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对 n-3 多不饱和脂肪酸反应的异质性。

Heterogeneity in the response to n-3 polyunsaturated fatty acids.

机构信息

Department of Nutrition, Gillings School of Global Public Health & School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Canada.

出版信息

Curr Opin Clin Nutr Metab Care. 2023 May 1;26(3):284-287. doi: 10.1097/MCO.0000000000000930. Epub 2023 Mar 20.

Abstract

PURPOSE OF REVIEW

A central goal in the study of long chain n-3 polyunsaturated fatty acids (PUFA) is to translate findings from the basic sciences to the population level to improve human health and prevent chronic diseases. A tenet of this vision is to think in terms of precision medicine and nutrition, that is, stratification of individuals into differing groups that will have different needs across the lifespan for n-3 PUFAs. Therefore, there is a critical need to identify the sources of heterogeneity in the human population in the dietary response to n-3 PUFA intervention.

RECENT FINDINGS

We briefly review key sources of heterogeneity in the response to intake of long chain n-3 PUFAs. These include background diet, host genome, composition of the gut microbiome, and sex. We also discuss the need to integrate data from newer rodent models (e.g. population-based approaches), multi -omics, and analyses of big data using machine learning and data-driven cluster analyses.

SUMMARY

Accounting for vast heterogeneity in the human population, particularly with the use of big data integrated with preclinical evidence, will drive the next generation of precision nutrition studies and randomized clinical trials with long-chain n-3 PUFAs.

摘要

综述目的:研究长链 n-3 多不饱和脂肪酸 (PUFA) 的一个核心目标是将基础科学研究的成果转化为人群水平,以改善人类健康并预防慢性疾病。这一愿景的一个原则是从精准医学和营养的角度思考问题,即根据个体的不同,将其分为不同的人群,这些人群在整个生命周期中对 n-3PUFA 的需求是不同的。因此,迫切需要确定人群对 n-3PUFA 干预的饮食反应中存在的异质性的来源。

最新发现:我们简要回顾了对长链 n-3PUFA 摄入量反应中存在的关键异质性来源。这些来源包括背景饮食、宿主基因组、肠道微生物组组成和性别。我们还讨论了需要整合来自新型啮齿动物模型(例如基于人群的方法)、多组学以及使用机器学习和基于数据的聚类分析对大数据进行分析的数据。

总结:考虑到人群中存在巨大的异质性,特别是使用与临床前证据相结合的大数据,将推动下一代长链 n-3PUFA 的精准营养研究和随机临床试验。

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