Abstract
Study Design
Mendelian randomization (MR) and gene-level analysis.
Purpose
To investigate the relationship between gut microbiota (GM) and spinal canal stenosis (SCS).
Overview of Literature
To date, no studies have demonstrated the relationship between GM and SCS or explored its racial differences.
Methods
This study utilized summary data from publicly available, large-scale genome-wide association studies to investigate the causal relationship between GM and SCS in interracial, European, and East Asian populations using a bidirectional two-sample MR approach. Reverse MR analysis was conducted to estimate the effect of SCS on GM taxa with significant associations. Additionally, gene-level analyses—including Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and protein-protein interaction network analysis—were performed to elucidate key biological processes and hub genes involved in both GM and SCS.
Results
In the interracial population, five bacterial taxa were identified as risk factors and four as protective factors for SCS. Among Europeans, three bacterial taxa were associated with increased risk and three with reduced risk. In the East Asian population, seven bacterial taxa demonstrated protective effects, while one taxon was associated with increased risk. Sensitivity analyses revealed no evidence of heterogeneity or horizontal pleiotropy. Cross-comparisons indicated significant differences in GM profiles between European and East Asian populations. Gene-level analysis identified key GO terms, enriched KEGG pathways, and hub genes potentially mediating the relationship between GM and SCS.
Conclusion
This study identified a potential causal relationship between GM and SCS, highlighting distinct microbial and genetic mechanisms in European and East Asian populations. The findings provide novel insights into the pathogenesis, prevention, and treatment of SCS. Gene-level analyses suggest potential molecular pathways underlying the GM–SCS association, providing a foundation for future mechanistic research.
Keywords: Gastrointestinal microbiome, Spinal stenosis, Mendelian randomization analysis, Association, Computational biology
Graphical Abstract
Introduction
Spinal canal stenosis (SCS) is characterized by a reduction in the volumetric capacity of the spinal canal, which may result in severe paresthesia and/or functional impairment [1]. Lumbar SCS is the most common indication for spinal surgery in individuals over 65 years of age and affects the quality of life of more than 2 million adults in the United States [2]. Patients with persistent symptoms often require spinal decompression surgery [3]. Given the global burden and debilitating nature of this common degenerative condition, it is imperative to develop strategies and provide novel insights to reduce its prevalence and impact.
The gut microbiota (GM) is a diverse community of microbial species that inhabit the digestive tract, playing a vital role in nutrient absorption and food digestion. Beyond these functions, it contributes significantly to primary physiological regulation and has been implicated in various systemic disorders [4,5]. A broad association exists between the human microbiome and musculoskeletal diseases. Moreover, the GM exerts a notable influence on neuropharmacology, with emerging evidence suggesting a potential role in the treatment of SCS [6]. Specifically, novel concepts such as the gut-spine axis, gut-disk axis, and gut-ligament axis have been proposed to describe the interconnections between the GM and spinal structures [7]. However, to the best of our knowledge, no studies have directly indicated a correlation between SCS and GM.
In the present study, Mendelian randomization (MR) was employed to investigate the relationship between GM and SCS in an interracial population. To account for potential ethnic variability, datasets from European and East Asian populations were analyzed separately. The MR analysis was conducted based on three key assumptions: (1) the selected instrumental variables (IVs) are significantly associated with GM; (2) the IVs are not related to confounding factors; and (3) the IVs influence SCS solely through their effect on GM, and not via alternate pathways. The study adhered to the STROBE-MR (Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization) guidelines, and the corresponding checklist was used. Subsequently, gene-level analyses were performed to explore potential mechanisms by which GM may influence key pathological factors involved in SCS at the gene level. The study design is illustrated in the workflow chart (Fig. 1).
Fig. 1.
The workflow chart of the study combining Mendelian randomization and gene-level analysis. SCS, spinal canal stenosis; SNPs, single-nucleotide polymorphisms; IVW, inverse variance weighted; MR Egger, Mendelian randomization Egger; GWAS, genome-wide association study; GM, gut microbiota; IVDD, intervertebral disc degeneration; DEGs, differentially expressed genes; OLF, ossification of the ligamentum flavum; HLF, hypertrophy of the ligamentum flavum; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI, protein-protein interaction; BP, biological process; MF, molecular function; CC, cellular component.
Materials and Methods
All data were obtained from publicly available databases, and no human participants were involved; therefore, consent for publication was not required. In addition, Institutional Review Board approval was not necessary for this research.
Data sources for the MR analysis
The GM database used in this study was obtained from a previously conducted large-scale genome-wide association study (GWAS), comprising a cohort of 18,340 individuals [8]. SCS data for an interracial population—including 454,787 individuals of European ancestry and 178,726 individuals of East Asian ancestry—were obtained from a prior cross-population research [9]. Specifically, the SCS dataset for East Asians included 661 cases and 178,065 controls and was retrieved from the GWAS Catalog (https://www.ebi.ac.uk/gwas/). The European dataset consisted of 1,910 cases and 359,284 controls and was obtained from the FinnGen project (https://www.finngen.fi/en/access_results).
Selection of instrumental variables
Four criteria were employed to identify IVs significantly associated with GM. First, the significance threshold for single-nucleotide polymorphisms (SNPs) associated with GM was set at 1×10−5 to enhance the effectiveness of the analysis. Second, a clumping method was applied to minimize the potential offset impact of linkage disequilibrium (LD) between genetic variables. The LD threshold was set as r2 <100 with a clumping distance of 1,000 kb. Third, Phenoscanner ver. 2.0 (http://www.phenoscanner.medschl.cam.ac.uk) was used to screen for and exclude SNPs associated with other phenotypes to reduce pleiotropy. Fourth, the F-statistic was calculated using the formula F=R2(n−k−1)/k(1−R2), where n is the sample size, k is the number of selected IVs, and R2 is the variance in the exposure explained by the IVs. IVs with F-values <10 were excluded to avoid weak instrument bias. Following these procedures, the final set of robust IVs was selected for MR analysis.
MR analysis and sensitivity analysis
In the MR analysis, GM data from the interracial population were treated as exposures, and SCS data as outcomes. Five MR methods were employed to assess the causal relationship between GM and SCS: inverse variance weighted (IVW), MR-Egger, weighted median, simple mode, and weighted mode. Among these, IVW served as the primary analytical method, while the other four were used as auxiliary methods. Following MR analysis, sensitivity analyses were conducted to evaluate the presence of horizontal pleiotropy and heterogeneity among the IVs. In addition, a leave-one-out analysis was conducted, wherein one SNP was removed at a time, and the MR analysis was repeated using the remaining SNPs to evaluate the impact of individual variants on the overall results. All MR analyses were conducted using R software ver. 4.3.2 (The R Foundation for Statistical Computing, Vienna, Austria), employing “TwoSampleMR” and “MR-PRESSO” packages.
Reverse MR analysis
After identifying GM taxa significantly associated with SCS through MR and sensitivity analyses, a reverse MR analysis was conducted to assess the possibility of a reverse causal relationship.
Collection of raw gene datasets
Ossification of the ligamentum flavum (OLF), hypertrophy of the ligamentum flavum (HLF), and intervertebral disc degeneration (IVDD) were selected as key pathological factors for SCS. The RNA-sequencing dataset for OLF (GSE106253) was obtained from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo) and included messenger RNA expression profiles from four OLF and four non-OLF tissue samples. The RNA-sequencing datasets for HLF (GSE113212) and IVDD (GSE34095) contained gene expression data from four HLF and four non-HLF tissues, and from three IVDD and three non-IVDD tissues, respectively. The GM-related gene datasets were retrieved from Genecards (https://www.genecards.org) using the keyword “intestinal bacteria.” Genes were filtered based on their relevance scores, with the median relevance score used as the threshold for selection.
Identification of the differentially expressed genes related to GM and gene-level analysis
The R package “limma” was used for the differential gene expression analysis for the OLF, HLF, and IVDD datasets. The thresholds for fold changes were set at 1.5, 1.5, and 1.0, respectively. Subsequently, the GM-related genes were intersected with the identified differentially expressed genes (DEGs) to obtain GM-related OLF-DEGs, GM-related HLF-DEGs, and GM-related IVDD-DEGs. These GM-related DEGs were then imported into the STRING online database (https://www.string-db.org) to predict protein-protein interactions (PPIs) and construct PPI networks. Interactions with scores greater than 0.4 were considered statistically significant. The resulting PPI networks were visualized using Cytoscape ver. 3.10.2 (https://cytoscape.org/), and hub genes were identified using the cytohubba plug-in.
Results
Selection of IVs
Based on the clumping analysis results, a total of 2,559 SNPs meeting the inclusion criteria were identified, among which 477 were duplicates (detailed in Supplement 1). After excluding bacterial taxa with unknown names, 196 bacterial taxa were retained for further analysis (Supplement 2). All selected SNPs had F-values greater than 10, indicating that they were strong IVs. To satisfy the independence assumption of MR, IVs associated with potential confounders (i.e., body mass index, occupational physical activity, computer usage time, and television viewing time) were excluded.
The impact of GM on SCS in the interracial population
In the IVW analysis, GM was considered the exposure and SCS the outcome. The results showed that nine bacterial taxa were significantly associated with SCS (p<0.05) in the interracial population. Among them, five taxa were estimated to be risk factors, namely, Class Clostridia and Class Gammaproteobacteria, Genus Eubacterium fissicatena group, Genus Lachnospiraceae UGG010, and Order Clostriales. The remaining four taxa were identified as protective factors, i.e., Family Bifidobacteriaceae, Genus Eubacterium rectale group, Genus Lachnospiraceae UGG004, and Order Bifidobacteriales (Figs. 2, 3).
Fig. 2.
Mendelian randomization (MR) results and forest plot of causal relationship between gut microbiota and the risk of spinal canal stenosis in the interracial population. OR, odds ratio; CI, confidence interval; NA, not applicable. p<0.05 was considered statistically significant.
Fig. 3.
(A–I) Scatter plots demonstrating the causal relationship between gut microbiota and the risk of spinal canal stenosis in the interracial population. SNP, single-nucleotide polymorphism; MR, Mendelian randomization.
The impact of GM on SCS in the European population
The IVW analysis revealed a significant relationship between six bacterial taxa and SCS in the European population (p<0.05). Among these, Class Gammaproteobacteria, Genus Oxalobacter, and Order Clostridiales were associated with an increased risk of SCS, while Genus Eubacterium rectale group, Genus Eubacterium ruminantium group, and Genus Ruminiclostridium5 were found to have protective effects (Supplements 3, 4).
The impact of GM on SCS in the East Asian population
The IVW analysis revealed a significant correlation between eight bacterial taxa and SCS in the East Asian population (p<0.05). Of these, seven were protective factors, including Class Erysipelotichia, Class Melaniabacteria, Family Erysipeloticheae, Genus Odoribater, Genus Oscillibacter, Order Erysipelotrichales, and Order Gastranaerophilales. Genus Lachnospiraceae UGG008 was identified as a protective factor (Supplements 5, 6). Notably, the bacterial taxa associated with SCS differed significantly between the European and East Asian populations.
Sensitivity analysis and reverse MR
Multiple methods were employed to evaluate heterogeneity and horizontal pleiotropy in the MR analysis, ensuring the reliability of the results. Cochran’s Q test and the intercept from the MR-Egger regression indicated no significant heterogeneity or horizontal pleiotropy, as the p-values for all bacterial taxa exceeded 0.05 (Supplement 7). In addition, MRPRESSO global tests detected no outliers (Supplement 8). The leave-one-out analysis further confirmed the robustness of the results, with no SNP identified as an influential outlier (Fig. 4, Supplements 9, 10). Subsequently, reverse MR analysis was performed to evaluate whether SCS exerts a causal effect on GM. In this analysis, the bacterial taxa previously identified as significantly associated with SCS were treated as outcomes, and SCS data from the interracial, European, and East Asian populations were used as exposures. In the interracial population, the Genus Eubacterium fissicatena group was significantly affected by SCS (Supplements 11–14). Sensitivity analysis for the reverse MR revealed no evidence of heterogeneity, horizontal pleiotropy, or outliers (Supplements 15, 16).
Fig. 4.
(A–I) Leave-one-out plots depicting the causal relationship between gut microbiota and the risk of spinal canal stenosis in the interracial population.
Identification of the GM-related DEGs
Following processing of the RNA-sequencing datasets using the limma package, a total of 5,898 OLF-DEGs and 2,878 HLF-DEGs were identified based on a fold change >1.5 and p<0.05. Among the OLF-DEGs, 3,129 were upregulated and 2,769 were downregulated, while the HLF-DLGs included 1,904 upregulated and 974 downregulated genes. For IVDD, a total of 570 DEGs were selected using a fold change >1.0 and p<0.05, consisting of 346 upregulated genes and 224 downregulated genes (Fig. 5A). A total of 3,982 GM-related genes were retrieved based on GeneCards relevance scores, with the median score used as the threshold. Following intersection with the DEGs, 802 GM-related OLF-DEGs, 494 GM-related HLF-DEGs, and 128 GM-related IVDD-DEGs were identified for downstream analysis (Fig. 5B).
Fig. 5.
Volcano plots of the differentially expressed genes (DEGs) of ossification of the ligamentum flavum (OLF), hypertrophy of the ligamentum flavum (HLF), and intervertebral disc degeneration (IVDD). (A) Venn diagrams demonstrating the gut microbiota (GM)-related DEGs. (B) Lollipop graphs of the significant Gene Ontology (GO) terms in biological process (BP), cellular component (CC), and molecular function (MF). (C) Chord plots depicting the significant pathways of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis. (D) Selected top 10 hub genes of the constructed protein-protein interaction (PPI) networks using the Maximal Clique Centrality algorithm (E).
Gene-level analysis
High-ranking Gene Ontology terms (p<0.05) are shown in Fig. 5C. Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed that the GM-related DEGs associated with OLF, HLF, and IVDD were enriched in different pathways (Fig. 5D). Following construction of the PPI networks, the Maximal Clique Centrality algorithm was applied to identify hub genes. In the GM-related OLF-DEG network, the top 10 hub genes included IL6, IL1B, AKT1, MMP9, GAPDH, CXCL8, IL10, ICAM1, CCL2, and EGF. The top 10 hub genes in the GM-related HLF-DEG network were GAPDH, HIF1A, TNF, FN1, STAT3, MAPK3, CTNNB1, EGFR, PPARG, and CCND1. In the GM-related IVDD-DEG network, the top 10 hub genes were SMAD2, BCL2, MAPK14, CALML4, FYN, SMAD4, SMAD3, MAPK11, PRKCD, and MSH6 (Fig. 5E).
Discussion
To the best of our knowledge, this is the first study to use publicly available GWAS data to explore the causal relationship between GM and SCS across interracial, European, and East Asian populations. Racial differences have been well-documented in various spinal disorders, such as ossification of the posterior longitudinal ligament and OLF [10]. A previous review elaborated on racial differences in bone density, attributing them to a combination of genetic, lifestyle, and cultural factors. In addition, differences in screening practices, treatment, and education were emphasized as contributors to these differences [11].
It is well recognized that GM is broadly associated with spinal diseases. For instance, one study employing advanced omics techniques demonstrated that intervertebral discs possess a distinct microbiome, and that GM dysbiosis may contribute to disc lesions and degeneration [12]. These findings suggest that GM may have an impact on SCS by promoting the pathological progression of IVDD. Additionally, low back ache, a common symptom among obese and overweight individuals, has also been linked to GM alterations, possibly through enhanced inflammatory responses mediated by the microbiota [13].
In the analysis of the association between GM and SCS in the interracial population, we identified Class Clostridia and Order Clostridales as risk-associated taxa. Previous studies have shown that certain GM, such as Clostridium and Streptococcus, can produce short-chain fatty acids (SCFAs) through amino acid fermentation [14]. SCFAs produced by GM have been shown to induce insulin-like growth factor 1 production and promote bone growth [15]. It is plausible that SCFA-induced increases in bone mass could lead to tissue compression within the spinal canal, thus leading to the development of SCS. Family Bifidobacteriaceae and Order Bifidobacteriaceae were identified as protective factors against SCS in the interracial population in the present study. Bifidobacterium species have been shown to promote leptin secretion and reduce brain serotonin levels by binding to the neuronal receptors in the brainstem [16]. Given that serotonin plays a regulatory role in bone metabolism, these findings suggest that the gut-brain axis may also influence the development of SCS.
Class Melainabacteria, a protective taxon in our analysis of GM and SCS in the East Asian population, has been shown to exert a protective effect against lower back pain in a previous MR study [17]. Furthermore, Genus Oscillibacter, which is related to calcium absorption, was found to be significantly associated with SCS in the Asian population [18]. A prior study reported that sarcopenia is linked to reduced GM diversity and increased levels of Lachnospira and Eubacteria. Given that muscle atrophy contributes to structural degeneration in the spine, one potential mechanism underlying SCS may be mediated by the so-called gut-spine axis, wherein changes in the gut microbiota affect musculoskeletal integrity through systemic pathways [19].
In our study, the nuclear factor-κB signaling pathway was enriched by the GM-related OLF-DEGs. This pathway has previously been implicated in the functional regulation of nucleus pulposus cells and the pathogenesis of IVDD, as well as in inhibiting heterotopic ossification following trauma [20,21]. However, its precise role in OLF still warrants further research. The GM-related IVDD-DEGs were also enriched in the advanced glycation end product (AGE)–the receptor for AGE (RAGE) signaling pathway in diabetic complications, which has been linked to bone mass regulation in obese mice [22]. Supporting this, another study has also demonstrated that AGEs promote hypertrophy, osteogenic differentiation, and ultimately ectopic calcification of the IVD [23]. Moreover, an analysis of key hub genes SMAD2 and SMAD3 in the GM-related IVDD-DEG network demonstrated that inhibition of the transforming growth factor-beta (TGF-β)-mediated SMAD2/3 phosphorylation accelerates nucleus pulposus cell apoptosis and IVD degeneration [24]. Conversely, activation of the TGF-β1/SMAD2/3 pathway in platelet-rich plasma has been shown to retard IVDD [25].
From a clinical perspective, modulation of GM shows promising potential in the prevention and management of SCS. In the preventive context, the well-established association between GM and systemic inflammation provides a viable target for early intervention. Microbiota enriched in SCFAs may help alleviate IVD degeneration and ligamentous ossification by suppressing pro-inflammatory mediators. This suggests that early microbiota-targeted interventions in metabolically compromised populations—such as individuals with diabetes or obesity—could potentially reduce the risk of developing SCS. As discussed earlier, taxa such as Bifidobacterium and Oscillibacter, which are associated with bone mass regulation and calcium absorption, may influence the osseous structure of vertebral bodies, thereby contributing to the pathogenesis and progression of SCS. From a disease management standpoint, recent studies also suggest a link between GM composition and perioperative pain, providing novel perspectives for perioperative pain management in patients undergoing spinal surgery [26].
The present study has several strengths and limitations. The use of sensitivity analysis following MR analysis—including tests for heterogeneity, horizontal pleiotropy, and SNP outliers—enhanced the robustness of the findings. However, certain limitations should be acknowledged. First, the original SCS cases from FinnGen and other GWAS datasets did not differentiate the anatomical location of the lesions (e.g., cervical spondylotic myelopathy [ICD-10: M47.101†G99.2* and M51.001†G99.2*], thoracic spinal stenosis [ICD-10: M48.003], or lumbar canal stenosis [ICD-10: M48.061]). This lack of specificity may have introduced bias. Second, phenotypic heterogeneity should be considered, as individuals with similar genetic backgrounds may exhibit different SCS phenotypes due to environmental factors, lifestyle influences, or epigenetic variations, which may confound causal inferences.
Conclusions
In summary, this study provides evidence of a potential causal relationship between GM and SCS across interracial, European, and East Asian populations. Specific bacterial taxa associated with SCS were identified, and key signaling pathways and hub genes potentially involved in its pathogenesis were elucidated. These findings offer new insights into the underlying mechanisms of SCS and suggest novel avenues for its prevention and treatment through microbiota-targeted interventions.
Key Points.
The study confirms a potential causal relationship between gut microbiota (GM) and spinal canal stenosis (SCS) across different ethnic groups.
We identifies specific GM taxa associated with SCS and reveals key signaling pathways and hub genes potentially involved in its pathogenesis.
These findings provide new insights and potential approaches for preventing and treating SCS through microbiota-targeted interventions.
Footnotes
Conflict of Interest
No potential conflict of interest relevant to this article was reported.
Funding
This work was supported by the National Natural Science Foundation of China (No. 82071667 and 81902209).
Author Contributions
Conceptualization: LZ, CW. Data analysis: WHZ, LL, YDW, YBW, ZHC. Literature search: WHZ, LL, YDW, YBW, ZHC. Funding acquisition: WC, LZ. Writing–original draft: WHZ, LL, YDW, YBW, ZHC. Writing–review & editing: TRW, LZ, CW. Final approval of the manuscript: all authors.
Data Availability
The datasets used for Mendelian Randomization analysis in the present study are available in the GWAS catalog database (https://www.ebi.ac.uk/gwas/) and FinnGen project (https://www.finngen.fi/en/access_results). The original RNA-Sequencing datasets were available in Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo). The related data about this are shown in supplementary files.
Supplementary Materials
Supplementary materials can be available from https://doi.org/10.31616/asj.2025.0235.
Supplement 1. Supplement 1. Selected instrumental variants.
Supplement 2. 196 bacterial taxa included.
Supplement 3. Mendelian randomization results and forest plot of casual relationship between gut microbiota and the risk of spinal canal stenosis in the European population.
Supplement 4. Scatter plots demonstrating the causal relationship between gut microbiota and the risk of spinal canal stenosis in the European population.
Supplement 5. Mendelian randomization results and the forest plot of casual relationship between gut microbiota and the risk of spinal canal stenosis in the East Asian population.
Supplement 6. Scatter plots demonstrating the causal relationship between gut microbiota and the risk of spinal canal stenosis in the East Asian population.
Supplement 7. Heterogeneity and pleiotropy results.
Supplement 8. MRPRESSO method results of the MR analysis (European).
Supplement 9. Leave-one-out plots depicting the causal relationship of the risk of spinal canal stenosis in the European population.
Supplement 10. Leave-one-out plots depicting the causal relationship of the risk of spinal canal stenosis in the East Asian population.
Supplement 11. Scatter plot and leave-one-out plot depicting the reverse Mendelian randomization results.
Supplement 12. Interracial reverse MR results.
Supplement 13. East Asian reverse MR results.
Supplement 14. European reverse MR results.
Supplement 15. Heterogeneity and pleiotropy of reverse MR results.
Supplement 16. MRPRESSO results.
References
- 1.Jensen RK, Harhangi BS, Huygen F, Koes B. Lumbar spinal stenosis. BMJ. 2021;373:n1581. doi: 10.1136/bmj.n1581. [DOI] [PubMed] [Google Scholar]
- 2.Lurie J, Tomkins-Lane C. Management of lumbar spinal stenosis. BMJ. 2016;352:h6234. doi: 10.1136/bmj.h6234. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Katz JN, Zimmerman ZE, Mass H, Makhni MC. Diagnosis and management of lumbar spinal stenosis: a review. JAMA. 2022;327:1688–99. doi: 10.1001/jama.2022.5921. [DOI] [PubMed] [Google Scholar]
- 4.Costello ME, Robinson PC, Benham H, Brown MA. The intestinal microbiome in human disease and how it relates to arthritis and spondyloarthritis. Best Pract Res Clin Rheumatol. 2015;29:202–12. doi: 10.1016/j.berh.2015.08.001. [DOI] [PubMed] [Google Scholar]
- 5.Le Roy T, Lecuyer E, Chassaing B, et al. The intestinal microbiota regulates host cholesterol homeostasis. BMC Biol. 2019;17:94. doi: 10.1186/s12915-019-0715-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Walsh J, Griffin BT, Clarke G, Hyland NP. Drug-gut microbiota interactions: implications for neuropharmacology. Br J Pharmacol. 2018;175:4415–29. doi: 10.1111/bph.14366. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Morimoto T, Kobayashi T, Kakiuchi T, et al. Gut-spine axis: a possible correlation between gut microbiota and spinal degenerative diseases. Front Microbiol. 2023;14:1290858. doi: 10.3389/fmicb.2023.1290858. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Kurilshikov A, Medina-Gomez C, Bacigalupe R, et al. Large-scale association analyses identify host factors influencing human gut microbiome composition. Nat Genet. 2021;53:156–65. doi: 10.1038/s41588-020-00763-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Sakaue S, Kanai M, Tanigawa Y, et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet. 2021;53:1415–24. doi: 10.1038/s41588-021-00931-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Ahn DK, Lee S, Moon SH, et al. Ossification of the ligamentum flavum. Asian Spine J. 2014;8:89–96. doi: 10.4184/asj.2014.8.1.89. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Wu Q, Dai J. Racial/ethnic differences in bone mineral density for osteoporosis. Curr Osteoporos Rep. 2023;21:670–84. doi: 10.1007/s11914-023-00838-y. [DOI] [PubMed] [Google Scholar]
- 12.Rajasekaran S, Soundararajan DCR, Tangavel C, et al. Human intervertebral discs harbour a unique microbiome and dysbiosis determines health and disease. Eur Spine J. 2020;29:1621–40. doi: 10.1007/s00586-020-06446-z. [DOI] [PubMed] [Google Scholar]
- 13.Li JS, Su SL, Xu Z, et al. Potential roles of gut microbiota and microbial metabolites in chronic inflammatory pain and the mechanisms of therapy drugs. Ther Adv Chronic Dis. 2022;13:20406223221091177. doi: 10.1177/20406223221091177. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Ferreira CM, Vieira AT, Vinolo MA, et al. The central role of the gut microbiota in chronic inflammatory diseases. J Immunol Res. 2014;2014:689492. doi: 10.1155/2014/689492. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Yan J, Charles JF. Gut microbiome and bone: to build, destroy, or both? Curr Osteoporos Rep. 2017;15:376–84. doi: 10.1007/s11914-017-0382-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Scott MM, Lachey JL, Sternson SM, et al. Leptin targets in the mouse brain. J Comp Neurol. 2009;514:518–32. doi: 10.1002/cne.22025. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Chen S, Han H, Sun X, et al. Causal effects of specific gut microbiota on musculoskeletal diseases: a bidirectional two-sample Mendelian randomization study. Front Microbiol. 2023;14:1238800. doi: 10.3389/fmicb.2023.1238800. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Whisner CM, Martin BR, Nakatsu CH, et al. Soluble maize fibre affects short-term calcium absorption in adolescent boys and girls: a randomised controlled trial using dual stable isotopic tracers. Br J Nutr. 2014;112:446–56. doi: 10.1017/S0007114514000981. [DOI] [PubMed] [Google Scholar]
- 19.Kang L, Li P, Wang D, et al. Alterations in intestinal microbiota diversity, composition, and function in patients with sarcopenia. Sci Rep. 2021;11:4628. doi: 10.1038/s41598-021-84031-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Zhang GZ, Liu MQ, Chen HW, et al. NF-κB signalling pathways in nucleus pulposus cell function and intervertebral disc degeneration. Cell Prolif. 2021;54:e13057. doi: 10.1111/cpr.13057. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Ju J, Yu D, Xue F, et al. Inhibition of Nf-κb prevents trauma-induced heterotopic ossification in rat model. Connect Tissue Res. 2019;60:304–10. doi: 10.1080/03008207.2018.1530771. [DOI] [PubMed] [Google Scholar]
- 22.Xia B, Zhu R, Zhang H, et al. Lycopene improves bone quality and regulates AGE/RAGE/NF-κB signaling pathway in high-fat diet-induced obese mice. Oxid Med Cell Longev. 2022;2022:3697067. doi: 10.1155/2022/3697067. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Illien-Junger S, Torre OM, Kindschuh WF, et al. AGEs induce ectopic endochondral ossification in intervertebral discs. Eur Cell Mater. 2016;32:257–70. doi: 10.22203/eCM.v032a17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Chen S, Lei L, Li Z, et al. Grem1 accelerates nucleus pulposus cell apoptosis and intervertebral disc degeneration by inhibiting TGF-β-mediated Smad2/3 phosphorylation. Exp Mol Med. 2022;54:518–30. doi: 10.1038/s12276-022-00753-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Yang H, Yuan C, Wu C, et al. The role of TGF-β1/Smad2/3 pathway in platelet-rich plasma in retarding intervertebral disc degeneration. J Cell Mol Med. 2016;20:1542–9. doi: 10.1111/jcmm.12847. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Minerbi A, Shen S. Gut microbiome in anesthesiology and pain medicine. Anesthesiology. 2022;137:93–108. doi: 10.1097/ALN.0000000000004204. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplement 1. Supplement 1. Selected instrumental variants.
Supplement 2. 196 bacterial taxa included.
Supplement 3. Mendelian randomization results and forest plot of casual relationship between gut microbiota and the risk of spinal canal stenosis in the European population.
Supplement 4. Scatter plots demonstrating the causal relationship between gut microbiota and the risk of spinal canal stenosis in the European population.
Supplement 5. Mendelian randomization results and the forest plot of casual relationship between gut microbiota and the risk of spinal canal stenosis in the East Asian population.
Supplement 6. Scatter plots demonstrating the causal relationship between gut microbiota and the risk of spinal canal stenosis in the East Asian population.
Supplement 7. Heterogeneity and pleiotropy results.
Supplement 8. MRPRESSO method results of the MR analysis (European).
Supplement 9. Leave-one-out plots depicting the causal relationship of the risk of spinal canal stenosis in the European population.
Supplement 10. Leave-one-out plots depicting the causal relationship of the risk of spinal canal stenosis in the East Asian population.
Supplement 11. Scatter plot and leave-one-out plot depicting the reverse Mendelian randomization results.
Supplement 12. Interracial reverse MR results.
Supplement 13. East Asian reverse MR results.
Supplement 14. European reverse MR results.
Supplement 15. Heterogeneity and pleiotropy of reverse MR results.
Supplement 16. MRPRESSO results.
Data Availability Statement
The datasets used for Mendelian Randomization analysis in the present study are available in the GWAS catalog database (https://www.ebi.ac.uk/gwas/) and FinnGen project (https://www.finngen.fi/en/access_results). The original RNA-Sequencing datasets were available in Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo). The related data about this are shown in supplementary files.