Nutrients, Vol. 18, Pages 533: Dietary Behavior Clustering and Cardiovascular Risk Markers in a Large Population Cohort
Nutrients doi: 10.3390/nu18030533
Authors:
Mauro Lombardo
Giovanni Aulisa
Fares M. S. Muthanna
Sercan Karav
Sara Baldelli
Gianluca Tripodi
Gilda Aiello
Background: Eating habits influence cardiometabolic health alongside traditional dietary measures. However, the links between dietary patterns, body composition, and heart-healthy food preferences remain under-explored in large cohorts. Methods: In this cross-sectional study, 2461 adults (aged 18 to 75 years) completed an online survey on eating behaviors, food preferences, and lifestyle. Principal component analysis (PCA) of seven behaviors identified dietary profiles. A heart-healthy diet score (range −2 to 10; higher = greater preference for fruit, vegetables, legumes, fish, and less meat/processed meat) was derived from these food preferences. ANOVA and adjusted regressions linked the profiles to BMI, fat mass, waist circumference, and diet score. Results: Four profiles emerged: structured, social, irregular, and disordered eaters. Structured eaters had the lowest BMI (26.8 ± 5.1 kg/m2), lowest fat mass (28.9 ± 9.4%), and highest dietary score (4.73 ± 2.0). Disorganized eaters had the highest BMI (29.0 ± 5.5 kg/m2), the highest fat mass (31.2 ± 8.8%) and the lowest score (3.93 ± 2.0); all p < 0.05. Dose–response analyses confirmed that greater disordered eating (PCA1) was associated with worse outcomes. Conclusions: Dietary profiles are associated with body composition and cardioprotective preferences. Behavioral assessment could refine the identification of cardiometabolic risk and personalize nutrition.
Background: Eating habits influence cardiometabolic health alongside traditional dietary measures. However, the links between dietary patterns, body composition, and heart-healthy food preferences remain under-explored in large cohorts. Methods: In this cross-sectional study, 2461 adults (aged 18 to 75 years) completed an online survey on eating behaviors, food preferences, and lifestyle. Principal component analysis (PCA) of seven behaviors identified dietary profiles. A heart-healthy diet score (range −2 to 10; higher = greater preference for fruit, vegetables, legumes, fish, and less meat/processed meat) was derived from these food preferences. ANOVA and adjusted regressions linked the profiles to BMI, fat mass, waist circumference, and diet score. Results: Four profiles emerged: structured, social, irregular, and disordered eaters. Structured eaters had the lowest BMI (26.8 ± 5.1 kg/m2), lowest fat mass (28.9 ± 9.4%), and highest dietary score (4.73 ± 2.0). Disorganized eaters had the highest BMI (29.0 ± 5.5 kg/m2), the highest fat mass (31.2 ± 8.8%) and the lowest score (3.93 ± 2.0); all p < 0.05. Dose–response analyses confirmed that greater disordered eating (PCA1) was associated with worse outcomes. Conclusions: Dietary profiles are associated with body composition and cardioprotective preferences. Behavioral assessment could refine the identification of cardiometabolic risk and personalize nutrition. Read More