Precision Nutrition: Your Diet, Optimized

Authored by: Ariana Dahi

Art by: Claire Ma

What if a doctor, or even an app, could tell you exactly what to eat to become your healthiest self, all based on your biological information? The NIH is currently funding Nutrition for Precision Health with the goal of creating diet plans tailored to participants’ unique biological makeup. To do this, researchers are examining the interactions of a variety of diets with genes, proteins, microbiomes, metabolism, and other factors. Combined with artificial intelligence, the project aims to create an algorithm that proposes precise dietary plans specified to different individuals’ needs. Precision nutrition holds promise in optimizing interventions for diseases like obesity, which alone affects over 650 million adults worldwide [1]. The challenge lies in the implementation of the project: ensuring that these innovations reach those who need them most and help bridge the gaps of health equity, not widen them.

The more widely known, "Personalized Nutrition" generally describes a nutritional plan based on a person’s attributes, and is a more tailored approach than any “one-size-fits-all’ advice [2]. "Precision Nutrition" builds upon this idea, as it dares to transform a mix of information from an individual’s psychological relationship with food and biological characteristics into quantitative information used to propose specific nutritional guidance [3]. The development of an effective precision nutrition device, however, demands extensive scientific knowledge and backing, which makes the NIH’s mission an ambitious one that requires a great amount of investment. In order to accelerate the project’s timeline, the NIH is using universities as partners such as Cornell University, where a $23 million was awarded to the Division of Nutritional Science in January of 2022 [4].

As promising as the project is, experts have raised various ethical concerns. One of the most prominent calls for caution are surrounding fears of the exacerbating health inequities. The promotion of health inequities may occur through biases, such as racial and gender biases, in the training data that could then lead to algorithms that are ineffective for minority groups [5]. Many groups have been including information such as ethnicity, gender, and education in their datasets to control for some biases [5]. Experts working with the NIH, like Hillard, contend that while race does not have any biological bases and should only be accounted for as a means of addressing discrimination, ancestry DNA is a valuable piece of information that can increase the precision of algorithms. She points to how many African Americans have ancestors either from the deep interior of West Africa or coastal West Africa and that because of historical differences in agriculture have significant genetic differences that cause for a 2- to 100-fold increased risk for kidney disease [3]. Another popular concern lies in ensuring the security of user data and the ethical usage of personal information. As partners, Cornell and RTI are working to create standard protocols to address these possible issues. These protocols are aimed at outlining how participants in the study should be enrolled and how the safety of their information will be monitored [4].

Precision nutrition remains a new and exciting field with lots of promise. As algorithms are being developed and leveraged by users, it is important for safeguards to be put in place and assessed to confront health equity and data security issues. Additionally, it is imperative that policies that promote the accessibility of affordable healthy foods are expanded in the coming and that people actually have the means to follow the dietary plans. Precision Nutrition has the potential to make waves in the medical community, as long as important social factors are taken into account.

Works Cited

1. Mehta, N. H., Huey, S. L., Kuriyan, R., Peña-Rosas, J. P., Finkelstein, J. L., Kashyap, S., & Mehta, S. (2024). Potential Mechanisms of Precision Nutrition-Based Interventions for Managing Obesity. Advances in nutrition (Bethesda, Md.), 15(3), 100186. https://doi.org/10.1016/j.advnut.2024.100186

2. National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Food and Nutrition Board; Food Forum; Callahan AE, editor. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop—in Brief. Washington (DC): National Academies Press (US); 2021 Dec 8. Available from:https://www.ncbi.nlm.nih.gov/books/NBK575794/ doi: 10.17226/26407.

3. National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Food and Nutrition Board; Food Forum; Callahan AE, editor. Challenges and Opportunities for Precision and Personalized Nutrition: Proceedings of a Workshop—in Brief. Washington (DC): National Academies Press (US); 2021 Dec 8. Available from:https://www.ncbi.nlm.nih.gov/books/NBK575794/ doi: 10.17226/26407.

4. Dean, J., & January 20, 2022. (2022, January 20). Cornell to co-lead NIH Center for Precision Nutrition Research. Cornell Chronicle. https://news.cornell.edu/stories/2022/01/cornell-co-lead-nih-center-precision-nutrition-research 

5. Xizhi Wu, David Oniani, Zejia Shao, Paul Arciero, Sonish Sivarajkumar, Jordan Hilsman, Alex E Mohr, Stephanie Ibe, Minal Moharir, Li-Jia Li, Ramesh Jain, Jun Chen, Yanshan Wang, A Scoping Review of Artificial Intelligence for Precision Nutrition, Advances in Nutrition, Volume 16, Issue 4, 2025, 100398, ISSN 2161-8313, https://doi.org/10.1016/j.advnut.2025.100398.

6. Ory Marcia G. , Adepoju Omolola E. , Ramos Kenneth S. , Silva Patrick S. , Vollmer Dahlke Deborah, Health equity innovation in precision medicine: Current challenges and future directions, Frontiers in Public Health, Volume 11, 2023, https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2023.1119736.