The gut microbiome’s role in the development and progression of post-traumatic osteoarthritis: A systematic review

Abstract

Objective

Gut microbiome dysbiosis is linked to osteoarthritis (OA), but its specific association with post-traumatic osteoarthritis (PTOA) is less understood. This systematic review synthesizes evidence linking PTOA and the gut microbiome to clarify its role in PTOA pathogenesis.

Method

Literature searches were conducted in PubMed, Embase, SPORTDiscus, and Web of Science through October 2025. Quantitative, original human and animal studies examining associations between the presence/severity of PTOA and gut microbiota composition and diversity were included. These measures were extracted and synthesized. Risk of bias was assessed using ROBINS-I for non-randomized and RoB 2 for randomized studies (PROSPERO registration: CRD42024496152).

Results

Thirteen studies met inclusion criteria, all utilizing small animal PTOA models. Generally, they found significant gut microbiome differences between PTOA and control groups. Collectively, these preclinical studies provided evidence that the gut microbiome can influence biological processes underlying PTOA, particularly via inflammatory and metabolic pathways, and vice versa. Two studies examined exercise, showing it can reduce PTOA severity by slowing articular cartilage degeneration and subchondral bone loss, increase microbiome diversity, and mitigate negative effects of poor diet.

Conclusions

Current literature from animal models suggests the gut microbiome may play a role in PTOA development and progression, emphasizing a bidirectional "gut-joint axis." Traumatic joint injuries may lead to systemic inflammation affecting the gut microbiome, which may exacerbate joint inflammation and PTOA progression. Significant gaps remain, particularly the lack of human studies. Future research should prioritize clinically relevant animal models and human studies to elucidate the gut microbiome's role in PTOA pathogenesis.

1. Introduction

Post-traumatic osteoarthritis (PTOA) frequently develops following major joint trauma, such as anterior cruciate ligament (ACL) tears [1,2]. Following an ACL injury, for example, approximately 20–40% of patients develop PTOA at 10–15 years [[3], [4], [5], [6]], rising to 50–75% by 20+ years post-injury [3,7,8]. PTOA can lead to joint replacement surgery within 10–15 years post-injury [[9], [10], [11]]. Given the significant impact of PTOA on joint health and quality of life, there is an urgent need to better understand its pathogenesis and develop effective strategies to prevent its progression and reduce the need for salvage procedures like total knee arthroplasty.

PTOA is associated with biomechanical factors, such as altered joint loading and joint instability [1], as well as injury severity, concomitant injuries, patient characteristics (e.g., age, body mass index), and treatment type [2,12,13]. While many of these factors are also linked to non-PTOA osteoarthritis (OA) [[14], [15], [16]], recent evidence suggests a significant role for biochemical processes, particularly inflammatory processes involving the gut microbiome (GMB), in the disease's progression [17,18]. Alterations in the GMB, or dysbiosis, have been linked to systemic low-grade inflammation, which may exacerbate OA progression through increased gut permeability and elevated circulation of bacterial endotoxins [19,20]. A working hypothesis is that dysbiosis disrupts tight junctions in the gut epithelium, leading to "leaky gut" syndrome, which, in turn, facilitates the systemic dissemination of inflammatory mediators and bacterial products, thereby contributing to joint inflammation and cartilage degradation [21,22]. Studies have linked an abundance of specific GMB bacterial taxa, such as Streptococcus and Bacillota (formerly known as Firmicutes), the Bacillota/Bacteroidota ratio [17,23,24], as well as GMB diversity to OA symptoms. Meanwhile beneficial bacterial strains might help mitigate the disease [17,25]. The Bacillota/Bacteroidota ratio compares the two most dominant bacterial phyla of the GMB and is an indicator of GMB balance or dysbiosis. The alpha diversity of the GMB is a measure of one's microbial richness and evenness; meanwhile its beta diversity is a measure of the differences in overall composition of the GMB between groups. Emerging research shows that gut-derived metabolites and factors can even directly interact with chondrocyte signaling pathways, potentially influencing OA pathogenesis [26]. Moreover, the exploration of microbiome-targeted therapies holds promise for addressing OA symptoms [21,22].

Given the established link between the GMB and the development of non-PTOA OA, it is plausible that a similar association may exist in PTOA; however, this relationship remains poorly understood. Current research has predominantly focused on non-PTOA OA, with minimal exploration of how microbial processes might influence PTOA specifically. Furthermore, there is a lack of a comprehensive reviews on this topic in the current musculoskeletal literature. Addressing these gaps will identify key research directions to deepen our understanding of the GMB's role in PTOA, ultimately guiding the development of novel preventive and therapeutic strategies for patients at risk of developing PTOA.

The purpose of this systematic review was to synthesize evidence on the relationship between PTOA status or severity and GMB composition and diversity. To inform a conceptual model of the GMB's role in PTOA pathogenesis, a secondary aim was to summarize the role of modulating factors (e.g., diet, exercise), as well as the effect of secondary outcome measures on PTOA, when assessed alongside GMB outcomes. For the context of this review, we defined secondary outcomes measures as variables other than, but related to, GMB composition and diversity that may affect PTOA outcomes, such as inflammatory markers, gut permeability, etc.

2. Methods

This systematic review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The protocol was registered with PROSPERO (CRD42024496152).

2.1. Search strategy

With the guidance of a licensed librarian, we performed a comprehensive search centered around the keywords “osteoarthritis”, “biome”, and “gut microbiota” in PubMed, Embase (Elsevier), SPORTDiscus (EBSCO), and Web of Science from inception to October 22, 2025. Detailed search strategies are provied in Appendix A. Inclusion criteria were human and animal studies exploring the relationship between the GMB and PTOA. Exclusion criteria were reviews, studies lacking a healthy comparator group, and other types of arthritic disease (non-PTOA OA, rheumatoid arthritis, ankylosing spondylitis). Comparator groups included comparisons across exposure groups, disease statuses, or intervention arms, as well as within-subject comparisons (e.g., pre-/post-PTOA event or microbiome intervention). Studies were classified as a “PTOA study” if they explicitly mentioned it or if they utilized a traumatic joint injury to induce PTOA (e.g., destabilization of medial meniscus, meniscal and/or ligamentous injury in animal models).

2.2. Study evaluation

Two reviewers (MLB and NBP) independently removed duplicates and then screened titles/abstracts followed by full-texts, strictly adhering to uniform inclusion/exclusion criteria. Disagreements were resolved by discussion or a third reviewer (EMW).

2.3. Risk of bias evaluation

Risk of bias was performed based on the methodology of the included studies. Studies with non-randomized designs (n = 4) and randomized designs (n = 9) were evaluated with the ROBINS-I [27] and RoB 2 [28] tools, respectively. Risk-of-bias visualizations were generated using robvis [29]. Details are provided in Appendix B.

2.4. Data extraction and synthesis

All data from the studies were extracted and evaluated following a strict protocol. Extracted data included: author information, study design, sex of subject/animal included, animal model, PTOA model, OA analysis method, method of biome sampling, time point at which sampling was done, DNA sequencing method, and main outcome measures. The main outcome measures were GMB alpha and beta diversity and composition. If present, we also included related measures, such as inflammatory markers and gut permeability, as well as modulating factors, such as exercise and diet, including their effect on the GMB and/or PTOA outcomes. The main independent variable of interest was the presence or severity of PTOA. For consistency, we used the taxonomy nomenclature from the National Center for Biotechnology Information Taxonomy Database.

GMB measures were synthesized according to the outcome measure. Alpha and beta diversity data were organized into a summary table, which excluded studies that did not assess at least one of these measures. This table included metrics and statistics for comparisons between PTOA and control groups. Where studies did not report the diversity metrics used or corresponding statistical values, this was clearly indicated within the summary table. Taxonomic differences in bacterial abundance between PTOA and control groups, or across varying levels of PTOA severity, were visually summarized using doughnut plots to highlight patterns of increased or decreased taxa prevalence. Only studies that reported such composition differences were included in these plots. Both the summary table and the doughnut plots were sorted by animal model. Findings with regard to the effect of modulating factors, such as exercise and diet, on the GMB and/or PTOA outcome and of secondary variables, such as systemic inflammation and gut permeability, were narratively synthesized.

3. Results

3.1. Study search and selection

Our comprehensive database search identified a total of 1862 distinct studies. Of those studies, we excluded 1774 studies based on their titles and abstracts. We screened the full texts of the remaining 88 studies and excluded an additional 75 studies, leaving us with 13 studies to include in this systematic review. The process used to select the studies included in the final synthesis is illustrated in Fig. 1.

Fig. 1.

Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow diagram.

3.2. Characteristics of included studies

The data extracted from each study are summarized in Table 1. This review included 13 animal studies [[30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42]], all of which utilized either mouse (n = 8) or rat (n = 5) models of PTOA and included a healthy control group that was either naïve or sham-operated, matched for strain, age, and sex when reported. Across studies, sex and age at the time of PTOA-inducing surgery varied by study and animal model (Table 1). In studies reporting animal age, mice underwent surgery at 8–18 weeks (average: ∼13.0 weeks), whereas rats underwent surgery at 6–9 weeks (average age: 8.2 weeks), an unweighted mean difference of nearly 5 weeks (Table 1). Although both these average ages represent young adult animals, they are not directly equivalent. In studies reporting animal sex, four mouse studies included males only and two included both sexes, whereas all rat studies included males only (Table 1). No studies involving explicitly identified PTOA patients were found. Although human OA studies may have included PTOA cases, PTOA status was not reported, precluding distinction between PTOA and non-PTOA OA.

Table 1.

Characteristics of included studies.

Study	Study design	Animal model (Strain)	Male: female ratio (M:F)	Agea	PTOA Model	OA analysis method	Sample type	Time point	Sequencing method	Data reported
Dyson et al. (2025) [30]	n = 6/group	Mouse (C57BL6/J)	All males	12 weeks	DMM	OARSI; histologic synovitis score; histologic osteophyte score	Cecal	8 weeks	16S rRNA gene sequencing	Relative abundance; Composition; diversity
	-sham + chow diet
	-OA + chow diet
	-sham + ketogenic diet
	-OA + ketogenic diet
Fu et al. (2024) [31]	n = 6/group	Mouse (C57BL/6J WT)	Not specified	12 weeks	ACLT	OARSI; synovitis score	Fecal	6 weeks	16S rRNA gene sequencing	Relative abundance; Composition; diversity
	-Sham
	-OA
	-OA + moxibustion
Guan et al. (2020) [32]	n = 18/group	Mouse (C57BL/6 N)	1:1	8 weeks	DMM	OARSI	Fecal	8 weeks	qPCR (targeting specific bacterial group)	Relative abundance; Composition
	-CO
	-OA
	-ABT + OA
Guan et al. (2023) [33]	n = 9/group	Mouse (strain not specified)	Not specified	Not specified	DMM	OARSI; bone microstructure (via micro-CT)	Fecal	8 weeks	16S rRNA gene sequencing	Relative abundance; Composition
	-Sham
	-OA
	-sham + Fer-1
	-OA + Fer-1
Huang et al. (2020) [36]	n = 6/group	Mouse (C57BL/6J)	1:1	10 weeks	MLI	OARSI; Safranin O score	Fecal	8 weeks	16S rRNA gene sequencing	Relative abundance; Composition; Diversity
	-Saline/MLI+
	-OA-METS-/MLI--OA-METS-/MLI + n = 12/group
	-OA + METS-/MLI+
	-OA + METS+/MLI+
	-SPF/MLI+
Izda et al. (2024) [37]	n = 6/group	Mouse (C57BL/6 N)	All males	16 weeks (young chow DMM group)	DMM	OARSI	Fecal	8 weeks	16S rRNA gene sequencing	Relative abundance; Composition; Diversity
	-Young, chow diet
	-Young, HFD
	-Old, chow diet
	-Old, HFD n = 5/group
	-Young, chow, DMM
Liu et al. (2025) [39]	n = 5/group	Mouse (C57BL/6)	All males	18 weeks	DMM	OARSI	Fecal	12 weeks	16S rRNA gene sequencing	Relative abundance; Composition; Diversity
	-Sham
	-OA
	-sham + colistin
	-OA + colistin
	-sham +E. coli
	-OA +E. coli
Ulici et al. (2018) [42]	n = 17/group -Younger SPF mice n = 19/group -Younger GF mice n = 6/group -Older SPF mice n = 7/group -Older GF mice	Mouse (C57BL/6J)	All males	12–18 weeks (younger mice) 37–48 weeks (older mice)	DMM	ACS; Safranin O stain; osteophyte size; synovial hyperplasia	Fecal	8 weeks	16S rRNA gene sequencing	Relative abundance; Composition; Diversity
Hao et al. (2022) [35]	n = 6/group	Rat (Sprague Dawley)	All males	8 weeks	ACLT + DMM	OARSI; modified Mankin; Safranin O score	Fecal	12 weeks	16S rRNA gene sequencing	Relative abundance; Composition; Diversity
	-sham + sedentary
	-PTOA + sedentary
	-PTOA + exercise
Hao et al. (2025) [34]	n = 9/group	Rat (Sprague Dawley)	All males	9 weeks	ACLT + DMM	OARSI; modified Mankin score	Fecal	12 weeks	16S rRNA gene sequencing	Relative abundance; Composition; Diversity
	-sham + sedentary
	-sham + exercise
	-PTOA + sedentary
	-PTOA + exercise
Kang et al. (2024) [38]	n = 10/group	Rat (Sprague Dawley)	All males	6 weeks	ACLT	Histopathological analysis, including Krenn's synovial pathology score	Fecal	6 weeks	Shotgun metagenomic sequencing	Relative abundance; Composition; Diversity
	-CO
	-OA
	-OA + GA low-dose
	-OA + GA medium-dose
	-OA + GA high-dose
Ran et al. (2025) [40]	n = 6/group	Rat (Sprague Dawley)	All males	9 weeks	ACLT	Mankin score; synovitis score	Fecal	8 weeks	16S rRNA gene sequencing	Relative abundance; Composition; Diversity
	-FMT (OA-/OB-) + OA
	-FMT (OA+/OB-) + OA
	-FMT (OA-/OB+) + OA
	-FMT (OA+/OB+) + OA
	-FMT (OA+/OB+) + sham
Tang et al. (2022) [41]	n = 5/group	Rat (Sprague Dawley)	All males	9 weeks	MLI	Not described	Fecal	30 days	16S rRNA gene amplicon sequencing	Relative abundance; Composition; Diversity
	-CO
	-OA
	-OA + probiotics

ABT: antibiotic-induced group; ACLT: anterior cruciate ligament transection; ACS: articular cartilage structure score; CO: control group; CT: computed tomography; DMM: destabilization of medial meniscus; Fer-1: Ferric Inhibitor-1; FMT: fecal microbiota transplantation; GA: gallic acid; GF: germ free; HFD: high-fat diet; METS: metabolic syndrome; MLI: meniscal/ligamentous injury; OA: osteoarthritis; OARSI: Osteoarthritis Research Society International histology score method; OB: with obesity; PTOA: post-traumatic osteoarthritis; SPF: specific pathogen free; WT: wild type.

^aage at time of PTOA-inducing injury/surgery.

3.3. Risk of bias of included studies

The risk of bias for each study is presented in Appendix B, including a summary, Figure B1 (non-randomized designs), and Figure B2 (randomized designs).

3.4. Data synthesis: gut microbiome diversity

GMB diversity in animal models of PTOA was commonly evaluated using alpha and beta diversity analyses. Alpha diversity, which measures within-sample microbial richness and evenness, was reported in 11 studies. Beta diversity, a measure of the differences in overall microbial composition between groups, was also assessed in 11 studies. However, one of these studies [39] did not assess the effect of PTOA on these measures of GMB diversity; rather, it compared the control group with intervention groups that were administered GMB dysbiosis-inducing substances, irrespective of PTOA status. Table 2 summarizes the alpha and beta diversity findings from studies that measured these metrics.

Table 2.

Summary of the gut microbiome diversity findings.

Study	Animal Model	Alpha diversity		Beta diversity		Notes/comments
		Result (metric)	Statistic	Result (metric)	Statistic
Dyson et al. (2025) [30]	Mouse	PTOA = controls (Pielou's evenness index)	p= 0.60	PTOA = controls (weighted UniFrac)	p= 0.40
Fu et al. (2024) [31]	Mouse	PTOA = controls (Simpson, Shannon, Chao1, and Pielou's evenness indices)	p> 0.05 (all metrics)	Groups clustered separately on PCoA (distance metric used not specified)	Not reported
Huang et al. (2020) [36]	Mouse	OA FMT (OA + METS+/MLI+) < controls (Shannon index)	OA + METS+/MLI + vs. OA-METS-/MLI-: p= 0.006, vs. OA-METS-/MLI+: p= 0.005, vs. OA + METS-/MLI+: p= 0.011	Groups clustered separately on PCoA (Jensen-Shannon)	Not reported
Izda et al. (2024) [37]	Mouse	PTOA = Controls HFD < chow (observed OTUs)	PTOA: p= 0.24; HFD: p= 2E-9 (young mice); p= 0.004 (old mice)	Groups clustered separately on PCoA (weighted UniFrac)	p= 0.001 (all groups); statistics for effect of PTOA or HFD not individually reported
Liu et al. (2025) [39]	Mouse	Control = colistin =E. coli (Chao1, Shannon, Simpson indices)	Not reported	Groups clustered separately on PCoA (weighted UniFrac distance)	Not reported	Effect of PTOA on alpha and beta diversity not assessed; only assessed between control, colistin, and E. coli groups, irrespective of PTOA status
Ulici et al. (2018) [42]	Mouse	SPF mice: Low severity PTOA < high severity PTOA (observed OTUs, Phylogenetic diversity whole tree index)	p= 0.041 (observed species) p= 0.049 (Phylogenetic Diversity)	SPF mice: Low severity PTOA = high severity PTOA (weighted UniFrac)	Not significant (p-value not reported)
Hao et al. (2022) [35]	Rat	PTOA < controls (Choa1 index) PTOA = Controls (observed OTUs; Shannon and Simpson indices)	p< 0.05 (Choa1 index) p> 0.05 (observed OTUs, Shannon, Simpson)	PTOA = controls (Bray-Curtis)	p> 0.05
Hao et al. (2025) [34]	Rat	Sham + sedentary ≠ Sham + exercise ≠ PTOA + sedentary ≠ PTOA + exercise (Shannon and Simpson indices) Sham + sedentary = Sham + exercise = PTOA + sedentary = PTOA + exercise (Chao1 index and observed OTUs)	Not reported	Sham + sedentary and PTOA + sedentary did not cluster separately; overlap present (weighted UniFrac distance)	Not reported	Pairwise comparison for effect of PTOA (Sham + sedentary vs. PTOA + sedentary) not explicitly reported; only differences across all four groups were reported
Kang et al. (2024) [38]	Rat	PTOA = controls = PTOA + gallic acid (Sobs, Chao1, ACE and Simpson's indices)	p= 0.1345 (Chao, Sobs, ACE) p= 0.0644 (Simpson)	Control, PTOA, and PTOA + gallic acid groups clustered separately on PCoA (metric not specified)	p= 0.001	PTOA group vs. control group not explicitly assessed; only differences across all three groups were reported
Ran et al. (2025) [40]	Rat	No differences between groups (metric not specified)	Not reported	PTOA + FMT (OA+/OB+) = Controls + FMT (OA+/OB+) (Bray-Curtis distance)	p= 0.056
Tang et al. (2022) [41]	Rat	PTOA = controls = PTOA + probiotics (Shannon, Simpson, and Chao1 indices)	p= 0.18 (Shannon)	PTOA = controls = PTOA + probiotics (Bray-Curtis)	Not reported	PTOA group vs. control group not explicitly assessed; only differences across all three groups were reported
			p= 0.20 (Simpson)
			p= 0.11 (Chao1)

OA: osteoarthritis; OB: obese; FMT: fecal microbiota transplantation from OA patients; METS: metabolic syndrome; MLI: meniscal/ligamentous injury; OTUs: Operational Taxonomic Units; HFD: high-fat diet; PCoA: principal coordinates analysis; SPF: specific pathogen-free.

3.4.1. Alpha diversity

Among studies utilizing either a mouse or a rat model of PTOA, the majority (mouse: 3/5; rat: 3/5) did not observe statistically significant differences in alpha diversity of the GMB between PTOA and control groups. For each animal model, two studies identified significant differences between groups, with one mouse study [36] and one rat study [35] reporting decreased alpha diversity in PTOA models relative to controls. Overall, alpha diversity findings were heterogeneous, with most studies reporting no significant differences between PTOA and control animals.

3.4.2. Beta diversity

As for links between PTOA and beta diversity of the GMB, findings across studies were mixed, with four studies reporting significant differences between PTOA and control groups [31,[36], [37], [38]] and six studies reporting no differences [30,34,35,[40], [41], [42]]. However, this pattern varied by animal model. In mouse models of PTOA, most studies (3/5) observed significant differences in beta diversity, indicating altered gut microbial composition in PTOA mice compared to controls [31,36,37] while two studies did not detect differences [30,42]. In contrast, the majority of studies utilizing a rat model of PTOA (4/5) found no significant differences in beta diversity between PTOA and controls [34,35,40,41], with only one study demonstrating a microbial composition difference between groups [38]. The specific microbial taxa differing between PTOA and control groups are summarized in the ‘Data Synthesis: Gut Microbiome Composition’ section (Fig. 2, Fig. 3).

Fig. 2.

Summary of the gut bacterial microorganisms found to be enriched (orange) and decreased in abundance (blue) in mice induced with post-traumatic osteoarthritis compared to control mice, as well as those found to be positively (orange) and negatively (blue) associated with the degree of cartilage damage. Microorganisms in gray are included to show lineage.

Fig. 3.

Summary of the gut bacterial microorganisms found to be enriched (orange) and decreased in abundance (blue) in rats induced with post-traumatic osteoarthritis compared to control rats, as well as those found to be positively (orange) and negatively (blue) associated with the degree of cartilage damage. Microorganisms in gray are included to show lineage.

Together, these findings suggest that differences in GMB alpha diversity between PTOA animal models and controls are relatively uncommon. As for beta diversity, findings were mixed. Although significant group differences in beta diversity were reported more frequently in mouse than rat studies, this pattern should be interpreted cautiously given several factors: the small number of studies; the lack of direct cross-species comparisons; and differences between mouse and rat studies in animal sex and age at the time of PTOA-inducing surgery.

3.5. Data synthesis: gut microbiome composition

A summary of the gut bacterial taxa found to be increased or decreased in abundance in mice induced with PTOA compared to control mice, as well as taxa positively or negatively associated with cartilage damage severity, is presented in Fig. 2. The corresponding summary for studies using rat PTOA models is presented in Fig. 3. The results demonstrated partial concordance across studies, with certain taxa repeatedly identified as significantly associated with PTOA in both mice and rats, alongside some discrepancies between studies and animal types. Most of the reported microorganisms were from the Bacillota phylum (formerly, and more commonly, known as Firmicutes), the most abundant phylum in mouse, rat, and human GMBs. Generally, taxa within this phylum decreased in abundance in rats induced with PTOA, while findings in PTOA mice were more variable, with different taxa showing both increases and decreases in abundance. Within the Bacilli class, however, results were more consistent among mouse and rat PTOA model studies, with most reporting decreases in abundance in PTOA animal groups (Bacilli class [37,40], its order Lactobacillales, [37,40] its family Lactobacillaceae, [37,40] and its genus Lactobacillus [37,40,41]). Consistent findings were also observed for microorganisms from the Oscillospiraceae family [37,41,42] and two of its genera (e.g., Oscillospira; [41,42] Ruminococcus [31,37,41]), which were enriched in PTOA animals.

The Bacteroidota phylum, the second most abundant in the mouse and rat GMB, was enriched in PTOA rats [34,41] and predominantly decreased in abundance in PTOA mice [33,39] compared to control animals. At the class and order levels, similar findings were reported. Increases in Bacteroidia abundance were associated with PTOA in rats [34] and mice [32,37]. In one of these studies, however, this increase was only noted for female mice, whereas the class decreased in male mice with PTOA [33]. Increases in the Rikenellaceae family were also associated with PTOA status or severity in rats [34] and most mouse studies [37,42]. Beyond the Bacillota and Bacteroidota phyla, taxa belonging to the Verrucomicrobiota phylum mostly decreased in abundance in PTOA mice [30,32,33,37], but increased in PTOA rats [40,41] compared to control animals.

In terms of the Bacillota/Bacteroidota ratio, findings were inconsistent. This ratio was positively associated with PTOA risk and severity in one mouse study, with the relationship being complex and influenced by factors such as sex [32]. However, it was negatively associated with PTOA severity in another mouse study that included only male animals [39], as well as in male rats (Hao et al., 2025) [34]. Taken together, while results varied between animal models and some degree of concordance was observed, there was consensus that a significant link exists between the relative abundance of various microorganisms in the GMB and PTOA in small animal models.

3.6. Data synthesis: secondary outcome measures and modulating factors

3.6.1. Inflammatory markers, gut permeability, etc

The studies included in this systematic review suggest that the GMB may influence PTOA through mechanisms involving inflammation and metabolic pathways. For instance, Guan et al. showed that altering the GMB to decrease systemic inflammatory markers like TNF-α and IL-6 led to improved PTOA outcomes [32]. In other studies, fecal microbiota transplantation from OA patients with and without metabolic syndrome worsened PTOA in mice and rats by increasing gut permeability [36] and systemic inflammation [36,40]. Similarly, Liu et al. showed that GMB dysbiosis led to increased intestinal permeability and systemic inflammation, resulting in more severe PTOA [39]. Additionally, capsiate (a compound from certain sweet peppers), probiotic supplementation with Bacillus subtilis and Enterococcus faecium, and gallic acid (a natural polyphenol found in plants such as fruits, nuts, and tea), were found to modulate inflammatory and metabolic pathways and GMB composition, leading to improved PTOA outcomes [33,38,41]. Interestingly, Izda et al. found that gut-originating bacterial DNA could be detected in mice knee cartilage, showing a physical link between the GMB and joint tissues affected by PTOA [37]. Collectively, these preclinical models indicate that the GMB can influence the biological processes underlying PTOA, both systematically through inflammatory and metabolic pathways and locally at the joint level.

3.6.2. Modulating factors

Lifestyle factors, such as exercise and diet, can influence PTOA by affecting GMB composition and related inflammatory markers [30,34,35]. Two studies included in this systematic review investigated the effect of exercise on the GMB and PTOA, both using a rat model [34,35]. Hao et al. found that exercise weakened the effect of traumatic joint injury on the GMB and PTOA severity, assessed at 12 weeks post-injury [35]. Specifically, eight weeks of treadmill-walking helped maintain cartilage-subchondral bone integrity, reduced systemic inflammation, and moderated increases in PTOA-associated bacterial genera. Building on these findings, a subsequent study by Hao et al. similarly reported that treadmill-walking mitigated the effect of traumatic joint injury on cartilage degeneration and bone loss in PTOA rats [34]. Exercise was associated with reduced abundance of taxa positively associated with PTOA severity, increased abundance of taxa associated with beneficial joint health outcomes, and elevated levels of beneficial metabolites. Taken together, exercise not only modified the GMB, but also decreased systemic inflammatory markers and metabolites and increased beneficial metabolites, helping to maintain the integrity of the cartilage-subchondral bone unit and attenuate PTOA-related changes [34,35].

4. Discussion

This systematic review provides preliminary evidence supporting a relationship between the GMB and the development and severity of PTOA. Collectively, the included studies suggest that the GMB may play a role in disease development and progression, possibly by influencing systemic inflammation and metabolic pathways. However, this evidence is limited to animal PTOA models and is based on only 13 included studies. Notably, no human studies have yet addressed this association; therefore, the applicability of these findings to human PTOA remains unknown. While this review underscores the potential role of the GMB as a contributing factor to PTOA, it also reveals the critical need for further research in this area. Should future studies further substantiate this mechanistic link, the GMB could represent a novel avenue for disease modification and prevention in PTOA.

Interpretation of this emerging preclinical literature on the GMB in PTOA can be affected by methodological differences, including the species studied, their age and sex, and the method of inducing PTOA. Across included studies, those using a rat model were conducted in younger, male-only animals, whereas those using a mouse model were conducted in older animals (by nearly 5 weeks, on average) with more variable, and sometimes unreported, sex distribution. These differences may contribute to the heterogeneous patterns reported across studies. Because few studies were designed to test sex- or age-dependent effects, the extent to which sex and age modify microbiome–PTOA relationships remains mostly unknown.

The primary finding of this review is that the GMB composition and diversity are associated with PTOA in preclinical models. Similar associations have been reported in the broader OA literature; however, the direction and specific taxa implicated are not always in agreement across PTOA models and OA cohorts [17,19]. For example, while findings for the Bacillota/Bacteroidota ratio were inconsistent across the PTOA studies included here, this ratio has been reported as more consistently increased in human OA compared with controls in several studies [17,23,24,43]. At the phylum level, our review suggested model-dependent patterns (e.g., Bacillota enriched in some PTOA mouse studies but decreased in PTOA rat studies), whereas Bacillota is more commonly reported as enriched in human OA cohorts [17,44]. At the genus level, Lactobacillus was generally decreased in PTOA animals; correspondingly, the broader OA literature has linked higher Lactobacillus abundance with better OA outcomes [17]. With respect to diversity metrics, significant decreases in alpha diversity were uncommon among the PTOA animal studies in this review, whereas reduced alpha diversity has been more consistently reported in OA cohorts in the broader literature [17,23,24,45]. Beta diversity findings were heterogeneous in both our PTOA review and the broad OA literature, with only a subset of studies reporting clear separation between OA/PTOA and control groups [17,46]. Overall, although individual taxa differed across PTOA models and human OA studies, the collective evidence supports an association between GMB alterations and OA-related phenotypes, including PTOA [19,47,48].

4.1. Altered gut microbiome in pathogenesis of PTOA

Findings of this review also underscore the so-called "gut-joint axis", and its bidirectionality, as an important research area in understanding PTOA pathogenesis (Fig. 4).

Fig. 4.

Conceptual model of the proposed role of the GMB in the pathogenesis of PTOA and its modulating factors. It focuses on a hypothesized bidirectional gut–joint axis and does not include all potential contributing factors to PTOA development and progression. In patients that develop PTOA following a traumatic joint injury, like an ACL injury, evidence suggests that there is a perpetuation of inflammation [49]. The initial injury triggers a local inflammatory response that may lead to a broader systemic inflammatory response in these patients. In turn, systemic inflammation can alter gut permeability and its microbiome. Evidence that the GMB changes following injury is plentiful [[30], [31], [32], [33], [34], [35],37,38,40,41]. This is a bidirectional relationship for which GMB health at the time of injury is also linked to PTOA outcomes [36,39,40,42]. This bidirectional link appears to be mediated by systemic inflammation [32,36,[39], [40], [41],64,65]. Modulating factors such as exercise [34,35] and diet [30,37] can affect PTOA outcomes via the GMB and inflammation. Obesity is also linked to PTOA [40]. Created in BioRender.

This bidirectional relationship is supported by small animal models of PTOA, where PTOA appears to have a detrimental effect on the GMB, notably shifting its microbial composition toward profiles linked to inflammatory pathways [32,33,35,37,41], In turn, the GMB at the time of injury significantly influences PTOA progression and outcomes [36,39,40,42]. In the broader literature on the pathogenesis of PTOA, Lieberthal et al. speculate that certain patients experience a pro-inflammatory response that lacks adequate control post-injury, perpetuating chronic inflammation and tissue damage leading to PTOA [49]. Jacobs et al. add that this post-injury dysregulation of the inflammatory response occurs irrespective of injury severity [50]. The initial injury triggers a localized inflammatory response characterized by the release of pro-inflammatory markers, such as cytokines IL-1, IL-6, and IL-8 [1,49,50]. These cytokines can enter the bloodstream and lead to a wider systemic inflammatory response. Taken together with the findings of this review, we hypothesize that in some cases this systemic inflammatory response may alter the gut permeability and its microbiome, but this warrants further investigation. Conversely, a dysbiotic gut at the time of injury, for example, can increase gut permeability (i.e., “leaky gut”), allowing microbial products like lipopolysaccharide (LPS) to breach the gut barrier, enter the bloodstream, and exacerbate systemic inflammation [36,39,51,52]. Notably, a recently published PTOA study provides a more specific mechanistic example of gut-joint axis [53]. Using a mouse PTOA model, Yang et al. [53] showed that gut microbes can change bile acid metabolites, which then signal through intestinal FXR (farnesoid X receptor, a bile acid–sensing receptor in the gut lining) and the GLP-1 pathway to influence OA severity. We propose that this could perpetuate local inflammation, and thus advance the progression of PTOA. On the other hand, optimal gut health may help mitigate these inflammatory processes and promote anti-inflammatory processes [41], essentially breaking the perpetuating pro-inflammatory cycle and halting or slowing PTOA progression.

4.2. Effect of modulating factors on role of gut microbiome in pathogenesis of PTOA

Results from this review also revealed that modulating factors like exercise [34,35], diet [30,37], and obesity [37,40] may influence PTOA outcomes through multiple pathways, one of which could involve their effects on the GMB and/or the inflammatory response (Fig. 4). While Ran et al. [40] suggest that mechanical loading was the predominant factor in obesity-related PTOA in their study, this does not preclude systemic factors (e.g., metabolic, immune) from also contributing to PTOA progression. In the broader OA literature, recent work has presented evidence supporting a paradigm shift toward OA as a systemic condition, arguing that mechanical overload alone cannot fully explain the progression of OA [54,55]. In terms of exercise, this review found that exercise may reduce the risk of developing PTOA by positively affecting the GMB and reducing systemic inflammation. In their rat PTOA model, Hao et al. [35] argued that exercise eliminates the microbial-relevant low-degree inflammation in PTOA, perhaps by modifying the PTOA-relevant shifts of the GMB. This is supported by studies beyond those included in this review that indicate that moderate exercise can positively modify the GMB by increasing microbial diversity [56,57], increasing beneficial bacteria [58], and reducing harmful bacteria [57,58]. Exercise can also improve the integrity of the gut barrier, which helps prevent harmful substances from entering the bloodstream (i.e., leaky gut) and causing a variety of symptoms and health issues, including chronic inflammation [57]. Additionally, exercise can have anti-inflammatory effects. In particular, aerobic exercise can lower inflammatory markers, such as C-reactive protein [59,60], IL-6,⁵⁹ and TNF-α [60]. Thus, preclinical evidence supports exercise as a modulating factor of PTOA progression by improving GMB diversity and composition and by reducing inflammation. Whether these mechanisms operate similarly in humans remains to be determined.

4.3. Limitations

Several limitations must be acknowledged. First, only studies utilizing small animal models of PTOA were included; no human studies were identified, limiting translatability. Second, heterogeneity in animal PTOA models, such as the use of different small animal species and methods for inducing PTOA, complicates direct comparisons. Third, all included studies employed an invasive procedure for inducing PTOA, and five studies did not incorporate a sham surgery group. This highlights the need for adopting non-invasive, clinically relevant joint injury models to enhance applicability to human PTOA, like a cyclic ACL fatigue loading model [61] or an ACL rupture model induced via tibial compression overload [62]. Finally, most studies used male animals only [30,34,35,[37], [38], [39], [40], [41], [42]]. Therefore, sex remains insufficiently examined as a modifying factor of the GMB in the pathogenesis of PTOA. Addressing this gap could reveal sex-specific responses in GMB and PTOA interactions that could explain the higher prevalence of PTOA in females compared to males [63].

4.4. Conclusions

In conclusion, this systematic review highlights a potential role of the GMB in the development and progression of PTOA, as one of several contributing factors. Evidence from 13 studies, all using small animal models of PTOA, supports the concept of a bidirectional "gut-joint axis" in PTOA. Collectively, they show post-injury changes in the GMB, often linked to increased systemic inflammation and gut permeability, as well as pre-injury changes in the GMB linked to PTOA progression. Despite promising insights from animal studies, significant gaps remain, particularly the absence of human studies focused on PTOA. Future research should prioritize clinically relevant animal models and human studies to enhance the applicability of these findings.

5. Contributions

EMW conceived the idea for the review. MLB, NBP, and EMW developed the protocol. NBP conducted the database searches. MLB and NBP screened titles, abstracts, and full-text papers; extracted and analyzed data; and drafted the manuscript. EMW resolved any questions or disagreements regarding study selection during screening. MLB, NBP, and EMW critically reviewed draft versions of the manuscript and approved the final version.

Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work, the authors used the AI Model ‘GPT-4o’ to language edit portions of the manuscript such as correcting grammatical and/or spelling mistakes. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Role of the funding source

No funding was received for this work.

Competing interests

The authors have no conflicts of interest to declare relating to this work.

Handling Editor: Professor H Madry

Appendix A. Full database search strategies

Database	Search strategy	Filters	Date
SPORTDiscus (EBSCO)	(SU osteoarthritis OR TI osteoarthrit∗ OR TI (degenerat∗ N2 arthrit∗) OR TI ("post-traumatic" N2 arthrit∗) OR AB osteoarthrit∗ OR AB (degenerat∗ N2 arthrit∗) OR AB ("post-traumatic" N2 arthrit∗)) AND (SU "gut microbiome" OR TI bacteria∗ OR TI bacteriu∗ OR TI biome OR TI biomes OR TI dysbio∗ OR TI gbm OR TI microbiom∗ OR TI microbiot∗ OR TI microflora∗ OR TI (gut N2 flora∗) OR AB bacteria∗ OR AB bacteriu∗ OR AB biome OR AB biomes OR AB dysbio∗ OR AB gbm OR AB microbiom∗ OR AB microbiot∗ OR AB microflora∗ OR AB (gut N2 flora∗))	None	10/21/2025
PubMed	(osteoarthritis[mh] OR "degenerative arthritis"[tiab:∼2] OR osteoarthri∗[tiab] OR "post-traumatic arthritis"[tiab:∼2]) AND (gastrointestinal microbiome[mh] OR bacteria∗[tiab] OR bacteriu∗[tiab] OR biome[tiab] OR biomes[tiab] OR dysbio∗[tiab] OR gbm[tiab] OR "gut flora"[tiab:∼2] OR "intestinal flora"[tiab:∼2] OR microbiom∗[tiab] OR microbiot∗[tiab] OR microflora∗[tiab])	Language: English Publication types excluded: Systematic review; review; letter; editorial	10/21/2025
Embase	(Osteoarthritis/exp OR osteoarthri∗:ti,ab OR ((degenerat∗ OR 'post-traumatic') NEAR/2 arthritis):ti,ab) AND (microflora/exp OR bacteria∗:ti,ab OR bacteriu∗:ti,ab OR biome:ti,ab OR biomes:ti,ab OR dysbio∗:ti,ab OR gbm:ti,ab OR microbiom∗:ti,ab OR microbiot∗:ti,ab OR microflora∗:ti,ab OR ((gut OR intestin∗) NEAR/2 flora∗):ti,ab)	Publication types: Article; article in press; clinical trial	10/21/2025
Web of science	(TI=(osteoarthri∗) OR TI=(degenerat∗ NEAR/2 arthrit∗) OR TI=(post-traumatic" NEAR/2 arthrit∗) OR AB=(osteoarthri∗) OR AB=(degenerat∗ NEAR/2 arthrit∗) OR AB=(post-traumatic" NEAR/2 arthrit∗)) AND (TI=(bacteria∗ OR bacteriu∗ OR biome OR biomes OR dysbio∗ OR gbm OR microbiom∗ OR microbiot∗ OR microflora∗) OR TI=(gut NEAR/2 flora∗) OR TI=(intestin∗ NEAR/2 flora∗) OR AB=(bacteria∗ OR bacteriu∗ OR biome OR biomes OR dysbio∗ OR gbm OR microbiom∗ OR microbiot∗ OR microflora∗) OR AB=(gut NEAR/2 flora∗) OR AB=(intestin∗ NEAR/2 flora∗)	Document types: Article, early access, correction	10/21/2025

Appendix B. Risk of bias evaluation of included studies

[Note: References in this appendix correspond to the reference list in the main manuscript]

Methods

Studies with non-randomized designs (n = 4) were evaluated with the ROBINS-I tool [27]. Each study was assessed in seven domains of bias including confounding, classification of interventions, selection of participants into the study/analysis, deviations from intended interventions, missing data, measurement of the outcome, and selection of the reported results. The risk of bias was then scored as either low, moderate, or severe in each domain and an overall risk of bias was assigned to each study.

The risk of bias of the studies that utilized randomized designs (n = 9) was evaluated with the RoB 2 tool [28]. Each study was assessed in five domains of bias including bias due to the randomization process, deviation from intended intervention, missing outcome data, measurement of outcomes, and selection of the reported results. The risk of bias was then scored as either low, some concern, or high in each domain and an overall risk of bias was assigned to each study.

Results

The risk of bias for each study with a non-randomized design is presented in Figure B1. Among the four studies assessed, one was identified as having a low overall risk of bias [36]. Huang et al. [36] achieved low bias scores across all evaluated domains. However, confounding bias was a recurring issue [32,40,42], which highlighted challenges in controlling for all potential influence factors. Overall, while one study showed a commendable robustness in methodology, the presence of moderate bias in the others underscores the variability in methodological rigor and reporting practices in the field.

Fig. B1.

Risk of bias analysis of studies with non-randomized designs using the ROBINS-I tool. Created with robvis [29].

The risk of bias for each study utilizing a randomized design is presented in Figure B2. Among the nine studies assessed, none demonstrated a low overall risk of bias. Instead, all studies [30,31,34,35,[37], [38], [39],41] exhibited at least moderate risk in two or more domains, most frequently arising from deficiencies in the randomization process and the measurement of the outcomes. By contrast, bias due to missing outcome data and selective reporting was generally low across studies. These findings highlight the need for enhanced methodological rigor and standardization in future randomized trials to mitigate such biases and improve the reliability of results.

Fig. B2.

Risk of bias analysis of studies with randomized designs using RoB 2. Created with robvis [29].