Skip to main content
NCBI home page
Medicine logoLink to Medicine
. 2026 May 22;105(21):e48970. doi: 10.1097/MD.0000000000048970

Causal association between gut microbiota and trigeminal neuralgia: A bidirectional two-sample Mendelian randomization analysis

Yingqiang Zhong a, Hong Yu a, Yang Wang a, Yaohui Jia a, Tao Jiang a, Jianbing Bai a,*
PMCID: PMC13201056  PMID: 42175460

Abstract

An increasing number of studies have found associations between the gut microbiota (GM) and neurological disorders. However, the causal relationship between the GM and trigeminal neuralgia (TN) remains unclear. Mendelian randomization (MR) is an effective method to link pathological changes in distant tissues, so we proposed to explore the potential causal relationships between GM and TN via MR analysis. We obtained genome-wide association study summary datasets for GM and TN from publicly available databases, respectively. After rigorous instrumental variable screening, 5 MR methods, including inverse variance weighting, MR-Egger, Weighted median, Weighted mode, and Simple mode, were used to assess the causal relationship between GM and TN. Various sensitivity analyses were also used to enhance the robustness of the results. In addition, we used multivariable MR (MVMR) to further assess the independent causal relationship between GM and TN. We initially found significant causal relationships between 5 GMs and TN, including Turicibacter (Genus), Roseburia_unclassified (Species), Acidaminococcaceae (Family), Eubacterium ruminantium group (Genus), and Bacteroides_plebeius (Species). We did not find reverse causation. In addition, MVMR suggested that only Acidaminococcaceae (Family) (odds ratio, 0.60; 95% confidence interval, 0.42–0.87; PMVMR = .006) remained a protective factor for TN. Sensitivity analyses of all negative results enhanced the robustness of our results. This study found the causal relationship between GM and TN, which may provide potential new insights to the gut-brain axis and contribute to the understanding of the TN. However, further experiments are needed to clarify the protective effect of GM on TN and its mechanism.

Keywords: causal relationship, gut microbiota, Mendelian randomization, odds ratio, trigeminal neuralgia

1. Introduction

Trigeminal neuralgia (TN) is a facial pain disorder characterized by sudden, severe, electric shock-like pain that typically affects one side of the face.[1] The pain originates from the trigeminal nerve and can be triggered by mild facial stimuli, resulting in significant distress and impairment in quality of life.[2] In a European population-based study, the lifetime prevalence of TN was approximately 0.16% to 0.3%, with an incidence of approximately 12.6 to 27.0 per 100,000 person-years.[1] TN is considered one of the most painful conditions and can often lead to anxiety, depression, and social withdrawal. The pathophysiology of TN is not yet fully understood, but it is thought to result from a combination of peripheral nerve injury and maladaptation in the central nervous system.[3]

Numerous studies have revealed the existence of the gut-brain axis, a bidirectional communication network that connects the enteric and central nervous systems.[4–6] This axis is now recognized as a crucial factor in regulating not only gastrointestinal functions but also central nervous system-related disorders, such as chronic pain syndromes.[7] The gut microbiota (GM), which is composed of trillions of microorganisms residing in the gastrointestinal tract, is an essential component of the gut-brain axis. Microbial populations play a crucial role in several physiological processes, such as immune system modulation, metabolism, and neurotransmitter production.[8]

Recent research indicates that changes in the GM may impact the onset and continuation of neuropathic pain conditions. Studies have shown that an imbalance in GM can cause heightened pain sensitivity and neuroinflammation through various mechanisms, such as microbial metabolites, immune responses, and neural pathways.[9–11] Most bacteria detected in fecal samples from the general population belong to 2 phyla: Bacteroidetes and Firmicutes, and their proportions are thought to be closely related to disease.[12,13] It has been found that the Firmicutes-to-Bacteroidetes ratio is higher in obese individuals, with a significant decrease after weight loss.[14] In a polycystic ovary syndrome model, there was a decrease in Bacteroides and an increase in Firmicus and Proteus.[15] The gut microbiota can influence pain through related neurotransmitters (GABA, norepinephrine, and glutamate) and metabolites. The main metabolites include short-chain fatty acids (SCFAs) and 5-hydroxytryptamine (5-HT).[11] Various bacteria such as Bifidobacterium, Collinsella, Bacteroides, and Prevotella can synthesize SCFAs by fermenting undigested carbohydrates.[16] SCFAs have been found to have a significant impact on neuropathic pain. Lanza et al[17] reported that oral administration of SCFAs alleviated nitroglycerin-induced hyperalgesia in mice while restoring intestinal barrier integrity and shifting fecal microbiota towards Lactobacillus and Bifidobacterium-enriched communities. Specifically, SCFAs reduce intestinal inflammation and tighten epithelial junctions, thereby lowering peripheral pro-nociceptive signaling that contributes to central sensitization. Thus, GM can connect with and influence distant tissues in multiple ways. However, we do not yet know the relationship between GM and TN, and the causal relationship between GM and TN is easily affected by confounding factors. Traditional observational studies are also unable to effectively control these factors, limiting causal inferences between GM and TN.

Mendelian randomization (MR) is an approach used to infer the relationship between exposure and disease based on the principle that alleles cluster independently during meiosis, thus avoiding confounding factors.[18] MR utilizes single-nucleotide polymorphism (SNPs) as instrumental variables (IVs) to assess causality.[19] Therefore, we proposed to evaluate the causal relationship between GM and TN with the support of MR.

2. Materials and methods

2.1. Study design

We used publicly available pooled data from genome-wide association studies (GWAS) to conduct a bidirectional 2-sample MR analysis using SNPs as instrumental variables for separate exposures. To obtain an accurate causal analysis, we relied on 3 assumptions: IVs are only associated with exposure; IVs do not influence outcomes through confounders; and IVs only influence outcomes by exposure and not with direct effects.[20] The study design is shown in Figure 1.

Figure 1.

Figure 1.

Open in a new tab

Flowchart of the entire MR analysis. MR = Mendelian randomization.

This study did not require additional informed consent or ethical approval as the publicly available data were approved by the relevant institutional review boards. In addition, our study was conducted according to the MR-STROBE guidelines[21] (Supplementary Table 1).

2.2. Data sources

First, we obtained GM’s GWAS summary data from the MiBioGen consortium.[22] This study performed 16S ribosomal RNA gene sequencing on 16 cohorts totaling 18,340 participants from multiple countries, which identified a total of 211 taxonomic units of the GM (15 unknown), including 9 phyla, 16 classes, 20 orders, 32 families and 119 genera. Second, to obtain a more detailed GM classification unit, we obtained GWAS summary data for GM from another study.[23] This study performed a genome-wide association study of 207 taxa and 205 pathways representing microbial composition and function in 7738 participants of the Dutch Microbiome Project. After screening, we obtained 105 species-level GMs. In addition, we utilized the FinnGen dataset for TN (N = 362,315) based ICD-10 as the corresponding exposure and outcome. Summary statistics for anxiety and depression were derived from the UK Biobank. The anxiety cohort comprised 337,159 individuals, including 4611 cases, while the depression cohort comprised 361,194 individuals, including 1145 cases. Details of the dataset can be found in Supplementary Table 2.

2.3. Selection of SNPs

We performed a rigorous screening of the IVs required for exposure to meet the assumptions. Firstly, we used SNPs with P < 1 × 10−5 to maximize the amount of initial genetic IVs for MR analyses.[24,25] Secondly, we applied the parameters r2 = 0.001 and windows size = 10,000 kb to establish linkage disequilibrium between IVs to eliminate chain disequilibrium relationships and to ensure the independence of the screened SNPs, and non-compliantSNPs were excluded.[26] Thirdly, if instrumental SNPs were not found in the ending data or if they had palindromic sequences they were also removed. Fourthly, the statistic F-values of the SNPs were calculated to estimate the strength of the IVs to strengthen the inference of assumption Ⅰ. Specifically, F = R2 (n-1-k)/ (1-R2) k, with R2 indicating the degree of variance elucidated by the instruments, n signifying the sample size, and k representing the number of chosen IVs. IVs with F < 10 will be excluded.[27] Then, to ensure the validity of assumption Ⅱ, we searched the NHGRI-EBI catalog and PhenoScanner for IVs related to potential confounding factors (including anxiety, depression, sleep, and diet). Meanwhile, to meet the criteria of assumption III, we removed the SNP that based on the following criterion: P outcome < P exposure.[28]

2.4. Statistical analysis

Five MR analysis methods were used, including inverse-variance weighted (IVW), MR-Egger, weighted median, weighted mode, and simple mode. IVW is the traditional standard method for summarizing data in MR, which can directly calculate the size of the causal effect using summary data without requiring individual-level data. While ensuring that all IVs are valid, IVW provides the most accurate assessment of causality. Therefore, in this study, we used IVW as the primary criterion for determining causality.[29] To enhance the robustness of the results, causality was only considered to exist if P IVW < .05, the 5 methods provided associations with Beta-values in the same direction, and the sensitivity analyses did not suggest anomalies.[30]

We used a series of sensitivity analyses methods to ensure the robustness of the results. Firstly, we used the Steiger test to check the validity of causality.[31] We then determined the robustness and validity of causality through MR-PRESSO analyses, Cochran’s Q test and MR-Egger intercept test.[32] MR-PRESSO identifies outliers in the selected IVs, with Global test P < .05 being considered an outlier, which requires that we need to perform the MR analysis again after removing the outliers.[33] For the Cochran Q test and the MR-Egger intercept test, P > .05 was considered not to be significant for heterogeneity and horizontal pleiotropy.[34] In addition, to ensure the unbiasedness of the causal estimation, we performed leave-one-out analyses by sequentially excluding each instrumental variable SNP and re-running the IVW analysis, thereby identifying any influential SNPs.[35] Finally, we performed a reverse MR analysis to see the relationship between TN and GM that were causally related in the forward MR analysis.

2.5. Multivariable MR

Some confounding factors (anxiety and depression) may affect the accurate causal relationship between GM and TN. To fully adjust for confounding factors and determine independent causal relationships, we conducted further analyses using multivariable MR (MVMR) analysis.[36] Similarly, multivariable IVW was predominantly used to assess the competing relationships between GM and TN, supplemented by multivariable Median, multivariable Egger for analyses. Multivariable Egger Intercept and Cochran Q test were also reported to validate horizontal pleiotropy and heterogeneity. P < .05 was considered significant.

All analyses were performed using the “TwoSampleMR,” “MendelianRandomization,” “MR-PRESSO” and “MVMR” R packages as well as the R Studio (version 4.3.0).

3. Results

Through rigorous screening of IVs, we selected the corresponding number of SNPs for TN based on the GM of 9 phyla, 16 classes, 20 orders, 32 families, 119 genera, and 104 species, with the number ranging from 1 to 21, and the total number of SNPs was 3016. The F-values of all SNPs were >10, indicating that the selected IVs were not subject to bias from weak instrumental variables. Additionally, we did not identify any SNPs associated with confounding factors (Supplementary Tables 3 and 4).

3.1. Causal effects of gut microbiota on trigeminal neuralgia

Based on the above criteria, we identified 5 GMs that were causally associated with TN when different groups of GMs were used as exposures. Specifically, Turicibacter (Genus) (odds ratio [OR], 1.42; 95% confidence interval [CI], 1.06–1.89; P = .019) and Roseburia_unclassified (Species) (OR, 1.21; 95% CI, 1.05–1.40; P = .008) act as risk factors for TN, in other words, they promote the occurrence of TN. On the other hand, Acidaminococcaceae (Family) (OR, 0.65; 95% CI, 0.45–0.95; P = .024), Eubacterium ruminantium group (Genus) (OR, 0.81; 95% CI, 0.66–0.98; P = .035), and Bacteroides_plebeius (Species) (OR, 0.85; 95% CI, 0.73–0.99; P = .042) were negatively associated with risk of TN (Fig. 2 and Supplementary Table 5).

Figure 2.

Figure 2.

Open in a new tab

Forest plots with significant causal relationships identified between gut microbiota and trigeminal neuralgia using univariable MR analysis. MR = Mendelian randomization.

We performed several sensitivity analyses to assess the robustness of our results. Data presented in Supplementary Tables 6 and 7 demonstrate horizontal pleiotropy, heterogeneity and MR-PRESSO tests, respectively. All P-values are >.05, indicating that our results are robust. The MR-Steiger test also verifies the correctness of all our causal directions. The leave-one-out method analyzes that the current results are not driven by a single SNP (Fig. 3).

Figure 3.

Figure 3.

Open in a new tab

Leave-one-out analysis of gut microbiota with significant causality in univariable MR. MR = Mendelian randomization.

3.2. Causal effects of trigeminal neuralgia on selected gut microbiota

In the reverse MR analysis, when TN was used as the exposure, we selected GM identified as significant causal relationships in the forward MR analysis as the outcome, respectively. The results showed no causal relationship between TN and GM (Supplementary Tables 8 and 9). Corresponding sensitivity analyses also failed to detect significant heterogeneity and pleiotropy, and leave-one-out analyses suggested that the current results were not driven by a single SNP, which further enhanced the robustness of the results (Supplementary Tables 10 and 11).

3.3. Causal effects of gut microbiota on trigeminal neuralgia by MVMR

To determine whether the selected GM directly affects the risk of TN or indirectly through potential confounding factors, we further conducted MVMR analysis. We included anxiety and depression as adjustment variables, and the final results are shown in Figure 4 and Supplementary Table 12. We finally found that only Acidaminococcaceae (Family) (OR, 0.60; 95% CI, 0.42–0.87; P MVMR = .006) remained a protective factor for TN. Also, the multivariable Cochran Q test and MR-Egger intercept test did not show significant heterogeneity and pleiotropy (Supplementary Table 13).

Figure 4.

Figure 4.

Open in a new tab

Independent causal relationship between gut microbiota and trigeminal neuralgia assessed using multivariable MR analysis. MR = Mendelian randomization.

4. Discussion

We performed a bidirectional, 2-sample and MVMR analysis to assess the causal relationship between GM and TN. Our findings suggest that Acidaminococcaceae (Family) could decrease the risk of TN, and reverse MR did not find a causal relationship between TN and GM. These results expand our understanding of the gut-brain axis and help us understand the contribution of GM to TN.

Although TN is not a highly fatal disease, the pain associated with TN leads to a significant reduction in the patient’s quality of life, and the chronic pain continues to worsen the patient’s mental and psychological state.[37] It has been shown that the incidence of depression and anxiety in TN patients is significantly higher than in the general population, even leading to suicide attempts, making it particularly important to explore the pathogenesis of TN.[38] The vascular compression hypothesis is thought to be the main pathogenesis of TN, and common vessels include the superior cerebellar artery, anterior inferior cerebellar artery, basilar artery, and cerebral pontine vein.[39] However, studies have reported that the long-term success rate of microvascular decompression is estimated to be about 70%, and the incidence of postoperative complications ranges from 0.2% to 5%,[40] implying that there are other potential mechanisms involved in the pathophysiological process of TN.

The GM has emerged as a key factor in neurological health, influencing a range of disorders from mood disorders to chronic pain syndromes.[9,41,42] GM can generate and release a range of secretagogues to remotely modulate central system-associated pain.[7,43] GM can produce secretion such as SCFAs, aromatic amino acids and 5-HT, which bind to receptors to promote activation of secretory cells and indirectly affect the gut-brain axis.[44] SCFAs, including acetate, propionate, and butyrate, exert profound regulatory effects on intestinal homeostasis, energy metabolism, and immune function.[45] Propionate, predominantly generated in the colon, plays a pivotal role in glucose metabolism.[46] Butyrate enhances epithelial barrier integrity by inhibiting histone deacetylase activity, while also exerting anti-inflammatory effects through augmenting Treg cell function and upregulating IL-10 expression.[47,48] In nitroglycerin-induced migraine mice, sodium butyrate and sodium propionate increased the Firmicutes/Bacteroidetes ratio and enriched SCFAs-producing genera (e.g., Lactobacillus, Bifidobacterium), thereby restoring intestinal barrier integrity (via occludin and ZO-1 expression) and suppressing intestinal inflammation (through downregulation of ICAM-1, P-selectin, and pro-inflammatory cytokines), ultimately reducing migraine frequency. Lactobacillus is recognized as a key probiotic in this process.[17] Secretory bacteria in the gut can directly produce neurotransmitters and their precursors to influence the levels of neurotransmitters in the brain.[49] Glutamate was discovered early on as a widely distributed excitatory neurotransmitter in the organism. Increased glutamate levels have been shown to induce nociceptive hypersensitivity in animal models.[50] As a major neurotransmitter in the central nervous system, 5-HT could further elicit a depolarizing response in neurons when 5-HT acts on its specific receptors.[51] And knockout of 5-HT receptors could cause reduction of pain sensitivity.[52] Meanwhile, activation of 5-HT-specific receptors in the basolateral amygdala also promotes neuropathic pain activity in the central nucleus.[53] Additionally, abnormal activation of inflammation may be involved in the development of pain. It has been demonstrated that pro-inflammatory cytokines can trigger spontaneous discharges by sensitizing A and C fibers, resulting in nociceptive hypersensitivity.[54] And GM has long been shown to be involved in the progression of inflammation in the body. As activators of Toll-like receptors, lipopolysaccharides are secreted by GM. The activated Toll-like receptor activates downstream inflammatory receptors and promotes the release of inflammatory factors, which in turn have a positive feedback effect on inflammatory cells, leading to an inflammatory cascade.[55] Therefore, the changes in the composition of GM may disrupt the balance of various secreted substances, thus affecting the development of TN. Our study found that Acidaminococcaceae (Family) could decrease the risk of TN, which may be related to metabolites, neurotransmitters, and inflammatory responses.

Members of the family Acidaminococcaceae, predominantly represented by the genera Phascolarctobacterium and Acidaminococcus, are obligate anaerobes occupying specific ecological niches within the human distal gut, where they participate in metabolic cross-feeding interactions.[56] These taxa are characterized by their ability to utilize succinate or amino acids as primary substrates, thereby producing SCFAs via the methylmalonyl-CoA pathway.[57,58] As noted above, SCFAs may alter pain sensitivity via modulation of intestinal homeostasis and inflammatory pathways, ultimately lowering the risk of TN. Importantly, Acidaminococcus transplantation has been shown to elevate serum nitric oxide and 5-HT levels in patients with chronic functional constipation, leading to marked clinical improvement and, in some cases, complete remission.[59] Mechanistically, following SCFA release, Acidaminococcus may enhance 5-HT biosynthesis and secretion via TPH1 upregulation in enterochromaffin cells, thereby influencing gut motility, sensory function, and potentially nociceptive processing.[60] Furthermore, Acidaminococcus gen. n. can metabolize glutamate as a growth substrate to produce SCFAs, a metabolic strategy that depletes glutamate while accumulating SCFAs, potentially reshaping the dynamic equilibrium of excitatory neurotransmitters.[61] Therefore, the Acidaminococcaceae family occupies a core metabolic node in the intestinal ecosystem, linking SCFAs, 5-HT, and glutamate, which affects immune regulation and neuronal signaling, further influencing the occurrence of TN. In addition, with the recent development of fecal microbiota transplantation technology,[62] selecting suitable genera within the Acidaminococcaceae family for targeted transplantation may modulate the structure of the GM, thereby reducing the risk of TN.

Traditional retrospective studies are limited by causality, and we confirmed the category of gut flora affecting TN with the support of MR. However, there are some limitations of this study. Firstly, the GWAS data for the study subjects were derived from European populations, which means that populations of other ethnicities may not be applicable. Secondly, to obtain sufficient SNPs, we did not choose the traditional GWAS significance thresholds (P < 5 × 10-8), which may cause some bias. Additionally, although we controlled for confounding factors, they cannot be completely eliminated, especially those that have not yet been clearly characterized. Finally, large randomized controlled trials and studies are still needed to uncover the role and mechanisms of GM in TN.

5. Conclusions

In this study, we found a causal association between Acidaminococcaceae (Family) and TN, which may reduce the risk of TN. The current findings add new insights to the gut-brain axis and contribute to the understanding of the pathogenesis of TN. However, further large-scale studies are needed to clarify the protective effect of probiotics on TN and its specific protective mechanisms.

Acknowledgments

We would like to thank the FinnGen consortium, MiBioGen consortium and Lopera-Maya et al. for providing complete GWAS data.

Author contributions

Conceptualization: Hong Yu, Jianbing Bai.

Data curation: Yingqiang Zhong, Hong Yu, Yang Wang, Yaohui Jia.

Methodology: Yingqiang Zhong, Hong Yu, Yaohui Jia, Jianbing Bai.

Supervision: Jianbing Bai.

Writing – original draft: Yingqiang Zhong.

Writing – review & editing: Hong Yu, Yang Wang, Yaohui Jia, Tao Jiang, Jianbing Bai.

medi-105-e48970-s001.docx (21KB, docx)
medi-105-e48970-s002.docx (30.5KB, docx)
medi-105-e48970-s003.docx (26.9KB, docx)
medi-105-e48970-s004.docx (17.3KB, docx)
medi-105-e48970-s005.docx (14.7KB, docx)
medi-105-e48970-s006.docx (25.8KB, docx)
medi-105-e48970-s007.docx (14.7KB, docx)
medi-105-e48970-s008.docx (21.7KB, docx)
medi-105-e48970-s009.docx (16.9KB, docx)
medi-105-e48970-s010.docx (26KB, docx)
medi-105-e48970-s011.docx (14.5KB, docx)
medi-105-e48970-s012.docx (16.7KB, docx)
medi-105-e48970-s013.docx (18.9KB, docx)

Abbreviations:

5-HT
5-hydroxytryptamine
GM
gut microbiota
GWAS
genome-wide association studies
IVs
instrumental variables
IVW
inverse-variance weighted
MR
Mendelian randomization
MVMR
multivariable MR
SCFAs
short-chain fatty acids
SNPs
single-nucleotide polymorphism
TN
trigeminal neuralgia

The authors have no funding and conflicts of interest to disclose.

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

Supplemental Digital Content is available in the online version of this article (http://dx.doi.org/10.1097/MD.0000000000048970).

How to cite this article: Zhong Y, Yu H, Wang Y, Jia Y, Jiang T, Bai J. Causal association between gut microbiota and trigeminal neuralgia: A bidirectional two-sample Mendelian randomization analysis. Medicine 2026;105:21(e48970).

YZ and HY contributed to this article equally.

Contributor Information

Yingqiang Zhong, Email: 929410434@qq.com.

Hong Yu, Email: 335682819@qq.com.

Yang Wang, Email: 448973910@qq.com.

Yaohui Jia, Email: jmeomim@163.com.

Tao Jiang, Email: hbmedicalu@163.com.

References

  • [1].Bendtsen L, Zakrzewska JM, Heinskou TB, et al. Advances in diagnosis, classification, pathophysiology, and management of trigeminal neuralgia. Lancet Neurol. 2020;19:784–96. [DOI] [PubMed] [Google Scholar]
  • [2].Allam AK, Sharma H, Larkin MB, Viswanathan A. Trigeminal neuralgia: diagnosis and treatment. Neurol Clin. 2023;41:107–21. [DOI] [PubMed] [Google Scholar]
  • [3].Araya EI, Claudino RF, Piovesan EJ, Chichorro JG. Trigeminal neuralgia: basic and clinical aspects. Curr Neuropharmacol. 2020;18:109–19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [4].Mayer EA, Nance K, Chen S. The gut-brain axis. Annu Rev Med. 2022;73:439–53. [DOI] [PubMed] [Google Scholar]
  • [5].Socała K, Doboszewska U, Szopa A, et al. The role of microbiota-gut-brain axis in neuropsychiatric and neurological disorders. Pharmacol Res. 2021;172:105840. [DOI] [PubMed] [Google Scholar]
  • [6].Ding M, Lang Y, Shu H, Shao J, Cui Li. Microbiota-gut-brain axis and epilepsy: a review on mechanisms and potential therapeutics. Front Immunol. 2021;12:742449. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [7].Guo R, Chen L-H, Xing C, Liu T. Pain regulation by gut microbiota: molecular mechanisms and therapeutic potential. Br J Anaesth. 2019;123:637–54. [DOI] [PubMed] [Google Scholar]
  • [8].Adak A, Khan MR. An insight into gut microbiota and its functionalities. Cell Mol Life Sci. 2019;76:473–93. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [9].Corriero A, Giglio M, Inchingolo F, Moschetta A, Varrassi G, Puntillo F. Gut microbiota modulation and its implications on neuropathic pain: a comprehensive literature review. Pain Ther. 2024;13:33–51. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [10].Zhang JD, Liu J, Zhu S-W, et al. Berberine alleviates visceral hypersensitivity in rats by altering gut microbiome and suppressing spinal microglial activation. Acta Pharmacol Sin. 2021;42:1821–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [11].Morreale C, Bresesti I, Bosi A, et al. Microbiota and pain: save your gut feeling. Cells. 2022;11:971. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [12].Qin J, Li R, Raes J, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464:59–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [13].Ley RE, Turnbaugh PJ, Klein S, Gordon JI. Microbial ecology: human gut microbes assocated with obesity. Nature. 2006;444:1022–3. [DOI] [PubMed] [Google Scholar]
  • [14].Gomes AC, Hoffmann C, Mota JF. The human gut microbiota: metabolism and perspective in obesity. Gut Microbes. 2018;9:308–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [15].Sun Y, Gao S, Ye C, Zhao W. Gut microbiota dysbiosis in polycystic ovary syndrome: mechanisms of progression and clinical applications. Front Cell Infect Microbiol. 2023;13:1142041. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [16].Morrison DJ, Preston T. Formation of short chain fatty acids by the gut microbiota and their impact on human metabolism. Gut Microbes. 2016;7:189–200. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [17].Lanza M, Filippone A, Casili G, et al. Supplementation with SCFAs re-establishes microbiota composition and attenuates hyperalgesia and pain in a mouse model of NTG-induced migraine. Int J Mol Sci. 2022;23:4847. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [18].Sekula P, Del Greco M F, Pattaro C, Köttgen A. Mendelian randomization as an approach to assess causality using observational data. J Am Soc Nephrol. 2016;27:3253–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [19].Bowden J, Holmes MV. Meta-analysis and Mendelian randomization: a review. Res Synth Methods. 2019;10:486–96. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [20].Burgess S, Davey Smith G, Davies NM, et al. Guidelines for performing Mendelian randomization investigations: update for summer 2023. Wellcome Open Res. 2019;4:186. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [21].Skrivankova VW, Richmond RC, Woolf BAR, et al. Strengthening the reporting of observational studies in epidemiology using Mendelian randomization: the STROBE-MR statement. JAMA. 2021;326:1614–21. [DOI] [PubMed] [Google Scholar]
  • [22].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] [PMC free article] [PubMed] [Google Scholar]
  • [23].Lopera-Maya EA, Kurilshikov A, van der Graaf A, et al. Effect of host genetics on the gut microbiome in 7,738 participants of the Dutch Microbiome Project. Nat Genet. 2022;54:143–51. [DOI] [PubMed] [Google Scholar]
  • [24].Li P, Wang H, Guo L, et al. Association between gut microbiota and preeclampsia-eclampsia: a two-sample Mendelian randomization study. BMC Med. 2022;20:443. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [25].Cao Y, Lu H, Xu W, Zhong M. Gut microbiota and Sjögren’s syndrome: a two-sample Mendelian randomization study. Front Immunol. 2023;14:1187906. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [26].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;51:600–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [27].Burgess S, Thompson SG. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011;40:755–64. [DOI] [PubMed] [Google Scholar]
  • [28].Shen Y, Liu H, Meng X, et al. The causal effects between gut microbiota and hemorrhagic stroke: a bidirectional two-sample Mendelian randomization study. Front Microbiol. 2023;14:1290909. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [29].Lawlor DA, Harbord RM, Sterne JAC, Timpson N, Davey Smith G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med. 2008;27:1133–63. [DOI] [PubMed] [Google Scholar]
  • [30].Chen X, Kong J, Diao X, et al. Depression and prostate cancer risk: a Mendelian randomization study. Cancer Med. 2020;9:9160–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [31].Hindy G, Engström G, Larsson SC, et al. Role of blood lipids in the development of Ischemic stroke and its subtypes: a mendelian randomization study. Stroke. 2018;49:820–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [32].Bowden J, Del Greco M F, Minelli C, Davey Smith G, Sheehan NA, Thompson JR. Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic. Int J Epidemiol. 2016;45:1961–74. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [33].Ong JS, MacGregor S. Implementing MR-PRESSO and GCTA-GSMR for pleiotropy assessment in Mendelian randomization studies from a practitioner’s perspective. Genet Epidemiol. 2019;43:609–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [34].Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017;32:377–89. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [35].Chen X, Hong X, Gao W, et al. Causal relationship between physical activity, leisure sedentary behaviors and COVID-19 risk: a Mendelian randomization study. J Transl Med. 2022;20:216. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [36].Sanderson E. Multivariable Mendelian randomization and mediation. Cold Spring Harb Perspect Med. 2021;11:a038984. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [37].Melek LN, Smith JG, Karamat A, Renton T. Comparison of the neuropathic pain symptoms and psychosocial impacts of trigeminal neuralgia and painful posttraumatic trigeminal neuropathy. J Oral Facial Pain Headache. 2019;33:77–88. [DOI] [PubMed] [Google Scholar]
  • [38].Wu TH, Hu L-Y, Lu T, et al. Risk of psychiatric disorders following trigeminal neuralgia: a nationwide population-based retrospective cohort study. J Headache Pain. 2015;16:64. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [39].Headache Classification Committee of the International Headache Society (IHS) The International Classification of Headache Disorders, 3rd ed. Cephalalgia; 2018;38(1):1–211. [DOI] [PubMed] [Google Scholar]
  • [40].Oesman C, Mooij JJ. Long-term follow-up of microvascular decompression for trigeminal neuralgia. Skull Base. 2011;21:313–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [41].Pan H, Liu C-X, Zhu H-J, Zhang G-F. Immune cells mediate the effects of gut microbiota on neuropathic pain: a Mendelian randomization study. J Headache Pain. 2024;25:196. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [42].Liu L, Wang H, Chen X, Zhang Y, Zhang H, Xie P. Gut microbiota and its metabolites in depression: from pathogenesis to treatment. EBioMedicine. 2023;90:104527. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [43].Lin B, Wang Y, Zhang P, Yuan Y, Zhang Y, Chen G. Gut microbiota regulates neuropathic pain: potential mechanisms and therapeutic strategy. J Headache Pain. 2020;21:103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [44].Wang Q, Yang Q, Liu X. The microbiota-gut-brain axis and neurodevelopmental disorders. Protein Cell. 2023;14:762–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [45].Martin-Gallausiaux C, Marinelli L, Blottière HM, Larraufie P, Lapaque N. SCFA: mechanisms and functional importance in the gut. Proc Nutr Soc. 2021;80:37–49. [DOI] [PubMed] [Google Scholar]
  • [46].Lange O, Proczko-Stepaniak M, Mika A. Short-chain fatty acids-a product of the microbiome and its participation in two-way communication on the microbiome-host mammal line. Curr Obes Rep. 2023;12:108–26. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [47].Eshleman EM, Rice T, Potter C, et al. Microbiota-derived butyrate restricts tuft cell differentiation via histone deacetylase 3 to modulate intestinal type 2 immunity. Immunity. 2024;57:319–32.e6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [48].Wei H, Yu C, Zhang C, et al. Butyrate ameliorates chronic alcoholic central nervous damage by suppressing microglia-mediated neuroinflammation and modulating the microbiome-gut-brain axis. Biomed Pharmacother. 2023;160:114308. [DOI] [PubMed] [Google Scholar]
  • [49].Yano JM, Yu K, Donaldson GP, et al. Indigenous bacteria from the gut microbiota regulate host serotonin biosynthesis. Cell. 2015;161:264–76. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [50].Watson CJ. Insular balance of glutamatergic and GABAergic signaling modulates pain processing. Pain. 2016;157:2194–207. [DOI] [PubMed] [Google Scholar]
  • [51].Cortes-Altamirano JL, Olmos-Hernandez A, Jaime HB, et al. Review: 5-HT1, 5-HT2, 5-HT3 and 5-HT7 receptors and their role in the modulation of pain response in the central nervous system. Curr Neuropharmacol. 2018;16:210–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [52].Shoji H, Ikeda K, Miyakawa T. Behavioral phenotype, intestinal microbiome, and brain neuronal activity of male serotonin transporter knockout mice. Mol Brain. 2023;16:32. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [53].Ji G, Neugebauer V. Contribution of corticotropin-releasing factor receptor 1 (CRF1) to serotonin receptor 5-HT(2C)R function in amygdala neurons in a neuropathic pain model. Int J Mol Sci. 2019;20:4380. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [54].Schäfers M, Sorkin L. Effect of cytokines on neuronal excitability. Neurosci Lett. 2008;437:188–93. [DOI] [PubMed] [Google Scholar]
  • [55].Park BS, Lee JO. Recognition of lipopolysaccharide pattern by TLR4 complexes. Exp Mol Med. 2013;45:e66. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [56].Simó C, Mamani-Huanca M, Hernández-Hernández O, Redondo-Río A, Muñoz S, García-Cañas V. Application of nanopore long-read sequencing and metabolomics in an in vitro dynamic intestinal digestion model: a genome-centric metatranscriptomic approach to investigating microbial TMA and SCFA metabolism. J Pharm Biomed Anal. 2025;262:116896. [DOI] [PubMed] [Google Scholar]
  • [57].Fernández-Veledo S, Vendrell J. Gut microbiota-derived succinate: friend or foe in human metabolic diseases? Rev Endocr Metab Disord. 2019;20:439–47. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [58].Cheng L, Shi L, He C, et al. Rutin-activated adipose tissue thermogenesis is correlated with increased intestinal short-chain fatty acid levels. Phytother Res. 2022;36:2495–510. [DOI] [PubMed] [Google Scholar]
  • [59].Tian Y, Zuo L, Guo Q, et al. Potential role of fecal microbiota in patients with constipation. Ther Adv Gastroenterol. 2020;13:1756284820968423. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [60].Reigstad CS, Salmonson CE, Rainey JF, et al. Gut microbes promote colonic serotonin production through an effect of short-chain fatty acids on enterochromaffin cells. FASEB J. 2015;29:1395–403. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [61].Rogosa M. Acidaminococcus gen. n., Acidaminococcus fermentans sp. n., anaerobic gram-negative diplococci using amino acids as the sole energy source for growth. J Bacteriol. 1969;98:756–66. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [62].Porcari S, Benech N, Valles-Colomer M, et al. Key determinants of success in fecal microbiota transplantation: from microbiome to clinic. Cell Host Microbe. 2023;31:712–33. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

medi-105-e48970-s001.docx (21KB, docx)
medi-105-e48970-s002.docx (30.5KB, docx)
medi-105-e48970-s003.docx (26.9KB, docx)
medi-105-e48970-s004.docx (17.3KB, docx)
medi-105-e48970-s005.docx (14.7KB, docx)
medi-105-e48970-s006.docx (25.8KB, docx)
medi-105-e48970-s007.docx (14.7KB, docx)
medi-105-e48970-s008.docx (21.7KB, docx)
medi-105-e48970-s009.docx (16.9KB, docx)
medi-105-e48970-s010.docx (26KB, docx)
medi-105-e48970-s011.docx (14.5KB, docx)
medi-105-e48970-s012.docx (16.7KB, docx)
medi-105-e48970-s013.docx (18.9KB, docx)

Articles from Medicine are provided here courtesy of Wolters Kluwer Health

RESOURCES