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.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
D’Agostino RB, Grundy S, Sullivan LM, Wilson P, CHD Risk Prediction Group. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA 2001; 286: 180–187.
Conroy RM, Pyörälä K, Fitzgerald AP, Sans S, Menotti A, De Backer G et al. SCORE project group. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J 2003; 24: 987–1003.
Wu Y, Liu X, Li X, Li Y, Zhao L, Chen Z et al USA-PRC Collaborative Study of Cardiovascular and Cardiopulmonary Epidemiology Research Group; China Multicenter Collaborative Study of Cardiovascular Epidemiology Research Group. Estimation of 10-year risk of fatal and nonfatal ischemic cardiovascular diseases in Chinese adults. Circulation 2006; 114: 2217–2225.
Ridker PM, Buring JE, Rifai N, Cook NR . Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. JAMA 2007; 297: 611–619.
Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, May M, Brindle P . Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study. BMJ 2007; 335: 136.
Melander O, Newton-Cheh C, Almgren P, Hedblad B, Berglund G, Engström G et al. Novel and conventional biomarkers for prediction of incident cardiovascular events in the community. JAMA 2009; 302: 49–57.
D'Agostino RB, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation 2008; 117: 743–753.
Cui J, Forbes A, Kirby A, Simes J, Tonkin A . Laboratory and non-laboratory-based risk prediction models for secondary prevention of cardiovascular disease: the LIPID study. Eur J Cardiovasc Prev Rehabil 2009; 16: 660–668.
Pandya A, Weinstein MC, Gaziano TA . A comparative assessment of non-laboratory-based versus commonly used laboratory-based cardiovascular disease risk scores in the NHANES III population. PLoS One 2011; 6: e20416.
Hu FB, Willett WC . Optimal diets for prevention of coronary heart disease. JAMA 2002; 288: 2569–2578.
Panagiotakos DB, Pitsavos C, Stefanadis C . Inclusion of dietary evaluation in cardiovascular disease risk prediction models increases accuracy and reduces bias of the estimations. Risk Anal 2009; 29: 176–186.
Baik I, Shin C . Prospective study of alcohol consumption and metabolic syndrome. Am J Clin Nutr 2008; 87: 1455–1463.
Baik I, Cho NH, Kim SH, Han BG, Shin C . Genome-wide association studies identify genetic loci related to alcohol consumption in Korean men. Am J Clin Nutr 2011; 93: 809–816.
Baik I, Abbott RD, Curb JD, Shin C . Intake of fish and n-3 fatty acids and future risk of metabolic syndrome. J Am Diet Assoc 2010; 110: 1018–1026.
Pencina MJ, D’Agostino RB, Larson MG, Massaro JM, Vasan RS . Predicting the 30-year risk of cardiovascular disease: the Framingham Heart Study. Circulation 2009; 119: 3078–3084.
Koenig W, Löwel H, Baumert J, Meisinger C . C-reactive protein modulates risk prediction based on the Framingham Score: implications for future risk assessment: results from a large cohort study in southern Germany. Circulation 2004; 109: 1349–1353.
Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 2010; 21: 128–138.
David W Hosmer and Stanley Lemeshow. Applied Logistic Regression. Wiley: New York, USA, 2000.
Lloyd-Jones DM . Cardiovascular risk prediction: basic concepts, current status, and future directions. Circulation 2010; 121: 1768–1777.
Carter SJ, Roberts MB, Salter J, Eaton CB . Relationship between Mediterranean diet score and atherothrombotic risk: findings from the Third National Health and Nutrition Examination Survey (NHANES III), 1988–1994. Atherosclerosis 2010; 210: 630–636.
Hooper L, Kroon PA, Rimm EB, Cohn JS, Harvey I, Le Cornu KA et al. Flavonoids, flavonoid-rich foods, and cardiovascular risk: a meta-analysis of randomized controlled trials. Am J Clin Nutr 2008; 88: 38–50.
Acknowledgements
This study was supported by grants from the Globalization of Korean Foods R&D program funded by the Ministry of Food, Agriculture, Forestry and Fisheries (911003-01-1-SB010) and by a research fund (2001-347-6111-221, 2002-347-6111-221, 2003-347-6111-221, 2004-E71001-00, 2005- E71001-00, 2006- E71005-00, 2007- E71001-00, 2008- E71001-00, 2009- E71002-00, 2010- E71001-00) from the Korea Centers for Disease Control and Prevention.
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Competing interests
The authors declare no conflict of interest.
Rights and permissions
About this article
Cite this article
Baik, I., Cho, N., Kim, S. et al. Dietary information improves cardiovascular disease risk prediction models. Eur J Clin Nutr 67, 25–30 (2013). https://doi.org/10.1038/ejcn.2012.175
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/ejcn.2012.175
Keywords
This article is cited by
-
Longitudinal trajectories of atherogenic index of plasma and risks of cardiovascular diseases: results from the Korean genome and epidemiology study
Thrombosis Journal (2023)
-
Early elevation of high-sensitivity C-reactive protein as a predictor for cardiovascular disease incidence and all-cause mortality: a landmark analysis
Scientific Reports (2023)
-
Validation of a Thai semiquantitative food frequency questionnaire (semi-FFQ) for people at risk of metabolic syndrome
Journal of Health, Population and Nutrition (2023)
-
The product of fasting plasma glucose and triglycerides improves risk prediction of type 2 diabetes in middle-aged Koreans
BMC Endocrine Disorders (2018)
-
The inherited real risk of coronary artery disease
European Journal of Clinical Nutrition (2013)