Causal roles of gut microbiota in OS etiology suggested by genetic study

doi:10.21203/rs.3.rs-5294517/v1

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Causal roles of gut microbiota in OS etiology suggested by genetic study

https://doi.org/10.21203/rs.3.rs-5294517/v1

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Objectives

Osteosarcoma (OS) represents a prevalent primary malignant bone tumor with limited treatment options at present. The relationship between gut microbiota and OS remains ambiguous, with uncertainty surrounding whether this association is causal or influenced by bias. To investigate the potential link between gut microbiota and the development of OS, we conducted a two-sample Mendelian randomization (MR) analysis.

Methods

We employed a two-sample MR study design to elucidate the causal association between gut microbiota and OS. Our analysis encompassed a total of 196 bacterial features across five taxonomic levels: Phylum, Class, Order, Family, and Genus. The MR analysis incorporated the inverse-variance-weighted (IVW), weighted median, MR-Egger, weighted mode, and simple median methods. The primary analysis was conducted using the IVW method, and the MR results were validated through heterogeneity tests, sensitivity analysis, and pleiotropy analysis.

Results

Our findings revealed significant associations between nine distinct bacterial traits and OS using the IVW method. Specifically, we identified that genus. Odoribacter.id.952 (OR: 15.19; 95% CI: 2.08, 111.18; p = 0.007) and genus.Clostridiumsensustricto1.id.1873 (OR: 10.78; 95% CI: 1.78, 65.32; p = 0.009) were associated with a significantly increased risk of OS. Conversely, genus.LachnospiraceaeUCG001.id.11321 (OR: 0.21; 95% CI: 0.06, 0.70; p = 0.011) was found to significantly decrease the risk of OS.

Conclusions

Research suggests specific gut microbiota affect OS risk, highlighting their role in disease development and paving the way for new OS treatment strategies.

Osteosarcoma

Gut Microbiota

Mendelian Randomization Analysis

Risk Factor

Single Nucleotide Polymorphism

Osteosarcoma is the most common and highly aggressive malignant primary tumor, with a high incidence in children and adolescents and a tendency to metastasize (5-year survival is less than 30%). Many studies have shown that gut microbiome is involved in the occurrence, development and even metastasis of tumors. The gut microbiome and its metabolites play crucial roles in nutrient absorption, immune regulation, and the gut‒brain‒bone axis. To investigate the potential connection between the gut microbiome and the development of osteosarcoma, we conducted a two-sample Mendelian randomization analysis. Our findings revealed significant associations between nine distinct bacterial traits and the risk of OS via the inverse variance-weighted method. These results suggest a causal relationship between specific gut microbiota and the risk of OS, highlighting their pivotal role in disease progression and contributing to the exploration of novel therapeutic approaches for osteosarcoma in the future.

Osteosarcoma (OS) is the most prevalent primary malignant bone tumor, characterized by a bimodal incidence pattern peaking at 18 and 60 years of age and a slight male preponderance[1]. This aggressive tumor is prone to malignant progression and metastasis. While significant improvements have been made in the treatment of OS, with event-free survival rates exceeding 60%, the outcomes for patients with metastatic disease remain poor, with event-free survival rates below 30%[2]. The mainstay of OS treatment involves surgical resection of the primary tumor, followed by a combination of chemotherapy and targeted drugs. However, patients with unresectable primary or metastatic disease at diagnosis, as well as those with disease relapse, have extremely poor outcomes. Therefore, there is an urgent need for new therapies and treatment strategies for OS[3, 4].

The gut microbiome comprises the microbial communities residing in our intestines, encompassing a diverse array of bacteria, fungi, viruses, and other microorganisms. These microorganisms play a crucial role in facilitating digestion, synthesizing essential vitamins, modulating the immune system, and maintaining the integrity of the intestinal barrier. Recent research has suggested that disruptions in the gut microbiome, known as dysbiosis, may be implicated in the development of various diseases, including obesity, diabetes, inflammatory bowel disease, and cancer[5, 6]. Notably, the gut microbiota has been shown to play a role in the development and progression of cancer, with extracolonic tumor-associated microbiota identified in numerous human cancers, such as breast cancer, lung cancer, pancreatic cancer, melanoma, and soft tissue sarcomas[7, 8]. Extensive investigation in this field has corroborated the association between gut microbiota and tumors.

Mendelian randomization (MR) is a method of data analysis that has recently gained popularity for inferring causality in epidemiology[9]. Traditional epidemiological causal inference is often hampered by reverse causation and confounding factors, and randomized controlled trials are difficult to implement due to ethical limitations in human medicine and trial design. MR complements these limitations by using gene variation as instrumental variables (IVs), typically single nucleotide polymorphisms (SNPs), to infer the causal effect of treatment exposure on outcomes[10]. MR relies on three core assumptions: (i) the genetic variants associate with the risk factor of interest; (ii) the genetic variant is not associated with confounders; (iii) the genetic variants affect the outcome only through their effect on the risk factor of interest[11].

Two-sample Mendelian randomization (TSMR) is an alternative method to randomized controlled trials that leverages genetic variation as an instrumental variable to investigate the causal relationship between exposure and outcome, minimizing the impact of confounding factors and reverse causation. In this study, we conducted a two-sample MR study based on genome-wide association study (GWAS) summary data to explore the potential causal relationship between gut microbiota and OS, and to identify specific pathogenic bacterial taxa. Our findings may contribute to the identification of novel therapeutic options for OS treatment.

While there have been remarkable achievements in research related to gut microbiota and bone-related diseases, such as osteoporosis, there have been relatively few advancements in the realms of primary bone tumors and bone metastases. Due to ethical constraints in human medicine and trial design, randomized controlled trials are challenging to implement. Mendelian Randomization (MR), which leverages genetic variants as instrumental variables, can mitigate the impact of confounding and reverse causation[11]. To date, no MR studies have examined the causal relationship between gut microbiota and OS. The objective of this study is to ascertain whether a causal relationship exists between gut microbiota and the incidence of OS. Elucidating the risk factors associated with the progression of OS will facilitate the development of novel treatment strategies to mitigate cancer risk.

Study design

In this study, we categorized each distinct bacterial taxon present in the gut microbiota as a separate exposure variable. A total of 196 gut microbiota taxa were selected for inclusion as exposures, with OS serving as the outcome variable. To identify which bacterial taxa were causally linked to OS, we conducted a TSMR analysis utilizing summary statistics derived from GWAS. The comprehensive study design is visually depicted in Fig. 1.

Data sources

Exposure data

SNPs related to the human gut microbiome composition were selected as instrumental variables (IVs) from a GWAS dataset of the international consortium MiBioGen[12]. This was a multi-ethnic large-scale GWAS that coordinated 16S ribosomal RNA gene sequencing profiles and genotyping data from 18,340 participants from 24 cohorts from the USA, Canada, Israel, South Korea, Germany, Denmark, the Netherlands, Belgium, Sweden, Finland, and the UK to explore the association between autosomal human genetic variants and the gut microbiome. A total of 211 taxa were included. We first removed 15 bacterial traits without specific name, leaving 196 bacterial traits, including 9 Phylum, 16 Class, 20 Order, 32 Family and 119 Genus. The details of the data source we used are listed in Supplementary Table S1.

SNPs linked to the composition of the human gut microbiome were chosen as instrumental variables (IVs) from the MiBioGen[12] dataset, an extensive multi-ethnic GWAS. This collaborative effort integrated 16S ribosomal RNA gene sequencing profiles and genotyping data from 18,340 participants across 24 cohorts spanning the USA, Canada, Israel, South Korea, Germany, Denmark, the Netherlands, Belgium, Sweden, Finland, and the UK. The aim was to investigate the relationship between autosomal human genetic variants and the gut microbiome, encompassing a total of 211 bacterial taxa. Initially, we excluded 15 bacterial traits lacking specific names, resulting in 196 bacterial traits for analysis. These traits encompassed 9 phyla, 16 classes, 20 orders, 32 families, and 119 genera. Detailed information about the data source we utilized is provided in Supplementary Table S1.

Outcome data sources

Briefly, genetic summary statistics for OS were generated from a FinnGen consortium GWAS (R11) (https://www.finngen.fi/en) including 69 cases and 345,118 controls served as the outcome. The diagnosis criteria of OS were consistent with the International Classification of Diseases Code 10 (ICD-10).

Selection of instrumental variables

However, applying a stringent threshold of 5 × 10^− 8 would have excluded the majority of these SNPs. Consequently, we opted for a relatively lenient yet still statistically significant threshold of 1 × 10^− 5, as supported by prior studies[13].

One of the principles of the MR approach is that there is no linkage disequilibrium (LD) among the included IVs, as the presence of strong LD might result in biased results. In order to obtain IVs from independent loci, we set the LD threshold at R² < 0.001 and clumping distance = 10,000 kb in 1000 Genomes EUR data using “TSMR” packages[14].

After extracted relevant information such as effect allele, effect size including β-value, standard error and P-value for each SNP, we calculated the proportion of variation explained (R²) and F-statistics to quantify the instrument strength, with the following equation: R² = 2 × MAF × (1 − MAF) × β², F = R² (n-k-1) / k(1-R²), where "MAF" is the minor allele frequency of SNPs used as IVs, "n" is the sample size, and "k" is the number of IVs employed[15, 16].

Weak IVs in MR studies can cause bias in the causal association between exposure factors and outcome events. The F-statistic, derived from the regression of exposure outcomes on instrumental variables, can respond to the degree of correlation between exposure factors and outcomes and detect weak IVs[17]. It is used to represent the degree of bias when estimating causal associations. An F-statistic less than 10 indicates the presence of weakly predictive instruments and was excluded[18].

SNPs with the lowest p-value for the associated trait were retained for clumping with 196 bacterial traits. A total of 114 independent SNPs were found to be associated with 196 bacterial traits.

MR analysis/ Statistical analysis

Five popular MR methods were used for features containing multiple IVs: IVW test[19], weighted mode[20], MR-Egger[21], weighted median[22], and simple mode[23]. The IVW method is reported to be slightly more powerful than the others under certain conditions; therefore, the results with more than one IV were mainly based on the IVW method, with the other four methods serving as complements.

The IVW method is commonly used for obtaining variant-specific causal estimates, and can combine the effect values of multiple IVs into one estimate and provide a more accurate analysis of the causal relationships among variables[24].

The MR-PRESSO test was employed to identify outliers and address heterogeneity. In instances of detected heterogeneity among genetic IVs, outliers were eliminated, and MR analysis was re-executed[25]. Sensitivity analyses were conducted via the leave-one-out method, Cochran’s Q statistic was utilized to assess potential heterogeneity, and the MR Egger intercept test was employed to estimate horizontal pleiotropy[26]. The outcomes revealed P values exceeding 0.05, indicating the absence of heterogeneity and horizontal pleiotropy.

Additionally, we also employed funnel plots and conducted leave-one-out sensitivity tests to assess heterogeneity. The leave-one-out analysis was utilized to identify potential pleiotropic effects originating from individual SNPs. Scatter plots, forest plots, funnel plots, and leave-one-out sensitivity tests serve as valuable tools for visualizing MR results in a comprehensive manner.

All statistical analyses were performed using the R packages: TSMR [27]. As the present study was based on public summary data, no additional ethics approval or consent to participate was required. The details of the data sources in this MR study are shown in Table 1.

Table 1

Details of the genome-wide association studies and datasets used in our analyses
Exposure or outcome	Sample size	Ancestry	Links for data download
Human gut microbiome	18,340 participants	Mixed	https://mibiogen.gcc.rug. nl
Osteosarcoma	345187 (69 cases; 345118 controls)	European	https://www.finngen.fi/en

SNP selection

After quality control steps by LD effects and palindromic, 114 SNPs (Supplementary Table S2) were identified as IVs associated with 211 bacterial taxa (based on the MiBioGen consortium) for OS. These taxa comprised 1 phylum (14 SNPs), 2 family (28 SNPs), and 6 genus (72 SNPs) (Table 2). Notably, each SNP showed sufficient strength as instruments with F statistics ranging from 17.17 to 32.49 (Supplementary Table S2), all exceeding the threshold of F > 10, suggesting less possibility to suffer from weak instrument bias. Exceeding this threshold indicates that a result based on a valid instrumental variable ought not to suffer substantially from weak instrument bias.

Table 2

Selection of IVs after quality control
Taxonomies	Taxa	IVs
Phylum	1	14
Family	2	28
Genus	6	72
Total	9	114
IVs = instrumental variables

Causal effects of gut microbiota on the development of OS

Out of the 196 bacterial traits analyzed, we identified 9 bacterial traits that showed significant associations with OS using the IVW method. Among these 9 traits, 5 were positively associated with the risk of OS, while the remaining 4 exhibited an inverse relationship with this risk (Fig. 2).

In particular, we found that genetically predicted genus.Odoribacter.id.952 (OR: 15.19; 95% CI: 2.08, 111.18; p = 0.007); genus.Clostridiumsensustricto1.id.1873 (OR: 10.78; 95% CI: 1.78, 65.32; p = 0.009);genus.Eubacteriumeligensgroup.id.14372(OR: 8.39; 95% CI: 1.42, 49.41; p = 0.019); genus.Ruminococcustorquesgroup.id.14377( OR:7.95; 95% CI: 1.14, 55.39; p = 0.036 ); family.Methanobacteriaceae.id.121(OR:2.57; 95% CI: 1.02, 6.48; p = 0.045)were positively correlated with the risk of OS in IVW method.

The results of weighted median method showed that the genus.Odoribacter.id.952(OR: 17.88; 95% CI: 1.57, 203.71; p = 0.02); genus.Clostridiumsensustricto1.id.1873(OR: 11.06; 95% CI: 1.09, 112.14; p = 0.042) significantly increased the risk of OS (Supplementary Table S3).

In contrast, genus. Escherichia.Shigella.id.3504(OR:0.14; 95% CI:0.03, 0.61; p = 0.008); genus.LachnospiraceaeUCG001.id.11321(OR:0.21; 95% CI:0.06, 0.70; p = 0.011); family. Peptostreptococcaceae.id.2042(OR:0.22; 95% CI:0.06, 0.89; p = 0.033); phylum. Bacteroidetes.id.905(OR:0.15; 95% CI:0.02, 0.98; p = 0.048) were negatively associated with OS risk using the IVW MR method. The results of weighted median method showed that the genus.LachnospiraceaeUCG001.id.11321(OR:0.1; 95% CI:0.019, 0.51; p = 0.006) significantly decreased the risk of OS.

Sensitivity analyses

Subsequently, a comprehensive sensitivity analysis was carried out to assess the stability and reliability of the inferred causal relationship between gut microbiota and OS. The detailed description is summarized in Table 3. The MR-Egger, weighted mode, simple mode, and weighted median methods yielded similar causal estimates for magnitude and direction. Cochran’s Q statistics revealed the absence of significant heterogeneity among the selected IVs, with P values exceeding 0.05 in both IVW and MR-Egger methods. Furthermore, our analysis, employing the MR-Egger intercept method, did not reveal any indications of a horizontal pleiotropic effect (P > 0.05). The outcomes from the MR-PRESSO trial indicated the absence of any horizontal pleiotropic outliers. The leave-one-out analysis results demonstrated that none of the SNPs were influential outliers (Supplementary Figure S4). Additionally, the use of funnel and forest plots to depict a symmetrical pattern serves to visually affirm the reliability of the study’s results (Supplementary Figure S5 and S6).

Table 3

Heterogeneity and pleiotropy analysis of the mendelian randomization study on gut microbiota and osteosarcoma
Exposure	Outcome	Method	Heterogeneity		Horizontal pleiotropy		MR-PRESSO
			Q	Q, P value	Egger intercept	P value	P value
genus. Ruminococcustorquesgroup.id.14377	OS	MR Egger	7.29	0.78	-0.08	0.73	0.84
		IVW	7.41	0.83
genus. Clostridiumsensustricto1.id.1873	OS	MR Egger	1.76	0.97	0.12	0.61	0.98
		IVW	2.05	0.98
genus. Escherichia.Shigella.id.3504	OS	MR Egger	9.21	0.76	-0.31	0.12	0.65
		IVW	11.99	0.61
genus. LachnospiraceaeUCG001.id.11321	OS	MR Egger	11.05	0.68	0.41	0.12	0.56
		IVW	13.75	0.54
genus. Odoribacter.id.952	OS	MR Egger	3.55	0.83	0.05	0.84	0.91
		IVW	3.60	0.89
phylum. Bacteroidetes.id.905	OS	MR Egger	4.37	0.93	-0.17	0.33	0.90
		IVW	5.42	0.91
family. Methanobacteriaceae.id.121	OS	MR Egger	7.25	0.70	0.17	0.58	0.78
		IVW	7.57	0.75
family. Peptostreptococcaceae.id.2042	OS	MR Egger	8.52	0.81	-0.03	0.83	0.87
		IVW	8.56	0.86
genus. Eubacteriumeligensgroup.id.14372	OS	MR Egger	9.72	0.29	0.05	0.85	0.43
		IVW	9.77	0.37

The OS is one of the most common types of bone malignant tumors, characterized by a bimodal distribution in its incidence pattern, with peaks at ages 18 and 60 years and a slight male preponderance. Multiple potential genetic factors linked to bone formation are involved in the underlying pathophysiology, ultimately leading to malignant progression and metastasis. While patients with localized disease have shown significant improvement in prognosis, with event-free survival rates exceeding 60%, those with metastatic disease still face a bleak outlook, with event-free survival rates below 30%. Pathological examination of bone biopsy samples remains the definitive diagnostic gold standard. However, due to the rarity of OS and the absence of reliable biomarkers, screening and monitoring methods are limited. The standard curative treatment combining chemotherapy and surgery often leads to side effects that can compromise patients' quality of life [1, 2]. Therefore, there is an urgent need for research into novel treatments. Currently, there is increasing attention on the relationship between the gut microbiome and various diseases emerging as a focal point in OS research.

Our research is the first large-scale, thorough MR investigation to our knowledge to look into the possible link between gut microbiome and OS from genus to phylum perspectives. We identified genetic predisposition to gut microbiome that are causally linked to OS. Overall, we were able to identify 9 distinct gut microbiome as potential risk factors for such disease. These results will contribute to the public health in prevention and treatment of OS.

Previous studies have confirmed that the abundances of Colidextribacter, Lachnospiraceae.NK4A136.group, Lachnospiraceae.UCG.010, Lachnospiraceae.UCG.006, and Lachnoclostridium are decreased in the feces of mice with OS, while Alloprevotella and Enorhabdus are correspondingly increased. Their results suggest that the microbiota may play a significant role in the onset and progression of OS [28]. Using Mendelian Randomization, we have identified that genus. Odoribacter.id.952, genus. Clostridiumsensustricto1.id.1873, genus. Eubacteriumeligensgroup.id.14372, genus. Ruminococcustorquesgroup.id.14377, and family. Methanobacteriaceae.id.121 are associated with increased risks of OS, whereas genus.Escherichia Shigella.id.3504, genus.LachnospiraceaeUCG001.id.11321, family.Peptostreptococcaceae.id.2042, and phylum.Bacteroidetes.id.905 are associated with decreased risks of OS. These findings may have predictive implications for the role of gut microbiota in the occurrence and development of OS.

The gut microbiota is intricately linked to the host's health and disease states[28]. It regulates metabolism, immunity, and the nervous system. Moreover, the gut microbiota has been implicated in cancer development[29]. Some metabolic and metabolomic studies have reported metabolites as potential diagnostic markers for diseases[30]. Research on the microbiome composition and metabolites related to OS is a nascent field. Metabolic changes, reflective of the host's health status, are considered crucial indicators of disease. Through 16S rRNA gene sequencing and metabolomic analysis, researchers have documented significant differences in the gut microbiota composition and metabolic profiles between OS model mice and healthy controls. Notably, in metabolic pathways related to bone metabolism, OS mice exhibit marked metabolic dysregulation. The gut microbiota and its metabolites may contribute to the pathogenesis of OS by influencing the host's metabolic pathways[28].

Alterations in microbial diversity and richness may further exacerbate tumor growth and metastasis. Changes in the gut microbiome may interact with the OS microenvironment through metabolites or immune regulatory mechanisms. In malignant and metastatic bone tumors, pathological fractures often occur, accompanied by bone remodeling characterized by an imbalance between osteoblasts and osteoclasts, as observed in Paget's disease, a condition considered a precursor to OS[2]. Despite extensive research on bone disease treatments, the lack of effective targets has heightened the therapeutic challenge. Currently, research primarily focuses on osteoporosis, with insufficient attention given to bone tumors[31, 32].

Chemotherapy is a common treatment for OS, but its impact on the gut microbiome is also significant. Studies have investigated the relationship between gut microbiome dysregulation and chemotherapy effectiveness in OS treatment. This suggests that the microbiome state may influence chemotherapy efficacy and patient prognosis. In mouse models of OS, gut microbiota diversity is markedly reduced, particularly the abundance of beneficial bacteria such as butyrate-producing bacteria, while harmful bacteria proliferate. Butyrate, a short-chain fatty acid, is the primary energy source for intestinal epithelial cells and exerts anti-inflammatory and intestinal barrier-enhancing effects. Its depletion may promote OS progression. Chemotherapeutic agents like cisplatin and doxorubicin also induce significant changes in the gut microbiota during OS treatment. Post-chemotherapy, the abundance of butyrate-producing bacteria increases, and butyrate metabolism levels recover, suggesting that gut microbiota changes may be linked to chemotherapy effectiveness. Modulating the gut microbiome may potentially enhance patient response to chemotherapy[28].

Immunotherapy represents a transformative advancement in cancer treatment, now integrated into the management of almost all cancer types. Specifically, immune checkpoint blockade therapy has significantly improved survival rates for multiple cancers[33]. Antibiotic treatment can influence patient response to immunotherapy, although the mechanisms are not fully understood. The microbiome's impact on the tumor microenvironment can modulate inflammation, innate immunity, and the tumor microenvironment itself. Vancomycin can enhance dendritic cell antigen presentation and cancer immunity by depleting gut bacteria that consume short-chain fatty acids[34]. Depletion of the gut microbiota through broad-spectrum antibiotics leads to the expansion of Actinobacteria. Studies have shown that patients receiving anaerobic antibiotic treatment combined with targeted therapy for B-cell malignancies have significantly reduced survival rates[35], highlighting the impact of antibiotic treatment on immunotherapy. What’s more, strategies such as fecal microbiota transplantation and dietary interventions have been used to improve the success rate of immunotherapy. Research has confirmed that in advanced melanoma, there is an increase in fecal abundance of key bacterial species belonging to the Actinobacteria phylum (Bifidobacteriaceae species). Ruminococcaceae and Lachnospiraceae are associated with favorable outcomes after anti-PD-1 treatment[36, 37]. These findings suggest that gut cells can influence the efficacy of immunotherapy by regulating the number and function of immune cells. Therefore, interventions targeting the gut microbiota hold promise as a new direction for immunotherapy in OS.

The gut microbiome is an emerging and crucial field in OS research. Although current research is limited, evidence suggests that changes in the gut microbiome are closely related to the occurrence, development, and treatment outcomes of OS. Future studies are likely to uncover deeper mechanisms and provide new insights for the prevention and treatment of OS. With advancements in science and technology, research on the gut microbiome will offer a more comprehensive understanding of cancer and drive the development of personalized medicine. Interventions targeting the gut microbiota, such as FMT and dietary interventions, are expected to become integral components of comprehensive OS treatment. As research progresses, the application prospects of the gut microbiome in cancer treatment will broaden further.

There exists a complex interplay between the gut microbiota and OS. Dysregulation of the microbiota not only affects the host's metabolism and immune function but may also promote the onset and progression of OS. Using MR techniques, we investigated the relationship between 196 different gut microbiota species and OS, uncovering nine nominally significant associations and strong causal relationships. Through genetic prediction, our study isolated microbiota that could serve as potential diagnostic biomarkers and therapeutic targets for OS. Future research should further elucidate the specific roles of the gut microbiota in the pathogenesis of OS and explore new strategies for treating OS by modulating the gut microbiota. This will not only contribute to a deeper understanding of the biological characteristics of OS but may also provide new ideas and methods for its diagnosis and treatment.

OS, Osteosarcoma

MR, Mendelian randomization

TSMR, Two-sample Mendelian randomization

IVW, Inverse-variance-weighted

IVs, Instrumental variables

LD, linkage disequilibrium

SNPs, Single nucleotide polymorphisms

GWAS, Genome-wide association study

MAF, minor allele frequency

AUTHOR CONTRIBUTIONS

Wei Wei: performed model building and statistical analyses; wrote the first version of article. Zihuan Zhou: performed model building and statistical analyses; contributed to article revision. Xiaolin Li: con-tributed to article revision. Haifeng Wei& Jianru Xiao: designed the study; contributed to article revision. Zhipeng Wu: contributed to article revision. All authors have reviewed and approved the final version of article. The work reported in the article has been performed by the authors, unless clearly specified in the text.

ACKNOWLEDGEMENTS

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by National Natural Science Foundation of China (General Program 82072971 and 82472647).The funding sources had no role in the study design, data gathering, analysis, and interpretation, writing of the report, or the decision to submit the report for publication. The corresponding author had full access to all the data and the final responsibility to submit for publication.

We thank Professor Haifeng Wei for great supports to this study and conscientious guidance. The authors also thank all the colleagues for their kind help.

CONFLICT OF INTEREST STATEMENT

There is no potential conflict of interests for authors.

DATA AVAILABILITY STATEMENT

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

DISCLAIMER

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

ETHICS STATEMENT

Ethical approval was not required for the study involving humans in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and the institutional requirements.

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No competing interests reported.

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Causal roles of gut microbiota in OS etiology suggested by genetic study

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Abstract

Objectives

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Novelty and Impact

Introduction

Materials and Methods

Study design

Data sources

Exposure data

Outcome data sources

Selection of instrumental variables

MR analysis/ Statistical analysis

Results

SNP selection

Causal effects of gut microbiota on the development of OS

Sensitivity analyses

Discussion

Summary

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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