Causal Machine Learning for Personalized Recommendation of Bioactive Peptides in Aging-Related Disease Prevention

Abstract

Aging-related diseases represent a convergent burden shaped by biological aging, chronic inflammation, metabolic dysregulation, microbiome shifts, and cumulative environmental exposures. Bioactive peptides derived from food and animal sources have attracted attention because they may modulate oxidative stress, immune signaling, metabolic pathways, and tissue repair, yet their effects are heterogeneous and context dependent. Most existing recommendation systems for nutrition and preventive interventions rely on associational learning, which limits their capacity to distinguish causal effects from confounding, selection bias, and reverse causation. This paper examines causal machine learning as a system-level architecture for personalized recommendation of bioactive peptides in aging-related disease prevention. It discusses how causal representation learning, heterogeneous treatment effect estimation, reinforcement learning, and network medicine can be integrated with multimodal data infrastructures spanning clinical records, omics, microbiome profiling, dietary behavior, wearables, and product composition databases. The analysis emphasizes structural trade-offs involving personalization, interpretability, robustness, fairness, privacy, and sustainability. It also considers governance requirements for deployment, including validation, monitoring, model updating, consent, equitable access, and regulatory oversight. The central argument is that causal machine learning can improve peptide recommendation only if it is embedded within a socio-technical system that treats evidence generation, data stewardship, and translational accountability as first-order design concerns. Such a system must remain modest about uncertain effects while enabling safe, adaptive, and equitable preventive pathways.