Machine learning-based investigation of the relationship between gut microbiome and obesity status

Abstract

Gut microbiota is believed to play a crucial role in obesity. However, the consistent findings among published studies regarding microbiome-obesity interaction are relatively rare, and one of the underlying causes could be the limited sample size of cohort studies. In order to identify gut microbiota changes between normal-weight individuals and obese individuals, fecal samples along with phenotype information from 2262 Chinese individuals were collected and analyzed. Compared with normal-weight individuals, the obese individuals exhibit lower diversity of species and higher diversity of metabolic pathways. In addition, various machine learning models were employed to quantify the relationship between obesity status and Body mass index (BMI) values, of which support vector machine model achieves best performance with 0.716 classification accuracy and 0.485 R² score. In addition to two well-established obesity-associated species, three species that have potential to be obesity-related biomarkers, including Bacteroides caccae, Odoribacter splanchnicus and Roseburia hominis were identified. Further analyses of functional pathways also reveal some enriched pathways in obese individuals. Collectively, our data demonstrates tight relationship between obesity and gut microbiota in a large-scale Chinese population. These findings may provide potential targets for the prevention and treatment of obesity.

Keywords: Gut microbiota; Machine learning; Metagenome; Obesity.