The realisation that diet and health are closely linked is nothing new. As early as 1850, the German philosopher Ludwig Feuerbach essentially stated: “You are what you eat”. Today, nutritional research is seeking to establish exactly how food influences our metabolome – that is, the metabolic processes in our bodies and their interactions. The gut microbiome also plays a crucial role in this. This is because the community of microorganisms is not only actively involved in digestion, but also produces essential vitamins, strengthens the intestinal barrier and trains the immune system. Exactly how this interaction works is still unclear, as the relationships between diet and health are complex. Furthermore, lifestyle factors and pre-existing conditions play an important role.

Dr Mattea Müller, a early-career researcher in the ‘Clinical Data Sciences’ research division at the Peter L. Reichertz Institute for Medical Informatics (PLRI) at Hannover Medical School (MHH), is addressing this issue. With her research group ‘Computational Precision Nutrition (CPN)’, she aims to gain a better understanding of the individual differences in metabolic responses to diet and their impact on health. The long-term aim is to develop an AI-based predictive tool that will enable doctors to provide personalised dietary recommendations for the prevention and treatment of their patients’ health conditions. The Federal Ministry of Research, Technology and Space (BMFTR) is funding the project with 1.8 million euros over five years.

Finding evidence of cause and effect

“Computer-aided approaches are revolutionising nutritional science by integrating data from wearables such as smartwatches or fitness trackers and from digital health platforms with multi-omics technologies, which provide insights into genes, their activity, proteins and metabolic processes in our bodies,” says Dr Müller. “Traditional statistical models, on the other hand, cannot adequately capture the temporal and individual variations inherent in such data.” This is because the data set itself is not uniform. For example, laboratory results from blood tests can yield different outcomes and are generally difficult to compare with one another due to non-identical testing procedures and reference ranges.

The project focuses on developing computer-based models that harmonise the data and highlight the interactions between diet, the microbiome and the metabolome, as well as their significance for health. “We want to use AI-based methods to derive scientifically verifiable cause-and-effect relationships that can explain why, for example, a particular diet has a positive effect on one person but not on another,” explains the molecular biologist and data scientist.

Not all apples are the same

One of the research group’s aims is therefore not only to understand individual metabolic responses to diet, but also to predict them. The influence of gender and any pre-existing conditions is also to be taken into account. To achieve this, the researchers must first collect data from as many people as possible, which they can use to train the AI. This data comes from databases in Germany, the UK and the Netherlands. The problem lies in the diversity – not only among people, but also in the food itself. “One apple is not necessarily the same as another; there are already enormous differences in nutrient composition between varieties and growing regions worldwide,” the molecular biologist points out.

Ultimately, an interactive platform for precision nutrition – the CPN-Map – is intended to be made available as a user-friendly tool, initially for research purposes. It remains to be seen whether the analyses actually always provide reliable predictions. If this is the case, the CPN-Map could be put into clinical use. With individually tailored, personalised nutritional recommendations, it aims to help improve patients’ metabolic health and prevent conditions such as obesity, type 2 diabetes and neurodegenerative diseases.

Peter L. Reichertz Institute for Medical Informatics (PLRI)

The Peter L. Reichertz Institute for Medical Informatics (PLRI) is a joint institute of the MHH and the Technical University of Braunschweig. Also involved in the project “Computational Precision Nutrition: Mapping the Interactions between Diet, Microbiome and Metabolome in Health and Disease (CPN-Map)” are Leibniz University Hannover and the Lower Saxony Centre for AI and Causal Methods in Medicine (CAIMed), as well as the University of Kiel and Maastricht University.

SERVICE:
Further information can be found here: https://plri.de/forschung/nachwuchsgruppen/computational-nutrician-science

Further information is available from Dr Mattea Müller, mueller.mattea@mh-hannover.de.