Exploring the interactions between serum free fatty acids and fecal microbiota in obesity through a machine learning algorithm

• Published in: Food research international Vol. 121; pp. 533 - 541
• Main Authors: Fernández-Navarro, Tania, Díaz, Irene, Gutiérrez-Díaz, Isabel, Rodríguez-Carrio, Javier, Suárez, Ana, de los Reyes-Gavilán, Clara G., Gueimonde, Miguel, Salazar, Nuria, González, Sonia
• Format: Journal Article
• Language: English
• Published: Canada Elsevier Ltd 01.07.2019
• Subjects: algorithms; artificial intelligence; Bacteroides; Bifidobacterium; blood serum; body mass index; colorimetry; decision support systems; Diet; docosahexaenoic acid; eicosapentaenoic acid; energy metabolism; EPA; feces; females; food frequency questionnaires; Free fatty acids; intestinal microorganisms; linoleic acid; males; Microbiota; Obesity; palmitic acid; prediction; protocols; quantitative polymerase chain reaction; regression analysis; Obesity; Microbiota; Bifidobacterium; Diet; EPA; Free fatty acids; Bacteroides
• ISSN: 0963-9969; 1873-7145; 1873-7145
• DOI: 10.1016/j.foodres.2018.12.009

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. [Display omitted] •Serum EPA level is the main factor linked to obesity, independently of gender•High EPA and Bacteroides level are good lean predictors, independently of gender•Gut Microbiota together with FFA play a gender-specific role in obesity•Fecal Faecalibacterium level is the best indicator of obese linked profile in males•Fecal Bifidobacterium complements FFA levels to predict obesity in women