P-376: Dietary patterns and cardiometabolic correlates in youth with prediabetes and type 2 diabetes
M Talbo, T Luehrs, R Johnson, D Garfias Silva, C Gardner, D Maahs, A AddalaAbstract
Introduction
Metabolic disease is rising among youth, along with the risk of type 2 diabetes (T2D). Eating patterns rich in whole grains, fruits, vegetables, legumes, and nuts are protective in adults, but evidence in youth is limited.
Objectives
We aimed to identify dietary patterns in youth (10-21 years) with T2D or prediabetes and evaluate their associations with sociodemographic and cardiometabolic variables.
Methods
In this cross-sectional study, youth with HbA1c >5.7% in the past year completed an online survey with a food frequency questionnaire (135 items; English/Spanish). Dietary data were combined into 20 food groups. Principal component analysis (PCA; with varimax rotation) was used to identify dietary pattern components (PC); sampling adequacy and sphericity assumptions were confirmed. Kendall’s tau [95% CI] assessed associations with clinical and sociodemographic factors.
Results
Among 47 participants (median age 16 (IQR: 13-17) years; 77% female; 51% Spanish-speaking; 60% diagnosed with T2D; median HbA1c 5.8% (5.5-6.5); 70% used antihyperglycemic agents [metformin (n=18), GLP-1 receptor agonists (n=18), and insulin (n=14)]), PCA identified 5 PCs explaining 70% of total variance. PC1 was associated with T2D diagnosis (τ= 0.29 [0.04, 0.50]), overweight/obesity (τ= 0.30 [0.08, 0.49]), and higher ALT (τ= 0.28 [0.02, 0.48]). PC2 was inversely associated with overweight/obesity (τ= -0.27 [-0.44, -0.08]). PC3 was inversely associated with male sex (τ= -0.33 [-0.50, -0.13]) and having a T2D diagnosis (τ= -0.23 [-0.45, -0.01]). There were no statistically significant associations between the clinical and sociodemographic variables and the remaining dietary patterns identified.
Conclusions
Several interpretable dietary patterns in youth with prediabetes and T2D were identified. These results suggest potential targets for tailored nutrition interventions. Whole-food, plant-forward patterns and lower processed food intake may represent potential intervention targets.