Using computer analysis to create diets that prevent disease

Using computer analysis to create diets that prevent disease
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New research has used computer analysis to help design specifically tailored diets that could help prevent disease.

Researchers from Chalmers University of Technology in Sweden have developed a new mathematical model for the interaction of gut bacteria which could aid the design of new probiotics and specially tailored diets to prevent disease.

The studies, published in the journal PNAS, involved regular measurements of health indicators, which the researchers compared with the predictions made from their mathematical model which proved to be highly accurate in predicting multiple variables, including how a switch from liquid to solid food in the Swedish infants affected their intestinal bacterial composition.

Modelling diets

The paper describes how the model performed when making predictions relating to two clinical studies, one involving Swedish infants, and the other involving adults in Finland with obesity. As well as accurately predicting multiple variables, the team also measured how the obese adults’ intestinal bacteria changed after a move to a more restricted diet, which also proved to be reliably accurate.

“Intestinal bacteria have an important role to play in health and the development of diseases, and our new mathematical model could be extremely helpful in these areas,” says Jens Nielsen, Professor of Systems Biology at Chalmers, who led the research.

“These are very encouraging results, which could enable computer-based design for a very complex system. Our model could therefore be used for creating personalised healthy diets, with the possibility to predict how adding specific bacteria as novel probiotics could impact a patient’s health.”

Understanding bacteria

How different bacteria grow and function in the intestinal system is impacted by a variety of factors such as which other bacteria are already present and how they interact with each other, as well as how they interact with the host and environmental factors such as the diet we eat. To make predictions about the behaviour of bacteria one must first understand how these bacteria are likely to act when they enter the intestine or how a change in diet will affect the intestinal composition, such as whether they will be able to grow there, how they interact with and possibly affect the bacteria that are already present in the gut, and how different diets affect the intestinal microbiome.

“The model we have developed is unique because it accounts for all these variables. It combines data on the individual bacteria as well as how they interact. It also includes data on how food travels through the gastrointestinal tract and affects the bacteria along the way in its calculations. The latter can be measured by examining blood samples and looking at metabolites, the end products that are formed when bacteria break down different types of food,” says Nielsen.

The data to build the model has been gathered from many years’ worth of pre-existing clinical studies. As more data is obtained in the future, the model can be updated with new features, such as descriptions of hormonal responses to dietary intake.

Preventing diseases

Working with bacterial composition offers the potential to influence the course of diseases and overall health through treatment with probiotics, which are carefully selected bacteria that are believed to contribute to improved health.

Neilson said: “Changes in the bacterial composition can be associated with or signify a great number of ailments, such as obesity, diabetes, or cardiovascular diseases. It can also affect how the body responds to certain types of cancer treatments or specially developed diets.”

Nielsen and his research group will use the model directly in clinical studies and are already participating in a study together with Sahlgrenska University Hospital in Sweden, where older women are being treated for osteoporosis with the bacteria Lactobacillus reuteri.

Cancer treatment with antibodies is another area where the model could be used to analyse the microbiome, helping to understand its role in why some patients respond well to immunotherapy, and some do not.

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