Interest in personalised nutrition is growing among researchers, healthcare practitioners, consumers, and the food industry. While there’s no single official definition, it generally refers to moving away from a one-size-fits-all approach to nutrition and providing advice tailored to individual needs (1).

Nutrition practitioners personalise guidance based on factors like dietary preferences, current diet, lifestyle, health status, and potential barriers to achieving dietary goals. This holistic approach can include exercise habits, stress levels, sleep patterns, gut health, medication use, nutrient deficiencies, and weight management. Practical considerations, such as time constraints, budget, and knowledge gaps, are also factored in to make recommendations sustainable and realistic.

Emerging technologies like genetic testing, microbiome analysis, glucose monitoring, and AI-driven tools are adding new layers of insight to help personalise diets even further.

This article explores the evolving field of personalised nutrition, highlights key areas of research, and reflects on the challenges of bringing these innovations into everyday clinical practice.

The foundation of personalised nutrition lies in understanding that individuals respond differently to foods based on their genotypic (genetic, epigenetic) and phenotypic (physical traits, disease markers) characteristics. This approach helps identify those who may be at increased risk for certain diseases or health issues and tailor nutrition to mitigate those risks (2).

🔬 Genotypic Characteristics

Genetic differences affect how people metabolise and respond to nutrients. Personalised nutrition uses genetic testing to explore variations in nutrient metabolism, food sensitivities, and preferences.

• Nutrigenomics studies how nutrients influence gene expression.
• Nutrigenetics looks at how genetic variation affects response to diet.

Understanding someone’s genetic profile can help inform dietary decisions that may impact metabolism and long-term health.

📊 Phenotypic Characteristics

Age, sex, ethnicity, body composition, symptoms, and activity levels all influence nutritional needs. The composition of the gut microbiota also plays a role in how food is digested and utilised.

Examples of Personalised Nutrition in Research

1. Food4Me Study

A randomised control trial led by Prof. Mike Gibney at UCD, the Food4Me study tested whether internet-delivered personalised nutrition advice (with or without genetic/phenotypic data) improved dietary behaviours better than general advice (3).

Participants were split into four groups:

1. General dietary advice
2. Personalised advice based on baseline diet
3. Baseline diet + phenotype (biomarkers, body measurements)
4. Baseline diet + phenotype + genotype

After 6 months, those receiving personalised advice made greater dietary improvements (e.g., reduced red meat, salt, and saturated fat, and increased folate). However, adding genetic or phenotypic data didn’t significantly boost results beyond personalised advice based on diet alone.

✅ Conclusion: Personalisation can improve diet quality, but the additional impact of genetic testing is still unclear.

2. Zeevi et al. – Glycaemic Response Prediction

In this landmark study, 800 participants wore continuous glucose monitors to track blood sugar responses to identical meals. Diet, lifestyle, and gut microbiome data were collected, and AI models were used to predict individual responses (4).

A group receiving personalised diet plans based on this data achieved better glycaemic control than those receiving general advice.

✅ Conclusion: Personalised diets based on glucose responses and gut microbiome data show promise, especially for conditions like diabetes, though benefits over general advice was not substantial.

3. PREDICT Study (ZOE Programme)

Led by Prof. Tim Spector at King’s College London, this ongoing research collects vast data on genetics, gut microbiome, blood sugar and fat responses, and lifestyle (5),(6). Machine learning is used to predict how individuals respond to different foods.

These findings support the ZOE programme (currently not in Ireland), which offers app-based, personalised nutrition plans. Participants can track how their body responds to food in real-time through at-home test kits.

✅ Conclusion: Big data and AI are opening exciting new doors in personalised nutrition, though these services are still evolving.

Can This Research Be Used in Everyday Life?

These studies support the potential of personalised nutrition, but there are still questions about its clinical relevance, cost-effectiveness, and scalability for public health. Key issues include:

• How many (and which) biomarkers are truly necessary?
• Will personalised nutrition create health inequalities based on access to technology?
• Is the benefit meaningful enough to warrant extra cost and complexity?

That said, the field is progressing quickly. Large-scale data, machine learning, and microbiome sequencing are creating promising pathways for tackling chronic diseases more effectively than general recommendations alone. For example, over the past few decades, numerous studies, including genome-wide association studies (GWAS) have identified genetic variants associated with various aspects of cardiovascular and diabetes health (7),(8),(9),(10),(11). This research has provided valuable insights and has the potential to inform risk prediction, prevention strategies and personalised treatment approaches.

An ongoing challenge is translating knowledge into action. Even if you know you’re at risk for a condition or would benefit from eating more vegetables, will that knowledge change your behaviour?

Personalised nutrition may help motivate behaviour change, especially when it feels directly relevant to your health, but this likely depends on factors like individual readiness, health status, and access to support.

Direct-to-Consumer Testing
These tests (e.g., for genetics or gut health) provide insights into potential risk factors. But without professional support, they can be difficult to interpret and even cause anxiety. And some tests overpromise what they can actually predict.

Working with a Nutrition Professional
Qualified nutrition practitioners can help select the most appropriate approach and interpret data in the context of your health, lifestyle, and goals. Some receive extra training in genetics or microbiome interpretation, and they can also offer practical, motivational strategies for sustainable change.

The approach to personalised nutrition can vary depending on whether you’re working with a dietitian, nutritionist, or nutritional therapist… something I’ll explore in a future post.

References

1.         Ordovas JM, Ferguson LR, Tai ES, Mathers JC. Personalised nutrition and health. The BMJ [Internet]. 2018 [cited 2025 Apr 23];361:bmj.k2173. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC6081996/

2.         Fenech M, El-Sohemy A, Cahill L, Ferguson LR, French TAC, Tai ES, et al. Nutrigenetics and Nutrigenomics: Viewpoints on the Current Status and Applications in Nutrition Research and Practice. J Nutrigenet Nutrigenomics [Internet]. 2011 Jul [cited 2025 Apr 23];4(2):69. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC3121546/

3.         Celis-Morales C, Livingstone KM, Marsaux CFM, Macready AL, Fallaize R, O’Donovan CB, et al. Effect of personalized nutrition on health-related behaviour change: evidence from the Food4Me European randomized controlled trial. Int J Epidemiol [Internet]. 2017 [cited 2025 Apr 23];46(2):578–88. Available from: https://pubmed.ncbi.nlm.nih.gov/27524815/

4.         Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, et al. Personalized Nutrition by Prediction of Glycemic Responses. Cell [Internet]. 2015 Nov 19 [cited 2025 Apr 23];163(5):1079–94. Available from: https://pubmed.ncbi.nlm.nih.gov/26590418/

5.         Berry SE, Valdes AM, Drew DA, Asnicar F, Mazidi M, Wolf J, et al. Human postprandial responses to food and potential for precision nutrition. Nat Med [Internet]. 2020 Jun 1 [cited 2025 Apr 23];26(6):964–73. Available from: https://pubmed.ncbi.nlm.nih.gov/32528151/

6.         Mazidi M, Valdes AM, Ordovas JM, Hall WL, Pujol JC, Wolf J, et al. Meal-induced inflammation: postprandial insights from the Personalised REsponses to DIetary Composition Trial (PREDICT) study in 1000 participants. Am J Clin Nutr [Internet]. 2021 Sep 1 [cited 2025 Apr 23];114(3):1028. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC8408875/

7.         Pérez RF, Santamarina P, Tejedor JR, Urdinguio RG, Álvarez-Pitti J, Redon P, et al. Longitudinal genome-wide DNA methylation analysis uncovers persistent early-life DNA methylation changes. J Transl Med [Internet]. 2019 Jan 9 [cited 2025 Apr 23];17(1). Available from: https://pubmed.ncbi.nlm.nih.gov/30626398/

8.         Jellema A, Zeegers MPA, Feskens EJM, Dagnelie PC, Mensink RP. Gly972Arg variant in the insulin receptor substrate-1 gene and association with Type 2 diabetes: a meta-analysis of 27 studies. Diabetologia [Internet]. 2003 Jul 1 [cited 2025 Apr 23];46(7):990–5. Available from: https://pubmed.ncbi.nlm.nih.gov/12819898/

9.         Xu M, Zhao J, Zhang Y, Ma X, Dai Q, Zhi H, et al. Apolipoprotein E Gene Variants and Risk of Coronary Heart Disease: A Meta-Analysis. Biomed Res Int [Internet]. 2016 [cited 2025 Apr 23];2016:3912175. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC5102878/

10.      Shi J, Liu Y, Liu Y, Li Y, Qiu S, Bai Y, et al. Association between ApoE polymorphism and hypertension: A meta-analysis of 28 studies including 5898 cases and 7518 controls. Gene. 2018 Oct 30;675:197–207.

11.      Xu M, Zhao J, Zhang Y, Ma X, Dai Q, Zhi H, et al. Apolipoprotein E Gene Variants and Risk of Coronary Heart Disease: A Meta-Analysis. Biomed Res Int [Internet]. 2016 [cited 2025 Apr 22];2016:3912175. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC5102878/