Training Perspectives from the NIH T32 Artificial Intelligence for Precision Nutrition Programs

IF 4.2 3区医学 Q2 NUTRITION & DIETETICS

Journal of Nutrition Pub Date : 2026-10-01 Epub Date: 2026-08-22 DOI:10.1016/j.tjnut.2026.101796

Saurabh Mehta, Samantha L Huey, Paraskevi Massara, Li Liu, Corrie M Whisner, Youping Deng, Rachel Novotny, Janet Antwi, Seungchan Kim, Salma Musaad, David Erickson, Deborah Estrin, Julia L Finkelstein, Thorsten Joachims, Catherine H Kagemann, Rulla Tamimi, Diana Thomas, Martin T Wells

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引用次数: 0

Abstract

Nutrition is one of the few modifiable risk factors for health status. The diet–disease relationship, however, is highly complex, with many endogenous and exogenous factors contributing to risk and with variation in responses to diet between individuals. The 2020–2030 National Institutes of Health (NIH) Strategic Plan for Nutrition Research states that precision nutrition is a unifying and holistic approach to developing comprehensive and dynamic nutritional recommendations relevant to both individual and population health. Recognizing the growing importance of advanced computational approaches for such precision nutrition research, in 2022, the NIH Office of Nutrition Research and participating NIH Institutes and Centers published a funding opportunity announcement (FOA) for training programs in Artificial Intelligence for Precision Nutrition (AIPrN) aiming to develop a new generation of the scientific workforce, literate in both nutritional and computer sciences, that leverages the growing data resources available for nutrition research to tackle complex and emerging health–disease associations. In this paper, we describe the approaches developed by the first 4 institutional awardees of the T32 AIPrN training grant, and we highlight applied strategies for interdisciplinary training, data integration, and methodological innovation as the research community prepares to analyze and interpret data generated by major initiatives such as the NIH-funded Nutrition for Precision Health Initiative.

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来自美国国立卫生研究院T32人工智能精密营养计划的培训观点。

营养是影响健康状况的少数可改变的危险因素之一。然而，饮食与疾病的关系是非常复杂的，许多内源性和外源性因素都对风险有影响，而且个体对饮食的反应也存在差异。《2020- 2030年美国国立卫生研究院营养研究战略计划》指出，精确营养是制定与个人和人口健康相关的全面和动态营养建议的统一和整体方法。认识到先进的计算方法在营养研究中的重要性日益增加，2022年，美国国立卫生研究院营养研究办公室和参与的美国国立卫生研究院研究所和中心发布了一份资助机会公告，用于人工智能精密营养（AIPrN）培训计划，旨在培养新一代具有营养和计算机科学知识的科学工作者。这就利用了营养研究中可用的日益增长的数据资源来处理复杂的和新出现的健康-疾病关联。在本文中，我们描述了T32 AIPrN培训资助的前四个机构获奖者所开发的方法，并强调了跨学科培训、数据整合和方法创新的应用策略，因为研究界准备分析和解释由美国国立卫生研究院资助的“精确健康营养倡议”等主要倡议产生的数据。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Nutrition 医学-营养学

CiteScore

7.60

自引率

4.80%

发文量

260

审稿时长

39 days

期刊介绍： The Journal of Nutrition (JN/J Nutr) publishes peer-reviewed original research papers covering all aspects of experimental nutrition in humans and other animal species; special articles such as reviews and biographies of prominent nutrition scientists; and issues, opinions, and commentaries on controversial issues in nutrition. Supplements are frequently published to provide extended discussion of topics of special interest.