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通过数学方法整合食物为基础的膳食指南的各个方面:2019 年 9 月 23 日至 24 日在德国波恩举行的 DGE/FENS 研讨会报告。

Integration of various dimensions in food-based dietary guidelines via mathematical approaches: report of a DGE/FENS Workshop in Bonn, Germany, 23-24 September 2019.

机构信息

German Nutrition Society, 53175Bonn, Germany.

Department of Nutrition and Food Sciences, Nutritional Epidemiology, University of Bonn, 53012Bonn, Germany.

出版信息

Br J Nutr. 2021 Sep 28;126(6):942-949. doi: 10.1017/S0007114520004857. Epub 2020 Dec 4.

Abstract

In the past, food-based dietary guidelines (FBDGs) were derived nearly exclusively by using systematic reviews on diet-health relationships and translating dietary reference values for nutrient intake into foods. This approach neglects many other implications that dietary recommendations have on society, the economy and environment. In view of pressing challenges, such as climate change and the rising burden of diet-related diseases, the simultaneous integration of evidence-based findings from different dimensions into FBDGs is required. Consequently, mathematical methods and data processing are evolving as powerful tools in nutritional sciences. The possibilities and reasons for the derivation of FBDGs via mathematical approaches were the subject of a joint workshop hosted by the German Nutrition Society (DGE) and the Federation of European Nutrition Societies (FENS) in September 2019 in Bonn, Germany. European scientists were invited to discuss and exchange on the topics of mathematical optimisation for the development of FBDGs and different approaches to integrate various dimensions into FBDGs. We concluded that mathematical optimisation is a suitable tool to formulate FBDGs finding trade-offs between conflicting goals and taking several dimensions into account. We identified a lack of evidence for the extent to which constraints and weights for different dimensions are set and the challenge to compile diverse data that suit the demands of optimisation models. We also found that individualisation via mathematical optimisation is one perspective of FBDGs to increase consumer acceptance, but the application of mathematical optimisation for population-based and individual FBDGs requires more experience and evaluation for further improvements.

摘要

过去,食物为基础的膳食指南(FBDG)几乎完全是通过对饮食与健康关系的系统评价,并将营养素摄入量的膳食参考值转化为食物来制定的。这种方法忽略了饮食建议对社会、经济和环境的许多其他影响。鉴于气候变化和与饮食相关的疾病负担增加等紧迫挑战,需要将来自不同维度的循证发现同时纳入 FBDG。因此,数学方法和数据处理正在成为营养科学的有力工具。通过数学方法制定 FBDG 的可能性和原因是德国营养学会(DGE)和欧洲营养学会联合会(FENS)于 2019 年 9 月在德国波恩联合主办的一次研讨会的主题。邀请欧洲科学家讨论和交流关于为制定 FBDG 进行数学优化的主题,以及将不同维度纳入 FBDG 的不同方法。我们得出的结论是,数学优化是制定 FBDG 的一种合适工具,可以在相互冲突的目标之间找到权衡,并考虑到多个维度。我们发现,对于不同维度的约束和权重设定到何种程度,以及对于适合优化模型需求的多样化数据的汇编,证据不足。我们还发现,通过数学优化进行个性化是提高消费者接受度的 FBDG 的一个视角,但为基于人群和个体的 FBDG 应用数学优化需要更多的经验和评估,以进一步改进。

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