# Metagenomic profiling of the gut microbiome to predict orthopedic healing responses in postmenopausal women

## TL;DR

This study shows that the gut microbiome can predict recovery after orthopedic surgery in postmenopausal women, with certain bacteria linked to faster healing.

## Contribution

The study introduces a machine learning model using gut microbiome data to predict post-surgical recovery outcomes in postmenopausal women.

## Key findings

- Increased Firmicutes and Bacteroidetes abundance correlates with improved recovery outcomes.
- Higher Proteobacteria and Escherichia levels are associated with delayed healing.
- A random forest model predicted recovery with 85% accuracy using microbiome data.

## Abstract

Recovery following orthopedic procedures in postmenopausal women is often prolonged and more complex due to age-related physiological changes, including reduced bone mineral density, altered hormonal profiles, impaired immune regulation, and delayed tissue regeneration. Conventional recovery assessment methods such as radiographic imaging, range-of-motion evaluation, and functional mobility tests provide valuable clinical information but offer limited insight into the underlying biological processes that influence healing. Emerging evidence indicates that the gut microbiome plays a critical role in regulating inflammation, immune homeostasis, and tissue repair, highlighting its potential as a predictive biomarker for post-surgical recovery outcomes. This study investigated the association between gut microbiome dynamics and recovery following orthopedic surgery in postmenopausal women.

Stool samples were collected from preoperative (baseline) and 6 weeks postoperative time points. Microbial profiling was performed using 16S rRNA gene sequencing on the Illumina MiSeq platform, and data processing and taxonomic analysis were conducted using QIIME2. Microbial diversity was evaluated through alpha diversity metrics to assess community richness and beta diversity to characterize compositional differences over time. Clinical recovery was assessed using radiographic imaging, the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and the Timed Up and Go (TUG) functional mobility test. To evaluate the predictive potential of the gut microbiome, a random forest machine learning model was trained using microbial abundance data and correlated with clinical recovery outcomes.

The results revealed significant temporal shifts in gut microbial composition during the recovery period. Bacterial diversity varied across time points, with Firmicutes and Bacteroidetes identified as the dominant phyla. Increased abundance of these taxa was strongly associated with improved functional outcomes and faster recovery. In contrast, elevated levels of Proteobacteria and Escherichia were linked to delayed healing and poorer clinical performance. The predictive model achieved an accuracy of 85%, demonstrating the robustness of gut microbiome signatures as indicators of postoperative recovery.

Overall, this study highlights the significant influence of gut microbiome composition on orthopedic recovery in postmenopausal women. Identification of microbial biomarkers associated with favorable healing outcomes provides a foundation for developing microbiome-guided, personalized therapeutic strategies to enhance postoperative recovery and improve long-term musculoskeletal health.