How Gut Microbiome and Blood Metabolites Drive Ossification of the Posterior Longitudinal Ligament of the Spine: A Genome-Wide Association Study Based on the East Asian Population

How Gut Microbiome and Blood Metabolites Drive Ossification of the Posterior Longitudinal Ligament of the Spine: A Genome-Wide Association Study Based on the East Asian Population | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article How Gut Microbiome and Blood Metabolites Drive Ossification of the Posterior Longitudinal Ligament of the Spine: A Genome-Wide Association Study Based on the East Asian Population Yazhou Li, Jiazhou Wu, Yanbin Wu, Zexian Liu, Junming Zhang, Tao Qian, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7893117/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Feb, 2026 Read the published version in European Spine Journal → Version 1 posted 9 You are reading this latest preprint version Abstract Objective This study explored the associations between the gut microbiome, blood metabolites, and ossification of the posterior longitudinal ligament of the spine(OPLL), and identified potential causal relationships mediated by blood metabolites. Method Using summary-level genome-wide association study (GWAS) data, including measures of the gut microbiome, blood metabolites, and OPLL, we employed a two-sample Mendelian randomization (MR) approach to identify gut microbial taxa and blood metabolites potentially associated with OPLL development. Additionally, we identified blood metabolites as mediators of the causal pathway by which the gut microbiome influences OPLL. The inverse-variance weighted (IVW) method served as the primary analytical approach in MR analysis. Sensitivity analyses including Cochran's Q test, MR-Egger regression, and MR-PRESSO were performed to assess the robustness of the results. Results MR analysis revealed unidirectional causal relationships between 20 gut microbiome taxa and OPLL. Among these, 6 taxa were positively associated with an increased risk of OPLL, while 14 taxa were negatively associated with risk. Additionally, 8 blood metabolites exhibited potential causal relationships with OPLL, with 5 showing positive and 3 showing negative associations. Mediation analysis demonstrated that 6 gut microbiome taxa influenced OPLL development through 4 intermediary metabolites. Specifically, manganese mediated the effect of Bacilli on OPLL, aspartic acid mediated the effects of Megasphaera and Prevotella oris, cystine mediated the effect of MF0036, and vitamin B2 (VB2) mediated the effects of MF0052 and MF0047 on OPLL. Conclusion This study provides evidence supporting potential causal links between specific gut microbiomes and OPLL, emphasizing the mediating role of blood metabolites. Gut Microbiome metabolites ossification of the posterior longitudinal ligament of the spine Mendelian randomization GWAS Figures Figure 1 Figure 2 Figure 3 Figure 4 1.Introduction Ossification of the posterior longitudinal ligament of the spine (OPLL) is characterized by abnormal ectopic ossification of the posterior longitudinal ligament, often leading to compression of the spinal cord and nerve roots, resulting in symptoms such as paralysis and myelopathy[ 1 ]. Although OPLL predominantly affects the cervical spine, it can also affect the thoracic and lumbar regions[ 2 ]. The prevalence of OPLL is as high as 3.0% in Asian countries[ 1 ], whereas it is lower in European and North American populations, ranging from 0.1% to 1.7%[ 3 ]. Epidemiological and familial studies indicate that OPLL is a multifactorial disorder resulting from the interaction of complex genetic (polygenic) and nongenetic (environmental) factors[ 4 – 6 ]. However, the precise mechanisms underlying its pathogenesis remain unclear and effective treatments to halt disease progression are lacking. Therefore, spinal cord decompression surgery is the standard therapeutic option for patients with advanced OPLL. The human gut microbiota comprises approximately 100 trillion microbial cells and contains roughly 3.3 million microbial genes, forming an extraordinarily complex microecosystem[ 7 ]. Through long-term co-evolution, this microbial community develops a mutually beneficial symbiotic relationship with the host, playing several crucial roles in maintaining host health. These roles include participating in substance metabolism, regulating immune function, and facilitating nutrient absorption[ 8 – 10 ].In recent years, an increasing number of studies have demonstrated that gut microbiota can influence spinal-related diseases through various potential mechanisms, including effects on bone, cartilage, intervertebral discs, ligaments, and muscles[ 11 , 12 ]. Specifically, gut microbiota dysbiosis may affect spinal health primarily through the following three mechanisms: (I) nutritional metabolic pathways, including calcium absorption, amino acid metabolism, and vitamin K synthesis; (II) immune regulatory pathways, involving estrogen metabolism, the actions of short-chain fatty acids (SCFAs), and regulation of systemic inflammatory responses; and (III) neuroendocrine pathways, affecting bone metabolism through neurotransmitters such as serotonin and leptin, ultimately leading to an imbalance between osteoblast and osteoclast activity[ 13 ]. Based on these findings, precise modulation of the biological composition of the gut microbiome has the potential to prevent and inhibit the onset and progression of these diseases. The interaction between gut microbiota dysbiosis and metabolic abnormalities has been shown to be closely associated with various pathological conditions. Gut microbiota primarily influence the host's immune and physiological functions through the production of metabolic products[ 10 ]. These metabolites not only play a role in the immune regulation of chronic spinal diseases but are also linked to the onset and progression of diabetes and metabolic syndrome[ 14 – 16 ]. Additionally, several studies have demonstrated that gut microbiota dysbiosis contributes to the development of certain cardiovascular diseases through various metabolic pathways[ 17 , 18 ]. A recent study comparing plasma metabolomics between 10 patients with OPLL and 10 healthy controls found significant differences in the plasma metabolic profiles. Specifically, the levels of six metabolites including acylcarnitine, palmitoylcarnitine, and fatty acids were notably higher in the OPLL group than in the control group. These finding suggest that metabolic abnormalities may be implicated in the onset of OPLL[ 19 ].However, research on the complex interactions between the gut microbiome, metabolites, and OPLL is lacking. Therefore, it is necessary to conduct a more comprehensive exploration to understand the complex mechanisms by which changes in the gut microbiota and associated metabolites influence the occurrence and development of OPLL. MR is based on the principle of using genetic variations, specifically single nucleotide polymorphisms (SNPs), as instrumental variables to assess causal relationships between exposures and outcomes. This approach allows researchers to overcome the limitations inherent in traditional observational studies, thereby improving the validity and reliability of causal inferences in epidemiological research[ 20 ]. In this study, we employed two-sample MR and mediation analyses utilizing summary statistics from the latest GWAS on the gut microbiome, blood metabolites, and OPLL. These analyses aimed to clarify the complex interrelationships between these variables and provide valuable insights into their potential causal links. 2. Materials and methods 2.1 Study Design This study utilized summary-level genetic data from GWAS. The instrumental variables in the MR analysis were selected based on three core assumptions required for valid MR analysis: (1) the genetic variants must be significantly associated with the exposure of interest, (2) these variants must not be linked to any potential confounders influencing the outcome, and (3) the genetic variants must influence the outcome solely through exposure[21]. A two-sample MR approach was used to assess the causal relationships between gut microbiota, blood metabolites, and OPLL. Mediation MR analysis was subsequently performed to evaluate the role of metabolites as mediators in the association between the gut microbiota and OPLL. The study design is shown in Figure 1. 2.2 Data Sources The gut microbiome data used in this study were integrated from a large-scale Chinese population cohort. This dataset, constructed via shotgun metagenomic sequencing of 2,545 fecal samples from 1,539 participants, provides 500 precisely defined microbial features. Furthermore, quantification of 112 blood metabolites, including amino acids, lipids, vitamins, and hormones, was conducted using multi-platform mass spectrometry techniques [22]. OPLL data were obtained from a major cohort study within the BioBank Japan project, published by Koike et al. in 2023. This study included 22,016 Japanese individuals (2,010 patients and 20,006 controls) and was conducted using a large-scale case-control design for the GWAS analysis[23]. To minimize potential biases arising from population heterogeneity, this study strictly confined all analytical data to individuals of East Asian genetic ancestry. Specifically, although the samples were obtained from different sources—with gut microbiome and blood metabolite data derived from a Chinese health cohort and ossification of the posterior longitudinal ligament (OPLL) data sourced from the Japanese BioBank—both datasets were uniformly restricted to East Asian populations (according to the 1000 Genomes Project classification, both Chinese and Japanese populations belong to the EAS group). Recent studies have confirmed high genetic similarity (r² > 0.85) in genome-wide association study (GWAS)-relevant single nucleotide polymorphism (SNP) loci between Chinese and Japanese populations, providing a theoretical basis for cross-cohort genetic analysis[24]. All data used in this study were publicly available resources, requiring no additional ethical review approval. 2.3 Selection of Instrumental Variables In the MR analysis, SNPs were chosen as instrumental variables because of their strong association with exposure variables. To address the limitation of the conventional genome-wide significance threshold (P = 5 × 10⁻⁸), which may result in an insufficient number of SNPs for analysis, we relaxed the threshold to P < 1 × 10⁻⁵[25]. To ensure the independence of the selected SNPs, we performed linkage disequilibrium (LD) pruning with an clumping R² < 0.001 and a physical distance threshold of 10,000 kb, removing SNPs in LD[26]. By applying these stringent criteria, we identified a set of instrumental variables that were strongly associated with the gut microbiota and blood metabolites and were mutually independent, thereby minimizing the risk of violating the core assumptions of MR analysis. The strength of the selected SNPs was assessed using F-statistics, and SNPs with F-statistics < 10 were excluded to mitigate the risk of weak instrument bias in MR analysis[27]. 2.4 Statistical Analysis Multiple MR methods were employed in this study, including the IVW, MR-Egger regression, weighted median (WM), simple mode, and weighted mode. Given the robustness of the IVW method in causal inference[28], it was selected as the primary approach for estimating causal associations. A p-value less than 0.05 was considered indicative of a statistically significant causal relationship between the exposure and outcome.To ensure the robustness and reliability of the results, several sensitivity analyses were conducted. First, Cochran’s Q test and the MR-Egger intercept were used to assess heterogeneity and horizontal pleiotropy in the causal estimates[29,30]. Second, the MR-PRESSO method was applied to detect pleiotropic bias; a p-value greater than 0.05 was interpreted as no evidence of significant horizontal pleiotropy[30]. Leave-one-out analysis was performed to evaluate the influence of individual SNPs on the overall causal estimate[31]. Furthermore, to examine the reverse causality between gut microbiota and OPLL, the methods described above were applied. To explore the causal pathways between gut microbiota, blood metabolites, and OPLL, a two-step MR analysis was conducted. First, the total causal effect of inflammatory factors on OPLL was estimated using a two-sample MR analysis. Two independent two-sample MR analyses were performed: the first assessed the causal relationship between gut microbiota and metabolites (effect size β1), and the second evaluated the causal association between metabolites and OPLL (effect size β2). The mediation effect was calculated as (effect size β1 * effect size β2), and the direct effect was derived by subtracting the mediation effect from the total effect, The mediation rate was determined as (mediation effect / total effect) × 100%[32]. All MR analyses were conducted using R software (version 4.4.1) and relevant packages, including "TwoSampleMR" and "MRInstruments." 3. Results 3.1 Causal Relationship Between Gut Microbiome and OPLL Using the IVW method as the primary analytical approach, we identified significant causal associations between genetically predicted levels of 22 gut microbial taxa and the risk of OPLL. Elevated levels of the following taxa were positively associated with an increased risk of OPLL: Erysipelotrichales (OR = 1.160; 95% CI: 1.049–1.283; P = 0.003), Clostridium hylemonae (OR = 1.094; 95% CI: 1.024–1.169; P = 0.007), Enterococcus faecalis (OR = 1.150; 95% CI: 1.036–1.276; P = 0.008), Acidaminococcus intestini (OR = 1.084; 95% CI: 1.019–1.153; P = 0.009), Bacilli (OR = 1.133; 95% CI: 1.029–1.248; P = 0.010), Neisseria subflava (OR = 1.063; 95% CI: 1.009–1.119; P = 0.021), and other related taxa. Conversely, higher levels of the following taxa were associated with a decreased risk of OPLL: Bacteroides dorei (OR = 0.871; 95% CI: 0.792–0.958; P = 0.004), Clostridium difficile (OR = 0.766; 95% CI: 0.613–0.956; P = 0.018), Enterobacter asburiae (OR = 0.944; 95% CI: 0.900–0.990; P = 0.019), Prevotella tannerae (OR = 0.919; 95% CI: 0.856–0.987; P = 0.020), Bacteroides caccae (OR = 0.938; 95% CI: 0.887–0.992; P = 0.026), and other related taxa.(Fig. 2 ).Reverse MR analysis showed no evidence of reverse causality between OPLL and the 20 gut microbial taxa (reverse P-value > 0.05). However, reverse causal relationships were observed for two taxa, Anaerococcus and Bacteroides dorei, with P-values 0.05), and the MR-PRESSO method revealed no substantial pleiotropic bias (Supplementary Table 1). Furthermore, leave-one-out analysis confirmed the robustness of the results, with no individual SNP exerting an undue influence on the overall causal estimates (Supplementary Fig. 1). 3.2 Causal Relationship Between Blood Metabolites and OPLL Eight blood metabolites were associated with the risk of developing OPLL. Higher levels of aspartic acid (OR = 1.490; 95% CI: 1.044–2.125; P = 0.027), manganese (OR = 1.322; 95% CI: 1.018–1.716; P = 0.035), and red blood cell distribution width (OR = 2.627; 95% CI: 1.134–6.088; P = 0.024) were positively associated with an increased risk of OPLL. In contrast, higher levels of dehydroepiandrosterone (OR = 0.703; 95% CI: 0.531–0.930; P = 0.013), HR (OR = 0.394; 95% CI: 0.185–0.839; P = 0.015), monocyte percentage (OR = 0.624; 95% CI: 0.419–0.927; P = 0.019), cystine (OR = 0.924; 95% CI: 0.856–0.997; P = 0.043), and VB2 (OR = 0.904; 95% CI: 0.818–0.999; P = 0.048) were identified as protective factors against OPLL (Fig. 3 ). Sensitivity analyses showed no evidence of horizontal pleiotropy or heterogeneity (Supplementary Table 3). 3.3 Mediation Analysis of Blood Metabolites To explore the mediating role of blood metabolites in the relationship between gut microbiota and OPLL, we conducted a mediation MR analysis using the identified gut microbiota and blood metabolites.We identified four metabolites involved in six mediation pathways between gut microbiota and OPLL(Fig. 4 ). Among the identified pathways, the mediation effect of manganese on the association between Bacilli and OPLL was (β = 0.012, P = 0.091), accounting for 10.00% of the total effect. Aspartic acid mediated the relationship between Megasphaera and OPLL (β = -0.006, P = 0.128), with a mediation proportion of -14.4%. Additionally, aspartic acid mediated the association between Prevotella oris and OPLL (β = 0.017, P = 0.101), accounting for − 14.2% of the total effect. The mediation effect of cystine on the relationship between MF0036 and OPLL was (β = 0.011, P = 0.116), representing 3.1% of the total effect. Furthermore, the mediation effect of VB2 on the relationship between MF0052 and OPLL was (β = -0.005, P = 0.147), with a mediation proportion of -8.33%. The mediation effect of VB2 on the overall association between gut microbiota and OPLL was (β = 0.058, P = 0.148), accounting for 7.2% of the total effect (Supplementary Table 5). Sensitivity analyses confirmed the absence of horizontal pleiotropy and heterogeneity (Supplementary Table S4). These findings suggest that gut microbiota may influence the development of OPLL through metabolite-mediated pathways. 4. Discussion This study is the first to analyze the causal relationship between the gut microbiome and OPLL using publicly available genetic data. The results support the mediating role of four blood metabolites in the pathogenesis of OPLL driven by six gut microbiome taxa. Bacilli may serve as a risk factor for OPLL by modulating blood manganese levels. Bacillus subtilis, a representative bacterium of the Bacilli class, has been shown to contain metal-sensing regulatory proteins such as Fur, MntR, and PerR, which collaboratively regulate iron and manganese homeostasis, thereby directly influencing the absorption and metabolism of metal ions [33,34]. Furthermore, transport proteins such as CitM and CitH in B. subtilis facilitate the transport of metal-citrate complexes, affecting the absorption process of metal ions [35]. These findings support the positive regulatory effect of Bacilli on manganese metabolism. Manganese, a trace element essential for bone development, acts as a cofactor for enzymes critical to bone health, including glycosyltransferases and manganese superoxide dismutase (MnSOD). It plays a pivotal role in regulating cartilage matrix synthesis and bone formation [36].In summary, we hypothesize that the regulatory effect of Bacilli bacteria on manganese metabolism may serve as a key biological bridge through which the gut microbiota influences the pathogenesis of OPLL. Although we have observed this potential causal relationship, its specific molecular mechanisms require further in-depth study and validation. Megasphaera may exert a protective effect in the pathogenesis of OPLL by regulating aspartic acid metabolism. Megasphaera is an anaerobic gut bacterium capable of utilizing amino acids as metabolic substrates and generating short-chain fatty acids through the acrylate pathway, thereby participating in host energy metabolism and immune function regulation [37].Aspartic acid is an important non-essential amino acid involved in various physiological processes, including neurotransmitter synthesis, gluconeogenesis, urea cycle, and cellular energy metabolism [38]. Notably, variants of the asporin protein, which encodes aspartic acid repeat sequences, have been shown to inhibit chondrogenesis by suppressing the TGF-β signaling pathway, potentially leading to bone metabolism disorders and promoting ectopic ossification under certain pathological conditions [39]. Our findings are consistent with this, as aspartic acid has been identified as a risk factor for OPLL. Further analysis suggests that Megasphaera may exert a protective effect on OPLL by downregulating its levels. Additionally, Prevotella species have been shown to be closely associated with chronic inflammation, autoimmune diseases, and metabolic disorders [40]. Our study further found that abnormal enrichment of Prevotella oris might contribute to the onset and development of OPLL by regulating host amino acid metabolism, particularly by upregulating aspartic acid levels. However, the potential mechanisms underlying these effects require further investigation. The gut microbiota encoded by MF0036 may serve as a risk factor for OPLL by modulating blood cystine levels. Cystine, an essential precursor of glutathione synthesis, plays a crucial role in maintaining cellular survival and function under oxidative stress conditions. Previous studies have demonstrated that glutathione’s antioxidant properties protect cells from oxidative damage, thereby inhibiting abnormal activation of osteoblasts in stressed environments [41]. We hypothesized that elevated cystine levels may reduce oxidative stress in bone metabolism through antioxidant effects, thereby preventing excessive osteoblast activation and slowing the progression of OPLL. Both existing research and our data support the notion that cystine exerts a protective effect on OPLL to some extent, with higher levels potentially reducing the risk of abnormal ossification.Although this functional module has not been assigned to a known specific genus, its association with metabolic pathways suggests that it may represent a class of key metabolic microbiota that plays a regulatory role in the gut-metabolism-bone metabolism axis. The gut microbiota encoded by MF0052 positively correlated with blood VB2 levels, whereas the microbiota encoded by MF0074 exhibited a negative correlation with VB2 levels. The previous studies have shown that VB2 exerts a protective role in various chronic diseases by enhancing glutathione metabolism and reducing oxidative stress levels.[42] In OPLL, a condition characterized by ectopic ossification and abnormal activation of osteoblasts, oxidative stress and chronic inflammation are recognized as key pathogenic mechanisms [43,44]. Therefore, upregulation of VB2 may reduce the risk of OPLL by boosting antioxidant defense mechanisms and maintaining cellular redox homeostasis, which in turn may inhibit abnormal activation of osteoblasts. Based on the directional association between MF0052, MF0074 and VB2, we hypothesize that MF0052 may exert a protective effect by promoting increased VB2 levels, while MF0074 may have a pathogenic effect by suppressing VB2 levels. However, our study had several limitations. First, in line with the relevant literature, we applied a significance threshold of P < 1 × 10⁻⁵ for SNP selection to ensure an adequate number of instrumental variables. This threshold, however, does not meet the conventional genome-wide significance threshold of P < 5 × 10⁻⁸. To address this, we rigorously evaluated the F-statistics of each SNP to mitigate potential weak instrument bias [45]. Second, our study sample was restricted to individuals of East Asian descent, which may limit the generalizability of our findings to other populations. Although MR is a robust tool for causal inference, it has some limitations. The validity of the MR results depends on three core assumptions, and violations of these assumptions can introduce bias. Therefore, our findings should be viewed as suggestive rather than conclusive.Despite these limitations, our study still provides the most compelling evidence to date regarding the causal relationship between gut microbiota, blood metabolites, and the risk of OPLL, offering theoretical support and potential intervention strategies for the prevention and treatment of OPLL. In conclusion, our study revealed potential causal relationships among the gut microbiome, blood metabolites, and OPLL. These findings may provide potential biomarkers for the prevention and treatment of OPLL. Furthermore, we discovered for the first time that the gut microbiota may influence the progression of OPLL through blood metabolites. These findings offer new perspectives for understanding the pathophysiological mechanisms of OPLL. Further experimental and clinical studies are warranted to validate these associations and explore their therapeutic implications. Declarations Ethics approval and Consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials All data generated or analysed during this study are included in this published article. Competing interests The authors declare that they have no competing interests. Authors' Contribution YL: Data curation, Methodology, Writing, original draft. JW: Data curation, Methodology, Writing – original draft. BY: Investigation, Writing – review & editing. ZL: Investigation, Writing – review & editing. JZ: Investigation, Writing – review & editing. TQ: Investigation, Writing – review & editing. JY:Investigation, Writing – review & editing. BM: Investigation, Writing – review & editing. LZ: Supervision, Writing – review & editing. EL: Writing – review & editing, Supervision. YB: Supervision, Writing – review & editing. XS: Supervision, Writing – review & editing. YH: Supervision, Writing – review & editing. WX: Writing – review & editing, Conceptualization, Funding acquisition. JP: Conceptualization, Writing – review & editing, Project administration. References Matsunaga S, Sakou T. Ossification of the posterior longitudinal ligament of the cervical spine: etiology and natural history. Spine (Phila Pa 1976). 2012 Mar 1;37(5):E309-14. Hirai T, Yoshii T, Hashimoto J, et al. Clinical Characteristics of Patients with Ossification of the Posterior Longitudinal Ligament and a High OP Index: A Multicenter Cross-Sectional Study (JOSL Study). J Clin Med. 2022 Jun 27;11(13):3694. Belanger TA, Roh JS, Hanks SE, et al. Ossification of the posterior longitudinal ligament. Results of anterior cervical decompression and arthrodesis in sixty-one North American patients. J Bone Joint Surg Am. 2005 Mar;87(3):610-5. Ren Y, Liu ZZ, Feng J, et al. Association of a BMP9 haplotype with ossification of the posterior longitudinal ligament (OPLL) in a Chinese population. PLoS One. 2012;7(7):e40587. Choi BW, Song KJ, Chang H. Ossification of the posterior longitudinal ligament: a review of literature. Asian Spine J. 2011 Dec;5(4):267-76. Yan L, Gao R, Liu Y, et al. The Pathogenesis of Ossification of the Posterior Longitudinal Ligament. Aging Dis. 2017 Oct 1;8(5):570-582. Qin J, Li R, Raes J, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010 Mar 4;464(7285):59-65. Bäckhed F, Ley RE, Sonnenburg JL, et al. Host-bacterial mutualism in the human intestine. Science. 2005 Mar 25;307(5717):1915-20. Round JL, Mazmanian SK. The gut microbiota shapes intestinal immune responses during health and disease. Nat Rev Immunol. 2009 May;9(5):313-23. Brestoff JR, Artis D. Commensal bacteria at the interface of host metabolism and the immune system. Nat Immunol. 2013 Jul;14(7):676-84. Behera J, Ison J, Tyagi SC, et al. The role of gut microbiota in bone homeostasis. Bone. 2020 Jun;135:115317. Seely KD, Kotelko CA, Douglas H, et al. The Human Gut Microbiota: A Key Mediator of Osteoporosis and Osteogenesis. Int J Mol Sci. 2021 Aug 31;22(17):9452. Chen Y, Wang X, Zhang C, et al. Gut Microbiota and Bone Diseases: A Growing Partnership. Front Microbiol. 2022 May 6;13:877776. Nagata N, Takeuchi T, Masuoka H, et al. Human Gut Microbiota and Its Metabolites Impact Immune Responses in COVID-19 and Its Complications. Gastroenterology. 2023 Feb;164(2):272-288. Guo Z, Pan J, Zhu H, et al. Metabolites of Gut Microbiota and Possible Implication in Development of Diabetes Mellitus. J Agric Food Chem. 2022 May 25;70(20):5945-5960. Feng K, Zhang H, Chen C, et al. Heptamethoxyflavone Alleviates Metabolic Syndrome in High-Fat Diet-Fed Mice by Regulating the Composition, Function, and Metabolism of Gut Microbiota. J Agric Food Chem. 2023 Jul 5;71(26):10050-10064. Moutsoglou DM, Tatah J, Prisco SZ, et al. Pulmonary Arterial Hypertension Patients Have a Proinflammatory Gut Microbiome and Altered Circulating Microbial Metabolites. Am J Respir Crit Care Med. 2023 Mar 15;207(6):740-756. Nemet I, Li XS, Haghikia A, et al. Atlas of gut microbe-derived products from aromatic amino acids and risk of cardiovascular morbidity and mortality. Eur Heart J. 2023 Aug 22;44(32):3085-3096. Tsuji T, Matsumoto M, Nakamura M, et al. Metabolite profiling of plasma in patients with ossification of the posterior longitudinal ligament. J Orthop Sci. 2018 Nov;23(6):878-883. Cao Z, Wu Y, Li Q, et al. A causal relationship between childhood obesity and risk of osteoarthritis: results from a two-sample Mendelian randomization analysis. Ann Med. 2022 Dec;54(1):1636-1645. Emdin CA, Khera AV, Kathiresan S. Mendelian Randomization. JAMA. 2017 Nov 21;318(19):1925-1926. Liu X, Tong X, Zou Y, et al. Mendelian randomization analyses support causal relationships between blood metabolites and the gut microbiome. Nat Genet. 2022 Jan;54(1):52-61. Koike Y, Takahata M, Nakajima M, et al. Genetic insights into ossification of the posterior longitudinal ligament of the spine. Elife. 2023 Jul 18;12:e86514. Takeuchi F, Akiyama M, Matoba N, et al. Interethnic analyses of blood pressure loci in populations of East Asian and European descent. Nat Commun. 2018 Nov 28;9(1):5052. Sanna S, van Zuydam NR, Mahajan A, et al. Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases. Nat Genet. 2019 Apr;51(4):600-605. Lee CH, Cook S, Lee JS, et al. Comparison of Two Meta-Analysis Methods: Inverse-Variance-Weighted Average and Weighted Sum of Z-Scores. Genomics Inform. 2016 Dec;14(4):173-180. Burgess S, Thompson SG; CRP CHD Genetics Collaboration. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011 Jun;40(3):755-64. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013 Nov;37(7):658-65. Bowden J, Del Greco M F, Minelli C, et al. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat Med. 2017 May 20;36(11):1783-1802. Verbanck M, Chen CY, Neale B, et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018 May;50(5):693-698. Sekula P, Del Greco M F, Pattaro C, et al. Mendelian Randomization as an Approach to Assess Causality Using Observational Data. J Am Soc Nephrol. 2016 Nov;27(11):3253-3265. Carter AR, Sanderson E, Hammerton G, et al. Mendelian randomisation for mediation analysis: current methods and challenges for implementation. Eur J Epidemiol. 2021 May;36(5):465-478. Hu G, Wang Y, Blake C, et al. Parallel genetic adaptation of Bacillus subtilis to different plant species. Microb Genom. 2023 Jul;9(7):mgen001064. Helmann JD. Specificity of metal sensing: iron and manganese homeostasis in Bacillus subtilis. J Biol Chem. 2014 Oct 10;289(41):28112-20. Krom BP, Warner JB, Konings WN, et al. Complementary metal ion specificity of the metal-citrate transporters CitM and CitH of Bacillus subtilis. J Bacteriol. 2000 Nov;182(22):6374-81. Taskozhina G, Batyrova G, Umarova G, et al. The Manganese-Bone Connection: Investigating the Role of Manganese in Bone Health. J Clin Med. 2024 Aug 9;13(16):4679. Du L, Li Q, Yi H, et al. Gut microbiota-derived metabolites as key actors in type 2 diabetes mellitus. Biomed Pharmacother. 2022 May;149:112839. Holeček M. Aspartic Acid in Health and Disease. Nutrients. 2023 Sep 17;15(18):4023. Kizawa H, Kou I, Iida A, et al. An aspartic acid repeat polymorphism in asporin inhibits chondrogenesis and increases susceptibility to osteoarthritis. Nat Genet. 2005 Feb;37(2):138-44. Iljazovic A, Roy U, Gálvez EJC, et al. Perturbation of the gut microbiome by Prevotella spp. enhances host susceptibility to mucosal inflammation. Mucosal Immunol. 2021 Jan;14(1):113-124. McBean GJ. Cysteine, Glutathione, and Thiol Redox Balance in Astrocytes. Antioxidants (Basel). 2017 Aug 3;6(3):62. Hristov BD. The Role of Glutathione Metabolism in Chronic Illness Development and Its Potential Use as a Novel Therapeutic Target. Cureus. 2022 Sep 28;14(9):e29696. Dash UC, Bhol NK, Swain SK, et al. Oxidative stress and inflammation in the pathogenesis of neurological disorders: Mechanisms and implications. Acta Pharm Sin B. 2025 Jan;15(1):15-34. García-Sánchez A, Miranda-Díaz AG, Cardona-Muñoz EG. The Role of Oxidative Stress in Physiopathology and Pharmacological Treatment with Pro- and Antioxidant Properties in Chronic Diseases. Oxid Med Cell Longev. 2020 Jul 23;2020:2082145. Pierce BL, Burgess S. Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators. Am J Epidemiol. 2013 Oct 1;178(7):1177-84. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable15.xlsx SupplementaryFigure1.doc Cite Share Download PDF Status: Published Journal Publication published 13 Feb, 2026 Read the published version in European Spine Journal → Version 1 posted Editorial decision: Revision requested 01 Dec, 2025 Reviews received at journal 27 Nov, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviews received at journal 18 Nov, 2025 Reviewers agreed at journal 10 Nov, 2025 Reviewers invited by journal 04 Nov, 2025 Editor assigned by journal 18 Oct, 2025 Submission checks completed at journal 18 Oct, 2025 First submitted to journal 18 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7893117","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":542746669,"identity":"0d5ee954-cd65-4e27-ab6f-741bb231fac9","order_by":0,"name":"Yazhou Li","email":"","orcid":"","institution":"Clinical Medical College of Hebei University of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Yazhou","middleName":"","lastName":"Li","suffix":""},{"id":542746670,"identity":"e2cadd92-99db-479c-92e8-924c72f92bf9","order_by":1,"name":"Jiazhou Wu","email":"","orcid":"","institution":"The Fourth Medical Center of Chinese PLA 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1","display":"","copyAsset":false,"role":"figure","size":367454,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy design overview.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7893117/v1/7560cc525701b6563253ffd7.png"},{"id":95936054,"identity":"d4de5235-bf2d-4d0a-ba33-0a13c6cfe615","added_by":"auto","created_at":"2025-11-14 15:37:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":500206,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe forest plot visualizes the significant causal impact of the gut microbiota on OPLL.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7893117/v1/e39b4ba922144b940143afa4.png"},{"id":95936055,"identity":"76b090c2-2ad6-4d99-a523-7d4808d6f394","added_by":"auto","created_at":"2025-11-14 15:37:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":197094,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe forest plot visualizes the significant causal effect of blood metabolites on OPLL.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7893117/v1/d832193bd84bf8367f428584.png"},{"id":95936061,"identity":"29a3709e-4c1b-464b-abb6-e4173bc57053","added_by":"auto","created_at":"2025-11-14 15:37:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":773103,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe forest plot visualizes the significant causal effects of 6 gut microbiota on OPLL mediated by 4 blood metabolites.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7893117/v1/f7ed414b44b5a969f5821ea7.png"},{"id":102786410,"identity":"9cc91ae7-2358-426c-ae37-ff97892b225e","added_by":"auto","created_at":"2026-02-16 16:13:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2343649,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7893117/v1/9276ad1d-b216-4639-9abd-2a093a10df1f.pdf"},{"id":96243967,"identity":"12f08780-7fae-4f7b-a3b0-135b43055e11","added_by":"auto","created_at":"2025-11-19 07:17:25","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":57997,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable15.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7893117/v1/9cadf9ea8320d2cd48fa915e.xlsx"},{"id":95936060,"identity":"496376c7-1de9-4132-a3f3-04538f819be5","added_by":"auto","created_at":"2025-11-14 15:37:04","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":362496,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.doc","url":"https://assets-eu.researchsquare.com/files/rs-7893117/v1/b80b6c29ac8b384576ff0230.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"How Gut Microbiome and Blood Metabolites Drive Ossification of the Posterior Longitudinal Ligament of the Spine: A Genome-Wide Association Study Based on the East Asian Population","fulltext":[{"header":"1.Introduction","content":"\u003cp\u003eOssification of the posterior longitudinal ligament of the spine (OPLL) is characterized by abnormal ectopic ossification of the posterior longitudinal ligament, often leading to compression of the spinal cord and nerve roots, resulting in symptoms such as paralysis and myelopathy[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although OPLL predominantly affects the cervical spine, it can also affect the thoracic and lumbar regions[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The prevalence of OPLL is as high as 3.0% in Asian countries[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], whereas it is lower in European and North American populations, ranging from 0.1% to 1.7%[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Epidemiological and familial studies indicate that OPLL is a multifactorial disorder resulting from the interaction of complex genetic (polygenic) and nongenetic (environmental) factors[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, the precise mechanisms underlying its pathogenesis remain unclear and effective treatments to halt disease progression are lacking. Therefore, spinal cord decompression surgery is the standard therapeutic option for patients with advanced OPLL.\u003c/p\u003e\u003cp\u003eThe human gut microbiota comprises approximately 100 trillion microbial cells and contains roughly 3.3\u0026nbsp;million microbial genes, forming an extraordinarily complex microecosystem[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Through long-term co-evolution, this microbial community develops a mutually beneficial symbiotic relationship with the host, playing several crucial roles in maintaining host health. These roles include participating in substance metabolism, regulating immune function, and facilitating nutrient absorption[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].In recent years, an increasing number of studies have demonstrated that gut microbiota can influence spinal-related diseases through various potential mechanisms, including effects on bone, cartilage, intervertebral discs, ligaments, and muscles[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Specifically, gut microbiota dysbiosis may affect spinal health primarily through the following three mechanisms: (I) nutritional metabolic pathways, including calcium absorption, amino acid metabolism, and vitamin K synthesis; (II) immune regulatory pathways, involving estrogen metabolism, the actions of short-chain fatty acids (SCFAs), and regulation of systemic inflammatory responses; and (III) neuroendocrine pathways, affecting bone metabolism through neurotransmitters such as serotonin and leptin, ultimately leading to an imbalance between osteoblast and osteoclast activity[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Based on these findings, precise modulation of the biological composition of the gut microbiome has the potential to prevent and inhibit the onset and progression of these diseases.\u003c/p\u003e\u003cp\u003eThe interaction between gut microbiota dysbiosis and metabolic abnormalities has been shown to be closely associated with various pathological conditions. Gut microbiota primarily influence the host's immune and physiological functions through the production of metabolic products[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These metabolites not only play a role in the immune regulation of chronic spinal diseases but are also linked to the onset and progression of diabetes and metabolic syndrome[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Additionally, several studies have demonstrated that gut microbiota dysbiosis contributes to the development of certain cardiovascular diseases through various metabolic pathways[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. A recent study comparing plasma metabolomics between 10 patients with OPLL and 10 healthy controls found significant differences in the plasma metabolic profiles. Specifically, the levels of six metabolites including acylcarnitine, palmitoylcarnitine, and fatty acids were notably higher in the OPLL group than in the control group. These finding suggest that metabolic abnormalities may be implicated in the onset of OPLL[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].However, research on the complex interactions between the gut microbiome, metabolites, and OPLL is lacking. Therefore, it is necessary to conduct a more comprehensive exploration to understand the complex mechanisms by which changes in the gut microbiota and associated metabolites influence the occurrence and development of OPLL.\u003c/p\u003e\u003cp\u003eMR is based on the principle of using genetic variations, specifically single nucleotide polymorphisms (SNPs), as instrumental variables to assess causal relationships between exposures and outcomes. This approach allows researchers to overcome the limitations inherent in traditional observational studies, thereby improving the validity and reliability of causal inferences in epidemiological research[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In this study, we employed two-sample MR and mediation analyses utilizing summary statistics from the latest GWAS on the gut microbiome, blood metabolites, and OPLL. These analyses aimed to clarify the complex interrelationships between these variables and provide valuable insights into their potential causal links.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized summary-level genetic data from GWAS. The instrumental variables in the MR analysis were selected based on three core assumptions required for valid MR analysis: (1) the genetic variants must be significantly associated with the exposure of interest, (2) these variants must not be linked to any potential confounders influencing the outcome, and (3) the genetic variants must influence the outcome solely through exposure[21]. A two-sample MR approach was used to assess the causal relationships between gut microbiota, blood metabolites, and OPLL. Mediation MR analysis was subsequently performed to evaluate the role of metabolites as mediators in the association between the gut microbiota and OPLL. The study design is shown in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Data Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe gut microbiome data used in this study were integrated from a large-scale Chinese population cohort. This dataset, constructed via shotgun metagenomic sequencing of 2,545 fecal samples from 1,539 participants, provides 500 precisely defined microbial features. Furthermore, quantification of 112 blood metabolites, including amino acids, lipids, vitamins, and hormones, was conducted using multi-platform mass spectrometry techniques [22].\u003c/p\u003e\n\u003cp\u003eOPLL data were obtained from a major cohort study within the BioBank Japan project, \u0026nbsp; published by Koike et al. in 2023. This study included 22,016 Japanese individuals (2,010 patients and 20,006 controls) and was conducted using a large-scale case-control design for the GWAS analysis[23].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo minimize potential biases arising from population heterogeneity, this study strictly confined all analytical data to individuals of East Asian genetic ancestry. Specifically, although the samples were obtained from different sources\u0026mdash;with gut microbiome and blood metabolite data derived from a Chinese health cohort and ossification of the posterior longitudinal ligament (OPLL) data sourced from the Japanese BioBank\u0026mdash;both datasets were uniformly restricted to East Asian populations (according to the 1000 Genomes Project classification, both Chinese and Japanese populations belong to the EAS group). Recent studies have confirmed high genetic similarity (r\u0026sup2; \u0026gt; 0.85) in genome-wide association study (GWAS)-relevant single nucleotide polymorphism (SNP) loci between Chinese and Japanese populations, providing a theoretical basis for cross-cohort genetic analysis[24]. All data used in this study were publicly available resources, requiring no additional ethical review approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Selection of Instrumental Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the MR analysis, SNPs were chosen as instrumental variables because of their strong association with exposure variables. To address the limitation of the conventional genome-wide significance threshold (P = 5 \u0026times; 10⁻⁸), which may result in an insufficient number of SNPs for analysis, we relaxed the threshold to P \u0026lt; 1 \u0026times; 10⁻⁵[25]. To ensure the independence of the selected SNPs, we performed linkage disequilibrium (LD) pruning with an clumping R\u0026sup2; \u0026lt; 0.001 and a physical distance threshold of 10,000 kb, removing SNPs in LD[26]. By applying these stringent criteria, we identified a set of instrumental variables that were strongly associated with the gut microbiota and blood metabolites and were mutually independent, thereby minimizing the risk of violating the core assumptions of MR analysis. The strength of the selected SNPs was assessed using F-statistics, and SNPs with F-statistics \u0026lt; 10 were excluded to mitigate the risk of weak instrument bias in MR analysis[27].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultiple MR methods were employed in this study, including the IVW, MR-Egger regression, weighted median (WM), simple mode, and weighted mode. Given the robustness of the IVW method in causal inference[28], it was selected as the primary approach for estimating causal associations. A p-value less than 0.05 was considered indicative of a statistically significant causal relationship between the exposure and outcome.To ensure the robustness and reliability of the results, several sensitivity analyses were conducted. First, Cochran\u0026rsquo;s Q test and the MR-Egger intercept were used to assess heterogeneity and horizontal pleiotropy in the causal estimates[29,30]. Second, the MR-PRESSO method was applied to detect pleiotropic bias; a p-value greater than 0.05 was interpreted as no evidence of significant horizontal pleiotropy[30]. Leave-one-out analysis was performed to evaluate the influence of individual SNPs on the overall causal estimate[31].\u003c/p\u003e\n\u003cp\u003eFurthermore, to examine the reverse causality between gut microbiota and OPLL, the methods described above were applied. To explore the causal pathways between gut microbiota, blood metabolites, and OPLL, a two-step MR analysis was conducted. First, the total causal effect of inflammatory factors on OPLL was estimated using a two-sample MR analysis. Two independent two-sample MR analyses were performed: the first assessed the causal relationship between gut microbiota and metabolites (effect size \u0026beta;1), and the second evaluated the causal association between metabolites and OPLL (effect size \u0026beta;2). The mediation effect was calculated as (effect size \u0026beta;1 * effect size \u0026beta;2), and the direct effect was derived by subtracting the mediation effect from the total effect, The mediation rate was determined as (mediation effect / total effect) \u0026times; 100%[32]. All MR analyses were conducted using R software (version 4.4.1) and relevant packages, including \u0026quot;TwoSampleMR\u0026quot; and \u0026quot;MRInstruments.\u0026quot;\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Causal Relationship Between Gut Microbiome and OPLL\u003c/h2\u003e\n \u003cp\u003eUsing the IVW method as the primary analytical approach, we identified significant causal associations between genetically predicted levels of 22 gut microbial taxa and the risk of OPLL. Elevated levels of the following taxa were positively associated with an increased risk of OPLL: Erysipelotrichales (OR\u0026thinsp;=\u0026thinsp;1.160; 95% CI: 1.049\u0026ndash;1.283; P\u0026thinsp;=\u0026thinsp;0.003), Clostridium hylemonae (OR\u0026thinsp;=\u0026thinsp;1.094; 95% CI: 1.024\u0026ndash;1.169; P\u0026thinsp;=\u0026thinsp;0.007), Enterococcus faecalis (OR\u0026thinsp;=\u0026thinsp;1.150; 95% CI: 1.036\u0026ndash;1.276; P\u0026thinsp;=\u0026thinsp;0.008), Acidaminococcus intestini (OR\u0026thinsp;=\u0026thinsp;1.084; 95% CI: 1.019\u0026ndash;1.153; P\u0026thinsp;=\u0026thinsp;0.009), Bacilli (OR\u0026thinsp;=\u0026thinsp;1.133; 95% CI: 1.029\u0026ndash;1.248; P\u0026thinsp;=\u0026thinsp;0.010), Neisseria subflava (OR\u0026thinsp;=\u0026thinsp;1.063; 95% CI: 1.009\u0026ndash;1.119; P\u0026thinsp;=\u0026thinsp;0.021), and other related taxa. Conversely, higher levels of the following taxa were associated with a decreased risk of OPLL: Bacteroides dorei (OR\u0026thinsp;=\u0026thinsp;0.871; 95% CI: 0.792\u0026ndash;0.958; P\u0026thinsp;=\u0026thinsp;0.004), Clostridium difficile (OR\u0026thinsp;=\u0026thinsp;0.766; 95% CI: 0.613\u0026ndash;0.956; P\u0026thinsp;=\u0026thinsp;0.018), Enterobacter asburiae (OR\u0026thinsp;=\u0026thinsp;0.944; 95% CI: 0.900\u0026ndash;0.990; P\u0026thinsp;=\u0026thinsp;0.019), Prevotella tannerae (OR\u0026thinsp;=\u0026thinsp;0.919; 95% CI: 0.856\u0026ndash;0.987; P\u0026thinsp;=\u0026thinsp;0.020), Bacteroides caccae (OR\u0026thinsp;=\u0026thinsp;0.938; 95% CI: 0.887\u0026ndash;0.992; P\u0026thinsp;=\u0026thinsp;0.026), and other related taxa.(Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).Reverse MR analysis showed no evidence of reverse causality between OPLL and the 20 gut microbial taxa (reverse P-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, reverse causal relationships were observed for two taxa, Anaerococcus and Bacteroides dorei, with P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05(Supplementary Table\u0026nbsp;2).\u003c/p\u003e\n \u003cp\u003eIn the MR analysis of the gut microbiota and OPLL, Cochran\u0026rsquo;s Q test showed no significant heterogeneity among microbial taxa (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and the MR-PRESSO method revealed no substantial pleiotropic bias (Supplementary Table 1). Furthermore, leave-one-out analysis confirmed the robustness of the results, with no individual SNP exerting an undue influence on the overall causal estimates (Supplementary Fig. 1).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Causal Relationship Between Blood Metabolites and OPLL\u003c/h2\u003e\n \u003cp\u003eEight blood metabolites were associated with the risk of developing OPLL. Higher levels of aspartic acid (OR\u0026thinsp;=\u0026thinsp;1.490; 95% CI: 1.044\u0026ndash;2.125; P\u0026thinsp;=\u0026thinsp;0.027), manganese (OR\u0026thinsp;=\u0026thinsp;1.322; 95% CI: 1.018\u0026ndash;1.716; P\u0026thinsp;=\u0026thinsp;0.035), and red blood cell distribution width (OR\u0026thinsp;=\u0026thinsp;2.627; 95% CI: 1.134\u0026ndash;6.088; P\u0026thinsp;=\u0026thinsp;0.024) were positively associated with an increased risk of OPLL. In contrast, higher levels of dehydroepiandrosterone (OR\u0026thinsp;=\u0026thinsp;0.703; 95% CI: 0.531\u0026ndash;0.930; P\u0026thinsp;=\u0026thinsp;0.013), HR (OR\u0026thinsp;=\u0026thinsp;0.394; 95% CI: 0.185\u0026ndash;0.839; P\u0026thinsp;=\u0026thinsp;0.015), monocyte percentage (OR\u0026thinsp;=\u0026thinsp;0.624; 95% CI: 0.419\u0026ndash;0.927; P\u0026thinsp;=\u0026thinsp;0.019), cystine (OR\u0026thinsp;=\u0026thinsp;0.924; 95% CI: 0.856\u0026ndash;0.997; P\u0026thinsp;=\u0026thinsp;0.043), and VB2 (OR\u0026thinsp;=\u0026thinsp;0.904; 95% CI: 0.818\u0026ndash;0.999; P\u0026thinsp;=\u0026thinsp;0.048) were identified as protective factors against OPLL (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Sensitivity analyses showed no evidence of horizontal pleiotropy or heterogeneity (Supplementary Table 3).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Mediation Analysis of Blood Metabolites\u003c/h2\u003e\n \u003cp\u003eTo explore the mediating role of blood metabolites in the relationship between gut microbiota and OPLL, we conducted a mediation MR analysis using the identified gut microbiota and blood metabolites.We identified four metabolites involved in six mediation pathways between gut microbiota and OPLL(Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAmong the identified pathways, the mediation effect of manganese on the association between Bacilli and OPLL was (\u0026beta;\u0026thinsp;=\u0026thinsp;0.012, P\u0026thinsp;=\u0026thinsp;0.091), accounting for 10.00% of the total effect. Aspartic acid mediated the relationship between Megasphaera and OPLL (\u0026beta; = -0.006, P\u0026thinsp;=\u0026thinsp;0.128), with a mediation proportion of -14.4%. Additionally, aspartic acid mediated the association between Prevotella oris and OPLL (\u0026beta;\u0026thinsp;=\u0026thinsp;0.017, P\u0026thinsp;=\u0026thinsp;0.101), accounting for \u0026minus;\u0026thinsp;14.2% of the total effect. The mediation effect of cystine on the relationship between MF0036 and OPLL was (\u0026beta;\u0026thinsp;=\u0026thinsp;0.011, P\u0026thinsp;=\u0026thinsp;0.116), representing 3.1% of the total effect. Furthermore, the mediation effect of VB2 on the relationship between MF0052 and OPLL was (\u0026beta; = -0.005, P\u0026thinsp;=\u0026thinsp;0.147), with a mediation proportion of -8.33%. The mediation effect of VB2 on the overall association between gut microbiota and OPLL was (\u0026beta;\u0026thinsp;=\u0026thinsp;0.058, P\u0026thinsp;=\u0026thinsp;0.148), accounting for 7.2% of the total effect (Supplementary Table 5). Sensitivity analyses confirmed the absence of horizontal pleiotropy and heterogeneity (Supplementary Table S4). These findings suggest that gut microbiota may influence the development of OPLL through metabolite-mediated pathways.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study is the first to analyze the causal relationship between the gut microbiome and OPLL using publicly available genetic data. The results support the mediating role of four blood metabolites in the pathogenesis of OPLL driven by six gut microbiome taxa.\u003c/p\u003e\n\u003cp\u003eBacilli may serve as a risk factor for OPLL by modulating blood manganese levels. Bacillus subtilis, a representative bacterium of the Bacilli class, has been shown to contain metal-sensing regulatory proteins such as Fur, MntR, and PerR, which collaboratively regulate iron and manganese homeostasis, thereby directly influencing the absorption and metabolism of metal ions [33,34]. Furthermore, transport proteins such as CitM and CitH in B. subtilis facilitate the transport of metal-citrate complexes, affecting the absorption process of metal ions [35]. These findings support the positive regulatory effect of Bacilli on manganese metabolism. Manganese, a trace element essential for bone development, acts as a cofactor for enzymes critical to bone health, including glycosyltransferases and manganese superoxide dismutase (MnSOD). It plays a pivotal role in regulating cartilage matrix synthesis and bone formation [36].In summary, we hypothesize that the regulatory effect of Bacilli bacteria on manganese metabolism may serve as a key biological bridge through which the gut microbiota influences the pathogenesis of OPLL. Although we have observed this potential causal relationship, its specific molecular mechanisms require further in-depth study and validation.\u003c/p\u003e\n\u003cp\u003eMegasphaera may exert a protective effect in the pathogenesis of OPLL by regulating aspartic acid metabolism. Megasphaera is an anaerobic gut bacterium capable of utilizing amino acids as metabolic substrates and generating short-chain fatty acids through the acrylate pathway, thereby participating in host energy metabolism and immune function regulation [37].Aspartic acid is an important non-essential amino acid involved in various physiological processes, including neurotransmitter synthesis, gluconeogenesis, urea cycle, and cellular energy metabolism [38]. Notably, variants of the asporin protein, which encodes aspartic acid repeat sequences, have been shown to inhibit chondrogenesis by suppressing the TGF-\u0026beta; signaling pathway, potentially leading to bone metabolism disorders and promoting ectopic ossification under certain pathological conditions [39]. Our findings are consistent with this, as aspartic acid has been identified as a risk factor for OPLL. Further analysis suggests that Megasphaera may exert a protective effect on OPLL by downregulating its levels. Additionally, Prevotella species have been shown to be closely associated with chronic inflammation, autoimmune diseases, and metabolic disorders [40]. Our study further found that abnormal enrichment of Prevotella oris might contribute to the onset and development of OPLL by regulating host amino acid metabolism, particularly by upregulating aspartic acid levels. However, the potential mechanisms underlying these effects require further investigation.\u003c/p\u003e\n\u003cp\u003eThe gut microbiota encoded by MF0036 may serve as a risk factor for OPLL by modulating blood cystine levels. Cystine, an essential precursor of glutathione synthesis, plays a crucial role in maintaining cellular survival and function under oxidative stress conditions. Previous studies have demonstrated that glutathione\u0026rsquo;s antioxidant properties protect cells from oxidative damage, thereby inhibiting abnormal activation of osteoblasts in stressed environments [41]. We hypothesized that elevated cystine levels may reduce oxidative stress in bone metabolism through antioxidant effects, thereby preventing excessive osteoblast activation and slowing the progression of OPLL. Both existing research and our data support the notion that cystine exerts a protective effect on OPLL to some extent, with higher levels potentially reducing the risk of abnormal ossification.Although this functional module has not been assigned to a known specific genus, its association with metabolic pathways suggests that it may represent a class of key metabolic microbiota that plays a regulatory role in the gut-metabolism-bone metabolism axis.\u003c/p\u003e\n\u003cp\u003eThe gut microbiota encoded by MF0052 positively correlated with blood VB2 levels, whereas the microbiota encoded by MF0074 exhibited a negative correlation with VB2 levels. The previous studies have shown that VB2 exerts a protective role in various chronic diseases by enhancing glutathione metabolism and reducing oxidative stress levels.[42] In OPLL, a condition characterized by ectopic ossification and abnormal activation of osteoblasts, oxidative stress and chronic inflammation are recognized as key pathogenic mechanisms [43,44]. Therefore, upregulation of VB2 may reduce the risk of OPLL by boosting antioxidant defense mechanisms and maintaining cellular redox homeostasis, which in turn may inhibit abnormal activation of osteoblasts. Based on the directional association between MF0052, MF0074 and VB2, we hypothesize that MF0052 may exert a protective effect by promoting increased VB2 levels, while MF0074 may have a pathogenic effect by suppressing VB2 levels.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, our study had several limitations. First, in line with the relevant literature, we applied a significance threshold of P \u0026lt; 1 \u0026times; 10⁻⁵ for SNP selection to ensure an adequate number of instrumental variables. This threshold, however, does not meet the conventional genome-wide significance threshold of P \u0026lt; 5 \u0026times; 10⁻⁸. To address this, we rigorously evaluated the F-statistics of each SNP to mitigate potential weak instrument bias [45]. Second, our study sample was restricted to individuals of East Asian descent, which may limit the generalizability of our findings to other populations. Although MR is a robust tool for causal inference, it has some limitations. The validity of the MR results depends on three core assumptions, and violations of these assumptions can introduce bias. Therefore, our findings should be viewed as suggestive rather than conclusive.Despite these limitations, our study still provides the most compelling evidence to date regarding the causal relationship between gut microbiota, blood metabolites, and the risk of OPLL, offering theoretical support and potential intervention strategies for the prevention and treatment of OPLL.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our study revealed potential causal relationships among the gut microbiome, blood metabolites, and OPLL. These findings may provide potential biomarkers for the prevention and treatment of OPLL. Furthermore, we discovered for the first time that the gut microbiota may influence the progression of OPLL through blood metabolites. These findings offer new perspectives for understanding the pathophysiological mechanisms of OPLL. Further experimental and clinical studies are warranted to validate these associations and explore their therapeutic implications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and Consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; Contribution\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYL: Data curation, Methodology, Writing, original draft. JW: Data curation, Methodology, Writing \u0026ndash; original draft. BY: Investigation, Writing \u0026ndash; review \u0026amp; editing. ZL: Investigation, Writing \u0026ndash; review \u0026amp; editing. JZ: Investigation, Writing \u0026ndash; review \u0026amp; editing. TQ: Investigation, Writing \u0026ndash; review \u0026amp; editing. JY:Investigation, Writing \u0026ndash; review \u0026amp; editing. BM: Investigation, Writing \u0026ndash; review \u0026amp; editing. LZ: Supervision, Writing \u0026ndash; review \u0026amp; editing. EL: Writing \u0026ndash; review \u0026amp; editing, Supervision. YB: Supervision, Writing \u0026ndash; review \u0026amp; editing. XS: Supervision, Writing \u0026ndash; review \u0026amp; editing. YH: Supervision, Writing \u0026ndash; review \u0026amp; editing. WX: Writing \u0026ndash; review \u0026amp; editing, Conceptualization, Funding acquisition. JP: Conceptualization, Writing \u0026ndash; review \u0026amp; editing, Project administration.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMatsunaga S, Sakou T. Ossification of the posterior longitudinal ligament of the cervical spine: etiology and natural history. Spine (Phila Pa 1976). 2012 Mar 1;37(5):E309-14.\u003c/li\u003e\n\u003cli\u003eHirai T, Yoshii T, Hashimoto J, et al. Clinical Characteristics of Patients with Ossification of the Posterior Longitudinal Ligament and a High OP Index: A Multicenter Cross-Sectional Study (JOSL Study). J Clin Med. 2022 Jun 27;11(13):3694.\u003c/li\u003e\n\u003cli\u003eBelanger TA, Roh JS, Hanks SE, et al. Ossification of the posterior longitudinal ligament. Results of anterior cervical decompression and arthrodesis in sixty-one North American patients. J Bone Joint Surg Am. 2005 Mar;87(3):610-5.\u003c/li\u003e\n\u003cli\u003eRen Y, Liu ZZ, Feng J, et al. Association of a BMP9 haplotype with ossification of the posterior longitudinal ligament (OPLL) in a Chinese population. PLoS One. 2012;7(7):e40587.\u003c/li\u003e\n\u003cli\u003eChoi BW, Song KJ, Chang H. Ossification of the posterior longitudinal ligament: a review of literature. Asian Spine J. 2011 Dec;5(4):267-76.\u003c/li\u003e\n\u003cli\u003eYan L, Gao R, Liu Y, et al. The Pathogenesis of Ossification of the Posterior Longitudinal Ligament. Aging Dis. 2017 Oct 1;8(5):570-582.\u003c/li\u003e\n\u003cli\u003eQin J, Li R, Raes J, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010 Mar 4;464(7285):59-65.\u003c/li\u003e\n\u003cli\u003eB\u0026auml;ckhed F, Ley RE, Sonnenburg JL, et al. Host-bacterial mutualism in the human intestine. Science. 2005 Mar 25;307(5717):1915-20.\u003c/li\u003e\n\u003cli\u003eRound JL, Mazmanian SK. The gut microbiota shapes intestinal immune responses during health and disease. Nat Rev Immunol. 2009 May;9(5):313-23.\u003c/li\u003e\n\u003cli\u003eBrestoff JR, Artis D. Commensal bacteria at the interface of host metabolism and the immune system. Nat Immunol. 2013 Jul;14(7):676-84.\u003c/li\u003e\n\u003cli\u003eBehera J, Ison J, Tyagi SC, et al. The role of gut microbiota in bone homeostasis. Bone. 2020 Jun;135:115317.\u003c/li\u003e\n\u003cli\u003eSeely KD, Kotelko CA, Douglas H, et al. The Human Gut Microbiota: A Key Mediator of Osteoporosis and Osteogenesis. Int J Mol Sci. 2021 Aug 31;22(17):9452.\u003c/li\u003e\n\u003cli\u003eChen Y, Wang X, Zhang C, et al. Gut Microbiota and Bone Diseases: A Growing Partnership. Front Microbiol. 2022 May 6;13:877776.\u003c/li\u003e\n\u003cli\u003eNagata N, Takeuchi T, Masuoka H, et al. Human Gut Microbiota and Its Metabolites Impact Immune Responses in COVID-19 and Its Complications. Gastroenterology. 2023 Feb;164(2):272-288.\u003c/li\u003e\n\u003cli\u003eGuo Z, Pan J, Zhu H, et al. Metabolites of Gut Microbiota and Possible Implication in Development of Diabetes Mellitus. J Agric Food Chem. 2022 May 25;70(20):5945-5960.\u003c/li\u003e\n\u003cli\u003eFeng K, Zhang H, Chen C, et al. Heptamethoxyflavone Alleviates Metabolic Syndrome in High-Fat Diet-Fed Mice by Regulating the Composition, Function, and Metabolism of Gut Microbiota. J Agric Food Chem. 2023 Jul 5;71(26):10050-10064.\u003c/li\u003e\n\u003cli\u003eMoutsoglou DM, Tatah J, Prisco SZ, et al. Pulmonary Arterial Hypertension Patients Have a Proinflammatory Gut Microbiome and Altered Circulating Microbial Metabolites. Am J Respir Crit Care Med. 2023 Mar 15;207(6):740-756.\u003c/li\u003e\n\u003cli\u003eNemet I, Li XS, Haghikia A, et al. Atlas of gut microbe-derived products from aromatic amino acids and risk of cardiovascular morbidity and mortality. Eur Heart J. 2023 Aug 22;44(32):3085-3096.\u003c/li\u003e\n\u003cli\u003eTsuji T, Matsumoto M, Nakamura M, et al. Metabolite profiling of plasma in patients with ossification of the posterior longitudinal ligament. J Orthop Sci. 2018 Nov;23(6):878-883.\u003c/li\u003e\n\u003cli\u003eCao Z, Wu Y, Li Q, et al. A causal relationship between childhood obesity and risk of osteoarthritis: results from a two-sample Mendelian randomization analysis. Ann Med. 2022 Dec;54(1):1636-1645.\u003c/li\u003e\n\u003cli\u003eEmdin CA, Khera AV, Kathiresan S. Mendelian Randomization. JAMA. 2017 Nov 21;318(19):1925-1926.\u003c/li\u003e\n\u003cli\u003eLiu X, Tong X, Zou Y, et al. Mendelian randomization analyses support causal relationships between blood metabolites and the gut microbiome. Nat Genet. 2022 Jan;54(1):52-61.\u003c/li\u003e\n\u003cli\u003eKoike Y, Takahata M, Nakajima M, et al. Genetic insights into ossification of the posterior longitudinal ligament of the spine. Elife. 2023 Jul 18;12:e86514.\u003c/li\u003e\n\u003cli\u003eTakeuchi F, Akiyama M, Matoba N, et al. Interethnic analyses of blood pressure loci in populations of East Asian and European descent. Nat Commun. 2018 Nov 28;9(1):5052.\u003c/li\u003e\n\u003cli\u003eSanna S, van Zuydam NR, Mahajan A, et al. Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases. Nat Genet. 2019 Apr;51(4):600-605.\u003c/li\u003e\n\u003cli\u003eLee CH, Cook S, Lee JS, et al. Comparison of Two Meta-Analysis Methods: Inverse-Variance-Weighted Average and Weighted Sum of Z-Scores. Genomics Inform. 2016 Dec;14(4):173-180.\u003c/li\u003e\n\u003cli\u003eBurgess S, Thompson SG; CRP CHD Genetics Collaboration. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011 Jun;40(3):755-64.\u003c/li\u003e\n\u003cli\u003eBurgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013 Nov;37(7):658-65.\u003c/li\u003e\n\u003cli\u003eBowden J, Del Greco M F, Minelli C, et al. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat Med. 2017 May 20;36(11):1783-1802.\u003c/li\u003e\n\u003cli\u003eVerbanck M, Chen CY, Neale B, et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018 May;50(5):693-698. \u003c/li\u003e\n\u003cli\u003eSekula P, Del Greco M F, Pattaro C, et al. Mendelian Randomization as an Approach to Assess Causality Using Observational Data. J Am Soc Nephrol. 2016 Nov;27(11):3253-3265.\u003c/li\u003e\n\u003cli\u003eCarter AR, Sanderson E, Hammerton G, et al. Mendelian randomisation for mediation analysis: current methods and challenges for implementation. Eur J Epidemiol. 2021 May;36(5):465-478.\u003c/li\u003e\n\u003cli\u003eHu G, Wang Y, Blake C, et al. Parallel genetic adaptation of Bacillus subtilis to different plant species. Microb Genom. 2023 Jul;9(7):mgen001064.\u003c/li\u003e\n\u003cli\u003eHelmann JD. Specificity of metal sensing: iron and manganese homeostasis in Bacillus subtilis. J Biol Chem. 2014 Oct 10;289(41):28112-20.\u003c/li\u003e\n\u003cli\u003eKrom BP, Warner JB, Konings WN, et al. Complementary metal ion specificity of the metal-citrate transporters CitM and CitH of Bacillus subtilis. J Bacteriol. 2000 Nov;182(22):6374-81.\u003c/li\u003e\n\u003cli\u003eTaskozhina G, Batyrova G, Umarova G, et al. The Manganese-Bone Connection: Investigating the Role of Manganese in Bone Health. J Clin Med. 2024 Aug 9;13(16):4679.\u003c/li\u003e\n\u003cli\u003eDu L, Li Q, Yi H, et al. Gut microbiota-derived metabolites as key actors in type 2 diabetes mellitus. Biomed Pharmacother. 2022 May;149:112839.\u003c/li\u003e\n\u003cli\u003eHoleček M. Aspartic Acid in Health and Disease. Nutrients. 2023 Sep 17;15(18):4023.\u003c/li\u003e\n\u003cli\u003eKizawa H, Kou I, Iida A, et al. An aspartic acid repeat polymorphism in asporin inhibits chondrogenesis and increases susceptibility to osteoarthritis. Nat Genet. 2005 Feb;37(2):138-44.\u003c/li\u003e\n\u003cli\u003eIljazovic A, Roy U, G\u0026aacute;lvez EJC, et al. Perturbation of the gut microbiome by Prevotella spp. enhances host susceptibility to mucosal inflammation. Mucosal Immunol. 2021 Jan;14(1):113-124.\u003c/li\u003e\n\u003cli\u003eMcBean GJ. Cysteine, Glutathione, and Thiol Redox Balance in Astrocytes. Antioxidants (Basel). 2017 Aug 3;6(3):62.\u003c/li\u003e\n\u003cli\u003eHristov BD. The Role of Glutathione Metabolism in Chronic Illness Development and Its Potential Use as a Novel Therapeutic Target. Cureus. 2022 Sep 28;14(9):e29696.\u003c/li\u003e\n\u003cli\u003eDash UC, Bhol NK, Swain SK, et al. Oxidative stress and inflammation in the pathogenesis of neurological disorders: Mechanisms and implications. Acta Pharm Sin B. 2025 Jan;15(1):15-34.\u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a-S\u0026aacute;nchez A, Miranda-D\u0026iacute;az AG, Cardona-Mu\u0026ntilde;oz EG. The Role of Oxidative Stress in Physiopathology and Pharmacological Treatment with Pro- and Antioxidant Properties in Chronic Diseases. Oxid Med Cell Longev. 2020 Jul 23;2020:2082145.\u003c/li\u003e\n\u003cli\u003ePierce BL, Burgess S. Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators. Am J Epidemiol. 2013 Oct 1;178(7):1177-84.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-spine-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esjo","sideBox":"Learn more about [European Spine Journal](http://link.springer.com/journal/586)","snPcode":"586","submissionUrl":"https://submission.springernature.com/new-submission/586/3","title":"European Spine Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Gut Microbiome, metabolites, ossification of the posterior longitudinal ligament of the spine, Mendelian randomization, GWAS","lastPublishedDoi":"10.21203/rs.3.rs-7893117/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7893117/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThis study explored the associations between the gut microbiome, blood metabolites, and ossification of the posterior longitudinal ligament of the spine(OPLL), and identified potential causal relationships mediated by blood metabolites.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e\u003cp\u003eUsing summary-level genome-wide association study (GWAS) data, including measures of the gut microbiome, blood metabolites, and OPLL, we employed a two-sample Mendelian randomization (MR) approach to identify gut microbial taxa and blood metabolites potentially associated with OPLL development. Additionally, we identified blood metabolites as mediators of the causal pathway by which the gut microbiome influences OPLL. The inverse-variance weighted (IVW) method served as the primary analytical approach in MR analysis. Sensitivity analyses including Cochran's Q test, MR-Egger regression, and MR-PRESSO were performed to assess the robustness of the results.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eMR analysis revealed unidirectional causal relationships between 20 gut microbiome taxa and OPLL. Among these, 6 taxa were positively associated with an increased risk of OPLL, while 14 taxa were negatively associated with risk. Additionally, 8 blood metabolites exhibited potential causal relationships with OPLL, with 5 showing positive and 3 showing negative associations. Mediation analysis demonstrated that 6 gut microbiome taxa influenced OPLL development through 4 intermediary metabolites. Specifically, manganese mediated the effect of Bacilli on OPLL, aspartic acid mediated the effects of Megasphaera and Prevotella oris, cystine mediated the effect of MF0036, and vitamin B2 (VB2) mediated the effects of MF0052 and MF0047 on OPLL.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study provides evidence supporting potential causal links between specific gut microbiomes and OPLL, emphasizing the mediating role of blood metabolites.\u003c/p\u003e","manuscriptTitle":"How Gut Microbiome and Blood Metabolites Drive Ossification of the Posterior Longitudinal Ligament of the Spine: A Genome-Wide Association Study Based on the East Asian Population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-14 15:36:59","doi":"10.21203/rs.3.rs-7893117/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-01T09:52:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-27T12:32:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175599115568785730916308722657379065541","date":"2025-11-25T11:20:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-18T19:33:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121614219143071433138897511008044556566","date":"2025-11-10T14:18:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-04T15:28:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-18T14:33:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-18T14:32:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Spine Journal","date":"2025-10-18T11:18:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-spine-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esjo","sideBox":"Learn more about [European Spine Journal](http://link.springer.com/journal/586)","snPcode":"586","submissionUrl":"https://submission.springernature.com/new-submission/586/3","title":"European Spine Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"20522e95-aab0-4100-8974-d39964ce4657","owner":[],"postedDate":"November 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T16:10:09+00:00","versionOfRecord":{"articleIdentity":"rs-7893117","link":"https://doi.org/10.1007/s00586-026-09792-6","journal":{"identity":"european-spine-journal","isVorOnly":false,"title":"European Spine Journal"},"publishedOn":"2026-02-13 15:59:31","publishedOnDateReadable":"February 13th, 2026"},"versionCreatedAt":"2025-11-14 15:36:59","video":"","vorDoi":"10.1007/s00586-026-09792-6","vorDoiUrl":"https://doi.org/10.1007/s00586-026-09792-6","workflowStages":[]},"version":"v1","identity":"rs-7893117","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7893117","identity":"rs-7893117","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}