# Personalising dietary advice for disease prevention: concepts and experiences

## TL;DR

Personalized nutrition aims to tailor dietary advice using genetic and biomedical data, but recent approaches are expanding to include lifestyle and environmental factors for better health outcomes.

## Contribution

The paper introduces new avenues for personalizing dietary advice that extend beyond biomedical data to include the individual's food environment and lifestyle.

## Key findings

- Personalized dietary advice based on biomedical measures often fails to provide significant health benefits.
- New approaches incorporate the individual's food environment and use AI for real-time, customized advice.
- These systems can also promote physical activity and healthier lifestyles.

## Abstract

Personalised nutrition (PN) as a new endeavour emerged in the background of the human genome project with the ease to analyse genetic heterogeneity. First commercial offers with recommendations for diet and lifestyle changes, usually based on a few polymorphisms, entered markets soon after the presentation of the human genome blueprint. Although PN has seen many attempts, meanwhile, with the inclusion of other biomedical measures such as microbiome and/or continuous glucose monitoring, scientific assessments of such approaches in various settings revealed limited success. Although personalisation improved general compliance over generic advice, particular benefits in referring to biomedical measures and individual risks did, in most cases, not provide any significant advantage. Moreover, scholars criticised such approaches as of limited impact from a public health perspective by attracting mainly technology-open individuals of high social status and proper financial capabilities. Based on these experiences, new avenues for personalising dietary advice are developed, and those are going beyond pure biomedical data by assessing the entire food environment of the individual with its capabilities and constraints in the given life setting. Embedded into digital environments for data collection but also for bidirectional communication, new possibilities emerge. Artificial intelligence methods allow for the multitude of input data and highly complex decision trees to be derived to customize advice. And that can be delivered on the spot and in time in any language whenever decisions are made on what to buy or what to eat. But systems can also be employed to increase physical activity levels and for the adoption of a more healthy lifestyle in general.