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个性化营养数据:衔接生物医学、心理行为学和食物环境方法以实现广泛的人群影响。

Data in Personalized Nutrition: Bridging Biomedical, Psycho-behavioral, and Food Environment Approaches for Population-wide Impact.

作者信息

Linseisen Jakob, Renner Britta, Gedrich Kurt, Wirsam Jan, Holzapfel Christina, Lorkowski Stefan, Watzl Bernhard, Daniel Hannelore, Leitzmann Michael

机构信息

Epidemiology, Medical Faculty, University of Augsburg, University Hospital Augsburg, Augsburg, Germany; Institute of Information Processing, Biometry and Epidemiology, Ludwig-Maximilians University, Munich, Germany.

Department of Psychology, University of Konstanz, Konstanz, Germany; Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany.

出版信息

Adv Nutr. 2025 Jul;16(7):100377. doi: 10.1016/j.advnut.2025.100377. Epub 2025 Jan 20.

Abstract

Personalized nutrition (PN) represents an approach aimed at delivering tailored dietary recommendations, products, or services to support both prevention and treatment of nutrition-related conditions and to improve individual health using genetic, phenotypic, medical, nutritional, and other pertinent information. However, current approaches have yielded limited scientific success in improving diets or in mitigating diet-related conditions. In addition, PN currently caters to a specific subgroup of the population rather than having a widespread impact on diet and health at a population level. Addressing these challenges requires integrating traditional biomedical and dietary assessment methods with psycho-behavioral, and novel digital and diagnostic methods for comprehensive data collection, which holds considerable promise in alleviating present PN shortcomings. This comprehensive approach not only allows for deriving personalized goals ("what should be achieved") but also customizing behavioral change processes ("how to bring about change"). We herein outline and discuss the concept of "Adaptive Personalized Nutrition Advice Systems," which blends data from 3 assessment domains: 1) biomedical/health phenotyping; 2) stable and dynamic behavioral signatures; and 3) food environment data. Personalized goals and behavior change processes are envisaged to no longer be based solely on static data but will adapt dynamically in-time and in-situ based on individual-specific data. To successfully integrate biomedical, behavioral, and environmental data for personalized dietary guidance, advanced digital tools (e.g., sensors) and artificial intelligence-based methods will be essential. In conclusion, the integration of both established and novel static and dynamic assessment paradigms holds great potential for transitioning PN from its current focus on elite nutrition to a widely accessible tool that delivers meaningful health benefits to the general population.

摘要

个性化营养(PN)是一种旨在提供量身定制的饮食建议、产品或服务的方法,以支持营养相关疾病的预防和治疗,并利用基因、表型、医学、营养及其他相关信息改善个体健康。然而,目前的方法在改善饮食或缓解与饮食相关的疾病方面取得的科学成效有限。此外,PN目前仅针对特定人群亚组,而非在人群层面上对饮食和健康产生广泛影响。应对这些挑战需要将传统生物医学和饮食评估方法与心理行为、新型数字和诊断方法相结合,以进行全面的数据收集,这有望缓解当前PN的不足。这种综合方法不仅能确定个性化目标(“应达成什么”),还能定制行为改变过程(“如何实现改变”)。我们在此概述并讨论“适应性个性化营养建议系统”的概念,该系统融合了来自三个评估领域的数据:1)生物医学/健康表型;2)稳定和动态行为特征;3)食物环境数据。个性化目标和行为改变过程预计将不再仅基于静态数据,而是会根据个体特定数据实时、就地动态调整。为成功整合生物医学、行为和环境数据以提供个性化饮食指导,先进的数字工具(如传感器)和基于人工智能的方法至关重要。总之,将既定的和新颖的静态与动态评估范式相结合,对于将PN从目前专注于精英营养转变为一种能为普通大众带来切实健康益处的广泛可用工具具有巨大潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bde3/12281445/0ad1f2754476/gr1.jpg

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