The consumption of highly processed foods, along with other dietary and lifestyle poor habits, has an impact on health by increasing the risk of several non-communicable pathologies, such as diabetes. Gut microbiome composition, in specific, can be modulated by nutrients, deriving in different metabolic outcomes that have an influence on this high disease susceptibility and making it a possible therapeutic target for these comorbidities. In this work, gut microbiome of 60 and 46 individuals from 2 different studies focused, among other aspects, on diet-microbiome interactions, was characterised. By means of differential abundance analyses and supervised machine learning techniques based on random forest, gradient boosting and support vector machines, a set of microbial genera that could be potential biomarkers for the differentiation of individuals with poorer dietary patterns was discovered, after comparing coincidences in these taxa among classifiers and testing them for significant differences. Among these, Dialister, Phocea and Pseudoflavonifractor were suggested to have a role in the way highly processed foods affect health negatively, along with Prevotellaceae NK3B31 group and an undetermined genus from Muribaculaceae in the opposite sense. Furthermore, all the identified genera in this study had already been linked to type 2 diabetes, among which Bacteroides and Pseudoflavonifractor proved to be differentially abundant in groups of individuals with different levels of biomarkers for this disease. Nevertheless, further research via longitudinal studies and experimental validation of these genera should be carried out to confirm the association of these taxa with diet and diabetes.

Obesity has an impact on health by increasing the risk of various diseases. However, these risks might also depend on the metabolic health status, as it seems that metabolically healthy obese subjects are under a reduced risk of suffering comorbidities such as colorectal cancer. The gut microbiome has an effect on obesity and metabolic disorders through several integration pathways, making it a potential therapeutic target for these diseases. In this study, we characterized the gut microbiota of 356 obese and non-obese European individuals with different comorbidities associated with obesity. Using approaches based on supervised machine learning and network biology, we found a set of biomarkers of interest for differentiating metabolically healthy from unhealthy subjects. Then, we performed a linear discriminant analysis of effect size on a population of 1593 colorectal cancer, adenoma and control subjects assembled by the COST Action ML4Microbiome to investigate their role in colorectal cancer risk. Four of our biomarkers appeared in both approaches, suggesting their possible role in colorectal cancer development, prognosis and follow up: Clostridium leptum, Gordonibacter pamelaeae, Eggerthella lenta and Collinsella intestinalis. Further research via longitudinal studies or experimental validation of these microbial species would be necessary to confirm this association.