The gut microbiome as non-invasive biomarkers for identifying overweight people at risk for osteoarthritis.

OBJECTIVE

To evaluate the potential for identifying overweight people at risk for osteoarthritis from a gut microbiome biomarker.

BACKGROUND

Osteoarthritis (OA) is the most common form of arthritis, affecting millions of people worldwide. Being overweight increases the load placed on the joints such as the knee, which increases stress and could hasten the breakdown of cartilage. Identifying overweight people at risk for osteoarthritis remains a challenge. However, emerging evidence indicates that microbial dysbiosis in the human gut might play an important role in many inflammatory diseases. Considering the role of inflammation in OA development, analysis of the gut microbiome might be a potential non-invasive tool for overweight individuals to evaluate their risk for OA.

RESULTS

In this prospective study, we collected 182 stool samples from overweight OA patients (n = 86) and overweight normal people (n = 96) (25 kg/m2<BMI<30 kg/m2). 16S ribosomal RNA gene sequencing for V3 and V4 regions on the Illumina MiSeq platform was used to identify bacteria at different levels and it showed that both the diversity and richness of the gut microbiome decreased in overweight OA patients. Correspondingly, 9 phyla and 87 genera had significantly differences between overweight OA patients and overweight normal people. Finally, we identified 7 optimal microbial biomarkers in genus levels as a panel, including Gemmiger, Klebsiella, Akkermansia, Bacteroides, Prevotella, Alistipes and Parabacteroides, to build the random forest model, and achieved a 83.36% area under the curve (AUC) of receiver operating characteristic (ROC).

CONCLUSION

We present the first 16S rRNA gene sequencing profiling study of stool microbiomes in overweight people to discover and validate microbial biomarkers indicating risk for OA. Our study successfully established a 7 biomarkers prediction panel, moving towards affordable non-invasive early diagnostic biomarkers for OA in stool samples from overweight individuals.