The internal validity of a dietary pattern analysis. The Framingham Nutrition Studies.

STUDY OBJECTIVES: To examine the internal validity of a dietary pattern analysis and its ability to discriminate clusters of people with similar dietary patterns using independently assessed nutrient intakes and heart disease risk factors. DESIGN AND PARTICIPANTS: Population based study characterising dietary patterns using cluster analysis applied to data from the semiquantitative Framingham food frequency questionnaire collected from 1942 women ages 18-76 years, between 1984-88. SETTING: Framingham, Massachusetts. MAIN RESULTS: Of 1942 women included in the cluster analysis, 1828 (94%) were assigned to one of the five dietary pattern clusters: Heart Healthy, Light Eating, Wine and Moderate Eating, High Fat, and Empty Calorie. Dietary patterns differed substantially in terms of individual nutrient intakes, overall dietary risk, heart disease risk factors, and predicted heart disease risk. Women in the Heart Healthy cluster had the most nutrient dense eating pattern, the lowest level of dietary risk, more favourable risk factor levels, and the lowest probability of developing heart disease. Those in the Empty Calorie cluster had a less nutritious dietary pattern, the greatest level of dietary risk, a heavier burden of heart disease risk factors, and a relatively higher probability of developing heart disease. Cluster reproducibility using discriminant analysis showed that 80% of the sample was correctly classified. The cluster technique was highly sensitive and specific (75% to 100%). CONCLUSIONS: These findings support the internal validity of a dietary pattern analysis for characterising dietary exposures in epidemiological research. The authors encourage other researchers to explore this technique when investigating relations between nutrition, health, and disease.

Validation of a dietary pattern approach for evaluating nutritional risk: the Framingham Nutrition Studies.

OBJECTIVE: To validate the use of cluster analysis for characterizing population dietary patterns. DESIGN: Cluster analysis was applied to a food frequency questionnaire to define dietary patterns. Independent estimates of nutrient intake were derived from 3-day food records. Heart disease risk factors were assessed using standardized protocols in a clinic setting. SETTING: Adult women (n = 1,828) participating in the Framingham Offspring-Spouse study. STATISTICAL ANALYSES: Age-adjusted mean nutrient intakes were determined for each cluster. Analysis of covariance was used to evaluate pairwise differences in intake across clusters. Compliance with published recommendations was determined for selected heart disease risk factors. Differences in age-adjusted compliance across clusters were evaluated using logistic regression. RESULTS: Cluster analysis identified 5 distinct dietary patterns characterized by unique food behaviors and significantly different nutrient intake profiles. Patterns rich in fruits, vegetables, grains, low-fat dairy, and lean protein foods resulted in higher nutrient density. Patterns rich in fatty foods, added fats, desserts, and sweets were less nutrient-dense. Women who consumed an Empty Calorie pattern were less likely to achieve compliance with clinical risk factor guidelines in contrast to most other groups of women. CONCLUSIONS: Cluster analysis is a valid tool for evaluating nutrition risk by considering overall patterns and food behaviors. This is important because dietary patterns appear to be linked with other health-related behaviors that confer risk for chronic disease. Therefore, insight into dietary behaviors of distinct clusters within a population can help to design intervention strategies for prevention and management of chronic health conditions including obesity and cardiovascular disease.