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

Area of Science:

• Metabolic Health
• Gut Microbiome Research
• Nutritional Science

Background:

• Serum free fatty acids (FFA) are elevated in obesity.
• Gut microbiota influences host energy metabolism and fat storage.
• A connection exists between diet, FFA, and intestinal microbiota.

Purpose of the Study:

• To investigate the interplay between serum FFA, gut microbiota, diet, and obesity.
• To identify predictive factors for obesity using a regression tree model.

Main Methods:

• Analysis of serum FFA by enzymatic assay and MTBE extraction.
• Gut microbiota determination using qPCR.
• Dietary assessment via food frequency questionnaire.
• Machine learning-based classification using decision trees.

Main Results:

• Obesity linked to decreased eicosapentaenoic acid (EPA) and increased linoleic, gamma-linolenic, and palmitic acids.
• Serum EPA and gender were the most significant predictors.
• Palmitic acid, Bifidobacterium, and Faecalibacterium were important factors, followed by Bacteroides and docosahexaenoic acid (DHA).
• A predictive model identified serum EPA and Bacteroides levels for non-obese status.

Conclusions:

• Specific FFA profiles and gut bacteria are associated with obesity.
• Serum EPA and gut microbiota composition are crucial for predicting obesity.
• Faecalibacterium and Bifidobacterium play complementary roles in obesity prediction across genders.