Interindividual Variability in Functional Food Clinical Trials: Statistical Challenges, Responder Phenotypes, and Implications for Precision Nutrition

Interindividual variability in response to dietary and functional food interventions is increasingly being recognized as a central issue in precision nutrition. However, individuals showing large pre–post changes are frequently classified as responders without adequately considering measurement error, within-person biological variability, regression to the mean, or distinction between the observed outcome variability and true heterogeneity in treatment effect. This review critically examines responder and non-responder concepts in functional food and dietary intervention trials, focusing on statistical interpretation, clinical trial design, biological determinants, metabolomics, metabotyping, multi-omics integration, and machine-learning approaches. We highlight the limitations of threshold-based responder classification, separate within-group significance testing, post hoc subgroup analyses, and high-dimensional predictive models without appropriate validation. Evidence from representative intervention studies is organized along a progression from retrospective responder classification and biological characterization to baseline predictor identification, predictive modeling, external validation, and prospective response-guided intervention. Particular attention is given to the distinction between explaining why individuals appeared to respond and predicting response before intervention. Based on the methodological and empirical evidence reviewed, we propose a structured hierarchy for responder research, ranging from descriptive heterogeneity to prospective response-guided intervention. The key challenge for precision nutrition is therefore not simply to identify who appeared to respond, but to determine whether heterogeneous responses are reproducible, treatment-specific, prospectively predictable, and useful for improving intervention selection. Moving from retrospective classification toward validated prospective prediction will be essential for translating responder research in functional foods into rigorous and clinically meaningful precision nutrition.