Could Gut Microbiota Composition Be a Useful Indicator of a Long-Term Dietary Pattern?

• Published in: Nutrients Vol. 15; no. 9; p. 2196
• Main Authors: Šik Novak, Karin, Bogataj Jontez, Nives, Petelin, Ana, Hladnik, Matjaž, Baruca Arbeiter, Alenka, Bandelj, Dunja, Pražnikar, Jure, Kenig, Saša, Mohorko, Nina, Jenko Pražnikar, Zala
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 05.05.2023; MDPI
• Subjects: Bacteria; biomarkers; Blood pressure; blood serum; Body mass index; Diet; DNA barcoding; eating habits; Fatty acids; Food habits; food intake; gastrointestinal system; gender; genes; Gut microbiota; high fat diet; intestinal microorganisms; lifestyle; Low-carbohydrate diet; Metabolism; Microbiota; Microbiota (Symbiotic organisms); omnivores; Participation; Prevotella; Proteins; RNA; Tumor necrosis factor-TNF; Type 2 diabetes; vegan diet; Vegetarianism; United States > US; low-carbohydrate; dietary pattern; high-fat; omnivorous; vegetarian; vegan; gut microbiota
• ISSN: 2072-6643; 2072-6643
• DOI: 10.3390/nu15092196

Despite the known effects of diet on gut microbiota composition, not many studies have evaluated the relationship between distinct dietary patterns and gut microbiota. The aim of our study was to determine whether gut microbiota composition could be a useful indicator of a long-term dietary pattern. We collected data from 89 subjects adhering to omnivorous, vegetarian, vegan, and low-carbohydrate, high-fat diet that were equally distributed between groups and homogenous by age, gender, and BMI. Gut microbiota composition was analyzed with a metabarcoding approach using V4 hypervariable region of the 16S rRNA gene. K-means clustering of gut microbiota at the genus level was performed and the nearest neighbor classifier was applied to predict microbiota clustering classes. Our results suggest that gut microbiota composition at the genus level is not a useful indicator of a subject’s dietary pattern, with the exception of a vegan diet that is represented by a high abundance of Prevotella 9. Based on our model, a combination of 26 variables (anthropometric measurements, serum biomarkers, lifestyle factors, gastrointestinal symptoms, psychological factors, specific nutrients intake) is more important to predict an individual’s microbiota composition cluster, with 91% accuracy, than the dietary intake alone. Our findings could serve to develop strategies to educate individuals about changes of some modifiable lifestyle factors, aiming to classify them into clusters with favorable health markers, independent of their dietary pattern.