An AI-driven evidence ecosystem for cardiometabolic nutrition: bridging the gap from multiomics to patient empowerment

Abstract

Cardiometabolic diseases remain a primary global health challenge, with nutritional interventions serving as a critical pillar of management. However, the rapidly expanding volume of nutritional research, spanning high-dimensional multiomics, real-world continuous monitoring, and highly heterogeneous clinical trials, has created fragmented knowledge silos, leading to a profound translation lag between scientific discovery and clinical practice. To overcome this methodological bottleneck, we propose a conceptual framework for an “AI-Driven Evidence Ecosystem.” This perspective outlines a four-phase closed-loop architecture: (1) AI-assisted mechanistic decoding of multiomics and the development of digital gut twins; (2) data-optimized clinical research integrating traditional trials with real-world digital biomarkers; (3) a methodologically agnostic, multi-agent living evidence synthesis engine capable of automating complex data extraction and mitigating the translation gap; and (4) the deployment of conversational AI for public knowledge translation and personalized patient empowerment. By addressing concurrent ethical challenges, algorithmic biases, and the risk of researcher de-skilling, this ecosystem paradigm leverages artificial intelligence not merely as an automation tool, but as an indispensable bridge. Ultimately, it transforms isolated, stagnant data into a dynamic, translatable evidence network, reshaping the future of precision cardiometabolic care.