Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Journal of internal medicine

INTRODUCTION : The gut microbiome may contribute to the development of obesity. So far, the extent of microbiome variation in people with obesity has not been determined in large cohorts and for a wide range of body mass index (BMI). Here, we aimed to investigate whether the faecal microbial metagenome can explain the variance in several clinical phenotypes associated with morbid obesity.

METHODS : Caucasian subjects were recruited at our hospital. Blood pressure and anthropometric measurements were taken. Dietary intake was determined using questionnaires. Shotgun metagenomic sequencing was performed on faecal samples from 177 subjects.

RESULTS : Subjects without obesity (n = 82, BMI 24.7 ± 2.9 kg m-2 ) and subjects with obesity (n = 95, BMI 38.6 ± 5.1 kg m-2 ) could be clearly distinguished based on microbial composition and microbial metabolic pathways. A total number of 52 bacterial species differed significantly in people with and without obesity. Independent of dietary intake, we found that microbial pathways involved in biosynthesis of amino acids were enriched in subjects with obesity, whereas pathways involved in the degradation of amino acids were depleted. Machine learning models showed that more than half of the variance in body fat composition followed by BMI could be explained by the gut microbiome composition and microbial metabolic pathways, compared to 6% of variation explained in triglycerides and 9% in HDL.

CONCLUSION : Based on the faecal microbiota composition, we were able to separate subjects with and without obesity. In addition, we found strong associations between gut microbial amino acid metabolism and specific microbial species in relation to clinical features of obesity.

Meijnikman A S, Aydin O, Prodan A, Tremaroli V, Herrema H, Levin E, Acherman Y, Bruin S, Gerdes V E, Backhed F, Groen A K, Nieuwdorp M


amino acids, gut microbiome, histidine, lipids, machine learning, metabolism, obesity