Dietary patterns associated with risk factors for cardiovascular disease in healthy US adults

Background: Certain nutrients are well established as dietary risk factors for cardiovascular disease (CVD), but dietary patterns may be a better predictor of CVD risk. Objective: This study tested the hypothesis that the complex dietary behaviors of US adults can be grouped into major dietary patterns that are related to risk factors for CVD. Design: With the use of food-frequency questionnaire data from the third National Health and Nutrition Examination Survey, dietary patterns of healthy US adults (greater than or equal to 20 y old; n = 13 130) were identified by factor analysis. Log-transformed biomarker data were associated with major dietary patterns after control for confounding variables in regression analyses. All statistical analyses accounted for the survey design and sample weights. Results: Of 6 dietary patterns identified, 2 patterns emerged as the most predominant: the Western pattern was characterized by high intakes of processed meats, eggs, red meats, and high-fat dairy products, and the American-healthy pattern was characterized by high intakes of green, leafy vegetables; salad dressings; tomatoes; other vegetables (eg, peppers, green beans, corn, and peas); cruciferous vegetables; and tea. The Western pattern was associated (P < 0.05) positively with serum C-peptide, serum insulin, and glycated hemoglobin and inversely with red blood cell folate concentrations after adjustment for confounding variables. The American-healthy pattern had no linear relation with any of the biomarkers examined. Conclusions: The identification of common dietary patterns among free-living persons is promising for characterizing high-risk groups at the US population level. The dietary patterns identified here are similar to those reported in other nonrepresentative samples and are associated with biomarkers of CVD risk, which confirms that dietary pattern analysis can be a valuable method for assessing dietary intakes when predicting CVD risk.