A two-cohort study on the association between the gut microbiota and bone density, microarchitecture, and strength

Abstract

The gut microbiome affects the inflammatory environment through effects on T-cells, which influence the production of immune mediators and inflammatory cytokines that stimulate osteoclastogenesis and bone loss in mice. However, there are few large human studies of the gut microbiome and skeletal health. We investigated the association between the human gut microbiome and high resolution peripheral quantitative computed tomography (HR-pQCT) scans of the radius and tibia in two large cohorts; Framingham Heart Study (FHS [n=1227, age range: 32 - 89]), and the Osteoporosis in Men Study (MrOS [n=836, age range: 78 - 98]). Stool samples from study participants underwent amplification and sequencing of the V4 hypervariable region of the 16S rRNA gene. The resulting 16S rRNA sequencing data were processed separately for each cohort, with the DADA2 pipeline incorporated in the16S bioBakery workflow. Resulting amplicon sequence variants were assigned taxonomies using the SILVA reference database. Controlling for multiple covariates, we tested for associations between microbial taxa abundances and HR-pQCT measures using general linear models as implemented in microbiome multivariable association with linear model (MaAslin2). Abundance of 37 microbial genera in FHS, and 4 genera in MrOS, were associated with various skeletal measures (false discovery rate [FDR] ≤ 0.1) including the association of DTU089 with bone measures, which was independently replicated in the two cohorts. A meta-analysis of the taxa-bone associations further revealed (FDR ≤ 0.25) that greater abundances of the genera; Akkermansia and DTU089, were associated with lower radius total vBMD, and tibia cortical vBMD respectively. Conversely, higher abundances of the genera; Lachnospiraceae NK4A136 group, and Faecalibacterium were associated with greater tibia cortical vBMD. We also investigated functional capabilities of microbial taxa by testing for associations between predicted (based on 16S rRNA amplicon sequence data) metabolic pathways abundance and bone phenotypes in each cohort. While there were no concordant functional associations observed in both cohorts, a meta-analysis revealed 8 pathways including the super-pathway of histidine, purine, and pyrimidine biosynthesis, associated with bone measures of the tibia cortical compartment. In conclusion, our findings suggest that there is a link between the gut microbiome and skeletal metabolism.

Keywords: 16S amplicon sequencing; aging; bone Density; bone microarchitecture; cohort study; gut microbiome.

Figure 1

Flowchart of stool samples processed for microbial profiling using 16S ribosomal RNA (rRNA) sequencing. Identical criteria were applied to exclusions of samples from the Framingham Heart Study and the Osteoporotic Fractures in Men Study cohorts including participants who reported use of antibiotics within 30 days of collection, a history of colon surgery in the year prior to collection, and samples from individuals lacking covariate data.

Figure 2

Microbiome diversity of the two cohorts. Both cohorts were separately evaluated for microbial composition and diversity. In each cohort, taxa abundances were glomed (merged) at the genus level. (A) shows a heatmap of the log relative abundance of the top ten most abundant genera within each cohort. In order to avoid 0 in the count table downstream during log transformation, 1 was uniformly added to the abundance table, and the abundance table was then converted to relative abundance within each cohort. Eight of the ten most abundant genera are the same in both cohorts. The group designated as "Other" includes all the remaining genera. For (B), non-metric multidimensional scaling (NMDS) was used to ordinate the data, using Bray-Curtis distance as the beta diversity metric (Stress = 0.21). Cohort explained 3.0% of the total variability in the microbial abundance profiles (PERMANOVA, R2 = 0.030, P=0.001). We adjusted for all covariates in the PERMANOVA test.

Figure 3

Associations between the gut microbial genera and bone measures in the FHS cohort. This includes all of the 67 associations with an FDR<0.1 that were calculated using MaAsLin2. We adjusted for all covariates in the association models. The X and Y axes were clustered hierarchically. The "+" and "-" signs indicate the direction of association between microbial abundance on the vertical axis and bone measures along the horizontal axis.

Figure 4

Associations between the gut microbial species and bone measures in the FHS cohort. This includes all of the 15 associations with an FDR ≤ 0.1 that were calculated using MaAsLin2. We adjusted for all covariates in the association models. The X and Y axes were clustered hierarchically. The “+” and “-“ signs indicate the direction of association between microbial abundance on the vertical axis and bone measures along the horizontal axis.

Figure 5

Meta-analysis of the associations between abundance of microbial genera and bone measures. Meta-analysis of genera - bone associations was conducted with MMUPHin and all associations at FDR ≤0.25 are shown.

Figure 6

Association between predicted metabolic pathways and bone measures. The metabolic pathways were predicted using PICRUSt2. Pathways - bone associations were calculated using MaAslin2 and all associations with an FDR<0.1 are shown. We adjusted for all covariates in the association models. For (A) the heatmap of associations in the FHS cohort are shown. The X and Y axes were clustered hierarchically. The "+" and "-" signs indicate the direction of association between microbial abundance on the vertical axis and bone measures along the horizontal axis. (B) is a table of the associations in MrOS.