutr     Publication Date:  2013 Jan 
Date Detail:
Created Date:  2013-01-09     Completed Date:  2013-06-24     Revised Date:  2013-07-24    
Medline Journal Info:
Nlm Unique ID:  8804070     Medline TA:  Eur J Clin Nutr     Country:  England    
Other Details:
Languages:  eng     Pagination:  25-30     Citation Subset:  IM    
Affiliation:
Department of Foods and Nutrition, College of Natural Sciences, Kookmin University, Seoul, Republic of Korea. ibaik@kookmin.ac.kr
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MeSH Terms
Descriptor/Qualifier:
Adult
Aged
Calibration
Cardiovascular Diseases / epidemiology,  ethnology,  etiology*,  prevention & control
Cohort Studies
Diet / adverse effects*,  ethnology
Female
Follow-Up Studies
Humans
Incidence
Male
Middle Aged
Models, Biological*
Proportional Hazards Models
Prospective Studies
Questionnaires
Republic of Korea / epidemiology
Risk Factors
Comments/Corrections
Comment In:
Eur J Clin Nutr. 2013 Jun;67(6):684   [PMID:  23612510 ]
Eur J Clin Nutr. 2013 Jun;67(6):683   [PMID:  23612516 ]

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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