Dietary information improves cardiovascular disease risk prediction models
DC Field | Value | Language |
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dc.contributor.author | Baik, I. | - |
dc.contributor.author | Cho, N. H. | - |
dc.contributor.author | Kim, S. H. | - |
dc.contributor.author | Shin, C. | - |
dc.date.accessioned | 2021-09-06T05:40:56Z | - |
dc.date.available | 2021-09-06T05:40:56Z | - |
dc.date.created | 2021-06-14 | - |
dc.date.issued | 2013-01 | - |
dc.identifier.issn | 0954-3007 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/104307 | - |
dc.description.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 ODD, 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. European Journal of Clinical Nutrition (2013) 67, 25-30; doi:10.1038/ejcn.2012.175; published online 14 November 2012 | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | NATURE PUBLISHING GROUP | - |
dc.subject | 10-YEAR RISK | - |
dc.subject | FUTURE RISK | - |
dc.subject | VALIDATION | - |
dc.subject | SCORE | - |
dc.subject | PREVENTION | - |
dc.title | Dietary information improves cardiovascular disease risk prediction models | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, S. H. | - |
dc.contributor.affiliatedAuthor | Shin, C. | - |
dc.identifier.doi | 10.1038/ejcn.2012.175 | - |
dc.identifier.scopusid | 2-s2.0-84872125436 | - |
dc.identifier.wosid | 000313527300006 | - |
dc.identifier.bibliographicCitation | EUROPEAN JOURNAL OF CLINICAL NUTRITION, v.67, no.1, pp.25 - 30 | - |
dc.relation.isPartOf | EUROPEAN JOURNAL OF CLINICAL NUTRITION | - |
dc.citation.title | EUROPEAN JOURNAL OF CLINICAL NUTRITION | - |
dc.citation.volume | 67 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 25 | - |
dc.citation.endPage | 30 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Nutrition & Dietetics | - |
dc.relation.journalWebOfScienceCategory | Nutrition & Dietetics | - |
dc.subject.keywordPlus | 10-YEAR RISK | - |
dc.subject.keywordPlus | FUTURE RISK | - |
dc.subject.keywordPlus | VALIDATION | - |
dc.subject.keywordPlus | SCORE | - |
dc.subject.keywordPlus | PREVENTION | - |
dc.subject.keywordAuthor | cardiovascular disease | - |
dc.subject.keywordAuthor | risk prediction | - |
dc.subject.keywordAuthor | dietary predictors | - |
dc.subject.keywordAuthor | prospective cohort study | - |
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