Authors
Qingyong Zheng, Jianguo Xu, Tengfei Li, Ming Liu, Yongjia Zhou, Min Tian, Shaojuan Huo, Li Zhou, Jinhui Tian
Published in
Frontiers in nutrition. Volume 13. Pages 1924184. Epub Sep 28, 2026.
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.
PMID:
42865675
Bibliographic data and abstract were imported from PubMed on 13 Oct 2026.
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