Differences in United States Adult Dietary Patterns by Cardiometabolic Health and Socioeconomic Vulnerability.

Background: Naturally occurring dietary patterns are not well described among individuals with cardiovascular disease (CVD) or cardiometabolic risk factors (i.e., diabetes, hypertension, obesity, and dyslipidemia), particularly considering socioeconomic vulnerability.

Objectives: We investigated major dietary patterns in the United States and their distribution by prevalent CVD, cardiometabolic risk factors, and socioeconomic vulnerability.

Methods: This cross-sectional study analyzed data from 32,498 noninstitutionalized adults who participated in the National Health and Nutrition Examination Survey (2009-2020). We used principal component analysis to identify dietary patterns. Using multiple linear regression, we tested the association of prevalent CVD, cardiometabolic risk factors, and socioeconomic vulnerability [number of social risk factors and Supplemental Nutrition Assistance Program (SNAP) participation status] with each pattern.

Results: Four dietary patterns were identified: processed/animal foods (high-refined grains, added sugars, meats, and dairy), prudent (high vegetables, nuts/seeds, oils, seafood, and poultry), legume, and fruit/whole grain/dairy, which together explained 29.2% of the dietary variance. After adjustment for age, gender, race and ethnicity, cohort year, and total energy intake, the processed/animals foods pattern associated (β-coefficient for difference in principal component score) positively with diabetes [0.08 (0.01, 0.14)], hypertension [0.11 (0.06, 0.16)], obesity [0.15 (0.11, 0.19)], higher social risk score (P-trend < 0.001), income-eligible SNAP nonparticipation [0.16 (0.09, 0.23)], and SNAP participation [0.23 (0.17, 0.29)]. The prudent pattern associated negatively with hypertension [-0.09 (-0.14, -0.04)], obesity [-0.11 (-0.16, -0.06)], higher social risk score (P-trend < 0.001), income-eligible SNAP nonparticipation [-0.14 (-0.21, -0.06)], and SNAP participation [-0.30 (-0.35, -0.24)]. The legume pattern was associated negatively with CVD [-0.09 (-0.15, -0.02)] and obesity [-0.08 (-0.12, -0.04)], and positively with income-eligible SNAP nonparticipation [0.11 (0.04, 0.18)]. The fruit/whole grain/dairy pattern was associated positively with diabetes [0.08 (0.01, 0.15)] and negatively with hypertension [-0.21 (-0.26, -0.15)], obesity [-0.23 (-0.28, -0.18)], higher social risk score (P-trend < 0.001), and SNAP participation [-0.19 (-0.25, -0.12)].

Conclusions: Empirical dietary patterns in the United States vary by CVD, cardiometabolic risk factors, and socioeconomic vulnerability. Initiatives to improve nutrition should consider these naturally occurring dietary patterns and their variation in key subgroups.