Perspective: The Application of A Priori Diet Quality Scores to Cardiovascular Disease Risk—A Critical Evaluation of Current Scoring Systems

Abstract

Healthy dietary habits are the cornerstone of cardiovascular disease (CVD) prevention. Numerous researchers have developed diet quality indices to help evaluate and compare diet quality across and within various populations. The availability of these new indices raises questions regarding the best selection relevant to a given population. In this perspective, we critically evaluate a priori–defined dietary indices commonly applied in epidemiological studies of CVD risk and mortality. A systematic literature search identified 59 observational studies that applied a priori–defined diet quality indices to CVD risk factors and/or CVD incidence and/or CVD mortality. Among 31 different indices, these scores were categorized as follows: 1) those based on country-specific dietary patterns, 2) those adapted from distinct dietary guidelines, and 3) novel scores specific to key diet-related factors associated with CVD risk. The strengths and limitations of these indices are described according to index components, calculation methods, and the application of these indices to different population groups. Also, the importance of identifying methodological challenges faced by researchers when applying an index are considered, such as selection and weighting of food groups within a score, since food groups are not necessarily equivalent in their associations with CVD. The lack of absolute cutoff values, emphasis on increasing healthy food without limiting unhealthy food intake, and absence of validation of scores with biomarkers or other objective diet assessment methods further complicate decisions regarding the best indices to use. Future research should address these limitations, consider cross-cultural and other differences between population groups, and identify translational challenges inherent in attempting to apply a relevant diet quality index for use in CVD prevention at a population level.

Food Liking-Based Diet Quality Indexes (DQI) Generated by Conceptual and Machine Learning Explained Variability in Cardiometabolic Risk Factors in Young Adults

“…Another important consideration of diet quality indexes is the scoring and weighting of the individual components, particularly in the examining of associations with cardiovascular disease risk [15]. The Healthy Eating Index scoring is complex, with significant dietary analysis to derive food group scores representing the whole diet, determining portion equivalents adjusted for total energy intakes, and weighting according to the Dietary Guidelines as the relevant standard [18].…”

“…Hybrid methods benefit from a priori methods with the underlying structure of dietary patterns [37]. The hybrid method allowed validation of the weighting of the conceptual food/beverage groups as recommended [15] and reduced the importance of 10 weighted groups in the theoretically-constructed DQI to a smaller number of food groups. Our hybrid diet quality index was derived from ridge regression analysis, which has been supported to produce a greater association with cardiovascular health than analysis with principal component analysis [42].…”

“…Higher scores on the indices indicated healthier behaviors. Thus, we had a balance of weights for healthy versus unhealthy foods/beverages, as seen with other diet quality indexes [15]. Table 1.…”

“…However, the index of diet quality must be sensitive enough to associate with chronic disease risk factors in young adults [14]. There is a need for examining the scoring and construction of diet quality indexes to improve the understanding of diet-cardiovascular disease risk [15]. The present study will focus on new methodologies to assess habitual food and beverage consumption and calculation of a diet quality index that may enhance the ability to see these relationships in young adults.…”

Second Version of a Mini-Survey to Evaluate Food Intake Quality (Mini-ECCA v.2): Reproducibility and Ability to Identify Dietary Patterns in University Students

“…However, it is important to notice that these surveys were designed to examine dietary intake and association with some health outcomes like cardiometabolic risk [21,22,29,71], which is not the aim of Mini-ECCA v.2. Our tool places special emphasis on evaluating the quality of foods consumed, either healthy or non-healthy [72].…”