Personalized Dietary Impact Assessment Using Advanced Statistical Modeling

Precision nutrition relies on understanding how individuals uniquely respond to dietary interventions. This study utilizes a robust N-of-1 trial design involving 80 participants to investigate postprandial glycemic responses to two distinct diets. A hierarchical mixed-effects modeling framework was employed to estimate individualized treatment effects and to quantify interindividual variability. The model incorporated gut microbiome data to explore interaction effects and conditional treatment effects (CATEs). Simulation-based power analysis confirmed the adequacy of the sample size for detecting significant treatment heterogeneity. Results demonstrated substantial variability in glycemic responses across individuals, with gut microbiome profiles accounting for a meaningful proportion of this variance. The proposed analytical framework supports the development of personalized dietary strategies informed by biological markers, thus contributing to the advancement of precision nutrition research.