December 2022: Cohen and Borenstein predict dietary habits based on microbiome
Yotam Cohen, Edmond J. Safra MSc fellow, and Prof. Elhanan Borenstein have developed the first algorithm of its kind, which is able to translate information about the composition of the microbiome into accurate information about the dietary habits of a person or a certain population.
Yotam Cohen, Edmond J. Safra MSc fellow (Computer Science; Borenstein lab) and Prof. Elhanan Borenstein, Edmond J. Safra member (Computer Science and Medicine), have developed the first algorithm of its kind, which is able to translate information about the composition of the microbiome (the population of bacteria in our gut) into accurate information about the dietary habits of a person or a certain population - in particular, with regard to plant-based foods that contain dietary fibers. This study was published in the journal BMC Biology.
The term 'dietary fiber' is well known to the general public, and frequently appears on food product packaging. These dietary fibers are complex carbohydrates that our digestive system is unable to break down, and are generally degraded by the bacteria in our gastrointestinal tract. However, it is important to note that there are in fact many different types of dietary fiber and that any type of plant, fruit, or vegetable contains different dietary fibers. Each type of fiber, in turn, may have certain bacteria that feed on it and know how to break it down. Studies in recent years have examined the mutual relationship between the gut bacteria and the host's diet, but until now, researchers have struggled to show a clear correlation between the composition of the gut bacteria and the types of fibers found in the host's diet.
Cohen and Borenstein sought to address this issue, using computational methods and big data. They used large metabolic databases and microbiome samples from around the world, and developed a computational algorithm that calculates for each microbiome sample an “inferred fiber degradation profile”. They then applied this algorithm to several populations of humans and animals around the world and showed how the composition of the microbiome and its degradation capacities accurately reflect the dietary habits of these communities.
The researchers explain that this approach enables a direct 'translation', which was not possible until now, of the microbiome structure of a person or a certain population to the dietary habits of that individual or that population, and could be used for nutritional advice to improve the habits and health of people around the world.