Exploring the interactions between serum free fatty acids and fecal microbiota in obesity through a machine learning algorithm.
Journal:
Food research international (Ottawa, Ont.)
PMID:
31108778
Abstract
Serum free fatty acids (FFA) are generally elevated in obesity. The gut microbiota is involved in the host energy metabolism through the regulation of body fat storage, and a link between diet, FFA and the intestinal microbiota seems to exist. Our aim was to explore the interaction among serum FFA levels, gut microbiota, diet and obesity through a model regression tree in 66 subjects (age 52.7 ± 11.2 y) classified according to Body Mass Index (BMI). Total and individual FFA were analyzed by colorimetric enzymatic assay and methyl-tert-butylether-based extraction protocol (MTBE), respectively. Microbiota was determined by qPCR and diet through a food frequency questionnaire. Statistical analyses were performed, and predictive factors for obesity were obtained via classification by decision trees using machine learning methods. An obese-linked FFA profile was characterized by decreased eicosapentaenoic (EPA) and increased linoleic, gamma-linolenic and palmitic acids levels simultaneously. Serum EPA and gender were identified as the most significant variables with 100% and 80% of importance, respectively. Palmitic acid, Bifidobacterium and Faecalibacterium explained >30%, followed by Bacteroides group with 20% and docosahexaenoic acid (DHA) almost with 15% of importance. Also, the regression tree model obtained for predicting obesity, showed a non-obese-linked profile, independently of gender, with serum EPA > 0.235 μg/mL and Bacteroides > 9.055 log n° cells per g of feces. Moreover, Faecalibacterium and Bifidobacterium seemed to play an important role by complementing the levels of FFA in predicting obesity in males and females, respectively.