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The need for dietary and physical activity assessment in the integration of cardiovascular risk scores

Comment by Demosthenes Panagiotakos, Population Science and Public Health Section, and Christina Vafia

Two decades have passed since the need for global cardiovascular disease (CVD) risk assessment (1) was highlighted. Meanwhile, and under the context of effective primary prevention strategies, many risk tools (or scores) have been developed to assess overall CVD risk based on multiple, but common risk factors. The Framingham Risk Score, developed in late 1990s, was the first tool attempted to assess the overall 10-year risk of coronary heart disease for Caucasian-American individuals without known heart disease. In 2003, the European Society of Cardiology, recognizing the need for local risk estimation models, supported a prediction model assembling a pool of datasets from 12 European cohort studies, the Systematic Coronary Risk Evaluation (SCORE) model. The proposed model was considered an innovation for the clinical management of cardiovascular risk in clinical practice for Europeans.

Preventive Cardiology
Risk Factors and Prevention

SCORE2 & Risk stratification

In addition to the traditional CVD risk factors assessed in previous risk models, such as age, sex, arterial blood pressure and lipids levels, smoking habits, many other environmental and lifestyle features have been found to exert an important influence on CVD risk prediction and stratification (2). Such features include ethnicity and genetic variability, as well as cultural (including nutritional and lifestyle), and socio- economic structures that show great variability among European regions. Based on the aforementioned considerations SCORE project investigators proposed in 2021 an updated version, i.e., the recalibration of SCORE2 (3), that incorporated some of the age- and sex-specific CVD variations in incidence rates among Europeans.

Diet & Physical activity as independent CVD risk factors. Does dietary and physical activity information improve CVD risk models’ classification ability?

Physical inactivity, together with unhealthy dietary habits are now strongly considered as the most important modifiable risk factors for CVD morbidity and mortality. Both affect CVD risk not only indirectly, through their effect on other risk factors (i.e., obesity, diabetes, hypertension), but also directly, through distinct mechanisms and pathways that promote atherogenesis (4).

The inclusion of dietary and physical activity information in CVD risk models has shown that it significantly improves the goodness-of-fit of the models. For example, the integration of a diet score which evaluated the level of adherence to the Mediterranean diet, i.e., the MedDietScore, in the SCORE model improved the classification ability of the final model by 56%. When physical activity assessment was also integrated in the risk model, the misclassification was once again significantly reduced, in both men and women at all ages (5), suggesting that more people at risk would have been identified and received the appropriate treatment.

How feasible is it to include dietary and physical activity assessment into health metrics during daily clinical practice?

There are several well-established health metrics for dietary and physical activity assessment, that have shown very good credibility. Measures of the quality of diet, like the Healthy Eating Index,  the Diet Quality Index-International, as well as specific indices for the Mediterranean countries, like the MedDietScore, have shown high reliability and validity in assessing dietary habits and relating them with various health outcomes. Regarding physical activity assessment, there are also several evaluation tools proposed, like the International Physical Activity Questionnaire (IPAQ), the General Practice Physical Activity Questionnaire (GPPAQ), or more specific, like the Community Healthy Activities Model Program for Seniors (CHAMPS), etc, that capture energy expenditure of individuals, and daily activities. Both dietary and physical activity assessment tools have been extensively used in research, with large amount of data available that can be used for an integrated CVD risk modeling approach. Moreover, lifestyle tools are not time-consuming, can be easily attained in daily clinical practice, gathering the necessary information about individuals’ dietary habits and physical activities.

Conclusively, lifestyle health metrics should be integrated into future CVD risk models, like the new … SCORE “3”, and, by this way, to improve the predictive ability and the correct classification rate of the model, offering to the public an important tool for primary prevention. 


D Panagiotakos and C Vafia commented on this article:

3. SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe. European Heart Journal, Volume 42, Issue 25, 1 July 2021, Pages 2439–2454, 

other references:

1. Grundy SM, Pasternak R, Greenland P, Smith S, Fuster V. Assessment of cardiovascular risk by use of multiple-risk-factor assessment equations: A statement for healthcare professionals from the American Heart Association and the American College of Cardiology. Circulation. 1999;100(13):1481–92.

2. Bhatnagar A. Environmental Determinants of Cardiovascular Disease. Circ Res. 2017;121(2):162–80.

4. Zhang YB, Pan XF, Chen J, Cao A, Xia L, Zhang Y, et al. Combined lifestyle factors, all-cause mortality and cardiovascular disease: A systematic review and meta-analysis of prospective cohort studies. J Epidemiol Community Health. 2021;75(1):92–9.

5. Georgousopoulou EN, Panagiotakos DB, Pitsavos C, Stefanadis C. Assessment of diet quality improves the classification ability of cardiovascular risk score in predicting future events: The 10-year follow-up of the ATTICA study (2002-2012). Eur J Prev Cardiol. 2015;22(11):1488–98. 

Notes to editor

Note: The content of this article reflects the personal opinion of the author/s and is not necessarily the official position of the European Society of Cardiology.