Exploring associations among pro-inflammatory cytokines, osteoarthritis, and gut microbiome composition in individuals with obesity using machine learning

Abstract

Objective

To investigate potentially novel and modifiable mechanisms of the effects of gut microbiome composition on obesity-related osteoarthritis (OA), focusing on cross-sectional relationships between microbiota, cytokines, and lipopolysaccharide (LPS).

Design

Johnston County OA Project participants (n ​= ​64) with (cases) and without (controls) OA in hands and knees, with age ≥55 years and obesity (BMI ≥30 ​kg/m²), provided samples for multiplex cytokine, LPS, and fecal microbiota analysis. Latent Dirichlet Allocation (LDA), a machine learning method to detect latent groups within data, was used to identify microbial enterotypes. LDA regression models were used to evaluate associations of enterotypes with demographics, cytokines associated with OA, and LPS.

Results

We identified 5 enterotypes. Enterotypes 3, 4 (most prevalent in our sample), and 5, dominated respectively by genera Akkermansia, Bacteroides, Ruminococcus/Phascolarctobacterium, were positively associated with control status, and inversely associated with levels of at least two cytokines associated with OA in our sample. We observed no associations of enterotypes with LPS levels. Enterotype 3 was inversely associated with thrombopoietin and IL-4 levels (b [95 ​% CIs] −0.19 [−0.43, 0.05] and −0.17 [−0.42, 0.08]), enterotype 5 with osteopontin and thrombopoietin (−0.23 [−0.49, 0.03] and −0.24 [−0.51, 0.04]), and enterotype 4 was inversely associated with all 3 of these cytokines (b −0.20 to −0.35).

Conclusion

Three of five identified enterotypes were inversely associated with OA status and levels of OA associated cytokines. These exploratory analyses revealed associations between the gut microbiome, cytokines, and OA outcomes, suggesting potentially cytokine-mediated mechanisms of the effects of gut composition on OA in obese individuals, and providing a basis for further investigation of the underlying causal mechanisms.